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.
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IO. OK, Ismael, welcome back.
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.