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- [00:04]
A
Hey, Bankless Nation. In this episode, we're going to talk to Vlad Novikoski from lidr. Vlad is a pretty interesting character and he's been around the podcast circuit quite a lot talking about Lighter as a platform, how it's different from hyperliquid and other per platforms and what 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 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 we. You start in the middle of the conversation because you kind of do. So let's go hear from him right now. Vlad, I want to know a little bit more about your background, because people know you as the founder of Lighter, the Perp DEX, Ethereum layer 2Perp DEX. But your background goes pretty far, pretty far back into your childhood, I think, with your first, like, relevant set of skills.
- [01:15]
B
Talk to me about particular set of skills. Right?
- [01:17]
A
Particular set of skills. Yeah. Talk to me about math. You won a Math and Physics Olympiad? Me and most of my listeners probably don't know what that means. What does that mean?
- [01:28]
B
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 US team to the International Physics Olympiad and the International Olympiad. Informatics on the math side.
- [01:46]
A
Informatics, yeah, that's the programming one. Okay.
- [01:48]
B
Programming math. I mean, I tried to do a hat trick as far as making all three. I was actually the first, or I think one of the first people in the US ever to make two teams. Didn't quite make three, but, you know, but it was good.
- [02:04]
A
What does it mean to compete in math and physics? Like, how does one actually. What it is to compete over?
- [02:10]
B
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, solve 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 Adrian. You have to actually, like, it's not theoretical. You actually have to write a program that would actually do it, right?
- [02:40]
A
It's deterministic. So there's a very clear correct answer.
- [02:43]
B
Yes.
- [02:43]
A
Okay.
- [02:43]
B
Yes. And so, like, you have, you know, three problems like that. Five hours, two days of, of that. So that's the, that's the programming one. Math at the. And I'm talking about the highest levels, right? And the ways leading up to that. It's, you know, it's not five hours, right? There's like, it's less intense, but the math that one looks like, it's also two days. And you have three problems where you have to find a solution with proof. You've probably heard of, like, the four color theorem. You can paint a map with four colors. It doesn't matter if it's states, countries, right? Now, that one's very hard. But let's say the problem is prove that you can do it with six colors, hypothetically, right? It's not enough just to show one example. You have to prove that you can always do it. And then you have to write proofs like that for three problems over five hours. Also, I think two days. Physics one, there's a theory part and experiment part. So the theory part is also similar. It's like, you know, like a block of ice is coming down the hill 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, bring in a bunch of different concepts together. But then there's also the experiment part and that. That's probably the most different of all these. You, like, you actually have to, like, do stuff with your hands and like, you know, measure something or like, build something, design something.
- [04:10]
A
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 entertaining. Entertaining for you.
- [04:22]
B
Yeah. So the, the thing was that, like, it was like a virtuous 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, and not only is winning fun, meeting other like minded people. And in the long run that's actually the most important part of all. This is the people you meet, right?
- [04:42]
A
Because this wasn't solo activity. This. You were on a team.
- [04:45]
B
Well, yes, but even the, I mean a lot of the, the competitions are individually measured and then the country, you know, once you get the international level that the countries have teams but you just like, you know, cause there's training camps like as 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, you know, students, but really just kids around the country.
- [05:10]
A
Right. Who did you meet?
- [05:11]
B
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 Intranics one, the first CTO of Facebook was, I mean they weren't the future CTO. So some people who's also on the board of OpenAI now. Right. So that's pretty interesting. And that's just like people who've achieved the kind of the most well known outcomes. But there are a bunch of others who write like, for example, like I think if 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,
- [06:05]
A
et cetera, 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 is it always like this?
- [06:21]
B
Yeah, it's a good question. I mean I think it goes there are bursts like I think the early 2000s for whatever reason. Maybe it's because we were the first to grow up with the Internet, that cohort in particular, and not just on the math and physics side, that cohort achieved a lot of success. I mean the vice president is part of that cohort too. Right. People who've done very well in politics have done very well in other forms of business. Like that early 2000s, I think there was a lot of great achievement that came out of that. But that being said, if you look back Even to the 90s, 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 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 2000s was. When that was a very high bar.
- [07:18]
A
Yeah, I would imagine the competition has plenty to do with it because, like, what was 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, you were. You were almost, as you said, did the hat trick. What kind of problems, whether. Whether it was math, physics or informatics. What kind of problems?
- [07:40]
B
What.
- [07:41]
A
What type of problems did you really enjoy the most? Is there. Is there something to discuss there?
- [07:46]
B
Yeah, yeah. This was always like, you know, when we would hang out after the competitions and talk, this would always come up like, it's 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 grok better than others.
- [08:03]
A
You were particularly strong at.
- [08:04]
B
Yeah. And this was always a debate. Like, some guy would be like, oh, like, I love, like, geometry is really cool. I have geometry. And then like, maybe somebody else like, oh, no, like that, like, trigonometry is really cool. Right. Et cetera. But like, for me, I really like the stuff where you needed, like, an unusual insight to solve it, where if you look at the problem one way, it looks like there's like, no solution or very difficult path to a 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.
- [08:39]
A
This stroke of genius problems.
- [08:41]
B
Well, I guess, Gene, you know, I think it's more of just.
- [08:45]
A
Or thinking outside of the box. Exactly. Thinking creatively, Right? Exactly. Yeah.
- [08:49]
B
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.
- [08:57]
A
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- [11:20]
B
So we were at 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, 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 in undergrad and then graduate, he was a PhD student under Terry Tao, who's probably like the best mathematician in the world, you know, ucla. And so anyway, so then he got into high frequency training later, as I did. So our paths actually got closer kind of later on.
- [11:57]
A
Was it obvious to you, being in these Olympiads and just in these cohorts of people, was it kind of just obvious amongst you and your peers that everyone was going to go and apply themselves in some great way, or were you guys just kids playing with numbers?
- [12:14]
B
For me, I saw it a little bit because I used to think of it like, okay, if you stack rank everybody, let's say you were whatever metric you come up with, 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 ranking doesn't change that much from one year to the next. I thought, okay, well, if you project that out 20 years, it's probably going to be about the same then. So these are the people who will contribute. And now I think the thing that was different is I think because these competitions, the people who organize them are academics. So the default career path is academia. So I think we weren't, obviously we weren't thinking about Facebook and Anthropic and, you know, OpenAI. 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.
- [13:16]
A
But yeah, when did, as you're growing up and just kind of getting into your adult years, did a particular direction for you open up and become clear? Or, 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?
- [13:34]
B
Well, one, one thing at some point around then is like, I really 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, 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 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, I actually had a pretty bad day on theory. Jet lag, I don't know. But in the experimental one, I was ranked second in the world. And so overall I still got a silver. So that was kind of the start of. Okay. I actually really like this data real world stuff. And that I think led to doing more with applied sciences later on, like economics, finance, trading and kind of so on and so forth.
- [14:36]
A
You met Ken Griffin sometime in your teenage years, correct?
- [14:39]
B
Yes, I finished high school at 16 and Harvard at 18. So yeah, I was.
- [14:45]
A
You finished Harvard at 18?
- [14:46]
B
Yeah.
- [14:47]
A
How does one do to Harvard in two years?
- [14:49]
B
Well, two and a half.
- [14:50]
A
Two and a half years, yeah. How does one do that?
- [14:53]
B
You know, there's like, you, you can get credit for one year from. Because I took a bunch. You know, I, 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 kid who. 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 a semester, I took six to get another semester.
- [15:17]
A
Was it about finishing Harvard? Like that, Was it another competition? It was like, who can finish Harvard quicker?
- [15:23]
B
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 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 the, I mean, and then of course one of us in that cohort did it, you know, with, with, with Zuck. But like.
- [15:55]
A
Right. Zuck hadn't done it yet because. Right, okay, yeah.
- [15:58]
B
But like, but anyway, 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. You can cross register. I can literally take the same exact class as a graduate student and be on the same exact campus. And it's like I was like, I might as well do that.
- [16:26]
A
So your form of dropping out was just doing it faster.
- [16:29]
B
Yeah.
- [16:30]
A
Why get so aggressive and competitive with the timelines? Why not just do Harvard in four years?
- [16:36]
B
Because of this thing, right? Because. Because it's strictly better to be a grad student than an undergrad.
- [16:41]
A
It was just an optimization problem.
- [16:43]
B
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?
- [16:50]
A
Sure.
- [16:50]
B
It's just strictly better.
- [16:51]
A
It was a sister.
- [16:52]
B
You can cross register, you can take the same exact classes. And this is just like a life hack that no one really thought about.
- [16:57]
A
I mean, so obviously you got an education out of Harvard, but what did you get out of Harvard beyond that?
- [17:01]
B
Lear. Learning how to, you know, 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 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 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, 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. I think I really learned like again, this is cliche, but like how to network. How do you like go into a room and like start conversations with different kinds of people, get to know them
- [17:47]
A
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.
- [18:09]
B
It all depends on the field. Right. If you're like. I mean, I guess like most scientific fields have some collaboration, but obviously there are. There are also others like that reward. 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.
- [18:29]
A
So your first step out of Harvard was working with Ken Griffith?
- [18:32]
B
Yes, I guess that was the original question. We got sideshow. 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 convincing me to join.
- [19:04]
A
Was that your first time applying your love of like, numbers and engineering and. And programming to finance?
- [19:12]
B
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.
- [19:22]
A
Yeah. Were you good at it?
- [19:24]
B
Like. Like trading as a college student?
- [19:28]
A
Yeah.
- [19:28]
B
Not particular. 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 strategies used to work system, but they don't work anymore because I'm arbitraging them away.
- [19:40]
A
So you maybe. Maybe you and Ken found these same strategies at the same time. But Ken already.
- [19:45]
B
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.
- [19:52]
A
Okay, so you did find something, but Ken was like, neener, neener, neener. I got there first.
- [19:56]
B
Yes, yes.
- [19:58]
A
Is that why.
- [19:59]
B
That's fine. I mean, I wasn't trying to. I was just like. You were trying to find interesting stuff to trade.
- [20:04]
A
Right, right, right, right. Is that why he hired you?
- [20:08]
B
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.
- [20:20]
A
Why is that correct?
- [20:21]
B
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. Or if you have like a team of people with somewhat different backgrounds and approaches, like, because markets are very close to being efficient. And so to find an efficiency is a bunch of different things. All have to be true. And it's easier to find those combinations in a group.
- [20:49]
A
Right? Sure, yeah. Add all the different perspectives together kind of with the same lesson from Harvard.
- [20:54]
B
Well, it's like, for example, if you were to tell me that you have a really interesting thesis on Ethereum, and then, I don't know, if I talk to Tom Wean, he gives me his thesis, then I put that together.
- [21:05]
A
Now you're right. Right. How long did you work at Citadel?
- [21:09]
B
Yeah, so I was there for around a year, and then another firm recruited me to kind of come in and continue, you know, to kind of actually build a trading desk there. So I kind of. Because I wanted ultimately, like, I wanted to do something more entrepreneurial. Like I was trying to do that in college.
- [21:26]
A
Did you know that you wanted to be an entrepreneur?
- [21:28]
B
Yes.
- [21:28]
A
Yeah.
- [21:29]
B
Yes.
- [21:29]
A
When did you first know that?
- [21:32]
B
When I was 8.
- [21:33]
A
Oh, really?
- [21:34]
B
Yeah.
- [21:34]
A
What happened when you were 8?
- [21:36]
B
My dad and I watched a TV program about Bill Gates. 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.
- [21:48]
A
So building a company has always been something that you wanted to do?
- [21:51]
B
Yes.
- [21:52]
A
Yeah. Okay. And then you were kind of like searching around throughout Harvard and working with Ken and then the next.
- [21:56]
B
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 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 if we're talking about interactions with famous people. One other interesting thing was when I moved from finance to Silicon Valley, I met with Peter Thiel at that point, and his advice was like, don't start a company yet. Like, get to know how startups work first.
- [22:26]
A
Right.
- [22:26]
B
And that was great advice too.
- [22:28]
A
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?
- [22:39]
B
That's right. That's right. 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 gonna be? And now some of the information that you're seeing in the markets may end up like, you know, your brain may come. May process that later, like in the shower and it's like, oh, actually, like, this is pretty cool. You can like, maybe there should be like a better risk management platform or something. But that may happen years later.
- [23:12]
A
Yeah. How did you meet Peter Thiel?
- [23:14]
B
Well, Peter I met through, you know, through my friend who was CTO of Facebook. Right. So.
- [23:18]
A
Ah, okay.
- [23:19]
B
Peter was their first.
- [23:21]
A
Yeah. Do you have an ongoing relationship with him or.
- [23:23]
B
They, you know, the last venture round we. We did before launching a token, you know, they co led that one.
- [23:28]
A
That was Founders Fund.
- [23:29]
B
Yes.
- [23:29]
A
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.
- [23:38]
B
And it was a little bit like they, you know, nowadays, you know, big companies use the terminology like startup within a startup or something started within a big company. And like, for me, building a trading desk within a larger firm was literally that. Right.
- [23:50]
A
That was your first experience actually building a squad and being a leader.
- [23:55]
B
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.
- [24:05]
A
Yeah. Right.
- [24:05]
B
So. Well, yeah, I mean, it's actually all this stuff has some interesting aspects of it. 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. 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.
- [24:26]
A
What firm was this that you were talking?
- [24:27]
B
Graham Capital. Graham Capital, yeah.
- [24:29]
A
Okay.
- [24:29]
B
So not far from where we are right now in New York.
- [24:30]
A
Oh, yeah, yeah, yeah, yeah. And that's also like high frequency trading stuff.
- [24:34]
B
Well, that's what they wanted me to build. They didn't have it before.
- [24:37]
A
I see. And so you built that.
- [24:39]
B
Yes.
- [24:39]
A
Yeah. When did you end up at Addepar? So.
- [24:42]
B
Right. So then 2012 is when I kind of took the swing to go from Wall street to Silicon Valley. So initially I was at Quora for a year. And a half because I was really interested in the problem space there of kind of like, I guess I'm attracted to like matching problems in general. Like in the Quora, we're like matching users with questions and answers to show them. Adderpar was really, you know, at the time, like, fast growing fintech company, right. Kind of building actually like aggregating data from various sources to build ways to analyze portfolios. Like, like a lot of Addepar was started by Joel Lansdale, 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.
- [25:32]
A
And so the. You were head of machine learning at Quora.
- [25:35]
B
That's correct, yeah.
- [25:36]
A
And then also Addepar, the head of all of engineering. Yeah, head of all of engineering. And so this kind of goes back to what you were talking about with like the skills that you were uniquely good at in your Olympia days, when your math competition days, where it's like you really liked getting your hands on data and that's what, that's what you were particularly good at. And then Quora is like, hey, we have all the. We have users looking for questions, we have people providing answers. How do we match these things?
- [26:00]
B
That's right.
- [26:01]
A
And that was the, that was the big question to answer at Quora.
- [26:03]
B
Yeah. And you know, machine learning. So interestingly 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 Olympiads. Like, that's where you get to meet the president and stuff like that. And so the project I did for that was essentially like using optics to, instead of chips to like run neural networks faster. And. But so that, 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 to like transformers and kind of modern AI.
- [26:46]
A
Was part of that actually physical, so using optics?
- [26:49]
B
Well, my project was physical, yes.
- [26:50]
A
Right, yeah. So part of the competition was a hybrid of being able to do math and coding, but then also build physical.
- [26:59]
B
Well, you can do whatever you want. You can submit any research project you want. So my project, like, I think probably at least half of the kids who competed in that did do some Actual, like hands on stuff in the lab. But the others, you know, you could just write like, you know, like, I placed 12th in that one. But like the guy who placed a second was like basically just sort of complete theoretical math paper.
- [27:21]
A
Okay, I see. But what you did was one part physical with the optics.
- [27:26]
B
And so you had basically what's now called photonics. That wasn't a term back then, but it was just like optics and neural. Neural networks.
- [27:33]
A
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.
- [27:41]
B
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.
- [27:57]
A
Right, right.
- [27:58]
B
But the idea of using optics instead of silicon is a thing and I think is like, that is a new form of computing, as is quantum.
- [28:04]
A
Right, right, right, right. And this was early stage machine learning,
- [28:07]
B
when you were doing this machine learning. Well, not really. I mean, machine learning was around since the 80s.
- [28:10]
A
Sure. Yeah, yeah, yeah. Well, I guess early stage by how we kind of know it today, which is just like AI.
- [28:15]
B
Yes. But what's interesting is that competition back then. I think now these Olympiads have become more famous because of success of some of the people who've done them. But back then this research competition was considered like the main competition, science competition in the US and we got to meet the president, the vice president, a bunch of Nobel laureates and the Nobel laureates, and they were, I think, expressing the opinion of the scientific community at the time. They were like, yeah, all this machine learning stuff, that's a dead end. It's cute that you did this work as a kid, but if you want to do science, do quantum. This neural network stuff is not going to work.
- [28:54]
A
Wow. Did you have a reaction to that when they said that?
- [28:59]
B
Yeah, I mean, 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.
- [29:04]
A
Sure.
- [29:04]
B
Like, you know, I was, I kind of, I did move away from it. Then I came Back to it 12 years after when kind of in my role at Quora. Yeah, I guess the big data kind of changed the game because I think, I mean, they weren't wrong what they were saying, given what existed at the time because this was pre Internet scale of data, right?
- [29:21]
A
Yeah. They didn't have the. You didn't have the clay to do the machine learning to actually manipulate and stuff into being useful.
- [29:27]
B
Yes.
- [29:27]
A
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 neuron neural connection coming together in your time at Quora where you realize that, you know, you actually I do have big data here and you're doing the matching, the matching problem, like connecting people at Quora and now you have the data and you have your experience with machine learning.
- [29:49]
B
Yes.
- [29:50]
A
Did some, something collide there? Did particles collide there in your head when you realize that, oh, there's actually a lot of clay here to work with from the machine learning perspective.
- [29:57]
B
Well, the machine learning, you know, we were using it in quant finance too, because after 2008, a lot like before that, you could trade in fairly simple ways and it was more around implementing like fast execution, you know, what's now known as high frequency trading. Right. But like pretty simple strategies worked. Then after 2008, those simple strategies stopped working and you had to find more complicated patterns in the market. So machine learning was used for that too. It just wasn't. See, the difference is like in the tech industry, if you come up with anything interesting, you want to create buzz around it and all that. But in quant trading it's the opposite. Right. If you actually have really interesting math model to trade.
- [30:32]
A
Yeah.
- [30:33]
B
You keep it secret and you want the competitors to think that you're doing something very basic, even if you're not. Right. 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, we're using a lot of this stuff. So I knew some of that already, including from the work I did as a kid. But yeah, with Quora, 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 is another kid I knew from Olympiads. Now he's running a company called Modal Netflix. 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 Quora. But what was funny is that Quora actually had a lot of the things you need to build LLMs, right? Because it was text data, unlike Spotify or Netflix, it was text data and
- [31:41]
A
it was also super information dense.
- [31:43]
B
Yes.
- [31:43]
A
Because it was all questions and answers.
- [31:45]
B
That's right, yeah. Like in principle we could have built the first good LLM. I mean like our, our founder 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, they didn't add much alpha to the prediction model.
- [32:02]
A
Yeah, yeah, yeah. So you also had the connections to the two anthropic founders from the Olympiad days, correct?
- [32:09]
B
Yes.
- [32:09]
A
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 Quora. Was it odd when, when AI finally happened, like we had the chat GPT moment in 2023 or whenever that was.
- [32:27]
B
Yeah.
- [32:27]
A
For you was that 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 ChatGPT?
- [32:37]
B
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 were 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 DeepMind back then, there was 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 building a great business. So I think I got a lot of the big picture right, like that AI was going to be big and investing in AI first companies was going to be a thing. And so like the exact path it took, I don't think I would have predicted it or I don't know if anyone predicted like the fact that. Because even within OpenAI 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 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 the stuff that, where they were competing with DeepMind on playing video games. And I'm not a video game person. And so I thought, okay, this is interesting, but this is not exactly the application of AI that I'm excited about. The reality is that they were working on that kind of stuff. They had like two guys on their team who were doing this LLM stuff Even with an OpenAI, that wasn't their main thing.
- [34:27]
A
Right.
- [34:27]
B
And then they found some application that was face shift different than, I think GPT. 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, 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, I guess when I first got a sense that this was going to be a big consumer product was in, 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 like homework, doing homework, even if that's all it is. I was like, this is, there's some product market fit there. This stuff is not just pure research anymore. But that's when I first thought, okay, this stuff is going to work out. Because there's a lot of even within AI, right? Even within what's used now. There's generative video, images. All these things are quite different.
- [35:31]
A
Right. Yeah. The whole kind of concept of AI has blossomed into basically everything.
- [35:35]
B
Yeah.
- [35:35]
A
I want to, I want to get to your time at lunch. Go. But first I want to talk about your time at Add a Par.
- [35:39]
B
Yeah.
- [35:39]
A
Which is the, as I understand it, the last company you're at before, before lunchclub, what was Add a Par.
- [35:45]
B
So Add a Par, as I was saying was like the fintech company that Joe Lonsdale co founded that brought together a lot of financial data and like you can understand your portfolio, like run analytics, run reports. What, 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 understanding 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 portfolio now, you'll think, well, why would you not know? Well because if you have many layers of maybe somebody owns Apple stock directly, but they also own an spv, they may also own a mutual fund that has Apple. The correlations are actually non obvious.
- [36:28]
A
And you ran an internship program there, so you hired interns to work at Addepar. Correct?
- [36:33]
B
Well, the, you know, it wasn't my idea. You know, Joe was hiring interns from day one. I mean they had interns at Palantir too. So the idea to hire smart interns was not new. What was new that I brought to the table was that we should source them from the Olympiads.
- [36:46]
A
Okay. Okay. So you, you picked your, an arena that you were familiar with to hire talent.
- [36:51]
B
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 getting the recruiting team to learn about is like these Olympiads you can actually like, you know it's legal to work in the U.S. you know, 17 years old. Like we, we can hire people at any age who did these Olympiads is going to be high signal hiring.
- [37:17]
A
Did that thesis work out for you?
- [37:21]
B
It did work out, yeah.
- [37:22]
A
How, how would you gauge that success?
- [37:24]
B
Well, it certainly, it worked out fairly well for Addepar, but it worked out really well for those kids.
- [37:28]
A
Yeah.
- [37:29]
B
And I mean I think you know, it worked out for those of us who angel invest in their future projects.
- [37:34]
A
Yeah. Who, who were some of those kids that you, that you worked with?
- [37:37]
B
Well, one of them I co founded Lunch Hub with. Right. Scott, Scott Wu, who is now co founder CEO of Cognition. But yeah, you know, like between, between the Addepar 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. A bunch of them went to trading firms. I mean I think it was very, I mean I think. But essentially we just got a lot of the early stage talent in one place and they just shooting fish in A barrel. And then they learn a lot from the full time folks, but they also form connections with each other and some of them started companies as a result of connections there.
- [38:37]
A
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?
- [38:47]
B
It was more of like, just like undiscovered alpha. Right? You know, because I think from my perspective, it always would have been like that. I mean, maybe some ebbs and flows depending on specific cycles, but like, 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.
- [39:32]
A
And so it's just you, you had the experience because you went through it and so you knew that it was out there.
- [39:36]
B
I mean, I did it there. I mean, I'm sure there are other, you know, there was a guy at early Jane street who was an Olympiad guy, like hrt, like all these trading firms and startups too, right? Like, you know, cto, Facebook. Did Olympiads Google 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
- [39:58]
A
and then the Alpha got, got squeezed out.
- [40:01]
B
Yeah, yeah.
- [40:02]
A
Let's talk about, let's talk about Lunch Club. So Lunch Club was the first company that you founded, correct?
- [40:06]
B
That's correct, yeah.
- [40:07]
A
Tell me about just how the spark came to you about like why Lunchclub was a good idea.
- [40:13]
B
Well, I think a lot of the. Even if you just. We think about our conversation right now, I think it's pretty clear that connections are very important. Right. Sometimes just like even a single connection can change the trajectory of someone's life or career in some cases an industry. Right. How much value was created from Peter Thiel meeting elon Musk in 97? The industries. We know this happens all the time, right?
- [40:36]
A
Right.
- [40:37]
B
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 do really valuable networking? Right. Making the right connections. So that idea was kind of. That was the mission. But the implementation detail that made it work, or at least made it work as well as it did, because there have been others that have tried this kind of stuff.
- [41:09]
A
Right.
- [41:09]
B
That didn't really go anywhere. But I mean, for us it worked pretty well, especially during COVID And even since then it's plateaued. It didn't die. But the key insight on the implementation side was how do we avoid adverse selection? Because if you have a system where you use the model from dating sites where you're like, okay, double opt in, that doesn't really work too well for networking, because unlike dating, where there's somebody for everybody in networking, it's like if you see if you could send requests to anybody, it's like, I don't know, Marc Andreessen would get like a million requests and someone who's like a college kid might not get any. 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. But that's the kind of condition that should happen instead of both of those kids being matched with Marc Andreessen and Mark never having the time to respond.
- [42:02]
A
Right, yeah, right, right, right. Yeah. Cut out the intermediary or the indifferent. An irrelevant point, actually, Mark, to be
- [42:09]
B
fair, Mark actually is very good at connecting people. So I don't want to do something maybe a bad example of. But I think you get the point, right. To avoid adverse selection. So you actually don't opt into a particular connection. You let the system make the match.
- [42:21]
A
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, people and their queries and their answers. And then, and then you were working at Addepar, working on like you were working on your own thing, but also
- [42:39]
B
more about aggregating data than matching data.
- [42:41]
A
But.
- [42:41]
B
But yes. And I guess a lot of the, like, I guess less on the product side, more on the people side of Adipar, of like building these teams. I guess a lot of that was kind of relevant.
- [42:50]
A
Yeah, yeah. And. And then, so then came Lunch Club, which is just kind of just the. You smash those two things together and,
- [42:56]
B
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. All come from me by any means.
- [43:07]
A
Yeah, yeah, yeah. But it just seems kind of, kind of like we're kind of your arc. The Vlad of the arcs. The arc of Vlad is kind of crescendoing here at this time. I feel like you're going. You're kind of shifting from, 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.
- [43:32]
B
Yes.
- [43:33]
A
Yeah. Talk about the transition from Lunch Club to Lighter. Because this is the same company, correct?
- [43:38]
B
Yeah. So in terms of, you know, the, the process that it took, like corporate entity wise, it is the same. And I mean, I can talk about that as well, but I think kind of like why Lighter? I think maybe is maybe even more interesting question because we Even like in 2017, when we settled on Lunch Club as idea, like we were thinking about crypto ideas then too. Like I think when we had a short list of like 5 IDE ideas, like 2 were in crypto. So, you know, it's not like if you just look at Launchcom, why would you go from this to crypto? But if you were to see the full picture of our idmas, crypto was always in the background. We just, like in 2017, a lot of the tech just wasn't ready to do what you needed to. Just how like 2014, AI wasn't ready for, you know, add a part. But like anyway. And I always really liked, like you asked earlier about what kind of math problems I really liked. Olympia is like one of the fields I like was number theory. Right. Because number theory had a lot of these opportunities to do stuff really creatively and out of the box thinking. And so 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 in cryptography. So anyway, so kind of I was always intrigued about the space and especially as it can address some of what I saw as missing in finance and being in TradFi. Right. But yeah, the actual process we went through though, because this is. It all sounds good and well now it's like, oh, yeah, we did this and we did that. It was not Easy, right? Not a lot of startups, we didn't think it was a sure thing that we could succeed with a pivot. But I think in space, where now there's a lot of negative 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 Robinhood were down a lot. Right. Even companies like SaaS, companies from obviously, crypto, companies like Coinbase, everything goes down a lot. Right. So it wouldn't have been easy to just start from scratch. So we were able to retain 80% of our engineering team through the pivot. The way we did it was like, okay, let's have everyone be part of the process. It's not just like, okay, we're doing this now, we're doing that. It's more like, let's run kind of like an internal YC within our company and let best idea win. So we actually were like testing three different ideas. Lighter was the one that was the best, and we kept building that and kind of took some interesting parts of the other ones, merged that into lighter.
- [46:32]
A
Who came up with the lighter idea or how did that idea come about?
- [46:36]
B
I think that this one, you know, was idea that, that I came up with. Yes.
- [46:43]
A
Okay, so the. What, what did you see in crypto that, that you. That really scratched your itch? Because crypto is, I think from somebody who's very into numbers and into systems, probably provides a lot of material for you to, to think about. So what about crypto? Before you even committed to crypto, Right. What did you see that was like, just intriguing or interesting to you?
- [47:04]
B
Right. So crypto, you know, like, I think there's this thing people talk about in startups. It's like a solution, looking for a problem. And I think crypto kind of had a little bit of that. I mean, I guess bitcoin was always providing value, but a lot of other stuff had more the feel of like, okay, there's this really interesting tech. Like, there has to be applications for it. But like, you know, like, I mean, you were around 2017, 2018. A lot of the projects back then were like, you know, Uber on chain
- [47:32]
A
or like, very more fake, very backwards. Yeah, yeah.
- [47:34]
B
There's stuff like, okay, can we build like a quora on chain? Or like, you know, can we like build Airbnb on chainos? 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 store value Bitcoin. Like there have, you know, it works as like, you know, decentralized computer with Ethereum. But like there, 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, let's look at exchanges. Why is it that we have these digital assets and they represent decentralized protocols for the most part, but the way they are traded, this was true when we started building. The way they're traded generally is not actually using the Rails that they themselves are for. There was uniswap. And I mean AMMs have their own inefficiencies, but that's certainly decentralized. But for the most part that was less than 1% of the volume of how digital assets were traded. They were actually traded in ways that didn't actually use the Rails that they were for. And so that was. Okay, how do we solve that problem? We knew a lot about the tech already, right? But the actual connecting that with 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 TradFi? That's not really how companies get built unless you're elon or something. But generally you have to start with a small customer base, build something that actually works and grow from there. And so that's where okay, perps is actually something people want. They want decentralized perps, but also that are secure and verifiable. No one's been able to do that like we, we think we can.
- [49:24]
A
Lunch Club pivoted into LiDAR in 2022ish. The first time I ever heard about LIDAR was sometime late in 2024. So what happened in those two years?
- [49:35]
B
Well, we were, I mean the cortech 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 because it had never been done before. We had to use ZK in kind of novel ways and come up with.
- [49:52]
A
So you guys are 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.
- [50:01]
B
Yes.
- [50:01]
A
Okay.
- [50:02]
B
Now we were talking to some small groups of customers and like showing them prototypes or like different versions, but like, we certainly weren't publicizing anything that we're working on in the open. It was more like we would go to conferences and meet traders and, you know, other builders and show them some stuff and build those relationships over time. Which actually proved really useful. Right. Because when we got to, when we did have the tech ready, we had a hundred 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.
- [50:44]
A
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. Starting with just like the, the physics and the informatics and the math Olympia, but then also just the matching engine work that you've been doing you did at Quora, the high frequency trading work that you did at Citadel. It seems to be like 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?
- [51:22]
B
Yeah. I mean, it, it, it, it definitely feels great to have. You know, it's. It's one thing to like, work on something that, that you know, you feel like you're like you're built to do it. I mean, it's nothing to actually build it and have real customers. All right. I mean, these things, there's like, levels to this stuff as far as the satisfaction you feel. I mean, I think we're still very early if you zoom out, so hopefully there'll be like more rewarding feelings in the future. But yeah, no, it's, it's been great. I mean, I think 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 would do well against competitors if we built it. And so like. Yeah, I mean, I mean, I think like lunchclub fit across all three at the time. Although I think where we Got it wrong was the market was actually not very big. It got big during COVID and then shrunk. Sure. But with lighter. I think all three things are true. It's like trading 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 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.
- [53:06]
A
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 10:10 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. They're 30 minutes, they're short, they're punchy. I think this crypto cycle is harder to navigate than most. So let's do it together. Go subscribe to this podcast, search the Defi Report. Wherever you get your podcasts, YouTube, Apple, Spotify, or find a link in the show notes. There's a new episode waiting for you now. Hey, Bankless Nation, it's David. If you're hearing this, that's because you are listening to the free Bankless podcast feed. Did you know that there is a premium Bankless RSS feed? The premium feed has extra interviews that I do for my own personal research and just deeper questions that I want answered about the crypto industry. Questions that I want to answer so I can be more informed as an investor, both at Bankless Ventures and also just in my own personal portfolio too. Also, there are no ads, which means if you listen to the premium feed instead of the free feed, you'll get about 20 hours of your life back every year because you choose to support Bankless 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 Bankless 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 Lighter?
- [54:43]
B
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's hard. I mean I don't know if we've 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 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 tradfi and, and in crypto, right? I mean we, we see like hacks and kind of, 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 Madoff, you wouldn't have, you know, ftx, you won't have a lot of these things that have been a problem for finance. And composable, that's the one where all these DEFI protocols have been composable. That's one of the nice things about Defi, but that's not new. But what is new is Defi and Tradfi being composable. All these financial primitives now we're talking about tokenized stocks and perps and options all on the same balance sheet. Like composability not just from like a software level but from like a, you know, a balance sheet level is really cool. So anyway, I mean I think this is all. 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. It's helpful to think about, like, what does this look like two, three, four, five years from now? But really, like, I think we know 1, 2/4 out and we listen to customers and we keep building. But I mean, ultimately it's like whatever the merge of Defi and Stratify looks like, we want to build the tech to make it happen.
- [56:54]
A
Well, my next question for you is going to be what does lighter want to be when it grows up? But I guess you think 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?
- [57:06]
B
Yeah, I mean, I think the saying, like the longer term kind of being the technology layer for defi, meaning tradfi, is a way to describe the mission for sure. But again, I think when you pick like a short statement like that, that misses something because then anytime you do that, somebody can be like, oh, well, what about this thing though, that I care about? Is that not part of the mission? No, it is.
- [57:28]
A
It's also that. Yeah, yeah.
- [57:31]
B
But, 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, not just to check the box, 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, Robinhood, et cetera. There's kind of 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.
- [58:22]
A
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, you know, the, the bittrexes and the bitmexes of the world. And we had this growth of centralized exchanges. So we like, we unlocked that part of the tech tree. Ethereum came along, we had Ether Delta which put that kind of technology, but put it on chain. Terribly slow, terribly inefficient, but nonetheless it worked. Uniswap came along, we had the AMM got a little bit better, we had L2s come along and there's been this just arc of exchange technology that has grown. And I see you had the big exchanges like Coinbase and Kraken and Binance kind of really plow the way of what actually compliant and compatible centralized exchanges works with tradfi and regulation and all this with Lighter. It seems to be that there is somewhat of a step function change in terms of the technology under the hood with the ZK circuits that optimize for latency but also providing the auditability that is something that Coinbase can't offer. Binance can offer, Kraken can offer, Uniswap can offer, but Uniswap can't offer the speed and the latency of the ZK circuits. And so it's like if Coinbase was like next generation Exchange and then Uniswap was like a next generation exchange. Seems like Lighter is like something like Gen 3 or the most latest generation of exchange technology, which to me kind of embodies a lot of the ideals that crypto has, the user verifiability, but it also has the product demands that traders want and must have. And it seems to be there's just like a synthesis of all of the things that we've ever learned in crypto all kind of being applied into the same spot to create a, create a product that has a lot of these, that satisfies some of the most hardcore users, 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, 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, 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.
- [61:01]
B
Well, we're first of all like we're, we're, we're glad to see, you know, really passionate, you know, support from, from early community members. I think you articulated it as well as anybody. So that's like, that, 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 I think as a technologist, you talked about how, okay, there have been aspects of this that have worked well in the past, but there's always a trade off and we found a way that, where we can actually kind of achieve all these things. And that's what's really fun, right? As a technologist, it's like sometimes, most of the time you're solving problems where you're optimizing X at the expense of Y and you're making calls on 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 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 crypto the ZK proofs were such a thing where with this technology you can actually 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 that ZK has real applications. And then I think the second part of what you described was like, how tradfi perceives it. You know, tratfi is interesting, right, because they, they understand the tech nowadays. They, they're testing it out in various forms. Ultimately you stop to remember, like, tradfi follows the money, right? So when they see product market fit, they want to make sure the stuff is legit and works in ways that are fair and verifiable and in many cases regulated. So that's all 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 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 way. Like it doesn't work like they're gonna do.
- [63:25]
A
They'll tell you to go Go build the mousetrap and then maybe we'll use it.
- [63:28]
B
Yeah, exactly. And then they'll say, okay, well, maybe it's like, you know, like maybe the path would looks like when you first try it. Like, I think the 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 Tradify looks at things. It's not like, okay, this 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. We'll use it for this part of the business, for that part of the business, and eventually it becomes the thing. Yeah, yeah.
- [64:14]
A
And from the margins.
- [64:15]
B
That's right, yeah. So that's how we see it playing out. But a lot of the stuff has to do with particular constraints that these businesses have. They all have shareholders, they have their own management structures, this and that, and we just have to be willing partners to engage and meet them where they are.
- [64:32]
A
Well, Vlad, we've got a particular set of skills. It's been fun watching you apply them, and I will continue to watch you apply them over the year. So thanks for coming on the show.
- [64:39]
B
Thank you.
- [64:40]
A
In New York,