What is edge in the context of the financial markets, and how does it come about? It can be many different kinds of edge, but in the end, something that makes you money is something that either you know that nobody else knows or something that you can do that many other people can't do. You managed the Jane Street cohort for Sam Bagden Fried. He was definitely noticeable. He challenged people to explain things. Sam was like, day 1, I'm gonna ask all the questions. I'm gonna figure things out. We've seen a huge growth in retail trading post COVID. Prediction markets are growing in the last 5 years. The world is going in a more pro gambling direction. Sports gambling is legal now. It's what I'm calling the gamblization of everything. Who is the type of person that should become a quant trader? I think you have to go all the way back to Augustin, thanks so much for doing this. What is edge in the context of the financial markets, and how does it come about? The way I define it in the book is edge is something that either you know or you can do that the marginal participant in that market either doesn't or can't. And so it can that that can mean many things depending on the context. For example, if you have really, really good technology that lets you trade really, really fast, that's a source of edge. If you have really good models developed by very good knowledgeable researchers, that's another source of edge. If you're the only company that has access to a particular market, maybe as a foreigner, that could be another source of edge, you know. So, like, it could be many different kinds of edge, but in the end, something that makes you money is something that either you know that nobody else knows or that many other people don't know or something that you can do that many other people can't do. And so I wanna ask a little bit about your time at Jane Street. You know, you worked there. You worked in the London office. And 1 of the things that I think fascinates a lot of people is they see these stories of the news of these top quant shops raking in billions and billions of billions of dollars. Mhmm. But it's it seems like a black box. Like, it just seems you know, we have the efficient hypothesis of the market, the markets are efficient. And, obviously, if these guys are capitalizing on all these different inefficiencies, that can't 100% be true. But I guess my real question is, how do these quant shops, you know, build these organizational structures and hire talent that can continuously generate and execute on their edges and make money? Yeah. I think a lot of it comes down to, like, knowing what you're good at and trying to be as good as you can at those things. Like, it has to start with something that you're particularly good at. I think every great trading firm has an origin story that looks something like, this is something we figured out how to do that was fairly simple, pretty self contained that we could start with, and build from there. I think that's that's 1 of those things where if I think if you start, trying to build a massive firm from day 1 that does everything, you'll probably fail. Trading is 1 of those things that yields to incremental improvements. I think 1 of Jane Street's biggest edges is that whatever it is that they do, and trading requires a whole lot of skills and a whole lot of, people involved, the whatever it is that's the worst thing that they're that they do, they're pretty decent at. Right? And so they're very good at some things, but even their worst thing, and I don't necessarily know what that is, they're still pretty decent at it. And I think that's that's the nature of trading. Right? Like, if you're exploitable, then you will be exploited. And so, kind of plugging those little holes over time is 1 of the best ways to sort of get better. And then colonizing adjacent markets is probably the other natural way to go about it. You start with a small thing that you know you can do well, and then what's the next thing? What's the thing that's most similar to it that we're not doing and start doing that thing? I guess this is more a specific question about, you know, executing on edge or rather well, it's just about trading in general. How do you know you have edge? How can you be sure? Let's say you have, you know, something that's there and the the pattern that was present. Let's say only in the last 12 months, you're thinking about it rationally. Maybe it's to do with crypto. And, you know, you're thinking rationally, and you you think that the trend will persist. How do you know that that that's something that's that's that's profitable and then you can actually tangibly trade in a way that you'll that to make sure that you'll make money? So I think if we're being honest with ourselves, I think the answer is you can't. You can't know for sure. And so all you can really say is statistical things. Right? Like, I did 1000 trades and I made money on 55% of them. And the amount of money that I made on the 55% was about the amount of money that I lost on the 45%. Like, I have some good statistical bounds on what is the probability that I I just got lucky. Right? And so, certainly, that's what pushes firms, quant trading firms in the direction of doing more trades, more high frequency stuff, in part because you have axe it gives you more data, but also because it gives you more statistical certainty that you have something. If you're doing something more discretionary, and I have lots of friends in crypto that do more discretionary type trades, it's a lot harder to say, yeah. Like, I definitely definitely have edge or I definitely can do this thing. An analogy that I would give, if you don't mind, is is with poker, where if you just look at the the statistics of win rates and that sort of thing and you're trying to figure out is this person a winner in this game, it takes, like, a 100,000 hands or more to really figure out, like, is this person actually making money or are they just on a big massive heater? But if you look at their hand histories, if you look at the individual decisions they made, a a really good poker professional can, in a span of 1,000, maybe at most 1000 hands, can tell, yeah, this person is good. This person's beating this game or not. And so you can sort of think about the that 2nd kind of studying a poker player as more applicable to discretionary trading. Like, what is the thought process? What is the way in which they're developing edge, thinking about it, improving it? How do they self regulate? How do they control the, you know, things that happen? Is that a statistical statistically significant sample size? Perhaps not. But we have all this other information we might be able to integrate to get get to a better sense. Do we know for sure? I don't think so. Oh, fascinating. I think that's a very interesting way of looking at discretionary trading, analyzing, you know, looking at the inputs that 1 has taken in, the information they were analyzing, and how they synthesize that. I think that's that's a great insight. You know, on that note, so I think 1 of the things you mentioned in your book, the laws of trading, is that there are extreme events that can occur. And another thing you mentioned is that there are quant shops that aren't truly fully systematic. And what I mean by that is it still requires someone to turn the thing on and and off and make a decision on whether or not the the market conditions are ones in which the model will behave as it has in the past and thus make money. Now I'm curious about that. You know, during crises moments of crises such as, let's say, .com crash or 2008 or even 2020 or or 2022, you know, these periods of of insane volatility. Let's say you're trading in the bid you know, you're trading market movements, but you're using data that maybe is not applicable to the current regime. How does a trader how does a skilled trader rather navigate that? Yeah. And I think it depends a lot on the kinds of strategies that you're running. If you're running something that's very automated, very systematic, you basically just have to make a judgment call about whether the the situation that you're in is a in sample of your training data, whatever you use to train your model. If it is, great. Keep going. If it isn't, then you have another judgment call to make about whether given that it's out of distribution, is this still is this going to be a good or a bad time to turn the the the machine or the system on? So if you're a systematic trader, there's still a couple of maybe decision points. It is worth noting that in times of crisis, like when things are very volatile, etcetera, etcetera, those are generally very good times to trade if you are not forced to trade Because kind of the nature of the volatility is that people are forced to trade. Maybe there's some liquidation somewhere that's forcing somebody to sell out of a position. Maybe there's, like, a margin call, something. Right? That's part of what's driving that volatility. Big moves drive big trades. If you're not constrained in that way, I if you have enough risk capital to deploy during those times, those are very often extremely good times to trade. And so maybe your system doesn't actually have to keep nearly as much of the relative edge because there's enough absolute edge to maybe make up for that. And so that's another thing to keep in mind. I I think in particular, of market making strategies. Right? Like, when things are very volatile, then the value of somebody always being able to buy and sell at a price goes up. Like, that is a more valuable service when things are volatile. So you should be, if you're doing things right, making more money when things are volatile. So that's maybe kind of on the on the systematic side. And then maybe on the more discretionary side, which I'll admit, to be clear, I'm not nearly as familiar. Again, it's about all those other soft things. Right? Like, am I making good decisions? Am I understanding the world properly? Consulting with people you trust. Like, all these sorts of things factor in. Now I wanna go back to what I kinda talked about towards the beginning of this conversation where you're talking about people seeing the news, seeing all these quant shops, quant trading firms, quant funds, you know, with these, you know, insane amounts of of profits, you know, just raking in huge sums of money. And it feels to me as an outsider looking in that these firms have all the money. They can hire

All the talent in the world. They have access to the best technology. And so if you're a smaller player trying to break into the scene, maybe you have a little bit of edge, it feels like well, even that, like, even having a little bit of edge seems to be something that is very, very difficult to to have when all these firms are raking you know you know, capturing all the edge, capturing all the inefficiency rather. And I'd like to hear it from you. Where do you see the future of quant trading shops, quant funds, maybe even multi managers? Do you think that they will continue to get bigger and that smaller players are gonna essentially be wiped out? I'd love to hear your thoughts. Yeah. So there's a couple of things there. 1 is I think there's an interesting question of why is it that the profitability of these companies, it's definitely gone up in the last 10, 15 years. Like, what is the source of that? We could maybe dive into that a little bit later. But to address your specific question, yeah, I think it actually the the waterline has risen. Like, it's much harder as a small shop with probably a big technological research cost disadvantage to compete with with these companies that are progressively getting more automated. Like, it used to be like, the standard answer was, like, go figure out how to trade the bottom end of the Russell because Bitcoin shops is kinda just not worth their time to to to focus on that. Right? I think that's still somewhat true probably, but less so than before because the marginal cost of adding some more marginal product just keeps going down and down as automation just keeps making things easier for for large firms. And also those things just trade more anyway, there's just probably more money there to begin with. And so I do think it gets harder and harder for individuals, for small shops to to compete. I I definitely believe that. I think there's definitely a consolidation that's happening. And you you probably the most obvious places in options where used to be there is probably, like, a couple of dozen options market making firms that could, you know, make a decent living at it. But the technological lift, like, the sort of the fixed cost of being able to be an options market maker just keeps going up and up and up, and that kind of prices out a lot of medium and small sized shops. And you've seen this consolidation in in the last, again, like, 5, 10 years. And I wanna bump I wanna, sorry, jump on that. The point that that you made earlier. So, you know, begging the question of why is it the profits of these firms has gone up and up and up in the last 10 years? Mhmm. We'd love to hear from you. Yeah. Look. I certainly don't know. It's something that I and my friends kinda wonder about. I think 1 plausible source is, like, the world has gotten richer, and so, like, there's just more money out there. That's fine. Right? The other thing is to the extent that, like, old line investment banks like, you know, like Goldman or, like, Morgan Stanley or or JPM, they used to internalize a lot of their client flow, and that money used to be made by these investment banks. I think that's changing. Right? Like, you see a lot of these quant firms, for example, entering into bond markets. It used to be very much controlled by investment banks. The investment banks are being disintermediated. Like, the the end customers, say it's like some pension fund in Brazil, is going straight to the the ETF market maker to say, hey. What's the price you can make me on a $100,000,000 of EM? Like, that like, the middleman is getting cut out. And so maybe a lot of those profits are just kinda getting shifted in the direction of the firms that are actually taking the risk as opposed to just being the middleman. So that that might be 1 possibility, or maybe the 2nd possibility in addition to the world getting richer. I do also think, sadly, that in some countries, certainly like India and The United States, we've seen a huge growth in retail trading in the last, you know, we'll say, like, post COVID if nothing else. And and so, like, there's a lot of people make there's a lot of quant firms making money off of a lot of small retail people gambling that they should or, I I don't wanna say that they shouldn't, but they're they're definitely not making money by through gambling. Right? And I'd I'd we definitely see a trend, I think, in the last 5 years of the world is going in a more pro gambling direction. Like, sports gambling is legal now. Like, lots of people trade now. Like, prediction markets are growing. Like, it's it's what I'm calling, like, the gamblization of everything. And, yeah, like, if you're making markets in a growing market, you're probably gonna make more money. You know, the thing you touched on there of the gamblization of everything, I I agree. And I think it's ridiculous that Kalshi is under partnership with Robinhood. And on your brokerage account, you can literally be sports betting. Like, for me, that's something that doesn't make any sense to me. I don't know how that's how that's doesn't call it sports betting. Right? They call it predicting events. Right? Yeah. But, like, you know, it it it seems to be that if you're betting OKC over the paces, that doesn't really seem like a, you know, a data driven, like, insightful I I don't think anyone really has has has edge there, especially a retail guy. And they're frankly, they've got they can gamble away their their life savings. You know, on the note of of trading prediction markets, I just finished my undergrad in London at UCL. I studied math and physics. And a lot of my friends, you know, studying math, studying physics, a lot of them, you know, were were interested in becoming quants. And, you know, in London, it's a financial hub. Whenever I'd meet someone who studies math, he'd always be like, oh, yeah. I wanna become a quant. I wanna work at 1 of these large market makers or maybe a big hedge fund. And 1 of the things I think you talk about a lot is that these firms hire for raw talent rather than knowledge. And I guess my question right now is who is the type of person that should become a quant trader versus the type of person that's better off seeking a career in something that's that's, I guess, easier to get into. Yeah. I think you you bring up a good point, which is the number of seats that are at good shops doing, like, interesting work is actually relatively small. Right? Like, it's probably only a couple thousand new hires per year tops around the world in what I would call, like, a good quant trading firm. There's lots of 2nd tier and 3rd tier firms that I don't know that you'd necessarily wanna go into, But I think you have to go all the way back to, like, why is it? Like, why do people want to do this? Why do students why are students so motivated and interested in doing this? I think if you ask and I've I've talked to a lot of them. If you ask students, like, why why this? Why why is this the interesting thing? They'll say, oh, some combination of, I think, the math and the science the math and the stats and the AI and the ML is interesting. But there are plenty of places to apply those sorts of skill sets, so it's not necessarily that. I think if you dig deep, a lot of the time, the real answer is, a, it's a high status thing to do. Right? Like, across like, if you go to MIT or Harvard or whatever, like, what are the high status internships to get? Well, it's the James Street internship. It's the HRT internship. Maybe it's the OpenAI internship. I don't know. So, like, people do it because it's the highest status thing to do, not necessarily because that's truly what interests them. And the other thing is, again, I talked to a lot of people who say, oh, I've wanted to be a a trader my whole life. Like, this is, you know, it's been my lifelong dream. And I would argue that probably the true answer is I wanted to be rich my whole life, and I think this is my best plan to get there. Right? Which is a very different thing. And I think that's that's, you know, getting back to your question, 1 of the things that's hard to appreciate unless you, you know, do an internship or work at these places is the vision from the outside of what the nature of the work is is very, very different from what the actual work is. Rightly so because it's very secretive. Like, you don't you don't get a sense of what the job is. And, yeah, some people find it super engaging and fun and and, yeah, they do make a lot of money, but for for other people, it's like, this really isn't my life's work here and but I'm kinda stuck here. Right? And so that's kind of a thing for, I think, for students to really look in the mirror a lot. Like, there are a lot of really, really interesting places to to work, a lot of interesting things to do. The world is a fascinating, huge place. Quant trading is just a tiny sliver of what's interesting. So going back to the concept of edge that we discussed earlier, how should someone apply the concept of Edge to navigating their own career or even life decisions? Yeah. Again, goes back to what is the thing that is I don't wanna say unique necessarily, but what is the thing that differentially you're you're kind of good at? Right? It has to start with that, I think. No matter how much I have a dream of becoming a professional sprinter, like, I just I don't have that edge. Right? This is I don't have the speed. It's not in me. I shouldn't try. Right? I mean, I should do it for fun. I should enjoy it as a as a, you know, pastime, but I shouldn't focus on trying to make a career out of being a professional sprinter. And so I think the idea of finding your own edge, that's really hard. I think part of what university gives you over the course of those 4 whatever years is an opportunity precisely to do that. You take some classes. It's like, oh, this is kind of interesting. Right? There's sort of a natural inherent interest in in understanding, I don't know, 17th century London or whatever. Right? Like, you didn't know you had that interest, but university gave you an opportunity to discover that. Right? And so where you're interested in something, you will spend the time to learn about it, to get better at it, etcetera, and that can become an edge for you over time, sort of like a like an emergent edge, let's say. Where does that turn into, like, an actual edge in terms of making money in a career? That's another thing you can kind of discover in university through internships, etcetera. Right? Probably you have at least 3 opportunities to do internships in university. You should probably do 3 fairly different things that seem reasonably interesting to you and then compare. Right? I think it's a very, I think the best way is a very sort of empirical experiential way

Figuring out what your personal edge is. And once you figure that out, then then a lot of things become easy. It's like, oh, I'm good at this, and I enjoy it. Okay. Well, I should probably go try to do that. Right? And once you're in those places so I guess let's take trading as an example. How do you acquire how do you learn the skills faster than anybody? So let's say, you know, 1 of the 1 of the things I've been seeing on X recently is you talking about how you were I think you were the head of or, like, you managed the the Jane Street cohort for Sam Beckman Fried. And, you know, I guess I guess we can touch on that later. But if there was a a trader from that cohort that would go up to you at the start of the internship, and he'd say, Augustine, I really wanna learn as much as possible. I want to become, let's say, the best trader among my my cohort. What would you tell them? So I think I would tell them the thing I I hope I did tell them during those those times, which is I think on day 1, the thing I like to say is on day 1, there is no question that is too stupid for you to ask. You should be asking more questions than you think you should be asking. You should be trying to deeply understand as much as you can. I have yet to see an intern that has gone too far and asked too many questions. I have seen many interns that haven't asked enough questions. And it's not just asking questions for the sake of asking them. I think a lot of the things that that you learn are learned, again, because you sort of self select into certain paths and certain kinds of trades or kinds of desks or kinds of ideas. And the only way you're gonna figure out what interests you and what you're good at is by asking questions and learning as fast as possible. I say that's thing number 1. The other thing is the internship is an opportunity for you to figure out both for the company to figure out whether you're you're a good fit for them, but also if this is a good fit for you. And so throughout that internship, you should be asking yourself, like, what is like, what's the best case thing here for me? Like, what is the kind of work that I'm gonna be doing that I'm gonna really enjoy 2 years from now, 3 years from now, 5 years from now? You may not know the answer to that, but I think that's again what guides, like, getting good at something. It has to come from some some baseline deep interest. Like, to give you an example, like, I I certainly don't like to read financial statements of companies. I don't like to read prospectuses of corporate actions and stuff like that, But some people love that. They just find it inherently fascinating. Right? They're just like they'll read this, like, 80 page prospectus on some, like, bond offering on or some, like, tender offer or something. They'll read and they'll read. And, like, on page 34, they'll notice something. It's like, that's kinda weird. That think that means x, y, and z, and and they'll go price it out. And they'll go look at the market and they'll be like, oh, wait a 2nd. That doesn't seem to make sense. And 90% of the time, you talk to somebody else and they tell you, oh, no. No. You misread this other thing on page 74 that totally counteracts that. But sometimes, like, oh, yeah. Actually, there's a trade to do. Right? You're never gonna find that thing until unless you're actually really interested in reading these prospectuses. That's an example of a thing that, you know, you could be a great trader at that thing. You're not gonna know until you start trying things out. And let's say, you know, you're seeing the internship cohort and let's say they're 4 weeks in, 5 weeks in, or maybe 6 weeks in. Can you tell from that moment who's gonna be a great who's a great fit for Jane Street 5 years down the road? Like, this guy is gonna be a superstar here, or is it still somewhat vague after after, you know, those 1st 3, 4, or 5 weeks? I think it's a distribution. I think some people you can tell. Like, they just get it day 1. Maybe they had some prior experience. Like, I don't know. They were sports bettors. When they actually made money or or, you know, they were poker players or it doesn't matter. I'm not saying they have to be game players, but there's something in their past that sort of kind of pre trained them for for for trading. And then they jump in and they just they just go. Right? And you think, yeah. This person's probably gonna be pretty good. Most people are are there's also the other extreme where, like, you have somebody who's super smart and, you know, you know, passed all of the interviews, Mick got the offer, etcetera, but they just they don't get it. Right? Either they don't get it or it's clear that it's not for them. There are a lot of people who are really, really good at math and solving problems. It turns out they just don't like trading. Like, the the the the reward system just isn't for that. Right? Like, they care a lot more about solving the math problem than making money. And in trading, in the in the end, you kinda have to make money. So maybe that's like another piece of the tail on the other on the other side. And then there's just the meaty middle where you don't really know. I think it's very easy to get overconfident both in interviewing and hiring, but also in in in running an internship program and saying like, oh, this person's gonna be great. This person's gonna be terrible. It's a lot harder than that. Like, people evolve over time in weird ways, and it's hard to predict. What was Sam Backman Fried like? Because, you know, you said you you were you know, you met him and he was he was an intern while you were managing the cohort. Yeah. Was was he like? Was he good? Yeah. So he he was he was definitely noticeable. He he was the most noticeable person in his in his class. Like, mostly in a good way, to be honest with you. Like, he asked lots of questions. He challenged people to, like, explain things and, oh, this doesn't make sense to me. And look. As an intern, you're coming into, you know, very well regarded trading firm. Most people are just kind of intimidated the 1st week, 2 weeks, etcetera. It's just like, let's just kinda be quiet and and, like, not make waves and and not get noticed too too much and and just, like, not screw up. Right? Like, we're all motivated by the not getting yelled at. Right? Sam wasn't like that. Sam was like, day 1, I'm gonna ask all the questions. I'm gonna figure things out. And yeah. So he was very noticeable in that respect. And he was sharp and and quick and everything. I think personality wise, there were questions, definitely. But but, yeah, he was he was a pretty good inter. Mhmm. Now this is a funny story, but so my my dad's office in in Hong Kong was on in the same coworking space as FTX's ex head office there. Mhmm. And so there's this photo of of Caroline Ellison that's, you know, is very iconic with, like, this green behind her. And I remember when that photo was going, like, you know, was was was going crazy on on the Internet. I remember seeing that and thinking, hey. I used to go there every now and then visit dad. But 1 of the craziest stories and I remember going down the elevator 1 time, and I see a guy with culmable shorts and slides and and and longish hair. And this is back when FTX was still doing quite well. And overclocked in the fact, oh, that's probably Sandbag McFried, and that was for me, that was like, woah. And then I saw the news later, and I was like, that's that's crazy. But my question, I guess, about SPF and I don't know if did you follow FTX closely? You know, having managed, you know, his batch and and knowing him? Were you, I guess, following, like, their story, crypto trading? Yeah. I knew plenty I knew plenty of people who worked at FTX. Yeah. Oh, wow. Were there any, like, red flags you saw, you know, before the whole thing, I guess, came crashing down? No. No. I I didn't see any red flags. I was on the outside. Like, it was like, you know, I think we all knew that Sam was incredibly ambitious and aggressive, and I think people who are and and he said he stated openly in many podcasts and interviews that he said, you know, he's risk neutral, not risk averse. And in my experience, people who say they're risk neutral, are not risk neutral. They're actually risk seeking. They just call it risk neutral. And so maybe that was, like, a thing that I would say you know, I think we we people who who knew him and saw tracked him at FTX thought, yeah, like, he's making a really big bet. Right? And it could be it could go really, really well or or not so great. I don't think any of us actually thought that billions doll billions of dollars of fraud. That's that came as a shock to me. With some of your ex colleagues from Jane Street, you know, the ones that have left, typically, what is the reason for leaving, and what do they tend to do afterwards? It's actually I I would say it's pretty heterogeneous. Like, some people leave and go back to, like, go back to academia, like, you know, go back to being a math professor or something. Some people just retire, obviously. Like, you know, life is good. Right? Why why am I gonna keep working into my forties, fifties, sixties, etcetera? Other people, you know, they go to other firms, right, competitors. I think there's been quite clearly a fairly steady drip of talent from Jay Street to other companies in the last 5 years. I think there's a very natural set of reasons for that. Some people just kinda go off and do stuff on their own, like trading on their own and kind of maybe bring some of that skill set into, like, a a a tiny like, I don't I don't have any bosses. I just kind of work for a few hours a day and and make money, and it's okay. So yeah. There's and then, yeah, like, people like me who do who the hell knows what it is that I do now? What do you do now for our audience? So, yeah, I I am currently I moved to Prague last summer, and I'm currently working at an AI startup that is applying reinforcement learning to financial markets. How much can you talk about it? I mean, I can talk about it a little bit. I think 1 of the things that that is true about quant trading is that, historically, at least, there's a very clear distinction between the thing that tries to figure out what something's worth and the thing that takes your belief

About what something's worth and turns it into a set of trades that you actually do. Right? You can kind of, like, divide the quant trading pipeline into these 2 pieces. Right? And so the 1st piece, like, yeah, it it gets a lot of AI and ML applied to it. Like, take a bunch of data and try to figure out what something like, what the fair price is, etcetera, etcetera. This other piece, the, like, okay. I have a fair value. How do I turn that into a set of trades has historically been very heuristic, very manual, very not manual in the sense of people actually executing, but in the sense of the rules that the system uses in order to to decide, like, what trades to do has historically been very yeah. We'll say, like, heuristic because it's hard to get a machine to learn that. Right? You have to sort of understand how to interact with the with the market, how the market reacts to your actions, etcetera. And so that's a very natural setting to use reinforcement learning techniques. And so this company is founded by a bunch of ex deep mind people who had applied it to poker and that sort of thing. And so it's kind of bringing that reinforcement learning background and trying to build a system that essentially can learn what actions it should do in a situation as opposed to, like, kind of instead of having these 2 little pieces, we just kind of do 1 end to end, figure out what to do from from the data. And so are you is the firm trying to trade its own money? Is it trying to raise money to trade, or is it trying to provide the service to, I guess, some trading firms that maybe aren't so good at you know, when they when they have a estimate for the fair value of an asset, for example, maybe they don't know how exactly to go about trading that. What are you trying to do? Yeah. So I I am a firm believer that if you have an edge in the market, then that edge is valuable in inverse proportion to the number of people who either know it or can use it. Right? And so the natural setup for something like this is essentially a trading firm where you're trading your own capital or some version of it and trying to make money the old fashioned way. Lovely. I wanna gear a conversation now towards advice for young people. There's so much changes happening in the world today. And, you know, even going on act or Instagram, there's jokes of computer science majors being unemployed. You know? There there's this 1 of, like, you know, you see your friend 20 years later, he studied computer science, and he's, like, on the side of a road, and you're like, woah. I I didn't recognize you. You know? All these jokes about how generative AI is coming for everyone and and and real serious, you know, serious discussion on it as well. I personally, I do believe that I mean, when I think about the middle class in the future or even young people now, I think it is very difficult for those who aren't in the top percentile of intelligence or or family background, you know, wealth connections, etcetera. How do what do you think about what I've just said, and how should young people navigate these changing times? Yeah. It's a great question. I, you know, I have 2 teenagers myself, and so I spend an inordinate amount of time thinking about this. And I will admit at the outset that I don't have any great answers. So and and I would even go further. I would say anybody who claims to have great answers is probably either trying to fool you or trying to fool themselves. Unclear. Because it's it's very unknowable. Right? Like, just just think about AI timelines. Right? If if your AI timeline is is like, oh, 2027 will have ASI and and, like, therefore, a machine will be able to do any work that a a human can do today, that's a very different world from it'll be 2040 and AIs will be useful assistance to humans, but, like, there's no ASI, you know, on the horizon. Right? These are 2 very different futures. And so, like, yeah, in the case of the former future, I don't know. Like, maybe maybe we all get turned into paper clips and who cares. Right? If that's the case, if we all get turned into paper clips, well, like, I'm not gonna worry about that branch of the probability tree because there's nothing I can do about it. Right? So let's think about what we can do something about. And I think I was I was talking to to a friend about this a couple weeks ago, and he said, look. You know, the thing I tell my kids is you have to figure out how to be adaptable. I mean, that's the number 1 thing. And I I at 1st, I thought that's kind of a cop out. Right? Like, yeah, it's always been useful and important to be adaptable. But I'd like as I thought about it more, I I do think that that is a key component of it. Like, it is when when the world is changing fast, it is really important to be able to understand what is going on, understand, like, little pockets of of interesting things to be able to do, and be able to jump into those quickly. I think well, I I think I I I wrote about this week on on x is that cognitive tools like AIs, like LLMs, etcetera, are like force multipliers. Right? Like, you can either use them for good or for bad. Right? You can you you don't have to, like, you don't have to write down and and do math by hand anymore. We have calculators. But if you don't literally know how to add 3 and 4, you're probably not doing so great. Right? And so you can use a calculator as a force multiplier. Right? It doesn't mean that it replaces you. It just helps you do whatever it is you do better. And I think these tools kind of look the same way to me. Right? How can I use these machines in order to make me better at what I like to do or or what I want to do and how to avoid the pitfalls of endless AI slop on social media? Right? Let's say you're in a position where you know your job can already be done by, you know, agents, you know, like a simple, I don't know, you're you're you're a junior software engineer and you're building the front end of which Cursor can already do, but yet but you still have a job. Mhmm. How do you navigate that situation knowing that full well maybe you're being kept on for goodwill and that you have no security whatsoever? Well, I would say you you don't have any security anyway. Right? Like, especially in The US. Right? At will employment is a thing. Right? Like, they can they can lay you off anytime they want. They don't have to give you a reason. So I in that sense, I don't think it's especially different. Rather give if if the thing that you're doing right now isn't obviously economically valuable, you should figure out, like, what to do that is economically valuable. In that particular instance, like, you should really think very, very hard whether you're at a startup or a big company. It doesn't matter. Like, where does the value come from? I think this is a really important thing that's not obvious, especially for young people because value comes from all sorts of weird places. Like, most I would say most, like, SaaS companies. Right? Let's just use a SaaS company as an example in the last 10 years. The value doesn't come from the technology at all. The technology is super mature. It's a complete like, you use, like, a a Postgres database on the back end, and then you do some GraphQL, and you write some front end and react, and, like, there's no edge there. There's no value there. The value in most SaaS companies, let's just say an enterprise SaaS company, is probably, like, in the in the biz dev and in the marketing. Like, it's probably the people who can who can pick up a phone and, like, call the person in the in the company that knows exactly the person in the in the in the potential client to call to set up the meeting, like, to show the value of your of your platform, to keep that thing sticky. Like, that's where the value is in a modern SaaS company. It's not in the technology at all. So it should be unsurprising maybe that if you're writing code at a at a enterprise SaaS company, like, you're probably pretty replaceable. You have been for a while. It just maybe hasn't happened yet. Right? You've been replaceable by by a team of developers in Ukraine for, you know, 10 years, 15 years. So I don't think there's anything new there. If you really wanna understand, like, what it is you should do, you should figure out where the value is and figure out how to increase that. Right? Maybe that's after development. Maybe it's something else. It completely depends on the situation. And if you're a young person today, where would you live to maximize your prospects of I guess, maximize your options and maximize your chance of success? And I ask you that question because you've lived in London, and you're now in Prague. You you're you're from The US. Where is the best geographical location today for becoming a successful person? Yeah. That's an interesting question. I think it depends it probably depends greatly on what it is that you want to do. It's kind of a transparently boring answer. Like, if I wanna be in manufacturing, I definitely want to be in China. Like, that's where all the best manufacturing happens. That's where I'm gonna learn the most. Right? As a young person, I probably want to optimize for learning more than anything else. Like, finding the company that will help me accelerate my knowledge and skill set the fastest is probably the thing to optimize for. And so if it's if it's AI and ML, yeah, like, I probably wanna be an SF. If it's, like I said, manufacturing is probably China. If it's, like, if it's drones, I probably wanna go to Ukraine. You know what I mean? Like, it it it completely depends on on on what it is that interests you and what it is you wanna get really, really good at. And final question. And it's kind of it's kind of the 1st question I've asked you about, you know, what to do as a young person. But I wanna flip it a little bit and because you mentioned you have 2 teenage, you know, 2 teenage kids. Mhmm. How are you raising them for this world that's changed so dramatically? And what actionable insights would you give from your experience to parents who want to make sure they can prepare their kids for

For the future. Yeah. I I am very reticent to give advice because I feel even though I think about this a lot, I feel like I don't know anything because so, like, let's just say the traditional path, work as hard as you can in high school and, like, dot every I and cross every t so you can get into the best university you can and, like, in order to get the best internships you can and get the best job you can. Like, that path still works. Right? It like, it's fine. But I do believe there are increasingly other paths that that are also that also make sense and hopefully don't suck the life out of you between ages 14 and 18. And so what does that path look like? I don't know. Again, I think it depends a lot on what you're optimizing for. If you're optimizing for, like, what is the highest status job that I can get? Yeah. That traditional path probably works pretty well. If you're optimizing for something else, and I don't necessarily know what that is, there's probably other ways. Like, I'll give you an example in The US just because I know this is the case. Like, if you're if you're a high school student in The United States, you can take community college classes. You can just do it. You just sign up and do it. And in most states, like, it's basically it's close to free if not exactly free. Right? So you have a choice. Right? You can either, like, just throw your life away in the stupid, like, history like, grade 10 history class, learning the same shit like everybody else is learning, or you can take a history class in a community college in an area that might be slightly more interesting to you because there's a wider variety of of possible courses. It counts towards your high school diploma. It also counts as postsecondary, like, education. You get 2 for 1, and it looks really good on a resume at a university application. That's like an example of a thing, like a like a small arbitrage that continues to exist where, like, if you if you sort of look around and think about what options are available to you, there's actually a lot more out there that that than you might think. You just have to kinda look for it and be okay with doing stuff that is weird. I think that's the thing. The world is becoming clearly weirder in the sense of, like, higher variance, and so, like, kinda staying in the median is probably not optimal anymore. Final final question. Since you're a young person today or, you know, the young people you've met, the interns or even the among you know, your kids and their friends, what is a question that you don't think is commonly asked by them that that should be? Yeah. It's a great question. Yeah. I think it's hard because a lot of, let's say, relatively ambitious young people probably don't ask enough questions, period. Like, there is a path and and there is their understanding of that path, and they just sort of follow it. I think a lot of the questions that ambitious young people don't ask are actually about themselves, which sounds counterintuitive because you think, like, oh, in the modern world, like, people are sort of more much more in touch with themselves. I don't think so. I think that I think that especially the prevalence of social media sort of shapes our perceptions about the things that we should be and care about and want and do in ways that are very hard to disentangle and avoid. And so, like, instead of being instead of, like, actually figuring out what you wanna do, you just sort of self select into, like, some weird niche. Right? Like, oh, like, I'm a furry. Right? Or I'm a I'm a I'm a finance bro. Right? Like, no. You're you. Right? You're not, like, a thing that that that falls into 1 of 1000 categories. And so I think a lot of the questions are, like, what is the what is the thing that truly interest me? And the only way you're going to figure that out is by going out into the world and meeting people and asking them questions about their lives. Like, especially, you know, the person who's 5 years older than you, the person who's, like, 6 years, 10 years older than you. Tell me about your life. Like, what's interesting? What's not interesting? What, you know, what do you what what would you wish you had done? And and the idea of, like, not seeing the world in terms of, like, do I fit in this box or this box or this box? But it's more like, well, there's no boxes. Like, just I need to figure out what's good for me is hopefully a more modern world compatible way to figure out things. I love that answer. Thanks a lot. And to all our audience watching, I loved Augustine's book, The Laws of Trading. I'll put a link in the Amazon, for to the Amazon link for for this for the book. And, thanks a lot, Augustine. Absolutely. Really enjoyed it again.