From Harvard at 18 to Building Lighter

Transcript

Hey, Bankless 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 Hyperlid 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 Daario from Anthropic and Vlad from Robin Hood and a bunch of other names that have gone on to raise massive companies. And so the 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 this episode, we just kind of start rolling. So maybe it feels like you started 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. Uh cuz people know you as the founder of Lighter, uh the Perpex, Ethereum layer 2 Perpex. Uh but your background goes pretty far pretty far back into your childhood I think with your first like relevant set of skills. Uh talk to me about >> particular set of skills.

>> Particular set of skills. Yeah. Talk to me about math. Uh you won a math and physics olympied. Me and most of my listeners probably don't know what that means. What does that mean? >> Yeah. Um well I started competing in you know academic competitions. Let's just call it that when I was around 12. And uh I guess some of the highlights of that were making the US team to the international physics olympiad um and the international Olympiad informatics. On the math side in informatics >> yeah that's the programming one.

>> Okay. >> The math I I mean I tried to do a hat trick as far as making all three. Um I was actually the first or one of the I think one one of the first people in the US ever to make two teams. Um didn't quite make three but you know but it was good. What does a uh what does it mean to compete in math and physics? Like how does one actually what 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's actually are like uh like these days like Twitch streamers and whatnot who do that where it's like you're you're writing code basically have a problem solved like I don't know the problem could be like here you know

here's location of two pizza places in New York find the shortest >> path given traffic how to get from A you have to actually like it's not theoretical you actually have to write a program that would actually do it >> right it's deterministic so there's a very clear correct answer >> yes okay >> yes and so like you have um you know three problems like that 5 hours, two days of of that. Uh so that's the that's the programming one. Um math at the I'm talking about the highest levels, right? And the ways leading up to that it's you know it's not 5 hours, right? There's like it's uh less intense. But and then the math um that one looks like it's also um two days and um you have three problems where you have to find a solution with

proof. You've probably heard of like um the four color theorem like 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 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 you have to like prove that you can always do 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 >> um 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 some bring in a bunch of different concepts together. But then there's also the experiment part. Uh, 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 I'm assuming you were like intrinsically just thrilled to do this math to to be in these competitions, right? Like math is entertain entertaining for you. Yeah. So the the thing was that um like it was like a virtual cycle cuz I was pretty good at

it even before competing and then once I started competing I saw that like winning is fun. >> Okay. >> And um and not only is winning fun, meeting other like-minded people. Mhm. 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 a solo activity. This you're on a team. Well, yes, but even I mean a lot of the the competitions are individually measured and then the country um you know once you get the international level the the countries have teams but you just like you know cuz there's training camps like as it was you know you sit around wait for results like you meet a lot of the the people it's like you know the opportunity to meet other like-minded uh you know students but really just kids

around the country >> right who did you meet funny because a lot of Uh 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 um what we see in industry like for example um at the US the training camp for the US physics Olympiad um two of the co-founders of anthropic were there. So I got to meet them got to know one of them quite well actually. Um and then at the Intronics one um you know the first CTO of Facebook was I mean they weren't they they future >> so some of the people in you know who's also on the board of open now right so like that's a pretty interesting uh and and that's that's just like >> people who've achieved

uh you know the kind of the most well-known outcomes but there are a bunch of others who write like for example like I think you look at people who run some of the big quant 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 is it is your cohort unique in that it 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 um 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. >> Mhm. >> That that cohort in particular, not and not just in on the math and physics side, like that cohort, you know, like achieved a lot of success. Um I mean, you know, the vice president is part of that cohort too, right? Like you know, people who've come who've done very well in politics, done very well in other forms of business. like that early 2000s, I think there's a lot a lot of great achievement that came out of that.

But but but that being said, like if you look back um even to the 90s, like one interesting thing was the 93 uh team that Harvard sent to the ICPC, which is like the equivalent of the Informatics Olympiad at the college level, Steam Competition team of three. So out of that three person team, not one but two became billionaires. >> And like in the 2000s, not not now. when was that was a very high bar. >> Yeah, I would imagine the competition has plenty to do with it because like what's the common denominator between physics, math, and informatics? You know, there there's a lot in common there, but really it's the competition side of things that if you're good, you were you were almost as you said did the

hattick. Um what kind of problems whether whether it was math, physics or or informatics, what kind of problems what what what type of problems did you really enjoy the most? Is there 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 as different even of the people who um did well, we all had like our own like favorites or like the kinds of things that like we thought we rocked better than others.

>> You were particularly strong at. >> Yeah. And and this was always a debate like some guy would be like, "Oh, like I love like geometry is really cool. I love geometry." And then like maybe somebody else like oh no like that like like trigonometry is really cool right etc. 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 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 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.

the stroke of genius problems. >> Well, I guess gen, you know, I think it's more of just or thinking outside the box, being thinking creatively, >> right? Exactly. >> Yeah. >> I mean, you didn't, you know, these problems solve them. You don't have to be, you know, Einstein. >> You're not thinking of something that no one's ever thought of before. But you do have to think outside the box. >> I've been trading crypto assets for almost a decade. And I've used so many wallets exchanges aggregators front ends, and I'm kind of always looking for the same thing. one interface with deep liquidity across a bunch of chains and assets and the ability to act in private and I still control my own funds and I've never really found it and I'm always switching wallets, juggling gas fees or getting eaten by slippage.

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>> So, we were um at the same high schools, uncle Thomas Jefferson, which is a math and science high school in Northern Virginia. >> Mhm. Um, yeah, we were the two lads. I was, you know, Russian immigrant. He was a Bulgarian immigrant. >> Um, >> our paths crossed more later in life like I mean we, you know, we were at the high school. Um, of course, I mean, he started doing math actually more >> deeply in undergrad and then graduate. He he was um a PhD student under Terry Tao who's probably like the best mathematician in the world. uh you know UCLA and you know so so anyway so then he got into high frequency trading 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 the these cohorts of people? Was it kind of just obvious amongst you and 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 cuz 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 come up with like you stack rank everyone let's say in the country right >> like whoever the top seed is second seed third seed if you like and that 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 not about the same then. So like these are the people who will contribute and now I think the the the thing that was different is like I think cuz 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 like Harvard and 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 uh as you're growing up uh and just kind of getting into your adult years, did a particular direction for you open up and become clear or or how did you 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 one thing that I realized um at some point around then is like I really like data >> and that's the the thing is that um 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 um the funny thing about that was like when I was training, I didn't do that well in the experiment part. I would always like 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 whatever I don't know but in the experimental one I was ranked second in the world and so overall I still got a 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 um applied sciences later on like economics, finance, trading and kind of

so on and so forth. >> You met uh Ken Griffin sometime in your teenage years correct? Um yes I finished high school um at 16 and u Harvard at 18. So yeah I was >> 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. >> Um >> how does one do that? >> You know there's like you you can get credit for one year from cuz I took a bunch you know like we were competing on a lot of things right including how many APs you took. >> Mhm.

>> Like I'm talking about among that competitive cohort. Right. Like I mean there's one kid doing 17 APs I think I did like 12. So that's enough to get one year of credit at Harvard and then and then I just like instead of taking five classes this semester I took six to get another semester. >> Was it about finishing Harvard? Like that was it another competition. Is like who can finish Harvard quicker? >> Um well that was more of a competition between me and myself. No one else was really trying to do that. The I mean I looked at it like I talked about thinking outside of the box, right? Like the way I looked at it was um you know assuming I now these days probably someone like me would have dropped out and gone to like uh 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 with with Zaga but like >> right Zuck hadn't done it yet because Right. Okay. Yeah, 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 the 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 can crossregister. I can literally take the same exact classes as a graduate student and be on the same exact campus. And it's 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 in four years? >> Well, because of this thing, right? Because because it's strictly better to be a grad student than an undergrad. >> It was just an optimization problem >> like in 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 the other way around. Right.

>> Sure. >> Strictly better. It was a sister cross where you can take the same exact classes >> and this is just like a life hack that no one really thought about. >> 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 the 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 were already doing that but like to think about a real world problem from like imagine thinking about real world problem as a mathematician as a philosopher as a 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 Again, this is cliche, but 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 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 Harvard is a super social environment, right? and need to be in in that game to really excel. I think coming out of Harvard, well, it

depends on the field, right? If you're like um I mean I guess like most scientific fields have some collaboration, but obviously there there also others like that were more like the lone wolf archetype too. So it really depends on the field, but yeah, for for a lot of the fields, the value you get out of Harvard is through like a collaboration, but b meeting people from other fields. So your first step out of Harvard was working with Ken Griffith. I guess that was the original question. We got so Ken >> um you know like he has he himself had started Citadel out of Harvard I believe he did he gradu he wasn't a dropout I think he he graduated but he was running the fund out of his dorm room in like 1988 1989 um like 15 years before I was there and uh 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 ground and uh spent a lot of time we convinced Sime to join. >> Was that your first time applying your love of like numbers and engineering and and uh 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 um as a college student? Not particular. I mean, I found some strategies that worked okay. Uh, and then Ken actually told me like, "Yeah, these strategies aren't as good as like like some of these strategies used to work if you back test them, but they don't work anymore >> because I'm arbiting them away." >> So maybe maybe you and Ken found these same strategies at the same time, but Ken already had >> I think I found them like five, six years too late. But yes, cuz I had some good back tests, but in in real trading, it was like close to zero. >> Okay. So, you did find something, but Ken was like, "Ner, Nener, Nener, I got there first."

>> Yes. Yes. Is that is that why he >> then that's fine. I mean that he like I wasn't trying to I I was just like trying to find interesting stuff to trade. Right. >> Right. Right. Right. Uh is that why he hired you? >> Well, I I think that um you know he part of like the 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 whereas if you have like a team of people with somewhat different backgrounds and approaches like um cuz like like for like the markets are very close to being efficient and so like to find in efficiencies 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 add all the different perspectives together kind of the with the same lesson from Harvard. >> 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 talked to Tom Lean he gives me his thesis then I put that together

>> now. Yeah. Right. Right. How long did you work at uh at Citadel? >> Yeah. So I I was there for around a year and then another firm recruited me to kind of come in and um continue you know to kind of actually build um a trading desk there. So I kind of cuz 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 what happened when you were eight? >> My dad and I watched the TV program about Bill Gates. Uh-huh. >> And uh you know that was I thought that was pretty cool that you could like you know build software and uh you know create a great business around that. >> Mhm. >> 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 >> Well, I didn'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 and like joining a firm and learning a lot of the lay of the land of an industry is important. I mean >> I guess um like if we're talking about

interactions with like famous people, one other interesting thing was like when I moved from finance to Silicon Valley like I met with Peter Teal at that point and his advice was like don't start a company yet like get to know how startups work first, right? >> And that was great advice too, >> right? Yeah. And I'm assuming you were you as as an entrepreneur, you were always kind of like looking around for an idea, but you 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, flash 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 may process that later like in the shower is 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 Tal? >> Uh well Peter I met through you know through my friend who was CTO of Facebook right. So >> ah okay >> Peter was their first Yeah. Do you have an ongoing relationship with him or did >> they uh they you know the last venture round we we did before launching a token you know they co-led that one >> that was founders fund.

>> Yes. >> Okay. Okay. Um 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 it to the >> And it was a little bit like they you know nowadays um you know big companies use the terminology like startup within a startup or something. 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 uh experience actually building a squad >> and being a leader. >> That's right. And and just like building a business in the sense that like Yeah. I mean we we were like you know 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 the stuff

has some interesting aspects of it 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 okay like who has data for you know order like um 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 capital yeah so not far from where we are right now in New York. >> Oh yeah yeah yeah and that's also like high frequencies trading stuff. Well, that's what they wanted me to build. They didn't have it before.

>> I see. And so you built that? >> Yes. >> Yeah. When did you end up at at a par? >> So, right. So, then 2012 is when I um kind of took the swing to go from um you know, Wall Street to Silicon Valley. So, initially I was at Quora for a year and a half. Um cuz um I was really interested in in the problem space there of um kind of like I guess I'm attracted to like matching problems in general, right? like in the core were like matching users with questions and answers that to show them. Mhm. >> Um, Adapar was uh really um you know at the time like fast growing uh 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 adapart was started by Joel Lansdale right who co-ounded Palunteer 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 A lot of the data that should have been there in one place wasn't. >> And so the you were head of machine learning at Quora. >> That's correct. >> Yeah. And then also at a part the >> Well, that part was head of all of engineering. Yeah. >> Head of all of engineering. And so this 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 was like you really liked getting your hands on data

>> and that's that's where you were particularly good at. And then Kora is like, "Hey, we have all the we have users looking for questions. We have people providing answers. How do we match these things? >> That's right. >> And that was that was a big question to answer at Kora. >> Yeah. And you know, machine learning um so interesting enough, right? Like actually worked on AI, what's now called AI going back to um 25 years ago. So cuz I did like in addition to the Olympiads, I also did a research competition called the Intel Science Talent Search. That was actually more prestigious competition than the Olympiads. Like that's where you get to meet the president and and stuff like that. And so the project I did for that was um essentially like using optics to um instead of chips to like run neural networks faster and but

so that 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 led to like transformers and kind of modern AI >> was part of that actually physical so using optics >> my project was physical yes >> right yeah so part of The competition was a hybrid of being able to do math and coding but then also build physical. >> You could do whatever you want. You could 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 um you know like I placed 12th in that one but

um like the guy who placed a second was like basically just sort of complete theoretical math paper. >> Okay, I see. But what you did was one part physical with the optics >> and so you had >> basically what's now called photonics that there wasn't a term back then but it was just like optics and neural 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 uh 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 using photonics like that is a thing I mean I mean I didn't come up with that whole idea I was like

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 you know quantum >> right right and this this was early stage machine learning when you were doing >> machine learning well not really I mean machine learning was around since the '8s >> 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 Olympiads have become more famous because of, you know, success of some of the people who've done them. But like back then this research competition was considered like the main science competition in in the US and we got to meet the president,

the vice president, bunch of Nobel laureates. Um, and the noble warriors, 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 did you have a reaction to that when they said that? >> Yeah. I mean, I at the time, I mean, I was like, who am I? I was like yeah like let's let's let's focus more on the quantum stuff. Sure. Like you know I was I kind of I did move away from it and then I came back to it 12 years after

when kind of in in Marora. >> Yeah. Big data I guess like the big data kind of changed the game >> cuz I think they I mean they weren't wrong what they were saying given what existed at the time because this was pre internet scale data >> right? Yeah. They didn't have the you have the clay to do the machine learning to actually manipulate stuff into being useful. >> Yes. >> Okay. And so tell me I'm I'm assuming I'll make an assumption here, but I'm assuming there was like an aha moment or like a a neuron neural connection coming together in your time at Kora where you realized that you know you actually I do have big data here and you're doing the the matching uh the matching problem like connecting people at Kora and now you have the data and you have your experience with machine learning.

>> Yes. >> Did some something collide there? Or did particles collide there in your head when you realized that oh there's actually a lot of clay here to work with from the machine learning? >> 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 difference is like in in the tech industry if you come up with anything interesting you want to like create buzz around it 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 even if you're not 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 this stuff so I knew some of that already, including from like the work I did as a kid. But like, yeah, with with with um Kora, you know, internet companies, we certainly weren't the only ones to do it. I mean, Spotify was matching users and and music recommendations. Um the guy worked on that is another kid I knew from Olympiads. Now he's uh running a company called Modal.

um you know um the Netflix you know this you know if you recall there was the Netflix prize back in 2010 that was when there was like a million dollar prize 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 the thing I mean we were the first to do it in the space of knowledge at Kora. Um, but what was funny is that core 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. >> Cuz it was all questions and answers. >> That's right. >> Yeah. >> Like in principle, we could have built the first good LLM. I mean like our our founder be you know joined the board of OpenAI for recently, right? like you know we I mean at the time language models were pretty bad though and so the we tried using them even like 12 years ago they they didn't add much alpha to the prediction model. >> Yeah. Yeah. Yeah. So you also had the connections to the two anthropic founders from the Olympia days. Correct. >> Yes.

>> And I don't know if how where 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 Kora. Was it a when when AI finally happened like we had the chat GBT moment in 2023 or whenever that was? >> Yeah. >> For you was that like oh finally like we've got this thing now or like what was it like being on uh having the kind of like the information that you had >> preacht? These companies had like interesting histories, right? So like OpenAI 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're working on a bunch of it was it was like

pretty open-ended 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 3 years before starting a company. >> So, that's probably the fastest anyone's gone from like being an academic >> to like building a great business. So, um I think I got a lot of the big picture, right? Like that, um AI was going to be big. >> Mhm. >> And investing in AI first companies was going to be a thing. Um and so like the exact path they 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 OpenAI like the LLM

stuff was not like I remember they gave a presentation at in 2018 to the startup. We were part of this group called South Park Commons and we were using AI for um 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. Um 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 OpenAI guys gave a presentation. It was actually Dario who did it. He was still at OpenAI back then, right? And so they were talking about this stuff that where they were competing with um 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 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 open that wasn't their main thing and then they found some um application that like was phase shift different than like I think GP for when they went from like GPT1 to GPT2 it was pretty big like like if you're in the field it all feels iterative right it's like the the stuff gets better and better >> but for the for it to be interesting to the consumer was more of a step function >> right

>> cuz like the Like I I guess when I first got a sense that this was going to be a big consumer product was in um 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 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 just like homework. Yeah. Yeah. I was like I even even if that's all it is I was like this is like there 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 is going to work out cuz 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 I want to get to your time at at uh Lunch but first I want to talk uh about your time at Adapar which is the as as I understand it the last company you were at before before lunch club what was adapar >> so adapar as I was saying was like the fintech company that Joanzel co-ounded that brought together a lot of financial data and like um you could understand your portfolio like run analytics run reports what what what I wanted to also do there is to use AI to think about like what should be in your portfolio >> um at the time the tech wasn't ready for

that so it was more around like understanding the what it is now versus what it should be but also very very important function Because if you don't know what's in your own portfolio now, you think, well, why would you not know? Well, 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 under like the the correlations are actually not obvious. >> Mhm. And and uh you ran an internship program there. So you hired interns to work at Adapar, correct? >> Well, the you know, it wasn't my idea. You know, Joe was hiring interns from day one. I mean they had interns at Palanteer or 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 Olympians. >> Oh okay. Okay. So you you picked your an arena that you were familiar with to to hire talent. >> We you know we were 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 and and what I kind of was >> um getting the recruiting team to learn about is like okay these Olympiads you can actually like you know it's legal to work in the US you know 17 years old like we can hire people at any age who did who did these Olympads it's going to be high signal hiring.

>> Did that thesis work out for you? >> It did work out. >> Yeah. How how would you gauge that success? Um well it certainly it worked out fairly well for Adapar but it worked out really well for those kids and uh I mean I think you know it worked out for those of us who angel invested in their future projects. >> Yeah. Who who were some of those kids that you that you uh worked with? >> Well one of them I co-ounded lunch club with right Scott uh Scott Woo who is now uh co-founder CEO of Cognition. Um but um yeah you know like between between the Adapar intern program and um you know the core intern program I mean like I think like so you know

co-founders of companies like scale perplexity u modal as I mentioned um um you know and that's just uh that's just an AI right um also founders of some crypto projects too a bunch of them went to trading firms. I mean, I I think it was uh, you know, very uh I mean, I think essentially we just like got a lot of the the early stage talent in one place and they >> they just they 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. Mhm. This is we're going backwards a little bit to but to what degree do you uh ascribe the reasons why there was such a strong hit rate of talent coming out of the Olympiads? >> It was more of like just like undiscovered alpha, right? You know, cuz I think from my perspective it always would have been like that. I mean, maybe some es and flows depending on specific cycles, but um like like for one one piece of context, right? Like so when I was at at Citadel in the early 2000s or the mid2000s I guess I should say like 2004 2005 like um 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 cuz 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 Mhm. And so it's just you knew you had the experience cuz you went through it and so you knew that it was out. >> I mean I did it there. I mean I'm sure there are other you know there's a guy at early Jane Street who was um an Olympiad guy like HRT like all these training firms and and startups too right like um you know CTO of Facebook did Olympiads. Google um their first CTO actually also did Olympiads

like so they they those people kind of slowly but surely educated their colleagues about this stuff but it took years >> and then the alpha got got squeezed out. >> Yeah. Yeah. >> Let's talk about Lunch Club. So Lunch Club was the first company that you founded correct? >> That's correct. >> Yeah. Uh tell me about just how the the spark came to you about like why Lunch Club was a good idea. Well, I think a lot of the if you like even if you just um >> we think about our conversation right now, I think it's it's pretty clear that connections are very important, right? Like sometimes 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 what how much value was created from Peter Tio meeting Elon Musk in 97.

>> The industries we know this happens all the time, right? Right. >> Um so the idea was like okay but this stuff is all random. just happen to meet like there's all this data on the internet now we use as 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 >> um networking right making the right connections and but the the so that that idea was kind of that was the the mission but the implementation detail that made it work or at least made it work as well as it did cuz There 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 co

>> and even since then it's like plateaued. It didn't it didn't die. But the key insight on the implementation side was like how do we avoid adverse selection, right? Cuz if you have a system where you use like the the model from you know dating sites where you're like okay like like double optin 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 could send requests to anybody like it's like you know I don't know Mark soon 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 who's thinking about AI and they can make the next great AI agent for

healthcare. Like that's the kind of that should happen instead of both of those kids being matched with Mark Andre and Mark never having the time to respond. >> Right. Yeah. Right. Right. Right. Yeah. Cut out cut out the intermediary or the the indifferent and irrelevant point. >> Actually Mark to be fair Mark actually is very good at connecting people. So I don't want to use you know maybe bad example you know but but I think you get the point right to avoid adverse selection. We 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 uh continuation cuz first you were working at Quora where you were matching between queries and answers.

>> Yes. >> People and their queries and their answers and then and then you were working at Adapar working on like you were working on your own >> about aggregating data than matching data but but yes and I guess a lot of >> the like I guess less on the product side more on the people side of part of like building these teams. I guess a lot of that was kind of relevant. >> Yeah. Yeah. And 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? So it wasn't, you know, it was only Yeah. Didn't all come from me by any means.

>> Yeah. Yeah. Yeah. Yeah. But it just seems kind of kind of like we're kind of your arc the Vlad the arcs uh the arc of Vlad is kind of crescendoing here at this time. feel like you're going you're kind of shifting from >> um you know acquiring skills and competing in skills to to applying skills. That's kind of like the inflection point that I see with Lunch Club. It was like okay Vlad Vlad is like kind of starting to find where he wants to really be an entrepreneur. >> Yes. >> Yeah. Talk about uh the transition from Lunch Club to lighter because this is the same company, correct?

>> Yeah. So in terms of um you know the um the process that it took um like corporate entity wise it is the same and I mean I can talk about that >> as well but uh I think the kind of like why lighter I think maybe even more interesting question uh >> cuz we even like in 2017 when we settled on lunch club is ideal 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 in crypto.

>> So, you know, it's not like if if you just look at launch, like why would you go from this to crypto? But if you were to see the full picture of our idea maze, crypto was always in the background. We just like in 2017, a lot of the tech just wasn't ready >> to do what you need to do. Just how like 2014 AI wasn't ready for, you know, adapar. But like anyway, um and I always really liked like you asked earlier about um what kind of math problems I really liked in Olympia. Like one of the fields I liked was number theory, right? because number theory had a lot of these like opportunities to do stuff really creatively uh 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 showed 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 we're kind of was always intrigued about the space and especially as it can um address some of what I saw as missing in finance and being in trfy Right. But yeah, the the the actual process we went through though cuz this is it 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 and like not a lot of startups like we didn't think it was like a sure thing that we could succeed with a pivot uh but our you know like I think in in in the space we're now there's a lot of like

>> negative negativity around VCs I mean we wouldn't have been able to do that without VC 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? Um even companies like um SAS companies from you know um obviously crypto companies like Coinbase, everything was down a lot, right? So >> it wouldn't wouldn't have been easy to just start from scratch. So we were able to retain 80% of our engineering team through the pivot.

>> Um the way we did it was like okay like let's have everyone be part of the process. >> Like it's not just like okay we're we're doing this now we're doing that. It was 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. Lighter was the one that was the best and we can 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? >> Um >> or how did that idea come about? I think that this one, you know, was idea that that I came up with. Yes.

>> Okay. So, the um what did you see in crypto that that you that really scratched your itch? Cuz crypto is I think from somebody who's very into numbers and into systems probably provides a lot of material for you to to think about. So, what about crypto? Before you even committed to crypto, >> 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 saying people talk about in startups. It's like a solution looking for a problem. And I 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 I mean, you were around 2017, 2018. A lot of the projects back then were like, you know, Uber on chain or like >> veryorphic, 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 there has to be like this this tech clearly works. It works as store of value Bitcoin. Like there have, you know, it works as like, you know, decentralized computer with Ethereum, but like there there has to be like other applications of this that are really valuable. But it's like hard to really put your finger on it. And so then the

things like well like let's look at exchanges like why is it that we have these digital assets and they're 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 unis swap and I mean amms have their own inefficiencies but but that's 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.

>> Mhm. >> And so I was like okay like how do we solve that problem? And we we knew a lot about the tech already, right? But like but the actual like connecting that with, you know, cuz you have to meet the customers where they are, right? It's like you could build you could sit in an ivory tower and say like, "Okay, how would you build the perfect system to replace Strati?" >> Yeah. >> Like that that's not really how companies get built, you know? um 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 where we're okay like per is actually something people want they want decentralized per but also that are

secure and verifiable like no one's been able to do that like we we think we can >> Mhm. Lunch Club pivoted into lighter in in 2022ish. >> 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 that took 18 months to build that >> cuz it had never been done before. We got to use ZK in kind of novel ways and come up with.

>> So you guys were just heads down engineering doing some like hardcore cuz nobody had ever built anything like that before. And so this was all just hardcore engineering work that you guys had done for 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. Um, but like we certainly weren't publicizing anything that working on in in in the open. It was more like, you know, 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? Cuz when we got to when we did have the tech ready, we had 100 traders ready to try it. And they were they weren't just like people who spent 5

minutes on it and left. Like they they knew us for a long time and they were they knew the grit. >> Yeah. And they really like committed to testing out and gave it giving us feedback over months. >> Mhm. the lighter the problem of lighter as in the the problem that lighter is trying to solve seems to be quite the amalgamation of all the other things that you've been doing uh starting with just like the the physics and the informatics and the math olympiats >> uh but then also just the matching engine work that you've been doing you did at Kora uh the high frequency trading work that you did at Citadel. It seems to be like the perfect problem for you and maybe that's why you picked the idea in the first place, but yeah. And for a guy who's hyper competitive and

just really into 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 it it it definitely feels great um to have you know it's it's one thing to like work on something that um you know you feel like you're like you're built to do. I mean it's another thing to actually build it and have real customers. All right. I mean these things there's like levels to this stuff um 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 uh rewarding feelings in the future. But yeah, no, it's it's been great. Um I mean, I 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 we would do well against competitors if we built it. And so like um yeah 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 like it got big during co and then shrunk.

>> Sure. But with with lighter I think all three things are true right it's totally like trading derivatives is a huge market you know the mission of doing that in a decentralized and secure way is a mission we care a lot about or you know especially after I saw how traiy 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 that are good at at solving this problem specifically because we have people on the team that know a lot about cryptography and people who know a lot 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 that background.

Some exciting news. We are launching a new podcast to help people figure out the crypto cycle, how to navigate it. The best crypto cycle investor I know, his name is Michael Nato. He runs the DeFi Report. This is the guy that sent me a sell alert before the 1010 price drop happened. His cycle analysis has been absolutely on point. I've been following him for years and this year we started recording weekly podcast episodes. Each one we get into his portfolio, what he's holding, the market structure, entry targets, fair market value of Bitcoin and Ether, and where we are in the cycle. There's new episodes that are released every Wednesday.

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premium feed instead of the free feed, you'll get about 20 hours of your life back every year because you choose to support Bank List directly. So, if you're interested in getting extra content all while skipping the ads, or you just appreciate what we do here and want us to keep doing it, we'd appreciate it if you signed up for Banklist Premium 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 in a long form way? Like what's the what is the big mission of letter? >> Coming up with like a short concise statement is like you know was the Mark Twain quote is like I I wish you know I was writing you short letter I didn't have time so I wrote you a long one. So that 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 um 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 think low cost and latency are pretty self-explanatory. Um 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 um you know both in in trfy and and in crypto right? I mean, we we see >> like hacks and um kind of um you know, even things like

assets being seized. 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, FTX, you wouldn't have a lot of these things that have been a problem for for finance. M >> um and composable that's that's the one where like all these D5 protocols I mean have been composable that's one of the nice things about D5 but I mean that's not new but what is new is like defi and stratfi being composable >> right like all these like financial primitives um you know now we're talking about like tokenized stocks and and pers and

options all in the same balance sheet like like composability not just from like a software level but from like uh you know a balance sheet level is really cool. So anyway, I mean I think this is all um what does how does this take shape over the years? I mean I'm a little bit I like what um 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 helpful to think about like what does this look like 2 3 4 5 years from now. 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 DeFi and Strati looks like, we want

to build the tech to make it happen. Well, my next question for you is 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 mean I I think the the saying like the longer term kind of being the technology layer for you know DeFi meaning fat fight is is a way to describe the mission for sure. >> Um >> 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 what about >> this thing though that I care about? Is that not part of mission? No it is like >> it's also that. Yeah. Yeah. So, um, but,

um, yeah, a couple quarters out, it's like, you know, one of the really nice 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 not just to check the box, but you're you're actually building something that by its nature can be both. M >> and so from that perspective like yeah we're building lighter core and lighter core is interoperable with other instances of lighter that were um that are um used by partners like telegram wallet Robin Hood etc. There's kind of many

more of those to come. Um so that's that's big but then also we're building products you know directly like like options like um you know stuff with AI agents like there's a lot of exciting stuff. >> Mhm. Well, Vlad, we're coming up on time, but let me take a moment to just kind of articulate about uh what about lighter excites me and maybe I can get you to to react to it. And really been 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 uh fork and fair launch phenomenon in 2013 and we had this new crop of exchanges. is we had you know the the Bitrexes and uh the BitMEXes of the world and we had this growth of

centralized exchanges. So like we unlocked that part of the tech tree. Ethereum came along. We had Ether Delta which put that kind of technology but put it on chain. Terribly slow terribly inefficient but nonetheless it worked. UniS swap came along. We had the AMM got a little bit better. We had L2s come along and there's been this just arc of exchange technology that has grown and I see and like you had the big exchanges like Coinbase and Kraken and Binance kind of really plow the way of like what actually like compliant and compatible centralized exchanges works with uh with like tradi regulation and all and all this >> with lighter it seems to be that there's a a a somewhat of a step function change

in terms of the techn technology under the hood uh with the ZK circuits that optimize for latency but also providing the uh the uh auditability that is something that Coinbase can't offer, Binance can offer, Kraken can offer, unis swap can offer but unis swap can't offer the speed and the latency of the zk circuits. So it's like if if Coinbase was like a next generation exchange and then UniS swap was like a next generation exchange. Seems like lighter is like something like Gen 3 or the 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 uh 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 create a product that has a lot of these that satisfy some of the most hardcore users and traders but also people like me who are more like idealistically driven. And so >> that's kind of like I just think of it as like the next generation. And I think like the the NASDAQ or the CME or the 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 that 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 you can react to that however you like. Well, we're first of all, like we're we're we're glad to see um you know, really passionate um you know, support from from early community members. Uh I think you articulated it as well as anybody. So that's um like that 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 um 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 trade-off and like we found a way that where we can

actually kind of achieve all these things and that 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 netn net is that the right thing to do or not but sometimes you find these solutions where you can actually improve of X and Y or X and Y and Z. >> Mhm. >> And that's from I think you those come about kind of once in a generation in a sense that like you know like I think in AI like the transformer was such thing, right? I think in 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 um that ZK has real applications. Um and then I think the second part of what you describe is like how Trafi perceives it. Um you know Trafi is interesting, right? Because they they understand the tech nowadays. they they're they're testing it out in various forms. Um ultimately you still have to remember like Pratt 5 follows the money, right? So when they see product market fit

>> um now 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 that's all um a prerequisite, but like ultimately TRFI also needs to see that it works. So you can't just go into trapfi and say okay like you know I have a better mousetrap 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 like it doesn't work like they're going to do >> tell you to go go build the mouse trap and then maybe we'll use it.

>> Yeah. Exactly. And then they'll say okay well maybe it's like you know like maybe the path would looks like where you first tried it. Like I think the uh example of um you know ICE making a strategic investment poly market is like a good example of this right 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 now if they work they may explore it further and so that's kind of I think how looks at things. It's not like okay this um I mean similar to AI right like when if um you know large bank it's not like large banks in 2023 said okay this AI stuff is interesting we're going to rebuild everything with it right now

it's more 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 yeah yeah >> mhm 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, to engage and and and uh meet them where they are. >> Well, Vlad, you'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 years. So, thanks for coming on the show and meeting in New York.