Renaissance Technologies

I always used to misspell Renaissance as I was typing it out at R-E-N, and then I would sort of like not really know what came from there, but I learned a mnemonic to make sure I get it right.

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

Well, there's that too, but are you ready for this? You can't spell Renaissance without A-I.

Oh.

All right, let's do it. Who got the truth? 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, who got the truth?

I'm David Rosenthal.

And we are your hosts. They say, David, that as an investor, you can't beat the market or time the market, that you're better off indexing and dollar cost averaging rather than trying to be an active stock picker. They say there's no persistence of returns for hedge funds, that this year's big winner can be next year's big loser, and that nobody gets huge outperformance without taking huge risk.

When I was in college, I actually took an economics class with Burton Malkiel, who, of course, you know, was involved in starting Vanguard and is a big proponent of all that. And that is what I learned, Ben.

Well, David, it turns out they were wrong. Today, listeners, we tell the story of the best performing investment firm in history, Renaissance Technologies or Rentech. 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? Or, if you have, why don't you know much about them?

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.

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? And while the resources that are out there are scarce, because, for one, employees sign a lifetime non-disclosure agreement, David and I are going to take you through everything we've learned about the firm from our research, dating all the way back before Jim Simons started as a math professor, to understand it all.

Yeah, just like how we say, every company has a story, every company's story is powered by payments. And JP Morgan Payments is a part of so many companies that we talk about on Acquired. It's not just the Fortune 500 too, they're also helping companies grow from seed to IPO and beyond.

Yep. So with that, the show is not investment advice. David and I may have investments in the companies we discuss, or perhaps wish we did. And this show is for informational and entertainment purposes only. David, where do we start our story today?

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

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. He helped us out a bunch. Thank you, Greg.

And helped fact check a few of our assumptions of what happened after the book came out.

So that was Jim's parents. But really a major influence on him growing up was his grandfather, Marsha's dad. 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. And Bezos, of course, would get his start in his career at D.E. Shaw, a quant fund coming.

up at the same time as Rentech.

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.

Which I think he probably would say that thousands of times the rest of his life.

I think so. 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.

Three packs of merits a day.

Unbelievable.

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.

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

Yes. Important to tease out the difference between those two things. Yes.

Very, very important. And what he means when he says he has no interest in business, it's because from a pretty young age, he gets really into math. So the legend has it. when Jim is four years old, he stumbles into one of Zeno's famous paradoxes from ancient Greek times.

Yep. This is great. The basic gist of 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. You need to do addition or subtraction to do that.

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. So pretty, you know, heady stuff.

Yeah. And Jim would go on to finish his undergrad at MIT in three years and get a master's in one year. Yeah.

Pretty, pretty smart. But it turns out that that freshman year grad seminar he took actually has a big impact on him, because he doesn't do well in the class. He can't keep up. And Jim's pretty self-aware here. There are other people at MIT who never run into problems.

Which is, you know, what do you do with that information? You realize you have to add a few of your skills together to become the best at something. You have to be smart and something else.

Yes. So Jim's own words on this are, I was a good mathematician. I wasn't the greatest in the world, but I was pretty good. But he recognizes, like you said, Ben, that he has a different advantage that most of the super geniuses lacked. And that's that, as he put it, he had good taste.

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. And he said, I'm smart, but I'm not that smart. And so switch to computer science.

I think the analogy here is like sports. There are all-star players, there are Hall of Famers, and then there's LeBron and MJ. And Jim ends up being a Hall of Famer mathematician. But he's not Tom Brady.

I mean, he's got a pretty important theorem named after him.

That goes on to become a foundation of string theory and physics, which isn't even Jim's field.

Crazy.

So this realization that Jim has about himself, though, both that he's not the smartest person in the room at a place like MIT, but he can hang with them, and that he has this taste concept, I think, becomes one of the most important keys 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. 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. 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.

He's the extroverted theoretical mathematician.

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.

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. 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.

It's honestly crazy that this group of people took this type of risk.

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 PhD with the professor Xing-Hsien Cheung. 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 and physics. But before Jim leaves for the West Coast, he meets a girl in Boston and they decide to get engaged in four days.

Which is so interesting to think about because at that point in time, there was such an advantage to just being there. This wasn't even the trading floor, but information is all so manual and also 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.

Exactly. Yeah. You couldn't just log into Yahoo Finance or something or open the stocks app on your iPhone, which even the information they were getting was God knows how long delayed from New York or from Chicago for the futures and commodities that are being traded that Jim gets into. He's as close to the action as he can possibly be, but he's a long, long way from the action. 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. It was kind of a rush.

I bet.

Except he ends up losing all of his profits just as quickly. Yeah.

Important to learn that lesson early.

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

Right. Effectively playing the ponies.

Yeah, exactly. So, Jim'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..

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. He just gets bored fast.

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.

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.

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.

He's really racking them up.

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

It was basically formed with this idea that, one, across various branches of our government, we need better collaboration and cross funding of the same initiatives. And two, there are 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.

Yep. So the IDA there in Princeton kind of functioned like the Institute for Advanced Study, which is also in Princeton. That's where Einstein went when he came to America, kind of an independent think tank research group, except it's solely focused on code breaking and signal intelligence with the Russians during the Cold War.

Yeah. It's a pretty wild charter, and especially how special of an organization it was, like. the way these people would spend their time is 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.

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 code breakers there. They would double their salaries.

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. 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, 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, 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.

It's crazy. 1964, this was published?

Yes. Now, at this point in time, fundamental analysis was then, as in most of the world, today still is, the primary way of investing in things of, 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.

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

Yes, fundamental investing. There also existed, in the 60s, technical investing, which kind of is voodoo.

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.

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.

And importantly, 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. You are looking for signal in the noise and trying to use computers and algorithms to mine signal from something that otherwise kind of looks random.

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

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

Yes, there's all this noise in this data, and it is impossible for a human to sit here and look at this data and say, oh, I know what the Soviets are saying. No, no, you have to use mathematical models and statistical analysis to extract the patterns.

So mathematical models, statistical analysis. We actually hear a lot of that in the world today, because machine learning is a thing.

Yes, what they are really doing here 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. Which makes this the perfect time to talk about our presenting sponsor for this season, JP Morgan Payments.

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Yep. So also unsurprisingly, JP Morgan was ranked number one in a recent global banking index of AI capabilities, with Fortune saying they were, quote, head and shoulders above the others. Their customers get AI-powered payment solutions for fraud prevention, customer insights, and treasury insights, all of which grows the bottom line. They can even analyze transaction data to predict and mitigate fraud patterns in real time, with their validation services, helping stop millions of dollars for customers in attempted fraud.

Yep. We were doing some research to prep for this segment, and we came across something pretty wild. The United States Treasury Department has started using AI to detect suspected check fraud and recovered over $375 million in 2023. utilizing the new tools. The U.S.

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Yep. So then the natural question is, okay, what is the model here? How are they going to do this? And it turns out that one of the employees of IDA at this time, and one of the members of this sort of rebel group, shall we say, within the organization, is a guy named Lenny Baum. And Lenny just happens to be the world expert in a mathematical concept called a Markov model.

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. It's going to be a strikeout. They're going to get on base.

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. 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, etc.

And obviously, they're much fancier than that. But that is kind of the underpinning of it all. I mean, I remember in my sophomore year of college computer science class, I had a Markov chain assignment. And it was basically write a Java program to ingest this public domain book. And then I would give it a seed word, you know, the first word of each sentence and press return, return, return, return, return.

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.

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. 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.

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.

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? 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. I mean, it was hard enough for Warren Buffett to raise money at this point in time for his fund, and he was Benjamin Graham's anointed, appointed disciple. And here are these academics who are working at some random, unknown non-profit, saying, give us money.

This was decades before it became high pedigree to come, from a technical computer science background in the world of investing.

Yes. So a bunch of kind of Keystone Cop style fundraising happens here. They're going around in secret. They're trying to keep the IDA bosses from knowing what they're doing. One of the group ends up leaving a copy of the investment prospectus on the copy machine at work one night and the boss discovers it and calls them all into his office and is like, guys, what are you doing here?

It's a little bit of a clown show on the operational side, even if the idea is good.

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. 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. Jim writes an op-ed in the New York Times denouncing the Vietnam War and saying like, he's sort of part of the Defense Department, but not everybody in the Defense Department is for the war.

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.

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. And when the Newsweek piece comes out, that's when the Department of Defense is like, all right, you got to fire this guy.

Yeah.

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 re-established, math department at the State University of New York, Stony Brook, which is the Long Island campus of the State University of New York. 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. 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 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. He wouldn't be able to recruit somebody like Jim otherwise, but because he's now kind of tarnished his career, here's a very talented mathematician that they can convince to come be chair of the department.

Yep.

And amazingly, it works. Jim starts getting a bunch of great talent, including James Axe, who is a superstar in algebra, and number theory from Cornell. And he ends up at Stony Brook recruiting and building one of the best math departments in the world.

Amazing.

Totally amazing. But in true Jim fashion, after a couple of years of this, and also his marriage with Barbara falling apart, he starts getting restless again. He decides that he wants to go on a sabbatical and go back to Berkeley and reunite with his old advisor there and go spend some time out on the coast in California. And this is where 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.

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. 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.

Yeah. I mean, it was really viewed in the math community as anyone who's going to do investing is throwing away their talent. And it wasn't even that it was common, the way that it sort of is today.

Right. Jim was the first one. But the idea that you would leave to do anything commercial, you're doing a disservice to humanity.

Yes, exactly. And leaving to do anything, sure. But leaving to do investing was almost just seen as dirty, like it's this rich person's game that provides no value to society.

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.

Academics tend to be much more motivated by prestige than money. So I could totally see this other people being like, oh, I could do that if I wanted. But I have this higher calling and everyone respects me for this higher calling. And my currency is the papers I publish and the awards that I win. And that's what I want.

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.

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. And he recruits his old IDA buddy, original partnering 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.

And how much did he make on that flooring sale?

I think, together with Jim, his partners and whatever money Lenny put in, they had a little less than four million dollars in this initial capital.

In 1978.

Yep. 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.

Yes. And those folks are more loyal to Jim than they are to Stony Brook.

But they're more loyal right now to academia than they are to finance. This is not a paved pathway until Jim paves this pathway.

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

So, having Baum and Axe and Simons, it's like suddenly this extremely credible team in the math world.

Yes, beyond credible.

Right. All the theorems that a lot of mathematicians are using every day are all named after these three guys who are now at the same firm trading.

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. He's out there being like, hey, I think we can make money.

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. 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.

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. 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.

The computers are by no means running loose at this point.

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? Or are we going to do something just totally different? that we think is what's going to happen?

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, which is sort of where they would end up many years later, once they had both the hardware you're referring to, sophisticated computers, the clean data that would be required to make all of those incredibly numerous and fast calculations, and also the real computer engineering architecture to build these scale systems, to actually act on large amounts of signals and understand them all to come up with results. They just didn't have any of that at the time. So it was hunches and chalkboards.

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, but I also want to put my money somewhere else too, for some diversification.

Which was essentially spun out of Renaissance. Like it was kind of the venture capital work that they were doing at Renaissance that didn't fit with the rest of Renaissance.

Yes. So here's how it all went down. And this is so poorly understood out there.

Yes.

Howard was a computer science and business school professor at the University of Pennsylvania. So he taught CS at Penn and business at Wharton. 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. And Jim gets excited about investing together with Howard.

And again, trading on currencies and commodities at this point.

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.

I could not, when we figured this out in the research, I could not believe that this is not a more widely understood story. That this is the origins of what is today a fantastic venture capital firm, first round capital. But you could not name two more different strategies. in investing. I mean, a long term illiquid thing like venture capital, highly speculative versus, you know, we're going to trade.

Totally. 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, and Lenny and James and Howard. There's not institutional capital here. They're not out pitching LPs of like, oh, you should invest in my diversified strategy of currency trading and private technology startups.

When they say multi-strategy, this is really multi-strategy.

Yeah, we'll get into what multi-strategy today means later. But in these early days of Rentech, 50% of the portfolio was venture capital and 50% was currency trading. And in fact, a couple of years after they get started, the currency trading side of the firm almost blows up. when Lenny goes super long on government bonds and the market goes against him and the whole portfolio drops 40%, which is wild. That ends up triggering a clause in Lenny's agreement with Jim and they sell off Lenny's entire portfolio and he leaves the firm.

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. Simon's going to Lenny and saying, hey, maybe we should cut some of our losses and it's okay to trade out of these positions. And Lenny was just very dug in on, I'm a true believer. And that's how you can get into a situation where you trigger a covenant like this.

Totally. And again, also shows 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 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.

What?

At this low point for the trading side. Yes.

I had no idea. That's crazy.

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.

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.

So I think, perhaps in part because of the trading losses, James Axe starts to get a little disillusioned too. And he tells Jim that he wants to move out to California with Sandor Strauss, who started working with them at this point. Sandor was another Stony Brook alum that joined them. And the two of them want to move out to California and do trading out there. Jim says, sure, fine.

So it's this interesting arm's length thing where Jim strikes a deal where he's going to own a part of Axe.

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.

And importantly, I don't think anyone had any idea what Axe.

No, nobody would have done what they did had they known what was coming.

Yes. Wouldn't have spun it out.

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. 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.

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. 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.

I mean, he's doing ETL on the data. 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.

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. And Berlekamp had studied with folks like John Nash and Claude Shannon at MIT.

I love that Claude Shannon is coming in again. I know. We talked about a lot on the Qualcomm episode, father of information theory, really the center of gravity for attracting tons of talent to MIT and kind of paving the way for what would become phone technology and telecommunications broadly in the future. But the fact that Berlekamp is crossing paths at MIT with Claude Shannon, so cool.

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.

So, with this combination now of much, much, much better and deeper data from Strauss 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. 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. This is the turning point.

Yeah. In these kind of mid 80s years, Axecom 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.

Well, that's the thing. They don't know how reliable yet. They know they've done it kind of a few years in a row here. But the question is how uncorrelated to the stock market over a long period of time and how predictable are these returns, or is it just super high variance?

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. Fun coda on this. When Howard starts first round, a number of years later with Josh Koppelman, Jim, of course, is a large LP.

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.

Yeah, it was super fun talking to Howard about this and just the history of how first round started and early super angel investing and everything that became.

I also didn't realize that first round's fund one was a 50x on $125 million fund.

First institutional fund, which I believe they called fund two.

I mean, wild, wild stuff.

Totally wild. So when Howard spins out the venture activities, Jim then decides to set up a new fund as a joint venture between Rentech and Axecom, 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.

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, and it is born out of this observation that, oh my God, what they're doing over there at Axecom is really interesting. Maybe they shouldn't be doing it all the way over there. Maybe that should be a deeper part of the fold here at Rentech, and we shouldn't have let that get away or frankly given up on the quantitative trading strategies too early.

Those ideas had all existed before. This is the first time that it's all brought together and actually working and operationalized.

And, frankly, that computers got good enough to actually do it too. That's another big piece of this.

I don't know that Strauss could have done his data engineering too much earlier in time. Yeah. But before we get into the just absolutely insane run that this Medallion Fund is about to go on, that continues right through to this day, now is the perfect time for another story about ServiceNow. ServiceNow is one of our big partners here in Season 14 and is just an incredible company.

Yep. ServiceNow digitally transforms your enterprise, helping automate processes, improve service delivery, and increase operational efficiency, all in one intelligent platform. Over 85% of the Fortune 500 runs on them, and they have quickly joined the Microsofts and the NVIDIAS as one of the most important enterprise software companies in the world today.

So we talked on our Novo Nordisk episode about how ServiceNow founder Fred Luddy discovered this core insight that software can transform and eliminate manual tasks. And on Hermes, we told the story of how current CEO Bill McDermott came in and turbocharged that into an absolute monster $150 billion market cap global behemoth. The key thread that connects those two eras is that from day one, Fred knew the ServiceNow platform could be used across the whole enterprise. But at the same time, he also knew from his decades of prior software experience that launching a broad horizontal offering right out of the gate as a startup was a recipe for failure. You need to start with a specific vertical use case.

And that's been true for us here on Acquired too. David, if we didn't name it Acquired and cover technology acquisitions that actually went well, we never could have broadened and become the podcast that tells the stories of great companies. You can't just start as that.

Totally.

Well, this is what's so cool and where I think the playbook lesson really is for listeners. Because you can't just pick any use case, you have to be strategic about it. And IT was the perfect vertical because every other department has to interface with them, from the CEO on down. So they're going to notice when IT service management rapidly improves. All of those support tickets that used to take forever are now just magically resolved.

Once those other departments do pull the trigger on joining the ServiceNow platform, who is in charge of rolling it out for them? Of course, it's IT, who are already true ServiceNow believers. I'm honestly not sure that there's a better enterprise software playbook in history than ServiceNows. So once they established the beachhead in IT, they then took the same platform to HR with employee experience. They took it to CSM with customer service requests.

So, if you want to learn more about the ServiceNow platform and playbook and hear how it can transform your business, head on over to ServiceNow.

So they've got this grand new plan and vision with the Medallion Fund.

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.. 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.

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

And I do think he's making up that number. I think it's illustrative.

Right. But we're 100% right. 50.75% of the time. You can make billions that way.

It's so true. When you have that little edge, it's about making sure that you're not betting so much that a few bets that don't break your way can take you down to zero, and to make sure you can just play the game a lot.

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

Yep. Now, of course, this is all great in the abstract. if it's that you're literally sitting at a casino and you're somehow perfectly making these bets and you're just sitting right there at the table and then you can walk over to the cashier. It gets a little bit different. in the market.

This is slippage.

There's all sorts of practical consideration. You could get front run by other people. It's not just a computer program that gets executed. You actually have to meet the constraints of the real world when you're deciding. instead of a few big bets, we're going to have 100,000 tiny bets.

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

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

Yeah. We're going to come back to this in just a minute. But this certainly for early Rentech and then even now still for all of quantitative finance, is a really, really, really important thing.

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. 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.

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

Exactly. Yes. Looking at the quoted price of an asset is wrong. You actually should be looking at what is the volume that is willing to buy and what is the volume that is willing to sell. And for all of those buyers and all of those sellers, what are the price at which they are willing to transact?

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.

Right.

Medallion was about $27 million under management. when Burley 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 and carry?

I mean, either. one of those numbers is shooting the freaking lights out. Assuming that this is not a crazy high risk strategy that they executed and will completely fall apart under different market conditions, like if this is an actual repeatable strategy that produces the numbers you just said, unbelievable, world changing.

Hell yeah, let's go. And indeed, it was a hell yeah, let's go situation.

So the numbers. you quoted me, the gross and the net, sounded quite different. Talk to me about the fees and carry.

So 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, 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.

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. 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 pay folks, and whatnot. Like, great, 5% management fee.

And so the pitch they're making to the investor base is like, if you believe that we should be able to massively outperform the market doing quantitative trading, well, we're going to need a lot of fees to do that. And so the investors basically took the deal, if they thought about it enough. Okay, so that's the fees. On the performance, that 20% or 25%, it's just not actually that far above market, if it's above market at all. What you're seeing is a high fee, normal-ish performance fee fund at this point in time.

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 Berlikamp, hey, you should move here to Long Island. Let's recentralize everything here. I want to go all in on this.

On the one hand, making a 6x in one year sounds great.

On the other hand, this is the equivalent of when Don Valentine sold Sequoia's Apple stake before the IPO to lock in a great game, but miss out on all the upside to come.

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

Yeah. That's just on the carry side of it. 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.

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.

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 going to turn Rentec into an even better, even more idealized version of IDA and the math department at Stony Brook. 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. So, of course, he starts raiding the Stony Brook department itself again.

And listeners, I imagine this is probably the point where you're starting to get confused and saying there are so many people in this story. I think we're on eight or nine. We just keep introducing more people. And that is the story of Renaissance. It is not this singular, clean narrative.

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.

And we haven't even introduced the two people who would become the co-CEOs of this company for 20 years.

Yes. 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. So not Jim's bogey of 80%, but still pretty freaking great.

And we should say the years of modest performance are behind them. From every single year forward, they shoot the lights out. From 1990 onward, they never lose money. And on a gross basis, they never even do less than 30%. It's working.

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

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.

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.

And that's sort of in the mid nineties.

Yep. As they're hitting this $250 million, half a billion dollar scale.

Right. The computer model spits out, we should go buy this huge amount of something at this price. They go to do it. They can only buy 10,, 20, 30 percent of the amount they want at that price. And then suddenly the price is very different.

Yep. 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 nineties was a different era, but half a billion dollar fund doesn't sound that big. How are they moving markets with half a billion dollars?

It's not the equity markets.

It's because they're in these thinner markets. It's not that commodities and futures are small markets. They're large, but they're thin compared to equities. There's just not that much volume and you just can't trade that much without slippage becoming a huge issue. And Medallion is now hitting that limit.

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. 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.

And this brings us to Peter Brown and Bob Mercer. And in 1993, one of the mathematicians that Jim had recruited to Rentec, a guy named Nick Patterson, gets especially passionate about going out and recruiting new talent along with Jim. And this is, I think, one of the keys to Rentec and the culture there. People want other smart people to come be there too. Nick's sitting there like, this is a joy.

Yep. 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? 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.

It's not just kind of, it's exactly the same signal processing.

Right. Speech recognition is a hidden Markov process where the computer that's listening to the sounds to try to turn it into language doesn't actually know English, right? Obviously. But what it does know is, when I hear this set of frequencies and tonalities and sounds, 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.

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. Because they're good on the math side, but they have this large systems experience. 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. And the current talent at Rentex coming from academia has just never experienced that or built anything like it.

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. 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 and every time after it. They're also, once they do, identify patterns, which this is key, the algorithms identify the patterns.

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

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

So 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. But it turns out, A, they're obviously very successful at that.

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. So if it's all part of the same model, we can 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. And because we're putting it all in one model and no one else in the world is, we can discover patterns that no one else knows about.

It turns out that this was actually the second most important innovation 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, 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. 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.

They did have the cultural element where it was encouraged that you share your learnings, but someone would have to take the time during their lunch break and go learn from you about those and then implement it in their version. There's a lag and it may actually not get implemented.

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 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.

Right.

What Bob and Peter do is they unify everything at Rentech. So all the wood is going behind one arrow.

Yes. And before we talk about the impact of that, we want to thank our long-time friend of the show, Vanta, the leading trust management platform. Vanta, of course, automates your security reviews and compliance efforts. So frameworks like SOC 2, ISO 2701, GDPR, and HIPAA compliance and monitoring, which is quite topical if you are in the heavily regulated finance industry and you need a lot of security and compliance. Vanta takes care of these otherwise incredibly time and resource draining efforts for your organization and makes them fast and simple.

Vanta 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.

Vanta takes care of all of it for you. No more spreadsheets, no fragmented tools, no manual reviews to cobble together your security and compliance requirements. It is one single software pane of glass, just like one model, that connects to all of your services via APIs and eliminates countless hours of work for your organization. There are now AI capabilities to make this even more powerful, and they even integrate with over 300 external tools. Plus, they let customers build private integrations with their internal systems.

And perhaps most importantly, your security reviews are now real-time instead of static, so you can monitor and share with your customers and partners to give them added confidence.

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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.

And yeah, just imagine the computers that you were using during 1994 and 1995.

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%. 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.

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. You kind of quickly get where I'm going here, which is, oh, they're scaling AUM, not by bringing in new investors.

It's closed to new investors.

It's all just compounding. 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.

Yes. And then in the year 2000, they just totally blow the doors off.

They grow the fund from 1.

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.

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. And, David, as you said, volatility actually makes their algorithms work even better. because what are they doing? 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.

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. 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. Part of it might also be, too, that it didn't perform well for those couple days.

Yep.

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. 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.

Fascinating. It's such a good insight. That illustrates Jim as a leader right there.

It totally does. 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 quantquake 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. 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. 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?

It is so true though. I even find this about myself that I will naturally take the position of the foil to the person across from me. So if somebody 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. But you naturally want to play the other side to balance out the person sitting across from you.

So back to the year 2000 and this incredible performance. Then 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. We got to introduce this concept of a sharp ratio now, which for all of you listeners that are in the finance world, you'll know this, but for everybody else, this is a really important concept.

And I think people grasp it intuitively. We've mentioned this concept a couple of times. this episode where, okay, great. It's amazing to have a fund that 25Xs or a year where you have 100% investment return, or I bought Bitcoin yesterday and it doubled overnight. Does that make you one of the best investors in the world?

Yes. And so the Sharpe ratio is a measurement combining these two concepts.

Exactly. So. it's named after the economist William F. Sharpe. It was pioneered in 1966..

Good.

1995 to 2000, Sharpe ratio of 2.

Good. Where do I sign up to invest?

At some point, they added foreign markets and achieved a Sharpe ratio of 6.

And I believe, if I have my research right, in 2004, they actually achieved a Sharpe ratio of 7.

Astonishing.

You know, again, back to our sports analogy here, these aren't Hall of Fame numbers. These are like, I don't know, make Tom Brady look like a third stringer.

Yes, exactly.

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. 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 raise the carry to 44%.

There's two interesting ways to look at this. One, they're just trying to jack it up so high that they just purge their existing investors out, where they're saying, we're not going to kick anyone out yet, but we've been closed to new business for a long time now. 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

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

Totally. So. I have to imagine it didn't go over well with the existing investors, but they just have so much leverage that what's going to happen?

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%.

And not all alumni get to stay. There's select alumni that get grandfathered in.

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. Even in the equities market, they are now hitting up against slippage.

Yep.

And so if they want to maintain this crazy, crazy performance, they just can't get that much bigger.

This is the problem that Warren Buffett talks about all the time and why he has to basically just increase his position in Apple rather than going and buying the next great family owned business. The things that move the needle for them are so big that that's really all they can do. And when you are big, you're going to move any market that you enter into.

Yep.

And the strategy that rent tech is employing right now, they're just deeming doesn't work at north of $5 billion.

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?

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. Well, the first one is sometimes there's just more profitable strategies than they had the capital to take advantage of in Medallion, but they weren't sure it would be on a durable basis. If they were sure that they could manage $10, $15, $20, $25 billion in Medallion all the time, then they would grow to that.

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. 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.

And these guys are very used to sharp ratios that are far better than the S&P.

Right. And so Jim says, yes, exactly. Now get to work on designing the product that they should invest in.

Right. And so that's basically what they come up with is, can we create something that's like an S&P 500 with a higher sharp ratio? Can we beat the market by a few percentage points or, frankly, even match the market each year with lower volatility than if they were buying an index fund? And you can see who? this would be very attractive to pensions, large institutions, firms that want to compound at market or slightly above market rate, but don't want to risk these massive drawdowns or, frankly, just big volatility in general, should they need to pull the capital earlier.

And the marketing is built in. It's not like there's any lack of demand of outside capital that wants to invest with Rentech.

Right. It's really funny. There's all these 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.

Clearly, this is a very different product.

But people did not perceive that. People were very excited. It's a Renaissance product. It's the same analysts. They're using all their fancy computers.

Yeah, that has not performed anywhere near how medallion has performed.

Correct. Has it served its purpose?

Yeah.

But is it medallion? No, it's not special in the way the medallion is special.

Yes.

A couple other funny things on the institutional fund. So I spent a bunch of time scrolling through 13 Fs over the last decade from the medallion filings. I think they have two institutional funds.

Yeah, there's institutional equities and diversified alpha.

So the funniest thing is they file these 13 Fs. And David and I are very used to looking at 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? And usually you see 15,, 25, maybe 50 different names on there. Well, the 13 F for Renaissance has 4,300 stocks in these tiny little chunks. And there's a little bit of persistence quarter to quarter.

Hey, they've been listening to a choir.

That's right.

That's one of the signals in the model. Yeah.

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. And, even though this is the end of quarter filing, of what their ownership was, if you had taken it a day or a week earlier, it could look completely different.

Yes. The way that some folks we talked to described the difference between the institutional funds and Medallion to us is that Medallion's average hold time for their trades and positions is call it like a day, maybe a day and a half. Whereas the average hold time for the institutional funds positions is like a couple months. So, across 4,300 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. Which makes sense.

Yep. And frankly, it just looks a lot like the S&P 500.. Like when you look at as of November 23,. so 11 of the 12 months of the year had happened, they were up 8.6%. Okay.

Zero percent 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.

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

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. They have effectively these really robust models that are highly unemotional, that are making these super intricate multi-security bets, and they are putting on exactly the right set of trades to achieve the risk and exposure that the system wants them to have. And who is on the other side of those trades? It's panic sellers.

Yes. Well, there's one slight nuance to that, but I don't know how much it holds water. And the apologist nuance would be, well, Warren Buffett could be on the other side of the trade, and Medallion could 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.

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. The direct counterparty of Medallion and the quant 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.

But I think the important thing is, can you and your adversary both benefit? And I think in this case, you and your counterparty, the person you're trading against, yes, you have two different objective outcomes. Like, can I get a penny over on Warren Buffett by managing to take him on this one trade? But his strategy is such that that is irrelevant.

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.

They gained confidence that they had enough profitable trades they could make, that they could raise the capacity without dampening returns.

Yes.

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

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, 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. 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%.

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. And if so, that just means that they continue to find more profitable strategies within Medallion to keep those same unbelievable returns at larger sizes.

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

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.

Yeah, I still know how to use Excel.

That's right. Okay, so $60 billion in total, carry.

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. So Bob Mercer, that name may have sounded familiar to many of you along the way, 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. 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.

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

Yeah, tens of millions of dollars, many tens of millions of dollars on all sides and through many campaign cycles here from Rentech employees and alumni. This did become a flashpoint for the firm. in the wake of the 2016 election. Mercer obviously became a controversial figure, both externally and internally within the firm.

Especially once people realized he was the through line through Breitbart, Cambridge Analytica, the Trump election and Brexit.

Yes. Ultimately, Jim asked Bob to step down as co-CEO in 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.

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.

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.

Yeah, I think unexpected is the best way to put it.

Like everything, with Renaissance, it works a little bit different than the rest of the world.

Yes. Okay. Speaking of, let's transition to analysis. 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.

Yeah, I mean, typically in kind of a friendly way, but yeah.

Let's take like in a venture firm, you've got your lead partner on a deal or a deal team. They're working that deal and maybe some of the other partners help a little bit, but mostly they're off prosecuting their own deals.

Yep.

And I think that's the most collegial way that this happens in finance. Yeah. Then you've got multi-strategy hedge funds out there. firms are being pitted against one another to be weighted in the ultimate trading model for the firm.

Yep.

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. 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.

Yeah, that's a good point. The whole code base is completely visible.

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.

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?

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.

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. Most of the people at Rentech are not going to have another job.

What did you find on LinkedIn? At least the median tenure of employees is like 16 years.

Yeah, I just got LinkedIn Premium and you can see median tenure. And it's crazy. There's only like 300, 400 employees at Renaissance and the median tenure, at least as reported by LinkedIn, is like 14 years.

Yes. Okay, this brings me to point number two, which you said, this is an absurdly small team. 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. So let's call it, I don't know, 150, 200 people max. who are like hands on the wheel here for Medallion.

Yep.

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 two to 5,000 people work at those places.

Wow, I didn't realize it was that big.

It is an order of magnitude more people who are working at the other firms versus who are working at Rentech.

And lest you think that it's like a capital-based thing, no, the institutional funds have gotten big. They peaked at over a hundred 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.

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.

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.

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. You're not comparing notes or measuring parts of your anatomy with someone else.

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, 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. 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.

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.

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.

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 performance returns that Rentech does. At Rentech, you're splitting that a couple hundred ways. At Citadel, you're splitting that 5,000 ways.

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.

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 Rentech so unique and defensible is Medallion's structure itself. That it is a LPGP fund with 5% management fee and 44% carry.

So it's not like a prop shop or like proprietary, it's just one pot of money. It's literally a GPLP, even though the GPs and the LPs are the same people.

So here's my thinking on this. 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, why?

Right. They've already kicked out most of the LPs, if not all. So why are they raising the carry?

Right. It's all themselves. It's all insiders. Why do they charge themselves 44% carry and 5% management fees? I think Jim talks about this, though.

Yes. It's always a funny argument. It's like, who are you paying the fees to? Right.

So I was like, what is happening here? So, okay, here's my hypothesis. This is not about having crazy performance fees. This is not about having the highest carry in the industry. This 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.

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

I mean, depending on your definition of set for life, I think it happens very, very quickly. Yep. 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?

Right. Especially when they all have access to the whole code base. The whole thing is meant to function like a university math department where everyone's constantly knowledge sharing, because we're going to create better peer-reviewed research. when we all share all the knowledge all the time. You would think that's a super risky thing to give everyone all the keys.

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, 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.

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

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. But over time, you become a meaningful investor in the firm and you sort of shift to that 51%.

And at the end of the day, I think these three pieces to me are the core of this sort of tapestry of Rentech. One model that everybody collaborates on together, 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. 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.

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.

Yeah, the unlevered returns, if you were running, this strategy, would be much lower.

Yeah, so a big piece of this playbook that we didn't talk about is leverage, but every quant fund does leverage, and so Renaissance was just more clever than everyone else.

Yeah, it's an important point, though. Nine out of every 10 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. Like, I'm scared of it.

Right. I mean, you could imagine, let's say it wasn't, they were right 50.25% of the time, but they were right 50.0001% of the time, they would need to do a ton of trades in order to generate enough profits. So that's why you need, you know, $60 billion of cash to actually execute the strategy, to produce the returns that they were looking for.

Yeah.

On $5 billion of equity. 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. 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.

Right, because that's not including the LP return on Medallion.

100%, it's not.

Which again, as we spent a long time talking about, it's all the same thing.

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, 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. 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.

I don't know. I mean, you're talking about a lot of data here.

Yeah, it says right on their website, they have 50,000 computer cores with 150 gigabits per second of global connectivity and a research database that grows by more than 40 terabytes a day. That's a lot of data.

Right. Is that $750 million a year? I don't know, but it's not zero.

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

I wonder too if the fee element of Medallion basically pays the base salaries for the current team.

That feels like it's right. 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. Okay, power.

This is a fun one.

Yeah. So, listeners who are new to the show, this is Hamilton Helmer's framework from the book Seven Powers. What is it that enables a business to achieve persistent differential returns, to be more profitable than their closest competitor on a sustainable basis? And the seven are counter positioning, scale economies, switching costs, network economies, process power, branding, and cornered resource. And, David, my question to you to open this section is specifically about Rentech's lifelong non-competes.

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.

Yep.

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

Yep.

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 Rentech. There's the legal layer, the base layer that you're talking about, it's like the agreements you sign. Then there's the economic layer of what we spent a long time talking about in Tapestry of. it would just be dumb to leave. You are better off staying there as part of that team with a smaller number of people than going to Sigma with a lot more people.

It's your community.

It's your community. Totally. You're not in New York City. You're not in the Hamptons. You're not in Silicon Valley.

All right. So classify it. What power does that fall under?

Well, I mean, I think the people specifically you would put into cornered resource, but I'm not actually sure that fully captures it here. I was thinking more process power, because I think it is the combination of the people and the model and the incentive structures.

Yeah, I think that's right. I also had my biggest one, being process power. You actually can develop intricate knowledge of how a system works and then build processes around that that are hard to replicate elsewhere. I think these systems have been layered over time, also, where anyone who's come into the firm in the last five years doesn't know how it works. start to finish.

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

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

I have a couple of nuances on this. So one, I think it probably is true that they have better data than any other firm, thanks to Sandor Strauss and the work that he started doing in the 80s before anybody else was really doing this. Yep. So they have that and other firms don't. That said, certainly all the other quant firms are throwing untold resources at all this too.

Right. They want to do this and money is not the issue.

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. 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.

There's one particular relationship between types of equities that they've just been exploiting and no one can figure out except them.

Right. And that may entirely be possible.

Isn't that crazy?

Right. Now, Rentech will say, they will all say that is a hundred percent, not the way that it works. It's not that at all. If that were the way that it works, they would, of course, still say that because they don't want anybody to know.

Don't look at the relationship between soybean futures and GM. Just don't do it.

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. 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.

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.

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.

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. 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. I think there might also be some argument to the data that that older data is helpful, but its value decays over time as markets evolve.

Definitely.

The broader point I 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.

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

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

Oh, yes. Totally, there's dis-economies of scale. Your strategies, stop working when you get too much, AUM.

Yep. You get slippage. I don't think there's any network economies here. I mean, they literally don't talk to anybody.

Although, well, they do have some very well-established relationships with electronic brokerages and different players in the trade execution chain. I think they have very good trade execution and very fast market data. Their ability to pull data out of the market is very high quality.

Do you think it's actually better than their competitors, though?

I don't know. That's probably not the secret sauce.

Yeah, I don't think so.

It's the table stakes.

Switching costs. I don't think apply. Branding maybe applies in their ability to raise money for the institutional funds, but that's not a big part of the business.

The fee stream on the institutional fund may entirely belong to branding.

Yes.

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. 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 going to have two episodes in a row of counter-positioning at scale.

Tell me about your counter-positioning. Who is being counter-positioned and in what way?

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 the same type of trading activity.

Like they're counterparties in trades.

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.

Well, but quant? funds are often counterparties to each other.

That's true. But I think, yes, adversaries in finding the similar types of trades. And I think the counter-positioning for Rentech, or for Medallion specifically, is one, I do think the single model approach versus the multi-model, multi-strategy approach that most others have, does have benefits, like I was talking about in the tapestries. But I think also, and maybe bigger, is, 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.

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.

Yes. Particularly because it's all the same people on the GP and LP side.

Oh, we keep going round and round that axle. I loosely buy the counter-positioning thing. I just think the answer is disgustingly simple and kind of annoying here, which is, they're just better than everyone else at this particular type of math and machine learning, and they've been doing it for longer. So they're just going to keep beating you.

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.

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

Yep. I think that could be. So maybe that feeds into process power.

Yep.

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.

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. 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. Well, if you buy the whole thing gets reinvented continuously every two years, then yes.

And there's remnant knowledge. 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. 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. So learning's compound is my answer.

Great.

Okay. Playbook. So, in addition to the three-part David Rosenthal tapestry that you have woven,

I have nothing more to add.

There are a handful of things that I think are worth hitting. So the first one is signal processing. is signal processing is signal processing. They, by not caring about the underlying assets, they literally don't trade on fundamentals, except in the institutional fund. when they trade on fundamentals a little bit, they use price earnings ratios and stuff like that in the institutional fund, which is kind of funny, because that's a completely different skillset.

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. 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. That's ridiculous. They don't teach anybody anything about investing. They're just doing signal processing.

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

Yes.

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. 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.

Yes. And I think when I've heard people from Rentech talk about this, they will all say 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. Like it can't tell you why, but that's okay.

But it does know it has a slight edge, and so it should trade on it, even though it can't explain why.

Yes.

Well, speaking of models, I've been trying to nail down an answer to this question. Do you think Rentech was the birthplace of machine learning?

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. They said, we don't know because they don't share anything.

Right. It's like. the principles certainly came out of the same math community that spawned machine learning, but is what Rentech has figured out over the last couple of decades in Google's Gemini model and in chat. No, it's not because they don't contribute any research back. 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.

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.

If that were the case, we should have very different answers to powers.

To illustrate this point, it's quite interesting. Peter Brown's academic advisor was Geoffrey Hinton.

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

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 Geoffrey Hinton is Ilya Sutskever, who is the co-founder of OpenAI. I mean, many years later, but still.

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

Yep. Okay. Another big one is this concept that you should trade on a secret that others are not trading on. So on the face of it, it seems obvious. Of course, I should come up with some strategy to trade on that other people aren't trading on.

Such a good point. And many, if not most, of the other quant firms are not doing that. 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.

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. 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.

Yep, totally. And that was surprising for me in the research. Like 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.

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. You have, on the one axis, fast and slow in terms of trade execution. And on the Y-axis, you have smart versus obvious.

Yeah, the way he phrased it to us was smart versus dumb, but dumb doesn't mean dumb.

Right, it's the obvious. trades. And the high-level point is, all quant funds are not high-frequency trading firms and vice versa. And this is something that I didn't know, not coming from this industry, and now makes total sense to me. I think I thought they were the same thing, but fast and obvious is your classic high-frequency trader.

Yeah, this is Jane Street.

Yes, there's fast and smart, which you kind of don't need to be both. You don't need the fastest connectivity in the world and the most clever trades to put on. So people kind of tend to pick a lane that they're either a high-frequency trader or they're trying to make the smartest, you know, most non-obvious trades possible. And that, of course, leads us to Medallion, which is in the slow and smart quadrant. All the machine learning systems discover the relationships in the data, so there's a huge amount of compute.

The non-obvious trades.

Exactly. That goes into finding the non-obvious trades, but then they're actually made reasonably slowly. They still have to happen within seconds or minutes, but the advantage isn't that they're high-frequency, the way that all the Flash Boys stuff is.

My sense is, Rentec is not a high-frequency trading shop. They are not front-running things, you know, 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.

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

Oh, this is another thing that we heard. Rentec is world-class at disguising their trades.

Yeah, they can make it so that they don't move the market and you don't know who is acting or when, and this is because in the early days, they weren't good at this, and people basically intercepted the trades that they were making and were front-running them, and they had to adapt and develop these clever systems to make it so. you don't know who's buying and you don't know in what quantities, and you don't know if they're going to keep buying.

Yep.

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? 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.

And it happened.

Yeah, and while it's never happened to Rentec, 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. 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. The systems were not looking at the same location and memory for the same bit.

Yeah, so, like the whole portfolio, gone, right?

Yes. 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.

Well, they probably got margin called by all their counterparties.

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, 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.

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.

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. 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.

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. Is this where you're going with this?

Well, I mean, the investment business, it sort of depends how you define investor. 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. And of course, lots of things are called investing that are not that. Is it investment? if I put money to work and then I get more money back later and I don't actually care how the money got made and it's actually zero sum, I'm just vacuuming it out of...

Right, right. Yeah, the money is not being invested in anything to produce.

Correct. But it's literally the same business model as a casino. You have a slight edge and you let a whole bunch of patrons come in and lose money to you in your slight edge.

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. 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, in that they are allowing trades that people want to make to happen faster and at much lower spreads.

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. 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.

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. Yep. None of which used to be true.

Nope. 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. They actually don't all have an edge, even though they might purport to.

Right. Lots of them lose money. You too, listeners, could beat the market. Not investment advice. Please don't try.

Right. On average, medallion will not lose money to you. But, you know, there are plenty of other hedge funds out there and high frequency shops and counterparties for you where you could take them. It's just not Jim Simons.

Oh, there's this great, great vignette at the end of Greg's book. When was it? It was during one of the like selloffs in the mid 20 teens in the market, where Jim calls the head of his family office. He's long retired from Rentech at this point, calls the head of his family office and says, what should we do with all the selloff in the market? And it's like, you're Jim Simons.

You're Jim Simons. What should we do?

What should we do?

All humans are fallible.

Totally.

A couple of other are squintable. The value creation exists. It's easy to knock that. all these smart people are going into finance and you wish they were doing something more productive for the world. At the end of the day, humans are going to do what they are incented to do.

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

Well, there's not much to it other than a huge amount of InfiniBand was used by high-frequency trading firms. And I don't know for sure, but I kind of think Mellanox built their business on quant finance.

Yes.

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.

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.

But does it create anywhere near as much as it captures?

That's it. They're really, really good at value capture.

Yes.

This is not Wikipedia here. This is about as far away on the spectrum as you can get.

There's a great Always Sunny in Philadelphia where Frank, Danny DeVito, sort of goes back to his whatever business he founded in the 80s. And he's like dressing in his pinstripes and stuff again. And he's taken back over. He brings Charlie with him. And Charlie, you know, he's like, so, Frank, what is the business?

Yeah, totally. Very, very good at value capture too.

Yes. Okay, bear bull. So this was a section that we had for a long time, that we did not put in the last episode. And boy, did we hear about it. So, listeners, thank you so much for expressing your concern.

Resurrected like a phoenix.

Resurrected. However, this is about the lamest episode. to resurrect it on. What's the bull case for Rentech?

Past performance is an indicator of future success.

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

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.

Yeah, the bear case is things are changing and I think things are changing basically on any axis. is the bear case for them. So things are changing where competitors are catching up. Maybe. Maybe the fact that the tech industry has figured out these large language models.

Right. There's no way for us to know any of this, because there's no way to know any of this.

Right. It's all the secret.

Yep. Okay. Our new ending section, the splinter in our minds, the takeaway,

the one thing you can't stop thinking about.

What is the one thing for each of us personally from doing this work over the past month on Rentech that sticks with us? 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.

In other words, early Google.

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.

Nope. Pure gasoline right into the veins.

Yeah. Which is not to say I would necessarily want to work there. I think I would. Totally not. Yeah.

Yep. The one thing I can't stop thinking about is the idea of the complex adaptive system that I was talking about earlier. I think from 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 correct most of the time.

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?

That's the bear case. I don't know.

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 arbitraged down.

All right. Should we have some fun? Carveouts?

Let's have some fun.

Sweet. All right, listeners. I have three. People have been expressing that they're loving the carveout section, so I decided to load them up a little bit more.

That's right. Let's indulge.

We'll spin off a whole new podcast called Carveouts.

44%. carry. Let's go.

So I have one announcement, one TV show, and one other fun thing for listeners. So first, the announcement. David and I are going to be emceeing Modern Treasury Transfer again this year, and so if payments are your thing, you should come join us. It was awesome last year. It'll be May 15th, 2024 in San Francisco, and we'll put a link in the show notes to register.

Can't wait.

My second one is a TV show, and it is actually acquired related. It is called The New Look on Apple TV+.

Oh, yes. But Christian, it is such a new look.

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. 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. 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.

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. I'm also very curious, for people who are watching, feel free to put a thing in the slack and carve out, 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.

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.

The Wertheimers, indeed. Oh, God, we get to do a Chanel episode at some point. But the new look on Apple TV+, I promise you whether or not fashion luxury is your thing, it's a beautiful and harrowing story.

Oh, as you and listeners know, I'm not a TV guy, but this is so up my alley.

The whole thing, it takes place in wartime Paris.

All right, I got to watch it.

You got to watch it. Okay. My third one is a fun thing for listeners. So, after our Nike episode, the president of Cole Haan, which if you listen to the episode, you now know is its own company. Out from Nike years ago, the president of Cole Haan reached out, and it turns out he, like all of you, is also an acquired listener.

To be clear, this is not a sponsorship. No, no. It's just like he's a fan and reached out to us.

And I've owned a bunch of Cole Haan products over the years and I've really liked them. So for 35% off anything, you can go to colehaan.com slash acquired or use the code acquired35 at checkout. And thank you to Dave for providing this to us. This is only live, I think, for a couple of weeks. So if you're listening to this episode soon after it drops, go check it out.

Super cool. He likes acquired and wanted to share the love back.

Love it.

I love it.

All right, David, your carve outs.

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.

David, you and I go to Palm Beach for two days and you get hooked on.

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.

Who, it is nice.

So great. We didn't knowingly spot any Rentech people there, but we may have.

We did knowingly spot some Birkin bags, though.

Yes, 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?

David's, like, I'm a thousand. I have no idea what you're talking about, Jenny. Yeah, right, right, right.

I live under a rock and I'm a dad. And she showed it to me. This is a woman who lives in Palm Beach and she goes around, she posts on Instagram and on TikTok and she just interviews people on the street about what they're wearing, what brands they're wearing, their style. It is magnificent. My favorite is, we'll see if we can find it and link to it.

Excess, truly excess.

And that's when I was hooked. I was just like, this is the greatest thing I have ever watched.

All right. If I used TikTok, I would subscribe.

No, you can get it on Instagram too.

Oh, all right, good.

I actually subscribed the acquired account on Instagram to class of Palm Beach. I don't know how many people we're following. It's not many, but we are following class of Palm Beach.

Look at David opening up our Instagram account. You're so youthful.

I know.

All right, listeners. Well, a huge, huge thank you to JP Morgan Payments, ServiceNow and Vanta. You can click the link in the show notes to learn more. David, I know you've got some thank yous from folks you talked with, and a few of them we did together.

Yes, for sources for this episode who were so generous with their time and thoughts. First, huge thank you to Greg Zuckerman, author of The Man Who Solved the Market. The canonical book out there about Rentech and Jim Simons. 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.

It is crazy. Everywhere you hear that 66% number quoted, and that is from Greg's analysis.

Yes, truly a service to us and to corporate historians and financial historians everywhere that he did that research and got those returns.

And there's a few other primary sources. There's really not much. So we can actually list all of them here. There's a congressional testimony of Peter Brown about the basket options thing. There's Peter Brown doing an interview at GS Exchanges, which again, many of the questions were straight out of Greg's book and the stories told.

Yeah, it was 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. 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.

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. Brett Harrison, who you mentioned, Ben. Brett, is now building Architect, which I love this.

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. We'll be including little tidbits that we learn after releasing each episode, including listener corrections. You can listen to ACQ2, search and subscribe in any podcast player and listen for our most recent episode with the, well, really creator or a person who led the team that created Lyra Glutide, which went on to become Semiglutide, which, of course, is Ozempic, Wegovy, et cetera.

All modern GLP ones.

Lata Bier Knudsen from Novo Nordisk was awesome to have her on the show. And after you finish this episode, come talk about it with other smart members of the Acquired community at acquired.fm slash slack. If you want some merch, we've got some. Acquired.fm slash store. And with that, listeners, we'll see you next time.

We'll see you next time.