Renaissance Technologies

0:00 I always used to misspell Renaissance as I was typing it out at R E N and then I would sort of like not really know what came from there, but I learned a mnemonic. to make sure I get it right. Oh. I thought you were gonna say you've typed it so many times now over the past month. Well there's that too.

0:16 But you ready for this? You can't spell renaissance without AI. Uh Touche, touche. Alright.

0:27 Let's do it. Who got the choice? Is it you, is it you, is it you Who got No. Is it you, is it you, is it you

0:39 Down. Another story on the way Welcome to season fourteen, episode three of Acquired, the podcast about great companies and the stories and playbooks behind them. I'm Ben Gilbert. I'm David Resenthal. And we are your hosts.

0:56 They say, David, that as an investor You can't beat the market. Or time the market. that you're better off indexing and dollar cost averaging rather than trying to be an active stock picker. They say there's no persistence of returns for hedge funds that

1:12 This year's big winner can be next year's big loser. And that nobody gets huge outperformance without taking huge risk. When I was in college, I actually took an economics class with Burton Malkiel. who of course, you know, was involved in starting Vanguard and his uh big proponent of all that and

1:29 That is what I learned, Ben. Well David, it turns out. They were wrong. Today, listeners, we tell the story of the best performing investment firm in history. Renaissance technologies.

1:41 Or Rentek. Their thirty year track record managing billions of dollars has better returns than anyone you have ever heard of. including Berkshire Hathaway, Bridgewater. George Soros, Peter Lynch, or anyone else. So why haven't you heard of them?

1:56 Or if you have, why don't you know much about them? Well their eye popping performance is match only by their extreme secrecy. And they are unusual in almost every way. Their founder, Jim Simons, worked for the US government in the Cold War as a codebreaker before starting Renaissance. None of the founders or early employees had any investing background, and they built the entire thing by hiring PhD physicists, astronomers,

2:21 and speech recognition researchers. They're located in the middle of nowhere in a tiny town on Long Island. They don't pay attention to revenues, profits, or even who the CEOs are of the companies that they invest in. And at any given time, they probably couldn't even tell you what actual stocks they own. Now you may be thinking, Okay, great, I just learned about this insane fund with unbelievable performance.

2:43 And to be specific, listeners, that's sixty six percent annual returns before fees. And you know, well I want to invest. Well You can't. To add to everything else that I just said, RenTech's flagship medallion fund that doesn't take any outside investors.

2:59 The partners of the firm have become so wealthy from the billions that the fund has generated that the only investors they allow in are themselves. Oh we are going to talk about it. A lot About that.

3:11 Towards the end of the episode. 'Cause I think it's kind of the key to the whole thing. Ooh. Cliffhanger David. I'm excited. So what exactly does Renaissance do? Why does it work? And how did it evolve to be the way it is today?

3:25 And while the resources are out there are scarce because for one, employees sign a lifetime non disclosure agreement. David and I are going to take you through everything we've learned about the firm from our research dating all the way back before Jim Simons started as a math professor to understand it all. This episode was selected by our acquired limited partners. And to be honest, I didn't think enough people knew what RenTech was to pick it, but when we put it out for a vote, the people have spoken. So if you want to become a limited partner and pick one episode each season and join the quarterly Zoom calls with us, you can join at acquired.fm slash LP. If you wanna know every time a new episode drops, sign up at acquire.fm slash email. These emails also contain hints at what the next episode will be and follow up facts from previous episodes. For example, we had a listener, Nicholas Cullen, email us this time, who found the actual document with the bylaws of Hermes's controlling family shareholder, H51.

4:23 Which we linked to in this most recent email. Come talk about this episode with us after listening at acquire.fm slash slack. If you want more from David and I, check out ACQ2. Our most recent episode was with Lata Bjerek Newton. who led the team that created the first GLP ones at Novo Nordisk. So awesome follow up to the Novo episode if you liked that one.

4:44 So with that, the show is not investment advice. David and I may have investments in the companies we discuss, or perhaps wish we did. And this show is for informational and entertainment purposes only. David, where do we start our story today? Ah well we start. In nineteen thirty eight.

5:00 In Newton, Massachusetts. Which is a fairly wealthy suburb just outside of Boston. Where one James Simons. Is born. Both of Jim's parents were

5:11 Very, very smart, especially his mother, Marsha. His dad was a salesman for Twentieth Century Fox, the movie company. His job was he went around the theaters in the northeast and Sold. Packages of movies to them. Super cool.

5:26 By the way, we knew all this because we have to think Greg Zuckerman, author of The Man Who Solved the Market. Which is the only book out there that is solely dedicated to Rentec and Jim Simons and We actually got to talk to Greg in our research. He helped us out a bunch. Thank you, Greg. And help fact check a few of our assumptions of what happened after the book came out.

5:44 So That was Jim's parents. But really a major influence on him growing up was his grandfather. Marcia's dad. This already kind of echoes of the Bezos story here with the grandfather, the mother's father, and spending a bunch of time with him and

6:00 rubbing off on young Jeff or young Jim in this case. And Bezos, of course, would get his start. In his career at D saw. A quant fund coming up at the same time as Rentek.

6:12 But Back to Jim here in the nineteen forties. His grandfather Peter Owned a shoe factory that made women's dress shoes. Jim spends a ton of time there growing up.

6:24 At the factory. So Jim's grandfather Peter. was quite the character. He was a Russian immigrant.

6:31 And He's Kinda like still more Russia than Boston at this point in time. As Greg puts it in the book, Peter reveled in telling Jim and his cousins stories of the motherland involving wolves, women, caviar, and vaga. And he teaches young Jim when he's a child here in the factory. to say Russian phras like give me a cigarette And kiss my ass.

6:54 Which I think he probably would say that thousands of times the rest of his life. I think so. His hands are always twitching. Because he has chain smoked his entire life, probably going back to like Age ten in the factory. Three packs of merits a day.

7:10 Unbelievable. Although I think he quit later in life, but he definitely chain smoked the better part of the first call it seventy five years or something. I mean the these famous stories of the conference rooms at Rentec and the war rooms when the market is going through like a crazy gyration and it's just filled with cigarette smoke and it's all gym. Different time. Different time.

7:29 So back to Jim's childhood though here in the Boston suburbs. He grows up. Certainly not uber wealthy or uber rich, but very, very solidly upper middle class. And especially he's an only child. He has all the resources of his parents, his family. His grandfather's this sort of well to do entrepreneur.

7:48 And Jim, you know, he gets to rub shoulders in the Boston area with people who are Really rich. And he says later, I observed that it's very nice to be rich. I had no interest in business.

8:00 Which is not to say I had no interest in money. Yes. Important to tease out the difference between those two things. Yes. Very, very important. And What he means when he says he has no interest in business.

8:12 Pretty young age. He gets really into math. So the legend has it, when Jim is four years old, he stumbles into one of Zeno's famous paradoxes from ancient Greek times. Yep, this is great. The basic gist of Xenos paradoxes if you are always taking a quantity and dividing it by two

8:32 You will never hit zero. You will asymptotically approach zero, but you will never actually touch zero. You need to do addition or subtraction to do that. Division won't cut it. And so, Jim, as a four year old When he observes they need to go to the gas station to fill up the tank. He throws out the idea, Well

8:49 Well let's just use only half the gas in the tank. Because then we'll still be able to after that only use half the gas in the tank. And you know, the funny thing that doesn't occur to a four year old is Well then we're just not gonna get very far. So Jim's dream is to go to MIT.

9:05 down the street in Cambridge and study math. He graduates high school in three years. And during the second semester of Jim's freshman year there. He enrolls in a graduate math seminar on abstract algebra. So pretty, you know, heady stuff.

9:19 Yeah, and Jim would go on to finish his undergrad at MIT in three years and get a master's in one year. Yeah. Pretty pretty smart. But It turns out that that freshman year grad seminar he took.

9:32 actually has a big impact on him because he doesn't do well in the class. He can't keep up. And Jim's pretty self aware here. There are other people at MIT who never run into problems. They never hit a limit. They never struggle understanding any concept.

9:50 And he realizes that Oh. I'm smart. I'm very, very smart. I'm smarter than most other people here. But I'm not one of Those people.

9:59 Right, which is, you know, what do you do with that information? You realize you have to add a few of your skills together to become the best at something. You have to be smart and Something else. Yes. So Jim's own words on this are I was a good mathematician. I wasn't the greatest in the world, but I was pretty good. But he recognizes, like you said, Ben, that he has a different advantage that most of the super geniuses lacked. And that's that as he put it,

10:23 He had good taste. So these are his words. Taste in science is very important. To distinguish what's a good problem. And what's a problem that no one's gonna care about the answer to anyway? That's taste. And I think I have good taste.

10:37 By the way, this is exactly the same thing as Jeff Bezos. in college realizing he wanted to be a theoretical physicist, he met some of the extreme brain power people that would go on to become the best theoretical physicists in the world. And he said I'm smart, but I'm not that smart. And so switch to computer science. I think the

10:56 Analogy here is like sports. There are all star players There are Hall of Famers. And then there's LeBron and MJ. And Jim ends up being a Hall of Famer mathematician.

11:09 But he's not Tom Brady. I mean he's got a pretty important theorem named after him. That goes on to become a foundation of string theory and physics, which isn't even Jim's field. Crazy. So this realization that Jim has about himself, though Both that he's not

11:24 The smartest person in the room at a place like MIT. But he can hang with them. And that he has this. Taste concept. I think becomes one of the most important keys.

11:36 to the secret sauce that ends up getting built at Rentek. Which is the He can relate. to everybody. He understands what's going on. Any person off the street probably couldn't even really have a conversation with these folks.

11:49 But he can. And yet He also has the perspective, maybe some of this is from his grandfather, of what is important out there in the real world. And as a result All of his friends at MIT and these super smart people, they look up to him.

12:03 Because You aren't like The kid in the corner at the high school dancing. You're cool. He's the extroverted theoretical mathematician. Yes. So he was elected class president in high school.

12:16 You know, he smokes cigarettes, he's popular with the ladies. He kinda looks like Humphrey Bogart. He's a popular dude, especially at this point in time. We're now in the late fifties when Jim's at MIT. You know, this is kinda James D and Rebel Without a Cause era.

12:31 Yeah. So after graduation Jim Leads his buddies. on a road trip with motor scooters. You can't make this stuff up. From Boston down to Bogota, where one of his classmates is from.

12:45 The idea is that they're gonna do something so epic that the newspapers are gonna have to write about it. So they all load up on scooters. And drive down to Bogota. They get into all sorts of adventures. There's knives and guns and they get thrown in jail. It's honestly crazy that this group of people took this type of risk.

13:03 Totally crazy. So After he's done at MIT and after the road trip. Jim heads out to Berkeley. In California.

13:11 So that he could do his PhD with the professor. Shing Shen Churn. And much later in life, Jim would collaborate with Churn. For the Chern Simon's theory that we talked about earlier, that becomes one of the foundational parts of string theory in physics.

13:25 But before Jim leaves for the West Coast He meets a girl in Boston. And they decide to get engaged. In four days. I mean this is this is him back then. These were the times.

13:39 And When they get To California. And they get married. Jim takes

13:45 The five thousand dollar wedding gift that I believe they got from her parents. And he decides I wanna multiply this. So he starts driving from Berkeley into San Francisco every morning. to go hang out at the Merrill Lynch brokerage office.

13:59 And just be a rat hanging around the brokerage and find ways to trade and turn this money into something more. Which is so interesting to think about because at that point in time There was such an advantage to just being there. This wasn't even the trading floor, but information is all so manual and all so relationship driven in the markets that there was basically no way to be in on the action unless you were physically in on the action. Exactly.

14:24 Yeah, you couldn't just log into Yahoo Finance or something or open the stocks app on your iPhone. Which even the information they were getting was God knows how long delayed from New York or from Chicago for the futures and commodities that are being traded that Jim gets into. He's as close to the action as he can possibly be, but he's a long, long way from the action. Yep. None the less.

14:45 When he starts out Doing this, Jim. It's a hot streak. And he goes up fifty percent. In a few days.

14:52 Trading is easy. Trading is easy. He says. I was hooked. It was kind of a rush. I bet.

14:58 Except he ends up losing all of his profits just as quickly. Yeah. Important to learn that lesson early. Yes. And also right around this time, Barbara, his wife

15:08 gets pregnant with their first child and is like You can't be driving into San Francisco every morning. at gambling our future like this. Right, effectively playing the ponies. Yeah, exactly. So Tim's like okay, okay

15:21 I'll stop, I'll focus on academia. For now. So he finishes his PhD in two years. They come back to Boston and he joins MIT as a junior professor at age twenty three. So they stay one year in Boston.

15:34 But Jim. Even though he's got a family, even though he's super successful as a young academic here, you know he's got kids. He's restless. So One of his buddies from the scooter trip to Bogota.

15:46 is from Bogota and lives there. His family's there. He has an idea to start a floor tile manufacturing company. 'Cause he's like, you know, the floor it MIT in Emboston. It's so much nicer than a Bogota. We should start a company and Make the same kind of floor. When I read this, I couldn't believe that this was Jim Simons' first

16:05 business venture. Like it's so random, but it really is emblematic of Just how much he was thrill seeking and just looking for anything that was unexpected, different, exciting. He just gets bored fast. Totally. Not just is this the start of his entrepreneurial career.

16:23 the seeds of this financially are what Go on to start RenTech. Wild. Totally wild. So Jim takes a year off.

16:31 It goes down to Bogota. This is a guy with an MIT. Undergrad And masters. And a Berkeley PhD in theoretical math. Who's now a professor at MIT.

16:43 Who is taking a year off to go work on a flooring company in Bogotaw? Yes. Accurate. So he does that for a year, they get it set up, he gets bored again. He's like all right, I don't want to just run this company. I've helped set it up. I have an ownership stake in it now.

16:56 He bounces back. To Boston, this time to Harvard. As a professor there. For a year. He's really racking'em up.

17:04 But He spends a year there and he's like ah Got the itch again and you know, the junior professor's salary isn't that much and Like we said about him back from his childhood days, he sees the appeal in being rich. He's like, This is not a path to being

17:20 So He's like, I'm gonna go put my skill out on the open market. He gets a job. In Princeton, New Jersey, not at Princeton University. But

17:30 Uh. The Institute for Defense Analyses. Which is a non profit organization

17:37 That consults Exclusively. For The U S government. Specifically

17:44 the defense department. And specifically The NSA. These are The civilian

17:50 Code breakers. Yes. It was basically formed with this idea that one Across various branches of our government. We need better collaboration and cross funding of the same initiatives and two there are gonna be a lot of people who don't work for the government that we're gonna wanna hire to do some pretty

18:09 Secret work. Yeah. So the IDA There in Princeton. Kinda.

18:15 functioned like the Institute for Advanced Study, which is also in Princeton. That's where Einstein went when he came to America kind of an independent think tank research group. Except it's solely focused on Code breaking and signal intelligence.

18:30 with the Russians during the Cold War. Yeah. It's a pretty wild charter. And especially how special of an organization it was. Like the way these people would spend their time. Is

18:41 part code breaking, but part kind of goofing around. Because the creativity of mathematicians working together on passion projects is is important. to discovering clever new algorithms. Yes, this is so, so key.

18:55 And this culture. ends up getting translated whole cloth. Right into Rent Tech. So the way IDA worked. And I assume still works to this day.

19:04 Is They recruited Top. Mathematicians and academics. to come be code breakers there.

19:11 They would double their salaries. And importantly, it couldn't have been a government division if they were gonna be doing that, because there's very specific congressionally approved budgets for payroll. Exactly. They figured out that they needed to attract the smartest people in the world who weren't gonna come just go work for the Department of Defense.

19:30 This was the way to do it. So Like you said, Ben. The charter of the group was that employees had to spend fifty percent of their time doing code breaking. But the other fifty percent of the time

19:42 They were free to do whatever they wanted. Like research, pursue whatever they were doing in academia, publish papers. Kinda the appeal of going there was Hey. It's the same thing as being a professor at MIT or Princeton or Harvard or whatever.

19:58 Except You're doing code breaking instead of teaching. And there's No bureaucracy to worry about. There's no politics. It's just like Hey, you do your codebreaking work and then you publish and you can collaborate with your colleagues there.

20:11 Yeah. Now. This is pretty crazy. Very quickly after Jim Arrives at IDA.

20:18 Remember, he's in money making mode at this point in time. He recruits a bunch of his very brilliant colleagues. To come work with him in their fifty percent free time. On an idea.

20:31 To apply the same work and technologies that they're using in Code breaking and signal intelligence. To trading in the stock market.

20:41 So they come together and they publish a paper called Probabilistic Models for and Prediction of Stock market behavior. And Everything that they suggest in this paper. Really?

20:54 Is Rentek. Just Twenty years before Rentec. It's crazy. Nineteen sixty four this was published. Yes.

21:03 Now At this point in time. Fundamental analysis was then, as in most of the world. Today still is. the primary way of investing in things of

21:13 Hey, I know this company, I'm gonna analyze their revenues, their price multiple, or I'm gonna think about what's happening in the currency markets or in the commodity markets and why Copper is moving here or the British pound is moving there and I'm gonna invest on those insights. You're effectively looking at the

21:31 intrinsic value of an asset, trying to assign it a value and make investments based on that. Yes, fundamental investing. There also existed in the sixties. Technical investing. Which

21:45 kind of is voodoo. This is like I'm looking at a stock chart and I've got a feeling. That it's gonna go up. Like I'm tracing this pattern and like it's going up, baby. Or no, no, no, this pattern is going down. Yeah, using the phrase technical might be a little generous. But what they're looking for basically

22:04 trying to mine trading behavior for signal about the way that it will trade in the future, rather than mining the intrinsic information about an asset for what you think it will do in the future. Right. And what Jim and his colleagues here are suggesting is That But just not really done by

22:22 humans. It's That with a lot more data and a lot more sophisticated. Signal processing. And importantly, you might say, Why is it this group of people that came to that conclusion of applying

22:37 computational signal analysis. to investing. Well, it's effectively the same thing as code breaking. You are looking for signal in the noise and trying to use computers and algorithms. to mine signal from something that otherwise kind of looks random. Totally. When Jim started working on code breaking,

22:56 I think he just looked right back to his experience trading in the markets and was like, Whoa this is the same thing. Which is not an insight other people had. That was the amazing thing about his background priming him to realize that. Yes, there's all this noise in this data, and it is impossible for a human to sit here and look at this data and say, Oh, I know what the Soviets are saying. No no, you have to use mathematical models and statistical analysis to extract The patterns.

23:24 So mathematical models, statistical analysis We actually hear a lot of that in the world today. Because machine learning is a thing. Yes. What they are really doing here

23:36 Add IDA and then Soon in Ren Tech. is early machine learning. And Jim just had this Incredibly brilliant insight.

23:46 That you can use these techniques and this technology. For making investments. All right listeners. Now is a great time to talk about a new partner of ours here on Acquired, Lagora. The agentic operating system that is redefining how the world's best legal teams work.

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26:10 And just tell'em that Ben and David sent you. Okay, David, so This paper is published. They're gonna trade and make a whole bunch of money in the stock market by applying this code breaking

26:23 signal processing data analysis approach to investing. Yeah. So then the natural question is Okay, what is the model here? How are they gonna do this? And it turns out that one of the employees of IDA at this time and one of the members of this sort of

26:39 Rebel group, shall we say, within the organization. is a guy named Lenny Baum. And Lenny Just happens to be The world expert.

26:48 in a mathematical concept called a Markov model. Specifically. A version of the Markov model called a hidden Markov model. Now. A Markov model

26:59 is a statistical concept that's used. To model. Pseudo random or chaotic situations. Basically it says. Let's abandon any attempt.

27:10 to actually understand what is going on in all of this data that we have. And instead. Just focus on What are the observable states? That we can see.

27:20 of the situation. Can we identify different states that the situation is in? And if we just do that. Can we predict future states? Based on what we've observed about the patterns of past states.

27:34 And the answer to that is Usually yes. Even if you don't know anything about fundamentally how the system operates. So the great example that Greg Zuckerman gives in the book is Yes, a baseball game. There's three falls and two strikes.

27:48 That state has a narrow set of states after it. It's gonna be a strikeout, they're gonna get on base, it's gonna be a walk. Or maybe they foul it off and it keeps going. There's only really a narrow set of things that could happen after that.

28:02 Whereas when it's zero balls and zero strikes There's a lot that could happen. They could just keep pitching. And if you don't know the rules, you're like, Why do they just keep pitching? And so it's this sort of great way to explain this idea of the black box that If nobody tells you the rules to the game by observing the outputs enough and observing, okay In this state, these outputs are possible, but you actually can kinda get pretty good.

28:27 Yeah. at least if not predicting, understanding the probability distribution of the outcomes for any given state in the game. So We brought up. machine learning and AI a minute ago.

28:39 This is a foundational concept. to modern day AI. If you think about large language models and predict what comes next. It's not like these large language models necessarily

28:50 understand English. They're just really, really good at predicting states. And the next state. i e characters and the next character, or pixels and the next set of pixels or framework.

29:02 Et cetera. And obviously they're much fancier than that, but that is kind of the underpinning of it all. I mean, I remember in my sophomore year of college computer science class, I had a Markov chain assignment, and it was basically write a Java program to ingest this public domain book. And then I would give it a seed word, you know, the first word of each sentence and press return, return, return, return, return. And it would scan through the probability tree and give me the most probable word based on the corpus of the book that it just read to create some sentence. And it feels like magic. And of course, in these early rudimentary Markov chain things like the one I did in college. It kind of spits out nonsense. But

29:39 that would evolve to be the LLMs that we know of today. Yes. Totally. And that is what they were using at IDA to do code breaking. And that's what they propose in this paper.

29:50 that they could use in the stock market too. Exactly. And the way that this applies to investing Is Just like you might not know the rules of baseball, but if you've watched enough baseball, you can kinda guess. at what the probabilities of the next thing to happen are based on the state.

30:08 Investing's kinda the same thing, or at least the stock market movements are, where You don't know the future. You don't know what's gonna happen. You don't know if stock X affects stock Y in some way because you don't know in what way those companies do business together or who holds both stocks. Are they overlapping investors? Like you don't know the relationship between those companies. So you can't forecast with a hundred percent certainty what is going to happen. However, if you suck in enough data about what has happened in the past and the probability distribution from every given state in the past, You probably could make some educated guesses.

30:43 or at least understand the probability of any individual outcome based on a state today of what could happen next. Yes. Exactly. So Jim

30:54 And Lenny And this whole little crew. They're pretty fired up. They're like, Oh, great. Let's go.

31:01 Raise a fund. And Invest in the markets using this strategy. Certainly we're gonna be successful at raising that fund and certainly we're gonna be very profitable because we've got this great idea. Totally. What could go wrong?

31:14 Well In the mid sixties. The idea that some wonky academics At some Random secretive agency in Princeton, New Jersey.

31:24 Could go raise money. was non viable. I mean, it was hard enough for Warren Buffett to raise money at this point in time for his fund. And he was Benjamin Graham's anointed, appointed disciple. And here are these academics who are working at some random unknown non profit.

31:43 Saying Give us money. We don't know anything about these companies that we're gonna invest in. We don't know anything about fundamentals. But we've got a really good algorithm.

31:53 What is an algorithm? So they just have no access to capital. Right. This was decades before It became high pedigree to come from a technical computer science background in the world of investing. Yes.

32:05 So A bunch of kinda Keystone cops style fundraising happens here. They're going around in secret. They're trying to keep the IDA bosses from knowing what they're doing. one of the group ends up leaving a copy of the investment prospectus on the copy machine at work one night and the boss discovers it and calls them all into his office and is like, guys, what are you doing here? Right. It's a little bit of a clown show on the operational side, even if the idea is good. Yes.

32:34 So They end up abandoning the effort. Both because they can't raise money and because IDA has found out about this and they're not too pleased. Shortly after all of this though, Jim ends up moving on anyway. Because

32:47 The Vietnam War starts. And He, as you can imagine from his background, is Not a supporter of the Vietnam War at this point in time. Jim writes an op ed.

32:57 In the New York Times. Denouncing the Vietnam War and say, like, yeah, he's, you know, sort of part of the Defense Department, but like not everybody in the Defense Department is for the war. Which is so naive, thinking you can write an open in the New York freaking times. And that's not gonna create issues for you in your job. Even more than that.

33:17 Amazingly, nobody really paid attention to it, except a reporter at Newsweek Who then comes to interview Jim and ask him some more questions and he just doubles down on this. And when the Newsweek piece comes out That's when the Department of Defense is like All right, you gotta fire this guy.

33:35 Yeah. Jim gets fired. In nineteen sixty seven. Even though he's a star codebreaker he made supposedly huge contributions to the group, which are still classified.

33:45 But at age thirty. With a wife and three kids. He's out on the street. And Even though he's super smart, his colleagues love him, clearly.

33:55 He's now bounced out of MIT. He's bounced out of Harvard. He's gone to This. Seemingly final home for him, great place at IDA. He gets bounced out of there too.

34:07 His job prospects are not great. Yeah. So he takes Pretty much. The only halfway decent paying job that he could get.

34:16 Which is to be the chair. Of the newly Established or maybe reestablished. math department at the state. University of New York Stonybrook.

34:27 Which is the Long Island campus of the State University of New York. This is not Harvard. This is not

34:36 M I D No it is not. But it did have one very important thing going for it, which is why Jim ended up there. And that is that Nelson Rockefeller Who was then the governor of New York.

34:48 had launched A campaign, a hundred million dollar campaign. To try and turn. This long island campus of the State University of New York. into a

34:59 Mathematical powerhouse to become the Berkeley of the East. I sort of thought MIT was the Berkeley of the East already, but Rockefeller is waging a campaign. That he wants to Stony Brook.

35:13 a math and sciences. Powerhouse. And Jim is the key. He wouldn't be able to recruit somebody like Jim. Otherwise, but because he's now kinda tarnished his career.

35:25 Here's the like very talented mathematician that they can convince. to come be chair of the department. Yeah. So

35:32 The basically give Jimmy An unlimited budget. And Leeway to go try and poach Math professors. from departments all over the country and the world.

35:41 And bring them there to Long Island. And Part of how Jim goes and recruits folks is money, like the old hey, I'll double your salary line. But the other part of it too is

35:52 He's given such leeway. And Stony Brook is so different from the politics of an MIT or a Harvard or a Princeton. He says, Hey, come here, I'll pay you more. But even more importantly

36:04 You can just focus on your research. You're not gonna have to deal with committees. You're not gonna have to do all this stuff. There is none of this stuff here. You might have to teach a little bit, but that's not even the point. Rockefeller doesn't want this necessarily become A great teaching institution. He just wants to assemble talent there. Yeah. And amazingly

36:23 It works. Jim starts getting a bunch of great talent. including James Axe, who is a superstar in algebra and number theory from Cornell. And he ends up At Stony Brook.

36:34 Recruiting and Building. One of the best math departments in the world. Amazing. Totally amazing.

36:41 But In true Jim fashion after a couple of years of this. And also his marriage with Barbara falling apart. He starts getting restless again. He decides that he wants to go

36:52 On a sabbatical and go back to Berkeley. And reunite with his old advisor there and go spend some time out on the coast in California. And this is where Turn and Simons end up collaborating and developing the Turn Simons theory. that ends up winning the highest award in geometry from the American Mathematical Society. And really kinda is

37:10 Jim's personal Mark on Mathematics. Yeah. Now also Right around the same time.

37:18 Remember the Colombian flooring company. Yeah. Gets acquired. And Jim and his buddies who are partners in it. Come into a good amount of money.

37:28 And Jim is newly divorced, he's restless in academia. He has back from when he was an IDA about what you could do.

37:39 in the markets if you had capital. He starts trading again. And he gets more. And more into it. Meanwhile, like we said, he's becoming disillusioned again and restless at academia.

37:51 And in nineteen seventy eight. He leaves To focus full time on training. Which is a huge shock to the academic community. Remember, he's assembled this superstar team there at Stonybrook. There's a quote in Greg's book from another

38:03 Mathematician at Cornell. We looked down on him when he did this. Like he had been corrupted and had sold his soul to the devil. Yeah, I mean it was really viewed in the math community as anyone who's going to do investing is throwing away their talent. And it wasn't even that it was common the way that it sort of is today. Right, Jim was the first one. But the idea that you would leave to do anything commercial.

38:26 Doing a disservice to humanity. Yes, exactly. And leaving to do anything. Sure, but leaving to do investing was almost just seen as dirty. Like it's this rich person's game that provides no value to society. Right. Yeah, I don't think it was that

38:41 the rest of the math world was skeptical that it could work. They probably were like, Oh yeah, this could work. But they were like Ew. Academics tend to be much more motivated by prestige than money. So I could totally see this other people being like, Oh, I could do that if I wanted, but I have this higher calling and everyone respects me for this higher calling. And my currency is the papers I publish and the awards that I win, and that's what I want. Yep.

39:04 Now, Stonybrook, we should say too, like it's a very nice place. Yes. But It's in the middle of Long Island on the North Shore. This is not the Hamptons. It's like The Long Island suburbs.

39:15 Yep. The wooded Long Island suburbs. Yes, the wooded Long Island suburbs. Here's Jim in a strip mall next to a pizza joint. Setting up his trading operation that he decides Very cleverly to call monometrics.

39:29 A combination of money And metrics or econometrics. And He recruits. His old IDA buddy.

39:38 Original. Partnering crime. On the trading idea. Lenny Baum. To come.

39:45 And join him. And This time though. They have some capital from the sale of the flooring company. And how much did he make on that flooring sale?

39:54 I think together With Jim, his partners, and whatever money Lenny put in. They had a little less than four million dollars. In this initial capital. In nineteen seventy eight.

40:06 Yep. Now Jim also has another advantage at this point in time. Which is he's right down the street from Stonybrook. And he's just recruit all of these superstar mathematicians. The table has been set.

40:19 Yes. And those Folks are more loyal to Jim than they are to Stonybrook. But they're more loyal right now to academia than they are to finance. This is not a paved pathway until Jim paves this pathway. Yes, in general. But some of them and in particular The superstar James Axe.

40:37 Jim convinces. Join him. in his trading operations. So having boom and axe.

40:45 And Simons, it's like suddenly this extremely credible team in the math world. Yes. Beyond credible. Right. All the theorems that a lot of mathematicians are using every day. Are all named after these three guys who are now at the same firm trading.

41:01 Yes. And it's led by Jim. who's somebody that they respect as an academic. But even more important Is somebody they want to work for.

41:10 And they look up to and they think is cool. And he's out there being like Hey, I think we can make money. Right. Now.

41:17 At this point They're primarily trading currencies. Not stocks. And currencies are Obviously large markets.

41:26 But they aren't. impacted by as many signals and as many factors. As Stocks are. Or really even slightly more complex commodities like

41:35 I don't know, soybeans or whatever. And it seemed to me like a lot of the trading of currencies they were doing was basically based on feelings that they had around how a central bank was acting. Like if the head of state of a certain country was gonna do something or not. It's basically like Betting

41:51 on how one single actor who was in control of currencies at governments would act. So to your point about very few signals impacting price. It's knowing what one person is gonna do. Yes. And this is super important.

42:06 At the end of the day, they build some models there. They're getting Yeah. Early Versions and infrastructure and scaffolding of this quantitative approach set up.

42:17 But in terms of the actual trades they're putting on, they're still doing all of it by hand. And they're still all Really going on. A fundamental type analysis. They'll take some signals from the model, they'll see it's interesting, what they spit out.

42:31 But they're not gonna act on anything unless they can be like Oh yeah, I see. what is going on here. I have a hypothesis. Right. The computers are by no means running loose at this point.

42:43 By no means at all. Yeah, they're just suggesting patterns and ideas. And Jim and Lenny and James, they have to then decide. Hey, are we gonna do this or not? Or are we gonna do something just totally different though? We think is what's gonna happen. Yep.

42:56 And this Actually. Does make sense. Really for two reasons. One.

43:02 Computers and computing power just Wasn't sophisticated enough yet. Two Really build AI

43:11 In a way that's powerful enough that it could work well enough you could really trust it. That's one part. The other part is These folks are mathematicians.

43:21 They're not computer scientists. Right. And they're really, really good at building models. Decoding signals, obviously. But They're much more from this realm of theory.

43:33 And I actually spoke with Howard Morgan, who's gonna come up here in a second, and he made this point to me. He's like In math. There's this concept of traceability. That's a really, really important cultural tenant.

43:45 It's like proving a proof or proving a theorem or something like that. You really need to understand why to get ahead in the field. It's not like you can just say, Oh, hey, the data suggests this. It's like no, you need proof. And that's the world that these guys are coming from. They're like, Oh, we can use data to sort of help us here, but ultimately We wanna have a rock solid theory of what is fundamentally happening here.

44:10 Fascinating. Which is very different than we'll cram a huge amount of data in and then whatever the data suggests, we know it's true'cause the data suggests it. Which is sort of where they would end up many years later, once they had both the hardware you're referring to, sophisticated computers, the clean data that would be required to make all of those incredibly numerous and fast calculations, and also the real computer engineering architecture to build these scale systems to actually act on large amounts of signals and understand them all to come up with results. They just didn't have

44:42 Any of that at the time. So It was hunches and chalkboards. Yes. M. So much so that

44:48 Even Jim is Ringleader here. He's far from convinced that he should put all of his wealth into this thing. He's like, Oh yeah, this is interesting. We're building. We're experimenting, like, great. But I also want to Put my money somewhere else too for some diversification.

45:03 So This is where Howard Morgan comes in. You know, we used to talk about this on old acquired episodes that it uh early days of Silicon Valley, there were only ten people out here and they all knew each other and they were all doing the same thing. This was also the case.

45:17 In East Coast. Finance and technology and early V C in these days. Howard Morgan would go on to be

45:24 One of the co founders of First Round Capital. Which was essentially spun out of Renaissance. Like it was kind of the venture capital work that they were doing at Renaissance that didn't fit With the rest of Renaissance? Yes. So here's how it all went down. And this is so poorly understood out there. Yes. Howard was a computer science and business school professor at the University of Pennsylvania. So he taught C S at Penn.

45:49 And business at Wharton. And He had been involved in bringing Arbanet.

45:57 Early, early Internet pioneer. And so as a result. He was super plugged into Tech.

46:05 And Early startups. And really early, early proto internet stuff. And Jim gets excited about Investing together with Howard.

46:14 So they say like Hey, maybe we should Partner together. And in nineteen eighty two Jim actually winds down monometrics.

46:23 And he and Howard co found a new firm together. That's gonna reflect both of their backgrounds and be a great diversification. Jim and

46:33 His group are gonna bring in the quantitative trading thing. And again trading on Currencies and commodities at this point. And Howard's gonna bring in private company technology investing.

46:46 And they pick. A name. For a firm that is gonna reflect this. Renaissance technologies. It's crazy. And that is why Ren Tech is called RenTech.

46:57 I could not when we figured this out in the research, I could not believe that this is not a more widely understood story that this is the origins of What is today a fantastic venture capital firm, first round capital, but you could not name Two more different strategies. in investing. I mean a

47:16 long term illiquid thing like venture capital highly speculative. versus, you know, we're gonna trade whether we think The French franc is gonna go up or down tomorrow based on the whim of Some government leader. It's unbelievable these were under the same roof.

47:31 Totally. But when you know the whole background in history, it kinda makes sense because This is their personal money. This is Jim and his buddies. And Lenny and James and Howard. There's not institutional capital here. They're not out pitching LPs of like, oh, you should invest in my diversified strategy of currency trading and private technology. Yeah, when they say multi-strategy, this is really multi-strategy. We'll get into what multi-strategy today means later.

47:59 But in these early days of Frentech Fifty percent of the portfolio was venture capital. And fifty percent was Currency trading. And in fact.

48:08 A couple of years after they get started. The currency trading side of the firm. almost blows up when Lenny goes super long on government bonds. And the market goes against him and the whole portfolio drops forty percent. Which is

48:24 Wild. That ends up triggering a clause in Lenny's agreement with Jim. And they sell off. Lenny's entire portfolio. And he leaves the firm.

48:34 This is Crazy. I mean Blow up risk is always an issue in the markets. But this happened to Rentec. And because we quickly got to this point in the story, it would be easy, Well, that's a clause that has a lot of teeth.

48:46 There were many sort of rumbles of something like this potentially happening, Simons going to Lenny and saying hey, maybe we should cut some of our losses and it's okay to trade out of these positions and Lenny was just very dug in on I'm a true believer, and that's how you can get into a situation where you trigger a covenant like this. Totally. And again also shows

49:06 They weren't. doing model based quantitative trading really at this point in time. No, so much gut. So As a result of that For a while Rentec.

49:17 is truly almost entirely a venture capital firm. At one point. On The venture side, just one investment. Franklin Dictionaries. Do you remember, Ben? The Franklin Electronic Dictionaries? Yeah, that was one of their biggest investments.

49:30 That one investment is half of Jim's net worth. What? At this low point for the trading side. Yes. I had no idea. That's crazy.

49:40 Yeah, so In the book Greg talks about Oh, Jim was focused on Venture capital and that's kind of the story out there. It's like Well, he was focused on venture capital'cause that was the only thing working and making money.

49:51 Well I mean it's the only thing where they actually had an edge. From Howard's access to deal flow,'cause they certainly didn't have an edge in the global currency markets. So I think Perhaps. In part because of the trading losses.

50:04 James Axe. Starts to get a little dissolution too. And he tells Jim that he wants to move out to California. with Sandor Strauss, who started working with them at this point. Sandor was another Stony Brook alum that joined them. And the two of them wanna move out to California.

50:19 and do trading out there. Jim says, Sir, fine. I'm here with Howard. I'm doing Venture capital stuff. Why don't you go move out to California? You can start your own firm.

50:31 Which they do. It's called Axcom. A X C O M. And Well

50:37 Contract with Axcom. to run what's left of the trading operations here for Rentec. So it's this interesting arm's length thing. Where Jim strikes a deal where he's gonna own a part of Axcom.

50:49 In exchange for this very favorable contractual relationship where they're gonna hire them to be the manager for this pot of money that Renaissance has raised. But you know, it's technically not Renaissance. It's Axcom. Right. It's another company that is now doing The quantitative training.

51:06 Yep. And I think Jim owned a quarter of it, is that right? Yes, that's right. And importantly. I don't think anyone had any idea what Axcom would become.

51:15 Or how unbelievably Profitable it would be. Uh no. Nobody would have done what they did had They known what was coming. Yes. Wouldn't have spun it out.

51:27 No. So Once Axe and Strauss. Get out to California.

51:34 Strauss. He's kinda on the computing data infrastructure side. That's what he was doing at Stony Brook. And that's what he came into renaissance. To build. He starts getting really into data.

51:45 And he starts collecting Intraday pricing movements. on securities. At this point in time. I think really the best

51:54 Data you could get from providers out there was Maybe open and close. Data on securities pricing. Strauss finds a way to get

52:04 Tick data. Like every twenty minute data. On the securities. Throughout the day. Not only that.

52:12 he's getting historical data that predates what your traditional data providers would give you. And then ingesting it into computers and cleaning the data to get it into the same format as the tick data. So he's getting early nineteen hundreds, even eighteen hundreds stuff. To try to just

52:28 say at some point hopefully we'll be able to make use of this and I want to have this just really, really clean data set about the way that these markets interact. Yeah, I mean he's doing ETL on the data. Yes. I think before anybody knew what ETL was. Again, no one told them to do that. That was just a self motivated, almost like obsession of like, well, if we're gonna have data, it should be well formatted and well understood and labeled and all that. So that's one thing that happens. The other thing is Jim says, Oh, you're going out to California. Let me hook you up.

52:59 with my buddy who's a Berkeley professor out there. Elwyn Berlekamp. And Burley Camp? had studied with

53:08 Folks like John Nash. And Claude Shannon? At MIT. I love that Clon Shannon is coming in again. I know. We talked about it a lot on the Qualcomm episode. Father of information theory, really the center of gravity for attracting tons of talent to MIT and kind of paving the way for what would become phone technology and telecommunications broadly in the future.

53:29 But The fact that Burlicamp is crossing paths at M I T. With Claude Shannon. So cool. So cool.

53:36 And Most importantly for This specific use case, Burlicamp had worked with John Kelly. who developed the Kelly criterion on bet sizing. Which poker players will likely be well familiar with.

53:49 Yep. So with this combination now of Much, much, much better and deeper data from Strauss. And Burley Camp coming in and working with Axe on the models and saying Hey.

54:00 We should be smart about the bet sizing that we're doing in the trades that are coming out of these models versus I don't know what they were doing before. Maybe it was naive of like every trade was the same or Just like we should actually be systematic about this. The model start. Really working. Yep, this is the turning point.

54:19 Yeah. In these kind of mid eighties years, Axcom is generating IRRs of like twenty plus percent. On the trading side. You know, not necessarily gonna beat venture capital IRRs, but

54:32 Liquid. Yes. Reliable. Well That's the thing. They don't know how reliable yet. They know they've done it kind of a few years in a row here. But the question is how uncorrelated to the stock market over a long period of time and how predictable are these returns? Or is it Just super high variance.

54:49 Yes. But the early results Are really good. And Jim and Burley Camp especially are

54:56 Very encouraged by this. So in nineteen eighty eight. Jim and Howard Morgan decide to spin out the venture investments. And Howard goes to manage those. With

55:07 Basically their own money. Fun coda on this. When Howard starts first round a number of years later with Josh Copelman. Jim of course is.

55:17 A large LP. And Howard, of course. remains An investor in Rentec. The

55:26 First Institutional fund. that first round ended up raising. was a fifty X on a hundred and twenty five million dollar fund. It had Roblox. Uber.

55:36 And square. So I believe this is right. I think Jim Made as much money From his investments in first round.

55:44 As Howard did from his L P Steak. No in RenTech. That's wild. Isn't that amazing? Wow, that is a untold story about Jim Simons. I think I read basically every primary source thing on

55:59 Gym or Renaissance on the whole internet. But I assume you got that from Howard. Yeah, it was super fun talking to Howard about this. And just the history of how first round started and early super angel investing and everything that became I also didn't realize that first rounds fund one was a fifty X on a hundred and twenty five million dollar fund.

56:17 First institutional fund. Which I believe they called fun two. I mean Wild. Wild stuff.

56:25 Totally wild. So When Howard spins out the venture activities Jim then decides to set up a new fund.

56:35 As a joint venture between Rentech and Axcom. And they decide to name it. After all of the collective Mathematical awards. that Jim and James and Berlicamp and all these prestigious mathematicians

56:49 Have won in their careers. They name it. The medallion fund. Panana. And listeners, we've arrived. This is the part of the story that matters. The medallion fund is the crown jewel.

57:02 Or you might even say actually the only interesting thing about Renaissance. And Yeah. Is born out of this. observation that oh my God, what they're doing over there at Axcom is really interesting.

57:14 Maybe they shouldn't be doing it all the way over there. Maybe that should be a deeper part of the fold here at RenTech and we shouldn't have let that get away or frankly given up. on the quantitative trading strategies too early. And again. Still just currencies, still just commodities futures, not play in the stock market at all. But the seeds and the ideas, the huge amount of clean data.

57:37 the robust engineering infrastructure to process all that data. the mining of signals from data to figure out what trading strategies to execute. That is really starting to form here. in this new joint venture, this medallion fund. Those ideas. Had all existed before.

57:55 This is the first time that it's all brought together. Yeah. And actually working and operationalized. And frankly, that computers got good enough to actually do it too. That's another big piece of this. Yeah, I don't know that Strauss could have done His Data engineering.

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1:00:00 That Ben and David sent you. So They've got this grand new plan and vision with the medallion fund. Unfortunately Right out of the gate.

1:00:12 The fund. Stumbles a bit. And Axe ends up getting Burned out. Burley Camp though is like no no no no.

1:00:20 This is an anomaly, like we're gonna fix this. I really, really believe. that what we're doing with these models is gonna be extremely profitable. So he Buy's out. Most of Axe's stake.

1:00:32 In the summer of nineteen eighty nine. And He moves the offices up to Berkeley. And there, he He comes up with the idea.

1:00:41 That hey. We should trade. more frequently. A lot more frequently. Because if what we're trying to do is understand the state of the market from the data we have and then predict the future state of the market.

1:00:54 And then combine that with figuring out the right bet sizing to make. We actually want to make a lot more trades. to get a lot more data points and learn a lot more about the bets we're making so that we can then size them up or size them down. It's that And it's two other things.

1:01:10 One is the further into the future you look, the less certain you can be about it. If you know something is worth ten dollars right now. what you know five minutes from now is it's probably gonna be worth about ten dollars. the most likely situation is it's within five percent of that. If you ask me three years from now, I have almost no intuition about that. And

1:01:30 A state machine is the same way. If you flash forward a whole bunch of states, you sort of lose predictability as you sort of continue down that chain. The second thing is If your models are showing that you're gonna be right. call it something like fifty point two five percent of the time. then the amount of money you can make.

1:01:48 Is gated by the number Of bets you can make. At a quarter percent edge. If I walk up to the casino and I think I'm right.

1:01:57 about this particular roulette wheel, which of course you're not. fifty point two five percent of the time and I decide to Play once or play twice or play five times. there's a chance I could lose all my money, or if I have tiny little bet sizes, then I'm just not going to make that much money. But if I walk up to said game with a little bit of edge, And I use small bet sizes and I play ten thousand times.

1:02:17 I'm gonna walk out with a lot of money. There is a great Bob Mercer quote about this later. He says. We're right.

1:02:24 Fifty point seven five percent of the time. And I do think he's making up that number. I think it's illustrative. Right. But We're one hundred percent right, fifty point seven five percent of the time.

1:02:37 You can make billions that way. It's so true. When you have that little edge, it's about making sure that you're not betting so much that a few bets that don't break your way can take you down to zero. And to make sure you can just play the game a lot. A lot, yes. And then back to the Kelly criterion, adjust your bet sizes over time as you're making those bets.

1:02:58 Yeah. Now, of course, this is all great in the abstract if it's that you're literally sitting at a casino and you're somehow perfectly making these bets and you're just sitting right there at the table and then you can walk over to the cashier. It gets a little bit different in the market. For example. There are real transaction costs, especially at this point in history before some of these more uh innovative trading business models with pay for order flow and zero transaction fees and all this stuff, there's real transaction cost to putting on these trades.

1:03:24 And of course, you're gonna move the market when you put on these trades. Yes. This is slippage. There's all sorts of practical consideration. You could get front run by other people. It's not just a computer program that gets executed. You actually have to meet the constraints of the real world when you're deciding instead of a few big bets, we're gonna have a hundred thousand tiny bets. Yes. And as time goes on and the whole quant industry emerges and becomes much more sophisticated.

1:03:50 I think it's particularly the slippage there that becomes the governor on how high velocity you can actually Beyond this. And the slippage is that Once you are at a certain scale, you are gonna move the market with your trades. So the deeper you get into the order book, like

1:04:04 Let's say you want to buy five million dollars of something. maybe your first hundred thousand dollars, you're pretty sure you can get the quoted price. But buy your last hundred thousand dollars of that five million dollar buy. The price might have gotten pretty different already.

1:04:17 Yeah. We're gonna come back to this in just a minute. But this certainly for early Rentek. And then even now still for all of quantitative finance is a really, really, really important thing. Yeah.

1:04:29 And David, in a very crude way, calls back to last episode on Air Mez. The idea that the price would be highest for the family member that is willing to sell now and sort of goes down over time. If

1:04:42 the family was gonna sell to Bernard Arnault, it would behoove you to be first in the order book, not last in the order book. Yes. I feel like there's this metal lesson that I've been learning through acquired and my own personal investing over the past couple of years. Every market is dependent on supply and demand.

1:04:59 You can see quoted valuations and quoted price streams. But oftentimes that's like the mistake of just looking at averages. Exactly. Yes. Looking at the quoted price of an asset. is wrong. You actually should be looking at what is the volume that is willing to buy and what is the volume that is willing to sell. And for all of those buyers and all of those sellers, what are the price at which they are willing to transact? And well, the way that tends to manifest on a stock chart is.

1:05:25 Here's the price of a share right now. But that's not actually what's going on under the surface. It's a whole bunch of buyers and sellers who have different willingness to pay and have different amounts that they're trying to buy or sell. Yes. Now at this point in time when the medallion fund is first starting to work in say late nineteen eighty nine, early nineteen ninety. It's small enough that

1:05:46 This isn't a big consideration yet. Yeah. Right. Medallion was about twenty seven million dollars under management. When Burley Camp bought out Axe.

1:05:55 In nineteen ninety, the first full year after that. The fund gains. Seventy seven point eight percent Gross. Which

1:06:04 After Fees and carry. was fifty five percent net. Now what were the fees and carry? I mean either one of those numbers is shooting the freaking lights out.

1:06:15 Assuming that this is not a crazy high risk strategy that they executed and it'll completely fall apart under different market conditions. Like if this is an actual repeatable strategy that produces the numbers you just said. Unbelievable. World changing. Hell yeah. Let's go. Yes.

1:06:33 And Indeed, it was a hell yeah, let's go situation. So the numbers you quoted me, the gross and the net, sounded quite different. Talk to me about the fees and carry. So Carrie, I've seen different sources of whether it was twenty or twenty five percent in the early days. But the management fee on the fund was five percent. Which is

1:06:51 Crazy. The top venture capital firms in the world charge a three percent. Management fee, and even that is like Everybody holds their nose and is like this is ridiculous. How on earth were these nobodies?

1:07:03 Charging a five percent Management fee. out the gate to their investors. Well. A couple things.

1:07:12 One, their investors were not sophisticated. It was mostly their own money. And their buddies' money. So they set that precedent. They said that president. But two, though.

1:07:20 They actually needed the money. Yes. Because Strauss's infrastructure costs were about eight hundred thousand dollars a year. So they just backed into the management fee based on like, Hey, we need eight hundred thousand dollars a year to run the infrastructure, plus we need some money to, you know Pay folks and whatnot. Like Great.

1:07:38 Five percent management fee. And so the pitch they're making to the investor base is like if you believe that we should be able to massively outperform the market doing quantitative trading. Well, we're gonna need a lot of fees to do that. And so the investors basically took the deal. If they thought about it enough. Okay, so that's the fees on the performance that twenty or twenty five percent. It's just not actually that far above market, if it's above market at all.

1:08:01 What you're seeing is a high fee, normal ish performance fee fund at this point in time. Yes. High management fee, normal ish carrier performance element. Yeah. So At the end of nineteen ninety.

1:08:14 Simons is So jazzed about what's going on. That he tells Burlicamp. Hey. You should move here to Long Island. Let's recentralize

1:08:24 Everything here. I wanna go all in on this. I think with some tweaks we can be up eighty percent after fees next year. Burley Camp is a little more circumspect.

1:08:36 A he wants to stay in Berkeley, he doesn't have any desire to move to Long Island. And B, I couldn't tell how much of this is just he's a little more conservative than Jim. Or how much of this actually might be his Hey whole poker bet sizing thing. He turns to Jim and he says, Well

1:08:53 If you're so optimistic. Why don't you buy me out? So Jim does. At six X

1:09:00 The basis. That Burley Camp had paid Axe a year earlier. On the one hand. Making a six sex in one year sounds great. On the other hand

1:09:09 This is the equivalent of when Don Valentine sold Sequoia's apple steak. before the IPO to lock in a great Game. But miss out. On all the upside.

1:09:22 To come. David. I think we should throw this out so people understand the volume of this. They've generated on the order of sixty billion dollars of performance fees. Four.

1:09:33 the owners of the fund. over their entire lifetime. So On the one hand, six X in a year ain't bad. On the other hand, you owned a giant part of something that has dividended sixty billion dollars in cash out to its owners. Oof.

1:09:49 Yeah. That's just on the parry side. I mean. The owners are The principles. So just like dollars out of the firm, it's probably

1:09:57 Twenty. I would estimate probably uh Hundred and fifty, two hundred billion dollars that have come out of medallion over the last thirty five years. So

1:10:07 Jim. Buys out Burley Camp. He rolls. Everything in the medallion fund. Back into Renteke itself.

1:10:16 Moves everything back to Stony Brook. Strauss moves to Stonybrook. So it's now the Jim Simons show in New York. with Strauss building the engineering systems. And Axe, I think, still had a small stake.

1:10:27 Yes, that's right. And Strauss had a stake as well. So once Jim takes control And moves everything back. He basically Decides.

1:10:37 That he's gonna turn Rent Tech. Into And Even better.

1:10:45 even more idealized version. of IDA And the math department at Stony Brook. He's gonna make this. An academics

1:10:55 Paradise. Where If you are One of the Absolute smartest.

1:11:02 Mathematicians or systems engineers. in the world. This is where You wanna be. So

1:11:10 Of course he starts reading. The Stony Brook department. Itself again. And this is when Henry Laufer Joins.

1:11:17 Full time. Laufer had been. consulting with Medallion in the early days and working with Barla Camp as they're doing bet sizing as they're making more frequent trades. But now Once the whole operation is moved back to Long Island.

1:11:32 Laufers like, Oh, okay, great, I'll come full time. I'm here at Stony Brook anyway. This is way more fun than teaching. And listeners, I imagine this is probably the point where you're starting to get confused and saying there are so many people in this story. I think we're on eight or nine. We just keep introducing more people. And that is the story of Renaissance.

1:11:50 It is not this singular, clean narrative. It is a Very Complex. Reality.

1:11:58 Of A whole bunch of different people that came in and out at different eras. where the firm was trying different things and eventually became phenomenally successful with a very particular approach. But while they were figuring it out along the way, it took a lot of people.

1:12:13 A lot of people and Just a lot of time too. This is twenty five years. This is a quarter century. From The time.

1:12:22 That Baum and Simons write the paper at IDA. Until medallion really starts to work. It takes a long time.

1:12:31 And we haven't even introduced the two people who would become the co CEOs of this company for twenty years. Yes. Well Let's get to that. Yeah.

1:12:42 So Jim moves everything back to Long Island, sets it up as this academic paradise, is recruiting the smartest people in the world. In nineteen ninety one, the next year, The firm does Fifty four point three percent

1:12:55 Gross returns. And thirty nine point four percent. Net returns. After fees. So Not Jim's bogey of eighty percent, but

1:13:03 Still pretty freaking great. And we should say the years of modest performance are behind them from every single year forward they shoot the lights out. From nineteen ninety onward, they never lose money. And on a gross basis, they never even do less than thirty percent.

1:13:22 It's working, it's going, the whole rest of the story. is about hold on. Keep the machine working. And were on the train. The historic run has begun, let's just say. Yep.

1:13:35 So Nineteen ninety two, gross returns are. Forty seven percent. Ninety three. They're fifty four percent.

1:13:43 At the end of nineteen ninety three Simons decides to close the fund and not allow new LPs in. So if you're an existing LP, you can stay in, but they're no longer. open for new inflows. He has so much confidence. in what they're doing. that he thinks they're all gonna make more money

1:14:01 without accepting new capital by just keeping it to the existing investor base. Nineteen ninety four, gross returns are ninety three freaking percent. Medallion. at this point is stacking up cash. It is a

1:14:17 Meetingful fund. It's about two hundred and fifty million dollars. Total at this point in time. Which It's small. But we're talking about nineteen ninety four. With a bunch of outsiders and academics.

1:14:29 That have managed to amass a quarter billion dollars here. People start to pay attention. And the performance fees on this are Seven milliн dollars, thirteen million dollars, fifty two million dollars. the free cash flow flowing to partners here is certainly becoming real too.

1:14:46 Yes. But as they get into that Call it on the order of magnitude of a billion dollar scale. They start bumping into The moving markets problem.

1:14:57 And the slippage. That we were talking about earlier. Yep. And that's sort of in the mid nineties. Yeah. As they're hitting this two hundred and fifty million, half a billion dollar scale. Right. The computer model spits out we should go buy this huge amount of something at this price, they go to do it. They can only buy ten, twenty, thirty percent of the amount they want at that price, and then suddenly the price is very different. Yeah.

1:15:19 Up to this point. The vast majority of what Medallion is doing. is trading currencies and commodities. Not

1:15:28 Equities. 'Cause you might be thinking. Okay. Yeah, I hear you the nineties was a different era, but Half a billion dollar fund doesn't sound that big. How are they moving markets with half a billion dollars? It's not the equity markets. It's because they're in these thinner markets. It's not that

1:15:45 Commodities and futures are small markets, they're large, but they're thin. Compared to equities. There's just not that much volume and you just can't trade That much without slippage becoming a huge issue. And Medallion is now hitting that.

1:15:57 Limit. So Simons decides. The only thing we can do here to expand, which I'm such a believer in what we're doing. We need to expand. is we need to move into equities.

1:16:10 Equities are the holy grail. If we can make this work there. the depth in those markets will let us scale. Way, way, way bigger than we are now. And there's so much more data that About

1:16:23 Equities pricing. that we can feed into our models and the signal processing that we can do and the signals that we can find. Are gonna be even better. Right. There's so many buyers and sellers every day showing up to trade so many different companies at such high velocity. It's almost this honeypot.

1:16:41 for Renaissance's systems. This is sort of their moment. This is what they were built for. And it's kinda funny that they've just been in Kid Glove land the whole time with these thinly traded markets with minimal data. Yes. And this brings us to Peter Brown and Bob Mercer.

1:16:57 And In nineteen ninety three. One of The mathematicians That Jim had recruited to Rentach.

1:17:03 A guy named Nick Patterson. gets especially passionate about going out and recruiting new talent along with Jim. This is I think one of the keys to Rentec and the culture there. People want other smart people to come be there too. Nick sitting there, like this is a joy.

1:17:20 I wanna go find other best people in the world to hang out with. And He had read in the newspaper. That IBM was going through cost cutting.

1:17:30 And was about to do layoffs. And he also knew that that the speech recognition group at IBM Had some. Absolutely.

1:17:38 Fantastic Mathematical talent. And really. What they were doing was again another vector in the early AI machine learning. Research.

1:17:49 Specifically. IBM's deep blue chess project. Had Come out of this group.

1:17:57 And Peter Brown there was the one That actually spearheaded the project. Yeah. And It's interesting that you talk about speech recognition.

1:18:06 As The perfect fit for what they were doing. And you might say, why is that? Well, the actual work that goes into speech recognition, natural language processing is kind of the same signal processing that Renaissance is doing to analyze the market.

1:18:21 It's not just kind of it's exactly the same signal processing. Right. Speech recognition is a hidden Markov process where The computer that's listening to the sounds to try to turn it into language. doesn't actually know English, right? Obviously. But what it does know is When I hear this set of frequencies and tonalities and sounds

1:18:42 There's a limited set of likely things that could come after it. And in Greg's book he greatly points out this perfect example. When I say Apple You might say pie. D.

1:18:52 And so these people who have spent their careers not only doing the math and the theoretical computer science behind speech recognition to help figure out and predict the next words that you have a narrow set of likely words to choose from. So when you're listening to those frequencies, you can say It's probably gonna be one of these three rather than search the entire dictionary for any word that it could be to narrow the processing power. It's not only the theoretical side, but it's also people who have built those systems at IBM, like a real operational computer company. Yes. Uh.

1:19:29 operational scale. And this is what's So important. And why the two of them become Probably the most critical hires in Rentek's history, even including all the great academics that came before them. Because they're good on the math side.

1:19:44 But They have this large systems experience. And Jim. And Nick know that if they're gonna move into equities

1:19:52 because of the volume of data and because of how much more complex that market is. They need More complex. Systems. And the current talent at Rent Tech coming from academia has just never experienced that or built anything like it.

1:20:05 And the world that they're entering is just exploding in complexity and dimensionality. And when I say that, here's what I mean. The data that they are mining. that they're looking for is this intraday tick data between

1:20:19 Every stock trading. So there in this sort of Trying to map the relationship between one stock and every other stock, not just at that moment in time, but every time before it and every time after it. They're also Once they do identify patterns, which this is key, the algorithms identify the patterns. It's not a human with a hunch saying, I think when

1:20:39 oil prices go up, the airline prices are gonna get hit. It's computers doing machine learning to discover the patterns in the data. Then there's the second piece of well What trades do you actually put on To Be profitable.

1:20:53 From the probabilities that you just discovered, all these weights of relationships between all of these different companies. You're not just putting on one trade, you're putting on 10, 100, thousands of simultaneous trades. both to hedge to be able to isolate some particular variable that you're looking for. Again, not you, but a computer is looking for. And you also need to do it. In such a

1:21:15 Specific byte sizes so that you don't move the market. So you're looking for a super multivariate, multi-dimensional. problem both on the data ingestion side and on the how do I actually react to it side. And all of this computation can't take a long time because you must act You know, not In milliseconds, it's not a high frequency trading that's front running the market. That's not actually what they do. A lot of people think it is, but we'll get to that later.

1:21:43 But they do need to act with Reasonable quickness, probably on the order of minutes. So these need to be really efficient computer systems too. Yeah. And the universe of equities is

1:21:54 So much more multidimensional and interrelated. There are only so many currencies in the world, and there are especially only so many currencies that are large enough trading markets that you can operate in. There's not infinite, but thousands and thousands of equities in the world that are deep enough markets that you can operate in. And to some degree they're all correlated with one another.

1:22:17 And Just keep adding layers of complexity here. Keep adding new things to multiply by. Many of these are traded on multiple exchanges. So you might also be looking for pricing disparities on the same Equity on different markets at different points in time. So there's just dimensions upon dimensions of things to analyze, correlate, and act upon. So

1:22:38 Patterson and Simons. go rate IBM. They're like Steve Jobs rating Xerox Park. They bring Peter. And Bob

1:22:48 And one of their programming colleagues, David Magerman. Over from IBM. Into Rent Tech. And they get started on building. The equities model.

1:22:57 But it turns out Hey there. Obviously very successful at that. But the impact that they have and what they build is even bigger than Because

1:23:07 Bob and Peter. Realize that Hey, actually We should just have one model. For everything here.

1:23:17 For currencies. For commodities. For equities. Everything is correlated. Everything is a signal. It's not like the equities.

1:23:26 is wholly independent and Separate. from what's happening in currencies or what's happening in commodities. There are relationships. Everywhere.

1:23:35 We really want just one model. This is like a fantastical undertaking, especially in the Early to mid nineties. Right. But if you can nail it.

1:23:45 It means that you can do interesting things like Hey, we don't have a lot of data on this particular market. But it looks a lot like something we do have data on. So if it's all part of the same model, we can kind of just apply all the learnings from this other thing. onto this brand new thing that we're looking at with little data for the first time.

1:24:05 And because we're putting it all in one model and no one else in the world is we can discover patterns that no one else knows about. It turns out that this was actually the second most important Innovation.

1:24:17 That Bob and Peter bring to Rentec. the actual product and performance of having one model. The most important thing Is that If you have only one model

1:24:28 One infrastructure. Everybody in the firm is working on That same model. You can all collaborate. Oh.

1:24:38 Together. Which is especially important when you have The smartest people in the entire world. All in one building. Before this, there were separate models within Renteke. So insights

1:24:50 And innovations and work that one team was doing on one model. wouldn't get applied or translate over to work that was happening by another team on another model. They did have the cultural element where it was encouraged that you share your learnings, but someone would have to take the time during their lunch break and go learn from you about those and then implement it in their version. There's a lag and it may actually not get implemented.

1:25:13 Yeah. This Is wholly unique and revolutionary. No other

1:25:20 At scale. investment firm Period, and especially Quant firm operates this way today with just one model. They're portfolio managers and teams and multi strategy. People are

1:25:33 culturally competitive with one another, but even if they're not, the work that you're doing on this side of Citadel is not impacting the work that you're doing on that side of Citadel. Right. What Bob and Peter do is they unify Everything at Red Tech. So all the wood is going behind one arrow. Yes. All right listeners.

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1:27:39 So David. The equities machine. Yes, and indeed a machine it is. So Peter and Bob come in in nineteen ninety three. And nineteen ninety four, nineteen ninety five.

1:27:50 They're building this. Rent tech is getting into equities. And yeah, just imagine the comput you were using during nineteen ninety four and nineteen ninety five. It is astonishing the level of computational complexity and coordination and results that they are pulling off. Again, in real time analyzing these markets.

1:28:09 With the technology that was available during those years. Yes. And here's what's amazing. Returns. Go down.

1:28:17 Maybe slightly. Certainly. A bit from the blowout year that nineteen ninety four was. But they're still above thirty percent. Every single year, most years above forty percent. This is unbelievable that they're maintaining this performance.

1:28:31 as they're going into this hugely more complex market. And they're scaling assets under management. So by the end of the nineteen nineties, Medallion has almost two billion dollars in assets under management. While maintaining roughly the same performance by getting into equities.

1:28:49 This is Huge. Yep. And David, if you just kind of look at this and do the math, okay, so ninety four Their AUM was two hundred and seventy six million and they grew ninety three percent.

1:29:02 And then their AUM the next year was four hundred and sixty two million, and then they grew fifty-two percent. And their AUM the next year was six hundred and thirty seven million, you kind of quickly get where I'm going here, which is Oh, they're scaling AUM not by bringing in new investors. Right. It's closed to new investors. It's all just compounding. This is the same capital that they had in nineteen ninety-three that has gone from a hundred and twenty two million at the beginning of that year. to nineteen ninety nine being one point five billion.

1:29:31 Yes. And then In the year two thousand. They just Totally blow the doors off.

1:29:37 A hundred And twenty eight percent. Gross returns. Net returns. After fees.

1:29:46 of ninety eight point five Percent. This is bananas. They grow the fund from one point nine billion to three point eight billion. Again Purely by investing gains, not by getting any new investors.

1:30:02 The year the tech bubble burst. Yes. While the whole rest of the market Is down big time. Medallion.

1:30:10 Is up a hundred and twenty eight percent gross. on the air. And this becomes a theme. High volatility is when medallion really shines. And here you go. uncorrelated. They have their final stamp of approval right here of

1:30:25 Not only are we a money printing machine, We are a money print machine in all environments, regardless of the state of the broad market. And David, as you said. Volatility actually makes their algorithms work even better. Cause what are they doing? They're looking for scenarios where the market's gonna act erratically and they can take advantage of people making decisions that they shouldn't. And any time any investors are under pressure.

1:30:50 there's a little bit of edge that's gonna accrue to a medallion that's saying, Oh, okay, you're fear selling right now. Well, I can determine if you should be fear selling or not. And if I determine that you shouldn't be dumping that asset, I'm buying it from you. So there's a really fun story around this that Really illustrates. Jim's genius. In managing

1:31:10 The firm and the people. And How This year was when they really figured this out. So the first

1:31:17 of the tech bubble bursting. Medallion actually takes A bunch of large losses. But

1:31:25 Part of it might be that the model wasn't tuned right yet because nobody at Rentek had seen this type of behavior in the market before. Part of it might also be too that it didn't perform well for those couple of days. It's a really stressful Time for everybody. You know, everybody's in Jim's office. Jim's smoking his cigarettes. It's a cloud of smoke.

1:31:45 And they're debating what to do. And Jim makes the call to take some risk off. He's worried about blowing up. We're not very far removed at this point from long term capital management. Uh the model may be saying we should stay long here, but let's not blow up the firm.

1:31:59 Yep. After this goes down. Peter Brown comes to Jim and offers to resign. Given. the losses that they incurred over these couple days.

1:32:10 And Jim says. What are you talking about? Of course you shouldn't resign. You are way more valuable to the firm now. That you've lived through this.

1:32:18 And you now know not to one hundred percent trust the model in all situations. It's fascinating. It's such a good insight. That illustrates Jim as a leader right there. It totally does.

1:32:29 There's a parallel story when Jim ultimately does retire and In two thousand nine. And Peter and Bob take over as co CEOs. Where a year or so before the quote unquote quantquake had happened.

1:32:43 Where similar to the tech bubble bursting. There was all of a sudden very large drawdowns among all quantitative firms in the market and Rentech gets hit. And During that period

1:32:55 Peter argued very strenuously that we should Trust the model. Stay a risk on. This is gonna be an incredibly profitable time for us. And Jim. Pumped the brakes and stepped in, intervened, and took a risk off.

1:33:08 And Peter goes. The gym. Again around the CEO transition, it says Hey Jim. Aren't you worried that with me running the place now, I'm gonna be too aggressive and blow it up one of these days?

1:33:19 And Jim says, No, I'm not worried at all. I know you were only so aggressive in that moment because I was there pushing back on you. And when you're in the seat. You're gonna be less aggressive. He's just such a master at insight into human behavior. It is so true, though. I even find this about myself that I will naturally take the position of the foil to the person across from me. So if somebody's being pushy in some way, I'll find myself taking a position where if I pause and reflect, I'm like I don't think I expected to take this position coming into this conversation, but you know, you naturally want to sort of

1:33:53 play the other side to balance out the person sitting across from you. Yeah. So Back to the year two thousand and this incredible performance. Ben, to what you were saying earlier about uncorrelated returns.

1:34:05 Not only did they shoot the lights out that year, They're doing it when the market is down. We got to introduce this concept of a sharp ratio now, which for all of you listeners that are in the finance world, you'll know this, but for everybody else, this is a really important concept. And I think people grasp it intuitively. We've mentioned this concept a couple of times this episode where Okay, great.

1:34:26 It's amazing to have uh fund that twenty five X's. or a year where you have a hundred percent investment return. Or I bought Bitcoin yesterday and it doubled overnight. Does that make you one of the best investors in the world? We all intuitively know. No, it doesn't. Because

1:34:42 Maybe that was a fluke. Maybe. you're taking on an extreme amount of risk. And then the question is always adjusting for the risk that you're taking, can you produce a superior return?

1:34:53 Taking the risk into that account. And so You basically can provide value to investors as a fund manager in two ways. You can outperform the market. Or you can be entirely uncorrelated with the market and get market returns. Or what you can do as Rentek is both.

1:35:08 You can be uncorrelated. and massively outperform, which is effectively the holy grail of money management. Yes. And so the sharp ratio is a measurement. Combining these two concepts. Exactly. So it's named after the economist William F. Sharp. It was pioneered in nineteen sixty six.

1:35:25 It is effectively the measure of a fund's performance relative to the risk free rate. So if you performed At fifteen percent that year and the risk free rate was three percent. then you know, your numerator is gonna be twelve percent. And it is compared against the volatility or the standard deviation is technically what it is, but effectively.

1:35:46 How volatile have you been the last X years? And typically it's looked at as a three year sharp or a five year sharp or a 10 year sharp. The sharp ratio represents the additional amount of return that an investor receives per unit. of an increase in risk. And so David, you're starting to throw out numbers. Low sharp ratios are bad. Negative sharp ratios are worse because that means you're underperforming the risk free rate. High sharp ratios are good because it means that you're producing lots of returns and your variance or your standard deviation or your sort of risk is low.

1:36:18 So in nineteen ninety They had a sharp of two point oh, which was twice that of the S P five hundred benchmark. Awesome. Yep. Good. Nineteen ninety five to two thousand, sharp ratio of two point five. Really starting to hum. Pretty unbelievable.

1:36:33 Good. Where do I sign up to invest? At some point they added foreign markets. and achieved a sharp ratio of six point three. Which is double the best quant firms.

1:36:43 This is a firm that has almost no chance of losing money, at least historically. and massively outperforms the market. On an uncorrelated basis. And I

1:36:54 believe if I have my research right in two thousand four. They actually achieved a sharp ratio of seven point five. Astonishing. You know, again, back to our sports analogy here. These aren't Hall of Fame numbers. These are like I don't know, make Tom Brady look like a third stringer. Yes, exactly.

1:37:13 So On the back of two thousand and this rise. The next year in two thousand one. They raise the The carried interest on the fund.

1:37:22 To thirty six percent. Up from either twenty or twenty five percent, whatever it was before. Now remember, they've already closed the fund. to new investors. So they're still outside investors in the fund. But no new investors are coming in.

1:37:36 And then the next year in two thousand two. They raise the carry to forty four. I mean Great work if you can get it. But for context.

1:37:47 The Sequoyas, the benchmarks out there. They have obscene carry of thirty percent. Forty four is unprecedented. There's two interesting ways to look at this. One, they're just trying to jack it up so high that they just purge their existing investors out. Where they're saying, We're not gonna kick anyone out yet, but We've been close to new business for a long time now. You should see yourself out at some point.

1:38:08 The other way to look at this, which I think is probably the right way to look at it, is Investors Our arbitragers. They see a mispricing, they come into the market, they fix that mispricing. So any time that there's an opportunity to bring the way that a currency is trading on two different exchanges closer together.

1:38:28 investors are serving their purpose. of coming in. Arbitraging that difference, taking a little bit of profit as a thank you, and then sort of fixing the market to make the market a true weighing machine, not a voting machine, but making it so that all prices reflect the value of what something is actually worth. And in some ways.

1:38:46 That's what Renaissance is doing here to themselves or to their investors. They're coming in and saying, look, This is obscene. We so clearly outperformed the market. you're still gonna take this deal, even if we take more of this, because there's just a mispricing here. This product should not be priced at 20, 25% carry. This product should be priced at a much higher carried interest, and you're still gonna love it.

1:39:08 You should pay twenty percent carry for a firm that delivers you fifteen percent annual returns. We're delivering you fifty percent annual returns. Totally. So I have to imagine it didn't go over well with the existing investors, but they just have so much leverage that What's gonna happen.

1:39:24 Okay. Once again. I'm sorry, audience. I have to say hold on one more minute. for another perspective that I have to offer. On the carry. Element.

1:39:34 But I want to finish the story first. Okay, so two thousand one they raised the carry to thirty six percent, two thousand two they raised it to forty four percent. And then in two thousand three They actually say, Hey, we can't incentivise you out of the fund outside investors. We are gonna kick you out. So starting in two thousand three.

1:39:50 Everybody who's an outside investor who's not part of the Rent Tech family, you know current employee or alumni of the firm. It's Kicked out. And not all alumni get to stay. There's select alumni that get grandfather in.

1:40:03 Yes. Now Why did we do this? I'm gonna talk about one reason in a minute. But one reason is super obvious. The medalli is now at five billion dollars in assets under management that they're trading. even in the equities market, they are now hitting up against slippage.

1:40:19 Yeah. And so if they want to maintain This Crazy, crazy performance. They just can't get that much bigger.

1:40:27 This is the problem that Warren Buffett talks about all the time and why he has to basically just increase his position in Apple rather than going and buying the next great family owned business. The things that move the needle for them are so big that that's really all they can do. And when you are big, you're gonna move any market that you enter into. Yeah. And the strategy that RenTech is employing right now, they're just deeming doesn't work at north of five billion dollars. So in two thousand three, they start kicking all the outside investors out of medallion.

1:40:57 But clearly there's still Lots of institutional demand. To invest. With Renaissance. So what do they do?

1:41:05 Well Time to start another fund. So They start the Renaissance Institutional Equiti Fund. And

1:41:13 There's a couple things to add a little bit of context to really why they decide to do this. Well the first one is Sometimes there's just more profitable strategies than they had the capital to take advantage of in medallion. But they weren't sure it would be on a durable basis. If they were sure that They could manage

1:41:30 ten, fifteen, twenty, twenty five billion in medallion. all the time, then they would grow to that. But if just sometimes there's these strategies that appear, well, we don't want to commit to a much higher fund size and then not always have those strategies available. The other thing is that a lot of the times those strategies aren't really what medallion is set up to do. They require longer hold times.

1:41:51 And so There's a little bit of downside to that,'cause these new strategies, the predictive abilities. are less because they have to predict further into the future to understand what the exit prices will be on these longer term holds. But they still figure

1:42:05 Hey, even though it's not quite our bread and butter with the short term stuff, we should be able to make some money doing it. Yeah, there's a fun story around this that Peter Brown tells. Of Jim came into his office one day and said Peter, I got a thought exercise for you.

1:42:21 If you married a Rockefeller Would you advise the family that they should invest a large portion of their wealth in the S P five hundred? And Peter says, No, of course not. That's not a great risk adjusted return. And these guys are very used to sharp ratios that are far better than the S P

1:42:39 Right. And so Jim says. Yes, exactly. Now get to work on designing the product that they should invest in. Right. And so that's basically what they come up with is

1:42:48 Can we create something that's like an S P five hundred with a higher sharp ratio? Can we beat the market by a few percentage points, or frankly, even match the market each year with lower volatility than if they were buying an index fund? And you can see who this would be very attractive to. Pensions, Large institutions Firms that want to compound at market or slightly above market rate, but don't want to risk these massive drawdowns or frankly just big volatility in general, should they need to pull the capital. earlier. And the nice thing about being invested in a hedge fund versus a venture fund is you can do redemptions. Like if you look at the thirteen F's, the SEC documents that the Renaissance Institutional Equities Fund files over time.

1:43:28 It changes every quarter because there's new people putting money in, there's people doing redemption. So it's a pretty good product. Or at least the theory behind it is a pretty good product of a Lower risk similar return thing to the S P five hundred. And the marketing is built in. It's not like there's any lack of demand of outside capital that wants to invest with

1:43:50 Rent Tech. Right. It's really funny. There's really stories about how the marketing documents literally say this is not the medallion fund. We don't promise returns like the medallion fund. In fact, we're not charging for it like the medallion fund. You know, David, you said that the fees and carry on medallion went up to what, five and forty-four. Well, on the institutional fund, the fees are one in ten. You're only taking one percent annual fee and ten percent of the performance. Clearly, this is a very different product. But people did not perceive that. People were very excited. It's a Renaissance product. It's the same analysts. They're using all their fancy computers. I'm sure we're gonna get this crazy outperformance. And At the end of the day, it is an extremely different vehicle.

1:44:26 Yeah. That has not. Performed. Anywhere near how Medallion has performed. Correct.

1:44:33 Has it served its purpose? Yeah. But is it medallion? No. It's not sp special in the way the medallion is special.

1:44:40 Yes. A couple other funny things on the institutional fund. So I spent a bunch of time scrolling through thirteen Fs over the last decade from the medallion filings, and they're all from I think they have two institutional funds. Yeah. There's institutional equities and diversified alpha. So the funniest thing is they file these thirtefs. And David and I are very used to looking at the thirteen Fs of

1:45:02 friends of the show who run hedge funds who we've had on as guests. or perhaps really just any investor where you want to see like, or what are they buying and selling this quarter? And usually you see fifteen, twenty five, maybe fifty different names on there. Well the thirteen F for Renaissance has forty three hundred stocks. In these tiny little chunks.

1:45:21 And There's a little bit of persistence quarter to quarter. For example, weirdly, Novo Nordisk has been one of their biggest holdings, biggest, I say at like one to two percent. That's their biggest position for several quarters in a row. Hey, they've been listening to a choir. That's right.

1:45:35 That's one of the signals in the model. You kinda get the sense From looking at these filings that

1:45:44 These things were flying all over the place. And this was just the moment in time where they decided to take a snapshot and put it on a piece of paper. And even though this is the end of quarter filing of what their ownership was. If you had taken it a day or a week earlier, it could look completely different. Yes. The way that some folks we talked to described the difference between the institutional funds and medallion to us.

1:46:07 is that medallions average hold time for their trades and positions. Is call it like A day. Maybe a day and a half. Whereas the average hold time for the institutional funds positions is like

1:46:21 A couple months. So across the Forty three hundred. Stocks in the portfolio. There's a lot of trading activity that happens on any given day.

1:46:30 But It's a lot. Slower. In any given name. than medallion would be.

1:46:36 Yeah. Which makes sense. Again it Gets back to the slippage concept. If you have a bigger fund and you're investing larger amounts, which the institutional funds are you can't be trading as frequently or all of your gains are gonna slip away. Yeah.

1:46:49 And Frankly, it just looks a lot like the S P five hundred. Like when you look at as of November twenty three, so eleven of the twelve months of the year had happened. They were up eight point six percent. Okay, that sounds like an index type return. You look at the first four months of twenty twenty, right after the crazy dip from the pandemic.

1:47:07 They were down 10.4%. Less than the broader market. But they still were sort of a mirror of the broader market. So I think the R I E F their institutional fund. Yes, it works as expected. No, it's not medallion.

1:47:20 And if it were standing on its own, there's zero chance that we would be covering the organization behind it on acquired. Zero percent chance. Speaking of the fund, that is the reason why we are covering this company on this show. We set up

1:47:35 during the tech bubble crash, the volatility is when medallion really shines. Well There's no more volatile periods than two thousand seven and two thousand eight. Yep. Two thousand seven.

1:47:47 medallion does a hundred and thirty six percent Gross. Two thousand eight. Medallion does a hundred and fifty-two percent gross. Like get out of here. Crazy. This is two thousand eight, while the rest of the financial world is melting down. And so this really does illustrate where they make their money from, who is on the other side of these trades. It's people acting emotionally. They have effectively these really robust models.

1:48:13 that are highly unemotional, that are making these super intricate multi-security bets and they are putting on exactly the right set of trades to achieve the risk and exposure that the system wants them to have. And who is on the other side of those Trades. It's panic sellers. It's dentists. It's hedge funds who don't trust their computer systems and are like, ah crap, we gotta just take risk off, even though it's a negative expected value move for us.

1:48:39 They're basically trading against human nature. And importantly, in this business versus every other business that we cover here on Acquired or most other businesses, this is truly zero sum. It's not like They're here in an industry that's a growth industry and lots of competitors can take different approaches, but the whole pie is growing so much that I don't care if no.

1:48:58 You're fighting over a fixed pie here. I'm trading against someone else. I win, they lose. Yes. Well there's one Slight nuance to that.

1:49:08 But I don't know how much it holds water. And The apologist nuance would be well Warren Buffett could be on the other side of the trade. And Medallion could

1:49:19 Make money on that trade with Warren. over its time horizon of a day and a half. And Warren could make money over his time horizon of, you know Fifty. Super fair. So I think the

1:49:30 Argument. Against that, though Is that medallion sold after a day and a half to somebody else who bought at that lower price. And so somewhere along the chain

1:49:43 That loss is getting offloaded to somebody. the direct counterparty of medallion and the quan industry. Writ large. might not take the loss, but somebody is gonna take the loss along the way. It is, as you say, a zero sum game.

1:49:57 Yeah, but I think the important thing is can you and your adversary both benefit? And I think in this case, you and your counterparty, the person you're trading against, yes, you have two different objective outcomes. Like Can I get a penny over on Warren Buffett by managing to take him on this one trade? Sure, but his strategy is such that that is irrelevant. So After the historic performance during the financial crisis, as I alluded to earlier.

1:50:23 Jim retires at the end of two thousand nine. And Peter and Bob become Co CEOs, co heads of the firm in twenty ten. They Take the

1:50:33 Portfolio size up to ten billion dollars. when they take over. It had been at five for the last few years. Jim's tenure. They take it up to ten. And

1:50:43 Really with No impact. Which I assume means that Rentek was getting better and the models were getting better'cause otherwise they would have gone to ten before. Right.

1:50:52 They gained confidence that They had enough profitable trades they could make that they could raise the capacity without dampening returns. Yes. And perhaps they could have done it earlier and they just didn't have the confidence that it would work at larger size, but

1:51:07 I bet they're very good at knowing how large can our strategy work up to before it starts having diminishing returns. Yeah. And Importantly, during periods of peak volatility, like say

1:51:19 twenty twenty. Medallion continues to shoot the lights out. So from at least the data that we were able to find on Medallion's performance over the past few years. twenty twenty. They were up a hundred and forty nine percent.

1:51:34 Gross. And seventy six. Percent. N So

1:51:39 The magic is still there. And one way to look at it, which may not be the be all and end all, but I think is a good way to compare Jim's era. At medallion versus Peter and Bob's era. During Jim's tenure, medallions

1:51:54 Total aggregate IR. From nineteen eighty eight. When The fund was formed. To two thousand nine when he retired.

1:52:01 was sixty three point five percent gross annual returns. And forty point one percent net annual returns. Which of course did Include.

1:52:12 many periods of lower carry, twenty percent versus the forty four percent. During the Post Jim era. The Peter and Bob era. From twenty ten.

1:52:21 to twenty twenty two was when we were able to get The latest data. IRRs are Seventy seven point three percent gross. And forty point three percent.

1:52:33 Yeah. So better on both fronts. Even with much higher average fees. So Yeah, I think Medallion is doing fine.

1:52:42 It's amazing. And we weren't able to tell there's some sources that report that they've grown from ten billion dollars in the last few years to being comfortable at a fifteen billion dollar fund size. And if so, that just means that they continue to find more profitable strategies within Medallion to keep those same unbelievable returns at larger sizes. Yeah. And

1:53:03 At the end of the day, this is all just insane. So as far as we can tell, Ben, you alluded to this a bit at the beginning of the episode. And as far as anybody else can tell. Medallion has by far

1:53:17 the best investing track record of any single investment vehicle in history. So give me those net numbers. So during the entire lifetime so far of Medallion from nineteen eighty eight

1:53:31 That's thirty four years. The total Net. Annual return number. is forty percent four zero.

1:53:40 Over thirty four years. After fees. It's sixty eight percent before fees. Which equates to total lifetime carry dollars for the whole firm. Of sixty billion dollars, just in carry, by our calculations.

1:53:56 Astonishing. That is a lot. Of money. Also, David Rosenthal, good spreadshe work on this. You have not done a spreadsheet for an episode in a while, so I admire your uh your work on this one. Yeah. I still know how to use Excel. Barely.

1:54:13 It's gonna be a dying art now with Copilot and That's right. Okay, so sixty billion in total carry. So sixty billion in total carry.

1:54:23 Is a lot of money. And Well. Speaking of a lot of money. We do need to mention before we

1:54:30 Finish the story here. That That Rent Tech Money has bought. A lot

1:54:36 Of influence in society. So Bob Mercer, that name may have Sounded familiar to many of you along the way. Bob was the primary funder of Breitbart.

1:54:47 And Cambridge Analytica. And one of the major financial backers of both the twenty sixteen Trump campaign And the Brexit campaign. in Great Britain. Now, lest you think that rent tech dollars are

1:55:00 solely being funnel into one side of the political spectrum. Jim Simons is a major democratic donor. As our Many other folks. Uh Rentek.

1:55:09 Yeah, Henry Laufer and other folks are also huge donors. approximately to the same tune as what Bob Mercer is on the right. Yeah. Tens of millions of dollars, many tens of millions of dollars. on all sides and through many campaign cycles here.

1:55:24 from Rentek employees and alumni. This Did become a flash point for the firm in the wake of the twenty sixteen election. Mercer.

1:55:34 obviously became a uh controversial figure. both externally and internally within the firm. Especially once people realized he was the through line through Breitbart, Cambridge Analytica, the Trump election and Brexit. Yes. Ultimately, Jim asked Bob to step down as co CEO in twenty seventeen, which he did.

1:55:54 But he did remain a scientist at the firm. And a contributor to the models, even though he wasn't leading the organization with Peter from a leadership standpoint any longer. Ultimately. The thing that surprised me the most is how These people all still work together.

1:56:10 Despite having about the most opposite political beliefs you could possibly have. Yeah. Understatement of the century. All being extremely influential and active In those political systems. Yes, Bob Mercer is no longer the CEO of Renaissance Technologies.

1:56:26 Or the co CEO, he still works there. He's still associated. They all still speak highly of each other. It's unexpected. Yeah. I think unexpected is the best way to put it.

1:56:37 Like everything with Renaissance, it works a little bit different than the rest of the world. Yes. Okay. Speaking of Let's transition to analysis.

1:56:47 And I have a fun little Monologue I want to go on, if you will bear with me. Ben I think this qualifies as The Rentek Playbook.

1:56:57 But I really kind of think of it as the Rent Tech Tapestry. And I was inspired by Costco here because we were talking to folks in the research and Everybody said, you know. Rentec.

1:57:08 It just has these puzzle pieces that fit together. On the surface Rentec. Does the same things that Citadel D Shaw.

1:57:19 Two sigma. Jane Street, others, et cetera, do. They Hire the smartest people in the world. And they give them the best data and infrastructure in the world.

1:57:29 To work on And they say Go to town and make profitable trades. Those are very expensive commodities, those two things, the smartest people in the world and the best data and infrastructure.

1:57:41 But they are commodities. Like Citadel can say the exact same things. Just the same as like Walmart and Amazon can say, we too have large scale supplier relationships that we leverage to provide low prices to customers, just like Costco. But it's underneath that where I think the magic lies. There are three very interrelated things that make RenTech unique. Hm.

1:58:03 So number one. They get the smartest people in the world to collaborate. And not Compete.

1:58:10 Pretty much. Every other financial firm. out there. employees and teams within the firm

1:58:18 quasi compete with one another. Yeah. I mean typically in kind of a friendly way, but yeah. Let's take like in a venture firm You've got your lead partner on a deal or a deal team.

1:58:30 They're working that deal. And Maybe some of the other partners help a little bit, but mostly they're off prosecuting their own deals. Yep. And I think that's the most collegial way that this happens in finance. Yeah. Then you've got multi strategy hedge funds out there where literally firms are being pitted against one another to be weighted in the ultimate trading model.

1:58:50 For the firm. Yep. At Rent Tech though. Because of the one model architecture. Everyone works together on the same

1:59:00 investment. Strategy. And the same investment infrastructure. That means Everyone sees everybody else's work.

1:59:07 Everybody who works at Rentec on the research team, on the infrastructure team. They have access to the whole model. That's not true anywhere else. Yeah, that's a good point. The whole code base is completely visible.

1:59:20 And that also means because it's Just one model. Just one strategy. When somebody else improves That model's performance.

1:59:30 That directly impacts you. As much as it impacts them. This is really different than any other hedge fund out there. So why is that different than if I roll some of my compensation into a multi strategy hedge fund that I work at? Don't I love other teams?

1:59:44 Creating high performance also. Sure, but you don't love it as much as Your team. Because either compensation or career wise You are much more dependent on

1:59:54 your performance than you are. Other people's performance. Oh yes. This is a big thing. you intend to have a job after that job at most places most of the time. So you care about credit.

2:00:05 And you care about smashing the pinata and then going elsewhere, or building reputation and then going elsewhere. Most of the people at RenTech are not gonna have another job. What did you find on LinkedIn at least the median tenure of employees is like sixteen years? Yeah, I just got LinkedIn premium and you can see median tenure. And it's crazy. There's only like three, four hundred employees at Renaissance. And the median tenure, at least as reported by LinkedIn, is like fourteen years.

2:00:31 Yes. Okay. This brings me to point number two. Which he said. An absurdly Small team.

2:00:38 There are less than four hundred employees that work at Rent Tech. Only half of which work in research and engineering. And the other half are either back office or institutional sales for the open funds. I don't know, a hundred and fifty, two hundred people max who are like

2:00:55 Hands on the wheel here for Medallion. Yep. Every other peer firm of Rent, you know.

2:01:02 Citadel. D Shaw, two Sigma, et cetera, all of them. You lump Jane Street, you know, jump the high frequency guys in here. minimum. Two to five thousand people work at those places.

2:01:13 Wow, I didn't realize it was that big. It is an order of magnitude, more people. who are working at the other firms versus who are working. At Rentek. Unless you think that it's like a capital based thing.

2:01:25 No, the institutional funds have gotten big. They peaked at over a hundred billion, but they're currently between sixty and seventy billion that they manage on top of the ten or fifteen that's in the Medallion Fund? Yeah, so A U M is like the same. Yep. As these big funds. This has

2:01:41 All sorts of benefits. Number one, there's like the Hermes Atelier Workshop benefit. Everyone knows each other by name. You know your colleagues' kids, you know your colleagues' families. Yep. They put right on their website there are 90 PhDs in mathematics, physics, computer science, and related fields. The about page has these 10 kind of random bullet points, and that's one of them.

2:02:01 Yes. Then there's the related aspect to all this. The firm is In the middle of nowhere on Long Island. You actually know your colleagues' families and kids because

2:02:11 You're not going out and getting drinks with Someone from Two Sigma in New York City. You're not comparing notes or measuring parts of your anatomy with someone else. You're like hanging out at the swimming pool. Totally. And since Renaissance doesn't recruit from finance jobs. It's kind of unlikely that you know someone else in finance.

2:02:30 You came out of a science related field. You now work in East to talk it. Long Island, which has it's like ten thousand people or something or less that live there. So you're in this little town You're not actually going into the city that often and if you are, it's Again, not to grab drinks with other finance people. So

2:02:46 Even if you didn't have a many page non compete. and a lifetime NDA. you're very unlikely to be in the social circles. You're just not getting exposed. Exactly.

2:02:59 And Rentec's hiring established scientists and PhDs. They're not hiring kids out of undergrad like Jane Street or Bridgewater is. My sense is that the place is like a college campus without any students. Have you seen the pictures online?

2:03:14 Yeah. If you look up Renaissance technologies at Google and you go and look at the photos on campus, it's Little courtyard and Winding walking path and Woods all around it.

2:03:24 Tennis courts. Yeah. So then there's the Last piece of the small team element, which is just the magnitude of the financial

2:03:33 Impact. Which I don't think is true. But let's say that there were another quant fund that made the same number of dollars of performance returns that Rentek does. At Rentek, you're splitting that a couple hundred ways.

2:03:46 At Citadel, you're splitting that five thousand ways. It just doesn't make sense to go. Anywhere else. We were chatting with someone to prep for this episode and they told us You can't ever compete with them, but they'll pay you enough that you won't want to. Yes.

2:04:00 Okay. So this brings me to What I've been kinda teasing that I'm super excited about. I think The third puzzle piece.

2:04:09 of what makes Rent Tech so unique and defensible. Is medallions structure. itself. That it is A LP G fund.

2:04:21 with five percent management fee. And forty four percent carry. So it's not like a prop shop or like proprietary it's just one pot of money. It's literally a GPLP, even though the GPs and the LPs are the same people. So here's my thinking on this. Now I don't know How it is actually structured, but

2:04:40 There was something about this whole crazy forty-four percent carry that just wasn't sitting with me right throughout the research. Cause I kept asking myself, Why? Right. They've already kicked out most of the LPs, if not all. So why are they raising the carry? Right. It's all themselves. It's all insiders. Why do they charge themselves forty four percent carry and five percent management fees?

2:05:02 I think Jim talks about this, though Oh, I pay the fees just like everybody else. Yes. It's always a funny argument. It's like who are you paying the fees to? Right. So I was like, what is happening here? So

2:05:13 Okay, here's my hypothesis. This is not about having crazy performance fees. This is not about having the highest carry in the industry. This

2:05:24 is a value transfer mechanism. Within the firm. From the tenure base. to the current people who are working on Medallion in any given year. So here's how I think it works.

2:05:37 When people Come into Rentek. They obviously have way less wealth. Than the people who've been there. For a long time.

2:05:45 Both from the direct returns that you're getting every year from working there. And just your investment percentage of The medallion fund. Which by the way I think they took

2:05:56 It was either the state of New York or the federal government to court to be able to have the four oh one K plan. At Ren Tech. be the medallion five. No way. Yeah. So like if you work there, you're four oh one K

2:06:09 Is the medallion fund. That's crazy. So it really doesn't take more than a few years before you're set for life. Totally. I mean it depending on your definition of set for life, I think it happens very, very quickly. Yeah. Okay. So Given that though. How do you avoid the incentive

2:06:25 for a group of talented younger folks to split off and go start their own Medallion fund. Right. Especially when they all have access to the whole code base. The whole thing is meant to function like a university math department where everyone's constantly knowledge sharing because we're gonna create better peer reviewed research when we all share all the knowledge all the time. You would think That's a super risky thing to give everyone all the keys.

2:06:50 Right. So I think it's the forty four percent carry structure that does it. Cause basically what you're saying is Every year Five percent management fee, so five percent off the top, and then forty four percent of performance.

2:07:04 So let's say Medallion is on the order of Call it doubling every year. Let's round that up and just add'em and say. Forty nine percent. of the economic returns in any given year.

2:07:16 go to the current team. And fifty one percent of the economic returns. Go to the tenure base. I was like, what is the equivalent here? I think it's kinda like uh academic tenure kind of thing. The longer tenure you are at the firm, The more your balance shifts.

2:07:33 to the LP side of things. Interesting. And the younger you are at the firm, The more your balance is on the GP side of things, but at the end of the day, it's fifty one forty nine. So there's this very natural value transfer mechanism to keep the people that are working in any given year

2:07:51 Super incentivized. And As you stay there longer, You Are paying your younger colleagues.

2:07:58 To work for you. Right. funny. I think it's a good insight that it's structured like a university department tenure. Well, I just kept asking myself why? Why? Why do they have this if there's no outside LPs? And this was The best thing I could come up with.

2:08:15 And I actually think it's kinda genius. Yeah, it's more elegant than It's all one person's money and they're deciding to bonus out the current team every year and just give them enough money to make sure you retain'em. Right, which is how I think most prop shops work. Like Jane Street is mostly a prop shop. I think it is mostly the principal's money. But that's a static situation. It's not like

2:08:36 You know, if that were true then Jim would just own this thing forever. And I don't think that's true here at Rentec. Yeah, so essentially, David, the real magic is they've got one fund. It's evergreen.

2:08:48 And When you start at the firm you're only getting sort of paid the carry amount, but over time You become a meaningful investor in the firm, and you sort of shift to that 51%, you're kind of the LP.

2:09:01 And then over time you eventually graduate out entirely and you're only an LP. And so you're right, it's a value transfer mechanism from The old guard to the new guard in a way that is clear, well understood, probably tax advantaged versus just doing I'm the owner and I'm giving everyone arbitrary bonuses. Yep. And at the end of the day.

2:09:20 I think these three pieces. to me are the core of this sort of tapestry of Frentec. One model. That everybody collaborates on together. A super small team.

2:09:31 Where we all know each other and the financial impact. That Any of us make to that one model is great to all of us. And three. this L P GP model with very high

2:09:44 carry performance fees. That creates the right set of incentives both for new talent on the way in and old talent on the way out. Yep. I think that's right. All right listeners.

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2:10:59 But these are Objectively fascinating historical events. that are totally worth knowing about. And the first one is called basket options. So

2:11:08 The year is two thousand and two. Rent Tech has thirteen years of knowing that They basically have a machine that prints money. So what should you do when you have a machine that prints money? Leverage.

2:11:21 Now, there are all sorts of restrictions around firms like this and how much leverage they can take on. You can't just go and say, I'm gonna borrow, you know, a hundred dollars for every dollar of equity capital that I have in here. So you need to sort of get clever to borrow a whole bunch of money. from banks or from any lender to basically juice your returns. If Again, you have a money printing machine that's reliable.

2:11:44 Most people don't. Most people probably shouldn't take leverage because they're just as likely to blow the whole thing up as they are to be successful. So Basket options. I am gonna read directly from the man who solved the market because Greg Zuckerman just put it perfectly.

2:11:58 Basket options are financial instruments whose values are pegged to the performance of a specific basket of stocks. While most options are based on an individual stock or a financial instrument. Basket options are linked to a group of shares. If these underlying stocks rise, the value of the option goes up.

2:12:15 It's like owning the shares without actually doing so. Indeed, the banks, who of course loaned the money, who put the money in the basket option, were legal owners of the shares in the basket. But for all intents and purposes, they were medallions property. So this is very clever. Medallion saying, Well, the way we're gonna lever up is there's a basket. We have an option to purchase that basket. Most of the capital in that basket is actually the bank's capital, but the bank has hired us to trade the options in the basket and then After a year when long term capital gains tax kicks in

2:12:47 We have the option to buy that basket. So anyway. All day medallions computers sent automated instructions to the banks, sometimes in order a minute or even a second. The options gave medallion the ability to borrow significantly more than it otherwise would be allowed to. Competitors generally had about seven dollars of financial instruments for every dollar of cash.

2:13:07 By contrast, Medallion's option strategy allowed it to have$12.50 worth of financial instruments for every dollar of cash, making it easier to trounce rivals assuming they could keep finding profitable trades. When medallion spied an especially juicy opportunity, it could boost leverage, holding close to twenty dollars of asset for every dollar of cash. In two thousand and two, medallion managed over five billion, but it controlled over sixty billion dollars of investment positions. David, this exposes something we haven't shared yet on the episode, which is It's not just that they could find five billion dollars worth of profitable trades.

2:13:39 It's that they wanted to lever the crap out of five billion dollars and find sixty billion dollars of profitable trades to make, and basket options gave them a legal way. to have an incredible amount of leverage in a way that they felt safe about. Yeah, the Unlevered returns if you were Running this strategy would be much lower.

2:14:00 Yeah. So a big piece of this playbook that we didn't talk about is leverage, but every quant fund does leverage. And so Renaissance was just more clever than everyone else. Yeah. It's an important point though. Nine out of every ten companies that we cover on acquired.

2:14:14 leverage is zero part of the story. Right. And for us coming from the world we come from in tech and venture capital Leverage is like a dirty word. Like I'm scared of it. Right. I mean you could imagine. Let's say it wasn't they were right 50.25% of the time, but they were right fifty point oh oh oh one percent of the time. They would need to do a ton of trades in order to generate enough profits. So that's why you need, you know, 60 billion dollars of cash.

2:14:39 actually execute the strategy to produce the returns that they were looking for. Yeah. on five billion dollars of equity. Anyway, there's a second chapter to this, which is

2:14:48 It's all well and good that this is how they get a bunch of leverage. That's one piece of it. The other piece is they thought this was a remarkably tax efficient vehicle. The way that they were filing their taxes said. Oh. Sure, there's stuff in that basket, but the thing that we actually own is an option to buy that basket or sell that basket. And we only exercise that.

2:15:07 Once every thirteen months or so. I don't know the exact number, but something like that, over a year. And so therefore We're buying something, we're holding it for a year, we're selling it. Oh, of course there's millions and millions of trades going on inside the basket, but we don't own that basket. The banks do. We're just advising them. You can kind of see the logic here. Over time, eventually in twenty twenty one, the IRS said No, you made all those trades. That was not a completely separate entity.

2:15:33 And so you guys owed six point eight billion dollars in taxes that you didn't pay. You're gonna need to pay that. with interest, with penalties. And by the way, Jim Simons, we're gonna want you and the other few partners to really bear the load of that. And they did. So for Simons alone, he paid six hundred and seventy million dollars to the IRS in back taxes for this basket option strategy that turned out not to be a long term capital gain.

2:15:58 Yeah. All right, some numbers on the business today, and then we will dive into power and playbook. So today we've talked about medallion 10 or 15 billion, depending on who you ask. Historically it was more like five or 10 billion. The institutional fund. is about sixty to seventy billion and at one point was a hundred billion. the total carry generated, David, you said is$60 billion. Forbes estimates that Jim Simons alone

2:16:23 is worth about thirty billion dollars today, which kind of pencils with a bunch of other stats over the years that he owned about half of Renaissance. The returns. Obviously the medallion fund generated approximately sixty-six percent annualized from nineteen eighty eight to twenty twenty after those fees. was about thirty nine percent.

2:16:42 Wild. So an interesting thing to understand I ran a hypothetical scenario. of how much money do you think Renaissance the business makes a year in revenue.

2:16:53 And so the institutional fund, let's call it ten percent on sixty billion of assets. So that's six hundred million from fees and six hundred million from performance. So one point two billion a year in revenue to the firm. From the institutional side of the business.

2:17:09 'Cause I always ask myself the question, does that actually matter? They did all this work to stand up the institutional side, who cares? Well Let's say Medallion does their average sixty six percent. gross on fifteen billion.

2:17:20 That is seven hundred and fifty million in fees. And four point three billion on performance. So A total of five billion from medallion and one point two billion from the institutional side of the business. Now, of course, the employees are the investors in medallion, so um you could just argue it's actually silly to cut them up, but I don't know, it's a seven, eight, nine billion dollar revenue business. Right,'cause that's not including the L P return on medallion. A hundred percent. It's not. Which again, as we spent a long time talking about, it's all the same thing. Yes.

2:17:53 But It's kind of interesting just to compare it against other companies to have this in the back of your head. This is a seven, eight billion dollar a year revenue business. Now I think there are a lot of expenses on the infrastructure side. Totally. That was another thing I wanted to talk about. The fact that they do let's say medallion alone. So they have seven hundred and fifty million dollars in fees. I don't think they come close to seven hundred and fifty million dollars a year in expenses, but

2:18:16 They are running. Who knows what infrastructure, some kind of supercomputing cluster. What does it cost to run one Amazon data center? I mean it's I think much smaller scale. I don't know. I mean

2:18:28 You're talking about a lot of data here. Yeah, it says right on their website, they have 50,000 computer cores with 150 gigabits per second of global connectivity. And a research database that grows by more than forty terabytes a day. That's a lot of data. Right.

2:18:44 Is that seven hundred and fifty million a year? I don't know, but it's not zero. I don't think so. They're certainly not losing money on the fees, but There are actual hard costs to this business. Right.

2:18:57 I wonder too if the fee element of medallion. Basically pays the base salaries for the current team. That feels like it's right.

2:19:08 If you're a someone who has done a data center build out before. Or has any way to sort of back into what the costs of medallions operating expenses are on the compute and data and network side.

2:19:20 We would love to hear from you. Hello at acquire.fm. Okay. Power. Power. This is a fun one. Yeah. So listeners who are new to the show, this is Hamilton Helmer's framework from the book Seven Powers. What is it that enables a business to achieve persistent differential returns to be more profitable than their closest competitor on a sustainable basis? And the seven are.

2:19:43 Counter positioning, scale economies, switching costs. network economies. Process power. Branding. And cornered.

2:19:51 Resource. And David My question to you to open this section is Specifically about Rentec's lifelong non competes. That feels like a big reason that they maintain their competitive advantage.

2:20:05 And I'm curious if you agree with that, what would you put that under? Well, I think it's lifelong N DAs. And non competes as long as the state of New York legally allows for. But that's not lifetime. I've heard various figures, six years, five years, something like that. Yep. I mean, at the end of the day, non competes are more like What is one side willing to go to court over? Right. But the reality is

2:20:30 People don't. Leave. People don't leave, period. And people especially don't leave and start their own firms. Yeah.

2:20:36 I was thinking about this. In the middle of the night. And I think there's three layers to The Effective non compete.

2:20:46 That happens. with Rentec. there's the legal layer, the base layer that you're talking about. It's like the agreements you sign. Then there's the economic layer. Of

2:20:56 What we spent a long time talking about in tapestry of It would just be dumb to leave. You are better off staying there as part of that team with a smaller number of people than going to Sigma with a lot more people. Yeah. I think that's the next level of and then I think the highest level is just probably the social layer.

2:21:13 You're there with the smartest people in the world in a collegial atmosphere. Where you're all working hard on something that has direct impact on you. Right, it's your community. It's your community, totally. You're not in New York City. You're not in the Hamptons.

2:21:26 You're not in Silicon Valley. You are selecting into that. And I think if that's what you want the like What better place in the world? All right, so classify it.

2:21:37 What power does that fall under? Well I mean I think the people specifically you would put into cornered resource, but I'm not actually sure that fulgtures it here. I was thinking more process power.

2:21:49 'Cause I think it is the combination of the people And the model. And the incentive structures. Yeah. I think that's right. I also had my biggest one being process power. You actually can develop intricate knowledge of how

2:22:04 a system works and then build processes around that that are hard to replicate elsewhere. I think these systems have been layered over time also, where anyone who's come into the firm in the last five years doesn't know how it works start to finish. I didn't ask anyone to verify that, but it's Over ten million lines of code. And

2:22:24 the level of complexity of the system of when it's putting on trades, what trades it's putting on, why, the speed at which they need to happen. I actually don't think Anyone holds the whole model in their head. And so I think

2:22:39 There's process power just because It's thirty plus years of complexity that's been built up. Yeah. I totally agree with that. Particularly in the

2:22:48 model itself. I mean, maybe you could argue the model is a cornered resource. I am going to argue that the data is a cornered resource. I don't know for sure about the model. Maybe. I mean, I guess that's the same thing as saying the knowledge of what the 10 million lines of code does. That's the model. But I actually think the fact that they have clean data and they've been creating systems. Like they have the best PhDs in the world.

2:23:12 Thinking about data cleaning. That's not a sexy job. And yet they have probably the treasure trove of historical market data in the best format. that nobody else has. That's an actual cornered resource.

2:23:27 I have a couple of nuances on this. So One, I think it probably is true that they have better data than any other firm, thanks to Sandor Strauss. And the work that he started doing. in the eighties before anybody else was really doing this. Yep. So they have that and other firms don't. That said

2:23:46 Certainly all the other quant firms. Are throwing untold resources at all this too. Right. They want to do this. And money's not the issue. So

2:23:56 In chatting with a few folks About this episode. I had more than one person say to me There's two ways. That

2:24:05 Rantec. Could work. And one version of how it works is they discovered something twenty plus years ago.

2:24:14 That is a timeless secret and they've been trading on that. For twenty plus years. Right. There's one particular relationship between types of equities that they've just been exploiting and no one can figure out except them. Right. And that may entirely be possible. Isn't that crazy?

2:24:28 Right. Now Rentek will say, they will all say that is a a hundred percent not the way that it works. It's not that at all. If that were the way that it works, they would of course still say that'cause they don't want anybody to know. Right. Don't look at the relationship between soybean futures. And GM. Just don't do it.

2:24:44 Right. So let's accept that there is a possibility that that might be true. More likely though is that what Rentek does say Is true, which is No, but

2:24:55 There is no holy grail. what we do here is we completely reinvent the whole system. continuously on a two year cycle. Two years is kinda what I heard. The the model is fully

2:25:07 restructured. every two years. It's not like on a date every two years. It's being restructured every day. But collectively it's about a two year cycle. So that would be an argument then that the people actually could of five people left, they probably could go recreate it. And all they would need is the data. It's also an argument that there is no actual cornered resource here in terms of either the model itself and maybe not the data either.

2:25:30 I bet the data is though. Let's say you've been working there for 10 years. You don't know how the nineteen fifty five soybean futures data ended up in the database. even if you're used to using that data and you're able to go recreate the model elsewhere, you don't know how it originally found its way. Yeah, no. I think that's fair.

2:25:49 I think there might also be some argument to the data that That older data is helpful, but its value decays over time as markets evolve. Definitely. The broader point I wanna make here is just that. Every other major quamp firm out there is also spending hundreds of millions, if not billions, on this stuff too.

2:26:06 And people are looking for alt data everywhere. the bridgewaters of the world are paying gobs of money for things that you would never dream could possibly have an effect on the stock market and yet They're paying millions or tens of millions or hundreds of millions of dollars for it. Yep.

2:26:20 So I think we can rule out scale economies for sure. If anything, there are anti scale economies here. Oh. Yes, there's totally there's diseconomies of scale.

2:26:30 Your strategies stop working when you get too much AUM. Yeah. You get slippage. I don't think there's any network economies here. I mean They literally don't talk to anybody.

2:26:41 Although well They do have some very well established relationships with electronic brokerages and different players in the trade execution chain. I think they have very good trade execution and very fast. market data. Their ability to pull data out of the market is very high quality. Do you think it's actually better than their competitors, though? I don't know. That's probably not the secret sauce. Yeah, I don't think so.

2:27:05 It's the table stakes. Switching costs I don't think apply. Branding maybe applies in their ability to raise money for the institutional funds, but That's not a big part of the business. The fee stream on the institutional fund may entirely belong to branding. Yes.

2:27:19 But I think there's a lot of public equity firms and a lot of hedge funds that have a lot of branding power that have On average market returns with decent sharp ratios. And are able to raise because they've built a brand. Yep. Venture firms the same way.

2:27:33 Totally. So for me this kinda leaves counter positioning. I actually think there's some counter positioning here, and I think we're gonna have two episodes in a row. Of counter positioning at scale. Tell me about your counter positioning. Who is being counter positioned in in what way?

2:27:48 They're direct competitors in the market, the other quant firms. And when I say direct competitors, I obviously don't mean for LP dollars. I mean for like the same type of trading activity. Like their counterparties in trades. I don't think they are counterparties. I think they are All seeking to exploit.

2:28:04 similar types of trades. I think the counterparties are the people there, the dentists that they're taking advantage of. Well, but quant funds are often counterparties to each other. That's true. But I think yes, adversaries in finding the similar types of Trades. And I think the counter positioning

2:28:20 For Rent Tech. Or for medallion specifically. is one, I do think the single model approach. versus the multi model, multi-strategy approach that most others have. Does have benefits like I was talking about in the tapestries.

2:28:35 But I think also in maybe bigger Is Every incentive at Rent Tech is fully aligned. to optimize Fun size for performance.

2:28:46 In a way that is not true. Just about everywhere else. Hm. I think They have the most incentive of anybody.

2:28:54 To truly maximise. performance we're able to achieve. Right. even though the dollars would continue to rise because they get fee dollars from more money in the door. They are incentivized in a unique way.

2:29:07 that makes it so they're not willing to trade V dampener on performance to get those dollars. Yes. Particularly because

2:29:17 It's all the same people on the G P and L P side. Oh, we keep going round and round that axle. I loosely buy the counter positioning thing. I just think The answer is disgustingly simple and kind of annoying here, which is They're just better than everyone else at this particular type of math and machine learning, and they've been doing it for longer. So they're just gonna keep beating you.

2:29:37 Oh, that's another argument I heard from people. And that RenTech basically is a math department in a way that None of these other Firms are. It could be culture.

2:29:47 Yeah, could be culture. I mean, honest to God, it could just be that the culture is set up in a way that continues to attract the right people and incentivise them. in a sort of fake altruistic way. Like this is just a fun place to do my work and yeah, the outcome is getting really rich, but I wouldn't go work at Citadel.

2:30:04 Yeah. I think that could be. So maybe that feeds into process power. Yeah. Okay. For me It is some combination of process power and counter positioning. And I don't think it's any of the other powers.

2:30:15 For me it is process power and cornered resource. Yeah. Okay, I buy that. And a thing that's not captured in seven powers. Is

2:30:23 tactical like execution. The whole point of seven powers is strategy is different than tactics. And I think Legitimately. Rent Tech may just

2:30:33 have persistently been able to out execute their competitors. There's part of it that's just like they're smarter than you. Yeah. Well if you buy the the whole thing gets reinvented continuously every two years, then Yes. And there's remnant knowledge. Like if you started building a machine learning system. In nineteen

2:30:53 Whatever it was. sixty four you're gonna be really good at machine learning today. And the people that you've been spending time with for the last 15 years learning all of your historical knowledge and working in your systems. are also gonna be better at machine learning than probably the other people who are out in the world learning it from

2:31:11 People that just got inspired to start learning machine learning based on The new hotness. So learning is compound is my answer. Right.

2:31:20 Okay. Playbook. So in addition to the three part David Rosenthal tapestry that you have woven. I have nothing more to add. There are a handful of things that I think are worth hitting. So the first one. is signal processing is signal processing is signal processing.

2:31:37 They By not caring. about the underlying assets. They literally don't trade on fundamentals, except in the institutional fund when they trade on fundamentals a little bit. They use price earnings ratio and stuff like that in the institutional fund, which is kinda funny because That's a completely different skill set.

2:31:54 But if you just look at medallion. It's all just Abstract. Numbers. You don't actually have to care about

2:32:03 what underlies those numbers. You just have to look for whether it's linear regression or any of the fancier stuff that they do, just relationships between data. And Once you reduce it to that. It is so

2:32:17 brilliant that they can just recruit from any field. It's not relevant how someone has done. sophisticated signal processing in the past, whether it's being an astronomer and trying to denoise a quote unquote photo of a star super far away, or whether they've tried to do like natural language processing, it's just signal. There's this really funny line that

2:32:39 Jim and Peter. And others will say when asked about why they only hire academics and not from you know Wall Street and whatnot, and they're like, Well, we found it's easier to Teach. Smart people, the investing business, then teach investing people how to be smart. Right. That's ridiculous. They don't teach anybody anything about investing. They're just doing signal processing. I bet at least half the people at Red Tech on the research side could not read a balance sheet.

2:33:05 It's so funny. It's a whole bunch of people who are in the investment business, none of which are investors. Yes. You can decide if this fits or not. I

2:33:14 was thinking a lot about complex adaptive systems. It's always been on my mind since we had the NZS Capital guys on a few years ago and read their work and the San Fe Institute's work on this. In a complex adaptive system, it's really difficult to actually understand how one thing affects everything else because the idea is the relationships are so combinatorially complex that you can't deterministically nail down. this one thing is the cause of that other thing. It's the butterfly flapping its wings.

2:33:41 But there are relationships between entities that You can't understand or see on the surface. Do you remember way back when we did our second NVIDIA episode, I opened with the idea that When I was a kid, I always used to look at fire. And think like

2:33:55 If you actually knew the composition of the atoms in the wood and you actually knew the way the wind was blowing, and you actually knew that like all the could you actually model the fire? And when I was a kid and you always just assume no. But actually the answer is yes. This is a known thing of what will happen when you light this log on fire for the next three hours and can you see exactly the flames.

2:34:18 I think RenTech has basically They haven't figured that out for the market. They can't predict the future. But if they have a fifty point oh one percent chance of being correct. then they can sort of take a complex adaptive system and say,

2:34:32 We don't really care that it's a complex adaptive system. Our models. understand enough about the relationships between all these entities that we're just gonna run the simulation a bunch of times and we're gonna be profitable enough from all the little pennies that we're collecting on all the little coin flips where we have a slight edge over and over and over and over again that they're sort of the closest in the world to being able to Actually

2:34:55 predict how the complex adaptive system of the market will work. Now I don't think they can back out to it. No person could explain it, but I think their computers can. Yes. When I've heard people from Rentek talk about this.

2:35:09 They will all say. The model. Does not actually understand. the market. But

2:35:15 It can Predict and we Can be So confident in its predictions. About

2:35:23 What the market will do. That we rely on it. Whether it understands or doesn't understand. Doesn't actually matter. Like it can't tell you why.

2:35:31 Right. But that's okay. But it does know it has a slight edge and so it should trade on it, even though it can't explain why. Yes. Well speaking of models. I've been trying to nail down an answer to this question. Do you think RenTech was the birthplace of machine learning?

2:35:45 This is such a tough answer to tell. We actually Emailed some friends who are very prominent AI researchers and AI historians and sort of asked this question. Yeah. The answer we got back is unsurprising, they said

2:35:59 We don't know. 'Cause they don't share anything. Right. It's like the principles certainly came out of the same math community that spawned. machine learning. But is what Rentek has figured out over the last couple decades.

2:36:14 in Google's Gemini model and in ChatGP No, it's not,'cause they don't contribute any research back. It may be the case that actually Rentek has beat everyone else to the punch and they have a strong AI or something that is actually much more sophisticated than all the A we have out in the world today. And they've just chosen that

2:36:33 They'd rather keep it locked up and Captive and make a bunch of money. I mean it could just be the case that Renaissance is just taking in as much unstructured data as it possibly can. And they sort of were just a decade or two ahead of everyone else and realizing that

2:36:49 you can have unstructured, unlabeled data and if you have enough of it. You can make it in the case of an LLM say things that sound right. Or sound true, or in the case of these trades. be right more than fifty percent of the time. Right. Make trades that

2:37:03 Sound right. Right, they figured out this big unsupervised learning thing before anybody else. All the way up until last year when the AI moment happened. If that were the case, we should have very different answer to powers. To illustrate this point, it's quite interesting.

2:37:17 Peter Brown's academic advisor was Jeffrey Hinton. Yes. Oh, I'm so glad we brought this up. Yeah. It was the exact same stew And the exact same cohort of people in social group and academic groups. that Rentech came out of, that AI came out of.

2:37:33 The other person, just for people who are like, Why are you saying that? To make it super explicit, the other person whose academic advisor was Jeffrey Hinton. is Ilya Sitzgever, who is the co founder of OpenAI. I mean many years later, but still. Yeah. I mean it's like we were talking about with Markov models and hidden markov models.

2:37:52 That is the foundation of Rent Tech. That is one of the foundations of AI and generative AI today. Yep. Okay, another big one is this concept that you should trade on a secret. that others are not trading on.

2:38:06 So On the face of it, it seems obvious. Of course I should come up with some strategy to trade on that other people aren't trading on. But I said a couple of words there, which is of course I should come up with. And therein lies the fallacy.

2:38:20 I think most investment firms try to get their ideas out of people. and then do an incredibly rigorous amount of data. analysis to figure out if they should put those trades on or not. I could be wrong.

2:38:33 But I do not think modern RenTech does that. I think all of their investment ideas come from Data. And come from signal processing.

2:38:44 And so therefore you are going to put trades on that make no intuitive sense. And so when you're putting trades on that are Profitable. And make no intuitive sense.

2:38:54 You aren't going to have competitors. If you find a relationship between two things that a human could never come up with or dream of those relationships, and I we're saying two. It end things, you know. Ten things, twenty things, a hundred things, and in various different weights at various different timescales. That is a killer recipe.

2:39:12 to exploit a secret that no one else knows and be able to beat other people in the market. Such a good point. And Many, if not most, of the other quant firms. Are not doing that. Some of them maybe, but

2:39:24 I think most of them The model is suggesting things. And there is a person or persons who are the Master Portfolio Allocators.

2:39:34 That pull the trigger or don't pull the trigger. Yes. And to be super illustrative,'cause I think your natural tendency is like, Oh, I can understand why these two things would be related. the relationship may not be what you figure. For example, there could be two things that always move together, Tesla stock and wheat futures.

2:39:52 And you might try to, because humans are storytellers, concoct some story in your head of why those move together. And if you believe it, then you might decide there's some date where they should stop moving together. Well It could very well be That some other big hedge fund just owns both of those things. And when they rebalance, it causes those assets to move together. But you would never think of that. You would think these things have a direct relationship with each other, not just that there's liquidity in the market from both of them at the same time because someone else owns both of them. So I think what RenTech sort of admitted is.

2:40:25 We have no idea why anything is actually connected, but it doesn't matter. Yeah. Totally. And that was surprising for me in the research. Like I sort of assumed that was the whole quant industry. And

2:40:36 It was very surprising to me to discover that I believe No. It is. Pretty much only Rentek and maybe a couple other people. Okay. My next one.

2:40:46 is brought to you by a friend of the show, Brett Harrison, who has worked in the quant trading industry for a long time and shared an idea that he has with us, which is that there's basically this two by two matrix. you have on the one axis fast and slow in terms of trade execution. And on the y axis. You have Smart versus obvious.

2:41:06 Yeah, the way he phrased it to us was smart versus dumb, but Dumb doesn't mean dumb. Right. It's the obvious trades. And the high level point is all quant funds are not high frequency trading firms, and vice versa. And this is something that I didn't know not coming from this industry and now makes total sense to me. I think I thought they were the same thing. But Fast and obvious is your classic high frequency trader. They're front running trades.

2:41:30 They're locating in a data center that's really near the, you know, this is Flash Boys. Or they've got a microwave line between New Jersey and Chicago and they're trying to arm the difference between two markets. You need to have the fastest connectivity in the world to pull this off. Yep, this is Jane Street. Yes.

2:41:46 There's fast And smart. Which You kind of don't need to be both. You don't need the fastest connectivity in the world and the most clever trades to put on. So people kind of tend to pick a lane that they're either a high frequency trader or they're trying to make the smartest, you know, most non-obvious trades possible. And that, of course, leads us to medallion, which is in the slow and smart. Quadrant.

2:42:10 all the machine learning systems discovered the relationships in the data. So there's a huge amount of compute. The non obvious trades. Exactly, that goes into finding the non obvious trades, but then they're actually made reasonably slowly. They still have to happen within seconds or minutes. But the advantage isn't that they're high frequency, the way that all the flash boys stuff is.

2:42:31 Rent Tech is not a high frequency trading shop. They're not. front running things, you know, they are not flash boys. Compared to you and me, they still operate. Incredibly fast.

2:42:43 But It's more about the smartness and less about the fastness. Greg has a quote in his book. They hold thousands of long and short positions at any given time, and their holding period ranges from one to two days or one to two weeks. They make between 150,000 and 300,000 trades a day, but much of that activity entailed buying or selling in small chunks to avoid impacting market prices rather than profiting by stepping in front of other investors.

2:43:07 Oh, this is another thing that we heard. Rent tech is world class at disguising their trades. Yeah. They can make it so that They don't move the market and you don't know who is acting or when. And this is because in the early days they weren't good at this and people basically intercepted.

2:43:25 the trades that they were making and were front running them. And they had to adapt and develop these clever systems to make it so you don't know who's buying and you don't know in what quantities and you don't know if they're going to keep buying. Yeah. My last one before we get into value creation, value capture, is that this is a terrifying business to be in. The amount of controls and risk models that you need and kill switches are just so important. What if the software has a bug?

2:43:49 Is it possible to make a ton of unprofitable trades in a matter of minutes and lose it all? You know, that wasn't possible in the old world where you're calling your broker. That totally is possible here. And It happened. Yeah, and while it's never happened to Rentec.

2:44:03 There was a company called Night Capital in twenty twelve. That lost four hundred and sixty million in a single day. There was a bug in their process to deploy the new code. And Basically what happened it was a simple flag error.

2:44:16 A misinterpretation of setting a bit from zero to one that caused this infinite loop to run, where once a certain trade happened it was supposed to flip the bit. It flipped a different bit. The systems were not looking at the same location in memory for the same bit. And so it basically thought it was never flipped. This infinite loop ran. four million trade executions in forty five minutes, and there wasn't the appropriate kill switches built in, and they basically watched it all to just drain out and there was nothing they could do. Yeah. So like the whole portfolio gone. Right. Yes. Uh well, I don't know if it's the whole portfolio, but it was enough that they lost a huge amount of the LP capital and then they were a publicly traded firm. Overnight their equity traded down seventy five percent and then someone stepped in and bought them.

2:45:01 Well, they probably got margin called by all their counterparties. So Whoever is in charge of the financial controls and safety systems at Ren Tech, that's a huge job for someone in this industry. Totally. Alright.

2:45:15 To kick off value creation, value capture. I have a provocative statement which is David. Renaissance technologies is actually not in the investment business. They are in the gambling business. And in particular They're the house.

2:45:29 Well, I would totally I thought where you thought you were going with this, I was like, Yes, I would totally agree. They're not in the investment business. They have no idea how to invest. The model does. I'll tell you this. They're not investors and they're not in the investment business. There is investment going on all around them in the markets that they trade in. But The fact that they're in those markets, they're not there as investors.

2:45:49 They're there setting up shop. as Caesar's palace. Letting everyone come in. And do business with them. Well they have a slight edge and they'll lose sometimes.

2:45:59 But most of the time they're gonna come out slightly ahead. And I think Let's say they do have a fifty point oh one percent chance of being right. They're just there to collect their Vig on everyone who is willing to trade with them over all these years.

2:46:15 And at scale it really worked. Jim Simons managed to drain thirty billion dollars into his own pocket. out of everybody that he ever traded with. Now I think where you're going with this

2:46:27 Is Perhaps similarly along the lines to Caesar's Palace or a Casino. They are not in the investment business, but they are providing a service. Sure. Is this where you're going with this? Well, I mean the investment business, it sort of depends how you define investor.

2:46:42 If you want to be like all hoity toity about it, which I'm, you know, in this illustrative example, I'm kind of being one and saying an investor is someone who provides capital, you know, risk capital to a business for that business to create value. in some way in the future. Where you lend money to some intrinsic underlying asset so that it can be productive with that capital and produce a return for you as an investor. And of course.

2:47:07 Lots of things are called investing. that are not that. Is it investment if I put money to work and then I get more money back later and I don't actually care how the money got made and it's actually zero sum. I'm just vacuuming it out of Right, right. Yeah, the money is not being invested in anything to produce correct. But it's literally the same business model as a casino. You have a slight edge and you let a whole bunch of patrons come in and lose money to you in your slight edge.

2:47:32 Well, where I was going with the service provider. I think casinos are service providers. They are providing entertainment to their Customers. Everybody knows that the Games are stacked in the casino's favor.

2:47:43 Similarly, I think you could make an argument. I think this is Probably quite accurate. That Rent Tech and All other quant firms like them.

2:47:52 are providing a service to the market. In that they are allowing trades that people want to make To happen faster and at much lower spreads. Absolutely. That is the

2:48:03 undeniable. Yes, quant funds create value in the world thing, which I think It's very easy to say quant funds provide no value because it's like it's zero sum. They're not actually providing the capital to businesses to do something with. They're purely looking to do an arbitrage or any of the strategies we've talked about this episode.

2:48:22 But You're totally right that there is a value to market liquidity. creating more depth to a market. Makes it so that If we go back to the era that Renaissance was started

2:48:33 There's no chance that retail is able to function like it does today with zero transaction fees and people able to invest in all these different companies that Near real time. And any single one of us. Can go. buy a security in just about any market.

2:48:51 At just about any time of day. Pretty much instantaneously. And get a very, very, very granular price on it. Yep. None of which used to be true.

2:49:01 No. The fact that there is a whole bunch of quant funds, hedge funds out there that are ready to be willing counterparties to anyone who wants to trade. That is a service. You're right. They're also not all

2:49:14 Medallion. They actually don't all have an edge, even though they might purport to. Lots of'em are gonna lose money to you. Right. Lots of'em lose money. You too, listeners, could beat the market. Not investment advice. Please don't try.

2:49:27 Right. On average, Medallion will not lose money to you, but you know, there are plenty of other hedge funds out there and high frequency shops and counterparties for you. Where you could take them. It's just not Jim Simons. Oh there's this great, great vignette at the end of Greg's book.

2:49:46 When was it? It was during one of the like sell offs in the mid twenty teens in the market. Where Jimmy calls the head of his family office. He's long retired from Rent Tech at this point calls the head of his family office and says.

2:49:58 What should we do? With all the sell off in the market. It's like You're Jim Simons. Right. You're Jim Simons. What should we do? What should we do? Yeah. Oh, all humans are fallible. Totally.

2:50:11 A couple of other are squintable the value creation exists. It's easy to knock that all these smart people are going into finance. And you wish they were doing something more productive for the world. At the end of the day, humans are going to do what they are incented to do. And so absent a larger

2:50:29 global concern that is incredibly motivating to people. I mean you look at World War Two people's level of patriotism and wanting to go save the world from evil. was a huge, unbelievable motivating factor to move mountains. When that is absent or when people feel that there's some existential thing that is absent.

2:50:46 They're gonna go do what's best for them and their family. And if they're an empire builder, go build empires. And if they're a fierce capitalist, go make a bunch of money. And so the system is set up the way that it is. So like you can be mad about that. Given that. Okay. People are gonna go engage in quantitative finance as a lucrative profession.

2:51:06 Fortunately. There's a bunch of valuable stuff that comes out of that. And I think that is Often missed. Is that

2:51:13 These really lucrative. Professions and businesses can often produce RD that becomes valuable elsewhere. For example, we just did this big NVIDIA series. What do you think Melanox was used for before large language models? Oh.

2:51:30 Yes. This is Such Oh Really mind blowing.

2:51:34 Point here in value creation, value capture. Go for it. Take it away. Well there's not much to it other than A huge amount of InfiniBand was used by high frequency trading firms, and I don't know for sure, but I kinda think Melanox built their business on quant finance. Yes.

2:51:50 That's one of many examples, but Now you know, that has limits. But I think it goes overlooked that there's a lot of technology innovation here. Yeah.

2:52:00 These are all great points. They all came up in the research. I Totally agree with all of them. It is In my opinion, false to say

2:52:11 That Quantitative finance. Does not create value for the world. It definitely does, in my opinion.

2:52:18 But does it create anywhere near as much as it captures? That said. They're really, really good at value capture. Yes. This is not Wikipedia here. This is about as far away on the spectrum as you can get. There's a great Always Sunny in Philadelphia where Frank, Danny DeVito, sort of goes back to his whatever business he found in the eighties and he's like dressing in his pinstripes and stuff again and he's taken back over. He brings Charlie with him. And Charlie, you know, he's like, So Frank, what is the business, uh what do we do here? What does the business make?

2:52:50 And Danny DeVito looks at me and he goes What you mean? We make money. He's like, no, no, like what do you build? Because

2:52:56 We build wealth. I think that's a pretty good meme for kind of what's going on here. Yeah. Totally. Very, very good at value capture too.

2:53:05 Yes. Okay. Bear bull. So this was a section that we had for a long time that we did not put in the last episode, and boy did we hear about it. So Listeners, thank you so much for expressing your concern. Bearer versus Bull is unkilled.

2:53:19 And it is back. Resurrected. Like a phoenix. Resurrected. However, this is about the lamest episode to resurrect it on. What's the bull case for RenTech? Past performance is an indicator of future success. Right. Like they're gonna keep attracting all the smartest people in the world. They're gonna have the ability to keep their incredibly unique culture. They're not gonna get tempted to let the business of institutional funds become the dominant business. You know.

2:53:45 Keep on keeping on is basically the bull case. Maybe uh that they're actually still ahead. The bullcase. For the GP and LP stakeholders in

2:53:54 medallion. Which is I don't know. Five hundred people in the world. And None of the rest of us can get any exposure to it.

2:54:01 Yeah. The bear case is things are changing and I think things are changing basically on any axis is the bear case for them. So things are changing where competitors are catching up. Maybe? Maybe the fact that the tech industry has figured out these large language models, maybe that trickles into making it easier to compete with RenTech. It's a blurry line, but it is plausible. Like maybe RenTech actually was here a decade before everyone else, and now everyone else has arrived to the party.

2:54:30 there's things that are changing maybe about their culture. Like Jim Simes has been gone for a long time. Bob Mercer is no longer a co CEO. Peter Brown is a co CEO, and they just announced that they're making the guy who was in charge of the institutional funds David Lippy, he is becoming a co CEO as well. So maybe there's a bear case around that that someone from the institutional side of the house

2:54:54 Is becoming the current co CEO and maybe eventually. If you believe the medallion is the special thing and the institutional funds are sort of a blemish on the business. You know, they're the uh Air Mez Apple Watch strap and David's parlance. Maybe that's a bear case. Maybe there's a bear case that

2:55:13 their talent is becoming kind of the same as everyone else's talent. When you look on LinkedIn, I recognize a lot of the companies that people worked at who are more junior at Ren Tech. And in the past, I think it would have been all people just out of university research shops. So I think If it's true that they're starting to

2:55:32 see the same talent flow as everyone else, that would be concerning. These things are all sort of narratives you can concoct and really no way to know if they're true or not. Right. There's no way for us to know any of this'cause There's no way to know any of this.

2:55:44 Right. It's all the secret. Yep. Okay. Our new ending section. The splinter in our minds, the takeaway. The one thing you can't stop thinking about.

2:55:55 What is the one thing? For each of us. Personally. From doing this work over the past month on RenTech.

2:56:03 That sticks with us. For me Perhaps this is obvious from my Little diatribe on the tapestry. I just think this is

2:56:13 Such a powerful example of the power of incentives and getting them right. And setting them up, right? And culture too. I don't wanna shortchange that. I think the culture of managing an academic environment. In a

2:56:27 Fashion like a lab. But without letting it spin into the frivolity of a lab that Jim Simon set up. Right. In other words, early Google. Yeah. This is like early Google. Exactly. They're

2:56:41 historically has not from our research, and as best as we can tell, currently Is not Anything going on at Rentek. That is Frivolous.

2:56:52 They are all very focused. Which again to me then speaks back to the power of incentives. When you're there With less than four hundred people And on the research and engineering side. Less than two hundred people.

2:57:03 And those colleagues who you work with Are the sole purveyors, supervisors and beneficiaries of All of this that you're doing, like that is so powerful. Yeah.

2:57:16 I can't think of anywhere else like that in the world. I mean maybe Some venture funds or other investment firms, but Not on a Day to day fulliquid with returns like this. There's nothing like it.

2:57:28 No. Pure gasoline right into the veins. Yeah. Which is not to say I would necessarily want to work there. I think I would not. But it is Truly unique.

2:57:38 Yeah. The one thing I can't stop thinking about is the idea of the complex adaptive system that I was talking about earlier. I think from what everything we can tell from the outside. Renaissance actually has built a large scale computer system that discovers

2:57:54 relationships. Between different entities in the world. Stocks. Commodities. Bond prices.

2:58:02 And whether it can explain them or not. It is correct most of the time. And it might be a small most But all you need is most, and then you can operate a casino business. That is my takeaway, is that they are the house.

2:58:15 And they have an edge. And that edge is predicated on a all the relationships between these entities that we think are just noise and they know the signal. It does make you wonder.

2:58:28 to what you were talking about with the tech industry Catch up, quote unquote. in recent years. How hard is it? To

2:58:37 Build this now, given the technology open source and otherwise that's available for sale out there. That's the bear case. I don't know. Yeah.

2:58:46 And then what's gonna happen. By nature, given that it's a complex adaptive system. If you can now buy and build this. Well, the returns will get arbitraged down. Yeah.

2:58:55 All right, should we have some fun? Carve outs. Let's have some fun. Sweet. So I have one TV show and it is actually acquired related.

2:59:04 It is called the New Look. On Apple TV plus. Oh yes. But Christian. It is such a new look. Exactly. So for anyone who listened to the L VMH episode, remember we were talking about the groundbreaking thing that Christian Dior did was his collection, The New Look. that was a post World War Two

2:59:25 Explosion onto the scene. Celebration of life. Yes. Gone are the days of the militaristic. Boxy clothing and now we're in with these Seductive and dare I say Sumptuous materials. War rationing is over. Exactly. Yes. Provocative.

2:59:43 Dresses. The Apple TV show is this incredible drama. Of kind of flashbacks to the wartime experiences

2:59:53 Harrowing wartime experiences. of Christian Dior. of Balenciaga, of Coco Chanel, and everything they went through and how all their paths crossed. Oh. Coco's in it. Yes.

3:00:05 Oh wow, how do they treat that? It will be very interesting if a lot of people watch this show to see if that affects product sales of Chanel. I'm also very curious for people who are watching, feel free to put a thing in the Slack and carve outs. Do you think she's a sympathetic figure? Do you think she's a villainous figure? I'm curious how you think of her portrayal. versus reality. So Well there's the whole crazy thing with Chanel where

3:00:28 The company ends up getting But By Chanel the perfume division, which is The two Jewish brothers in New York.

3:00:35 The Worthheimers, indeed. Oh God, we gotta do a Chanel episode at some point. But the new look on Apple TV Plus, I promise you, whether or not fashion luxury is your thing. It's a beautiful and harrowing story. Uh. As you and listeners know, I'm not a T V guy, but this is so up my alley. The whole thing it takes place in wartime Paris.

3:00:54 Oh all right, I gotta watch it. You gotta watch it. All right, David, your car vats. Mike Carve out is related to the new look in a very different way, but both video consumption and Fashion and luxury and style.

3:01:10 It is The class of Palm Beach. Instagram and TikTok account. This is so great. Two days and you get hooked on This is amazing. So

3:01:26 Ben and I went to Palm Beach for a couple of days for a speaking event recently, which was amazing. I'd never been to Palm Beach before. Oh it is nice. So great. We didn't knowingly spot any Rentek people there, but We may have.

3:01:38 We did knowingly spot some Birkin bags, though. Yes. The style in Palm Beach. We had just recorded The Hermes episode. And Oh man.

3:01:49 I was so pleased to be there. And Then I got home. And Jenny, my wife, was like Do you not know the class of Pond Beach TikTok account? And David's like, I'm a thousand. I have no idea what you're talking about, Jenny. Yeah, right, right, right. I live under a rock. I'm a dad.

3:02:04 And she showed it to me. This is a woman who lives in Palm Beach. And she goes around, she posts on Instagram and on TikTok. And she just interviews people on the street about what they're wearing, what brands they're wearing, their style It is. Magnificent. My favorite is we'll see if we can find it and link to it in the show notes. There's a video of one woman who's being interviewed.

3:02:25 Who has A mini Kelly. Inside her birken. Excess. Truly access. And that's when I was hooked. I was just like

3:02:34 This is the greatest thing I have ever watched. Uh, I'm obsessed. Alright. If I used TikTok, I would subscribe.

3:02:43 No, but you can get it on Instagram too. Oh, all right, good. I actually subscribe the acquired account on Instagram to Class of Palm Beach. I don't know how many people we're following. It's not many, but we are following Class of Palm Beach. Look at David opening up our Instagram account. You're so youthful. Uh no.

3:02:59 David, I know you've got some thank yous from folks you talked with and a few of them we did together. Yes, for sources for this episode who were so generous with their time and thoughts. First, huge thank you to Greg Zuckerman, author of The Man Who Solved the Market. the canonical book out there about Rentec and Jim Simons. Yeah Greg was super generous spending time talking to us, emailing with us. Making sure we're getting things right. He also he and the book is

3:03:26 The canonical source of medallions investment returns. And I know he worked so hard to Get that table. Together that is now all over the internet as it should be.

3:03:37 It is crazy, everywhere you hear that sixty six percent number quoted, and that is from Greg's analysis. Yes. Truly a service to us and to corporate historians and financial historians everywhere that he did that research and got those returns. And there's a few other

3:03:52 primary sources. There's really not much. So we can actually list all of them here. There's a congressional testimony of Peter Brown about the basket options thing. There's Peter Brown doing an interview at GS Exchanges, which again, many of the questions were straight out of Greg's book. And the stories told. Yeah. It's a funny moment where Peter's like, Where are you getting these questions? How do you know all this stuff? And I'm like Come on. Clearly.

3:04:17 Yeah. There's a great book called The Quants, which is a little bit earlier. I think it's twenty eleven. So it's not as updated as The Man Who Solved the Market. And there's only sort of a couple chapters about RenTech, but some good stuff in there. And then there's a good Bloomberg piece from 2016 that we'll link to that I think between that and the quants, it was sort of the first time there was really anything at all that was published about RenTech. So all those will be in the show notes. Other people to think, David.

3:04:43 Other people to thank Howard Morgan, who we spoke to, which was So fun to get a bunch of the first round history from him and then of course the You know, founding of Red Tech and partnering with Jim and Investing in each other's funds and all that. So fun. Brett Harrison, who you mentioned been

3:04:58 Brett is now building Architect, which I Love this. This is so needed in the world. It's the interactive brokers for the 21st century. Well. Anybody who uses interactive brokers knows exactly the opportunity there. So Thank you, Brett.

3:05:14 And then Matthew Grenade, who I spoke with. Matt is the co-founder of Domino Data Lab, which is a great enterprise AI ops platform backed by Sequoia and many others. It allows model driven businesses and products to accelerate research, increase collaboration, rapidly deliver new machine learning models. All of the sorts of things that we were talking about here with Rentek.

3:05:36 Matt. before starting domino data. came out of the quant world. He was at point seventy two and Bridgewater, which isn't really quant, sort of a its own thing, but He was a longtime senior employee at both of those firms. And He gave us great, great.

3:05:51 perspective on The landscaped of everybody out there and where Rentek fits in. Awesome. Well, if you liked this episode, you should check out our Berkshire Hathaway episodes from a few years ago for a very different style to investing. You can sign up for new episode emails at acquire.fm slash email. We'll be including little tidbits that we learn after releasing each episode, including listener corrections. You can listen to ACQ two, search and subscribe in any podcast player, and listen for our most recent episode with

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3:06:45 If you want some merch, we've got some. Acquired dot fm slash store. And without listeners. We'll see you next time. I'll see you next time. Who got the truth?

3:06:56 Is it you, is it you, is it you Who got the truth now

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