He Finished Harvard at 18 — Now He's Building Crypto's Fastest Exchange | Vlad, Lighter

TRANSCRIPT
David:
[0:03] Hey Bankless Nation, in this episode, we're going to talk to Vlad Nowakoski from Leiter. Vlad is a pretty interesting character, and he's been around the podcast circuit quite a lot talking about Leiter as a platform, how it's different from Hyperliquid and other PERP platforms and what its 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 Robinhood 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 Lightyear. 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 in the middle of the conversation because you kind of do.

Vlad:
[0:59] So let's go hear from him right now.

David:
[1:01] Vlad, I want to know a little bit more about your background because people know you as the founder of Leiter, PerpDex, Theorem Leiter 2, PerpDex. But your background goes pretty far back into your childhood, I think, with your first relevant set of skills. Talk to me about...

Vlad:
[1:15] Particular set of skills, right?

David:
[1:17] Particular set of skills, yeah. Talk to me about math. You won a math and physics Olympiad.

David:
[1:24] Me and most of my listeners probably don't know what that means. What does that mean?

Vlad:
[1:28] 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 of Informatics. On the math side...

David:
[1:45] Informatics.

Vlad:
[1:46] Yeah, that's the programming one.

David:
[1:47] Okay, programming.

Vlad:
[1:48] The math, 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.

David:
[2:03] What does it mean to compete in math and physics? Like, how does one actually... What it is to compete over?

Vlad:
[2:09] 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 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 a location of uh two pizza places in new york find the shortest path given traffic how to get from maybe 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

David:
[2:40] So there's a very clear correct answer yes okay.

Vlad:
[2:43] Yes and so like you have you know three problems like that five hours two days of of that uh so that's the that's the programming one math at the i'm talking about the highest levels right on the ways leading up to that it's you know it's not five hours right there's like it's less intense but and then the math that one looks like so 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 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 one is very hard, but let's say the problem was like prove that you can do it with like six colors.

David:
[3:22] Okay.

Vlad:
[3:22] Hypothetically, right? And it's like, it's not 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. Mm-hmm. 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 gonna get there while 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 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,

Vlad:
[4:08] you know, measure something or like build something, design something.

David:
[4:10] And this was all fun for you. I'm assuming you were like intrinsically just thrilled to do this math, to be in these competitions, right? Like math is entertaining for you?

Vlad:
[4:22] 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. In the long run that's actually the most important part of all this is the people you meet

David:
[4:41] Right because this wasn't solo activity this you're on a team.

Vlad:
[4:44] Well yes but even I mean a lot of the the competitions are individually measured and then the country is You know, once you get the international level, the countries have teams, but you just like, you know, because there's training camps, 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.

David:
[5:10] Who did you meet?

Vlad:
[5:11] 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 US, the training camp for the US 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 Intratix one, you know, the first CTO of Facebook was, I mean, they weren't, they didn't.

David:
[5:39] Right, the future CTO. Yeah, the future, yeah.

Vlad:
[5:41] So some people, you know, who's also on the board of OpenAI now, right? So, like, that's pretty interesting. And 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 too, right? Like, for example, like, I think if you look at people who run some of the big quant trading shops,

Vlad:
[6:01] like, you know, you see a lot of people from those competitions there, et cetera.

David:
[6:06] People that come out of these like math and physics and informatics Olympiads, 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?

Vlad:
[6:21] 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 that cohort in particular and not just on the math and physics side that cohort achieved a lot of success I mean the vice president is part of that cohort too right like people who've done very well in politics, who've done very well in other forms of business like that early 2000s i think there's a lot of a lot of great achievement that came out of that but but 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 same composition team of three so out of that three-person team not one but two became billionaires and like in the 2000s not not now Yeah, yeah. But when 2001 was, that was a very high bar.

David:
[7:17] Yeah. I would imagine the competition has plenty to do with it, because like, what is the common denominator between physics, math, and informatics? You know, there's a lot in common there, but really it's the competition side of things. If you're good at, 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?

Vlad:
[7:45] 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 did well, we all had like our own like favorites or like the kinds of things that like we thought we grogged better than others.

David:
[8:02] You were particularly strong.

Vlad:
[8:03] 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 have 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

David:
[8:38] The stroke of genius problems.

Vlad:
[8:41] Well i guess gene you know i think it's more of just

David:
[8:44] Or thinking outside.

Vlad:
[8:45] Exactly thinking creatively right exactly yeah yeah you didn't you know these problems solving 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

David:
[8:58] Your teenage years were all super heavy in math. And this is also where you also met Robin Hood's lad, correct?

Vlad:
[9:03] So we were in the same high school, school called Thomas Jefferson, which was a math and science high school in Northern Virginia. Yeah, we were the two vlads. I was, you know, Russian immigrant. The two vlads, yeah. He 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 an undergrad and then graduate he he was a phd student under terry tau who was probably like the best mathematician in the world you know ucla and so 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

David:
[9:40] 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.

Vlad:
[9:56] 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 well 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 be not about the same then so like these are the people who will contribute and now i think the the thing that was different is like i think because 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 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 yeah.

David:
[11:00] 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 a kid playing with numbers and competing with other kids into like thinking about a career, thinking about being an entrepreneur?

Vlad:
[11:17] Well, one thing that I realized... At some point around then is like are we like data and that's the 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 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 a 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 to 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.

David:
[12:18] You met Ken Griffin sometime in your teenage years, correct?

Vlad:
[12:22] Yes. I finished high school at 16 and Harvard at 18. So yeah, I was...

David:
[12:28] 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?

Vlad:
[12:36] 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 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 of credit at Harvard. And then I just, like, instead of taking five classes a semester, I took six to get another semester.

David:
[13:00] Was it about finishing Harvard? Like, that was another competition? It was like, who can finish Harvard quicker?

Vlad:
[13:06] Well, that was more of a 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 an 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.

David:
[13:38] Zuck hadn't done it yet because, right. Okay.

Vlad:
[13:40] 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 to pay 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 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.

David:
[14:08] So your form of dropping out was just doing it faster.

Vlad:
[14:11] Yeah.

David:
[14:13] Why get so aggressive and competitive with the timelines why not just do harvard in in four.

Vlad:
[14:19] Years because of this thing right because because it's strictly better to be a grad student than an undergrad

David:
[14:23] It was just an optimization.

Vlad:
[14:25] 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 the way around right sure okay it's just strictly better it's just strictly you can take the same exact classes yeah and this is just like a life hack that no one really thought about uh-huh

David:
[14:39] I I mean, so obviously you got an education out of Harvard, but what did you get out of Harvard beyond that?

Vlad:
[14:43] 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, 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.

David:
[15:30] 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, Harvard is a super social environment and need to be in, in that game to really excel. I think coming out of Harvard.

Vlad:
[15:51] 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,

Vlad:
[16:09] but B, meeting people from other fields.

David:
[16:11] So your first step out of Harvard was working with Ken Griffith.

Vlad:
[16:14] Yes, I guess that was the original question we got. 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 he convinced me to join

David:
[16:47] Was that your first time applying your love of like numbers and engineering and and programming to finance.

Vlad:
[16:55] 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 And it didn't work out that well.

David:
[17:05] Yeah. Were you good at it?

Vlad:
[17:07] Like trading... As a college student yeah not particularly i mean i found some strategies that worked okay and then ken actually told me like yeah these strategies aren't as good as like like some of these charges used to work you back to some but they don't work anymore because i'm arbitraging them away

David:
[17:23] So you maybe maybe you and ken found these same strategies at the same time but ken already.

Vlad:
[17:28] I think i found them like five six years too late but yes because i had some good back tests but in real trading it was like close to zero

David:
[17:35] Okay so you did find something but ken was like neener neener neener i got there first yes yes is that why.

Vlad:
[17:41] He hired you and that's fine i mean that he like i wasn't trying to i was just like trying to find interesting stuff to trade right right

David:
[17:48] Right right is that why he hired you.

Vlad:
[17:51] Well i think that 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

David:
[18:03] Why is that correct.

Vlad:
[18:04] 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 we're, if you have like, a team of people with somewhat different backgrounds and approaches like because like for like the markets are very close to being efficient and so like to find inefficiencies like a bunch of different things all have to be true and it's like easier to find those combinations in a group

David:
[18:31] Right sure yeah add all the different perspectives together kind of the same lesson from harvard.

Vlad:
[18:36] Just 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

David:
[18:47] Now you have two yeah right right how long did you work at uh citadel.

Vlad:
[18:52] 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 want ultimately like i wanted to do something more entrepreneurial like i was trying to do that in college

David:
[19:09] Did you know that you wanted to be an entrepreneur yes yeah yes when did you first know that when.

Vlad:
[19:14] I was eight

David:
[19:15] Oh really yeah what happened when you were.

Vlad:
[19:18] Eight my dad and i watched the tv program about bill gates uh-huh and you know that was i thought that was pretty cool that you could like you know build software and you know create a great business around that

David:
[19:31] 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.

Vlad:
[19:39] 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 it's like 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 other interesting thing was like when i moved from finance to silicon valley like i met with peter thiel at that point and his advice is like don't start a company yet like get to know how startups work first right and that was great advice too

David:
[20:11] 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 don't want to force an idea you want the idea to come to you correct.

Vlad:
[20:22] 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 process that later like in the shower and was like, oh, actually, 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.

David:
[20:55] Yeah. How did you meet Peter Thiel?

Vlad:
[20:57] Well, Peter, I met through, you know, through my friend who was CTO of Facebook, right? Ah, okay. Peter was their first VC and S.

David:
[21:03] Yeah. Do you have an ongoing relationship with him?

Vlad:
[21:05] Well, they, you know, the last venture round we did before launching a token, you know, they co-led that one.

David:
[21:11] That was Founders 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 it to the new firm.

Vlad:
[21:22] And it was a little bit like, you know, nowadays... 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

David:
[21:33] That was your first experience actually building a squad and being a leader.

Vlad:
[21:38] That's right 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 but yes like the or like i didn't like 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 L2 order book data for like this market. Like I was like doing that, right? It's not like it was all there.

David:
[22:08] What firm was this? It was called Graham Capital.

Vlad:
[22:10] Graham Capital? Yeah. Okay. So not far from where we are right now in New York.

David:
[22:13] Oh yeah. Yeah, yeah, yeah. And that's also like high frequency trading stuff.

Vlad:
[22:17] Well, that's what they wanted me to build. They didn't have it before.

David:
[22:20] I see. And so you built that. Yes. Yeah. When did you end up at Adapar?

Vlad:
[22:25] 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... Was really interested in the problem space there of kind of like i guess i'm attracted to like matching problems in general like in the core we're like matching users with questions and answers that to show them adapar was a 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 adapar was started by joel ansdale 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

David:
[23:14] And so the you were head of machine learning at Quora that's correct yeah and then also at a part.

Vlad:
[23:20] The that far was head of all of engineering head of

David:
[23:22] 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 where you were particularly good at and then quora has like hey we have all the we have users looking for questions we have people providing answers how do we match these things.

Vlad:
[23:42] That's right

David:
[23:43] And that was that was a big question to answer at quora.

Vlad:
[23:45] Yeah and you know 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 olympiads 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 yeah 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 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 that lets like transformers and kind of modern ai

David:
[24:28] Was part of that actually physical so using.

Vlad:
[24:31] Optics so my project was physical right

David:
[24:33] Yeah so part of the competition was a hybrid of being able to do math and coding but then also build.

Vlad:
[24:41] Physical you can do whatever you want you 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. You know, like I placed 12th in that one, but like the guy who placed a second was like basically just sort of complete theoretical math paper.

David:
[25:04] Okay, I see. But what you did was one part physical with the optics. And so you had...

Vlad:
[25:10] Basically, it was now called photonics. That wasn't a term back then, but it was just like optics and neural networks.

David:
[25:15] 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?

Vlad:
[25:24] 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 didn't come up with that whole idea. I was like optimizing a particular aspect of it.

David:
[25:39] Right, right.

Vlad:
[25:40] 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.

David:
[25:47] Right, right, right, right. And this was early stage machine learning when you were doing this.

Vlad:
[25:51] Well, not really. I mean, machine learning was around since the 80s.

David:
[25:53] Sure. Yeah. Yeah. Well, I guess early stage by how we kind of know it today, which is just like AI.

Vlad:
[25:58] 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 the US. And we got to meet the president and the vice president, bunch of Nobel laureates. 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, 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.

David:
[26:39] Did you have a reaction to that when they said that?

Vlad:
[26:42] 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 Marola Quora.

David:
[26:53] Yeah.

Vlad:
[26:54] Because big data, I guess 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.

David:
[27:03] Right. Yeah, they didn't have the, you didn't have the clay to do the machine learning to actually manipulate stuff into being useful. Okay. 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 realized 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 something collide there? 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 perspective?

Vlad:
[27:40] Well, the machine learning, 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, in the tech industry, if you come up with anything interesting, you want to like create buzz around it and all that. In quant training it's the opposite right like you don't like if you actually have a really interesting math model to trade

David:
[28:15] Yeah you keep it secret.

Vlad:
[28:17] And you want the competitors to think that you're doing something very basic even 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 the work I did as a kid, but like, yeah, with, with, with core, 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, this, you know, if you recall, there's a 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 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

David:
[29:23] Was text data and it was also super information dense yes because it was all questions and.

Vlad:
[29:27] Answers that's right yeah like in principle we could have built the first good LLM I mean like our founder you know joined the board of OpenAI for recently right but 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 didn't add much alpha to the prediction model yeah

David:
[29:46] Yeah so you also had the connections to the two anthropic founders from the Olympiad days correct yes and I don't know if how where how you kept up with

David:
[29:56] 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 AI finally happened, like you had the chat GPT moment in 2023 or whenever that was?

Vlad:
[30:10] Yeah.

David:
[30:11] For you, was it like, oh, finally, like we've got this thing now? Or like, what was it like being on, having the kind of like the information that you had pre-chat GPT?

Vlad:
[30:21] These companies have 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 were working on a bunch of it was like pretty open-ended lab right like they're working on a bunch of different things i mean there's 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 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 open ai 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 OpenAI guys gave a presentation was actually Dario who did it he was still at OpenAI back then

Vlad:
[31:48] Right and so they were talking about the stuff that where they were competing with deep mind on like playing video games and you know 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 the 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 ad that wasn't their main thing right and And then they found some 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 because like the like i i guess when i first got a sense that this was going to be a big consumer product was in it 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 homework yeah i was like even 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.

David:
[33:14] Right. Yeah. The whole kind of concept of AI has blossomed into basically everything. Yeah.

David:
[33:18] I want to get to your time at Lunch Club. First, I want to talk about your time at Adapar, which is, as I understand it, the last company you were at before Lunch Club. What was Adapar?

Vlad:
[33:28] So Adapar, as I was saying, was like the fintech company that Joe Lonsdale co-founded. Right. That brought together a lot of financial data and like you can 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 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 a very very important function because if you don't know what your own portfolio now you think 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

David:
[34:11] And when you ran an internship program there so you hired interns to work at a park correct.

Vlad:
[34:15] 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 Olympians.

David:
[34:29] Okay, okay. So you picked an arena that you were familiar with to hire talent.

Vlad:
[34:34] Well, we, you know, we were open to talent from anywhere, but like we were specifically, you know, that was like, you know, because we, it was like, it wasn't just me, we had a recruiting team. And what I kind of, was 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, at 17 years old, like we can hire people at any age who did these Olympiads is going to be high signal hiring.

David:
[35:00] Did that thesis work out for you?

Vlad:
[35:03] It did. It did work out. Yeah.

David:
[35:05] How would you gauge that success?

Vlad:
[35:07] Well, certainly it worked out fairly well for Adipar, but it worked out really well for those kids. And I mean, I think, you know, worked out for those of us who angel invest in their future projects yeah

David:
[35:18] Who were some of those kids that you that you worked with.

Vlad:
[35:20] Well one of them ice co-founded lunch club with right scott uh scott who is now co-founder ceo of cognition but um yeah you know like between between the out of our 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 i mean and that's just that's just an ai right also founders of some crypto projects too uh you know a bunch of them went to trading firms i mean i think it was 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 like working 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.

David:
[36:21] 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?

Vlad:
[36:31] It was more of like just like undiscovered alpha, right? And, you know, because I think from my perspective, it was always... Would have been like that I mean maybe some ebbs and flows depending on specific cycles but like like for one 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 olympiads 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

David:
[37:16] And so it's just you you had the experience because you went through it and so you knew that it.

Vlad:
[37:20] Was 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 an olympiad guy like hrt like all these training firms and and startups too right like you know a CTO of Facebook did Olympiads. Google, their first CTO actually also did Olympiads. Like, so they, those people kind of slowly but surely educated their colleagues about this stuff, but it took years.

David:
[37:43] And then the alpha got squeezed out.

Vlad:
[37:45] Yeah, yeah, yeah.

David:
[37:47] 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 the spark came to you about like why Lunch Club was a good idea.

Vlad:
[37:57] Well, I think a lot of the, if you, 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? 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 how much value was created from Peter Thiel meeting Elon Musk in 97? The industries we know, this happens all the time, right? So the idea was like, 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 things?

Vlad:
[38:40] 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 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 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 when 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, Marc Andreessen would get like a million requests. 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. Like, that's the kind of question that should happen instead of both of those kids being matched with Marc Andreessen and Marc never having the time to respond.

David:
[39:47] Right. Yeah, right, right, right. Yeah, cut out the intermediary or the indifferent and irrelevant.

Vlad:
[39:53] Point of the data. To be fair, Mark actually is very good at connecting people. So I don't want to use that, you know, maybe a bad example, but I think you get the point, right? So with adverse selection, so you actually don't opt into a particular connection. You let the system make the match.

David:
[40:06] 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 Adapar working on, like you were working on your own thing, but also that.

Vlad:
[40:24] Well, yeah, Adapar's more about 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 of Adapar, of like building these teams, I guess a lot of that was kind of relevant.

David:
[40:35] Yeah, and then, so then came Lunch Club, which is just kind of just the, you smash those two things together.

Vlad:
[40:41] 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 Scott your co-founder yeah didn't all come from me by any means yeah yeah but it just

David:
[40:53] Seems kind of like we're kind of your arc the Vlad 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

David:
[41:16] be an entrepreneur. Yes. Yeah. Talk about the transition from Lunch Club to Lighter because this is the same company, correct?

Vlad:
[41:24] 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 the kind of like why lighter i think maybe it's maybe even yeah more interesting question uh because we even like in 2017 when we settled on lunch club as 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 lunch club 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 used to just how like 2014 ai wasn't ready for you know out of power but like anyway and i always really liked like you asked earlier about what kind of math problems i really liked in olympia is like one of the fields i like 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

Vlad:
[42:27] Again the only reason i knew about it is because of a friend i made olympians show me the paper so i was like this is actually a cool way to use number theory in your 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 trad fi right but yeah the actual process we went through though because this is all 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 like not a lot of startups

Vlad:
[42:59] We didn't think it was like a sure thing that we could succeed with a pivot, but our... You know, like, I think in the space where now there's a lot of like negative around VCs. I mean, we wouldn't have been able to do that without the 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 Robinhood were down a lot right even companies like SaaS companies from you know 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 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'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 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

David:
[44:17] Who came up with the lighter idea or how did.

Vlad:
[44:20] That i think that this one you know was idea that that i came up with yes

David:
[44:28] Okay so the what did you see in crypto that you, that really scratched your itch because crypto is i think from somebody who's very into numbers and into systems right probably provides a lot of material for you to think about so what about crypto before you even committed to.

Vlad:
[44:45] Crypto right

David:
[44:46] What did you see that was like just intriguing or interesting to.

Vlad:
[44:48] 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 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

David:
[45:17] Very skew more fake very backwards.

Vlad:
[45:19] 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 jane knows all this kind of stuff right and it's like there there has to be like this this tech clearly works and 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 have 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 thing is 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 uniswap and i mean amms have their own inefficiencies but but that's certainly decentralized but for the most part like that was less than one percent of the volume

Vlad:
[46:16] 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's okay like how do we solve that problem we 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 ivory tower and say like okay how would you build the perfect system to replace stratfi yeah like that 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 where 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 we think we can

David:
[47:09] Lunch club pivoted into lighter in in 2022 ish the first time i ever heard about lighter was sometime late in 2024.

David:
[47:18] So what happened in those two years?

Vlad:
[47:20] Well, we were, I mean, the core tech 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 because it had never been done before. We got to use ZK in kind of novel ways and come up with.

David:
[47:36] 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.

Vlad:
[47:47] 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 in the open it was more like I don't 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 because 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 five minutes on it and left like they they knew us for a long time and they knew the grit yeah and they really like committed to testing out and gave it giving us feedback over months

David:
[48:29] The lighter the problem of Leiter, as in the problem that Leiter 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 Olympiads, but then also just the matching engine work that you've been doing. You did a Quora, the high frequency trading work that you did at Citadel. It seems to be like it's a perfect problem for you. And maybe that's why you picked the idea in the first place. But 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 feels?

Vlad:
[49:07] Yeah, I mean, it definitely feels great to have, you know, it's one thing to like work on something

Vlad:
[49:15] That, you know, you feel like you're like, you're built to do it. I mean, it's another thing to actually build it and have real customers, right? 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.

Vlad:
[49:35] But yeah, no, it's been great. I mean, I think the way we thought about the idea maze was like, I 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 would do well against competitors if we built it and so like yeah i mean i think like lunch cup 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 clearly like trading droid 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 tradfi 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

Vlad:
[50:42] 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.

David:
[50:51] How would you articulate the mission of Leiter in a long form way? Like what's the, what is the big mission of Leiter?

Vlad:
[50:59] Coming up with like a short, concise statement is like, you know, the Mark Twain quote is like, I wish, you know, I was writing you short letter. I didn't have time to write you a long one. So that, I mean, 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 in trad fi and and in crypto right we see like hacks and

Vlad:
[51:47] 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 ftx you wouldn't have a lot of these things that have been a problem for for finance um and composable that's that's the one where like all these defy protocols i mean have been composed but that's one of the nice things about defy but i mean that's not new but what is new is like define trad fi being composable right like all these like financial primitives you know now we're talking about like tokenized stocks and perps and options all in the same balance sheet like 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 helpful to think about like what does this look like two three four five years 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 defy and stratify looks like we want to build the tech to make it happen

David:
[53:10] 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 gonna what lighter wants to be in two quarters.

Vlad:
[53:22] Yeah i mean i think the the saying like the longer term kind of being the technology layer for you know d5 meaning stratfi 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 sure anytime you do that somebody can be like oh well what about this thing though that i care about is that that part of the mission no it is it's

David:
[53:44] Also that yeah.

Vlad:
[53:45] Yeah and so but yeah a 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 i feel like with crypto you can actually be both and not not not just to check the bugs but you're you're 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 were that are used by partners like telegram wallet robin hood etc there's many more of those to come. So that's, that's big. But then also we're building products, you know, directly like, like options, like, you know, stuff with AI agents, like there's a lot of exciting stuff.

David:
[54:38] Well, Vlad, we're coming up on time, but let me take a moment to just kind of articulate about what about LIDAR excites me. And maybe I can get you 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 fork and fair launch phenomenon in 2013. And we had this new crop of exchanges. We had the Bittrexes and the BitMEXs of the world. And we had this growth of centralized exchanges. So we unlocked that part of the tech tree. Ethereum came along. We had EtherDelta, 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 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 Kraken 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.

David:
[55:44] With LiDAR, 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't offer. Kraken can't offer. Uniswap can't 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 Leiter 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. It's 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.

David:
[56:43] 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. 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 LIDAR. And I just want to kind of like react. You can react to that however you like.

Vlad:
[57:18] Well, first of all, like 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, that's really rewarding as an entrepreneur, right? To hear kind of early users really understand in some cases better than we do why we're building is important. But, but I think, you know, as a technologist, like you talked about how, okay, like there've 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're going to 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 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 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

Vlad:
[58:37] 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

Vlad:
[58:50] And then i think the second part of what you described was like how trad fi perceives it you know trad fi is interesting right because they they understand the tech nowadays they they're they're testing it out in various forms ultimately you have to remember like tradfi follows the money right so when they see product market fit

Vlad:
[59:11] 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 a prerequisite but like ultimately tradfi also needs to see that it works so you can't just go into tradfi 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 gonna do they're gonna tell

David:
[59:41] You to go go build the mousetrap and then maybe we'll use.

Vlad:
[59:44] 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 try it like i think the uh example of you know ice making a strategic investment polymarket 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 triadify 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 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 simple yeah yeah

David:
[1:00:30] And from the margins.

Vlad:
[1:00:31] That's right yeah so that's That's how we see it play 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 meet them where they are.

David:
[1:00:48] 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 years. So thanks for coming on the show.