No Priors: Artificial Intelligence | Technology | Startups
00:07:56 · Vlad Tenev, Sarah Guo
So there was this closet in my grandfather's house where, you know, he worked as a doctor kind of in the shipbuilding and naval industries, so he was on a boat a lot of times. So he had his contacts at the port, and as soon as he would get salary, he would exchange it for copper cookware. So it was kind of the first real world asset that I encountered people Like, investing [00:08:24] S1: sorry, stacked like pans. [00:08:25] S0: Exactly. Yeah, In the closet you'd have. [00:08:28] S1: Holds value,
00:08:29 · Vlad Tenev
[00:08:29] S0: Pots and pans, much better than the local currency at the time. And so, I just, I grew up with that. I grew up understanding, you know, what happens in a broken system where you don't have free markets and good healthy capital markets. And I'd say for me, I pretty quickly discovered that academically I was quite strong, and also independent. Basically by choice, you know, my parents couldn't really afford babysitters or after school activities or any of that stuff. They both working multiple jobs and my dad was a full time graduate student. So it was kind of like, you know, when I was in kindergarten, five years old, they would, I would walk back from school by myself. They would tell me, okay, go to the neighbor's house. We lived in apartment buildings at the University of Maryland. Knock on knock on the door, your neighbor will watch you for, you know, a couple of hours. And then they realized, alright, our son's going through this complicated ritual to get home, we might as well just give him the key, and pretty soon after that I would let myself in, do my homework, go outside, play with my friends, so I was very independent. And there was always this pressure of having to succeed and do well in school, probably for a couple reasons. One, I was good at it and so everyone just, everyone around me was like, do more school, skip grades, like do better in math, more math, you know, and the teachers would have these meetings with my parents like, hey, your son, know, do you know he's really good at math? You should put him in this thing. So I think that was one of the benefits of growing up around the University of Maryland, which is very academically oriented community and they would try to sort of like identify the kids that had potential and accelerate them. The other thing was my parents would just sit me down and say, look, the financial status of this family is very insecure. We're not making any money, so you're gonna have to basically get a scholarship if you wanna go to college, and to get a scholarship you are gonna have to do very very well in school, and also, by the way, we might get, we're like visa holders here, so, you know, if we lose our jobs, we're gonna have to get sent back to Bulgaria.
00:11:00 · Vlad Tenev, Sarah Guo
So there was all this pressure to succeed academically, and for me, my math PhD program, at some point it became kind of this challenge, like what's the hardest thing, how can I do better at the hardest thing, and it was this almost like competition with myself, with my parents who were also very academically strong, like, oh, what was the hardest subject for you at school, dad? Oh, chemistry. Let me just do really well at chemistry. Yes. Know, what was the most advanced math that you did? And I think math has this, like, ladder of achievement. It's almost like playing a really good video game, like, you always know what the next hardest thing is, and it's almost like how quickly can you go to algebraic geometry or algebraic topology and can can you can you learn that earlier and kind of understand it. And I think what appealed to me when I got to grad school was this idea that if you are... If you create a new mathematical theory, you know, maybe you'll be remembered for a really long time and you know, just like kids in school everyone learns you know, Gauss and Riemann and all these greats. If you create a theory, maybe kids will be learning and sort of like thinking about your work hundreds of years from now. So that was kind of what appealed to me. [00:12:25] S1: Just given that background and, you know, you should also explain what Harmonic is as an effort, what is your reaction to the revolution in math that's happening with AI today?
00:12:38 · Vlad Tenev
[00:12:38] S0: I think on the one hand it's very exciting and sort of predictable because the rise of AI models and the ability to verify the outputs put together has led to kind of automation of the math pipeline and I think it's really really exciting, right? I think that the power and the tools, I mean, the math is changing so quickly, and, you know, math as a field hasn't changed very rapidly at all in thousands of years. I mean, when I was in grad school, most of the work was still being done in person with like chalk and a chalkboard. It's like there was no collaboration. People weren't even using computers. So now math has kind of converged with coding. Mathematicians are using the same tools, they're using version control, they're using Lean for verification, and the field is accelerating very rapidly. About a year ago, so Harmonic, which is a startup that I co founded a couple years ago with the goal of building mathematical super intelligence. So last year we created a model called Aristotle that was one of three that got a gold medal on the International Math Olympiad. And then subsequently to that, we rolled it out to the public and we had people using Aristotle to tackle unsolved math problems. And so it was used to solve these Erdosche problems. There were these other mathematicians that were using it for new results and so forth. And so then I started thinking a lot about, okay, what's the future of math? How should these tools be used? And I think for me, it was very important for the tools that I build, not just Harmonic, but also Robinhood to be empowering to individuals, to people. And so the one thing I don't love about this latest wave of AI models solving math problems is that it's too centralized, right? It's like AI companies with their big data centers and their employees using secret models to sort of do all this work. And I think what's more attractive and what would ultimately win out in the end as a model is rolling these things out and actually putting them in the hands of people and having the users make interesting discoveries and inventions?
00:15:29 · Sarah Guo, Vlad Tenev
[00:15:29] S1: So, I am probably like a pretty fundamental believer in decentralization as as a philosophical premise, and I would love to believe that's true. I think that there are a lot of people who work at large centralized AI companies who would say, like, if I'm if I may push back a little bit that this is Cope and Centaur play will just be outclassed by, you know, more compute in an agent loop. Like, how do you, like, do you believe that's true, or what will be the human input here that is productive in math? [00:16:04] S0: Well, I think I think it's sort of like two separate things, right? One is what's the role of the individual mathematician? And the other is, is this gonna be like a product that people use, or are the AI labs gonna be producing the products? I mean, have thoughts on both of them, but on the second one, it's much more interesting. I think that a world where you have the AI labs on a proprietary basis making all the discoveries, and you have like the OpenAI or Anthropic data centers that are just running code, you know, managed by the employees of those companies, and you know, they're vertically integrating and producing all the drugs and selling the drugs to people, making all the mathematical and physical discoveries and publishing all the papers. If all of that is happening and it's owned by the AI labs themselves, I think that's a bit of like a dystopian outcome and unlikely to happen for a couple of reasons. Not just like technological and market, but I think also if that happens to a significant degree, people will, they'll
00:16:04 · Vlad Tenev, Sarah Guo
[00:16:04] S0: Well, I think I think it's sort of like two separate things, right? One is what's the role of the individual mathematician? And the other is, is this gonna be like a product that people use, or are the AI labs gonna be producing the products? I mean, have thoughts on both of them, but on the second one, it's much more interesting. I think that a world where you have the AI labs on a proprietary basis making all the discoveries, and you have like the OpenAI or Anthropic data centers that are just running code, you know, managed by the employees of those companies, and you know, they're vertically integrating and producing all the drugs and selling the drugs to people, making all the mathematical and physical discoveries and publishing all the papers. If all of that is happening and it's owned by the AI labs themselves, I think that's a bit of like a dystopian outcome and unlikely to happen for a couple of reasons. Not just like technological and market, but I think also if that happens to a significant degree, people will, they'll [00:17:28] S1: lose their It is license not acceptable. To Yes. I [00:17:31] S0: think it's much more, I think you could have the same outcome of Navier Stokes being proven or the partial Riemann hypothesis or any one of these things, and it'll be much more accepted if it was like a random lay person using [00:17:52] S1: It might be celebrated. Yeah.