Solving The AI Black Box: ZK-Proofs in Defence Tech

Welcome to The Epicenter, the show which talks about the technologies, projects, and people driving decentralization and the blockchain revolution. I'm Sebastian Kucho, and I'm here today with Ismail, CEO of LaGrange Labs. LaGrange believes that there is a gap in cryptography within national security and defence. We realized there was an opportunity to take things that were developed in crypto that are frontier hard tech and to purpose that outside of crypto.

The zero knowledge proofs provide this way to glimpse inside the black box to determine that the output that you got back has come from the correct model with these set of correct inputs which allows you to determine why the decision was made, under what circumstances decision was made and under what circumstances decision should be adjusted or things should be changed going forward. I'm here today with Ismail, CEO of LaGrange Labs. Hey, how are you doing? Thank.

You so much for having me, Sebastian. I was on Epicenter recently and there's been so much exciting progress we've had as a company since then. I'm so excited to be back and talk about a lot of that today. This episode is brought to you by Nosis, building the open Internet one block at a time. Nosis was founded in 2015 and it's grown from 1 of Ethereum's earliest projects into a powerful ecosystem for open user owned finance.

Nosis is also the team behind products that had become core to my business and that are so many others like Safe and Cow Swap. At the center is Nosis Chain. It's a low fee layer one with 0 downtime in seven years and secured by over 300,000 validators. It's the foundation for real world financial applications like Nosis Pay and Circles.

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You've been on just a couple months ago, but so much has been happening over at the grounds that I want to get you back on to talk about some of the interesting military and industrial applications for ZKML that you guys have been working on. A lot of like exciting partnerships have been announced.

But I guess like before we before we start like kind of bouncing off of that conversation from from July where we talked about deep proof for folks who have who have not listened to that episode, we should go back and listen to it. Let's maybe, you know, set the context here for what is LeBron building and what is deep proof and what are you building in the space around ZK machine learning?

Yeah. So LaGrange likes to think of itself as the preeminent company for frontier research in applied cryptography across both commercial and national security defense. And what that means is we are uniquely positioned to build 0 knowledge proofs, things like fully homomorphic encryption and then consensus in a way that no other commercial company has the capacity to do really across anywhere in crypto or or defense right now.

And so the the tech we build, the core of this is something called deep proof, which is a zero knowledge machine learning library. Effectively, it takes a model. Think of an arbitrary machine learning model all the way from an LLM to a computer vision model used in a drone, or to a simple MLP used decision. The movement of assets on chain

and we're able to do 2 things. We're able to firstly prove that that model has executed correctly by effectively generating A0 knowledge proof of the correctness of the execution of that model. The same way that AZK roll up proves that all the transactions executed correctly, ZK machine learning proves that AI executes correctly. And secondly, we can do this over a configurable set of private inputs. The model can be private or the

input data can be private. And this is a very powerful feature because it in bakes privacy into how AI can be used across both commercial and then obviously government defense and national security settings. Privacy of AI is a very, very big question. And a lot of the incumbent attempts to add privacy to AI are based on you know, air gap systems, sequestering data, sequestering cloud environments

but or hardware based security. But they aren't based on 1st principles, innovation and cryptography. LaGrange takes the approach that it's going to go to 1st principles, it's going to rebuild the mathematical fundamentals of how we can bake privacy into AI, and then we're going to purpose it as we do across dual use applications. Let's let's maybe just kind of talk about one of the use cases that people are most familiar with, which would be like chat

bots, right? Talk to Chachi, BT or Gemini and how, how the inference is generated in in those cases versus say some of the, you know, other more private or privacy preserving chat bots like like Venice, for instance. And, and contrast that to some like to a first principles approach, because it sounds like what you're saying that those systems don't necessarily use a first principles approach. Is that correct? Yeah, I think Venice uses Tees.

I mean, Venice is like a, they take open source models, they deploy that on a network and they make it private, which that's great. I, I think that's fine for some people. You're, you're not going to be having a drone swarm that needs to maintain privacy over computer vision inputs. That's using Venice, right?

You're not going to have an LLM used for, you know, by a field operator to determine a plan of action for some type of defense purpose that uses Venice. These are interesting curiosities that I think are built by crypto companies, but they tend not to be commercial applications.

I would argue that for something like Open AI or Enthropic or DeepMind to actually take one of their closed source proprietary models and add privacy on top of that, it requires a solution that is low touch, doesn't degrade performance and doesn't result in the underlying model IP being leaked, right. If you deploy Open AI's model on Jema, sorry, on Venice, you're effectively just going to just lose all the IP on that. The way it's the biases, the architecture all to be public,

right? It's it's a very different question, right? So if you want to use Frontier closed source models, you have to be able to bake cryptographic security in at the level of the model developer. And that's what 0 knowledge machine learning does. It lets you add privacy on top of models and lets you add verifiability on top of models wherever they're they're deployed. So just just so we understand this correctly, a a model that has ZK properties has to be

trained. So from the ground up, it has to be. Trained as AZK model, you can take an existing open source model and make AZK. No, that's not what I'm saying. What what I'm contending is that you don't want to use an existing open source model because the performance of existing open source model is not good. The majority of commercially interesting models are not existing open source models,

right? Most people when they, they use chatbot, they're using clawed or they're using, you know, O5 or they're using, you know, Gemini, they're not using Gemma and GBT 2, right. Those are interesting applications. If you really want to ask your LLM something you don't want the world to know, but you, you know you, you so care about privacy. What you're asking that you, you, you, you probably shouldn't be asking these questions to

begin with. Generally where I see ZK machine learning useful is in applications where these are frontier proprietary models that need to protect both the two aspects of privacy, both the privacy of the person who wants to input data sent in and the privacy of the model. OK, interesting. So what you're saying is that a in order for AZK model to be any model, any model, right? Any model? The efficiency of that model and it's ability to be to be a private model is very much tied

to how it's trained. Or is that? Are those two things uncorrelated? Yeah, I, I think for any AI model in the world, right, if you develop the best AI model to do something, you're not going to open source it, right, Unless you run a cherry. But you know, if, if you develop a great generative model for video, right, VO3 or Nano Banana two, you're not going to open source that you're not going to send it over to China, you're not going to give it to your competitor.

You're going to deploy a, a website and you're going to have an API. And behind that API, your model is going to sit on a server and people are going to come in, they're going to ping it and you're going to charge them for credit. You're not going to give it to them for free. So you effectively need to protect privacy of two things. We're going to talk about AI. You need to protect privacy of IP intellectual property for the actual model. And you also in many cases want

to protect consumer privacy. So the the data that someone is sending to that model, a lot of the private AI solutions are just taking open source models that are publicly available and deploying those in trust execution environments or in a

cloud somewhere that they call air gapped. And they are not actually adding privacy on top of commercially relevant models, right? OK. They're they're not taking the Frontier models, Nano Banana 2, VO 3 clawed and they're not adding privacy on those. They're taking Gemma 3 with a billion parameters that nobody's going to use for anything, and they're deploying it as a curiosity. How much of ZK machine learning and the the ability to do privacy within models is

hardware dependent? None of it, none of it. It's it's not I mean you can you get faster inference on GPU and you get faster proofing on GPU, right. So there there is obviously some questions of hardware that but. What I meant by hardware dependent is like how much of that is dependent on the availability of Ttes? None of it, not at all. No Zeke. And machine learning is entirely just. Mathematics got it. OK, which which?

Which is arguably why it's more interesting because it doesn't require any specific hardware to have provable AI that's also private. Precisely interesting. OK. So a lot of the focus around Lebron's and what you guys were sort of like been focused on in the last couple months and certainly the like announcements that front of the company have been around military and industrial applications. Why is there such an importance here within these to the very highly sensitive applications

for AI and privacy? What, what's the, what's the kind of edge that you guys have there? I love this question. So LaGrange for the majority of its history has been a commercial focused company. We've developed privacy solutions and and cryptographic assurance for AI usage as well as other types of computation that that were relevant database computation early in our history for solely commercial applications.

There's a belief that I have though, and this is something that I've seen talked about by Alex Karp and the Technological Republic. He's talked about publicly by Palmer Luckey, a lot of businesses pursuing incremental and trivial applications of technology that marginally improve consumer life instead of materially influencing national security defense, right? This is a generation of businesses that have been, you know, developing chat apps.

It's a generation of food delivery and, you know, marginal convenience improvements in in the Bay, right? It, it, it's the, the generation of engineers who were brought up to build a slightly better delivery app instead of trying to close the hypersonic missile gap with China. It, it, it, it effectively has separated arguably the most competitive and technologically advanced area of this country from all areas of national security and defense.

And this has been like what US tech was for the better part of 15 years right before Andrel and Shield and some of the the more American dynamism and and reindustrialization companies have started raising very large rounds the last 3-4 years. There was very little venture financing that deployed into anything in traditional sectors that was doing aerospace, defense or national security. And that's not a successful

state of a country, right? What we require as a nation is for the most innovative people of that country, the people who build the best technology, who push the state-of-the-art, who compete to to develop the things that make our nation special. To purpose that not just to improve the the quality of life of people in Silicon Valley who want their food delivered on a drone, but to purpose it to increase freedom, to increase the hegemony of the United States globally.

And to do that you require dual use applications of frontier tech. And so when I look at crypto, I see very much that the state of Silicon Valley 10 years ago, right? We've had an entire industry that put a billion dollars into frontier cryptography development for zero knowledge proofs, fully homomorphic encryption and consensus well over a billion. If you include consensus, probably a billion for ZK couple

100 million for FHE and funding. And maybe you know, 5-6 billion for things that are raised with novel consensus, maybe 3 or 4 billion. But all of that was purpose for one thing in crypto. How can I launch a more

efficient token? And so we've put effectively the the America's best in cryptography, consensus mechanism design, distributed systems, all of this not the building things that make America safer, make America more secure, allow us to dominate internationally, but we've instead purposed at the things that just flip a quick buck for

investors wanting a token. When we looked at this, we realized there was an opportunity to take things that were developed in crypto that are frontier hard tech and to purpose that outside of crypto, not just to commercial, but for dual use for defense, national security, military and government, and and that's why LaGrange started doing this.

We have 3 professors in applied cryptography in the US, Our research teams led by Babis Papa Monttu, who chairs the cryptography department at Yale. We have 8 PhDs in applied cryptography on our team. We have multiple patents we file

in the US on novel proof systems. We have 5 or 6 papers we've authored on the company's history. And yet like many other companies, all we were purposing is for was launching tokens. Now I think there's a tremendous commercial opportunity to instead purpose that not just us but the whole industry, the things that are a lot more important. Yeah. I mean, I think it resonates with me this this idea that a lot of our a lot of the innovation in tech in the last 20 years has been around

increasing convenience. Maybe this is also sort of a reflection of just like how cheap capital has been in the last, you know, 20-30 years and also like a relatively peaceful state of the world in the last 20 years. I mean, certainly since the Cold War. Debatable. Yeah, right.

I mean debatable, but I mean like people's everyday sort of like considerations were not were no longer whether or not, you know, a nuclear bomb would be dropped on their heads where which argued it was the case, you know, for like a generation before us now that that has that that the world dynamic is definitely different now. And I think that the that is reflected in the fact that there's more capital for, for military and industrial applications.

But the, the return profile on those, I think are much different than the, you know, the delivery app type of things for investors. How do you think that's going to change? I mean, already I think we're seeing that that change in, in, in capital funding in crypto.

How do you think that's going to change the entire sort of VC landscape where you know, the, the time to return is probably a lot longer in a defense company that's building, you know, drumstorm technology over, you know, delivery app that's going to be sold at private equity in like a couple years or maybe sold off on like the next funding round? Yeah, I I would actually disagree.

I, I think that the reforms in procurement have allowed defense and national security focus companies to see substantial growth at rates that are comparable to even the delivery apps, right? You look at public markets, Palantir has been one of the fastest growing companies on the

equity markets. I mean, Palantir is a comp of, you know, what you want your public company to do and you compare Palantir's performance over the last, you know, let's say 48 months compared to Uber, DoorDash and other more convenience oriented companies there. There's there's no question that you would you would you would want your money parked in Palantir. It's it's a hard tech problem, right? You look at another dual use company, SpaceX, right?

SpaceX is IPO and it's going to be one of the largest IP OS of all time, right? I'm here people saying 1 1/2 trillion has to be the valuation, right? I mean, these are, these are incredibly hard to fathom outcuts, right? I mean, Uber is what like a 50 billion market cap business? And you know, Palantir, I think is over a trillion, SpaceX could be over a trillion and the rolls going to are going to come out probably in, within 24 months.

And it's a once again going to be a fantastically large business. Their growth has just been tremendous. I I think the, the fact that the prime defense contractors, the, the Lockheed, the Raytheon, the Northrop Brummins, the General Dynamics, the General Atomics, they have much more competition now and is a much larger willingness within the Department of War and other agencies to purchase technology and to, to, to solicit bids for technology from non traditional defense contractors.

And that is going to mean significantly more innovation, significantly more alignment between US private sector innovation and U.S. military and US defense capabilities. I think that just starts with, with the willingness of any administration to to, to purchase defense relevant technology from from non prime contractors. We've seen that, that, that that is a trend. So I do think the return profile there is going to be very, very

strong. And I think that's why we've seen so much investment in this, right. I think last week it was, I can't remember the name hypersonic missile company raised that was closing the gap, you know, 3400 million and you don't see rounds like that for things outside of American dynamics that are just, you know, foundation models in Web 2 venture right now.

As as as you were talking, I was looking at the market cap for Lockheed Martin and Raytheon. I had no idea what these were, but yeah, Raytheon is like 240 billion, Lockheed's 100 billion. Kind of interesting though. That's. That software companies in the space like Palantir are, you know, chasing valuations that's like over a trillion. Or I think 4:50. I just looked it up. I was wrong. Oh, OK, 450 billion for yeah, 450 billion, yeah for Palantir, Yeah.

While the, you know, the guys building jets are, are far below that. Like, I guess like in terms of the, the, you know, the geopolitical aspect here. You, you wrote somewhere, I think it was like a corn market cap article. I've got the quote here that was interesting. So superiority and software has become the most efficient way to sustain US leadership worldwide.

I mean, I think that's true. I mean, to a large extent, a lot of that software superiority has gone into things that are maybe more convenience driven and, and you know, now that the world is changing or like people are, there's a reaction to that.

And that is going now into more military and industrial applications, you know, to the extent that that has been possible because of the availability of chips due to, you know, somewhat from the commercial relationships with China and you know, by extension Taiwan. The ability for the US to be superior in software has been dependent on its on access to chips.

And that access is now, you know, possibly compromised in 2027. Yeah, I, I, I, I would say broadly that the, the, the ability to be superior in software does come out of chips, but it also comes down to energy independence, which which China also has risks if they, if they

move on Taiwan, right. The majority of Chinese oil moves through the Malacca Strait, which you know, effectively if, if, if Chinese energy was was significantly impaired, having large amounts of, of chip access or would not necessarily improve their, their global position, software or AI

development. But yes, it would substantially harm the United States position in AI development if, if, if chip access from Taiwan was significantly reduced or cut off and chip manufacturing in Taiwan was cut off. And I think there's there's a recognition of this and there's efforts that, that both private companies and then obviously the US government is taking to improve domestic chip manufacturing and build foundation for US electronics

chip manufacturing. But it's very unlikely that this will catch up to the capacity that that that that Taiwan can produce within a reasonable period of time. There's there is, there is the, I mean, there is like the time it takes to reach capacity, which I think, you know, we're probably talking about, you know, in, in the decades, decades. But there's also access to the rare minerals that make those trips possible to produce.

And to I think mostly are Chinese is, is the West kind of by that by by that fact doomed long term unless there are there are, you know, sustainable business relationship with China, commercial relationship with China that allow them to purchase these these rare metals and and also manufacture chips at a scale that allows us all to grow. No, I, I would, I would go the other way.

And I would say that if China is not able to affect a military action against Taiwan by 20-30 and they've, they've, they've publicly said 2027 is the, the, the point that they're going to do this. And most intelligence analysts expect 2027. China has a substantial risk of never being able to and therein significantly reducing its prominence internationally. The Chinese naval's effectively bottled up in the South China Sea.

They're blocked off by the ring of islands consisting of Japan, Taiwan, and the Philippines. All of their oil moves to Malacca Straits. Their population is aging. Within 15 years, the average population in China is going to be the same as the average population in Japan, which is not very good. Chinese equities have not been

doing well. The problem with the SEO system, with state owned enterprises that that that China has is that they are they are not as competitive at implementing and developing new technology as US counterparts. They don't have an open and free market economy. There is brain drain from China. There's a lot of intellectual talent in China that would rather study and and work in the United States in a free country than they would within the hierarchy of a state owned enterprise in China.

I actually don't think that China's position is necessarily very strong. We also have seen Chinese military equipment fail. Pakistan was mostly using Chinese military equipment during the recent conflict with India and most of the equipment didn't work. With the recent strikes by Israel on Iran, most of the anti aircraft systems that were being used by Iran were Chinese and

most of them didn't work. So there's levels of grift and corruption and inefficiencies in the Chinese system that are intrinsic to how the Chinese government structures control and enterprise. And I think a lot of that boils down to historical issues, issues over freedom and issues over free markets. And so I, I think that the reindustrialization narrative that the US private sector has been financing very aggressively.

So building US domestic manufacturing capacity, building AI manufacturing, building the capacity to produce the mined refined rare minerals in the United States, building chip production in the US, building drone production in the US, building decentralized manufacturing so that they can tested time. There's a very hard to knockout manufacturing base plus energy independence, small modular

reactors. US is very far ahead, I would argue, in the economic structures that will allow it over significant periods of time to innovate and outperform China. One area that that I think is, is, is, is kind of interesting is this is well, I mean, I guess a military development that has really accelerated since the war in Ukraine. Is, is, is drone technology and, and, and particularly they the use of a low cost drones to achieve like all, all of the, sort of like all the, all the

positive advancements we've seen in, in, in, in Ukraine there. And, and it seems like, I mean, from where I'm sitting, the US approach has been to kind of like throw lots of money at this problem and with, with like no boots on the ground, right? Whereas like Ukraine has been actively pursuing this problem and, and demonstrating that they're able to like, utilize

cheap commercial drones for all sorts of military applications. Yeah. What's the like, to what extent is the bottoms up approach that we're seeing in Ukraine a more effective approach to, to generating outcomes over like spending billions of dollars on like new drone technology that may or may not be, I mean, maybe used in conflict, But like by the time that technology gets used in conflict, there's been like billions of dollars spent on that technology.

We'll we'll only know if it's effective, right? Like when it's been used. See what I'm saying? Yeah, I, I, I would say it's slightly different than that, right. So a lot of the drones being used in Ukraine right now are repurposed civilian drones,

right? So DJI drones like the DJI Mavic and a lot of these are FPV drones that are being, you know, flown by an operator like the racing drones and FPV view that they strap the RPG on top of and in contested environments strap a fiber optic cable on. And so these are like very unsophisticated, but very effective improvised munitions, right, which are, I think in many ways a great piece of evidence on how warfare is evolving and how battlefield

strategy is evolving. And I would argue that U.S. drone doctrine has historically prioritized very large platforms, right? The US Predator drones and the Reaper drones have been highly effective at pinpoint strikes on targets in the Middle East on the War on Terror. You know, the, the, the, the General Atomics, Reaper platforms and Predator platforms.

Now we're seeing both Air Force and Navy prioritize new CCA initiative, the Collaborative combat aircrafts, which are effectively unmanned UAVs that pair with existing US fires.

So you'll have an F35 and you'll have 5 CCA drones, which are very large combat aircrafts flying alongside of it, which is going to hopefully increase the capacity of, of, of US aerial programs to defend and to affect missions in highly adversarial environments, both in terms of combat with other aircrafts and then combat on, on ground strikes. But so it is true that a lot of the, the US platforms are, are much larger than the platforms that are being used in Ukraine

right now, right? The the, the majority of the, the programs, the the shield, AI expat and V bat, the Andrel Fury, the Andrel Omen, these are like group three, group 5 UAVs. You know, the, the US has groupings of what they'd consider UAVs. Most of the ones used in Ukraine right now are the Group 1 and the group 2 UAV. So very small, managed by a single operator.

Now it is true that the US has less of a of a small man, less of a manufacturing capacity in small drones than China does and also has less competitive swarm technology than China does. Which is actually one of the problems that LeBron just is currently working on, which is how can you have swarms that operate in highly contested environments when base links are

knocked out? How do they effectively form consensus and reorganize themselves to be able to affect emission outcome even if half the drones get shot down, lit on fire, jammed, etcetera. But I would say that US battlefield doctrine is changing and there's going to be a prioritization of both very large platform and small platforms. Hag Seth has talked publicly a lot about that. I think there's needs to be catching up that the US has to do here.

But I I wouldn't say the US isn't learning from from the Ukraine more. I think it's probably one of the, the areas that that the US is studying most aggressively. It's also worth mentioning that that probably the one of the most effective and under mentioned pieces of, of battlefield technology in the war in Ukraine is electronic warfare, jamming things that knock out drones. And the US has substantial work that it's doing in this end,

right? So how can you effectively knock down large amounts large swarms of of of unmanned vehicles as they try to effect some action on the battlefield? So let's talk about verifiable AI for defense and some of the work you've been doing with with Andrel. You, you announced recently that that the Grange was building within their Lattice SDK, which I'm not super familiar with.

Maybe we've been talking about what what that SDK is and and also how LaGrange is is integrating with an SDK and and and and how how relevant is DKML in that context? Yeah. So to take a step back, one of Lagrange's core theses is that there is a gap in cryptography within national security defense. Effectively the the, the modern paradigms for implementing cryptographic security and defense systems are based on air

gap, air gap in the system. So just private clouds, private networks, private links and the assumption that these these communication links can't be broken since they're encrypted, which broadly is true. Where we believe this type of framing falls down is in highly decentralized combat environments, which is what we see in modern drone warfare.

When you have hundreds, if not thousands of different drones across potentially different manufacturers that need the capacity to form consensus or form agreements on the state of a battlefield. And then be able to effect action subject to the onboard AI or whatever command and control system provided them with, with a mission objective.

And so when we look at that problem and it's very analogous in our mind to the problems we see in crypto, which is you have 10,100 thousand nodes globally that need to reach consensus in the state of a shared Ledger very, very quickly based on a set of transactions that could be submitted at any single node at any point in time. It's very similar in our view to to the question of how do drones reach coordination in a tested

environments. And so a lot of the technology that LaGrange is building orients itself around adding enhanced cryptographic security to the use of autonomous weapons and drones in highly adversarial environments. And so that's things as simple as proving the computer vision models that run on top of drones and edge devices, or proving the correctness of command and control systems and AI that runs

on command and control systems. So Anderl's Lattice SDK is an open SDK that is developed by Anderl to coordinate across assets and drones that are built by Anderl as well as by other companies, wherein drones can interface, coordinate and allow an operator to control those drones and complete downstream mission objectives. This is everything from their Seabed Sentry and Uvs all the way through Omen and Fury and a lot of their their larger airborne platforms.

And So what LaGrange built was a proof of concept demonstrating how command and control system that made relevant battlefield decisions with AI could be made accountable and provably correct. We've open sources, we've released it publicly, and we think this is hopefully the start of enhancing how cryptographic security is going to be used in defense relevant programs that involve autonomous systems. You you guys have written about accountable autonomy and and why that's important.

So what is that? What does that mean and and and why is it relevant to to the context of AI and and defense? Yeah. There's a few places where accountable autonomy really matters, right? If your baseline assumption is that in a contested environment you only have crash fault tolerant requirements, so there's no Byzantine actors, then you you assume that the drones on board AI can't be compromised.

So if the groans onboard AI can be compromised and in theory someone can jam the thing, they can, they can spoof communication with it, take it over, update the software. Then it's very, very important that you can prove the correctness of the model running there such that the output that you're getting back, if you know another UAV is communicating and, and, and, and, and interacting with, with the, the first asset is provably correct, right? It's the same thing as crypto, right?

You have, how do you verify that an AI model was run correctly on the blockchain? We can't have everybody rerun it. So you have to have the person who ran it prove it. That's very true. If you're assuming Byzantine assumptions. Now it's debatable if you should assume Byzantine assumptions in a war zone or not. We're not going to get into that. There's some people who think you should, some people think you shouldn't. Someone once said to me, we can trust the hardware.

The hardware blows up, so there you go. But now, if you have a command and control system and you want to ensure that the outputs that are being consumed by the end device or the edge device are correct, that's another place where you need 0 knowledge machine learning.

Effectively, how can you ensure that one system that's controlling a bunch of downstream assets or one system that an operator is using and one AI model of an opera is using to control a bunch of downstream assets is in fact only making for examle kill chain relevant decisions as a result of the correct AI models being used in the command and control system? That's one of the use cases that we think is the most relevant, right?

So assume you have an LLM that's job is to coordinate submission objective. It's supposed to take in a bunch of sensor input from a bunch of UU VS and then coordinate some substantive UU VS to go in, you know, effect some some battlefield action. In this case, you would require that the Uuvs that are affecting that battlefield action would only affect that battlefield action if there is accountability that the correct model using the correct inputs

has in fact recommended action. Otherwise, the kill chain decision shouldn't be made unless the correct chain of command, the correct chain of of both data custody and inference

custody has been followed. This is when we talk about accountable autonomy, that autonomous systems are not just operating as these black boxes making, you know, random battlefield decisions, but that they're made entirely with an operator's decision or entirely by the decision of a model wherein there's hard constraints put in place by an operator. Oh, sorry, what's a kill chain decision?

A decision to so to affect, let's say, a kill, a kill action, to to, to fire a missile, to to to to, to use some armament for a battlefield purpose. OK, and in and in this context? I guess the assumptions that the AI is providing that kill chain decision and then yeah, an operator is deciding to execute that decision or not. That's often how it works, right? So it's a structure of multi stage process describing how

adversaries can conduct attacks. So you know under what circumstance, under what assumptions, under what sensor feed inputs should a autonomous system engage in an attack, right? Should an operative in a loop should just entirely be AI? If it's entirely AI, this is a place where we highly believe that verifiability is paramount. Right. I I guess like so I'm not super filming with this, but I I suspect there are some sort of Geneva Convention laws that relate to the use of AI in

military applications. I'll have to check. I don't believe. Yeah. I don't like let's let's let's let's assume that you know, there are or there may be like in the future, some Geneva Convention rules around like the use of AI in war. To what extent is provability and, and to what I sense is provability and accountable autonomy important then in ensuring that decisions made by AI are in fact lawful?

And I guess this this could also extend maybe to, to domestic use of AI in, in policing, for example, it's not only in the military context, but also inside like national like sort of policing. Yeah, this is a great question. So I would say that the the the use of autonomous weapons. Is very, very contentious right now, especially autonomous weapons that don't have an operator to loop.

So effectively a say a drone that can make a kill train decision without an operator saying, hey, yes, I approve this to occur. The USI believe doesn't have autonomous weapons that can make kill train decisions about operators being in the loop that that as part of U.S. military doctrine, there always must be an operator in the loop.

That's my current understanding. I don't know if that's changed, but you know, to be honest, and Andrew has talked about this shield has talked about this, the use of autonomous weapons is not a new thing. It just feels new, right? You know, when, when Caesar was, was fighting versus Gedricks, he dug a spike pit surrounding the the, the Gaelic army, right? That that is nothing more than autonomous weapon. You walk and you fall into the spikes, right?

They don't have to be there right there. You can go anywhere you want. The middle of the night, there's a spike that you can step on, right? World War One, World War 2, the use of mines, all of these are autonomous weapons. You get too close to a mine, you could be a school bus, you could be a family of, of, of refugees and horrific, there's horrific implications. So the, the idea that you know, an armament now is has a computer vision model that says, hey, if this is a Russian tank,

yes, explode. If this is a school bus, don't that that's a net positive that's substantially better the state-of-the-art of a of a mind that doesn't have any type of AI usage right. But just the the question over, hey, we're going to we're going to to outright ban the use of AI and weapons is is is actually much worse than the alternative. I'd much rather that a missile has on board guidance to to reroute and avoid hitting the wrong target, right.

If it's if it's firing at a target, then the, the, the comms link is jammed. I'd much rather correctly rearm and hit the correct target instead of, you know, hitting a school. All of this stuff I think is super important. The more precise weapons can be and AI is a is incredible way to increase precision of weapons.

The the, the, the less collateral damage and casualties. You'll, you'll, you'll see. And so that that's something I do firmly believe, but I, I, to your point, I do believe that there has to be conventions and organization around what or how these decisions can be made such that they're lawful. How is auditability conducted? How can we quickly audit to make sure that all of the use of autonomous weapons are following a predefined several rules, right? Is the AI operating correctly

right? If if a weapon missed, is it the fault of an operator? Is it the fault of the onboard AI? Is it the fault of an adversary? Can taking over the system? Is the fault of the system being jammed or having a kinetic interference? Now all of this stuff is super important. And when you add AI, you open up a bunch of questions over what verifiability and auditability of AI means. And that's where zero knowledge machine learning is super, super

important. And that's been one of our very large pitches that we've made to to almost everyone we can talk to in defense that zero knowledge proofs provide this this way to glimpse inside the black box to determine that the output that you got back that indicated that an action should be taken has come from the correct model with these set of correct inputs, which allows you to determine why the decision was made, under what circumstances decision was made

and under what circumstances decision should be adjusted, the model should be adjusted or things should be changed going forward. Yeah, yeah. I was thinking about this like, as with a lot of things with AI, I think they, it quickly becomes a race to the bottom where, you know, if we have increasingly more, more and more AI in military decision making, then like at the bottom, it just becomes about taking out the other person's AI, right?

If, if we're narrowing every military decision to like, is this, is this a military target? And at the same time you have those like increasing use of AI, then at the end, you're just trying to take out the other person's AI or the other country's AI And, and you're no longer trying to take out any one of their like military or, you know, infrastructure. It's all about just taking out the AI. But I, I would say taking out AI has a lot to do with

infrastructure, right? I mean, I think that's the whole room industrialization. It's the energy grid. It's the the where you train the AI, it's where you run the

inference and data centers. It's it's it's where you I met I. Met Milac area infrastructure, but Oh yeah, I got you of course, like yeah, they're you know, electricity infrastructure, IT infrastructure, but like yeah, at the end of the day, it comes down to that where then I guess like, you know, having having decentralized AI that's able to function autonomously and in a more of a mesh system becomes more interesting.

It does. I want to talk about the token here because, you know I know you guys still have some applications and kind of products that are geared to more towards the more crypto application crypto use cases. But you know, when you're talking to Andrew or or Lockheed about implementing LaGrange technology and the token has a lot to do and a lot to sort of do in the, in the decentralized nature of the of the of the ZK verification aspect of the product. How's that conversation going?

And I mean does the token have anything to do with the military applications or is it strictly for for the crypto related use cases? Yeah. So the token is staked into Lagrange's Prover network with which is a decentralized network of operators that can generate proofs within an auction mechanism that that has been designed and developed by our team where work is assigned into the network. The network generates proofs and sends those proofs back to whoever requested them.

The decision of whether or not you want to deploy Deep Proof or any of Lagrange's technology and decentralized or centralized capacity just depends on whether or not the person in question would prefer one of the other. I would say for all of our crypto use cases, which is a lot, we work with probably every AI X crypto company in the space right now. We've announced stuff with Sentient, Gaia, Mira, 0G, Open Ledger, really all of these crypto X AI companies.

It all is going to be running through and runs through our proven network and where our proven network powers all of the ZK machine learning that we do crypto. Now the defense applications, it'll just depend on the on the decisions that our defense counterparties want to make. I can't promise one way or the other. We have decentralized deployments and our software also works on centralized places as well.

LaGrange Foundation is the foundation of issues of token and LaGrange Labs is a development entity for the the LaGrange Prover network as well as deep proven a bunch of other technology and cryptography and we are very excited to see growth of our technology across dual use purposes, right. Think of Palantir, there's very different deployments of Palantir for public sector and for private sector.

You know, Gotham for example, is a platform that Palantir develops entirely for police forces. Something like Foundry and AIP are really strongly commercial, but also have government application as well. And I think that LaGrange is committed to being a dual use company, which means that we will continue to expand our adoption in crypto, we'll continue to expand our adoption in enterprise and we will continue to expand our adoption

in government defense. What are your thoughts on Europe in the sort of military geopolitical landscape, specifically when it comes to AI driven military technology?

Yeah. I mean, look, I think there's some, there's some great military and defense contractors out of out of the EU, out of Europe, Rafael Talis in France, BA Systems in the UKI would say that these are more legacy style primes and more so than they are, you know, up and coming highly competitive manufacturers of, of, of, of new drone

platforms, new AI platforms. But you know, we know the teams, we've met the teams at, at, at these companies and we have, you know, think they're fantastic, frankly. But I, I would say that, you know, where I think a lot of the innovation is happening in Europe in, in, in modern military doctrine or on drones, obviously in, in Ukraine's, we touched on before, right, Ukraine uses AI and drones.

One of the very useful things you can use for AI and drones is, is allowing those drones to operate in jammed environments, right? So, you know, assume you have a bunch of electronic warfare jammers that are trying to knock out and operate like, right. The, the, the one way you can do that in a very naive way is you just attach fiber optic cable to this. This is what a lot of the FTV

drones are being done. And you know, now, now Russia has launched these like big mothership drones that have like

6 kilometers of, of, of fiber optic cable attached on smaller drones. You know, I personally don't think that that's a very future proof strategy. There's, you know, kinetic issues when you have a bunch of fiber optics attached. But AI provides another alternative, which is you can still jam the operator link. You can have a bunch of electronic warfare jamming it. But if the drone can complete its objective in the last mile without needing an operator link because it has onboard AI, then

it can then it can do this. This is if you remember the the Ukraine operation when they attacked the Russian jets, it took out the Russian jets. All of those drones, I believe were using on board AI since the Russian air base was obviously jamming. And so these models were trained to recognize weak points in the Russian jets. They got close enough and then the jet was able to kind of loiter as required, then be able to, to affect that nation on top of the Russian jet at the weak spots.

And that wouldn't have been possible without onboard AI. So I, I think that there is substantial military innovation

in, in Europe right now. And I think a lot of it's driven by the war in Ukraine. But I'd say like the the world leader right now, I'd argue in drone innovation is the US and China. With the advent of of AI in military and military technologies, it, it feels like a huge turning point to most of the military technologies that we've had up until now that have been mostly kinetic.

To what extent do you think there's this sort of like getaway phenomenon or a runaway phenomenon where when one sort of country or, or military alliance really takes off, the other ones can no longer catch up just because their their superiority that that AI superiority just makes them so much more powerful. Is is there a new sort of dynamic at play here that really changes the trajectory of military advancement in a way that we haven't seen previously? I mean, I think. Military.

Developments I, I think warfare continually evolves, right? And I, I think the, the, the strategies that are employed by nations to counter the strengths of other nations militaries just continually change, right? I mean, the entire US power projection platform often predicates on the US Air Force or US aircraft carriers, wherein, you know, US carrier group can effectively go very deep blue waters and, and project the US authority and US power anywhere in the world.

And So what has China done to try to combat that? Well, they've developed, you know, cutting edge hypersonic missile technology and in the conflict with China. And then Pete Hags has talked about this. So this is not me making it up. It would be very, very hard for for for US carrier groups to be able to combat just a large barrage of hypersonic missiles that were fired by China at US

aircraft carriers. And so if the 16 US aircraft carriers could be knocked out in the 1st 20 minutes of a conflict with China, it would substantially harm EU s s position, right? And so there's obviously a lot of work been done in the US to improve the US domestic hypersonic missile manufacturing capacity as well, right? You look at, for example, the role of tanks in the war in Ukraine, I would argue is analogous to this, right?

A lot of these have been effectively left to become artillery more so than they are now kind of these forward moving heavily armored platforms since FPV drones that cost $1000 in RPG attached can disable and take out these tanks at will. So a lot of these tanks have been now both on the Ukrainian side and the Russian side set back under effectively like mosquito netting to stop drones from getting through where they can lob long range effectively artillery shells.

But they aren't kind of these forward advancing units. And it's an entirely different paradigm. And I, I think this stuff continually changes. So I, I, you look at like what I would argue makes the US so special and what result in the US being the strongest country in the world and for, for hundreds of, you know, a couple 100 years is effectively the, the, the market. It's the fact the US can commercially compete by having a tight alignment between both public sector and private

sector. This is the same story of the US military manufacturing base for US manufacturing base for World War 2. It's the same story of, of DARPA funding research across, across, you know, Massachusetts and San Francisco areas during, during the Cold War. And it's what we're seeing now with, with the manufacturing of, of, of weapons and autonomous weapons in these kind of reindustrialization in American dynamism companies.

When you align these things and you have one of the largest, most productive and most competitive free market in the world that is putting resources behind it, you, you're able to produce stuff that other countries cannot, right? This is, This is why the, the, you know, I tend to be very short on the, the whole state of enterprise durability long term. I don't think you can centrally plan an economy long term. I agree and we'll have to leave it there.

Thank you so much, Ismail, for coming back on and sharing your thoughts on on this like really interesting topic, which I think is only going to get more interesting as time goes on. Unfortunately, I mean, fortunately or unfortunately, you know, war, war continues to be something that happens across the world. And I don't think that's going

to go away anytime soon. And if if the Western world can continue to project its values through through these technologies, then I guess that's a better state of affairs than than the alternative. Yeah, I, I don't think anyone in crypto wants to live in a world where the West doesn't win. It just is not amenable to the quality of life to standard of living and, and the the, the freedoms and civil liberties that that we've grown accustomed to.

And I think there's a moment where there's an opportunity for companies in crypto like LaGrange and hopefully others to assist with ensuring that all of us and as well as all of our descendants can benefit from the same liberties that we have. Thank you.