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On the episode, we discuss:

🇹🇼 Why Taiwan matters: The strategic importance of Taiwan goes far beyond semiconductors, and its future has enormous implications for the U.S. alliance system in the Indo-Pacific.
⚔️ Deterrence and gray-zone warfare: Why China may prefer a gradual campaign of economic coercion, cyberattacks, and other sub-threshold activities over a conventional military invasion.
🚀 Emerging defense technologies: The critical roles of autonomous maritime systems, resilient communications, integrated air defense, and AI in a potential conflict over Taiwan—and why the lessons from Ukraine don't always translate to the Pacific.
🏭 The defense industrial base: Why the U.S. and its allies need to rethink defense production and build integrated, allied supply chains to compete with China's manufacturing capacity.

One of my biggest takeaways from the conversation was Eyck's argument that deterring a Chinese invasion of Taiwan is both possible and achievable—but deterring an invasion alone isn't enough. The U.S. also needs a strategy for responding to the growing range of coercive activities that fall below the threshold of conventional warfare.

As always, please reach out if you or anyone you know is building at the intersection of technology and national security. And please fill out the form below if you are interested in working in defense tech (in or out of the Shield Capital portfolio).

You can listen to the podcast on Spotify, YouTube, Apple, the Shield Capital website, or right here on Substack.

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Transcript

Maggie 00:45

In this episode of the Mission Matters podcast, we sit down with Eyck Freymann to discuss strategies to preserve peace and protect US interests and values in an era of systemic competition with China. On this podcast, we often discuss the kinds of technologies that we’re building to improve our national security, but we rarely dig into the details of what the adversary actually looks like and why we need these new technologies to remain competitive. So this is an episode I’m really looking forward to. I just read Eyck’s newest book, Defending Taiwan: A Strategy to Prevent War with China, and learned quite a lot about the actual tactics and situation on the ground of what it might actually look like if we had some kind of a conflict with China.

Matt 01:27

In addition to his most recent book, Eyck is the author of several other books, including The Arsenal of Democracy: Technology, Industry, and Deterrence in an Age of Hard Choices, and One Belt, One Road: Chinese Power Meets the World. This conversation today covers everything from the current state of US-China-Taiwan relations, different pillars of deterrence to prevent a conflict with China, and the role that emerging technologies would play in a potential future conflict. Eyck, thanks so much for coming on the podcast.

Eyck 01:56

Thanks for having me on. Let’s get into it.

Maggie 01:58

Yeah, well let’s just start with a basic question. Why does China care about Taiwan, and why should America care about Taiwan?

Eyck 02:07

Well, thanks for starting there. That’s the way to start. The first thing to say is that both China and America cared about Taiwan before Taiwan made semiconductors. In fact, we almost went to nuclear war over it twice, in the first and second Taiwan Straits crises. And the reason has to do first and foremost with Taiwan’s geographic position. Taiwan sits right in the center of the first island chain, which is an archipelago extending down from Japan all the way to Southeast Asia via Taiwan and The Philippines. There are not that many navigable waterways between those islands. That partly has to do with the topology of the seabed. It’s known as the Ring of Fire, right? There’s volcanoes. It’s a place where two tectonic plates collide. And as a result, the United States learned this geography lesson in the Pacific War and the Korean War, if our friends and partners are all astride this first island chain, then a Chinese or any other continental Eurasian navy will have a very hard time breaking out into the wider waters of the Pacific, crossing the ocean and threatening the homeland. But if China can somehow flip Taiwan and use its Pacific-facing coast as a forward base to project naval power, surface ships or submarines forward, they can effectively surround Japan. They can surround the Philippines, they can surround South Korea, and these are economies that ultimately are almost entirely dependent on imported energy, food, and other inputs to survive. So a world in which Taiwan falls by force or coercion is an unmitigated catastrophe for the entire alliance system in the region. In fact, the current status quo in our relations with Japan and the others becomes unsustainable. So that’s why China wants Taiwan for geostrategic reasons. It obviously has this enormous propaganda value for the CCP because not to retread the history in too much detail, this is the unfinished business of China’s civil war. This is the question of whether there is such a thing as one China that includes Taiwan, and whether the Chinese Communist Party is the sole legitimate government, or whether there is some second China, a free China, which is richer and more technologically advanced, and just better in a lot of ways, or whether Taiwan got separated at some point along the way. So for Beijing, this is an incredibly sensitive issue that relates to the Communist Party’s legitimacy, Xi Jinping’s personal legitimacy within the regime, and also this is this massive opportunity, because if Taiwan can be made to submit, the dominoes might very quickly fall, and then China’s pathway to national rejuvenation and eventually regional and global hegemony would be well underway.

Maggie 05:15

One of the things I find really interesting is actually learning about the domestic situation in Taiwan. So can you tell us a little bit about some of the different political opinions in Taiwan about China and its relationship with China?

Eyck 05:29

Well, how much time do we have? I’m kidding. I’m kidding. So, it’s hard to answer this question without drawing caricatures because Taiwan is a thriving democracy with an incredibly robust and vibrant political debate. And they understand China reasonably well because they can absorb CCP material in the original language because they’re flooded with propaganda, from every direction, because they’ve been living under threat in one form or another for the last 70-odd years, and because they’re not stupid. Like everyone else, they can see that Donald Trump is unpredictable, and they wonder what that means for their security and their personal future. So, Taiwan is divided between two basic camps that they call green and blue. The green camp is the Democratic Progressive Party, it’s the current ruling party, which basically takes the view Taiwan is an independent country. We became independent in 1949, and we’re not going to rock the boat, but we are in fact an independent country with our own distinct traditions, heritage, history, culture, and we have a place in the US-led security architecture, and the United States wants to protect us for the reasons we’ve discussed. And therefore, if we keep the Americans on board and we play our part and we are a good partner, the Americans will deter this for us, and they will not abandon us. And then there’s the Blue camp, which is the Guomindang. This is the party of Chiang Kai-shek, the party that fought the Chinese communists on the mainland in the 1930s and ‘40s and then decamped to Taiwan after, which sometimes gets branded as the pro-China party. That’s kind of an oversimplification. The KMT would say, “Who are we kidding?” We speak Chinese. We eat Chinese food. Most of us practice Chinese religion or Chinese philosophy in some form. Most of us have ancestry on the mainland, an ancestral hometown on the mainland. We are Chinese, right? There is one China that includes Taiwan, but for historical reasons that we all understand, there is a free part of China and an unfree part of China, and the free and unfree parts have different definitions of what the ruling framework for China ought to be. But on that basis, we can talk to the CCP. We can deliver each other’s mail. We can trade, and furthermore, the Americans may not be reliable. The Americans may not be coming. The cavalry may not be there, and if that’s the case, we don’t wanna be used as a pawn in someone else’s game, and we don’t want Beijing to get the wrong idea that we’re going to provoke a crisis. We need to do diplomacy with Beijing to reassure them that the best way to handle this is through grown-ups sitting down at a table. And now I’m caricaturing, and within each of these parties, there’s a huge range of perspectives which range from the loco-coco, independence now, dare Beijing to attack us, which is a rounding error of the population, less than ten percent, not zero, loud on Twitter, but less than ten percent. These people have blown up my DMs since I’ve written the book. And then on the other side, the loco-coco or paid off united front, unification now, we can become a glorious province of the PRC and advance Chinese communism. Basically, no one in Taiwan believes that. But plenty of people in Taiwan believe, due to China’s size and scale and geographic proximity, that some accommodation has to be had. So these two camps totally hate each other. They inhabit completely different information environments. It divides families. It divides communities. And unlike in the United States where there’s this, you could call it a deep state, or you could just call it a centrist core of technocratic professionals, which is most of our military, most of our intelligence community, a lot of our diplomatic corps, people who basically consider the partisan stuff to be noise, a distraction from the mission, Taiwan is a younger democracy, is very polarized across all of its institutions. They don’t really have nonpartisan think tanks, for example. And both sides caricature the other side and try to get the Americans, in some cases China, to intervene in Taiwan’s politics to their advantage. This is a very noisy and challenging information environment, and the reason I’m harping on this is because Taiwan’s going into an election. In 2028, January 2028, they will elect their next president, and it will probably be the incumbent, Lai Ching-te of the DPP, the green side, versus some yet to be determined KMT. And there will be a temptation for some in the United States government to just assume, well, the DPP is the pro-America party superficially, so they’re our team. And one of the arguments in the book is this stuff is way more delicate than we would think, and trying to meddle in Taiwan’s politics is more likely to backfire than to work. We should respect that the people of Taiwan can make their own choices, that they are sane and don’t want their kids to grow up under communism, and that they will make good decisions for themselves. But you have to remember that the people of Taiwan will respond to the signals they get from the United States, and if they believe that the United States does not have their back and will not be there for them, then they may decide that the only solution is some kind of negotiated surrender. And the goal of the US policy should be to back up Taiwan, whoever they happen to democratically elect, to allow this process to play out peacefully through diplomacy and not in a more violent or coercive way.

Matt 11:43

So speaking of the US position on Taiwan, help us understand, like let’s suppose the US decided that the risks of conflict with China weren’t worth it, and that, whether it was the military conflict or just an economic crisis that coming to Taiwan’s defense, in an invasion or blockade scenario was just not worth the cost. Help us understand what would be the worst case scenario for the type of outcome that could lead to, from a US perspective. What would you want the US government and citizenry to have in mind, as it evaluates that decision and thinks about what its posture will be toward Taiwan?

Eyck 12:31

Assuming we’re unwilling to fight?

Matt 12:33

Yes.

Eyck 12:33

Well, if you’re unwilling to fight, you’re choosing between bad options, because you definitely don’t wanna get backed into fighting a war that you don’t wanna fight. So if it turns into a crisis and you’re intending to fold, the more chips you have on the table when you eventually fold, the worse a position you’re in. And in other words, if you’re holding a bad hand in poker, fold early. Everyone knows that. On the other hand, the United States has maintained a, what we call this One China policy. Technically, it’s a One China policy guided by the Three Communiques, the Six Assurances, and Taiwan Relations Act. It’s a whole litany to recite, but we have this policy that has grown over half a century that we have reiterated a zillion times, and that is part of what creates stability in the region. So if we were to change our policy in an arbitrary way, it would scare the daylights out of all of our regional partners. It would make them doubt whether our promises to them, which have never really been tested, are still good, and it may invite provocations and further tests, not just from China against Taiwan, but from North Korea on the peninsula or in contested maritime space, from Russians all around the region. The Russians are out there on a regular basis harassing our Japanese friends. China may see whether it extends into the Southern Ocean. They recently fired a nuclear-capable ICBM towards Australia. So the possibility that walking away from Taiwan could actually set in motion a catastrophe that brings us in is something we can’t ignore, and that’s one of the reasons for strategic ambiguity because, frankly, different presidents have different views on this, and we don’t have a window into the heads of past presidents, and it’s possible that some of them would have fought for Taiwan and others actually wouldn’t. But maintaining the current ambiguous position seems like the wise thing to do in that scenario. But obviously you don’t want to be led to the brink and then have to walk down in a humiliating way.

Matt 15:06

Yeah, I mean, in the book, you draw a really interesting historical comparison to talk about US resolve, during the Cold War, to Berlin, and the symbolic positioning that Berlin had in the Cold War, and how it’s analogous in some sense to the position of Taiwan today. I was wondering if you could explain that comparison and what we can learn from it as the US kind of continues or modifies its strategy and adapts to the new situations going forward.

Eyck 15:34

So there’s lots of ways in which Taiwan is not like Berlin, that we have to get to understand the limits of the analogy, but let’s talk about the ways in which they are potentially instructive. So Berlin is this weird case at the end of the Second World War because it’s in the heart of East Germany. It’s an exclave. It’s in the heart of East Germany, but it’s divided into four parts, a Soviet sector, an American, a British, and a French. And over the course of 1945 to 1948, it is unclear whether the Soviets will do as they’ve promised at Yalta and actually allow these Eastern European countries that were liberated from the Nazis by the Red Army to elect their own leaders. And it becomes clearer and clearer that Stalin is not intending to do this, and the Western Allies decide to pursue increasing integration for their sectors in Berlin and turn it into more of a garrison. But it’s very hard to defend because it’s a pinprick on a map completely surrounded by Brandenburg and East Germany, which is controlled by the Red Army. And the Soviets believe it should be trivial to take it through blockade. In 1948, they try. And the Truman administration, with help from the British and others, decides to launch this airlift to prove our commitment. And various other contingencies are discussed that would involve the use of nuclear weapons, but the United States decides we’re not gonna go there yet, but we’ll threaten the Soviets that if you shoot the planes which are resupplying Berlin, then you’ll likely have nuclear war. And in 1948, ‘49, only we have nukes. So there’s lots of reasons why we would not wanna fight the Soviets, but we do happen to have nukes and they don’t. And we supplied Berlin, and it is an epic public relations victory as well as an operational success. Because not only do we supply Berlin through the winter with just a never-ending flood of planes landing, like, on the tarmac in Tempelhof, like a Swiss watch, but crowds of German schoolchildren, you know, stand and wave flags for them. And it’s clear that we are the good guys and the Soviets are the bad guys, and that we’re gonna continue this, and they give up. Now, it’s tempting to think, “Oh, awesome. So if China blockaded Taiwan, we would just do that.” Wrong. And the reason is the distances are much greater, there’s 10 times as many people, and the GDP is much bigger, and a lot of what they do is, like, industry, like making chips and things that the world needs, which is super energy intensive, and a lot of Taiwan’s energy system runs on gas, which you can’t fly in a plane. So for various reasons, it’s much, much harder to resupply Taiwan through an airlift than it was for Berlin. The other way in which the Berlin crisis is revealing is in the late ‘50s through 1962, under Eisenhower and Kennedy. At this point, the Soviets have nukes, and they decide to try again. And Khrushchev’s idea is, “Well, now that I have nukes, you wouldn’t dare.” And he ends up linking Berlin and Cuba, and he pushes us to the brink of a Cuban missile crisis, believing that if he stares us down over Cuba, he can also make a deal over Berlin. And that doesn’t work in part because he underestimates our willingness to go to the nuclear brink. There’s a bunch of lessons you can take from this history, but one of the big ones is Taiwan feels a bit like Berlin in this respect, and if you’re not prepared to go to the brink, you’re lost. But it is much more likely, given the fact that the US and China are nuclear powers, that Taiwan’s fate is determined by a crisis than by a war. Because if we do go to the brink, given the unimaginable stakes, it is quite possible that one side or the other just backs down with all of the reputational disaster that follows from that.

Maggie 19:52

So coming back to the present, luckily today we are not on the brink, but that’s not to say that there isn’t anything happening in—

Matt 20:00

We’re getting closer.

Maggie 20:00

In conflict. Yeah, so can you give us what is the state today of conflict between Taiwan, China, the US, and, you know, in particular, what’s happening in the gray zone kind of beneath the threshold of a hot war?

Eyck 20:17

There’s a few different levels to this. Let’s talk at the conventional military level, which is where most of your listeners are probably paying the most attention, which is this question of deterrence by denial. China’s building this capability to try to show that they could take Taiwan through an amphibious invasion. Now, an amphibious invasion will probably have multiple parts and multiple sequences. It would probably begin with cyberattacks and bombardment to soften the target, to trap some of Taiwan’s air force in their mountain hangars, maybe to disable some of their surface fleet, some of their air force. It would try to degrade Taiwan’s command and control infrastructure, probably try to take out some of their leadership, sow chaos, all of that. But then ultimately, if China wants to take Taiwan through an invasion, they have to deliver some number of tens of thousands of men across 100 miles of very challenging water, either by ships on the surface or by air. And this is very challenging to do because 10, 20, 30,000 men have to be resupplied with a whole lot of stuff, like millions of tons of stuff: vehicles, fuel, spare parts for the vehicles, medical, tents, other kinds of equipment, blood. And these things have to be, seriously, these things have to be delivered on schedule in what may turn out to be dozens or hundreds of crossings back and forth across the strait. And there’s only so many of these vessels that can do that job, and if a certain number of them are taken out of commission, damaged, destroyed, sunk, then you can have a scenario where China ends up with several thousand men on the island, but no ability to sustain them. They get massacred, and this is the worst possible outcome reputationally for China. So, the contest that the United States and its partners on one side and China on the other are engaged in on the deterrence by denial question is trying to change each other’s risk calculus or assessment of how likely it is that this could succeed. And so much of this depends on assumptions, on assumptions, on assumptions stacked on top of each other. You know, how much operational surprise do they get? How resilient is Taiwan’s communication system when certain nodes get whacked? What countries allow the US military access basing and overflight? And on and on and on. But both sides are presumably studying this with an incredible degree of granular detail, and they’re plugging it into simulations, and they’re running those simulations. They’re all probabilistic, and they’re running the simulations zillions of times and then seeing what percentage of the time it comes out with a win and with a loss. Obviously a real war, way more complex. These are not crystal balls, but just they’re indicative. And what the US and its partners are trying to do is multiply the sources of catastrophic risk that this kind of operation would face. And it’s even if Beijing has an assessment, it is something-something percent, they’re gonna have confidence intervals on that number. So it’s partly about shaping what that number is and partly about shaping their confidence intervals based on classified capabilities that you may or may not have. But that is obviously not the whole story. There is a whole other basket of activities that China’s doing to Taiwan short of kinetic attack that they hope will get Taiwan to submit without fighting. And I think obviously China would prefer to get Taiwan for free than to go through all of this cost and challenge and risk in an operation that could not possibly have 100% chance of success.

Matt 24:15

Where are the conflicts in Ukraine and the Middle East instructive for how to think about deterrence in Taiwan, in the Taiwan Strait, and where are they not instructive?

Eyck 24:26

Oh, that’s an interesting question. So I think the danger of over-indexing on the Ukraine case is that we assume that Xi Jinping’s personality is like Vladimir Putin’s. It’s not. It’s totally different, and his political system works differently, and that it’s a binary between whether he invades or whether he doesn’t. Let’s talk about personalities. Vladimir Putin is a risk-taker. He’s a gambler, and he’s invaded countries before. He invaded Georgia in 2008, and he got away with it. We let him get away with it. And then he took Crimea and started what was essentially a low-level war in Eastern Ukraine in 2014 that dragged on for years and years. And we also basically let him get away with it. We put sanctions on, but we did not resist what he was doing from a military point of view. So he did learn from those two experiences that he would probably not face serious military costs directly from NATO if he moved again against Ukraine. And he also revealed that that’s part of his pattern of behavior, so it should not have come as that much of a surprise when he went for the whole thing in 2022. Xi Jinping has a different personality. His whole modus operandi is different. He is more of a risk-taker when it comes to taking out threats to his rule at home, taking out rivals. But that’s because the stakes if he gets that wrong are existential for him. When it comes to moving in foreign policy, he tends to act in a much more gradual and systematic way, trying to change facts on the ground over time. Look at how he handled the South China Sea. Look at how he handled Hong Kong. In fact, he probably believes that he was extremely restrained letting the Hong Kong protests develop as they did over more than five years before he finally decided to put the kibosh on it in 2019. So everything we know about Xi Jinping’s mode of operating suggests that he would prefer a slowly tightening noose than some blitzkrieg over Taiwan. There’s also a difference between land war and maritime war, which is pretty significant. And maybe this is a separate question, but clearly drones matter a lot. Unmanned systems are changing the battlefield in Ukraine, and there will be significant read-throughs to the Indo-Pacific. But it’s just different when you’re operating across an enormous body of water, and the battle space is hundreds of times as big, and you’re routinely using either carrier-based aviation or very long-range precision missiles to deliver mass to target. Command and control, your ability to see and understand a battle space that is hundreds of thousands of square miles of size, is an entirely different technical problem than the one Ukraine and Russia are facing in their land war today. Relatively speaking, C4ISR matters much more, and the lesson from historical maritime war seems to be because of that. If you can see your enemy and deliver an effective first shot, that can be it. So maritime wars, once they start, tend to have much more decisive conclusions that come very quickly. And you can tell a story either way about what that means. But it does suggest that a lot of the things that we believe we have to build in Ukraine to protect the rest of Eastern Europe may not be exactly the things we have to build in the Pacific.

Maggie 28:34

Yeah, I think you were reading my mind for the next question, which is one of the major themes, of course, coming out of the conflict in Ukraine and the Middle East is the importance of commercial space like Starlink, the importance of drones, the importance of AI for targeting, the importance of electronic warfare, et cetera. What role will some of these emerging technologies play in a future conflict, potentially in the Indo-Pacific between China, Taiwan, and other allies?

Eyck 29:03

Well, immensely important role. Let’s start with the invasion scenario. So Taiwan has to... Let’s assume that Taiwan is being invaded, they’re being bombarded. They have to sustain the resilience of their command and control, so they actually know what is happening and they can coordinate their forces, and then they have to identify which are the most dangerous approaching craft, whether in the air or on the surface, and then they have to take them out with an economy of resources because Taiwan during an invasion is gonna be very hard to resupply. Ukraine has depth, they have geographic depth, and they have a land border with NATO, and Taiwan is something like one seventeenth the size. So they will have to conserve their forces and use their missiles, unmanned systems, and other fires in a much more disciplined way. So that means in a context where Taiwan doesn’t have access to Starlink, the technologies that will enable Taiwan’s armed forces to have truly resilient C4ISR are fundamental. There’s a number of reasons why Taiwan doesn’t have Starlink. One is that they don’t trust Elon, and they probably have good reasons not to trust him, given Elon’s recent comments in China. But the other thing is, unmanned systems where unmanned surface vessels are probably gonna matter relatively more. There is a maritime fight in the Black Sea. The Ukrainians have been very effective at it. But if you assume that China will generally, in invasion, try to take most of the men across the strait on slow-moving ferries, the question is how you can create risks and complications for those ferries at a relatively low cost. And an indigenized supply chain to make USVs of various sizes is, I think, 100% essential, mission-critical for Taiwan. They’re doing that. They’re ramping, and if Taiwan can do it right, they could potentially become a provider of those capabilities to others in the region. And I could go on all day about this. I think these are two of the big ones. The third is air defense. Taiwan is close enough to China’s mainland bases that they would be saturated with missile and drone strikes, just enormous strike packages against their critical infrastructure, against military targets, possibly against civilian targets. And force conservation is gonna be an incredibly important principle in absorbing that, those opening barrages. Taiwan cannot afford to expend its precious air defenses on cheap, low-value drones. And having an integrated air and missile defense system is non-negotiable for them. Probably exploring some creative counter-UAS capabilities as well. The good news is that Japan needs exactly the same thing, South Korea needs exactly the same thing, the Philippines needs exactly the same thing. So we have a basis, if we can get our act together, to create one great big allied market and create an overwhelmingly powerful demand signal for whatever producer anywhere in the world can generate these capabilities. And I’d like to see us move in that direction.

Matt 33:05

Yeah, are we moving in that direction, or what are the hindrances to having a allied demand signal for these capabilities across the Indo-Pacific?

Eyck 33:18

Well, I think we’re not yet at a point in the American debate where we’ve accepted that we’re just not gonna make most of this stuff ourselves. I mean, it seems obvious to me, and I argued in my previous book, The Arsenal of Democracy, that literally the only way that we can get the capabilities we would need to fight and win a war with China is gonna involve buying at least components, if not finished stuff, from Japan, Taiwan, and South Korea, ‘cause we can’t make anything in this country. And if we make anything, it comes in late and over budget and often poor quality. And with no offense to the many exciting defense techs that are popping up everywhere, including at Stanford, I mean, there was a wonderful Wall Street Journal piece the other day about, I think it was in the Journal, about trying to make the board game Monopoly in the United States, and the conclusion was it cost twice as much, the pieces were crap, and they had to buy the dice from China. So if you can’t make a Monopoly set, why would we think that we can become a globally competitive drone power? We’re just not going to be. But if that is true, then the implications for our strategic posture are enormous because it means you are gonna become dependent on someone else for a capability that is life and death in fighting any future war. So which of our partners are we willing to become that dependent on? And our partners are gonna ask the same question. So I think there’s an overwhelmingly strong case for involving the Japanese. But do we want the Taiwanese to play a role in this as well? We already depend on them for TSMC. We haven’t spent much of this conversation talking about chips, but, like, clearly one of the reasons we care about Taiwan is that they make all the GPUs that are powering AI. So if we’re depending on them for the GPUs, why not also depend on them for the motors or the actuators or the sensors or the other ingredients of making a drone? It’s one thing to say that on a podcast. It’s another thing for the Pentagon to put in writing, “Yeah, we’re gonna become strategically dependent on Taiwan to make drones in the United States.” But the co-production thing is the only way to do it, and this is a conversation that has to include at minimum the Indo-Pacific allies plus Taiwan, but ideally also the British, whichever European militaries are interested in being involved, because they’re struggling to make drones too, the Ukrainians, who have their own supply chain challenges, who would struggle to do bilateral cooperation with Taiwan but are totally happy to access our capital markets, and frankly the Canadians too, because if the club is that big, maybe the Canadians will come to the table. So the approach should be you get all of the allies together and you say, “We want to become an integrated block to make drones so we don’t depend on China for the drone supply chain. Who are we comfortable including in the club? Do we not wanna become more than 70, 80% dependent on any one place? Okay, fine.” What follows from that in terms of setting specifications to make parts interoperable across different kinds of unmanned systems? Who’s gonna be allowed to buy from whom? How are the co-production supply chains going to work? It just seems obvious to do the economy of scale, but actually accomplishing that is a diplomatic lift, which is easier said than done. And that is why it hasn’t happened yet.

Maggie 36:59

Can you tell us what is the current state of the Chinese military’s investment in some of these emerging commercial technologies, and how do we, you know, the US and Taiwan stack up?

Eyck 37:10

Well, that’s a broad question. So China is investing in a whole array of technologies. Now, how good those actually are is another question. Now, I like to say the CCP loves things that it can count, and it hates things that it can’t count. And the way that their system works is they’ll have a meeting and they’ll decide we’re gonna make X many missiles, Y many drones, Z many submarines, and then they go out and do that. In the same way that every year they have a meeting and they say our GDP is going to be 5%, and if their GDP is actually only on track to be 2%, they dig holes and fill them up again or build bridges to nowhere. That’s how their system works. These numbers are inputs, they’re not outputs. So there is this lingering question about quality that applies to everything that they do. And quality is harder to measure, and this is a source of insecurity for them.

Maggie 38:16

I mean, we hear stories, right, of missile silos filled with water instead of gasoline, right?

Eyck 38:21

Well, if you wanna think about, like, what levers we can pull to make Xi Jinping think twice about the assuredness of his military forces. Like, you’re not gonna make him doubt that he has X many drones, Y many missiles, Z many submarines, and you’re not going to make X plus one, Y plus one, Z plus one because they’re the Chinese Communist Party. So if you can’t beat them in quantity, you must beat them in quality, and you must inject uncertainty into his thinking about the extent of that qualitative gap, that you must show him that you lead technologically, and you must make him insecure about what that lead means, and so he doesn’t know how much quantitative overmatch he needs to compensate. That is, by the way, to put it in a different way, what we did in the last 15 years of the Cold War that beat the Soviet Union, is that starting in the second half of the ‘70s, heading into the ‘80s with a bunch of visionaries, Harold Brown and others, we decided that we would bet on going to space for a lot of our ISR, and this was not a sure bet at the time. It was a moonshot. GPS was almost canceled multiple times. And this is a whole saga which I talk about in a previous book. But the fact that we did it, and then we displayed those capabilities to the Soviets by doing deterrence patrols that shocked them, that we could just go dark and pop up someplace else, made them unsure about what it would actually take to catch up and conclude the only possible approach was arms control to rein in our capabilities. We need to do something similar for China. And arguably, if you want to get China to the table in AI arms control too, we have to do something like that. But in cyber, in other domains where quality is hard to measure, it’s very important to signal our lead when we have it. And we need to invest in the sources of our technological strength because they are marshaling a lot of resources to catch up.

Matt 40:41

One major stakeholder in all this we haven’t talked about much is NATO and Europe. Obviously that’s a frequent topic of discussion and debate today. What role do you see NATO playing, especially now in deterrence in the Indo-Pacific, not just militarily, but kind of across the different pillars you talk about in the book, and especially economically? And what’s your outlook on their role in all this?

Eyck 41:11

NATO remains fundamental because there is one continental scale land war that is happening in the world right now, and it’s on NATO’s eastern border. And if we don’t win it or settle it on favorable terms, it will spread. There is no doubt in my mind about that. Putin will not stop. Therefore, NATO needs to remain active. NATO members need to pay their way. More important than spending the money, they have to spend the money on the right stuff. They have to move towards more efficient co-production of the critical stuff that’s needed. They have to have a better pipeline to get the learnings from Ukraine and integrate them into European militaries. I mean, who among us really believes that if, God forbid, Putin were to roll tanks across the border, that NATO members would fight as effectively with drone warfare as the Ukrainians? I’m not convinced. Who among us believes that if North Korea were to try this, try out some of these tactics, techniques, and procedures on the South Koreans, that the South Koreans would be prepared to fight as effectively as the Ukrainians? I don’t know. So NATO has a role to play in bringing these learnings that Ukraine is acquiring and paying for in blood, to the rest of the coalition. I think NATO also has this globally stabilizing role, even if NATO allies would not be directly involved in the conflict in the Pacific, because China doesn’t want to initiate a chain of events that breaks their relation with Europe. But for reasons of geography, for reasons of politics, for reasons of economy, the European Union is not really much of a player in the Taiwan issue, and they are not part of the core coalition that would have to go toe-to-toe with China if it came down to it in the Pacific. So we should not delude ourselves, and we should also not pretend that you can just copy and paste the NATO model to the Pacific. We should be wiring together our Asian allies more effectively. I hope that’s come through in the conversation. I really think we should, and I think we can, but the way to do it is not, like, to build a cathedral and then try to wire it. You build the wiring first, and then if you want to later, you put some masonry and stained glass windows in. And so the interesting thing to me is what the US, Japan, and Australia, for example, are doing on a trilateral basis to conduct joint fires, and we’re demonstrating that in exercises like Balikatan. Bringing the UK in through the AUKUS pact, which is not just about subs, but it’s about hypersonics and all these emerging technologies. How do we fold Japan in or get Japan to a place where they can join that pact? And then because this is not an all or nothing proposition, how do we wire our respective systems so we can collaborate with the Indians where convenient, even if it’s just for ISR? How do we wire ourselves up to talk to the Vietnamese? Obviously, we will not integrate with Taiwan’s military the way we do with Australia’s, but how far can we go? Rethinking this as a sliding scale. A sliding scale or as a ladder that you can build, climb step by step, I think that kind of federated architecture is much more attractive to everyone, including us, than a NATO model, which has benefits but is also kinda cumbersome, right? Because NATO has to move by consensus. So NATO is wonderful. We gotta protect it, we have to preserve it, and in the Pacific we need to build something a little different.

Matt 45:11

Yeah. One other topic I wanna make sure we went into a little bit more depth about was AI. I mean, you have an entire section of your book where you talk about the role of AI in strategic deterrence specifically. And obviously the US and China both depend on Taiwan for the most advanced chips. I think most of our listeners know about that at this point, so we don’t need to talk about that in a ton of depth specifically. And I think you wrote that 99% of NVIDIA’s most advanced GPUs are made in Taiwan. But this particular issue strikes me as one of the most confusing parts, one of the most confusing dynamics of the Taiwan scenario, just given that the government’s understanding of the role of AI is changing really fast and the models themselves are continuing to improve, like, super rapidly. So kind of across the board, and it’s a broad question, but if you were briefing US policymakers, what framework would you use to explain what role AI is gonna play in all this, especially as it’s just evolving so fast?

Eyck 46:13

Okay. That’s a nice way to end. I wanna make three points. First, I wanna talk about strategic deterrence and how it’s become fuzzy in a very scary way. Next, I wanna talk about hybrid war and the gray zone. And then third, I wanna use those two things to talk about AI. The strategic deterrence used to be simple. If I have nukes and you have nukes, I can nuke you, bad for you. But if you can absorb that and nuke me back, it’s bad for me. And if both of us have a secure second strike, it’s in neither of our interests to move first. So I just explained that in 15 seconds. Tom Schelling won a Nobel Prize for explaining that and other things, and building a game theoretic architecture around it, and that was nuclear strategy basically for most of the Cold War. Now, that doesn’t end nuclear competition. We’re, you know, competing with the Soviets over intermediate range missiles and air defense, ballistic missile defense all through the end of the Cold War. But basically, in a land where strategic deterrence is nuclear deterrence and second strike capabilities exist, it’s simple. We don’t live in that world anymore because our nuclear command and control systems are now multimodal. They’re based on space-based architectures that can be attacked through all kinds of means, computer systems which can be attacked using all kinds of means, and the number of tools that you can potentially use to go after an adversary’s critical infrastructure, including chem, bio, and other things, are becoming easier to access. And as cyber enables all sides, including non-state actors, to reach into others’ critical infrastructure and tinker, it raises real questions of what strategic deterrence even means in the 21st century. And that’s a very scary question, and it brings us back to some debates in the Cold War about what tactical nuclear weapons might do to strategic stability, like whether it might be tempting to use a tactical nuclear weapon that could take out just a building or a city block. Maybe it won’t be such a taboo if it can be targeted in that way. Second point, hybrid war. We are not at war with China and Russia, but we’re not at peace with them either. Hybrid war is relentless. It’s happening globally. It basically is a euphemism for acts of terrorism, and it’s happening all over the place. China cutting undersea cables in the Baltic, cutting undersea cables connecting to Taiwan, using lasers to jam satellites. Russia sending explosive packages to major German cargo airports. I mean, I could go on. This is ramping up in a number of domains simultaneously, and it’s really scary because what it is really targeting are the sinews of commerce in the commons, particularly the sea lanes through which the energy and people and supplies on which our economy depends for its lifeblood depend. And we see an example of this in Hormuz. Hormuz isn’t fully open, and it isn’t fully closed. And what do you do with that if you’re no longer in a world of binary open and closed? This is how China plans to move against Taiwan, through an all-around strangulation campaign that targets the shipping and eventually aims not to shut it down but to take it over, to dictate the terms on which it can flow. We do not yet have a doctrine for how we deal with that, and the proof that we don’t have a doctrine is that the bad guys’ use of it is going parabolic. And if it continues to go parabolic, eventually basic functions of the global economy will stop working. So this is not a peripheral question. This matters just as much as deterring an invasion. Now, how does AI play in? AI just turbocharges that problem because it offers all of these ways to make trouble for other countries in ways that are actually really damaging or socially destabilizing, but hard to attribute and below the level of kinetic, actually kinetic attack. You can potentially deliver strategic effects with AI. An agent that goes rogue, “Oh, so sorry it escaped from a lab and took out your power grid. Sorry about that.” I mean, it introduces so many hundreds of scary scenarios at the same time, and we should think of it as an arms control problem from that point of view. But I think we also have to be realistic that the Chinese and the Russians think they’re winning in this fight and won’t change their minds unless they think we have big-time qualitative overmatch. So I think AI is very scary for this reason, because it makes this definition of strategic deterrence fuzzier because it intensifies the hybrid war. I don’t see a clear solution other than pacing the frontier.

Maggie 51:54

Eyck, one last question to close us out. What is the thing that you most see startups and other American technology firms and policymakers most misunderstand or get wrong about the situation with Taiwan?

Eyck 52:07

That we can deter Xi Jinping, that this is not a lost cause. How do I know? Because we’ve deterred him so far. The guy’s been in power for, like, 14 years. Every single day he has woken up and said, “Not today.” So if the question is, can you deter him from pressing the button? This is the big one. When the missiles fly and we’re gonna whack American bases, and we’re going to blockade the island and send the global economy into free fall, I don’t think he’s gonna press that button. Not because he wouldn’t under the right circumstances, but because the United States and Taiwan and our other friends and partners are stepping up and continue to step up so that he doesn’t see a clear pathway to success. So if that’s your definition of victory, I think we are succeeding and we can continue to succeed. What people don’t fully understand is that that is not the whole story. This hybrid gray zone question of the boundary between peace and war getting blurred and pushed, we lack a doctrine for this, and we lack an understanding of the kinds of effects we can deliver in response that will be equal and opposite and proportionate, that will make our adversaries think twice. Some of these may not be kinetic military in nature. Some of them may be economic tools. A lot of them may be cyber-related. Some of the most valuable in China’s case may be information war capabilities. But until we have a doctrine and then a set of capabilities that we can deploy in service of that doctrine, this feeling that the international architecture that holds the commons up is breaking down, that’s gonna accelerate. Our supply chains are gonna get less secure. Our societies are gonna get less resilient. And this is ultimately something that the Pentagon has to lead on by clarifying that this is a priority. But if and when they do, and I hope they do, we are gonna need industry to step up, with ideas, with innovations, with products that can be made available to the allies to respond.

Maggie 54:33

Well, thank you so much for coming on the show. I think our listeners probably learned a lot about the current state of Taiwan deterrence, and all things China. So thank you so much for your time.

Eyck 54:43

Thanks for having me on. This was great.

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Transcript

Maggie 00:36

On this episode of the Mission Matters podcast, we sit down with Jesse Kallman, the co-founder and CEO of Danti AI. Danti is building the future of AI-enabled intelligence analysis. Its platform runs a team of AI intelligence analysts to answer questions by integrating and parsing through diverse data sources, including open source intelligence like social media data, geospatial intelligence like satellite and drone imagery data, sensor data, and more. Their product is used by customers across the US government, including the Space Force and National Geospatial Agency. Before founding Danti, Jesse had a long career in the defense industry across both startups and defense primes, including Northrop Grumman, Booz Allen Hamilton, Airbus, Xwing, and more. Jesse, thank you so much for joining us on the Mission Matters podcast.

Jesse 01:27

Thanks for having me.

Maggie 01:28

So first question, a little bit of a softball. You all of course have .ai in your URL, which is very hot these days with startups. Everywhere you look, people are talking about AI, AI agents, both inside and outside of the US government. So I wanna ask, what does AI agents for defense mean to you, and in the actual context of Danti, what all are you actually doing with AI?

Jesse 01:56

Yeah, it’s a great question. We get asked about this so often. Sometimes people even laugh when they hear the phrase AI agent because it can mean so many different things to so many different people. And so from our perspective and how I think about AI agents, it’s essentially tools that can execute tasks that are sometimes quite complex, in a sequence. And so they understand how to run the sequence. They understand the steps of the sequence. They understand, based on key phrases from a person, what they’re trying to accomplish — how to actually go and do that work. And so how Danti thinks about that in the context of intelligence analysis, or just supporting war fighters overseas that just need key information, we like to bring a variety of different tradecraft and skill sets into these AI agents that understand, based on a person’s question: is this a maritime question? Are they trying to solve for a natural disaster? Is this a border issue? Or is this just a simple data request that can be solved from a summarization of 50 tweets, two news articles, and a coincidental image? And so that’s how we think about agents — tools that can go, once tasked, and execute multimodal or multidisciplinary tasks for the user.

Maggie 03:10

So perhaps by design, I think your average person is not super familiar with what an intelligence analyst in the intelligence community or in the US military actually does. So can you paint a picture? What does an intelligence analyst job look like pre-Danti, and then where does Danti fit into their workflow?

Jesse 03:30

Sure. So a lot of our team comes from the defense and intelligence community, and they’ve worked in many different agencies across many different disciplines. I myself have seen a variety of different disciplines, but I think it boils down to a few key things. It comes from: someone somewhere needs information for some specific purpose, and something like a request for information, or an RFI, will come in to an organization — says, “I’m looking for X,” or you’re providing a briefing to Y. And typically that analyst will, one, take that request, and based on their prior background and knowledge and their level of experience, will know: to solve this and answer this question, I have to execute these steps. I’ll typically go across a variety of different systems. So let’s say in the case of a GEOINT analyst — they’re being asked about activities in a port overseas. What they’ll typically do is, that port gives them a key buzzword, where they’ll know: okay, we have things like port boundary information. What is the actual boundary of that port? What’s encapsulated there? What other data sets? Do we have any visual imagery data sets? Are we talking unclassified, classified? Say, at the unclassified level, what data repositories do I have access to? Do I have the logins that I know, based on my background, I can now go and pull information? The next thing is more of that temporal context. When are we talking about? Are we talking yesterday, two months ago, three months ago? Is this a pattern thing, or a series over time? And they have to start to build that temporal context as well. So now they’ve got the spatial, they’ve got the temporal, and then they start sourcing. So let me pull together the key information that I have across that spatial-temporal context, and this can take days in many cases. And once they’ve pulled all that together, then they can begin the analysis work. So what’s actually happening here? What am I observing? What’s happening in the surrounding areas? And can I determine what it is this person is ultimately asking for? And if I can, I then bring that all together with supporting evidence into a report that’s then delivered. And so that’s one example of how we’ve observed it over the last many years.

David 05:38

Awesome. Well, maybe taking it from the abstract level — SpaceWorks just named you a success story, I think last month. So maybe go into a little bit of detail about what you’ve been doing with Space Force and various combatant commands, and how they’re leveraging Danti today.

Jesse 05:55

Yeah, great. We were happy to see that story come out on some of the great work we’ve been doing, initially across the Space Force’s TAC-SRT program. It’s since been renamed to, I believe, BATS. But—

David 06:08

And what does TAC-SRT stand for?

Jesse 06:10

It’s Tactical Surveillance Reconnaissance, I believe. I forget the exact terminology. But yeah, it’s supposedly for responding quickly to combat and command needs for commercial data that’s publicly available and publicly shareable with allies and partners. And some of the key use cases there are things like supporting global natural disasters, maritime activities, border conflict issues. But anywhere the US really needs to be able to provide this information at a fully unclassified level to its allies and partners in the region, so that the US can provide that information to support whatever needs there might be. And so that’s been the primary focus of that program. I think it’s been pretty revolutionary, being able to bring in so many different commercial vendors — whether they be imagery providers, analytics providers, analysis services companies — to, again, take in-turn requests that could take a week before, to maybe a couple days. And where Danti’s been supported is: now, how do we take that request and potentially automate it all the way, initially? Or how do you understand the data landscape before you go and execute those tasks? And maybe to give you a specific example, since this is recent, with US SOUTHCOM. So the earthquakes in Venezuela — that was something that Space Force supported pretty heavily. One, in providing a lot of information very quickly to local NGOs and other governments in the region. Obviously not able to provide classified information, but a lot of their requests are: what are the status of the ports? Are there slips to bring aid in via ship? What’s the status of the airport? Are the runways destroyed? What sort of hospital infrastructure — what does that look like? And so what Danti provided in that case is, an analyst at US SOUTHCOM, TAC-SRT, the Joint Commercial Operations Cell, can just come in, in natural language, type: “What are the status of the top three ports in Venezuela in the last forty-eight hours?” And the system, in a matter of seconds, will synthesize thousands of news articles, tweets, information across the Internet. It’ll cross-correlate what it finds when it determines where those ports are. I mentioned before pulling that information on the port boundaries, so understanding where the ports are. It’ll look at broadcasting ships via AIS. It’ll pull coincident imagery, so you get a visual understanding. And then it provides a simple synthesized summary that someone, in a couple clicks, can put into a briefing or PowerPoint slides and send as an email to an NGO. And so that’s what Danti provides — that ability to really automate across those different government and commercial systems and get the user directly to that end report they need.

David 08:51

Maybe just tell me — but for Danti, in the case of this Venezuelan earthquake and being able to pull in a lot of information quickly, how would this otherwise go down? What are the analysts using today without Danti, or yesterday without Danti?

Jesse 09:11

Yeah, I was actually there in the ops room at SOUTHCOM for some of this as well, and it was interesting to see the ops center, where you have a bunch of different teams — mostly staff from different parts of SOUTHCOM, from mission partners, other agencies — and they were fielding requests either by phone call, WhatsApp message, whatever the form, and they’re bringing that into the ops center. The ops center is typically, again, very manually going by and saying, “Okay, based on this request, who has this information? Who can get on the phone with another partner agency and bring it together?” And it was being pieced together by different commercial companies, contractors. They have a common operating picture up in the ops floor where they’re integrating different datasets to see if they could do it there. And so that’s how it’s been done. Where Danti provided value was, we showed up with a lot of automated integrations to a lot of the systems they were pulling from already, in a lot of the modalities: air traffic data, maritime data, imagery, open-source intelligence. We would simultaneously take those RFIs coming in, also type them in to see if the system could actually just spit the answer out completely automated — completely alleviating all those people having to waste time calling across their different networks to piece the information together.

David 10:29

So, Jesse, maybe building off of the work that you’ve been doing with SpaceWorks, talk to us a little bit about some of the international cooperation and working with some of the combat commanders. It seems as though international partners are starting to leverage a lot more of the unclassified data that’s coming off of a lot of different capabilities, whether it’s commercial GEOINT providers or otherwise. And just how do you see the international market shaping up?

Jesse 10:58

Yeah, I think that’s a great thing, and it’s probably one of the biggest growing areas we see in general right now. And maybe a more US-centric example would be — we actually did a very rapid deployment with USSOCOM, with NATO, during an exercise called Trojan Footprint this past summer, where we got contacted one morning and there was a need for equipping a lot of allied forces in Europe with information tools, but there was nothing that they could do quickly. And within two or three hours, we had a deployment available for SOCOM that allowed them, plus a number of different NATO countries — I believe it was about eight or nine — and all of their special operations forces to connect unclassified information from open source, new social, imagery, a variety of different things. And now any SOF operator in any language could come into one terminal and ask a question and get provided the entire coalition’s information in seconds. And we saw anyone from Polish and Hungarian to French forces just get dropped into our software and start running things like GEOINT, SIGINT, OSINT workflows all together. And I think, more broadly in the international market, this is a function of — most countries do not have the infrastructure the US has. They don’t have tens of thousands of highly skilled analysts sitting in offices waiting to support and answer questions and fuse information. A lot of these foreign partners know the information exists. They want control. And I think what we’re hearing from a lot of the space companies — a lot of even companies like Palantir have coined this data sovereignty thing around the world — is that all of these international organizations now, they want their own data capabilities. They want their own satellites. They want their own feeds to the key information they need, but they just don’t have the massive trained analyst force to do it. And I think that’s a great opportunity for companies like Danti, where we’re already providing a lot of that encoding of when is it the right time to use a synthetic aperture radar satellite versus a bunch of tweets, versus the beacon off a ship. And so how does someone like the Korean government or the Philippines government better leverage all of this information with an analyst — doesn’t really matter how well trained, or their background, whatsoever. They’re able to control their data environment and control their own information that’s getting out to key users.

David 13:20

That’s awesome. I guess a lot of what you’re talking about, with all this data aggregation and fusing, I will say, seems a bit reminiscent of Palantir, and I do feel like they’re kind of an 800-pound gorilla across the software landscape within the department. How do you view Palantir, and where does Danti fit into the tech stack or ecosystem of tools available to end users?

Jesse 13:47

Yeah, I think Palantir obviously has been around a long time, and I think they’ve done a lot of amazing work in revolutionizing how complex organizations connect silos of data and build useful applications on top of that, and coining some of these phrases like data ontologies and how do you build relationships. So I think they’ve really advanced how a lot of different applications and different folks in the community can operate. And I think where Danti fits in — we, as a company, provide capabilities, one, that help organizations do a lot of these data integrations connected in more of an automated way, where you can just tell the system what this data is. We provide a lot more in-depth, very detailed workflows for that end user, who is likely not going to use a more complex system like a Maven Smart Systems or something like that. But really, we deploy within that ecosystem. So today, we’re actually even co-deployed with Palantir in organizations like the US Army, and in that Venezuela example. We are able to feed information into those common operating pictures like a Maven Smart Systems, where those are really powerful views of the entire visual data landscape. Whereas in Danti, you’re asking an agent — essentially an analyst — to start running cross-modal workflows for you, doing a lot of fusion, doing some of that analysis, to then spit it back into those common operating pictures. So I think Palantir has laid the landscape to making a lot of those data feeds more easily integratable and common across the entire enterprise. And I think it’s paved the way for a lot of companies at the application layer like ourselves, who are doing a lot of great work, to take the last step with a lot of these organizations.

Maggie 15:40

So Jesse, from the use cases you’ve been describing, it sounds like you’re able to work with a number of different combatant commands, a number of different services on different use cases, but you’re able to use one platform for all of those. So can you talk a little bit about how Danti is flexible and able to work on each of these different tasks?

Jesse 16:01

Yeah. I think we talked a little bit about some of the bigger players in the space, but there’s a lot of organizations that don’t need that level of infrastructure rebuild, where, again, we can step into some of these mission partners who have a very simple... I mentioned maritime domain awareness, where they already have some of their own infrastructure, their own ecosystem of vendors. Danti will allow them, in a matter of hours or even a couple days, to completely connect that disparate information data environment, and then have someone of any skill level now be able to run the workflows of what were highly expert, 15-year subject matter experts. And now it alleviates those subject matter experts to focus on those really difficult tasks, like the ones that absolutely need a human spending hours researching and analyzing. We can now alleviate that, so we provide that data integration infrastructure layer for them. We provide all the workflows on top of it. We provide a lot of the encoded tradecraft so that, again, anyone from a mission partner that can’t spell, or doesn’t know what SIGINT means or OSINT or any of those things are — they’re able to run those workflows across those different tradecraft areas.

Maggie 17:13

And then expanding that further, so CENTCOM can use this, PACOM can use this, but you also have commercial customers using this. Can you tell us about some of the commercial use cases for a tool like this?

Jesse 17:24

Yeah, just like the US government has issues connecting highly disparate and very spatial, anchored-in-the-real-world datasets, so do insurance companies, so do commercial real estate companies, so do global logistics companies. And so some of the things we’ve seen are anything from, again, global logistics companies that wanna understand impacts to their facilities due to ongoing conflicts around the world — where cargo may be stuck in the seas due to conflicts outside of Iran — to even real estate investors that are trying to understand the complexities and nuances between things like foot traffic or traffic data around a location and how that relates to demographics of a local neighborhood or population changes, or even the structural information of a building, and how do you use all of that information together to help with things like real estate acquisition that today also require a team of expert brokers to pore across this information. So that’s how we think about it, where you have this anchored-in-the-real-world problem that is a huge mix of information across different disciplines, where you’re just trying to help someone use all of it interchangeably to answer whatever their key concern is.

David 18:42

So maybe, it seems as though the use cases for the military are not so dissimilar from those on the commercial side. What does the setting-up process look like for a commercial vendor versus the department? Maybe we’ll circle back to the department, and you can provide the listeners some of your trials and tribulations and what it’s been like. But just curious — how have you seen the differentiation there?

Jesse 19:11

Yeah, I would say, for the differentiation on the government side versus commercial — in the government landscape, there’s a lot of security restrictions, data access restrictions; there’s a lot of sensitivities into where that data can be shared. I would say it’s not extremely dissimilar to commercial, where a lot of enterprises are also thinking about how do we secure our data. There’s a big topic now around data sovereignty for both governments and enterprises. And so in the enterprise market, a typical startup, or a typical new start — a lot of times these organizations are just using our software out of the box. It’s cloud deployed. It’s got tons and tons of information around the world. They’re able to get comfortable with what the system can do before they start integrating it with their own infrastructure. So, being able to monitor global facilities to start, you just need to tell the system where the facilities are. We don’t need deep integrations with their own enterprise systems, but we’ve done other applications where insurance companies can directly either integrate or run automated exports of things like policy data, more sensitive commercial or customer information, into the system, because we deploy the system in nodes where every organization and every customer’s data stays secure locally in one environment. They have complete access and control on it. It doesn’t send data out to external AI models or anything like that. Everything is locally deployed and secured, and so they can be assured. But anyway, they can start with a more public and open system, but they can also have even their own on-prem deployed systems behind the firewall.

David 20:54

Very cool. And so you’ve obviously had a decent amount of success with the military contracts. What does that look like on the networking side, and getting access to government network information and data?

Jesse 21:08

Yeah, for those that listen to this that are maybe also looking at getting into deploying software at the government, it’s not an easy road. There are so many things required — from facilities clearances, authorities to operate, being able to actually have the credentials and do the deployments. We joked earlier today about even having CAC cards sometimes to be able to access certain network terminals, and sometimes the hurdle’s there. And so we’ve been around a little over three years, and we’ve been able to get through a lot of those hurdles, where great partners like the National Geospatial Intelligence Agency supported us to get our facilities clearance. So now the company itself can hold classified contracts. We can have cleared staff that can be cleared to go into buildings and access the right networks and systems. The system’s already gone through provisional ATOs, and that’s where you essentially have the government review the security infrastructure around the software and how it’s tested and its deployment, to even something that sounds as simple as getting military IDs. But a lot of these terminals require all of these things to be able to then just deploy your software directly into that government network, to inherit the controls of that network, to inherit things like user rights and permissioning. And that’s critical for an application like ours — to know when someone puts their ID card into a network terminal, the system is gonna provide them only the data that they have access to see, and they’ll only be able to leverage it in a way that keeps that information secure. So we understand why the hoops are there, but yeah, for a lot of new entrants, it’s quite difficult to navigate.

Maggie 22:46

Yeah. What’s remarkable to me — I saw genai.mil recently got stood up, but if you actually look at the models on the back end of something like that, I believe they’re models that are two or three years old. So the department is just now getting access to the ChatGPT that we all had access to three years ago. Which does bring me to another question, which is: how does something like Danti compare to a ChatGPT or Claude just straight out of the box? How much are they able to do versus what is the special sauce that you all are bringing to the table?

Jesse 23:16

Yeah, it’s interesting, ‘cause when we deploy in different environments — we’re just deploying in a different environment now with the Army, where I think in that case it’s an OpenAI model, where in some other cases it’s a very different model. Our system is architected to be able to leverage the best as these models improve. They get better at information synthesis tasks, or they get better at whatever the task might be. We can leverage that model, but there’s a lot of parts of our system that don’t require LLMs at all. Some of it is graph technology, some of it is learning graphs that are really built on Danti’s own IP that can understand and learn this tradecraft. Whereas the models that are just so broad and trained on a very broad set of data — once you start to go into these physical-world environments that have much more nuanced and complex data sets, things like signals from satellites and organizations like a HawkEye 360 — this is complicated information that is super powerful. A lot of these models just have no prior training or understanding of how that data is used and how it’s used across a variety of different tasks. And so what Danti does is more of the orchestration — based on what the user’s trying to accomplish, understanding what the tradecraft and use is there, and knowing when the right steps to introduce a model are. The other thing is, there’s very little tolerance for hallucinations or incorrect information. And so we have to provide a lot of standardization in how results are presented back to users, and citations. And so you can only introduce some of those non-deterministic models at certain parts in those sequences, and knowing also when you can’t use those models is also very important. So we provide a lot of that, so that the end user shouldn’t have to care nor think about that. They can trust that when they go into a system like Danti, they’re asking their question and they’re getting a cited-back answer every time, consistently, knowing the provenance of all the data — data that is their government’s information living on their government server completely securely — and then just leveraging the AI to speed up certain portions of that task.

Maggie 25:21

So Jesse, looking back at your career, you’ve worked at defense primes, you’ve worked at startups. From all this experience, what do you think startups are uniquely good at in the government space versus where do you think more traditional primes will still win? And maybe a secondary question: how do you think about opportunities for startups to partner with primes to accelerate the chance of bringing technology to the warfighter?

Jesse 25:45

Yeah, I think that’s a really important question, myself being a Booz Allen alumni and seeing all the great work they do across so many different agencies and their ability to have that deep customer understanding. They’re involved in them and many others. You guys mentioned some of the deployment environments. They’re supporting a lot of the build-outs of those environments as close, trusted partners of those organizations. And for a startup like us to just walk into an intelligence agency and pretend to understand the complexities and nuances of their IT environment, their procurement environment, the user infrastructure, and the applications and workstations? These defense primes have already built it. They understand it. They understand where the issues are, and they can help accelerate companies, I think, much more quickly versus a company trying to come in by themselves and do everything new. I think there’s definitely some nuances in things like shipbuilding or autonomous aircraft or things like that, where you’re building a complete end-to-end system. But sure, they do have C2 environments and others that maybe some of the primes are engaged in. But from a software perspective, it’s really, I think, important to leverage a lot of partnerships with those prime contractors, again, because they so deeply understand the customer environment.

Maggie 27:04

Yeah, absolutely. So you’ve been at this now three years. You’ve been working in the defense space longer than that. What advice do you have for startups embarking on a journey, the kind of journey that you’ve been on? Or what advice would you have for yourself if you could go back in time and talk to yourself three years ago?

Jesse 27:21

Yeah. Don’t underestimate how long things may take. There’s a lot of buzzy news stories out there about companies getting massive contracts and going huge, and I would say that that is not the standard. And I would say, maybe look into how long ago those organizations actually started. And I think my advice to folks is, definitely over-index on the amount of experience you bring into the team early on. Don’t think that it’s just simple — like a commercial industry — to go and build something and disrupt everybody, and you’re gonna grow like a Snapchat would, but inside the government domain. It’s definitely a different animal. So heavily rely on a lot of experience as soon as you can get it. Relationships are critically important, so make sure that you over-index on those things as well. And just make sure that you plan for the right time horizon, because things are complicated. Things take time. There’s things outside of your customer’s control. It’s not like in the commercial market, where your customer can just like you and buy it. There’s so many other things behind the scenes that they cannot control, and you just have to be patient and understand that there’s just frustrating things that you’re gonna encounter that are well outside of you and your own customer’s control.

Maggie 28:36

Great. Well, Jesse, with that, thank you so much for coming on the Mission Matters podcast, and we’re super excited to be investors in Danti.

David 28:42

Yeah, thanks, Jesse.

Jesse 28:43

Thanks for having me. It’s great to work with you guys.

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As the name implies, robotics foundation models are foundation models specifically designed to control robots.1 They take in sensor data (video, audio, etc), natural language instructions, robotic position data, and (optionally) task demonstration data as inputs and output direct robotic motor controls. Like large language models (LLMs), these models are designed to generalize to many different, potentially unseen, scenarios and embodiments, with minimal demonstration or instruction data required. Ideally, the same RFM could be used to control a robot arm in a factory, a quadruped robot on the battlefield, and a humanoid robot in a household, in all kinds of weather, terrain, and seasons.

Source: NVIDIA, “GR00T N1: An Open Foundation Model for Generalist Humanoid Robots”

Over the last year, companies like Pi and Skild AI have released impressive demos of RFMs capable of folding laundry, cleaning dishes, and even cooking. Rather than manually programming each robotic control task, in these demos, users simply give the robot an open ended task in natural language (ex: “cook potatoes in an air fryer”), and the learned model determines the actions the robot needs to take to complete that task. Performance improves even further if a user provides the model with a demonstration of the task or natural language step by step instructions (ex: open the air fryer, pick up the potato, put the potato in the air fryer, close the air fryer).

These models are trained on large, diverse, multimodal real world and synthetic data sets, including teleoperated robotics data, egocentric human data, YouTube videos, physics simulation data, and more, across a range of embodiments, tasks, and environments. The need for large, diverse data sets to train RFMs has spurred an entire industry of companies collecting and labeling this data for robotics labs. The hope is that the scaling laws observed in language and coding models will extend to robotics, with ever larger models trained on increasingly diverse datasets and refined through reinforcement learning in simulation environments developing emergent capabilities that generalize across tasks and environments. This hope appears to be coming true. Recent advances in in-context learning2 from Skild AI and Generalist suggest that, like language models, as pre-training datasets and hours scale, RFMs are proving capable of conducting tasks outside of pre-training distribution with just a few seconds of demonstration sensor data, the same way language models can answer questions outside of pre-training distribution with just a few natural language sentences of context provided by the user.

Source: Physical Intelligence, “π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities”

If they can be made to work reliably, RFMs will make it meaningfully easier to deploy robotics in the real world, as they will reduce the need for labor-intensive, manual robotics programming and teleoperation and allow a single model to generalize across many different tasks, rather than relying on brittle, hand-engineered algorithms that break when conditions shift even slightly from what they were designed for.

Today, most robots are “dumb”: they are either programmed to perform predefined tasks in controlled environments or operated remotely, with machine learning confined to discrete functions such as perception. For example, the vast majority of drones used in Ukraine and the Middle East are remote controlled, with limited autonomy for tasks like position hold, terminal guidance, or waypoint navigation. Where true autonomy appears on the battlefield, it is usually born of necessity: adversary systems move too fast for human decision-making, and electronic warfare denies the communications and navigation that human control depends on.

Similarly, most industrial robots are hard-coded for narrowly defined tasks, with only basic computer vision, and break when faced with conditions they were not explicitly programmed to handle, such as changes in lighting, color, or object size. Much of the cost of deployment therefore lies not in the hardware but in programming robots to conduct specific tasks. Setting up an industrial robot for an assembly operation can take months of engineering work and cost more than the robot itself, and even minor product changes can require extensive manual reprogramming. As a result, robotic automation has largely been limited to highly controlled, predictable environments, such as low-mix high-volume factories, where they can perform narrowly defined, repetitive tasks with minimal variation at a scale that can justify the high upfront integration costs. Autonomous driving is the main exception, with companies like Waymo and Tesla relying on machine learning across the full autonomy stack, from perception through prediction and planning, to operate on uncontrolled public roads.3

RFMs aim to eliminate much of this manual programming, enabling robots to execute complex tasks from simple natural language instructions. Rather than manually programming each step, a human could simply tell a robot to “assemble this drone,” and the RFM would determine how to complete the task.

However, today, for most use cases, RFMs do not yet work out of the box. Current RFMs feel like they are at a GPT-2 level of performance: it is amazing what the models are able to do, and the demos are cool, but the models still frequently make mistakes, move slowly, and aren’t yet capable of delivering a whole lot of value. It seems we’re still a few years away from a true ChatGPT breakout moment in robotics where these models are good enough for use in everyday life.

Still, a number of companies have emerged to build on top of this new crop of models to make them useful for specific tasks, fine tuning foundation models on domain specific data and deploying them on increasingly cheap off the shelf hardware (“RFM wrappers”). While the U.S. government is not yet a customer for these RFM-powered robots, many of these companies are applying these technologies to use cases that strengthen U.S. critical industries and the industrial base, across industries including manufacturing, scientific discovery, logistics, and critical infrastructure construction and management.

This new crop of startups differentiates itself from the large general purpose robotics labs by adapting general purpose models to specific customer use cases. Rather than collecting large, diverse data sets like the large labs, these startups focus narrowly on data specific to their industry, robot embodiment, and target tasks, collected through a mix of in-house operations, third-party data vendors, and enterprise partnerships. These startups also differentiate themselves from large labs by choosing industry-specific hardware embodiments for the robots themselves and by building custom integrations with industry-specific software and hardware tools to ensure their robots can work with existing toolsets (ex: lab robotics startup Medra has built integrations to allow their robots and software agents to operate more than 75% of the instruments scientists already use in labs).

The large RFM labs are taking different approaches to this ecosystem. Some, like Pi, actively encourage startups to build on their models: Pi recently launched an API that lets companies fine-tune and host Pi models for their own domain-specific use cases. Others, like Skild AI, keep their models proprietary and own the customer relationship themselves, adapting the technology for domains such as manufacturing, food preparation, and logistics.

Manufacturing

By far the most common RFM use case relevant to national security I’ve come across is manufacturing – particularly, assembly. While factories have employed robots for years, adoption has largely been concentrated in low-mix high-volume industries such as electronics and semiconductors, automotive, plastics and chemicals, and food and beverage. These applications typically rely on expensive, highly customized robotic production lines that require significant upfront engineering and capital investment.

In contrast, robotics are seldom used for high-mix low-volume manufacturing and other tasks that require dexterous manipulation or that handle deformable materials. Due to relatively low production volumes, most aerospace & defense manufacturing is still primarily manual, as are most contract manufacturers, and manufacturers of deformable products like wire harnesses.

However, there are a host of new startups fine tuning RFMs deployed on COTS hardware for manufacturing tasks. Companies like Foundry Robotics, Matter, Nidus, Deft Robotics, Dream Machines, and Formic Robotics are fine tuning RFMs for high-mix low-volume assembly, developing robots capable of assembling everything from wire harnesses to battery packs to robotic components without requiring extensive manual re-programming for each new assembly. Thus far, most of the companies in this space have focused on assembly rather than other manufacturing processes, such as welding, grinding, or painting, likely because today’s RFMs have been trained predominantly on dexterous manipulation tasks, such as folding laundry and handling objects, which translate more naturally to assembly than to processes requiring specialized tools, precise force control, or an understanding of material properties.

Scientific Discovery

Several startups are fine tuning RFMs to automate lab work needed for scientific discovery. Like manufacturing, labs have employed robots to automate simple, repetitive work, like pipetting, for years, but have failed to automate more complex and variable tasks. Companies like Medra are building autonomous scientific research labs, leveraging RFMs to operate lab equipment and robots for customers like Genentech. There are also a handful of academic projects like LabVLA exploring how VLAs and RFMs can automate scientific labwork.

Logistics

Many startups are fine tuning RFMs to automate logistics tasks, another area critical to national security. Historically, logistics automation tasks like warehouse automation have been engineered around predictable throughput and SKU4 profiles. Fixed systems are costly to reconfigure when demand or product mix shifts, and robotic picking has often required item-specific engineering to handle new or irregular SKUs. However, companies leveraging RFMs can autonomously adapt to new SKUs without manual reprogramming and have flexibility as throughput seasonally ebbs and flows throughout a year.

Companies like Remy AI, Ultra, inLoop, Yondu, and Frontier Machines are building custom RFMs for warehouse automation tasks like picking, receiving, kitting, sorting, packing, box assembly, visual inspection, labeling, wrapping, and unpacking. Companies in China have already begun to scale deployments of RFMs for logistics – Chinese robotics company RobotEra recently deployed its VLA-controlled humanoid robots at close to a dozen logistics hubs for tasks like bar code alignment.

In the more distant future, autonomous trucking and delivery companies could leverage RFMs to improve logistics, as autonomous driving companies like Wayve, Tesla, and Waymo make progress on full end-to-end learned autonomous driving models.

Critical Infrastructure Construction and Maintenance

Several startups are fine-tuning RFMs to build and maintain critical infrastructure, strengthening systems that underpin national security. Companies like Roma are building construction-specific foundation models to power purpose-built robots for framing, pouring, and finishing on jobsites where Roma itself acts as the general contractor and developer, so that each building generates the deployment data the next one learns from. Actor is fine tuning RFMs built by Pi to operate heavy construction machinery including excavators and bull dozers.

A number of companies are focused on building construction robots for specific critical industries, including data center and energy infrastructure. Shephard Robotics, Cybernetic Physics, General Instinct, and Proprio Robotics are fine tuning RFMs specifically for data center construction and management, focusing on tasks like cable handling and inspection; equipment checks and evidence capture; cable connection and assembly; mechanical and PCB assembly; server assembly; kitting; packaging; fan insertion; switch recabling; RAM extraction; GPU cabling; disk hot-swap; and heatsink removal. In the energy space, Cosmic Robotics is also building autonomous robots focused on solar farm and data center construction leveraging RFMs and other models for tasks like robotic planning and control.

Managing Models’ Shortcomings

Despite the years of research and billions in funding, RFMs are still not fully ready for most real world deployments, and companies building in the space must find ways to manage the technology’s shortcomings. Some companies, like Cosmic Robotics, continue to rely on more classical robotics controls technologies for parts of the stack, with plans to swap out those modules when this next-generation of technology is ready. Others like inLoop employ human teleoperators who are ready to take over control of the robot if the underlying model makes a mistake, while still others have teams of humans working alongside robots, capable of completing any tasks robots cannot.

Business Models

These companies are pursuing several different business models, each with its own tradeoffs. On the one hand, some companies sell or lease robots to external customers to help those customers automate their own operations. For example, Formic is selling robots to manufacturers to build different products, but they are not manufacturing products of their own. On the other hand, many startups are vertically integrated, using the robots they build in house to operate businesses, with the hope that the robotics technology affords them a structural advantage (speed, quality, cost, etc) over competitors. For example, rather than selling robots to construction companies, Roma functions as a construction company itself, operating a team of AI agents, robots, and humans as a general contractor running the construction site. Similarly, Medra has built a scientific lab that uses robots and AI agents to conduct research, while Matter Robotics and Foundry Robotics are both operating as full stack contract manufacturers, using a mix of humans and robots to build for customers, rather than selling robots to manufacturers.

There are several benefits to this kind of vertical integration. First, companies can collect data from their own operations to fine tune and improve their foundation models. In contrast, when selling robots to a third party, many customers (particularly those in sensitive industries like A&D and medical) may be unwilling to share performance data from the robots. Second, it allows for imperfect technology. When shipping a robot to a customer, that robot needs to work — mistakes, debugging, and maintenance all impact customers’ margins and willingness to buy and use a system. In contrast, startups deploying imperfect robots in their own operations can rely on human workers to perform tasks the robots cannot yet handle, while quickly intervening to resolve edge cases and other operational challenges as they arise. Operating as a vertically integrated facility allows startups to “dogfood” their own product and perfect the technology, and can be a bridge to one day selling robotics technology to third parties when the technology is ready.

Additionally, vertically integrated operators can scale revenue more quickly, as they are selling into well-established budget lines (ex: companies have well-established budgets for contract manufacturing, but NOT for manufacturing robotics) and do not need to go through the process of selling robots to outside customers, which tends to be an extremely slow process, especially when working with large corporations,5 often requiring years of pilots, cybersecurity accreditations, and quality testing.

Further, one of the major challenges robotics companies have faced over the years is generating positive ROI for their customers. Often, the ROI calculation looks good on paper, but can fall apart when faced with a real world deployment. Consider an example: on paper, a particular manufacturing robot may be able to automate a specific part of a manufacturing process that previously required three people. Let’s say the robot costs $100K / year to operate, and the cost of human labor to complete the same task is $150K / year ($50K / person / year). At first it seems the robot can save the customer $50K / year! Perhaps it can even unlock more revenue by increasing throughput! However, when the customer actually deploys the robot, they realize there are many additional costs including the cost of:

1. Transporting the robot to the customer site

2. Integrating with existing hardware and software systems

3. Repairing the robot

4. Tending the robot (does it a require a human to hand it materials? Set it up each day? Charge it or change its batteries?)

5. Fine tuning for customer use cases

6. Customer safety and reliability testing

7. Cybersecurity certifications

8. Site preparation (floor leveling, safety cages or light curtains, power drops, network/Wi-Fi coverage, clearing fixed obstacles)

9. Process redesign

10. Insurance and liability

11. Operator training (how hard is it to use the robot? Do you need any specialists to manage the robot?)

Operating as a vertically integrated facility allows companies to sidestep the challenges of working with risk-averse, cost sensitive third party customers.

Of course, the downside to operating as a vertically integrated facility is the operational complexity that comes with running these facilities. Pure play robotics companies can focus all their resources on building excellent robots, whereas vertically integrated companies will also need to manage the day to day operations of running contract manufacturers, construction sites, laboratories, etc, which come with a whole host of human resources, regulatory, supply chain, and financial complexities.

Startups planning to sell robotics technology directly to customers rather than operating in a vertically integrated fashion can do so in several ways. First, they can sell integrated hardware-software systems directly to customers, with customers paying an upfront cost to outright purchase the robotics hardware (perhaps with a recurring fee to pay for software).

Second, they can employ a “robotics-as-a-service” (RaaS) model, in which the startup maintains ownership of the robot and charges the customer a recurring fee to use it. Owning the fleet lets the startup push software updates and swap in hardware improvements over time, much like a SaaS company that ships new features to existing customers. Some startups may choose to combine these two models, structuring the financing as a loan, in which the customer pays the cost of the product over time and ultimately owns the system outright after the loan is repaid.

Third, some startups may choose to sell only software, charging a recurring fee, which they can deploy and integrate onto hardware already owned by the customer. Many customers have already purchased robotics hardware for experimentation or other automation tasks, and may prefer to leverage the hardware they already own, rather than invest additional capital in more hardware.

Conclusion

While RFMs are not yet ready to control any robot embodiment across any task in any environment, they are showing real promise to automate tasks critical to national security. Fine-tuned on narrow, domain-specific data, deployed on domain-specific hardware, and paired with human fallbacks, today’s models are already good enough to start automating the high-mix, low-volume work that has long resisted automation: defense assembly lines, lab benches, warehouse floors, and construction sites.

Most potential customers, especially those whose only experience is with the previous generation of brittle, hand-programmed robots, face a blank-slate problem. They don’t know where these models can generate a return or how to integrate them into their operations. Startups that own the domain data, hardware integrations, and operational know-how are positioned to close that gap and capture value as the underlying models improve.

The stakes go beyond commercial returns. China already leads in deploying traditional industrial robots and dominates robotics hardware manufacturing. Beijing has made robotics a national priority backed by substantial capital and subsidies, and Chinese firms are already deploying RFM-controlled robots across logistics and manufacturing networks.6 Staying competitive will require the U.S. to sustain investment in frontier research, inside and outside universities. It will also require backing the companies that turn that research into deployed systems. In robotics, the advantage will go not to whoever builds the best demo, but to whoever deploys at scale first.

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Transcript

Maggie 00:36

On this episode of the Techquisition Edition of the Mission Matters podcast, we’re back with another emergency pod. On September 30th, just last week, Secretary Pete Hegseth signed a memo titled Project Agincourt, Pathway to the Autonomous Warfare Command. It lays out a plan to stand up a brand-new functional combatant command built around autonomous warfare. This has not happened in a very long time, so David and I wanted to break it down quickly to explain what this means, what a combatant command is in the first place, and how all of this is going to be affecting startups.

David 01:13

Certainly a way to end the fiscal year with a bang, and the name is not so subtle. The Battle of Agincourt was in 1415, where outnumbered English longbowmen with cheap, massed ranged weapons cut down the heavily armored French knights, the exquisite platform of the day. And if you’re wondering what the department thinks about a swarm of attritable drones and what it can do to a hundred-million-dollar platform, well, there’s your answer.

Maggie 01:42

If you listened to our episode on the DRPM UXS — that is the Direct Reporting Portfolio Manager for Unmanned Systems — this should feel like a continuation of a familiar theme. In fact, it is the DRPM UXS who is leading Project Agincourt. Establishing the DRPM UXS centralized how the department buys unmanned systems, but Agincourt is really about how we’re going to fight with them.

David 02:07

Exactly. But before we get to the memo, we need to level set. What exactly is a combatant command, and how is it different from a service? Who does it report to? What kind of authorities and responsibilities do they have? And when was the last time the Pentagon actually created one?

Maggie 02:24

To help explain what it will take to stand up this new combatant command, and whether we should be standing one up in the first place, we’re joined by Bill Greenwalt.

David 02:33

Bill is a friend, but he’s also a senior fellow at the American Enterprise Institute. And if you’ve read anything about acquisition reform of the last twenty-plus years, you’ve definitely read some of Bill’s work. He served as a Deputy Under Secretary of Defense for Industrial Policy, and he spent years as a professional staff member on the United States Senate Armed Services Committee under John McCain, where he shaped much of the acquisition reform legislation that we enjoy today. And he’s also worked in industry on the other side of the table. And then finally, he co-authored Competing in Time with Dan Patt, which every founder in this space should read.

Maggie 03:13

But before we bring in Bill, let’s level set with a quick primer on combatant commands.

David 03:19

All right, Maggie. Combatant commands. The simplest way I can say it: the services raise the force and the combatant commands fight with the force.

Maggie 03:29

Right. So the Army, Navy, Air Force, Marine Corps, and Space Force organize, train, and equip. They recruit people, they run budgets, they buy most of the stuff that soldiers fight with, then they hand those forces to a combatant commander who actually employs them in combat. So if we look at the war in Iran, CENTCOM Commander Admiral Brad Cooper — he’s a four-star admiral — he’s in charge of actually directing troops in theater, telling them when, where, and how to fight. But CENTCOM did not hire the soldiers, train them, or buy any of the equipment that they’re fighting with. They did not buy the aircraft carriers, ships, fighter jets, munitions, et cetera. That is the responsibility of the services, the Navy, Air Force, Army. The combatant command structure as we know it today was established by the Goldwater-Nichols Act back in the 1980s to ensure that US forces fight as a single joint force under one commander, rather than as separate services all coordinating by committee. Congress passed this act after there were a string of failures in Vietnam, Iran, Beirut, Grenada, that showed that service rivalry and divided command were actually costing lives and missions. In some of these failure cases, different services weren’t even able to communicate with each other during the conflict, which led to some serious combat failures.

David 04:52

Absolutely. I feel like you’re bringing me back to my ROTC days where we learned about military history. But staying on theme, we have 11 combatant commands today, and there’s a difference between them. We have seven that are geographic, so they own a piece of the map. You know these as AFRICOM, CENTCOM, EUCOM, USPACOM, NORTHCOM, SOUTHCOM, and SPACECOM. So note SPACECOM is not a functional command, as space is considered a geographical area starting at 100 kilometers up, otherwise known as the Kármán Line. Also, SPACECOM should not be confused with Space Force. As Maggie already stated, Space Force is the service — the organize, train, and equip. It trains and hires the guardians, and they buy the satellites. But SPACECOM is the warfighting command in charge of actually directing an operation in space. So Maggie, why don’t you tell us about the functional commands, the non-geographic combatant commands?

Maggie 05:52

Yeah. So in addition to these geographic commands that control parts of the globe, we have four functional commands which each own some mission that cuts across every geography. So we have SOCOM for special operations, STRATCOM for nuclear deterrence, TRANSCOM for actually moving things all around the world, and then CYBERCOM, which is responsible for cyber operations.

David 06:16

And so what about the chain of command? Well, it’s really flat. You have the President, the Commander-in-Chief, and then you have the Secretary, and then you have the combatant commanders. And some might ask, well, what about the Chairman of the Joint Chiefs of Staff? Well, the Chairman advises, but they are not actually in the chain of command during an operation.

Maggie 06:37

And that’s the same with the service chiefs, right? The Secretary of the Army, Secretary of the Navy, they advise, but they do not actually command as part of a chain of command. Typically, a functional command — CYBERCOM, SOCOM, et cetera — will be supporting geographic commands during an operation. So there’s only ever one commander actually in charge. Again, if we think about what’s happening in Iran, CENTCOM is the commander in charge of the whole operation, but then we have functional commands in the chain of command providing operational support. So Cyber Command supporting cyber operations, TRANSCOM supporting mobility, and so forth.

David 07:14

And it should be noted that Space Command did employ some operations in the very early days of the conflict with Iran. And so for founders, there’s some additional nuance that not all functional commands are created equal. Some have special authorities. For example, United States Special Operations Command, or SOCOM, actually operates quasi as a service. Like the services, they have their own budget line, otherwise known as Major Force Program 11, or if you’re down in Tampa, they just call it MFP 11. They also have delegated acquisition authority, so you might be familiar with SOCOM Acquisition, Technology and Logistics, so SOF AT&L. They have their own program executive offices, not so dissimilar from the services. With TRANSCOM, it’s a little bit more of a contrast, but they kind of operate like a business. Congress does not appropriate a budget line to move things around. Instead, TRANSCOM runs off of what’s called a working capital fund, or in the case of TRANSCOM, TWCF, TRANSCOM Working Capital Fund. The services, when they need to move things around, or the other combatant commanders, they actually pay into this TWCCF fund, and then that’s how TRANSCOM pays for the gas and the people and the aircraft to move parts and people around. So when EUCOM asks for an armored brigade, the Army operations and maintenance account pays TRANSCOM to move it there. And finally, for CYBERCOM, they’ve slowly but surely been earning their own budget control. They have some acquisition authority, though they primarily rely on the services for that organize, train, and equip.

Maggie 09:03

So the last time we stood up a functional command from scratch was in 1987, where both SOCOM and TRANSCOM were stood up. Notably, STRATCOM and Cyber Command were created later, but they were basically rebranded earlier organizations that already existed, so they were not really created from scratch. It’s also important and interesting to point out Congress actually established SOCOM against the Pentagon’s wishes, and gave them that additional acquisition authority against the Pentagon’s wishes.

David 09:35

All right, so hopefully now everyone can take credit for some professional military education after this synopsis of combatant commands. Let’s get in with our subject matter expert. All right, with that, it’s a huge welcome to Bill Greenwalt. Welcome to the Techquisition Edition.

Bill 09:54

Well, thanks for having me, David and Maggie. I really appreciate being on, and hopefully we can have a great conversation here.

Maggie 10:02

Yeah. So Bill, first question. What was your reaction when you read this memo? What jumped out to you?

Bill 10:10

Well, my first reaction was I thought of Ukraine. And I thought of a recent Wall Street Journal interview with the commander of Magyar’s Birds, which was about a week ago, in the Wall Street Journal. And in that interview, Major Brovdi stated that something fundamental has changed about the threat that requires institutional changes to Western militaries. And he talked about creating an ecosystem for drones, and that the Western acquisition system is currently incapable of doing that. He criticized us as being too focused on building the best drone and putting it on the shelf, versus what is in essence serial, iterative, operational prototyping deployment that I’ve been trying to make the foundation of time-based acquisition now for over a decade. So when I saw this memo, I said, this is potentially a first step to really doing that. Maybe we’re actually going to get this right. And the Pentagon seems to be channeling and maybe trying to learn the right lessons from Ukraine, that the US needs an ecosystem for autonomy that is completely different from our current, sclerotic acquisition system. So there were a lot of positive thoughts I had when I first read it.

David 11:36

So Bill, with that, maybe walk us through what the memo actually directs. Are we establishing a command now, or how does that come to be?

Bill 11:47

Not yet. They’re establishing a pathway. So this is aspirational — the aspiration to create such an entity. And so the Direct Reporting Portfolio Manager for Unmanned Systems is tasked to lead the effort and to create this framework for a four-star command. Much of this will need legislation, but it’ll also need the department to think through how it wants to do this. And so I think this is the start of the thinking of what this entity is going to look like. They wanted to possess directive manpower, budget, acquisition authorities, and create dedicated career pathways for officers and enlisted personnel. So I think they’ve got the ideas, and frankly, Congress has actually already put some of this framework out there, so it’s also reacting to that. So we can talk about that a little bit later as we go.

Maggie 12:47

Yeah, in a previous episode a couple months ago, we did discuss the DRPM, Direct Reporting Portfolio Manager for Unmanned Systems, which was really designed to centralize the coordination of acquisition of unmanned systems under the Deputy Secretary of War. So how should we think about this autonomous warfare command compared to the DRPM? And similarly, how does it compare to some of these other initiatives like JIATF-401 — that’s the Joint Interagency Task Force 401, focused on counter-drone capabilities — the DAWG, the Defense Autonomous Working Group, each of which has tens of billions of dollars requested in the President’s budget request?

Bill 13:24

Yeah. I think I’m looking at this more akin to DRPM and such being the Space Development Agency, and the creation of Space Command in 2019, and a lot of what Will Roper was trying to do from the Air Force at the time. So there was the beginning of trying to figure out how to change acquisition to take advantage of these acquisition authorities in space, and they knew they needed someone outside of the bureaucracy to go faster, and a different culture needed to be established. And I think there’s an understanding that you need to marry up acquisition authority and budget, flexible budgeting, and then most importantly, the operators who are going to drive the requirements and provide the real-time testing and feedback to really make a serial, iterative, operational prototype system actually work, and that’s time-based. I kind of look at the Joint Interagency Task Force and the DAWG — they’re the money pieces that eventually have to pull in the concept. But the important part here is the operators and the acquisition side of the house, and I think that’s what they’re trying to do here. At least I hope that’s what they’re trying to do here.

David 14:46

Yeah, I think that’s right. And the way I have also been thinking about it as a functional combatant command interacting with the geographic commands: they’re submitting their capability gaps, right? Their integrated priority lists, the requirements. They’re clearly not satisfied with what they’re getting. I think Admiral Paparo is on the record for saying, I’m not getting any of this autonomous stuff out into the field. And so instead of submitting it directly back to the services, and then the services pick and choose amongst each other what activities they’re going to work on — ideally coordinated — what I’m sensing is that this will now be the combatant command that is testing, iterating, giving direction to the services on what to buy through the DRPM, and then getting it out there to each regional combatant commander based on prioritization of needs and efficacy of capability. So I’m almost seeing it as more of a centralizing force around this critical technology area that we know is necessary based on the results from Ukraine and some of the issues we’ve been experiencing in Iran.

Bill 16:11

This is a long-standing issue and problem, working the seams of the operators and the acquisition and budget system. And from my time on the Hill, we debated a lot on how much authority — how can we solve this? Can we bring in acquisition authority to the combatant commanders? And several decades ago, we gave it to the old Joint Forces Command, and that was the idea of how can we do experimentation and prototyping and actually get capability to the combatant commanders faster? Getting rid of Joint Forces Command got rid of the authority. Obviously, SOCOM and Cyber Command have gone forward on this model. But there’s this need to how do we get the requirements that are coming out of the geographic commands and get capability faster? JCID reform is designed to do just that. In other words, to bring in and start with experimentation in the geographical commands to drive new capabilities. And I see this as one functional way to do that, but there is a lot more that can be focused on to try to solve this disconnect between the urgent needs of the geographic combatant commanders and what is actually being produced in the acquisition system, which is frankly not meeting their needs.

David 17:46

Not fast enough, not enough of it.

Bill 17:49

Yeah, exactly.

David 17:51

So maybe, slightly switching gears. I’ve had the pleasure of being at Defense Innovation Unit and helping get it stood up. I’ve been in the Pentagon taking over a program that had a $5 billion budget and zero validated requirements, and working through the bureaucracy. But I’ve never had the pleasure of being boots on the ground at what would be the designs of the next combatant command. Bill, maybe from your storied history, both inside the department and as a professional staff member — what does it take to actually stand one of these up? Is it just a memo and a go-do and a POA&M, and now you’re off to the races?

Bill 18:33

Well, it’s got to start that way, but the reality is you need money, and of course you’re going to need Congress to pay for it, whatever it is you want to do. First you’ve got to figure out what you want to do, then you’ve got to figure out how you’re going to budget for it, and Congress needs to pay for it, and give really tacit approval and buy-in through the authorization and appropriations laws. So you’re talking time right there. And then when we start talking about acquisition authority, that’s not something that has been given freely to the geographic and functional commands. There was a huge debate on SOCOM decades ago. Cyber Command started off with a $75 million authority in 2015, 2016, when I was there, tried to bootstrap it and create this. So yeah, it takes a long time. You need staffing, you need personnel. All the problems you had at DIU at that level just magnify 100x.

Maggie 19:57

You brought up this piece around acquisition authority, and I know the memo stated, quote, “This command will possess directed manpower, budget, acquisition authorities, and create dedicated career pathways.” This seems reminiscent maybe of SOCOM’s acquisition authorities. SOCOM of course has its own money and acquisition authority under 10 USC 167. Does an autonomous warfare command need this kind of authority to matter?

Bill 20:23

Absolutely. I think this is going to be critical, because the services’ processes — budget, acquisition, and requirements — are just too slow to turn the cycles that are necessary for what we’re seeing in Ukraine. I think we really need to look at how the Ukrainians are doing what they’re doing. And that’s why I said that’s the first thing that hit me here, because they need to iterate and bring out new capability based on what the warfighter is seeing in days, perhaps in hours — not in decades or years. And not buy a bunch of capability, put it on the shelf, and wait for something to happen. They have to do it now. And we have to get into that mindset. And so what that mindset means is: here’s an operator doing something, here’s the requirement that’s not working. I need to turn to my contractor who’s sitting right next to me and make sure that that contract is getting paid to do this, to then put in the next iteration incredibly quickly. That’s not a model that we’re used to. We can do it, but it’s not a model we’re used to, and I think that’s what has to happen. And they have to have acquisition authority to do that, and put a lot of contractors on essentially speed dial, and various other transactions approaches and things like that to really make this work.

Maggie 21:58

And where do you expect these resources to come from? Of course, the budget has to come from somewhere. The people and billets and HQ all have to come from somewhere. Is this the services going to have to give this up, or are other combatant commands going to have to give something up?

Bill 22:12

In a static budget, that is absolutely right. It has to come from somewhere. And we can talk about whether we’re going to have a static budget or we’re actually going to have an increase, which you can then do more things with. But still, talent, while it may be fungible, there’s a limitation on it. And so you’re going to have to figure out how to bring the right kind of talent from wherever it is, and that’s obviously going to hurt someone who enjoys having that talent working for them. And so if we had an unlimited amount of personnel resources, that’d be great, but it’s not just billets. It’s like, who are the qualified people who can actually think this way and can work with the culture here? And they are probably in the current system few and far between. And so that’s going to be a real challenge when they set this up.

David 23:08

Yeah. When we talk about budget authority — so it seems like a lot of what the DRPM Unmanned Systems is planning on leveraging is the President’s budget request that the DAWG put forward, $54.6 billion. Most of that money is in reconciliation. It doesn’t look very promising for the — what, $350 billion reconciliation package to get through, especially with the midterms barreling down. So how is this gap going to be received on the Hill? Putting your professional staff member hat on, we’re all seeing the lessons learned of Ukraine and Iran. We know we need to move here, but shoot, now we’re in a continuing resolution, so we’re stuck to the spend from the previous year’s budget, and that was at $1 billion for the DAWG, and they’re trying to get to $54 billion. Something’s got to give. So how does this get resolved between the Pentagon and the Hill?

Bill 24:14

So the first thing I will say is that at least on the authorization side, in the Senate, because of Section 917 in this year’s bill — which essentially authorizes this type of command, this four-star command, or they call it, I think, Robotics and Autonomy Command — many of the principles from Secretary Hegseth’s speech are already in this year’s NDAA, at least on the Senate side. Now, we’ll see what the House thinks about it. And that to me will be an interesting thing in Congress, in conference on this year’s bill, if they get to an NDAA. Because there have been a lot of individuals who think that maybe this is the year the NDAA stops. I’m a little more optimistic, but it might take a while to get there, let’s put it that way. So that’s on the authorization side. So maybe we’ll see limited acquisition authority. In the bill, there’s a provision for limited acquisition authority at $20 million. That’s not sufficient, but it’s a start. And so the Pentagon and the Congress can actually work together to massage this particular provision. But none of this is going to work without flexible money to move within year of execution onto things that work, and move money away from things that don’t work. And the one benefit of the large pots of money in the reconciliation process is the flexibility inherent in those pots of money. And so a lot of this is going to kick back to the appropriators, and it’s going to kick back to whether the appropriators are willing to allow such a thing to happen. And if they don’t, they will essentially be subjecting the United States to technological inferiority and put us behind. And so this is a staff issue with the appropriators, and they have to get their arms around what they want to do in this particular area. And I don’t know yet, because they have not been forthcoming on where they really want to go and how they want to actually deal with the way technology is moving ahead.

Maggie 26:50

You’ve written for years about the importance of speed and competing in time. Do you see this new four-star command as a speed play, or is this just going to add more bureaucracy?

Bill 27:01

It can go either way. It really can go either way. I do think that the rationale for doing this is because the services have failed. This is an indictment of the services. This is an indictment of the acquisition system, the requirement system, the budgeting system, that we cannot do this. We cannot succeed in this without doing something different. And so I applaud the Pentagon for doing this, but I also applaud the US Senate for considering this as well, because this is something that is really needed. We need to focus on this area. Now, without agile requirements, without agile budgeting, without agile acquisition, this will be a bureaucratic nightmare, okay? And so they need all three of those. But having a central commander to do this, who’s working with the operators in the field to essentially bring this to fruition — it has a shot. And if they think through not in long-term, five-to-ten-year major systems acquisition, but in terms of near-term, rapid, serial, iterative, operational prototypes, minimal viable product that serially improves based on warfighter and operator input, they have a chance, because that’s what time-based acquisition is all about.

Maggie 28:38

So I want to turn to a question around the pros and cons of this, and let’s start with the cons. What are going to be the critiques of this kind of approach? Why might a geographic combatant commander or a service chief or someone on the Hill hate this idea?

Bill 28:51

Because it gores a lot of oxen. The traditional acquisition system — and budget system and requirement system — is the source of many, many jobs and many important positions of power. And this is designed to go completely around it. That’s what all acquisition, budget, and requirements reform is all about: going around those institutions that all they do is suck time and do not support the warfighter. But they live because they support the individuals’ jobs and self-esteem and power structure who are in them. And we have to change it. And so you’re going to hear all sorts of antibodies. Oh, this is going to — well, we’re not testing, we’re taking on too much risk. And risk is something that just scares everybody around here. Well, risk — we’ve got to throw at least five more years on time to make sure that we de-risk the whole thing. So I think you’re going to see that. But again, I have to applaud the Pentagon for trying something, just as they did with the Space Development Agency, which I think they then created problems in its implementation. Just as they tried in DIU, which I thought was a great operation, and they’ve still done things to slow that down and haven’t implemented it the way it should be. So yeah, this could end up in an implementation purgatory, but we need to try it, and we need to move forward. But yes, there’s going to be individuals in the command saying, oh, this is just another seam I have to deal with, another bureaucracy, just give me the money and I can do it. The services obviously, because this is an indictment of their inability to do something, are not going to like this. Congress, the appropriators, may not like it because it takes away from flexibility. They would rather essentially do something in ten years if they’ve had complete control over it than do something in the year of execution that actually helps the warfighter. That’s the indictment of the system we have to deal with, and we have to change it.

David 31:09

Yeah. One thing that always does bother me, though, when we stand up new organizations is we don’t actually apply the remedy. It’s an indictment of the status quo, but we don’t get rid of the entity that isn’t getting the job done, or we don’t call them out in the memo. So even with the standing up of a new four-star command — you’re elevating and shining a light on a systemic problem, which is we cannot get mass autonomous capability out to the operational warfighter, but we’re not getting rid of the thing that was inhibiting it in the first place. And whether that is the structures of the various commands, or a specific office, I don’t know. So Bill, you’ve been inside these discussions. Is it possible to get rid of legacy organizations that are in the way, and thus requiring us to work outside the system?

Bill 32:18

Perhaps we need an organizational BRAC. But no, it’s incredibly difficult. And the whole premise — and it may be the wrong premise — behind reformers is, if you stand up a new authority, if you stand up a way to go around the system, the reformers will move to that system, and the other part will atrophy and die. The problem is, that other part does not atrophy and die. It actually sees itself as existentially in jeopardy, and therefore does whatever it can to kill the new authority and to kill the new way of doing business. And this is a leadership problem. Leadership has to essentially DOGE it, okay? And the Pentagon is not ready to essentially bring in DOGE, or something like it, and start trimming away at the fat. And the fat is actually very productive, in a sense that they can justify they’re doing something. But they’re doing something that is not achieving the effects that we need. It’s not at speed. It’s not supporting the operator. We should be looking at everything as: is this supporting the operator today and in the future? And if it doesn’t, we should probably get rid of it. But that’s something that the Secretary and Congress need to really try to address. And it’s going to be hard, because these are all about jobs, money, and current power over the system.

David 34:03

Yeah. So Bill, we are a podcast that’s geared towards helping founders navigate this very complex system, trying to make it simple for them. Personally, I think that bringing in this new joint force command, aligning it to the unmanned systems DRPM — which is all about leveraging, in a lot of cases, venture-backed technology. If I’m a founder building autonomy software or systems, does this change who my customer is? Or what are the signals I ought to be looking for so I can plug into this?

Bill 34:44

I hope it does. I hope it’ll change who your customer is. And the reason for that is — the test of this is, are the founders, the entrepreneurs, the contractors being asked to come in and work side by side with the operators? Not the acquisition folks, not the prime contractor, not the FFRDC, not the systems integrator, but the operators, to essentially achieve what they need to do. I think when that happens, we will have success. But if we’re going through three or four chains to get to the operator to find out what it is they really need from Silicon Valley, or from autonomous drone companies, or from the commercial marketplace, we are failing. And that is what we do constantly on a daily basis, because we are not getting to the core of the problem. Again, that’s what JCIDS reform was about. The services haven’t even started to do that. We need flexible money to put to those problems that the operators are driving. We are not close to doing that. We need time-based acquisition that supports that. When your guys see that yes, we’re using other transactions in a commercial way, in a time-based way to essentially buy things and iterate the same way that the commercial marketplace does, then yes, we’re having success. But until that happens, we’re just going through the motions, and frankly, we’re wasting a lot of money, and putting ourselves in jeopardy, because we think we’re doing something, and we’re really not.

David 36:37

Amazing. I agree with you. And I think also, if I’m a founder, one thing when reading some of the documents — which I would assert as the tea leaves for where the department is going — if you read the DRPM UXS, which is all about autonomous capability, which is obviously aligned with this command: in it, it calls out Defense Innovation Unit as sort of the commercial liaison to industry. So they’re the ones that are expected to be engaging with the best in breed of what commercial technology can provide. And then you recognize that the former director of DIU, as of the other day, Owen West — who was critical in standing up the drone dominance program during his time leading the department’s DOGE efforts — he’s now been elevated specifically to the DERPM-UXS CEO-type role. And if you assume that the DERPM-UXS is going to be tightly aligned with this new autonomous warfare command, it feels like DIU is in a way getting elevated to be able to support this maturation of technology. So I feel as though if you’re a founder, yeah, you need to look into the budget line items, but I would be very keen to checking out the solicitations coming out of DIU or associated with DIU.

Bill 38:16

Agreed. And even more importantly, my criticism of DIU over the decade-long history is, we need to scale that. In other words, it can’t just be we’re doing a bunch of science projects and we’re getting some capability that’s not essentially getting out there. We need to use production OTs in the way that they’re supposed to be used, at high levels, and get the acquisition people to stop doing FAR 15, designing and managing these companies like they’re Lockheed Martin, but essentially treating them like the commercial companies that they truly are. And DIU needs to lead that charge, but understanding that they’ve had to go to the services before this. In this particular function, they can go to a different pot of money, and that might actually succeed, because the services are just not willing to scale up on these things. And we haven’t seen the success of DIU because the services — again, culture is broken. This is a bigger issue as far as where should acquisition authority go. Ten years ago, we tried to decentralize to the services, thinking that the centralized bureaucracy of OSD was just keeping innovation from happening, and they had their chance, and they blew it. They absolutely blew it. The service chiefs were given responsibility. Major acquisition authority was devolved to the services, and frankly, their culture just couldn’t deal with it. We’re seeing the same thing in requirements, and this department needs to reevaluate all that. We’re probably looking at another centralization trend. But the question is whether they can get centralization right. And I look at this as another centralization movement, and it’s all because the services have failed in their ability to manage and to take advantage of new technologies and the trends that are out there. And I hope that they can actually get their act together, but I’m not confident that they will.

David 40:30

Yeah. I agree with you. I would say that the standing up of all these DRPMs is essentially putting the services in receivership mode, where they no longer control the milestone decisions around the major programs of importance. You look at what General White from the Air Force leads under his DRPM, and he’s reporting directly to the Deputy Secretary of War, General Gutelein. When you think about some Space Force and Missile Defense Agency type activities, and then the DRPM for subs — the list goes on and on, and now we’ve got this new one with unmanned systems. I think you’re spot on. Of course, I remember coming into the services, into the Air Force as an acquisition officer way back when, and every major decision program was reporting up through what was then AT&L. And then it was divested. Maybe what’s old is new again. Well, Bill —

Bill 41:35

This was Senator McCain’s vision, to break up AT&L, to decentralize, to give the services — I mean, he trusted the Navy. He trusted that they were going to do the right thing, and they didn’t. And so what we’re seeing now is the split of AT&L into R&E and A&S, essentially our principal staff officers to the new AT&L, which is the Deputy Secretary of Defense. And so Congress doesn’t need to change it. They don’t need to resurrect AT&L. They have to understand that AT&L now exists at the Deputy Secretary’s office. It’s a different world, and it’s being centralized. And it’s a very interesting new phenomenon.

David 42:16

Yeah. Agreed. Well, Bill, we’ve definitely enjoyed having you on with us. It’s always a pleasure for me to get to talk to you. I don’t get to do it nearly enough, but I do get to read your awesome work. So for anybody who loves Bill’s verbal words, you’ll love his writing even more, so go check out his work at AEI, or just type in his name, Bill Greenwalt, and it’ll come right up. But we’re starting a new tradition here at the Mission Matters Techquisition Edition podcast. We want to have a bit of a sign-off question. So you’re our first guinea pig on this, but you won’t be our last. If you were king — had king authorities as the Secretary of the department — what is the one thing that you would want to do? And Maggie and I are going to keep a list of this and continue to publish it over time and see if these are truly the needed remedies.

Bill 43:15

So last year I would’ve told you kill JCIDS, because that’s what Dan Patt and I recommended, and the Secretary did. So this year I will say kill the PPBE process. It’s in the authority of the department to do that. They can formulate the way they want to do the budget in any way they want to. Congress can reject it or whatever — that’s up to them. But the process is driving so much bad behavior in the department. It’s a 50-, 60-year legacy of centralized planning. They should move off of it. And the first thing, if I had the ability to be king for the day, would essentially be to go to the Comptroller’s office and say, it’s over. And maybe put that level up to the Deputy Secretary, I don’t know, and start over with a new budget process.

David 44:09

Well, it was Secretary McNamara who gave us what was then PPBS, now PPBE. And his other major thing that he did was oversee the war in Vietnam. That wasn’t very successful. So are you saying the two might be related?

Bill 44:28

I’ll even say that this process came from the Ford Motor Company that Mr. McNamara had run into the ground after that. So yes, no one has a process like PPBE. Nowhere in the world — maybe the North Koreans do, I don’t know. I’m not an expert in North Korean budgeting. But it doesn’t exist anywhere, for a very good reason. And we should basically adopt a different process, and adopt the flexible authorities that the other agencies in the US government have that DoD does not. And that’s the one thing that came out of the PPB commission — how many flexibilities, many of them self-imposed, that the department puts on itself. And for those that are not self-imposed, it should go to Congress and say, why does the Department of Homeland Security have this? Why does the Department of Health and Human Services have this other authority? Why does NASA have this that we don’t have? And frankly, if the department was given the authorities that already exist out there to the rest of the government, it could essentially flexibly implement not just drone dominance, but a lot of other dominance as well.

David 45:46

Okay. I think we know exactly who the right man to implement that job is.

Bill 45:51

If only I could. It would be great.

Maggie 45:54

Well, Bill, thank you so much. This was fantastic. It sounds like there’s a lot more work to be done to stand up this new combatant command. There’s probably a fight ahead of us over people and money and authority. But it sounds like if this is successful, it could really push the needle on the adoption of autonomous weapons systems for our warfighters, and make us more competitive on the geopolitical stage. So thank you so much for your time, and hopefully we can get you on another Emergency Pod someday in the future.

Bill 46:26

I’d love to, and thank you very much for having me.

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Transcript

Maggie 00:36

On this episode of the Techquisition Edition of the Mission Matters podcast, we sit down with our next guest who’s spent the last several years at the Defense Innovation Unit bridging the military and Silicon Valley. Sarah Pearson is currently the director of strategy and recently launched DIU’s latest initiative called The Bridge out of stealth. The Bridge is built to tear down the barriers that keep commercial technology from ever reaching the warfighter, things like access to classified spaces, access to military networks, and access to military test ranges. Before her current role at DIU, Sarah spent the preceding five years as DIU’s commercial engagement lead, helping the organization source companies to participate on DIU solicitations.

David 01:21

Sarah represents a new model of government leadership. She started at the United States Naval Academy, and upon graduation, spent five years as a surface warfare officer. After separating, Sarah joined the private sector, first as a senior product manager at General Electric and then at Google, working on Google Assistant, before getting the itch once more for public service. Sarah, thank you so much for joining us on the Mission Matters podcast. Welcome.

Sarah 01:47

Thanks for having me. This is exactly where I wanted to sit down and share what we’re building within DIU. So I appreciate the time.

David 01:55

It is super exciting, and I think it cannot be understated some of the boogeymen that you are tackling with this Bridge initiative. but as we were sort of prepping a little bit ahead of this, like, actual recording, you know, you made it clear that this has been in the works for some time. But before we get into the exactly what’s happening, maybe just spend a little bit of time breaking down the three initiatives in your words for our listeners.

Sarah 02:27

Happy to. I think, I know you guys have an awesome pool of listeners, and I think a lot of them are the very people that frankly I wanna reach, DIU tries to reach, right? We try to find the builders, the operators that want to disrupt the system. They look at the modern battlefield, and they see their technology there, or at least they have the vision for where and how their technology can fit, but they’re facing this numerous walls, numerous barriers to try to drive those capabilities to scale. So yeah, I’m excited to be sharing this all with you. We, as Maggie shared, came out of stealth a couple weeks ago to share this new strategic pillar for DIU. Truthfully, we feel that this is akin to the CSO. When you go back a decade ago, 11 years. DIU just celebrated its, 11th birthday. It’s Owen reminded us all maybe the week before last When you go back and you look at the origin story and what you all were trying—I say you, I’m looking at David, what our early, our early folks who are leaders were doing at DIU, and then the evolution from connecting technologists to the government With investors in the mix, how do we contract? How do we prototype? And how do we wash, rinse and repeat that over and over? That took us about a decade, right? From when we had OT authority to running, I think we’ve now ran over 600 prototypes.

David 03:58

And Sarah, just to interject really quickly. So you’re talking about the CSO, the commercial solutions opening, the primary mechanism that DIU leverages to issue solicitations around projects and programs critical to various department customers, whether it be in the Army and Marine Corps, the combatant commanders. And then just to, for the audience, OTs other transaction, that’s the legislative authority for the contracting pathway that the CSO resides under. So just wanted to get our audience up to, up to date. But yes, I agree with you that what this portends to be, the Bridge, is probably just as revolutionary as what the CSO was to, you know, contracting pathways for the department.

Sarah 04:46

Thank you for defining this. David’s my go-to acquisition SME in so many ways. I appreciate that. Look, at the end of the day, I believe DIU has been engineered and is operated to break barriers. It is to look at a problem, whether it’s a problem that resides with a mission partner or a customer, in our, in our language, in commercial speak. With a customer who thinks they can only solve that problem the way that the machine, the institution has taught them to, right? DIU shows up and says, “Actually, hold on. There’s capability that exists that doesn’t leverage the resources, government-derived resources. It actually leverages private capital, and the technology matures at a rate that we don’t see within the department. Let’s go look there first and see if there’s something that we can tap into.” I mean, that culture of thinking differently and being comfortable in measuring risk, in taking on risk, that has informed what the Bridge is. So what is the Bridge? The Bridge is a platform to systemically bring down the barriers that are inhibiting our ability to scale commercial tech inside the department. Through 600 programs that DIU has run, we have had a front row seat to exactly where and why and how prototypes stop. Whether they stop at month nine because we’ve determined now we should clear the vendor, issue an FCL, a PCL clearances for the company and the people within it Or if it comes to a pause because we determined we should accredit, do some cybersecurity accreditation on the hardware or software stack, it’s inhibiting our ability to move quickly and get commercial technology validated in the hands of a war fighter and then ultimately scaled. So that is what Bridge wants to do. There’s one key thing I want to call out here, though. There’s a way to solve those problems with Band-Aid solutions There’s a way to throw money at a problem, to artificially juice a system. That’s not what we’re doing. What Bridge is driving is, yes, reducing barriers, and we’ll get into the areas, but how do we do that with institutionalization in mind? How do I ensure there is staying power to the approaches and the solutions that we bring online to reduce these barriers?

David 07:23

So Sarah, if I’m understanding, you know, after 10 years of operating, 10 years or more of operating a myriad of different projects, 600 plus, the systemic issues that are inhibiting companies from moving from the prototype phase to scaled wide adoption across the operational enterprise, you all have deduced it’s likely access to classified information, so fixation on the facility clearance process, FCL. And then the access to networks, whether they’re classified or unclassified through the authority to operate, procedures under risk management framework. And then also then the test ranges, or how do we test and evaluate technology to ensure that it meets whatever military standards that it has to. Is that correct, those three things?

Sarah 08:21

It’s correct, but I’m gonna elaborate even more.

David 08:24

Perfect.

Sarah 08:25

About a year, year and a half ago, when we were looking at this space and we said, “Hey, if we are serious about driving change and true adoption of commercial technology, it goes far beyond a success memo. It goes far beyond us validating that the commercial tech could solve a government problem. We have to test and clear and accredit.” As we were making this list of challenges, it’s long. I mean, I think our total list was around 30, 35 things were inhibiting dual use or defense tech founders from prototyping and ultimately transitioning their capability. Then as we began to quantify some of those things, like where does that really hit the pockets and the capabilities for the war fighter? And we rallied around three central themes. Theme number one, and therefore initiative number one, is not just how do we efficiently clear companies within the innovation base. Like thing one, we need to clear more companies. The bench has to be deeper. We have to have a bias to clear companies and the key personnel there sooner in the process. The second theme within the, this first bucket is then access to classified infrastructure. I mean, we’ve watched time and time again, we sign a prototype OT contract with two, three vendors for a specific program. We’ll attach DD 254s, forms that say there’s a need to know and we’re gonna take you through the clearance process We then walk the vendors through it. We’ve been giving them services, free services to help them navigate that. that, I mean, on the fast end, I think we’ve gotten one through around six weeks from when they were given 254 to when they had a full FCL TS level, I believe for that, this vendor I’m thinking of. But then we came across the challenge of, sorry, where do I, where do I actually put your engineers? Where do I have a SCIF available for you to pop in there and work? Spoiler alert, it’s not the DIU SCIF in a Conex container in Mountain View. So where and how do I put our portfolio companies to work? so a key element of this first category is changing the model for classified infrastructure, which we’ll get into more. But how do I physically put companies to work? How do I get them in the collateral, the SEI spaces, the SAP spaces, or unclass? Okay. Not overlook that. How do I get them in a space so they can be fingers on keyboard and actually deliver the capabilities that we’re buying instead of waiting 12 months to maybe slide into somebody else’s SCIF?

Maggie 11:15

Okay. So category one is really focused on helping companies get their facilities clearance or FCL faster, helping them get access to SCIFs, really just helping accelerate the process of getting access to some of this classified infrastructure that companies need to do business with the department. Next, can you turn and tell us a little bit more about category two, which with my understanding is really focused on reforming some of the key IT security processes the department has in place, like the Risk Management Framework, or RMF, and Authority to Operate, ATO processes.

Sarah 11:48

Category two, the ATO. There’s so many headlines. Google ATO. Gemini probably has a better perspective on how the, you know, we should fix the ATO process. it’s always—there’s always been a lot of talk. Hey, does it actually render cyber resilience? What is the value to the companies that are assessing and embedding mitigations to these controls that are outlined in the RMF Risk Management Framework process? But the piece that DIU is really interested in driving forward is looking at the instructions, of where and how we do that, and then how do I weave AI into that? It’s a very, very manpower-heavy process today. Can tell you, I just staffed a whole, a brand-new AO shop within DIU. It is hard to find these people, by the way. They’re brilliant cyber folks, but a lot of their time is spent in systems, maybe one in particular, the eMass system, and there’s a lot of manual entry, and there’s just you know, it reminds me of what software looked like in the early 2000s, early twenty—you know. You’re like, “Wow, we’re still doing this. Hold on. we’ve progressed past this.” So agentifying, if I can—I don’t know if that’s a word or not. David would know better than me. But like, how do I agentify RMF one through six in a way that in some ways is more robust than the manual way? And still deliver an opportunity to an AO or whoever they designate to assess risk on behalf of the government and certify it or not When you do that right, you can handle much greater throughput than we have available today. So that’s category two.

Maggie 13:33

Okay. So it really sounds like category two is focused on finding ways to speed up and automate the ATO and RMF processes to really help companies get their cybersecurity accreditations more quickly. Next, can you tell us a little bit more about category three, working to improve the department’s test and evaluation infrastructure for startups?

Sarah 13:54

Test is, first of all, a beast that I did not fully appreciate a few years ago. I think I appreciated it, or I know I appreciated it from industry, from kind of playing, you know, different product and program roles in AI world. But then you enter the test apparatus, which is the Department of War, and this is a very different, this is a very different game. You know, DIU, when we were prototype, we are. We prototype commercial capabilities, right? Typically, these are higher TRL capabilities. Something exists. Many founders that show up on DIU’s front step say, “Oh, my thing’s, you know, in production for Bank of America. I think it can solve these problems for you guys. I see you’re, you’ve got a solicitation out here. I think it’s in the same space,” right? This is technology in production. However, for the department to really field these capabilities, there’s a whole slew of developmental and operational tests that needs to happen. In pockets at DIU, our customers, the mission partners, would bring their test teams to the table, and they would help design the test and execute it and capture the data, so we had very clear performance artifacts that support how the capability delivered. So that was happening, but not enough. We did not have that approach really scaled, first of all, one. Two, it, in those instances, relied it on the mission partner, right? Relied on the Navy to come forward with those things, to tap into their test ranges, to tap into their, you know, all of their awesome capabilities in that space. However, we knew not every mission partner can come to the table with that. So that’s—So we began to assess where does DIU need to step up in this world of commercial tech, not tech derived inside the government or, you know, anywhere else. Where does DIU need to come up and at the very beginning start designing test plans? Because we know that drone is going to need to go through DT and, developmental tests and operational tests before it can get fielded with the Army. Got it. Let’s front-load. And by the way, let’s do that as effectively and as efficiently as it’s possible. Let’s leverage all of the capabilities that we have. Are those government ranges, commercial ranges, public lands, all of it? Yes. How do I book it? How do I book it easily? Great. I’m now shaving days, weeks off of someone’s many people’s jobs inside the department. So test, really the end state of what we’re trying to do is we’re trying to bring test left, make sure it is part of prototyping when we’re prototyping commercial capabilities, because at the end of the day, we will produce more buyable products. Also don’t know that this is a word, but actually give the PEOs and PAEs something they can buy and field immediately

Maggie 16:45

Sarah, that was great. I mean, it’s clear that you guys have thought extremely deeply about where some of these bottlenecks are for adopting this next generation of technology and have thought really hard about the best way to try and solve those bottlenecks. You know, one of the questions that comes to me, it sounds like you’ve already done a lot of really hard and great work. You know, will these concepts stay within DIU, or will the outcomes of what you learn and these processes move to some of these other legacy organizations traditionally responsible for things like RMF, FCL, test, et cetera?

Sarah 17:19

It’s funny you ask that, Maggie. look, all of these solutions, all the solutions that nest within the problems we just talked about, the way we’re devising the solutions are generally a blend of four inputs. There’s only four inputs, and I’m kind of toggling these up and down based on the scenario. Input number one, commercial tech. Where is there a tool I can use to fix part of this problem? Input number two, cultural. What is the cultural barrier that I have to overcome to drive change inside the department? Mostly that’s inside the department, but there’s some scenarios where it’s outside the department. Three, where do I need to lean on policy? Some of our efforts, we’re changing policy. We have to, right? or you need to clarify policy, or do you need an exception to policy, et cetera. And then the fourth lever that I lean on here is procedural. Where is there a process? You know, process engineering, where when we look at something like a SKIF accreditation workflow, okay? A lot of that’s pretty detailed and it’s clearly prescribed what that looks like. But what about the areas that I don’t? How do I kind of tweak the procedural aspects of these barriers or bottlenecks? because those are the key four inputs for all of them, the transition pathway, it varies. At the end of the day, DIU, again, I believe, DIU is meant to break barriers, think differently, have a bias for speed, and do everything with commercial cost in the center of our minds, right? We don’t own FCLs. That’s, that’s not my metric. That is, amazing leader, Joe Tonon, the director of DCSA, love what he’s doing over there. that’s his metric to own, though. But am I able to show up as a teammate, and partner with him and his organization to help get the best technology in? Yes, and that’s what we’re doing. So in some scenarios, nope, the owner resides with who owns the mission set. In other scenarios, as I mentioned, classified infrastructure is hard, right? It’s hard for us to get our companies... Any kind of dual use defense tech company, there is a bottleneck around getting into classified spaces. as part of that, DIU’s sponsoring a dozen plus, probably more, classified WeWorks, if you will. In fact, just use an easy term that folks understand. Will we own dozens of classified WeWorks? No. Are we the sponsor today? Yes, because that’s what was needed to go prototype a new model, a model where classified infrastructure doesn’t need to sit on the government’s balance sheet. Shift it. Let somebody else carry that asset. I just wanna pay for usage. I wanna help you get the thing operational as quick as possible so a company can get in, book a SIPR terminal, sit down and do their work on Mondays every week that they’re doing for the government. Great. The way we institutionalize the, institutionalize these different solutions that we’re trying will depend on the solution set, on where the mission sits today, and frankly, what will set us up for the greatest degree of success in the long term.

David 20:34

So Sarah, this is amazing work, and I agree, you know, institutionalizing, whether it’s a process change, an instruction change to help showcase that things can be done differently. You’re saying that these things are in motion, right? ‘Cause you’re coming out of stealth. So maybe just walk us back over the last year, 18 months. Like, what are the things that DIU has put in place today, and where can companies or people that are interested get access to these, I guess, call them services, if you will?

Sarah 21:10

So I’m actually gonna take the second part of that first, kinda who can utilize, right? So big picture up and out, I always tell the team, right? We’ve got a phenomenal team, by the way. Over and over, I keep telling them, “We will or we will succeed based on people,” 100%, and I believe we have a winning team. we’re really now just getting all the right folks in the right seats. That took a few months, which I understand in defense time, that’s actually rather quick. But, and to your, to your point, your question on what services, who can benefit. So the up and out piece, like I said, I wanna show the department there’s a new way to do ATOs, and there’s a way to do it on commercial timelines and at commercial price points. And when I say commercial price points, I’m talking about the burden we place on companies, right, that are maintaining infrastructure pending an ATO, and it’s costing them millions of dollars a year. That needs to come down, so that’s what I mean when I say commercial price points. look, us showing the department a new, there’s a new approach here, and working with all of the forward-leaning, brilliant AOs in the department, and they’re watching. “Okay, this is how DIU is gonna play with the model and see what they can do and how they can change it.” That type of up and out leadership, my hope is folks emulate, frankly, the way... Not my hope. We are sowing the seeds so folks then can follow the playbook the way they have with the CSO. To emulate that is that is a massive win, and that goes back to every single person that’s worked at DIU, every mission partner that has trusted us, every company that has signed up, taken money, and delivered to give you light. So that’s phenomenal. I think that can be akin to some of the work in the RMF space, for example. Let’s do that for RMF. Let’s show people there’s a different way, and maybe we blink one year from now, two years from now, people are all doing ATOs in weeks, and we’re not sacrificing any cyber resilience in the process. That’s the up and out piece, and just a silly, you know, one example with RMF. The down and in, ‘cause I get this question a lot since it came out of South a few weeks ago. A lot of folks are landing on our website. We do now have a capture mechanism there, by the way. So you can go there. If you have a cool technology that you think can help us solve some of these problems, ‘cause I mentioned commercial tech is one of the four inputs to the solutions. If you’ve got tech in that area, hit our website, go through it. It populates obviously on our side. We diligence those, and we’ll reach out. So, thank you for that. Quick side note. But for the companies that are reaching out saying, “Hold on, I need an FCL. can you get me an FCL in weeks? I need an ATO. My ATO’s been stalled with so and so for one and a half years My answer to them so far is we are bringing these services online for DIU portfolio companies to start. That is my test pool. DIU maintains about 100, 100-ish, let’s say, active portfolio companies at any given time. Portfolio companies, by the way, to us, just companies that are actively performing on a contract. Just, talked about classified infrastructure. the vendors we’ve—Some of our, performers have gone public, that they’ve won a segment of the business for this. They’re not creating those spots just for DIU portfolio companies. No, that’s for, that’s for anybody, right? So, as those sites come online, that’s a B2B play that luckily no need for government interference on that at all. Companies can go book spaces and utilize those classified WeWorks. so that would be maybe an exception to anybody can really leverage that, pool.

David 24:53

So maybe just to switch from the facilities and access to classified spaces, let’s move to the networking side. ‘cause I’ll be honest, I polled all of my, like, sweat equity participants, and asked them of these, like, three pillars under the bridge, which one do they think would resonate the most with, you know, the emerging non-traditional companies? Yeah. And about 70%, ranked the kind of networking side of it as number one and then facilities number two, test ranges number three. But maybe they’re all just a bunch of software developers and they’re super biased. But for my sweaties out there, can you maybe highlight what are the things you have in motion going on with the networks today?

Sarah 25:42

Yeah. Isn’t that funny? I wouldn’t have anticipated that actually. Do you know... Hold on. Question to your question. Do we know which areas? Like, are they looking for more, you know, government networks and environments to drop their code in to leverage to tap into operational data sooner for validation? Like, do we know, when they talk about the networking piece, which elements of that they’re really trying to get after?

David 26:11

So for some of them it’s, “Hey, my technology can perform a function for you at the open source level.”

Sarah 26:20

Yep.

David 26:21

And because I can do that, the government is less likely to give me an ATO for fear that they’ll no longer get access to publicly available information on, like, potentially dirty networks or, non-NIPR, non-SIPR.” Others have just highlighted a real challenge in getting access to data to train their models absent of going through the RMF process, and it taking a really long time to get sponsorship and some authorizing official to approve it. And then I’d say there’s a third bucket who’ve maybe been successful in receiving an authority to operate from one entity, but then not getting the reciprocity to join a different program and getting on their network, without having to go through the whole process. So, you know, I would say at the end it’s this, there’s just a huge schism in getting commercially developed software or commercially developed models access to information and insights, or getting operators to use it, as part of their workflow.

Sarah 27:31

To me, there’s a few things. I think, maybe I ought to break this up into, The RMF piece we’ve, we’ve dwelled on for a second here on... The DOD is really clear on what needs to happen when. eMASS of all the controls, you know, the assessment of which ones apply to this specific technology, you can automate a lot of that, and we’re gonna do that. So I think that gets after at least the pieces of we don’t have enough manpower. There’s a manpower problem inside the department for the people that work within kind of the RMF framework. That’s a true thing. There’s a reason everyone’s beg, borrowing, and stealing access to AOs. Since we, DIU, brought in, a second AO. So there’s a, there’s a shortage of people, first of all, across all the levels, the ISMS, the ISOs, the SKAs, the A... Like all the way up. so fixing that process and automating it, that’s gonna help with some of the throughput problems that the department’s seeing for the people that choose to adopt these different approaches. And of course, we’re designing the contracts with the vendors that are helping us do this in a way that’s highly scalable. The reciprocity piece, my hunch, and I totally could be wrong, I’m not a cyber person, but as we drive a degree of standardization in how we assess for risk and therefore mitigate risk against the controls that apply, if we’re automating that to a really large degree, and then these eMASS records start to look a bit more similar, I actually think that’s going to drive trust across the AO community in a way that I don’t know is there today. I’m not an AO, I’m not an AO by background. but here’s an example. If I hop into eMASS and I look at an ATO package for, I look at someone’s record for a comp—you know, some, doesn’t matter, defense tech company, and you see that it was initially accredited at the Air Force. And say I’m the new AO looking at that, and maybe the ask is for the DIU AO to take on that. You look through and there’s a lot of gaps, right? There’s a lot of just differences across the two different AOs of, “Hmm, why is this empty and this empty?” And that answer doesn’t address that. What... You know. So there’s variation, and I think it’s, it’s some of that variation that leads to a degree of the lack of reciprocity. Now, to the point on networks, how do I get access to operational data sooner? Here’s something I witnessed firsthand. when I first joined—When I first left Google, I came to DIU, was leading commercial for AI. So, you know, three, four thousand companies a year met over. We curated probably a dozen we were generally curating a dozen projects a year. Our bias at that point, this is early COVID days, our bias was to do everything unclass, right? We would even take a classified use case, obfuscate it with an unclassified narrative, and our bias was to then buy unclass data, make dummy data if we needed to, but like prove the thing works without ever having to touch government data. One of the things we learned in that process, though, was by kicking the can down the road, it didn’t actually make our products any more buyable, because at the end of the day, when you go back to the DoD or IC and say, “Hey, look, I have this knowledge graph. It can do all the things for you.” One of the first things they say is, “Sorry, is it ATO’d?” Like, “Can I use this now?” And we’d say, “No, no, but we validated it unclass.” And they’d be like, “I don’t have time. That’s 18 months, 24 months. Not interested. Thank you, bye.” So part of our hypothesis and what we’ve put into practice is, no, no, we’re gonna credit the software sooner because we can do it faster. Let’s do that sooner so the company can see how their system performs in a true operational environment or with operational data at least, with government data, and now we have something to really iterate and improve upon. giving access to DIU portfolio companies to networks to be able to do that, maybe spots they inherit some controls, to condense that timeline even further or not, right? but creating those environments for DIU portcos to be able to move quickly and implement their tech in is another key aspect of our second, a second team around accreditation.

Maggie 32:00

Yeah, Sarah, I also wanted to dive just a little bit more into how you all are thinking about the physical testing of systems as well. I mean, I have friends who work at drone companies or otherwise, they’re constantly, you know, dragging all their kit out to the middle of the desert in order to, you know, do testing in a place where they’re allowed to do it. Obviously, even, when you’re in the middle of the desert, in the middle of nowhere, but not on a military base, there’s still certain kinds of testing you’re not able to do anywhere but on a military base. So just would love to hear a little bit more of the details of how you all are thinking about accelerating the testing process for a lot of these vendors.

Sarah 32:37

So some of this, I’m gonna sound a little like a broken record because frankly, some of these changes that you implement in pillar one for classified infrastructure, they translate to the other ones because at the end of the day, these are systems, these are processes that have been developed decades ago in some instances. first I think it’s moving test and test planning left. Thing number one, I already made this point, so I’m not gonna get into it a ton, but how do I ensure that I have test engineers, test planners, and test managers here at DIU from the moment we curate a program? It’s that the Air Force comes to us and says, “We wanna do X, Y, Z. What do you think, DIU? Is there commercial tech available? Is it on commercial timelines and commercial price points, and does it drive lethality, i.e. align to DIU’s mission?” Those are yeses, and we’re gonna be taking on this program jointly. I need a, I need a test expert in the loop right away to be looking at that program plan that we’re developing before we ever even go to market to solicit and look for capabilities. I need to have a test plan for developmental and operational tests right alongside. so I think that’s, that’s number one, just a process change. That’s in category of kind of process changes. there’s some work in there too that bumps into the authorities that we have at DIU or authorities we can lean on to do that. So that’s a, let’s call that a paperwork exercise with, memorandums, with agreements and understandings and all these things. So making sure we can tackle the, authority piece to be able to do things in a legitimate manner. Another element, when we look at the infrastructure around test, to the example you just shared, Maggie, of like people go out in the middle of the desert. it’s astounding how manual the tooling is and process. but there’s some really awesome companies in the venture-backed space that have reimagined what test can look like, for digital and physical environments and are helping us really bring that infrastructure into the 21st century. I think that’s a very important piece. How can I design a test plan, in, you know, minutes, in a very short period of time relative to the months, nine, 12 months that historically for some types of capabilities and technologies it takes to develop a test plan? How do I, how do I condense that and bring the goodness of AI and other things to the table? I think that’s a key piece. And then another theme, we’ve got a lot of test ranges. We’ve got a lot of test environments open. You know, I was just down in Austin, Texas for Fed Supernova and had the privilege of going out to a ranch private citizen, who’s been a key, an avid supporter of the DoW and other, other services or branches as well, departments as well, You know, the authorities that we bring, the authorities, we bring the capabilities, and they’re—and the environment is there. We’ve done everything in accordance with FCC, FAA, all the, you know, all the other branches or departments that are important. great. There’s another example of kind of private lands that we can lean it, lean on.

Maggie 35:55

In a year or two from now, what do you want success to look like for The Bridge? You know, what kinds of success stories do you want people to be telling right here on the Mission Matters podcast?

Sarah 36:06

you guys, again, have such an awesome collection of founders, builders. I would love for those people to have access, to have had one of these things fixed for them. And like, you know what? Before, I could have never dreamed of getting my company and my people cleared so we can apply for classified solicitations. I’d love for them to be able to do that. I’d love for them to be able to book, space within a classified facility to deliver on a contract they’re already on. I’d love for them to be actual practitioners and users of the solutions that we’re implementing right now in the department. In some of these ways, I think I shared earlier, the timeline to build a SCIF, that timeline is still, like, over four years from A contract being let to full operational capability. The, I’m trying to bring that to a year. But I think, yeah, make your listeners practitioners. That’s, that’s, probably the win within 12 months.

David 37:10

Well, Sarah, you’ve been super generous with our time. I’m sure your email inbox is probably just nonstop with different requests, but maybe just to build off of that last call to action, what else can we be doing here in the community? You know, the, all the listeners of the “Mission Matters” podcasts and our friends and allies across the venture ecosystem. You know, how do we help you at The Bridge be successful in implementing these much-needed and much-appreciated changes?

Sarah 37:41

You know, the solution will always, like success will always reside right at the intersection of the builders, you know, the operators, the people, those of you building companies and capability, the policymakers, the investors that are making that building feasible, and then the buyers, the government utilizing it all. Each of us, and many of us, have worn hats and we kinda rotate, right? We’re all obsessed. We have the North Star. We wanna see democracy persevere, so we hop, and wear different hats at different times in this ecosystem. but it really will take all of those working in concert to overcome even one of these barriers. I would say if you’re, if you’re on the builder side of that and you’re working with a PM within government, you’re on contract, and you listen to this on your commute, ask your PM. You know, “Hey, we’re, we haven’t talked at all about ATOs, but I’m building software. When...” Like, applies to both software, hardware, but like, “When are, when are we gonna go through the cyber things? What do I need to be baking into my system now so that I can satisfy those controls? Do you have an AO? Do we not? what’s my test plan? What about a clearance? I hear some cool kids at DIU are trying to, you know, change the way we do these things. Can I connect you with them? Can I...” Like, it really, I think, will be a little bit of a foot soldier exercise to start with, and it needs to come from all sides. That’s for the operator side. The policymakers, man, they were out ahead, right? It was Congress that first put Section 874 and the ‘25 NDAA, forcing the department to think about what classified infrastructure as a service would look like. So hats off to them. They continue to be phenomenal supporters, I just, I couldn’t ask for better partners on that side, so I would say we stay the course there. The investors, it’s you guys that are taking a chance on these things. You guys, it, and it’ll be even more critical. Talk about FCLs, as we bring investors to the table, and we start to clear them in a concerted way, which I’m really excited. Stand by for some news in that area in the next week or two. you guys are critical. You need to know what the demand signal is. What are the things the department needs to solve now, so you can efficiently direct capital, do what you guys do best. And then on the buy side, on the government side, do what our leaders have said. Have a bias for commercial tech. Have a bias to move quickly. Use flexible contracting mechanisms. and just be as impatient as we are, because we don’t have the time. We don’t have 10 years to show everyone how to use an OTA and actually adopt it. We have, far less time, you know, to really drive these commercial capabilities into adoption, so.

David 40:33

Well, Sarah, thank you so much for coming onto the podcast. We’re excited for The Bridge coming out of stealth, and look forward to its success in slaying these bureaucratic dragons, right? And unleashing American innovation across the operational, workforce.

You can listen to the podcast on Spotify, YouTube, the Shield Capital website, or right here on Substack.

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Transcript

Maggie 01:12

Offensive cyber attacks are officially moving at machine speed. Just last month in July, OpenAI reported a cyber incident in which one of its AI agents broke out of its sandbox and hacked into Hugging Face in an attempt to obtain solutions to a test that OpenAI was running against the model. Notably, during this Hugging Face attack, the Hugging Face team reported that they actually couldn’t use closed source frontier AI models to help defend themselves against this attack because all of the frontier AI labs have put up guardrails on their models to prevent people from using the models for cybersecurity tasks. Now, of course, they’re trying to prevent offensive attacks like the attack Hugging Face themself was actually experiencing, but ultimately, these guardrails prevent any kind of defensive use of these models as well. The Hugging Face team ultimately ended up needing to use a Chinese open source model, specifically ZAI’s GLM 5.2 model, to defend themselves in this situation. How can companies defend themselves today against automated machine speed attacks in the age of AI? In this episode of the Mission Matters podcast, we sit down with Lou Manousos, the co-founder and CEO of Ent Security. Ent is building the future of what they call intent-aware cybersecurity, leveraging edge deployed AI agents to prevent cyber attacks in real time at the endpoint. This kind of technology is absolutely crucial for companies to have to defend themselves in this age of AI for offensive cyber. Ent is working to bring security tooling into the AI era. By deploying AI agents on the edge at the operating system layer, Ent can detect and prevent attacks as they happen at machine speed with full context of a user’s typical behavior on a system. Since it was founded in 2025, Ent has raised more than $100 million and started working with enterprises across financial services, defense, hospitality, and more. This conversation covers everything from the current state of AI for offensive and defensive cyber, what it takes to build AI agents for cybersecurity, the debate over closed versus open source models, what it takes to deploy AI agents at the edge, and what it takes to build a successful cybersecurity business. All right, Lou, thank you so much for joining us on the Mission Matters podcast. This has been one of the interviews I’ve been most excited to do back since Akhil and I started this podcast more than a year ago. There’s been a lot of hype and discussion publicly recently about the role that AI agents can play in both cyber defense and of course, offense. I mean, lots of discussion around Anthropic’s Fable models, OpenAI’s newest models, even some of these Chinese open source models about their ability to conduct offensive cyber. Akhil and I, and Lou, you yourself, have all sat through many, many pitches for all kinds of uses for AI and security, whether it’s automated pen testing, SOC management, incident response, threat intelligence analysis, and much more. But I wanna hear it from you, what do you see as the state of AI for cybersecurity today? What is actually real versus what is just hype around what AI agents can do in this space today?

Lou 04:32

Look, I mean, it’s amazing. All offense will be AI entirely, I think by the end of this year. Mythos really gave us a window into that. So a lot of the hype really is people who worked with these models hands-on and have seen some of the capabilities. And so it’s pretty clear to me where this is heading. The models are really great at coding, they’re really great at technical tasks, and that’s what a lot of the security incidents are built around. So I think that Frontier AI labs, they’ve been talking a lot about this through this lens of safety, and I think they’re taking it seriously, but the cat’s kinda out of the bag, and that means those capabilities are gonna flow to open models, and attackers are gonna be able to increasingly use offensive AI. And what that means for security teams and the whole industry is it’s a speed question. So it all comes down to speed. If an offensive attack is operating at machine speed, people operate at human speed. We’re humans, we can only type so quickly, we can only move the mouse around so fast. So even if you have a defensive agent as a human operator, you may already be too late, even by the time you start to triage these incidents. And if you can’t understand the incident, really wrap your arms around what’s going on... I mean, frankly, if you look at some of these attacks, like 27 seconds, under a minute, that amount of time, just to reason in your own mind as a human, it’s unlikely. So to me, it’s pretty obvious that as offense is all AI, defense is gonna have to be mostly automated and able to execute in under a second and really prevent these attacks. So we’ve heard about all these different dramatic things that have happened recently, and whether it’s discovering vulnerabilities... I mean, you mentioned a bunch of them. Software inventory, hacking a system. These are all real risks that we’ve seen AI successfully pull off. So at this point, whether you’re a defender or somebody doing offense, you’re using AI at this point. It’s just a matter of how much the human is in the loop versus out of the loop. And I don’t think it’s about convincing anyone about the impact here. So that’s my view of it. It’s not hype, it’s happening. I think what’s maybe under-hyped is just how important it’s gonna be to stop thinking about response and focus more on prevention.

Akhil 07:21

Hey, Lou, I love that framing of offense versus defense. Actually, Maggie, I think on our first podcast we were talking about, obviously, here at Shield Capital, we talk a lot about and discuss revolution, military affairs generally. What I find interesting about this conversation, whether it’s in the digital world or whether it’s drone, counter-drone, there’s always this balance of power and velocity when it relates to technology as applied to the offense versus the defense. Correct me if I’m wrong, Lou. What I’m hearing from you is that where we are with AI today, you are arguing that that naturally favors the offense right now. Is that a correct statement?

Lou 07:58

Yeah. The investment certainly has gone into offense and, you know, even the first wave of companies in cybersecurity largely were around pen testing vulnerability. You know, that’s what the models were really good at, whether that’s in source code or in actually scanning live systems. And then the second wave went into understanding SOC operations, but that was really much more of a response function. So does it favor offense or defense? I think it’s really hard to build real-time defense and prevention. These agents are actually, while they’re faster than humans, they’re slower than a normal prevention system which operates in under a second. The agents do need to reason over larger quantities of data. And so I don’t think that the frontier labs are focused on that type of use case right now, and most security companies are just adopting what they can get from these frontier labs. I wouldn’t characterize it as the offenders have the advantage. They almost always have the advantage, right? Even before AI. It’s just much easier to find one hole in a system than it is to defend a system.

Maggie 09:28

Now, Lou, one of the big debates that I know we’ve discussed and a lot of the industry is discussing right now is really around the models’ capabilities for offense and defense, and really around the model providers putting in these guardrails to try and ensure that these models are being used in a safe way. But, of course, a lot of these guardrails maybe actually make it harder for defenders to use the model for defensive cyber operations. So I just wanted to get, what is your take on this debate around, to what extent the model providers should be building these safety guardrails in, especially in this world where we’re seeing this proliferation of open models that don’t necessarily have those same guardrails in place?

Lou 10:14

Yeah, this is kinda how we kicked it off. I think some of the recent incidents, Hugging Face and others that have happened in the last couple weeks really underscore that when you give an agent an objective, it’s... You’re not telling it, like, “Go hack this system.” It’s trying to get to its objective, and you didn’t have to give it step-by-step instructions like, “Go do this.” And it finds a way to work around the controls that you put in place. As long as you’ve got the tokens, it’s just gonna keep going. And so as a defender, if you’re specifically asking the model to do things and you’re using a frontier lab or an API provider that’s blocking you, I mean, that’s very troubling. And you don’t have control over that. You don’t really know at what point you might be blocked. That might happen once it understands what you’re trying to do, that might happen later. So I think organizations, defenders have to have some ability to run local models and switch out their model provider. I mean, that really became painfully clear last week when several people blogged about how they were blocked while they were defending themselves, and that caused them to spin up local capabilities. Now, what’s interesting about that, and Hugging Face, I mean, they’re like the experts in running local models, so they were able to do it. But how many organizations really have the capability to deploy and run a local model in that way? Probably not many. So yeah, to me, it is the digital right to bear arms or the right to bear technology. As a defender, if you’ve been given a guardrail, it’s like fighting a fight with one hand tied behind your back. So I feel pretty strongly that, as a security expert, you need to have exact knowledge as to when you’re gonna be blocked and what the model’s gonna do, and frankly, what it’s not gonna do when you ask it to do that. I think that’s where it ultimately ends up is you’ll need to remove guardrails for defenders. There will need to be... At least the frontier labs are gonna be challenged by this. But it will drive the best security teams towards open-weight models and open local models. At least as a backup plan, you’ll need to have that capability. I don’t see a way around it.

Maggie 12:59

Yeah, can you tell us more about what you think the proliferation of these open source and open-weight models will mean for both offensive and defensive cybersecurity?

Lou 13:11

Well, it lowers the costs, whether it’s social engineering, deception, just basic types of phishing, credential theft, data leakage. I mean, really you go through all the bad things that can happen in a security program. The models are trained on all this amazing research that’s been published. I have background in threat intelligence, published thousands of articles over the years. All of that information is in the memory of the model, so they’re quite good at this. And that I think is... Yeah, I think that’s the basic problem that you’re facing as a defender is, like, you’ve just lowered the bar for offense. I do think... We saw this in the early days of web exploits where just average cyber criminals could go online and buy a web exploit. They didn’t have to know very much about exploiting a web browser. They just go buy this exploit, they run the exploit kit, and now they’re able to target, whether it’s a nation state threat actor or just your average cyber criminal. It just made it way harder as a defender when you could get your hands on these. I think we’re entering a new phase now where that’s gonna become the new normal, where fairly effective attacks will be in the hands of ordinary cyber criminals. So in a world where we know that that’s gonna happen, and we have the speed of the attack being driven by AI, you have to be able to prevent and actually go beyond just detection and response. And this is gonna require a shift in thinking on architecture and how we’ve architected these systems. They’re currently architected to deal with human-driven attacks, which are, again, relatively slow. Once I go back to the exploit kit example, once I had a foothold on your network, as an attacker, I’d go to this panel, be like, “Oh, great, I hacked Maggie’s computer.” And now I would start tasking and going to look for my objective. That’s very slow. That might take hours, at least many minutes. So if you had an EDR system in place, your threat hunters would see that activity, and then they’d be able to isolate that machine. When that occurs in like under a minute, by the time your security architecture alerts you and your team starts responding, the damage has been done, the data has already leaked. So the answer is to move back to something where as soon as you detect this behavior that is potentially malicious or suspicious, that machine must be isolated immediately. You have to intervene at the moment that you see this behavior. And that’s a change in terms of how we’ve been behaving as a security industry the last almost 15 years now.

Maggie 16:29

Yeah. So maybe we’ve been hinting at it a little bit, but let’s move to discussing Ent itself. Can you tell us a little bit about what Ent is building and how it relates or compares to this old generation of security tools?

Lou 16:45

So we’re building an intent-aware security platform, and it’s designed to secure both human and AI-driven work. Our goal is to move the industry back to prevention because it’s only with prevention that you can see productivity and secure it and really increase the adoption of this AI technology faster. So we deploy an intelligent agent directly on the endpoint, and we model what people are doing, what applications are doing, what the AI agents are doing, and how that accrues to the work that’s happening. And we understand that in context. And so by understanding what the intent is, what humans and AI agents are doing, we’re able to see if there’s any risk there, if it’s deviating, and then we can intervene before that becomes an incident. What makes the endpoint super interesting in this AI world is I believe that humans are always gonna be in control at some point. And so you have to think where’s the place where humans and machines are going to meet. That’s how I define an endpoint. That is the place where a human and an AI agent or some future AI comes together. Once you start using your endpoint, you’re moving around a lot. You move between browsers, chat, other business applications. These agents are working hand in hand with users at that endpoint layer. So we provide security for this modern workspace. Instead of looking at each application separately, we look at the entire endpoint, from the operating system up, and that’s why operating systems were built. They are supposed to provide a platform and common primitives like security. So that’s Ent in a nutshell.

Akhil 18:54

No, Lou, that’s awesome, and exciting just how quickly too you have moved, and a lot of that obviously based upon your background experience and the team that you could bring together. Let me dig into a couple comments you made. The first is you talk about this shift back to prevention. So what has been missing, Lou, that you really felt like, “Hey, this is the one”? You’ve done many startups in the cyberspace. You decided to focus in this particular... Why this approach and why this vector?

Lou 19:25

You know, again, on why is the endpoint so special, I was just thinking about all these risks that are now playing out. I mean, I thought we’d have a little bit more time, but they’re happening now. Where’s the right place to build security? You have the cloud. So definitely a lot of things happening there. You have the network layer, and you have the endpoint. Again, the endpoint is where humans and machines meet, and that is the logical place to put the security control. That’s where the information’s gonna leak back to the user. That’s where the user is actually prompting or tasking their agents. And so when I thought about... I looked across the market and say, who’s doing this, and who’s got the capabilities? There’s plenty of great security companies out there, but they’re challenged. The incumbents are challenged because their model is based on detection and response, and we moved away from prevention about 13 years ago when the EDR market took off. And it would require a complete reboot, reset as to how prevention is done, which really relies on detecting a threat, using threat intelligence to hunt and then sending that as rules back to endpoints. The latency there is just too long. So that’s why we stepped up here. It’s just a ground up first principles rewrite of prevention and security.

Akhil 21:09

Yeah. Thanks, Lou. We’ve got some folks who listen to this podcast who are deep in cyber and know what EDR and SOC automation is, and then some that don’t, might be coming from different domains, but this certainly applies to them regardless of industry. Can you give an example, maybe a tangible one, of a threat that Ent agents have or were or will be able to detect and prevent?

Lou 21:35

So one example might be, you have an employee who is working on sensitive data on their local machine, and you wanna move that data from one machine to the next. In this example, this is kind of an anonymized example. This organization uses WhatsApp to communicate with some of their customers. So WhatsApp is an approved application in this environment. The data that the employee is using is approved. What’s not approved is for the AI model built inside WhatsApp, which there’s Meta AI, should not have access to this specific data. They’re only supposed to use WhatsApp to communicate with customers. So what happened was this approved communications app all of a sudden now had proprietary data put into it, and that was kinda cut and paste from another application on the desktop. So unless you have local AI capabilities that are watching the sequence of events, like data is taken from a Word document and pasted into WhatsApp, but into this portion of WhatsApp that has Meta AI for summarization, you would not be able to detect a company policy violation and potential regulatory violation and exposure. So it’s only when you have the context of the work, the legitimate work that that person is doing and the knowledge of the data, and putting that whole picture together, which is very difficult to do when you don’t have AI running directly at the operating system, watching not just the web browser, but communication applications, productivity applications, and you can design an adaptive policy that protects that entire environment. So that’s just one example.

Maggie 23:49

And so moving to a little bit of the discussion of the technology behind Ent, one of the things I found really interesting about it is that you guys did decide to deploy this on the edge, on the endpoint. Can you talk a little bit about what that decision gets you, and maybe also some of the challenges with deploying on the edge?

Lou 24:07

Yeah. The edge gives you context of what the user is doing. I’ll go back a little bit to, like, intent aware. One of the inspirations for Ent was autonomous vehicles. Riding in an autonomous vehicle here in San Francisco, I was, like many people, amazed at how the car can adapt to the world around it. So they have a world model, and then the vehicle intervenes. If that worked like a security system, right, it would log accidents, and then some analyst would come in and then tell it not to do that again. That’s not how it works, right? It doesn’t log an accident. It just intervenes so that there isn’t an accident. And so when you’re kind of at that point, at the edge, you actually have the ability to intervene in sub-second time. So you have the context of the environment, you have speed, and you have total control. And so that, in the world of cybersecurity really only exists at the endpoint itself, at the edge. And that’s where all these applications interact. That’s where human work is handed from your mind and translated through the keyboard or through your voice. It could be different modalities, right? It will be at that point where we hand off control to AI agents. And so if you don’t have that context, if you’re not living at that point, if you’re living sort of in application logs, or through network events or through the logs at the cloud, you completely miss the intent of the operator and what they’re trying to do. And so that’s really was one of the key pieces. And the other is that when you run locally, you can run very quickly, so that removes this sort of roundtripping that happens where I have to log the event, send it to the server, the server processes the information, sends it back. When you have something that has to complete in less than a second, you’re gonna gate the user and intervene, you’re gonna lock process execution or something. Like it has to be quick, otherwise the machine will crash or the user will complain. So that’s a really critical second reason. And then the last one is we really believe in the data boundaries. We wanna have a decentralized system. There’s a lot of sensitive data that happens on the endpoint, and we wanna keep as much of that information there and allow customers and their organizations to decide what they collect, how they analyze that. So all these things now can happen within the memory and compute of a modern endpoint. So we’re designing Ent around this future where we have a lot more happening locally, and these handoffs between humans and AI are just increasingly sort of the norm. So we just thought that’s the logical place, is the endpoint.

Akhil 27:37

Oh, that’s awesome, Lou. Hey, Lou, can I follow up on, as you decentralize and take action at the edge, there’s some trade-offs associated with potentially the decision-making or something broader. So two questions. One, as you look at proliferating at the edge, how do you find commonalities between different attacks if you’re trying to prevent and engage with those? And number two is, if you have to make some major refinement to how your agents respond or prevent at scale, how do you think about doing that? Where I’m going with this is, what is the trade-off between being able to prevent at the edge and being able to have a little bit of that centralized pivot or adjustment at scale?

Lou 28:27

Look, there’s some trade-offs, but there’s also barriers. I think the first one is are we sort of ready to hand off control to some local, very fast, Ent-like agent that can prevent. I mean, that’s just a change in the way security systems work now. So that I think is the first one. The second part of it is, are end users themselves ready for this security system to intervene and potentially coach them away from risky behavior? ‘Cause if I can predict the next thing you’re gonna do will put your computer at risk, or you’re falling for some social engineering scam, or you’re gonna run a command that could compromise your machine... I can’t just block it. Again, going back to the examples we talked about earlier, if it’s an agent, it’ll just keep trying to go through the steps. And also, humans do the same thing, by the way. If they get blocked, they just keep trying to go around you. And so having a system in place that intervenes and can also convince the operator, like, the next thing you do is gonna be really bad, it’s a total change in the relationship that end users have with security systems. And then the third thing is local hardware and the speed of the models. And we’ve done a lot here to build our own models. We can use what customers have access to. A lot of this relies on the local compute, which is changing. It’s getting faster. We’re getting more capabilities at the edge. And that I think has been playing out. It’s driven by broader market forces than just cybersecurity. In terms of what are the trade-offs, there really are no trade-offs because you can still go grab threat intelligence. You can still share within your own company or within cohorts. Whether you’re in financial services or government, there’s no reason you cannot share the threat intelligence of the types of attacks that you’re seeing with your peers, what is now magnified by the fact that you have both local reasoning capability and global threat intelligence or market threat intelligence. So I think this is sort of the best of both worlds.

Maggie 31:05

How do you think about combining both deterministic and non-deterministic models together in your platform?

Lou 31:12

Yeah. Basically, to answer the question on deterministic and non-deterministic, security controls generally are deterministic. And for example, access control. I’m either granted access or denied access, very strict, constrained. But I think where it becomes difficult is where you have unconstrained language models. How do you use such a thing in security? So the way we do it is we combine three forms. We start with the deterministic controls, which provide clear policy, clear, predictable enforcement, and that’s usually regulatory driven. If these things happen, I must block it. And then we layer in behavioral. So what action is normal for this user or the types of users that work in the company like this user works with, their role, we try to derive the role, and the generative models and multimodal models can interpret what’s ambiguous in this context and map it to intents, and then those intents will help you really understand, again, what is normal for this user. So a policy might start with, again, a deterministic, like, all right, I’ve got clipboard activity, and inside the clipboard is sensitive data. That’s fairly easy to determine deterministically. But now the action, like what is the user doing with that data, is non-deterministic, and then the destination is deterministic. Like, oh, I’m pasting it into Meta AI, I’m pasting it into WhatsApp. That’s very easy to determine. So, and then finally, looking at the entire action, and understanding the boundary conditions of the policy and reasoning over it and reasoning over larger time periods, for that we use reasoning models. And so we combine all three approaches, again, under a second, and that’s a very powerful thing because now you get the compliance things you need, but you also get this flexibility to deal with benign false positives and try to capture more recall and make sure you can broaden the net for situations which you’re unsure if you can act deterministically.

Maggie 34:01

And are these models that you all built yourself to be able to fit on an edge device, are these open source models that you’ve used or distilled?

Lou 34:09

Yeah. We distilled and we started with things that we can get, and there are certain things that we could build from scratch that are really useful for data classification and are becoming easier to build on your own. But for the larger reasoning tasks, we’re taking off-the-shelf models, just quantizing and distilling those down. But it’s still very, very hard work and you have to have training data and other scenarios. And the way we do that is we do have a synthetic environment which allows us to take real world information that we’ve populated with these virtual workers, these virtual workers that represent what you commonly see in the enterprise, and they are repeating tasks, and they’re repeating adversarial tasks as well. And so we can take that data without using any customer-sensitive information to help train and fine-tune our models.

Maggie 35:15

And are these reasoning models multimodal models? Do they have, like, vision capabilities to see the screen, or are they more relying on text data that’s coming in or current data?

Lou 35:26

Yeah, the model itself combines being able to operate on the operating system telemetry, which is very technical telemetry. The application layer, user layer telemetry, which is more human content, mouse moves, copy and paste actions, the text that’s actually on the screen, and what’s in a document, and then the visual aspect of what’s on the screen, using vision capabilities when that’s appropriate. And then many customers, they wanna change the way this behaves for different reasons, so we also allow customers to turn off some of these capabilities or tune some of these capabilities. And then the thing that we always do is we use very lightweight embeddings models and specialized small language models, and yes, those also can optionally have these vision capabilities. So we’re pretty deliberate in this, and we attempt to stay model agnostic when a customer is saying, “No, I wanna use this particular frontier model.” We choose the best model depending on the customer constraints, and also the limitations of hardware and other things within that customer environment. So that’s what we’ve been doing. I don’t think I’ve talked to anybody who says that, “Hey, this isn’t a problem.” It’s just a matter of how they’re gonna tackle it.

Maggie 37:01

Yeah, one question I have, and I ask this of a lot of companies that are building these AI solutions for mission-critical use cases. What have you done to build trust with customers to actually allow you to have these autonomous agents operating on these mission-critical systems?

Lou 37:21

Well, I think, again, it goes back to the way you’ve architected the system. Have you built a product so that you as the author of that product cannot see the customer data, you can’t control the environment? Once you’ve deployed it, it is completely in the hands of your customer. So we’ve designed... And some of this goes back to my background. I’ve done a lot of work in governments and critical infrastructure, worked at some of the largest tech players in the industry. And in those environments, these are table stakes things. Like, sensitive data must stay in the customer’s data boundary. Then it’s all about when the product is running, like customers see what we would have detected and how the product would have responded before we actually turn on interventions or prevention capabilities. And that creates a real proof, like this is what this product will detect, this is how it will respond. And once that happens, then... You know, we had just a kind of a story. We had a customer who was worried about inside risk and they told us, like, Ent is the first product that made them feel like an expert on the first day, like, explained what was happening in their environment in terms that they understood as an inside risk expert. And it took all these technical signals and put them together in a narrative that made sense to them. So some of that trust comes from just building the product properly for not just security experts, but the business leaders and the end users. It should feel... The product should feel natural. That’s one of the amazing things about this gen AI movement, is we’re actually able to build products that speak in the language of real humans. And so it’s a magical moment in building things.

Maggie 39:29

You mentioned that at some of your past companies, you’ve done work with the government, and in past roles you’ve had. Is that something that you all are focused on for Ent? Are there any defense or government customers that you’re working with?

Lou 39:41

So Ent is an American company. We’re built and headquartered in the United States. That actually matters quite a bit in this AI race, and it matters to us. We’re here for our government customers, and we care about accountability, having a supply chain that you could verify, like, where we wrote the code and how it operates. I also think the legal jurisdiction matters. Alignment with national security really matters quite a bit. Government and defense customers, they face the same challenges that we’ve been talking about in this podcast, but the impact is just so much greater. Like, AI attacks just have much bigger consequence in those environments. And it’s not that they want local control, they absolutely require it. They have to have a very decentralized system. Obviously, it has to be sovereign. The provenance of the data, everything has to work within very strict environments.

Maggie 40:52

You’ve been working on building AI agents for cybersecurity longer than probably almost anybody else on the planet. After your last company, RiskIQ, was acquired by Microsoft, you stayed on at Microsoft to lead their initial security co-pilot product development. I’m curious, where have you seen the tech change the most these last four years, and what have been the biggest surprises watching this tech evolve over the past few years?

Lou 41:19

Yeah, I mean, it’s been... It’s wild, but it’s five years of thinking about gen AI and cybersecurity, which is an awfully long time. So during that, I think the biggest change is we were using AI to help security analysts and make their life better, and that was more around summarizing incidents, querying systems, like things that security experts do or threat analysts do, really important stuff. And we’ve seen the models evolve from being great at writing code to now these agentic flows, which are amazing. The multimodal stuff has come an awfully long way in the last year. And then finally, these small models and being able to distill and quantize and run them locally, which makes all of the local security possible. So the biggest surprise to us really has been how much you can do locally now. I mean, I wish that was possible five years ago. It just wasn’t. And so that’s what we’re really focused on, and that’s the biggest change, is the speed and just how much you can squeeze into a smaller compute memory footprint. I’ve seen the whole thing evolve, and it’s been one of the more rewarding parts of my career, to be able to work on this technology, which I think is a dream for so many who’ve been in cyber and been sci-fi fans and dreamed of seeing the robots. It’s finally happening, so fun time to be working.

Maggie 43:15

Lou, I saw you made a post the other day that AI agents are going to kill the browser. Could you just talk a little bit more about what you mean by that?

Lou 43:25

So when you think about the modern endpoint, there was this trend with SaaS that got everyone thinking that, like, the web browser, and maybe this was Google pushing this narrative, I’m not sure, that the web browser was the center of the universe. Like, everything was gonna happen in the web browser. And frankly, as somebody who built RiskIQ, I’d say a ton of what we did was web-related. It was all around websites. What’s just totally fascinating about AI is it’s broken this concept of the web browser being the center of the universe. Like, I do not immediately go to Google and type in a search query anymore or go try to find a web page for a company. I start with my AI tool that runs locally. I type my query into that system, and my objective, and I let it go do the thing that it does best, and it comes back with a good summary. And then from there, if I have to fetch the page, I go fetch it. But usually, the AI agent has done that for me. And I’m probably on the more bleeding edge, but more and more people I talk to, they’re like, “Yeah, that’s exactly what my day looks like.” A lot of the security that we’ve built the last 15, 20 years has been around this narrative of external threats coming through the web. And if that vector is shrinking, you look at where we spend money, we should be allocating money very differently than protecting a usage modality of web browsing that’s actually decreasing. It’s not that we’re not using HTTP. We are. It’s just that the agents are actually running it, which gives you... It really contains the blast radius within that application layer. So I have more desktop applications now than I’ve had in a long time. You mentioned earlier, everyone’s coding again. I’m coding again. I’m bringing down local packages. So my machine has a lot more going on than just a Chrome browser running on the desktop, and I think the future is gonna look a lot more like many applications and agents running locally, running in the cloud, all talking to each other. And so that just changes the gates that we traditionally protected. So yeah, thanks for that. Thanks for reading my post.

Maggie 46:09

Of course.

Akhil 46:10

Lou, I have so many follow-ups. Can I ask one on that? Okay. Then how do you decide what runs locally and what runs in the cloud?

Lou 46:17

I don’t think you decide at all. I think the AI’s seemingly deciding, right? You describe to the AI what you want, and it goes ahead and writes code. It fires up a container, whether it’s inside WSL or Ubuntu containers. A lot of these local agents then run programs, compile programs, and run them locally. You could see a future where the user experience is then generated by that program. So instead of it being generated by the website in a DOM object that comes across the browser, the local AI agent is just gonna develop an application for you that’s purpose-built based on your intent, and that’s why that stack is going to increase at the edge, at the... or whatever the modern endpoint ends up looking like. So this is a reality. Anyone who’s been using some of the latest Anthropic or OpenAI local capabilities, you’re living in this future right now.

Maggie 47:25

You know more about building security startups than almost anybody else. This is your fifth cybersecurity company, right?

Lou 47:33

Yeah, that’s right.

Maggie 47:34

What advice do you have for early-stage founders building technology for some of these critical industries?

Lou 47:41

I mean, choose a problem where there’s a real cost to... where failure has a real cost. It’s a loss of something that you care about deeply, and I think that that creates passion and conviction for you and your co-founders and for your team. And then you gotta spend a lot of time with customers who respect that. And when you’re solving problems that have major impact, working hand in hand with customers is just very, very rewarding. And I think in terms of mission-driven customers and mission-critical industries, these types of customers are amazing ‘cause they know that... It’s really the passionately curious that can solve any problem with them. Yeah, so they make some of the best customers, and I would encourage founders who listen to the podcast, lean in on that. You probably have a lot more in common with your customer than you realize. And then the last thing for me is it’s always important to be hands-on with the technology. I mean, I’m an engineer. That’s my natural way of thinking, and I’ve seen these problems firsthand, and I’ve seen how customers have to deal with the problem, and I know how difficult it is to solve the problem, and that gets your brain thinking in the right way, how to understand the technology and build essentially a better mousetrap. So there’s really no excuse to not get hands-on with AI today. You can learn almost anything, and I would encourage everyone who’s thinking about starting a company, you wanna be, as a leader in that company, you have to really understand not only the problem that you’re solving, but really understand underneath the hood, how this thing works so you can...

Maggie 49:46

You said you learned Rust for Ent.

Lou 49:49

Yeah.

Maggie 49:49

Is that right?

Lou 49:50

Yeah, exactly. I actually never... I mean, in my programming career, I’d stopped programming when Rust took off, and then when I went back to work on Ent, Brandon and I were talking about what the right way to build this was. So I’m like, “All right, I’ll write a little Rust program.” It’s amazing what you can do with the help of AI or with our Clanker friends. And then the other thing I’d say is, don’t build a company around the models, like whatever the frontier labs have because these things are gonna change. So these models are gonna come and go, but durable value will come from the insight that you have working with your customers, the data you have access to, the evals that you’re building, the distribution that you’ve built, and the customer trust, which was a big theme in this talk here. And with those things, you build the right team that can execute and deliver consistently, and that’s kinda how you get to the promised land and build something super special. And then the top talent in the industry will be attracted to that. You’ll be able to hire the best, and turns out that those people also care deeply about the mission.

Maggie 51:12

I know some people are worried that the frontier labs are going to eat all other software companies. Obviously you don’t believe that, otherwise you wouldn’t be here building Ent. But what’s your take on how the future of these models and these labs are going to play out?

Lou 51:27

I think, if you look at Microsoft and what they did with cybersecurity, just as an example, they got very serious about security and have a great security business. Like, I would not, just speaking for myself in cybersecurity, I do think, will OpenAI have a security business? Probably. Anthropic already has a security product inside Claude. So obviously the frontier labs are looking for markets where they can apply their models, and they’re gonna have fantastic distribution. So there’s no question that that will continue to be the case. I feel like the harness and understanding these applications is equally important now, and we’re seeing that play out in other industries. So I would not fear as much the frontier labs. I would just focus on the customers and the use case, and I think you’ll build a great business.

Akhil 52:40

That’s right. That’s right. Yeah. Was there anything... I mean, obviously you’ve seen where Microsoft has been going. You’ve got these big providers here that have been on the front. What to you is a sustaining advantage to some of these larger security providers? Like what’s one area that you’re like, “You know what? It makes sense that Microsoft or CrowdStrike or name your entity are doing relative to the types of things that Ent is doing”?

Lou 53:11

I think there’s a major advantage that Microsoft has in their ecosystem of, with Windows and the O365 productivity suite and Azure. It’s just so much easier to adopt technology. They’ve made it extremely valuable. There’s so much value in those products. The cost, because they run Azure, there’s no way to beat the cost on storage and compute. When you’re a hyperscaler, that’s a tough thing to beat. So for us, we deploy our technology directly in the customer’s data boundary. If that data boundary is Azure, then great. We’ll deploy it inside Azure, and we’ll deploy it on the endpoint within Windows. I think it’s very difficult for anyone to go toe-to-toe and say, “I’m gonna beat Microsoft, Google, Amazon at the inference game.” I mean, that makes no sense at all. Or, “I’m gonna beat on storage.” Those are things that I think you let the hyperscalers take and you work with your customers, where you can add value. Generally, when I look at the security incumbents, the value that they have is they’ve been collecting all of this data on the security events that the customer’s been seeing. That data has value. It’s just at a different level in the stack. It’s describing device failures more than what’s happening where modern work is occurring. So modern work is occurring really in the application stack. I’m running these co-work models. ChatGPT has one, Anthropic has one, Microsoft has one, and a lot of the security risk is happening in that application layer, which the security incumbents traditionally have stayed away from. So modern work is all about moving up that stack. But that doesn’t mean that that traditional device-level malware and the network attacks, they’re still real too. So our view is we’re not gonna go back and compete with Microsoft Defender or with CrowdStrike as an antivirus or an EDR provider. Companies are relatively happy with what they get from those capabilities. It’s where they’re lacking visibility and prevention is one layer up that stack, and then unifying all of the other applications that sit on that stack. So it’s very much a complementary thing, and I think, time will tell, if that remains durable for those incumbents. It probably is gonna change too, but for the time being, we coexist with them, and I think it’s very much a better together story.

Maggie 56:31

Great. Well, Lou, thank you so much for coming on the podcast and sharing all your insights.

Lou 56:35

Thank you, guys.

As always, please reach out if you or anyone you know is building at the intersection of technology and national security.

You can listen to the podcast on Spotify, Apple, YouTube, the Shield Capital website, or right here on Substack.

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Transcript

Maggie 00:36

In this episode of the Mission Matters podcast, we sit down with Secretary Ben Kohlmann, the Assistant Secretary of the Navy for Manpower and Reserve Affairs. As ASN for M&RA, he is responsible for the oversight of manpower and reserve component affairs of the Department of Navy, including the full spectrum of policy and programs for military, that is active duty, reserve, retired, their families, and the civilian workforce.

David 01:04

Before his appointment, Secretary Kohlmann held a variety of roles both in and out of government. He served in the Navy for 20 years, where he was deployed into combat as an F-18 carrier pilot, flew as an instructor with the US Marine Corps, and worked in several tech-focused roles at the Office of Naval Research, Defense Innovation Unit, and Chief of Naval Operations Rapid Innovation Cell. In the private sector, Secretary Kohlmann worked as a consultant at McKinsey, as an investor and CEO at Cubic Capital, and as the CEO of New American Industrial Alliance.

Maggie 01:37

In this conversation, we cover everything from how the Navy can recruit tech talent, how they can upskill their existing workforce to prepare for the future of software-enabled warfare, how the Navy is modernizing its manpower management systems, what the private sector and public sector can learn from each other, and much more. So Secretary Kohlmann, thank you so much for joining this podcast. We’re really excited to have you.

Sec. Kohlmann 02:00

David and Maggie, honored to be here. Thank you for having me.

Maggie 02:03

So let’s start with just a simple question. I think most of our listeners are probably not familiar with your role and organization. However, it’s clearly a critical role. Organizations are only as good as the people within them. So can you tell us a little bit about what your responsibilities are as the Assistant Secretary of the Navy for Manpower and Reserve Affairs?

Sec. Kohlmann 02:23

Yeah, the government likes to put huge titles on what are... Have far shorter titles in the private sector. This is basically chief talent officer, Department of Navy. So a million personnel across our whole organization between active duty, reserve, military folks, and then all the civilian workforce, including the 200,000 civilians across the world, and a contractor workforce about 150,000. So everything from policy, promotions, retention, recruitment, all the things that go into fielding this workforce fall under my remit.

David 02:54

That’s awesome. And, so Secretary Kohlmann, it’s great to have you on the podcast. We’ve known each other, gosh, for over a decade, I think, when I met you, through the Defense Entrepreneur Forum, on Stanford’s campus 2016, I think it might have been. I’m sure we’ll dive into some of the DEF stuff and everything that your career’s entailed. But, in preparation for this podcast, I was, reading your Senate confirmation hearing notes, and clearly you come from a long lineage of military pilots, and so to say that this job is core to your DNA I think would be a bit of an understatement. But now having been in the role for, gosh, seven-ish months, eight months, what are your top priorities, for the Navy as far as manpower and readiness?

Sec. Kohlmann 03:45

Yeah, there are three things I’m focusing my time and attention on. The first is broad talent management and kind of updating our approaches for the 21st century. Military manpower and even the civilian manpower force that I’ve, have the honor of serving and leading has an industrial age approach. Single widget comes in on the entry. There’s career pipeline things that happen. For the most part, they’re interchangeable, then you retire on the back end We’re trying to change that into an into an information age approach, where an individual has unique skills and talents that they have inherent to who they are, the things they are gifted at, the things that they like to do. The organization has specific problems that they need solved. Figuring out ways to better match the specific skill sets with the problems the government has, and then providing promotion paths and options through that is somewhere where I’m trying to push the department. The second category really gets into really ensuring we have a strong foundation, and this gets into our civilian workforce. You know, when I was in the military, I didn’t realize we had, you know, 200-plus thousand civilians that were doing a lot of great work behind the scenes. How do we ensure that they have the right promotion pathways, the right recruitment opportunities, the right baseline of interesting things that we can leverage?And then more importantly is... Not more importantly, but equivalent to that, is the shipyard workforce. You know, shipbuilding has become, front of mind all the way from the president down to where we’re at right now. And to be honest, we’ve kind of hollowed it out over the past 20 or 30 years. And we’ve realized that the American shipbuilding industry and our maintenance approaches could use some improvement. And so we’re spending a lot of time trying to figure out, do we have the right incentives in place to recruit, retain, and then attract the next generation of shipyard workers, to do the maintenance activities that are in our four public shipyards right now. And then the last one is what I call white glove service for those who have served and those who serve. And this is about the day-to-day processes that folks might experience. So I’m also responsible for a number of boards that we have, the Board of Naval Corrections, being one of them. And if someone who is of, former service comes in and says, “Hey, there’s something wrong with my record. I wanna have that corrected,” they send a mail package into this mailbox, and it kind of gets in the system. And the system does its thing and might pop out six months later or three years later or some amount of time. We have all these processes in the department that if we can map and understand better, we can compress that time in order to deliver the outcomes that our service members both in uniform and out of uniform deserve. Those three areas are spending a lot of my time right now.

Maggie 06:19

Yeah, so I’d love to pull a thread on that first one, on, you know, talent management and modernizing that process. Can you talk a little bit about, you know, what does this industrial age approach look like today, and what will it take? You know, what is your vision for bringing it into the 21st century?

Sec. Kohlmann 06:35

Yeah, I mean, on the military side, there’s been what we call golden path career paths for a long time, especially in the officer community, where if you’re an aviator, surface warfare officer, a submarine officer, you kind of know what every step of the hierarchy is from the time you enter service to the time you retire, whether it’s at, you know, 10 years and you transition out, or 20 years, or 30 years. And There, there’s a lot of flexibility though that isn’t really built into the system. So my personal story, I chose to leave the military at about 12 years of active service to go get a graduate degree. At the time, graduate degrees were not really encouraged in the aviation community. And if I wanted to do it on the government’s dime, I’d basically be pushed off track and would go into a different category of officer. That didn’t necessarily seem right to me. On the civilian side, we kind of don’t really have any track whatsoever. There are some for highly technical areas, but most folks, you know, kind of feel their way around promotions and opportunities and people they know, as opposed to, you know, clearly defining what their outcome they wanna be and then knowing how to get there from step first to step last. And especially in the reserves as well, one of the things we’re seeing is there’s an immense amount of talent in the commercial sector that has a deep desire to serve their country How do we slot that in to the organization so that they can get recognized for kind of their skills on the outside, but also bring those skills directly into what’s happening in the military? This is what we’re doing with the Naval Innovation Unit and other organizations. But it comes down to that fact, you gotta find the right person in the right job to solve the right problem. And I think up until recently, we’ve had more of a, “Hey, anybody will do for this specific job. There’ll be a good enough outcome that happens,” as opposed to take, as opposed to taking advantage, like the asymptotic upside that comes when finding the perfect genius for the perfect thing to be solved. With kind of a full force data analytic view, we can start parsing through and finding more signal amidst the noise to really match the right people into those jobs that really will accelerate the outcomes that we have for the organization.

Maggie 08:34

Yeah, I wanted to ask one question on something you brought up, which is this, you know, how do we get more people from the commercial sector into the Navy and into the armed forces more broadly? I mean, I know I have some friends who have looked into it. It’s a pretty long process to get in, and even once you’re there, you’re not guaranteed to necessarily get a role that is gonna fit your particular skill set. I know this is something you’ve had some initiatives around, like with the Navy Innovation Unit, so would love to hear a little bit more about how you’re thinking about getting people from the commercial sector into either the Reserves or other parts of the government.

Sec. Kohlmann 09:09

Yeah, it’s challenging, but it’s, now’s the time to do it. So we’ll use the Naval Innovation Unit as a great example. So the Army actually led the way here about a year and a half ago now with what they’re calling Debt 201, which was taking senior executives from Silicon Valley and quickly getting them through a commissioning process, through an application, and then applying them directly against the problems the Army had. The Navy’s taken a little bit more expansive view with a larger footprint, but taking 10 folks from the outside, direct commission them in as 0-3s or 0-4s or 0-5s, so lieutenants, lieutenant commanders, or commanders, commensurate with the level of skill, and then putting them within the Rapid Capabilities Office, which is a group in our Research, Development, and Acquisition organization, and then using them combined with existing Naval Reservists that have, working in Silicon Valley but have risen through the ranks to kind of have that cultural, insight that they already have, then applying them against direct problems. Bit of an expedited pathway, kind of an experiment where it’s an application process. Hey, we have 10 slots available, got about 300 folks who have thrown their name in the hat right now. Have a filtering, select those 10 folks, and then very quickly commission them and put them through a five-week, what we call, ODS, so Officer Development School, that gets them up to speed on the militarization pipeline faster than you would normally. And what we’re trying to seek out is like, hey, the Navy knows that we have gaps in particular areas. How do we then, again, take the unique individual to solve that problem and slot them in more quickly? You know, there are a number of other areas the Navy has as well that are professional in nature. We already do this with doctors a lot. One of the biggest challenges and shortages that we have in the Navy is with high-skill surgeons and other skillsets that, you know, people on the outside are making a lot of money on. They don’t necessarily wanna deploy, but if they do, we can direct commission them, ‘cause really what we’re solving for is their specific skillset as opposed to their military rank equivalent of militarization. We have things as the Supply Corps or the JAG Corps or others that at the end of the day, you have to have some level of militarization, but if you have the right skillset and you’ve proven the outside, we should be able to bring you in at a relatively senior level to actually apply your insights to solve that problem for the military. So for me in the organization, NIU is a way to start that with the tech scene, but I can see that if it works, kind of expanding it to broader segments of the reserve population in particular, to have that kind of revolving door between the civilian and military sectors to get the best of both worlds.

David 11:36

Yeah, it’s fascinating. And, you know, this translation between going from the commercial sector into the military apparatus and back and forth to try to take advantage of some of the cultural norms within the commercial innovation ecosystem, apply it to the national security use case. I think that’s something that, you know, we stumbled upon with Defense Innovation Unit so many years ago when we brought in these hi-highly qualified experts, essentially entrepreneurs, and, you know, put them on some of our most vexing problems across the department, and then just see the magic happen. And it’s really cool to see the Navy and the Army, and I’m hoping my, beloved Air Force at some point also adopts these types of innovative approaches as well. But maybe, you know, moving gears to your second pillar on the civilian workforce and incentives especially, you know, whether we’re talking about those in acquisition program offices, which is the world I came from. And so, yeah, upon commissioning and getting out to Peterson Air Force Base, you know, the military people were the minority, and the majority of people that you interacted with on a day-to-day basis were civil servants. And then you go out to the shipyards, and it’s even more dramatic, right? As far as seeing a uniformed, member versus a civilian corps. So how are you thinking about incentives and, you know, especially with a very robust private sector that you’re ultimately competing against and trying to take advantage of the reindustrialization excitement that’s happening, especially in the Navy around shipbuilding. You know, I’m curious how, you know, how you’re taking a very, like, strategic vision but then a tactical approach towards making it better, right? And it can always be better, but certainly, you know, we have a lot to work with here.

Sec. Kohlmann 13:29

Yeah. I mean, incentives absolutely matter, and increasingly over time, the patriotism premium has gotten higher and higher for those who choose to serve their country and their government. I use the shipyard example in some of the things that we’ve tried to do recently to kind of bridge the gap. You know, up until a couple years ago, it was in some cases better for someone, say, in Pascagoula, Mississippi, to actually work at a Chipotle across the street than be in the shipyard because the wages they were paying just weren’t competitive with the market. And so, we had to figure out how to, how to incentivize them and make sure that they were getting paid what they were worth. So a number of things that we’ve done, include more pay-for-performance opportunities. We’re working with a number of organizations, both within the executive branch and legislative branch, to think about how do we restructure the pay scale, to reflect specific skill sets but also reward people for outstanding performance? What’s been interesting is many people heard about the Department of War awards that happened at the end of 2025, kind of, 15,000 or $25,000 bonuses that were given to a set number of folks, for high performance. And as I walked around to some of the shipyards getting some feedback on that experience from some of the frontline workers and even the supervisors, there was one supervisor in particular that said, “Hey, this has actually transformed our workforce.” In one instance, it was life-changing money for some of these shipyard workers, but it also was an opportunity for those that didn’t quite meet the bar to have hard conversations with their supervisors, asking, “Hey, why wasn’t I eligible for this?” Or, why did I just not meet the cut?” And a lot of the supervisors are gonna lay out expectations in a way that they hadn’t been able to do before, ‘cause now there was a motivation to get to that higher compensation level. And got a report a couple weeks ago from this individual, that I’d followed up with kind of earlier in the year, and he said, “Hey, output’s basically doubled on some of our core projects because people now realize there’s an opportunity for them to be rewarded for hard work.” Another example that we looked at is the way that the government does wage scales and GS employees, one’s kind of the touch labor, individual contributor, the other are supervisory roles in the GS scales, is there was a crossover point where if you had a... If you, if you were sufficiently advanced in your individual skill, like a welder, for instance, 10 or 15 years in, you kinda capped out on your pay, and you had to then jump over to the GS scale to be a supervisor to make more money. But not everyone’s built to be a supervisor or manager, and David, as you and I know from our work in Silicon Valley, sometimes the IC software engineer is making more than their manager ‘cause the value they’re providing to the force is that much higher. And so you look at some of the Korean shipyards and Japanese shipyards, one of the reasons they’re so good is they have this cadre of folks who have been there 20, 30, 40 years that are absolute exceptional people in their craft in one specific thing. How do we take that mindset and bring it to the government workforce as well? How do we create those master welders that they can stay on the front lines doing that job for decades and get rewarded for that without having to hop over to be a supervisor? So we’re exploring ways to kind of fix that gap And then on the, on the, on the civilian side, just writ large, I think we gotta we gotta be more intentional how we reward people. Right now, the government has a lot of authorities with, you know, what it can do for spot awards or special purpose awards, and being more intentional about being clear of, “Hey, if you do X, Y, and Z and excel at a great level, there will be a bigger award bonus pool.” We have authorities to do that right now, but it’s a matter of shifting it and then shaping our budget in such a way that we have the right amount of money given to the right people to drive that incentive loop moving forward. So a lot of it has to do with incentives within existing authorities, kind of restructuring the way that... Again, things were designed in the ‘50s and ‘60s for a reason, but, you know, 2026 is different than what it was 60, 70 years ago.

Maggie 17:24

Yeah. It makes a lot of sense. You know, I wanna turn to a question. This podcast, we typically focus on our discussions around technology. I think we’re all tech nerds here. So I wanted to hear, how do you see the role of technology helping to support your priorities when it comes to managing talent in the Navy?

Sec. Kohlmann 17:43

Yeah, I’m gonna go in a little bit different direction. We can circle back to that question as well. You know, I’m a, was a venture capitalist, ran a hard tech startup, flew F-18s, another value of technology. Technology’s a critical enabler, especially in warfare, and even our broader American economy. But at the end of the day, technology is always in service to humans, and technology does not actually work or accelerate unless it’s solving a human problem. And on the battlefield, it’s men and women who are on the front lines using technology who find the most creative uses in that crucible of combat, something I call weapon-warfare fit. If you have weapon-warfare fit, it means that there’s a system that’s actually doing something in service to fighting and winning that war. And the most tangible example recently is Ukraine. You know, it’s the tens or hundreds of thousands of folks on the front lines who are having, you know, their friends killed beside them with these unmanned systems. And on a daily and weekly cycle, they’re taking that technology and modifying it to sort to human ends, and that requires a very creative human to get that, to get that question right. So as I think about, like, talent development, it’s about how do you think about that technology-human fusion such that technology is built to solve those human problems, but at the same time, the human is in or on the loop to be able to utilize it in the best way possible and use creativity to kind of break the rules when needed because it’s a life or death situation. And so as we think about talent development in the, in the, in the Department of the Navy, on the military side, it’s about creating great leaders who can use technology to the best of their abilities. But on the civilian side, it’s about how do you find those exceptional people that then can be put in positions to drive change and opportunity? And I’ll end this, kind of this theme with a story. So we ran a data challenge back in the, in the spring, and we kinda gave open access to a much of our HR data. As long as you had clearance to view it, you were allowed to do it, and self-generated teams across the force. And we probably had 10 teams kind of develop themselves, and one of the, one of the winning teams, one of the top three teams, they were sharing their development story with me, and what was interesting is their senior leader actually didn’t want them to participate. They said, “This is taking away from your day job.” And for one of these individuals, their boss actually put more workload on them to prevent them from taking part. And yet this individual, with their team, still managed to do exceptional things and developed a solution that’s now very useful for us at the Department of the Navy. Like to me, it wasn’t the tool or technology that was the problem, it was the human systems that elevated the individual capability to a level that could take advantage of the technology that we had right now. So as I think about talent management in the department, it’s about how do we find those nodes of genius and make sure that they’re empowered to solve the problems they can and align with the, with the technology advancement we have that’s developing. So it’s gotta be a parallel process.

David 20:31

Yeah, and what you talked about with that anecdote also highlights a lot about culture and, you know, no amount of tools can fix culture. Though, you know, I’ve been in the military now for a long time on the active duty and the reserve side, and Lord knows I’ve interacted with any number of HR admin tools I honestly feel as though it’s sort of a potpourri of at some point I get to the right conclusion of the task I’m trying to figure out, but it was never as obvious and intuitive as it should’ve been. And I feel as though part of the issue when it comes to the military, and you touched upon this a little bit with your people are thought of like fungible assets that are just interchangeable cogs of the, of the machine. And really, we should be treating individuals for the unique talent that they are so they can reach their highest potential. And I find that a lot of our HR and admin systems are trying to boil the ocean and treat everyone, regardless if you’re a fighter pilot or an acquisition officer or something in between the tip and the blunty part of the spear, one system to solve everything. And I’m curious if in part of this, you know, priorities of execution that you’re trying to maybe subjugate the career fields and find the right tools given, their culture, the lifestyle, whether or not they’re in an office or on a computer more often than not, or if they’re behind, you know, a joystick in a cockpit. Curious how you’re kind of thinking about that piece of the equation instead of one size fits all for the entire workforce.

Sec. Kohlmann 22:12

Yeah, I mean, it’s truly a balance. I think one of the eye-opening experiences in this job is you kinda do have to look at the macro to some extent. You know, the three hundred and sixty thousand Navy active duty folks and another sixty thousand on the Reserve side. There is a degree in which People are a part of the whole, and so you wanna have the broad trends. Do we have enough, you know, department heads in the surface warfare community? Do we have enough commanding officers in the, in the submarine community? Those are somewhat interchangeable. And yet each submarine is gonna have a different setup of the commanding officer, the executive officer, the wardroom. If you inject a new person, that person has a personality, and that person has a unique skill set. What if you could have an algorithm that inherently knew, a, I’ll call it a digital twin or digital human, that, hey, Ben is an INTJ. He had, you know, one and a two on the working genius, and he was X, Y, or Z on another personality profile. And oh, by the way, he’s really good at kind of high-level strategy. He loves playing Axis and Allies and Risk, loves to read on the weekends. And then you have person X who’s very gregarious and outgoing, an X, Y, or Z thing. Well, how do Ben and person X interact? Well, you can imagine a world in which it’s not just about putting Ben in the same squadron because he’s a body, but, like, how does this, how does this interactive team work together, across the whole? If you can have the quantum of data of a digital human that has characteristics about their performance, about their skills and whatever, you can, you can do the macro-level aggregation. Hey, I know I’ve got 11,000 financial analysts in the Department of the Navy. I’ve got 8,000 contracting specialists. But you can also say, okay, when Ben goes to apply for this job or when Ben goes to in this squadron, here’s the characteristics of people around him, you know, based on trends or performance reasons. We realize there might be a mismatch in terms of how he fits in. Maybe he’s better suited over here. Or we know that he wants, his family is in Dallas, and he should be stationed in Dallas, and so our matching algorithm kind of highlights that in whatever capacity he thinks is important. I think there’s a world in which we get there, where we can take the macro and the micro and kind of find the right happy medium. And we’re starting to do this right now with detailing for the enlisted force, and this is to the deep credit of Admiral Hames and some of the folks down there, is there’s right now an algorithm that a bunch of sailors can, apply for basically, and it’ll send them, “Hey, these are the top 10 jobs we think that you’re eligible for based on what your career progression needs to be, what your skill sets are, what you care about from a, from a location standpoint.” You don’t have to take these 10, but it’s a great starting point to have that conversation with your detailer. What we found is even though that’s like a human on the loop with a computer doing the system, there’s, there’s a higher level of uptake rate and retention because the sailor now realizes there is some intentionality around their careers. They may not leave the service. We’re seeing anywhere from 30% to 40% of the people who would’ve left staying because of that more, that more high-touch detailing approach. So I think we’re seeing a convergence of technology and insights right now where in the medium term, maybe a year or two if we build it right, we could start having these nascent systems to kind of do this matching better to ensure people are in the right job.

David 25:33

Yeah. And so it sounds like, you know, you are trying to incorporate AI and just a ton of big data into helping identify, you know, jobs and roles and opportunities for folks. I guess I’m curious, you know, where from a Navy standpoint are you guys taking inspiration, right? Obviously, the Navy it’s the best navy in the world, and I think probably potentially the largest. Not sure exactly what China’s numbers are relative to the US. But, you know, you’re not the only large corporation trying to figure out a diverse set of skillsets across a diverse set of locations to ultimately, you know, get mission readiness, mission effectiveness. And so where, you know, when you think about solving these problems, are you looking towards large private corporations, whether they be, you know, big energy, you know, from the state that you hail from, or other Fortune 10 type entities, or maybe the Air Force?

Sec. Kohlmann 26:33

Yeah. I mean, to be honest, this is probably a gap in where we’ve spent our time to just know what’s happening in the outside world. You know, in my consulting days, I did a number of HR studies for large organizations, so taking some of the lessons learned there of how to look at the workforce, how to think about restructuring, how to do some of this data analytics. But I think there’s actually a big opportunity for us to improve, and I think there’s a, there’s a couple cultural things that, you know, we can, we can lean more heavily into The first is this belief like the government is different, and the military is different, and the broader skillsets that are over there don’t apply to us. I think one of the things I learned in consulting, having worked in the government, having worked in a bunch of industry is a lot of the problems are the same, and the insights are like 80 to 90% there. There’s the 10% that’s important, you gotta get it right, but the foundational of how to manage humans, how to think about structuring, they’re all similar. So we could reach in there and grab some more information. The other thing too is more cross-pollination, and we talked about this earlier in the conversation. More cross-pollination for business ideas coming into government to just, you know, raise the awareness. There is a different way to do things, and I think this is where the Department of War has been leading forward in the bond program especially, where they’ll bring in senior executives who are post-economic in many cases and just want to serve, and bring them in executive roles to kind of partner up with their equivalent to kind of Hey, this is what’s happening in the private sector. This is what we did at Energy Company X. This is what we did at Walmart or whatever, and help that, help that, help that kind of exchange of information flow. I can’t help but think there’s probably value in more secondments, you know, mid-grade GS’s goes over to work in industry for a couple years knowing they’re gonna come back to solve a problem, to kind of get that experience, the... I think there’s a, the, what is it? Secretary of Defense Technical Fellows or something like that goes to industry. That cross-pollination is critical. Not as good as we could be at it. I’ll also say one other thing. You know, the Chick-fil-A model of high customer experience I think is one I wanna see about integrating into our reserve centers. You know, who is the customer of a reserve center? It’s the reservist who’s coming in on the weekend, and right now the experiential level isn’t as high, which kind of drives misintegration. How do we get the lesson learned from high customer service organizations and apply them into high customer service facing military functions to drive that experience and also the outcomes better? So a lot of work we could do, but we’re thinking about it.

Maggie 29:07

I recently read, an article with some pretty concerning statistics, or statistics I found concerning. It said, you know, fewer than 25% of Americans age 17 to 24 are eligible to serve in the military without a waiver. The number of 18-year-olds is actually projected to decline by 13% by 2041, and the propensity of Americans to serve in the first place has fallen from 16% in 2003 to 10% in 2022. However, I also saw the Navy is actually ahead of schedule on recruiting this year. So I just wanted to hear a little bit about how you all are thinking about recruiting and some of the challenges that the department as a whole is facing.

Sec. Kohlmann 29:50

Yeah, it’s a paradox. I attribute it to probably three things. The Navy Marine Corps, you know, I have the honor of helping to oversee both. 2022, 2023 was a real nadir for us, and Navy Recruiting Command saw that deficit and started to apply kind of best practice from industry with Salesforce and others. And the current commander of Navy Recruiting Command, Jim Waters, he’s basically instituted a daily ops update that has incredible dashboards to highlight and pinpoint which specific recruiting stations are falling short on which specific line items they need, they need to fill for. And as a result, it’s driven attention to leadership level to areas of improvement. And it’s, you know, it’s an accountability measure, but it’s also like, “Hey, what’s going wrong here? How do we problem solve that?” So that deep dive execution and being relentless at the basics, I think, has gotten the Navy back on track On the Marine Corps, it just comes back to the core ethos they’ve always had, the few, the proud, the Marines. Like, that mindset continues to resonate, that are you good enough? Can you meet the standard? There’s a lot of young men and women who wanna try out for that, and they-- If I do that, if they achieve that, like, that’s something they can hold their head high about. And I think that has driven kind of a resurgence in recruiting, across both the Navy and Marine Corps right now. You know, looking to the future, the demographic cliff is real. You know, 2028, I think that’s when kind of the 17, 18-year-olds kind of fall off, given what happened in the early 2000-- or, you know, kind of post global financial crisis era. And I saw this when I was at the board of a university down in Texas. We were kind of looking at that number. It’s like, “Hey, how do we make this math work?” there will still be the cohort of 17 to 23-year-olds that will be a target market. But the question is, how do we think about then augmenting it from folks who are kind of in career one and a half or two? Maybe the more reserve option. You got five to 10 years in, how do you slot that experience in a different capacity? Who knows where AI takes us, but as those jobs become maybe less secure, there will always be a need to have people defend their country and make it high enough status and high enough paying that it’s a, it’s a, it’s an option for people to flow back in. Maybe there’s a world in which we have some sort of loan repayment program for 25 to 30-year-old folks who tried the private sector out, couldn’t quite get traction. It’s an incentive bonus to kind of get those folks back in the, in the realm. I don’t know. There’s a bunch of stuff that we’re thinking about to try to, try to accelerate. But I’ll also say, whenever the American public realizes there’s a need, and a legitimate one, they tend to, they tend to fill that gap. You know, we saw it in 9/11, we saw it, you know, kind of 2024, 2025. So I’m optimistic, but, like, w-we are watching it closely, and there are some changes that will have to be made down the line.

David 32:31

Yeah. So, you know, you highlighted on the top of the funnel with the next generation of uniform wearers. I’m kinda curious on the other side, right? So you and I are kind of anomalies getting out at the 12, 13 year point, or at least separating. Normally, people would stay in for the full 20, right? The whole cliff vesting strategy that we were under. But about 10 years ago, they implemented the blended retirement system, which makes it a little less obvious to stay in from a financial reward standpoint as you become a, you know, mid-career officer. I’m curious if there’s anything you could speak to on whether or not recruitment at the later stages of officers to get to go for O-five and O-six, or whether or not there’s a bit of a fall off there too.

Sec. Kohlmann 33:24

Yeah. Broadly speaking, we’re hitting our retention numbers, in many cases are exceeding them. But there are specific communities where we’re struggling. These are kind of the more high skill communities, cyber, aviation, submarines, places where there are really clear and tangible, financial benefits on the other side that even if you stayed at 20 to get your pension, you know, you’re missing out on millions of dollars potentially. I think the integration of the Blended Retirement System, if anything, forces us to be better to make sure that what do we have to do to keep these folks? And it’s not the same thing that took in the ‘90s or the 2000s or even the ‘80s, but, like, it forces the organization to shift their approach to try to retain these people with the salary caps that have been given to us by Congress and others. And I think we’re kind of going through those growing pains right now, especially with aviation. This is something near and dear to my heart One of the reasons I got into this, you know, 15 years ago was I saw the flight of fighter pilots from the Navy and wanted to figure out why, and it’s only snowballed from there. This is getting increased congressional attention, attention elsewhere in the Department of War. And it comes down to quality of service, what’s the experience they have, do they have other options? A lot of people are putting a lot more stake, especially men, into families, and so they’re not maybe taking the 8 to 10-year carrier pathway ‘cause they wanna spend time with their kids between 18 and 20 in a way that maybe they didn’t 20 to 30 years ago. So that’s having an increased effect on that as well. So what does that mean for what it means to be a fighter pilot in 2026 and beyond when you need that exceptional skill set? I think we’re wrestling through that question in particular. We have some things that we’re thinking about, but it’s becoming more and more apparent in that community. But writ large, we’re actually meeting a lot of our ‘04, ‘05, and ‘06 goals. It’s just those highly skilled roles where there’s a deep market, competition, that it’s becoming trickier to retain those folks.

Maggie 35:19

You’ve now spent time in government, in the military, active duty, as an investor working with startups. Having seen all sides of this equation, what advice do you have for startups looking to work with the Navy and the U.S. Military as a whole?

Sec. Kohlmann 35:36

First of all, find exceptional talent. I mean, I think at the end of the day, like, this job just reinforces something I learned being an investor, is that the best companies win ‘cause they have the best talent. They can solve the hardest problem. They can figure it out. And that talent in government comes down to understanding how the government buys things, how Congress works, who the true decision-makers are in the department, and what the non-intuitive problems are, you know, downstream of what happens. I’ll just give an example. You know, submarine supply base, not just about building the submarine itself. It’s about the tier two and tier three suppliers that have to supply the parts to both maintain the submarines and build them. Well, if we know that we’re gonna have an increase in the total number of submarines and, maintenance capability, we’re probably gonna have a submarine supply shortage. You’re seeing a couple startups that have kinda sneaked into that realm in a very impressive way and are filling that gap. So where do you see the puck going that is gonna be a funded source that is not yet filled yet? So thinking about it that way. There’s a little bit of luck. Two, three, four years ago, UASs weren’t... Well, I guess they’ve always been big. But there were some capabilities and systems that if you were ahead of the curve building then, are now emerging right now. Are you ready to jump on that, take advantage of it? But honestly, David’s probably the best expert here. He’s written the most on it. I know you have too, Maggie. You guys are, you guys are well-versed in this. But it comes down to finding the right people to find the other right people in the organization to then win those contracts and kinda find the scaling opportunities.

David 37:12

Awesome. Well, thank you. And I guess, as we wrap up, we just have one special question that we did not prep you for, and we were wondering what kind of insight you can provide us about Top Gun 3 coming out, and will you be playing a starring role as a wingman to, Tom Cruise?

Sec. Kohlmann 37:30

Well, I can’t say much, but there won’t be any people in it ‘cause it’s gonna be all unmanned, so.

David 37:35

Will it, though? I mean, gosh, the magic of Top Gun: Maverick was, you know, going back to the third gen fighters, right? Or fourth gen fighters to take out whatever adversa- fictitional adversarial was.

Sec. Kohlmann 37:48

No, that’s right. That’s right. Well, the sad part is when they come out with Top Gun 3, the old airplane will be the Super Hornet, which is what I flew, which makes me feel really old. So no, like I can’t wait for the third one to come out if they choose to do it. So I don’t know if you guys have any tipper, but that’d be a great one.

David 38:01

Awesome. Well, Secretary Kohlmann, thank you so much for spending your time with us, and really look forward to all the great work that you’re doing to help increase readiness, get the talent pool ready to go, and make sure that we can deter our adversaries that, not today, not tomorrow. That’s right.

Sec. Kohlmann 38:17

Thanks, David. Thanks, Maggie.

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Here are just a few of the awesome roles our portfolio companies are hiring for:

• Autonomous Cyber is hiring a Forward Deployed Engineer to work with customers to build the future of AI for offensive cyber (for more on Autonomous Cyber, check out our podcast episode with the founders).

• Code Metal is hiring AI research engineers, deployment strategists for CENTCOM, PACOM, and USMC, modeling and simulation engineers, a recruiting coordinator, and much more to build the future of AI code translation for national security (for more on Code Metal, check out our podcast episode with the CEO).

• Starfish Space is hiring electrical, GNC, and software engineers, as well as business development, supply chain, and mission engineering managers (for more on Starfish, check out our podcast episode with the CEO).

You don’t need to already work in defense. You just need to be exceptional at what you do and want work on a Mission that Matters. 🇺🇸

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While many startups ultimately aim to become prime contractors and secure government programs of record or large commercial market shares of their own, partnering with established defense primes and commercial corporations as subcontractors, vendors, or suppliers can provide an important bridge in the early years. These relationships can create the order volumes needed to scale production, reduce unit costs, and help startups cross the proverbial “valley of death” on the path to mass production.

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Last summer, Shield Capital operating partner Pat O’Reilly and I wrote an article “demystifying” defense corporation sales for startups. This summer, we decided to update that piece with additional lessons learned over the past year. Pat has over four decades of experience working as a customer, consultant, and executive within the Aerospace and Defense (A&D) industry, and he works closely with startup CEOs selling to A&D and large commercial corporations. In his experience, it is critical for startups to understand how defense primes and other large corporations make purchasing and partnership decisions as they craft their Go-To-Market (GTM) strategies and work to efficiently allocate a startup’s limited resources. This article shares Pat’s insights into increasing the probability of overcoming a large corporation’s sales hurdles.

There are many lucrative opportunities for startups to work with large corporations. Many internal software and hardware tools are legacy and inefficient, existing suppliers are expensive and slow, and many corporations’ engineering teams lack enough strong software, AI, and robotics talent needed to deliver on new contract opportunities.

However, corporations also tend to have slow, deliberate, and proprietary decision processes for adopting new technologies, or services, creating a significant and often opaque barrier that can feel impenetrable for startups seeking to navigate the organization and secure sales. Corporations’ procurement decisions are often intertwined with non-disclosable but enforceable agreements with existing suppliers, and many corporations view their procurement process as a differentiating factor in competitive solicitations. Additionally, they want to limit disclosure to government agencies that they may have to negotiate contracts with in the future.

Unlike small or mid-sized companies where a few individuals can rapidly make procurement decisions with transparent rationale, large corporations’ decision processes typically take 9 months or more due to the large number of stakeholders and disaggregated functional staffs involved. Recent earnings reports (scrutinized by Wall Street analysts which put a premium on cash on hand), on-going proposals, the general economy, and the success or failure of recent solicitation pursuits can all impact corporate decision-making. Additionally, corporations need to balance competing technology investment opportunities with capital and Internal Research and Development (IR&D) budgets. Furthermore, they do not want to expose themselves to the scrutiny of why one startup’s technology was chosen over another’s.

Regardless of the industry, successful startup engagements with large corporations typically follow a pattern of eight sequential steps:

1. Identify and Engage the Target Customer

2. Introduce the Technology and Demonstrate its Capabilities

3. Engage the Business Unit and Secure Agreement for a Pilot

4. Plan and Execute the Pilot

5. Validate Pilot Results, Support Building the Business Case, and Secure Budget Approval

6. Negotiate and Finalize the Purchase Order or Contract

7. Manufacture and Prepare the Product for Delivery

8. Deliver the Product and Receive Payment

While the scope of each step may vary, the pattern of engagement steps is common across large corporations and industries. Typically, each step has different corporate stakeholders, influencers, and decision makers participating; therefore, success in one step does not necessarily indicate success in the following step. A key to success is identifying and cultivating a “champion” within the target corporation who can provide early guidance and help the startup navigate the company’s technology adoption and procurement processes.

Understanding Corporate Customers’ Perspectives

Before attempting to sell to a large corporation, startups must first understand corporate customers’ perspectives and incentives, which often differ significantly from their own. While startups tend to be focused on improving quality, lowering cost, or maximizing performance, corporations are primarily motivated by achieving announced profitability forecasts that are updated in quarterly earnings calls.

Startups and large corporations differ significantly in their tolerance for risk. Traditional startup guidance is to deliver a “Minimum Value Product (MVP)” that can be improved over time by feedback and collaboration with their customers. Unlike selling to businesses that can accommodate incremental, ongoing improvements to the product or take part in an R&D effort, A&D corporations need products to be delivered with proven reliability and performance and in their final configuration, so as not to jeopardize the quality or brand of their products. Corporations’ production contracts do not allow for time or resources to incrementally improve their sub-contractors’ products once delivered.

Corporations place a premium on winning large competitive contracts and then complying with the terms, conditions, and scope of those contracts with the minimum execution cost and risk. Therefore, A&D corporations have little incentive to introduce a new supplier or technology to a contract if it increases the risk, modestly reduces costs, or exceeds the performance of an already compliant contract execution plan. Since most corporations’ business units manage portfolios of multiple ongoing contracts, resources and expenses are allocated, and often re-allocated, to optimize the collective profitability of all contracts reported quarterly by that business unit instead of optimizing the performance of an individual contract. Similarly, due to cross-contract teaming agreements, it is unlikely that a supplier will be replaced on a contract or proposal team if they are also participating on other contracts and proposals in that business unit. Therefore, simply exceeding a contract’s performance requirements usually does not provide sufficient motivation to disrupt the execution of an existing contract or on-going proposal, particularly for cost-plus contracts which do not incentivize contractor efficiency. Likewise, if a corporation is confident in their Probability of winning (Pwin) a solicitation, they are not incentivized to add a new entrant onto a proposal team even if it improves their products, if they deem it could introduce additional, unnecessary risk to the project. Finally, A&D corporations may be unable to adopt new technologies without first securing end-customer approval when introducing new major subcontractors or changes to established systems.

Startups often underestimate a corporation’s cost of evaluating, much less adopting, a new technology.

Evaluating and adopting a new technology can be costly and time-consuming, requiring employees across multiple functions to test, evaluate, and integrate the product, often while the organization is under pressure to keep its workforce focused on production. Management may also be reluctant to involve the unions or labor groups that would ultimately use the product, even though their participation may be critical to successful adoption.

Given the negative incentives for a corporation to adopt new technologies and services, when would a startup’s product have a high likelihood of being adopted? Our experience indicates there are three cases when a corporation would be driven to adopt a startup’s new technology:

1. It solves a serious problem causing a large loss of revenue (ex: one of our portfolio companies is working with a large corporation to help solve quality assurance problems that can cost the customer millions of dollars each year in product recalls, fines, etc.)

2. It significantly reduces operations expenses, or Cost of Goods Sold, on the order of 10X (ex: Apex Space builds satellite buses an order of magnitude cheaper and faster than its legacy competitors, and as such has won contracts with a number of defense primes)

3. Their largest customers, or competitors, force them to adopt the technology (ex: many U.S. military programs are now requiring contractors to translate millions of lines of legacy code into memory safe coding languages, which has driven some defense primes to adopt AI code translation tools like Code Metal)

Thus, startups should prioritize developing and selling products to corporate customers where one or more of these situations apply. Developing a solution that is only slightly better is not enough to break through corporate customers’ buying cycles.

Step 1. Identify and Engage the Target Customer

Startups should invest the time and energy upfront to identify corporate customers with a compelling and urgent need for their product. Focusing on highly motivated buyers increases the likelihood of a successful sale and prevents scarce time and resources from being wasted on prospects with little urgency to act.

The corporate information required to determine a customer’s level of need for a product is often confidential. Despite this, insights into a corporation’s problems and needs can be gained through open-source research and “probing” engagements. For public corporations, quarterly earnings reports, press announcements, speeches and articles by corporate leaders, and annual 10-K reports are good open sources for research. For private companies, public information is more limited, but startups can still glean relevant information from press releases and interviews. In our experience, ultimately the most effective approach to identify a corporation’s level of need for a product is through repeated in-person engagements.

When evaluating potential corporate customers, it is important to consider timing. Government Requests for Proposals (RFPs), corporate annual budget cycles, and SEC earnings reports drive corporate decision schedules. Startups should track major RFPs that are relevant to their technology and engage a corporation soon after the RFP has been released by the government, as early outreach creates more time to establish a teaming relationship and shape a competitive joint proposal.

Recently announced startup-defense prime partnerships and contracts also illustrate the importance of aligning with urgent administration priorities. For example, earlier this month, GrayMatter Robotics and Path Robotics announced a $900M contract with HII to automate Navy shipbuilding, a major priority for this administration. Similarly, Northrop Grumman and Apex Space announced a partnership to develop space-based interceptors, a novel space platform critical to the administration’s “Golden Dome” plans. Picogrid and Northrop Grumman announced a partnership to develop software for air and drone defense, as did General Dynamics and Anduril, another major government priority in the face of the conflicts in Iran and Ukraine. Likewise, manufacturing startups including Hadrian, Deterrence, and Machina Labs have partnered with major defense primes to expand production of missiles, artillery, and other munitions as recent conflicts have exposed critical shortages across U.S. weapons inventories.

Unlike the U.S. government, most publicly owned corporations’ fiscal years run from January to January. Because of their size and complex governance structures, corporations often develop and approve annual budgets well before funds are deployed, which can create a significant lag between procurement approval and actual spending. For example, if a startup’s product is considered a capital expense, decisions to budget for that capital procurement often occur in the summer before the following year’s budget. Thus, if a successful pilot leads to a purchase decision in September, the funds to execute that procurement may not be allocated until the following January. Privately owned corporations generally do not adhere as strictly to annual budget cycles and, therefore, are more agile to react to opportunities to adopt new technologies throughout a year.

The timing of capital programs is also important. For example, if a corporation just purchased a license for a suite of engineering tools on a three-year contract with multiple two-year contract extensions, it is highly unlikely they would be receptive to a pitch on new engineering tools, regardless of how much money it would save or its level of performance. Most corporations have a “refresh rate” for replacing and upgrading tools and capital expenditures, so, during an introduction session, startups should ask when their “window of sales opportunity” is open to identify the best time to follow up and engage a customer on a new product. Additionally, corporations often have manufacturing and tool upgrade cycles tied to large program pursuits, so startups should track potential customers’ published awards to know when it is best to engage with them on new technologies.

Unless a founder has a pre-existing relationship with a corporation’s employee, founders should begin their outreach through groups that are positioned to evaluate emerging technologies, such as a corporate venture capital (CVC) team,1 the offices of the Chief Technology Officer (CTO) or Chief Information Officer (CIO), or a Manufacturing Technology (MANTECH) organization. Often CVC teams can connect startups with relevant business units or technology evaluation organizations within a corporation. However, not all CVCs are created equal – while some CVC teams have excellent connectivity into relevant stakeholders in the parent organization and a demonstrated ability to transition new technologies into business units, others amount to little more than “innovation theater.” Before engaging, founders should examine a CVC’s track record of successful technology adoption and speak with its portfolio companies to assess whether the team can provide meaningful access and support. For some organizations, it may be more effective to work directly with the business units and technology organizations themselves.

Step 2. Introduce the Technology and Demonstrate its Capabilities

Often, a startup’s first engagement with a corporation is not with the persons who ultimately determine whether the organization will adopt its technology. Thus, a startup’s introductory chart decks should simply and clearly describe the technology and its specific, quantifiable, value proposition (ex: lowers execution time and cost by 10x, decreases defect rate by 5x, etc.), so that the meeting participants can easily repeat the main points to other corporate stakeholders. Unlike an investment pitch, corporation employees are keenly aware of their own problems, so it is recommended that no more than one chart address the need for solving a problem. Instead, the presentation should focus on how the startup proposes to solve a problem and benefit a customer.

If the initial corporate contacts identify a strong fit within a particular business unit or use case, the startup will need to deliver a second presentation to the relevant stakeholders who will ultimately champion the startup’s products. These champions connect startups with the managers and operators who will use the product and help navigate the corporation’s evaluation, budgeting, and procurement processes. After this second call, startups should offer to conduct an in-person product demonstration at the corporate customer’s facilities. In-person product demonstrations often attract a broader group of stakeholders, generate more substantive feedback, elicit comparisons with competing products, and reveal the personnel, approvals, and remaining steps involved in the corporation’s procurement process.

Step 3. Engage the Business Unit and Secure Agreement for a Pilot

Demonstrating a product to a corporate-level technology group, such as the CTO’s office, is important to build internal support and identify potential champions within a corporation. However, the ultimate evaluation and justification of a specific use case often rests with the MANTECH, technology, or software teams embedded within a relevant business unit. In addition to a demonstration, these groups typically require a pilot program ranging from a few days to several months to validate a product’s performance in an active production environment.

Pilots are an expensive undertaking for a corporation given the time needed for managers, engineers, and operators to learn how to deploy the product. Further, pilots can introduce risks, as unsuccessful pilots can cause quality problems or diminish production rates. Therefore, securing corporate approval for a pilot often requires additional demonstrations and planning sessions with the relevant business units and functional stakeholders.

Step 4. Plan and Execute the Pilot

Once an agreement is reached to conduct a pilot, startups should actively participate in the planning of the pilot. When planning a pilot, corporate champions will survey production managers, operators, and other key stakeholders to establish evaluation metrics that support the business case needed to request capital budget allocation to ultimately purchase the product. Additionally, it is important to work with production planners to determine when a production process can host a pilot with minimum disruption and risk to their production quotas.

To prepare for a pilot, startups should spend time observing their potential customer’s current operations to ensure they fully understand how their product will be used and how evaluation metrics will be measured and recorded during the pilot. Because corporations already bear the internal costs of running a pilot, startups should not expect to receive funding to loan their products or to participate in a pilot; however, when it is offered, it signals a genuine commitment to evaluating the product. When funding is offered by a customer, startups should consider offering deeply discounted prices for pilot projects in exchange for the customer’s comprehensive feedback and testimonials if the pilot is successful.

Startups should avoid “blind pilots,” in which products are tested without their direct participation, whenever possible. Offering complimentary training and on-site support usually allows the startup to ensure proper product use, gather accurate feedback, and verify that performance and cost data are measured correctly.

Given pilots are often executed on an active production line, startups should be prepared for contingencies such as supply chain delays, union strikes, customer order changes, and other exigencies to significantly delay pilots at the last minute.

Once the pilot’s scope, objectives, and measurement parameters are agreed to, the burden is on the startup to ensure the final configuration of their product is completed on time. Startups should view a pilot as a single opportunity to prove the maturity and production readiness of the technology. Thus, startups should invest significant resources in product quality control and testing before the pilot and thoroughly rehearse any required training materials.

In order to accelerate the initial product procurements after a pilot, when possible, startups should request a very small Purchase Order (PO) associated with the pilot. Even a “no-cost” PO can initiate supplier registration early and reduce onboarding delays after a decision is made to procure a product, as it allows the corporation to perform early due diligence on the financial, government compliance, and legal criteria required by a corporation’s policies to purchase goods from the startup. It also provides the startup with transparency around payment terms, which helps with revenue planning.

During the execution of a pilot, especially in the early days, startups should endeavor to have on-site support personnel present when products are operated. Backup resources should be available to support repairs or recalibration of a product without disrupting production. If the pilot is going well, champions should highlight its progress and results to the decision-makers responsible for ultimately approving the purchase. If the pilot is not going well, the startup should pause execution to address product issues rather than complete the pilot with conclusive negative results.

Step 5. Validate Pilot Results, Support Building the Business Case, and Secure Budget Approval

Once a pilot is complete, startups should offer to provide technical support while a corporation analyzes the results to ensure performance data is interpreted correctly. Large corporations often outsource the analysis that informs procurement decisions, particularly decisions that require specific expertise, to business or engineering consulting firms like Deloitte, Boston Consulting Group, and McKinsey. Ensuring these consulting firms understand the product and pilot data collected is critical for the accurate analysis of a pilot’s results.

The ultimate decision to procure a new product is based on the product’s performance, reliability, ability to solve a severe problem, and the magnitude of the Return On Investment (ROI). In general, a new technology must offer the potential for a 10x ROI to justify the management attention, organizational effort, and risk required to adopt it. Clear ROI is particularly important when selling to P&L leaders at public companies, where quarterly earnings face close scrutiny from equity analysts and executives are incentivized to preserve free cash reserves by minimizing capital expenditures.

When conducting this business case analysis for a product’s ROI, corporations include both the cost of technology adoption and the long-term cost of ownership of the new product. This includes the cost of:

1. The product’s selling price (whether it is a product, service fee, or license)

2. Maintenance and future upgrades to the product

3. Cancelling contracts early with incumbent suppliers

4. Training employees to use a new product (including the resistance to learning a new tool)

5. Disposing current assets that a new product will displace

6. Disruption to existing programs and revenue streams

7. Impacts on other services or products provided by terminated suppliers

8. Qualifying components to meet customer specifications, reliability, and compliances with statutes, regulations, and customer policies

9. Meeting government cyber security, environmental, safety, quality, or other regulatory requirements

10. Acquiring data rights from the customer’s 3rd party vendors (e.g. the data from a customer’s 3rd party digitally controlled manufacturing machines)

Once the sponsoring organization within a corporation develops the business case, it submits a budget request proposal to procure a startup’s product for financial review and presents it to the relevant P&L leader. The P&L leader then compares it with competing capital expenditure requests (most of which the startup will never know about) and determines which projects will receive funding in the following fiscal year. Until corporate leadership approves a final, corporate-wide, capital budget (which requires multiple management layer reviews and board approval), the decision to procure a new product or service is not guaranteed. P&L leaders may decide to approve a different purchase quantity than the sponsoring organization requested or allocate Internal Research and Development (IR&D) funds for a limited purchase to continue evaluating the product during the next fiscal year.

Each quarter, business units assess the status of their finances, and if they are exceeding profit expectations, or if other capital expenses are delayed, there are occasionally opportunities to accelerate capital expenditures into the current fiscal year. Securing additional funds typically requires approval from senior management, which may instead reallocate the excess funds to a business unit with a more urgent need. Thus, staying closely connected to champions in a corporation is important even after a P&L has made a capital budget allocation decision on a startup’s product.

Step 6. Negotiate and Finalize the Purchase Order or Contract

After securing funding for a new project, startups must negotiate a PO with the corporation, a potentially time consuming process that can delay startups’ ability to quickly deliver to customers. Unfortunately, corporations often have many backlogged procurement actions at the start of each quarter, so even though funding may be approved and available, it can take more than a month before a PO is issued to a startup. Finally, startups must clearly understand delivery terms, marking and packaging requirements, inspection criteria, and warranty obligations to avoid costly surprises that can significantly reduce the anticipated profit from a large corporate sale. As stated earlier, when possible, to reduce the negotiating time to execute a PO, startups should begin the process to register as a corporation’s certified supplier during a pilot through a small PO or a “no cost” PO.

When selling to an A&D corporation, PO negotiations can be delayed if the startup’s legal counsel is not familiar with U.S. government Defense Federal Acquisition Regulation Supplement (DFARS) T&Cs mandated by congressional appropriations and authorizations. Startups can avoid paying hourly for their legal counsel’s education if they select legal counsel that is already familiar with DFARS mandates and with waivers to DFARS T&Cs such as much less onerous Other Transactional Authorities (OTAs).

Step 7. Manufacture and Prepare the Product for Delivery

While selling to large corporations is a great achievement for generating revenue, it also is a significant opportunity for startups to establish a reputation for quality and on time delivery with other large corporations. Thus, once a corporation approves the capital budget, the startup should begin planning workforce expansion, quality assurance, configuration management, cybersecurity support, and other long-lead activities required to scale production after receiving the purchase order. When scaling production, startups frequently encounter new, unexpected supplier delays and quality issues. Therefore, when possible, startups should secure potentially long-lead-time items early by establishing working capital or exploring debt options, while negotiating exclusive supplier agreements based on anticipated future order volumes to gain higher priority order status with suppliers.

After landing an initial division as a customer within a corporation, startups should prioritize expanding into adjacent divisions with similar use cases, as growing within an existing corporate account typically accelerates revenue more quickly than pursuing entirely new corporate customers. Testimonials for on-time, high quality production performance greatly accelerates sales to other business units and corporations.

Step 8. Deliver the Product and Receive Payment

Startups should witness in-person the receipt of the initial delivery and operation of their product for large POs, as feedback from the handling of products often informs more efficient transportation and packaging that can reduce a startup’s future operating expense.

The T&C of the PO will dictate the payment timing for a product. Typically, payment is made 30 days after the receipt of the product.

Conclusions

The intent of this article is to shed light on the pattern of activities and decisions encountered when selling a new product, technology, or service to a large corporation. Understanding a corporation’s motivations and procurement process enables founders to develop informed Go-To-Market plans and business strategies. Success first requires research to find a potential corporate customer with either a large problem, need to dramatically reduce costs, or comply with ultimate customer or government mandates that the startup’s product can solve. Then a startup must commit time and resources to endure a prolonged corporate procurement process prior to consuming their available cash, or runway. Despite these hurdles, successful sales to a corporation can provide a stable and sufficiently large revenue stream to propel a startup over the “valley of death” and into mass production.

As always, please reach out if you or anyone you know is building at the intersection of technology and national security. And please let us know what you think and how this analysis compares with your own experiences working with large corporations and startups.

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Transcript

Maggie 00:18

In this episode of the Mission Matters podcast, we sit down with Pratap Ranade, the CEO of Arena Physica.

Akhil 00:43

Really excited for this conversation. Pratap’s building something truly revolutionary. Arena Physica is really focused on the future of what he will describe as electromagnetic super intelligence. What exactly is that? It’s developing an AI-native platform for electrical and RF engineering, and it’s applicable across domains. It’s an agentic platform, Atlas, that pairs non-deterministic AI agents with deterministic hardware, physics, and simulation models to autonomously debug, verify, test, and optimize electrical designs. And as part of Atlas, Arena has also developed the first foundation models for radio frequency, or RF design.

Maggie 01:23

As more and more hardware systems become software-defined, which is a term we’ve talked about on many past episodes, electromagnetic engineering will be more important than ever. However, the tools available to electrical engineers remain slow and fragmented, largely relying on computational physics simulations that can really take hours to run. Many of these tools really don’t talk to each other. The data remains siloed and fragmented in all kinds of different tools, really slowing down some of the most crucial engineering that we’re doing today.

Akhil 01:54

And what’s really exciting as Maggie and I and the Shield Capital team look at technologies that truly do serve the national security market as well as a host of others throughout commerce and industry writ large. That’s customers like Amkor, semiconductor customers like AMD, healthcare customers like Bausch + Lomb across the gamut. And, last time I think, Pratap, when I came to the office, I mean, you’ve been at this since 2019, raised more than 60 million, but most importantly have an incredible 80-person team all in-person in Manhattan working collectively on both the future of hardware and how it integrates with AI agents, and evolving and developing faster. So excited for this discussion.

Pratap 02:35

Great. Guys, it’s great to be here. Thank you for having me.

Maggie 02:38

So, Pratap, I wanna start with just a basic question. What exactly does it mean to you to be building the future of electromagnetic super intelligence? And maybe part of that, can you tell us a little bit about what Arena is building today?

Pratap 02:51

Yeah, for sure. Like, let me answer your first question, electromagnetic super intelligence first. So the thesis very simply is, you know, there is, I would say, consensus view that language models equal intelligence. So ultimately make a language model very big, and it will kind of reason about everything. We actually think language is not the only primitive of intelligence, and we’re seeing, you know, this fringe belief getting a little bit more popularity under the moniker of world models, right? So an animal doesn’t learn to move by reading books about it, right? Like movement is probably another primitive in and of itself. You know, protein structure may actually also be one, and we’re seeing some really great work out of DeepMind on this. Our belief is, for physics, the fundamental primitive is fields. And, you know, Isaac Newton did not come up with calculus, because it was fun. He came up with it because language was a very poor description of what’s happening with physics. And so if you think about electromagnetism, of the four fundamental forces of physics, which is the strong force and the weak force, which are nuclear forces, gravity and electromagnetism — electromagnetism is the one that humans have industrialized the most. We use it to make, you know, our entire semiconductor revolution. Think about radars, photonics, it’s all powered by that. One of the core beliefs we have is if you were to build a very large model trained on electromagnetic fields. So imagine your training tokens aren’t words, but they’re EM fields. You would be able to understand how particular materials and geometries, create certain electromagnetic behavior. And this is basically what’s used when people are designing, phased array systems for satellite communication, for radar, for high-speed chip-to-chip communication, and interconnects. And even as you move into light and photonics. You asked about software-defined hardware. We’ll talk more about that, I’m sure. But you think about a lot of these companies, like our customer set, including companies like Anduril and AMD, Akhil, like you mentioned, they’re fusing the benefits of advanced electrical design, mechanical, and software all together under one h- one roof to create remarkable new machines, right? And so here you have problems that part of them can be solved with LLM-based agents, part of them require physics models. And so our Atlas product is an agentic product allowing advanced hardware engineering across those three disciplines when they’re enmeshed together. And actually Heaviside, our EM foundation model, becomes available through Atlas.

Maggie 05:21

So maybe just to level set for people who aren’t as familiar with the world of electrical and RF engineering, you know, what is the current state of design tools for these kinds of engineers? And then maybe just contrast that with your Atlas platform.

Pratap 05:34

Yeah, for sure. So the current state of design tools is highly fragmented and old, right? So if we look at it, you have... Let’s say you’re building a new, autonomous aircraft, right? You are designing electrical circuit, you’re designing the logic of it, then the physical layout. You’re putting that in time, in a housing, like probably a metal box of some sort that has to go through, withstand G-forces, go through various, you know, temperature regimes, probably like do some degree of insulation to sort of EW. And then you’re running specific software on it, probably low-level software, on the flight computer. You’re not writing, you know, you’re not writing like a Python app, right? And so all those three interact. If I change how the software behaves, I might heat up my, you know, I’m gonna generate more heat, right? I’m gonna generate different behavior. I’m gonna change how much current is even going into a servo. So these three disciplines, interact very deeply, right? And you can imagine, you know, someone once mentioned to me, the problems always emerge at the seams between teams. And so the problem here is you have a mechanical, engineering group, an EE group, and a software group. Now, software, they’re running on GitHub, which is owned by Microsoft. Mechanical might be running on NX by Siemens, or SolidWorks by Dassault, and then electrical might be running on Altium, which is owned by Renesas or, a Cadence or Synopsys product. So you have three different, very large incumbent tool chains that you can imagine aren’t the most sophisticated and don’t play nice with each other. And then oftentimes, you actually have siloed organizations as a consequence. These guys, you know, they’re not having lunch together, even if they’re in the same office, right? And so, but yet we need all these three disciplines to come together for, like, that first autonomous flight to go successfully. And so structurally, we think there’s actually a problem with the fragmentation and the age of those tool chains. Plus, you know, the organizational mirroring of that, which is three siloed orgs. If the future is more companies doing software-defined hardware, more and more vertical integration, moving really fast, we need a way for this to look much more integrated, much more seamless, sort of like the way software engineering is done.

Akhil 07:48

Pratap, I love the seams between teams connotation. It brings up so many things, and I think maybe highlights we’ve been hearing a lot about this, you know, software-defined hardware, or in the national security domain, right, the software-defined warfare. But that inherently involves both technically and operationally combining those two seams together. What does software-defined actually mean to you? We’ve had this conversation on a couple podcasts, and I think it’s an amorphous term, right? There’s a technical aspect to it. There’s clearly, you know, a cultural, a team aspect to it as well. But how do you see the software-defined future and really the role of electronics in the work that you’re doing evolving? And where do you want it to be? If you can paint a vision or day in the life of someone working at that seam, what does it look like right now, and what should it look like?

Pratap 08:37

Yeah. It’s a great question. So I think software-defined is this broad term that has come to mean in many things, but it’s definitely like it’s a powerful trend. A simple example is a Tesla with an over-the-air software update. You’ve got the same hardware, new software update lands, it’s suddenly capable of more. And I think this is a beautiful idea because you can take advantage of the sort of, you know, rapid ways in which we can evolve and adapt software to give your hardware new capabilities. And so this theme, I think we started to see with software-defined radios, but just continues to be sort of the dominant theme. I think one thing that’s not talked about often is, you know, we can draw up this image in our heads of, great, I’ve got hardware, now I’m just gonna put some software on top and software eats the world. I think the reality is it’s like you’ve got your brain, you’ve got your body. There’s actually a very complicated nervous system and spinal cord and a bunch of stuff that is really important to get these things to work together really well. And I think this is where we’re seeing a lot of the fracturing, both on the tool chain side and the sort of, you know, employee side. Now let’s say in a simple example, I’m an individual engineer with all of these skills and I’m working on my own project. I’m taking files out of one system, I’m putting them in another system. You know, my code, and even if I’m using my coding agent, is unaware of the hardware environment it’s going into. My electrical software is unaware of the mechanical. We’ve literally seen cases where you’ve made a board, an electrical flight computer board. It’s great. It’s designed to spec perfect, turns out beautifully. You’ve-- your mechanical guys have done your mechanical housing, also designed to spec really great. You literally have a case where then the copper on the board touches the metal from the mechanical, they’re both in tolerance, and you short the board. And this is not a-- this is not rocket science. This is very knowable, but again, it’s a function of those sort of, separation of teams and separation of tool chains. You know, in software, we have, this idea of like, you know, continuous integration, you know, like CI/CD. You’re shipping your code, tests are autonomously running, it’s getting merged in, and of course, this is taken to the next level with agents. Why can’t you have something like that on the hardware side, right? Why can’t you have sort of like, “Hey, I’ve updated my firmware,” automatic compatibility checking with everything else? “Oh, I’ve updated my board design,” why can’t it check for coherence across the mechanical things? I think these are things that hold our engineers back, and I think what’s super exciting to be doing this at this moment in time is, you know, for the first time, it feels like you have-- these companies have an imperative to move fast and ship new hardware. This hasn’t always been the case in the last several decades in hardware. But with companies like Anduril, leading the charge on the aerospace and defense side. Also, companies like AMD, putting the most advanced AI inference chips in the world now out there and building data centers, at like breakneck speed. There’s this massive race which is creating extraordinary demands on engineering teams that are already pushing the limits of collaboration, but now they’re going an order of magnitude faster. They’re already building frontier hardware. You know, the only solution here is AI and automation. This is not one of those things where we’re coming in to take a job. This is one of those things where literally our customers are like, “There is no way this roadmap gets done without AI as part of it.” And I think that’s what’s-- it’s creating a very natural place to plug in. And personally, as a big science fiction fan, you know, we want a future with like sentient factories, growing machines and stuff. How are we gonna get to that world? We want space elevators. I think this is one of those key unlocks. I think if we’re successful, we get to see that science fiction future come centuries sooner.

Akhil 12:11

Yeah, absolutely, Pratap. In the last 10, 15 years, most people didn’t go become double Es. They became CS majors. And so you’re not taking jobs because actually we just don’t have enough of folks at that layer to go support that.

Pratap 12:24

No, exactly. Everyone went into software engineering. We lost a lot of hardware talent.

Maggie 12:29

Yeah, so Pratap, I wanted to, you know, switch gears a little bit here and dig into a couple of just technical questions on how exactly you’re able to make a system that’s capable of doing some of the work of electrical engineers. And one of the things I find really interesting about your platform is that it’s not like today you just have AI models that are magically able to do everything. You know, if you actually look back at the history of a lot of these electrical engineering tools, you know, there’s some very sophisticated tools that have had decades and decades and decades of engineering put into them to make these simulations work really well, you know, whether that’s Ansys, Altium, Cadence, Keysight, et cetera. So I’m curious to hear, could you talk a little bit about how you combine these non-deterministic foundation models alongside these deterministic simulation and solver tools in order to 10X the performance of both of them?

Pratap 13:26

The way we think about it is, so if you think about the sort of life cycle of engineering, right? You have-- You could think about it as kind of a grid, right? Where the rows are, you’ve got electrical, you’ve got mechanical, and you’ve got software, and they’re all interacting as you sort of co-evolve through this sort of evolution of developing a new software-defined hardware product. So you’d start with design, right? And then you would do some degree potentially of simulation. Oftentimes it’s quick calculation, not quite simulation in the design verification, because obviously it’s fastest to iterate on the design. The minute you start making it, you start incurring this longer cycle time ‘cause you have to make the first version physically. So you get that back and you enter like what people would call engineering validation testing or design validation testing. You have the first prototypes. You know something’s wrong, you wanna iterate it and tune it. Then you get to, okay, I’ve got like a production instance, my testing is good, and then you move into kind of mass production and operations. So if we think about this as our life cycle, what we believe is the current system of tooling, right, EDA tools, simulation tools, you mentioned Keysight and test equipment. These are amazing companies that have built very deep, sophisticated solutions that are trusted by pretty demanding engineers for a reason, right? Our view is not that we’re gonna roll in and, like, do an agentic EDA platform. I think that’s a... You know, maybe one day one will kind of exist and replace it. But I think the idea is you as an engineer are often, like, flipping. You’re moving across those stages, and as you move across stages, if you’re a small company, you may be doing all of it. If you’re a larger company, that’s usually a different team. And again, in our grid, you’re either going up and down, like talking to mechanical, talking to software, or you’re moving left to right as you go from design to validation to test, you know, into production. And here, like the simple case is you’re alt tabbing, switching between tools, trying to give them context, right? And so how can we take the power of all these tools? You know, there was a statistic I read, I think recently, I think it was a couple weeks ago now, agentic traffic on the internet has now surpassed human traffic, right? So we’re seeing like the advent of agents. So now if I’m an engineer and I’m going to invoke my agents, how do I get an agent to effectively help me across this thing? The idea is not like I don’t need to replace my EDA software, but I want my agent to be able to do work in my EDA software. I want that agent to be aware of what the mechanical implications are. And I think here is where your question is very, like, particularly becomes like very pointed, because you do want this flexible reasoning capability of an agent. You do wanna be able to ask it a question. You don’t wanna have to spell everything out. You wanna be like, “Hey, can you check if this is gonna work well under the conditions that you know I’m going to operate in?” It can inherit like, you know, the ConOp and requirements and specs. So it’s like, oh, I know you’re operating under these G-forces, these temperature gradients, this sort of electromagnetic environment. So, you know, it can fill in the blanks, and this is where you need to be very precise. And so the first piece Atlas has is a meta graph which spans those three layers and is deterministic in nature. So it enables graph traversal, hierarchical graph traversal, so you know what nodes this is connected to. Like I mentioned the automotive example, it’s literally tracing the wire paths and saying, “This is the most likely place where you’re probably seeing a short.” So you really want that to be just, you know, a deterministic picture of the world, right? The second place is then sometimes you need to say, “Okay, what is-- what am I seeing and how does it match with expectation?” And those expectations are still determined by physics, right? And so this is where, you know, in the current case, you would agentically invoke a physics simulation. And now I think anyone who’s used physics simulators, unlike Apple products that are really built to make it easy for use, because you have a very sophisticated user group, these products are terrible to use. They’re slow, they’re ugly, they’re... I mean, it’s not a fun experience, right? And so how do you actually execute that simulation workload or that physics workload correctly? So these are the two places where if we get this right, you can pair the flexibility of the language model with deterministic lookups on the graph, and then deterministic invocation of the physics simulator. And then if we play this movie forward and we say, okay, agents are gonna continue to get better and this is gonna get easier. You solve the efficiency problems. This is when we start hitting the hard physics wall, and then the next inevitable solution has to be a change in how we do physics simulation or train AI models for physics. And this is where we strongly believe LLMs are not the answer here.

Maggie 18:02

Yeah. So can you talk a little bit more about what it means to build a foundation model for RF? I think most people when they hear foundation model, they think of a language model, you know, maybe a coding model, maybe they can understand like an image generation model. You know, we’ve all played around with ChatGPT and Gemini and others to generate images. But what does that mean to build this for RF or electrical engineering? Like, what is the data that you’re training this on? Are you starting from scratch? Are you building on a base model?

Pratap 18:30

Yeah. So in order to answer your question well, I might take a one-minute detour and talk about language models, right? So before language models, we had a whole field of natural language processing and where we’re really solving these sort of use case-specific problems. We were using different types of models to group words together and, you know, kind of like dismantle sentence structures and create n-grams and really like think about that kind of problem set. What we found with language models was an architecture with the transformer that let us scale it up very easily and, an attention mechanism that let us fetch all of the right context around a particular word to understand what it means. And I think as someone said, like, the meaning of a word is entirely determined by the words around it, right? And so effectively, your cross-attention mechanism is scanning every other word, associating that and saying, “Okay, great. If I can understand the full context around this word, I can predict the next word.” So what’s interesting is that that problem setup is very general, right? That would apply to any word, any language, like, and as we’ve seen even in things that we don’t traditionally think of as languages, but are languages like coding. And so as you scaled it up, you know, OpenAI in January 2020 published this seminal paper on scaling laws, also followed by a DeepMind paper on the Chinchilla scaling laws, and that was a profound moment, right? It was the first time in machine learning history where a model got better, as the model got bigger, not just as a function of data. So, as you scale compute, model size, and data size, you’re getting more capability. As we pushed those parameters to very large values, we saw this emergent behavior where you could do more general learning, generalize across languages, generalize across tasks, right? So that’s the current state. Now, if we think about electromagnetism, and you mentioned RF, is like actually determined by how Maxwell’s equations kind of determine how like, you know, any current that basically creates an electric field — electric field creates current creates electric field. But you have an electric field that’s changing, that induces a magnetic field. And, you know, electric, changing electric and magnetic fields are also like literally fully describe light, which is a photon. If you actually have a model that’s going to learn and generalize, it will learn the behavior of electromagnetic fields as a function of the geometry and the material that’s an input. So the premise is if we can build a large model trained on EM fields, you learn that relationship. So just like you’ve seen with language models, you’d be able to basically just say, “Hey, give me a seven gigahertz band pass filter,” and it gives you back. And so what we achieved in March, end of March this year, was what we consider the GPT-1 milestone for electromagnetism. Fully autonomous design for 2.5D RF structures. So an RF structure being like some combination of a metal wire and an insulator, a few layers of it, and then they’re connected together with these little vias that connect the different layers, and the structure should, you know, behave in a certain way. And I think what was super interesting is, one is it was done autonomously. You-- There’s actually a public preview. You could play with it online. You could type it in, and it will generate you a design. But two is we saw some very strange designs coming out of this, and there was a few that looked more like QR codes than something a human would draw. Now, these are not superhuman designs. Those designs did not have a capability that human designs couldn’t do, but it’s super interesting. At this current scale, GPT-1 scale of the model, you’re seeing weird designs that we wouldn’t have come up with that work automatically. If we scale this up, our bet is the best answers live in those weird designs that we couldn’t even think of. And, you know, GPT-3 version of an EM foundation model is probably a breakthrough EM capability that, like, just hasn’t been done by people today. And like, you know, We got a copy of the book about Lockheed Martin Skunk Works, for everyone on the team because I think that’s probably the first example of people wielding EM in an original way and actually creating stealth as a national security advantage. So, you know, you think about where that goes, I think there’s a lot of, like, technology that we could build that we can’t really do today that this would basically unlock.

Maggie 22:44

So I guess one question that always comes to mind for me when I hear about these sorts of specialized foundation models is just where do you even get the training data to build something like this? Like, where do you get the EM fields that allow you to build these kinds of models at scale?

Pratap 23:02

There is no EM training data publicly available. We looked really hard. On the one hand, that makes it difficult for us, but on the other hand, that’s why, one of the reasons why it’s very valuable. Anyone who would need to do this would have to create their own data. And so there’s a few ways in which we build this. We think about it as a layer cake. So the first layer is you create a lot of expert-guided templates. And so the reason here is there’s 10 to the several hundreds in the power of possible EM designs. So if you just go and explore the space and simulate it, most of the space is garbage and not interesting, so let’s not waste our time. How do we get the interesting parts of the space? You know, in human language, we were training off of something someone wrote on Reddit or something someone wrote on GitHub, so it was automatically meaningful. We were training on the meaningful set. So here we’ve hired a few of the world’s leading RF experts, and they’re actually creating seed designs. These seed designs then we scale up parametrically, similarly to how video games procedurally generate forests and trees with varieties based on like one initial template. So that creates like a big part of the initial layer of the cake. Then around that, we insert random deviation and noise because we wanna get some of the interesting stuff. So you start exploring the entire wider universe, rate limited by spend and compute, but like starting with the interesting regions and distorting from there. So that’s all part of the simulation stack. We use a mix of different open simulators to do this. I think one of the great insights from the language models is all of your data doesn’t need to be great data. Like a lot of bad data is still useful to learn language. So then we take the very top candidate designs, we fabricate them, and then we measure them in our lab. And so that’s the smallest amount of data, but that is the highest quality data. And again, if we look back to language models as an analogy, you know, that last layer for alignment for the ChatGPT model was, I believe it was only a few hundred thousand data points, but it was very high quality data points. So the view is that’s sort of the layer cake, that we’re building for training the model.

Akhil 25:06

Thanks, Pratap. Maybe switching gears again, you’ve got a range of folks that are using and pretty rapidly, right? You’ve got a lot of inbounds that have just come in being like, “Oh, this is a huge bottleneck. Let me go try and run with it,” and then seeing some amazing expansion based upon some initial pilots. Any immediate lessons on, you know, one of the first pilot that comes to mind and just how quickly it scaled? What’s kind of interesting about the initial use case that came about? And then, is that generalizable across some of these which seem kind of niche, right? Like going from Anduril to Bausch and Lomb on initial surface don’t seem that near to each other, but at the end of the day, we’re making an embedded system and a set of systems and devices to go do a variety of things.

Pratap 25:53

Yeah. So even though the industries are different, you know, those are a few of the public customers we could talk about this more in each of these industries. But even though the industries are different, it-- they’re-- what they share in common is they’re all building software-defined hardware at the frontier of their field in a hurry, right? And so this is kind of, you know, you have, all of them have this property of a mechanical, a software, and electrical team coming together to create something brand new. And I think where we’re focused today is really people on that frontier because they’re the ones who are really pushing the limits. So, you know, AMD with their next generation chips, Anduril obviously with next generation hardware, you know, BNL actually innovating a lot of like, surgical hardware as well, like actually on the med device side. So you look at the-- those-- the functionally, the problems actually look very similar, and the teams actually are structured in similar ways with similar tool chains. Obviously some of the details are different. The degree of electromagnetics in each one is different, but you actually have this sort of like common set. I think the pattern that we’ve seen from pilot to rollout, that’s very exciting to me, is- We’re either getting pulled in where there’s, of course, a pilot would usually begin where there’s the most acute pain. So either you have acute pain, which is you’re trying to get a brand new product out the door, you’re having challenges in design validation or in testing, or you have like, let’s say, a production product and you have like a troubleshooting or operational like repair problem. These are often two acute places where we will get drawn in. What I’m super proud of is every time, once we’re through the pilot and it’s deployed and people understand and see what Atlas can do, it gets pulled upstream. So if we started on the right side, which is like test and production, it’s continuously getting pulled into R&D, which is the next question is, “Oh, wow, it helped me...” Like, we have had multiple cases where the time to root cause a failure has dropped from six hours to about 20 minutes. And that’s pretty profound change, but it’s able to pinpoint the problem, and not just that, but because of the meta graph, link it back to the specific design area of interest. And so what we’ve seen now is those companies now injecting Atlas into the design review process so that you basically have, oh, great, I want to connect those two. So not just gonna help the technician in the field root cause, it can actually help my next version of the product get better. And so we’ve seen this in automotive, we’ve seen this in aerospace and defense, we’ve seen this in semiconductors, and we, you know, we’ve also seen the other version of it, like expanding right, which is starting in test and design and saying, “Oh, wow, can I now give this expert capability to a field technician?” And that will often, from the design team is like, context switching is expensive. I don’t wanna take a designer off of their next project to go field an L1 or an L2 support request, but could I give kind of Geek Squad in a box to an operator in the field, right? And so you’re seeing this sort of intelligence getting initially deployed in one place, and then sort of radiating out. And I think this is actually one of the organizational benefits is you have, you know, there has been this quest for shared learning and all of the data going into one place, for a long time, right? Ever since like the big data days, and obviously, you know, given, my experience at Palantir, you know, that’s a core kind of, value prop of Palantir, for example. But I think what you see very often is people are busy. You can interpret that as people are lazy, but they’re busy. I may have all the data there. Am I really going to go and spend my evening now going and like searching through it to see if there’s a way I can make my design better? Probably not. I probably have better things to do. And so what’s different now is we’ve changed what used to be like a pull into a push. The AI is doing that for you, and it’s injecting in your natural course of work, “Hey, Akhil, this next design, by the way, this part of the battery management system has had challenges in the past. You might wanna consider this additional piece of pre-charge circuitry.” Right? And that’s just being pushed to you in your natural flow before you were gonna go to design review. That’s where you want it, right? And so that’s actually what we’re seeing is like this, the organization now as an organism starts to learn and have memory in this way that’s totally new.

Akhil 30:05

Makes sense. And, Meg and I can definitely share some stories on battery management. Definitely a hot topic these days. Can you, Pratap, share an example? You know, have us come into a sales pitch or customer call or an on-site visit where you had just that one person in the room that was totally not with the program, that was just like, “This will never work. I can’t trust this.” What was that conversation like? How did you build trust with someone like that?

Pratap 30:35

Yeah, happens all the time. And I think especially with our target set, you can imagine these engineers are remarkably good at their job, and they got there by being right, right? These-- They’re not software engineers. Software engineers, we’re wrong all the time. Oh, ship something, it breaks. Move fast, it breaks. You can’t move fast and break stuff if you’re building a plane, right? Like, you don’t do that. It is a very, very different culture, very different mindset with a very good reason. And especially like we mentioned the customer, we’re lucky enough that, and, you know, by good fortune and also by deliberate choice, we’re working with customers that are at the cutting edge. These are very, very good engineers. So skepticism is high. Barrier of entry is very high, right? And so what we found — I’ll give you one example — is we came in with a very hard problem around, like, a new type of chip. And, you know, we’d scoped it down to, “Let’s help you test this chip faster. Let’s help you, like, do the testing faster.” And, you know, we had, like, a larger set of things we wanted to do, and we narrowed it down to one where there was one engineer who was, let’s say, slightly more open-minded than the rest to, like, give something a shot. And the skepticism was very deep, and it was very grounded in the principle that I’m an engineer, this is how I think, this is how I reason. This is not a probabilistic game. There’s no way some AI model is gonna come in and do that. Like, it was like... And in fact, they’ve all then used ChatGPT and Claude at this point and been like, “I can’t trust it,” right? So you have this immediate out-of-the-gate trust problem. And in this environment, it started with basically just creating usefulness, just creating utility, which is saving a ton of time around their core problem. So not yet touching the core that we wanted to go after of that intelligence, but all the little stuff around it, making it easier, like, oh, kick off your testing job, execute your testing job, parse your data. Like, make it all really easy to use. And one of the principles has been show me your work. So Atlas evidences everything it’s doing. It annotates exactly the calculation it used, where it got the value from, so it’s auditable. So Atlas is like, it’s like a good student showing you its work. It’s one of the features that our engineer users love. They literally call it show me your work, and so that’s been powerful. And then it’s just been a lot of, like, good old startup, like, grit and grind and being in it with our customers, which is something we learned from Palantir, which is, you know, you’re not going to be trusted with the customer’s biggest problem overnight. No matter how great your innovation is, they’re not gonna give access right away. Like, we’ve been working with these customers now for several years in many cases. And over time, you know, we’ve been with them sort of through thick and thin, and we find that’s earning access to the problem. And for us, that’s earning more degrees of freedom for the agent. So in the semiconductor example of it was probably maybe, like, nine months in, 10 months in, they give us a call. They said, “Hey, can you help us boost performance of the chip?” But like it’s basically a natural consequence once you’re done with testing and you’re getting the results and it’s working, how do you get it to sort of hill climb in performance? And they’re like, “We know you didn’t exactly build it for that, but could we, like, could we do it?” And we said, “Look, we didn’t build it for that, but do you have some sort of verification environment the agent can access?” And they actually had a device farm of chips, new ones that had come back that were set up on boards, and, it was a performance optimization game, performance per watt being a big unit. And we said, “Look, give us the weekend. Give us access. Let Atlas run.” It took about a year of trust-building to even be in the place where they would ask us, and they would give Atlas that secure access. And then Atlas just went to town. And that’s when seeing is believing, and like it set a new performance frontier, superior to what was possible by people alone. And that triggered a really interesting sort of watershed moment where, you know, eyes were opened in terms of, oh, wow, this is the new way to kind of do performance optimization. So I would say it’s very like starts humble, we’re in it with our customers for a long time, building in a transparent way with show me your work, and then ultimately, there are these moments where they’re like, “Hey, can you try?” And then we’re having these moments of surprise, and then actually our greatest skeptics have become our biggest champions.

Maggie 34:49

So Pratap, I’m curious, do you guys ever use your own product internally for any projects?

Pratap 34:56

Yeah. You know, we use it for a number of things. I think one recent one has been we’ve been tearing down some DJI drones and actually basically using sort of a little bit of a reverse engineering approach to build that meta graph that I was telling you that Atlas uses. And then in our lab, hooking the components up and characterizing them by connecting them to our test equipment like oscilloscopes and spectrum analyzers and even like, you know, in the thermal chamber, seeing how they perform under different temperatures. And actually it’s been really interesting just looking at, you know, the entire BOM of components and how they’re connected, how they’re organized, and just seeing how, like even if you take DJI as an example, how as their drones have evolved, how the architecture, has shifted towards more vertical integration, where they’ve vertically integrated, where they’ve collapsed different electrical packages onto a single piece of silicon, and where they have, either chosen not to or struggled to insource. So yeah, it’s been a really fun project we’ve been working on, using Atlas internally.

Maggie 36:04

Anything particularly surprising or interesting from those teardowns that you can share right now?

Pratap 36:08

Sure. I mean, I think, you know, if you compare a version from about 2019 to like kind of like 2023, 2024, you know, the, you know, top five or six components make up the bulk of the cost. A good old Pareto principle turns out to be true. They were largely non-Chinese components, Western, many Western components actually in 2019, and now it’s predominantly Chinese. The second thing is a lot of the independent components have moved onto their own proprietary silicon. There’s definitely a weight saving, so we know at least that’s like definitely driving a performance gain, potentially cost as well. It’s definitely more secure as well. The one piece which hasn’t moved, which is almost surprising ‘cause it’s in the top five, is the GNSS receivers. So, you know, something interesting for us to keep digging into there.

Maggie 36:59

Pratap, I wanna move to talk just a little bit more about your vision for the company and for this technology. On your website, you say the vision is to ultimately become the world’s first AI electromagnetics laboratory. Can you tell us more about what that means to you?

Pratap 37:15

Yeah. So, you know, I started out my journey as a physicist. I was at Stanford, and then I went on to do my PhD at Columbia, and yeah, I was deeply interested in just like science, like how does physics work? It’s actually quantum electromagnetism, so like weirdly a full circle moment for me. But I always felt like I had to make this choice. I could pursue science, right? Or I could go to industry. And, you know, definitely my advisor was very unhappy when I left the academy, right? But if we look at what’s happening today with AI broadly, a lot of the barriers to building software are going away. It’s easier than it’s ever been. A lot of that work is going away. And there’s definitely one, like, negative side that people are looking at, which is, “Oh, where are all the jobs?” But there’s a positive side, which is we can up the ambition of what we’re able to build with the same team at the same price point in the same unit time. And I think what’s happening — and what we want to usher in — is almost a new variant of what was once something like Bell Labs, right? Which is, I think you can pull the physics much closer to the engineering. That chasm between science and application can close. And so I believe that there’s a future where if you are pushing at the frontier of applied physics, with advanced models and applied physics, and you’re in a connected loop that’s very close with customers working at the bleeding edge, which is why we’ve been very deliberate at picking customers working on frontier technology, you can create a new type of science and industrial collaboration. Like if I look at my critique of academia is it’s slow. It, you know, there’s no real motivation. It’s not necessarily targeted at like the most important problems. And my critique of industry is we chase the low-hanging fruit, and we don’t go for like the ambitious, hard stuff. Like, I think there’s a new company, and I think the time is finally right to emerge, where you could actually be pushing increasingly into sort of breakthrough science with commercial application and create an engine where like if, you know-- I wanted to create a place I would like to work. Coming out of my physics program, I wanted to work with smart people in a high-intensity environment where I was shipping things that had impact that I could be proud of, with real people who are benefiting from that impact, you know, connected to the real world. But I wanted to do something that was fundamentally rooted in science. And I think my hope is those don’t need to be binary choices anymore. And Arena Physica is a place where with Heaviside pushing the frontier and Atlas as the delivery vehicle today, you actually have the beginnings of a loop that will start with like electromagnetic super intelligence. But inevitably, as we climb, that will have to become a more multi-physics capability. And so that’s a roadmap. If we can create something that’s a new kind of Bell Labs, I would be, you know, just super happy.

Maggie 40:10

Yeah, I think that’s an amazing vision. And, you know, also came out of Stanford, had a lot of friends working in the engineering and physics space, and it seems like their only options were either to go get a PhD, go work at like a super slow, big legacy, you know, defense prime or company, or go work at a software startup and kinda give up on the world of hard science if they wanted to move fast and, you know, really make a big impact on the world. So it’s great to see that maybe there will soon be another option for all of those people. What’s been the biggest challenge building Arena Physica over the past seven years?

Pratap 40:44

Yeah... I think, well, part of it is seven years — You know, we started in kind of a field applying machine learning, when the challenge was getting people to care and pay attention to what was an esoteric, you know, nerdy field. And then we flipped into the opposite problem, which is everything is AI, what’s different, right? And so I think the beauty is we’ve sort of stayed the course. It’s funny, which is, we actually executed a large pivot of the company where we focused entirely on hardware engineering, whereas in the original days we were more broad across industries. That was a tough moment. Like, you know, just to be clear, we purposely deleted a double-digit million revenue stream that we’d worked tirelessly to build. And you can imagine those phone calls, and we’re doing that off of conviction in something that was barely a six-figure revenue stream, right? Like, because we believed in the meaning of what that small six figures represented and the capability set it represented of what we could do in hardware. And, you know, it goes back to the origin — when we first founded the company, that was always the end state on the website. And we’d said, “Hey, we were gonna start with like accessible commercial problems to prove economic viability and then literally work with companies building robots and in space.” And so we’ve exhibited the plan, although the way there was different. I would say that was probably one of the singular hardest moments of the company, which was it was, you know, it’s rare where you have to make such a clear bet based on conviction. And, you know, something that, like, one of my friends had taught me that stuck with me is he’s like, “The only decisions that matter are the decisions that have a cost.” And in this case, we had to make a very specific, pointed decision with a very clear cost on the one side and an unclear outcome on the other side, right? And, yeah, that was hard. I would say we definitely were kind of-- We just silently built for like a while after that. Like, you’re not in a loop getting feedback. It’s just you’re just betting on the dream with a few people, betting on that dream and building that dream. And, yeah, that was probably, if I were to think about it, probably the single hardest moment. But actually now looking back, you know, probably the moment I’m most proud of.

Akhil 43:10

Your gut told you a pain point that customers really need across a variety of industries, ones that really matter right now, whether that’s in aerospace or defense, or even for just improving our daily lives and society’s lives, by first advancing, you know, how we think about embedded systems and doing it in a way that is fundamentally better. So thanks so much for taking time, Pratap. Any last shared wisdom for folks listening in that might be thinking about working on a technology set or just a problem that really matters?

Pratap 43:44

No. I would say if you’re interested in our mission, definitely come join us. We’re hiring actively. But yeah, I would say now is the time to just really be ambitious. I think I’m excited for more folks who wanna push the boundaries. I think foundation models are gonna continue to get better. More foundation models are gonna start to exist, like ours for physics. There’s so much more we can build and, you know, it’s actually necessary. We need gainful employment for humanity. We think there’s plenty to do, but that’s gonna come down to entrepreneurs, going and building stupidly ambitious things, and we could probably just up that level a few degrees. So yeah, you know, let’s do it. Let’s build it.

Akhil 44:23

Thanks, Pratap.

Maggie 44:24

Thank you so much.

Pratap 44:25

Thanks, guys. Appreciate being here.

Earlier this week, SECWAR Pete Hegseth announced a major shakeup for how DoW will procure, test, and field unmanned and autonomous systems, establishing a new Direct Reporting Portfolio Manager (DRPM) for UxS (see full memo here). So, as always, David Rothzeid and I have a new episode of the Techquisition Edition of the Mission Matters Podcast to break down what exactly this change means for startups.

We cover:
🇺🇸 What exactly is a DRPM?
🇺🇸 How does this DRPM compare to others like Golden Dome, Submarines, and Critical Major Weapon Systems?
🇺🇸 What falls under the new DRPM's portfolio and what doesn't (mUSV + CCA are not included)?
🇺🇸 The history of U.S. military drone programs from GWOT to Replicator to DAWG to Drone Dominance to JIATF-401

As always, please let us know what you think. And please reach out if you or anyone you know is building at the intersection of technology and national security.

You can listen to the podcast on Spotify, Apple, the Shield Capital website, or right here on Substack.

Subscribe now

Transcript

Maggie 00:36

In this episode of the Techquisition Edition of the Mission Matters podcast, we discuss the Pentagon’s new memo establishing a Direct Reporting Portfolio Manager, DRPM, for unmanned and autonomous systems, UXS. This is a major shakeup in the way the Department of Defense will procure, test, and field unmanned and autonomous systems. So of course, David and I are here with an emergency pod to break down the memo and what it means for startups. Just as a disclaimer, we’re going to use the term Department of Defense in this podcast. We recognize the department is doing business as the Department of War, but statutorily it’s still referred to as the Department of Defense, so that’s the nomenclature we’ll be using. David, thanks so much for getting this put together on such short notice. I know we were all scrambling to figure out exactly what this memo meant last night and this morning as we were preparing for this podcast. Let’s start with a basic question: can you explain what exactly a Direct Reporting Portfolio Manager is, and why create this new DRPM UXS in the first place?

David 01:46

Yeah. Awesome. Thank you, Maggie. It’s great to be here on an emergency podcast, and I guess I’ll save talking about the US versus Bosnia and Herzegovina matchup for somebody else as we dive into what’s happening in our world. But a DRPM is basically a high-level Pentagon official with extremely wide authority across all the services and other programs that might be doing similar activities. I think the term people like to throw around in the media is a “czar” for a broad acquisition area. This memo from Secretary Hegseth establishes a DRPM for unmanned systems, or as you said, UXS. And it calls this person the single joint integrator for all unmanned and autonomous system programs across the department. In practice, this means this person officially reports to the deputy secretary and oversees every drone or robot program, from micro unmanned aerial vehicles, or UAVs, to large autonomy projects. Though, notably, it did carve out the Collaborative Combat Aircraft, which will still retain ownership inside the United States Air Force, and then the Medium Unmanned Surface Vehicle, MUSV, will still reside, I believe, in the portfolio acquisition executive, or... remote autonomous systems, robotic —

Maggie 03:21

Autonomous systems.

David 03:22

— DAE/RAS, in the Navy.

Maggie 03:26

So how exactly is a DRPM different from, say, a normal PEO, portfolio executive office, which have now been rebranded portfolio acquisition executives, or PAEs, or something like a joint program office?

David 03:41

Sure, yeah. Lots of changes in the acquisition world, and at times like this, things might feel like they remain the same, but certainly the terminology and lexicon is changing all around us. Normally, each service will have its own PEOs. Now, as you identified, PAEs, or across the Fourth Estate you see these joint offices focused on one weapon or platform, and now we’re taking this more portfolio approach. The most famous example of a joint program office would be the F-35, or in the Pentagon we call it the JPO. And they report directly to the Under Secretary of Acquisition and Sustainment. So the DRPM is actually even higher up in the food chain, as they report directly to the Deputy Secretary of Defense. On the PAE side, each service has lots of these, and they’re now organized around these larger buckets of portfolios, whether that’s fighter vehicles on the Air Force side or different sets of technologies grouped together. And they report through their service secretary. On the Air Force side, they’d report through the Assistant Secretary for Acquisition, or as I know it, SAF/AQ. And then, as kind of alluded to, these DRPMs are across all the service chains and can downward-direct on priorities and standards, and even reprogram funding within certain limits as designated by Congress. So in the example of the DRPM UXS, they’ll take precedence on all acquisition matters related to unmanned system programs, and report directly to the Secretary of Defense and the deputy. So effectively, they’re becoming their own milestone decision authorities — almost above your traditional program acquisition and portfolio acquisition executives, who are also milestone decision authorities for their respective programs. So there’s a lot of crosscutting and matrices across these different reporting chains.

Maggie 05:53

So are the DRPMs going to be the organizations in charge of the budgets for these different programs? Are they the ones making decisions on who actually wins these contracts?

David 06:03

So in the short term, yes. They’re going to be working in concert with a lot of these program offices, or PAEs, that are already underway. I recall listening to General Guetlein — that was the first DRPM stood up, for Golden Dome, around our missile defense architecture — and he has highlighted the fact that he can reach into Space Force or into the Missile Defense Agency and redirect a lot of their initiatives and activities to make sure it’s tracking towards a holistic architecture. And in the case of these unmanned systems, whether it’s aerial, ground, or maritime, making sure that we are leading up to certain standards — data links, not allowing the services to have as much autonomy, which then might lead to a situation where certain assets are unable to communicate with each other because those platforms or programs were developed in isolation from the other services. And as we know, in a joint force, if our systems and platforms aren’t able to communicate with each other, then the whole is less than the sum of our parts. And so for this DRPM, the intent is they can downward-direct certain things to occur that maybe otherwise wouldn’t, or would have to be reworked after they’re already fielded, which is a huge problem across the department.

Maggie 07:39

Is there any precedent for this kind of setup?

David 07:42

I like the example of JIEDDO — the Joint Improvised Explosive Device Defeat Organization — which was established in 2006. So go back to 2006, and we’re at the height of the wars in Iraq and Afghanistan, and our adversaries are using a lot of IEDs, improvised explosive devices, and causing havoc across our ground forces. And so JIEDDO was stood up to figure out a solution, and came up with the Mine-Resistant Ambush Protected vehicle, the MRAP. Now JIEDDO no longer exists — eventually it was turned into JIDA, which then got folded into DTRA, the Defense Threat Reduction Agency. I recall at the end with JIDA that they were doing a lot of counter unmanned aerial systems work, counter-UAS. So it’s kind of ironic that now we’re skipping forward, and the department is once again putting forward a joint organization — now what we’re calling a DRPM — to tackle some of these same issues we’ve been dealing with all the way back to the mid-2010s.

Maggie 09:05

And are there any more recent examples of these DRPMs being used?

David 09:10

So I alluded to General Guetlein, who is leading Golden Dome, and that was the first DRPM stood up last year. And then there were a few others. We had the submarine acquisition DRPM, and then in the Air Force, General Dale White — he’s in charge of strategic air systems, which includes the B-21, the F-47, Sentinel, and maybe a few other programs. But essentially, what we’re seeing is a prioritization of certain programs that the deputy secretary has decided to put their thumb on the scale for, taking it out of the services themselves. So with the B-21 and the F-47, you’d expect the Department of the Air Force to have senior control and just report to the deputy and the secretary. But in this case, it’s like: “Not so fast — actually, we’re going to pull it out.” The funding still resides in the budget line of the Air Force in the case of the B-21 and F-47, but OSW, or OSD, has decided we’re going to take a hard, fast look at this alongside you.

Maggie 10:23

What are some of the other changed authorities with this new DRPM?

David 10:27

Yeah, so the memo grants pretty broad planning and oversight powers. The DRPM, for example, will provide oversight and authoritative direction for programming, planning, and budgeting for all unmanned system programs. It gets direct input related to all the different program elements, or PEs, across the president’s budget, and can direct changes to the service budget submissions in coordination with DCAPE and the comptroller. So it certainly gives them a lot of latitude across existing budgets and funding. You might recall that the Defense Autonomous Warfare Group, otherwise known as the DAWG — the president’s budget requested, I think, fifty-four billion for fiscal year ‘27, starting October 1st. We’ll see how that ends up shaking out in the appropriations bill. I know HAC-D came out with a mark, and we’ve seen some marks from HASC and SASC, and it’s not nearly to the level of fifty-four billion. So whether or not the unmanned systems DRPM has the ability to execute across these different lines of effort really depends on whether Congress is putting funding into these program elements that the DRPM is taking oversight and control over.

Maggie 11:52

Let’s back up here for a minute. I feel like every six months or so, there seems to be a new initiative coming out related to how the Pentagon is going to acquire and field drones and other autonomous systems. During the Biden administration, we had Replicator, that ultimately turned into the Defense Autonomous Warfare Group, or DAWG, under Special Operations Command. We had the Drone Dominance Initiative. We had JIATF-401 — that’s the Joint Interagency Task Force 401 — which is specifically focused on counter-unmanned systems. How does this DRPM fit into all those other initiatives, and why is the Pentagon so focused right now on unmanned systems in the first place?

David 12:34

Maybe I’ll answer the second part first. Realistically, drones have gone from fairly niche to ubiquitous, and I think we’re seeing a lot of that because of the war in Ukraine. Certainly on the counter side, we’re experiencing that firsthand with Iran and across the Strait of Hormuz. And so I think this memo is sort of a reflection of that speed — it even calls that out in the memo — to start fielding these systems even faster. Now, whether the problem is organizational or not, I think it just shows the importance the department is putting on getting these unmanned systems out there faster. And as the memo highlights, the DAWG, JIATF 401, and Drone Dominance — given that its focus is on group one, group two UASs — are all going to now fall directly inside the purview of this DRPM. So the Army is going to have a major role, specifically on Drone Dominance, which was also being co-led with the Defense Innovation Unit. Same thing goes for JIATF 401. And then a lot of these unmanned surface vehicles for the maritime dominance domain, which was spearheaded by Replicator and now, I think, falls under the DAWG and Task Force Able — that’s all going to come under the oversight of this DRPM, which I think just gives it more emphasis across the secretary.

Maggie 14:11

One of the things I find so interesting about drones and unmanned systems is this is not a new technology. We’ve been using these unmanned systems in warfare for several decades now, at least since the War on Terror, if not before that, in Vietnam and elsewhere. But the way we’ve used these systems has not really changed, despite there being huge leaps in the underlying technology — the cost of robotics hardware like motors, cameras, actuators. They’re much cheaper, much better than ever before. We have meaningfully better edge compute than ever before. We’ve seen huge improvements in computer vision, in AI, in controls software — and yet we still seem to be treating these systems like the old Reaper and Predator drones we had in Afghanistan, Iraq, and elsewhere.

David 15:02

Yeah, I think you bring up a good point. It feels like we just took a hard paper copy and digitized it — you fill it out the exact same way, just on your computer instead of by pencil — not really taking advantage of the underlying opportunities that being digitally native would provide. And trust me, I’ve been on programs where the MQ-9, the Reaper drone, has certainly gotten better. But it’s a far cry from where we’re seeing commercial technology advancing. And a lot of this comes down to the tactics, techniques, and procedures, and the concept of operations, and how we think about employing these systems. So my hope is that one piece of this unmanned systems DRPM is really thinking through how we exercise and operationalize this technology, and ultimately change the nature of how we train and think about fighting. It’s certainly changed the battlefield in Ukraine, and I’ve got a lot of friends who have just come back from there not too long ago — and certainly we have an investment that was born out of Ukraine in the company called UFORCE. The drones have changed the character of war in a major way, and I’d say this DRPM is maybe getting after that in a more emphatic manner.

Maggie 16:33

What exactly is the Defense Innovation Unit’s, or DIU’s, role in this new DRPM’s initiatives? And more broadly, how is DIU’s role changing in terms of managing autonomous systems and unmanned systems for the department?

David 16:48

Yeah, so going back to one of our earlier Techquisition Edition episodes, we talked about the acquisition transformation memo that had come out, I think, in the November timeframe. Emil Michael, the Under Secretary for Research and Engineering, has been very fixated on consolidating some of the innovation organizations. The Defense Innovation Unit was made a field activity, reporting directly to the CTO, or Emil Michael himself. But for the purposes of this program, the memo actually explicitly calls out DIU at least twice, and the main piece is that DIU will serve as the primary industry engagement interface between the Department of War and the commercial industry for all unmanned and autonomous system programs within the DRPM unmanned systems portfolio. So I think what you’re going to see is DIU playing a front-and-center role — the front door for industry — engaging across a lot of these different contract opportunities, solicitations, and prize challenges — the things DIU does best — as a means to, one day, show off your capabilities to the department, and ideally that results in prototype contracts, production contracts, and maybe, at some point, programs of record.

Maggie 18:15

Let’s talk about the budget. What has Congress actually funded for Replicator, DAWG, JIATF 401, and the other programs that are now going to be under this DRPM?

David 18:27

Sure. Yeah, I think we’ve got to go back to the beginning with Replicator, when it was announced by then-Deputy Secretary of Defense Kathleen Hicks. They had to repurpose fiscal year ‘24 RDT&E funding, and then they tried to reprogram significant other pots of funding. It wasn’t until fiscal year ‘26 that the budget for the Pentagon added $225 million for the DAWG, and now FY27 is coming around. We already mentioned the president’s budget, which tried to ramp it up to over $54 billion — with a B — for the DAWG. At the same time, the JIATF 401 program is getting hundreds of millions of dollars to execute its counter-UAS program. And whether or not programs like the Medium Unmanned Surface Vehicle are tied to any of these is a little challenging to understand and figure out. But needless to say, I think what we’re going to see is significantly more funding going after these projects and programs, and that aligns with the $1.5 trillion top line — the base budget of $1.15 trillion plus the $350 billion in reconciliation. Of course, it’s still yet to be seen how that plays out for FY27. But needless to say, if you’re a company, this is definitely a space to look out for, where I think greenfield activities are going to thrive, assuming your technology works and is poised to scale.

Maggie 20:11

And of course, this all relies on Congress actually passing a budget this year.

David 20:15

Indeed. With the midterms — I’m no prognosticator — but we’ve had a continuing resolution most years, unfortunately a few years with a government shutdown, and very few budgets passed on time. So a betting man would likely say the continuing resolution. I put it to my sweat-equity counterparts a few weeks ago, and most people thought we wouldn’t get a budget until after Thanksgiving.

Maggie 20:46

What should startup founders and investors make of all of this? Any immediate advice?

David 20:53

Yeah, at the end of the day, if you’re a company, ideally you’ve found a champion or a stakeholder somewhere in the department who is aligned to the mission of your unmanned system. Keep them close. But recognize that there are going to be more stakeholders engaged in some of the decision-making. With that burden, I think comes additional opportunity, because these stakeholders are now probably going to get faster-aligned around the meritocratic solutions that are poised to scale. So it might be more work, and things might be a little bumpy in the near term, and it wouldn’t surprise me if certain contracts that were deep into an RFP cycle, and maybe out for negotiations, potentially get held up. At the same time, for those ongoing competitions, seeing additional oversight from OSW and other stakeholders tied to this DRPM probably means that moving to production is going to be of significantly more value. At the same time, we’re in July, so we’re headed towards the end of the fiscal year. The two-year fiscal year ‘25 RDT&E funding is poised to expire, and the FY26 operations and maintenance funding is set to expire. So maybe contracts being negotiated right now will still get awarded because of all these parameters around fiscal law. But come next fiscal year, I’d expect to see some larger programs being announced, and with it, a large, sizable slate of stakeholders. So make sure your program office or your user advocate is engaged in this new DRPM. And if the Defense Innovation Unit isn’t part of your engagement outreach, and you’ve got a capability that’s ready to be demonstrated, you should definitely be engaging.

Maggie 23:07

Great. Well, David, thank you so much for sharing all these insights. As we always end these podcast episodes — what do you think will be the next Techquisition Edition of the Mission Matters podcast?

David 23:19

Well, truth be told, Maggie, I think we’re maybe a little delinquent on behalf of our listeners — we probably should have done one on some of the NDAA bills that came out of HASC and SASC, and then there was a HAC-D bill as well. What I will tell our listeners is Maggie and I are planning something special, which is maybe why it’s taking a little longer. So maybe not an emergency podcast, but it should be fairly informative. That’s what I think our next one will be.

Maggie 23:52

Great. Thanks so much, David.

David 23:54

Thank you.

Do defense tech founders need to have military or intelligence community experience? Do they need to have a PhD and/or an MBA? Do they need to have experience working at a VC backed startup? Or a big tech company? Or a big defense prime? Or both? Do they need to have experience working at SpaceX, Palantir, or Anduril?

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Of course, every great founding team is unique, perhaps even more so in defense. Defense tech founders will need to navigate bureaucratic government procurement and security certification processes and build at the technical frontier, while moving at startup speed. They need to attract excellent talent, sell investors on their vision, lobby Congress, and deploy cutting edge technology to operators in mission-critical environments.

So, where have previous successful defense tech founding teams come from? To better understand what successful national security startups have in common, I analyzed the founding teams of the companies featured in SVDG’s 2026 NatSec 100 list,1 looking for patterns and recurring characteristics. I used Pitchbook to collect the list of founders and key executives (CEO, CTO, COO) for each company, and I used Claude to enrich each founder’s profile with the following information:

1. Primary professional background (e.g., engineer, researcher, academic, founder, military operator, business leader)

2. Highest educational degree achieved (high school, bachelor’s, master’s, PhD, MBA, etc)

3. Technical vs. non-technical background

4. Prior military service

5. Prior intelligence community (IC) experience

6. Prior aerospace, defense, or national security company experience

7. Previous employers (3–5 most significant)

Note that Claude may have made some mistakes, as I did not carefully check every piece of information it collected, but this analysis should show broad directional trends.

What kinds of professional roles did defense tech founders have?

Perhaps unsurprisingly, almost half of all national security startup founders were previously engineers, researchers, or academics before starting their companies, and close to 75% have some kind of technical background.

Looking at founding teams as a whole, most combine founders with complementary technical “builder” backgrounds (engineers, researchers, and academics) and non-technical “commercializer” backgrounds (founders, executives, and operators). Successful national security startups need both exceptional technical leadership to build differentiated products and strong commercial leadership capable of navigating complex government and defense procurement processes.

Do defense tech founders need prior defense experience?

Notably, the majority of national security founders have no past military, IC, or aerospace and defense (A&D) private company experience. Fewer than one-third of founders have prior experience at a private A&D company (e.g., SpaceX, Anduril, Lockheed Martin), and less than 15% have actual military or IC experience (of course, many with military and IC experience do not publicly advertise their past, so it is possible that they are under-represented in the publicly available data as a result).

However, looking at founding teams as a whole, most companies have at least one founder with prior defense experience, whether through military service, the IC, or the A&D private industry. For example, Shield AI’s founding team combined CEO Brandon Tseng’s military experience as a Navy SEAL together with the robotics expertise of CTO Nathan Michael, a CMu robotics professor with no military experience. Similarly, Anduril’s founding team brought together Palmer Lucky’s technical vision with Trae Stephen’s government and finance experience, as well as Matt Grimm and Brian Schimpf’s experience developing technology for government customers at Palantir.

Only about one-third have founding teams with no prior defense experience. Given the complexity and idiosyncrasies of the national security market, that is still a surprisingly large share. It suggests that while deep domain expertise is valuable, it is not a prerequisite for building a successful defense technology company. For instance, led by MIT dropout Alexandr Wang, Scale AI’s founding team had no prior government experience, yet the company has since won hundreds of millions of dollars in military contracts. Meanwhile, Applied Intuition, founded by venture capitalist Qasar Younnis and Google product manager Peter Ludwig, has won more than $100M in military contracts.

Which organizations produce defense tech founders?

Nearly half of all prior employers are from the broader technology ecosystem (including venture-backed startups, academia, and big tech companies), while just 34.5% come from defense-focused organizations, such as traditional defense contractors, the U.S. government, or venture-backed national security companies.

The top previous employers skew toward the broader technology ecosystem rather than the traditional defense industrial base. SpaceX and Palantir stand out as major sources of national security startup talent, producing the founders of companies like Impulse Space (SpaceX), Anduril (Palantir), Cape (Palantir), Hermeus (SpaceX), and K2 Space (SpaceX). Large technology companies like Google, IBM, Amazon, Meta, and Microsoft are also heavily represented, producing the founders of companies like Applied Intuition (Google), OpenAI (Google), Chainguard (Google), Code Metal (Microsoft), Dataminr (IBM), and Re:Build (Amazon). Tesla’s presence also suggests that founders with experience in advanced manufacturing, autonomy, and hard-tech scaling are increasingly moving into national security markets, producing several founders of OpenAI, Epirus, and JetZero.

The U.S. Air Force (USAF) in particular produces many more defense tech founders than the other military services, likely due to its history of partnering with innovative startups through organizations like AFWERX, programs like STRATFI / TACFI, and its active use of SBIR contracts for cutting edge technology.

Interestingly, traditional defense primes appear far less frequently: Raytheon is the only legacy defense contractor in the top 10, having produced founders of both Epirus and Castellion.

Where educational backgrounds do defense tech founders have?

More than one-quarter of founders in the dataset hold a PhD (and nearly one-third of companies have at least one PhD co-founder), compared with roughly 2% of the U.S. population, underscoring the deep technical expertise that underpins many successful national security startups like Skydio, Shield AI, Databricks, and Starfish Space which are commercializing frontier research for defense applications.2

However, two-thirds of companies do not have any PhD co-founders, showing that not all national security startups require exquisite, research-level technical expertise. Rather than commercializing academic research, many national security startups focus on applying existing technologies to solve defense problems. For example, Neros was founded by two champion FPV drone racers without college degrees and is adapting mature, decades-old technology to build FPV drones for the U.S. military. Similarly, Apex Space is applying modern manufacturing techniques long proven in commercial industries like automotive to efficiently produce modular satellite buses.

By contrast, fewer than 10% of founders hold an MBA, and only about one-fifth of companies have an MBA co-founder. The data suggests that deep technical expertise is far more common among successful national security founders than formal business training.

Similar to the broader startup ecosystem, founders from Stanford, Harvard, MIT, and UC Berkeley dominate the national security startup landscape. One notable difference, however, is that MIT produces more founders than any other university in the dataset, likely reflecting its strengths in engineering and applied research, as well as its longstanding ties to the national security community through MIT Lincoln Laboratory. Carnegie Mellon and Georgetown are also more heavily represented than in the broader startup landscape, likely due to their respective strengths in robotics, artificial intelligence, and foreign policy. Similarly, USC appears more frequently, reflecting its close proximity to Southern California’s aerospace and defense ecosystem.

Conclusion

Overall, there is no one “right” background for a defense tech founding team. Startups can be successful with and without military experience, with and without teams of academic researchers. The most successful national security startups are typically built by founding teams that combine technical, military, and business expertise.

Perhaps the most important takeaway is that great defense founders increasingly come from outside the traditional defense industrial base. The best founders tend to bring world-class technical talent from universities, venture-backed startups, and leading technology companies, then combine it with just enough defense expertise to navigate the unique demands of the national security market. As software, AI, robotics, autonomy, and advanced manufacturing continue to reshape warfare, that blend of frontier technical capability and mission understanding is likely to become more important than ever.

I’m more excited than ever to find and partner with the best founding teams building the technology needed for the U.S. and its democratic allies to remain competitive on the geopolitical stage. As always, please reach out if you or anyone you know is building at the intersection of technology and national security. And please let me know what you think – this data and analysis certainly is not perfect, and I’m very open to any thoughts on how to improve or refine this kind of analysis in the future.

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In the latest Mission Matters Podcast episode, David Rothzeid and I sat down with Oleg Rogynskyy, CEO of UFORCE, Ukraine's first defense tech unicorn, to talk about what it takes to build systems that actually work when lives are on the line.

A few things that stuck with us:
• A PACOM general told Oleg: "If you can't fix it with a coconut on an island, I don't want it." Most Western defense tech can't pass that test.
• UFORCE has flown more combat missions than all Western defense tech companies combined, and is expanding to NATO allies and the US market now.
• Ukraine's acquisition model is outcomes-based in real time. Units rate their weapons like Amazon reviews, updated from missions flown that morning.
• The lessons from the Black Sea are directly applicable to the Strait of Hormuz, the Baltic, and the Indo-Pacific.

As always, please let us know what you think. And please reach out if you or anyone you know is building at the intersection of technology and national security. 🇺🇸

You can listen to the podcast on Spotify, Apple, YouTube, the Shield Capital website, or right here on Substack.

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Transcript

Maggie 01:18

In this episode of the Mission Matters podcast, we’re joined by Oleg Rogynskyy, the CEO of UFORCE, Ukraine’s first defense tech unicorn.

David 01:21

UFORCE builds some of the most consequential battle-tested autonomous systems in the Russia-Ukraine war. Its platforms have executed tens of thousands, nay, hundreds of thousands of real missions in highly contested environments, informing not just autonomy software, but manufacturing, supply chains, deployment, and sustainment at scale.

Maggie 01:44

Perhaps most notably, UFORCE builds the Magura Unmanned Surface Vessel, or USV, the first ever naval drone to sink an enemy warship in combat, destroying eight Russian warships in the Black Sea in its first year of operation and causing hundreds of millions or even billions of dollars of damage to other ships. UFORCE also develops several other defense systems, including the Nemesis Unmanned Aerial Vehicle, UAV, Liut Unmanned Ground Vehicle, UGV, and a command control software platform.

David 02:14

In addition to its Ukrainian customers, UFORCE is expanding to NATO allies and the United States, where it will bring its combat-proven systems to an industrial base in dire need. In this conversation, we dive into what it takes to build defense technology that actually works on a real-world battlefield, the lessons the US, the Middle East, and the rest of Europe should take from Ukraine, what it takes to scale this technology beyond Ukraine and more. And with that, Oleg, thank you so much for joining us. I guess first things first, like how are Ukrainian forces using UFORCE systems today, and where are they creating the biggest operational advantage?

Oleg 02:53

David, thank you for having me here, Maggie. So to your question, we have a number of platforms, and they’re all used in different ways in Ukraine. We’re most known for the Magura USV, and in the beginning when there were still, Russian naval assets in the Black Sea in the open, Magura was used in a number of operations, both as, one-way attack strike platform to strike those ships, but also as a short-range air defense platform to defend the swarms of Maguras from incoming aircraft, incoming drones, et cetera. Magura was also used to find subs, to find sea mines, to deliver UAVs to the enemy shore against the hard targets in fully denied environments, and so on and so on. So that’s just one chapter. The second product we have is Nemesis, which is the workhorse of Ukrainian mid strike capabilities. So every night there is thousands of these drones flying to deliver tens of thousands of payloads, to the distances of 20 to 50 kilometers, return back, reload, and go back out again. All of that driven by software that kind of works like DoorDash, where launch teams just go from spot to spot to spot and relaunch, reload, change the battery of the drones. We also have the ground vehicle, called Liut, that, allows Ukrainian forces to, do first ever, trench assaults without any humans on the line. These are fully remotely controlled UGVs, that have AI capabilities in them, have turrets with guns that both shoot down incoming FPVs as well as help, complete unmanned trench assaults, kind of smoking out enemy soldiers out of the trenches. There’s a mo- a lot more I can tell you, but that’s, those are the three main CONOPs we are running.

Maggie 04:52

Oleg, what are the actual combat effects that UFORCE has had in Ukraine to date?

Oleg 04:59

Great question. So, on the maritime front, we have affected latest count, I believe 19 Russian warships, out of which 14 are verified sunk. And then in terms of aircraft, because as you remember, we do, USV, unmanned surface vessel-based short-range air defense and counter-UAS. We’re the first company ever to take down fighter jets. We got two Su-30s, as well as we have two confirmed and a number of unconfirmed, as in we didn’t film them, helicopter kills, over the Black Sea, and that’s the maritime side. The land side, we have flown over 220,000 missions within all versions of Nemesis drones, and that has allowed us to destroy, over 10,000 Russian vehicles And I mean, the body count is really high, as you can imagine. And then, we’re talking over 2,000 armor, we’re talking number of logistics trucks, very large number of logistics trucks. But more importantly, we have been the artillery hunter for the Ukrainian side. So we mow down that thing all day long, every day, all night long, every, all the time. And then we had the technology that got close to 100% of the kills for the North Korean, howitzers that they gave to Russians. So that’s one of the claims to fame.

Maggie 06:32

You know, I think Oleg, I think UFORCE is probably the first company that we have invested in at Shield that had actually been used on the battlefield before we made the investment. What’s it been like building and operating a defense company in Ukraine during wartime, and how has that environment shaped the culture and urgency of the business?

Oleg 06:53

So that’s really interesting because I’m not a founder. I joined as a CEO, two and a half years into the company when the company was already north of $100 million in revenue, and the difference was stark. In Ukraine, everything is real time. You just go and do it right now. You don’t wait. Obviously, there’s downsides in terms of longer term planning, longer term system thinking, et cetera, but at the same time, the sense of urgency, the sense of why it is right now and why it is extremely important is nothing like I’ve seen before. The systems, the communication patterns, everything else in the company is organized around that real-time communication. And that’s different. Now, if you layer on what I’m trying to do, layer on knowledge management systems onto real-time communication, some really good things are happening.

Maggie 07:46

Can you tell us a bit about the origin story of the company and how you came to lead it?

Oleg 07:50

Yeah, absolutely. So the company came together through a merger of initially two companies. One was making Nemesis drone, another one was making a Magura C Drone. And then as those two came together, then became clear that, they were sharing a C2 system by a third company that got brought in. And then turns out there were other companies like a UGV company running on the same C2. So the interesting part is there was a whole cluster of companies and teams that were operating together even pre-merger that kind of were acting as one team, and it was just a very obvious move to bring those together using Western capital and, identify synergies, create scalability across them, start normalizing the tech stack, code base, training, ConOps, TTPs, business development, and so on and so on. So in the end, we now have, I think the latest count was nine companies that have come together, but they all have been operating as one for a while.

David 08:56

Well, like it’s so interesting, and it feels like very organic and like Silicon Valley-esque, right? That everyone’s doing their thing. They’ve got these core competencies. You find these opportunities to sort of collaborate, and then lo and behold, the whole is greater than the sum of its parts, so you start to incorporate raising money, and obviously now headquartered in London and trying to proliferate the combat-proven technology across multiple domains, multiple geographies. And I guess the basis of my question is like how replicable is this, you know, across the Ukraine, entrepreneurial, you know, which has really shown itself, in a pretty inspiring way. But, you know, is UFORCE going to be a one of one, or is this a model that we’ll see happening over and over again?

Oleg 09:47

So I think there is two answers to that one. From one side, it’s replicable. It has to be replicated because the consolidation to achieve scale, especially as the war in Ukraine ebbs and flows, will need to happen. Like at our size and scale, we can, not only continue leading in Ukraine but also start expanding to other markets. On the other hand, the bottleneck is actually in the leadership talent And there’s only so many, leaders out there who have done, and it’s not just me, it’s my whole leadership team, who have done work with Ukraine before, but have also seen Western large scale companies that allows them to not figure out things on the spot, but also bring in Western governance, scalability, finance, and other capabilities to the bear, to combine it with the product market fit that was achieved by the Ukrainian, ingenuity, iteration, and exposure to a real war.

Maggie 10:51

Yeah, Oleg, I think that’s an interesting point that you make. I know that your experience previous to this was as a CEO of a software company in Silicon Valley. Can you talk a little bit about how you bring some of the principles from running a business like that, which as far as I know, had no connection to defense into this new market area?

Oleg 11:11

Yeah. Running a software in AI company in Silicon Valley taught me a lot. I mean, what’s happening in defense right now is that there is a massive amount of growth opportunities happening all the time, and that means you gotta be hiring fast, but with really, consistent quality. You have to be working with capital markets. You have to be thinking about, capacity models, both on the manufacturing but also on the sales side, on customer support, success, et cetera side. And SaaS and AI were the places where those skills had to be developed at significant, scale because of the templatization of, SaaS industry that happened in between 2010 and 2020 or so. So having learned those pathways, having learned those patterns, has been extremely helpful in order to scale up this business very quickly. I mean, we went from over, from about 800 people to 13, 1400 people in less than Nine months, 10 months, something like that. So the growth is staggering. The bookings growth, I don’t wanna talk about it, but it’s also staggering. And all of that is only happening if you’re able to hire people at consistent, significant scale and consistent quality. And then one more thing to actually to bring to that is we learn how to run efficient companies in Silicon Valley between 2020 and 2024, kind of through the downturn, through COVID, et cetera. And so scaling up a business like UFORCE is very hard on its own, but scaling it up efficiently without lighting massive piles of money on fire on stupid things is a lot harder. And then doing that while introducing AI capabilities in every part of the business, both in terms of personnel efficiency, documentation, engineering, sales, everywhere, is also a challenge of its own, especially in high OpSec environment where every minute Russians are trying to get you, and not just Russians. So it’s been probably the most challenging and the most exciting thing I’ve ever done, but man, it’s a lot of work

Maggie 13:33

Yeah, I mean, speaking of some of those OPSEC challenges and, you know, just actually what it takes to build for real combat conditions, can you tell us about some of the biggest technical and operational challenges that you’ve had to work through in order to actually harden these systems for real-world battlefield deployments?

Oleg 13:51

Yeah. I mean, first of all, in Ukraine, everything is kind of move fast and break things because Ukraine knows, Ukrainian soldiers know that whatever is the weapons are today, it’s not gonna be the same tomorrow. So nobody’s focusing on building that highly certified, highly perfect, highly integrated set of systems, just the rate of change is so fast. And so, on one hand, that leads to, really fast iteration. On the other hand, it leads to kind of more dynamic environment that what Western militaries are used to. And then from OPSEC perspective, you have to approach it very differently. If everything’s changing all the time, you cannot defend your perimeter as well, ‘cause you cannot think through every little bit of operational security. And so what you gotta focus on is a lot more prevention of the lateral movements. You assume they’re in, you gotta stop them from moving to other places. Very different, approach in the West, where you have to really focus on your perimeter security, you have to really focus your interoperability to your NATO certification, other certifications, and other integrations and slow things that you can afford when you’re not at war. So completely two different cultures, and you have to be very conscious about what’s relevant from wartime to peacetime and the other way.

Maggie 15:12

Kind of following up on some of the technical deployments here, you know, something I found interesting when you read about what’s happening in Ukraine, in the news and elsewhere, there’s a lot of discussion of autonomy, you know, completely human out-of-the-loop action. But if you actually look at a lot of the technology being used today, most of it is still mostly remotely operated, you know, maybe there’s some small pieces of autonomy in terminal guidance or elsewhere. What will it take to move towards greater autonomy, and how do you see this future of software-defined warfare and UFORCE role in that?

Oleg 15:46

Yeah. So there is an interesting stat for why autonomy is not picking up in Ukraine, and it’s actually harder for it to pick up elsewhere. There’s not enough targets, and so it would be akin to over-engineering if people went for full autonomy right away. There is pockets of it developing really rapidly, such as terminal guidance in denied, in fully denied environments, autonomous resupply using aerial ground and sea drones, Swarm intelligence, but in a very basic way. Like, how do you queue up multiple drones in case one drone gets distracted to another target, opportunistic target? How do you make sure the others are following and both missions get completed? So kind of mission queuing, et cetera. But nobody in Ukraine is thinking about, “Well, I’m gonna send in drones over there and have them figure it out.” The kill boxes are too undefined, the terrain is too complex, the assets are too sparse, the battery ranges are too low. And so from that perspective, it basically would be an overkill. Now, when you look at Indo-Pacific, for example, where distances are massive, you know roughly you expect better ISR, but then at the same time you expect denial and kind of peer-to-peer, fight, autonomy would play a much bigger role there. And so you have to modulate what’s relevant and where. In fact, that leads to the base mantra that UFORCE carries with it everywhere, which is we start with the customer. I call us, we are the first bottoms-up adoption defense tech company. Other defense techs go and go straight to the ivory tower and push the top down. Okay, capture the Congress, shove down $20 billion of contracts down your throat, and then see what COCOMs do. We try to do the opposite, and Ukraine taught us that. We got the users who love the product and ask for more, and then Ukrainian government has enabled commanders of individual units to have a say in what weapons they get to fight the war, specifically of the type that they are facing. And so that’s the approach we’re taking elsewhere. We have combatant commanders of different theaters in the US being very excited about what we do before the Pentagon figures out. We have individual war fighters of war-fighting leaders kind of ask for our hardware because it works, because it’s battle proven before we show up to the procurement teams. And that has been very different Of an approach, and it’s a much more wartime approach. But unfortunately, the world is becoming less stable than more, so that approach is gonna be taking more and more space.

David 18:47

Yeah. Oleg, I definitely wanna jump into it, and I know you brought up a little bit about the Indo-Pacific Theater and INDOPACOM. And then, you know, saying a little bit of heresy there, that centralized control and decision-making at the world’s largest bureaucracy, the Pentagon, maybe isn’t the best path to creating innovation and combat-proven technology. But before we jump into that, maybe just like one quick anecdote for our listeners about how the Ukrainian acquisition system and its fixation on outcomes-based, capability, development and funding versus this requirements-based that we sort of have here in the US, where you have to think about what it is you want to build, then you go and build it, then you test and you iterate, and then at some point it reaches the operational environment. But in Ukraine, it’s obviously been different ‘cause you don’t have the luxury of test and evaluation. It’s just outcomes-based. So can you maybe just highlight a little bit of that?

Oleg 19:53

Yeah, absolutely. So in Ukraine, you don’t have the time. You don’t have the time to develop a thing and then spend couple years certifying it and getting it, and then perfecting it, et cetera. Everything gets to the front line in Ukraine in days, weeks. It gets iterated within hours, gets upgraded. If you don’t ship code same day as you got the mission data, you’re out. And so that’s the speed at which Ukraine operates nonstop, and that’s the only way to play. And so that speed is actually enabled by a number of inventions that only Ukraine so far has. Now, some of them are only possible because Ukraine is in an active fight, and we... at the same time, we’ve seen lack of those innovations, slow things down in the Gulf, for example And what I mean by that is, so in Ukraine, both vendors and units get points for kill videos that they get submitted. So basically, you fly a mission, you got a kill, you submit the video to a government website. There is like a government Amazon for weapons. And the AI verifies that this video is unique and not AI generated. And then, you basically get points, and you can use those points to buy weapons that you want for your next mission. They even have Amazon Prime for that. You can spend more points to get it by tomorrow. And so that is very interesting because it pushes down the decision-making to a unit commander level, and at the same time, it gives the government perfect data of what works. Because if it doesn’t work, people won’t order it again. There is literally reviews you can go and read on every drone and from missions that were flown this morning. I’m not even talking about, like, last year on a slick sheet. And then from there, the government is now starting to layer on more and more data, like how many missions did this system actually fly? How many kills did they have? How many... What kind of units are using it? What kind of operators are more successful with them? So they are now collecting the ecosystem metadata and segmenting the procurement data about what can be bought with factual battlefield data to an insane extent. And then on the other side of this invention, they have created very tight partnerships between the tech partners like us and the units themselves. And so for example, we are closely paired up with Unit 412 Nemesis, which started as a battalion, and then it became a regiment, and now it’s a brigade. And rumors are, unconfirmed rumors, that it’s gonna become a corps at some point And what it means is we started with 100 operators, which became 1,000, which then became 5,000 operators, who could become 25,000 operators that are sitting and whose job is not just to fly missions, but to deliver feedback to us. And our job is to iterate on that feedback in real time with them. There is tens of thousands of code commits and design changes that happen all the time based on that real-time feedback. So now that you know there are units that can access the data and influence procurement in real time through the government system, and all this kit is being co-developed with people that are trusted in real time, things are changing very quickly and improving all the time.

David 23:17

Yeah. That’s fascinating. I mean, it’s a real markets-based approach to capability development and acquisition procurement, and definitely some lessons that could be learned over here in the United States military. Maybe from, like, the operational standpoint, right? Like, the last couple months, the US has been embroiled in a number of different conflicts. You know, there was Venezuela, which went fairly quickly, and now nobody talks about that. And of course, you know, the situation with Iran, Given where our capability deployments have been, and there was a great chart within the first couple weeks of the Iranian conflict that kind of highlighted that there were very few capabilities developed in the last 10 years that were actually operationally being used, in Iran. And I’m wondering, you know, at some point, did the Pentagon come calling or people in Central Command or European command about, “Hey, how can we get some of this stuff over there?” And then, you know, could Ukrainian combat-developed technology be useful to helping the situation with the Strait of Hormuz?

Oleg 24:30

I can’t really comment on specific engagements, but as I mentioned in one of the interviews, the phone had been ringing and has been ringing off the hook, with interest because when you are dealing with the challenges that we saw in Iran, the lessons from Ukraine are directly transportable. These are the same Shahed drones flying. The tactics and concepts of operations that Iranians are using against Dubai and Qatar is no different than what they’re doing against Odesa in Ukraine. Russians are doing it against Odesa in Ukraine. The mining, demining in Hormuz is no different than when Russians tried to mine the grain routes to bring Ukrainian grain out of Ukraine. So the analogy is all there. Maybe the temperatures are slightly different, but the sea is also similar. Black Sea and, the Gulf both go maximum sea state three, and so on and so on. So lots of lessons to be learned from there. Ukrainian experience is extremely, effective there. As you know, there was a delegation from Ukraine helping take down a number of Shahed drones in Middle East. That has been extremely successful, and lots of lessons learned from there, including in design of interceptor drones. Ukraine was able to change how interceptor drones are designed in less than two weeks to match the Middle Eastern requirements, which was very interesting. So yeah. I think, also there is a very effective knowledge transfer that happened from those examples of how Ukraine fights to people on the ground in Bahrain and other parts of US military installations in Middle East right now. People are learning. We saw the Lucas program. There is many other programs where Ukrainian experience is being implemented right away. Now, when is it gonna arrive to Pentagon? Great question. Hopefully soon.

David 26:30

Yeah. And hopefully, your investor at Shield Capital is helping, you know, accelerate that whole process, which I know can feel a bit mind-numbing. Now maybe just real quick, speaking of engaging with the Pentagon, you just hired your US-based CEO, Sean Planke. Maybe talk a little bit about how you met Sean and what you were looking for in somebody to help sort of, push out the US, UFORCE, exposure.

Oleg 27:01

Yeah. So, we got our introduction to Sean through our investors. And then what’s interesting is at least three of our investors triangulated the same person to us around the same time, which was, a great way to know that we’re talking to someone who is for real, where, all of the US really plugged in defense investors we have on the cap table at once said, “That’s the guy.” And then at the same time, just the level of expertise that we saw, the level of ability to navigate, Washington DC, which is not easy to navigate. But also the knowledge of having served, the knowledge of having met a number of people in a number of missions and just being super mission-aligned is something that really caught our eye. Level of energy is more than mine, which is really hard to imagine. But guess what? We were just in a, literally in a bunker with a number of commanders a couple days ago. It was day eight on the job, and I was sitting in the back of the room. I’m like, “Huh, I’m not really needed here anymore. That’s amazing.”

Maggie 28:14

What have been some of the biggest challenges, you know, or unexpected surprises in scaling UFORCE beyond Ukraine to international markets?

Oleg 28:24

That’s a really good question. So early on, about a year and a half ago, it was financing, not private capital financing, but like walking up to a bank and saying, “Yeah, we want some working capital to make some weapons,” was a difficult thing. In the UK, even opening a bank account was a very difficult thing. We had to go through a chairman of a banking group to get someone to open a bank account for us there. Not an issue anymore. Scaling, talent has been an issue because especially in Europe, where there is much less density of venture scale outcomes and so there’s very few, much fewer people who have seen unicorn growth, 10Xing year over year, et cetera. And so, finding those people was hard. And it’s like I’m juxtaposing them with people who come from defense world, which actually historically has been barely growing has been difficult. How do you get hypergrowth people who know defense? In the US, there is a bit more of that because you have hardware companies that have scaled, they have seen hypergrowth that are not technically defense. But, in Europe it’s very much harder. And even the US defense stuff, like we are talking to number of folks we’re trying to recruit from the larger defense tech companies in the US, and all of them are like, “Yeah, we’ve built shit. Nobody has used it, so we don’t really know if we built the right thing. So we know how to build stuff, we don’t know if anybody’s gonna use that stuff.” And how do you hire that? And they’re like, “And how about hypergrowth? Well, we know how to hypergrow a company on investment capital, but it’s not really revenue-driven hypergrowth.”

Maggie 30:20

Yeah, I think it’s an interesting point that you’re making that a lot of these, you know, international companies have not actually deployed technology yet on the battlefield. And I’m curious, what do you think are some of the things that maybe Western, international defense tech companies misunderstand about what it takes to actually deploy on the battlefield? And what will it take for them to get over that hurdle?

Oleg 30:46

Well, like, everything. There’s only two Western companies that I can think of that have real battlefield effects in Ukraine. It’s Quantum Systems and it’s Tekever, and Destinus. Everybody else, I’m sorry, there’s two wars going on right now, and this is, like, my favorite question for investors to ask other defense tech companies. Ask them how many daily active users they have. Like, there is two active wars right now, and you don’t have daily active users, like people logging in and using your stuff daily? What are you doing? And so to that point, the challenges to deploy are, first of all, just even getting your stuff to the frontline is a whole thing of its own. Like, I was just talking to someone who is like, “Yeah, the, this defense tech company showed up and said, ‘Hey, here is our product. Use it.’ Like, great, I wanna modify something. ‘Oh, you can’t do that. Our agreements and everything doesn’t allow. If you wanna pay us more, we can send in our four deployed techs, and they’re gonna try to do something here.’ Most likely they’re gonna have to ship it back.” That was a real discussion with the top three largest defense tech company in the US. And I’m not even talking Prime, I’m talking, like, hard defense startup company. So that doesn’t work in Ukraine. A general in the PACOM during one of the exercises told me something. He’s like, “Hey, when I’m on an island and you can’t fix your thing with a coconut, I don’t want it.” And so that’s the stuff that Ukraine has figured out, that everything is simple, modular, deployable, doesn’t need experienced MIT PhD field technicians, doesn’t need to be shipped back to the HQ for a software upgrade, or whatnot, and it just works. And unfortunately, defense tech outside of Ukraine is not getting that.

Maggie 32:44

How do you actually scale a manufacturing process or manufacturing facility in the middle of an active war when you yourself as a weapons manufacturer may actually be a target for the enemy?

Oleg 32:58

Well, you approach building manufacturing facilities very differently. Like, nobody in Ukraine builds large, beautiful, brand-new facilities with a big company logo on the roof and kind of people wearing white suits, white space suits or whatever it is, inside, and you can hear a pin drop. Like, in Ukraine, if you do that, you’re gonna get a ballistic missile in 40 seconds, and we’ve, uh... Even without doing that, we’ve lost a number of facilities over the years because you become the prime target. If your kit works, Russia is gonna use their 40 million dollar ballistic missiles to take out the manufacturing facility for your kit. And trust me, every one of the Western factories for weapons is already mapped out by China. It’s already mapped out by whoever needs to map them out, and those things will be gone in the first 30 seconds of a hot exchange. Now, what we do in Ukraine is we use, secondhand or underground facilities for pretty much every critical element so that we can withstand missile strikes. There is a lot of operational security protocols on how to stay under the radar, how to not endanger your people, how to know how to behave when an airstrike is imminent, and how to not use your, not lose your expensive equipment, or don’t use the expensive equipment. So the way we operate our manufacturing processes is it, the factory needs to be built from a local Home Depot using components from a local Best Buy, because you will not have the time to wait for nine months for the machine to arrive, and you’re not gonna have, continuous power, you’re not gonna have continuous supply chain technicians to come fix the machine, et cetera. So it has to be something simple, it has to be something scalable, easy to repair, field deployable, and fast.

Maggie 34:58

How did you all think about designing your systems to be able to be manufactured at scale during this kind of environment?

Oleg 35:06

Well, you take the tools you have, and you build what you can with them, and then when they ask for more, you figure out how to do more of it, and you keep on iterating until you get to large scale. Again, Ukraine doesn’t allow you to build for scale right away has to be... Manufacturing is as much of an iterative process as design and as fielding in Ukraine. You cannot plan for it. Spreadsheets don’t work. You, and the way Ukrainian government is purchasing, it’s all supply-constrained right now. So they walk up to you and they say, “Hey, look, how many can you make? We’re gonna give you margin on top, and we’re gonna buy everything you can make,” versus kinda shelfware and stuff like that.

Maggie 35:49

How have you all thought about managing your supply chain during this conflict? I know today a lot of the systems that Ukraine fields do still come from China or have a lot of componentry coming from China. So how have you thought about that, you know, right now, and then where do you see that going in the medium to long term?

Oleg 36:05

Well, if your stuff really works, your supply chain is gonna be targeted. So we had our supply chain targeted multiple times, and because of that, as a company, we made a decision to insource most of our supply chain. So we make our own drone motors, we make our own cameras, gimbals, battery packs, frames obviously, hulls, you name it. And so, we had to grow up to be fully NDAA compliant and not depend on Chinese and other non-fully aligned national, sources. And so PCBs, circuit boards, all that stuff has to be made in-house. Now, costs are different, but over time you can drive it down, and at our scale, it’s not impossible to execute.

Maggie 36:52

So how are you seeing international demand for battle-tested systems like UFORCE? I guess, you know, geographically, where are you seeing the most demand, and what kinds of products are people most eager to purchase? I mean, it sounds like you have a pretty unique offering on the market as being one of the only scaled or scaling combat-tested defense tech startups.

Oleg 37:15

Yeah. We think about the world in terms of four theaters that are active right now. You have Indo-Pacific, you have CENTCOM, you have EUCOM, and Ukraine, kind of overall. And from that perspective, the, each of these theaters has different problem sets. And so what we are doing, we are figuring out what are the problem sets we can actually solve with the same platform capabilities across the board. It turns out Magura is relevant in all four theaters. In Ukraine, it’s done its job mostly. But in the Baltic and in the Gulf and in the Pacific, Magura can do different things for each one of them. In the Gulf, it’s demining and counter-UAS against Shaheeds. In the Pacific, it’s all kinds of things, from one-way attack to ISR to demining to sub-hunting to C-UAS, you name it. Same in the Baltic. And so, demand is skyrocketing everywhere. There’s a lot more water outside of Ukraine than in Ukraine. At the same time, the distances are much larger, and we are very good at long distances, high endurance, loitering, delivering payloads, working as a truck to get things over there where nothing else can get through. And then the other part is we’re starting to see more and more demand for our Nemesis bomber drones. You know what’s interesting? I don’t think US understands bomber drones. Two days ago, for the first time, I was meeting with a very senior general in the US Army, and he basically for the first time brought up their interest in droppers and how they’re thinking about it. In Ukraine, droppers are Probably more cost-effective than FPVs in some way. And the battlefield damage for droppers is on par with FPVs, it’s just at the longer standoff distance. And so we’re seeing more and more adoption for the dropper technology in Europe. A major European country right now is deploying, is basically upgrading one of their light armor brigades to full CONOPS and TTPs and software and hardware and doctrine we developed for 412 Nemesis, and then rolled out across 40 brigades in Ukraine. And the moment that started happening, other countries with light, started thinking about what is gonna change about what needs to change about their light armored brigades in order to increase their survivability and effectiveness. Because in the world of FPVs, light armored brigades don’t have a future. And so we are seeing a whole queue right now of light armor brigades, which is leading to a whole NATO FLE, the field lethality experiment, which is kind of like a, when NATO as a whole decides, “Hey, we need to change something as a NATO, recommendation across all forces at once.” And so to summarize, demand for Magura where there is more water, outside of Ukraine, and then demand for Nemesis, where they expect a lot of land fighting and a lot of, kind of 20 to 50 kilometer, effects needed.

David 40:29

Well, yeah, I think, what the picture you paint is both terrifying but also in certain respects, like reassuring that opportunity for the US to modernize its capability, its kit, is very much available and very proven. And so maybe just, you know, Oleg, as we kinda come to the end of this, interview, you know, what is the future of UFORCE? We’ve seen some pretty lofty valuations for a couple of these other defense tech companies that have gotten started in the last 10 years, three years in some cases, right? Anduril just raised that, I think 61 billion, and, Saronic raised, I think just sub of 10 billion. Where do you see UFORCE fitting into this picture, across this emerging defense tech landscape?

Oleg 41:19

Well, at some point, customer is gonna ask, “Does this really work?” And that’s kind of where we come in. We’re seeing more and more war fighters and commanders really focus on being combat proven for their kit. And so in terms of valuations cannot comment on it as you can imagine, but, we’re already a unicorn. At some point we’ll be a decacorn, as they say. But the valuation is not the point. What the point is that battle proven kit is how you save war-fighters’ lives and help them achieve their mission. And, the amount of experience we bring to the table is absolutely unparalleled. I think UFORCE has flown together like an order of magnitude more missions than all of the Western defense techs combined and I haven’t done the numbers, but just eyeballing it looks significantly larger. We’ve flown 220,000 combat missions so far. That’s more than all of US forces have flown in, over Iran, for example. Way more. I think it’s, like, 10 times more. And so the future of this is we’re just gonna keep on scaling. We are building stuff that works. It’s not gonna be the most expensive stuff, it’s not gonna be the most exquisite, but you can always rely on it to do the right thing.

Maggie 42:44

Oleg, if you were the American Secretary of War for a day, what changes would you make to the department, the way it buys and what it buys, to ensure that the United States is ready for this next generation of warfare?

Oleg 42:58

That’s a great question. I actually think there are some really good positive dynamics happening at the DOW. The whole team that came in from private equity background is moving fast, and it’s very encouraging. But, what’s less encouraging is just the complexity of procurement with, COCOMs, combatant commanders, basically being in full dependency of Pentagon, where the actual war fighting leaders don’t decide what they need for their region. They can submit a vote, but they don’t really guarantee you get that, is something that needs to change, it needs to change ASAP. Like, Admiral Paparo knows better than anyone else on the planet how to fight the war in the Pacific, and so he should be getting what he wants and needs. Same with the leader of CENTCOM, same with other leaders. And that’s something that I think, only US has for its size. Like, when we work with European countries, the linkage between the combatant commander and the procurement office is extremely tight.

Maggie 44:07

Great. Well, Oleg, thank you so much for coming on the show. We really appreciate it, and we’re super proud to be investors in your business.

Oleg 44:15: Thank you, Maggie. Thank you, David. Appreciate being here. Thank you, Oleg.

A few things that stood out:

• The legacy code problem is bigger than people realize. US weapons systems still run on COBOL, Fortran, and Ada. These are decades-old languages maintained by a shrinking pool of specialists. Modernizing a single codebase has historically taken teams of engineers months.

• LLMs alone aren't enough for safety-critical code. Code Metal pairs AI code generation with formal verification.

• The shift Peter is betting on: Code generation is becoming a commodity. Verification is where the value is moving. Whichever foundation model wins, mission-critical industries will pay for the layer that proves the output is correct.

As always, please let us know what you think. And please reach out if you or anyone you know is building at the intersection of technology and national security.

You can listen to the podcast on Spotify, Apple, YouTube, the Shield Capital website, or right here on Substack.

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Transcript

Maggie 00:36

In this episode of the Mission Matters podcast, we sit down with Peter Morales, the CEO and co-founder of Code Metal AI. Code Metal is building the future of code translation. That is taking code written in one coding language and translating it into a different coding language, and then verifying the accuracy of that translation.

Pat 00:57

Code Metal is focused on building code translation and verification tools for several major use cases, including code-based modernization, edge hardware deployment optimization, and code portability. Code translation projects that previously took teams of engineers months of work can now be completed in just minutes with Code Metal. Code Metal’s capabilities are a crucial part of the future of efficiently developing software-defined commercial products and has many military applications, helping engineers build software-defined hardware products and bringing modern software capabilities to the US government and other mission-critical customers

Maggie 01:41

Since it was founded in 2023, Code Metal has raised close to $200 million and signed contracts with groups across national security and commercial sectors, including L3Harris, RTX, Toshiba, and the US Air Force. The founding team has deep experience in building AI tools for defense, hailing from MIT Lincoln Labs, BAE Systems, Microsoft, IBM, Data Miner, and more. Peter, thank you so much for joining us.

Peter 02:08

Yeah, thanks so much for having me, guys.

Maggie 02:10

So I wanna start with just a softball question. Can you tell us, why would somebody actually want to translate code in the first place?

Peter 02:18

Yeah. Um, that’s a great question, and I think, um, you know, we’ve gotten that even, even some of the folks that have been here a long time have gotten that question that worked on some languages themselves, so the authors. Uh, you know, language is essentially really how you talk to the hardware and command what it does, and sometimes you wanna move really fast, and you wanna speak really abstractly, and that’s what we call, like, high-level languages. They’re great for prototyping. They’re great for maybe writing some different algorithms you wanna try. But then sometimes you wanna run really exquisitely fast on hardware. You want it to be really efficient, and that means having a lot of say in how you control things. If you think of C++, that’s a language where you actually get to decide what you do with the memory, where a lot of other languages maybe don’t worry about memory. Um, that level of control gives you optimization, gives you speed, and so being able to translate code into more optimal code, uh, is, you know, one of the hard use cases of what we do.

Maggie 03:15

And I should think of this of, like, if I’m trying to put a computer vision algorithm on a drone or on a satellite or something like that where I may not have access to a whole lot of compute capabilities, that’s where I really care about this kind of optimized code. Is that the right way to think about it?

Peter 03:28

Yeah, yeah. I mean, it doesn’t have to be a drone, right? You want battery life on your phone. You might want battery life on your laptop. But yeah, being able to be efficient is a real reason for translating code. And then on top of that, you might even have different hardware providers, so maybe you’re efficient on NVIDIA, but you wanna be efficient on Qualcomm. Um, having to rewrite your code that, you know, uses their proprietary libraries is a big pain in the butt, but something that I think AI is letting us automate now.

Maggie 03:57

So what does it look like today sans Code Metal? If I am an engineer at a car company or a laptop company or a chip company and I need to build software for these hardware products, or if I’m working in some of these legacy code bases, what does it take to deploy code to these systems, and how can Code Metal fit into accelerating these processes?

Peter 04:19

Well, I, I think there’s a really interesting spectrum here. There’s sort of, you know, I’m building a product and I need to pick and, and really get locked into hardware. So one of the early use cases that came up was folks were asking not necessarily what hardware c- we could get to, but what’s the minimum amount of hardware to make their product as cheap as possible that they needed to run certain code. Um, so, you know, traditionally, that would be maybe you over-spec a bit. You spend a lot of time writing it and hoping it all fits on there, and that means hiring a whole team to do that. Or on the other end of the extreme, we’ve, uh, we’ve talked to companies that actually outsource all of their development. They write specs, and they hire contracting houses that are really specialist in the hardware that they’ve chosen. So maybe they’re gonna write FPGA code on a Xilinx FPGA. They’ve got a favorite shop, and they basically write instructions, wait months for that code to come back, and then have to spend time integrating it back into their product. So it’s a wide gamut, but a really, really slow loop that essentially, you know, our mission is to make that instant.

Maggie 05:21

So I know another major use case you all have hit on is the code-based modernization use case. Could you talk a little bit about some of the customer interest there?

Peter 05:30

Yeah. Where, where we’ve seen a lot of growth, uh, recently is in the Air Force and, you know, DOD writ large or DOW. Uh, you can think of they’ve invested a lot, right? They pay a lot for software. Um, there’s a lot of latent potential in all the different software they’ve spent But these are old code bases. Sometimes they’re reusing systems from the ‘80s, ‘90s, even older. Getting those into something that can be cloud distributed across the entire DOW, uh, is something that I feel like is a consistent use case, where they wish they had access to XYZ tool, um, but right now it takes a specialist with hardware that’s a pain in the butt to get access to, um, and software, and they need to, you know, basically wait months to get answers back. So we’ve been taking a lot of code bases in these sort of older formats, or at least more expert-driven formats, and getting them ready to deploy, uh, you know, sometimes in an easy language like Python.

Maggie 06:22

Pat, I know you’ve talked about how a number of our key weapons systems are written in old programming languages like Ada, which is a programming language from the 1980s. I don’t know how many Ada engineers there still are out there today, but it seems like if we wanna have any chance of modernizing these systems or even just maintaining them, these kinds of capabilities that Code Metal is developing are really going to be crucial.

Pat 06:44

Yeah. Actually, it’s, it’s a mixture of codes. It’s even, it’s not just one old code. It’s COBOL, uh, Fortran, Ada, you name it, uh, uh, develop- large projects the government and industry has developed over many decades is, uh, this in- in- inconsistent, uh, set of, uh, codes that makes it highly inefficient maintaining compilers that are, you know, been out of production for decades, all of that across the board. So that’s, that’s a real world application of this.

Peter 07:16

Yeah, and I, I think you mentioned the mixture of languages, and it’s, that’s, that’s so true ‘cause it’s like they’re... DOW will buy capabilities, and they’re not gonna tell you how to do it. They’re gonna tell you, “Meet these requirements.” Um, and so, you know, some of these systems we’ve built that are mixtures of different code bases, it’s the contractor’s favorite language, and it’s, like, something random like a data science language like R, uh, that they were doing. Uh, uh, I think one of my favorite examples ever is we found a, a, a web app. So it had a Leaflet front end and all these sort of features, but it was an R code base, which is, uh, I guess for all the coding nerds out there, just a very crazy choice that when I tell people, they’re like, “How did that happen?” And it was really ‘cause it was the contractor’s favorite language. That’s what they knew. And they, like, just kinda squeezed the use case into the code base and the, the software tools they knew. So not necessarily always the best choices even to start with. I’ve heard people basically looking to start retraining engineers in Ada ‘cause exactly that. It’s memory safe. They can’t hire enough Rust engineers. There’s enough legacy Ada code that They’re just kind of biting the bullet and, um, for anybody not familiar with Ada, it’s really cool formal methods and, um, sort of security built in, but very old language that never got the momentum of Rust, so not a lot of public support out there for libraries. So really these enclaves of hidden code bases that you kind of have to be an expert in to use.

Maggie 08:38

Yeah. So I want to turn to a couple questions about the technical details of the product you’re building, something that’s really interesting. So unlike a lot of the, the hype out there today, which is, you know, LLMs, AI is everything, you all use a mix of both, you know, LLMs, artificial intelligence, as well as more traditional formal methods and formal verification. So can you talk more about how you architect your system using all these different kinds of technologies to really ensure, uh, trust and verifiability of the code that you’re translating?

Peter 09:11

Yeah, I, I think if you step back and it’s, it’s, it’s really, again, a spectrum of, of validation and verification tools. On, on the high end, you have formally verified. That’s essentially saying, “I’ve proven mathematically these things are equivalent.” And you can’t always do that, but for some use cases, you have to in order to have that confidence. Um, and then on the other spectrum of that, you’ve at least built up enough evidence that you’re giving the customer, you know, sufficient trust that the AI did the right work. And if you think about where we are today in AI, like code almost costs nothing if you’re a company that can use some of these copilots. Where you are spending a lot of time is actually validating now this mountain of code. And so the task has moved to human verification and va- validation of, you know, what the code has written or that the AI has written. And so what we’ve done is we’ve essentially started pairing together, um, these sort of little powerful code generation tools with a suite of test harnesses. That’s where a lot of our IP lives, actually, like validating that these tasks that we think can be fully verified, um, actually are by creating this evidence. This essentially third-party, you know, auditor that isn’t AI-driven that’s able to check the AI in a way that’s automated and lets the teams move really, really fast.

Maggie 10:24

Many of the coding li- languages that you all are working with, and whether it’s Rust or Ada or, um, VHDL or others, these are not super common open source programming languages like a Python or React or something along those lines. How well do these AI models work with the languages out of the box, and how much customization is required to actually get these systems working with these more esoteric languages?

Peter 10:50

Yeah, I think that’s, that’s something that it’s been encouraging to see how much better the language models are getting out of the box with just some form of explanation of the languages. Um, but yeah, there is a huge gap between what it’s used to working with and sort of these more esoteric... I think VHDL is a great example of a, a language that’s more of a hardware spec masquerading as a language, um, that the language models just have trouble grokking because it’s so different than, um, the, the normal languages that they’re used to working with. So What we do is we actually decompose these really difficult tasks using sort of static analysis techniques into really bite-sized, manageable, provable steps. Um, and again, that sort of pulls in the, uh, the non-AI component of the, the product that we’re building. So I think we, we-- You know, our hope is actually that the tools get good enough, the AI tools that is, where we’re not doing as much customization as we are at, ‘cause there’s still the need for validation, verification, all the things that we’ve invested effort in. Um, it’ll just converge a lot faster. So right now, uh, on some of these trickier languages, we do a lot of fine-tuning of models. We do a lot of breaking down the problem into bite-sized steps. We do a lot of, uh, other sort of tricky in-house secrets on getting these things to converge. Um, but you know, we view us to be in synergy with like basically the growth and the capabilities of these models.

Maggie 12:14

And then how do you think about what models to use on the back end? And, you know, maybe carrying a little bit to some of the customers that you work with, a lot of them, you know, are working in air-gapped environments, maybe even classified environments. How do you think about, um, the models that you use that you’re able to actually deploy on those systems, and what are some of the, the trade-offs of deploying in some of those high sensitivity systems?

Peter 12:36

Yeah, and I, and I don’t want to undersell the commercial space either. If you talk to a Japanese automotive company on their treasured code base, like they won’t even tell you what they’re doing. So it feels like you’re, you know-

Maggie 12:46

They’re, they’re not all deployed in the cloud

Peter 12:48

Yeah. It feels like a TSSCI SAP program when like you’re not even allowed to know what they want to do with your, your product. So, um, def-definitely I think writ large our customers want to have a lot of control of where their data’s going, and that means deploying on-prem. I think they’re all looking towards deploying in cloud in some way, and some of the business side has deployed some of those tools. But in terms of like, I’ll call it their treasured code base, like, you know, AMD isn’t putting the IP cores for their, you know, GPUs necessarily in a cloud, um, or at least anything that isn’t locally controlled. So I, I think the way we’ve looked at it is we want to be open to plug and play with any model. Um, you know, from our training stacks to our deployment stacks, we try and support everything. I mean, I think the kind of crowd that we have that work here is The second a model drops, everybody’s excited to immediately plug it into our pipeline and see what the baseline sort of results are. So I think there’s a fun sort of nerdy culture here in terms of like some new diffusion models are dropping, and immediately the team’s figuring out how to get it plugged in and running, uh, benchmarks. But, uh, it, it’s keep up with things. Um, you know, defense definitely wants to run US models, which is getting tougher. Uh, China’s definitely run quite ahead in terms of performance there, so there’s a little bit of a handicap working in that space. But, uh, on-prem and whatever’s the latest is the, you know, the way we’ve taken, uh, our product.

Pat 14:11

Uh, you had mentioned, uh, you are working, uh, in Japan. Uh, what are your thoughts on, uh, navigating international markets that you’ve...lessons learned?

Peter 14:22

Yeah. Yeah. I think for us, that market is one that, you know, we got excited because there is a real hunger for US technology and moving fast, and there’s a lot of socioeconomic reasons where they are looking to adopt AI and looking to adopt new technology. So it’s been a country ready and excited for the types of things we’re working on. All that being said, time zones are hard. Field deployed engineering is hard. Um, you know, and for us that, you know, less than three years old now, um, it, it’s something that I definitely think you wanna go there with a, a, a really sort of locked down playbook. And I think it’s something where, um, we had to kind of slow down the, uh, demand that we had because we wanted to make sure the people that we are working with there, uh, we’re nailing it for them. So we almost, you know, we had to treat Japan a bit like a wait list situation where we’ve got our key sort of tent pole customers, um, that we’re partnering with now. But I think next year we’re gonna be focused on growing that area quite a bit more.

Pat 15:19

Well, speaking about growth, you’re pursuing a, uh, dual use strategy with commercial and, uh, government, uh, customers. Uh, what is the difference you see, and what is your advice on how to manage the, those differences?

Peter 15:36

Yeah, it’s really interesting, and I think it comes into how you talk about what you’re doing. Uh, when I’m talking to commercial space, it really is how are you gonna use Code Metal and sort of fitting to their use case and really focusing in on what our product does. Um, because we’re sort of in the productivity tool suite, a lot of the times when we work with DoD, what makes the things we do possible is our technology, but we’re talking about the capability it enables for them. And so it’s, it’s, I guess it’s a higher level up, um, in terms of when we’re selling DoW versus when we’re selling, um, you know, let’s say Bosch or something. Talking to each client, you know, I have to tell DoW the ultimate use case, the benefit to the warfighter, and it’s just a little bit, you know, higher level in terms of the impact. And with that comes into the way we execute, the way we prepare material, more on site, more sort of, you know, um, longer sort of contract cycles. But all of those things I think, you know, help us because one thing I do think DoW is great for is essentially on those early bets that we’re taking at Code Metal, we’re looking for DoD to sort of springboard and de-risk and build a technology base that then we take to commercial space.

Pat 16:47

And what are the unique challenges you’ve found that, uh, founders should prepare for if they’re gonna work with national security?

Peter 16:54

Uh, well, contracting is something I didn’t realize how little I understood. Um, I think, you know, it was interesting in that like luckily because I have a background in defense, uh, you know, I used to work at BAE Systems, uh, worked at MIT Lincoln Laboratory. Um, but I didn’t-- I wasn’t necessarily on the contracting side. I was in like the make the customer excited by the things we were doing side. And, um, it was interesting when we had customers who wanted to buy and it, it-- When they don’t understand how to buy, and you have to actually go help them navigate, like, their own organization, it becomes really, really interesting. And some of the successful, I think, you know, uh, I’m not sure if neo-primes is the right word, but some of the, the newer defense companies, uh, where I’ve sort of discussed with them and we’ve talked about what’s worked, it’s really being able to help them with that contracting side, helping them with that, like even up to the legislative lobbying side so that they can get what they want. But it’s a really weird situation versus commercial, where you have somebody, like, dying for the thing that you’re doing, and you have to go help them walk all these steps in order to actually pay you for it. So-

Pat 18:03

Yeah. Besides contracting, another area that’s unique to the government that, uh, startups have to str-struggle with is the, uh, working with classified information and the authority to operate. Uh, what was your journey like getting approval for authority to operate on your, for your classified customers?

Peter 18:24

Yeah. I, a lot of this, again, is gonna be driven, and I hate to keep hitting the contract, it’s like we had to do so much work in order to even get the permission to submit for ATOs, um, or submit for classifications, and it was all waiting for essentially the contract to say the words that, like, “You shall need this.” And then that kinda got everything running, which is its own headache, and it, it’s a lot of work, and there’s a lot of great companies, uh, spinning up, I think, to help startups with these problems. But at the core, you need somebody to buy in that, “Hey, Code Metal needs a top secret clearance, and Code Metal, uh, you know, needs to submit it to this, let’s say, cloud provider,” and to really kick off all those processes. And so it was pretty much a crawl in terms of getting these things done, and the second we had the contract in place, uh, I think it was like three months later, we had an FCL, um-

Pat 19:18

Which is a facility clearance.

Peter 19:21

Yeah. And, and I had gone from, you know, basically an inactive TS clearance to now I have my TSSCI. So it was a really fast process once this painful contracting piece had been done in place. And, and friends of mine and, you know, other founders I’ve talked to, similar sorts of journeys in terms of like either getting a prime, maybe they’re subcontracting to you to help them with that, uh, or, you know, their own direct contract.

Maggie 19:46

Yeah. I wanna know, I think you guys have really had a lot of success here just because the customers did get so excited about your product and were ready to go run through walls to help you get the ATO and the FCL. Maybe can you tell us about a moment when you showed the product to a customer and you felt like they really got it, and, you know, what was that experience like and what were they experiencing?

Peter 20:08

Yeah. I think, look, uh, I think as, as venture capital companies in dual use, we have this advantage that we can sort of build ahead of what our customers want and show them and not tell them. And I think a lot of the traditional industry out there is sort of selling through slides, selling through SBIRs. Um, I think you have the advantage of like, you have a customer who has needs, uh, and I don’t see enough founders actually think in the dual use space talking to the customer first. And so we-- I’ll give you an example of, of what really kicked this off We were working a, a project that was more of a, a favor due to expertise of the company to a friend. Um, but the, the customer that they had essentially wanted to come and visit Code Metal. And so as part of that, uh, I briefed them on sort of code translation and what we do before I got into, like, the detail thing we were helping them with. Uh, and sorry to be a little vague. But we, uh, we, we briefed them on the core capability, and the customer asked, like: “Oh, you know, we have this modernization thing. We’re getting bids for about a year to actually take our code base and update it to new. Uh, would you guys be able to do that with your tool?” And I said, “Yeah, sure. Can I get access to it?” And they’re like: “Yeah, it’s Git owned. We’ll send it to you.” Um, and I was... Originally, I was asking for more of, like, a, “Can I, like, see it to scope out the work?” And we just went ahead and did it and sent them back the code. And there was this, like, “Holy shit.” It was that, like, uh, they went from thinking, “We’re gonna take a year to, to modernize this one module,” to like, “Hey, we’ve got about 19 that we wanna do, and, like, we were dreaming about connecting these things and building this bigger platform.” And we were like: “That’s great.” And we, instead of just, like, waiting and trying to do this other end, and, and look, this might not work for everybody, but this is the way we did it. Uh, we just got to work, and we’re like: “We’ll figure out how you guys pay us. We’re just gonna do good work and focus on, on, on starting to build this system.” And, and it’s paid back in spades. So we, you know, I, I can say fast-forwarding to where we are today, um, you know, they’ve done a great job in terms of, like, coming back to us and, and paying for the work and then also expanding the opportunity. And they see it, and the sort of reputation, it’s a small industry, starts rippling around. You know, we were at an event just now supporting and deploying, and folks were coming by from the other services saying: “Hey, you need to come out to visit me. I see the way you guys are here day and night, day and night. Um, I see what you’ve done with them, and, like, this is six months from, like, when we talked to you to IOC that we’re now running. That’s initial operating capability, uh, your platform.” So I think- What I can say is we have an asymmetric advantage in that we’re trying to build, uh, you know, companies that, that scale and, and sort of play this long game. And you have these customers who have needs, so talk to them. Um, don’t try and just invent a thing and then, like, sell it to DOW. Like, there’s amazing problems that they have that, like, I don’t see people tackling. So I guess that’s my advice on selling to them and some of those things.

Maggie 23:06

How have you thought about building trust with some of these mission-critical customers? I mean, I know there’s a lot of distrust out there about AI broadly, so how have you approached that?

Peter 23:18

I think what we get a little bit of inherent in what we do is kind of like a practical thing we’re solving. So we’re not trying to boil the ocean, uh, or, you know, we, we kind of have to limit a bit sort of even the pipelines we do. Like, the North Star is any language to any language. Right now, we have very practical Python to C++, C++ to Rust sort of targets that we’re working. And so I think just hear-them hearing the proof points and sort of all the non-AI bits, um, like sometimes I actually get reminded like, “Oh, you can at least also mention that we’re using AI,” is sort of what we’re selling them. Um, and, and we’ve had pitches and they’ve seen the product where they’ve gone like, “Oh, you’re using LLM, I didn’t know that.” Um, and yeah, I, I think that’s, that’s been some of that trust building, that we’re just really trying to hear what their problem is and focus in on, “Here’s what we do, here’s how we do it, here’s the evidence we show you.” Um, and, and that’s been, you know, a huge part of it.

Pat 24:11

You have a broad experience with, uh, not only national labs but, uh, federally funded research development companies and, uh, academia and industry. Uh, what made you decide to start your own company?

Peter 24:25

Uh, that’s a great question. So I was, uh, I was at Microsoft during COVID working on the HoloLens, very, very frustrated in leadership, working very, very hard, um, to, to essentially solve a lot of problems that CodeML solves today. But, um, uh, 100% transparency, Code Metal wasn’t the first company that I started. I started another one after that, and I was very naive at that point. I was an engineer first, didn’t know anything about startups. I, uh, m-met a fellow who, you know, great sort of partner in business, but maybe not what a VC company would be looking for, and, um, ended up starting a company that, like, got into Techstars accelerator and did these things. And really, I just assumed a startup was like, “Oh, I’m in a job I want and make money.” That was, like, the extent of my business knowledge. Um, I remember being in, like, the Techstars whatever, uh, like, cohort, and they asked me, like, “How’d you get in?” I was like, “Oh, I didn’t know what this was,” but they were talking about stuff I was doing. So, uh, it was, it was a, a big learning curve, and I learned a lot about what a startup was. But the thing I pulled out of that, that, that got me excited was this idea of like, oh, the problems have to be big enough and the founder has to really fit the market to actually, you know, be reasonable as a venture-backed company. And so even though we had a great, you know, I consider it great success that we sold the company that, that I’d started, like, I really got a sense of what is, like, a true venture-backed, venture-scale company look like from that first kinda you know, I’ll call it pseudo failure. Um, and, and, you know, that’s-- a lot of those lessons went into Code Metal. So part of it was the autonomy of tackling the problems I wanted to tackle. Um, that’s a big part of it, like, uh, rather than sort of selling the company to do the things I wanted to do. Uh, and then really understanding that actually that’s a great fit for a venture-backed company. When you have the background, you have a problem that’s big enough to be r- you know, sort of worth venture backing. Um, you know, it seemed like a great fit and all the things I like to do and the ways I like to work.

Pat 26:22

And you mentioned the, uh, challenges of people working in, in venture companies and, uh, uh, venture-backed companies and, uh, uh, dealing with work-life balance comes up a lot, uh, when you’re retaining and growing and motivating, uh, teams. Uh, what have you found is, uh, successful, or how do you approach recruitment, uh, so that folks know, realize, uh, that working in a startup has a huge benefit, but at the same time, it, it’s not a normal nine-to-five, uh, five-day-a-week job?

Peter 26:56

There is a great description of people that fit, um, Valve, which is a video game company that I’ve stolen in, in everywhere I’ve tried to hire, and it was they look for T-shaped people. These are people with, like, a deep expertise through, like, um, you know, one core topic, but, like, broad exposure to a lot of things, willingness to be flexible. And I think, uh, the reason we look for that sort of T-shaped person is we, we look for people that, like, the thing that they like to do is almost their, like, life mission. Like, one of our pillars is passion. And the reason being is, like, I want these people rambling about formal methods or rambling about optimizing some, you know, embedded CPU core and, like, looking for signal on things where they’re doing this in their free time or that they’re, like, taking the extra mile on their own to do it. Because then my job is to give a really good North Star that we can all rally around so that, like, on their off days... I’ll give you an example. We spent a grueling, like, uh, deployment where it was, like, eleven PM, you know, we were wrapping up, and we were back in there at six AM for about two weeks solid and on the weekend. And, you know, coming at the end of that, I told the team, like, “Get out of here. I don’t want to see you for a few weeks. So, like, don’t wait to die. Go, go take a break.” They’re like pushing me things that they like, they still wanted to like fix the problem or, or still work on it. And so I’m getting like architecture ideas or improvement ideas like as they’re going away on vacation. Um, so it’s, it, it’s tough. Like it’s a competitive market. Like you need to have people motivated to put those hours and do that work. Um, I don’t think you can pretend that like you don’t have to put it, but it has to be good work. It has to be thoughtful work. It’s not just work for work’s sake. If you get your work done and you are, you know, you can leave the office, leave the office. Like we take sort of just get your job done attitude to how we do things. So, and a lot of that just comes from passionate people with broad exposure to things.

Maggie 28:47

You’ve worked in the defense space for more than a decade at national labs, at defense primes, elsewhere. What have been some of the biggest changes you’ve seen in the industry over your career, and what are you most looking forward to for the next five to 10 years or most excited about?

Peter 29:01

Maybe this one isn’t as like novel, but it’s still one I think about a lot. Um, it’s been a lot cooler to do defense stuff. And so, um, uh, you know, that, that sort of has its ups and downs. But I remember being, you know, Project Maven kicking off while I was at MIT Lincoln Laboratory and the Google employees sort of walking out, and now you have something similar now with, uh, sort of Claude and what happened, but I think it’s a little bit different. Um, but yeah, I, I think just the fact that people see the sort of value in, you know, doing this work, uh, has been something exciting to me. It’s easy to recruit. It’s, uh, a lot easier to walk on a Stanford campus and, you know, find kids that are excited by the stuff that you’re doing. Um, I think that was one change that I still sort of really appreciate. Uh, the other is I think more recent in this idea of like trying to figure out new ways to actually fix these issues in acquisitions and, and contracting and, um, I’m, I’m actually super excited by it. I think it’s one of the things we are getting right, at least trying things. Um, and so yeah, I’m looking forward to seeing the next two, three years where that goes.

Pat 30:11

Today, you’re having a board of directors meeting. Uh, what are your thoughts about, the benefits or how best to leverage a board for founders and investors that are, um, listening to this podcast?

Peter 30:27

Yeah, I, I think it’s really hard when you’re a founder and you’re waking up every day and thinking, “What’s the most important thing I need to do?” And at some point you’re gonna need to raise funds and build your board. And it’s like, it feels like a luxury when you get told like, “Hey, pick board members you like. Pick-- Work with people that you wanna work with.” Um, I, right now I do, which is what I feel very like lucky that we get to do that. But, um, in terms of like horror stories I hear from other founders and, and things that like basically they blame the company going wrong on, it, it’s a non-insignificant percentage where it’s like board dynamics. So I think it’s, it’s really important to actually like when it feels desperate and you’re like, “Oh man, I really should take this check.” And like I, I get it, as like a founder you might, you know, wanna take money for a certain reason from a certain fund. If there isn’t that fit with like, “Hey, I can work with this person,” um, it, it’s really stupid sort of repetitive advice that every founder gets, but like I’m just stressing it as like, yes, like listen to that. Figure out how to like extend your runway a bit or whatever it takes to like take the right capital in because you are tethered to these people. Um, and at least the way I’ve looked at it is at different stages of the company, we’ve needed different sort of help, and we’ve looked for people that, one, fit the, you know, had the background to provide that type of help, whether it be like technical product guidance or it be, um, you know, sort of moving to commercial is, is another example. Um, you know, giving us connections and top cover as we do defense work. All of those things were the criteria in how we targeted who we targeted, but first and foremost was, hey, I gotta work with this person forever maybe. You know, I have to pick people that I wanna work with.

Maggie 32:15

What lessons, if any, have you taken or has Code Metal taken from overseas conflicts in Ukraine and the Middle East? Or has it changed your approach to the ecosystem and your product?

Peter 32:27

Yeah, I think in, at least in Ukraine, there’s this notion of like, hey, the acquisitions process does not work for a war that’s adapting, you know, every week, every, you know, month. How do we actually start building, uh, ways of acquiring and reacting to, um, immediate sort of situations? So without getting into too much detail, obviously a company that’s like built around like immediately deploying hardware, the latest idea, you know, benefits from, from that sort of change in thinking. So it, it’s one of the things I think DoD still hasn’t quite figured out how to acquire and pay for that consistent adaptivity. Um, but it’s something that’s an open question and, and obviously first and foremost important.

Maggie 33:10

As I say, you guys have done some work as a, a subcontractor to larger primes. Is that right?

Peter 33:15

Yeah, absolutely.

Maggie 33:16

I’m curious how you think about those kinds of arrangements as a way to get access to some of these government customers that might be difficult to access otherwise. Could you talk a little bit about your experience with subcontracting?

Peter 33:29

Yes. There, I mean, there are calls for proposals, customers that you won’t get to see as a startup. Like, they won’t exist to you, they won’t be in the market to you, but maybe you actually solve exactly what they do. Uh, thinking about that early when you’re partnering with the primes, like wanting to be in the room when things get presented, wanting to get that connection is something that maybe when you’re trying to close a deal doesn’t feel like a order one priority, but it kind of is if you’re trying to build a long-term company. So I can say from experience, um, that was one of the things that we pushed back on wanting with some of the agreements that we had. Like, “Hey, we’ll work together on this, but we wanna be in the room for certain aspects of this.” And again, to get exposure to the company or the, the customer who doesn’t have the luxury of... You know, I think there’s a lot of saltiness in the DLE space about like, oh, they’re giving it to the same people or whatever, but they need stability. They need like repeatability. Like they can’t just be trying everything in a lot of these critical industries. So, you know, there’s some empathy to the customer in terms of like going with the same solutions over and over again. And the primes are a great way to actually start bootstrapping that exposure and, and relationship.

Pat 34:39

And what’s your vision for how you see your company growing and the product, the use and adaption, uh, adopting, um, the products that you’ve developed?

Peter 34:50

Yeah. I view we’re starting with a, I think, a really strong baseline in our defense clients, and it’s, it’s just industries that we know it’s a great place for developing, uh, deep tech technology. Uh, but it is a general use technology. Like it doesn’t just impact, uh, DOW. And so you do this first tranche about growth and, and defense of our, you know, sort of core positioning and the things that we wanna care about. Uh, and then, you know, we’ve been growing now out into more sort of initial landing and exploratory work in the industries like automotive, like semiconductor, uh, that skew more pure commercial. So I think that’ll be our phase two. And you know, personally, one of the mantras I’ve said to the team is we don’t wanna be a series Z company. Like we wanna get to, you know, reasonable, predictable revenue and IPO in, you know, three to four years.

Maggie 35:38

What have been some of the biggest surprises building Code Metal?

Peter 35:42

The AI market was moving Fast, but, like I said, the last six months to me have been, like, absolutely insane. So I think, um, I’ve liked it ‘cause we’ve had a clear focus on who our customer is and who our market is, so we’ve gotten to, like, kinda avoid the noise. But man, does it sound crazy out there. So, uh, that’s been, um-- It’s been interesting watching, to tie back to us, like, how smart our customer has gotten in a very quick amount of time. So I can think about two years ago what it was like to talk to them about AI code generation to today, and it’s interesting, like, seeing their evolution across these sort of legacy industries.

Maggie 37:44

Great. Well, Peter, thank you so much for the time, and thanks so much for coming on the podcast.

Peter 37:48

Yeah, thanks. It was great to talk to you guys.

I’m now almost exactly three years into my time on the investment team at an early stage, national security-focused VC firm, and I wanted to share the rhythm I’ve developed on how I spend my time (with the caveat that I am always learning and iterating).

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Ultimately, my primary job is to 1) see, 2) pick, 3) win, and 4) support early stage technology companies building in national security that have the potential to deliver outsized returns to my firm’s limited partners (LPs). To me this means:

1. Seeing: Sourcing and identifying the most compelling investment opportunities before they become obvious to the broader market.

2. Picking: Evaluating hundreds of pitches to select the few companies with the strongest potential to become category-defining outcomes.

3. Winning: Earning the right to partner with top founders in competitive rounds against other leading investors.

4. Supporting: Helping portfolio companies accelerate growth through strategic guidance, customer access, recruiting, fundraising, and operational support.

Fundamentally, everything I spend my time on (professionally) is in service of those four goals.

I spend the bulk of my time on the following tasks:

1. Sourcing and founder meetings

2. Investment diligence

3. Portfolio company support

4. Relationship building and ecosystem engagement

5. Market research and thesis development

6. Podcast and blog writing

7. LP fundraising and investor relations

For those curious, here is an anonymized screenshot of my calendar from a few weeks ago, which represents a pretty typical week (although, of course, every week is a bit different):

In general I start working between 8 and 9AM (sometimes earlier if there are meetings with folks based on the East Coast or internationally) and wrap up meetings around 5 or 6. I typically attend one or two evening events (happy hours, dinners, etc) each week. In general, I travel around the country at least once a month to attend events or meetings in person (the main cities I frequent are LA, SF, DC, Boston, and occasionally New York and Seattle). I try to reserve at least one morning each week for reading, writing, and focus time, as I’ve found I focus best before noon (however, often I end up doing this focus work in the evenings after a workout / dinner or on the weekends, as my weeks tend to fill up with meetings).

Sourcing and Founder Meetings (~5-10 hours / week)

Taking first pitches (“sourcing”) is the core bread and butter of my job. On average, I take between 5 and 20 first pitches each week. I meet companies through all kinds of different channels:

• Other VCs

  • Pre-seed investors whose portfolio companies are raising their next round

  • Generalist VCs that do not specialize in defense or deep tech

  • Trusted VC peers interested in co-investing or co-leading opportunities

• Portfolio company founders / employee references

• References from other friends, former classmates, professors, and broader professional networks

• Events and ecosystem engagement:

  • Happy hours

  • Dinners

  • Hackathons

  • Conferences

  • Tech talks

• Accelerators and demo days

• Online channels such as Twitter and LinkedIn

• Cold inbound outreach through email and LinkedIn

Investment Diligence (~5-20+ hours / week)

If a pitch is particularly interesting, we will conduct “due diligence” on the company to determine whether it is a good investment opportunity. On average, we conduct diligence on ~10% of all the companies that we meet. A diligence sprint typically lasts 3-14 days. Some weeks involve relatively light diligence activity if there are no active opportunities advancing through the pipeline. Other weeks can become almost entirely consumed by diligence, particularly when we are moving quickly to evaluate and win a highly competitive deal. Our diligence process primarily consists of the following steps:

1. Founder meetings: We aim to spend as much time with founders as possible before making an investment decision to determine whether there is a strong mutual fit. Founder-investor relationships often last a decade or longer and must withstand the inevitable highs and lows of building a company. Mutual trust, respect, rapport, and alignment are critical, and the best way to evaluate that is by spending time together. During this process, we also introduce founders to multiple members of our investment team to gather a range of perspectives on both the team and the business.

2. Founder references: As early stage investors, we are primarily underwriting the strength of the founding team. So, we conduct reference calls with former colleagues, managers, employees, classmates, and others who know the founders well. These conversations help us evaluate attributes such as grit, intelligence, work ethic, leadership ability, domain expertise, sales and recruiting ability, and the overall capacity to build a billion-dollar company.

3. Customer diligence: If the company has existing customers or design partners, we speak with them to understand the value the company is delivering and how the product is perceived in the market. We also engage with prospective customers to understand how they evaluate the company relative to alternatives. In many cases, we introduce the company to potential future customers, which allows us to observe the company’s sales process firsthand and gather direct feedback on the product and vision.

4. Tech diligence: We frequently bring in subject matter experts from our network to evaluate the company’s underlying technology, technical differentiation, scalability, and defensibility.

5. Market and competitive research: Outside of calls and meetings, we conduct independent research using public sources, expert call transcript libraries, industry reports, academic papers, think tank reports, and tools such as Claude and ChatGPT. This work helps us better understand the technology landscape, market dynamics, competitive environment, and the broader opportunity available to the company.

For more on how we evaluate companies in the national security space, see this blog post. For reference, we ultimately invest in less than 1% of all the deals that cross our desk.

Portfolio Company Support (~1-5 hours / week)

My firm typically leads financing rounds and often takes board seats in our portfolio companies. As a result, we take a “hands on” approach to investing, partnering closely with founders to provide strategic guidance, leverage our network, and help navigate key decisions throughout the company’s growth journey. While we do not have a large dedicated platform team, supporting portfolio companies is a shared responsibility across the firm. Every member of our investment team spends time helping founders, whether through customer introductions, recruiting, fundraising support, government engagement, strategic planning, or tackling whatever challenges are most critical to the business at a given moment.

I am a board observer for two of our portfolio companies and work closely with several others. The main ways I spend time supporting portfolio companies are:

1. Board engagement: Serving as a board observer and participating in ~quarterly board meetings, providing strategic input and ongoing support to management teams.

2. Fundraising support: Advising founders on fundraising strategy, conducting pitch preparation sessions, facilitating introductions to relevant investors, and participating in diligence calls with prospective investors.

3. Go-to-market strategy: Working with companies to navigate the federal and enterprise landscape, refine government and enterprise customer acquisition strategies, and identify pathways to procurement and programmatic adoption.

4. Recruiting: Leveraging my network to identify and connect high-quality candidates with portfolio companies. I also conduct targeted outreach, participate in first-round interviews during key hiring initiatives, and engage directly with finalists to help companies successfully close top talent.

5. Market intelligence: Engaging regularly with prospective customers, subject matter experts, policymakers, and industry leaders relevant to our portfolio companies to understand the current market dynamics, emerging technologies, policy environment, and competitive landscape.

Relationship Building and Ecosystem Engagement (~10-20 hours / week)

I am always trying to expand my “luck surface area” by building relationships and expanding my network. I make a deliberate effort to build relationships with exceptional founders, operators, investors, researchers, and government leaders. Over time, those connections often create unexpected opportunities: a future founder starting a company, a candidate who becomes a key hire at one of our portfolio companies, an expert who strengthens a diligence process, a new customer for a portfolio company, a differentiated market or technology insight, a follow-on investor for a portfolio company, a co-investor on an exciting new deal, a future LP, or a trusted advisor who helps a portfolio company navigate a challenge. Occasionally, those relationships become something even more valuable: a true friendship 🙂

You never know where new opportunities are going to come from – I’ve sourced portfolio companies from random happy hours (or even introductions from people I met at random happy hours), and key portfolio company engineering hires from random LinkedIn cold outbound messages! Venture capital is ultimately a relationship business, and the returns from investing in people often compound in unexpected ways.

To continuously expand and strengthen that network, I host ~monthly events around the country (happy hours, hackathons, small dinners, tech talks, etc), and regularly attend events organized by investors, government organizations, startups, industry groups, and research institutions.

The communities I spend the most time engaging with include:

• Founders, engineers, and operators outside of our portfolio at startups, national security technology companies (including the defense primes), and commercial technology firms to identify emerging trends, potential investments, and future talent.

• Researchers across university, government, and industry research labs to stay informed on technological breakthroughs and emerging areas of innovation.

• Government leaders and operators across acquisition, technology, and mission organizations to better understand capability gaps, operational challenges, and evolving government priorities.

• Venture investors across stages. Venture is a team sport, and building relationships with other investors helps generate co-investment opportunities, identify promising companies earlier, and support portfolio companies throughout future fundraising rounds.

• Job seekers and emerging talent, including students, transitioning military personnel, and professionals interested in joining mission-driven technology companies. I regularly connect talented individuals with relevant opportunities across our portfolio and broader ecosystem. Please feel free to reach out if you’re interested in our portfolio

Market Research and Thesis Development (~5-10 hours / week)

In addition to learning from conversations across the ecosystem, I spend time conducting independent research (apart from targeted due diligence) on emerging technologies, markets, and industry trends. My research draws from a wide range of sources, including newsletters, podcasts, books, expert network calls, academic papers, government reports, think tank reports, and industry analyses.

Much of this work serves as the foundation for investment theses, blog posts, and podcasts. It also frequently evolves into deeper due diligence efforts as we evaluate specific investment opportunities and seek to better understand the markets, technologies, and competitive dynamics surrounding potential portfolio companies.

For a full list of podcasts, newsletters, and some of my favorite books on national security, technology, and investing, check out my reading list.

Podcast and Blog (~5-20 hours / week)

My goal is to publish one blog post and two podcast episodes each month. Each blog post takes ~5-20 hours to write and edit (research-heavy deep dives like my piece on AI and offensive cyber are more time consuming, while shorter posts like this one take less time). Each podcast takes ~5 hours to prepare, record, and edit. I typically spend ~1-2 hours preparing for each episode, 2 hours recording, and another 2 hours reviewing the footage and providing editing notes (from there, our fantastic editor takes over, handling the audio production and creating the visuals for the podcast).

While the podcast and blog are time consuming, they’ve dramatically expanded my luck surface area. I’ve formed genuine friendships with people who first reached out after reading an essay or listening to an episode, helped portfolio companies attract customers, investors, and top talent, sourced deals at the earliest stages, and even connected with LPs who ultimately invested in the fund.

LP Fundraising and Investor Relations (~1-5 hours / week)

It’s not just startups who need to raise money – VC firms do too! VC firms rely on limited partners (LPs) to provide the capital that ultimately gets invested into startups. Fundraising and investor relations are not the primary focus of my role, as we have an amazing capital formation and investor relations team leading these efforts, but I still participate in several LP meetings each week and attend the ~quarterly events we host for current and prospective LPs.

My role is typically to discuss our portfolio, investment strategy, and market and technology themes we’re most excited about. I also spend a fair amount of time with existing LPs exchanging perspectives on emerging technologies, market trends, and the evolving national security landscape.

Conclusion

Overall, I genuinely love what I do and feel incredibly fortunate to spend my days learning from exceptional founders, LPs, engineers, operators, researchers, investors, warfighters, and policymakers. No two days (or two weeks) are ever the same. What I enjoy most is that the job is a constant exercise in learning and iteration. I’m always refining how I spend my time, searching for better ways to identify and support the most consequential national security companies at the earliest stages of their journey (so if you have any suggestions on what I can be doing better or differently, please let me know!)

As always, please reach out if you or anyone you know is building technology for national security. As you can see, my calendar is always open to learn more about the cutting edge of technology and national security.

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Note: The opinions and views expressed in this article are solely my own and do not reflect the views, policies, or position of my employer or any other organization or individual with which I am affiliated.

In this episode, we break down the $1.5T FY27 President’s Budget Request, the Pentagon’s infamous “J-books,” appropriations vs. reconciliation vs. supplemental funding, and what all of it means for startups, investors, and the broader defense innovation ecosystem.

We cover:

• How the PBR and J-Books relate to Congressional budget bills (appropriations, reconciliation, Iran supplemental)

• Why the J-books are one of the most important tools for founders building in defense

• Where the DoW is requesting for AI, autonomy, space, and other commercial technologies relevant to startups

As always, please let us know what you think. And please reach out if you or anyone you know is building at the intersection of technology and national security.

You can listen to the podcast on Spotify, Apple, the Shield Capital website, or right here on Substack.

Some obligatory PBR charts courtesy of Obviant:

And of course, if you aren’t already doing so, you should subscribe to Matt’s newsletter Defense Tech and Acquisition:

Defense Tech and Acquisition

Enabling defense and industry professionals deftly navigate the world's biggest bureaucracy to deliver better solutions faster.

By Pete Modigliani

Transcript

Maggie 00:36

In this episode of the Techquisition Edition of the Mission Matters podcast, we’re diving into an important ritual in the defense innovation ecosystem. That is the president’s budget request, the Pentagon’s program objective memorandum process, AKA the POM, and the release of the infamous J-books, or justification books.

David 01:11

And unlike a lot of defense acquisition conversations that happen behind closed doors, this is one of the few times the government essentially publishes a roadmap of priorities, programs, spending assumptions, and capability needs in plain sight. Though publicly available and well understood are two different things. These budget docs are strewn across multiple websites and dozens of PDFs. Until recently, it took real insiders to understand what was going on and how to make sense of this Byzantine process.

Maggie 01:41

To be clear, the president’s budget request is not the appropriations bill or reconciliation bill. It’s not a law that requires that money is allocated in a particular way. It’s simply what the executive branch is requesting that Congress fund in the ensuing fiscal year. But ultimately, it’s still up to Congress to actually write and pass a budget bill officially appropriating the funds to the executive branch. And while the president’s budget request is a guide to Congress, Congress often does not actually follow the exact numbers written into the request. If you wanna learn more about the congressional appropriations process, check out our past Techquisition Edition episode, that is episode number 17, that we did with Johnny Kaberle, where we really break down how the congressional appropriations process works. But to help us decode the president’s budget request, we’re joined by Matt McGregor, who spends quite a lot of time living inside these budget documents. Matt McGregor and his co-conspirator, Pete Modigliani, are the founders of Creative Defense, and they happen to publish one of the best defense acquisition newsletters out there called Defense Tech and Acquisition News. I personally read it every single week and am a huge fan, have learned quite a lot by reading their newsletters.

David 02:56

Indeed, it is required Saturday morning reading over a cup of coffee. Now, the reason why we are just getting to talking about the president’s budget request is that the timing matters. And while the president’s budget request, or the PBR, has been out for a while, we intentionally wanted to wait until the justification books, or as Maggie alluded to, the J books, dropped, because that is where the budget finally starts to become operationally useful for companies trying to understand where the money may actually flow to. The president’s budget request really talks about a top-line number across the various agencies, not just the Department of Defense, but also including other federal agencies. The J books really start to get into the individual programs to help identify over the next five years or the future year defense plan exactly how much funding is gonna be going and whether or not that budget is intended to increase or decrease.

Maggie 03:53

So today we’re gonna break down how the federal budget process actually works, why the president’s budget matters, what the J books are and how to use them, what stood out to us most in this year’s defense budget, and what all this means for startups trying to navigate the defense market.

David 04:10

And because this is a Techquistion Edition, we’re also gonna talk about where the hype is real and where programs may actually have money behind them, and where founders should be careful not to confuse PowerPoint strategy with executable budget authority.

Maggie 04:23

All right. Well, with that setup, let’s bring in Matt.

David 04:27

Well, Matt McGregor is a co-founder of Creative Defense and a co-author of the Defense Tech and Acquisition newsletter, which has become required reading for a lot of people trying to understand how defense budgeting, acquisition, and modernization actually works.

Maggie 04:42

Defense Tech and Acquisition news, it’s not just a weekly headline roundup. They actually spend an enormous amount of time digging into the underlying budget materials like the J books other, documents that are coming out to really identify where priorities are actually translating into real dollars.

David 05:02

Okay. So with that it’s an honor to welcome my longtime friend, Matt. Welcome to Techquistion Edition.

Matt 05:09

Yeah. Thanks for having me, you guys. Appreciate it.

Maggie 05:11

So Matt, let’s just start out with a basic question. At a high level, how should people be thinking about this $1.5 trillion defense-related spending package? What exactly is the president’s budget request? And then how does this relate to the emerging reconciliation package as well as the annual appropriations bill? What are all these different documents?

Matt 05:37

Yeah. We used to live in a simpler world where there was one budget and we got to track that. And then this year is even a little crazier because there’s gonna be three pieces, I think, that potentially play into what the total ‘27 budget’s gonna be The base budget of course, is 1.1 trillion, so even, by itself that’s still big. If we got that, I think that’d be, a huge win. Definitely getting in the right direction for all the challenges that we have. They added 350 billion on top of that for reconciliation, and then the other piece of that is there’s a supplemental, right? The Iran war. So there will be money in there that will impact the investment accounts for sure on the procurement of munitions. But also some other things in there. I think there is some debate on the Hill. I’m not the expert on all that, but there is some debate on the Hill about is a second reconciliation even possible? ‘Cause they just got the DHS one they’re working now, and there’s a lot of thought that the second one’s gonna be too hard. So there’s some push of well, do we try to cram some of the defense stuff into this first reconciliation bill this year and make that the big package, or do they do a second one and push it, or they push more on the supplemental? So still a lot of moving parts, but the only thing we officially know right now is there, there is a 1.5 trillion, a big part of it, the 1.1 is discretionary. The rest is up for debate on one of these other mandatory accounts.

Maggie 07:12

So just to make sure I’m understanding this right, so we have the president’s budget request, which is out, and this is just a request to Congress. It’s not actually the budget. Then we have the $1.1 trillion appropriations bill, or what we expect would come out in the appropriations bill, which is just the normal spending bill, and then another 3 to 400 billion that would come through some combination of this reconciliation bill, which is not a normal funding process, and then potentially a special bill just for the Iran war.

Matt 07:41

Yeah, the really only difference is that the, for the reconciliation, is that in the Senate you typically need 60 votes to, to pass a bill. With reconciliation you just need a majority. So that’s the difference is the 60 votes in the Senate primarily is the difference.

Maggie 07:57

And so when we look at the president’s budget request, I know one of the big topics of conversation is there’s different colors of money, right? So you have procurement money, RDT&E money, and O&M, which is operations and maintenance money. So maybe could you talk about what are those three different kinds of budget, and what is the breakdown between those categories in this budget request?

Matt 08:20

Yeah. So yeah, the big difference O&M is a big pot of very flexible money. Some of that can be used for, capability for sure especially as we move into an age with buying services, capabilities of services. But generally the O&M, and it does fund a lot of modifications to legacy platforms and things like that, but generally a lot of that goes to sustaining, right? Just keeping the existing things alive maintained, ready, all the readiness stuff, all the, you know- Just general housekeeping kind of things that, that DoD needs to do. But where we really focus, for the founder groups and for all these defense tech companies that are coming out, they’re generally more focused on the investment accounts because that’s where you’re gonna see your RDT&E dollars. This is all your prototyping, moving into a program of record, all the development test is funded on that. There is some big modification money too in there that sometimes, will be maybe become available for founders as they mature a capability that can be fielded on an existing platform. So RDT&E has a lot of potential for people that are entering the defense space. Procurement, of course, is more of those m- mature things where there’s we’re actually fielding them. So that’s the one where it gets down to there’s actually a production line or there are some software kind of things in there too, but generally it’s the big stuff that you see flying around tanks and missiles and all those ships and all those kind of things in there that that are there. But just going back to the overall breakdown, so within the base budget, and that’s the discretionary, there is $1.1 trillion. 218 of that is RDT&E, 257 is procurement, and then a big chunk in O&M, 382, and then some of these other accounts like military personnel and military construction. So of the base budget, you have, basically, 470 billion of the 1 point trillion. But in the reconciliation package, there’s a much bigger chunk. A lot of that is investment.

Maggie 10:37

And then Matt, I also wanted to ask, how do we think about the ratio between procurement to O&M to RDT&E funding? I know this is something that you’ve written about before. What can we read into the ratio of the current budget request?

Matt 10:51

Yeah, no, this one is a really exciting thing because we, we wanna see more in procurement. I know it’s not always the right thing for some of the smaller companies coming through ‘cause they, they really want that RDT&E money to seed some of their products. But as a military force, right, we definitely wanna see procurement to get the joint force up and ready for the future fight. We know in the China fight that there’s, we’re gonna, we’re gonna go through a lot of stuff really fast if that happened, and so we wanna be prepared to be able to stay in the game. So yeah, this year there was a big push towards increasing procurement 80 per, 82%. Now this is combined, combining like we talked about discretionary, mandatory. It’s combining both of those. But when you look at the combined piece, 82% increase in procurement from last year and 62% increase in RDT&E. While the percentages are not totally different from past years, like we always measure that- And they’re still in that roughly 57% procurement, 43% RDT&E. A lit- 1% bump up in procurement. So the ratio is not totally off, but because the numbers are so substantial it’s promising to see that much went into procurement. But a lot of those big procurement dollars were in reconciliation were basically driven by a lot of the missile stuff, a lot of the Golden Dome stuff, a lot of the space stuff, and things like that, that were in those accounts. We definitely need reconciliation for some of these big items.

David 12:18

Yeah, I think that is an interesting indicator. For a long time it seemed like the R&D spending, outpacing procurement not necessarily what, a venture-backed startup is looking for. And there’s almost a recognition that some of the R&D costs can be underwritten by the private capital markets, right? Especially when you introduce OSC that’s de-risking some of these loans, allows more money to pile in. And then really what the signals that are needed from the commercial innovation ecosystem are just buy the thing, operationalize it, use it, right? And that’s where you get the procurement and then O&M funding on the follow-on. But Matt, that was awesome. Thank you for helping us understand the top line, right? I think we call that the TOA, ... inside in, in building parlance. Maybe let’s dive into the actual nitty-gritty of the J books or justification books, which are essentially, the government or the executive branch saying, “This is why I am requesting this amount of money.” Right? What else can you see inside of these J books, and how are they broken down, and what’s useful information to extract from them?

Matt 13:41

Yeah, that’s a really good question because we were talking offline about, if you’re an AI company, for instance it can be a little bit confusing because you could look across all the J books, and I just did a quick search, and there’s 431 lines, right, across artificial intelligence. So clearly if you’re an AI company, there’s... That shows there’s a lot of potential but you do have to do the hunting for where is your particular thing going to fit into one of the broader efforts. So there is some detective work, if you wanna call that here that you have to do. But one of the helpful things with the J books that I would encourage, companies that are trying to figure out where their piece might fit in- Is if you go into the JBook, they can be a little daunting because there’s kind of these PDF files that’s hundreds of pages. But you can, do some keyword searches. Aviant is a company that y- also has a great tool that you can do searching and it’s a little much easier to m- navigate. But if you wanna do it the hard way, go in that PDF file and find s- a line that looks y- like something that you could support. So you’ll have to, you’ll have to flip through and you’ll see things like artificial intelligent reinforcements autonomy AI-enabled tools, AI-enabled DW. Like I said, there’s a little bit of detective work, but when you click in that line that maybe has some applicability, you think, to, to what you’re doing one of the helpful things is that there are m- things called major thrusts. And what that essentially just means is in that bigger budget line that maybe is like $100 million, you can actually go down and see more discrete projects under there, and you can say maybe this project has $10 million against it, this project has $20 million against it, and there’s a little bit more detail about what that’s about. And then sometimes they even have a helpful schedule with some milestones. Their schedules are weak generally, but you can at least see generally the flow of what they’re trying to do. And then I think the next step is if that’s something that really looks like, hey, I could really help here, is go figure out is that something that’s already been awarded? So you can start to tie that information, do some other searching and find out if there’s, already contracts against that. And what’s their period of performance? Maybe they’re coming up for a re-compete, or maybe it was just a prototype and there’s still, there’s still money in the FY DIP that, that you could potentially get after. So I think those the J-books are good signals for companies to be able to filter down and find their target areas for where they can see potential for contracts or maybe target customers. “Hey, I should really talk to this customer a little bit more because they’re planning this big thing that has exactly what I’m working on.” So they won’t tell you everything, but I see them as a signal to direct your attention. But yeah.

David 16:28

Yeah. And as a former author of my own RDocs for specific program elements, PEs, which are associated with these wonky, string numbers and letters, they’re not all, RDocs or R2s, which get down to the specific program elements, are made the same. Some have a lot better detail than others. Oftentimes you can see which other program elements one is related to, and so you can go check out them. And when you’re talking about, looking at the schedules or potential contract structures, it’s in there. I would say, maybe you brought up Aviant. I think a really nice thing about some of these tools that are helping aggregate all this publicly available information Is it’s now also creating the relationships between the budget, the program office, and the contracts. Because in these justification books, it’ll tell you, “This is the money we intend to spend against these priorities,” but it doesn’t necessarily tell you who or where, like what program office. Now, in certain cases it’ll say, Defense Innovation Unit, and Defense Innovation Unit has a program element ascribed to this, like this amount of money. Okay, DIU is expected to execute it. Of course, in those instances it doesn’t necessarily tell you exactly what projects or programs or who the end customers are that are going to be receiving it. This is one piece of the puzzle, but I think if you’re looking for market demand signal from the department about whether or not the technology you’re building is important, you will see that laid out over the future year defense plan if, in fact, it’s expected to grow over years. Well, that was awesome. Thank you, Matt. Maybe we just start talking a little bit about w- where and different line items that we thought were interesting and stood out. And Maggie, maybe if you wanted to start us off here and anything that you found that was interesting, we can go around the table.

Maggie 18:39

Yeah, of course sitting in San Francisco I always have to look for, do my keyword search for artificial intelligence. Where is the department looking to fund that? It’s something the administration has, of course, made a priority. The top line, it says there’s $53 billion for AI broadly. A lot of this was actually going to sovereign AI. They did not specify exactly what sovereign AI here means, but said something like 50, $46 billion for multi-year mandatory investment in a sovereign AI arsenal prioritizing enterprise-scale AI infrastructure investment to build an enduring strategic advantage. They actually have not released the J-books yet for the Chief Data and AI Office, at least as of the time of this recording. So there’s not more details on what exactly that means, but definitely will be interesting to see. Of course, a lot of these big AI data center build-outs, AI training runs are extremely expensive, so it would be interesting to see if there’s a way for the US government to play into some of the investment in American AI development. I also saw AlphaOne, which is one of the platforms that Chief Data and AI Office, CDAO, runs, got a big increase in funding. But then at the same time it appeared that CDAO itself actually got a decrease in funding. So I’m not entirely sure what to make of that. Matt, I don’t know if this is something that you’ve tracked at all or have more insight into.

Matt 20:07

Yeah, I do think with CDAO getting pulled under R&E I think and DIU for that matter- I think both of them are being recalibrated and having a little bit more targeted portfolio. So yeah. So I think, for CDAO, I think you’re gonna see, the OpenDagger stuff continue the guide experiments and the, data integration layer sort of efforts. And then I think AlphaOne, I think that- that’s probably something they’re gonna own as well ‘cause this could have some frontier AI and some theater cloud sort of efforts. So yeah, I still think CDAO is gonna be highly relevant. I wouldn’t take too much signal that they’re losing too much, but I think you’re right that there’s the department, as part of its whole requirements revamp, they definitely have refocused some efforts and so yeah, I think there’s a lot more lot more of that focused on the multi-domain joint operations stuff and long-range kill chains and different things like that, that maybe took a little energy away from CDAO but I think they’re still quite strong. One other interesting thing on the AI front, though since you brought that up, that I noticed is I didn’t see-- I looked at the J-books. The J-books did not-- were... When they when they first released the budget without the J-books, I saw this OSD equipment account, which historically has been ATFWIT money, been the predominant piece of it, and it got this huge plus-up, and I was really excited because I was like, “Oh, yeah, ATFWIT’s finally gonna get its huge bump,” because it’s been in the $100, $200 million range, and I was like, “Oh, maybe that’s gonna go to a billion.” Anyway, I finally saw the details on that, and there’s $29.5 billion that’s going to this effort called Next Generation Technology and Autonomy. And the way it’s described there is that it’s basically to trans- transition from funding scattered clusters of GPUs to more organized AI infrastructure for strategic to tactical AI compute, so in- it’s gonna include SCIFs and data centers and all this stuff. So anyway, that’s something interesting to watch on the AI side is that OSD equipment account which this was buried under.

Maggie 22:17

Yeah, and then maybe something similar I also saw this is actually under the Cyber Command J-books. They have a large line item for AI for offensive and defensive cyber operations. I think it’s $140 million, so maybe not a lot for the DoD, but a lot for some of these early-stage startups. And part of that for them was also building out GPU capability and other compute capability to actually run some of these AI models. I know to date when working with some of these more classified customers, I’ve heard startups talk about literally bringing GPUs in suitcases or in Pelican cases loaded with open-source models to the customer ‘cause they know they can’t rely on the customer having the right infrastructure to run these models. So I think it will be interesting to see just more of the infrastructure build-out for DoD to actually take advantage of a lot of these AI models. And then one more AI related item I’ll call out. I saw Maven’s smart system has $2.3 billion budgeted. This is, of course, one of the big AI initiatives. They have played a large role in Iran in targeting and battle space management. I think there’s also talk that it played a role in Venezuela. This is a Palantir effort, although it also has some Anthropic Claude models built in, and it was announced last month that it’s officially becoming a program of record and now has $2.3 billion going towards it.

Matt 23:42

Yeah, no, I think that’s a good rundown.

David 23:44

Maybe one that I’ll call out right from my alma mater at Defense Innovation Unit. So I think this is a good example of where the president will submit a budget to Congress, but, Congress, as we know, has power of the purse, and they get to then ultimately decide how much funding is going to go into these specific accounts, and then the department is responsible for executing it as authorized, right? We don’t want any Misappropriation Act violations, right, Matt? But if you look at the DIU account, and of course, they haven’t released the specific J books, but we’re seeing the top line program element n- numbers for the OSD accounts. DIU, right, came in at five hundred and twenty-two million. Last year, DIU got one point eight billion, and so you would say, “Oh, well, that’s a far cry from what they got,” except the president’s budget request last year for fiscal year ‘26 was a hundred and forty million. So what we’re seeing is Congress has stated with congressional plus-ups that programs being executed under DIU are very important, and now we’re finally seeing the president’s budget start to catch up, though that is a huge delta between one point eight billion and five hundred and twenty-two. So it’ll be interesting to see if Congress for the third year in a row pluses up the DIU account, right? And that started to really occur I guess j- two years ago when DIU’s budget and program element really got congressional plus-up big time. Those are things that I think are interesting.

Matt 25:21

I was initially discouraged by that one too, David, but then my understanding is that a lot of the DAWG funding, they will be intimately involved with executing. If that passes, they will have more work than they can handle. So maybe taking a little cut to, a little cut in relative terms to the DAWG request maybe it’s not such a big deal ‘cause we’ll have, fifty, fifty billion to possibly do some thing, things with over the next few years.

David 25:51

And that’s why I think seeing the J books is so interesting ‘cause you can start to see how are the different program elements interrelated with each other, right? So like another one that, I think is a closely watched item right now is Golden Dome And, it’s really-- There are definitely some explicit program elements tied to it. In fact, you saw a $1.3 billion request for space-based interceptors, specifically under a PE inside the Golden Dome apparatus. But if you look across a lot of other Space Force programs like Next Gen, Overhead Persistent, Infrared, e- and OPIR, or the, Matt, you referenced the Satellite Defense Network. They’re all talking about how these programs support initiatives within Golden Dome, and that’s a political crutch that you’ll see often within these budget documents, that you tie these programs intricately together so that if Congress wants to just cut one, you know that there’s a reciprocal effect to maybe a higher priority program that they don’t want to impact. And so you see those types of machinations. And again, you can extract that through the J books, where there is just a little bit of nuance across these, otherwise stovepipe program elements. But Matt, you highlighted the Defense Autonomous Working Group, the DAWG, right, which is all about mass and attritable capabilities. Was there anything else, like maybe on drone dominance or the JIATF, the counter-UAS programs that were worth highlighting?

Matt 27:31

Yeah, no, a lot of lot of stuff with the DAWG is definitely gonna be a lot of the autonomous systems, definitely drones, small UAS small USVs things like that. But yeah, if I go through, just if I do a quick run-through of the services maybe just call out a couple things here. One o- one overall is that and this is another one, David, you mentioned about how Congress sometimes steps in and helps, is the S&T accounts. Th- Congress is definitely known for giving more money back in the S&T account, but I do feel like there was definitely some slashing going on there across the Army and Navy. There was a 30% kind of reduction in some of those accounts. But on the Army side, while they slashed a bunch of other things across a bunch of different areas, they really went hard on counter-UAS, counter-small UAS. So I think you’re definitely gonna see more focus on that with all of the things we’ve seen in Iran. Long-range precision fires is definitely a priority, so for the Army and EW. So I think you can start to look at some of these accounts, and even when there’s big reductions overall, when you start to dig in, you start to see, okay, this is where the Army is probably heading. Their leadership is thinking is they need to get after and focus their efforts a little bit more on counter-UAS, long-range pr- precision fires, and EW and then when you look at like prototyping stuff, you can start to see where some of these accounts are starting to be a little bit maybe less prioritized because there’s other programs that are growing, and so maybe they need a little less prototyping money because they’re gonna be programs of record. But you can start to see that they actually do wanna get after some, new types of missiles and improve their ability to do more mobile stuff. So I think that’s one of the big things too, is like there’s a lot more focus now on being able to operate in a distributed manner. I think you guys nailed it with the AI thing about being able to get to the tactical edge and pushing things forward. So some of these things you start to see as they’re really starting to focus 100% on the INDOPACOM theater, and a lot of those things reflect it. And hypersonics is a theme throughout all of this that, if you have anything to do with hypersonics realm, there’s definitely a lot more money going in that direction. Yeah, some of the other cuts you do see, I think there are some opportunities here for some of the legacy programs. I I saw things like the Army Tactical Command and Control where, you know, some of these existing efforts just had money at year after year and there was some big pullback there. So that’s another thing to look for in the J books as an opportunity, is if you see a legacy program that’s maybe gotten a lot less money than it normally would that could be an opportunity for you to step in and maybe offer some solutions that, for that. So yeah. So that’s one I would call out. UAS Launched Effects got a huge plus-up from the Army so four, four hundred and forty-three million there. So if you’re in the loiter ammunition business that’s definitely, a signal that I would take as they’re definitely more serious about that. That’s like over two hundred percent increase from last year. And then yeah, going down to the Navy. Yeah, Navy took a big, real big cut to S&T as well. So Marine Corps, had a big cut on their advanced technology demonstrations, which I think you will see with this administration, and it’s something to expect maybe over the next couple years, is there is a really big focus, I think I think you saw this with the critical technology areas update, where we had sixteen amorphous tech areas, and they really winnowed those down to things that are applied, things that could be fielded, really focusing on getting things out in the field and not doing these sort of long science projects. So I think some of these cuts in the S&T world reflect that. But definitely on the Navy side, big push on shipbuilding. If there’s any contributions that you can have to the shipbuilding helping in the industrial base in any way whether it’s from workforce development, to new kind of materials and new manufacturing processes and things like that, there definitely seems to be a big push to make help in any way on that as part of the maritime industrial base. There was one point eight trillion that was added to this new line called Advanced Shipbuilding Industrial Base and Future Ship Experimental. So that’s something to keep an eye on. Big increases on Marine Corps ground combat support systems. So if there’s-- you have a system that’s more focused on supporting those Marine type, d- expeditionary type units, they’re definitely looking for new ideas. If that’s any indication in terms of how much more money they added there. Directed energy i- is getting a lot more money. You start to see that. There’s more programs coming online. Navy has some, Army has some, so that’s another area if you’re in that business. F-35 probably not a lot there out there for small funders. F-35 definitely got a lot of money this year. EW across multiple accounts got some big plus ups. So I think if you’re in the EW space, there’s probably... You could look across all the J-books, and you could probably find multiple places that you could start to have conversations at the right levels and start to play in that space. EW sup- readiness support was one line in the Navy, one that got a big increase, and then Marine Corps also got a big increase in that line, three hundred and fifty-seven percent.

Maggie 33:12

Yeah. I was gonna say, one thing I was curious about, I think we’ve seen a lot of talk in the last year or so about acquisition reform, moving from PEOs to PAEs, program acquisition executives, and I’m curious to what extent, if at all, that is showing up in the budget. I saw that PAE robotic autonomous systems in the Navy had its own call-out in the Navy budget overview. They had a couple hundred million each for unmanned underwater vessels, small unmanned surface vessels, unmanned aerial vehicles. So I’m curious how we’re seeing the PAEs show up in this budget.

Matt 33:49

Yeah David mentioned the Golden Dome stuff. That was one of the big ones. But yeah, for the so I didn’t see a lot of it at the like the Army and Air Force because i-i-it, there’s a lot of nuance there with those services. The Navy, because they stood up a brand new robotic autonomous system, was a little bit more clear. I think on the Space Force, on some of the new PEs that have been stood up there, like the space-based interceptors going to combat power and then the all the space data transport and AMTI and GMTI going to the SB, space-based targeting PAE. You could start to see some correlations there but I do think you’re right. I think the PAE RAS was the most clear and explicit and they actually laid out the specific programs under that and the amount of money across the FYDP, which was which was really helpful to see, small USVs, medium USVs- the one sad thing about this is that we’re still not seeing the amount of money go. While they called it out and it’s very well organized, we’re still not seeing the increases that we wanna see, so they do have 2.2 billion across all the unmanned accounts, but it’s a lot less than we would’ve liked to see given the focus on building a hedge force. But yeah.

Maggie 35:09

And David, how’s the Air Force looking?

David 35:13

Yeah. So Matt, you talked about all the other services and components except for your alma mater, right? The Air Force- Oh yeah, I was about to-

Matt 35:19

I know. I was about to. I was getting there…

David 35:21

I, well, I have some opinions here, right? So collaborative combat aircraft, which seems to be the Air Force’s big push-

Matt 35:29

Ah. I know. Where’s the money? Where’s the money?

David 35:32

I mean, 1.3 billion in the fiscal year ‘27 request, but that’s less than what the B-52- Yep ... is getting in budget activity seven, I think for their engine upgrades, which is 1.4 billion in RDT&E. So we are spending more money to maintain an aircraft that’s been around for multiple generations, and then when you contrast it to the F-47 right, which is, went from 2.3 billion in FY25, now is up to five billion in ‘27. To me, these are, like, the specific trade-offs between incorporating autonomy versus relying on manned fighter aircraft, and it’s so much more expensive, and we don’t really get to invest the budgets that we need to get to a force structure where we have more mass. And so I don’t know what’s your kind of take on the Air Force overall? I also saw AFWERX got completely decimated. Yes. They’re basically just a SBIR shop at this point, which is really sad to see though there have been some announcements coming out of AFRL that they’re reorganizing and there’s a big meeting happening in a couple weeks to help engage industry. But yeah, curious on your takes with our beloved Air Force.

Matt 36:48

Yeah no, you’re right. I was gonna say that about the AFWERX is I am hopeful that the reorg that General Bartholomew has will hopefully keep the AFWERX mission alive if it’s, if not the organization. No, yeah, I think overall I think there was disappointment that there wasn’t more of a focus on autonomy, but I think you do have to look at where this administration’s focus has been, and it, it has been on mainly building on the, getting the big ships fielded, getting some of these big new aircraft programs going, like the F-47 and accelerating the B-21. And then on the Army side there, there’s, there, there is a shift there at least from the big tanks to moving to- towards more distributed kind of things, but still not fully leaning into the autonomous vehicle space. So I think overall with autonomy, it is a little disappointing. The Air Force put a little bit of down payment there on the CCA, but would’ve liked to see that be triple that. I would’ve liked to seen the Navy go, at least triple to really get after the MUSV, which they just announced a new marketplace. A better demand signal there would’ve been a lot more money next time. And then, yeah, for the Air Force overall, they’re retaining a lot of the same aircraft. They’re gonna extend the B-1. They’re gonna, extend the F-15 EX they’re buying a lot more of. And there’s one other aircraft. Yeah, they’re gonna keep the A-10 till 2033 or something. So yeah, there’s a, there’s definitely a more higher prioritization on the big platforms that historically we thought would be maybe less relevant in the Pacific Theater, but I think they are leaning into that, and hopefully we’ll see in ‘28 that’s the one gonna be the one to watch. If these programs, maybe they were not mature enough to make the case in the building, people could say, “Well, the MUSVs are still haven’t fielded that many of them. We haven’t matured the CONOPs.” Same with CCA, right? The experimental operations unit’s still playing around with the CONOPs piece. So maybe this is the year, ‘26, ‘27 is the year they see data as maturing all that, and then they’ll go hard in ‘28. So I have high hopes for the ‘28 budget on those fronts.

David 39:04

At least they did try to zero out Wedgetail the E-7. But we’ll see.

Matt 39:09

Oh yeah they did kill the E-7. Yeah.

David 39:11

We’ll see. I’m sure Congress is gonna plus that back up, so of course that’s a trade-off, and we’ll see where that goes. But yeah, okay. So I think we, we hit a lot of areas. Of course, for our founders out there, this is certainly far from everything, that’s in this defense tech innovation ecosystem. But again, this is a huge marker to help identify is the thing that you’re building an emerging or an already solved pain point, right? And whether or not there are active programs of record with specific vendors that you would need to break into. This is a good data point to, to bring into your marketing materials, whether you’re trying to raise from investors or talk to customers or differentiate your technology versus what’s currently available. So yeah, this is a big step in the process, but, where do we go, where do we go next? So we’ve got the president’s budget. We’re already seeing senior leaders testifying on Capitol Hill, to defend the budget, and so you’re seeing a parade of generals and senior executive civil servants and appointees articulating the merits of this budget. And then what? Congress goes into a black box, and they start figuring things out. What happens next, Matt?

Matt 40:37

Yeah, I think you’ll see some of the initial markups on the approps bill in the June timeframe. So hopefully we’ll get to see... Yeah, your point on the E7s a great one because, yeah, we know some of these things they will add back in. I think by June we’ll also have a little bit better sense of where reconciliation is going. So one thing that you could see the Hill do is really have to make that balance of if they can’t get that 350 billion, they’re gonna have to fit all of their priorities in with all of the other budget requests. DAWG, maybe DAWG if the reconciliation bill doesn’t look like it’s gonna happen, maybe they take a piece of that. It’s not 43 billion, but maybe they, support five billion or something like that. So you could see some of these accounts shrink, but hopefully move from reconciliation into the base budget. And then, yeah, they’ll pr- they’ll bump up all their priorities, but we’ll probably get a first look at that sometime later in June. And then yeah, we’ll see how things go with the supplemental, which will help, also help inform do they have to move more money to missile accounts to, to compensate if the supplemental hits a snag? Will they try to push maybe some of these really big priorities they know for the President? I don’t know if you picked up on that, but when you looked at the one of the things that I’d never seen before in the budget overview from OSD, they actually had the table, the pi- the pie chart, and they had a slice of it that was, like, presidential priorities, and I think that was, like, maybe a little signal that if you don’t fund these things you’re gonna get a call from the White House. So it will be interesting to see as they’re moving through there what things, what are the, what part of the presidential, President’s priorities move from reconciliation possibly into the base budget, and then what things get decremented. So yeah, I would say June and then there’ll be there’ll be some defense of that, and we’ll see the we’ll see the final hopefully before October. But time will tell.

Maggie 42:30

Great. Well Matt, thank you so much. This was incredibly informative. I know the federal budget process can feel very opaque and archaic to those of us on the outside. I was feeling a little bit of despair last night downloading the J books, trying to figure out what all the different acronyms mean, trying to figure out which of the 900-page PDFs, each item that I was interested in fell into. So it’s always great to talk to an expert like you. And of course I read Defense Tech Acquisition news like the Bible to really help me figure out everything that is happening in this space.

Matt 43:08

Thank you. Appreciate it. Thanks for having me.

David 43:10

Yeah. No better way to start your Saturday. Though I would be remiss not to highlight, right? This, the President’s budget, and we still haven’t seen all the J books come out, very late to Congress, right? Yes. This is something that’s supposed to happen in February traditionally. And so we’ve squeezed Congress who already seems to be-

Matt 43:29

Actually by law.

David 43:31

Okay, by law. So we’ve already squeezed Congress on their timelines- to get out the NDAA and the appropriations bill to do all of their horse trading and back and forth with the department. So all but I can say this is an interesting time for people at the Pentagon as they now have what’s called staffer days, where they start to defend the budgets that they’ve been putting together for the better part of 18 months encapsulating this entire POM process. So yeah. Well, Matt, thank you so much for joining us. It’s a pleasure to have a real subject matter expert like you. We missed having Pete on the podcast as well, but we’ll try to maybe we’ll get him next time for a different topic.

Matt 44:16

You bet. Nice to be here. Thanks for having me.

Maggie 44:19

Wow, Matt was really great. He definitely knows his stuff. The federal budget process can feel incredibly opaque, but conversations like these really help translate what’s happening inside government into actionable insights for the broader defense innovation ecosystem.

David 44:36

And for the founders listening, the key takeaway here is simple: The budget is strategy translated into resource allocation, and if you wanna understand where defense markets may emerge over the next several years, you need to understand where the government is placing its money, not just where officials are giving speeches.

Maggie 44:53

And of course, if you aren’t already reading Defense Tech and Acquisition, you definitely should be. Again, Matt and Pete are seriously consistently doing some of the best work breaking down what these budget documents actually mean. So David, I have to ask the question that we end every Techquistion episode with. What do you think the next Techquistion Edition episode is going to be about?

David 45:14

Well, I think we alluded to it a little bit given that we’ve got three budgets in one that are being bandied about, right? Between the president’s budget or the base budget or the discretionary budget, then you’ve got the mandatory budget or what they’re calling the reconciliation bill, and then this extra funding, geared towards specifically the actions in Iran. And I would imagine that we may be talking about one of the latter two, either the reconciliation budget and what was in it and what does that mean for startup founders and the programs that money’s tied to, or for any supplemental funding that’s tied to the Iran war.

Maggie 45:53

So sounds like there will be no shortage of content for future Techquistion episodes here. Well, David, thank you again, as always, for helping break down some complex issues, and looking forward to the next one of these.

David 46:05

Always a pleasure, Maggie. Thank you.

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Despite this massive increase in private capital funding for defense, in the past, American defense tech companies have fallen flat when faced with real-world battlefield conditions. Infamously, American drone companies struggled to operate in Ukraine during the early days of the conflict, as their design and testing was inadequate to operate in areas where electronic warfare was deployed.

However, the war in Iran appears to be a turning point in U.S. military use of commercially-developed unmanned systems and AI technologies. The conflict’s economics demand a shift toward innovation. Currently, the U.S. relies on million-dollar missiles to intercept $20,000 Shahed drones – a strategy that is both financially unsustainable and depleting our stockpiles. While legacy U.S. military systems still dominate the battlefield in Iran, U.S. forces are starting to use commercial unmanned aerial and maritime vehicles, low-cost counter drone systems, commercial space assets, and AI decision support tools out of necessity in order to counter cheap, asymmetric Iranian capabilities.

Unmanned Systems

First and foremost, like the Russia-Ukraine conflict, cheap, unmanned systems are playing a major role in the Iran conflict. Perhaps most notably, Iran has launched thousands of Shahed one way attack drones against U.S. and allied forces since the start of the conflict (Russian and Ukrainian forces have also made extensive use of Shahed-type drones, which cost ~$30,000-$50,000 depending on the exact make and model).

In December 2025, CENTCOM established Task Force Scorpion Strike (TFSS), a new task force dedicated to the U.S. military’s first one-way attack drone squadron in the Middle East. In February 2026, TFSS debuted the Low-cost Unmanned Combat Attack System (LUCAS) – a $35,0001 American-made Shahed alternative – during combat operations in Iran. LUCAS is an excellent success story for the power of innovative small businesses and innovation focused contracting mechanisms. LUCAS was developed by SpektreWorks, a small business based in Phoenix, Arizona, that reverse engineered an Iranian Shahed airframe that was captured by the U.S. military. LUCAS development was funded by an APFIT award, a contracting mechanism designed to enable rapid award of production contracts with novel vendors, and CENTCOM’s Rapid Employment Joint Task Force (REJTF) quickly delivered the system to deployed forces who used the system in combat less than a year after the initial APFIT award.2 While SpektreWorks developed the first set of LUCAS systems, the Department of War (DoW) plans to award contracts to multiple vendors to build LUCAS. According to Pentagon CTO Emil Michaels, LUCAS drones were not being mass produced before Operation Epic Fury, and only a few dozen have been used in combat to date, but is likely that the DoW will scale manufacturing and procure many more LUCAS drones in the months to follow, alongside initiatives like the Drone Dominance Program.

CENTCOM also appears to have some FPV drones present in theater, although it is not clear if the drones are being used in combat (most FPV drones have limited ranges that are not conducive to large scale naval operations). An X post from U.S. Marine Corps Forces Central Command shows Marines testing a Neros Archer FPV drone on the USS Tripoli, which is actively operating in the Arabian Sea as part of a U.S.-led blockade against Iran. The USMC previously awarded Neros a $17M contract for 8000+ FPV drones. There have not been any additional details released on how the DoW is using these drones in the conflict or if other drones procured via the Drone Dominance Program are in use in Iran.

In addition to aerial drones, the DoW has also turned to defense tech companies for aerial drone defense. Before the conflict began, the U.S. was woefully underprepared to defend against cheap drone threats like Shaheds. The U.S. is largely reliant on multi-million dollar missile interceptors, including Patriot and Terminal High Altitude Area Defense (THAAD) batteries, both fired from land, and Standard Missiles launched from Navy warships at sea, for Shahed drone defense.3 However, as stockpiles for these expensive munitions dwindle, the DoW has begun to turn to lower cost alternatives developed by startups.

For example, Matt Steckman, the CBO and President of Anduril, stated that Anduril is a “heavy participant in the current conflict in the Middle East, mainly on the defensive side,” responsible for one of the “principal” systems used against Iranian Shahed drones during Operation Epic Fury. Anduril’s Wisp infrared detection sensor, jet-powered Roadrunner drone, and Lattice battle management system could all potentially play a role in countering Shaheds. In March 2026, the Army’s Joint Interagency Task Force 401 (JIATF-401), the DoW’s primary group responsible for developing and deploying counter UAS technologies, awarded Anduril an $87M contract for Anduril’s Lattice software to serve as the tactical command and control solution for C-UAS.

In addition to Anduril’s C-UAS solutions, the U.S. Army has also purchased 13,000 low-cost Merops Surveyor drone interceptors, each of which cost ~$15,000, from Perennial Autonomy, an American defense tech company founded by former Google CEO Eric Schmidt. Notably, Merops interceptors have been successfully deployed on the battlefield in Ukraine against Russian Shahed-type drones. Army Secretary Dan Driscoll stated that he expects the cost of Merops to drop to $3,000-$5,000 per interceptor as they are manufactured at scale.

Both U.S. and Iranian forces have also employed maritime drones during the conflict. Iran has used small, explosive-laden unmanned surface vessels (USVs) disguised as wooden fishing vessels to attack several oil tankers in the Strait of Hormuz. The Iranian USVs are not autonomous, relying on basic remote control capabilities and potentially basic GPS waypoint navigation.

The U.S. Navy has deployed BlackSea’s Global Autonomous Reconnaissance Craft (GARC) USVs, which are capable of hosting kinetic, communications, and surveillance payloads, for patrol missions during Operation Epic Fury. The Navy also plans to use unmanned underwater vessels (UUVs) for mine clearance missions in the Strait of Hormuz, however, they did not specify which UUVs they will use.

Commercial Space

Commercial space companies have played a meaningful role in the Iran conflict. When the war began, the first confirmation that U.S. forces had launched strikes against Iran came from commercial space companies Planet Labs and Vantor, even before the U.S. government itself announced the mission. Planet Labs is now indefinitely withholding all imagery of Iran from non-government customers, while Vantor has implemented “enhanced access controls” to imagery from parts of the Middle East, restricting who can purchase and view certain data. Both Planet Labs and Vantor have significant DoW contracts. It is likely that other commercial earth observation providers are also privately providing valuable data to DoW.

On the satellite communications side, DoW is likely using SpaceX’s Starlink (and / or Starshield4) communications products. In one photo of a LUCAS drone, the drone appeared to be equipped with a Starlink terminal (or perhaps a Starshield terminal) for communications.

Artificial Intelligence

Palantir CTO Shyam Sankar described the war in Iran as “the first large-scale combat operation that was really driven, enhanced and made substantially more productive with technology, with AI.” Palantir’s Maven Smart System (MSS), which is expected to become an official program of record later this year, with $2.3B in funding requested in the FY27 defense budget, has played a central role in the Iran conflict.

During the Iran conflict, the U.S. military has used MSS to automate parts of the “targeting” process – that is, the process in which DoW identifies objects or installations to be attacked, taken, or destroyed in warfare. The targeting process has six functional steps: identify entities of interest, locate them, filter to lawful targets, prioritize, assign to firing units, and fire. Previously, intelligence analysts would have manually combed through data to identify and prioritize targets. MSS can automate many steps of this process, identifying, filtering, and prioritizing entities of interest based on a certain mission set. MSS has clearly helped to accelerate the targeting process – in the first 24 hours, CENTCOM managed to strike 1000 targets, with another 10,000 targets struck in the first month.

At its core, MSS is an AI-enabled command-and-control (C2) data platform that ingests operational data from many data sources (ex: satellites, drones, sensors) and uses AI classifier models like computer vision models to detect, classify, and geolocate objects like military vehicles, buildings, and weapons systems.5 Within the DoW, Maven has more than 20,000 users, and according to Navy Rear Adm. Liam Hulin CENTCOM is “heavily using MSS.”

In 2024, Maven integrated Anthropic’s Claude models into the platform as a natural language interface and unstructured data processing tool, and since then, Claude has been used within MSS to generate proposed targets, track logistics, and provide summaries of intelligence coming in from the field.6 Note that Claude and other LLMs do not perform tasks like object detection, which are conducted by more traditional classifier models (ex: computer vision models); rather, LLMs are used for natural language processing and workflow automation like query generation. Palantir has built “AIP agents” on top of MSS which can automate workflows like data science tasks. For example, rather than writing a complex query to search for particular objects in the MSS database, users can ask an AIP agent a natural language question like: “Show me detections of tum22s” (a specific type of Russian airframe), and the agent will automatically generate and run a query to find the specified data.

Conclusion

While the Iran conflict marks a clear inflection point in the U.S. military’s adoption of AI and startup-built unmanned systems, these capabilities remain in an early stage of deployment and still represent only a small share of overall military platforms used in theater. Outside of a handful of unmanned systems and AI decision support tools, the U.S. military is still heavily reliant on traditional exquisite platforms rather than new technologies developed by innovative contractors.

In mid-March, CENTCOM released a list of the primary U.S. weapons systems used in Operation Epic Fury. As defense acquisitions experts Pete Modigliani and Matt MacGregor point out, LUCAS is the only major weapon system on the list that was designed in the last 15 years that has been used in combat in Iran. U.S. forces have only used a few dozen LUCAS systems to date (in contrast, Russia and Ukraine deploy hundreds of Shahed-type drones Russia-Ukraine conflict every day), and thus far USVs have only been used for relatively limited ISR missions. According to a CENTCOM spokesman, GARC boats had logged 450 hours in the first month of the conflict, which suggests only single digit numbers of GARC boats are deployed in the region. Again, compare this to Ukraine where Ukrainian Magura USVs operating in one-way attack mode reportedly destroyed eight Russian warships and damaged six more, causing more than $500M in damages to the Russian fleet.

It is clear that relying on legacy systems alone is not sustainable, as Iranian forces deploy cheap, asymmetric capabilities against exquisite American forces. Expensive U.S. drone defense systems are economically unsustainable. Further, the Strait of Hormuz is closed, in part due to swaths of mines deployed from small, cheap ships, and ongoing mine clearing operations are dull, dangerous, and time consuming – a task that could clearly benefit from scaled deployment of unmanned maritime systems.

U.S. forces must quickly take the lessons learned from the Iran war and put them into practice, scaling manufacturing of cheap unmanned systems and counter-drone technology to rival adversary forces and extending the usage of AI to improve targeting, intelligence analysis, military decision making, and back office tasks.

The Iran conflict has made one thing clear: the question is no longer whether commercial technology belongs on the battlefield, but whether the United States can scale and integrate it fast enough to keep pace with adversaries who already have.

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Dan has a phrase that stuck with us: “AI drags infrastructure.” Everyone sees the value of AI. The hard part is deploying it where it’s actually needed, like oil rigs, mine sites, aircraft carriers, and national labs. These are places where the cloud simply can’t reach, and that’s where Armada comes in.

In this episode, we cover:

• Why the cloud breaks at the edge (latency, sovereignty, and cost)

• How Armada is enabling real-time AI in fully disconnected environments

• Armada’s role in the DOE’s Genesis Mission, the most ambitious AI project in U.S. history

• Why winning the AI race is just as much about infrastructure as it is models

• What it takes to build mission-critical technology for mission-critical customers

As always, please let us know what you think. And please reach out if you or anyone you know is building at the intersection of technology and national security.

You can listen to the podcast on Spotify, Apple, YouTube, the Shield Capital website, or right here on Substack.

One more quick reminder that registration is now open for the third annual National Security Hackathon, taking place May 1-3 in San Francisco! We are partnering with the U.S. Army to award $50,000+ in cash prizes to hackers building solutions to crucial U.S. military problem sets. 🇺🇸 Register now here.

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Transcript

Maggie 00:36

In this episode of the Mission Matters podcast, were joined by Dan Wright and Pradeep Nair the founding CEO and CTO of Armada AI. Armada is building the future of distributed AI at the edge.

Akhil 00:50

Armada is a quintessential company addressing both a commercial and national security pain point, and that is how to get connected compute to the edge, to these potentially remote or disconnected environments for mission critical applications. That’s whether it’s an oil rig, a mining site, or even the US military and its allies. You can think of Armada as orchestrating both the compute via its containerized mobile data centers, as well as the connectivity associated with connecting those together to enable and accelerate AI applications, data analysis, where it is needed and in the timelines in which it’s needed.

Maggie 01:31

I mean, essentially you can think of Armada’s Galleon product as a bunch of CPUs and GPUs in a hardened Conex box with Starlink connectivity and all of the software infrastructure that you need to actually put AI applications on that edge. Armada’s technology is particularly useful in situations where you cannot assume reliable connectivity back to the cloud. Today, if you look at LLMs ChatGPT, the vast majority of these applications rely on having unbroken access to large cloud data centers to do all of the processing that you need to use those AI models. However, you know, you can imagine if you’re deployed out on an aircraft carrier in the middle of the Pacific during a conflict or on an oil rig, or even at a mine site or an oil and gas site, you might not have access to those cloud compute capabilities, but you still want to be able to use these powerful AI models to make your tasks your life easier.

Akhil 02:27

Yeah, Maggie. I think a lot of us here at Shield Capital, whether we served in the military or have been deep in the space, recognize that. A lot of these environments, you just can’t connect back to the cloud and you have to be able to analyze, assess, and compute near the point of friction or the point of influence, so where it needs to be done. Even if you have that connectivity back to the cloud, it’s critical that analysis, that timeliness associated is done nearest to those who are actioning.

Maggie 02:57

Yeah, it’s also valuable having this edge compute capability as it allows you to process the data potentially where it’s actually collected, and it allows you to have those data centers right near your energy sources. Dan and Pradeep have both had long careers as AI and data infrastructure, technology company executives. Dan served on the executive team at AppDynamics and DataRobot while Pradeep was a vice president at Microsoft Azure for 10 years, and a VP of engineering at VMware. Now onto the conversation. Dan and Pradeep, thank you both so much for coming on the Mission Matters podcast. I wanna start out with a question, Dan, where is the Department of War today in terms of AI adoption and infrastructure build out, you know, what’s actually being used? There’s lots of buzzwords, lots in the news right now about how the DOW is actually using ai, but what are you seeing are some of the specific. Use cases that you’re excited about that are driving real value for the war fighter?

Dan 04:01

Yeah, I mean, we have a saying at Armada, which is that AI drags infrastructure and what is meant by that is that typically how things work, not just with the DOW, but with any customer that we work with, is first they see the value of the ai and then they’re like, oh, how do I enable this everywhere? And so that’s kind of the stage we’re at now, where now the DOW has obviously understood the value of drones. I mean, I think everything that’s happened in Ukraine and other conflicts has really highlighted the necessity of not just drones, but drones at massive scale, you know, drone swarms. And then the obvious question becomes how do we process all of that data? Locally, including in these more remote areas. And then on top of that, you see a lot of the foundation model companies now working with the DOW in a very close way that wasn’t happening in the past. And those capabilities too, it becomes, okay, well how do we actually deploy this? So then you heard Secretary Hegseth recently at Star Base talking about, you know, deploying these types of technologies at the edge. So the Edge is now really a focus, but that infrastructure rollout is happening as we speak right now.

Maggie 05:25

Yeah, Pradeep, we’re starting to see some of these use cases expand drones, others, what are some of the existing technical challenges that still exist for actually adopting AI in these mission critical organizations?

Pradeep 05:39

I think a couple of things it’s what we are seeing with a lot of our customers, we are actually deploying into countries or places where there’s an existing cloud region, right? So that, that’s something interesting to notice like most of our deployments are starting to happen and hey, there’s a, you know, one of the existing hyperscaler cloud region, but they want an edge deployment and that’s driven by. Primarily three things. One latency. They want to have you know, the compute close to where the data is generated. The second portion is sovereignty, right? That’s where the AI stuff is very important because if you see what’s happening as Dan was mentioning, a lot of large language models being, you know. But many of the enterprise customers or even government customers want to take it, you know, fine tune it on their you know, confidential or sensitive data and then push it to the in front for the edge, right? So that’s what we are enabling. One of the struggle has been, Hey, I have all these models, but I want to have the compute on the edge. Now how do I bring all this together? Which is one of the pain points we are solving is, Hey, okay, you can have all these AI models, you know, generated in the cloud, but how do you put to action on the edge that requests like, you know, distributed compute, a single amazing customer experience that allows customers to focus, to switch between cloud and edge. So that’s one of the things. And the last one we see is cost as well. Like in terms of the problem we are solving overall. Hey. Yes, I have the models in the cloud. I don’t want to keep pushing all the data to the cloud if I want to like, you know, run inference and get output out the edge and then push the post-process data to the cloud. That’ll save obviously you know, significant on my, cost as well, because you know, people are spending lots still on ai, cost, all the training, et cetera. Now how do we start, you know, optimizing it so that you don’t keep spending just on the large models or the large data and push all of that to the cloud.

Akhil 07:43

Pradeep, you’ve experienced this built on the cloud side and now the Edge side. And Dan, you mentioned I love that sort of statement, AI applications are dragging the infrastructure. I think something we’ve observed, and this is not just in the national security spaces, when that does happen, I mean it takes a hot minute for the, in infrastructure to get developed, in fact, much longer than it does necessarily to take an AI application, right? And to build the potential use cases. And so Pradeep kind of curious on, as we think about the challenges, not just in the application development, but you know, as we think about that dragging infrastructure. What are the challenges of actually building and adopting that scaled infrastructure in a distributed manner for some of these mission critical organizations? And that’s not just national security, that’s oil and gas. These organizations that that, that have similar mandates when it comes to the deployability and challenges associated there.

Pradeep 08:36

Yeah so interesting. I think as a, as you said, I was you know, fortunate to be part of the, you know, building the cloud journey for over a decade. What I significantly saw was what Cloud enabled was, hey, you can think about developers around the world could actually focus on the application and start deploying anywhere in the world, right? They don’t really care where the infrastructure is sitting. It’s just a dropdown for them in the broader experience. And I just go focus on my application deploying that. That is a similar thing we are doing here. Hey, you can think about distributed edge compute sitting across everywhere around the world. You focus on the application now what challenges we see? It’s literally if your application is modernized running on Kubernetes, like you’re able to do that. Then, you know, moving to the edges relatively straightforward. Where we see challenges, if it’s set of legacy applications that have been done that has very legacy dependencies, then you actually have to go through sort of a transformation or the second thing we have seen is. If you are actually too much tied to one of the cloud provider, right? You have so much dependency on some of the cloud services or specific cloud services coming from a cloud provider, then you have challenges transforming. But what we are also seeing a lot of the like, and if you think about you know, journey of multi-cloud like enterprise customers are deploying on multiple clouds, which means that. The app developers are like transforming the application to say that, oh, I should be able to run my applications on an Azure or A GCP or on Oracle, et cetera, right? That’s how they are thinking. Which makes our job easier because, hey, if you have you know, transform your application or you built your application from the scratch, that it can run on primarily any infrastructure, any cloud provider, then running it on engine infrastructure becomes relatively very easy. For example, we are at, we are one of the applications we actually, they were running on Azure. We were able to actually, you know, you know, enable them to run on the edge within less than 24 hours, right? Because it’s all you know, native Kubernetes stuff. So it is very easy for us to, you know, migrate them over.

Akhil 10:51

Thanks for that, that’s great. And I wanna pull on something you mentioned with a lot of these legacy industries. You know, legacy is not necessarily the right word, but with a lot of industries, like in national security. Basic dev tools, infrastructural tool just don’t exist in those environments. And what we’ve been finding, Maggie and I, with our portfolio companies here at Shield Capital that are on the AI application side is they’re allocating 20, 30% of their critical engineering talent. Not to the application, but to the deployability infrastructure and how it relates to the edge. And so I’m curious a little bit about how do you think about working with startups in these. Really advanced AI application companies, figuring out what that edge deployability looks like and maybe taking the burden off of them so that they can focus on, I think as I’ve heard before, focused on just making really good beer or making that really good application.

Pradeep 11:43

Yeah. So super interesting. You know, we, you know, we spend a lot of time between, you know, Nvidia of the world and then, you know, and the cloud providers like Microsoft. If you think about the stack, what we are seeing is there’s obviously the infrastructure layer which is you know, hardware and all of those things. Then you have the past layer with Kubernetes. Then you have a lot of ML ops platforms, and then you have the AI applications, right? That’s. The stack is starting to work, and this is where some of the customers struggle because if they have to do it all by themselves, they have to go all you know, together by themselves. And then there are startups, to your point, is working on couple of these portions and oh, they have a really good ML off platform, or they are, you know, developing really apps. So what we have been is like we think about partnership very key. Like how do we make the experience simpler and easier for our customers? Is the partnership with these, you know, ML ops and AI apps. And we work with the, for example, have a specific team what we call as the marketplace team. What they are actually, their full-time focus is to like, you know, work with partners, onboard them onto our platform, make their deployment very easy. They don’t have to worry about Hey, what infrastructure, et cetera. Very similar as I explained in the cloud. So they can actually scale faster, right? And what that gives us the abilities, Hey, we deploy the infrastructure. The number of ML ops platform integration or the number of AI apps we support goes really well. So that’s and my team is. Deep infrastructure specialists and you bring in the application teams from the, you know, the startups or the app companies is what we are focused on. So that’s really what we are focused on and how we do through is partnership. We see that struggle like very much. They e even for example, even if I’m an app developer company and I can deploy it in cloud, then I don’t know how to move to edge, right? So we actually take care of that easily for them. So that’s really a problem. And through partnership is what we are solving. That

Dan 13:49

Just to piggyback on that, I mean, we say pretty, but I like to say our value prop is the three S’s. Speed, scale, and sovereignty. And the speed part is really important for the end customers that we’re serving, where it’s all about. Hey, I’ve got, you know, mission critical problems in these edge locations. I need them solved yesterday. And you know, for example, with the Navy, and this is public, we did a UNITAS exercise where we were able to rapidly deploy one of our Galleons on a ship. And we were able to show, Hey, you can take these really powerful models and for the first time, it’s never been done before. Run them at scale, you know, totally disconnected from the public cloud in these very remote locations. The speed piece of that is really important. And then once you do that, you create a ton of value, both for the end customer and then these model companies, which also look at us as a fast way to get deployed in these sites. And then you start to scale. And then the sovereignty piece is really important. I mean, I think what we’re seeing is that you need to take a broader lens when it comes to national security. And we work not just with defense, but we work in, you know, energy. Also thinking about like other critical infrastructure, like airports, right? All of it is really national security where you need to be able to do 24 7 monitoring of these sites, secure them, and then run all of these really powerful AI models securely locally at the edge.

Akhil 15:20

That’s awesome. Thanks Dan. And yeah, I think very much aligns with how we think we, I mean, it’s mission critical, right? It’s about how do we. Safeguard and enable the varied use of AI applications with the right associated security measures if and when uncertainties happen. And that could be just a commercial cloud outage, which happens more frequently than we care to admit, or something more nefarious that affects not just the national security domain but our businesses and commercial enterprises. Dan, can you gimme another example? Maybe not from the military. Going to Pip’s comment about how you think about partnering with the best in breed of AI applications.

Dan 15:58

Yeah. One example I really love ‘cause of the impact that it has is the example with Alaska Department of Transportation. And this started with us managing all of the connectivity as well as other connected assets for the state, like drones. They use Skydio drones, which are really great for emergency response, specifically using it for things like avalanches and floods. And obviously Alaska’s a huge, very remote state, and so a lot of times when these natural disaster strike people are affected, stranded in very remote locations and you can’t wait. For intelligence to respond, you need that in split seconds, just like you do in a defense scenario. And so what we did is for the first time, we deployed one of our Galleons, these modular data centers in Alaska, and we were able to cut their latency from over a day to near real time to respond to these things using all the data from the drones. Right, and that’s using AI models that are very, people are very, you know, familiar with. But the difference between deploying those at the edge locally versus deploying those in the cloud is literally, you know, life and death. You know, 2020 over 24 hours. I mean, people cannot wait in that scenario. You have to have the intelligence in the moment for it to be useful if you don’t, responding the moment the data’s almost like useless. And so that is our focus. And I like that example because I think that is something that is true everywhere in the world. And you’d be surprised how many different locations have this same problem around the world. I’ll just give you another example. I’m about to fly out to Australia. We’re making a big announcement with a customer in Australia and. I was talking to one of the former Prime Minister there yesterday in preparation for that trip, and they said, Hey, we have that exact same problem, you know, very remote part of the world. There are no large data centers outside of, you know, maybe Sydney and Melbourne. And you know, when fires break out or you know, even just to provide the opportunity to you know, educate the population, provide infrastructure for education, obviously huge area for mining and oil and gas as well. So going back to energy, your stuff is mission critical and we don’t have anything like it. So we just see a huge amount of opportunity with those types of high impact use cases in these more remote locations that have been underserved. From infrastructure historically.

Maggie 18:50

Yeah. Dan, an another pretty interesting use case. I saw you all announced it was late last year. You announced that Armada is going to be an official collaborator on the Department of Energy’s new Genesis mission. So I wanted to see if you could talk a little bit about what that mission is and the role that Armada will play.

Dan 19:07

We are really excited about Genesis mission. I’ve really advocated for a project like this for my whole career, so I’m just. Pumped that we’re doing this as a country. The things that we’re doing there are twofold. So one is we’ve been working with secretary Wright and Dario Gill and the whole team Anthony Pugliese over there. And they’ve been great about just connecting us to all of the 17 national labs so that we can go there. We can meet with them and we can understand what are the gaps in infrastructure across the labs. Because again, AI drags infrastructure. Genesis’s mission is the most ambitious AI project in the history of the country. We’re talking about doubling productivity when it comes to you know. Science and engineering within a decade. That’s the goal. So if you’re going to do that, you’re going to need to deploy AI at a scale that has never been done before. But you can’t do that unless you have the right infrastructure to enable it. So that’s the first thing we’re doing, is we have gone to all of the national labs and we found out. Where are those gaps in infrastructure? And again, our value prop is speed, scale, and sovereignty. We can fill those gaps in infrastructure right away to enable us to. Accomplish the objectives of Genesis mission, which include getting, you know, big wins this year, right? We can’t wait, you know, years for traditional brick and mortar data centers to be built. So that’s number one. The second thing is there is a goal to create a common AI platform across all of the national labs. They don’t wanna operate in a siloed fashion where each Napa National Lab is doing its own thing, right? The power of Genesis mission is taking all of the data from all of the national labs and bringing it all together and having them work in unison. And so what’s really unique about our platform is the full stack nature of it. The fact that not only can we deploy. These Galleons you know, faster, more flexibly where you can scale up versus overbuilding and do it totally sovereign and disconnected from the public cloud in a way that nobody else can. But the full stack piece of it where each one of these things acts like a node to a distributed private infrastructure that can be leveraged locally, but then also. Coordinated across sites. And a very tangible example, getting into the weeds of this a little bit is take you know, take one of these model companies, right? If you look at who are the collaborators, we’re one of them for Genesis Mission, but also all of the Frontier model companies are part of it, all of the major ones anyway, you’ve got pretty much all of the GPU. Know, companies participating in it as well, but then the question becomes, okay, if I deploy those models on one of my sites for cybersecurity and other reasons, I’m gonna wanna use that locally. It also helps with cost, which is a big issue. Running the models is much more cost effective at the edge versus running it in the cloud. And we can get into that. But then I want to take. All of the improvements that I get, fine tuning the models on all of that data, and there’s a huge amount of data at each one of these labs, and I wanna leverage that across all of my different sites. And so what you can do is you can take one of those models, whether it’s from OpenAI or Xai, or you name it, you can run it on that data locally. You can fine tune it to the data, and then you don’t have to ship all the data back to leverage that across. All of the other labs, you just ship the fine tuned model back and then you leverage that across all of the other labs.

Akhil 22:57

Hey Dan, you hit on something that I think some folks first learning about Armada may not entirely know or have a perception around. It’s not just about the data center in a box, right? Armada is not, Hey, I’ve got this galleon and I’m just gonna plop it somewhere and it’s gonna have some compute, some GP, you know, a couple of server racks. And have some power. The whole thesis is how do you interconnect those in a way that allows for this nearly hybrid compute environment, depending on the type of application and depending on whether you’re optimizing for cost or resiliency.

Dan 23:32

Yeah, exactly. And I’ll let pretty comment on this ‘cause he, he saw this firsthand at Microsoft where, what, like when PIP and I were first talking at the beginning of Armada, the opportunity was. Oh, wow. There’s 30% of the world that has these big hyperscale data centers. But what about the other 70%? There’s all these gaps globally in infrastructure, and that’s why we call ourselves the hyperscaler for the edge. And you know, the Galleons are super cool and they’re very impressive, which in some ways is a big advantage for us. But in some ways, you know, we try to really make it clear it’s not just about one galleon. It’s about. The full stack nature of it in the fact that each one of them is a node to a distributed system that can plug all of these gaps in infrastructure globally, and then allow you to leverage your intelligent across all of your different sites.

Pradeep 24:27

So I think Al interestingly, we talk about the world as, I mean, hey, there is you know, cloud for training. And then there is like what we see the world evolving to fine tuning and inference, right? That’s really it where we are with this thing. So what Armada is really playing a key role as being the platform for fine tuning and inference right now. All the edge boxes we are talking about is mostly for inference deployment. So that’s where we, basically, what we do is. We give the platform, but we also put the entire full stack solution with hardware and with the modeler data center. That’s what we’re doing. And there are cases in fine tuning, we are actually partnering with, you know, data center providers or colo providers where hey, they will actually bring in the hardware and it sits in a colo. And we give the platform, right? So what we are focused on is that customer wants a single platform for both fine tuning and inference, and then I bring down the models from the cloud then fine tune it like on my sensitive data. And then I click a button and I push it to inference for the edge. So that’s really like what. What Dan is talking about, that’s where an edge is actually evolving with that per premises. An edge can be like a small, you know, box sitting in a, you know, department of transportation vehicle, a police car. Then we are thinking about an edge box sitting at the bottom of a cell tower. And then you are starting to expand into a co, right? So that’s really like what we are looking at as, hey. That can be small to you know, a colo. Definitely not a cloud, but how do we have a single platform that spans across multiple? And where we are doing the hardware are both those like ruggedized boxes where we also have partnered with, you know, some of the folks on smaller boxes because they are already deploying those things. Then we want to run our software stack on top of it. So that’s how we are you know, helping customers.

Akhil 26:27

Thanks, Pradeep. I wanna nerd out for a second here on fine tuning and inference, if that’s all right. I think we have a group, and I’m doing this because I think our listeners tend to be somewhat technical, but also a lot of functional experts, right? That recognize that the current models and what the applications look like cannot get to the level of fidelity for their specific use case and deal with the tail of outcomes without some of that. Inference or fine tuning towards the edge, but then comes in all the factors, cost, resiliency, et cetera. So maybe my first question here, Pradeep, is what is wrong with the current state of infrastructure to be able to support this fine tuning and inference and five years from now? What do you want that to look like with, as you said, Dan, with Armada as the edge hyperscaler?

Pradeep 27:13

Pradeep: Yeah so I’ll give you a real example. We work with one of the you know, logistics customer. They had some physical security challenges. They had some like specific worker safety rules. And they had like literally somebody sitting in front of a camera or like a monitor looking to see if they were detecting some sort of violations, right? And they were like, you know, looking at maybe trying to look at their own models, et cetera. And their long-term plan was, Hey, it’s not just developing the models. They have you know, hundreds of sites and all of these models needs to run on those edge sites, right? So this is where hey, they tried a bunch of models. They said, Hey, there’s a bunch of consulting companies who came in, but everybody tries to solve the R piece. They’re like, oh, I will create the models for you, right? And then like then the next thing they have to deal with this. What’s the accuracy of the model, right? How do you keep you know, retraining the models or fine tune the models and then are you going to enable me to deliver it end to end, right? Many companies says, oh no, I don’t do this. You ought to go talk to somebody else and then it’s your job, right? So this is a case where you know, our AI team came in, we, you know, sat with the customer, they gave us like two weeks of data. Like we had we had our base model developed on the cloud. We, you know, we fine tune it or it drained the model on that. We actually pushed it onto the edge and then you won’t believe, like the first week we were able to detect seven out of 10 scenarios. Like before it was like a one or a one out of 10 or maximum two, because when the person looks away from the screen, they don’t see it. And from there we went to seven out of 10. But what the customer loved it and said, Hey, it’s not this one time. I wanna make sure that because new scenarios will pop up. How do you do that continuous training and then make sure that hey you can push it to the edge because we know what is the capacity available on the like edge. So it is not going to be hundreds of GPUs. So that is something which we are seeing, like where people are. Like in some ways when people are developing models, they assume that there’s infinite amount of capacity, right? And now how do you take that and run at hundreds of sites with a finite capacity is like the transition and that’s where. You know, we are helping customers or even partners are starting to look at saying that, yes. Okay, if I say I have got 4G Ps on the site, right? If I take like a small suitcase a deployment, you are barely one GPU, right? How can you like run the same models you know, what you need to optimize is what we are looking at.

Maggie 29:48

Dan. I’m curious, turning back to, you know, what it’s like actually engaging with some of these customers. You know, when I think of oil and gas, national security, energy, I don’t think of the most tech forward-leaning customers. So I’m curious, you know, how much customer education have you all had to do with these customers? How forward-leaning have you found them to be and how do you get them to trust you to actually help them deploy, you know, really valuable, meaningful technology.

Dan 30:17

Yeah. The good thing is when we first start talking to customers in general, they already have a clear set of goals at the edge, and they already have a backlog of use cases that are low hanging fruit. Once we deploy the infrastructure, there is an initial bridge that you have to cross, which is you have to demonstrate the value of each one of those use cases at these sites. And then they also almost every single one of them has a very stringent cybersecurity review that you have to get across. But then once you do that, and once you deploy at one site, going from one to many is much, much easier. And the reason is there is a huge amount of value that directly ties to their objectives. And if you look at what is a common pattern to go to, back to Pip’s point on pattern each one of these. Customers, whether you’re talking about defense or you’re talking about you know, oil and gas, mining, any one of them, they have a common set of things that are happening. So one is they have very distributed remote sites where they have a lot of data being generated and they understand that the value of that data is dependent upon it being used on the site. For primarily latency reasons, but also a lot of these are critical national infrastructure. And for that and cybersecurity reasons, you actually can’t take the data off the site. So you have to do all the data processing locally. And then once you deploy, there’s a huge amount of value to be generated. And I gave the, you know, the defense example and I gave the, you know, emergency response example with Alaska. But if you think about. An industrial example, like a remote oil rig or mine refinery manufacturing facility. I could go on. Each one of those things has a lot of value to be delivered in. Number one, not sending all of the data to the cloud because one, you’re reducing risk and secondly, you’re typically, if you’re, especially if you’re running AI on that data, saving a significant amount in terms of cost. But number two. Just making sure that those sites work 24 7 as they’re intended, has a huge amount of value. Every single one of them, whether you’re talking about an oil rig or a mine, or a refinery or a factory, they have the concept of unplanned downtime and unplanned downtime costs a lot of money. Then at the same time, each one of them is moving towards automation. And so you can help them on that journey as well. And just to give you a sense, like a large energy company, we work with some of the largest in the world. They’re all moving towards fully autonomous operations over the next three years. And so immediately by deploying this, we’re able to avoid unplanned downtime. And a lot of them have. Days or weeks of unplanned downtime every year, which can cost hundreds of millions of dollars or billions of dollars at scale. So that’s an easy business case right there. And then there’s a bunch of other use cases. But then the second thing is all of them are planning to go towards full autonomy at these sites, or nearly full autonomy. Maybe there’ll be one person on these sites. Within the next few years between now and 2028. And the only way to do that is to have local compute. So we have this same pattern across all these different areas. And Akhil, to go back to your point on inference, if you take a step back, that’s why you’re seeing this super cycle shift from just the training of the models now to the fine tuning and the inference of models is that everybody sees that they have now reached A level where if you do that fine tuning on all of that data at the edge. Then you can actually move towards autonomy and the use cases there are through the roof and the values through the roof when you do that.

Akhil 34:18

Super helpful. Dan, lemme just recap for the audience here. What I’m hearing, I think coming into some of these discussions, I think a lot of folks might see this as, hey, this is, you’re really paying for resiliency. You’re paying for whatever it is. What you’re saying is it’s not resiliency, it’s efficacy. There’s a matrix associated with, some people do want that resiliency, right? In a disaggregated, disconnected environment. Others are actually like, wait a second, this is the most effective way to do fine tuning and inference. And to be able to get that last, you know, not to use a football analogy, the last, you know, third and five, the last five yards to the application efficacy. The only way to do that with all the data sovereignty, all of the. Local nuances is to do it at that edge, and you need to be able to do it in a way that is connected across multiple nodes.

Dan 35:05

Yeah, I would almost say it’s first about enabling all of these models, enabling local data processing at the edge in the way that was never possible before, which creates a huge amount of value. And then the resiliency is like a nice bonus. It’s oh, and by the way, these are a part of a really resilient. Distributed infrastructure that if something ever happens with your cloud or something ever happens, even with one of the nodes, you have really resiliency between nodes. But that’s like a bonus. The thing that gets people to say, Hey, I’m all in, is number one, there’s like a huge amount of immediate value to be generated. And then secondly, it aligns with these you know, multi-year initiatives to move towards autonomy in each one of these edge locations.

Pradeep 35:56

Yeah, and I, just to add to what Dan said, we have a mining customer, like they have around 30 mining sites, a large mining customer. They are going through this whole process right now. They have all of these IOT assets sitting in. They all push that to a cloud. It goes to some central region. They were like, Hey. This is not the way I want to do modern edge or modern, like a distributed architecture. So they’re like working with us to re architect the whole thing from a distributed you know, architecture where hey, you want all of these it devices to actually send, you know, data to be processed in the gall. And then post-process data that pushes to the cloud for, you know, you know, for their compliance reasons, backup, et cetera. And that would also be used for larger training as well, right? You get you know, you know, the what the events which are really useful rather than pushing everything to go sit in some storage account and keep paying for the storage, right? So that’s why say, hey, it helps me in also that when you do this. At the edge, you can actually take actions because you’re immediately processed within seconds. You have the output so the people on site can take action. At the same time like I’m not trying to pay, I also, in many of these cases, egress cost is high as well, right? If you think about the network, if you’re pushing all of this data, raw data, it is you know, somewhere you’re paying the network bill too, right? So that’s, those are the way they are reaping at the architecture from a distributed computer is what they’re looking at.

Dan 37:28

Yeah, this was actually a big eye-opener for us and our co-founder John, when we started Armada. You go actually talk to these mining companies just to use that example, and they have what’s called a hard segregation of it and ot it from operations. And you might ask what, why does that matter? What is the practical implication of that? Well, in mining, like in most businesses, especially industrials, time is money, right? And we work with a lot of the largest mining companies in the world. They’re constantly doing exploration for new projects, right? And a lot of times those things can take years. Well, why do they take years? They’re almost all in very remote locations. Then because of this hard segregation between IT and ot, they have to do the exploration work at the site. And then they ship this stuff in batch, like the same way they did decades ago to, you know, hq. And they analyze it and then they send it back. And we talk to a lot of companies where we ask, how long does that take you? Well, every time they do that, it takes them a month or maybe more. And so by actually bringing. Something like a galleon to the edge and deploying it behind the firewall on the site. You go from more than a month to real time, right? Which speeds up these projects, which creates a ton of value and also allows them to out-compete the competition. And so it’s really eye-opening where a lot of people just assume Hey, everybody’s doing things. You know, the kind of real time way now. Not with a lot of these types of businesses because of this hard segregation between IT and ot, and the only way to actually enable real time is to deploy behind the firewall on the site.

Akhil 39:18

Very helpful. And Dan, as you were saying, that can’t help but think about the national security parallels of, you know, your analogy between the IT and OT world, right? We’re listening and observing what has happened over the past couple weeks in the Middle East. We’re recognizing that the ability to quickly sense what is in the environment and make use of it before it gets into the large IT systems or in the process of is absolutely just as critical. And a lot of those systems too are far. I’m reminded by some of the stories Raj here at Shield Capital shared about coming back from, you know, a, a fight or flight, a fighter pilot flight, and you know, manually downloading the sensor data. Just think about the latency associated with that. If you could do that in a way that that did not require that for the immediate analysis that needed to be done.

Dan 40:12

Yeah. And Shield’s, obviously, one of our founding investors, you guys co-led our seed rounds, so thanks for all your support over the years. But I remember talking to RA about that when we started the company. Literally, it’s and that is happening globally. You cannot have a situation where you have. Very important data just sitting there and you have, you know, days or weeks or months of latency to try to do anything useful with it. You have to be able to use it in real time at the edge. And that’s the common thing we see playing out across all of these different areas, whether you’re talking about, you know, defense or you’re talking about energy, or you’re talking about you know, any other area.

Maggie 40:53

Dan, I know that you all recently released a white paper on what it’s gonna take to build US AI dominance, and we know that a lot of our, you know, potentially adversary nations like China are investing very heavily across the stack in talent, in compute, build outs, in mining, in energy. What is it gonna take for the United States to maintain our AI dominance? What are some of the sectors that we’re going to need to invest in over the next five, 10 years?

Dan 41:24

Yeah. It was funny how that played out. We didn’t plan it this way, but when we released that white paper, I was in Washington DC with some of our team and we were at the. Winning the AI race summit that the president spoke at last July, and that was the same day that the White House released its AI action plan. And we had been talking to them a lot and so I don’t think it’s coincidence, but they were very closely aligned our white paper to that. And the idea is number one, one of the biggest blockers to AI is energy, right? And if you look at how that is playing out globally, China has made very fast progress there. And that is a national security risk, right? We have to solve the energy problem. How do you do that? Number one, you use all of the energy that is available to you and I mean all of it. And one of the things that we highlighted is that in the US there’s about six gigawatts of stranded energy. And it’s not being used for ai. Why is it not being used? Well, it’s because we’ve been requiring that energy to somehow travel to these far away data centers, and that is very difficult to do. And so what we are instead saying is, let’s flip the script a little bit and let’s actually bring the data centers to the energy and enable that energy to be used right there. And a nice thing is that’s also where a lot of the data lives. And so that’s kind of our philosophy with Armada. The data centers are an enabler and they should go to where the energy and the data lives, rather than this dynamic where you’re having to have the energy and the data sail to some far away data center. That’s one of the reasons why we call the company Armada, is like each one of these things is a ship sailing to the data. And that’s why we call the data centers galleons. And then the other thing that we need to do is we need to use all different types of energy. And that includes, you know, stranded natural gas that includes things like some of these, you know, micro nuclear reactors. We think that you gotta invest in nuclear, you gotta invest in solar. You gotta, you know, be willing to look at things like data centers in space because the sun is like the ultimate source of energy. We need to use all of these different. Energy sources and then have the data centers go to those energy sources and enable ai. And we need to do it really fast ‘cause China’s moving fast. And so that’s what we’re focused on. The other thing, and this is the third pillar of the AI action plan and also in our white paper is you need to work with allies. The allies also have huge amounts of energy that is available for ai, and we can utilize that. And that also has other good impacts, like making sure that we don’t end up in a 5G situation like we did, you know, with China last time, where you’ve got other nations adopting that technology. We need to make sure that the world does run on the US AI stack, and so you kill two birds with one stone. When you work with allies, you are able to utilize all of that energy. Constantly improve these models, improve the entire stack, and then, you know, five years from now, instead of running on Chinese infrastructure, the world be running on the US AI stack.

Akhil 44:46

Yeah. And the AI race, as we think about in this geopolitical competition, is not just about the large language models, right? It is also about the infrastructure and the deployability across the globe. And I mean, this is what you’re seeing, right? Pradeep, both your prior experiences and what you’re seeing as you deploy Armada, not just. The United States, but across the globe.

Pradeep 45:08Yeah. I think, actually great question. I’ll tell you an example. In 2006 I was working on some of the telecom infrastructure stuff in Africa. What, interestingly I saw was like, you know, Huawei and some of the Chinese providers were actually setting up almost giving away free infrastructure. So they would set up all the telco infrastructure. You know, I didn’t realize that point of time, but they were actually taking sort of the backbone of the country, right? When you have all the infrastructure in your control, you have you know, the data and everything flowing through under sort of your control. I’m actually seeing similar thing. If you look at you know, the minerals industry, the mining, et cetera be like in a, if you have to build a lot of infrastructure to run AI models, you need all the, like silicons you for the silicons, you need all the minerals, right? So it’s a super interesting thing while I look at it as a LLM race, I also think about it’s a, you know, a race. For grabbing control of all the minerals and the mining sites and the oil and gas, right? So it’s very interesting to see that’s what I’m seeing. Hey you want those minerals in hand to produce more compute infrastructure to run better models, but, and also you want those to be deployed. So who has controlled all of this becomes super critical. And that’s where we have been talking about, hey, having a full American AI stack is super important from that perspective.

Akhil 46:39

Yeah, interesting. Pradeep, if you actually tie that line together, you can start with AI dominance and the geopolitical aspect there. Then you go to the infrastructure dominance and then you tie to the use cases. I mean critical minerals, right? I mean we’re talking about that right now. We’ve been talking about it. Your experiences from 2008 honestly predates some of the news around Huawei. And we’re only just now seeing or have for the past couple years the. The real challenges and limitations and the desire to have a critical mineral access point, and all of that is predicated upon knowing where they are and being faster about acquiring those resources.

Pradeep 47:18

Yeah. And I think that’s what to like really underline Dan point. It’s not just about within America, it’s about the allies too, right? You have to help and be partnering there to win the stack. Not because there are a lot of natural resources outside of us two, so we gotta be helping the allies to that.

Maggie 47:39

Last question to wrap up, what advice do you have for founders looking to build mission critical technologies for mission critical customers?

Dan 47:49

I’ll start and then Pradeep you chime in my first piece of advice is very simple. Be clear on what your mission is and write it down before you ever hire anybody. Like the first thing that Pretty, and John and I did when we started our motto before we hired a single employee was we sat in a room at Founders Fund and we said, okay, let’s write down our mission, our reason for existence. Let’s write down our vision and let’s write down our values and let’s make sure that every single thing we do from this moment forward aligns to that. And let’s make sure that everybody who joins our company understands what we’re about. And it’s a simple thing to say, but 99% of companies do not do that. So if you do that, you will stand out and you’ll get the best talent in the world because people want to work for companies that have missions. They wanna work for, especially the best people they wanna work for companies that are solving the biggest, hardest, most complex problems. And so we did that. We, we had our manifesto that we published, you know, while we were still in stealth, people were like, who’s this company? But apparently they stand for something. And then we even did a founder’s film and we did a launch film when we launched outta Stealth. And it was all about our mission. And it’s funny how. You know, those things are now a few years old, but you go back and look at ‘em. It’s all the things that we’re doing now. It’s all the things that we’re talking about in this interview. It’s just real. But being very clear about what you’re gonna do, what you stand for, and then, you know, walking the walk is really critical.

Pradeep 49:27

Yeah. Just to add, actually, as, when we were writing that thing, I remember in in one of our investors office, that time we didn’t even have an office. It’s funny that we actually took some of the hardest industries to go after, right? Because we believed that was the right focus area. And in some of the, you know, some of those industries are very hard to move because as earlier mentioned, they are legacy. So you’ve gotta believe in it and then you gotta be at it every day. So don’t do it if you’re not passionate about it. Because solving the hardest problem, you have to have the belief, passion, and you have to grind through it in many ways.

Maggie 50:07

Great. Well, Dan Pradeep, thank you both so much for your time and for coming on the podcast. We’re super excited to be investors and can’t wait to see what you all do next.

Dan 50:17

All right, thanks a lot. Great talking to y’all. See good. Thanks. Bye.

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Hey everyone!

I’m excited to announce that registration is now open for the third annual National Security Hackathon, taking place May 1-3 in San Francisco!

This year, we’re proud to be formally partnering with the US Army FUZE team, Stanford DEFCON Technology and National Security Student Network, and UC Berkeley’s new Defense and Dual Use Technology Club to bring together the brightest minds across defense, academia, and industry to tackle the challenges that matter most.

$50,000 in cash prizes is on the table across four critical challenge tracks:

• Sensor Analysis and Integration

• Edge Deployments and Drone Operation

• Mission Command and Control

• Digital Defense and Cybersecurity

Something special about this hackathon: because we are using Army prize challenge authority to award prizes, hackathon winners will be able to win follow on prototyping OT contracts from DoW following the event, as the hackathon counts as a competitive process. This event is a real opportunity to connect with DoW stakeholders to build needed technology that can lead to long term contract opportunities.

Here’s what’s happening:

• Hackathon Kickoff Event – May 1 @ Stanford

• Hackathon Weekend – May 2-3 in SF

Shield Capital is proud to co-host this event alongside Cerebral Valley, Palantir, IQT, DCVC, OpenAI, Scale AI, Armada, Deloitte, TENEX, Shack15, and Skyrun.

If you’re a builder, coder, designer, student, engineer, or analyst looking to apply your skills to problems that actually matter—this is your moment. Please forward to anyone in your network who may be interested in joining!

🇺🇸 Register now here.

Hope to see you there. Please reach out with any questions or comments.

As a result, many national security startups are rethinking how they operate, developing novel business structures to more effectively deliver capabilities to the warfighter. This article explores several of these approaches, including M&A-driven conglomerates, tech-enabled services, holding companies, “Americanization” strategies, roll-ups, contractor-owned/contractor-operated models, and factory-as-product.

Unlike traditional business to business (B2B) and consumer markets, the defense market is a heavily regulated monopsony market (a market dominated by a single buyer) with multi-year budgeting processes, often dominated by special interest groups beyond the control of the Department of War (DoW), that do not neatly align to the priorities of the end-users. Product end users (soldiers and intelligence analysts) do not get to make final product purchasing decisions, and the DoW has limited control over its own budget, as Congress ultimately determines appropriations1 (at times in ways that diverge from the Department’s stated priorities).2

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The DoW is not accustomed to buying technology products off the shelf or as a service. Instead, the DoW tends to award services and integration contracts, hiring contractors to build and integrate custom, exquisite capabilities like fighter jets, missiles, and even travel management software.3 Many leading defense contractors like Lockheed Martin are as much systems integrators as product manufacturers, combining in-house engineering with vast subcontractor ecosystems to deliver complex, exquisite platforms. These systems integrators typically charge the government under Cost Accounting Standards, billing for direct labor, overhead, general and administrative expenses, and other costs (such as materials), with a fixed profit margin of around 9% under cost-reimbursement (often called cost-plus) contracts, rather than offering a firm fixed price for a defined product.

In a single-buyer market for high-risk, exquisite first-of-its-kind systems, cost-plus contracting becomes the rational choice: development costs are uncertain, and with few potential customers to amortize upfront R&D, systems integrators need a structure that ensures cost recovery and preserves predictable margins. This model is the inverse of the quintessential VC-backed startup, which heavily invests its own capital into R&D upfront and then makes profits by selling products into large markets with strong operating profit margins. As a result, startups in the defense ecosystem have had to chart their own path, building businesses that depart from both cost-accounting-driven contracting and traditional SaaS or consumer playbooks to align with how the government actually buys and funds technology.

M&A Driven Conglomerate

Over the past three decades, all traditional defense primes have aggressively pursued mergers and acquisitions as a major source of growth. Companies like RTX, Lockheed Martin, and Northrop Grumman – each bearing combined names formed through successive mergers – are large conglomerates made up of many different business lines acquired over decades. Conglomerates are able to add additional value to each acquired asset by integrating disparate systems together, and building inter-operability, ultimately making the whole system more valuable than the sum of its parts.

Several new entrants, most notably Anduril and UForce have pursued this M&A-focused conglomerate approach. Since its founding in 2017, Anduril has completed nine acquisitions that now underpin much of its core IP. For example, its Fury platform, which is now competing for the Air Force’s CCA contract, was originally developed by Blue Force Technologies and acquired in 2023, while its Dive-LD platform, which was selected for the Navy’s XL-AUV program, was developed by Dive and acquired in early 2022. A core element of Anduril’s acquisition strategy is integrating disparate hardware into its Lattice software platform, creating a unified, interoperable system. Anduril also centralizes go-to-market and business development functions, giving each platform far greater reach into the DoW than a standalone company could achieve on its own.4

UForce is a Ukrainian defense company which has taken a similar approach. During the Russia-Ukraine conflict, a wave of small Ukrainian defense companies emerged, each focused on developing specialized battlefield systems. While individually these companies lack the scale and resources to become multi-billion-dollar businesses or navigate complex international defense procurement processes, when merged into one company they can reach a much broader market. UForce is a strategic consolidation of nine specialized Ukrainian defense companies, whose portfolio of products include some of the most important battle-tested platforms in Ukraine. Its most well-known is the Magura unmanned surface vessel, which was responsible for sinking a significant portion of the Russian Black Sea fleet. Its other products include Nemesis strike drones, Liut unmanned ground vehicles, Sunray counter-UAS laser system, and Bucha fixed-wing UAS interceptor. Similar to Anduril, UForce integrates all its product lines into a centralized command and control (C2) software and autonomy layer. Since consolidating these product lines into one company, UForce has onboarded an American-Ukrainian CEO, moved its headquarters to the U.K., raised more than $50M from international investors (becoming Ukraine’s first defense tech unicorn), and struck deals with European NATO allies.

Tech Enabled Services

A significant amount of the defense budget goes to services providers – that is, businesses whose primary product is human labor which they bill by the hour. Over the past few years, a new generation of startups have emerged using AI to provide services much more efficiently while achieving high gross margins. This new crop of businesses is poised to disrupt a number of services industries, including legal, HR, and finance – and defense is no exception.

For example, consider cyber operations: both defensive and offensive DoW cyber operations are dominated by contractors. Services firms provide the government with everything from security operations analysts to incident response teams to vulnerability researchers to exploit developers. Already, startups are using AI to disrupt these services-heavy operations. Companies like TENEX provide AI-enabled security operations, detection, and response services, while companies like Autonomous Cyber use AI to automate vulnerability research and exploit development services.

Advanced code generation models are also enabling a new set of companies to disrupt traditional engineering services businesses. The DoW often hires engineering services firms to manage projects like codebase modernization, custom software development, system of record implementations and migrations, and more. However, as code generation models have improved, the need for large, legacy teams of software engineering service providers has decreased. Palantir, known for its engineering-services-oriented “forward deployed engineer” (FDE) model, has introduced an “AI FDE” offering, deploying AI systems to perform work traditionally done by human FDEs, and is now using these models to execute engineering services tasks such as data migration. Similarly, companies like Code Metal and Cognition5 are exploring using their automated code translation capabilities to work on government codebase modernization and optimization contracts. Projects which previously would have required armies of contracted software engineers now require just a few engineers armed with powerful coding models.

The power of AI to disrupt services extends beyond engineering and cybersecurity services. For example, Hedral develops tools that automate architectural engineering design, a traditionally human labor heavy industry, and, according to Obviant data, has won several contracts to provide automation-enabled architectural engineering services to the DoW for building design. One could envision a future where legal AI startups like Legora and Harvey provide the DoW with automated solutions that displace traditional law firms for low-value, routine legal work.

Roll Ups

In recent years, a number of startups have raised VC funding to “roll up” mature, cash flowing companies in fragmented industries and drive operational efficiencies using AI and automation, taking a new, tech-enabled spin on a traditional private equity investment strategy. The defense industry, particularly defense manufacturing, is no exception.

Much of the U.S. defense manufacturing sector is fragmented and composed of a number of small, family owned businesses, with predictable cash flows, many of which have aging owners looking to exit their businesses. Modernizing the playbook of private equity roll-ups like aerospace component manufacturer TransDigm,6 several startups are buying up various manufacturing facilities, particularly those focused on tier 1 and below component manufacturing, and outfitting them with systems like AI-native ERP, MES, MRP, supply chain management, procurement, and quoting tools, as well as with robotics capable of automating key parts of the manufacturing process.

Re:Build Manufacturing, which has raised more than $120M, has acquired a number of small engineering and manufacturing firms, including battery designers, precision component manufacturers, composite manufacturers, and more, leveraging expertise and economies of scale to streamline marketing, product commercialization, supply chain management, and manufacturing processes. Wilder Industries, which initially started out as a manufacturing robotics automation company, has since acquired several manufacturing and aerospace component companies across CNC machining, sheet metal fabrication, and UAV manufacturing, and is working to equip these acquired assets with more efficient manufacturing technology. Amca has raised more than $75M of VC funding to acquire legacy aerospace component manufacturers including a pressure switch company, an electromechanical avionics company, and a magnetics company, bringing modern manufacturing techniques and business practices to these legacy businesses. In March 2025, Jeff Bezos announced that he is launching a $100B fund to acquire manufacturing firms and transform them with AI.

Factory as the Product

Rather than selling individual products, acquiring legacy facilities, or retrofitting incumbents with new technologies, a number of startups are pursuing a “factory-as-product” model – building next-generation factories designed to produce custom goods for aerospace and defense customers using more efficient, lower-cost, and modern manufacturing processes. In particular, many “factory-as-product” businesses automate processes like quoting, tooling design, and tooling manufacturing, which can take up the majority of time needed to manufacture complex parts.

For example, Hadrian has built a large, software-defined CNC machine shop that uses automation to improve margins and reduce lead times. Its proprietary software streamlines CAM programming, quoting, and quality inspection, while integrated robotics handle material movement across the factory floor. Rangeview has built a factory that uses advanced investment casting techniques, centered on 3D-printed molds, to produce precision parts for aerospace and defense customers. Its software automatically designs and prints casting molds, while integrated robotics automate much of the physical casting process.

Atomic Industries uses AI and generative physics simulation and modeling to efficiently design and build optimized molds for injection molded parts crucial for automotive, aerospace, and defense industries, significantly reducing tooling costs (which can run $50K+ per mold) and lead times. It operates a vertically integrated “factory-as-product” business model where customers purchase injection molded parts directly from Atomic Industries.7 Layup Parts sells composite parts to aerospace and defense customers, leveraging techniques like automated quoting, generative tooling design, automated layering, and factory robotics to reduce lead time from several months to several days.

Contractor-Owned Contractor-Operated (COCO)

Several startups are exploring contractor-owned, contractor-operated (COCO) business models. Rather than selling a product directly to the DoW, COCO businesses maintain ownership of their products and operate the products on behalf of their customers. For example, consider an ISR8 drone use case: in a non-COCO model (also known as GOGO – government-owned, government-operated), the DoW would purchase an ISR drone, and U.S. military personnel would fly and operate that drone. In a COCO model, the vendor retains ownership of the drone and operates it on behalf of the military customer, delivering ISR data to the DoW rather than selling the drone itself.

This business model is attractive for several reasons. First, DoW does not need to spend the money to purchase and maintain an expensive platform. Second, DoW does not need to spend the time and resources to train their own personnel to operate potentially complicated technology. Third, it allows the DoW to have access to the most cutting edge technology, without going through complicated contracting processes every time technology is updated. Because the contractor owns the underlying technology platform, they can continuously update the platform (or even replace it entirely) with the latest technology and capabilities without needing the DoW customer to purchase the new technology system.

Several startups have already begun operating COCO services — Shield AI and Aerovironment were both selected for a Navy ISR drone COCO contract. Earlier stage startups, like Atropos, are also exploring this business model for aircraft (Atropos “is building the future of autonomous flight as a vertically integrated, design-build-test-and-operate service”).

Similar to COCO, some companies leverage a contractor-owned, government-operated “as-a-service” model. Vector Defense, for example, offers “modern-warfare-as-a-service” by integrating hardware and software to develop attritable, autonomous systems, and selling those systems “as-a-service” under operations and maintenance (O&M) services contracts.9 Instead of selling drones outright, it sells a subscription that provides continuous access to drone capabilities, replaces attrited systems, and keeps fleets updated with the latest technology. Beyond hardware, Vector delivers upgrades, training, and tactical integration, drawing on field insights from active conflict zones to refine TTPs and operational effectiveness.

“Americanization” Companies

Today, significant military technology innovation is happening outside of the U.S. on the battlefield in places like Ukraine and the Middle East. However, generally, outside of a handful of exceptions, the U.S. military does not purchase technology directly from foreign companies, unless that company has a U.S. subsidiary. Often, when the DoW is interested in some foreign capability, U.S. defense primes establish teaming agreements with foreign companies to bring that capability to the U.S. For instance, in 2015, Kongsberg and Raytheon announced a teaming arrangement to bring the Norwegian RGM-184A missile to the U.S.

Several companies have entered the market to procure battle-tested technology from live conflict zones abroad (most notably Ukraine and the Middle East) and adapt that technology to the U.S. military by helping shape doctrine, tactics, techniques, procedures, and training for these foreign-developed systems. For instance, Revenant Technologies, which is founded and operated by U.S. veterans, partners with select foreign technology firms with battle-tested systems, enables U.S.-based demonstrations, pilots, and training, for those systems, and manages U.S. sales, program integration, and on-shoring of manufacturing for those systems.

Holding Companies

Unlike most traditional VC-backed technology startups, several young national security startups have structured themselves as holding companies. A holding company is a parent entity that owns controlling stakes in other businesses (referred to as subsidiaries), typically without managing their day-to-day operations. For example, Alphabet is a holding company that owns multiple subsidiaries including Google, Waymo, Google Ventures, Nest, and Google X.

Holding companies are common in the defense industry. For example, L3 Communications Holdings, which later merged with Harris to become L3Harris, the sixth largest U.S. defense contractor, was initially established as a holding company to acquire and hold certain business units from Lockheed Martin in the 1990s. L3 Communications managed a wide range of businesses across training and simulation, command and control, communications, intelligence, surveillance and reconnaissance (C3ISR) systems, ocean products, navigation products, and more.

This model offers several advantages: legal separation between subsidiaries limits risk while the parent company centralizes functions like legal, compliance, IT, and facilities to reduce duplication and create leverage, especially valuable in the complex regulatory environment of defense contracting. It also simplifies government engagement by providing a single counterparty instead of many small vendors. Scale further improves access to capital and purchasing power, allowing the parent to raise debt and equity more efficiently, secure better vendor terms, and achieve manufacturing economies of scale. Finally, consolidated finances provide flexibility, enabling profits in one business to offset losses in another and allowing the parent to redeploy capital from mature units into newer efforts without relying on external funding.

Holding companies can provide scale to more market-limited business. Many defense products are crucial for the warfighter, but have limited market sizes that make them unattractive for VC investment. Ultimately, VC investors are looking to invest in companies that can have multi-billion dollar exits (either via acquisitions or going public) within a 10-12 year time frame – a model that most defense companies do not fit.

In response, a new class of startup holding companies has emerged, assembling portfolios of smaller, mission-critical businesses whose combined scale can meet venture expectations. A leading example of this new wave of defense holding companies is Valinor. Valinor has raised over $85M and is developing a suite of defense products including a mobile modular medical system, modular charging nodes for unmanned systems, and an augmented-reality platform. While each subsidiary maintains an independent engineering team, Valinor centralizes functions like defense business development, go-to-market, compliance, security, and marketing, allowing its portfolio companies to share infrastructure and scale more efficiently.10

Conclusion

In order to successfully deliver the technology needed for the U.S. to remain competitive in the face of peer adversary competition, startups must be creative in how they structure their businesses to deliver value to customers. Like the legacy defense primes before them, national security startups cannot rely on traditional VC-backed business models like SaaS to succeed with national security customers. Instead, they must adapt to the realities of a regulated, single-buyer market, experimenting with models that better align with how the government and defense primes buy, fund, and deploy capability. The companies that win will not just build superior technology; they will design business structures that navigate procurement constraints, accelerate adoption, and ultimately translate innovation into fielded capability at scale.

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A modern defense and government technology company requires a modern corporate infrastructure. We are at an inflection point in the defense and broader government technology industry - finally, technologists are flocking to build for the mission. Engineers do not just want to build e-commerce solutions and social media apps. They want to build drones, s…

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2 years ago · 39 likes · Valinor Enterprises