I think AIX crypto by and large is a scam.
The majority of businesses I see building an AIX crypto are doing
nothing more than trying to launch and sell a token to
unsophisticated retail market participants.
When I started LaGrange, the cost of generating a proof for
AZKEVM was like a dollar or in the range of 10s of cents per
transaction. Ridiculous.
Now it's about 100th of a cent. It allows you to ensure that the
correct model is being used for the inference that a system is
receiving, and it also lets you ensure that there are properties
of privacy over the use of that aim.
There's a subset of public market participants in crypto
who trade charts behaving the same way when they're trading
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Welcome to The Epicenter, the show which talks about the
technologies, projects, and people driving decentralization
and the blockchain revolution. I'm Sebastian Coutier, and today
I'm here with Ismael from LaGrange Labs.
How's it going, man? Doing well.
Thanks so much for having me, Sebastian.
Yeah, so. I mean, we've known each other
for a while. This is actually your first time
on Epicenter though. I think you were probably on the
Interop at some point, like a while back.
We we've done some podcasts before.
I was this. Few times.
First, first episode or episode disclaimer I'm an Angel investor
in LaGrange Labs like to get that out of the way early on,
but you know I'm going to grill you any anyway.
But yeah, let's let's. I wouldn't.
Expect anything different? Yeah, let's let's dive right
into it. So like LaGrange has been a
while, has been around for a while and like you guys like
started as this highly research driven ZK proof ZK project that
has now evolved into all sorts of verticals, including AI and
defy and and scaling. Yeah.
But first, like, yeah, let's talk a little bit about your
journey and what sparked the idea for LaGrange and how does
your background kind of fit in this verifiable computations
narrative? Yeah, that's a fantastic
question. So LaGrange has from day one
been hyper focused as a zero knowledge proof company.
And throughout our history, we have targeted a variety of
problems that we solve with 0 knowledge proofs.
In the very early days of LaGrange, this was things like
Interop or Defy Co processing. And over time, we have scaled
the business, scaled the go to market motion and tackled
increasingly large problem spaces and increasingly large
10's. The current version of LaGrange,
the business that we are today has a very large part of our go
to market focus oriented towards AI.
Both the application of 0 knowledge proofs to improve the
trust and safety of AI in crypto, as well as to improve
the trust and safety of AI in traditional sectors using
advanced cryptography and 0 knowledge proofs.
But as a team, we've always been laser focused on ZK, and that's
been in our DNA. Our chief scientist, Babis
chairs the cryptography department at Yale and under him
we have a very large research team in applied cryptography
with a bunch of world class researchers, people like
Demetrius Papadopoulos, who's a professor at HKUST, Shravan
Trinvasin, Nicola Gaye, and a bunch of great, great people on
the team. And so unfortunately I'm not a
ZK researcher myself. I was a venture investor before
and then I worked in financial services before then leading
digital asset strategy for large insurance company.
But you know, the bread and butter, the DNA of the business
has always been ZK, and that's embodied with the research team
that we've built at La Grange. And how much of the team now,
like if you were to sort of, you know, look at the different
people on the team, how much of the team comes from the ZK
research background versus now like the AI component?
Yeah. So I would say that everyone at
the company is a ZK person. We're a cryptography company, so
we don't build new foundation models.
We don't build LMS or agents or, or really anything besides 0
knowledge proofs. What we do is we apply advanced
cryptography and 0 knowledge proofs to AI.
And so we do have people on the team who have familiarity with
AI. We have people in the team
who've worked at companies doing AI engineering.
But you know, as a business, what differentiates us isn't the
AI talent and the AI skills, it's the cryptography.
And it is, how do we take cryptography and apply that
cryptography to companies that have AI expertise?
The same way that like you don't have to be a insurance expert or
a, you know, consumer expert to build AI that's used for
consumer or or financial service purposes.
You don't, we don't think you have to be an AI expert or
shouldn't have to be an AI expert to build cryptography.
That can be very impactful to AI.
It allows AI companies not to have to deal with cryptography
when they work with us. All they have to do is use our
technology. We don't necessarily they have
to deal with AI either. They just have to be able to use
very simply, a technology that adds a a real zero to 1
improvement in the security and trust properties of what they
built. So there's always been an AI
crypto convergence narrative since as going as far back as
20/15/2016. You know, guys like Trent
McConaughey who've been on the podcast multiple times have been
kind of pushing like different narratives around AI and crypto,
whether it's for private AI or user own data or provable AI.
And you guys are like now at the forefront of that.
How much of this is sort of hype, right?
And what's the genuine, the genuine signal versus noise sort
of thing that people should look at when analyzing or sort of
observing projects, the building in the AI space within crypto?
Yeah. So you know, you started this by
saying you're going to ask some tough questions.
So I'm going to give some tough answers.
I think AIX crypto by and large is a scam.
The majority of businesses I see building an AIX crypto are doing
nothing more than trying to launch and sell a token to
unsophisticated retail market participants.
The, the reality is that there are some things that have been
financed and built in crypto that are actually very, very
relevant for AI. And so one of those we believe
is, is 0 knowledge proofs, right?
It's a technology that comes from academia, but has been
productionized in crypto because of the demand for scalable,
provable block space, right? ZK rollers and because of that,
you know, private capital has flowed into ZK in crypto and
about a billion dollars of venture dollars have been spent
on R&D 40 knowledge proofs in the space of crypto.
Now great, we can scale blockchains more, but what are
the other applications of this technology that we have as an
industry plugged a billion dollars into and AI and adding
trust and safety to AI, we would argue as one of the largest
markets. So it's not that AIX crypto
where we're doing the centralized agents to move your,
you know, to rebalance your yield aggregator on chain.
It is how do we take a fundamental piece of
infrastructure that's useful in crypto and that is a technical
breakthrough financed by crypto and apply that to other sectors
and other areas. That's how we think of where our
business is positioned. Crypto is a capital formation
mechanism. Cryptography is just
mathematics. It isn't a crypto, a crypto
thing, right? Cryptography has existed well
before crypto and it will exist for the entirety of crypto.
It secures the Internet and now it secures AI.
Now where I don't think crypto XAI is a scam is in some very
specific applications such as, you know, I think sourcing of
GPU's from, you know, very large subsets of users who may have
latent compute sitting around. I think is a very interesting
thing the the ethers of the world, the prime intellects of
the world. I also think it's a fantastic
market and AIX crypto for for certain types of agentic things,
right where you want to have like natural language based
wallets, I think is very interesting.
It decreases the user experience or improved user experience of
using crypto. I think those things are very
cool. But by and large, I think the of
the the 100 companies that you see that are announcing stuff
every week building an AIX crypto, maybe two of them are
not scams. Yeah, that I think that
resonates with me. And I think one of the things
that stands up from what you just said is that AI in crypto
is, is not just one vertical, it's different types of problems
that are trying to be solved. You know, whether that is, you
know, scaling access to GP us applying LLMS to user
interactions, or you know, in your case, providing provability
and verifiability to to AI inference.
Those are all like very different problems that use
crypto and cryptography in very different ways.
Let's like maybe dive into the CKML use case a little bit and
like for people who are not familiar with this particular
technology and how you guys are solving some really tough
problems there, like what is what is CKML and how does
LaGrange sort of fit in this, in this use case?
Yeah. So what ZKML lets you do is, is
effectively 2 things. It allows you to ensure that the
correct model is being used for the inference that a system is
receiving. And it also lets you ensure that
there are properties of privacy over the use of that AI.
Now, where is this valuable? Well, most places that use AI
have a remote system where that model is running, that it's
communicating with something else that is dependent on it.
You can think of this in aerospace defense as, you know,
command and control systems. You can think of this in
healthcare as a user who's interacting with a diagnostic
LLM, or a doctor that's interacting with a diagnostic
LLM run by a third party company.
Or you can think about this Encrypto as a user who wants to
generate a bunch of transactions from a natural language prompt
for, you know, buying an asset on Ethereum and bridging it onto
BNB and then swapping it into something else, right?
In all of these situations, you have someone with a materially
amount, a material amount of financial value tied to the
correctness of an AI output. What 0 knowledge proofs lets you
do is to generate a proof that effectively with this model and
this input, this is the output, and you can be 100% sure of
that. That's the first property you
get from ZKML. It's a very, very powerful one
in the field of applying security to AI and safety to AI.
Now, the second property is privacy, right?
There's a lot of conversations about AI ethics, and privacy is
generally central to all of those conversations.
But what privacy is is, is twofold.
It is how do I ensure that the model that's being used doesn't
potentially, or the person who's running the model doesn't
potentially have access to the underlying user data?
So you say, hey, I have this, you know, this weird chest pain
and I want to interact with diagnostic model, but I don't
want the the mega Corp running this diagnostic model to be
like, hey, I have chest pain. Let me serve, you know,
Sebastian adds for chest pain medication that that's kind of a
dystopian future of all of your health data becomes just, you
know, dispersed across the Internet with whoever's running
these models. And so that's where privacy is
like very, very important in AI. And the second place where
privacy is very important is in keeping the models private,
right? So closed sourcing of models and
closed sourcing of weights generally is, is significant in
fields like healthcare and financial services, we're
fine-tuned models. The weights of those will be
considered PII or client information.
And so being able to keep the model private actually allows
you to use it in some interesting ways.
And so the two things you get from ZKML is being able to keep
a lot of information private that otherwise would have to be
public and being able to add security on top of the use of
AI. OK, right.
So we have inference verifiability, which is like a
very important use case in military and industrial settings
where you, you want assurance that this query, this prompt has
been sent to a particular model and that the inference comes
from that particular model. And then you have privacy, I
think. I think so I want to maybe just
kind of zoom in on the inference verifiability part, because I
think for, for most people who think about ZK and, and kind of
0 knowledge circuits, what comes to mind is like a computation
environment that is, that's very limited, right?
So the, the, the types of computations that one can do
inside AZK proof are like quite simple and rudimentary.
While when thinking about AI inference, it's like this very
complex compute problem. So can you maybe like clear,
clear a little bit of that, that misconception and, and how we
actually get to do inference in a ZK proof?
Like how does that actually work?
Yeah, this is, this is a, this is a really a very good
question. So I, I would say that part of
the hard thing about staying on top of ZK for, for the broader
market is how fast ZK changes, right.
So, you know, I, I, since I started LaGrange 4:00-ish years
ago, about four years ago, we've seen an order of magnitude
improvement per year in the performance of ZK.
And that's consistently been every single year all the way
from improvements in the core cryptography, improvements in
tricks and circuit writing that make things faster, improvements
in hardware acceleration. All of this has just drastically
improved the performance of the space.
When I started LaGrange, the cost of generating a proof for
AZKEVM was like a dollar in the range of 10s of cents per
transaction. Ridiculous.
Now it's about 100th of a cent. I saw from from ZK Sync's newest
benchmark for Bluejam too. Fantastic improvements in speed
now. AI would have been a pipe dream
to prove in ZK four years ago. Today it is actually quite
performance. So our library deep proof that
we built can actually generate proofs of GPT 2, Llama and
Gemma, which are two, which are three open source models that
are are open source LLMS and and we can do those.
And obviously I'm not going to claim the performances like
anywhere near real time, but we can do those with relatively
reasonable performance. And for a lot of very from a lot
of much smaller model architectures, we can generate
proofs in the order of seconds. And that's without the
specialized hardware that we expect to be available in the
next year, which should add a one to two order of magnitude
improvement in the proving times of these systems.
Well, and so but. You're saying GPT 2 and Llama,
those are fairly old models, right?
I mean, yeah. So what what we like is it
expected that you know, we'll be able to do verifiability on you
know, large like very performant models like like Gemini 2.5 Pro
or or like Grok 4 super heavy like is it, is it reasonable to
think that ZK can also verify inference on on these very
complex models? Right.
So like GPT 2, Llama, Gemma, those are like let's say 10
figure parameter models, right? So, you know, Gemma, I think is
this version of Gemma that are 1 billion parameter, 5 billion
parameter. There's versions of Llama that
are like 6-7 billion parameter. GBT 2 I think is sub 1 billion
parameter. It's like 600700K.
But you, you you're, you're talking about, you know how many
orders of magnitude you need to reach in a performance
improvement to run those models efficiently.
So getting from a, a 5 billion parameter model to a 50 billion
parameter model. SO1 order of magnitude
improvement in memory optimizations and proving time
getting from a 10,000 parameter model to a 5 billion parameter
model or a 10 million parameter to a 5 billion parameter is what
it's four or three orders of magnitude.
So we're closer to being able to run a frontier model with 5060,
seventy, 100 billion parameters than we were to being able to
run Gemma or LLAMA or GBT 2A year and a half ago.
So we can run. In the current version of the
proof a variety of LMS that are transparently smaller in size
than what would be used for a lot of you know chat apps today.
But we're probably about a year, 18 months from being able to run
the, the frontier models that people are familiar with.
Generally we're actually, So what I'm seeing, not seeing an
increase in parameter count proportionate to the
improvements we're seeing in ZK performance every year, right?
We, we, we did not go from 50 billion parameter, 60 billion
parameter models last year to, you know, 500 billion parameter
models, trillion parameter models this year, right?
And we did go from being able to prove, you know, 8 figure
parameter models being able to prove low 10 figure parameter
models in a year. So right the the rate of ZK
improvements a lot faster than the rate with which models are
on. Interesting.
So, so you think that ZK will be able to continuously catch up
with the speed at which AI models are are are also
improving? At least inference, right.
I think there's a question of whether or not, you know, if
you're training a, a model on like, I think it was the newest
Grok one. They're training on that, that,
that giant data center that they did, you know, get financing for
in the range of of, you know, several billion dollars.
I, I don't think that you'll be able to generate efficiently a
proof of the training of Brok 4 or Brok 5 in those types of
environments for, for a very long time.
But I, I do believe you will be able to generate proofs of
inference in the next 1218 months for any model that you
want to with reasonable performance.
And I actually don't think it's a Volt prediction.
I think it's a rather conservative prediction.
So what? What is the?
What is the incentive for closed source model providers to
implement ZK proving of their models?
So you think this is something that will, you know, can at some
point be included in all models or does it, you know, will, will
it remain some sort of like a premium feature that only sort
of enterprise and governments and military clients would have
access to? Yeah, I mean, I think it depends
on who wants to pay for it, right.
The number one, the, the deciding factor of what people
will integrate I, I generally find is, is the economics of it,
right. And so if you are a user who is
very privacy concerned and verifiability concerned, there's
obviously a subset of users who are, you know, there's always
going to be open source models that you can use that you can
run your ZK on on top of yourself, right?
You can use DeepSeek with ZK proofs at some point in the
reasonable future and you know, have privacy private guarantees
of works. And that's great, that's
exciting. And then there are applications
where you're like, OK, I want Rock to be used for defense
purposes. And how do I know that a remote
system that's communicating with XAI servers, it hasn't been
tampered with? How do I know that nobody in the
back end at XAI has pushed to change the code that's going to
take down, you know, an entire fleet of US defense rooms,
right? That's a situation where you
really, really do need verifiability.
And there's no shortage of money that will be willing to be paid
for that. The great thing about ZK, and we
cushioned this into privacy earlier, it's actually very well
suited for closed source models because you can keep the model
private. So I can prove to you that a
commitment to the correct model was used to generate this
inference output for you without actually having to ever show you
the model. So you can have a commitment to
Grok that just says, hey, this is Grok and here's a proof it
came from Brok. And you never have to actually
see the weights to bias use the model architecture, anything of
the closed source model. And so it's very, very relevant
to use Zcane enterprise applications because you
actually can have guarantees of correctness over AI output and
privacy over the underlying models that are kept closed
source. Yeah, this has made me think
like I recently finished reading Nexus, the You've All Known
Harare book. And you know, part of his thesis
is that AI poses a risk to democracy in its current form.
And the way that AI is being used like on social media, that
could like create sort of like misinformation and can be used
adversarially by, by our, by our, by our enemies to create
social unrest. And, you know, I, I think ZK
could be in, in, in this context, could be used to curb
some of that, but it would have to be sort of a regulatory
requirement for AI companies to also include ZK proofs for all
of the inference. So that, you know, when you're
looking at a social media post, you know, that this is like an
AI generated thing versus something that's not.
Have you guys given any thought to that?
And what's your view on like having ZK being sort of part of
the AI stack from a, from a sort of like a regulatory
perspective? Yeah.
I mean, I think it, I think there will be increasing
regulation surrounding AI trust and safety all as well as the
trust and safety of data used to train AI.
Some of that, those problems can be addressed with ZK and I'd
like to see them addressed with ZK.
And some of those problems can't be right.
Things like, you know, preventing AI providers from
scraping private user data and using user chats to train next
generation of models that, you know, has potential actually
generative capacities that were predicated on non public
information or sensitive information that they shouldn't
have had access to a training. Those things are always going to
be concerns and they require regulation and ZK proofs.
You know, maybe in some architecture could solve it, but
it would be very, very complex. And the simplest answer is just,
you know, having somebody with a clipboard run after the 10
companies that are actually doing this and pointing at them
and saying stop doing that. That's probably the cheapest way
to solve that. Maybe not the most durable long
term, but probably in the short term the cheapest.
Where I think the ZK is uniquely positioned is in applications
that actually have an imperative that is not established by a
government, but is established by an economic motivation to use
ZK. And this is where I think the
most value in technology comes from, right?
You know, why do we have the centralized systems and block
chains? It wasn't because, you know,
some government bureaucrats said you have it, you have to have
it. It's because there was an
economic motivation to build the centralized block chains to
protect, you know, non custodial user assets and that that was
the entire basis of our industry.
What was the basis of financing for ZK?
Wasn't the, you know, government bureaucrats saying, hey, you
know, you should build private and verifiable scalability.
It's because there was massive hacks in crypto and then people
go, hey, maybe we should scale block chains in a more secure
way so we stopped losing our money, right?
And so where there is a, where there's a market for ZK in AI is
applications that cannot actually even use AI in the
current form because there's lack of safety and there's lack
of privacy over it, right? Healthcare is an example of
that, Aerospace defense an example of that, institutional
finances, an example of that, right?
Like there's a bunch of companies that can't use grok
because they can't just pass, you know, insurance participant
data over to X AI. And there's, you know, a team of
lawyers there say, no, you can't do that.
We're going to go to jail or we're going to get sued to
oblivion. So these are the places where
there's an actually a very large market for, for ZK in AI as well
as like actually in crypto, right?
How do you ensure that the, you know, agentics LLM you're using
to construct your transactions won't rug you?
These are these are where it's very, very valuable in my view.
And I hope there's regulation that also pushes things in our
favor. But I don't think those are the
driving motivations that that is going to transform this
industry. Yeah, can can you talk a little
bit about your your collaboration with NVIDIA?
Yeah. So, yeah, we recently announced
some really big collaborations, one of them with NVIDIA, one of
them with Intel and one of them with a very large hyperscaler
cloud provider. And so in all of these, the kind
of the central point is very simple.
It is there is a imperative on the use of AI and confidential
AI within a bunch of sectors these companies sell to.
And so there has not been a company before LaGrange that has
had a commercially viable product that has the capacity to
actually be able to start addressing these problems.
Now, I wouldn't claim that the version of the proof we have now
is the version of the proof that we'll have in 12 months, 18
months, 24 months. But directionally, it is moving
faster than anything has been able to move previously to
address these problems. And that's opened up a lot of
opportunities to us commercially to actually be able to work with
some very, very large AI companies to start exploring
what it looks like to use AI to improve trust and safety of of
deployments that they have. All of these AI companies have
healthcare, defense, institutional finance, relevant
contracts, kind of services, relevant contracts.
They have international contracts, you know, very
complex legal requirements surrounding how data can be
transited between countries and how AI can use between
countries. And what we have is a technology
that's uniquely positioned to address many of those problems.
LaGrange has recently launched its token.
It's the LAW token or the LA token and LA.
So yeah, it's got the finance listing.
Coinbase, what's the role of the LA token and what's planned here
for like staking and governance, etcetera?
Yeah, So, you know, we were very, very excited to finally be
able to unveil and to launch the LA Token.
The LaGrange Foundation did a fantastic job orchestrating and
coordinating that whole process. And so, you know, we were very
lucky as well to be listed on a variety of top liquidity venues,
Binance, Coinbase, Upbeat, and many others.
And we were very excited to see an overwhelming community
support behind the launch of the token.
The utility of a token as designed by the LaGrange
Foundation is as a fuel for the cryptographic engine that
LaGrange builds effectively. There is a network of provers
that generate proofs for Deep proof, RCK, machine learning, as
well as a bunch of other commercial applications we
target as well, ranging from roll ups to Co processing to
more as well as verifiable database infrastructure.
And at the end of the day, the token is used and staked into
individual provers in the network who have an economic
motivation to generate proofs correctly.
If, for example, they don't generate a proof on time, or
they failed to participate in an auction the way they were
supposed to, they can face a penalty in the form of slashing
or non payment. In the current version, you know
if it's possible to stake the LA token into provers and there is
programs designed by the LaGrange Foundation to
incentivize the staking of LA tokens based on fees that the
network collects from being able to render inference or render
proofs of inference to many of our counterparties.
And you, you tweeted something a little while back, which which I
thought was was kind of interesting.
You said if your intra protocol has no revenue, it's just a meme
coin. Can can you unpack this, this
thought and you know why? Why do you think?
I mean, I think it's it's it's obvious, right that the crypto
needs to move to more towards a more revenue generating model
than a simply like up only model.
How? How will revenues flow back to
token holders in the case of the LA token?
Yeah, this is a great question and I'm glad you asked it
because that was one of my favorite tweets.
But there's a subset of of public market participants in
crypto who trade charts. And all they do is they trade
listings and charts. And those listing and chart
traders are behaving the same way when they're trading banc
versus, when they're trading Pengu versus, when they're
trading with versus, when they're trading DOGE versus,
when they're trading LA. All they care about is trading
on price action and trying to catch a runner or momentum in
the chart. And if the only participants in
your market are trading on those characteristics, whether or not
you are in for protocol or you are a meme coin, you effectively
have converged the same market dynamics.
The meme coin right people trade goes up, people sell goes down
and that is really not an inspiring or long term durable
way to build a infrastructure protocol.
The objective of an infrastructure protocol should
be to create net new value such that the economics broadly of
that infrastructure protocol are creative to the network dynamics
that include the underlying asset.
And that is for example, why hype has done so well in market.
That is for example why you know the many other L ones that have
high demand Solana, Ethereum have done well by and large in
market. It is the hope that many
investors have brought to something like pump in the last,
you know 30 days. But anyway, to get back to the
point, if you do not have revenue and you do not have
traction, your token is nothing more than the meeting point.
And so LaGrange, as you know, we've talked a lot about today
has material traction both outside of crypto and within
crypto in the adoption of our technology in both enterprise,
AI, financial services, aerospace, defense and crypto
asset sectors. And because of that, we've tried
to design our network in a way where the fees that accrue from
the generation of proofs and from agreements that we have for
the generation of proofs agreement, the foundation has
for the generation of proofs accrue back to people who have
staked and who are generating proofs within our network.
And so This is why we, you know, we're very excited with with
many of the traction numbers that we have right now that are
are publicly verifiable on chain wherein you can see the movement
of fees for the generation of proofs to prove as a network and
very strong demand for the generation of proofs that's
visible in the network. And so long term, we think that
the majority of fees that accrue for staking the LA token will
come from fees that are paid directly for the generation of
proofs such that is a positive economic market wherein there is
an incentive to hold and stake the LA token into the network
that isn't simply just trading chart action and isn't simply
just trading on a meme coin. What So when when operating in
the enterprise space and selling LaGrange products to enterprise
customers, how is the crypto component perceived and how do
you get over some of the objections that people might
have simply by virtue of like Larache having a crypto
component? You know, some companies or like
clients might see that as a risk.
And, you know, I know that like working with an impressed
clients, it could be complicated to disassociate, you know, the
technology from a lot of the negative press that crypto gets.
Yeah. Yeah, that's a great question.
So Deep proof is a library. Our ZK machine learning
technology is a library. You could run it on top of our
Prover network with the same security guarantees as you
running it on top of an edge device used in a battlefield.
There is no difference in where you choose to operate that
library. The library will operate with
the same safety guarantees over proof generation anyway.
And so some people really like the centralized proof
generation, but they go and they seek that out.
So when we work with enterprise clients, we don't sell them on
the centralized proof generation, we sell them on core
cryptography. The entirety of the Internet has
been secured with cryptography, right?
The, the, so TLS on top of HTTP is what enables online banking.
It's what enables payments infrastructure.
It's what enables, you know, everything you do on your phone.
That's what enables social media.
It's one enables online dating at what it is.
It's the modern society that we have today is predicated on the
use of cryptography to add safety and privacy on top of web
connections. What LaGrange does with ZKML is
adding those same 2 properties, safety and privacy on top of AI
and that is what we sell when we interact with enterprise
clients, Web 2 customers, etcetera.
It is 2 properties that unambiguously need to be
included on top of AI for us to have a robust and functioning
and safe economy that predicates itself on top of AI.
The same way those two properties had to be added on
top of, you know, ICT Internet connectivity technology to be
able to add those properties on top of the web.
And so that is what Deep Proof and RZKML work is sold as and
what it sells. Now there is a subset of
customers, right? Wallet providers, for example,
or people who really like the centralized proof generation
because they think the properties you get over liveness
guarantees, remove dependencies on cloud providers who might
shut off, right? And in that case, you know, we
have a prover network that's fantastic and it can be used for
that. But when we sell to web two, we
don't sell the crypto token. We don't sell, you know, people
having to use or interact in any way with the crypto token.
We sell core technology and impactful technology.
And as a business, we have also our crypto network, which we
think probably is the best way to generate proofs long term.
We think the whole world's going to use the centralized proof
generation long term, but we want to see people using proof
generation 1st and then they'll eventually, in our view, start
moving to the centralized deployments.
So, so far we've talked a lot about AI, but you guys are also
doing a lot of interesting work on on the scaling side,
particularly like those recently announced.
So you guys are working with Matter Labs to handle a lot of
the proofs on on, on the on ZK sync.
Can you talk a little bit more about like the Co processor and
and some of the other products that are in the LaGrange product
line? Yeah.
So it's a really good question. So as I started with a little
bit today we're AZK company and where we see the largest Tam for
ZK today is on adding trust and safety on top of the use of AI.
But that's not the only thing that effectively we sell that
uses ZK, right. So we we sell verifiable
database infrastructure where you effectively are able to have
a database that is represented by a commitment to that data.
It's like a hash of all of that data that we can prove the
correctness of queries on top of which is very useful for a lot
of contexts where you want correct provenance over data.
It's very useful if you want to introspect into a chain and
query over the history of chain. And we have a very large market
for that that we we sell to within D Phi and NFT protocols.
Many of those we've announced like Gearbox, Azuki, etcetera.
We also have work that we've done on using our Prover network
to generate proofs for roll ups. Right.
And so we have a very large deal that we've signed and we've
announced Matter Labs, we're up to 75% of Matter Labs proof
generation for the next two years to be done on La Branche.
And for us that's a very exciting market opportunity.
We we think the Matter Labs team and the ZK sync ecosystem is,
you know, one of the, the, the, the largest and one of the most
important ZK roll up ecosystems in crypto and we love being a
part of supporting them in their growth ambitions.
So just switching gears a little bit, I want to, I want to ask
you some questions about, about your personal journey as a, as a
founder and you know, what's the, what's the thing that
you're the most proud of at LaGrange, but that most people
either don't know or don't care about?
Yeah, I think that there is a fallacy in founding companies
that the journey to be successful is linear and that
you catch lightning in a bottle, you become successful and then
all of a sudden you're off to the races and everything goes
great. The truth is that at LaGrange,
we've had very many periods where things were going our way
and very many periods with things weren't going our way.
And the resilience of the company and the team is the
thing that has allowed us to continue to XLS a business.
And that's the thing that we're that I'm the most proud of about
our business. You see a lot of companies in
crypto that they come up with a cool idea, they raise a big
round, they launch a token, things don't go their way and
they go to zero and then the team goes on to the next thing.
Or you see companies that you know, they had come up with a
great idea. They raise a first round.
They, you know, everything's very exciting for them.
They never end up catching that momentum again.
They never raise a second round. Nothing ever happens.
We are. I have to spend significant time
raising my first round. People don't know this.
I actually failed to raise my first seed round twice before
the third time that I succeeded on it.
We had many periods in the history of LaGrange where you
know the market was swinging away from ZK.
People weren't excited about Co processing, people weren't
excited about roll up proving and consistently what the
research team at LaGrange has done, the engineering team, the
business team, everyone was stick to the fundamentals that
we believe works, which is building technology that our
customers love and then aggressively commercializing
those into large Tam verticals. And through that strategy, we
have been able to weather very many bad periods and get to very
many very positive periods. And that's a resilience that I
think too few companies in crypto prioritize.
They prioritize the fast exit, the hot trade, the cool
narrative, and they don't build aggressively on a fundamental
that carries them through both bear and in the bull markets.
Yeah, I think fundamentals are highly, highly underrated in
crypto. I mean it, it's, it seems so
obvious, right? By like more and more I'm, I'm
finding that the, the, the thing that sets high performing teams
and successful teams apart from the rest is just, you know,
fundamentals and, and 1st principles thinking.
You know, you, you talked about the team and, and how it's grown
and everything. And what what is a a piece of
advice that you would give to aspiring crypto founders that
are building a great team, that want to build a great team for
the long term? Yeah.
I mean, the only way I think to be successful is to hire the
best people, especially in a very research oriented sector,
you need to go out preemptively and find the best people to work
with, hire them and then be able to retain them.
And so a lot of the early hiring at LaGrange, not even early
hiring, a lot of the hiring until today is done by me for a
lot of the research sectors, right?
I've run all of the, the interview processes for anyone
who's interviewed with LaGrange for the first three years of our
history, the first person I met was me.
I, I took a very high amount of ownership in trying to run the
interview process the way that I thought had to be run to attract
the best talent. And because of that, we were
able to get a lot of very, very, very good talent.
And now as we've grown, we've changed processes.
There's some roles I interview for, some roles I don't
interview for. But for anyone who's starting
out, I, I, I would recommend that they take as much ownership
and as possible on trying to run their interview and hiring
process and then do as much work as possible in one on ones.
I, I have one on ones with everyone on the team at LeBron
still at a very regular cadence. We like to keep our team small.
We like to make sure that we we have offer packages that are
competitive with the best companies in the space and we've
done that since day one. And we make sure that people who
join LaGrange have a very, very, very high retention rate when
they're at the company as well. What, what stands out in your
interview process do you think from other teams?
Like what's the, what's the one thing in your interview process
that, that, that, that stands it out as a a great way to find the
best talent? So I'll give these secrets
because because obviously we, you know, I think, I think, I
think founders should know this, but early on I wouldn't have
shared these secrets. But the one that really was
helpful was I, I was the first person who everyone would talk
to. So when someone is interviewing
with the company and the founder comes on for the first interview
and says this is a one-on-one interview with me.
And this role is so important to us and the role that you're
interviewing for is so important to us.
You will directly interact with me throughout this entire
process and I'll guide you through it.
Generally, people who are top of line and are trying to take a
bet on an earlier stage company enjoy that level and appreciate
that level of attention. Secondly, when we were competing
against larger companies for very, very good talent, I would
fly out to the city of that person who we made the offer to,
to meet them in person in as part of making that offer.
And we would spend time, we would take them to dinner.
We would get to know them. We would get to know them
personally. We make it very clear that if
they were to join the bronze, they were joining a company that
prioritized them and prioritized winning and that very few
founders even today I see are willing to fly out and meet a
compass someone in person who they make an offer to.
This is one of the best ways I used to try to close deals when
I was a venture investor, right? It's a hot company and a hot
founder we're trying to invest in.
Then I would fly out to meet you in person.
And I see no difference where if you're, you know, one of the
main differentiators you have as a founder is your talent that
you're able to hire, that you shouldn't be doing the same
thing. And I've told this to dozens of
founders and secrets and none of them have done it.
And so maybe I'll say it publicly and people will start
doing it, but after all this time, I rarely see any founders
doing it still. Yeah, it seems like such a
simple thing, right? It's all about relationship
building. And if you're competing against
whether it's, you know, other VCs for a deal or other
companies for talent having having that that like building
that sort of personal relationship with that person
early on can can make it sort of make or break the deal and like
flying out to meet that person. It's definitely, you know, it's
yeah, it's an effort thing. Totally.
Yeah. And then another question like
starting, starting a company, you know, and scaling that
company to, you know, 10s of people can be challenging for
some people. And there's a lot of founders, I
think that have a hard time getting over that, that that
sort of getting over that scale. So they may be able to run their
company when there's a handful of people, but then it gets
harder and then they might, they might kind of cap out and then
you have another, another CEO sort of like come in and take
that company to the next level. Like how do you as a founder
think about operating at all of those different levels?
Like if LaGrange, you know, scales now to, you know, over
100 people you know in sometime in the future, how, how do you
think about your role as ACEO operating at those different
levels of scale? Yeah.
So I, I have two answers for this.
Firstly, I think it ties into what we talked about before.
It's an effort thing, right? Nobody wants to spend in the
first year of their company 6 hours to 8 hours a day
interviewing candidates, right? Nobody wants to.
It's a lot of work and you know, if you, if you have to also run
your business and you want to win the best talents, you're
interviewing everyone personally and you're flying out
everywhere. It's a lot of work.
And it's like, it's, it's, it's kind of a pain in the butt.
But if you want to win, it's a decision you have to make if
you're going to do it. And so I think there's a subset
of founders who accept what being a founder is.
Which is doing what's required. At any stage in the business,
that business succeed, right, Even if you don't like it,
right. Your, your job as a founder
isn't to be an engineer. It isn't to be a salesperson.
It isn't to be a tweeter, it isn't to be a head of HR.
It's to it's to win. And every point in the business
or something you have to do to win.
The most important thing is going to be different at every
stage. And early on it requires a lot
of effort along the things we just talked about in my view,
and later on it requires a lot of effort along different axis.
And so, you know, I, I think as you scale your business, you
have to accept that, that, that things change.
And, you know, there's been a bunch of bumpy roads in my
journey as a founder getting to the company scale that we are
now. And there's going to be a bunch
of other bumpy ones getting to the next scale as well.
I'm, I'm, I'm sure of, and it just, you know, it will require
a significant amount of effort for me from the management team,
from the, from everyone at the company to continually hit the
milestones we need to grow these scales.
And, you know, I think teams should be cognizant of that.
They should accept the reality ahead of time so that they're
equipped to be able to tackle it when they face it in the moment.
How do you juggle with sort of remote versus in person?
Are are you guys mostly a remote team or do do most people sort
of come to an office? Yeah, we, we, we're a fully
remote team, which is a decision that we made because of our
requirement to optimize for talent quality.
Because we're a research organization, You know, a lot of
our researchers are based all over the world.
They're some of the top people who've authored and published
papers and, and applied cryptography and computer
science, specifically in things like ZKML and verifiable
database design. Our chief scientist, Babis
Papamanthu, who chairs the Cryptography department of Yale,
Dimitrius Papadopoulos, one of our distinguished researchers,
is a professor at HKUST in Hong Kong.
Obviously it would be very hard based the whole company in Yale,
in Hong Kong, you know, Nikola Gayi and Franklin the Leahy, two
of our fantastic, Nikola Gayi is a fantastic senior researcher on
the team and and Franklin's our head of engineering.
They're both based in Paris. So we have clusters of people
all over the world, but it would be very, very hard to force
everyone to move to one city. There's personal things that
just would be very hard. So we are a remote team.
I think there's something, you know, very special of being in
person team. It is very special in person
time you get as a team, especially if your remote team
is very little. In person time you get, but you
know, just something you have to work around.
We've always hired very, very, very high agency people.
Everyone in the team has a tremendous amount of autonomy
and that has just, you know, been very positive for some
people who really, really enjoy that and some people don't.
But we've been very lucky at the ones we've hired really do enjoy
level of autonomy. They like to be able to not have
someone, you know, breathing over their neck in an office.
They like to be able to execute at the highest level on their
work on their own time and then be able to contribute to a team
also doing the same thing. And for us, we've been able to
make it work. How often do you guys get
together as a team? You know, do you have sort of
quarterly retreats and how do you structure those so that you
guys can get the most kind of out of that FaceTime during
those moments when you see each other in person?
Yeah, I, I think we always try to do off sites.
I think I think companies should do off sites.
It's very important. We also have smaller team off
sites where people, you know, coalesce or on a conference, a
subset of team. Generally the business facing
people and the more go to market facing people are are often a
more of the commercial crypto conferences.
The research people are generally more at research
conferences and have, you know, kind of smaller meetings there
around publications. The the engineering team has,
you know, kind of meet ups that they they sometimes do to do in
person hacking together. And yeah, I mean, just very
broadly, we also do off sites as as team as well.
I think meeting people in person and you know, having the team
meet in person in a regular cadence, there's no replacement
for that. Yeah.
So before we wrap up, I wanted to ask.
You about some of the cryptography research that you
guys are working on. You mentioned that that there's
a paper coming out this year. Yeah, the paper came out last
year Dynamic Starks. It's a fantastic work that was
authored by a research team on a new paradigm for zero knowledge
proofs, fully updatable 0 knowledge proofs and this work
was published or accepted into SBC Science and blockchain
conference, one of the top academic conferences in crypto
for as K and consensus and generally science blockchain
designs, hence the name of the conference.
This is hosted generally either at Stanford for a bunch of
years, last year's at Columbia, this year's it's at Berkeley.
And so we're actually the only team in crypto that's had work
accepted two of the last three years.
We were wait listed unfortunately on the third year.
But the the, the, the dynamic stock work is is going to be
presented next week at Science and blockchain conference.
Wei J Wong, one of our our our PhD interns last summer, is the
lead author and then Travon, Dimitris and Babis are three of
the other authors on the team. And so, yeah, we're anyone who's
going to be at Berkeley next week for science and blockchain
or or whenever this airs. If you're at Berkeley for
science and blockchain, hopefully you saw the talk.
And there's a bunch of other fantastic work coming from our
research team this summer as well all the way like from
things like new start constructions, the things like
privacy preserving inference and privacy preserving MPC based
inference, like Coast arc work. And then some other stuff I
can't talk about that are kind of more foundational to ML and
applications of of of ZK within kind of some more foundational
constructs of ML. And so across the board, you
know, I think one of the things we prioritize the company is to
actually do fundamental ZK research alongside our
commercialization aims for deep proven other ZK technologies.
You know, I'd like to think of, you know what, what deep mind or
open AI were to AI research. We are to ZK research.
We hire the best people, we retain the best people.
We have fantastic groups of people who are, you know, active
professors or are active PhD students who are joining the
company for different periods of time on sabbatical or on
continuous, you know, part time basis to be able to construct
systems and research into improvements in systems that
are, you know, a standard deviation more advanced than
what you would get from purely commercially minded team.
Cool. Well, where can people go to
learn more about LaGrange? What's your what's your?
What's your CTA? Your call to action for the
audience. Yeah, so I would, I would
suggest anyone who wants to build a deep proof, go on
GitHub, go to LaGrange and look at deep proof.
It's, it's up there. Anyone who wants to follow us
and you know, learn about some exciting partnership updates and
research updates, follow us on Twitter at you know, LeBron dev
or if you are interested in kind of having a more personal
relationship with a team, I'd recommend you join our telegram,
I'm sorry, our Discord channel, which is linked on the website
and is a great way to interact with our community team and to
interact with the founders at a team as well as a whole.
And so I also would say anyone who really wants to, you're
welcome to reach out to me on, on Twitter or on Telegram and
anyone who's really, really motivated will be able to find
me. Cool.
Ismael, thank you so much for coming on.
Likewise, thank you so much for having me.