Bankless - From Harvard at 18 to Building Crypto's Fastest Exchange | Vlad, Lighter

Episode Date: July 27, 2026

What does it take to build an exchange that refuses to choose between speed, cost, security, verifiability and composability? Lighter founder Vlad Novakovski joins David to trace his path from international Olympiads and Harvard at 18 to Citadel, early machine learning, Lunchclub and an 18-month ZK engineering bet. --- 📣SPOTIFY PREMIUM RSS FEED | USE CODE: SPOTIFY24 https://bankless.cc/spotify-premium --- BANKLESS SPONSOR TOOLS: 🔓NEAR | TRADE CONFIDENTIALLY, GET 20% BACK https://bankless.cc/near-20 📊BITGET | TOKENIZED STOCKS 2.0 https://bankless.cc/bitget-stocks 🧭OKX | TRADE, EARN, PAY to OKX | 120M+ USERS WORLDWIDE https://app.okx.com/join/USBANKLESS 🎯THE DEFI REPORT | ONCHAIN INSIGHTS https://thedefireport.io/bankless 👑BANKLESS PREMIUM | AD-FREE & BONUS EPISODES https://bankless.cc/spotify-premium --- TIMESTAMPS 0:00 A Particular Set of Skills 11:33 Harvard at 18 19:03 Citadel and the Search for Alpha 25:14 Machine Learning Before the AI Boom 36:46 ​​Finding Elite Talent Early 41:39 The Lunchclub Matching Problem 45:11 Pivoting Into Lighter 51:18 The 18-Month ZK Engineering Bet 56:38 Lighter’s Five Pillars 1:00:04 A New Generation of Exchanges --- RESOURCES Vlad Novakovski https://x.com/vnovakovski --- Not financial or tax advice. See our investment disclosures here: https://www.bankless.com/disclosures

Hey, Bangladesh Nation. In this episode, we're going to talk to Vlad Novakoski from Lighter. Vlad is a pretty interesting character, and he's been around the podcast circuit quite a lot talking about lighter as a platform, how it's different from hyperliquid and other per platforms and what his goals are. But I don't think too many people have really explored the beginnings of Vlad, going all the way back to his teenage years when he was just a math Olympiad competing with alongside some of the brightest minds of our age, including Dario from Anthropic and Vlad from Robin Hood and a bunch of other names. that have gone on to raise massive companies. And so who Vlad is, I think, is a pretty unique and interesting character. And I just really wanted to ask and answer the question, why do investors love Vlad? And so this is what you're going to hear,

a different side of Vlad from Lighter. So let's go ahead and get right into it. We record this in person in a studio in Manhattan. And the beginning of the episode, we just kind of start rolling. So maybe it feels like you start at the middle of the conversation because you kind of do. So let's go hear from him right now. Vlad, I want to know a little bit more about your background because people know you as the founder of Lider, the PerpDex, Theorem Layer 2, PerpDX. But your background goes pretty far back into your childhood, I think, with your first, like, relevant set of skills.

Talk to me about... Particular set of skills, right? Particular set of skills, yeah. Talk to me about math. You won a math and physics Olympiad? Me and most of my listeners probably don't know what that means. What does that mean? Yeah.

Well, I started competing in, you know, academic competitions, let's just call it that when I was around 12. And I guess some of the highlights of that were making the U.S. team to the International Physics Olympiad and the International Olympiad at Informatics. On the math side, I... Informatics. Yeah, that's the programming one. Okay, programming.

I mean, I tried to do a hat-trick as far as making all three. I was actually the first or one of the, I think, one of the first people in the U.S. ever to make two. teams didn't quite make three but you know but it was good what does it mean to compete in math and physics like how does one actually what it is to compete over well

so the Olympiads all have a different format the programming one is maybe the most would be like the most exciting to watch there actually are like these days like Twitch streamers and whatnot who do that where it's like you're writing code basically have a problem solve like

I don't know the problem could be like here, you know, your location of two pizza places in New York find the shortest given traffic how to get from maybe and you have to actually like, it's not theoretical, you actually have to write a program

that would actually do it. Right. It's determined a stick. So there's a very clear correct answer. Yes. Okay. Yes. And so like you have,

you know, three problems like that, five hours, two days of that. So that's the programming one. Math, at the, I'm talking about the highest levels. I don't know the ways leading up to that, it's, you know, it's not five hours, right? There's, like, it's less intense.

But then the math, that one looks like, it's also two days and you have three problems where you have to find a solution with proof. You've probably heard of, like, the four-color theorem that you can, like, paint like a map with, like, four colors, like any, like, it doesn't matter if it's states, countries. Right. Now, that one is very hard, but let's say the problem is, like,

prove that you can do it with, like, six colors. Okay. Hypothetically, right? And it's like, it's not enough just to show one example. We have to, like, prove that you can always do it. it. And then you have to write proofs like that for like three problems over five hours also, I think two days. Physics one, there's a theory part and experiment part. So the theory part is also similar. It's like, you know, like a block of ice is coming down the hill, you know, with, you know,

with kind of assumptions like, you know, how fast is it going to get there while it melts, you know. So it's not just like basic, it's not like physics 101 where it's like just like a rock. But like, because it's ice and melting, you have to, like, do something, bring in a bunch of different concepts together. But then there's also the experiment part. And that,

that's probably the most different of all these. You, like, you actually have to, like, do stuff with your hands and, like, you know, measure something or, like, build something,

design something. And this was all fun for you. You, this, I'm assuming you were, like, intrinsically just thrilled to do this math, to be in these competitions, right? Like, math is entertaining,

entertaining for you? Yeah. So the thing was that, Like, it was like a virtual cycle because I was pretty good at it even before competing. And then once I started competing, I saw that like winning is fun. Okay. And not only is winning fun, meeting other like-minded people. Mm-hmm.

And in the long run, that's actually the most important part of all this, is the people you meet. Right, because this wasn't solo activity. This, you're on a team. Well, yes, but even, I mean, a lot of the competitions are individually measured. And then the country, you know, once you get the international level, the countries have teams, but you're just like, you know, because there's training camps. You're like, as it was, you know, you sit around wait for results. Like, you meet a lot of the people.

It's like, you know, the opportunity to meet other like-minded, you know, students, but really just kids around the country. Right. Who did you meet? It's funny because a lot of the, I mean, back then, these are people that I just met at the camps. I mean, they were impressive people then, right, just because of their skills. But some of them now are like shaping what we see in industry.

Like, for example, at the U.S. training camp for the U.S. Physics Olympiad, two of the co-founders of Anthropic were there. So I got to meet them, got to know one of them quite well, actually. And then at the intranics one, you know, the first CTO of Facebook was, I mean, they weren't, they didn't. The future CTO, yeah. So some people and, you know, who's also on the board of OpenEI and right. So like, that's pretty interesting. And that's, that's just like people who've achieved, you know, the kind of the most well-known outcomes, but there are a bunch of others who right, like, for example, like, I think you look at people who run some of the big

quand trading shops, like, you know, you see a lot of people from those competitions there, etc. People that come out of these like math and physics and informatics Olympiads, does it, was your cohort unique in that? A handful of them grew up to be incredible founders shaping the future of the world. Or, like, is it always like this? Yeah. It's a good question.

I mean, I think it goes, there are. bursts, like, I think the early 2000s, for whatever reason, maybe it's because we were the first to grow up with the internet. That cohort in particular, and not just on the math and physics side, like, that cohort, you know, like, achieved a lot of success. I mean, you know, the vice president is part of that cohort, too, right? Like, you know, people who've done very well in politics, have done very well in other forms of business. Like that early 2000s, I think there was a lot of great achievement that came on that. But that being said, like, if you look back, even to the 90s, like, one interesting thing was the 93 team that Harvard sent to the ICPC, which is like

the equivalent of the Informatics Olympiad at the college level, team competition, team of three. So out of that three-person team, not one, but two became billionaires. And like in the 2000s, not now. Yeah. But in 2000s, that was a very high bar. Yeah. I would imagine the competition has plenty to do with it because, like, well, it's the common denominator between physics, math, and informatics, you know,

there's a lot in common there, but really is the competition side of things. If you're good, you were at, you were almost, as you said, did the hat trick. What kind of problems, whether it was math, physics, or informatics, what kind of problems? What type of problems did you really enjoy the most? Is there something to discuss there?

Yeah, yeah. This was always like, you know, when we would hang out after the competitions and talk, this would always come up, like is different even of the people who did well. We all had like our own like favorites or like the kinds of things that like we thought we grog better than others. You were particularly strong at.

Yeah. And this was always a debate. Like some guy would be like, oh, like I love like geometry is really cool. I have geometry and then like maybe somebody else. Like oh no. Like that like trigonometry is really cool, right? Et cetera. But like for me, I really like the stuff where you needed like an unusual insight to solve

it. Or if you look at the problem one way, it looks like. like there's like no solution or very difficult path to solution and if you look at it like sideways like oh actually like you can just solve this in two lines and I mean I didn't always find those but when I did I think that's when I learned the most and that's why I got the most satisfaction out of it. This stroke of genius problems. Well I guess gene, you know, I think it's more of just.

Or thinking outside of the box. Exactly. Exactly. Exactly. Right. Yeah. Yeah. I mean you didn't, you know, these problems solve them.

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Your teenage years were all super heavy in math. And this is also where you also met Robin Hood's Lad, correct? So we were at the same high schools, school called Thomas Jefferson, which was a math and science high school in Northern Virginia. Yeah, we were the two lads. I was, you know, Russian immigrants. The two lads, yeah. She was a Bulgarian immigrant.

Our paths crossed more later in life. Like, I mean, we, you know, we were at the high school. Of course, I mean, he started doing math actually more deeply in undergrad and then graduate. He was a PhD student under Terry Tao, who was probably like the best mathematician in the world. you know, UCLA and, you know, so anyway, so then he got into high frequency training later as I did. So we, our paths actually got closer kind of later on. Was it obvious to you being in these Olympiads and just in these cohorts of people? Was it kind of just obvious amongst you and your peers that everyone was going to go and apply themselves in some great way?

Or were you guys just kids playing with numbers? For me, I saw it a little bit because I used to think of it like, okay, if you, if you, like, stack rank everybody, like, you know, like the people, like, let's say you were, like, whatever metric you went up with, like, you stack rank everyone, let's say, in the country, right? Like, whoever the top seat is, second seed, third seed, if you, like, and that ranking doesn't change that much from one year to the next. If you, I thought, okay, well, if you project that out 20 years, it's probably going to be not about the same then. So, like, these are the people who will contribute. And now, I think

the thing that was different is like, I think because these competitions, the people organize them are academics. So you're, the default career path is academia. So I think like we weren't, obviously we weren't thinking about like Facebook and anthropic and

you know, open AI. We were thinking more like, okay, these people are probably going to become like harbored MIT professors down the road in their field. So how exactly they applied their skills was not something anyone was really thinking about. But But yeah. When did, as you're growing up and just kind of getting into your adult years, did a particular direction for you open up and become clear?

Or how did you go from, you know, a kid playing with numbers and competing with other kids into like thinking about a career, thinking about being an entrepreneur? Well, one thing that I realized at some point around then is like, are we like data? And that's the thing is that you would think, well, yeah, like numbers data, of course. but actually the competitions don't really involve data. The only out of the competitions I described, the only one that does is the experimental physics one where you're actually dealing with the real world data, right? And the funny thing about that was like,

when I was training, I didn't do that well in the experiment part. I would always be the first at the training camp in the US on the theory, and then I would like not do that well in the experiment. I would hope that I would still make the top five even with that. But the actual international one, like, I actually had like pretty bad day on theory. Jet lag, I don't know. But in the experimental one, I was ranked second in the world.

And so overall, I still got the silver. So that was kind of the start of like, I mean, okay, I actually really like this like data real world stuff. And that I think led to doing more with applied sciences later on, like economics, finance, trading and kind of so on and so forth. You met Ken Griffin sometime in your teenage years, correct? Yes. I finished high school at 16 and Harvard at 18. You finished Harvard at 18. Yeah.

How does one do Harvard in two years? Well, two and a half. Two and a half years. Yeah. How does one do that? You know, there's like, you can get credit for one year from, because I took a bunch, you know, like we were competing on a lot of things, right, including how many APs you took. Like, I'm talking about it among that competitive cohort.

So, like, I mean, there's one can do 17 APs. I think I did like 12. So that's enough to get one year credit at Harvard. And then I just, like, instead of taking five classes, semester, I took. six to get another semester. Was it about finishing Harvard? Like that was it another competition? It was just like who can finish Harvard quicker?

Well, that was more competition between me and myself. No one else was really trying to do that. I mean, I looked at it. Like I talked about thinking outside of the box, right? Like the way I looked at it was, you know, assuming I, these days probably someone like me would have dropped out and gone to like, you know, start, you know, build a protocol or build AI model or something. But back then, that was rare.

Like, the only case we really knew about who did that was Bill Gates, right? So, I mean, and then, of course, one of us in that cohort did it, you know, with Zuck. But, like. Right. Zuck hadn't done it yet because, right, okay. But, like, but anyway, like, so that wasn't really a path I was considering, right? And so it was like, okay, well, like, I might do something in industry, but if I'm going to stay in school, the way I look at it is like, it's strictly better to be a grad student than an undergrad. Because you get undergrad, you have paid them, grad student, they pay you.

So I was like, I should finish the undergrad as quickly as possible. And then I can actually, you can cross-register. I can literally take the same exact classes as a graduate student and be on the same exact campus. And it's like, I was like, I might as well do that. So your form of dropping out was just doing it faster. Yeah. Why get so aggressive and competitive with the timelines? Why not just do Harvard in four years?

Because of this thing, right? Because it's strictly better to be a grad student than an undergrad. It was just an optimization problem. Let's say I wanted to stay for seven years. I would have spent two and a half as an undergrad and four and a half as a grad student versus all the way around, right? Sure. Okay.

It's just strictly better. It's just a cross-bed. You can take the same exact classes. And this is just like a life hack that no one really thought about. Uh-huh. I mean, so obviously you got an education out of Harvard, but what did you get out of Harvard beyond that? Learning how to, you know, like there was this thing they said, which sounded like

cliche, but I think ended up being true, which is like learning to think in different ways. So, you know, it was one thing in like, within. a certain field like math to look at a problem within two different mathematical approaches. Like, we're already doing that. But like to think about a real world problem from, like, imagine thinking about a real world problem as a mathematician as a philosopher, as, you know, biologist, you know, like thinking about problems in completely different ways, I think was like on the academic side what I learned. I think on the personal side, just like met a lot of great people. Like,

I think I really learned like, again, this is cliche with like how to network. Like how do you like go into a room and like start conversations with different kinds of people get to know them. Kind of in the same way that you were competing in the physics and the math and the informatics Olympiads, but really the common denominator of all of those is competition. At Harvard, you're learning all these different ways to think. But really the common denominator of Harvard is you're hyper social at Harvard. Like Harvard is a super social environment and need to be in that game to really excel, I think, coming out of Harvard. Well, it depends on the field, right?

if you're like, I mean, I guess like most scientific fields have some collaboration, but obviously there are also others like that were more like the lone wolf archetype too. So it really depends on the field. But yeah, for a lot of the fields, the value you get out of Harvard is through like a collaboration, but B, meaning people from other fields. So your first step out of Harvard was working with Ken Griffith. Yes, I guess that was an original question. We got such like, so Ken, you know, like he had, he himself had started Citadel.

out of Harvard. I believe he did, he graduated. He wasn't a dropout. I think he graduated, but he was running the fund out of his dorm room in like 1988, 1989, like 15 years before I was there. And so I think he, once he got to know my background, I mean, I first interviewed with the firm like everybody else, but then, you know, they kind of, you know, he got to know my background and spent a lot of time and convinced me to join. Was that your first time applying your love of like numbers and engineering and and programming to finance well i was doing trading from my dorm room like i was trying to be like someone like ken or jim simons you know when i was there so i mean it didn't work out that well yeah were you good at it like like trading

as a college student yeah not particularly i mean i found some strategies that worked okay and then can actually told me like yeah these strategies aren't as good as like some of these started as used to work in the back system, but they don't work anymore. Because I'm arbitraising them away. So maybe you and Ken found these same strategies at the same time, but Ken already had like a whole... I think I found them like five, six years too late, but yes. Because I had some good back test, but in real trading, it was like close to zero. Okay.

So you did find something, but Ken was like, I, Neer, Neer, Neerner, I got there first. Yes, yes. Is that why he hired you? And that's fine. I mean, like, I wasn't trying to, I was just like trying to find interesting stuff to trade, right? Right, right, right. Is that why he hired you? Well, I think that, you know, he, part of, like, his rationale for why I should join is that, like, it's much better to find these sources of alpha on a team versus doing it on your own, which I think is correct.

Why is that correct? You know, when you're finding alpha, you have to bring a lot of different ideas together, and it's like unlikely that one person is going to see all of that at once. And if you have, like, a team of people with somewhat different backgrounds on a person. approaches like because like like for like the markets are very close to being efficient and so like to find inefficiency is like a bunch of different things all have to be true and it's like easier to find those combinations in a group right sure yeah add all the different perspectives together kind of the same lesson from Harvard just like for example if you were to tell me that um like you have a really interesting thesis on you know Ethereum and then you know I don't know if I talk to Tom lean he gives me his thesis then I put that together Now you have two CETA. Right, right. How long did you work at Citadel? Yeah, so I was there for around a year

and then another firm recruited me to kind of come in and continue, you know, to kind of actually build a trading desk there. So I kind of, because I wanted, ultimately, like I wanted to do something more entrepreneurial. Like I was trying to do that in college. Did you know that you wanted to be an entrepreneur? Yes.

Yeah. Yes. When did you first know that? When I was eight. Oh, really? Yeah. What happened when you were eight? My dad and I watched a TV program about Bill Gates.

And, you know, I thought that was pretty cool that you could like, you know, build software and, you know, create a great business around that. So building a company has always been something that you wanted to do. Yes. Yeah. Okay. And then you were kind of like searching around throughout Harvard and working with Ken and then the next firm. I don't know. I mean, right.

I mean, it's not like that was the goal. Ultimately, that doesn't mean I had to do it right away. And joining a firm and learning a lot of the lay of the land of an industry is important. I mean, I guess like if we're talking about interactions with like famous people, one of the interesting thing was like when I moved from finance to Silicon Valley, like, I met with Peter Thiel at that point. And his advice was like, don't start a company yet, like get to know how startups work first. Right. Right.

And that was great advice too. Right. Yeah. And I'm assuming you were you as an entrepreneur, you were always kind of like looking around for an idea. Yeah, but you don't want to force an idea. You want the idea to come to you, correct? That's right. That's right.

Uh-huh. Well, I mean, you're looking around at some level, but I mean, when you're like on a trading desk and the markets, especially like we lived through 2008, 2010, fast crash, all that stuff. Like, when you're in the thick of it there, you're not thinking about like, what's my startup idea four years from now going to be. And now some of the information that you're seeing in the markets may end up, like, you know, your brain may process that later, like in the shower.

It's like, oh, actually, like, this is pretty cool. You can, like, maybe there should be, like, a better risk management platform or something. But that may happen years later. Yeah. How did you meet Peter Thiel? Well, Peter, I met through, you know, through my friend who was CTO of Facebook, right? So. Ah, okay.

Peter was their first VCN. Yeah. Do you have an ongoing relationship with him? Well, they, they, you know, the last venture round, we did before launching a token, you know, they co-led that one. That was founder's fund? Yes. Okay. Okay.

Okay. So then worked at Citadel, got picked up by a new firm to build out the trading desk. So took what you learned at Citadel applied to the firm. And it was a little bit like they, you know, nowadays, you know, big companies use the terminology like startup within a startup or something. They started within a big company. And like for me, building a trading desk within a larger firm was literally that, right? That was your first experience actually building a squad and being a leader. That's right. And just like building a business in the sense that like, yeah, I mean, we were like, like, I didn't have to set up HR or payroll or like the silly stuff. Yeah.

Right. So, well, yeah, I mean, it's actually all of the stuff has some interesting aspects. But yes, like the or like I didn't like we had obviously relationships with like prime brokers. But I remember like on my first day doing that like calling like data vendors like who has data for, you know, order like L2 order book data for like this market. Like I was like doing that right. It's not like it was all there. What firm was this that you were sure? Graham capital.

Graham capital. Yeah. Okay. So not far from where we are right now. New York. Oh yeah, yeah, yeah, yeah. And that's also like high-frequency straighting stuff. Well, that's what they wanted me to build. They didn't have it before. I see. I see. And so you built that. Yes. Yeah. When did you end up at Apar? So, right. So then 2012 is when I kind of took the swing to go from, you know, Wall Street to Silicon Valley. So initially I was at Quora for a year

and a half because I was really interested in the problem space there of kind of like, I guess I'm which I get to like matching problems in general. Like in the core, we're like matching users with questions and answers to show them. Adapar was really, you know, at the time like fast growing

FinTech company, right, kind of building, actually like aggregating data from various sources to build ways to analyze portfolios like. And like a lot of where Adapar was started by Joel Onsdale, right,

who co-founded Palantir and he's like actually like applying some of these ideas to finance. Like if you look at 2008, a lot of the reason why risk was miscalculated is because a lot of the data that should have been there in one place wasn't. And so you were ahead of machine learning at Quora. That's correct.

Yeah. And then also Adapar. Well, Atapar was head of all of engineering. Head of all of engineering. And so this kind of goes back to what you were talking about with like the skills that you were uniquely good at in your Olympia days, when your math competition days, where it's like, you really liked getting your hands on data. And that's what that's what you were particularly good at.

And then Quora has like, hey, we have all the, we have users looking for questions. we have people provide the answer is how do we match these things? That's right. And that was the big question to answer at Quora. Yeah, and machine learning. So interesting enough, right, like actually worked on AI, what's now called AI going back to 25 years ago. So because I did, like, in addition to the Olympians, I also did a research competition called the Intel Science Talent Search. That was actually more prestigious competition than the Olympians.

Like, that's where you get to meet the president and stuff like that. And so the project I did for that was essentially like, using optics to, instead of chips, to like run neural networks faster. And but so that's like the, you know, that whole field was like the precursor to, to deep neural networks, which then, you know, called deep learning.

And then that led, let's like transformers and kind of modern AI. Was part of that actually physical? So using optics. Well, my project was physical. Right. Yeah. So part of the competition was a hybrid of being able to do math.

encoding, but then also build physical... Well, you can do whatever you want. You can submit any research project you want. So my project, like, I think probably at least half of the kids who competed in that did do some actual, like, hands-on stuff in the lab, but the others, you know, you could just write, like, you know, like, I placed 12th in that one, but, like, the guy who placed a second was, like, basically just wrote a complete theoretical math paper. Okay, I see.

But what you did was one part physical with the optics. And so you had... Basically, it was now called photonics. That wasn't a term back then, but it was just like optics and neural networks, yeah. Yeah, right. Okay. And it was just really trying to balance the actual optics engineering and, like, all the math behind it to come up with a machine learning program? Well, it's to implement neural networks.

Like, you know how... I mean, to be honest, like, the project would be relevant today, too, because, like, instead of throwing all this compute at silicon chips, like, to using photonics. Like, that is a thing. I mean, I didn't come up with that whole idea. I was optimizing a particular aspect of it. Right, right. But the idea of using optics instead of silicon is a thing, and I think is like that is a new form of computing

as is quantum. Right, right, right, right. And this was early stage machine learning when you were doing this. Well, not really. I mean, machine learning was around since the 80s. Sure. Yeah, yeah, yeah. Well, I guess early stage by how we kind of know it today,

which is just like, you know, AI. Yes. But what's interesting is like that competition back then, I think now these Olympians have become more famous because of, you know, success of some people who've done them. But like back then, this research competition was considered like the main science competition in the U.S. And we got to meet the president, the vice president, a bunch of Nobel laureates. And the Nobel laureates, you know, and they were,

I think, expressing the opinion of the scientific community at the time. They were like, yeah, all this like machine learning stuff, like, that's a dead end. Like, you, like, it's kind of, it's cute that you did this work as a kid, but like, if you want to do science, like, do quantum. Like, this neural network stuff is not going to work. Wow. And so, yeah. Did you have a reaction to that when they said that? Yeah, I mean, at the time, I mean, I was like, who am I?

I was like, yeah, like, let's focus more on the quantum stuff, sure. Like, you know, I was, I kind of, I didn't move away from it. And then I came back to it 12 years after when kind of in Marilla Quora. Yeah. Because big data, I guess the like the big data kind of changed the game. Because I think, I mean, they weren't wrong with they were saying given what existed at the time, because this was pre-internet scale of data. Right.

Yeah, they didn't have the, you didn't have the clay to do. the machine learning to actually manipulate and stuff into being useful. Yes. Okay. And so tell me, I'm assuming, I'll make an assumption here, but I'm assuming there was like an aha moment or like a neural connection coming together in your time at

Quora where you realize that, you know, you actually I do have big data here and you're doing the matching problem, like connecting people at Quora, and now you have the data and you have your experience with machine learning. Yes. Did some, something collide there? Did particles collide there in your head?

when you realize that, oh, there's actually a lot of clay here to work with from the machine learning perspective. Well, the machine learning, you know, we were using it in quant finance too. Because after 2008, a lot, like before that, you could trade in fairly simple ways, and it was more around implementing, like, fast execution, you know, what's now known as high-frequency trading, right? But, like, pretty simple strategies worked. Then after 2008, those simple strategies stopped working, and you had to find more complicated patterns in the market. So machine learning was used for that, too. It just wasn't, see, the differences, like, in the tech industry, if you come up with

anything interesting, you want to create buzz around it and all that. In quant trading, it's the opposite, right? Like, you don't, like, if you actually have really interesting math model to trade. Yeah, you keep a secret. And you want the competitors to think that you're doing something very basic. Yeah. I mean, if you're not, right? Right.

So, like, so we were using a lot of this machine learning stuff, whether it's my team or other teams at my firm or our competitors. Like, we're using a lot of the stuff. So I knew some of that already, including from, like, the work I did as a kid. But, like, yeah, with Quora, you know, the internet companies, we certainly weren't the only ones to do it. I mean, Spotify was matching users and music recommendations. The guy worked on that as another kid I knew from Olympiads. Now he's running a company called Modal.

You know, the Netflix, you know, if you recall, there was the Netflix prize back in 2010. That was when there was like a million dollar price to come up with a better algorithm to rank movies. So this kind of stuff like, you know, structured learning of like user. data and user preferences and products to recommend. Like that was a thing. I mean, we were the first to do it in the space of knowledge at Quora. But what was funny is that Quora actually had a lot of the things you need to build LLMs, right? Because it was text data, unlike Spotify or Netflix.

It was text data, and it was also super information dense. Yes. Because there was all questions and answers. That's right. Yeah. Like in principle, we could have built the first good LLM. I mean, like, our founder joined the board of OpenEI for recently, right? but like, you know, we, I mean, at the time, language models were pretty bad, though.

And so we tried using them even like 12 years ago. They didn't add much alpha to the prediction model. Yeah. Yeah, yeah. So you also had the connections to the two entropic founders from the Olympiad days, correct? Yes. And I don't know if how you kept up with them or if you kept up with them throughout the years. But you had those connections early on.

You worked with machine learning relatively early on. You had your time at Quora. was it when when AI finally happened like we had the chat GPT moment in 2023 or whenever that was for you was it like oh finally like we've got this thing now or like what was it like being on having

kind of like get the information that you had pre chat CBT these companies had like interesting histories right so like open AI was started and you know they had like a bunch of co-founders right like Elon funded it you know it was like I think they were working on a bunch of it was like pretty open end of lab right

like they're working on a bunch of different things. I mean, there was deep mind back then. There's Google Brain. Anthropic was actually like its team was interesting because some of them were actually academics up until like three years before starting a company. So that's probably the fastest anyone's gone from like being an academic to like, you're building a great business. So I think I got a lot of the big picture, right, like that AI was going to be big.

And investing in AI first companies was going to be a thing. and so like the exact path it took I don't think I would have predicted it or I don't know if anyone predicted like the fact that because even within OpenEI like the LLM stuff was not like I remember they gave a presentation in 2018 to the startup we were part of this group called South Park Commons

and we were using AI for what we were building at the time right which was lunch club like matching again matching problem like kind of matching people with other people for professional connection. And anyway, so there were all these, like, presentations people gave either people who were part of our startup community or external guests, right? So like the Open AI guys gave a presentation

was actually Dario who did it. He was still at OpenEI back then. Right? And so they were talking about the stuff where they were competing with Deep Mind on like playing video games. And, like, I'm not a video game person. And so I thought, okay, this is interesting,

but like, this is not exactly the application of AI that I'm excited about. The reality is that they were working on that kind of stuff. They had like two guys on their team who were doing this LLM stuff. Like even with an opening out, that wasn't their main thing. Right. And then they found some application that like was face shift different than like I think when they went from like GPT1 to GPT2, it was pretty big.

Like if you're in the field, it all feels iterative, right? It's like the stuff gets better and better. but for it to be interesting to the consumer was more of a step function because like the can like I guess when I first got a sense that this was going to be a big consumer product was probably like two years

I had like in late 2021 I remember some of our interns were like you know we don't really do homework anymore I was like what do you mean? I'm like this like GPT2 is actually like good enough to do our homework I was like that's even just that is a big market right just like homework

doing homework yeah I was like even if that's all it is. I was like, this is, like, there's some product market fit there. Like, this stuff is not just pure research anymore. But, like, that's like when I first thought, okay, this stuff was going to work out. Because there's a lot of even within AI, right? Like, even within what's used now, there's like generative video images, like all these things are quite different. Right. Yeah. The whole kind of concept of AI has blossomed into basically everything. Yeah. I want to get to your time at lunch club. First, I want to talk about your time at Aipar.

Yeah. Which is the, as I understand it, the last company you were at before, before lunch club. What was Atapar? So Atapar, as I was saying, was like the FinTech company that Joe Lonsdale co-founded. That brought together a lot of financial data and like you can understand your portfolio, like run analytics, run reports. What I wanted to also do there is to use AI to think about like what should be in your portfolio. At the time, the tech wasn't ready for that.

So it was more around like understanding what it is now versus what it should be, but also very, very important function because if you don't know what's your own portfolio now, you think, well, why would you not know? Because if you have many layers of, like, like maybe somebody owns Apple stock directly, but they also own an SPV, they may also own a mutual fund that has Apple. Like the correlations are actually not obvious.

And you ran an internship program there, so you hired interns to work at Ata Par, correct? Well, you know, it wasn't my idea. You know, Joe was hiring interns from day one. I mean, they had interns at Palantir, too. So the idea to hire smart interns was not new. what was new that I brought to the table was that we should source them from the Olympiads.

Okay, okay, so you picked your, an arena that you were familiar with to hire talent. We, you know, we, we're open to talent from anywhere, but, like, we were specifically, you know, that was a, like, you know, because we, it was like, it wasn't just me, we had a recruiting team. Yeah. And what I kind of

was getting the recruiting team to learn about is like, okay, these Olympiads that you can actually, like, you know, it's legal to work in the U.S., you know, at 17 years old, like we can hire people at any age who did these Olympiads is going to be high signal hiring. Did that thesis work out for you?

It did. It did work out. Yeah. How would you gauge that success? Well, certainly it worked out fairly well for Atapar, but it worked out really well for those kids. Yeah. And I mean, I think, you know,

it worked out for those of us who angel invested in their future projects. Yeah. Who were some of those kids that you worked with? Well, one of them I co-founded lunch club with, right? Scott Wu-hoo. is now co-founder's CEO of Cognition. But yeah, you know, like between the Adaparte intern program and, you know, the core

intern programming, like, I think like, so, you know, co-founders of companies like scale, perplexity, modal, as I mentioned, you know, that's just, that's just an AI, right? Also founders of some crypto projects, too, a bunch of them went to trading firms. I mean, I think it was, you know, very, I mean, I think, but essentially we just, like, got a lot of the early stage talent in one place. And they just like shooting. Yeah, they, yeah, and then like, they learn a lot from the full-time folks, but they also, like, form connections with each other. And some of them, like, started companies as a result of connections there. This is, we're going backwards a little bit.

But to what degree do you ascribe the reasons why there was such a strong hit rate of talent coming out of the Olympiads? It was more of just like undiscovered alpha, right? You know, because I think from my perspective, it always would have been like that. I mean, maybe some ebbs and flows depending on specific cycles. But like for one piece of context, right, like so when I was at Citadel in the early 2000s or the mid-2000s, I guess I should say, like 2004, 2005, like, you know, and Citadel had a great recruiting team, one of, you know, top firms in the industry,

but like when I talked to the recruiting team, like they didn't know what the Olympians were. Like, that's crazy to think now, because now all these firms, like, that's like all they do is like, focus on this talent pool. But back then, like, I told them from, like, from first principles,

like what these competitions even were. And so it's just you, you had the experience because you went through it. And so you knew that it was alpha. I mean, I did it there. I mean, I'm sure there are other, You know, there was a guy at early Jane Street who was an Olympiad guy, like HRT, like all these training firms and startup soon, right?

Like, you know, CTO, Facebook did Olympiads. Google, their first CTO actually also did Olympiads. So they, those people kind of slowly but truly educated their colleagues about this stuff, but it took years. And then the alpha got squeezed out. Let's talk about lunch club. So lunch club was the first company that you founded, correct? That's correct.

Yeah. Tell me about just how. how the spark came to you about like why lunch club was a good idea. Well, I think a lot of the, like even if you just, if we think about our conversation right now, I think it's pretty clear that connections are very important, right? Sometimes it's just like even a single connection can change the trajectory of someone's life or career,

in some cases an industry, right? Like, you know, like how much value was created from Peter Tio meeting Elon Musk in 97? The industries we know that this happens all the time, right? Right. So the idea was like, okay, well, But this stuff is all random. People just happen to meet, like, there's all this data on the internet.

Now, we use this data to shop. We use it to listen to music to find recommendations for movies, for content to read. Why not use it as a way to, like, do really valuable networking, right, making the right connections. And but the, so that idea was kind of, that was the mission, but the implementation detail that made it work, or at least made it work as well as it did. Because there have been others that have tried this kind of stuff, right, that didn't really go anywhere. But, I mean, for us, it worked pretty well, especially during COVID.

And even since then, it's like plateaued. It didn't die. But the key insight on the implementation side was, like, how do we avoid adverse selection, right? Because if you have a system where you use, like, the model from, you know, dating sites where you're like, okay, like double opt in, that doesn't really work too well for networking. Because unlike dating where there's like somebody for everybody, like in networking, it's like if you see, if you can, could send requests to anybody. It's like, you know, I don't know, Mark Andreessen would get like a million requests. Right.

And someone who's like a college kid might not get any. Right. Even though what should happen is the college kid who's thinking about biology maybe should be matched with a college kid thinking about AI and they can make the next great AI agent for healthcare. Sure. But like that's the kind of question that should happen instead of both of those kids being matched with Mark Andreessen and Mark never having the time to respond.

Right. Yeah, right, right, right. Yeah, cut out the intermediary or the indifferent and everything. relevant point of the data. To be fair, Mark actually is very good at connecting people, so I don't want to use it. It may be a bad example, but I think you get the point right to

with adverse selection. So you actually don't opt into a particular connection. You let the system make the match. Right, right. So this was your first company, and it seems to me just like a pretty logical continuation, because first you were working at Quora, where you were matching between queries and answers. Yes. People and their queries

and their answers. And then you were working at Aipar working on, like, You were working on your own thing, but also that aggregating data than matching data. But yes. And I guess a lot of, like, I guess less on the product side, more on the people side out of part of like building these teams. I guess a lot of that was kind of relevant. Yeah. Yeah.

And then so then came lunch club, which is just kind of just the you smash those two things together. And, you know, I should point out like a large part of that was also the efforts of Scott too, right? Sure. Yeah. You know, it was only. Scott or co-founder. Yeah. Yeah.

All come from me by means. Yeah, yeah, yeah. But it just seems kind of like your arc, the arcs, the arc of Vlad is kind of crescendoing here at this time. It's like you're going, you're kind of shifting from, you know, acquiring skills and competing in skills to applying skills. That's kind of like the inflection point that I see with lunch club. It was like, okay, Vlad is like kind of starting to find where he wants to really be an entrepreneur. Yes. Yeah.

Talk about the transition from lunch club to lighter. because this is the same company, correct? Yeah, so in terms of, you know, the process that it took, like, corporate entity-wise, it is the same. And, I mean, I can talk about that as well. But I think kind of like why lighter, I think maybe is maybe even more interesting question. Because we, even like in 2017, when we settled on lunch club as an idea, like, we were thinking about crypto ideas then, too. Like, I think when we had a short list of, like, five ideas, like, two weren't crypto. So, you know, it's not like, if you just look at launch coming, like, why would you go from this to crypto?

But if you were to see the full picture of our IDMAs, crypto was always in the background. We just, like, in 2017, a lot of the tech just wasn't ready to do what you do, just how, like, 2014 AI wasn't ready for, you know, out of part. But, like, anyway, and I always really liked, like, you asked earlier about what kind of math problems I really liked in Olympias. Like, one of the fields that I liked was number theory, right? because number theory had a lot of these, like, opportunities to do stuff really creatively and out-of-the-box thinking. And so, like, when I read the Bitcoin paper 2012, again, the only reason I knew about it is because of a friend I made through Olympiads show me the paper.

So I was like, okay, this is actually a really cool way to use number theory and, you know, cryptography. So anyway, so kind of I was always intrigued about the space and especially as it can address some of what I saw is missing in finance and being in Tradfi, right? but yeah the the actual process we went through though because this is all sounds good and well now it's like oh yeah we you know we did this and we did that you know it was it was not easy right and like not a lot of startups like we didn't think it was like a sure thing that we could

succeed with the pivot but our you know like I think in in in in the space where now there's a lot of like negative negative around VCs yeah I mean we wouldn't have been able to do that without VCs supporting the pivot. Because if you think, you know, this was in 2022 when the markets were weak across pretty much every industry. It was right after the collapse of kind of earlier crypto, even before AI started to pick up. So, you know, it was not a good environment for capital markets. Even companies like Robin Hood were down a lot, right? Even companies like SaaS companies from, you know, obviously crypto companies like Coinbase, everything, it was down a lot, right? So

it wouldn't have been easy to just start from scratch. So, we were able to retain 80% of our engineering team through the pivot. The way we did it was like, okay, let's have everyone be part of the process. It's not just like, okay, we're doing this, now we're doing that. It's more like, let's run kind of like an internal YC

within our company and let best idea win. So we actually were like testing three different ideas. Later was the one that was the best. And we kept building that and kind of took some interesting parts of the other ones merged that into lighter. Who came up with the lighter idea? Or how did that idea come about? I think that this one, you know, was an idea that I came up with, yes.

Okay. So what did you see in crypto that really scratched through itch? Because crypto is, I think, from somebody who's very into numbers and into systems, probably provides a lot of material for you to think about. So what about crypto before you even committed to crypto? right what did you see that was like just intriguing or interesting to you right so crypto you know like I think there's this thing people talk about in startups it's like a solution looking for a problem and I think crypto kind of had a little bit of that I mean I guess Bitcoin was always providing value

but a lot of other stuff had more the feel of like okay there's this really interesting tech like there has to be applications for it but like you know like you're around 2017 2018 a lot of the projects back then were like, you know, Uber on chain or like... Very skemorphic, very backwards. Yeah. Yeah, there's stuff like, okay, can we build like a Quora on chain or like, you know, can we like build Airbnb on chain? It was all this kind of stuff, right?

And it's like there has to be like this tech clearly works. It works as sort of value Bitcoin. Like there have, you know, it works as like, you know, decentralized computer with Ethereum. But like there has to be like other applications of this that are really valuable. but it's like hearts are really with your finger on it. And so then the thing is like, well, like let's look at exchanges. Like why is it that we have these digital assets and they represent decentralized protocols

for the most part, but the way they are traded, this was true when we started building. The way they're traded generally is not actually using the rails that they themselves are for. So they're actually, yeah, I mean, there was uniswap and, I mean, AMMs have their own inefficiencies, but that certainly decentralized. But for the most part, like, that was less than 1% of the volume of how digital assets were traded. They were actually traded in ways that didn't actually use the rails that they were for. And so that was like, okay, like, how do we solve that problem?

And we knew a lot about the tech already, right? But like, but the actual, like, connecting that with, you know, because you have to meet the customers where they are, right? It's like, you could build. you could sit in the ivory tower and say like, okay, how would you build the perfect system to replace stratify? Yeah.

Like that's not really how companies get built, you know, unless you know, unless you're Elon or something. But like generally, like you have to start with a small customer base, build something that actually works and grow from there. And so like that's what we're like, okay, like perps is actually something people want. They want decentralized perps, but also that are secure and verifiable. Like no one's been able to do that.

Like we think we can. Mm-hmm. Lunch Club pivoted into Lighter in 2022-ish. Yeah. The first time I ever heard about Lighter was sometime late in 2024. So what happened in those two years? Well, we were, I mean, the core tech, the solution we came up with, to the problem of building an exchange that's low latency, low-cost, secure, verifiable,

and composable, right? Like, it took 18 months to build that. Because it had never been done before. We had to use ZK in kind of novel ways and come up with. So you guys were just heads down engineering, doing some like hardcore because nobody had ever built anything like that

before and so this was all just hardcore engineering work that you guys had done for like 18 months. Yes. Okay. Now we were talking to some small groups of customers

and like showing them prototypes or like different versions but like we certainly weren't publicizing anything that we're working on in the open. It was more like we would go to conferences and meet traders

and you know other builders and show them some stuff and build those relationships over time. which actually proved really useful, right? Because when we got to, when we did have the tech ready, we had 100 traders ready to try it. And they weren't just like people who spent five minutes on it and left.

Like, they knew us for a long time. And they really, yeah. And they really like committed to testing out and giving us feedback over months. The lighter, the problem of lighter, as in the problem that lighter is trying to solve, seems to be quite the amalgamation of all the other things that you've been doing, starting with just like the physics and the informatics and the math Olympias, but then also just the matching engine work that you've been doing. You did at Quora, the high-frequency trading work that you did at Citadel.

It seems to be like it's a perfect problem for you. Maybe that's why you picked the idea in the first place. And for a guy who's hyper-competitive and just really into numbers and math, It seems to be the perfect substrate to solve a very fun problem. Is that how it feel? Yeah, I mean, it definitely feels great to have, you know, it's one thing to work on something that, you know, you feel like you're built to do it.

I mean, it's not a thing to actually build it and have real customers, right? I mean, these things, there's like levels to this stuff as far as the satisfaction you feel. I mean, I think we're still very early if you zoom out. so hopefully there'll be like more rewarding feelings in the future but yeah no it's it's been great i mean i think think the way we thought about the idea maze was like want to build something that sits at the intersection of three things one like something that there's like a large market for right another thing is a mission we're excited about and third something we would actually be we would be like good at building like we were we would do well against competitors if we built it and so like yeah i mean i mean

I think like lunch club fit across all three at the time, although I think where we got it wrong was the market was actually not very big. It got big during COVID and then shrunk. Sure. But with lighter,

I think all three things are true. It's like trading. DRODA is a huge market. You know, the mission of doing that in a decentralized and secure way is the mission we care a lot about. Or, you know, especially after I saw how TadFi works,

what works, what doesn't work. And then, yeah, I think we're kind of particularly, you know, we have a particular set of skills that are good at solving this problem, specifically because we have people on the team that know a lot about cryptography and people who know about quant trading. And like, if you're building a system for traders, including quant traders, which is most of the market makers are, like, it's really helpful to have that background.

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and there is a link in the show notes to get started. Cheers to a good 2026. How would you articulate the mission of lighter in a long form way? Like, what is the big mission of lighter? coming up with like a short concise statement is like you know it was the mark twain quote is like i i wish you know i was writing you short letter i didn't have times or which you long one so i mean that that's that's hard i mean i don't know if we've like cracked that i mean the way i like

to think about it is these like five pillars right like low cost low latency secure verifiable composable and we can zoom in on which what those are and why they're important i mean i think low cost and latency are pretty self-explanatory. I mean, I think secure, this is like after, you know, security isn't just theoretical concept. Like, we've seen, you know, we see different, you know, both in TradFi and in Crypto, right? I mean, we see like hacks and kind of, you know, even things like assets being seized. I mean, there's like security and being able to like actually like hold on to your assets no

matter what is really important, right? But verifiability is equally important, right? Because with verifiability, you wouldn't have made off, you wouldn't have, you know, FTCs, you wouldn't have a lot of these things that have been a problem for finance. And composable, that's the one where, like, all these defy protocols, I mean, have been composed, well, that's one of the nice things on defy, but, I mean, that's not new. But what is new is, like, defy and trotify being composable. Right? All these financial primitives.

You know, now we're talking about like tokenized stocks and perps and options all on the same balance sheet. Like composability, not just from like a software level, but from like a, you know, a balance sheet level is really cool. So anyway, I mean, I think this is all what does, how does this take shape over the years? I mean, I'm a little bit, I like what, you know, Jensen has to say, you know, from Nvidia about how like, you know, we don't think about five year plans.

We think about what we're going to do tomorrow. So like, it's, I mean, it's, I mean, it's, helpful to think about like what does this look like two three four five years or not but really like I think we know one two quarters out and we listen to customers and we keep building but I mean ultimately it's like whatever the merge of defy and stratify looks like we want to build the tech to make it happen well my next question for you was going to be what does lighter want to be when it grows up but I guess I guess you think in in one or two quarters and so you can only tell me what you're going to what lighter wants to be in two quarters yeah I

I mean, I think the saying like the longer term kind of being the technology layer for, you know, D5 meaning stratify is a way to describe the mission for sure. But again, I think when you pick like a short statement like that, that misses something because then anytime you do that,

somebody can be like, oh, well, what about this thing, though, that I care about? Is that part of the mission? No, it is. It's also that, yeah. Yeah. Yeah. And so, but yeah, a couple quarters out, it's like, you know, one of the really interesting things that I really like

about kind of, you know, the crypto space is that like, you know, there's this question that comes up when you start building a company in software. It's like, are you a product company or are you an infrastructure company? And I feel like with crypto, you can actually be both and not just to check the bugs, but you're actually building something that by its nature can be both. And so from that perspective, like, yeah, we're building lighter core.

And lighter core is interoperable with other instances of lighter that are used by partners, like telegram wallet, Robin Hood, et cetera. There's kind of many more of those to come. So that's big, but then also we're building products, you know, directly like options, like, you know, stuff with AI agents. Like there's a lot of exciting stuff.

Well, Vlad, we're coming up on time, but let me take a moment to just kind of articulate about what about lighter excites me. And maybe I can get you to react to it. And really all throughout my time in crypto, we've seen an. evolution of exchange technology. And first we had like the, all the different proliferation of blockchains in the

fork and fair launch phenomenon in 2013. And we had this new crop of exchanges. We had, you know, the bitrexes and the bitmexes of the world. And we had this growth of centralized exchanges. So we like, we unlocked that part of the tech tree. Ethereum came along. We had an ether delta, which put that kind of technology, but put it on chain. Terribly slow, terribly inefficient.

But nonetheless, it worked. Uniswap came. along, we had the AMM, got a little bit better. We had the L2s come along. And there's been this just arc of exchange technology that has grown. And I see, and then like you had the big exchanges like Coinbase and Crackin and Binance kind of really plow the way of like what actually like compliant and compatible centralized exchanges works with like tradified regulation and all this.

With lighter, it seems to be that there is somewhat of a step function change in terms of the technology under the hood with the ZK circuits that optimize for latency, but also providing the auditability. That is something that Coinbase can't offer. Binance can offer, Krakken can offer. Uniswap can offer, but Uniswap can't offer the speed and the latency of the ZK circuits.

And so it's like if Coinbase was like a next generation exchange and then Uniswap was like a next generation exchange, seems like lighter is like something like Gen 3 or the most latest generation of exchange technology, which to me kind of embodies a lot of the ideals that crypto has, the user verifiability, but it also has the product demands that traders want and must have. And it seems to be there's just like a synthesis of all of the things that we've ever learned in crypto, all kind of being applied into the same spot to create a product that has a lot of these, that satisfies some of the most hardcore users and traders, but also people like me who are more like idealistically driven.

Yeah. And so that's kind of like, I just think of it as like the next generation. I think like the NASDAQ or the CME or the New York Stock Exchange would hopefully one day be built on that same kind of like ZK circuit substrate because it's just like it's just better for everyone. I'm sure the regulators also want it too because it's better for them as well, the transparency of it all.

And so that's kind of like why I've gotten excited about lighter. And I just want to kind of like react. You can react to that however you like. Well, first of all, like we're, we're glad to see, you know, really passionate, you know, support from, from early community members. I think you articulated it as well as anybody. So that's, like, that's really rewarding as an entrepreneur, right, to hear kind of early users really understand, in some cases, better than we do, why what we're building is important. But, but I think, you know, as a technologist,

like, you talked about how, okay, like, there have been aspects of this that have worked well in the past, but there's always a tradeoff. And, like, we, found a way that where we can actually kind of achieve all these things. And that's what's really fun, right, as a technologist. It's like sometimes, like most of the time you're solving problems where like you're optimizing X at the expense of Y and so, you know, you're making calls and like, okay, is that still like net, net is that the right thing to do or not? But sometimes you find these solutions where you can actually improve X and Y or X and Y and Z. And that's from, I think you, those come about kind of once in a generation,

sense that like, you know, like I think in AI, like the transformer was such thing, right? I think in crypto, the ZK proofs were such a thing where like with this technology, you can actually like do better across many dimensions relative to the previous frontier. And so, so that's kind of on the technical side, what it is. And I think right now we're the biggest ZK project. Like I think arguably like lighter is kind of what proves that there's, the ZK has real applications. And then I think the second part of what you describe was like how TrotFi perceives it. You know, Trotify is interesting, right, because they understand the tech nowadays.

They're testing it out in various forms. Ultimately, you stuff to remember, like, Trotify follows the money, right? So when they see product market fit, they want to make sure the stuff is legit and works in ways that are fair and verifiable and, you know, many cases regulated. so that's all prerequisite, but ultimately, TratFi also needs to see that it works. So you can't just go into Trotify and say, okay, like, you know, I have a better mouse trap.

Like, you should just, you know, you're running trillions of dollars of, you know, volumes, and you've done it for 150 years, but I have a better way you switch to my way. Like, it doesn't work. Like, they're going to do crawlwalk run. They're going to tell you to go build the mousetrap and then maybe we'll use it.

Yeah, exactly. And then it's like, okay, well, maybe, It's like, you know, like maybe the path would looks like where you first try it. Like, I think the example of, you know, ICE making a strategic investment in polymarket is like a good example of this. It's like, like prediction markets weren't something that ICE was doing before. So it's like for a new space, they're going to try new technologies. And if they work, they may explore it further. And so that's kind of, I think, how tried if I looks at things.

It's not like, okay, this, I mean, similar to AI, right? like when if, you know, a large bank, it's not like large banks in 2020, we said, okay, this is interesting. We're going to rebuild everything with it right now. So like, let's try it out in different pockets. Like we'll use it for this part of the business, for that part of the business. And eventually it becomes the thing. Simple.

Yeah, yeah. And from the margins. That's right. Yeah. So that's how we see it playing out. But, you know, a lot of the stuff has to do with like particular constraints that these businesses have, you know, they all have shareholders. They have their own management structures, this and that.

And we just have to be willing partners, you know, engage and and meet them where they are. Well, Vlad, we've got a particular set of skills. It's been fun watching you apply them, and I will continue to watch you apply them over the year. So thanks for coming on the show and meet me in New York.