Dwarkesh Podcast - Agustin Lebron - Trading, Crypto, and Adverse Selection

Episode Date: June 23, 2022

Agustin Lebron began his career as a trader and researcher at Jane Street Capital, one of the largest market-making firms in the world. He currently runs the consulting firm Essilen Research, where he is dedicated to helping clients integrate modern decision-making approaches in their business. We discuss how AI will change finance, why adverse selection makes trading and hiring so difficult, & what the future of crypto holds.Watch on YouTube, or listen on Spotify, Apple Podcasts, or any other podcast platform.Episode website here.Buy The Laws of Trading.Follow Agustin on Twitter. Follow me on Twitter for updates on future episodes.Subscribe to find out about future episodes!Timestamps:(00:00) - Introduction(04:18) - What happens in adverse selection?(09:22) - Why is having domain expertise in trading not important?(15:09) - How do you deal when you're on the other side of the adverse selection?(21:16) - Why you should invest in training your people?(25:37) - Is finance too big at 9% of GDP?(31:06) - Trading is very labor intensive(36:16) - Overlap of rationality community and trading(48:00) - The age of startup founders(50:43) - The role of market makers in crypto(57:31) - Three books that you recommend(58:47) - Life is long, not short(1:03:01) - Short history of Lunar SocietyPlease share if you enjoyed this episode! Helps out a ton! Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Okay, today I have this pleasure speaking with Augustine LeBron, who is the author of The Laws of Trading, a trader's guide to better decision-making for everyone. This is one of those books. You know, Tyler Cowan calls these quake books that completely shift the models you have of the world. I really, really enjoy reading this book. So, yeah, I'll let you describe your background, Augustine. But before that, let me just, let me ask this question. So Peter Thiel says that the Strassian reading of zero to one is. that you shouldn't start a startup.

And I think that, tell me what you think about this. I think the Strassian reading of the laws of trading is that you shouldn't trade, right? Because you probably don't have edge because you're not better than a marginal trader. And if you think you have edge, it's probably because you haven't factored in risks

and other costs. So don't trade. Is that what I should take away from this book? I think you pretty much hit the nail on the head. Like a lot of the times that people sort of start thinking about trading seriously, they start realizing more and more how hard a job it really is to do well. And the answer is probably, look, if you're smart enough

and good enough and hardworking enough to make a go at it and make a living at it in financial markets, there's probably an easier way to make money and, you know, have a satisfying life most of the time. Okay, yeah. So do you want to talk about your background and then what you've been working on in the past and what you're working on now? Yeah. So my background is engineering. That's kind of what I did in university. I did engineering for about six years professionally. I was a chip designer. At the time, I was playing a lot of online poker back when that was a profitable and arguably

legal thing to do. And so engineering was getting kind of boring and I wanted to do something else. And so I thought, well, what's halfway between engineering and poker? And of course, that's quant trading. So January 2008 walked into my boss's office and I said, I want to quit. and he said, oh, where are you going? And I said, I'm going to go into finance. And he's like, are you sure this is a good time to be doing that?

He said, yep, no, I'm dead set on it. And a few months later, managed to get a job at Jane Street and wrote out the implosion of Western civilization from the seat of a trading desk. So we did that for a few years. And then left Jane Street a few years ago and started my consulting company, basically just helping tech companies. with growth things like management and hiring and that sort of thing. And in the last few months, started a new company in the crypto space. How much are you willing to give up your edge by telling us what this is? Or if you're not willing to talk about it, that's okay as well.

Yeah, no, I mean, big picture, we're building a crypto protocol that is kind of new and has some pretty cool cryptographic guarantees against things that people don't like when they trade in crypto. Yeah, so let's get into some of the topics in the book. So, yeah, first I want to talk about adverse selection because this was, you know, this was the most interesting part of the book for me. So let me ask this question. If we think of hiring workers as, you know, placing bids on them, if you're like an employer and then multiple employers can place bids on them, doesn't winners curse imply that the average worker is probably overpaid because the true value of the employee is not the highest bid, but the average bid that they would get paid on the market? Yeah, you're right. From the employer side, it's definitely adverse selection all around.

Like, first of all, if you're looking for, if you're just sort of posting a job at, the applicants that apply are, you know, selected against in the sense that you're selected against that pool because, you know, the people who are really, really good, probably their employers know they're really good and so they're really incentivized to keep them. And so the people who are kind of on the market are probably at the margin not as good. Not only that, but even just the mechanics of hiring, the person who has the final say in terms of whether this happens or not is the employee. So you're going to get adversely selected there because the people who are really, really good are going to have lots of job offers, and so they're going to pick from one of many offers. The people who aren't so good are going to pick from few offers, and so employers just systematically get adverse selected that way. Now, whether that means that they're sort of systematically overpaid, I think that's a different question because in the end, companies have a pretty good idea, or at least should have a pretty good idea of what the marginal value of an additional employee is.

it's true certainly that people by and large give up things in order to get the security of working at a company. So maybe that counteracts that sort of adverse selection in terms of pay. It's not clear which way it washes out, I think, to me. Yeah, Bern-Hobart recently wrote a blog post about this chapter in your book about adverse selection. And so one of the things he said in a footnote, almost in passing, was that there should be more adverse selection in industries like finance, where the motivation for people to work in them is money because in industry, like, if a worker wants to work for SpaceX, there's a store you can tell about like why they're working for you and nobody else. In finance, you know, there's a lot of people, obviously, as you know,

who are like, might be bidding for really talented people. So if they're working for you, there's something suspicious about that. No, I think there's something to that. Certainly, you know, doing a lot of the hiring that I used to do, one of the biggest almost red flags is when somebody comes to you and says, oh, I've been wanting to be a trader my whole life. Because they're not, like, first of all, they don't know what trading is, right? They haven't known what trading is their whole life. They don't know what the job really involves.

It's not tangible in the way that being a doctor is tangible. And so what they're really telling you is, I've been wanting to make a lot of money my whole life, which is generally a pretty, well, let's say like in some jobs it's a good motivation, but it's not necessarily the motivation you're 100% looking for out of the gate in hiring someone. Oh, interesting. Because in your chapter on motivation, it seemed like you were implying that that is the motivation you should be looking for because if their motivation is emotional, then they're going to be losing to people whose motivation is to make money. So yeah, I love for you to talk more about

what is the motivation you are looking for. Yeah. So I mean, I think so that the motivation of like winning the game of like making money and that is sort of how we determine who wins the game. I think that that part of the making money motivation makes a lot of sense for a trader. But the like all I want to do is make the most money possible is correlated to things that that aren't maybe so great. Like, because a lot of the job is, um, is sort of having an inherent curiosity about random things, for example. Um, and, um, it, like, if your, if your whole motivation is like, where can I sort of make the most money today? It's not necessarily optimal over the long haul. And so you kind of need to sort of balance that against these other things, like enjoying the game

for its own sake, uh, enjoying the game for like, you know, sort of as an exploratory. kind of thing. So maybe that's like maybe a little bit inconsistent with something that I wrote in the book, but I think at the margin people need to hear the other thing more. Yeah, okay, interesting. And then how do you figure out if somebody enjoys the game for its own sake? I think you said in another interview that it's a company like Jane Street would hold it against you if you have like retail trading experience because I guess you can talk more about why that is.

But yeah, so if that's not what you're, that's not how you judge whether they would intrinsically enjoy the job. How is it that you would judge that? So one of the things I've always said, maybe you've heard me say this before, is I would love to talk to the person who is the third best player in the world at some weird obscure chess variant, because that is probably very correlated with things that I care about, such as a willingness to really, like, grind and try to get really, really good at something. And to do so, not because there's a huge pot of gold at the end of the rainbow, but because you just find inherent enjoyment and getting really, really good.

really good at something. So I think that's that's pretty good. But yeah, just general, again, aside from sort of the mathematical and and sort of risk-taking parts, which are sort of maybe independent from this, certainly a strong desire to be in a competitive environment and to enjoy being in that environment. I think that's, you know, that can take many forms, but I think that's a big part of it for sure. So then why is having domain expertise and trading not important because usually in other industry it's just like the more experience you have in the industry the better and it seems like you guys are often hiring people

who are just very analytically smart but maybe you haven't been traders before so like how do you guys manage it to do that I guess the thing I'm thinking of is that the concept of a domain is probably a lot narrower than people understand it to be like if I'm there sitting there on my Robin Hood account punting stocks back and forth like that is not the same domain

as what a trader at a market market maker or a top trading firm would do. And in fact, to the extent that you think that that's the same domain, that is a thing that you have to unlearn when you come work at, at, you know, we'll say a real company. And, you know, that can happen, but it's just, it's kind of a problem. Like, it's just a thing you have, you have in the back of your mind, right? Like, you'd rather take a blank slate, a really smart, motivated blank slate and sort of teach them what they need to know, then undo something and then teach them the thing they need to know.

you see this a lot of the time. The other thing is, from, again, at a meta level, probably in expectation the person who's doing trading in their personal account isn't doing positive edge trades. Like, they're probably on average losing money. And so you would like the person to realize that maybe this is not a winning game for them, and so they shouldn't be playing it. And so again, there's sort of this adverse selection of,

well, if they can't realize they're playing a losing game here, then that's probably not great. So you said in the book, it takes like six to 18 months before you can train a trader to be net positive. What is happening in that time? What are the skills you're teaching them? Yeah, so this varies from company to company and even has varied over the course of the history of Jane Street, certainly.

Like when I started, it was very much the Socratic method, right? You sit next to a senior trader and their jobs to teach you everything they know. And so it's just a continuous stream of questions, answers, conversations, et cetera. Jane Street, to their credit, has improved on that. There's now sort of a boot camp that you go through where you basically just intensively learn the fundamentals of everything that the firm feels like you need to know as a trader. So that, again, accelerates the process. But it is very much sort of putting people in situations to sort of experience the decision-making process and iterating on that decision-making process. Like, what are you thinking about here?

What do you think about that? Hey, did you think about that? What would you do in this situation? Why? Why not? et cetera and that just that just takes time i wonder so as you mentioned you've done a lot of um you've helped done a lot of hiring for tech companies i wonder if uh how applicable this model is to the tech industries i mean um could a company like google just have a very effective boot camp where they get like

people who study like physics or math at mit um and maybe not necessarily computer science but you know if you don't know that much programming you can still come in and then we'll make you you know 10x in a very short amount of time or is that something special about finance and trading I don't think so. In fact, I think that the most common failure mode I see in tech company hiring is hiring for skills instead of hiring for abilities and potential. And it's just because skills are very legible. Like, it is fairly straightforward to spend an hour somebody and understand whether they can write code in Python. Right. And so it's like the drunk looking for the keys near the lamp post, like you just evaluate what's easy to evaluate. My dream in some sense, and this is something that I can't really work on right now, but who knows, someday I could, is the idea of doing, mass, mass screening for people around the world. Like, what I'd love to find is the smartest

point one percent of high school graduates around the world, India, Nigeria, all these countries that are being massively underserved by their educational system and their opportunities. And putting them in these sort of boot campy situations for, you know, six months or something where they learn, you know, useful skills. And at the end of it, there's like a six-figure job with a Western company. Like, there's no reason that companies like Infosys should be taking the lion's share of that arbitrage opportunity. Like, there's this incredible need in the world

for people that are, you know, smart and motivated. And there's this incredible supply that we're just systematically undertapping. So my answer to your question is yes. There is, I strongly believe there's a trillion-dollar business potentially. Or maybe it's a nonprofit, I don't know, in closing this. arbitrage gap. Your former colleague, Sam Beckman-Fried, he, you know, obviously CEO of FTX, and he has, you know, started a big charity called the Future Fund. And one of their project ideas is exactly what you're talking about, where you would, there would be like large gains if you

could enable talent from the developing world. So what is it that you would look for when you were like scouting out this talent? Yeah. So I think one of the things that maybe isn't, uh, isn't terribly polite to talk about, but I think is critical is just, gee, intelligence. Like, it strongly predicts outcomes across jobs, across industries. And so that is some element of it. That is certainly some element of it. But also, I would say, I think in an ideal world, you would build this process, the selection process, kind of like a game, like maybe like a mobile game or something, where you're sort of people are sort of incentivized to kind of keep trying it stuff and maybe it's it's a little bit of a grind and again you're sort of selecting for that hardworkingness

stick-to-tuitiveness whatever you want to call it to use a principal skinner term and so yeah like some combination of those two things I think are pretty are almost definitely predictive of actual value have you have you heard of pioneer the thing started by daniel gross uh yes i have heard of it. I don't know much much about it. Yeah, this sounds a lot like it. I don't know too much about it either, but yeah, this is, this sounds very similar. I think they're trying to make building a startup like a video game, so, um, with, you know, the associated risk, uh, rewards and stuff. Um, how do you deal with adverse selection in cases where theoretically, adverse selection should work for you, um, um, but, you know, like the counterparty prices in

the possibility of getting a lemon. So like, an example would be I'm 21 years old and I'm a mail. So like car insurance premiums for me are huge, even if I'm, um, if, even if I'm a good driver because, you know, there's like, um, there's the adverse selection the insurance company faces. And like going back to another example, we're talking about if there's like a great employee, um, who's, he might be getting underpaid because the company that's hiring him doesn't know how good of an employee he is before he's hired. Um, so how do you, how to deal with such just scenarios when you're on the other side of the adverse selection. Yeah, certainly I think the car insurance situation, I am fairly sure there are now car insurances that essentially put

like an accelerometer and a GPS on your car, and they essentially monitor how safely you drive, or whatever, how jerkily you drive probably. And I imagine that you can sort of decrease your address selection by taking advantage of those kinds of things. In the case of the employment thing, that's a tougher one. At some level, the most important thing you can do is select your coworkers as a potential employee. And so getting really, really good at evaluating your interviewers, I think it's an undervalued skill. Not so much because you want to tell, like, are they good or not?

But it's more like, are they a good fit for me? Is this company a good fit for me? And the best signal of whether the company is a good fit for you is who the people are that are interviewing you and what do they ask you to do? If a company is at all sensible, what they ask you to do in the interview is highly correlated to what you do in the job. And so that's kind of maybe like a baseline. Don't, don't adverse select yourself by just kind of being like, me, yeah, I think this will probably work out or perhaps more importantly, this is a high status company.

I am told that it is a high status company and that letting that override your personal understanding of what the experience was. I think that happens very, very frequently. So once you get past that, then you're probably in good shape already. And at that point, I think it just comes down to, you know, putting yourself in the right place. positions and I think that's that's maybe a skill that's that you learn over time hopefully. Yeah. So I, um, there's a common thing that my friends complain about who are programmers, which is that when they're interviewing, they get asked questions that are very unlike their actual jobs. So, you know, questions that are almost brain teasers. But there's a kind of a Chester's fence argument you can make that it's like if all the tech companies are doing it, there must be

some important reason why they are. So, um, have you figured out the reason why such brain teasers are so uncommon? Is it just like G is so important that this is the best way to measure it? Exactly. So this is the thing, right? The dirty secret of all of this stuff is that explicitly testing for IQ is illegal in the United States as a, as an employment practice. However, you can kind of drive a truck through it because companies do. Like, for example, Wonderlick is a company. Maybe people have heard of Wonderlake because it's the test they give quarterbacks in the NFL. Wonderlick is a company that is dedicated, for example, to building employment testing that is essentially IQ testing but has the, you know, whether it's a figly

for actually legitimate justification that as long as you can show that it is important for job performance, then you can kind of do the testing, right? And so essentially I feel like a lot of these brain teaser type questions are, as you say, you know, IQ tests disguised. I think oftentimes they are badly misapplied by the interviewers. Like I think it takes actually a lot of really, really hard training and experience to ask these sorts of questions in a way that gets you the signal you want. But I think that's a big part of it. The extent to which you view your job as vocational is the extent to which you're going to hate those brain teasers. So if I'm a programmer and I want my job to be, I'm just going to write code all day and sit down and just write

code, then you're not going to like those brain teasers because you don't think of them as part of your job. Whereas if you think of your job as a programmer, as somewhat more expansive in the sense of like, well, I'm here to really think about hard problems and I happen to implement them in code, then maybe you're going to think of the brain teasers as more correlated to the thing you want to be doing. So again, select for what you like. Yeah, and maybe it makes sense to select for the latter type of person as well, right? Or I don't know, which is preferable to hire. But, um... So I think this is the thing about companies. Again, there's a lot of schizophrenia and tech hiring.

one of the things that's clear is everybody says they want to hire A players, but only a small fraction kind of by definition can hire those sort of high percentage or a high percentile kinds of people. And so what ends up happening is a lot of startups have the failure mode where they try to build these incredibly selective processes. But the people who they really, really want are never going to accept their offers. They're going to go somewhere sort of more high status or more high paying. particular. And so you try to select for like an 80th percentile person, but you end up selecting like a set of 50th percentile people who look like 80th percentile people, which is really,

really bad. And so what you should actually do as a startup is be very clear-eyed and say, look, if I have a team of 10, I probably need one or two like 90th percentile people, and I should evaluate for and in particular pay for that. And then the rest, I should try to hire a kind of 40th percentile people and, you know, put them in situations where they can be effective. That's a much, much more cost-effective way and more stable way to build the company, but nobody wants to hear that. Nobody wants to build a company like that. That's a great example of like a barbell strategy. So I'm wondering, do you have any ideas of what good arbitrage opportunities in tech hiring might be? I know I think SpaceX, some of their

early engineers were from the gaming industry because they're very used to doing optimization problems there, but it's not necessarily a high status career. So there's like arbitrage there. Are you, do you have any ideas now of like what is a good place you would be looking for really talented potential future programmers if you were, if you couldn't compete with pay at Google or something? Yeah. So I think one of the things I always tell companies is go more junior. Like if you look at, if you look at the salary of somebody who just comes out of school, and I'm not talking about somebody who just came out of Stanford. I'm talking about somebody who just came out of like a reasonable CS program. right? And you look at their salary three years later, like it could be almost double sometimes, right? It's just a

crazy, crazy jump. And that is kind of unjustified. I mean, you can sort of see the argument for it, but it's just like, there's definitely a kink at the two to three year point because every startup, or every, I mean, every tech company seems to want to have two years of experience. And a lot of it is because companies just don't want to or can't see themselves investing in the training of those first two years. And if they do, they tell themselves, well, they're just going to leave after two years to go for a higher paying job somewhere else. But I think those are terrible answers by and large to the problem. Like, you should be investing in training your people. You also get the benefit of training them exactly the way you want. And if you put in that work and you think carefully about what it is that people are coming to work to do for you day to day, probably they're not going to leave. Right. Like if,

if you give them a reason to not leave, they're probably not going to leave. Switching jobs is incredibly costly and risky. People don't go out of their way to do so. So, like, you're kind of getting the, the inertia working in your favor anyway. So, like, let's work on these things. Sounds very similar to the sheepskin effect of the last semester of college. So Brian Kaplan has a really good argument about this in the case of the education, which is that the last semester of college, like, boost your earnings,

many times more than the percentage of college you spend in that last semester. And it can't be because you're learning that much more in the last semester, which I guess sets up an arbitrage opportunity for hiring people right before they're about to finish their last year or something. But you see, like, I'll give you a perfect example here in San Diego where where startups in San Diego, tech companies in San Diego love to hire Intuit employees that have two to three years experience because Intuit hires a bunch of people and they train them and they train them pretty well and and then like they get poached but of course like nobody really actually thinks about the idea that like intuit knows who the good and the bad are after two years and like you're not

seeing the really really good ones into it's keeping those right so so you say in the book that you've traded over your long career in trading you've traded all kinds of different financial instruments I wonder what is the reason so is this just I guess you just have to do the you had to trade whatever market that you have to at the moment? Because I would think, you say in the chapter on Edge that one of the ways you can actually get Edge is to specialize. So is it a mistake of firms to let their traders over their career trade in multiple different categories?

Or is that necessary in order to build your general aptitude as a trader? Yeah. So I think it's a balance. Certainly I don't think that, again, it depends on how big the reference class is. Certainly, I have never done any trading that looks. like look at a balance sheet in an income state and listen to an earnings call and make a bet on that. Like that's sort of fundamental trading. I have never done any of that. And I think it would be a pretty big mistake to put me in that situation. But within we'll say that the well-defined realm

of like quantitative trading, I think a lot of the same skill sets apply in different markets. Like you're kind of bringing the same skill set to different markets. And having that experience of going around and looking at different kinds of markets and how they work, informs, like, it sort of informs how you think about things and gives you that, that wider vision that I think makes you a better trader. So, yeah, I think it's a balance. So I think finance is 9% of GDP. So I understand the argument that, you know, finance helps allocate scarce resources

to where they're needed most. But if we're giving up, like, a tenth of our resources to, make the allocation of the rest of the resources more efficient, is that too high a price to be paying for liquidity and price discovery? So is finance too high a fraction of GDP? I go back and forth on this question. I really do. Because when you see it from the inside, a lot of it is zero-sum competition. And it feels like, come on, there's got to be a more efficient way to do this. But at the same time, kind of, outside view, we haven't come up with a more efficient way to do this. And it's hard to argue

with GDP growth. And so I kind of go back and forth on it. Certainly, I think the other thing about it is there's two countervailing forces. You can sort of be inside something and be really, really familiar with it. And just the act of being very, very familiar with something just gives it legitimacy kind of automatically. But at the same time, like if you look at something from afar, you're like, oh, that's ridiculous, right? Like, that's, that's not, that's not a thing that should exist, right? And so it's sort of this perverse thing where the people most, like, the most well-informed people, the people really could or should be making these decisions about, like, is this a legitimate thing that we should be doing, are biased towards thinking like,

yeah, you know what, this is probably a good thing to be doing, or there's value to this. And so it's hard to sort of disentangle the, like, the experience and, and the biases that that experience sort of gives you. And then would that, would that fraction shrink without, without harming efficiency if, um, like, are there inefficiencies created by garden regulation or by restrictions on capital flow? Um, or is that like basically what you should expect it to be even in a free market or in an, in an optimally regulated market, let's say. That's also a tough one. Um, and it's, and it's not that I haven't, uh, thought a lot about these. It's just I feel like I don't have, I don't have a great answer. Like, at the margin, what would I, like, if you, if you sort of

made me, like, regulator of the world, like, at the margin, what would I do? There are some things that I would regulate more. And this is probably going to be a very unpopular opinion among my financial friends, but, like, I think leveraged DTF should be banned from retail trading. Like, I think they're just kind of a bad instrument. In particular, like, all the volatility products. So I feel like that should probably be regulated some more. But at the same, same time the sort of qualified investor status thing that people are driving a truck through, like, that seems weird. Like, should there be, should we just eliminate the qualified investor status and let people invest in whatever they want? Or should we make it even more restrictive?

I'm not sure about that one. And certainly, the other thing about it is, like, a lot of the regulations, especially around capital requirements for banks, are incredibly baroque. And they feel like job Ponzi's a lot of the time. Like, we need to figure out a way to employ all these people and like, okay, we're just going to create like Basel 3 and that's going to be like an extra thousand employees for every large bank in the world. That's probably kind of a deadweight loss, but doing things more simply doesn't seem like it's going to get you the thing, like the sort of the stability outcomes you want. And so, yeah, it's just, I feel like it's just kind of poor tradeoffs all around. What does the long run future of trading firms look like?

So if economic growth continues to stay low, then you would expect, like, other financial instruments to stop growing at high rates as well. But even if economic growth speeds up, if markets get more efficient over time, then, again, you would expect the profits that any one trading firm can get to decrease. So is there a future for highly profitable trade firms like Jane Street, like in the far future? So I think to the extent that Jane Street and companies like it provide a service to the world, and I really do think they provide a service to the world, then they're going to be around and they're going to be profitable. Now, are they going to gain, like we'll call them excess returns? Even that's not so obvious because the thing about trading firms is, especially market makers and that sort of thing, like most of the time the business is pretty good if you're really good at it,

but sometimes it's really good, like when there's lots of market volatility and that sort of thing. but that's precisely because you are the person, you are the entity that is willing to take the risks that nobody else is willing to take. And to the extent that we're going to still continue to have volatility in terms of either like market volatility or, you know, economic downturns or whatever, there's always going to be a service that these companies are going to provide. Now, over the long run, I feel like probably there's going to be more consolidation. that seems unlikely to stop, just because you sort of gain the benefits of, the economies of scale just kind of keep going up. But then again, you have sort of new things that come up,

like crypto and that sort of thing where, like, it's the Wild West right now, and there's going to be like a big consolidation over the next 10 years. I think that's the natural arc of things. Oh, interesting. So, yeah, can you describe what these economies of scale look like in finance? And then what does a tradeoff? where if you're like too big, then it's not even worth your time to like look at smaller,

smaller investments where you can't take as big estate without moving the market. Yeah. So the thing about finance or like market making, trading in general is, um, it's very labor intensive, right? So you should think of it almost like the value of a seat or the value of a person's time. And so are there going to be, are there going to be inefficiencies in the market like pockets in the, you know, pink sheets or something where it's just not worth a large companies or large successful companies trader time to look at. Yes, like those will always exist and they'll get

slowly competed away by by the by the mom and pop trading operations or even just the like the former Jane Street traders who are now at home and kind of doing it on their own for fun. So I think those will always kind of be there. Is there a potential that markets can get like way way more efficient if we have we develop much stronger AI and at what point will the work that even traders do, that's like much more, I don't know, much more model generation and like thinking abstractly, at what point can that even get automated away and not just like the wrote calculations? Yeah, I would say it's already getting and gotten like more efficient. Like when when my former boss started, the idea of an options market maker having 10 stocks that they

were market makers in was like, that was kind of the limit, right? When I was doing it, like, we could handle like 100 stocks, right? Market making and 100 stocks. Again, technology just made everything more efficient or more efficient in human time. That will continue. Like you can sort of set up things where I'm looking at some data and I can run a bunch of different models

and just select the good ones and make sure that I'm not overfitting because I have all these overfitting productions. This is all stuff that you can do now that maybe you couldn't do 20 years ago. That will definitely happen. I think when people talk about AI and trading, I think it's very hard to, like, we have to define terms. I think that's the hard part, is defining terms when we talk about AI.

Because if we talk about, like, if you ask a reasonably aware person what AI means, probably today in 2022, 90% of people are going to say, oh, we're talking about large language models. Of course, that's what AI is, right? And so is the question like, is GPTN going to be a significant force in markets? Like, I'm honestly kind of skeptical about that. I don't know that the, let's just keep making larger transformers is the way that we're going to get to AI, but that's my personal parochial opinion. But if we think of AI more broadly as slowly but surely increasing the range of things that things that machines can do, the humans can do, like the more we sort of creep into the things that humans can do as well, then, yeah, then like the human part is going to slowly start to get disappeared away. I think the natural analogy is what happened in the 20th century with manufacturing, where

like it used to be kind of all human power and a little bit of machine power, where you had kind of this big central, like, why did factories in the 19th century and early 20th century, why were they kind of tall and thin? Well, it's because they had one steam plant and they had to like all these belts and stuff to like use the power from that one steam plant, right? And then like electric motors happen and it's like, okay, now factories are horizontal, right? But over time, the trend is for it to be sort of less human power and more machine power. And I think the analogy is perfect. I think AI over time is going to take more and more of that sort of cognitive load from the human. That seems inevitable to me. I'm curious why you're skeptical that like a scaled-up

GPT3 or other language, large language model. I'm curious, so why does it not have applicability in financial markets? Like, at a there's like a toy version where you have like GPT10 and you ask it to complete the sentence. The best trade I can make today is and then. So why is that unlikely to happen? So there's a couple things that I might say. One is the concept of sample efficiency. Like these things are incredibly sample inefficient in a way that the way the humans learn are not.

And so there's something fundamental there that we're not getting, right? And the thing that I think we're not getting is the things that our brains have, which are structures for semantic understanding. To the extent that these large language models have semantic understanding, it's kind of by accident, right? It's just like, it's the clever Hans thing, right? It's just like a super clever Hans and it's super impressive and I'm not criticizing the models. Like, they're incredibly impressive. But it's still a clever Hans thing. And so there surely must be a better architecture out there, much like our brains have these sort of architectures that, that sort specialize in certain things that give these machines like semantic understanding, or at least

give them the potential to have semantic understanding that I don't think GPT3 certainly has evidenced. So Jane Street seems like a mysterious place, but what's interesting to me is there seems to be a large overlap with the rationality and EA community. So obviously you have Sam McEnfreyed. He's, you know, he went into Jane Street with the explicit goal of earning to give. Yep. Tyler Cowan announced that $20 million have been donated to his Emergent Ventures grant program from Jane Street Traders. And, you know, even reading your book, like you reference so many thinkers that are prominent in, like, rationality spears. And so there's just to be a big overlap with this community and with at least a part of the trading world that I'm familiar with. No, that could just be selection effects. But what is it going on here? Yeah, it's a great question. I think maybe at two levels. One is the idea of being very rational and not fooling yourself and to use a Yudkowski term, just shut up and multiply. I think that that is a thing that is very common, I think, in the two circles, or at least probably it should be. Try to really understand the real world and it matters to do so and doing so using kind of rational, mathematical, logical approaches. I think that there's a lot of overlap just inherently there. But I think you could say that about any number of finance, Wall Street, whatever, trading firms. I think the one thing that Jane Street has going for it differentially from those other firms maybe is a culture of collegiality.

I think that's kind of an important thing that Jane Street has developed over the years and continues, I think, to have. And so I think there's a lot of overlap there. It's the kind of place that if you are an EA person thinks about things rationally and just enjoys the process of kind of this collegiality and working with people and thinking interesting thoughts together, Jane Street is going to be a very natural fit for you. And I think maybe that's some of it too. When I had Bern Hobart on the podcast, we talked about whether debugging or finance was a better application of like rationality principles because in each case you had like update your beliefs and so on. And one interesting point he brought up was in finance you have, you not only have to

model like a static system as you would have in debugging, but you also have to model other Asians and their incentives and their motivations, which makes it a much more like a dynamic system to get a hold of in your brain, which I guess it could even mean that like the tools are like the current rationality movement are not good enough to, you know, be able to think about those things as well as probably you guys that have natively developed in the industry. Yeah. And look, the cross-pollination goes both ways. But yeah, the idea of, of you being an agent in the world you're trying to study is fundamental in trading. And it makes it like so much more interesting.

I think that's one of the getting back to the AI thing, just because it occurs to me, is one of the big failure modes is to think that, okay, well, yeah, I'm just going to like throw some AI or machine learning or something at this data set and I'm going to get a trading strategy. And, okay, that's great. Like, let's say you've figured out something that predicts the price movement, 55% of the time, like, that thing can still actually lose a lot of money in production because of the, again, so there's the adverse selection effect of you're only going to do a small fraction of the good trades, and you're going to do all the bad trades you want. But also, if you are actually making money at it,

this is like a big shining signal to the rest of the world, like, hey, there's money over here, like, why don't you compete it away? And so, yeah, that's definitely a huge component of it. So you'll have a very interesting chapter on software and technology in the book, And one of the things you argue for is that we should take the concept of technical debt seriously in a financial sense. So is one implication of this interpretation that you should be willing to accept technical debt more if you're a rapidly growing company? Because, you know, if you're a startup that's growing fast, it makes sense to maybe take out a lot of loans because you can pay back the interest plus way more. But maybe if you don't take it financially, maybe that's, you would think that if you're like scaling rapidly, that's the worst time to take on all the technical debt because you're just going to be hampered

the entire way along. So yeah, so more generally the question is what kinds of firms should be more willing to take on technical debt? Yeah, certainly startups is the classic example. And it's non-recourse debt, right? Like if it goes belly up, like you don't have to pay it back, right? You're done. So, so yeah, like startups should definitely do this. And you see it all the time, right, this concept of an MVP where, you know, let's just get something out there, let's get some feedback from the users, with the understanding that, hopefully with the understanding that you're going to have to essentially rewrite it from scratch if it's successful. I think it's a very useful and very, very productive way to do softer startups. Because, yeah, like the implied

interest rate that you're willing to pay is incredibly high. Larger companies, it's interesting. Like if you ask yourself, this is kind of a conversation I had with one of my good friends, who I actually did consulting with. He worked at Qualcomm for a lot of years. And I asked him because he worked very closely with Microsoft. Like, Microsoft employs tens of thousands of software engineers. Like, what do they do all day? And he said to me, like, look, I don't actually know for a fact.

But I'm pretty sure the vast majority of them are like, well, this library is deprecated. We need to upgrade this thing. Let's change all this code and all these different little play. places, right? So, like, there's just sort of a, like a, like a, like a, sort of an archaeology of software that occurs where, where, you know, if you build, if you've been building a software, piece of software for like 20 some odd years, like there's just all this cruft in there that you're just continually trying to maintain so that it's functional as you go from, you know, this OS to this other OS to the cloud to whatever, right?

So I think that's that's kind of like an accumulated debt that the large companies certainly have. Yeah, that's so interesting. They're just like servicing the debt they accumulated in like the 80s and 90s when they're growing rapidly. And you can even think of like them moving to a new platform or like rewriting their code as like refinancing their debt or something. Right, exactly. In fact, like I would say probably the best, maybe probably the best book I have ever read about software development is actually science fiction. Werner Vinge, a deepness in the sky. I feel like is very crucially about, like, it sort of takes this idea, like, what if we've

been building on the same software stack for 6,000 years? What does that look like? Like, what does that world look like? And I think it teaches us a lot about how to think about large software projects, large, long-term software projects. Yeah, so I'm super interested in how you guys think about software in the financial industry. I know Jane Street uses O Camel. So because I mean there's like safety, you can tell me more why this is, but from what I understand, it's like there's, there's more safety in a functional, functional programming language.

Yeah, so how do you think about like obviously there's a much more reason to want to have like safe code because you're dealing with an adversary there in some sense? So yeah, I'm curious like how do you guys make engineering decisions and what are the tradeoffs involved when you're doing when you're working in finance? Yeah, so as you said, like, James Street uses O'Camill. I think one of the, one of the biggest advantages of using that language is it is strongly and statically typed. And so you can put a lot of things in the, like you can use the type system to make impossible states unrepresentable. This is like a really good software engineering thing you should do. And it makes it sort of very easy and enrich environment to do that in. And so this like, oh, I didn't know I had to handle this.

problem is kind of minimized. But yeah, like, you know, Jane Street and companies like it obviously optimized for avoiding hot loops and code that incinerate money really, really fast. And that is not what your average, whatever SaaS startup optimizes for, or it shouldn't be anyway. But the thing I keep coming back to in talking to, you know, technology leaders and that sort of thing is software development is fundamentally an exercise in sociology, like in organizing teams and in creating processes and culture and conventions around the building of software.

Like, you know, software development is fundamentally the management of complexity, like the science of managing complexity, because it is incredibly complex, right? And so all that sociological stuff ends up being some of the most important stuff to think about. Now that you're working in finance, but you have a startup. So you have to think very carefully about this tradeoff. How are you managing this given that you have to like, I guess, move fast, but you also need to be safe? Hire really, really good people, honestly.

Like, don't skimp on those first few employees is, I think, a really important thing. Like, where the bar is kind of, like, the bar is kind of weird. Like, it's not, it's not like there's sort of one total ordering over a quality of engineer, right? There's, like, they're incredibly multivariate. But certainly, one really, really good. thoughtful engineer who can build correct code is worth for not so thoughtful people in a spot like that. And so that's kind of the thing we're optimizing for right now. And such engineers, do you expect or give them a lot of knowledge about finance or can they just function

knowing about engineering, just about engineering? And then you can just like tell them, we need a program that does this or do they need to have an understanding of how trading and finance works. So need is probably a hairstrong, but certainly the culture that I want to build is one where it's almost need. Like it's almost like want, right? Like I would want to hire somebody. I want to hire somebody for whom understanding the problem domain deeply is a critical part of the job they feel they're doing. And so is it possible to build something like this another way? Probably, but that's not the company I want to build. So in your career, you've done so many different things, engineering, trading, consulting.

Yeah, so how much carryover and lessons do you feel like you've had between these different domains or do you feel like they have like self-contained pools of knowledge? So I think if there's one constant for me, it's I am surprised by how much my previous careers inform my next careers. like when I when I wanted to to move from engineering to trading it did continually surprise me how useful like the the engineering training as opposed to just kind of like me hopefully being just generally smart and being able to figure things out like the actual engineering training was was useful and then coming back to the consulting with companies again really surprising how like I expected that you know

when we were doing kind of the management and hiring consulting that it would be about the nuts and bolts of, okay, well, what does a good hiring process look like? What kind of interview questions do you want to build? How do we evaluate them, et cetera, et cetera? And there's a component of that, but all of the other trading stuff, like how to think about the market for candidates and that sort of thing. Like, it surprised me how non-obvious a lot of that stuff was to the people I was talking to. And so now, yeah, like hopefully bringing all of those experiences to the table in this new startup that I'm doing, you know, I'm, I'm, I'm optimistic that that'll occur again.

You would think that people like you who have so much experience in so many different industries, they would be the most common archetype of a startup founder because they have so many general skills. At least in popular culture, maybe this isn't represented in like who are empirically the most successful founders. At least in popular culture, it seems like the trope is, you know, somebody who like has no particular skills as the person who like starts a startup out of college. and why are there not more founders who have a broader skill set and lots of experience?

I think there actually are. Like, if I remember correctly, maybe this is something I read maybe a year ago, the average startup founder is actually significantly older than sort of the popular conception. It's just that the young, flashy startup founder gets all the press, right? And perhaps rightly so. Like I'm not besmirching, you know, the young founders' press. but I think there's a lot of people kind of just doing it possibly with similar backgrounds to mine I think it works.

There was one question I forgot to ask about adverse selection, which is if you let's take a company like Jane Street. If the counterparty knows that they're trading against Jane Street and they know that Jane Street has a great reputation of making profitable trades, why does anybody even make that trade? And I mean, as a follow-up, does that mean that's a follow-up? Does that mean that Jane Street has to pay a higher cost to make the same trade because it has like this reputation of making really profitable trades, which means that there's almost a negative feedback loop of if you become too successful, like the market makes it really hard for you to continue being successful?

No, the answer is no, and I'm pretty sure the answer is no. And the reason is because, again, getting back to this idea that Jane Street provides a service to the world, right? So who are they? James Street doesn't want to trade against other market makers. And other market makers don't want to trade against Jane Street because they're in the same business and they know that's not who they're going to make their money from. Who they're going to make their money from is people who need the service that Jane Street

provides. So, for example, like if I am a pension fund or if I'm a, you know, a large hedge fund or something and I want to put on a bet in some random country, maybe I should just buy that country's ETF, right? It's certainly a lot easier, more straightforward, convenient to just buy the ETF than to go to that country's stock market and buy all the individual. stocks, right? And so that's not a thing that they're an expert in, right? They're not an expert in trading Vietnam stocks, right? They're just an expert in making these macro bets, let's just say, right? And so Jane Street provides them the service of being able to sell them that ETF. And then Jane Street takes care of all the little details, right? That's the thing that Jane Street is

really good at. And so there's gains from trade there. It's not it's not zero sum in that sense. And what is the role of market makers in crypto if you have automated market makers? like Uniswap or something. So then what is the what is like the compared advantage of I guess smart market makers? Well, so I think the thing I would argue is and perhaps you've seen this paper from like last fall, but that shows that at least half of liquidity providers on Uniswap V2 lose money. They just lose money. And that's on prior is what you would expect. Right. Like let's say that there was no fee on Uniswap, right? Like let's do liquidity providers just toss their money in. Then.

like liquidity providers are systematically getting adverse selected against by every trade that happens, right? And so the fee that you collect as a liquidity provider is a compensation for the adverse selection that you are undertaking by being a liquidity provider. But of course, that fee is sort of set by fiat, right? Like, it's either the five-bit pool or the 30-bit pool or whatever. Like, it is not adaptive to like market conditions, right? And so I am personally long-term skeptical about CFMMs as a market mechanism that is going to work. I just, I don't see how, I don't see how it makes sense for somebody to just, like, throw some money in a pool and expect to get sort of outsized returns by just doing nothing, right? Like, outsized returns come from you knowing how to do something

or being able to do something nobody else does, right? And so it just, it doesn't strike me as a, as an exciting thing very long term. Does that mean that you're also pessimistic about passive investing in the long term of somebody just like putting a certain amount with their money and the difference there i think the difference there is passive investing is at least again over a long haul you are providing risk capital to companies that are hopefully sources of discounted future profits right and so there's like there's a reason that you might expect that to make money for you whereas when you're when you're trading either fx or something that doesn't like earn yield from from like actually providing value to the world, then no, you probably shouldn't expect to make money

by passively investing in that. What made you interested in getting into crypto at this time, transitioning to that industry? Yeah, so I think the thing about crypto that I like is to the extent that you believe in this somewhat stagnationist theory that, look, whether it's through regulation or just cultural changes or whatever, that we're not doing bold, new, exciting, weird things. the extent to which crypto is a shelling point around which everybody has decided, look, all of the crazy weird stuff that we want to try, we're going to do it here. I like that. I think that that is a very good thing for the world. And if, and I'm not saying this is going to be the case, but if all crypto goes to zero and all that's happened is we've had a large wealth transfer from the rich

olds to the young people who want to build cool stuff, like, that's still good for the world. you know like i think it's going to be more than that i think there's a lot of interesting exciting things that are going to that are going to come out of the crypto world um but you know we get at least that right like a coordination around trying new things if if that doesn't work um and in like taking your negative example as um let's say that's a hypothetical i actually do wonder what is the actual wealth transfer that's happened here is it actually been from because like if um if institutional investors have not gotten that much into crypto as compared to like, you know, some grandma, maybe not grandma, but like, I don't know, some middle-aged guy.

So then has the actual wealth transfer been from wealthier to poor or I wonder if it's been the other way around? I think it has to have been. Like, look at all these, you know, look at all these VCs, raising all these funds. Like the LPs and those VCs funds are old, right? Yeah, there's a good story to be told about adverse election and venture capital as well. So, but yeah, so that's, it's basically a transfer. wealth from like VCs to VCs to like 21-year-olds.

The other thing about, like, the other thing about crypto, like people always, especially people from Jane Street, like whenever I meet them again and, you know, say, hey, how's it going? Like, almost the first question they ask me when they find out what I'm doing is like, so what? Like, are you all laserized now? Like, what's your deal? And I think by crypto standards, I think I'm very non-lazer eyes in the sense that this is probably going to be an unpopular opinion within the crypto world.

But I think success for crypto definitely looks like integration into the financial system. Like, it just, it's not. like it's going to replace it. It's not going to be like, oh, Goldman and Chase are going to go to zero and Coinbase is going to crush it. Like that's not what it looks like. Success for crypto looks like traditional finance integrates, takes the best ideas, and crypto companies are incredibly successful in that process, but we end up with something that's kind of a hybrid of the best of both. Like, I think that's success. Yeah, and I'm here. So like, what is, what does the future look like in a world where crypto is very successful. Like, for example, what would, what would, uh,

something like the stock market look like if, um, would it be like far more efficient if it's over crypto or would it be less efficient because of gas fees, um, or, you know, maybe it's like payments internationally. But yeah, I'm curious what you think like 20 years down the line, the success case where crypto looks like. Um, so I'm going to leave the financial markets to last. Like, I think Western Union is out of business. It's probably a good outcome. Like, it's probably good. All those stupid, um, Like all those stupid Thomas Cook money exchange in the airport things are out of business. Like that's probably good. So like if only that happens, I think we're already in good shape.

Certainly the idea of NFTs as transferable signals of facts about you or facts about whatever persona or avatar you want to have. I think it's pretty exciting. Like the idea that I have to like call my university and get a transcript from them and stuff, like that seems insane to me. And how some of these kind of credentialing systems can work with NFTs strikes me as a fairly natural thing. I think to the extent that crypto is breaking the oligopoly of a few large financial participants in the market today. I mean, I don't know if you followed the saga of the CFTC review of FTX's proposal to do sort of a different kind of futures margining process online. on actual real futures. I think this is a good thing. Like there's a lot of vested interest and entrenched interests that are kind of getting their bell wrong. And that's a good thing. So that's

that's kind of the direction that I would take it. Sort of the financial infrastructure or the plumbing of finance is probably going to be sort of cryptified. That doesn't mean it's all going on chain. I think you know, all the blockchain is is a very slow weird database, but it is a very slow weird database that has some useful properties in some situations. What are three books you would recommend? A deepness in the sky. A deepness in the sky by Werner Vinge. If you're a software engineer and you like science fiction,

you know, read it with the eye towards thinking of it as a softer book, as a thing I would say. If you want to think about kind of risk-taking in general, Aaron Brown's Red-Blooded Risk is... I'm trying to think of books that probably people haven't heard of. Red-blooded Risk by Aaron Brown is really, really, really good. In fact, all of his books are really good. Like, the thing that got me interested in finance was reading his book,

The Poker Face of Wall Street, because I was playing poker at the time. And it's kind of like, hey, maybe Wall Street. Maybe that's a thing. So the poker face of Wall Street by Iron Brown or the Red Blooder Risk. And one that's kind of off the wall a little bit is it's called Kalima Stories by Varlam Shalamov. And it is a collection of stories about people who sort of lived in the Gulag in Siberia during sort of Stalinist times. I think it is possibly the most revealing book about human nature that I've ever read.

It's depressing. I don't read it in a sunny place, you know, but it's revealing. Is this a covert way of telling us about the working conditions at Jane Street? No, not at all. Yeah, so a final question is, you know, you've been successful in so many different industries and you know, you know the lessons of, you know, working in so many of them. So is there advice you would give to, like, somebody who's in their early 20s? I guess most of my audience is probably going to, to the extent that they're working in

these industries, are probably going to be programmers. But I don't know, maybe after interviewing you, I'll have, like, a few traders or want-to-be traders who are listening as well. So, yeah, if you have, like, some advice you think would be useful for somebody who's very young. Yeah, I would say, like, number one thing I always, I tell my kids this all the time, like, life is long. Like, life is not short. Left is long. And what that means is you should think of yourself as having many opportunities

to learn things and try things and do things. And so, again, this is just my own experience, but I feel like I'm sort of sequentially obsessive. Like I will sort of block off six years of my life, it turns out, like empirically this is what has happened to like get really, really good at a thing. And then like, okay, next six to seven year period, I'm going to try to get really, really, really good at another thing that is kind of different. And that's worked out for me because it's easy to undervalue the importance of deep, deep, deep expertise in a thing and the process that it, that is required to get really, really good at something. And so this idea of kind of sequential excellence, I think, is a thing that I like to think about a lot. Because you have the

time, right? Like, spend five years being a, you know, a front end developer and get just incredibly good at that. And then, you know, go do something else. Maybe it's not programming. Maybe it's something else, right? And maybe come back to it, right? And you'll have this other perspective. Yeah, that's kind of my thought. Yeah, that's super interesting. I'm curious if you think, like, let's say you had, I guess it wouldn't be possible with crypto, but like, let's say you had been a trader, like you had, instead of doing electrical engineering and computer science, who had just like done trading from the very get-go, would you have been, by the end of your trading career, would you have been more successful in the counterfactual where you've

been the trader the whole time or one where you have the experience from engineering? And then, you know, same with consulting. I mean, yeah, so I guess is the career path with a lot of deep specialties, but changing what that specialty is over time? Is that, does that lead to a higher peak in the end or the one where you just focus on one career? Yeah, it's a good question. I think it probably varies by person. Like if you are, if you are destined or have the capacity to be a world-changing physicist, then probably you don't do any of the sequential weirdness. You just kind of go down that road. But, well, maybe I might edit that because one thing that I do believe about discoveries, whether it's in trading or in science or anything like that, is

there's sort of two types. There's the evolutionary type, which is take a body of work or a feel and just sort of push the boundaries out on it a little bit. And then there's the revolutionary type. There's the Albert Einstein, there's the Claude Shannon, these sorts of people where it's like, I'm just going to invent a whole new field, right? And so actually, Claude Shannon's kind of an informative case because he famously just basically played games all day

and just thought about random things and kind of tried to have as broad an exposure to things as he could. I mean, road unicycles and that sort of thing. So, you know, maybe like you need to be a little bit self-aware about kind of which of the two you might be. Yeah, okay. So the book is The Laws of Trading available on Amazon. And yeah, do you want to give your Twitter handle plus any other place where viewers can find you? Sure.

So, yeah, it's Augustine LeBron 3. I don't know why 3, but that's what it is. I mostly talk about trading, sometimes talk about software. sometimes talk about random things in the world. Yeah, that's, that's, I'm not much of a social media guy. In fact, I've hadn't been for the book, I would still be a social media, non-existent person. But I've kind of gravitated towards Twitter. It's where I end up having interesting conversations.

Yeah, yeah, I've enjoyed being a follower. Is there anything, is there anything we didn't touch on in the conversation that you think might be, might be interesting to close on? Well, I mean, I'm selfishly, I want to ask you, Dorcas, like, tell me. me about what you're doing? Like, what is this that you're building here? That's a good question. Yeah, so this was, to give you the backstory on this, this was, I think my sophomore year of college or maybe a junior year, COVID hit, and I was really bored because classes went online. And so I just, you know, started the podcast. I just cold emailed my first guest.

Yeah, actually, the time I was releasing, I didn't even have a name for the podcast because I just had like a recorded episode. But so anyways, I just kept it up. And then I graduated like four months ago. Actually, technically graduated like two weeks ago, but I was done with classes four months ago. And then I thought, all right, I have a little bit of money saved up from an internship and then another grant. And so I thought, all right, let me just do this like full time for a few months and see what happens and got some traction. So I don't know where this leads, but actually the comments you made about, you know, going deep on one particular thing. and then maybe using the skills you learn there to transition to another,

that makes you feel a lot better because I don't think my long-term, term trajectory is being a podcast host or writing a newsletter. But I do, so, you know, I would like love to go back to tech and startups, you know, like in the future, I have a computer science degree. But yeah, so we're hoping to learn as much as possible through the podcast and writing. and then hopefully use the skills I learned there to do some cool things in other fields. Love it. Sounds like a great plan.

Yeah, yeah. Thanks. Awesome. Yeah, thanks for coming on, Augustine. This was one of my favorite podcasts I've done. It was so many insights. Awesome. Thanks, Dorcas.

Really enjoyed it.