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Dr John Seo is co-founder and a managing director at Fermat Capital Management, LLC. He has over 30 years’ experience in fixed income bond and derivatives trading and has been active in the Insurance-Linked Securities (“ILS”) market for over 25 years. Prior to forming Fermat with his brother Nelson in 2001, Dr Seo was senior trader in the Insurance Products Group at Lehman Brothers, an officer of Lehman Re, and a state-appointed advisor to the Florida Hurricane Catastrophe Fund. Dr. Seo’s work in catastrophe funds was featured in a cover article for the New York Times Magazine (‘In Nature’s Casino’ by Michael Lewis, 26 August 2007), and he has also testified before US Congress as an expert on the catastrophe bond market (‘Hearings from the 110th Congress’, 6 September 2007). Dr Seo holds a PhD in Biophysics from Harvard University and a BS in Physics from MIT. He is based in Connecticut.
00:36 Early Career and Influences
02:10 The Investor Choice Problem
07:21 Academic Background and Family Challenges
12:43 First Steps in Finance
30:39 Lehman Brothers
37:29 Introduction to Cat Bonds
44:53 Parallels Between Derivatives and Insurance Markets
01:03:22 Building Fermat Capital
01:09:51 Future of Catastrophe Bonds
Music used with permission from Blade Runner Blues Livestream improvisation by State Azure.
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Steve Hsu is Professor of Theoretical Physics and of Computational Mathematics, Science, and Engineering at Michigan State University. Previously, he was Senior Vice President for Research and Innovation at MSU and Director of the Institute of Theoretical Science at the University of Oregon. Hsu is a startup founder (SuperFocus, SafeWeb, Genomic Prediction, Othram) and advisor to venture capital and other investment firms. He was educated at Caltech and Berkeley, was a Harvard Junior Fellow, and has held faculty positions at Yale, the University of Oregon, and MSU.
Please send any questions or suggestions to manifold1podcast@gmail.com or Steve on Twitter @hsu_steve.
Please send any questions or suggestions to manifold1podcast@gmail.com or Steve on Twitter @hsu_steve.
Creators & Guests
What is Manifold?
Steve Hsu is Professor of Theoretical Physics and Computational Mathematics, Science, and Engineering at Michigan State University. Join him for wide-ranging conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.
Steve Hsu: Welcome to Manifold. My guest today is John Seo. He is the founder and CEO of Fermat Capital. His hedge fund invests in cat bonds. He'll explain what that is later in the podcast. John, welcome to the show.
John Seo: All right. It's great to be here, Steve.
Steve Hsu: So John, I don't know if you remember, but about almost 15 years ago, I invited you to give a colloquium in the physics department at the University of Oregon. And you were kind enough to come out to Eugene and give a very interesting talk not just about cat bonds, but about a kind of theory of risk.
And I think something like discounting in the space of probability functions, something of that nature.
John Seo: Yes.
Steve Hsu: Was a little bit theoretical, but I think the physicists really enjoyed hearing about it. So I still remember our conversations from back then. And since I started the podcast, I've been meaning to touch base with you because I thought you would be a real interview for my audience.
John Seo: Yes, absolutely. I mean, well, you, you, you remember it well.
So it's, it's something that I, that I learned. But I was quite young. My father and I can't figure out exactly when it was that but I was probably about like 10 years old.
We know because he was working on a paper, we know its publication date. He explained to me that he was a mathematician by background. Algebraic number theory, but retooled as an economist. And so naturally what they did in those days, they tend to stick people like that into this field called decisions under uncertainty because it's very mathematical.
And he said that there was a particular problem, what he called the investor choice problem that is actually technically unsolved. And as you know, too, it was like math, my mother's a mathematician, too. All they do is talk about unsolved problems, right? Like, oh, if you can solve this problem, that's a 300-year-old problem, 100-year-old problem, etc.
So here's an unsolved problem. I thought it was interesting. He was explaining it to me, just to talk it out loud, really. And so I was like, okay, so like, what's, what's your strategy? Like what, you know, you're going to like chip away at that. And he said, Oh, I'm going to do even better. I'm going to prove it.
That it's unprovable. I'm going to prove that there is no solution. I mean, you know, that's the type of proof too, right? It doesn't exist. And I grew, I don't know why I got upset and I just said, no, dad, you can, you can solve this one. This can be solved. He was like, well, no, I don't know. And I remember he, he would, he, he told me, you know, again, growing up said, Oh, there are all these problems that people thought are unsolvable.
But in the end, this guy, you know, you know what he did? It's classic divide and conquer, you know, he divided and conquered and he tried to explain it to me. I was like, yes, yes. You know, these things can be, I go, dad, divide and conquer. You can see, you can divide and conquer this. And So I was really intrigued and it turned, so it was just after dinner and it ended up that we argued all night until the sun came up the next morning.
My mother got up to make breakfast for everybody. She couldn't believe that we were still in the living room, like heatedly arguing about this. So she broke it all up. And that was it. And we never talked about it again. So, but I always remember that. And so really what, when I, when I came to you and talked to your students, I was talking about actually the solution to that problem.
You know, it's actually, you know, a trade secret. It's very proprietary. They should have killed everybody in the room, but I thought they would, I thought, you know, they didn't, they'd enjoy seeing it, but really that was actually, you know, something from a very early age is planted in my mind that's like, how can you solve this problem?
Well, you know, the field at that time really thought it was unsolvable. And it turns out that like a lot of these things, when you see it and understand it, you realize that the parallels to other solutions that were used in finance going back, almost a thousand years, are there naturally. And so that's and of course that gave me courage to enter the field of finance that I'm in, called catastrophe bonds, right?
Steve Hsu: Yeah. So what I'd like to do is let's loop back and talk more about this specific question, the investors choice problem and how you price expected loss risk tail risk. and we'll come back to that. but before we do that, what I'd like to do is talk a little bit more about your childhood and your education and the story of how you maybe started out as someone who was going to become perhaps a biophysicist but ended up actually in finance. So maybe we could talk about that a little bit.
So when you were in high school, what did you think you were going to do for a living later in
John Seo: Actually, biophysics. I thought I'd be a biophysics professor. So that was, it was like seventh or eighth grade, you know, I was giving it great thought, you know, what I wanted to do, you know, like don't want to be a doctor or an astronaut or something like that. And then I, I don't know why I just had this great, you know, realization that what I wanted to do is study physics. Learn all I could about that. And then and then study biology. I didn't know that the field of biophysics existed. And I thought that would be, that would be the interesting thing to do. You know, research for the 21st century. If you could just take that hard science of physics, and then, and then deeply understand biology, you know, you could solve some interesting problems.
And so forth. And so, yeah, I, I told my mother that is like a three hour rant, just like, Oh, good. That's, you know, that's, that's great. And I didn't change my mind. I mean, I went to school and I studied physics. And as you know, when you get into that mode, you know, you would think it's like, Oh, of course, I'm going to study this for the rest of my life.
It's, it's beautiful and, and so forth. But any classmate would ask me, Yeah. What are you gonna do for grad school and so forth. I said, oh, biophysics. I know that. But it is like, but you don't seem to be studying bio. I took organic chemistry, but you know, I go, no, no, no. You study physics first, then you study biology.
That's biophysics. So I, I knew since maybe seventh or eighth grade.
Steve Hsu: So if I recall correctly, you did your undergrad at MIT and would this be the eighties or late eighties?
John Seo: Yeah, 84 through 88.
Steve Hsu: Okay. At MIT. And then for graduate school, you went across Cambridge to Harvard.
John Seo: Yes, that's right. They have a biophysics program there.
Steve Hsu: Yeah. And I think you and I probably overlapped. I came to Harvard as a postdoc in 91. So we might have overlapped
John Seo: Yeah. Yeah. Yeah. Yeah, I was there 88 to
Steve Hsu: never played this game, but I bet if if we tried, I could find a lot of people that I'm, so I'm probably one degree of separation from you in many respects
John Seo: I'm sure.
Steve Hsu: MIT and Harvard people.
So as you're coming to the end, though, of your PhD year, you're married and you've had a child. Is that right?
John Seo: correct. Yeah,
Steve Hsu: that child was premature and you ran up some huge medical bills because of the premature pregnancy or premature
John Seo: Well, they had, they had a strap my wife in a, in a bed for a little over a month so that he wasn't overly premature, but he was tiny. It was the size of a ketchup bottle.
Steve Hsu: Wow. And I hope everything's okay with him.
John Seo: He's yeah. Oh, he's terrific. Yeah.
Steve Hsu: Great, so I guess the health insurer told you that you wouldn't be covered anymore because of this. So if you were to have another child you'd have trouble getting it covered.
John Seo: Yeah. I don't really know. I mean, so I had my second on the way and then I went from. my grad school, the postdocs, I got my doctorate and I was also confused cause you know, my, my, the card said, you know, a particular insurance company, I guess I'm not going to name it now. It's a fine, it's a fine company.
It's one I use right now, actually. And at the, at my, from my postdoc. It was the same company in a way, but someone called me up and said oh, hey, listen, you know, I understand that your wife is pregnant again. Congratulations. We're not going to cover that. It's a pre-existing condition. I said, but I have the same insurance.
I said, Oh, that's a different insurance than that, you know, that's whatever. And I, I think unfortunately they really told the fib, you know, it's just, it wasn't true. Okay. But they convinced me that it wasn't covered and that, you know, you know, and then the, the woman, the woman actually whispered in my ear, listen.
Just find a corporate job somewhere, anywhere, and then they'll, they'll cover the kid. That was it. So, that's probably if not for that, yeah, I'd be a professor now.
Steve Hsu: yeah, I was gonna ask you that so it wasn't any disappointment or disgruntlement in what you were doing as a biophysicist It was really literally the search for better insurance coverage,
John Seo: Yeah, that's right. I loved, you know, I loved what I was doing back then. I still love the field, I think. You know, it's, it's just terrific, too, to see that after all these decades, because, you know, back then, frankly, biophysics wasn't capable of much. In my opinion, it wasn't so, but those who are in the field were thinking, you know, we're practically futurists, you know, and say, look, you know, a few, a few decades from now, you know, we're, we're going to be able to start to do this and that, you know, and a lot of stuff like that.
Steve Hsu: Throwing this out there because when we, when you and I met in Eugene 15 years ago, I, my research was 100 percent focused on elementary particle physics, quantum field, cosmology, and I didn't do anything in biophysics, but since then, so about five years after you came to visit, I started getting interested in genomics, and my rationale was that sequencing costs were going down so fast, that I could reliably predict that we would soon have huge data sets, millions of genomes to study.
And so I've since devoted a chunk of my research time to this problem of predicting phenotype directly from genotype. So we've now done research in which the training data set is a million people. And we know, for example, the height or the eye color or the diameter status of each of those people.
And we also have their genome and through machine learning and AI, we can build actually pretty good predictors. So we can predict the height of a person plus or minus a few centimeters from their DNA alone.
John Seo: Wow.
Steve Hsu: So that's, that's like what would have been science fiction in the nineties, because in the nineties, we didn't even have the first human, right?
So, eventually things kind of paid off and this field computational genomics is 100%, it's basically a hundred percent quantitative, you know, AI ML type stuff, but the data sets are huge and we only have those datasets because of huge advances in the technology for gene sequencing.
John Seo: Right. Oh, that's fantastic. Good for you.
Steve Hsu: Yeah. So we should, we should talk about that offline sometime because my, I'm sure my listeners have heard this before, but um, so you ended up, did you go to Lehman brothers first?
John Seo: No, actually, where'd I go? I mean, at first there was a, there was a, it was then a small trading firm that was working out of the Chicago board of trade. The building itself, which is 141 West Jackson, West Jackson, if I remember that, and Chicago has a, it had a lot of, not only, but it had a lot of MIT people in it. And so and there were some
Steve Hsu: O'Connor. It wasn't O'Connor.
John Seo: Yeah, that's correct.
Steve Hsu: Oh, you went to O'Connor. That's so funny. Okay, great. Yeah.
John Seo: Oh, okay. So, yeah, you know, yeah, for some people that are kind of, that know the options and drills, Mark, they know, they, they know O'Connor, you know. It actually had a nickname, they call it O'Connor University. So, I mean, still today, if you look at, I mean, the large banks or hedge funds, the heads of the, the options trading desks are O'Connor trained again, not exclusively, but it's, it's not unusual.
So, you know, I'd actually heard stories about O'Connor when I was in high school, because a person that was in the fraternity was working at Texas Instruments at the time. I grew up in Dallas, Texas. And so that fraternity tried to speak to every incoming freshman male just to kind of check them out.
So we had lunch and he told me stories about this. trading firm. And of course, I thought it was really fascinating. It was very, very interesting.
Steve Hsu: You know, O'Connor, I, I'm class of 86 from Caltech and O'Connor already at that time was interviewing on campus. So I
John Seo: Is that right?
Steve Hsu: Yeah, I attended one of their recruiting visits and I think some of my friends actually went there to work.
John Seo: Right.
Steve Hsu: Yeah, so it's familiar to me, even though I think maybe the firm, I don't know if the firm still exists, but it's definitely
John Seo: You know, interesting. It still does, you know? So it was, in a sense, bought by Swiss bank corporation, SBC, although all 19 partners were turned into managing directors. You know, in a, in a soft way, it was kind of a reverse takeover. So they wanted the O'Connor people to scale up the bank's operations and derivatives, and it worked in the sense that the Swiss bank at the time was maybe, I don't know, third place among the Swiss banks, let's just say. And the biggest was UBS and their goal was to become the biggest. And they, they essentially did. I mean, they grew so fast. And so boldly with these O'Connor, 19 O'Connor partners that they ended up merging with UBS, but it really was a reverse takeover. All the senior management at Swiss Bank were the ones that actually held all the senior roles.
So, UBS was the older, more prestigious bank. And so I think initially Swiss Bank was saying the new entity is going to be called Swiss Bank. They said, come on, look for national pride and so forth. So I do remember this, that UBS, it had a little period in between the letters, you know, because Union Bank of Switzerland, the logo actually had the periods in it.
And what they did is that after the merger, they stripped the periods out and they changed the corporate name from Union Bank of Switzerland to UBS. And stated specifically the U B and S don't stand for anything anymore.
Steve Hsu: Interesting.
John Seo: That was their compromise, you know, but they had O'Connor in there. So it was UBS O'Connor and and UBS in a sense went on to absorb hundreds of boutiques and for a good couple of decades, those boutiques, you could say, it would say UBS and then name of the boutique, you know but I don't know, it was like maybe like 10 years ago or something like that.
They said, look, it's just one firm now. So everybody just has UBS on the card. Except for O'Connor. I'm told that globally it's the exception because they think the name is so good that they actually preserve it. So UBS O'Connor still lives on.
Steve Hsu: I see. So, so tell me a little bit about, I have had many friends go through this transition from academic physicist or scientist to financier. In your case, it happened at O'Connor and you had to move to Chicago, I guess. And just tell me a little bit about those years, maybe the next five years after you left physics, like.
How did your psychology change? I think you probably had a big advantage over most physicists because you talked to your dad when you were a kid already about, I guess, utility functions. So just tell me how your psychology and your focus were changing during those years.
John Seo: I mean, it was kind of crazy. So I mean, I didn't know anything about finance. And, you know, even worse, I didn't care about money. You know, my parents, as academics said, have programmed that into me. And I happily accepted it. I think, you know, it's, I think it's not a bad way to go through life, to not make money your number one objective. You know, these were all really poor preparations for, for, for getting into it. I was just, you know, I'd always worked a bunch of odd jobs when I was, while I was growing up. And so I was a good employee in that sense. So it's just like, Hey, you know, just give me something awful that nobody else wants to do or something really, you know, like that.
And so they gave me a project they'd say so, look, to find out what the project is, go and speak to so and so forth on the 7th floor. So I show up, and the person's like, so you're the poor bastard that they gave the project to. Then he explained, it's like, this is the biggest dysfunctional mess I've ever seen in a project.
And I was like, listen, you're going to have to work with people across four different floors. And you should know this, this project's like one and a half years over deadline and it's just mired hopelessly. So, you know, they gave me kind of what I asked for. So I went on all the floors and people just ventured nonstop.
So when I was very young, by the way, I didn't really talk. So I automatically became a good listener. I just didn't talk. It's just super quiet. And so it was just listen mode and they'd just like to rant forever and just say, who sent you? It's like John on the seventh floor. It's like, well, you go tell John that, you know, You know, I'm sorry, you know, you're just caught in the middle, but you know, those guys want this, this, this, this, this, and that's gonna shift responsibility to me. And you know what? You know, F. That, you know, because they're the only ones, I don't, it was all like this. So I just listened, listened, listened up and down all those floors.
By the way, I needed to build a tool, you know, it's thousands of lines of code. But I couldn't begin because, you know, I'm talking to the end users or the people that need to cooperate. And I didn't understand anything they were saying. I had a lab book. So I write down all the phrases. Then I roam the floor and ask people to explain these phrases.
And after a while, I kind of got the idea. You know, I did that for six weeks before writing a single line of code. and then I coded it all up, you know, and it worked, you know? And what was interesting to me is that I finally realized that, you know, if you come out outside and you're saying math, physics or electrical engineering or whatever your discipline is, you always think about these unsolved problems or technical problems.
Right. And I realized that there's no technical problem. Unsolved problem here. It was actually that there was so much money in the business and it's all relatively new and dynamic, which I liked. I thought that's interesting, you know, cause you know, like I was used to thinking about science in terms of like, and then by the 2010s we'll probably have this, you know, so it's, it's, you're building that you're preparing for that.
This stuff moves really fast, right? evolves very quickly. It's like hyper. You know evolution. And so I thought it was all very, very interesting, but I was just intrigued that there, I don't, there, there wasn't a technical problem. It was like a cultural problem. And that there was so much money that rather than actually causing cooperation, it actually allowed fragmentation, you know. um, and then everything was so proprietary. I mentioned the four different floors. The reason why is that at one time, the security guard just grabbed me when I was going in on the 15th floor and he pulled me back forcefully. He said, I've been watching you.
Because you went in on the seventh floor. The eighth floor or a floor in the, I forgot the other four in the 15th floor. He goes, he goes, I happen to know no one has access to all those floors unless they're a partner. So he was looking at my badge to figure out how I had access there. So they ran it like the department of defense.
It's sort of like everybody was, even though it was inefficient, was actually separated and not even allowed to go to another floor. So that nobody could piece it all together, but for this special project, it needed all be pieced together, you know, and then he put me in, they had kind of like a, like a holding cell. And then he came back and he threw the badge at my face. And he was like, why didn't you tell me, you know, you're a cafeteria worker. So they had it as a big deal back then. They delivered lunch to you, to your desk. So nobody on the business side had access, but the cafeteria worker and partners had access to all floors.
So they, they, they gave me that status. Oh, also I wasn't being paid. So because I don't, I'll work for nothing except for health insurance.
Steve Hsu: Oh, wow.
John Seo: So like, okay, I did throw in though, because they made all these huge speeches about money and how money drives you as important as money me, motivates you and so on.
So I said, no, no, no. And I'm like, these guys are pretty obsessed with money. So I did throw in at the end. I said, okay, that's fine. But at the end. I asked that you pay me whatever you think it was worth. That's completely up to you. You assign that. And so I was paid zero, actually zero salary. And so I, I worked away at this thing here, made it, it worked.
And as I said, it was interesting because it really wasn't a technical problem. It was actually, in a sense, a people division type problem. Everybody was divided and not fully cooperative. There was so much money at stake. To be fair to these people, I understood what they were talking about. The way things were being done is that one group was shifting responsibility and downside to another while maintaining some kind of the upside or benefits of that risk.
Steve Hsu: Yep.
John Seo: And they were hypersensitive to that. Even if the shift wasn't very obvious, If it was the slightest and keep in mind these people, you know, Connor and the OTC options market and FX, for example, they could be 20 to 30 percent of the global trading volume because they were so precise about how to price the risk, right?
They were the most efficient. It's almost in a, in a funny way, if you abstract it, it's almost like high frequency trading before there's high frequency trading in the, in the good sense, right? They knew how to spread everything off of everything. So they had, they had a laser accurate bead on where the market was, where value was at any given time.
Those two things can be different. Right. And then they use that in order to create liquidity, you know? so it was really fabulous. Very interesting what they did. I always say this now that you say the modern market is very obsessed with finding liquidity. That's mispriced and then consuming it. I think it's a consumer people in those days, not just O'Connor.
They actually created liquidity. They made markets appear where there were none before. So, okay. So the, so in a sense, the culture is hypersensitive to the pricing of these options in a way of risk. So if one group was kind of just pushing over something a little bit, and the other group was not pushing over.
Properly compensated for it. Their culture was hypersensitive to that. If you're from the outside, like, you know, we're all one team. Like it's not, that's, that's, that's trivial, but they're all hypersensitive. So I thought that was kind of interesting too. So yeah, I mean, that, that was my entree. I discovered that it wasn't boring.
It was very dynamic. Of course I had my kid, third kid, fourth kid, fifth kid. The last straw for me, though, is that I actually maintain my postdoc which I, which I, which I enjoyed immensely, right? But you know how research is, right? So, you know, there's no 9 to 5 Monday to Friday, you know, it's, it was just 24-7, right?
You know, and, and I would happily, you know, feverishly be thinking about my problem and so on and so forth until midnight, and then go to sleep still thinking about it, and then you're still working on it in your sleep, and you get up, So, you know, it's just this, it's just really, you know, wonderful dedication.
I was doing that at the same time that I was working the finance job. And it was just as obsessive. I mean, they had intense deadlines. There, there, there are all these big, you know, programming challenges in order to write all those things I was talking about. I realized that you could act, you could actually rebalance everything, you know, politically across all these groups, just through simple, you know, software.
And and so I was like, I don't know, I was, I was really probably working like 110 hours a week.
And the last straw was that it was Christmas and I didn't have a Christmas tree. For my kids. And so I went to the lot and it was empty and it hit a little paper sign that just said, help yourself. And it was just like this little, you know, it was, it wasn't a real tree.
Brought it home and my oldest son's cheering. He's like, yay, Christmas tree. You know, I'm, I'm walking up the front steps and I just broke down crying. Because I realized it's like, okay, you know, like I was having a good time, right, you know, working in these two worlds, doing what I enjoy. That's like, this is too much.
I mean, you know, no Christmas tree, you know, come Christmas Eve and just dragging this twig home. It was just too much. So I made a decision, but by then I think I was, I was, I was ready. I just thought, You know, on the grand scheme of things with all those kids, it's sort of like, I thought of science as like my hobby,
Steve Hsu: Yep.
John Seo: a hobby that I just really love dearly.
And, you know, and wish I could pursue and there's a bit of the scientific approach if you want it, you know, on the finance side,
Steve Hsu: Yep. I often look back at my science career and I occasionally think like, man, what was, what did this cost me in terms of net worth to practice this hobby for the last?
John Seo: You don't do that calculation, but, you know, money's not everything. Right?
Steve Hsu: Yep. Yep. So let's, let's talk a little bit about how you got interested in cap bonds. because O'Connor, you know, you might be trading commodities, options on commodities, things like that. What, and, and you did mention this idea of like back in the day, people would create liquidity, create new markets in a way I view you as somebody who played a big role in creating a new market, right, for a new type of risk.
So maybe just talk about how that happened.
John Seo: Oh gosh, I mean, that's a really long road. I mean, so I did, you know, like, two or three tours of duty on Wall Street, so to speak, and you learn a lot. I kept moving closer towards what you would call investing to see, you know, like when you're, when you're doing like high trading volume, tiny bid ask spread massively liquid markets, or you made them liquid and all, you know, from that point of view, it's very micro.
I mean, it's all just bid ask. And there's a mentality of zero sum game, right?
Steve Hsu: Yes. Mm
John Seo: it's not necessarily true, but it's overwhelming because that's the first order approximation, it's a zero sum game. And I was, you know, and the techniques around that are interesting, but I was fascinated by this because I could see that there's a whole part of finance that's not a zero sum game.
And it's like, and why is it not a zero sum game? Because there's actually an excess return and you didn't get it by just viciously ripping it out of somebody else's hands. It's inherent there. So it's, it's a fundamental proposition of finance where it's kind of like, look, kind of, it's a win win if it's done right. You know, it may not be happy with the terms, but in the end, you know, things got done that wouldn't have otherwise, et cetera. So I just kept moving closer and closer to that, of course, to like all things in the end, it's employment. So you got to offer a skill that somebody wants and is willing to pay for back in those days, the investment side was getting creamed by the quants on wall street.
So the investment side kind of wanted a Wall Street quant, you know, almost purely for a defensive, you know, asset, right? And so I kind of made it over to the investment side that way, and eventually ended up at the Harvard endowment.
Steve Hsu: Oh, I didn't know you had worked there. That's amazing. Okay. Yes.
John Seo: I was back in the Jack Jack Meyer days and it was like a really golden age of that, of that place.
I think most, most would agree. No disrespect to the current setup, but yeah, it's, it's, it's, by the way, it's, it's not about that endowment in particular, right? It's about the evolution of finance, investing, the relationship between investors and wall street, et cetera, right? Constantly evolving. But like all things, there's, you know, there's a, there's a, And every moment's almost transitional, right?
Nothing Is permanent. But there are going to be some moments that you might call golden, and they can't last because it's all transitional. And it was a really, really interesting time. I mean, it's very intellectual and yet, and yet practical. big macro views and zooming all the way down to the micro.
I really learned a lot being there. I enjoyed it very much. But there I was with my quant skills, you know, on the, on the so called buy side, the investment side. And, they really, really helped crystallize my thinking about not just markets, but about investments and how the global economy works, because that's all that they talked about. and I got the phone call. That's it. Got a phone call from Lehman. I'd never worked for them before. And You know, they said, you know, we're interested in starting a proprietary trading group around something called catastrophe, catastrophe bonds or something like that. We want to make catastrophe bonds. I don't know anything about catastrophe bonds. That's the beautiful thing. Nobody does. And of course, I love stuff like that. I was like, oh, okay, that's great. And it was like a year. It was practically like a year. and I finally agreed to come on. They did something very interesting and very honorable, I have to say.
That I'm not sure if I ever told this story before publicly. At the time I was about to join, what was that at that time? Was that the, yeah, I think that was the, that was the collapse of long term capital. Yeah,
Steve Hsu: or something?
John Seo: 98 exactly, you know, and my first day of work is like October 1st, whatever like that. And then I knew I was leaving a good place. I mean, up to that point Harbor Management Company, it's, it's sort of like, you know, it's like, you know, It's a, it's a, it's a job for life, basically. And they called me up and they said, think of this, I hadn't yet joined, there's no NDA. But they said, listen, there's a possibility that we're going to go under. The CEO's out there telling everybody it's impossible and so on and so forth. But, you know, in the end, it's a, it's a, it's a, It's a game of confidence. If they lose confidence in us, it doesn't matter what our, what our balance sheet is, et cetera. We're dead. And they said, it's not clear whether we're going to survive. So, you know, we thought you should know that before you come over. I was supposed to come over in like a week or two. And we completely understand if you want to back out of your commitment and so on and so forth. Preserve yourself.
Steve Hsu: Those are good guys. Good guys.
John Seo: They were, I got to tell you, you know, every one of them that I interacted with and worked with were incredibly honored, honorable, the person that actually headed that group, a guy named Mike Gelban and Mike Gelban is actually officially the I believe the last CEO of Lehman people don't know that because he put his foot down when they were doing all those crazy trades that sunk them.
Steve Hsu: Yep.
John Seo: And he probably has been there for like 35 years. He was the inside guy and they basically, you know, fired him or pushed him out for cowardice
Steve Hsu: Wow.
John Seo: Now back then, so Lehman management was loaded, packed to the gills with equity options. That's kind of how they aligned interest. And, you know, so people have their entire fortune tied up, tied up in that. You didn't just cash out and walk, walk, walk away. They're doing risk taking. but that's all, it's all locked up.
You can't leave. You can't leave unless you're, unless you're basically fired without cause. Of course, cowardice is not a cause.
Steve Hsu: Yeah.
John Seo: So I believe, I believe they forced them to liquidate all his shares of Lehman. So yeah, I think, I think he looked, I believe that he basically was forced to liquidate at the very, very top, but he was, he was so visible and on record with putting his foot down like that, that as they were winding the whole thing up, I believe treasury asked him to come back in and be the last CEO and help wind it down.
Steve Hsu: So
John Seo: This person is just of immense integrity.
Steve Hsu: So interesting that in 98 they were warning you, being honorable guys, about what could happen, and then 10 years later they were gone.
John Seo: Yeah. I mean, there's a lot of, the demise is very, very complex too, right? Like, how it all, how that all happened and so forth. So, I wasn't around, so I can't really speak to it. But, yeah. You know, what was interesting too is that the original approach I got from Lehman's actually to be uh, a head mortgage trader, mortgage backed security trader.
Steve Hsu: Great.
John Seo: So that was before the cap bonds and, you know, eventually, and we talked about credit derivatives and I told them that that was the biggest blow up in the history of Wall Street where it happened. And not even jokingly, we both had a serious conversation about it and they understood where I was coming from.
They said, Oh, you're talking like that. I said, And he goes, Yeah, sure. But I mean, you know, you'll be rich by then. What do you care about? You'll be retired. I was like, Ah, yeah, I know. This is 97. It's like, I know. It could be like, It's going to take probably like 10 years or so for the mortgage market to blow up.
But you know, it's just not, it's just not, I'm not, I'm not interested. I want to kind of, yeah, I want to be in a market that I know it's going to be around longer than 10 years and because I'm going to devote myself to it. And they understood it, but it was interesting. They, you know, even they, in a very cold eyed way, it's like, if you're talking like that, I'm not saying I agree with you, but I do understand.
And yeah, it's probably about 10 years and then it blows
Steve Hsu: Yep.
Yeah, so this may be a good time for us to introduce the listener to the idea of a cat bond. And let me give it a shot and then you can fix what I say. so imagine that you're offering an insurance product. So, let's say you put up 100 Million dollars against an event, and the event might be an earthquake above a certain magnitude hitting Los Angeles, and maybe you have some idea that this type of earthquake only happens once in a 100 years.
And so there's an expected loss, which is that 1 percent chance that it happens times the amount of principal you're putting up.
John Seo: thing you can
Steve Hsu: expected loss against what the other, the counterparty would have to pay you to endure that risk. This kind of instrument is useful because it allows insurance companies to basically distribute risks so that they're not all holding it themselves on the book.
John Seo: said, I'm not
Steve Hsu: It also allows the person who's putting up the bond to make some kind of return on their capital. Which is maybe uncorrelated to ordinary instruments like the stock market, the bond market. So it's an example of a win-win kind of mechanism for risk distribution in which everybody's happy. Is
John Seo: Yes,
Steve Hsu: That's fair, John?
John Seo: That's perfect. I just want to make it clear. So if the earthquake happens, then we, the investor, loses 100 million bucks, right? So we're not making, we're not betting that we're not profiting from the death and destruction. It's perfect. Now this is the key thing here. So you say okay You got a 1 percent chance of losing your hundred million in any given year, you know So the the phone call that came from from Lehman was because Warren Buffett had done a trade This type of a trade with the California earthquake authority that was just formed because the earthquake market had collapsed actually.
And I'm going to use round numbers here. So on that a hundred million, you know, Buffett was paid 10 million. So, and if you're, you know, numbers type, you just say, well, 1 percent times a hundred million is 1 million. So the expected loss on average is 1 million a year. So, well, you got to get paid that at least, right?
Otherwise, you know, the expected value of this investment is negative. You gotta have a positive expected value. And most people kind of walked around and they thought, I don't know. I think if, you know, if we believe the science 1 percent and I'm fine, you know, I'm okay with it, but let's just say that that's just an agreed upon uncontroversial number, maybe.
That investment should pay you a million and a half or 2 million. Let's make it 2 million. He's a round number. So it's kind of like, it's like a retail markup. Like the, the cost of the goods is a million a year on that contract. Let's just charge 2 million and move on, but it was paying 10. So that's it. You know, but keep in mind, the Wall Street mentality actually even thinks, well, we trade options all day long with a, with a one Delta and we'll trade it maybe at like one and a half or two times here.
It's paying much more, but I've been studying this issue. Keep in mind as I, as I moved away from Wall Street and while I was at, at Harvard management company and, the numbers that they actually gave me, so it was, it was more, I'm rounding here. They were very precise. There were coincidentally three significant figures, practically the same numbers that I was imputing or calculating was present in the investment markets.
If you, if you, if you, if you invested your money wisely, you know, nothing risk free that you were kind of, you were getting this very healthy return. and it's more than what a market maker and the OTC options market would see, and it's persistent and consistent. So Lehman, I don't know, it was a phone call, so I couldn't see their faces, but it was kind of like.
You know, it was one of those moments and say, okay, prepare yourself here. Okay. You know, this is, you know, what, what Buffett got paid or something like that. And I calmly said, you know, that is fascinating because it's, coincidentally , what I've been seeing. You know, over here in traditional markets, it's like, well, that's not how it's like, no, no, that's not how traded markets work.
Let you know, when you're, when you're a market maker, bid ask and so forth there, but at a very high level, investing kind of looks like that. So I told him, I go, you know, in, you know, whenever you see these numerical coincidences, a lot of times it's just that, right. But I had a feeling that there was something deeper going on.
I was really intrigued. And, and really, of course, you know, what worked in my mind, I was explicitly thinking, this is the investor choice problem that my father had been talking about. And I said, you know, I, I, I combed wall street for the answer. I really did actually, because my father always said this, he said, You know, son, actually, the practitioners, they're, they're kind of just way ahead of the theoreticians when it comes to stuff like this. If you look at modern probability theories attributed to these letters that Pascal and Faramat exchanged with each other, right? Well, what were they talking about? Pascal was, you know, he was obviously crazy about gambling. A gambler, a very astute gambler said, you know, your math doesn't work. He's like, what are you talking about?
He's like, according to your math, these two, you know, gambles have the same probability. Same relative expected value, but they don't, you know, by the way, no one's ever done this. I've, I've, I've combed the literature. If you were to actually just empirically determine that these two bets don't have the same probability of success, you would have to gamble over 5, 000 times each just to have any statistics.
And these gambles are actually very laborious. You have to keep throwing dice. One of them is you have to, you get to throw it over 24 times. Actually yeah, the 24 times minimum. So you're throwing the dice over and over. It's an ungodly amount. And I suspect that actually this gambler had not collected that data and seen it that much.
That he could, he could intuitively understand with data that a statistician would say is insufficient. Right. We see this all the time. There's a guy named Dr. Jarecki, you know, you know about this guy. He
Steve Hsu: I don't
John Seo: Yeah. So that's a whole story unto itself. But I mean, he was a doctor and he was kind of like Hannibal Lecter.
He was like this evil genius and he could actually detect the anomalies and in roulette wheels. He told people that he had a computer, but he didn't. It was just in his head. And it's weird, the most subtle anomalies in the probabilities of a roulette wheel he could just detect by
Steve Hsu: wow Yep
John Seo: his observations are insufficient to see it.
So my father actually always told me that. And he said, he goes, he says, the gamblers, the people in the front line, they, they know things that the mathematicians don't. Mathematicians just figure it out. So, okay, it's like, oh, that makes sense. So, on Wall Street, you're there, it's a giant casino, so you're just, you know, I was searching for the answer, but couldn't find it.
But then I realized, wait, there's a whole nother parallel market. You know, and by the way, the parallels are stunning, you know today, globally, there's roughly 600 and 680 trillion derivatives according to the bank of international settlements, right? And just Google search it. B I S derivatives outstanding boom should be right there at the top of the Google search.
So it's like, let's, it's rough, you know, roughly 600 to 700 trillion in derivatives notional outstanding. If you back out of the envelope, the amount of insurance notional outstanding. It's 600 to 700 trillion dollars. So really, you know, derivatives actually are, you know, often actually just a form of financial insurance, right?
But there's this original insurance market, right? That exists. And most people think it is boring. That's just as big. It's been around forever and it has its own parallel culture that's kind of, in a sense, untainted by, you know, unmixed with what was going on Wall Street. So, and, you know, and I know you, every good scientist does this.
You're always thinking that way. It's like, what are all the tribalisms present? You know, in my field, right? And you actually, you know, it's like, well, this person's PhD advisor was this person, you know, all the lineages, you know, you know, all the schools that come, you know, all their biases, what they, you know, what they hold precious and all their principles.
And so you see all these tribal divisions in a field and you use that to inform yourself about where you might be able to. You know, put a dent in the
Steve Hsu: I could comment on that You know
John Seo: Yeah,
Steve Hsu: Now, there's this sort of actuarial culture. Those people know some math and statistics. And then you have this newer culture, which is a bunch of quant traders who know black shoals and stuff like this. They often come from physics or math backgrounds, but there is obviously some convergence in what these people do.
Right? But different, very different cultures.
John Seo: Very much so. The, all right, there's, there, there, there is some convergence, but I was, you know, like, so I was, you know, again, still obsessed about this problem. And I thought also, if we go, if I go over to the insurance market, maybe I'll find the answer there, right? Maybe they'll know. They may not have formalized it mathematically, but you know, that's, that's easy to do once you see it.
And so that was actually a, a, a big part also of my motivation to, to come on over because I thought, okay, I'm seeing this kind of big macro behavior where you see this excess, you know, return. Which is, you know, which can be converted into a legitimate investment. But it's sort of like, but, but why is it that way?
And why that numerically, right? You know, why, you know, why is it, you know, you know, 6.18 instead of 3.12, you know, and sometimes there is no answer. It's kind of like, it's just the way it is. That's the market price of risk. But there was, there was, there was something going on. I thought, and maybe I'd, I found it out on the insurance side and, you know, and I was right, actually.
That's the, you know, I found the answers. Can I say. Almost empirically on that side and then turn and then write up the math.
Steve Hsu: So for the audience, let me take a stab at this again. So if, let's say notionally, it was a hundred million dollars that I was putting up in one of these contracts. And if it's locked up for a year and I can't invest it during that year, I could have earned a risk free rate on that money, right?
So there's some base rate like LIBOR or some kind of risk free rate that I could have earned on that money. I should earn an excess over that. And certainly the excess has to be greater than the expected loss, one would think. And so I think your empirical rule, which you told me 15 years ago, I still remember is that the coefficient is something like four to six times.
The expected loss should be the excess return over LIBOR that you would expect for that contract. Is that about right?
John Seo: Exactly. And it all bends on market conditions. I mean, recently that number got up to like 10 or something like that. So it's kind of back to the old Warren Buffett days, but four to six is a good rule of thumb. And keep in mind what this a hundred million, what happens is that a hundred million actually sits in a bankrupt remote.
Trust actually, and is invested essentially in T bills, you know, shortening, you know, one month, you know, basically treasuries. So you actually get that. So it's, it's kind of like you, you know, you just put into a CD, let's just say. So you, you get that plus that four to six percent.
Steve Hsu: Yep. Yep. And you either have to surrender it or you get it, you get it back. Right.
John Seo: That's right. Right.
Steve Hsu: So I think you, what you just said a moment ago is that empirically this rule of thumb. So, so 1st of all, someone coming in with 1st principles from 1st principles, like your dad might say, why is it 4 to 6? Like, what determines that coefficient?
Is that a universal thing? Like, does it not depend on the distribution of utility functions or risk tolerances of all the gamblers in the market? What determines that coefficient? And I think what you're saying is you both Saw the empirical pattern that there is a pattern and then subsequently worked out a theoretical kind of justification for why the number is what it
John Seo: Correct. Right. Exactly. So, I mean, you know, the world now officially just says, well, Like utility, you know, that's or in a sense, that's, that's the risk aversion that the market shows or something like that. Well, everybody's got their own language for it. Wall Street people are, well, it's supply demand.
There was enough demand for these bonds. You wouldn't pay us a high and so on and so forth. And there's truth to that. But they would say that, so it's very kind of, well Kahneman and Tversky, for those who are kind of into this and, you know, is there a movie or there's a show? I haven't seen it yet, but people say it's out.
And it's, it's, it's, it's obviously a nobel prize for Kahneman. Tversky had passed away by then, but it's sort of like, you know, it's like, well, you know, just interview a bunch of grad students and play games with them and say like, here's a 10 bill. That's yours. Thanks for participating in this study, by the way.
Now, would you like to gamble that 10? You know, there's a 10 percent chance that this and this and the, and they're like, Hmm, I don't know. Yeah. And then they just. And then they, in the end, they get these graphs and they say, Hey, it's a, this is kind of what risk aversion looks like, you know, and it's psychological. There's a, you know, afraid of losing a hundred dollars like that in such a traumatic manner. So that's what they charge. And that's, that's actually weirdly the prevailing academic. Description at Wall Street, which is to say, well, you know, the supply of cap bonds, the demand for this, this catastrophic coverage outstrips the supply of capital, the number of people like John.
So they are willing to take it on. So that's, of course, why it's there. And so, but none of that actually explains anything, right? And in particular, what it is is that you'll actually notice that, well, one in a hundred years, California is like four to 6%. One in a hundred years, Florida is more like eight to 10 percent now.
One in a hundred years in Japan is like three to three and a half. And it goes on and on. So it's like, why is that? And they would just use these words and sort of like, well, the risk aversion is different there or its supply demand is different than all. But. What I saw in a moment, where was I? I think I was, I was, I was, I was on the train, you know, doing my two and a half hour commute from, you know from the wall street area back up to, to Connecticut. And suddenly it's like, Oh my God, I think I can, I can piece all those numbers together. I can actually show that if Florida is at 8. 2%, then Japan should be at 3%. 1%. Two or something like that, you know, it's just piecing it all together. It's like, that's actually pretty damn close to what's going on. And you can just see the whole structure of the market.
And so that's, what's missing in a way, if these explanations about it's just risk aversion or supply demand, they don't really explain the, well, they don't explain the structure of the market, right? But there was a structure of the market that I could see empirically. And I had an, you know, an insight into how mathematically that all knitted together.
Steve Hsu: I just ask? So these are like, would there be cases where let's suppose that just for assumption, just to make the assumption that the expected loss is something that can be known.
John Seo: Mm hmm.
Steve Hsu: So you could have a market like Florida and a market like Japan where let's suppose we look at two contracts and they have exactly the same expected loss, but the premium that's required to get someone to buy that contract is quite different in those two different markets.
So, yeah. So what's the other variable that could influence that premium?
John Seo: Yeah, it's, it's, it's a, it's not, I'm not just saying because it's a, it's a proprietary thing, but it's a, you know, in short, actually, a, the, the most succinct way of putting it is that the supply of the, of the Japan earthquake risk in the private market, the government absorbs an ungodly amount of itself is, is, is less than the supply of the California earthquake or the Florida one there.
So there are the wall street explanations. And it's like, well, you know, you know, or put it this way here, they're, they're all desirable investments, but the Japanese risk is much more rare. Thing to get right
Steve Hsu: Got it. Okay.
John Seo: Then when they were asked, Florida hurricanes are plentiful, if you want some, you got it so there's a supply and demand thing there.
But again, supply and demand doesn't necessarily connect these two separate things.
Steve Hsu: hmm.
John Seo: And so, but then how can you look at the different supplies and arrive at these numbers? You know, typically what they have to do is just like, you know, calibrate separate parameters for Japan and for California, et cetera.
Yeah. You can call those parameters whatever you want to risk aversion or just kind of like, you know, supply demand type, you know, parameters, but I could, in a sense, see unifying everything with a single parameter. As you know, that's the ultimate if you can do that.
Steve Hsu: So, so 15 years later, it's still proprietary since I talked to you about it last. And your competitors, do you think they know it or they don't even now? Like, it's still a source of competitive advantage for mom.
John Seo: Interesting. Fundamentally, I have to say, I don't really know what my competitors are doing, but. You know, we can observe their behavior and from
our observations of their behavior, no, they don't do it. You know, they don't, they don't have the secret formula or anything like that. And it goes back to my earlier story.
Whenever there's an area where there's enough excess wealth or, you know, returns being generated, I've noticed that behavior people are like. Why do I want to geek out, you know, it's sort of like Wall Street, you know, they were, they were waving in PhDs like ourselves. Well, it went in stages, you know, but you know, at one point it was kind of like, Jesus at this rate, the margins are just going to go to, you know, they're going to be a hundred times lower.
So we got to quant up
Steve Hsu: hmm.
John Seo: If we're going to survive in that, you know, kind of interesting, right? Makes sense. So, you know you know, it's kind of like, yeah, let's get some particle physicists in here because the, the, the quantum of the, the profit margins are going to be like a subatomic particle. So we need, so we're going to, we're going to, we need people like that.
But in general, as I say, if, you know, times are good and just, You know, money's rushing in. I think there's less of an inclination to do that. They don't, they don't quant it out. So, in a certain sense, what we do here is, it's more culturally driven, right? A business person might look at what we're doing and say, why, why are you doing that?
And I said, well, you know, it's. Intellectually stimulating. We like it. But actually more than that, you know, O'Connor really taught people how to take in a quantitative advantage,
Steve Hsu: Mm
John Seo: um, and understanding, but also, translate that into a commercial stance. Market approach, et cetera, how to actually carry through with that.
Not just be all smug. It's like, Oh, we, you know, we, you know, we think, or we know we're the only one with the, with the, the magic formula. And so that's it, you know, we're just superior to everybody and we should just do better. O'Connor is like, no, there's actually ramifications of that knowledge and how you're, you're, you're supposed to act, your position, yourself.
You know what your limits are, what your limits aren't actually, and, and, and on and on. And so really, but it does start with that because if you don't have that view of the structure of a marketplace, it deeply affects your behavior, right? Because essentially what you do is that you, you become, can I say, I don't know, it's kind of a funny way of phrasing it, it's that you become more social because, because, because there's a huge social aspect to the dynamics of a market, right?
Right. So if you don't have any kind of math to inspire you to a structural view of the marketplace, then it's just all about relationships and whether or not you're positioned up here and your competitors are seen down here. And so now you get first shot at all the good deals and stuff like that.
That is a way of going at a market, right? And it doesn't necessarily, you know, mean that you can't quantify it, but people who do that, it tends to be different, right? It's a power game. You know, where are you in the hierarchy and so forth there? And I don't think there's anything wrong with it. You know, it's a very valid way to go about business, but that has nothing to do with math.
Right? So, you know, you, so there's a fork in the road at the beginning. It's like, do you, yeah. You have this kind of scientific view. And typically if you do, it's because you were a scientist, that's all right. But the great thing about O'Connor is that they taught, can I say scientists and engineers and mathematicians how to take that insight.
And can I say make it commercially viable? And I still think it's, it's very, very telling that out of the hundreds of boutiques and, and a lot of more quant boutiques and so forth that UBS absorbed, only O'Connor is allowed to maintain its name on the business card as a distinct entity.
It's so strong. Its reputation is essentially unblemished.
Steve Hsu: You know, some years ago I had a PhD student who was going to leave physics for finance and I tried to teach him some financial math and we did a little project before he left. And we looked at it. I think deep out of the money options and the applied you know, the valuations and it looked like there was a pretty big premium,
John Seo: So part of you
Steve Hsu: um, for that kind of tail risk, selling that kind of tail risk.
And I never checked to see if it, if it obeys your rule of thumb, I should go back and look at
John Seo: Very, it probably does now, actually. So, in the old days it didn't, because Black Shoals doesn't really guide to that, right? Because you know, like, the so-called fat tails and the adjustments that you make to the Black Scholes model and the tail are effectively one giant fudge.
You know, fudge. It's sort of like, well, we just empirically see it's there
and so we gotta, we gotta fudge it.
And then you backfill all the reasoning. It's like, well, maybe it's, it's stochastic volatility, whatever you say, and I know it sounds like I'm making fun of it. I'm, I'm, I'm really trying not to, but you have to admit that the structure of black souls really doesn't. Doesn't lead to fat tails. So, okay.
So fine. So in the old days, it wasn't that way. And it started to change because, you know, and I'm aware of this. It was an actuary that got ahold of the options book at, I think it was at Merrill Lynch at the time. And he was just an actuary. So actuaries are used to seeing any anomalies like that and just saying, Hey, that's just value, but then created a giant short option position, which is Delta hedging, to be fair, and then the market gapped down and you just blew sky high.
Of course, back in those days, Wall Street was always fascinated with people. And if he fails small, then you're a loser. Right. But if you lose like 300 million bucks or something like that, it's like, wow. So it was, it was kind of like, I, I, I know the story because after the blow up, everybody on the street wanted to hire this guy.
And so the firm that, where I was at that time, the seat, I had to interview the person, you know, I was like, well, it's going to be, have to be after hours and can't tell anybody it's like, Well, I'm always here after hours and I usually don't tell anybody anything because I have no social life. Thank you.
It's like, okay, anyway, so this guy is going to come in and he's going to speak to you. We went, we want to know what you think about his quant skills. And he basically just told me that story. So it's like, yeah, I don't know what the deal is. And like these out of the money options actuarially. You know, they have a very, very high premium on them.
That's not explainable in any other way. So I shorten them, you know, I'd do it again. I don't care. I don't apologize. But of course, after enough of those blowups, the market, you know, actually reprices it. So people, you know, and if you listen to that history, it's actually, why is it priced there? And it's like, well, yeah, career risk.
That's why, you know, but it's, it's, it's, it's not true. And actually every time I glance over my shoulder at the pricing out there, It looks very much like the cap on the market. So, you know, that convergence I was kind of talking about is, has really taken hold.
Steve Hsu: Super interesting. Now we've gone over an hour and I said we were going to shoot for about an hour. maybe I can close out or start to close out a little bit.
Just ask you a little bit about reflecting on it because I think for me it was relatively new. I think actually what maybe when I knew you or you were running it out of like a house behind your house or something, maybe in Boston or something or Cambridge.
And now it's a, it looks like a big firm with 30 or 40 employees or whatever in Connecticut. Just talk a little bit about your journey. Like what were the challenges? What are you most proud of in building your firm? What does the future look like? Anything you want to say about that?
John Seo: That's interesting. Okay. Yeah. Oh boy. I mean the, you know, doing something like this, the, the challenges that first dollar in, you know, getting somebody the back, you, you know, so, you know, the finance is all about, you know. Well, now you got a billion. Okay. So now you, you know, that's the hardest, then you can get the five and you get the 10 and then you can get the, but how do you get from zero to, you know, non zero that's, that's the hardest part, you know, and I still don't understand it, I don't still, I just don't understand in a sense how firms like ours actually are able to survive.
Now there's, there's a huge element of luck to everything in this, right? It's not all just hard work and brains. It's just. And you have to be lucky, you know, the stars have to align. You're at the right place and naturally you're prepared. Right. So. I'll, I'll, I'll tell the story. I mean, I was, so I was out there trying to raise money and it's just like, it's impossible.
I mean, people are literally like, we're scheduled for a 30 minute or one hour phone call and they terminate the call after three minutes. Yeah. Everyone, it was a famous endowment, right? And it's like, Oh, okay. So he used to be at another endowment. Great. We'll take the call. it's like, okay, before we like, you know, like, I don't want to hear all the razzmine, it's like, what are we even talking about?
I said, catastrophe. I said, okay, you know, forgive me. John, but I thought the idea was to keep catastrophe out of the portfolio, not to deliberately bring it in. So this call is over and you have a good day. That was it. Now, back then we didn't have, I just had the cell phone and Nelson was pressing his ear up on the other side there and just like a clunk.
It's just so hung up the phone like it was like that. So, and then you have in-person meetings too, and you know, it was just terrible. And so like, I'm walking around Midtown Manhattan and um, you know, it's, my feet hurt, you know, I've been rejected, you know, a hundred times over and I get a phone call, , you know, so it was a, it was a hedge fund manager that had a giant wait list.
Billions of dollars, right? On a waitlist and he was trying to get his money to his traders. He had a bunch of traders and all kinds of different strategies, but those traders are all disciplined. You know, they, they didn't want to run more money than they could and he couldn't force them and kind of didn't want to, I was like, come on, man.
Just take the money. You can, you can do it. And I go, no, no. So I didn't, I found this out years later. So he was like, You know, I basically have every strategy that exists. Like if there's a profitable strategy, I find the person, pay him triple what he's been, you know, paid at wherever he is and then he just comes over and that's it, he works for me.
So it's like, I got to find something, I got to find something new, that something that doesn't actually exist was a term apparently he used. So apparently he had his staff pull up a list of a thousand candidates, whittled it down to a hundred and he personally interviewed every one of those for an hour, but they were kind of getting desperate because he was getting super irritable.
You know, the guy was very sharp, sharp as they came. So he'd be interviewing these people and then he'd like to tear into them and they'd be in tears and they'd run out of the building and stuff like that. And so finally the one of the guys I was tasked with finding somebody to feed, feed to this manager got me to come in.
I didn't have any PowerPoint slides. I just had a bunch of lab notebooks. I'd never been in an office like that. It's something I like out of a Hollywood movie. And yeah, my brother and I started out of my garage, the whole thing. So, you know, and he just, I said, okay, Mr. So and so forth, we'll be with you.
And the guy comes in in a beautiful three piece suit with a silk tie, magnificent silver hair. And he goes, you have one hour to go. And he just sat down like that. So I spoke for like 50 minutes. He gave me no feedback. Just talk for 50 minutes. And at the end of it, he said, I love it. I'm going to give you a hundred.
Now, normally my old nerdy self would sit there and try to make small talk with him for the next 30 minutes out of excitement. But I'd learned that in business, when you get an answer, it's very unexpected. Exactly. So I stopped and said, thank you very much, Mr. So I just walked out and I was like, how does this work?
What are we going to do?
So it's like, you know, but that's lucky, right? It's just, I found out years later at a Christmas party, I think out of the hundred that he interviewed, he gave money to two because it almost sounds like, like a Y Combinator team type thing. There's like, it's like you have all these things, this giant funnel in the end, you know, but that's luck too.
Right. You know, who knows? I mean, he got up on the right side of the bed. That morning for me, you know, so, you know, and if that and not it happened, I don't know. I don't know if I actually would have raised the money. I'm pretty determined, but it, but it requires stuff like that. You know, when people ask me about their advice in science, because I get that question, I don't recommend it.
And I don't think I ever do. Now, as you know, a lot of people have already made up their mind to go. So you do the best you can to advise them, but they haven't made up their mind. I don't necessarily recommend, you know, it's just like, everybody's just looking at the, you know, people like myself that made it through,
but it's massive.
Yeah. There's, yeah. There's a massive element of luck to that.
Steve Hsu: appreciate your time and uh, wish you guys continued success at Fermat.
Are you, are you bullish on the future of the cap on, on market?
John Seo: Oh, yeah, absolutely. It sounds terrible because it's like, oh, yes, there's going to be lots more, you know, destruction and so on and so forth there. Actually, I will say this simultaneously. It's really a lot of these risks that are very similar to what fire risk was catastrophic fire risk was before.
Ultimately, actually, it's a building code. Issue right? So, you know, back in the 1600s, 1700s, 1800s, they suddenly discovered it's like, holy cow, these cities are flammable. It's like, yeah, no, duh. That's a ruse. Everything's made out of wood. You use candles and stuff like that. The light everything, but it took him like a couple hundred years.
Like, huh? Well, that's crazy. That means that I could be asleep in my building here in a major city and then die in my sleep from a fire that happens 20 blocks down. Right. And if the wind is blowing just in the right direction, so, you know, insurance got involved and eventually just developed all the building codes, basically all the fire codes, which still persists to this day.
Right. to actually fix the problem. And so all of our, you know, so I'm obviously an optimist, you know, all these problems are very fixable from the vulnerability side. But the mean, in the meantime, you've got, you know, 10 trillion. 10 trillion. And physical property that's exposed and right now cap bonds is the most efficient solution for actually dealing with that while that, that, that, that resiliency transition happens.
Steve Hsu: Now that you mentioned that, maybe let me ask you one final question. So are your contracts of sufficient duration that the market you're in actually tells you something about what the market thinks about climate change? Can you, can you see that people are pricing in really significant changes in the next few years Yeah.
John Seo: Yeah. They're, they're, they're pricing is significant. We don't need it to be long dated, you know, so, so the intention of the market and what it's pricing and not pricing is always very clear. You don't need a contract, not, not in a niche market like ourselves. And they price in a tremendous amount of climate change.
I mean, they're basically taking the worst fears. of a hundred years of climate change, worst fears, upper bound, the whole thing there, and then pricing it in as, as the here and now,
you know, so they are, but it doesn't, it's maybe it's a whole nother talk, but I'll just tell you in some, I don't subscribe to those views, the, the, the, the, the empirical science and even the developing science does, does not support that.
But they're not going to, they're not going to listen to me and it doesn't matter. So it's just like, it's, it's, it's bigger, um,
Steve Hsu: mean there's some excess returns to be made there? So it sounds like it sounds like people are pricing in. Really big effects from climate that maybe won't materialize in the short term. Is that
John Seo: Yes, correct. For sure.
Steve Hsu: Yeah, great. Okay. Well, hey, John, I appreciate your time. I know you're super busy.
It was great catching up with you. I hope I will catch up with you before another 15 years go
John Seo: Yeah, please. In person too. It was terrific to see you. You're, you're looking terrific. And I'm gonna look up that T-shirt. What, what, what, what do you have on there? Su, super
Steve Hsu: Oh, this is an A. I. Company. I started building A. I. S. that use large language models, but they're narrowly focused. So they don't hallucinate.
John Seo: Mm-Hmm. ,
yeah. Feel like
Steve Hsu: employ them typically in call center or customer service environments. They can fully replace people in those environments.
John Seo: right now. Very nice. Very nice. Everybody
Steve Hsu: Fun stuff.