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Episode: Renaissance Technologies

Author: Ben Gilbert and David Rosenthal
Duration: 03:05:52

Episode Shownotes

Renaissance Technologies is the best performing investment firm of all time. And yet no one at RenTec would consider themselves an “investor”, at least in any traditional sense of the word. It’d rather be more accurate to call them scientists — scientists who’ve discovered a system of math, computers and

artificial intelligence that has evolved into the greatest money making machine the world has ever seen. And boy does it work: RenTec’s alchemic colossus has posted annual returns in the firm’s flagship Medallion Fund of 68% gross and 40% net over the past 34 years, while never once losing money. (For those keeping track at home, $1,000 invested in Medallion in 1988 would have compounded to $46.5B today… if you’d been allowed to keep it in.) Tune in for an incredible story of the small group of rebel mathematicians who didn’t just beat the market, but in the words of author Greg Zuckerman “solved it.”Sponsors:ServiceNow: https://bit.ly/acqsnaiagentsHuntress: https://bit.ly/acqhuntressVanta: https://bit.ly/acquiredvantaLinks:The Man Who Solved the MarketThe QuantsBloomberg’s 2016 RenTec profileQuartr's visualization of RenTec's returnsAll episode sourcesCarve Outs:Modern Treasury’s Transfer Conference RegistrationThe New LookCole Haan x Acquired!Class of Palm Beach (and the Mini Kelly inside the Birkin!!)More Acquired!:Get email updates with hints on next episode and follow-ups from recent episodesJoin the SlackSubscribe to ACQ2Merch Store!‍Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.

Summary

In this episode of 'Acquired' by Ben Gilbert and David Rosenthal, the extraordinary success of Renaissance Technologies is examined, particularly its flagship Medallion Fund, which boasts a remarkable 68% gross annual return and has never recorded a losing year. Founded by Jim Simons, the firm employs a unique quantitative investing approach, relying on mathematicians and scientists to analyze market data through advanced mathematical models and machine learning, rather than conventional financial metrics. The episode emphasizes Renaissance's secretive nature, its focus on data-driven strategies, and how it has transformed the investment landscape by turning quantitative finance into a scientific discipline.

Go to PodExtra AI's episode page (Renaissance Technologies) to play and view complete AI-processed content: summary, mindmap, topics, takeaways, transcript, keywords and highlights.

Full Transcript

00:00:00 Speaker_00
I always used to misspell Renaissance as I was typing it out, 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.

00:00:10 Speaker_01
Oh, I thought you were going to say you've typed it so many times now over the past month.

00:00:15 Speaker_00
Well, there's that too. But you ready for this? You can't spell Renaissance without A-I. Oh. Touche, touche. All right, let's do it.

00:00:31 Speaker_02
Is it you, is it you, is it you who got the truth now? Is it you, is it you, is it you? Sit me down, say it straight, another story on the way

00:00:45 Speaker_00
Welcome to Season 14, Episode 3 of Acquired, the podcast about great companies and the stories and playbooks behind them. I'm Ben Gilbert. I'm David Rosenthal. And we are your hosts.

00:00:56 Speaker_00
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.

00:01:09 Speaker_00
They say there's no persistence of returns for hedge funds, that this year's big winner can be next year's big loser, and that nobody gets huge outperformance without taking huge risk.

00:01:20 Speaker_01
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 is a big proponent of all that. And that is what I learned, Ben.

00:01:30 Speaker_00
Well, David, it turns out they were wrong. Today, listeners, we tell the story of the best-performing investment firm in history, Renaissance Technologies, or Rentech.

00:01:42 Speaker_00
Their 30-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?

00:01:56 Speaker_00
Or if you have, why don't you know much about them? Well, their eye-popping performance is matched only by their extreme secrecy, and they are unusual in almost every way. Their founder, Jim Simons, worked for the U.S.

00:02:09 Speaker_00
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 Ph.D. physicists, astronomers, and speech recognition researchers.

00:02:24 Speaker_00
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.

00:02:32 Speaker_00
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.

00:02:43 Speaker_00
And to be specific, listeners, that's 66% 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 doesn't take any outside investors.

00:02:59 Speaker_00
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.

00:03:08 Speaker_01
Oh, we are going to talk a lot about that towards the end of the episode, because I think it's kind of the key to the whole thing.

00:03:15 Speaker_00
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?

00:03:25 Speaker_00
And while the resources 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.

00:03:42 Speaker_00
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.

00:03:52 Speaker_00
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 want to know every time a new episode drops, sign up at acquired.fm slash email.

00:04:06 Speaker_00
These emails also contain hints at what the next episode will be and follow up facts from previous episodes.

00:04:12 Speaker_00
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, which we linked to in this most recent email.

00:04:26 Speaker_00
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 Bjerg-Nudsen, who led the team that created the first GLP1s at Novo Nordisk.

00:04:40 Speaker_00
So awesome follow up to the Novo episode if you liked that one. 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.

00:04:50 Speaker_00
And this show is for informational and entertainment purposes only. David, where do we start our story today?

00:04:57 Speaker_01
Ah, well, we start in 1938 in Newton, Massachusetts, which is a fairly wealthy suburb just outside of Boston, where one James Simons is born. And both of Jim's parents were very, very smart, especially his mother, Marsha.

00:05:14 Speaker_01
His dad was a salesman for 20th Century Fox, the movie company. His job was he went around to theaters in the Northeast and sold packages of movies to them. Super cool.

00:05:26 Speaker_01
By the way, we know all this because we have to thank Greg Zuckerman, author of The Man Who Solved the Market, which is the only book out there that is solely dedicated to Rentech and Jim Simons. And we actually got to talk to Greg in our research.

00:05:38 Speaker_01
He helped us out a bunch. Thank you, Greg.

00:05:40 Speaker_00
and help fact-check a few of our assumptions of what happened after the book came out.

00:05:44 Speaker_01
So that was Jim's parents, but really a major influence on him growing up was his grandfather, Marsha's dad.

00:05:53 Speaker_01
There's 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 rubbing off on young Jeff or young Jim in this case.

00:06:04 Speaker_01
And Bezos, of course, would get his start in his career at D. Shaw.

00:06:09 Speaker_00
A quant fund coming up at the same time as Rentek.

00:06:12 Speaker_01
But back to Jim here in the 1940s, his grandfather Peter owned a shoe factory that made women's dress shoes. Jim spends a ton of time there growing up at the factory. So Jim's grandfather Peter was quite the character.

00:06:29 Speaker_01
He was a Russian immigrant and he's kind of 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 vodka.

00:06:45 Speaker_01
And he teaches young Jim when he's a child here in the factory to say Russian phrases like, give me a cigarette and kiss my ass.

00:06:54 Speaker_00
which I think he probably would say that thousands of times the rest of his life. I think so.

00:06:59 Speaker_01
If you watch interviews with Jim, his hands are always twitching because he has chain-smoked his entire life, probably going back to like age 10 in the factory.

00:07:08 Speaker_00
Three packs of merits a day.

00:07:09 Speaker_01
Unbelievable.

00:07:11 Speaker_00
Although I think he quit later in life, but he definitely chain-smoked the better part of the first, call it 75 years or something.

00:07:17 Speaker_01
I mean, there's 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 Jim. Different time. Different time.

00:07:29 Speaker_01
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.

00:07:42 Speaker_01
He has all the resources of his parents, his family, his grandfather's this sort of well-to-do entrepreneur. And Jim, you know, he gets to rub shoulders in the Boston area with people who are really rich.

00:07:55 Speaker_01
And he says later, I observed that it's very nice to be rich. I had no interest in business, which is not to say I had no interest in money.

00:08:03 Speaker_00
Yes, important to tease out the difference between those two things.

00:08:06 Speaker_01
Yes, very, very important. And what he means when he says he has no interest in business, it's because from a pretty young age, he gets really into math.

00:08:16 Speaker_01
So the legend has it when Jim is four years old, he stumbles into one of Zeno's famous paradoxes from ancient Greek times.

00:08:23 Speaker_00
Yep, this is great. The basic gist of Zeno's paradox is if you are always taking a quantity and dividing it by two, you will never hit zero. You will asymptotically approach zero, but you will never actually touch zero.

00:08:37 Speaker_00
You need to do addition or subtraction to do that. Division won't cut it.

00:08:41 Speaker_00
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, 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.

00:08:57 Speaker_00
And, you know, the funny thing that doesn't occur to a four-year-old is, well then, we're just not going to get very far.

00:09:02 Speaker_01
So Jim's dream is to go to MIT 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.

00:09:17 Speaker_01
So pretty, you know, heady stuff.

00:09:19 Speaker_00
Yeah, and Jim would go on to finish his undergrad at MIT in three years and get a master's in one year.

00:09:26 Speaker_01
Yeah. Pretty, pretty smart. But it turns out that that freshman year grad seminar he took 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

00:09:43 Speaker_01
who never run into problems. They never hit a limit. They never struggle understanding any concept. 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.

00:09:59 Speaker_00
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.

00:10:09 Speaker_01
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.

00:10:21 Speaker_01
And that's that as he put it, 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 going to care about the answer to anyway, that's taste.

00:10:35 Speaker_01
And I think I have good taste.

00:10:37 Speaker_00
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 brainpower people that would go on to become the best theoretical physicists in the world.

00:10:51 Speaker_00
And he said, I'm smart, but I'm not that smart. And so switch to computer science.

00:10:55 Speaker_01
I think the analogy here is like sports, right? 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, but he's not Tom Brady.

00:11:11 Speaker_01
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.

00:11:19 Speaker_01
So this realization that Jim has about himself, though, both that he's not the smartest person in the room at a place like MIT, but he can hang with them, and that he has this taste concept.

00:11:32 Speaker_01
I think becomes one of the most important keys to the secret sauce that ends up getting built at Rentech, which is that he can relate to everybody. He understands what's going on.

00:11:45 Speaker_01
Any person off the street probably couldn't even really have a conversation with these folks, 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.

00:11:57 Speaker_01
And as a result, All of his friends at MIT and these super smart people, they look up to him because you aren't like the kid in the corner at the high school dance. You're cool.

00:12:09 Speaker_00
He's the extroverted theoretical mathematician.

00:12:11 Speaker_01
Yes. So he was elected class president in high school. You know, he smokes cigarettes. He's popular with the ladies. He kind of looks like Humphrey Bogart. He's a popular dude, especially at this point in time. We're now in the late 50s when Jim's at MIT.

00:12:27 Speaker_01
You know, this is kind of James D in Rebel Without a Cause era. Yep. 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.

00:12:45 Speaker_01
The idea is that they're going to do something so epic that the newspapers are going to have to write about it. So they all load up on scooters and drive down to Bogota. They get into all sorts of adventures.

00:12:55 Speaker_01
There's knives, guns, and they get thrown in jail.

00:12:59 Speaker_00
It's honestly crazy that this group of people took this type of risk.

00:13:03 Speaker_01
Totally crazy. So after he's done at MIT and after the road trip. Jim heads out to Berkeley in California so that he could do his Ph.D. with the professor Xing-Hsien Cheung.

00:13:15 Speaker_01
And much later in life, Jim would collaborate with Cheung for the Cheung-Simons theory that we talked about earlier that becomes one of the foundational parts of string theory in physics.

00:13:25 Speaker_01
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.

00:13:39 Speaker_01
And when they get to California and they get married, Jim takes the $5,000 wedding gift that I believe they got from her parents. And he decides, I want to multiply this.

00:13:52 Speaker_01
So he starts driving from Berkeley into San Francisco every morning to go hang out at the Merrill Lynch brokerage office and just be a rat hanging around the brokerage and find ways to trade and turn this money into something more.

00:14:05 Speaker_00
Which is so interesting to think about because at that point in time, there was such an advantage to just being there.

00:14:11 Speaker_00
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 there in on the action.

00:14:23 Speaker_01
Exactly.

00:14:23 Speaker_01
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.

00:14:38 Speaker_01
He's as close to the action as he can possibly be, but he's a long, long way from the action. Yep. Nonetheless, When he starts out doing this, Jim hits a hot streak and he goes up 50% in a few days. Trading is easy. Trading is easy. He says, I was hooked.

00:14:55 Speaker_01
It was kind of a rush. I bet. Except he ends up losing all of his profits just as quickly.

00:15:02 Speaker_00
Yeah, important to learn that lesson early.

00:15:04 Speaker_01
Yes, and also right around this time, Barbara, his wife, gets pregnant with their first child and is like, you can't be driving into San Francisco every morning and gambling our future like this.

00:15:16 Speaker_00
Right, effectively playing the ponies.

00:15:18 Speaker_01
Yeah, exactly. So Tim's like, okay, okay, 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 23. So they stay one year in Boston.

00:15:34 Speaker_01
But Jim, even though he's got a family, even though he's super successful as a young academic here, he's got kids, he's restless. So one of his buddies from the scooter trip to Bogota is from Bogota and lives there. His family's there.

00:15:48 Speaker_01
He has an idea to start a flooring tile manufacturing company because he's like, you know, the flooring at MIT and in Boston, it's so much nicer than a Bogota. We should start a company and make the same kind of flooring here.

00:16:01 Speaker_00
When I read this, I couldn't believe that this was Jim Simon's first 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.

00:16:16 Speaker_00
He just gets bored fast.

00:16:18 Speaker_01
Totally. Not just is this the start of his entrepreneurial career, the seeds of this financially are what go on to start Rentech. It's wild. Totally wild. So Jim takes a year off and goes down to Bogota.

00:16:33 Speaker_00
This is a guy with an MIT undergrad and master's and a Berkeley PhD in theoretical math. Who's now a professor at MIT. Who is taking a year off to go work on a flooring company in Bogota.

00:16:47 Speaker_01
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.

00:16:56 Speaker_01
He bounces back to Boston, this time to Harvard as a professor there for a year. He's really racking him up. But he spends a year there and he's like, ah, the itch again, and you know, the junior professor's salary isn't that much."

00:17:11 Speaker_01
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 rich. So he's like, I'm going to go put my skills out on the open market.

00:17:24 Speaker_01
He gets a job in Princeton, New Jersey, not at Princeton University, but at the Institute for Defense Analyses, which is a nonprofit organization that consults exclusively for the U.S.

00:17:41 Speaker_01
government, specifically the Defense Department, and specifically the NSA. These are the civilian codebreakers. Yes.

00:17:51 Speaker_00
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.

00:18:02 Speaker_00
And two, there are going to be a lot of people who don't work for the government that we're going to want to hire to do some pretty secret work.

00:18:11 Speaker_01
Yep. So the IDA there in Princeton kind of functioned like the Institute for Advanced Study, which is also in Princeton.

00:18:19 Speaker_01
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 with the Russians during the Cold War.

00:18:32 Speaker_00
Yeah, it's a pretty wild charter.

00:18:34 Speaker_00
And especially how special of an organization it was, like the way these people would spend their time is part code breaking, but part kind of goofing around, because the creativity of mathematicians working together on passion projects is important to discovering clever new algorithms.

00:18:53 Speaker_01
Yes, this is so, so key. And this culture ends up getting translated whole cloth right into Rentech. So the way IDA worked, and I assume still works to this day, is they recruited top mathematicians and academics to come be codebreakers there.

00:19:11 Speaker_01
They would double their salaries.

00:19:13 Speaker_00
And importantly, it couldn't have been a government division if they were going to be doing that, because there's very specific congressionally approved budgets for payroll. Exactly.

00:19:22 Speaker_01
They figured out that they needed to attract the smartest people in the world who weren't going to come just go work for the Department of Defense. This was the way to do it. So like you said, Ben.

00:19:34 Speaker_01
The charter of the group was that employees had to spend 50% of their time doing code breaking, but the other 50% of the time, they were free to do whatever they wanted, like research, pursue whatever they were doing in academia, publish papers.

00:19:50 Speaker_01
Kind of the appeal of going there was, hey, It's the same thing as being a professor at MIT or Princeton or Harvard or whatever, except you're doing code breaking instead of teaching. And there's no bureaucracy to worry about. There's no politics.

00:20:05 Speaker_01
It's just like, hey, you do your code breaking work and then you publish it. You can collaborate with your colleagues there.

00:20:11 Speaker_00
Yep.

00:20:12 Speaker_01
Now, this is pretty crazy.

00:20:14 Speaker_01
Very quickly after Jim arrives at IDA, 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 50% free time on an idea to apply the same work and technologies that they're using in code breaking and signal intelligence to trading in the stock market.

00:20:41 Speaker_01
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 is Rentech.

00:20:56 Speaker_00
Just 20 years before Rentech. It's crazy. 1964 this was published? Yes.

00:21:03 Speaker_01
Now, at this point in time,

00:21:06 Speaker_01
fundamental analysis was then, as in most of the world today still is, the primary way of investing in things of, hey, I know this company, I'm going to analyze their revenues, their price multiple, or I'm going to 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 going to invest on those insights.

00:21:29 Speaker_00
You're effectively looking at the intrinsic value of an asset, trying to assign it a value and make investments based on that.

00:21:36 Speaker_01
Yes, fundamental investing. There also existed, in the 60s, technical investing, which kind of is voodoo. This is like, I'm looking at a stock chart and I've got a feeling that it's going to go up.

00:21:53 Speaker_01
Like, I'm tracing this pattern and it's going up, baby, or no, no, no, this pattern is going down.

00:21:59 Speaker_00
Yeah, using the phrase technical might be a little generous, but what they're looking for basically 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.

00:22:16 Speaker_01
And what Jim and his colleagues here are suggesting is that, but just not really done by humans. It's that with a lot more data and a lot more sophisticated signal processing.

00:22:31 Speaker_00
you might say, why is it this group of people that came to that conclusion of applying computational signal analysis to investing? Well, it's effectively the same thing as code breaking.

00:22:43 Speaker_00
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.

00:22:53 Speaker_01
Totally. When Jim started working on code breaking, I think he just looked right back to his experience trading in the markets and was like, whoa, this is the same thing.

00:23:02 Speaker_00
Which is not an insight other people had. That was the amazing thing about his background, priming him to realize that.

00:23:09 Speaker_01
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.

00:23:24 Speaker_00
So mathematical models, statistical analysis, we actually hear a lot of that in the world today because machine learning is a thing.

00:23:33 Speaker_01
Yes. What they are really doing here at IDA and then soon in Rentech is early machine learning. And Jim just had this incredibly brilliant insight that you can use these techniques and this technology for making investments.

00:23:51 Speaker_00
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00:23:57 Speaker_01
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00:24:13 Speaker_00
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00:24:25 Speaker_01
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00:25:00 Speaker_01
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00:25:12 Speaker_00
Yep. So learn how you can put AI agents to work for your people by clicking the link in the show notes or going to servicenow.com slash ai-agents. Okay, David, so this paper is published.

00:25:27 Speaker_00
They're going to trade and make a whole bunch of money in the stock market by applying this code-breaking, signal-processing, data-analysis approach to investing.

00:25:38 Speaker_01
Yep. So then the natural question is, okay, what is the model here? How are they going to do this?

00:25:44 Speaker_01
And it turns out that one of the employees of IDA at this time, and one of the members of this sort of rebel group, shall we say, within the organization, is a guy named Lenny Baum.

00:25:53 Speaker_01
And Lenny just happens to be the world expert in a mathematical concept called a Markov model. Specifically, a version of Markov model called a hidden Markov model.

00:26:07 Speaker_01
Now, a Markov model is a statistical concept that's used to model pseudo-random or chaotic situations.

00:26:16 Speaker_01
Basically, it says, let's abandon any attempt 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 of the situation?

00:26:31 Speaker_01
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?

00:26:44 Speaker_01
And the answer to that is usually yes, even if you don't know anything about fundamentally how the system operates.

00:26:51 Speaker_00
So the great example that Greg Zuckerman gives in the book is, yes, a baseball game. There's three balls and two strikes. That state has a narrow set of states after it.

00:27:03 Speaker_00
It's going to be a strikeout, they're going to get on base, it's going to 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. Whereas when it's zero balls and zero strikes,

00:27:14 Speaker_00
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?

00:27:20 Speaker_00
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,

00:27:34 Speaker_00
you actually can kind of get pretty good at at least, if not predicting, understanding the probability distribution of the outcomes for any given state in the game.

00:27:45 Speaker_01
So we brought up machine learning and AI a minute ago. This is a foundational concept to modern day AI. If you think about large language models and predicting what comes next, it's not like these large language models necessarily understand English.

00:28:03 Speaker_01
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 frame, et cetera.

00:28:13 Speaker_00
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.

00:28:22 Speaker_00
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.

00:28:33 Speaker_00
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.

00:28:42 Speaker_00
And of course, in these early rudimentary Markov chain things like the one I did in college, it kind of spits out nonsense. But that would evolve to be the LLMs that we know of today.

00:28:52 Speaker_01
Yes, totally. And that is what they were using at IDA to do code breaking. And that's what they propose in this paper that they could use in the stock market too.

00:29:02 Speaker_00
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 kind of guess at what the probabilities of the next thing to happen are based on the state.

00:29:18 Speaker_00
Investing is kind of the same thing, or at least the stock market movements are, where you don't know the future. You don't know what's going to happen.

00:29:24 Speaker_00
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?

00:29:35 Speaker_00
You don't know the relationship between those companies, so you can't forecast with 100% certainty what is going to happen.

00:29:42 Speaker_00
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 or at least understand the probability of any individual outcome based on a state today of what could happen next.

00:30:00 Speaker_01
Yes, exactly. So Jim and Lenny and this whole little crew, they're pretty fired up. They're like, oh, great. Let's go raise a fund and invest in the markets using this strategy.

00:30:17 Speaker_00
Certainly we're going to be successful at raising that fund and certainly we're going to be very profitable because we've got this great idea. Totally. What could go wrong?

00:30:24 Speaker_01
Well, in the mid 60s, The idea that some wonky academics at some random secretive agency in Princeton, New Jersey could go raise money was non-viable.

00:30:38 Speaker_01
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.

00:30:47 Speaker_01
And here are these academics who are working at some random unknown nonprofit saying, give us money. We don't know anything about these companies that we're going to invest in.

00:30:57 Speaker_01
We don't know anything about fundamentals, but we've got a really good algorithm. People are probably like, what is an algorithm? So they just have no access to capital.

00:31:06 Speaker_00
Right. This was decades before it became high pedigree to come from a technical computer science background in the world of investing.

00:31:14 Speaker_01
Yes. So a bunch of kind of 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.

00:31:25 Speaker_01
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?

00:31:38 Speaker_00
Right. It's a little bit of a clown show on the operational side, even if the idea is good.

00:31:43 Speaker_01
Yes. 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.

00:31:52 Speaker_01
Shortly after all this though, Jim ends up moving on anyway because 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.

00:32:04 Speaker_01
Jim writes an op-ed in the New York Times denouncing the Vietnam War and saying 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.

00:32:17 Speaker_00
which is so naive, thinking you can write an op-ed in the New York freaking Times and that's not going to create issues for you in your job.

00:32:25 Speaker_01
Even more than that, 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.

00:32:37 Speaker_01
And when the Newsweek piece comes out, that's when the Department of Defense is like, all right, you got to fire this guy. So, Jim gets fired in 1967.

00:32:48 Speaker_01
Even though he's a star codebreaker, he made supposedly huge contributions to the group, which are still classified. But at age 30, with a wife and three kids, he's out on the street.

00:33:00 Speaker_01
And even though he's super smart, his colleagues love him clearly, 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.

00:33:16 Speaker_01
His job prospects are not great.

00:33:19 Speaker_00
Yeah.

00:33:20 Speaker_01
So he takes pretty much the only halfway decent paying job that he could get, which is to be the chair of the newly established or maybe reestablished math department at the State University of New York, Stony Brook, which is the Long Island campus of the State University of New York.

00:33:42 Speaker_01
This is not Harvard. This is not MIT. No, it is not. But it did have one very important thing going for it, which is why Jim ended up there.

00:33:53 Speaker_01
And that is that Nelson Rockefeller, who was then the governor of New York, had launched a campaign, a hundred million dollar campaign, to try and turn this Long Island campus of the State University of New York

00:34:08 Speaker_01
into a 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 Stony Brook to become a math and sciences powerhouse. And Jim is the key.

00:34:28 Speaker_01
He wouldn't be able to recruit somebody like Jim otherwise, but because he's now kind of tarnished his career, Here's a like very talented mathematician that they can convince to come be chair of the department. Yep.

00:34:41 Speaker_01
So they basically give Jim an unlimited budget and leeway to go try and poach math professors from departments all over the country in the world and bring them there to Long Island.

00:34:53 Speaker_01
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 he's given such leeway. And Stony Brook is so different from the politics of an MIT or a Harvard or a Princeton.

00:35:10 Speaker_01
He says, hey, come here, I'll pay you more. But even more importantly, you can just focus on your research. You're not going to have to deal with committees. You're not going to have to do all this stuff. There is none of this stuff here.

00:35:22 Speaker_01
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. Yep. And amazingly it works.

00:35:34 Speaker_01
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 recruiting and building one of the best math departments in the world. Amazing.

00:35:49 Speaker_01
Totally amazing. But in true Jim fashion, after a couple of years of this, and also his marriage with Barbara falling apart, starts getting restless again.

00:35:59 Speaker_01
He decides that he wants to go 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.

00:36:08 Speaker_01
And this is where Chern and Simons end up collaborating and developing the Chern-Simons theory that ends up winning the highest award in geometry from the American Mathematical Society and really kind of is Jim's personal mark on mathematics. Yep.

00:36:24 Speaker_01
Now also, right around the same time, Remember the Columbian Flooring Company? It gets acquired, and Jim and his buddies who are partners in it come into a good amount of money.

00:36:38 Speaker_01
And Jim is newly divorced, he's restless in academia, he has these ideas back from when he was an IDA about what you could do in the markets if you had capital. He starts trading again, and he gets more and more into it,

00:36:56 Speaker_01
Meanwhile, like we said, he's becoming disillusioned again and restless at academia. And in 1978, he leaves to focus full-time on trading, which is a huge shock to the academic community.

00:37:08 Speaker_01
Remember, he's assembled this superstar team there at Stony Brook. There's a quote in Greg's book from another 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.

00:37:21 Speaker_00
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.

00:37:32 Speaker_01
But the idea that you would leave to do anything commercial, you're doing a disservice to humanity.

00:37:38 Speaker_00
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.

00:37:48 Speaker_01
Right. Yeah, I don't think it was that 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.

00:37:58 Speaker_00
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.

00:38:08 Speaker_00
And my currency is the papers I publish and the awards that I win. And that's what I want. Yep.

00:38:14 Speaker_01
Now, Stony Brook, 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. Yep. The wooded Long Island suburbs.

00:38:27 Speaker_01
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, a combination of money and metrics or econometrics.

00:38:44 Speaker_01
And he recruits his old IDA buddy, original partner in crime on the trading idea, Lenny Baum, to come and join him. And this time, though, they have some capital from the sale of the flooring company.

00:39:01 Speaker_00
And how much did he make on that flooring sale?

00:39:04 Speaker_01
I think together with Jim, his partners, and whatever money Lenny put in, they had a little less than $4 million in this initial capital. In 1978. Yep.

00:39:16 Speaker_01
Now Jim also has another advantage at this point in time, which is he's right down the street from Stony Brook, and he's just recruited all of these superstar mathematicians. The table has been set.

00:39:29 Speaker_01
Yes, and those folks are more loyal to Jim than they are to Stony Brook.

00:39:33 Speaker_00
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.

00:39:42 Speaker_01
Yes, in general, but some of them, and in particular, the superstar James Axe, Jim convinces to come join him in his trading operations.

00:39:52 Speaker_00
So having Baum and Axe and Simons, it's like suddenly this extremely credible team in the math world. Yes, beyond credible. Right.

00:40:04 Speaker_00
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.

00:40:10 Speaker_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, 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.

00:40:26 Speaker_01
Now, at this point, they're primarily trading currencies. not stocks.

00:40:33 Speaker_01
And currencies are obviously large markets, but they aren't impacted by as many signals and as many factors as stocks are, or really even slightly more complex commodities like, I don't know, soybeans or whatever.

00:40:46 Speaker_00
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 going to do something or not.

00:40:59 Speaker_00
It's basically like betting 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 going to do.

00:41:13 Speaker_01
Yes. And this is super important. At the end of the day, they build some models there. They're getting the early versions and infrastructure and scaffolding of this quantitative approach set up.

00:41:27 Speaker_01
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.

00:41:37 Speaker_01
They'll take some signals from the model, they'll see it's interesting what they spit out, 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.

00:41:50 Speaker_00
The computers are by no means running loose at this point.

00:41:53 Speaker_01
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 going to do this or not?

00:42:01 Speaker_01
Or are we going to do something just totally different that we think is what's going to happen? Yep. And this actually does make sense. Really for two reasons.

00:42:11 Speaker_01
One, computers and computing power just wasn't sophisticated enough yet to really build AI 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.

00:42:30 Speaker_01
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. And I actually spoke with Howard Morgan, who's going to come up here in a second.

00:42:47 Speaker_01
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. It's like proving a proof or proving a theorem or something like that.

00:42:59 Speaker_01
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, no, no, you need proof. And that's the world that these guys are coming from.

00:43:10 Speaker_01
They're like, oh, we can use data to sort of help us here. But ultimately we want to have a rock solid theory of what is fundamentally happening here.

00:43:20 Speaker_00
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 because the data suggests it.

00:43:26 Speaker_00
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

00:43:46 Speaker_00
act on large amounts of signals and understand them all to come up with results. They just didn't have any of that at the time. So it was hunches and chalkboards.

00:43:55 Speaker_01
Yes. And so much so that 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.

00:44:08 Speaker_01
But I also want to put my money somewhere else too for some diversification. So this is where Howard Morgan comes in.

00:44:15 Speaker_01
You know, we used to talk about this on old acquired episodes that in the early days of Silicon Valley, there were only 10 people out here and they all knew each other and they were all doing the same thing.

00:44:25 Speaker_01
This was also the case in East coast finance and technology and early VC in these days, Howard Morgan would go on to be one of the co-founders of first round capital.

00:44:36 Speaker_00
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.

00:44:45 Speaker_01
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 CS at Penn and business at Wharton.

00:45:01 Speaker_01
And he had been involved in bringing ARPANET to Penn and was kind of like early, early internet pioneer. And so as a result, he was super plugged into tech and early startups and really early, early proto internet stuff.

00:45:19 Speaker_01
And Jim gets excited about investing together with Howard. So they say like, Hey, maybe we should partner together. And in 1982, Jim actually winds down monometrics and he and Howard co-found a new firm together.

00:45:36 Speaker_01
That's going to reflect both of their backgrounds and be a great diversification. Jim and his group are going to bring in the quantitative trading thing.

00:45:46 Speaker_00
And again, trading on currencies and commodities at this point.

00:45:51 Speaker_01
And Howard's going to bring in private company technology investing, and they pick a name for a firm that is going to reflect this. Renaissance technologies. It's crazy. And that is why Rentech is called Rentech.

00:46:07 Speaker_00
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

00:46:21 Speaker_00
two more different strategies in investing.

00:46:24 Speaker_00
I mean, a long-term illiquid thing like venture capital, highly speculative versus, you know, we're going to trade whether we think the French franc is going to go up or down tomorrow based on the whim of some government leader.

00:46:39 Speaker_00
It's unbelievable these were under the same roof. Totally.

00:46:42 Speaker_01
But when you know the whole background in history, it kind of makes sense because this is their personal money. This is Jim and his buddies, Lenny and James and Howard. This is not institutional capital here.

00:46:54 Speaker_01
They're not out pitching LPs of like, oh, you should invest in my diversified strategy of currency trading and private technology startups.

00:47:01 Speaker_00
Yeah. When they say multi-strategy, this is really multi-strategy.

00:47:06 Speaker_01
We'll get into what multi-strategy today means later. But in these early days of rent tech, 50% of the portfolio was venture capital and 50% was currency trading.

00:47:16 Speaker_01
And in fact, a couple 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 40%, which is wild.

00:47:35 Speaker_01
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. This is crazy. I mean, blow up risk is always an issue in the markets, but this happened to Rentech.

00:47:50 Speaker_00
And because we quickly got to this point in the story, it would be easy to say, well, that's a clause that has a lot of teeth. There were many sort of rumbles of something like this potentially happening.

00:48:00 Speaker_00
Simon's going to Lenny and saying, hey, maybe we should cut some of our losses and it's OK to trade out of these positions. And Lenny was just very dug in on I'm a true believer.

00:48:10 Speaker_00
And that's how you can get into a situation where you trigger a covenant like this. Totally.

00:48:14 Speaker_01
And again, also shows they weren't doing model-based quantitative trading really at this point in time. Now, so much gut. So as a result of that, for a while, Rentech is truly almost entirely a venture capital firm. At one point,

00:48:31 Speaker_01
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. That one investment is half of Jim's net worth.

00:48:44 Speaker_00
What?

00:48:45 Speaker_01
At this low point for the trading side.

00:48:46 Speaker_00
Yes. I had no idea. That's crazy.

00:48:50 Speaker_01
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 because that was the only thing worth it and making money.

00:49:01 Speaker_00
Well, I mean, it's the only thing where they actually had an edge from Howard's access to deal flow because they certainly didn't have an edge in the global currency markets.

00:49:09 Speaker_01
So I think perhaps in part because of the trading losses, 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.

00:49:23 Speaker_01
Sandor was another Stony Brook alum that joined them. And the two of them want to move out to California and do trading out there. Jim says, sure, fine. I'm here with Howard. I'm doing venture capital stuff. Why don't you go move out to California?

00:49:38 Speaker_01
You can start your own firm, which they do. It's called Axcom, A-X-C-O-M. And we'll contract with Axcom to run what's left of the trading operations here for Rentech.

00:49:52 Speaker_00
So it's this interesting arm's length thing where Jim strikes a deal where he's going to own a part of Axcom in exchange for this very favorable contractual relationship where they're going to hire them to be the manager for this pot of money that Renaissance has raised.

00:50:09 Speaker_00
But you know, it's technically not Renaissance. It's Axcom. Right. It's another company that is now doing the quantitative trading. Yep. And I think Jim owned a quarter of it. Is that right? Yes, that's right.

00:50:20 Speaker_00
And importantly, I don't think anyone had any idea what Axecom would become or how unbelievably profitable it would be.

00:50:29 Speaker_01
Uh, no. Nobody would have done what they did had they known what was coming.

00:50:35 Speaker_00
Yes. Wouldn't have spun it out.

00:50:37 Speaker_01
No. So once Axe and Strauss get out to California, Strauss, he's kind of 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.

00:50:53 Speaker_01
he starts getting really into data and he starts collecting intraday pricing movements on securities. At this point in time, I think really the best data you could get from providers out there was maybe open and close data on securities pricing.

00:51:12 Speaker_01
Strauss finds a way to get tick data, like every 20 minute data on these securities throughout the day.

00:51:21 Speaker_00
Not only that, 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.

00:51:33 Speaker_00
So he's getting early 1900s, even 1800s stuff to try to just 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.

00:51:47 Speaker_01
Yeah, I mean, he's doing ETL on the data.

00:51:49 Speaker_00
Yes. I think before anybody knew what ETL was. Again, no one told him to do that. That was just a self-motivated, almost like obsession of like, well, if we're going to have data, it should be well formatted and well understood and labeled and all that.

00:52:02 Speaker_01
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 with my buddy who's a Berkeley professor out there, Elwin Berlekamp.

00:52:13 Speaker_01
And Berlekamp had studied with folks like John Nash and Claude Shannon at MIT.

00:52:21 Speaker_00
I love that Claude Shannon is coming in again! I know! We talked about it a lot on the Qualcomm episode.

00:52:26 Speaker_00
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.

00:52:39 Speaker_00
But the fact that Burlicamp is crossing paths at MIT with Claude Shannon, so cool.

00:52:45 Speaker_01
So cool. And most importantly for this specific use case, Berlekamp had worked with John Kelly, who developed the Kelly criterion on bet sizing, which poker players will likely be well familiar with. Yep.

00:52:59 Speaker_01
So with this combination now of much, much, much better and deeper data from Strauss,

00:53:05 Speaker_01
And Berlekamp coming in and working with Axe on the models and saying, hey, 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.

00:53:17 Speaker_01
Maybe it was naive of like every trade was the same or just like we should actually be systematic about this. The models start really working. Yep.

00:53:26 Speaker_00
This is the turning point.

00:53:29 Speaker_01
Yeah. In these kind of mid eighties years, Axcom is generating IRRs of like 20 plus percent on the trading side. You know, not necessarily going to beat venture capital IRRs, but liquid. Yes. Reliable.

00:53:44 Speaker_00
Well, that's the thing. They don't know how reliable yet.

00:53:46 Speaker_00
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?

00:53:59 Speaker_01
Yes, but the early results are really good, and Jim and Berlekamp especially are very encouraged by this. So in 1988, Jim and Howard Morgan decide to spin out the venture investments, and Howard goes to manage those with basically their own money.

00:54:19 Speaker_01
Fun coda on this, when Howard starts first round a number of years later with Josh Koppelman, Jim, of course, is a large LP. And Howard, of course, remains an investor in Rentech, the first institutional fund that First Round ended up raising.

00:54:40 Speaker_01
was a 50x on $125 million fund. It had Roblox, Uber, and Square. So I believe this is right. I think Jim made as much money from his investments in first round as Howard did from his LP stake in Rentech.

00:54:58 Speaker_00
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 Jim or Renaissance on the whole internet, but I assume you got that from Howard.

00:55:13 Speaker_01
Yeah. It was super fun talking to Howard about this and just the history of how first round started at early super angel investing and everything that became.

00:55:21 Speaker_00
I also didn't realize that first rounds fund one was a 50 X on $125 million fund.

00:55:27 Speaker_01
first institutional fund, which I believe they called Fund 2.

00:55:32 Speaker_00
I mean, wild. Wild stuff.

00:55:35 Speaker_01
Totally wild. So, when Howard spins out the venture activities, Jim then decides to set up a new fund as a joint venture between Rentec and Axecom.

00:55:48 Speaker_01
and they decide to name it after all of the collective mathematical awards that Jim and James and Berlekamp and all these prestigious mathematicians have won in their careers. They name it the Medallion Fund.

00:56:06 Speaker_00
And listeners, we've arrived. This is the part of the story that matters. The medallion fund is the crown jewel, or you might even say actually the only interesting thing about Renaissance.

00:56:16 Speaker_00
And it is born out of this observation that, oh my God, what they're doing over there at Axcom is really interesting. Maybe they shouldn't be doing it all the way over there.

00:56:26 Speaker_00
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.

00:56:35 Speaker_00
And again, still just currencies, still just commodities futures, not playing the stock market at all, but the seeds and the ideas, the huge amount of clean data,

00:56:47 Speaker_00
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.

00:57:02 Speaker_01
Those ideas had all existed before. This is the first time that it's all brought together. Yeah.

00:57:08 Speaker_00
And actually working and operationalized. And frankly, that computers got good enough to actually do it too. That's another big piece of this.

00:57:16 Speaker_01
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00:59:35 Speaker_00
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00:59:38 Speaker_01
So they've got this grand new plan and vision with the Medallion Fund. Unfortunately, right out of the gate, the fund stumbles a bit. and Axe ends up getting burned out.

00:59:53 Speaker_01
Berlekamp, though, is like, no, no, no, no, this is an anomaly, we're gonna fix this. I really, really believe that what we're doing with these models is gonna be extremely profitable.

01:00:04 Speaker_01
So he buys out most of Axe's stake in the summer of 1989, and he moves the offices up to Berkeley.

01:00:14 Speaker_01
And there he comes up with the idea 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, 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.

01:00:43 Speaker_00
It's that, and it's two other things. One is, the further into the future you look, the less certain you can be about it. If you know something is worth $10 right now, what you know five minutes from now is it's probably going to be worth about $10.

01:00:58 Speaker_00
The most likely situation is it's within 5% of that. If you ask me three years from now, I have almost no intuition about that. And a state machine is the same way.

01:01:08 Speaker_00
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 going to be right, call it something like 50.25% of the time.

01:01:22 Speaker_00
then the amount of money you can make is gated by the number of bets you can make at a quarter percent edge.

01:01:30 Speaker_00
If I walk up to the casino and I think I'm right about this particular roulette wheel, which of course you're not, 50.25% of the time, and I decide to play once or play twice or play five times,

01:01:41 Speaker_00
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.

01:01:47 Speaker_00
But if I walk up to said game with a little bit of edge, and I use small bet sizes, and I play 10,000 times, I'm gonna walk out with a lot of money.

01:01:55 Speaker_01
There is a great Bob Mercer quote about this later. He says, we're right 50.75% of the time.

01:02:03 Speaker_00
And I do think he's making up that number. I think it's illustrative.

01:02:06 Speaker_01
Right. But we're 100% right 50.75% of the time. You can make billions that way.

01:02:15 Speaker_00
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.

01:02:27 Speaker_01
A lot. Yes. And then back to the Kelly criterion, adjust your bet sizes over time as you're making those bets. Yep.

01:02:34 Speaker_00
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.

01:02:44 Speaker_00
It gets a little bit different in the market.

01:02:46 Speaker_00
For example, there are real transaction costs, especially at this point in history before some of these more innovative trading business models with pay for order flow and zero transaction fees and all this stuff.

01:02:56 Speaker_00
There's real transaction costs to putting on these trades. And of course, you're going to move the market when you put on these trades.

01:03:03 Speaker_01
Yes. This is slippage.

01:03:05 Speaker_00
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.

01:03:12 Speaker_00
You actually have to meet the constraints of the real world when you're deciding instead of a few big bets, we're going to have 100,000 tiny bets.

01:03:20 Speaker_01
Yes. And as time goes on and the whole quant industry emerges and becomes much more sophisticated, I think it's particularly the slippage there that becomes the governor on how high velocity you can actually be on this.

01:03:32 Speaker_01
And the slippage is that once you are at a certain scale, you are going to move the market with your trades.

01:03:38 Speaker_00
So the deeper you get into the order book, like let's say you want to buy $5 million of something, maybe your first $100,000, you're pretty sure you can get the quoted price.

01:03:46 Speaker_00
But by your last $100,000 of that $5 million buy, the price might have gotten pretty different already.

01:03:53 Speaker_01
Yeah, we're going to come back to this in just a minute. But this certainly for early rent tech, and then even now still for all of quantitative finance is a really, really, really important thing.

01:04:04 Speaker_00
Yep. And David, in a very crude way, calls back to last episode on Hermes. 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.

01:04:17 Speaker_00
If the family was going to sell to Bernard Arnault, it would behoove you to be first in the order book, not last in the order book. Yes.

01:04:24 Speaker_01
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.

01:04:35 Speaker_01
You can see quoted valuations and quoted price streams, but oftentimes that's like the mistake of just looking at averages.

01:04:42 Speaker_00
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.

01:04:53 Speaker_00
And for all of those buyers and all of those sellers, what are the price at which they are willing to transact? And the way that tends to manifest on a stock chart is here's the price of the share right now.

01:05:03 Speaker_00
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.

01:05:13 Speaker_01
Yes. Now, at this point in time, when the Medallion Fund is first starting to work in, say, late 1989, early 1990, it's small enough that this isn't a big consideration yet.

01:05:24 Speaker_00
Yeah, right.

01:05:25 Speaker_01
Medallion was about $27 million under management when Burle Camp bought out Axe. In 1990, the first full year after that, the fund gains 77.8% gross, which after fees and carry was 55% net. Now what were the fees in carry?

01:06:04 Speaker_00
Unbelievable. World-changing.

01:06:06 Speaker_01
Hell yeah, let's go. And indeed, it was a hell yeah, let's go situation.

01:06:13 Speaker_00
So the numbers you quoted me, the gross and the net, sounded quite different. Talk to me about the fees in carry.

01:06:18 Speaker_01
So carry, I've seen different sources of whether it was 20 or 25% in the early days. But the management fee on the fund was 5%, which is crazy. The top venture capital firms in the world charge a 3% management fee.

01:06:32 Speaker_01
And even that is like, everybody holds their nose and is like, this is ridiculous. How on earth were these nobodies charging a 5% management fee out the gate to their investors? Well, a couple things. One, their investors were not sophisticated.

01:06:49 Speaker_01
It was mostly their own money and their buddies' money. So they set that precedent. They set that precedent. But two, though, they actually needed the money because Strauss's infrastructure costs were about $800,000 a year.

01:07:04 Speaker_01
So they just backed into the management fee based on like, hey, we need $800,000 a year to run the infrastructure. Plus, we need some money to, you know, pay folks and whatnot. Like, great, 5% management fee.

01:07:16 Speaker_00
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 going to need a lot of fees to do that.

01:07:24 Speaker_00
And so the investors basically took the deal if they thought about it enough. Okay, so that's the fees. On the performance, that 20 or 25%, it's just not actually that far above market, if it's above market at all.

01:07:37 Speaker_00
What you're seeing is a high fee, normal-ish performance fee fund at this point in time.

01:07:42 Speaker_01
Yes. High management fee, normal-ish carrier performance element. Yep. So at the end of 1990, Simons is so jazzed about what's going on that he tells Burlicamp, hey, you should move here to Long Island. Let's recentralize everything here.

01:08:02 Speaker_01
I want to go all in on this. I think with some tweaks, we can be up 80% after fees next year. Burlicamp is a little more circumspect. A, he wants to stay in Berkeley. He doesn't have any desire to move to Long Island.

01:08:16 Speaker_01
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.

01:08:26 Speaker_01
He turns to Jim and he says, well, if you're so optimistic, why don't you buy me out? So Jim does at 6X the basis that Burlekamp had paid Axe a year earlier.

01:08:41 Speaker_00
On the one hand, making a 6x in one year sounds great.

01:08:44 Speaker_01
On the other hand, this is the equivalent of when Don Valentine sold Sequoia's apple steak before the IPO to lock in a great gain, but miss out on all the upside to come.

01:08:59 Speaker_00
David, I think we should throw this out so people understand the volume of this. They've generated on the order of $60 billion of performance fees for the owners of the fund over their entire lifetime. So, on the one hand, 6x in a year ain't bad.

01:09:16 Speaker_00
On the other hand, you owned a giant part of something that has dividended $60 billion in cash out to its owners.

01:09:24 Speaker_01
Yeah, that's just on the carry side. I mean, the owners are the principals. So just like dollars out of the firm, it's probably twice that. I would estimate probably $150, $200 billion that have come out of Medallion over the last 35 years.

01:09:41 Speaker_01
So Jim buys out Burlicamp. He rolls everything in the medallion fund back into Rentec itself, moves everything back to Stony Brook. Strauss moves to Stony Brook.

01:09:55 Speaker_00
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.

01:10:03 Speaker_01
Yes, that's right. And Strauss had a stake as well. So once Jim takes control and moves everything back, he basically decides that he's gonna turn Rentech into an even better, even more idealized version of IDA and the math department at Stony Brook.

01:10:27 Speaker_01
He's going to make this an academics paradise, where if you are one of the absolute smartest mathematicians or systems engineers in the world, this is where you want to be.

01:10:45 Speaker_01
So of course he starts raiding the Stony Brook department itself again and this is when Henry Laufer joins full-time.

01:10:54 Speaker_01
Laufer had been consulting with Medallion in the early days and working with Burlakamp as they're doing betsizing, as they're making more frequent trades.

01:11:04 Speaker_01
But now, once the whole operation is moved back to Long Island, Laufer's like, OK, great, I'll come full time. I'm here at Stony Brook anyway.

01:11:11 Speaker_00
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.

01:11:22 Speaker_00
And that is the story of Renaissance. It is not this singular clean narrative.

01:11:29 Speaker_00
It is a very complex reality 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.

01:11:45 Speaker_00
But while they were figuring it out along the way, it took a lot of people.

01:11:49 Speaker_01
A lot of people and just a lot of time, too. This is 25 years. This is a quarter century from the time that Baum and Simons write the paper at IDA until Medallion really starts to work. It takes a long time.

01:12:07 Speaker_00
And we haven't even introduced the two people who would become the co-CEOs of this company for 20 years. Yes.

01:12:15 Speaker_01
Well, let's get to that. So Jim moves everything back to Long Island, sets it up as this academic paradise, is recruiting the smartest people in the world. In 1991, the next year, the firm does 54.3% gross returns and 39.4% net returns after fees.

01:12:28 Speaker_01
So not Jim's bogey of 80%, but still pretty freaking great.

01:12:41 Speaker_00
And we should say, the years of modest performance are behind them. From every single year forward, they shoot the lights out. From 1990 onward, they never lose money, and on a gross basis, they never even do less than 30%.

01:12:56 Speaker_00
It's working, it's going, the whole rest of the story is about, hold on, keep the machine working, and we're on the train.

01:13:07 Speaker_01
The historic run has begun, let's just say. Yep. So, 1992, gross returns are 47%, 93, they're 54%. At the end of 1993, Simons decides to close the fund and not allow new LPs in.

01:13:26 Speaker_01
So if you're an existing LP, you can stay in, but they're no longer open for new inflows.

01:13:31 Speaker_01
He has so much confidence in what they're doing that he thinks they're all going to make more money without accepting new capital by just keeping it to the existing investor base. 1994 gross returns are 93 freaking percent.

01:13:47 Speaker_01
Medallion at this point is stacking up cash. It is a meaningful fund.

01:13:54 Speaker_01
It's about $250 million total at this point in time, which is small, but we're talking about 1994 with a bunch of outsiders and academics that have managed to amass a quarter billion dollars here. People start to pay attention.

01:14:10 Speaker_00
And the performance fees on this are $7 million, $13 million, $52 million. The free cash flow flowing to partners here is certainly becoming real too.

01:14:22 Speaker_01
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 and the slippage that we were talking about earlier.

01:14:36 Speaker_00
Yep.

01:14:36 Speaker_01
And that's sort of in the mid-90s? Yep. As they're hitting this 250 million, half a billion dollar scale.

01:14:42 Speaker_00
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 10, 20, 30 percent of the amount they want at that price. And then suddenly the price is very different.

01:14:54 Speaker_01
Yeah, up to this point, the vast majority of what Medallion is doing is trading currencies and commodities, not equities. Because you might be thinking, okay, yeah, I hear you, the 90s was a different era, but

01:15:11 Speaker_01
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 commodities and futures are small markets.

01:15:23 Speaker_01
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 limit.

01:15:34 Speaker_01
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. Equities are the holy grail.

01:15:48 Speaker_01
If we can make this work there, the depth in those markets will let us scale way, way, way bigger than we are now.

01:15:56 Speaker_01
and there's so much more data about 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.

01:16:08 Speaker_00
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 for Renaissance's systems. This is sort of their moment. This is what they were built for.

01:16:21 Speaker_00
And it's kind of funny that they've just been in kid glove land the whole time with these thinly traded markets with minimal data.

01:16:28 Speaker_01
Yes. And this brings us to Peter Brown and Bob Mercer. And in 1993, one of the mathematicians that Jim had recruited to Rentech, a guy named Nick Patterson, gets especially passionate about going out and recruiting new talent along with Jim.

01:16:47 Speaker_01
This is, I think, one of the keys to Rentech and the culture there. People want other smart people to come be there too. Nick's sitting there like, this is a joy. I want to go find other best people in the world to hang out with.

01:17:00 Speaker_01
And he had read in the newspaper that IBM was going through cost cutting and was about to do layoffs. And he also knew that the speech recognition group at IBM had some absolutely fantastic mathematical talent.

01:17:17 Speaker_01
And really, what they were doing was, again, another vector in the early AI machine learning research. Specifically, IBM's Deep Blue chess project of the time had come out of this group.

01:17:33 Speaker_01
And Peter Brown there was the one that actually spearheaded the project.

01:17:38 Speaker_00
And it's interesting that you talk about speech recognition as the perfect fit for what they were doing. And you might say, why is that?

01:17:47 Speaker_00
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.

01:17:57 Speaker_01
It's not just kind of, it's exactly the same signal processing.

01:18:00 Speaker_00
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.

01:18:13 Speaker_00
But what it does know is, when I hear this set of frequencies and tonalities and sounds, 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.

01:18:25 Speaker_00
When I say apple, you might say pie. the probability that pi is going to be the next word following apple is significantly higher.

01:18:33 Speaker_00
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.

01:18:46 Speaker_00
So when you're listening to those frequencies, you can say it's probably going to be one of these three rather than search the entire dictionary for any word that it could be to narrow the processing power.

01:18:56 Speaker_00
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.

01:19:04 Speaker_01
Yes, at operational scale. And this is what's so important and why the two of them become probably the most critical hires in Rentex history, even including all the great academics that came before them.

01:19:18 Speaker_01
because they're good on the math side, but they have this large systems experience.

01:19:24 Speaker_01
And Jim and Nick know that if they're going to move into equities, because of the volume of data and because of how much more complex that market is, they need more complex systems.

01:19:36 Speaker_01
And the current talent at Rentech coming from academia has just never experienced that or built anything like it.

01:19:41 Speaker_00
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 every stock trading.

01:19:57 Speaker_00
So they're 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.

01:20:06 Speaker_00
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 win. oil prices go up, the airline prices are going to get hit.

01:20:18 Speaker_00
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 from the probabilities that you just discovered?

01:20:32 Speaker_00
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

01:20:41 Speaker_00
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 specific byte sizes so that you don't move the market.

01:20:55 Speaker_00
So you're looking for a super multivariate, multidimensional 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.

01:21:12 Speaker_00
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. But they do need to act with reasonable quickness, probably on the order of minutes.

01:21:23 Speaker_00
So these need to be really efficient computer systems, too.

01:21:27 Speaker_01
Yeah. And the universe of equities is so much more multi-dimensional 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.

01:21:42 Speaker_01
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.

01:21:53 Speaker_00
And just keep adding layers of complexity here. Keep adding new things to multiply by. Many of these are traded on multiple exchanges.

01:22:01 Speaker_00
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.

01:22:13 Speaker_01
So, Patterson and Simons go raid IBM. They're like Steve Jobs raiding Xerox PARC. They bring Peter and Bob and one of their programming colleagues, David Magerman, over from IBM into Rentech. And they get started on building the equities model.

01:22:33 Speaker_01
But it turns out, A, they're obviously very successful at that. But the impact that they have and what they build is even bigger because Bob and Peter realized that, hey, actually, we should just have one model for everything here.

01:22:53 Speaker_01
For currencies, for commodities, for equities, everything is correlated. Everything is a signal. It's not like the equities market is wholly independent and separate from what's happening in currencies or what's happening in commodities.

01:23:08 Speaker_01
There are relationships everywhere. We really want just one model. This is like a fantastical undertaking, especially in the early to mid 90s.

01:23:19 Speaker_00
Right. But if you can nail it, 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.

01:23:32 Speaker_00
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.

01:23:41 Speaker_00
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.

01:23:48 Speaker_01
It turns out that this was actually the second most important innovation that Bob and Peter bring to Rentech, the actual product and performance of having one model. The most important thing is that if you have only one model, one infrastructure,

01:24:06 Speaker_01
Everybody in the firm is working on that same model. You can all collaborate all together, which is especially important when you have the smartest people in the entire world, all in one building.

01:24:21 Speaker_01
Before this, there were separate models within Rentech. So insights 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.

01:24:36 Speaker_00
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.

01:24:46 Speaker_00
There's a lag and it may actually not get implemented.

01:24:48 Speaker_01
Yeah. This is wholly unique and revolutionary. No other at-scale investment firm, period, and especially quant firm, operates this way today with just one model. There are portfolio managers and teams and multi-strategy.

01:25:08 Speaker_01
People are 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.

01:25:18 Speaker_01
What Bob and Peter do is they unify everything at Rentech. So all the wood is going behind one arrow. Yes.

01:25:26 Speaker_00
We want to thank our longtime friend of the show, Vanta, the leading trust management platform. Vanta, of course, automates your security reviews and compliance efforts.

01:25:37 Speaker_00
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01:25:49 Speaker_01
Yep, Fanta is the perfect example of the quote that we talk about all the time here on Acquired, Jeff Bezos, his idea that a company should only focus on what actually makes your beer taste better, i.e.

01:26:00 Speaker_01
spend your time and resources only on what's actually gonna move the needle for your product and your customers and outsource everything else that doesn't. Every company needs compliance and trust with their vendors and customers.

01:26:11 Speaker_01
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01:26:18 Speaker_00
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01:26:25 Speaker_00
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01:26:35 Speaker_00
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01:26:45 Speaker_01
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01:26:53 Speaker_00
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01:27:09 Speaker_00
And thanks to friend of the show, Christina, Vanta's CEO, all Acquired listeners get $1,000 of free credit. Vanta.com slash Acquired. So David, the equities machine.

01:27:22 Speaker_01
Yes, and indeed a machine it is. So Peter and Bob come in in 1993, and 1994, 1995, they're building this, Rentec is getting into equities.

01:27:33 Speaker_00
And yeah, just imagine the computers that you were using during 1994 and 1995.

01:27:38 Speaker_00
It is astonishing the level of computational complexity and coordination and results that they are pulling off, again, in real time, analyzing these markets with the technology that was available during those years.

01:27:52 Speaker_01
Yes. And here's what's amazing. Returns go down maybe slightly, certainly a bit from the blowout year that 1994 was, but they're still above 30% every single year. Most years above 40%.

01:28:07 Speaker_01
This is unbelievable that they're maintaining this performance as they're going into this hugely more complex market and they're scaling assets under management. So by the end of the 1990s, Medallion has almost $2 billion in assets under management.

01:28:24 Speaker_01
while maintaining roughly the same performance by getting into equities. This is huge.

01:28:31 Speaker_00
Yep. And David, if you just kind of look at this and do the math, okay, so 94, their AUM was $276 million and they grew 93%. And then their AUM the next year was $462 million, and then they grew 52%. And their AUM the next year was $637 million.

01:28:50 Speaker_00
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.

01:29:00 Speaker_00
This is the same capital that they had in 1993 that has gone from $122 million at the beginning of that year to 1999 being $1.5 billion.

01:29:11 Speaker_01
Yes. And then in the year 2000. They just totally blow the doors off. 128% gross returns, net returns after fees of 98.5%. This is bananas.

01:29:31 Speaker_00
They grow the fund from 1.9 billion to 3.8 billion of assets under management. Again, purely by investing gains, not by getting any new investors. The year the tech bubble burst.

01:29:44 Speaker_01
Yes, while the whole rest of the market is down big time, Medallion is up 128% gross on the year. And this becomes a theme. High volatility is when Medallion really shines.

01:29:59 Speaker_00
And here you go. uncorrelated. They have their final stamp of approval right here of not only are we a money printing machine, we are a money printing machine in all environments regardless of the state of the broad market.

01:30:13 Speaker_00
And David, as you said, volatility actually makes their algorithms work even better because what are they doing?

01:30:19 Speaker_00
They're looking for scenarios where the market's going to act erratically and they can take advantage of people making decisions that they shouldn't. And anytime any investors are under pressure,

01:30:30 Speaker_00
there's a little bit of edge that's going to accrue to a medallion that's saying, Oh, okay, you're fear selling right now. Well, I can determine if you should be for your selling or not.

01:30:39 Speaker_00
And if I determine that you shouldn't be dumping that asset, I'm buying it from you.

01:30:42 Speaker_01
So there's a really fun story around this that really illustrates Jim's genius in managing the firm and the people. and how this year was when they really figured this out.

01:30:56 Speaker_01
So the first couple days of the tech bubble bursting, Medallion actually takes a bunch of large losses. And part of it might be that the model wasn't tuned right yet because nobody at Rentec had seen this type of behavior in the market before.

01:31:13 Speaker_01
Part of it might also be too that it didn't perform well for those couple days. It's a really stressful time for everybody. Everybody's in Jim's office. Jim's smoking his cigarettes. It's a cloud of smoke. And they're debating what to do.

01:31:27 Speaker_01
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. The model may be saying we should stay long here, but let's not blow up the firm. Yep.

01:31:41 Speaker_01
After this goes down, Peter Brown comes to Jim and offers to resign, given the losses that they incurred over these couple days. And Jim says, what are you talking about? Of course you shouldn't resign.

01:31:54 Speaker_01
You are way more valuable to the firm now that you've lived through this, and you now know not to 100% trust the model in all situations.

01:32:03 Speaker_00
It's fascinating. It's such a good insight. That illustrates Jim as a leader right there.

01:32:08 Speaker_01
It totally does.

01:32:09 Speaker_01
There's a parallel story when Jim ultimately does retire in 2009 and Peter and Bob take over as co-CEOs, where a year or so before the quote unquote quant quake had happened, 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.

01:32:32 Speaker_01
And during that period, Peter argued very strenuously that we should trust the model, stay risk on. This is going to be an incredibly profitable time for us. And Jim pumped the brakes and stepped in, intervened and took risk off.

01:32:48 Speaker_01
And Peter goes to Jim again around the CEO transition and says, Hey, Jim, aren't you worried that with me running the place now, I'm going to be too aggressive and blow it up one of these days. And Jim says, no, I'm not worried at all.

01:33:03 Speaker_01
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.

01:33:14 Speaker_00
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.

01:33:21 Speaker_00
So if somebody is 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.

01:33:30 Speaker_00
But, you know, you naturally want to sort of play the other side to balance out the person sitting across from you.

01:33:37 Speaker_01
Yeah. So back to the year 2000 and this incredible performance. Ben, to what you were saying earlier about uncorrelated returns, not only did they shoot the lights out that year, they're doing it when the market is down.

01:33:50 Speaker_01
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.

01:33:59 Speaker_00
And I think people grasp it intuitively. We've mentioned this concept a couple of times this episode where, okay, great.

01:34:06 Speaker_00
It's amazing to have a fund that 25 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?

01:34:19 Speaker_00
Y'all intuitively know, no it doesn't, because maybe that was a fluke.

01:34:24 Speaker_00
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, taking the risk into that account?

01:34:35 Speaker_00
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 rent tech is both.

01:34:48 Speaker_00
You can be uncorrelated and massively outperform, which is effectively the holy grail of money management.

01:34:55 Speaker_01
Yes. And so the Sharpe ratio is a measurement combining these two concepts.

01:34:59 Speaker_00
Exactly. So it's named after the economist William F. Sharpe. It was pioneered in 1966. It is effectively the measure of a fund's performance relative to the risk-free rate.

01:35:11 Speaker_00
So if you performed at 15% that year and the risk-free rate was 3%, then your numerator is going to be 12%. And it is compared against the volatility, or the standard deviation is technically what it is, but effectively

01:35:26 Speaker_00
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.

01:35:34 Speaker_00
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 Sharpe ratios are bad.

01:35:46 Speaker_00
Negative Sharpe ratios are worse, because that means you're underperforming the risk-free rate.

01:35:50 Speaker_00
High Sharpe 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. So, in 1990, they had a Sharpe of 2.0, which was twice that of the S&P 500 benchmark.

01:36:05 Speaker_00
Awesome. Yep. Good. 1995 to 2000, Sharpe ratio of 2.5. Really starting to hum. Pretty unbelievable. Good. Where do I sign up to invest? At some point, they added foreign markets and achieved a Sharpe ratio of 6.3, which is double the best quant firms.

01:36:23 Speaker_00
This is a firm that has almost no chance of losing money, at least historically, and massively outperforms the market on an uncorrelated basis.

01:36:33 Speaker_01
And I believe, if I have my research right, in 2004, they actually achieved a Sharpe ratio of 7.5. Astonishing. You know, again, back to our sports analogy here, these aren't hall of fame numbers.

01:36:47 Speaker_01
These are like, I don't know, make Tom Brady look like a third stringer. Yes, exactly. So on the back of 2000 and this rise, the next year in 2001, they raise the carried interest on the fund to 36% up from either 20 or 25%, whatever it was before.

01:37:07 Speaker_01
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. And then the next year, in 2002, they raised the carry to 44%. I mean, Great work if you can get it.

01:37:26 Speaker_01
For context, the Sequoias, the benchmarks out there, they have obscene carry of 30%. 44% is unprecedented.

01:37:34 Speaker_00
There's two interesting ways to look at this.

01:37:36 Speaker_00
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 going to kick anyone out yet, but we've been closed to new business for a long time now.

01:37:46 Speaker_00
You should see yourself out at some point. The other way to look at this, which I think is probably the right way to look at it, is investors are arbitrageurs. They see a mispricing, they come into the market, they fix that mispricing.

01:38:01 Speaker_00
So anytime that there's an opportunity to bring the way that a currency is trading on two different exchanges closer together

01:38:08 Speaker_00
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.

01:38:25 Speaker_00
And in some ways, 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.

01:38:35 Speaker_00
You're still going to 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.

01:38:43 Speaker_00
This product should be priced at a much higher carried interest, and you're still going to love it.

01:38:48 Speaker_01
You should pay 20% carry for a firm that delivers you 15% annual returns. We're delivering you 50% annual returns.

01:38:57 Speaker_00
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 going to happen?

01:39:04 Speaker_01
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, but I want to finish the story first. Okay, so 2001, they raised the carry to 36%.

01:39:17 Speaker_01
2002, they raised it to 44%. And then in 2003, They actually say, Hey, we can't incentivize you out of the fund outside investors. We are going to kick you out.

01:39:29 Speaker_01
So starting in 2003, everybody who's an outside investor, who's not part of the rent tech family, you know, current employee or alumni of the firm gets kicked out.

01:39:39 Speaker_00
And not all alumni get to stay. There's select alumni that get grandfathered in.

01:39:44 Speaker_01
Yes. Now. Why did we do this? I'm going to talk about one reason in a minute, but one reason is super obvious. The Medallion Fund is now at $5 billion in assets under management that they're trading.

01:39:55 Speaker_01
Even in the equities market, they are now hitting up against slippage. And so if they want to maintain this crazy, crazy performance, they just can't get that much bigger.

01:40:07 Speaker_00
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.

01:40:16 Speaker_00
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 going to move any market that you enter into.

01:40:25 Speaker_00
And the strategy that Rentech is employing right now, they're just deeming doesn't work at north of $5 billion.

01:40:32 Speaker_01
So in 2003, they start kicking all the outside investors out of Medallion. But clearly, there's still lots of institutional demand to invest with Renaissance. So what do they do?

01:40:45 Speaker_00
Well, time to start another fund. So they start the Renaissance Institutional Equities Fund. And there's a couple of things to add a little bit of context to really why they decide to do this.

01:40:57 Speaker_00
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.

01:41:07 Speaker_00
If they were sure that they could manage 10, 15, 20, 25 billion in Medallion all the time, then they would grow to that.

01:41:15 Speaker_00
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.

01:41:23 Speaker_00
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.

01:41:32 Speaker_00
And so there's a little bit of downside to that because 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.

01:41:44 Speaker_00
But they still figure, 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.

01:41:51 Speaker_01
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.

01:42:01 Speaker_01
If you married a Rockefeller, would you advise the family that they should invest a large portion of their wealth in the S&P 500? And Peter says, no, of course not.

01:42:12 Speaker_00
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. Right.

01:42:19 Speaker_01
And so Jim says, yes, exactly. Now get to work on designing the product that they should invest in.

01:42:26 Speaker_00
Right. And so that's basically what they come up with is, can we create something that's like an S&P 500 with a higher Sharpe ratio?

01:42:33 Speaker_00
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,

01:42:45 Speaker_00
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.

01:42:58 Speaker_00
And the nice thing about being invested in a hedge fund versus a venture fund is you can do redemptions.

01:43:02 Speaker_00
Like if you look at the 13Fs, the SEC documents that the Renaissance Institutional Equities Fund files over time, it changes every quarter because there's new people putting money in, there's people doing redemptions. So it's a pretty good product

01:43:14 Speaker_00
Or at least the theory behind it is a pretty good product of a lower risk, similar return thing to the S&P 500.

01:43:23 Speaker_01
And the marketing is built in. It's not like there's any lack of demand of outside capital that wants to invest with rent tech.

01:43:30 Speaker_00
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.

01:43:40 Speaker_00
You know, David, you said that the fees and carry on medallion went up to what? Five and 44. Well, on the institutional fund, the fees are one in 10. You're only taking 1% annual fee and 10% of the performance.

01:43:52 Speaker_01
Clearly, this is a very different product.

01:43:54 Speaker_00
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 going to get this crazy outperformance.

01:44:03 Speaker_00
And at the end of the day, it is an extremely different vehicle.

01:44:06 Speaker_01
Yeah. That has not performed anywhere near how Medallion has performed.

01:44:12 Speaker_00
Correct. Has it served its purpose? Yeah. But is it Medallion? No, it's not special in the way that Medallion is special. Yes. A couple other funny things on the institutional fund.

01:44:24 Speaker_00
So I spent a bunch of time scrolling through 13Fs over the last decade from the medallion filings and they're all from, I think they have two institutional funds.

01:44:33 Speaker_01
Yeah, there's institutional equities and diversified alpha.

01:44:37 Speaker_00
So the funniest thing is they file these 13 Fs.

01:44:39 Speaker_00
And David and I are very used to looking at the 13 Fs of 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?

01:44:50 Speaker_00
And usually you see 15, 25, maybe 50 different names on there. Well, the 13F for Renaissance has 4,300 stocks in these tiny little chunks. And there's a little bit of persistence quarter to quarter.

01:45:04 Speaker_00
For example, weirdly, Novo Nordisk has been one of their biggest holdings, biggest, I say, at like 1% to 2%. That's their biggest position for several quarters in a row. Hey, they've been listening to a choir. That's right.

01:45:16 Speaker_01
That's one of the signals in the bottom.

01:45:19 Speaker_00
You kind of get the sense from looking at these filings that 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.

01:45:31 Speaker_00
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.

01:45:39 Speaker_01
Yes. The way that some folks we talked to described the difference between the institutional funds and Medallion to us is that Medallion's average hold time for their trades and positions is call it like a day, maybe a day and a half.

01:45:56 Speaker_01
Whereas the average hold time for the institutional funds positions is like a couple months.

01:46:03 Speaker_01
So across 4300 stocks in the portfolio, there's a lot of trading activity that happens on any given day, but it's a lot slower in any given name than medallion would be. Yep. Which makes sense. Again, it gets back to this slippage concept.

01:46:20 Speaker_01
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 going to slip away. Yep.

01:46:29 Speaker_00
And frankly, it just looks a lot like the S&P 500. As of November 23, so 11 of the 12 months of the year had happened, they were up 8.6%. Okay, that sounds like an index type return.

01:46:43 Speaker_00
You look at the first four months of 2020, right after the crazy dip from the pandemic, they were down 10.4%, less than the broader market, but they still were sort of a mirror of the broader market.

01:46:53 Speaker_00
So I think the RIEF, their institutional fund, yes, it works as expected. No, it's not medallion. And if it were standing on its own, there's zero chance that we would be covering the organization behind it on acquired.

01:47:06 Speaker_01
0% chance. Speaking of the fund that is the reason why we are covering this company on this show, we set up during the tech bubble crash that volatility is when Medallion really shines. Well, there's no more volatile periods than 2007 and 2008. Yep.

01:47:26 Speaker_01
2007, Medallion does 136% gross. 2008, Medallion does 152% gross. Like, get out of here. Crazy. This is 2008 while the rest of the financial world is melting down.

01:47:44 Speaker_00
And so this really does illustrate where do they make their money from, who is on the other side of these trades. It's people acting emotionally.

01:47:50 Speaker_00
They have effectively these really robust models that are highly unemotional, that are making these super intricate multi-security bets.

01:47:59 Speaker_00
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,

01:48:11 Speaker_00
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. They're basically trading against human nature.

01:48:21 Speaker_00
And importantly, in this business versus every other business that we cover here on Acquired or most other businesses, this is truly zero-sum.

01:48:28 Speaker_00
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, you're fighting over a fixed pie here.

01:48:41 Speaker_00
I'm trading against someone else. I win, they lose.

01:48:44 Speaker_01
Yes. Well, there's one slight nuance to that, but I don't know how much it holds water.

01:48:50 Speaker_01
And the apologist nuance would be, well, Warren Buffett could be on the other side of the trade, and Medallion could 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 50 years.

01:49:08 Speaker_01
Super fair. So I think the 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, that loss is getting offloaded to somebody.

01:49:26 Speaker_01
The direct counterparty of Medallion and the Quan industry, writ large, might not take the loss, but somebody is going to take the loss along the way. It is, as you say, a zero-sum game.

01:49:38 Speaker_00
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.

01:49:48 Speaker_00
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.

01:49:56 Speaker_01
So after the historic performance during the financial crisis, as I alluded to earlier, Jim retires at the end of 2009, and Peter and Bob become co-CEOs, co-heads of the firm in 2010.

01:50:11 Speaker_01
They take the portfolio size up to $10 billion when they take over. It had been at five for the last few years of Jim's tenure. They take it up to 10.

01:50:23 Speaker_01
And really with no impact, which I assume means that Rentech was getting better and the models were getting better because otherwise they would have gone to 10 before.

01:50:32 Speaker_00
Right. They gained confidence that they had enough profitable trades they could make that they could raise the capacity without dampening returns. Yes.

01:50:42 Speaker_00
And perhaps they could have done it earlier and they just didn't have the confidence that it would work at larger size. But I bet they're very good at knowing how large can our strategy work up to before it starts having diminishing returns.

01:50:54 Speaker_01
Yeah. And importantly, during periods of peak volatility, like, say, 2020, 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,

01:51:10 Speaker_01
2020 they were up 149% gross and 76% net so 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

01:51:32 Speaker_01
During Jim's tenure, Medallion's total aggregate IRR from 1988 when the fund was formed to 2009 when he retired was 63.5% gross annual returns and 40.1% net annual returns, which of course did include many periods of lower carry, 20% versus the 44%.

01:51:57 Speaker_01
During the post-gym era, the Peter and Bob era, from 2010 to 2022 was when we were able to get the latest data. IRRs are 77.3% gross and 40.3% net. So better on both fronts, even with much higher average fees. So yeah, I think Medallion is doing fine.

01:52:22 Speaker_00
It's amazing. And we weren't able to tell. There's some sources that report that they've grown from $10 billion in the last few years to being comfortable at a $15 billion fund size.

01:52:33 Speaker_00
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.

01:52:42 Speaker_01
Yeah, and 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,

01:52:54 Speaker_01
Medallion has by far 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 1988 to 2022, that's 34 years.

01:53:13 Speaker_01
The total net annual return number is 40%, 4-0, over 34 years after fees. It's 68% before fees, which equates to total lifetime carry dollars for the whole firm of $60 billion, Justin Carey, by our calculations. Astonishing.

01:53:37 Speaker_00
That is a lot of money. Also, David Rosenthal, good spreadsheet work on this. You have not done a spreadsheet for an episode in a while, so I admire your work on this one.

01:53:47 Speaker_01
Yeah. I still know how to use Excel. Barely. It's going to be a dying art now with Copilot and GPTs. That's right.

01:53:58 Speaker_00
Okay, so $60 billion in total carry.

01:54:01 Speaker_01
So $60 billion in total carry is a lot of money. And, well, speaking of a lot of money, we do need to mention before we finish the story here that that Rentech money has bought a lot of influence in society.

01:54:18 Speaker_01
So Bob Mercer, that name may have sounded familiar to many of you along the way.

01:54:24 Speaker_01
Bob was the primary funder of Breitbart and Cambridge Analytica and one of the major financial backers of both the 2016 Trump campaign and the Brexit campaign in Great Britain.

01:54:38 Speaker_01
Now, lest you think that Rentech dollars are solely being funneled into one side of the political spectrum, Jim Simons is a major Democratic donor, as are many other folks at Rentech.

01:54:50 Speaker_00
Yeah, Henry Laufer and other folks are also huge donors, approximately to the same tune as what Bob Mercer is on the right.

01:54:57 Speaker_01
Yeah, tens of millions of dollars, many tens of millions of dollars on all sides and through many campaign cycles here from Rentech employees and alumni. This did become a flashpoint for the firm in the wake of the 2016 election.

01:55:13 Speaker_01
Mercer obviously became a controversial figure, both externally and internally within the firm.

01:55:19 Speaker_00
Especially once people realized he was the through line through Breitbart, Cambridge Analytica, the Trump election and Brexit.

01:55:27 Speaker_01
Yes. Ultimately, Jim asked Bob to step down as co-CEO in 2017, which he did, 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.

01:55:44 Speaker_00
Ultimately, the thing that surprised me the most is how these people all still work together despite having about the most opposite political beliefs you could possibly have. Yeah, understatement of the century.

01:55:57 Speaker_00
And all being extremely influential and active in those political systems. Yes, Bob Mercer is no longer the CEO of Renaissance Technologies or the co-CEO. He still works there. He's still associated. They all still speak highly of each other.

01:56:12 Speaker_00
It's unexpected.

01:56:14 Speaker_01
Yeah, I think unexpected is the best way to put it.

01:56:17 Speaker_00
Like everything with Renaissance, it works a little bit different than the rest of the world.

01:56:22 Speaker_01
Yes. Okay, speaking of, let's transition to analysis. 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 Rentech playbook, but I really kind of think of it as the Rentech tapestry.

01:56:41 Speaker_01
And I was inspired by Costco here because we were talking to folks in the research and everybody said, you know, Rentech, it just has these puzzle pieces that fit together.

01:56:52 Speaker_01
On the surface, Rentech does the same things that Citadel, D. Shaw, Two Sigma, Jane Street, others, etc. do.

01:57:02 Speaker_01
They hire the smartest people in the world, and they give them the best data and infrastructure in the world to work on, and they say, go to town and make profitable trades.

01:57:15 Speaker_01
Those are very expensive commodities, those two things, the smartest people in the world and the best data and infrastructure, but they are commodities.

01:57:23 Speaker_01
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.

01:57:35 Speaker_01
But it's underneath that where I think the magic lies. There are three very interrelated things that make Rentech unique. So number one, they get the smartest people in the world to collaborate and not compete.

01:57:50 Speaker_01
Pretty much every other financial firm out there, employees and teams within the firm quasi compete with one another.

01:58:00 Speaker_00
Yeah. I mean, typically in kind of a friendly way, but yeah.

01:58:05 Speaker_01
Let's take like in a venture firm, you've got your lead partner on a deal or a deal team. 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.

01:58:19 Speaker_01
And I think that's the most collegial way that this happens in finance. 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 for the firm. Yep.

01:58:32 Speaker_01
At Rentech though, because of the one model architecture, everyone works together on the same investment strategy and the same investment infrastructure. That means everyone sees everybody else's work.

01:58:47 Speaker_01
Everybody who works at Rentech on the research team, on the infrastructure team, they have access to the whole model. That's not true anywhere else.

01:58:56 Speaker_00
Yeah, that's a good point. The whole code base is completely visible.

01:59:00 Speaker_01
And that also means because it's just one model, just one strategy, when somebody else improves that model's performance, that directly impacts you as much as it impacts them. This is really different than any other hedge fund out there.

01:59:18 Speaker_00
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 creating high performance also?

01:59:26 Speaker_01
Sure, but you don't love it as much as your team, because either compensation or career-wise, you are much more dependent on your performance than you are other people's performance.

01:59:37 Speaker_00
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 and you care about smashing the pinata and then going elsewhere or building reputation and then going elsewhere.

01:59:51 Speaker_00
Most of the people at Rentech are not going to have another job.

01:59:54 Speaker_01
What did you find on LinkedIn? At least the median tenure of employees is like 16 years.

01:59:59 Speaker_00
Yeah. I just got LinkedIn premium and you can see median tenure and it's crazy. There's only like three, 400 employees at Renaissance and the median tenure, at least as reported by LinkedIn is like 14 years. Yes.

02:00:12 Speaker_01
Okay, this brings me to point number two, which he said, this is an absurdly small team.

02:00:18 Speaker_01
There are less than 400 employees that work at Rentech, only half of which work in research and engineering, and the other half are either back office or institutional sales for the open funds.

02:00:31 Speaker_01
So let's call it, I don't know, 150, 200 people max who are like hands on the wheel here for Medallion. Yep.

02:00:38 Speaker_01
Every other peer firm of Rentech, you know, Citadel, D. Shaw, Two Sigma, et cetera, all of them, you lump Jane Street, you know, jump the high-frequency guys in here. Minimum, 2,000 to 5,000 people work at those places.

02:00:53 Speaker_00
Wow, I didn't realize it was that big.

02:00:55 Speaker_01
It is an order of magnitude more people who are working at the other firms versus who are working at Rentech.

02:01:03 Speaker_00
And lest you think that it's like a capital-based thing, no, the institutional funds have gotten big.

02:01:08 Speaker_00
They peaked at over $100 billion, but they're currently between $60 and $70 billion that they manage on top of the $10 or $15 that's in the medallion fund.

02:01:16 Speaker_01
Yeah, so AUM is like the same as these big funds. This has 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.

02:01:32 Speaker_00
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.

02:01:41 Speaker_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 you're not going out and getting drinks with someone from Two Sigma in New York City.

02:01:56 Speaker_01
You're not comparing notes or measuring parts of your anatomy with someone else.

02:02:00 Speaker_00
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. You came out of a science-related field.

02:02:12 Speaker_00
You now work in East Setauket, Long Island, which has, it's like 10,000 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.

02:02:22 Speaker_00
And if you are, it's again, not to grab drinks with other finance people. So 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.

02:02:39 Speaker_01
And Rentech'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.

02:02:52 Speaker_00
Have you seen the pictures online? Yeah. If you look up Renaissance Technologies at Google and you go and look at the photos on campus, it's a little courtyard and winding walking path and woods all around it. Tennis courts.

02:03:06 Speaker_01
Yep. So then there's the last piece of the small team element, which is just the magnitude of the financial 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

02:03:21 Speaker_01
performance returns that Rentech does. At Rentech, you're splitting that a couple hundred ways. At Citadel, you're splitting that 5,000 ways. It just doesn't make sense to go anywhere else.

02:03:33 Speaker_00
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.

02:03:40 Speaker_01
Okay. So this brings me to what I've been kind of teasing and I'm super excited about. I think the third puzzle piece of what makes Rentek so unique and defensible is Medallion's structure itself. That it is a LPGP fund

02:04:01 Speaker_01
with 5% management fee and 44% carry.

02:04:06 Speaker_00
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.

02:04:15 Speaker_01
So here's my thinking on this. Now, I don't know how it is actually structured, but there was something about this whole crazy 44% carry that just wasn't sitting with me right throughout the research. Because I kept asking myself,

02:04:29 Speaker_00
Why? Right. They've already kicked out most of the LPs, if not all. So why are they raising the carry?

02:04:35 Speaker_01
Right. It's all themselves. It's all insiders. Why do they charge themselves 44% carry and 5% management fees? I think Jim talks about this, that, oh, I pay the fees just like everybody else.

02:04:46 Speaker_00
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?

02:04:52 Speaker_01
So, okay, here's my hypothesis. This is not about having crazy performance fees. This is not about having the highest carry in the industry.

02:05:03 Speaker_01
This 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.

02:05:17 Speaker_01
When people come into Rentech, they obviously have way less wealth than the people who've been there for a long time, 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, it was either the state of New York or the federal government to court to be able to have the 401k plan at Rentech be the medallion fund.

02:05:46 Speaker_00
No way.

02:05:47 Speaker_01
Yeah. So like if you work there, your 401k is the medallion fund.

02:05:51 Speaker_00
That's crazy. So it really doesn't take more than a few years before you're set for life.

02:05:55 Speaker_01
Totally. I mean, depending on your definition of set for life, I think it happens very, very quickly.

02:05:59 Speaker_00
Yeah.

02:06:00 Speaker_01
Okay. So given that though, how do you avoid the incentive for a group of talented younger folks to split off and go start their own medallion fund?

02:06:11 Speaker_00
Right. Especially when they all have access to the whole code base.

02:06:15 Speaker_00
The whole thing is meant to function like a university math department where everyone's constantly knowledge sharing because we're going to create better peer-reviewed research when we all share all the knowledge all the time.

02:06:25 Speaker_00
You would think that's a super risky thing to give everyone all the keys.

02:06:30 Speaker_01
Right. So I think it's the 44% carry structure that does it. Because basically what you're saying is every year, 5% management fee, so 5% off the top, and then 44% of performance. So let's say Medallion is on the order of

02:06:48 Speaker_01
call it doubling every year, let's round that up and just add them and say 49% of the economic returns in any given year go to the current team and 51% of the economic returns go to the tenure base. I was like, what is the equivalent here?

02:07:05 Speaker_01
I think it's kind of like an academic tenure kind of thing. The longer tenure you are at the firm, the more your balance shifts to the LP side of things. And the younger you are at the firm, the more your balance is on the GP side of things.

02:07:21 Speaker_01
But at the end of the day, it's 51-49. So there's this very natural value transfer mechanism to keep the people that are working in any given year super incentivized.

02:07:33 Speaker_01
And as you stay there longer, you are paying your younger colleagues to work for you.

02:07:40 Speaker_00
Right. It's funny. I think it's a good insight that it's structured like a university department tenure. Well, I just kept asking myself, why?

02:07:48 Speaker_01
Why? Why do they have this if there's no outside LPs? And this was the best thing I could come up with.

02:07:55 Speaker_00
And I actually think it's kind of 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 them.

02:08:06 Speaker_01
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, you know, if that were true, then Jim would just own this thing forever.

02:08:20 Speaker_01
And I don't think that's true here at Rentech.

02:08:23 Speaker_00
Yeah. So essentially, David, the real magic is they've got one fund. It's Evergreen. And when you start at the firm, you're only getting sort of paid the carry amount.

02:08:33 Speaker_00
But over time, you become a meaningful investor in the firm and you sort of shift to that 51 percent. You're kind of the LP. And then over time, you eventually graduate out entirely and you're only an LP.

02:08:45 Speaker_00
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.

02:08:58 Speaker_01
Yeah. And at the end of the day, I think these three pieces, to me, are the core of this sort of tapestry of rent tech. One model that everybody collaborates on together.

02:09:10 Speaker_01
a super small team 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.

02:09:19 Speaker_01
And three, this LPGP model with very high 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.

02:09:31 Speaker_00
Yep. I think that's right. Okay, there's a few other parts of the story that we skipped along the way because there was no real good place to put them in, but these are objectively fascinating historical events that are totally worth knowing about.

02:09:44 Speaker_00
And the first one is called basket options. So, the year is 2002. Rentech has 13 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.

02:10:00 Speaker_00
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 going to borrow $100 for every dollar of equity capital that I have in here.

02:10:11 Speaker_00
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, most people don't.

02:10:24 Speaker_00
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.

02:10:32 Speaker_00
I am going to read directly from the man who solved the market because Greg Zuckerman just put it perfectly. Basket options are financial instruments whose values are pegged to the performance of a specific basket of stocks.

02:10:42 Speaker_00
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. It's like owning the shares without actually doing so.

02:10:57 Speaker_00
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 Medallion's property. So this is very clever.

02:11:08 Speaker_00
Medallion saying, well, the way we're going to 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.

02:11:21 Speaker_00
And then after a year, When long-term capital gains tax kicks in, we have the option to buy that basket. So anyway, all day, Medallion's computer sent automated instructions to the banks, sometimes in order a minute or even a second.

02:11:35 Speaker_00
The options gave Medallion the ability to borrow significantly more than it otherwise would be allowed to. Competitors generally had about $7 of financial instruments for every dollar of cash.

02:11:46 Speaker_00
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.

02:11:57 Speaker_00
When Medallion spied an especially juicy opportunity, it could boost leverage, holding close to $20 of asset for every dollar of cash. In 2002, Medallion managed over $5 billion, but it controlled over $60 billion of investment positions.

02:12:11 Speaker_00
David, this exposes something we haven't shared yet on the episode, which is it's not just that they could find $5 billion worth of profitable trades.

02:12:18 Speaker_00
It's that they wanted to lever the crap out of $5 billion and find $60 billion 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.

02:12:32 Speaker_01
Yeah, the unlevered returns if you were running this strategy would be much lower.

02:12:39 Speaker_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.

02:12:47 Speaker_01
Yeah. It's an important point, though. Nine out of every ten companies that we cover on Acquired, 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.

02:13:01 Speaker_01
Like, I'm scared of it.

02:13:02 Speaker_00
I mean, you could imagine, let's say it wasn't, they were right 50.25% of the time, but they were right 50.0001% of the time, they would need to do a ton of trades in order to generate enough profits.

02:13:14 Speaker_00
So that's why you need $60 billion of cash to actually execute the strategy to produce the returns that they were looking for. Yeah. On $5 billion of equity.

02:13:25 Speaker_00
Anyway, there's a second chapter to this, which is, 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.

02:13:36 Speaker_00
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 once every 13 months or so.

02:13:48 Speaker_00
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.

02:13:55 Speaker_00
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.

02:14:04 Speaker_00
Over time, eventually in 2021, the IRS said, no, you made all those trades. That was not a completely separate entity. And so you guys owed $6.8 billion in taxes that you didn't pay. You're going to need to pay that with interest, with penalties.

02:14:22 Speaker_00
And by the way, Jim Simons, we're going to want you and the other few partners to really bear the load of that. And they did.

02:14:28 Speaker_00
So for Simons alone, he paid $670 million to the IRS in back taxes for this basket option strategy that turned out not to be a long-term capital gain.

02:14:37 Speaker_01
Yep.

02:14:38 Speaker_00
All right. So 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 $5 or $10 billion.

02:14:50 Speaker_00
The institutional fund is about $60 to $70 billion, and at one point was $100 billion. The total carry generated, David, you said is $60 billion.

02:14:59 Speaker_00
Forbes estimates that Jim Simon's alone is worth about $30 billion today, which kind of pencils with a bunch of other stats over the years that he owned about half of Renaissance.

02:15:10 Speaker_00
The returns, obviously, the medallion fund generated approximately 66% annualized from 1988 to 2020. After those fees, it was about 39% wild.

02:15:22 Speaker_00
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? And so the institutional fund, let's call it 10% on 60 billion of assets.

02:15:37 Speaker_00
So that's 600 million from fees and 600 million from performance. So, $1.2 billion a year in revenue to the firm from the institutional side of the business. Because I always ask myself the question, does that actually matter?

02:15:50 Speaker_00
They did all this work to stand up the institutional side, who cares? Well, let's say Medallion does their average 66% gross on $15 billion. That is $750 million in fees and $4.3 billion on performance.

02:16:06 Speaker_00
So, a total of $5 billion from Medallion and $1.2 billion from the institutional side of the business.

02:16:14 Speaker_00
Now, of course, the employees are the investors in Medallion, so you could just argue it's actually silly to cut them up, but, I don't know, it's a $7, $8, $9 billion revenue business.

02:16:23 Speaker_01
Right, because that's not including the LP return on Medallion. A hundred percent, it's not. Which, again, as we've spent a long time talking about, it's all the same thing.

02:16:31 Speaker_00
Yes. 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 $7-8 billion a year revenue business. Now, I think there are a lot of expenses on the infrastructure side. Totally.

02:16:44 Speaker_00
That was another thing I wanted to talk about. The fact that they do, let's say, Medallion alone. So they have $750 million in fees.

02:16:51 Speaker_00
I don't think they come close to $750 million a year in expenses, but 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.

02:17:05 Speaker_00
I don't know. I mean, you're talking about a lot of data here.

02:17:09 Speaker_00
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 40 terabytes a day. That's a lot of data.

02:17:22 Speaker_01
Right. Is that 750 million a year? I don't know, but it's not zero.

02:17:28 Speaker_00
I don't think so. They're certainly not losing money on the fees, but there are actual hard costs to this business. Right.

02:17:36 Speaker_01
I wonder too if the fee element of Medallion basically pays the base salaries for the current team.

02:17:45 Speaker_00
That feels like it's right.

02:17:47 Speaker_00
If you're someone who has done a data center build out before or has any way to sort of back into what the costs of Medallion's operating expenses are on the compute and data and network side, we would love to hear from you. Hello at acquired.fm.

02:18:03 Speaker_00
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.

02:18:13 Speaker_00
What is it that enables a business to achieve persistent differential returns to be more profitable than their closest competitor on a sustainable basis?

02:18:21 Speaker_00
And the seven are counter positioning, scale economies, switching costs, network economies, process power, branding, and cornered resource. And David,

02:18:32 Speaker_00
My question to you to open this section is, specifically about Rentech's lifelong non-competes, that feels like a big reason that they maintain their competitive advantage. And I'm curious if you agree with that, what would you put that under?

02:18:47 Speaker_01
Well, I think it's lifelong NDAs and non-competes as long as the state of New York legally allows for. But that is not lifetime. I've heard various figures, six years, five years, something like that.

02:19:01 Speaker_01
I mean, at the end of the day, non-competes are more like, what is one side willing to go to court over? But the reality is people don't leave. People don't leave, period. And people especially don't leave and start their own firms.

02:19:15 Speaker_01
I was thinking about this in the middle of the night, and I think there's three layers to the effective non-compete that happens with rent tech. There's the legal layer, the base layer that you're talking about, it's like the agreements you sign.

02:19:32 Speaker_01
Then there's the economic layer of what we spent a long time talking about in tapestry, 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.

02:19:46 Speaker_00
Yep.

02:19:47 Speaker_01
I think that's the next level of, and then I think the highest level is just probably the social layer.

02:19:52 Speaker_01
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.

02:20:04 Speaker_01
You're not in the Hamptons. You're not in Silicon Valley. You are selecting into that. And I think if that's what you want there, like what better place in the world?

02:20:14 Speaker_00
All right, so classify it. What power does that fall under?

02:20:18 Speaker_01
Well, I mean, I think the people specifically you would put into cornered resource, but I'm not actually sure that fully captures it here.

02:20:26 Speaker_01
I was thinking more process power because I think it is the combination of the people and the model and the incentive structures.

02:20:36 Speaker_00
Yep. I think that's right. I also had my biggest one being process power. You actually can develop intricate knowledge of how a system works and then build processes around that that are hard to replicate elsewhere.

02:20:48 Speaker_00
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 10 million lines of code.

02:21:03 Speaker_00
And 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.

02:21:17 Speaker_00
And so I think there's process power just because it's 30 plus years of complexity that's been built up.

02:21:24 Speaker_01
Yeah, I totally agree with that, particularly in the model itself. I mean, maybe you could argue the model is a cornered resource.

02:21:32 Speaker_00
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.

02:21:44 Speaker_00
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 thinking about data cleaning. That's not a sexy job.

02:21:55 Speaker_00
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. I have a couple of nuances on this.

02:22:07 Speaker_01
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 80s before anybody else was really doing this. So they have that and other firms don't.

02:22:23 Speaker_01
That said, certainly all the other quant firms are throwing untold resources at all this, too.

02:22:31 Speaker_00
Right. They want to do this, and money is not the issue.

02:22:34 Speaker_01
So in chatting with a few folks about this episode, I had more than one person say to me, there's two ways that Rentech could work.

02:22:47 Speaker_01
And one version of how it works is they discovered something 20 plus years ago that is a timeless secret, and they've been trading on that for 20 plus years.

02:22:57 Speaker_00
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.

02:23:06 Speaker_01
Isn't that crazy? Right. Now, Rentech will say they will all say that is 100% 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 because they don't want anybody to know.

02:23:17 Speaker_00
Right. Don't look at the relationship between soybean futures and GM. Just don't do it.

02:23:24 Speaker_01
Right. So let's accept that there is a possibility that that might be true. More likely, though, is that what Rentech does say is true, which is, no, there is no holy grail.

02:23:36 Speaker_01
What we do here is we completely reinvent the whole system continuously on a two-year cycle. Two years is kind of what I heard. The model is fully restructured every two years. It's not like on a date every two years.

02:23:49 Speaker_01
It's being restructured every day, but collectively, it's about a two-year cycle.

02:23:54 Speaker_00
So that would be an argument then that the people actually could, with five people left, they probably could go recreate it and all they would need is the data.

02:24:02 Speaker_01
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.

02:24:09 Speaker_00
I bet the data is, though. Let's say you've been working there for 10 years. You don't know how the 1955 soybean futures data ended up in the database.

02:24:19 Speaker_00
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 in. I think that's fair.

02:24:28 Speaker_01
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.

02:24:36 Speaker_00
Definitely.

02:24:37 Speaker_01
The broader point I want to make here is just that every other major quant firm out there is also spending hundreds of millions, if not billions, on this stuff too.

02:24:45 Speaker_00
And people are looking for alt data everywhere.

02:24:47 Speaker_00
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.

02:24:58 Speaker_01
Yep. So I think we can rule out scale economies for sure. If anything, they're anti-scale economies here.

02:25:05 Speaker_00
Oh, yes. There's totally, there's dis-economies of scale. Your strategies stop working when you get too much AUM.

02:25:11 Speaker_01
Yeah, you get slippage. I don't think there's any network economies here. I mean, they literally don't talk to anybody.

02:25:20 Speaker_00
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.

02:25:35 Speaker_00
Their ability to pull data out of the market is very high quality.

02:25:38 Speaker_01
Do you think it's actually better than their competitors, though?

02:25:41 Speaker_00
I don't know. That's probably not the secret sauce. Yeah, I don't think so. It's the table stakes.

02:25:45 Speaker_01
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.

02:25:53 Speaker_00
The fee stream on the institutional fund may entirely belong to branding. Yes.

02:25:58 Speaker_00
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.

02:26:10 Speaker_01
Yep.

02:26:11 Speaker_01
Venture firms the same way totally so for me this kind of 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 what way?

02:26:27 Speaker_01
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.

02:26:36 Speaker_00
Like they're counterparties in trades.

02:26:39 Speaker_01
I don't think they are counterparties. I think they are all seeking to exploit similar types of trades. I think the counterparties are the people there, the dentists that they're taking advantage of.

02:26:49 Speaker_00
Well, but quant funds are often counterparties to each other.

02:26:51 Speaker_01
That's true. But I think, yes, adversaries in finding the similar types of trades. And I think the counter positioning for Rentech, or for Medallion specifically, is one.

02:27:04 Speaker_01
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. But I think also and maybe bigger is

02:27:18 Speaker_01
Every incentive at Rentech is fully aligned to optimize fund size for performance in a way that is not true just about everywhere else. I think they have the most incentive of anybody to truly maximize performance we're able to achieve.

02:27:37 Speaker_00
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 that makes it so they're not willing to trade the dampener on performance to get those dollars.

02:27:53 Speaker_01
Yes. Particularly because it's all the same people on the DP and LP side.

02:27:59 Speaker_00
Oh, we keep going round and round that axle. I loosely buy the counter positioning thing.

02:28:04 Speaker_00
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 going to keep beating you.

02:28:16 Speaker_01
Oh, that's another argument I heard from people. in that Rentech basically is a math department in a way that none of these other firms are.

02:28:25 Speaker_00
It could be culture.

02:28:26 Speaker_01
Yeah, it could be culture.

02:28:28 Speaker_00
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 incentivize them in a sort of fake altruistic way. Like, this is just a fun place to do my work.

02:28:39 Speaker_00
And yeah, the outcome is getting really rich, but I wouldn't go work at Citadel.

02:28:43 Speaker_01
Yep. I think that could be. So maybe that feeds into process power.

02:28:47 Speaker_00
Yep.

02:28:48 Speaker_01
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.

02:28:54 Speaker_00
For me, it is process power and cornered resource. Yep. Okay. I buy that. And a thing that's not captured in seven powers is tactical, like execution. The whole point of seven powers is strategy is different than tactics.

02:29:08 Speaker_00
And I think legitimately Rentech may just have persistently been able to out-execute their competitors. There's part of it that's just like, they're smarter than you. Yeah.

02:29:19 Speaker_01
Well, if you buy the whole thing gets reinvented continuously every two years, then yes.

02:29:25 Speaker_00
And there's remnant knowledge. Like if you started building a machine learning system in 19- whatever it was- 64. you're going to be really good at machine learning today.

02:29:38 Speaker_00
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 going to be better at machine learning than probably the other people who are out in the world learning it from people that just got inspired to start learning machine learning based on the new hotness.

02:29:56 Speaker_00
So learning's compound is my answer. 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.

02:30:11 Speaker_00
So the first one is signal processing is signal processing is signal processing. 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.

02:30:26 Speaker_00
They use price to earnings ratios and stuff like that in the institutional fund, which is kind of funny because that's a completely different skill set. But if you just look at Medallion, It's all just abstract numbers.

02:30:40 Speaker_00
You don't actually have to care about 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.

02:30:52 Speaker_00
And once you reduce it to that, it is so brilliant that they can just recruit from any field. it's not relevant how someone has done sophisticated signal processing in the past.

02:31:04 Speaker_00
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.

02:31:15 Speaker_01
There's this really funny line that Jim and Peter and others will say when asked about why they only hire academics and not from Wall Street and whatnot.

02:31:24 Speaker_01
And they're like, well, we found it's easier to teach smart people the investing business than 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.

02:31:39 Speaker_01
I bet at least half the people at Rentech on the research side could not read a balance sheet.

02:31:44 Speaker_00
It's so funny. It's a whole bunch of people who are in the investment business, none of which are investors.

02:31:48 Speaker_01
Yes.

02:31:49 Speaker_00
Another one that you can decide if this fits or not. I 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 Cenefe Institute's work on this.

02:32:02 Speaker_00
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.

02:32:17 Speaker_00
It's the butterfly flapping its wings. But there are relationships between entities that you can't understand or see on the surface.

02:32:26 Speaker_00
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, 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?

02:32:43 Speaker_00
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.

02:32:57 Speaker_00
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 50.01% chance of being correct,

02:33:07 Speaker_00
then they can sort of take a complex adaptive system and say, we don't really care that it's a complex adaptive system.

02:33:14 Speaker_00
Our models understand enough about the relationships between all these entities that we're just going to run the simulation a bunch of times and we're going to 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.

02:33:29 Speaker_00
they're sort of the closest in the world to being able to actually 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.

02:33:43 Speaker_01
Yes. And I think when I've heard people from Rentech talk about this, they will all say,

02:33:49 Speaker_01
The model does not actually understand the market, but it can predict, and we can be so confident in its predictions about what the market will do that we rely on it. Whether it understands or doesn't understand doesn't actually matter.

02:34:08 Speaker_01
It can't tell you why, but that's okay.

02:34:11 Speaker_00
But it does know it has a slight edge, and so it should trade on it even though it can't explain why. Yes. 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?

02:34:24 Speaker_01
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. And the answer we got back is unsurprising.

02:34:37 Speaker_01
They said, we don't know, because they don't share anything.

02:34:42 Speaker_00
Right. It's like the principles certainly came out of the same math community that spawned machine learning. But is what Rentech has figured out over the last couple decades in Google's Gemini model and in chat.

02:34:56 Speaker_00
No, it's not because they don't contribute any research back.

02:34:59 Speaker_00
It may be the case that actually, Rentech has beat everyone else to the punch and they have a strong AI or something that is actually much more sophisticated than all the AI we have out in the world today.

02:35:10 Speaker_00
And they've just chosen that they'd rather keep it locked up and captive and make a bunch of money.

02:35:16 Speaker_00
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 in realizing that 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 50% of the time.

02:35:41 Speaker_00
Right. Make trades that 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.

02:35:51 Speaker_01
If that were the case, we should have very different answers to powers.

02:35:54 Speaker_00
To illustrate this point, it's quite interesting. Peter Brown's academic advisor was Geoffrey Hinton.

02:36:00 Speaker_01
Yes. Oh, I'm so glad we brought this up. Yeah. It was the exact same stew and the exact same cohort of people and social group and academic groups that Rentech came out of, that AI came out of.

02:36:12 Speaker_00
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 Sutskever, who is the co-founder of OpenAI. I mean, many years later, but still.

02:36:26 Speaker_01
Yeah. I mean, it's like we were talking about with Markov models and hidden Markov models. That is the foundation of Rentech. That is one of the foundations of AI and generative AI today.

02:36:36 Speaker_00
Yep. Okay, another big one is this concept that you should trade on a secret that others are not trading on. So, on the face of it, it seems obvious.

02:36:48 Speaker_00
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.

02:36:59 Speaker_00
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, but I do not think modern Rentech does that.

02:37:16 Speaker_00
I think all of their investment ideas come from data and come from signal processing, and so therefore you are going to put trades on that make no intuitive sense.

02:37:28 Speaker_00
And so when you're putting trades on that are profitable and make no intuitive sense, you aren't going to have competitors.

02:37:35 Speaker_00
If you find a relationship between two things that a human could never come up with or dream of those relationships, and we're saying to end things, you know, 10 things, 20 things, 100 things, and in various different weights at various different time scales, that is a killer recipe to exploit a secret that no one else knows and be able to beat other people in the market.

02:37:55 Speaker_01
Such a good point. And many, if not most of the other quant firms are not doing that.

02:38:01 Speaker_01
Some of them maybe, but I think most of them are, the model is suggesting things and there is a person or persons who are the master portfolio allocators that pull the trigger or don't pull the trigger.

02:38:15 Speaker_00
Yes. And to be super illustrative, because 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.

02:38:25 Speaker_00
For example, there could be two things that always move together, Tesla stock and wheat futures. And you might try to, because humans are storytellers, concoct some story in your head of why those move together.

02:38:37 Speaker_00
And if you believe it, then you might decide there's some date where they should stop moving together.

02:38:42 Speaker_00
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.

02:38:53 Speaker_00
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.

02:39:02 Speaker_00
So I think what Rentech sort of admitted is, we have no idea why anything is actually connected, but it doesn't matter.

02:39:09 Speaker_01
Totally. And that was surprising for me in the research. I sort of assumed that was the whole quant industry, and it was very surprising to me to discover that I believe no. It is pretty much only Rentech and maybe a couple other people.

02:39:23 Speaker_00
Okay. Okay, my next one 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.

02:39:36 Speaker_00
You have, on the one axis, fast and slow in terms of trade execution, and on the y-axis, you have smart versus obvious.

02:39:45 Speaker_01
Yeah, the way he phrased it to us was smart versus dumb, but dumb doesn't mean dumb.

02:39:49 Speaker_00
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.

02:40:01 Speaker_00
I think I thought they were the same thing, but fast and obvious is your classic high frequency trader. They're front running trades. They're locating in a data center that's really near the, you know, this is flash boys.

02:40:14 Speaker_00
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.

02:40:25 Speaker_00
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

02:40:39 Speaker_00
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.

02:40:49 Speaker_00
All the machine learning systems discovered the relationships in the data, so there's a huge amount of compute. The non-obvious trades.

02:40:56 Speaker_00
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

02:41:09 Speaker_01
My sense is, Rentech is not a high-frequency trading shop. They are not front-running things. They are not flash boys. Compared to you and me, they still operate incredibly fast, but it's more about the smartness and less about the fastness.

02:41:26 Speaker_00
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.

02:41:35 Speaker_00
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.

02:41:46 Speaker_01
Oh, this is another thing that we heard. Rentech is world class at disguising their trades.

02:41:53 Speaker_00
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.

02:42:02 Speaker_00
And people basically intercepted the trades that they were making and were front running them.

02:42:06 Speaker_00
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.

02:42:15 Speaker_01
Yep.

02:42:16 Speaker_00
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?

02:42:28 Speaker_00
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.

02:42:40 Speaker_00
And while it's never happened to Rentech, there was a company called Knight Capital in 2012 that lost $460 million in a single day. There was a bug in their process to deploy the new code

02:42:52 Speaker_00
And basically what happened, it was a simple flag error, 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.

02:43:06 Speaker_00
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 45 minutes, and there wasn't the appropriate kill switches built in.

02:43:19 Speaker_00
And they basically watched it all to just drain out and there was nothing they could do.

02:43:23 Speaker_01
Yeah. So like the whole portfolio gone, right?

02:43:26 Speaker_00
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 75% and then someone stepped in and bought them.

02:43:40 Speaker_01
when they probably got margin called by all their counterparties.

02:43:43 Speaker_00
So whoever is in charge of the financial controls and safety systems at Rentech, that's a huge job for someone in this industry. Totally. All right.

02:43:54 Speaker_00
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.

02:44:08 Speaker_01
Well, 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.

02:44:15 Speaker_00
I'll say 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.

02:44:28 Speaker_00
They're there setting up shop as Caesar's Palace, letting everyone come in and do business with them while they have a slight edge. And they'll lose sometimes, but most of the time, they're going to come out slightly ahead.

02:44:41 Speaker_00
And I think let's say they do have a 50.01% 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. And at scale, it really worked.

02:44:56 Speaker_00
Jim Simons managed to drain $30 billion into his own pocket out of everybody that he ever traded with.

02:45:02 Speaker_01
Now, I think where you're going with this 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?

02:45:17 Speaker_00
Well, I mean, the investment business, it sort of depends how you define investor.

02:45:21 Speaker_00
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, or 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.

02:45:45 Speaker_00
And of course, lots of things are called investing that are not that.

02:45:49 Speaker_00
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.

02:45:58 Speaker_01
Yeah, the money is not being invested in anything to produce.

02:46:02 Speaker_00
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.

02:46:11 Speaker_01
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.

02:46:22 Speaker_01
Similarly, I think you could make an argument, and I think this is probably quite accurate, that Rentec and all other quant firms like them are providing a service to the market

02:46:34 Speaker_01
in that they are allowing trades that people want to make to happen faster and at much lower spreads.

02:46:41 Speaker_00
Absolutely. That is the 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.

02:46:53 Speaker_00
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. But you're totally right that there is a value to market liquidity.

02:47:06 Speaker_00
Creating more depth to a market makes it so that if we go back to the era that Renaissance was started, 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 at near real time.

02:47:22 Speaker_01
And any single one of us can go buy a security in just about any market at just about any time of day, pretty much instantaneously, and get a very, very, very granular price on it. None of which used to be true.

02:47:41 Speaker_00
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 medallion.

02:47:54 Speaker_00
They actually don't all have an edge, even though they might purport to. Lots of them are going to lose money to you. Right.

02:48:00 Speaker_01
Lots of them lose money. You too, listeners, could beat the market. Not investment advice. Please don't try.

02:48:06 Speaker_00
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.

02:48:21 Speaker_01
Oh, there's this great, great vignette at the end of Greg's book. When was it? It was during one of the like sell-offs in the mid-twenty-teens in the market where Jim calls the head of his family office.

02:48:32 Speaker_01
He's long retired from Rentec at this point, calls the head of his family office and says, what should we do with all the sell-off in the market? And it's like, you're Jim Simons. Right.

02:48:41 Speaker_03
You're Jim Simons.

02:48:43 Speaker_01
What should we do?

02:48:44 Speaker_00
What should we do? Yeah. All humans are fallible. Totally. A couple of other squintable 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.

02:49:01 Speaker_00
At the end of the day, humans are going to do what they are incented to do.

02:49:06 Speaker_00
And so absent a larger global concern that is incredibly motivating to people, I mean, you look at World War II, people's level of patriotism and wanting to go save the world from evil was a huge, unbelievable motivating factor to move mountains.

02:49:21 Speaker_00
that is absent or when people feel that there's some existential thing that is absent, they're going to go do what's best for them and their family. And if they're an empire builder, go build empires.

02:49:31 Speaker_00
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 you can be mad about that. Given that, okay, people are going to go engage in quantitative finance as a lucrative profession.

02:49:45 Speaker_00
Fortunately, there's a bunch of valuable stuff that comes out of that. And I think that is often missed, is that these really lucrative professions and businesses can often produce R&D that becomes valuable elsewhere.

02:50:01 Speaker_00
For example, we just did this big NVIDIA series. What do you think Mellanox was used for before large language models?

02:50:08 Speaker_01
Oh, yes. This is such a really mind-blowing point here in value creation, value capture. Go for it. Take it away.

02:50:17 Speaker_00
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 kind of think Mellanox built their business on quant finance. Yes.

02:50:29 Speaker_00
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.

02:50:39 Speaker_01
Yep. 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 that quantitative finance does not create value for the world. It definitely does, in my opinion.

02:50:57 Speaker_00
But does it create anywhere near as much as it captures? That said.

02:51:03 Speaker_01
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.

02:51:11 Speaker_00
There's a great always sunny in Philadelphia where Frank Danny DeVito sort of goes back to his whatever business he founded in the eighties. And he's like dressing in his pinstripes and stuff again.

02:51:21 Speaker_00
And he's taken back over and 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? And Danny DeVito looks at me, he goes, what do you mean we make money?

02:51:33 Speaker_00
He's like, no, no. Like, what do you build? He goes, we build wealth. I think that's a pretty good meme for kind of what's going on here.

02:51:40 Speaker_01
Yeah, totally. Very, very good at value capture too. Yes.

02:51:45 Speaker_00
Okay. Bearer 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 and it is back.

02:51:59 Speaker_00
Resurrected like a Phoenix. Resurrected. However, this is about the lamest episode to resurrect it on. What's the bull case for Rentech?

02:52:06 Speaker_01
Past performance is an indicator of future success.

02:52:09 Speaker_00
Right, like they're going to keep attracting all the smartest people in the world. They're going to have the ability to keep their incredibly unique culture.

02:52:16 Speaker_00
They're not going to get tempted to let the business of institutional funds become the dominant business. You know, keep on keeping on is basically the bull case. Maybe that they're actually still ahead.

02:52:28 Speaker_01
The bull case for the GP and LP stakeholders in Medallion, which is, I don't know, 500 people in the world, and none of the rest of us can get any exposure to it.

02:52:40 Speaker_00
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.

02:52:51 Speaker_00
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.

02:53:03 Speaker_00
Like maybe Rentech actually was here a decade before everyone else, and now everyone else has arrived to the party. There's things that are changing maybe about their culture, like Jim Simons has been gone for a long time.

02:53:15 Speaker_00
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 Lippe, he is becoming a co-CEO as well.

02:53:28 Speaker_00
So maybe there's a bear case around that, that someone from the institutional side of the house is becoming the current co-CEO and maybe eventually CEO if you believe the medallion is the special thing and the institutional funds are sort of a blemish on the business.

02:53:43 Speaker_00
You know, they're the Hermes Apple Watch strap in David's parlance. Maybe that's a bear case. Maybe there's a bear case that their talent is becoming kind of the same as everyone else's talent.

02:53:55 Speaker_00
When you look on LinkedIn, I recognize a lot of the companies that people worked at who are more junior at Rentech. And in the past, I think it would have been all people just out of university research shops. So I think

02:54:08 Speaker_00
If it's true that they're starting to 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.

02:54:18 Speaker_01
Right. There's no way for us to know any of this because there's no way to know any of this.

02:54:23 Speaker_00
Right. It's all the secret.

02:54:25 Speaker_01
Yep. Okay. Our new ending section, the splinter in our minds, the takeaway. The one thing you can't stop thinking about. What is the one thing for each of us, personally, from doing this work over the past month on Rentek that sticks with us?

02:54:44 Speaker_01
For me, perhaps this is obvious from my little diatribe on the tapestry, I just think this is such a powerful example of the power of incentives and getting them right and setting them up right. And culture too. I don't want to shortchange that.

02:55:01 Speaker_01
I think the culture of managing an academic environment in a fashion like a lab, but without letting it spin into the frivolity of a lab that Jim Simons set up.

02:55:15 Speaker_00
Right. In other words, early Google.

02:55:17 Speaker_01
Yeah. This is like early Google. Exactly. There historically has not from our research, and as best as we can tell currently, is not anything going on at Rentech that is frivolous.

02:55:31 Speaker_01
They are all very focused, which again to me then speaks back to the power of incentives. When you're there with less than 400 people, and on the research and engineering side, less than 200 people, and those colleagues who you work with

02:55:46 Speaker_01
are the sole purveyors, supervisors, and beneficiaries of all of this that you're doing. That is so powerful. I can't think of anywhere else like that in the world.

02:55:57 Speaker_01
I mean, maybe some venture funds or other investment firms, but not on a day-to-day, fully liquid, with returns like this. There's nothing like it.

02:56:07 Speaker_00
Nope. Pure gasoline right into the veins.

02:56:10 Speaker_01
Yeah. Which is not to say I would necessarily want to work there. I think I would not. Totally.

02:56:15 Speaker_00
Yeah.

02:56:15 Speaker_01
But it is truly unique.

02:56:17 Speaker_00
Yep. The one thing I can't stop thinking about is the idea of the complex adaptive system that I was talking about earlier.

02:56:25 Speaker_00
I think, from everything we can tell from the outside, Renaissance actually has built a large-scale computer system that discovers relationships between different entities in the world – stocks, commodities, bond prices – and whether it can explain them or not, it is

02:56:44 Speaker_00
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.

02:56:52 Speaker_00
That is my takeaway, is that they are the house, and they have an edge, and that edge is predicated on a graph of all the relationships between these entities that we think are just noise, and they know the signal.

02:57:05 Speaker_01
It does make you wonder to what you were talking about with the tech industry catching up quote unquote in recent years. How hard is it to build this now, given the technology open source and otherwise that's available for sale out there?

02:57:22 Speaker_00
That's the bear case. I don't know.

02:57:24 Speaker_01
Yeah. And then what's going to happen by nature, given that it's a complex adaptive system. If you can now buy and build this, well, the returns will get arbitrage down.

02:57:33 Speaker_00
Yep. All right. Should we have some fun? Carveouts? Let's have some fun. Sweet. So I have one TV show and it is actually Acquired related. It is called The New Look on Apple TV+.

02:57:46 Speaker_01
Oh, yes. But Christian, it is such a new look.

02:57:53 Speaker_00
Exactly. So for anyone who listened to the LVMH 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 II explosion onto the scene. Celebration of life. Yes.

02:58:08 Speaker_00
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 dresses.

02:58:24 Speaker_00
The Apple TV show is this incredible drama of kind of flashbacks to the wartime experiences, harrowing wartime experiences of Christian Dior, of Balenciaga, of Coco Chanel and everything they went through and how all their paths crossed.

02:58:42 Speaker_00
Oh, Coco's in it. Yes. 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.

02:58:52 Speaker_00
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.

02:59:04 Speaker_01
Well, there's the whole crazy thing with Chanel where the company ends up getting bought by Chanel, the perfume division, which is the two Jewish brothers in New York.

02:59:14 Speaker_00
The Wertheimers, indeed. Oh God, we get to 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.

02:59:25 Speaker_01
Oh, as you and listeners know, I'm not a TV guy, but this is so up my alley.

02:59:30 Speaker_00
The whole thing, it takes place in wartime Paris. Oh, all right. I got to watch it. You got to watch it. All right, David, your carve-outs.

02:59:38 Speaker_01
My carve-out is related to the new look in a very different way, but both video consumption and fashion and luxury and style. It is the class of Palm Beach Instagram and TikTok account. This is so great.

02:59:58 Speaker_00
David, you and I go to Palm Beach for two days and you get hooked on.

03:00:03 Speaker_01
This is amazing. So 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. Ooh, it is nice. So great. We didn't knowingly spot any Rentec people there, but we may have.

03:00:17 Speaker_00
We did knowingly spot some Birkenbags though. Yes.

03:00:21 Speaker_01
The style in Palm Beach, we had just recorded the Hermes episode and oh man, 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 Palm Beach TikTok account?

03:00:37 Speaker_00
And David's like, I'm a thousand. I have no idea what you're talking about, Jenny.

03:00:39 Speaker_01
Yeah, right, right, right. I live under a rock. I'm a dad. And she showed it to me.

03:00:45 Speaker_01
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.

03:00:58 Speaker_01
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 who has a mini Kelly inside her Birkin.

03:01:08 Speaker_00
Ugh, excess. Truly excess.

03:01:11 Speaker_01
And that's when I was hooked. I was just like, this is the greatest thing I have ever watched. Uh, I'm obsessed.

03:01:19 Speaker_00
All right. If I used to tick talk, I would subscribe.

03:01:22 Speaker_01
No, you can get it on Instagram too. Oh, all right. Good. I actually subscribed the acquired account on Instagram to class upon beach. I don't know how many people were following. It's not many, but we are following class upon beach.

03:01:33 Speaker_00
Look at David opening up our Instagram account. You're so youthful. I know. David, I know you've got some thank yous from folks you talked with and a few of them we did together.

03:01:43 Speaker_01
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. canonical book out there about Rentech and Jim Simons.

03:01:55 Speaker_01
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 the canonical source of Medallion's investment returns.

03:02:09 Speaker_01
And I know he worked so hard to get that table together that is now all over the internet as it should be.

03:02:16 Speaker_00
It is crazy. Everywhere you hear that 66% number quoted, and that is from Greg's analysis.

03:02:22 Speaker_01
Yes. Truly a service to us and to corporate historians and financial historians everywhere that he did that research and got those returns.

03:02:30 Speaker_00
And there's a few other 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.

03:02:40 Speaker_00
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.

03:02:49 Speaker_01
There'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. They read the book. Clearly. Yeah.

03:02:56 Speaker_00
There's a great book called The Quants, which is a little bit earlier. I think it's 2011, 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.

03:03:08 Speaker_00
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.

03:03:21 Speaker_00
Other people to thank, David?

03:03:22 Speaker_01
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 founding of Rentech and partnering with Jim and investing in each other's funds and all that. So fun.

03:03:35 Speaker_01
Brett Harrison, who you mentioned Ben, 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.

03:03:47 Speaker_01
Well, anybody who uses interactive brokers knows exactly the opportunity there. So thank you, Brett. And then Matthew Grenade, who I spoke with.

03:03:56 Speaker_01
Matt is the co-founder of Domino Data Lab, which is a great enterprise AI ops platform backed by Sequoia and many others.

03:04:04 Speaker_01
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 Rentech.

03:04:15 Speaker_01
Matt, before starting Domino Data, came out of the quant world. He was at Point72 and Bridgewater, which isn't really quant, sort of its own thing, but he was a longtime senior employee at both of those firms.

03:04:28 Speaker_01
And he gave us great, great perspective on the landscape of everybody out there and where Rentech fits in.

03:04:36 Speaker_00
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 acquired.fm slash email.

03:04:49 Speaker_00
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03:04:54 Speaker_00
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03:05:10 Speaker_00
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03:05:24 Speaker_00
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03:05:31 Speaker_01
We'll see you next time.

03:05:32 Speaker_02
Who got the truth? Is it you? Is it you? Is it you? Who got the truth now?