Transcript: (disclaimer: may contain unintentionally confusing, inaccurate and/or amusing transcription errors)

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

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

Ben: Well, there’s that too. But you ready for this? You can’t spell Renaissance without AI.

David: Oh, touche, touche.

Ben: All right, let’s do it.

Welcome to season 14, episode 3 of Acquired, the podcast about great companies and the stories and playbooks behind them. I’m Ben Gilbert.

David: I’m David Rosenthal.

Ben: 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.

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

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? Well, their eye-popping performance is matched only by their extreme secrecy, and they are unusual in almost every way. Their founder, Jim Simons, worked for the US government in the Cold War as a codebreaker before starting Renaissance.

None of the founders or early employees had any investing background, and they built the entire thing by hiring PhD physicists, astronomers, and speech recognition researchers. They’re located in the middle of nowhere in a tiny town on Long Island. They don’t pay attention to revenues, profits, or even who the CEOs are of the companies that they invest in. And at any given time, they probably couldn’t even tell you what actual stocks they own.

Now you may be thinking, okay, great, I just learned about this insane fund with unbelievable performance—to be specific listeners, that’s 66% annual returns before fees—and I want to invest. Well, you can’t. To add to everything else that I just said, RenTech’s flagship Medallion fund doesn’t take any outside investors. The partners of the firm have become so wealthy from the billions that the fund has generated that the only investors they allow in are themselves.

David: Oh, we are going to talk a lot about that towards the end of the episode, because I think it’s the key to the whole thing.

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

This episode was selected by our Acquired limited partners. To be honest, I didn’t think enough people knew what RenTech was to pick it, but when we put it out for a vote, the people have spoken. So if you want to become a limited partner and pick one episode each season and join the quarterly Zoom calls with us, you can join at acquired.fm/lp.

If you want to know every time a new episode drops, sign up at acquired.fm/email. These emails also contain hints at what the next episode will be and follow-up facts from previous episodes.

For example, we had a listener, Nicholas Cullen, email us, this time who found the actual document with the by-laws of Hermes’ controlling family shareholder H51, which we linked to in this most recent email.

Come talk about this episode with us after listening at acquired.fm/slack. If you want more from David and I, check out ACQ2. Our most recent episode was with Lotte Bjerre Knudsen, who led the team that created the first GLP-1s at Novo Nordisk, so awesome follow-up to the Novo episode if you liked that one.

Before we dive in, we want to briefly share our presenting sponsor this season, is J.P. Morgan, specifically their incredible payments business.

David: Just like how we say every company has a story, every company’s story is powered by payments, and J.P. 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.

Ben: Yup. 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?

David: 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.

Ben: Super cool.

David: 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. We actually got to talk to Greg in our research. He helped us out a bunch. Thank you, Greg.

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

David: So that was Jim’s parents, but really a major influence on him growing up was his grandfather, Marsha’s dad. There are already echoes of the Bezos story here with the grandfather, the mother’s father, spending a bunch of time with him, and rubbing off on young Jeff, or young Jim in this case. Bezos, of course, would get his start in his career at D. E. Shaw.

Ben: A quant fund coming up at the same time as RenTech.

David: 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.

Jim’s grandfather, Peter, was quite the character. He was a Russian immigrant and he’s still more Russia than Boston at this point in time. As Greg puts it in the book, Peter reveled in telling Jim and his cousins stories of the motherland involving wolves, women, caviar, and vodka. He teaches young Jim when he’s a child here in the factory to say Russian phrases like, give me a cigarette and kiss my ass.

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

David: 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 age 10 in the factory.

Ben: Three packs of Merits a day.

David: Unbelievable.

Ben: 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.

David: There are these famous stories of the conference rooms at RenTech and the war rooms when the market is going through a crazy gyration, and it’s just filled with cigarette smoke and it’s all Jim.

Ben: Different time.

David: 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 grandfathers, this well-to-do entrepreneur.

Jim gets to rub shoulders in the Boston area with people who are really rich. He says later, “I observed that it’s very nice to be rich. I had no interest in business, which is not to say I had no interest in money.”

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

David: Yes. Very, very important. 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. Legend has it when Jim is four years old, he stumbles into one of Zeno’s famous paradoxes from ancient Greek times.

Ben: Yup. This is great. The basic gist of Zeno’s paradoxes, if you are always taking a quantity and dividing it by two, you will never hit zero. You’ll asymptotically approach zero, but you will never actually touch zero. You need to do addition or subtraction to do that. Division won’t cut it.

Jim, as a 4-year-old, when he observes they need to go to the gas station to fill up the tank, he throws out the idea, well, let’s just use only half the gas in the tank because then we’ll still be able to, after that, only use half the gas in the tank. The funny thing that doesn’t occur to a 4-year-old is, well then, we’re just not going to get very far.

David: 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. Pretty heavy stuff.

Ben: Jim would go on to finish his undergrad at MIT in three years, and get a master’s in one year.

David: 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. Jim’s pretty self-aware here.

There are other people at MIT who never run into problems. They never hit a limit, they never struggle understanding any concept. He realizes that, oh, I’m smart, I’m very, very smart. I’m smarter than most other people here. But I’m not one of those people.

Ben: Which is 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.

David: 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, as he put it, he had good taste.

These are his words. “Taste in science is very important. To distinguish what’s a good problem and what’s a problem that no one’s going to care about the answer to anyway, that’s taste. And I think I have good taste.”

Ben: 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. He said, I’m smart, but I’m not that smart, so switched to computer science.

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

Ben: He is got a pretty important theorem named after him.

David: That goes on to become a foundation of string theory in physics, which isn’t even Jim’s field.

Ben: Crazy.

David: 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.

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 look up to him because you aren’t like the kid in the corner at the high school dance. You’re cool.

Ben: He’s the extroverted theoretical mathematician.

David: Yes. He was elected class president in high school. He smoked cigarettes. He’s popular with the ladies. He looks like Humphrey Bogart. He’s a popular dude, especially at this point in time—we’re now in the late 50s when Jim’s at MIT—this is kind of the James Dean and Rebel Without A Cause era.

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. They all load up on scooters and drive down to Bogota. They get into all sorts of adventures. There are knives, guns, and they get thrown in jail.

Ben: It’s honestly crazy that this group of people took this type of risk.

David: Totally crazy. After he is done an MIT and after the road trip, Jim heads out to Berkeley in California so that he could do his PhD with the Professor Shiing-Shen Chern. Much later in life, Jim would collaborate with Chern for the Chern-Simons theory that we talked about earlier that becomes one of the foundational parts of string theory in physics.

Before Jim leaves for the West coast, he meets a girl in Boston, and they decide to get engaged in four days. This is him back then, these were the times. When they get to California and they get married, Jim takes the $5000 wedding gift that I believe they got from her parents and he decides, I want to multiply this. So he starts driving from Berkeley into San Francisco every morning to go hang out at the Merrill Lynch brokerage office, just be a rat hanging around the brokerage and find ways to trade and turn this money into something more.

Ben: 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.

David: Exactly. 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.

Ben: Trading is easy.

David: Trading is easy. He says, I was hooked. It was a rush.

Ben: I bet.

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

Ben: Important to learn that lesson early.

David: Also right around this time, Barbara (his wife) gets pregnant with their first child and is like, you can’t be driving into San Francisco every morning and gambling our future like this.

Ben: Effectively playing the ponies.

David: Exactly. Jim’s like, okay, okay, I’ll stop. I’ll focus on academia for now. He finishes his PhD in two years. They come back to Boston and he joins MIT as a junior professor at age 23. They stay one year in Boston. But Jim, even though he’s got a family, even though he is super successful as a young academic here because he’s got kids, he’s restless.

One of his buddies from the scooter trip to Bogota is from Bogota and lives there, his family’s there, has an idea to start a flooring tile manufacturing company. He’s like, the flooring at MIT and in Boston is so much nicer than in Bogota. We should start a company and make the same kind of flooring here.

Ben: When I read this, I couldn’t believe that this was Jim Simons’ first business venture. 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.

David: Totally. Not just is this the start of his entrepreneurial career, the seeds of this financially is what goes on to start RenTech.

Ben: It’s wild.

David: Totally wild. Jim takes a year off and goes down to Bogota.

Ben: This is a guy with an MIT undergrad and masters and a Berkeley PhD in theoretical math.

David: Who’s now a professor at MIT.

Ben: Who is taking a year off to go work on a flooring company in Bogota.

David: Yes, accurate. He does that for a year, they get it set up, he gets bored again. He is like, all right, I don’t want to just run this company. I’ve helped set it up. I have an ownership stake in it. Now, he bounces back to Boston, this time to Harvard as a professor there for a year.

Ben: He’s really racking them up.

David: But he spends a year there and he’s like, ah, got the itch again. The junior professor’s salary isn’t that much. We said about him back from his childhood days, he sees the appeal in being rich. He’s like, this is not a path to being rich. He’s like, I’m going to go put my skills out on the open market.

He gets a job in Princeton, New Jersey, not at Princeton University, but at the Institute for Defense Analyses, which is a nonprofit organization that consults exclusively for the US government, specifically the Defense Department, and specifically the NSA. These are the civilian code breakers.

Ben: Yes. It was basically formed with this idea that: (1) across various branches of our government, we need better collaboration and cross funding of the same initiatives, and (2) 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.

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

Ben: It’s a pretty wild charter, and especially how special of an organization it was. The way these people would spend their time is part code breaking but part goofing around, because the creativity of mathematicians working together on passion projects is important to discovering clever new algorithms.

David: This is so, so key, and this culture ends up getting translated whole cloth right into RenTech. The way IDA worked—I assume still works to this day—is they recruited top mathematicians and academics to come be codebreakers there. They would double their salaries…

Ben: 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.

David: 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.

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.

The appeal of going there was, hey, it’s the same thing as being a professor at MIT or Princeton or Harvard or whatever, except you’re doing code breaking instead of teaching, and there’s no bureaucracy to worry about, there’s no politics. It’s just, hey, you do your code breaking work, and then you publish. You can collaborate with your colleagues there.

Now, this is pretty crazy. Very quickly after Jim arrives at IDA—remember he’s in money-making mode at this point in time—he recruits a bunch of his very brilliant colleagues to come work with him in their 50% free time on an idea to apply the same work and technologies that they’re using in code breaking and signal intelligence to trading in the stock market.

They come together and they publish a paper called Probabilistic Models for and Prediction of Stock Market Behavior. Everything that they suggest in this paper really is RenTech, just 20 years before RenTech.

Ben: It’s crazy. 1964 this was published?

David: 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.

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

David: Yes, fundamental investing. There also existed in the 60s technical investing, which is kind of voodoo. This is like I’m looking at a stock chart and I’ve got a feeling that it’s going to go up, like I’m tracing this pattern and it’s going up baby. Or no, no, no. This pattern is going down.

Ben: 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.

David: 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.

Ben: 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 looks random.

David: 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.

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

David: There’s all this noise in this data, and it is impossible for a human to sit here, 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.

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

David: What they are really doing here at IDA, and then soon in RenTech, is early machine learning. 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, J.P. Morgan payments.

Ben: The finance industry has a rich history of innovating, dating all the way back to the literal renaissance, where double entry bookkeeping and letters of credit revolutionized global trade and economic development. J.P. Morgan payments really continues that tradition in their technology investments today.

They move $10 trillion a day securely. That is a quarter of all US dollar flows globally. Just think about the sheer volume of data at 5000 transactions per second and how important that is to the global economy.

David: Unsurprisingly, J.P. Morgan payments has been in the AI game for years now. Similar to RenTech, they were also early to recognize the value of AI to gather process, and analyze those massive troves of data to provide solutions for their customers and mitigate risk, like when they incorporated AI into their cashflow forecasting tool, which helps businesses manage liquidity, and that proved especially valuable during the pandemic.

Ben: Also unsurprisingly, J.P. Morgan was ranked number one in a recent global banking index of AI capabilities with Fortune, saying they were “head and shoulders” above the others. Their customers get AI-powered payment solutions for fraud prevention, customer 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.

David: 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 US Treasury Department disperses trillions of dollars annually. If they continue to employ new technologies like this, it could really add up to the tune of billions.

How does this fit in? Well, the Treasury Department recently selected J.P. Morgan to provide account validation services for federal agencies. Obviously, payment integrity and this issue of improper payments is top of mind for them and at enormous scale. So whether you are one of the largest institutions in the world or a small business like us here at Acquired, J.P. Morgan offers you peace of mind and protection.

Ben: One more playbook theme in common between RenTech and J.P. Morgan payments, they both analyze data to uncover patterns and insights you may never think to look for.

One of their clients, a furniture store, discovered a correlation with customers who also shop at pet stores where shoppers spent 76% more than the average customer when this was the case. So the furniture store launched a line of pet-friendly furnishings for that audience. These are the insights that drive growth with J.P. Morgan payments as your partner.

David: When it comes to payments technology, businesses of all sizes can benefit from having end-to-end AI-powered solutions that are constantly learning. And J.P. Morgan’s API first infrastructure across all aspects of treasury and payments is a one-stop shop solution.

Ben: To learn more, check out jpmorgan.com/acquired. And fun fact listeners, it is fraud prevention month, so listeners can learn even more by following at J.P. Morgan on LinkedIn.

Okay, David, this paper is published. They’re going to trade and make a whole bunch of money in the stock market by applying this code-breaking, signal processing, data analysis approach to investing.

David: Then the natural question is, okay, what is the model here? How are they going to do this? 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. Lenny just happens to be the world expert in a mathematical concept called a Markov model, specifically a version of the Markov model called a hidden Markov model.

Now, a Markov model is a statistical concept that’s used to model pseudo-random or chaotic situations. Basically it says, let’s abandon any attempt to actually understand what is going on in all of this data that we have. Instead, just focus on what are the observable states that we can see of the situation.

Can we identify different states that the situation is in? If we just do that, can we predict future states based on what we’ve observed about the patterns of past states? The answer to that is usually yes, even if you don’t know anything about fundamentally how the system operates.

Ben: The great example that Greg Zuckerman gives in the book is…

David: A baseball game.

Ben: There are 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, it’s going to be a walk, or maybe they foul it off and it keeps going. There’s only really a narrow set of things that could happen after that.

Whereas when it’s zero balls and zero strikes, there’s a lot that could happen. They could just keep pitching. And if you don’t know the rules, you’re like, why do they just keep pitching?

It’s this great way to explain this idea of the black box that if nobody tells you the rules to the game by observing the outputs enough and observing, okay, in this state these outputs are possible, you actually can get pretty good at (at least) if not predicting, understanding the probability distribution of the outcomes for any given state in the game.

David: 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, et cetera.

Ben: Obviously, they’re much fancier than that, but that is the underpinning of it all. I remember in my sophomore year of college computer science class, I had a Markov chain assignment. It was basically write a Java program to ingest this public domain book, then I would give it a seed word, the first word of each sentence, and press return, return, return, return, return. It would scan through the probability tree and give me the most probable word based on the corpus of the book that it just read to create some sentence.

It feels like magic. Of course, in these early rudimentary Markov chain things like the one I did in college, it spits out nonsense. But that would evolve to be the LLMs that we know of today.

David: Totally. That is what they were using at IDA to do codebreaking, and that’s what they propose in this paper that they could use in the stock market too.

Ben: Exactly. 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 guess at what the probabilities of the next thing to happen are based on the state.

Investing’s 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. You don’t know if stock X affects stock Y in some way because you don’t know in what way those companies do business together or who holds both stocks. Are they overlapping investors? You don’t know the relationship between those companies, so you can’t forecast with 100% certainty of what is going to happen.

However, if you suck in enough data about what has happened in the past and the probability distribution from every given state in the past, you probably could make some educated guesses or at least understand the probability of any individual outcome based on a state today of what could happen next.

David: Exactly. Jim and Lenny and this whole little crew are pretty fired up. They’re like, oh, great, Let’s go raise a fund and invest in the markets using this strategy.

Ben: 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.

David: 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.

It was hard enough for Warren Buffet 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. We don’t know anything about these companies that we’re going to invest in. We don’t know anything about fundamentals, but we’ve got a really good algorithm. People are probably like, what is an algorithm? So they just have no access to capital.

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

David: A bunch of Keystone Cops–style fundraising happens here. They’re going around in secret, 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. The boss discovers it, calls them all into his office, and is like, guys, what are you doing here?

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

David: So they end up abandoning the effort, both because they can’t raise money and because IDA has found out about this and they’re not too pleased. Shortly after all of this, though, Jim ends up moving on anyway because 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, yeah, he’s part of the defense department, but not everybody in the defense department is for the war.

Ben: 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.

David: 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. When the Newsweek piece comes out, that’s when the Department of Defense is like, all right, you got to fire this guy.

So Jim gets fired in 1967, even though he’s a star codebreaker. He made supposedly huge contributions to the group, which are still classified, but at age 30 with a wife and three kids, he’s out on the street. Even though he’s super smart, his colleagues love him clearly, he’s now bounced out of MIT, he’s bounced out of Harvard, he’s gone to this seemingly final home for him, great place at IDA, he gets bounced out of there too. His job prospects are not great.

He takes pretty much the only halfway decent–paying job that he could get, which is to be the chair of the newly established or maybe reestablished Math Department at the State University of New York, Stony Brook, which is the Long Island campus of the State University of New York. This is not Harvard, this is not MIT.

Ben: No, it is not.

David: 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 $100 million 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 thought MIT was the Berkeley of the East already, but Rockefeller is way too good 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 tarnished his career, here’s a very talented mathematician that they can convince to become chair of the department. They basically give Jim an unlimited budget and leeway to go try and poach math professors from departments all over the country in the world and bring them there to Long Island.

Part of how Jim goes and recruits folks is money. The old, hey, I’ll double your salary line. But the other part of it, too, is he’s given such leeway, and Stony Brook is so different from the politics of an MIT or a Harvard or a Princeton. He says, hey, come here. I’ll pay you more.

But even more importantly, you can just focus on your research. You’re not going to have to deal with committees. You’re not going to have to do all this stuff. There is none of this stuff here. You might have to teach a little bit, but that’s not even the point. Rockefeller doesn’t want this necessarily to become a great teaching institution. He just wants to assemble talent there.

Amazingly it works. Jim starts getting a bunch of great talent, including James Ax, 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.

Ben: Amazing.

David: 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, go back to Berkeley, reunite with his old advisor there, and go spend some time out on the coast in California.

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 is Jim’s personal mark on mathematics.

Now also right around the same time, remember the Colombian flooring company? It gets Acquired. Jim and his buddies who are partners in it, come into a good amount of money. Jim is newly divorced, he’s restless in academia, he has these ideas back from when he was at IDA about what you could do in the markets if you had capital. He starts trading again, and he gets more and more into it.

Meanwhile, like we said, he is becoming disillusioned again and restless at academia. And in 1978, he leaves to focus full-time on trading, which is a huge shock to the academic community. Remember, he’s assembled this superstar team there at Stony Brook.

There’s a quote in Greg’s book from another mathematician at Cornell. “We looked down on him when he did this, like he had been corrupted and had sold his soul to the devil.”

Ben: It was really viewed in the math community as anyone who’s going to do investing is throwing away their talent. It wasn’t even that it was common the way that it is today.

David: Jim was the first one, but the idea that you would leave to do anything commercial, you doing a disservice to humanity.

Ben: 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.

David: 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, eww.

Ben: Academics tend to be much more motivated by prestige than money, so I could totally see 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. My currency is the papers I publish and the awards that I win, and that’s what I want.

David: Now Stony Brook, we should say too, it’s a very nice place. 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.

Ben: The wooded Long Island suburbs.

David: 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 Moneymetrics—a combination of money and metrics or econometrics. He recruits his old IDA buddy, original partner in crime on the trading idea, Lenny Baum, to come and join him. This time though, they have some capital from the sale of the flooring company.

Ben: How much did he make on that flooring sale?

David: I think together with Jim, his partners, and whatever money Lenny put in, they had a little less than $4 million in this initial capital.

Ben: In 1978.

David: Yup. 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.

Ben: The table has been set.

David: Yes, and those folks are more loyal to Jim than they are to Stony Brook.

Ben: 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.

David: Yes, in general. But some of them, and in particular the superstar James Ax, Jim convinces to come join him in his trading operations.

Ben: So having Baum and Ax and Simons, it’s like suddenly this extremely credible team in the math world.

David: Beyond credible.

Ben: 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.

David: 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, they look up to, and they think is cool. He’s out there being like, hey, I think we can make money.

Now at this point, they’re primarily trading currencies, not stocks. Currencies are obviously large markets, but they aren’t impacted by as many signals and as many factors as stocks are, or really even slightly more complex commodities like soybeans or whatever.

Ben: 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. To your point about very few signals impacting price, it’s knowing what one person is going to do.

David: And this is super important. At the end of the day, they build some models there. They’re getting the early versions, 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.

They’ll take some signals from the model, they’ll see what’s interesting, what they spit out, but they’re not going to act on anything unless they can be like, oh yeah, I see what is going on here. I have a hypothesis.

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

David: By no means at all, yeah. They’re just suggesting patterns and ideas, and Jim, Lenny, and James 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?

This actually does make sense, really for two reasons. One, computers and computing power just wasn’t sophisticated enough yet to really build AI in a way that’s powerful enough that it could work well enough, you could really trust it. That’s one part.

The other part is these folks are mathematicians. They’re not computer scientists. They’re really, really good at building models, decoding signals (obviously), but they’re much more from this realm of theory.

I actually spoke with Howard Morgan who’s going to come up here in a second, and he made this point to me. He’s like, in math, there’s this concept of traceability that’s a really, really important cultural tenant. It’s like proving a proof, or proving a theorem, or something like that. You really need to understand why to get ahead in the field. It’s not like you can just say, oh, hey, the data suggests this. It’s like, no, no, no, you need proof.

That’s the world that these guys are coming from. They’re like, oh, we can use data to help us here, but ultimately we want to have a rock solid theory of what is fundamentally happening here.

Ben: Fascinating, which is very different from, 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 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.

David: 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, ah, yeah, this is interesting. We’re building, we’re experimenting. Great. But I also want to put my money somewhere else, too, for some diversification.

This is where Howard Morgan comes in. We used to talk about this on old Acquired episodes that in the early days of Silicon Valley, there were only 10 people out here. They all knew each other and they were all doing the same thing. This was also the case in East Coast finance and technology, and early VC in these days. Howard Morgan would go on to be one of the co-founders of First Round Capital.

Ben: Which was essentially spun out of Renaissance? It was the venture capital work that they were doing at Renaissance that didn’t fit with the rest of Renaissance?

David: Yes. Here’s how it all went down, and this is so poorly understood out there. Howard was a computer science and business school professor at the University of Pennsylvania. He taught CS at Penn and business at Wharton.

He had been involved in bringing ARPANET to Penn and was early, early internet pioneer. As a result, he was super plugged into tech, early startups, and really early, early proto internet stuff. Jim gets excited about investing together with Howard. They say like, hey, maybe we should partner together.

In 1982, Jim actually winds down Moneymetrics, and he and Howard co-found a new firm together that’s going to reflect both of their backgrounds and be a great diversification. Jim and his group are going to bring in the quantitative trading thing,

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

David: And Howard’s going to bring in private company technology investing. They pick a name for a firm that is going to reflect this—Renaissance Technologies.

Ben: It’s crazy.

David: And that is why RenTech is called RenTech.

Ben: 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.

A long-term illiquid thing like venture capital, highly speculative, versus we’re going to trade whether we think the French Franc is going to go up or down tomorrow based on the whim of some government leader. It’s unbelievable these were under the same roof.

David: Totally. But when you know the whole background in history, it makes sense because this is their personal money. This is Jim and his buddies, Lenny, James, and Howard. There’s no 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.

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

David: 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. 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. This is crazy. Blow up risk is always an issue in the markets, but this happened to RenTech.

Ben: 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 rumbles of something like this potentially happening. Simons going to Lenny and saying, hey, maybe we should cut some of our losses and it’s okay to trade out of these positions. Lenny was just very dug in on I’m a true believer. That’s how you can get into a situation where you trigger a covenant like this.

David: And again, also shows they weren’t doing model-based quantitative trading really at this point in time.

Ben: So much gut.

David: As a result of that, for a while RenTech is truly almost entirely a venture capital firm. 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 at this low point for the trading side.

Ben: I had no idea. That’s crazy.

David: In the book, Greg talks about, oh, Jim was focused on venture capital, and that’s the story out there. It’s like, well, he was focused on venture capital because that was the only thing working and making money.

Ben: 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.

David: I think perhaps in part because of the trading losses, James Ax starts to get a little disillusioned too, and he tells Jim that he wants to move out to California with Sandor Straus, 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. I’m here with Howard. I’m doing venture capital stuff. Why don’t you go move out to California? You can start your own firm, which they do—it’s called Axcom—and we’ll contract with Axcom to run what’s left of the trading operations here for RenTech.

Ben: It’s this interesting arm’s length thing where Jim strikes a deal where he’s going to own a part of Axcom in exchange for this very favorable contractual relationship where they’re going to hire them to be the manager for this pot of money that Renaissance has raised. But it’s technically not Renaissance. It’s Axcom.

David: It’s another company that is now doing the quantitative trading.

Ben: And I think Jim owned a quarter of it, is that right?

David: Yes, that’s right.

Ben: And importantly, I don’t think anyone had any idea what Axcom would become or how unbelievably profitable it would be.

David: Nobody would’ve done what they did had they known what was coming.

Ben: Wouldn’t have spun it out.

David: Once Ax and Straus get out to California, Straus is 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. Straus finds a way to get tick data, like every 20 minute data on these securities throughout the day.

Ben: Not only that. He’s getting historical data that predates what your traditional data providers would give you, then ingesting it into computers and cleaning the data to get it into the same format as the tick data. He’s getting early 1900 even 1800 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.

David: He’s doing ETL on the data (I think) before anybody knew what ETL was.

Ben: Again, no one told him to do that. That was just a self-motivated, almost obsession of, well, if we’re going to have data, it should be well-formatted, well-understood, labeled and all that.

David: 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, Elwyn Berlekamp. Berlekamp had studied with folks like John Nash and Claude Shannon at MIT.

Ben: I love that Claude Shannon is coming in again.

David: I know.

Ben: 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 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.

David: So cool. 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.

With this combination now of much, much, much better and deeper data from Straus, and Berlekamp coming in and working with Ax 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 every trade was the same, or we should actually be systematic about this. The models start really working.

Ben: This is the turning point.

David: In these mid-80s years, Axcom is generating IRRs of 20+ percent on the trading side. Not necessarily going to beat venture capital IRRs, but liquid, reliable…

Ben: Well, that’s the thing. They don’t know how reliable yet. They know they’ve done it 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?

David: But the early results are really good, and Jim and Berlekamp (especially) are very encouraged by this. 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 Kopelman, Jim of course is a large LP. Howard, of course, remains an investor in RenTech.

The first institutional fund, that first round ended up raising, was a 50X on $125 million fund. It had Roblox, Uber, and Square. I believe this is right. I think Jim made as much money from his investments in first round as Howard did from his LP stake in RenTech.

Ben: That’s wild.

David: Isn’t that amazing?

Ben: Wow. That is an 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.

David: Yeah. It was super fun talking to Howard about this, and just the history of how First Round started at early super angel investing and everything that became.

Ben: I also didn’t realize that First Round’s Fund I was a 50X on $125 million fund.

David: First institutional fund, which I believe they called Fund II.

Ben: Wild, wild stuff.

David: Totally wild. When Howard spins out the venture activities, Jim then decides to set up a new fund as a joint venture between RenTech and Axcom. They decide to name it after all of the collective mathematical awards that Jim, James, Berlekamp, and all these prestigious mathematicians have won in their careers. They name it the Medallion fund.

Ben: 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 Axcom 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.

And again, still just currencies, still just commodities futures, not playing the stock market at all. But the seeds and the ideas, the huge amount of clean data, the robust engineering infrastructure to process all that data, the mining of signals from data to figure out what trading strategies to execute, that is really starting to form here in this new joint venture, this Medallion fund.

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

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

David: I don’t know that Straus could have done his data engineering too much earlier in time. 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.

Ben: 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.

David: 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 in this case, he chose IT service management.

Ben: 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.

David: Totally.

Ben: 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. IT was the perfect vertical because every other department has to interface with them from the CEO on down.

They’re going to notice when IT’s service management rapidly improves. All of those support tickets that used to take forever are now just magically resolved. That greases the wheels for the other departments to say, hey, maybe we should adopt ServiceNow to turbocharge and digitally transform our service levels, too.

David: 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 ServiceNow’s. 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, they took it to finance with regulatory reporting, audit and expense approvals, and now they’re adding AI, which will take everything to the next level.

Ben: 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.com/acquired. When you get in touch, just tell them that Ben and David sent you.

David: So they’ve got this grand new plan and vision with the Medallion fund. Unfortunately, right out of the gate, the fund stumbles a bit, and Ax ends up getting burned out. Berlekamp though is like, no, no, no, no. This is an anomaly. Like we’re going to fix this. I really, really believe that what we’re doing with these models is going to be extremely profitable.

He buys out most of Ax’s stake in the summer of 1989, and he moves the offices up to Berkeley. There he comes up with the idea that, hey, we should trade more frequently, a lot more frequently, because if what we’re trying to do is understand the state of the market from the data we have and then predict the future state of the market, and then combine that with figuring out the right bet sizing to make. We actually want to make a lot more trades to get a lot more data points, and learn a lot more about the bets we’re making so that we can then size them up or size them down.

Ben: 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. A state machine is the same way. If you flash forward a whole bunch of states, you lose predictability as you continue down that chain.

The second thing is, if your models are showing that you’re going to be right, call it something like 50.25% of the time, then the amount of money you can make is gated by the number of bets you can make at a quarter percent edge.

If I walk up to the casino and I think I’m right about this particular roulette wheel, which of course you’re not 50.25% of the time, and I decide to play once or play twice or play five times, there’s a chance I could lose all my money. Or if I have tiny little bet sizes, then I’m just not going to make that much money. But if I walk up to said game with a little bit of edge, and I use small bet sizes, and I play 10,000 times, I’m going to walk out with a lot of money.

David: There is a great Bob Mercer quote about this later. He says, “We are right 50.75% of the time.”

Ben: And I do think he’s making up that number. I think it’s illustrative.

David: But we’re 100% right, 50.75% of the time. You can make billions that way.

Ben: 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.

David: A lot, yes. And then back to the Kelly criterion, adjust your bet sizes over time as you’re making those bets.

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

For example, there are real transaction costs, especially at this point in history, before some of these more innovative trading business models with pay for order flow and zero transaction fees and all this stuff. There are real transaction costs to putting on these trades. And of course you’re going to move the market when you put on these trades.

David: This is slippage.

Ben: There are 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 a hundred thousand tiny bets.

David: 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.

Ben: 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.

David: 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.

Ben: 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 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.

David: I feel like there’s this meta 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 the mistake of just looking at averages.

Ben: Exactly. 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 is the price at which they’re willing to transact?

The way that tends to manifest on a stock chart is here’s the price of the share right now. But that’s not actually what’s going on under the surface. It’s a whole bunch of buyers and sellers who have different willingness to pay and have different amounts that they’re trying to buy or sell.

David: 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. Medallion was about $27 million under management when Berlekamp bought out Ax. 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?

Ben: 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. If this is an actual repeatable strategy that produces the numbers you just said, unbelievable. World-changing.

David: Hell yeah, let’s go. And indeed it was a ‘hell yeah, let’s go’ situation.

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

David: 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 are like, this is ridiculous. How on earth were these nobodies charging a 5% management fee out the gate to their investors? Well, a couple of things. One, their investors were not sophisticated. It was mostly their own money and their buddy’s money.

Ben: So they set that precedent.

David: They set that precedent. But two, though, they actually needed the money. Straus’ 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. Great, 5% management fee.

Ben: And 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. 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.

David: High management fee, normal-ish carrier performance element.

At the end of 1990, Simons is so jazzed about what’s going on that he tells Berlekamp, hey, you should move here to Long Island. Let’s recentralize everything here. I want to go all in on this. I think with some tweaks, we can be up 80% after fees next year.

Berlekamp is a little more circumspect. (a) He wants to stay in Berkeley, he doesn’t have any desire to move to Long Island, and (b) I couldn’t tell how much of this is just, he’s a little more conservative than Jim, or how much of this actually might be his, hey, whole poker bet sizing thing.

He turns to Jim and he says, well, if you’re so optimistic, why don’t you buy me out? So Jim does at 6X the basis that Berlekamp had paid Ax a year earlier.

Ben: On the one hand, making a 6X in one year sounds great.

David: 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 gain, but miss out on all the upside to come.

Ben: 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. On the one hand, 6X in a year isn’t bad. On the other hand, you owned a giant part of something that has dividend $60 billion in cash out to its owners.

David: That’s just on the carry side. The owners are the principles. 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.

Jim buys out Berlekamp, he rolls everything in the Medallion fund back into RenTech itself, moves everything back to Stony Brook, Straus moves to Stony Brook.

Ben: So it’s now the Jim Simons show in New York, with Straus building the engineering systems, and Ax (I think) still had a small stake.

David: That’s right. And Straus had a stake as well. Once Jim takes control and moves everything back, he basically decides that he’s going to turn RenTech into an even better, even more idealized version of IDA and the math department at Stony Brook. He’s going to make this an academic’s paradise, where if you are one of the absolute smartest mathematicians or systems engineers in the world, this is where you want to be.

Of course he starts raiding the Stony Brook department itself again. This is when Henry Laufer joins full-time. Laufer had been consulting with Medallion in the early days and working with Berlekamp as they’re doing bet sizing as they’re making more frequent trades. But now, once the whole operation is moved back to Long Island Laufer’s like, oh, okay, great. I’ll come full time. I’m here at Stony Brook anyway. This is way more fun than teaching.

Ben: 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.

That is the story of Renaissance. It is not this singular clean narrative. It is a very complex reality of a whole bunch of different people that came in and out at different eras, where the firm was trying different things, and eventually became phenomenally successful with a very particular approach. But while they were figuring it out along the way, it took a lot of people.

David: 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.

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

David: 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. Not Jim’s bogey of 80%, but still pretty freaking great.

Ben: 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, it’s going. The whole rest of the story is about, hold on, keep the machine working, and we’re on the train.

David: The historic run has begun, let’s just say. So 1992 gross returns are 47%, 1993, they’re 54%. At the end of 1993, Simons decides to close the fund and not allow new LPs in. If you’re an existing LP, you can stay in, but they’re no longer open for new inflows. He has so much confidence in what they’re doing that he thinks they’re all going to make more money without accepting new capital by just keeping it to the existing investor base.

1994 gross returns are 93 freaking percent. Medallion at this point is stacking up cash. It is a meaningful fund. It’s about $250 million total at this point in time, which is small, but we’re talking about 1994 with a bunch of outsiders and academics that have managed to amass a quarter billion dollars here. People start to pay attention.

Ben: 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.

David: 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.

Ben: And that’s in the mid-90s?

David: Yup. As they’re hitting this $250 million–$½ billion scale.

Ben: 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% of the amount they want at that price, and then suddenly the price is very different.

David: Up to this point, the vast majority of what Medallion is doing is trading currencies and commodities, not equities. Because you might be thinking, okay, yeah, I hear you. The 90s was a different era, but a $½ billion fund doesn’t sound that big. How are they moving markets with $½ billion?

Ben: It’s not the equity markets.

David: 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.

Simons decides the only thing we can do here to expand—which I’m such a believer in what we’re doing, we need to expand—is we need to move into equities. Equities are the holy grail. If we can make this work there, the depth in those markets will let us scale way, way, way bigger than we are now. And there’s so much more data about equities, pricing that we can feed into our models and the signal processing that we can do in the signals that we can find are going to be even better.

Ben: There are 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 their moment. This is what they were built for. And it’s funny that they’ve just been in kid glove land the whole time with these thinly-traded markets with minimal data.

David: This brings us to Peter Brown and Bob Mercer. In 1993, one of the mathematicians that Jim had recruited to RenTech, a guy named Nick Patterson, gets especially passionate about going out and recruiting new talent along with Jim. And this is (I think) one of the keys to RenTech and the culture there. People want other smart people to come be there, too. Nick sitting there like, this is a joy. I want to go find other best people in the world to hang out with.

He had read in the newspaper that IBM was going through cost-cutting and was about to do layoffs. He also knew that the speech recognition group at IBM had some absolutely fantastic mathematical talent. Really what they were doing was, again, another vector in the early AI machine learning research, specifically IBM’s Deep Blue chess project of the time had come out of this group. Peter Brown there was the one that actually spearheaded the project.

Ben: It’s interesting that you talk about speech recognition as the perfect fit for what they were doing. 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.

David: It’s not just kind of. It’s exactly the same signal processing.

Ben: 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.

In Greg’s book, he greatly points out this perfect example. When I say apple, you might say pie. The probability that pie is going to be the next word following apple is significantly higher, so these people who have spent their careers not only doing the math and the theoretical computer science behind speech recognition to help figure out and predict the next words that you have a narrow set of likely words to choose from, so when you’re listening to those frequencies, you can say it’s probably going to be one of these three, rather than search the entire dictionary for any word that it could be to narrow the processing power. It’s not only the theoretical side, but it’s also people who have built those systems at IBM, like a real operational computer company.

David: At operational scale. This is what’s so important and why the two of them become probably the most critical hires in RenTech’s 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.

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 RenTech coming from academia has just never experienced that or built anything like it.

Ben: The world that they’re entering is just exploding in complexity and dimensionality. 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. They’re 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. It’s not a human with a hunch saying, I think when oil prices go up, the airline prices are going to get hit. It’s computers doing machine learning to discover the patterns in the data.

Then there’s the second piece of, well, what trades do you actually put on to be profitable from the probabilities that you just discovered? All these weights of relationships between all of these different companies.

You’re not just putting on one trade, you’re putting on 10, 100, thousands of simultaneous trades both to hedge, to be able to isolate some particular variable that you’re looking for—again, not you, but a computer is looking for—and you also need to do it in such specific bite sizes so that you don’t move the market.

You’re looking for a super multi-variate, multi-dimensional problem, both on the data ingestion side and on the how-do-I-actually-react-to-it side. All of this computation can’t take a long time because you must act, not in milliseconds. It’s not a high frequency trading that’s front running the market. That’s not actually what they do. A lot of people think it is, but we’ll get to that later. But they do need to act with reasonable quickness probably in the order of minutes. So these need to be really efficient computer systems too.

David: 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.

Ben: 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. There are just dimensions upon dimensions of things to analyze, correlate, and act upon.

David: So Patterson and Simons go raid IBM. They’re like Steve Jobs raiding Xerox Parc. They bring Peter, 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 they’re obviously very successful at that, but the impact that they have and what they build is even bigger because Bob and Peter realized that, hey, actually we should just have one model for everything here. For currencies, for commodities, for equities, everything is correlated. Everything is a signal.

It’s not like the equities market is wholly independent and separate from what’s happening in currencies or what’s happening in commodities. There are relationships everywhere. We really want just one model. This is like a fantastical undertaking, especially in the early- to mid-90s.

Ben: 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.

David: 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. Insights, 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.

Ben: 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.

David: 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. Their portfolio managers, 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. What Bob and Peter do is they unify everything at RenTech, so all the wood is going behind one arrow.

Ben: And before we talk about the impact of that, we want to thank our longtime friend of the show, Vanta, the leading trust management platform. Vanta, of course, automates your security reviews and compliance efforts, so frameworks like SOC 2, ISO 27001, 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 training–efforts for your organization and makes them fast and simple.

David: 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.e., spend your time and resources only on what’s actually going to move the needle for your product and your customers, and outsource everything else that doesn’t.

In RenTech’s case, this would be the model. Every company needs compliance and trust with their vendors and customers. It plays a major role in enabling revenue because customers and partners demand it, but yet it adds zero flavor to your actual product.

Ben: 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.

David: 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.

Ben: So whether you’re a startup or a large enterprise, and your company is ready to automate compliance and streamline security reviews like Vanta’s 7000 customers around the globe and go back to making your beer taste better, head on over to vanta.com/acquired and just tell them that Ben and David sent you. And thanks to friend of the show, Christina, Vanta CEO, all Acquired listeners, get $1000 of free credit. vanta.com/acquired.

So David, the equities machine.

David: And indeed a machine it is. Peter and Bob come in in 1993, and 1994–1995 they’re building this. RenTech is getting into equities.

Ben: Just imagine the computers that you were using during 1994 and 1995. It is astonishing the level of computational complexity, coordination, and results that they are pulling off, again in real time, analyzing these markets with the technology that was available during those years.

David: 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. By the end of the 1990s, Medallion has almost $2 billion in assets under management while maintaining roughly the same performance by getting into equities. This is huge.

Ben: And David, if you just look at this and do the math, okay, so 94, their AUM was $276 million and they grew 93%. Then their AUM the next year was $462 million and then they grew 52%. Then their AUM the next year was $637 million. You quickly get where I’m going here, which is, oh, they’re scaling AUM not by bringing in new investors.

David: 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.

David: And then in the year 2000, they just totally blow the doors off. A hundred twenty-eight percent gross returns, net returns after fees of 98.5%. This is bananas.

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

David: While the whole rest of the market is down big time, Medallion is up 128% gross on the year. This becomes a theme. High volatility is when Medallion really shines.

Ben: 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.

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. Anytime any investors are under pressure, there’s a little bit of edge that’s going to accrue to a Medallion that’s saying, oh, okay, you’re fear-selling right now. Well, I can determine if you should be fear-selling or not. If I determine that you shouldn’t be dumping that asset, I’m buying it from you.

David: 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. The first couple of days of the tech bubble bursting, Medallion actually takes a bunch of large losses.

Part of it might be that the model wasn’t tuned right yet because nobody at RenTech had seen this type of behavior in the market before. Part of it might also be, too, that it didn’t perform well for those couple of days. It’s a really stressful time for everybody.

Everybody’s in Jim’s office. Jim’s smoking his cigarettes, it’s a cloud of smoke, they’re debating what to do, and Jim makes the call to take some risk off. He’s worried about blowing up. We’re not very far removed at this point from long-term capital management. The model may be saying we should stay long here, but let’s not blow up the firm.

After this goes down, Peter Brown comes to Jim and offers to resign given the losses that they incurred over these couple of days. 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.

Ben: It’s fascinating. It’s such a good insight. That illustrates Jim as a leader right there.

David: 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 “quant quake” had happened, where similar to the tech bubble bursting, there was, all of a sudden, very large drawdowns among all quantitative firms in the market and RenTech gets hit.

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. Jim pumped the brakes and stepped in, intervened, and took risk off. 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? Jim says, no, I’m not worried at all. I know you were only so aggressive in that moment because I was there pushing back on you. And when you’re in the seat, you’re going to be less aggressive. He’s just such a master at insight into human behavior.

Ben: It is so true, though. I even find this about myself, that I will naturally take the position of the foil to the person across from me. So if somebody’s being pushy in some way, I’ll find myself taking a position where if I pause and reflect, I’m like, I don’t think I expected to take this position coming into this conversation. But you know you naturally want to play the other side to balance out the person sitting across from you.

David: Back to the year 2000 in this incredible performance. Ben, to what you were saying earlier about uncorrelated returns, not only did they shoot the lights out that year, they’re doing it when the market is down.

We got to introduce this concept of a Sharpe 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.

Ben: 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 25X’s, or a year where you have a 100% investment return, or I bought Bitcoin yesterday and it doubled overnight.

Does that make you one of the best investors in the world? We all intuitively know, no it doesn’t, because maybe that was a fluke, maybe you’re taking on an extreme amount of risk, and then the question is always adjusting for the risk that you’re taking. Can you produce a superior return taking the risk into that account?

You basically can provide value to investors as a fund manager in two ways. You can outperform the market, or you can be entirely uncorrelated with the market and get market returns. Or what you can do, as RenTech is, both. You can be uncorrelated and massively outperform, which is effectively the holy grail of money management.

David: The Sharpe ratio is a measurement combining these two concepts.

Ben: Exactly. It’s named after the economist William F. Sharpe. It was pioneered in 1966. It is effectively the measure of a fund’s performance relative to the risk-free rate. If you performed at 15% that year and the risk-free rate was 3%, then you know your numerator is going to be 12%.

It is compared against the volatility (or the standard deviation is technically what it is), but effectively, how volatile have you been the last X years? Typically, it’s looked at as a 3-year Sharpe or a 5-year Sharpe or a 10-year Sharpe. The Sharpe ratio represents the additional amount of return that an investor receives per unit of an increase in risk.

David, you’re starting to throw out numbers. Low Sharpe ratios are bad. Negative Sharpe ratios are worse because that means you’re underperforming the risk-free rate. High Sharpe ratios are good because it means that you’re producing lots of returns, and your variance or your standard deviation or your risk is low.

So in 1990 they had a Sharpe of 2.0, which was twice that of the S&P 500 benchmark. Awesome. 1995 to 2000, Sharpe ratio of 2.5 really starting to hum. Pretty unbelievable.

David: Good. Where do I sign up to invest?

Ben: At some point they added foreign markets and achieved a Sharpe ratio of 6.3, which is double the best quant firms. This is a firm that has almost no chance of losing money, at least historically, and massively outperforms the market on an uncorrelated basis.

David: I believe if I have my research right, in 2004, they actually achieved a Sharpe ratio of 7.5.

Ben: Astonishing.

David: Again, back to our sports analogy here, these aren’t hall of fame numbers. These make Tom Brady look like a third stringer.

Ben: Exactly.

David: 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. There are still outside investors in the fund, but no new investors are coming in.

Then the next year in 2002, they raised the carry to 44%. Great work if you can get it, but for context, the Sequoia and the Benchmarks out there, they have obscene carry of 30%, 44% is unprecedented.

Ben: There are 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 investors are arbitragers. They see a mispricing, they come into the market, they fix that mispricing. Anytime that there’s an opportunity to bring the way that a currency is trading on two different exchanges closer together, investors are serving their purpose of coming in, arbitraging that difference, taking a little bit of profit as a thank you, and then fixing the market to make the market a true weighing machine. Not a voting machine, but making it so that all prices reflect the value of what something is actually worth.

In some ways, that’s what Renaissance is doing here to themselves or to their investors. They’re coming in and saying, look, this is obscene. We so clearly outperform the market, you’re still going to take this deal even if we take more of this because there’s just a mispricing here. This product should not be priced at 20%–25% carry. This product should be priced at a much higher carried interest, and you’re still going to love it.

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

Ben: Totally. 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?

David: 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. I want to finish the story first. 2001, they raised the carry to 36%, 2002, they raise it to 44%, and then in 2003 they actually say, hey, we can’t incentivize you out of the fund outside investors. We are going to kick you out. Starting in 2003, everybody who’s an outside investor, who’s not part of the RenTech, family—current employee or alumni of the firm—gets kicked out

Ben: And not all alumni get to stay. There are select alumni that get grandfathered in.

David: Now why did we do this? I’m going to talk about one reason in a minute. 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. If they want to maintain this crazy, crazy performance, they just can’t get that much bigger.

Ben: 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. The strategy that RenTech is employing right now, they’re just deeming doesn’t work at north of $5 billion.

David: In 2003, they start kicking all the outside investors out of Medallion. But clearly there are still lots of institutional demand to invest with Renaissance. What do they do?

Ben: Time to start another fund. They start the Renaissance Institutional Equities Fund. There are a couple of things to add a little bit of context to really why they decide to do this.

The first one is sometimes there are 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. But just sometimes there are these strategies that appear, oh, we don’t want to commit to a much higher fund size and then not always have those strategies available.

The other thing is that a lot of the times, those strategies aren’t really what Medallion is set up to do. They require longer hold times. There’s a little bit of downside to that because these new strategies, the predictive abilities are less because they have to predict further into the future to understand what the exit prices will be on these longer term holds. But they still figure, hey, even though it’s not quite our bread and butter with the short-term stuff, we should be able to make some money doing it.

David: There’s a fun story around this that Peter Brown tells. 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? Peter says, no, of course not. That’s not a great risk adjusted return.

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

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

Ben: That’s basically what they come up with. Can we create something that’s like an S&P 500 with a higher Sharpe 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?

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. The nice thing about being invested in a hedge fund versus a venture fund is you can do redemptions.

If you look at the 13Fs, the SEC documents that the Renaissance institutional equities fund files over time, it changes every quarter because there are new people putting money in, there are people doing redemption. It’s a pretty good product, or at least the theory behind it is a pretty good product of a lower risk similar return thing to the S&P 500.

David: 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.

Ben: It’s really funny. There are always 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.

David, you said that the fees and carry on Medallion went up to what, 5% and 44%? Well on the institutional fund, the fees are 1% and 10%. You’re only taking 1% annual fee and 10% of the performance.

David: Clearly, this is a very different product.

Ben: But people did not perceive that. People were very excited. It’s a Renaissance product, it’s the same analysts, they’re using all their fancy computers. I’m sure we’re going to get this crazy out performance. And at the end of the day, it is an extremely different vehicle.

David: That has not performed anywhere near how Medallion has performed.

Ben: Correct. Has it served its purpose? Yeah. But is it Medallion? No. It’s not special in the way that Medallion is special.

A couple of other funny things on the institutional fund. I spent a bunch of time scrolling through 13Fs over the last decade from the Medallion filings. They’re all from (I think) two institutional funds.

David: There’s institutional equities and diversified alpha.

Ben: The funniest thing is they file these 13Fs. David and I are very used to looking at the 13Fs 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 what are they buying and selling this quarter, and usually you see 15, 25, maybe 50 different names on there.

The 13Fs for Renaissance has 4300 stocks in these tiny little chunks. There’s a little bit of persistence, quarter-to-quarter. For example, weirdly Novo Nordisk has been one of their biggest holdings. Biggest, I say at 1%–2%. That’s their biggest position for several quarters in a row.

David: Hey, they’ve been listening to Acquired.

Ben: That’s right.

David: That’s one of the signals in the model.

Ben: You 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. 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.

David: 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) a day, maybe a day-and-a-half. Whereas the average hold time for the institutional funds positions is a couple of months.

Across 4300 stocks in the portfolio, there’s a lot of trading activity that happens on any given day, but it’s a lot slower in any given name than Medallion would be, which makes sense. Again, it gets back to this slippage concept. If you have a bigger fund and you’re investing larger amounts, which the institutional funds are, you can’t be trading as frequently or all of your gains are going to slip away.

Ben: And frankly, it just looks a lot like the S&P 500. When you look at as of November 2023, so 11 of the 12 months of the year had happened, they were up 8.6%. That sounds like an index type return. You look at the first 4 months of 2020, right after the crazy dip from the pandemic, they were down 10.4%. Less than the broader market, but they still were a mirror of the broader market.

I think the RIEF, their institutional fund, yes it works as expected. No, it’s not Medallion. If it were standing on its own, there’s zero chance that we would be covering the organization behind it on Acquired.

David: 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, the volatility is when Medallion really shines. Well, there are no more volatile periods than 2007 and 2008. 2007 Medallion does 136% gross. 2008 Medallion does 152% gross. Get out of here. This is 2008 while the rest of the financial world is melting down.

Ben: 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. They’re putting on exactly the right set of trades to achieve the risk and exposure that the system wants them to have.

Who is on the other side of those trades? It’s panic sellers, it’s dentists, it’s hedge funds who don’t trust their computer systems and are like, ah, crap, we got to just take risk off even though it’s a negative expected value move for us. They’re basically trading against human nature.

Importantly, in this business versus every other business that we cover here on Acquired or most other businesses, this is truly zero sum. It’s not like they’re here in an industry that’s a growth industry and lots of competitors can take different approaches, but the whole pie is growing so much that I don’t care if no, you’re fighting over a fixed pie here. I’m trading against someone else. I win. They lose.

David: There’s one slight nuance to that, but I don’t know how much it holds water. 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. Warren could make money over his time horizon of 50 years.

Ben: Super fair.

David: I think the argument against that, though, is that Medallions sold after a day-and-a-half to somebody else who bought at that lower price. 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.

Ben: 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. Can I get a penny over on Warren Buffet by managing to take him on this one trade? Sure. But his strategy is such that that is irrelevant.

David: 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 take the portfolio size up to $10 billion when they take over. It had been at $5 billion for the last few years of Jim’s tenure. They take it up to $10 billion, and really with no impact, which I assume means that RenTech was getting better and the models were getting better, because Otherwise they would’ve gone to $10 billion before.

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

David: Importantly, during periods of peak volatility, say 2020, Medallion continues to shoot the lights out. 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. The magic is still there.

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%.

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

Ben: It’s amazing. And we weren’t able to tell. There are 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. If so, that just means that they continue to find more profitable strategies within Medallion to keep those same unbelievable returns at larger sizes.

David: At the end of the day, this is all just insane. 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.

Ben: So give me those net numbers.

David: During the entire lifetime so far of Medallion from 1988 to 2022—that’s 34 years—the total net annual return number is 40%. Four-zero over 34 years after fees. It’s 68% before fees, which equates to total lifetime carry dollars for the whole firm of $60 billion just in carry by our calculations.

Ben: Astonishing.

David: That is a lot of money.

Ben: 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.

David: I still know how to use Excel. Barely. It’s going to be a dying art now with Copilot and GPTs.

Ben: That’s right. Okay, so $60 billion in total carry.

David: $60 billion in total carry is a lot of money. Speaking of a lot of money, we do need to mention before we finish the story here, that RenTech money has bought a lot of influence in society.

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

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

David: 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.

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

David: 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.

Ben: 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.

David: Understatement of the century.

Ben: And all being extremely influential and active in those political systems. Yes, Bob Mercer is no longer the co-CEO of Renaissance Technologies. He still works there. He’s still associated. They all still speak highly of each other. It’s unexpected.

David: I think unexpected is the best way to put it.

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

David: Speaking of, let’s transition to analysis. 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 think of it as the RenTech tapestry.

I was inspired by Costco here because we were talking to folks in the research, and everybody said RenTech just has these puzzle pieces that fit together. On the surface. RenTech does the same things that Citadel, D. E. Shaw, Two Sigma, Jane Street, others, et cetera do. They hire the smartest people in the world, they give them the best data and infrastructure in the world to work on, and they say, go to town and make profitable trades.

Those are very expensive commodities, those two things, the smartest people in the world and the best data and infrastructure, but they are commodities. Citadel can say the exact same things, just the same as Walmart and Amazon can say, we too have large scale supplier relationships that we leverage to provide low prices to customers just like Costco.

But it’s underneath that where I think the magic lies. There are three very interrelated things that make RenTech unique. Number one, they get the smartest people in the world to collaborate and not compete. Pretty much every other financial firm out there, employees and teams within the firm quasi compete with one another.

Ben: Typically in a friendly way, but yeah.

David: Let’s take in a venture firm, you’ve got your lead partner on a deal, or a deal team. They’re working that deal. Maybe some of the other partners help a little bit, but mostly they’re off prosecuting their own deals. I think that’s the most collegial way that this happens in finance. Then you’ve got multi-strategy hedge funds out there where literally firms are being pitted against one another to be weighted in the ultimate trading model for the firm.

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, have access to the whole model. That’s not true anywhere else.

Ben: That’s a good point. The whole code base is completely visible.

David: 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 from any other hedge fund out there.

Ben: 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?

David: 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.

Ben: 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.

David: What did you find on LinkedIn, at least the median tenure of employees is like 16 years?

Ben: Yeah. I just got LinkedIn Premium and you can see median tenure. It’s crazy. There are only 300–400 employees at Renaissance, and the median tenure, at least as reported by LinkedIn, is 14 years.

David: 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. Let’s call it 150–200 people max who are hands-on the wheel here for Medallion.

Every other peer firm of RenTech, Citadel, D. E. Shaw, Two Sigma, et cetera, all of them—you lump Jane Street, jump the high frequency guys in here—minimum 2000–5000 people work at those places.

Ben: Wow. I didn’t realize it was that big.

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

Ben: Unless you think that it’s a capital-based thing, no. The institutional funds have gotten big. They peaked at over $100 billion, but they’re currently between $60 and $70 billion that they manage on top of the $10 or $15 billion that’s in the Medallion fund.

David: AUM is the same as these big funds. This has all sorts of benefits. Number one, there’s the Hermès Atelier workshop benefit. Everyone knows each other by name. You know your colleagues’ kids, you know your colleagues’ families.

Ben: They put right on their website. There are 90 PhDs in mathematics, physics, computer science, and related fields. The about page has these 10 random bullet points and that’s one of them.

David: 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. You’re hanging out at the swimming pool.

Ben: Totally. Since Renaissance doesn’t recruit from finance jobs, it’s 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 10,000 people or something or less that live there. 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. 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.

David: You’re Just not getting exposed.

Ben: Exactly.

David: 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.

Ben: Have you seen the pictures online?

David: Yeah.

Ben: If you look up Renaissance Technologies at Google and you go and look at the photos on campus, it’s little courtyard, winding walking path, and woods all around it, tennis courts.

David: 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 of hundred ways. At Citadel you’re splitting that 5000 ways. It just doesn’t make sense to go anywhere else.

Ben: 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.

David: This brings me to what I’ve been teasing that 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 an LP-GP fund with 5% management fee and 44% carry.

Ben: It’s not like a prop shop or proprietary just one pot of money. It’s literally a GP-LP, even though the GPs and the LPs are the same people.

David: Here’s my thinking on this. Now, I don’t know how it is actually structured, but there was something about this whole crazy 44% carry that just wasn’t sitting with me right throughout the research, because I kept asking myself why.

Ben: They’ve already kicked out most of the LPs, if not all, so why are they raising the carry?

David: It’s all themselves, it’s all insiders. Why do they charge themselves 44% carry and 5% management fees? I think Jim talks about this that, oh, I pay the fees just like everybody else.

Ben: It’s always a funny argument. Who are you paying the fees to?

David: What is happening here? 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.

Here’s how I think it works. When people come into RenTech, they obviously have way less wealth than the people who’ve been there for a long time, both from the direct returns that you’re getting every year from working there and just your investment percentage of the Medallion fund, which by the way, I think they took, it was either the state of New York or the federal government to court to be able to have the 401(k) plan at RenTech be the Medallion fund.

Ben: No way.

David: If you work there, your 401(k) is the Medallion fund.

Ben: That’s crazy. It really doesn’t take more than a few years before you’re set for life.

David: Totally. Depending on your definition of set for life, I think it happens very, very quickly. 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?

Ben: 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.

David: I think it’s the 44% carry structure that does it. Basically what you’re saying is every year 5% management fee, so 5% off the top and then 44% of performance.

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.

What is the equivalent here? I think it’s like academic tenure kind of thing. The longer tenure you are at the firm, the more your balance shifts to the LP side of things. The younger you are at the firm, the more your balance is on the GP side of things. But at the end of the day, it’s 51-49.

There’s this very natural value transfer mechanism to keep the people that are working in any given year super incentivized. As you stay there longer, you are paying your younger colleagues to work for you.

Ben: It’s funny, I think it’s a good insight that it’s structured like a university department tenure.

David: Well, I just kept asking myself, why? Why? Why do they have this if there are no outside LPs? And this was the best thing I could come up with. I actually think it’s genius.

Ben: 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.

David: 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 if that were true then Jim would just own this thing forever. I don’t think that’s true here at RenTech.

Ben: 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 paid the carry amount, but over time you become a meaningful investor in the firm and you shift to that 51%. You’re the LP, and then over time you eventually graduate out entirely and you’re only an LP.

You’re right, it’s a value transfer mechanism from the old guard to the new guard in a way that is clear, well understood, probably tax-advantaged, versus just doing, I’m the owner and I’m giving everyone arbitrary bonuses.

David: And at the end of the day, I think these three pieces to me are the core of this 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.

Three, this LP-GP 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.

Ben: I think that’s right. There are 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.

The first one is called basket options. The year is 2002. RenTech has 13 years of knowing that they basically have a machine that prints money. What should you do when you have a machine that prints money? Leverage.

Now, there are all sorts of restrictions around firms like this and how much leverage they can take on. You can’t just go and say, I’m going to borrow $100 for every dollar of equity capital that I have in here. You need to get clever to borrow a whole bunch of money from banks or from any lender to basically juicier returns if again, you have a money printing machine that’s reliable. Most people don’t. Most people probably shouldn’t take leverage because they’re just as likely to blow the whole thing up as they are to be successful. So basket options.

I am going to read directly from the man who solved the market because Greg Zuckerman just put it perfectly. “Basket options are financial instruments whose values are pegged to their performance of a specific basket of stocks. While most options are based on an individual stock or a financial instrument, basket options are linked to a group of shares. If these underlying stocks rise, the value of the option goes up.

It’s like owning the shares without actually doing so. Indeed, the banks who of course loaned the money, who put the money in the basket option were legal owners of the shares in the basket. But for all intents and purposes, they were Medallion’s property.”

This is very clever Medallion saying, well, the way we’re going to lever up is there’s a basket. We have an option to purchase that basket. Most of the capital in that basket is actually the bank’s capital, but the bank has hired us to trade the options in the basket. Then after a year when long-term capital gains tax kicks in, we have the option to buy that basket.

Anyway, all day Medallion’s computers send automated instructions to the banks. Sometimes in the order of a minute or even a second. The options gave Medallion the ability to borrow significantly more than it otherwise would be allowed to.

Competitors generally had about $7 of financial instruments for every dollar of cash. By contrast, Medallion’s option strategy allowed it to have $12.50 worth of financial instruments for every dollar of cash, making it easier to trounce rivals, assuming they could keep finding profitable trades. When Medallion finds an especially juicy opportunity, it could boost leverage holding close to $20 of asset for every dollar of cash. In 2002, Medallion managed over $5 billion, but it controlled over $60 billion of investment positions.

David, this exposes something we haven’t shared yet on the episode, which is it’s not just that they could find $5 billion worth of profitable trades. It’s that they wanted to lever the crap out of $5 billion and find $60 billion of profitable trades to make. Basket options gave them a legal way to have an incredible amount of leverage in a way that they felt safe about.

David: The unlevered returns, if you were running this strategy would be much lower.

Ben: A big piece of this playbook that we didn’t talk about is leverage, but every quant fund does leverage. Renaissance was just more clever than everyone else.

David: It’s an important point, though. Nine out of every ten companies that we cover on Acquired leverage is zero part of the story. For us, coming from the world we come from in tech and venture capital, leverage is a dirty word, like I’m scared of it.

Ben: 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. That’s why you need $60 billion of cash to actually execute the strategy to produce the returns that they were looking for on $5 billion of equity.

Anyway, there’s a second chapter to this. 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. We only exercise that once every 13 months or so.

I don’t know the exact number, but something like that over a year. Therefore, we’re buying something, we’re holding it for a year, we’re selling it. Oh, of course there are millions and millions of trades going on inside the basket, but we don’t own that basket. The banks do. We’re just advising them. You can see the logic here.

Over time, eventually in 2021, the IRS said, no. You made all those trades. That was not a completely separate entity. You guys owed $6.8 billion in taxes that you didn’t pay. You’re going to need to pay that with interest, with penalties, and by the way, Jim Simons, we’re going to want you and the other few partners to really bear the load of that. And they did. For Simons alone, he paid $670 million to the IRS in back taxes for this basket option strategy that turned out not to be a long-term capital gain.

All right. Numbers on the business today and then we will dive into power and playbook. Today, we’ve talked about Medallion, $10 or $15 billion depending on who you ask. Historically it was more like $5 or $10 billion. The institutional fund is about $60 to $70 billion, and at one point was $100 billion. The total carry generated, David, you said is $60 billion.

Forbes estimates that Jim Simons alone is worth about $30 billion today, which pencils with a bunch of other stats over the years that he owned about half of Renaissance. The returns, obviously the Medallion fund generated approximately 66% annualized from 1988 to 2020. After those fees, was about 39%. Wild.

An interesting thing to understand, I ran a hypothetical scenario, of how much money do you think Renaissance the business makes a year in revenue. The institutional fund, let’s call it 10% on $60 billion of assets. That’s $600 million from fees and $600 million from performance, so $1.2 billion a year in revenue to the firm from the institutional side of the business.

I always ask myself the question, does that actually matter? They did all this work to stand up the institutional side. Who cares? Well, let’s say Medallion does their average 66% gross on $15 billion. That is $750 million in fees and $4.3 billion on performance. So a total of $5 billion from Medallion and $1.2 billion from the institutional side of the business.

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

David: That’s not including the LP return on Medallion.

Ben: A hundred percent. It’s not.

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

Ben: But it’s 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.

David: Now, I think there are a lot of expenses on the infrastructure side.

Ben: Totally. That was another thing I wanted to talk about. The fact that they do, let’s say Medallion alone, 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 super computing cluster. What does it cost to run one Amazon data center? It’s (I think) much smaller scale?

David: I don’t know. You’re talking about a lot of data here.

Ben: 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.

David: Is that $750 million a year? I don’t know, but it’s not zero.

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

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

Ben: That feels like it’s right. If you’re someone who has done a data center build out before or has any way to back into what the costs of Medallion’s operating expenses are on the compute, data, and network side, we would love to hear from you, hello@acquired.fm. Okay, power.

David: Power. This is a fun one.

Ben: 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? The seven are counter positioning, scale economies, switching costs, network economies, process power, branding, and cornered resource.

David, my question to you to open this section is specifically about RenTech’s lifelong non-competes. That feels like a big reason that they maintain their competitive advantage. I’m curious if you agree with that, what would you put that under?

David: I think it’s lifelong NDAs and non-competes as long as the state of New York legally allows for, but that it’s not lifetime. I’ve heard various figures, six years, five years, something like that. At the end of the day, non-competes are more like what is one side willing to go to court over? But the reality is people don’t leave. People don’t leave, period, and people especially don’t leave and start their own firms.

I was thinking about this in the middle of the night, and I think there are 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’re better off staying there as part of that team with a smaller number of people than going to Sigma with a lot more people. I think that’s the next level up. And then I think the highest level is just probably the social layer. You’re there with the smartest people in the world in a collegial atmosphere where you’re all working hard on something that has direct impact on you.

Ben: It’s your community.

David: It’s your community, totally. You’re not in New York City, you’re not in the Hamptons, you’re not in Silicon Valley. You are selecting into that. I think if that’s what you want, then what better place in the world.

Ben: All right, classify it. What power does that fall under?

David: 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.

Ben: 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.

I didn’t ask anyone to verify that, but it’s over 10 million lines of code and the level of complexity of the system of when it’s putting on trades, what trades it’s putting on, why, the speed at which they need to happen, I actually don’t think anyone holds the whole model in their head. I think there’s process power just because it’s 30+ years of complexity that’s been built up.

David: I totally agree with that, particularly in the model itself. Maybe you could argue the model is a cornered resource.

Ben: I am going to argue that the data is a cornered resource. I don’t know for sure about the model. Maybe? 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. They have the best PhDs in the world thinking about data cleaning. That’s not a sexy job, and yet they have probably the treasure trove of historical market data in the best format that nobody else has. That’s an actual cornered resource.

David: I have a couple of nuances on this. One, I think it probably is true that they have better data than any other firm thanks to Sand or Straus and the work that he started doing in the 80s before anybody else was really doing this. They have that and other firms don’t. That said, certainly all the other quant firms are throwing untold resources at all this, too.

Ben: They want to do this, and money’s not the issue.

David: In chatting with a few folks about this episode, I had more than one person say to me, there are two ways that RenTech could work. One version of how it works is they discovered something 20+ years ago that is a timeless secret, and they’ve been trading on that for 20+ years.

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

David: And that may entirely be possible.

Ben: Isn’t that crazy?

David: Right. Now, RenTech will all say that is 100% not the way that it works. It’s not that at all. If that were the way that it works, they would of course still say that because they don’t want anybody to know.

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

David: 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 what I heard. That the model is fully restructured every two years. It’s not like on a date every two years, it’s being restructured every day. But collectively it’s about a two-year cycle.

Ben: That would be an argument then that the people actually could—five people left—go recreate it and all they would need is the data.

David: 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.

Ben: I bet that 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.

David: 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.

Ben: Definitely.

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

Ben: 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, yet they’re paying billions or tens of millions or hundreds of millions of dollars for it.

David: I think we can rule out scale economies for sure. If anything, there are anti scale economies here.

Ben: Oh yes. There’s totally economies of scale. Your strategy stop working when you get too much AUM.

David: You get slippage. I don’t think there are any network economies here. They literally don’t talk to anybody.

Ben: 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.

David: Do you think it’s actually better than their competitors, though?

Ben: I don’t know. That’s probably not the secret sauce.

David: Yeah, I don’t think so.

Ben: It’s the table stakes.

David: 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.

Ben: The fee stream on the institutional fund may entirely belong to branding. But I think there are 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 Sharpe ratios and are able to raise because they’ve built a brand. Venture firms are the same way.

David: Totally. For me, this leaves counter-positioning, I actually think there’s some counter-positioning here. I think we’re going to have two episodes in a row of counter-positioning at scale.

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

David: There are direct competitors in the market, the other quant firms. When I say direct competitors, I obviously don’t mean for LP dollars. I mean for the same type of trading activity.

Ben: Like they are counterparties in trades.

David: 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.

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

David: That’s true. But I think yes, adversaries in finding the similar types of trades. I think the counter-positioning for RenTech or for Medallion, specifically, is 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.

Ben: 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.

David: Particularly because it’s all the same people on the GP and LP side.

Ben: 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 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.

David: 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.

Ben: It could be culture.

David: Yeah, it could be culture.

Ben: 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 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.

David: I think that could be, so maybe that feeds into process power. Okay, for me it is some combination of process power and counter positioning. I don’t think it’s any of the other powers.

Ben: For me it is process power and cornered resource.

David: I buy that.

Ben: A thing that’s not captured in seven powers is tactical, execution. The whole point of seven powers is that strategy is different from tactics. 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.

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

Ben: And there’s remnant knowledge. If you started building a machine learning system in 1964, you’re going to be really good at machine learning today. 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. Learnings compound is my answer.

Okay, playbook. In addition to the three-part David Rosenthal tapestry that you have woven…

David: I have nothing more to add.

Ben: …there are a handful of things that I think are worth hitting. The first one is signal processing, is signal processing, is signal processing. 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 funny because that’s a completely different skillset.

But if you just look at Medallion, it’s all just abstract numbers. You don’t actually have to care about what underlies those numbers. You just have to look for whether it’s linear regression or any of the fancier stuff that they do, just relationships between data. Once you reduce it to that, it is so brilliant that they can just recruit from any field.

It’s not relevant how someone has done sophisticated signal processing in the past, whether it’s being an astronomer and trying to denoise a “photo” of a star super far away, or whether they’ve tried to do natural language processing. It’s just signal.

David: There’s this really funny line that Jim, Peter, and others will say when asked about why they only hire academics and not from Wall Street and whatnot. They’re like, well we found it’s easier to teach smart people the investing business then teach investing people…

Ben: How to be smart.

David: Right. That’s ridiculous. They don’t teach anybody anything about investing. They’re just doing signal processing. I bet at least half the people at RenTech on the research side could not read a balance sheet.

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

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 Santa Fe Institute’s work on this.

In a complex adaptive system, it’s really difficult to actually understand how one thing affects everything else because the idea is the relationships are so combinatorially complex that you can’t deterministically nail down this one thing as the cause of that other thing. It’s the butterfly flapping its wings.

But there are relationships between entities that you can’t understand or see on the surface. Do you remember way back when we did our second NVIDIA episode? I opened with the idea that when I was a kid, I always used to look at fire and think if you actually knew the composition of the atoms in the wood, and you actually knew the way the wind was blowing, and you actually knew that… all the, could you actually model the fire?

When I was a kid and you always just assume no, but actually the answer is yes. This is a known thing of what will happen when you light this log on fire for the next three hours, and can you see exactly the flames.

I think RenTech has basically, they haven’t figured that out for the market. They can’t predict the future. But if they have a 50.01% chance of being correct, then they can take a complex adaptive system and say we don’t really care that it’s a complex adaptive system. Our models understand enough about the relationships between all these entities that we’re just going to run the simulation a bunch of times, and we’re going to be profitable enough from all the little pennies that we’re collecting on all the little coin flips where we have a slight edge over and over and over and over again, that they’re the closest in the world to being able to actually predict how the complex adaptive system of the market will work.

Now, I don’t think they can back out to it. No person could explain it, but I think their computers can.

David: 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. It can’t tell you why, but that’s okay.

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

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?

David: This is such a tough answer to tell. We actually emailed some friends who are very prominent AI researchers and AI historians and asked this question. The answer we got back is unsurprising. They said, we don’t know because they don’t share anything.

Ben: 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 ChatGPT…? 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, and they’ve just chosen that they’d rather keep it locked up, captive, and make a bunch of money.

It could just be the case that Renaissance is just taking in as much unstructured data as it possibly can, and they were just a decade or two ahead of everyone else in realizing that you can have unstructured, unlabeled data, and if you have enough of it, you can make it, in the case of an LLM, say things that sound right or sound true. Or in the case of these trades, be right more than 50% of the time.

David: Make trades that sound right.

Ben: They figured out this big, unsupervised learning thing before anybody else, all the way up until last year when the AI moment happened.

David: If that were the case, we should have very different answer to powers.

Ben: To illustrate this point. It’s quite interesting. Peter Brown’s academic advisor was Jeffrey Hinton.

David: I’m so glad we brought this up. Yeah, it was the exact same stew, the exact same cohort of people, social group, and academic groups that RenTech came out of, that AI came out of.

Ben: Just for people who are like, why are you saying that? To make it super explicit, the other person whose academic advisor was Jeffrey Hinton is Ilya Sutskever, who is the co-founder of OpenAI. Many years later, but still.

David: 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.

Ben: Another big one is this concept that you should trade on a secret that others are not trading on. On the face of it, it seems obvious. Of course I should come up with some strategy to trade on that other people aren’t trading on. But I said a couple of words there, which is ‘of course I should come up with.’ Therein lies the fallacy.

I think most investment firms try to get their ideas out of people, and then do an incredibly rigorous amount of data analysis to figure out if they should put those trades on or not. I could be wrong, but I do not think modern RenTech does that. I think all of their investment ideas come from data and come from signal processing. Therefore you are going to put trades on that make no intuitive sense.

When you’re putting trades on that are profitable and make no intuitive sense, you aren’t going to have competitors. If you find a relationship between two things that a human could never come up with or dream of those relationships—and we’re saying too it end things, 10 things, 20 things, 100 things and in various different weights at various different timescales—that is a killer recipe to exploit a secret that no one else knows and be able to beat other people in the market.

David: Such a good point. 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.

Ben: 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. You might try to, because humans are storytellers, concoct some story in your head of why those move together, and if you believe it then you might decide there’s some date where they should stop moving together.

Well it could very well be that some other big hedge fund just owns both of those things, and when they rebalance it causes those assets to move together. But you would never think of that. You would think these things have a direct relationship with each other, not just that there’s liquidity in the market from both of them at the same time because someone else owns both of them. I think what RenTech admitted is we have no idea why anything is actually connected, but it doesn’t matter.

David: Totally. That was surprising for me in the research. I 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 of other people.

Ben: 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.

David: The way he phrased it to us was smart versus dumb, but dumb doesn’t mean dumb.

Ben: It’s the obvious trades. The high level point is all quant funds are not high frequency trading firms and vice versa. 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.

Fast and obvious is your classic high frequency trader. They’re front-running trades, they’re locating in a data center that’s really near the, this is Flash Boys, or they’ve got a microwave line between New Jersey and Chicago, and they’re trying to arb the difference between two markets. You need to have the fastest connectivity in the world to pull this off.

David: This is Jane Street.

Ben: Yes. There’s fast and smart, which you don’t need to be both. You don’t need the fastest connectivity in the world and the most clever trades to put on. People tend to pick a lane that they’re either a high frequency trader or they’re trying to make the smartest, most non-obvious trades possible.

That of course leads us to Medallion, which is in the slow and smart quadrant. All the machine learning system discovered the relationships in the data, so there’s a huge amount of compute…

David: The non-obvious trades.

Ben: 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.

David: My sense is RenTech is not a high frequency trading shop. They’re not front running things, they’re 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.

Ben: Greg has a quote in his book. “They hold thousands of long and short positions at any given time, and they’re 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.”

David: This is another thing that we heard. RenTech is world class at disguising their trades.

Ben: They can make it so that they don’t move the market and you don’t know who is acting or when. 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. They had to adapt and develop these clever systems to make it so you don’t know who’s buying, you don’t know in what quantities, and you don’t know if they’re going to keep buying.

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? That wasn’t possible in the old world where you’re calling your broker. That totally is possible here.

David: And it happened.

Ben: While it’s never happened to RenTech, there was a company called Night Capital in 2012 that lost $460 million in a single day. There was a bug in their process to deploy the new code.

Basically, what happened was a simple flag error, a misinterpretation of setting a bit from zero to one that caused this infinite loop to run. 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. It basically thought it was never flipped.

This infinite loop ran 4 million trade executions in 45 minutes. There wasn’t the appropriate kill switches built in. They basically watched it all to just drain out, and there was nothing they could do.

David: The whole portfolio gone, right?

Ben: Yes. Well, I don’t know if it’s the whole portfolio, but it was enough that they lost a huge amount of LP capital. They were a publicly-traded firm. Overnight, their equity traded down 75%, and then someone stepped in and bought them.

David: They probably got margin-called by all their counterparties.

Ben: Whoever is in charge of the financial controls and safety systems at RenTech, that’s a huge job for someone in this industry.

David: Totally.

Ben: To kick off value creation, value capture, I have a provocative statement. David, Renaissance Technologies is actually not in the investment business. They are in the gambling business. In particular, they’re the house.

David: 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.

Ben: I’ll say you this. They’re not investors and they’re not in the investment business. There is investment going on all around them in the markets that they trade in. But the fact that they’re in those markets, they’re not there as investors. 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. They’ll lose sometimes, but most of the time they’re going to come out slightly ahead.

Let’s say they do have a 50.01% chance of being right. They’re just there to collect their vig on everyone who is willing to trade with them over all these years. At scale it really worked. Jim Simons managed to drain $30 billion into his own pocket out of everybody that he ever traded with.

David: 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’re providing a service. Is this where you’re going with this?

Ben: Well, the investment business, it depends how you define investor. If you want to be all hoity-toity about it—which in this illustrative example I’m being one—and saying an investor is someone who provides 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.

Of course, lots of things are called investing that are not that. Is it investment if I put money to work, then I get more money back later, I don’t actually care how the money got made, and it’s actually zero sum, I’m just vacuuming it out of…?

David: Right, right. The money is not being invested in anything to produce.

Ben: 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.

David: Where I was going with the service provider, I think casinos are service providers. They’re 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 RenTech 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.

Ben: 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 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.

But you’re totally right that there is a value to market liquidity. Creating more depth to a market makes it so that if we go back to the era that Renaissance was started, there’s no chance that retail is able to function like it does today with zero transaction fees and people able to invest in all these different companies at near real time.

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

Ben: 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, lots of them are going to lose money to you.

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

Ben: On average, Medallion will not lose money to you. But there are plenty of other hedge funds out there, high frequency shops, and counterparties for you where you could take them. That’s just not Jim Simons.

David: There’s this great, great vignette at the end of Greg’s book. When was it? It was during one of the 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.

Ben: Right. You’re Jim Simons. What should we do?

David: What should we do, yeah.

Ben: All humans are fallible.

David: Totally.

Ben: A couple of other 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’re incented to do, absent a larger global concern that is incredibly motivating to people.

You look at World War II, people’s level of patriotism and wanting to go save the world from evil was a huge, unbelievable motivating factor to move mountains. When that is absent or when people feel that there’s some existential thing that is absent, they’re going to go do what’s best for them and their family. If they’re an empire builder, go build empires. If they’re fierce capitalists, go make a bunch of money. The system is set up the way that it is. You can be mad about that.

Given that, okay, people are going to go engage in quantitative finance as a lucrative profession. Fortunately, there’s a bunch of valuable stuff that comes out of that. I think that is often missed is that these really lucrative professions and businesses can often produce R&D that becomes valuable elsewhere.

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

David: This is such a really mind-blowing point here in value creation, value capture. Go for it, take it away.

Ben: There’s not much to it other than a huge amount of InfiniBand was used by high frequency trading firms. I don’t know for sure, but I think Mellanox built their business on quant finance. That’s one of many examples. Now you know that has limits, but I think it goes overlooked that there’s a lot of technology innovation here.

David: These are all great points they all came up in the research. I totally agree with all of them. It is, in my opinion, false to say that quantitative finance does not create value for the world. It definitely does in my opinion.

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

David: That said, they’re really, really good at value capture. This is not Wikipedia here. This is about as far away on the spectrum as you can get.

Ben: There’s a great, always sunny in Philadelphia, where Frank—Danny DeVito—goes back to his whatever business he founded in the 80s. He’s in his pin stripes and stuff again, taken back over, and he brings Charlie with him. Charlie like, so Frank, what does the business? What do we do here? What does the business make? Danny DeVito looks at him and he goes, what do you mean? We make money? He’s like, no, no. What do you build? He goes, we build wealth. I think that’s a pretty good meme for what’s going on here.

Dvid: Totally. Very, very good at value capture, too.

Ben: Yes. Okay, bear-bull. 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. Listeners, thank you so much for expressing your concern. Bear versus bull is unkilled and it is back.

David: Resurrected like a phoenix.

Ben: Resurrected. However, this is about the lamest episode to resurrect it on. What’s the bull case for RenTech?

David: Past performance is an indicator of future success.

Ben: 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. Keep on keeping on is basically the bull case. Maybe that they’re actually still ahead.

David: 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.

Ben: The bear case is things are changing. I think things are changing basically on any axis is the bear case for them. Things are changing where competitors are catching up, maybe. Maybe the fact that the tech industry has figured out these large language models, maybe that trickles into making it easier to compete with RenTech. It’s a blurry line, but it is plausible.

Maybe RenTech actually was here a decade before everyone else, and now everyone else has arrived to the party. There are things that are changing maybe about their culture. Jim Simons has been gone for a long time. Bob Mercer is no longer a co-CEO. Peter Brown is a co-CEO and they just announced that they’re making the guy who was in charge of the institutional funds, David Lippe, is becoming a co-CEO as well.

Maybe there’s a bear case around that, that someone from the institutional side of the house is becoming the current co-CEO, and maybe eventually CEO if you believe the Medallion is the special thing and the institutional funds are a blemish on the business. They’re the Hermès Apple watch strap in David’s parlance. Maybe that’s a bear case.

Maybe there’s a bear case that their talent is becoming the same as everyone else’s talent. When you look on LinkedIn, I recognize a lot of the companies that people worked at, who are more junior at RenTech and in the past. I think it would’ve been all people just out of university research shops. I think if it’s true that they’re starting to see the same talent flow as everyone else, that would be concerning. These things are all narratives you can concoct and really no way to know if they’re true or not.

David: There’s no way for us to know any of this because there’s no way to know any of this.

Ben: It’s all a secret.

David: Our new ending section, the splinter in our minds, the takeaway.

Ben: The one thing you can’t stop thinking about.

David: 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, getting them right, and setting them up right. And culture, too. I don’t want to shortchange that. I think the culture of managing an academic environment in a fashion like a lab, but without letting it spin into the frivolity of a lab that Jim Simons set up.

Ben: In other words, early Google.

David: 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. They are all very focused, which again, to me then speaks back to the power of incentives.

When you’re there with less than 400 people, and on the research and engineering side less than 200 people, and those colleagues who you work with are the sole purveyors, supervisors, and beneficiaries of all of this that you’re doing, that is so powerful. I can’t think of anywhere else like that in the world. Maybe some venture funds or other investment firms, but not on a day-to-day fully liquid with returns like this. There’s nothing like it.

Ben: Pure gasoline right into the veins.

David: Which is not to say I would necessarily want to work there. I think I would not, but it is truly unique.

Ben: The one thing I can’t stop thinking about is the idea of the complex adaptive system that I was talking about earlier. I think from what everything we can tell from the outside, Renaissance actually has built a large scale computer system that discovers 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 might be a small most, but all you need is most, then you can operate a casino business.

That is my takeaway is that they are the house and they have an edge, and that edge is predicated on a graph of all the relationships between these entities that we think are just noise, and they know the signal.

David: It does make you wonder to what you were talking about with the tech industry “catching up” 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.

Ben: That’s the bear case. I don’t know.

David: 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.

Ben: Yup. All right. Should we have some fun, carve outs?

David: Let’s have some fun.

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

David: That’s right. Let’s indulge.

Ben: We’ll spin off a whole new podcast called Carve-outs.

David: Forty-four percent carry. Let’s go.

Ben: I have one announcement, one TV show, and one other fun thing for listeners. First the announcement. David and I are going to be emceeing Modern Treasury Transfer again this year. 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. We would love to see you there.

David: Can’t wait.

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

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

Ben: Exactly. 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.

David: Celebration of life.

Ben: Gone are the days of the militaristic, boxy clothing, and now we are in with these seductive and dare I say…

David: Sumptuous materials. War rationing is over.

Ben: Exactly. Provocative dresses. The Apple TV show is this incredible drama of flashbacks to the harrowing wartime experiences of Christian Dior, of Balenciaga, of Coco Chanel, everything they went through, and how all their paths crossed.

David: Oh, Coco’s in it?

Ben: Yes.

David: Oh wow. How do they treat that?

Ben: It will be very interesting if a lot of people watch this show to see if that affects product sales of Chanel. I’m also very curious for people who are watching—feel free to put a thing in the Slack and carve-outs—do you think she’s a sympathetic figure? Do you think she’s a villainist figure? I’m curious how you think of her portrayal versus reality.

David: 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.

Ben: The Wertheimers. Indeed. Oh God, we got 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.

David: As you and listeners know, I’m not a TV guy, but this is so up my alley.

Ben: The whole thing. It takes place in wartime Paris.

David: All right. I got to watch it.

Ben: You got to watch it. My third one is a fun thing for listeners. After our Nike episode, the president of Cole Haan—if you listen to the episode, you now know is its own company spun out from Nike years ago—reached out, and it turns out, he, like all of you, is also an Acquired listener. We were chatting and he brought up the idea that they’d be happy to create a specific Cole Haan deal for Acquired listeners. I told them, frankly, if it’s good enough, then I’ll share it on air.

David: To be clear, this is not a sponsorship. This is just he’s a fan and reached out to us.

Ben: And I’ve owned a bunch of Cole Haan products over the years and have really liked them. For 35% off anything, you can go to colehaan.com/acquired or use the code ACQUIRED35 at checkout. 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. I think they intentionally want to cut it off at some point, so it doesn’t get shared around all the coupon sites. But fun thing for Acquired listeners.

David: Super cool. He likes Acquired and wanted to share the love back.

Ben: Love it.

David: I love it.

Ben: All right, David, your carve outs.

David: My carveout is related to The New Look in a very different way, but both video consumption, fashion, luxury, and style. It is the Class of Palm Beach, Instagram, and TikTok account. This is so great.

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

David: This is amazing. 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.

Ben: It is nice.

David: So great. We didn’t knowingly spot any RenTech people there, but we may have.

Ben: We did knowingly spot some Birkin bags, though.

David: Yes, the style in Palm Beach. We had just recorded the Hermes episode, and oh man, I was so pleased to be there. Then I got home and Jenny (my wife) was like, do you not know the Class of Palm Beach TikTok account?

Ben: And David’s like, I’m a thousand. I have no idea what you’re talking about, Jenny.

David: Right, right, right. I live under a rock. I’m a dad, and she showed it to me. This is a woman who lives in Palm Beach. She goes around, posts on Instagram and on TikTok, and she just interviews people on the street about what brands they’re wearing, their style. It is magnificent.

My favorite is—we’ll see if we can find it and link to it in the show notes—there’s a video of one woman who’s being interviewed who has a Mini Kelly inside her Birkin.

Ben: Excess, truly excess.

David: And that’s when I was hooked. I was just like, this is the greatest thing I have ever watched. I’m obsessed.

Ben: All right. If I use TikTok, I would subscribe.

David: You can get it on Instagram too.

Ben: Oh, all right. Good.

David: 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.

Ben: Look at David opening up our Instagram account. You’re so youthful.

David: I know.

Ben: All right, listeners, well, a huge, huge thank you to J.P. 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.

David: Yes. For sources for this episode who are so generous with their time and thoughts. First, a 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 and the book is the canonical source of Medallion’s Investment Returns. I know he worked so hard to get that table together that is now all over the Internet as it should be.

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

David: Truly a service to us, to corporate historians, and financial historians everywhere that he did that research and got those returns.

Ben: There are 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.

David: It is a funny moment where Peter’s like, where are you getting these questions? How do you know all this stuff? And I’m like…

Ben: Come on.

David: They’re in the book.

Ben: Clearly.

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. There are only a couple of chapters about RenTech, but some good stuff in there. Then there’s a good Bloomberg piece from 2016 that we’ll link to. I think between that and The Quants, it was the first time there was really anything at all that was published about RenTech. all those will be in the show notes.

Other people to thank, David.

David: 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. Then of course the founding of RenTech, 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; this is so needed in the world—the Interactive Brokers for the 21st century. Well, anybody who uses Interactive Brokers knows exactly the opportunity there, so thank you Brett.

Then Matthew Granade, who I spoke with. Matt is the co-founder of Domino Data Lab, which is a great enterprise AI ops platform backed by Sequoia and many others. It allows model-driven businesses and products to accelerate research, increase collaboration, rapidly deliver new machine learning models, all of the things that we were talking about here with RenTech.

Matt, before starting Domino Data, came out of the quant world. He was at Point72 and Bridgewater, which isn’t really quant sort of its own thing, but he was a long-time senior employee at both of those firms, and he gave us great, great perspective on the landscape of everybody out there and where RenTech fits in.

Ben: Awesome. Well, if you liked this episode, you should check out our Berkshire Hathaway episodes from a few years ago for a very different style to investing.

You can sign up for new episode emails at acquired.fm/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 person who led the team that created Liraglutide, which went on to become Semaglutide, which of course is Ozempic, Wegovy, et cetera.

David: Yup. All modern GLP-1s.

Ben: Lotte Bjerre Knudsen from Novo Nordisk was awesome to have her on the show. After you finish this episode, come talk about it with other smart members of the acquired community at acquired.fm/slack. If you want some merch, we have some, acquired.fm/store.

And with that, listeners, we’ll see you next time.

David: We’ll see you next time.