Renaissance Technologies (Audio)

Ben Gilbert 0:00 I always used to misspell Renaissance as I was typing it out. I'd R-E-N, and then I would sort of, like, not really know what came from there, but I learned a mnemonic to make sure I get it right.

David Rosenthal 0:10 Oh! I thought you were gonna say you've typed it so many times now over the past month.

Ben Gilbert 0:15 Well, there's that, too, but you ready for this? You can't spell Renaissance without AI.

David Rosenthal 0:20 Oh, [laughing]

Ben Gilbert 0:22 [laughing]

David Rosenthal 0:24 Touché, touché.

Ben Gilbert 0:25 Uh... All right, let's do it.

Speaker 0:28 Who got the truth? Is it you? Is it you? Is it you? Who got the truth now? Hmm. Is it you? Is it you? Is it you? Sit me down, say it straight, another story on the way. Who got the truth?

Ben Gilbert 0:45 Welcome to season fourteen, episode three of Acquired, the podcast about great companies and the stories and playbooks behind them. I'm Ben Gilbert.

David Rosenthal 0:53 I'm David Rosenthal.

Ben Gilbert 0:54 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 Rosenthal 1:20 When I was in college, I actually took an economics class with Burton Malkiel, who, of course, you know, was involved in starting Vanguard and is a big proponent of all that, and that is what I learned, Ben.

Ben Gilbert 1:30 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 thirty-year track record, managing billions of dollars, has better returns than anyone you have ever heard of, including Berkshire Hathaway, Bridgewater, George Soros, Peter Lynch, or anyone else. So why haven't you heard of them? 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 [chuckles] 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," and to be specific, listeners, that's sixty-six percent annual returns before fees, "and, you know, well, I want to invest!" Well, you can't. To add to everything else that I just said, RenTech's flagship Medallion Fund 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 Rosenthal 3:08 Oh, we are going to talk a lot about that towards the end of the episode, 'cause I think it's kind of the key to the whole thing.

Ben Gilbert 3:15 Ooh, cliffhanger, David. I'm excited. So what exactly does Renaissance do? Why does it work, and how did it evolve to be the way it is today? And while the resources that are out there are scarce, because, for one, employees sign a lifetime non-disclosure agreement, David and I are going to take you through everything we've learned about the firm from our research, dating all the way back before Jim Simons started as a math professor, to understand it all. This episode was selected by our Acquired limited partners, and to be honest, I didn't think enough people knew what RenTech was to pick it, but when we put it out for a vote, the people have spoken. So if you want to become a limited partner and pick one episode each season and join the quarterly Zoom calls with us, you can join at acquired.fm/lp. If you wanna 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 bylaws of Hermès's controlling family shareholder, H fifty-one, 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 Rosenthal 4:51 Yeah, 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 five hundred, too. They're also helping companies grow from seed to IPO and beyond.

Ben Gilbert 5:06 Yep. So with that, the show is not investment advice. David and I may have investments in the companies we discuss or perhaps wish we did, and this show is for informational and entertainment purposes only. David, where do we start our story today?

David Rosenthal 5:20 Ah, well, we start in 1938 in Newton, Massachusetts, which is a fairly wealthy suburb just outside of Boston, where one James Simons is born. And both of Jim's parents were very, very smart, especially his mother, Marsha. 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 Gilbert 5:48 Super cool.

David Rosenthal 5:49 By the way, we know all this because we have to thank Greg Zuckerman, author of The Man Who Solved the Market, which is the only book out there that is solely dedicated to RenTech and Jim Simons, and we actually got to talk to Greg in our research. He helped us out a bunch. Thank you, Greg.

Ben Gilbert 6:03 And helped fact-check a few of our assumptions of what happened after the book came out.

David Rosenthal 6:07 ... So that was Jim's parents, but really a major influence on him growing up was his grandfather, Marsha's dad. There's already kind of echoes of the Bezos story here with the grandfather, the mother's father, and spending a bunch of time with him, and rubbing off on young Jeff or young Jim, in this case. And Bezos, of course, would get his start in his career at D. E. Shaw.

Ben Gilbert 6:32 A quant fund coming up at the same time as RenTech.

David Rosenthal 6:35 But back to Jim here in the 1940s. His grandfather, Peter, owned a shoe factory that made women's dress shoes. Jim spends a ton of time there growing up at the factory. So Jim's grandfather, Peter, was quite the character. He was a Russian immigrant, and he's kind of, like, still more Russia than Boston at this point in time. [laughs]

Ben Gilbert 6:59 [laughs]

David Rosenthal 6:59 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. And he teaches young Jim, when he's a child here in the factory, to say Russian phrases like, "Give me a cigarette," and, "Kiss my ass."

Ben Gilbert 7:17 Which I think he probably would say that thousands of times the rest of his life. [laughs]

David Rosenthal 7:20 I think so. If you watch interviews with Jim, his hands are always twitching because he has chain-smoked his entire life, probably going back to, like, age ten in the factory.

Ben Gilbert 7:31 Three packs of Merits a day.

David Rosenthal 7:33 Unbelievable.

Ben Gilbert 7:34 Although I think he quit later in life, but he definitely chain-smoked the better part of the first, call it, seventy-five years or something.

David Rosenthal 7:40 I mean, there are these famous stories of the conference rooms at RenTech and the war rooms when the market is going through, like, a crazy gyration, and it's just filled with cigarette smoke, and it's all Jim. [chuckles]

Ben Gilbert 7:50 Different time.

David Rosenthal 7:51 Different time. So back to Jim's childhood, though, here in the Boston suburbs. He grows up certainly not uber wealthy or uber rich, but very, very solidly upper middle class, and especially, he's an only child. He has all the resources of his parents, his family. His grandfather's this sort of well-to-do entrepreneur, and Jim, you know, he gets to rub shoulders in the Boston area with people who are really rich, and he says later, "I observed that it's very nice to be rich. I had no interest in business, which is not to say I had no interest in money." [chuckles]

Ben Gilbert 8:26 Yes, important to tease out the difference between those two things.

David Rosenthal 8:29 Yes, very, very important. And what he means when he says he has no interest in business, it's because from a pretty young age, he gets really into math. So the legend has it, when Jim is four years old, he stumbles into one of Zeno's famous paradoxes from ancient Greek times.

Ben Gilbert 8:47 Yep, this is great. The basic gist of Zeno's paradox is if you are always taking a quantity and dividing it by two, you will never hit zero. You will asymptotically approach zero, but you will never actually touch zero. You need to do addition or subtraction to do that. Division won't cut it. And so Jim, as a four-year-old, when he observes they need to go to the gas station to fill up the tank, he throws out the idea, "Well, let's just use only half the gas in the tank, because then we'll still be able to, after that, only use half the gas in the tank." And you know, the funny thing that doesn't occur to a four-year-old is, well, then we're just not gonna get very far.

David Rosenthal 9:25 So Jim's dream is to go to MIT, down the street in Cambridge, and study math. He graduates high school in three years, and during the second semester of Jim's freshman year there, he enrolls in a graduate math seminar on abstract algebra. So pretty, you know, heady stuff.

Ben Gilbert 9:42 Yeah, and Jim would go on to finish his undergrad at MIT in three years and get a master's in one year.

David Rosenthal 9:49 Yeah, pretty, pretty smart. But it turns out that that freshman year grad seminar he took actually has a big impact on him because he doesn't do well in the class. He can't keep up, and Jim's pretty self-aware here. There are other people at MIT who never run into problems. [chuckles] They never hit a limit. They never struggle understanding any concept, and he realizes that, "Oh, I'm smart. I'm very, very smart. I'm smarter than most other people here, but I'm not one of those people."

Ben Gilbert 10:22 Right, which is... You know, what do you do with that information? You realize you have to add a few of your skills together to become the best at something. You have to be smart and something else.

David Rosenthal 10:32 Yes. So Jim's own words on this are, "I was a good mathematician. I wasn't the greatest in the world, but I was pretty good." But he recognizes, like you said, Ben, that he has a different advantage that most of the super geniuses lacked, and that's that, as he put it, he had good taste. So these are his words: "Taste in science is very important. To distinguish what's a good problem and what's a problem that no one's gonna care about the answer to anyway, that's taste, and I think I have good taste."

Ben Gilbert 11:00 By the way, this is exactly the same thing as Jeff Bezos in college, realizing he wanted to be a theoretical physicist. He met some of the extreme brainpower people that would go on to become the best theoretical physicists in the world, and he said, "I'm smart, but I'm not that smart," and so switched to computer science.

David Rosenthal 11:18 I think the analogy here is like sports. There are all-star players, there are Hall of Famers, and then there's LeBron and MJ, and Jim ends up being a Hall of Famer mathematician, but he's not Tom Brady.

Ben Gilbert 11:34 I mean, he's got a pretty important theorem named after him-

David Rosenthal 11:36 ... That goes on to become a foundation of string theory in physics, which isn't even Jim's field.

Ben Gilbert 11:41 Crazy.

David Rosenthal 11:42 So this realization that Jim has about himself, though, both that he's not the smartest person in the room at a place like MIT, but he can hang with them, and that he has this taste concept, I think becomes one of the most important keys to the secret sauce that ends up getting built at RenTech, which is that he can relate to everybody. He understands what's going on. Any person off the street probably couldn't even really have a conversation with these folks, but he can, and yet he also has the perspective, maybe some of this is from his grandfather, of what is important out there in the real world, and as a result-... all of his friends at MIT and these super smart people, they look up to him, because you aren't like the kid in the corner at the high school dance. You're cool.

Ben Gilbert 12:32 He's the extroverted theoretical mathematician.

David Rosenthal 12:34 Yes. [laughing] So he was elected class president in high school. You know, he smokes cigarettes, he's popular with the ladies, he kinda looks like Humphrey Bogart. He's a popular dude, especially at this point in time. We're now in the late '50s when Jim's at MIT. You know, this is kinda James Dean, Rebel Without a Cause era.

Ben Gilbert 12:54 Yep.

David Rosenthal 12:55 So after graduation, Jim leads his buddies on a road trip with motor scooters [chuckles] ., 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 gonna do something so epic that the newspapers are gonna have to write about it. So they all load up on scooters [chuckles] and drive down to Bogota. They get into all sorts of adventures. There's knives, guns, and they get thrown in jail.

Ben Gilbert 13:22 It's honestly crazy that this group of people took this type of risk.

David Rosenthal 13:26 Totally crazy. So after he's done at MIT, and after the road trip, [chuckles] Jim heads out to Berkeley in California so that he could do his PhD with the professor Shiing-Shen Chern, and 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. But before Jim leaves for the West Coast, he meets a girl in Boston, and they decide to get engaged in four days. [laughing] Uh, I mean, this is, this is him back then. These were the times. And when they get to California, and they get married, Jim takes the $5,000 wedding gift, that I believe they got from her parents, and he decides, "I wanna multiply this." So he starts driving from Berkeley into San Francisco every morning to go hang out at the Merrill Lynch brokerage office, and just be a rat hanging around the brokerage and find ways to trade and turn this money into something more.

Ben Gilbert 14:28 Which is so interesting to think about because, at that point in time, there was such an advantage to just being there. This wasn't even the trading floor, but information is all so manual and all so relationship-driven in the markets, that there was basically no way to be in on the action unless you were physically there in on the action.

David Rosenthal 14:46 Exactly. Yeah, you couldn't just log into Yahoo Finance or something, or open the Stocks app on your iPhone, which even the information they were getting was God knows how long delayed from New York or from Chicago for the futures and commodities that are being traded that Jim gets into. He's as close to the action as he can possibly be, but he's a long, long way from the action.

Ben Gilbert 15:06 Yep.

David Rosenthal 15:06 Nonetheless, when he starts out doing this, Jim hits a hot streak, and he goes up 50% in a few days.

Ben Gilbert 15:15 Trading is easy!

David Rosenthal 15:16 Trading is easy. He says, "I was hooked. It was kind of a rush."

Ben Gilbert 15:20 I bet.

David Rosenthal 15:21 Except he ends up losing all of his profits just as quickly. [chuckles]

Ben Gilbert 15:25 Yeah, important to learn that lesson early.

David Rosenthal 15:28 Yes, and also right around this time, Barbara, his wife, gets pregnant with their first child, and is like, "You can't be driving into San Francisco every morning and gambling our future like this."

Ben Gilbert 15:39 Right, effectively playing the ponies.

David Rosenthal 15:41 Yeah, exactly. So Jim's like, "Okay, okay, I'll stop. I'll focus on academia for now." So he finishes his PhD in two years. They come back to Boston, and he joins MIT as a junior professor at age 23. So they stay one year in Boston, but Jim, even though he's got a family, even though he's super successful as a young academic here, you know, he's got kids, he's restless. So one of his buddies from the scooter trip to Bogota is from Bogota and lives there, his family's there. He has an idea to start a flooring tile manufacturing company 'cause he's like, "You know, the flooring at MIT and in Boston, it's so much nicer than in Bogota. We should start a company and [chuckles] make the same kind of flooring here."

Ben Gilbert 16:24 When I read this, I couldn't believe that this was Jim Simons' first business venture. Like, it's so random, but it really is emblematic of just how much he was thrill-seeking and just looking for anything that was unexpected, different, exciting. He just gets bored fast.

David Rosenthal 16:41 Totally. Uh, not just is this the start of his entrepreneurial career, the seeds of this financially are what go on to start RenTech.

Ben Gilbert 16:50 It's wild.

David Rosenthal 16:51 Totally wild. So Jim takes a year off and [chuckles] goes down to Bogota.

Ben Gilbert 16:56 This is a guy with an MIT undergrad and master's, and a Berkeley PhD in theoretical math-

David Rosenthal 17:04 Who's now a professor at MIT.

Ben Gilbert 17:06 Who is taking a year off to go work on a flooring company in Bogota.

David Rosenthal 17:10 Yes, accurate. So he does that for a year, they get it set up, he gets bored again. He's like: All right, I don't wanna 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 Gilbert 17:25 He's really racking them up.

David Rosenthal 17:27 But he spends a year there, and he's like, "Ah, got the itch again," and, you know, the junior professor's salary isn't that much, and like we said about him back from his childhood days, he sees the appeal in being rich, and he's like: This is not a path to being rich. [laughing]

Ben Gilbert 17:42 [laughing]

David Rosenthal 17:44 So he's like, "I'm gonna 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. [chuckles] These are the civilian code breakers.

Ben Gilbert 18:14 Yes, it was basically formed with this idea that, one, across various branches of our government, we need better collaboration and cross-funding of the same initiatives, and two, there are gonna be a lot of people who don't work for the government that we're gonna wanna hire to do some pretty secret work.

David Rosenthal 18:34 ... Yep. So the IDA there in Princeton kind of functioned like the Institute for Advanced Study, which is also in Princeton. That's where Einstein went when he came to America, kind of an independent think tank research group, except it's solely focused on code breaking and signal intelligence with the Russians during the Cold War.

Ben Gilbert 18:55 Yeah, it's a pretty wild charter, and especially how special of an organization it was. Like, the way these people would spend their time is part code breaking, but part kind of goofing around because the creativity of mathematicians working together on passion projects is important to discovering clever new algorithms.

David Rosenthal 19:16 Yes, this is so, so key, and this culture ends up getting translated whole cloth right into RenTech. So the way IDA worked, and I assume still works to this day, is they recruited top mathematicians and academics to come be code breakers there. They would double their salaries-

Ben Gilbert 19:36 And importantly, it couldn't have been a government division if they were gonna be doing that because there's very specific congressionally approved budgets for payroll.

David Rosenthal 19:44 Exactly. They figured out that they needed to attract the smartest people in the world who weren't gonna come just go work for the Department of Defense. This was the way to do it. So like you said, Ben, the charter of the group was that employees had to spend fifty percent of their time doing code breaking, but the other fifty percent of the time, they were free to do whatever they wanted, like research, pursue whatever they were doing in academia, publish papers. Kind of the appeal of going there was, "Hey, it's the same thing as being a professor at MIT or Princeton or Harvard or whatever, except you're doing code breaking instead of teaching, and there's no bureaucracy to worry about. There's no politics." It's just like, "Hey, you do your code breaking work, and then you publish it. You can collaborate with your colleagues there."

Ben Gilbert 20:34 Yep.

David Rosenthal 20:35 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 fifty percent 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. So they come together, and they publish a paper called Probabilistic Models For and Prediction of Stock Market Behavior. And everything that they suggest in this paper really is RenTech, just twenty years before RenTech.

Ben Gilbert 21:21 It's crazy, 1964, this was published?

David Rosenthal 21:25 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 gonna analyze their revenues, their price multiple," or, "I'm gonna think about what's happening in the currency markets or in the commodity markets and why copper is moving here, or the British pound is moving there, and I'm going to invest on those insights."

Ben Gilbert 21:52 You're effectively looking at the intrinsic value of an asset, trying to assign it a value and make investments based on that.

David Rosenthal 21:59 Yes, fundamental investing. There also existed, in the '60s, technical investing, which kind of is voodoo. [laughing]

Ben Gilbert 22:11 [laughing]

David Rosenthal 22:11 This is like I'm looking at a stock chart, and I've got a feeling-

Ben Gilbert 22:15 [chuckles]

David Rosenthal 22:16 ... that it's gonna go up. Like, I'm tracing this pattern, and, like, it's going up, baby, or, "No, no, no, this pattern is going down."

Ben Gilbert 22:22 Yeah, using the phrase technical might be a little generous, but what they're looking for, basically trying to mine trading behavior for signal about the way that it will trade in the future rather than mining the intrinsic information about an asset for what you think it will do in the future.

David Rosenthal 22:38 Right. And what Jim and his colleagues here are suggesting is that, but just not really done by humans. It's that with a lot more data and a lot more sophisticated signal processing.

Ben Gilbert 22:53 And importantly, you might say, why is it this group of people that came to that conclusion of applying computational signal analysis to investing? Well, it's effectively the same thing as code breaking. You are looking for signal in the noise and trying to use computers and algorithms to mine signal from something that otherwise kind of looks random.

David Rosenthal 23:16 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 Gilbert 23:25 Which is not an insight other people had. That was the amazing thing about his background priming him to realize that.

David Rosenthal 23:32 Yes, there's all this noise in this data, and it is impossible for a human to sit here and look at this data and say, "Oh, I know what the Soviets are saying." No, no, you have to use mathematical models and statistical analysis to extract the patterns.

Ben Gilbert 23:47 So mathematical models, statistical analysis, we actually hear a lot of that in the world today because machine learning is a thing.

David Rosenthal 23:56 Yes. What they are really doing here at IDA and then soon in RenTech is early machine learning, and Jim just had this incredibly brilliant insight that you can use these techniques and this technology for making investments, which makes this the perfect time to talk about our presenting sponsor for this season, J.P. Morgan Payments.

Ben Gilbert 24:20 Yes. 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, and J.P. Morgan Payments really continues that tradition in their technology investments today. They move ten trillion dollars a day securely. That is a quarter of all US dollar flows globally. Just think about the sheer volume of data at five thousand transactions per second and how important that is to the global economy.

David Rosenthal 24:51 ... 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 cash flow forecasting tool, which helps businesses manage liquidity, and that proved especially valuable during the pandemic.

Ben Gilbert 25:15 Yep. So also unsurprisingly, J.P. Morgan was ranked number one in a recent global banking index of AI capabilities, with Fortune saying they were, quote, "head and shoulders above the others." Their customers get AI-powered payment solutions for fraud prevention, customer insights, and treasury insights, all of which grows the bottom line. They can even analyze transaction data to predict and mitigate fraud patterns in real time with their validation services, helping stop millions of dollars for customers in attempted fraud.

David Rosenthal 25:44 Yep. We were doing some research to prep for this segment, and we came across something pretty wild. The United States Treasury Department has started using AI to detect suspected check fraud and recovered over three hundred and seventy-five million dollars in 2023 utilizing the new tools. The US Treasury Department disperses trillions of dollars annually, so if they continue to employ new technologies like this, it could really add up to the tune of billions. So 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 Gilbert 26:36 Yeah. 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 seventy-six percent 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 sorts of insights that drive growth with J.P. Morgan Payments as your partner.

David Rosenthal 27:05 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 Gilbert 27:19 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 J.P. Morgan on LinkedIn. Okay, David, so this paper is published. They're gonna trade and make a whole bunch of money in the stock market by applying this code-breaking, signal processing, data analysis approach to investing.

David Rosenthal 27:44 Yep. So then the natural question is, okay, what is the model here? How are they gonna do this? And it turns out that one of the employees of IDA at this time, and one of the members of this sort of rebel group, shall we say, within the organization, is a guy named Lenny Baum, and Lenny just happens to be the world expert in a mathematical concept called a Markov model, specifically a version of 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, and 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? And if we just do that, can we predict future states based on what we've observed about the patterns of past states? And the answer to that is usually yes, even if you don't know anything about fundamentally how the system operates.

Ben Gilbert 28:57 So the great example that Greg Zuckerman gives in the book is-

David Rosenthal 29:00 Yes, a baseball game.

Ben Gilbert 29:01 There's three balls and two strikes. That state has a narrow set of states after it. It's gonna be a strikeout, they're gonna get on base, it's gonna be a walk, or maybe they foul it off, and it keeps going. There's only really a narrow set of things that could happen after that, whereas when it's zero balls and zero strikes, there's a lot that could happen. They could just keep pitching, and if you don't know the rules, you're like: Why do they just keep pitching? And so it's this sort of great way to explain this idea of the black box, that if nobody tells you the rules to the game, by observing the outputs enough and observing, okay, in this state, these outputs are possible, you actually can kind of get pretty good at at least, if not predicting, understanding the probability distribution of the outcomes for any given state in the game.

David Rosenthal 29:51 So we brought up machine learning and AI a minute ago. This is a foundational concept to modern-day AI. If you think about large language models and predicting what comes next, it's not like these large language models necessarily understand English. They're just really, really good at predicting states and the next state, i.e., characters and the next character, or pixels and the next set of pixels or frame, et cetera.

Ben Gilbert 30:19 And obviously, they're much fancier than that, but that is kind of the underpinning of it all. I mean, I remember in my sophomore year of college computer science class, I had a Markov chain assignment, and it was basically write a Java program to ingest this public domain book, and then I would give it a seed word, you know, the first word of each sentence, and press Return, Return, Return, Return, Return. And it would scan through the probability tree and give me the most probable word based on the corpus of the book that it just read to create some sentence, and it feels like magic. And of course, in these early rudimentary Markov chain things like the one I did in college, it kind of spits out nonsense, but that would evolve to be the LLMs that we know of today.

David Rosenthal 30:58 ... Yes, totally. And that is what they were using at IDA to do code breaking, and that's what they propose in this paper that they could use in the stock market, too.

Ben Gilbert 31:08 Exactly. And the way that this applies to investing is just like you might not know the rules of baseball, but if you've watched enough baseball, you can kind of guess at what the probabilities of the next thing to happen are based on the state. Investing'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 gonna happen. You don't know if stock X affects stock Y in some way, because you don't know in what way those companies do business together or who holds both stocks. Are they overlapping investors? Like, you don't know the relationship between those companies, so you can't forecast with a hundred percent certainty what is going to happen. However, if you suck in enough data about what has happened in the past and the probability distribution from every given state in the past, you probably could make some educated guesses, or at least understand the probability of any individual outcome based on a state today of what could happen next.

David Rosenthal 32:06 Yes, exactly. So Jim and Lenny and this whole little crew, they're pretty fired up. They're like: "Oh, great. Let's go raise a fund and invest in the markets using this strategy."

Ben Gilbert 32:23 Certainly, we're gonna be successful at raising that fund, and certainly, we're gonna be very profitable because we've got this great idea.

David Rosenthal 32:28 Totally! What could go wrong? Well, in the mid-'60s, the idea that some wonky academics at some random secretive agency in Princeton, New Jersey, could go raise money was non-viable. I mean, it was hard enough for Warren Buffett to raise money at this point in time for his fund, and he was Benjamin Graham's anointed, appointed disciple. And here are these academics who are working at some random, unknown nonprofit saying, "Give us money. We don't know anything about these companies [chuckles] 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 Gilbert 33:12 Right, this was decades before it became high pedigree to come from a technical computer science background in the world of investing.

David Rosenthal 33:20 Yes. So a bunch of kind of Keystone Cops-style fundraising happens here. They're going around in secret. They're trying to keep the IDA bosses from knowing what they're doing. One of the group ends up leaving a copy of the investment prospectus on the copy machine [laughing] at work one night, and the boss discovers it and calls them all into his office and is like: "Guys, what are you doing here?" [chuckles]

Ben Gilbert 33:45 Right. It's a little bit of a clown show on the operational side, even if the idea is good.

David Rosenthal 33:49 Yes. So they end up abandoning the effort, both because they can't raise money and because IDA has found out about this, and they're not too pleased. Shortly after all this, though, Jim ends up moving on anyway because the Vietnam War starts, and he, as you can imagine from his background, is not a supporter of the Vietnam War at this point in time. Jim writes an op-ed in The New York Times denouncing the Vietnam War and saying, like, yeah, he's, you know, sort of part of the Defense Department, but, like, not everybody in the Defense Department [chuckles] is for the war.

Ben Gilbert 34:23 Which is so naive, thinking you can write an op-ed in The New York freaking Times, and that's not gonna create issues for you in your job.

David Rosenthal 34:31 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. [chuckles] And when the Newsweek piece comes out, that's when the Department of Defense is like: "All right, you gotta fire this guy." [laughing]

Ben Gilbert 34:51 [chuckles] Yep.

David Rosenthal 34:51 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, and even though he's super smart, his colleagues love him, clearly, he's now bounced out of MIT, he's bounced out of Harvard, he's gone to this seemingly final home for him, great place at IDA. He gets bounced out of there, too. His job prospects are not great.

Ben Gilbert 35:25 Yeah.

David Rosenthal 35:26 So he takes pretty much the only halfway decent paying job that he could get, which is to be the chair of the newly established, or maybe reestablished, math department at the State University of New York, Stony Brook, which is the Long Island campus of the State University of New York. This is not Harvard. This is not MIT.

Ben Gilbert 35:53 No, it is not.

David Rosenthal 35:54 But it did have one very important thing going for it, which is why Jim ended up there, and that is that Nelson Rockefeller, who was then the governor of New York, had launched a campaign, a hundred million dollar campaign, to try and turn this Long Island campus of the State University of New York into a mathematical powerhouse, to become the Berkeley of the East. I sort of thought MIT was the Berkeley of the East already, but Rockefeller is waging a campaign that he wants Stony Brook to become a math and sciences powerhouse, and Jim is the key. He wouldn't be able to recruit somebody like Jim otherwise, but because he's now kind of tarnished his career, here's a, like, very talented mathematician that they can convince to come be chair of the department.

Ben Gilbert 36:46 Yep.

David Rosenthal 36:47 So they basically give Jim an unlimited budget and leeway to go try and poach math professors from departments all over the country and the world and bring them there to Long Island. And part of how Jim goes and recruits folks is money, like the old, "Hey, I'll double your salary," line.... But the other part of it, too, is 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 gonna have to deal with committees. You're not gonna have to do all this stuff. There is none of this stuff here." You might have to teach a little bit, but that's not even the point. Rockefeller doesn't want this necessarily to become a great teaching institution. He just wants to assemble talent there.

Ben Gilbert 37:37 Yep.

David Rosenthal 37:38 And 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 Gilbert 37:54 Amazing.

David Rosenthal 37:56 Totally amazing. But in true Jim fashion, after a couple of years of this, and also his marriage with Barbara falling apart, he starts getting restless again. He decides that he wants to go on a sabbatical and go back to Berkeley and reunite with his old advisor there and go spend some time out on the coast in California. And this is where Chern and Simons end up collaborating and developing the Chern-Simons theory that ends up winning the highest award in geometry from the American Mathematical Society, and really kind of is Jim's personal mark on mathematics.

Ben Gilbert 38:29 Yep.

David Rosenthal 38:30 Now, also, right around the same time, remember the Colombian Flooring Company? It gets acquired, and Jim and his buddies who are partners in it come into a good amount of money. And Jim is newly divorced, he's restless in academia, he has these ideas back from when he was 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's becoming disillusioned again and restless at academia, and in 1978, he leaves to focus full-time on trading, which is a huge shock to the academic community. 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 Gilbert 39:27 Yeah, I mean, it was really viewed in the math community as anyone who's going to do investing is throwing away their talent, and it wasn't even that it was common the way that it sort of is today.

David Rosenthal 39:37 Right. Jim was the first one, but the idea that you would leave to do anything commercial, you're doing a disservice to humanity.

Ben Gilbert 39:44 Yes, exactly. And leaving to do anything, sure, but leaving to do investing was almost just seen as dirty. Like it's this rich person's game that provides no value to society.

David Rosenthal 39:55 Right. Yeah, I don't think it was that the rest of the math world was skeptical that it could work. They probably were like, "Oh, yeah, this could work," but they were like, " Ew!" [chuckles]

Ben Gilbert 40:04 Academics tend to be much more motivated by prestige than money, so I could totally see this, other people being like, "Oh, I could do that if I wanted, but I have this higher calling, and everyone respects me for this higher calling, and my currency is the papers I publish and the awards that I win, and that's what I want."

David Rosenthal 40:19 Yep. Now, Stony Brook, we should say, too, like, it's a very nice place.

Ben Gilbert 40:23 Yes.

David Rosenthal 40:23 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 Gilbert 40:31 Yep, the wooded Long Island suburbs.

David Rosenthal 40:33 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 Monemetrics, a combination of money and metrics or econometrics. And he recruits his old IDA buddy, original partner in crime on the trading idea, Lenny Baum, to come and join him. And this time, though, they have some capital from the sale of the flooring company.

Ben Gilbert 41:07 And how much did he make on that flooring sale?

David Rosenthal 41:10 I think together with Jim, his partners, and whatever money Lenny put in, they had a little less than four million dollars in this initial capital.

Ben Gilbert 41:20 In 1978.

David Rosenthal 41:22 Yep. Now, Jim also has another advantage at this point in time, which is he's right down the street from Stony Brook, and he's just recruited all of these superstar mathematicians. [chuckles]

Ben Gilbert 41:34 The table has been set.

David Rosenthal 41:35 Yes, and those folks are more loyal to Jim than they are to Stony Brook.

Ben Gilbert 41:39 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 Rosenthal 41:48 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 Gilbert 41:58 So having Baum and Ax and Simons, it's like suddenly this extremely credible team in the math world.

David Rosenthal 42:07 Yes, beyond credible.

Ben Gilbert 42:09 Right. All the theorems that a lot of mathematicians are using every day are all named after these three guys who are now at the same firm trading.

David Rosenthal 42:17 Yes, and it's led by Jim, who's somebody that they respect as an academic, but even more important, is somebody they want to work for and they look up to and they think is cool, and he's out there being like, "Hey, I think we can make money."

Ben Gilbert 42:31 Right.

David Rosenthal 42:32 Now, at this point, they're primarily trading currencies, not stocks. And currencies are obviously large markets, but they aren't impacted by as many signals and as many factors as stocks are, or really even slightly more complex commodities like, I don't know, soybeans or whatever.

Ben Gilbert 42:53 And it seemed to me like a lot of the trading of currencies they were doing was basically based on feelings that they had around how a central bank was acting, like if the head of state of a certain country was gonna do something or not. It's basically like betting on how one single actor who was in control of currencies at governments would act.... So to your point about very few signals impacting price, it's knowing what one person is gonna do.

David Rosenthal 43:19 Yes, and this is super important. At the end of the day, they build some models there. They're getting the early versions and infrastructure and scaffolding of this quantitative approach set up. But in terms of the actual trades they're putting on, they're still doing all of it by hand, and they're still all really going on a fundamental type analysis. They'll take some signals from the model, they'll see it's interesting what they spit out, but they're not gonna act on anything unless they can be like, "Oh yeah, I see what is going on here. I have a hypothesis."

Ben Gilbert 43:55 Right. The computers are by no means running loose at this point.

David Rosenthal 43:59 By no means at all. Yeah, they're just suggesting patterns and ideas, and Jim, and Lenny, and James, they have to then decide, "Hey, are we gonna do this or not? Or are we gonna do something just totally different that we think is what's gonna happen?"

Ben Gilbert 44:11 Yep.

David Rosenthal 44:12 And 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.

Ben Gilbert 44:38 Right.

David Rosenthal 44:39 And they're really, really good at building models, decoding signals, obviously, but they're much more from this realm of theory. And I actually spoke with Howard Morgan, who's gonna come up here in a second, and he made this point to me. He's like, "In math, there's this concept of traceability that's a really, really important cultural tenet." 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." And that's the world that these guys are coming from. They're like, "Oh, we can use data to sort of help us here, but ultimately, we want to have a rock-solid theory of what is fundamentally happening here."

Ben Gilbert 45:26 Fascinating. Which is very different than we'll cram a huge amount of data in, and then whatever the data suggests, we know it's true because the data suggests it, which is sort of where they would end up many years later once they had both the hardware you're referring to, sophisticated computers, the clean data that would be required to make all of those incredibly numerous and fast calculations, and also the real computer engineering architecture to build these scale systems to actually act on large amounts of signals and understand them all to come up with results. They just didn't have any of that at the time, so it was hunches and chalkboards.

David Rosenthal 46:01 Yes. And so much so that even Jim is ringleader here. He's far from convinced that he should put all of his wealth into this thing. [chuckles] He's like, "Oh, yeah, this is interesting. We're building, we're experimenting, like, great. But I also wanna put my money somewhere else, too, for some diversification." So this is where Howard Morgan comes in. You know, we used to talk about this on old Acquired episodes, that in the early days of Silicon Valley, there were only ten people [chuckles] out here, and 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. [chuckles]

Ben Gilbert 46:42 Which was essentially spun out of Renaissance? Like, it was kind of the venture capital work that they were doing at Renaissance that didn't fit with the rest of Renaissance?

David Rosenthal 46:52 Yes. So here's how it all went down, and this is so poorly understood out there.

Ben Gilbert 46:57 [chuckles] Yes.

David Rosenthal 46:58 Howard was a computer science and business school professor at the University of Pennsylvania, so he taught CS at Penn and business at Wharton. And he had been involved in bringing ARPANET to Penn and was kind of like early, early Internet pioneer. And so as a result, he was super plugged into tech, and early startups, and really early, early proto-Internet stuff. And Jim gets excited about investing together with Howard. [chuckles] So they say, like, "Hey, maybe we should partner together." And in 1982, Jim actually winds down Monometrics, and he and Howard co-found a new firm together that's gonna reflect both of their backgrounds and be a great diversification. Jim and his group are gonna bring in the quantitative trading thing.

Ben Gilbert 47:52 And again, trading on currencies and commodities at this point.

David Rosenthal 47:57 And Howard's gonna bring in private company technology investing, and they pick a name for a firm that is gonna reflect this: Renaissance Technologies.

Ben Gilbert 48:09 It's crazy!

David Rosenthal 48:10 And that is why RenTech is called RenTech.

Ben Gilbert 48:13 I could not... When we figured this out in the research, I could not believe that this is not a more widely understood story, that this is the origins of what is today a fantastic venture capital firm, First Round Capital. But you could not name two more different strategies in investing. I mean, a long-term illiquid thing like venture capital, highly speculative, versus, you know, we're gonna trade whether we think the French franc is gonna go up or down tomorrow based on the whim of some government leader. It's unbelievable these were under the same roof.

David Rosenthal 48:47 Totally. But when you know the whole background in history, it kind of makes sense because this is their personal money. This is Jim and his buddies, and Lenny, and James, and Howard. There's not institutional capital here. They're not out pitching LPs of like, "Oh, you should invest in my diversified strategy [chuckles] of currency trading and private technology startups."

Ben Gilbert 49:07 Yeah, when they say multi-strategy, this is really multi-strategy. [laughing]

David Rosenthal 49:12 Yeah. [laughing] We'll get into what multi-strategy today means later. But in these early days of RenTech, fifty percent of the portfolio was venture capital, and fifty percent was currency trading.... And in fact, a couple years after they get started, the currency trading side of the firm almost blows up when Lenny goes super long on government bonds, and the market goes against him, and the whole portfolio drops forty percent, 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. I mean, blow-up risk is always an issue in the markets, but this happened to RenTec.

Ben Gilbert 49:57 And because we quickly got to this point in the story, it would be easy to say, "Well, that's a clause that has a lot of teeth." There were many sort of rumbles of something like this potentially happening. Simons going to Lenny and saying, "Hey, maybe we should cut some of our losses, and it's okay to trade out of these positions," and Lenny was just very dug in on, "I'm a true believer," and that's how you can get into a situation where you trigger a covenant like this.

David Rosenthal 50:20 Totally, and again, also shows they weren't doing model-based quantitative trading really at this point in time.

Ben Gilbert 50:26 No, so much gut.

David Rosenthal 50:28 So as a result of that, for a while, RenTec is truly almost entirely a venture capital firm. [chuckles] At one point on the venture side, just one investment, Franklin Dictionaries. Do you remember, Ben, the-

Ben Gilbert 50:42 Yes

David Rosenthal 50:42 ... Franklin Electronic Dictionaries? Yeah, that was one of their biggest investments. That one investment is half of Jim's net worth-

Ben Gilbert 50:50 What?

David Rosenthal 50:51 - at this low point for the trading side. Yes.

Ben Gilbert 50:54 I had no idea. That's crazy.

David Rosenthal 50:56 Yeah, so in the book, Greg talks about, "Oh, Jim was focused on venture capital," and that's kind of the story out there. It's like, well, he was focused on venture capital 'cause that was the only thing working and making money. [chuckles]

Ben Gilbert 51:07 Well, I mean, it's the only thing where they actually had an edge from Howard's access to deal flow 'cause they certainly didn't have an edge in the global currency markets.

David Rosenthal 51:15 So 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, A-X-C-O-M, "And we'll contract with Axcom to run what's left of the trading operations here for RenTec."

Ben Gilbert 51:59 So it's this interesting arm's length thing, where Jim strikes a deal where he's gonna own a part of Axcom in exchange for this very favorable contractual relationship where they're gonna hire them to be the manager for this pot of money that Renaissance has raised, but, you know, it's technically not Renaissance. It's Axcom.

David Rosenthal 52:18 Right. It's another company that is now doing [chuckles] the quantitative trading.

Ben Gilbert 52:22 Yep, and I think Jim owned a quarter of it. Is that right?

David Rosenthal 52:25 Yes, that's right.

Ben Gilbert 52:26 And importantly, I don't think anyone had any idea what Axcom would become or how unbelievably profitable it would be.

David Rosenthal 52:35 [chuckles] Uh, no. [laughing]

Ben Gilbert 52:38 [laughing]

David Rosenthal 52:38 Nobody would've done what they did had they known what was coming.

Ben Gilbert 52:41 Yes. Wouldn't have spun it out.

David Rosenthal 52:43 No. So once Ax and Straus get out to California, Straus, he's kind of on the computing data infrastructure side. That's what he was doing at Stony Brook, and that's what he came into Renaissance to build. He starts getting really into data, and he starts collecting intraday pricing movements on securities. At this point in time, I think really the best data you could get from providers out there was maybe open and close data on securities pricing. Straus finds a way to get tick data, like every twenty minute data on these securities throughout the day.

Ben Gilbert 53:27 Not only that, he's getting historical data that predates what your traditional data providers would give you, and then ingesting it into computers and cleaning the data to get it into the same format as the tick data. So he's getting early 1900s, even 1800s stuff, to try to just say, "At some point, hopefully, we'll be able to make use of this, and I want to have this just really, really clean data set about the way that these markets interact."

David Rosenthal 53:53 Yeah, I mean, he's doing ETL on the data [chuckles] -

Ben Gilbert 53:56 Yes

David Rosenthal 53:56 ... I think before anybody knew what ETL was.

Ben Gilbert 53:58 Again, no one told him to do that. That was just a self-motivated, almost like obsession of like, "Well, if we're gonna have data, it should be well-formatted and well-understood and labeled and all that."

David Rosenthal 54:08 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," and Berlekamp had studied with folks like John Nash and Claude Shannon at MIT.

Ben Gilbert 54:27 I love that Claude Shannon is coming in again!

David Rosenthal 54:30 I know.

Ben Gilbert 54:30 We talked about it a lot on the Qualcomm episode. Father of information theory, really the center of gravity for attracting tons of talent to MIT and kind of paving the way for what would become phone technology and telecommunications broadly in the future. But the fact that Berlekamp is crossing paths at MIT with Claude Shannon, so cool.

David Rosenthal 54:51 So cool, and most importantly, for this specific use case, Berlekamp had worked with John Kelly, who developed the Kelly Criterion on bet sizing, which poker players will likely be well familiar with.

Ben Gilbert 55:05 Yep.

David Rosenthal 55:06 So 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 and the trades that are coming out of these models," versus I don't know what they were doing before. Maybe it was naive of, like, every trade was the same or just like, "We should actually be systematic about this."... the models start really working.

Ben Gilbert 55:32 Yep, this is the turning point.

David Rosenthal 55:35 Yeah. In these kind of mid-'80s years, Axcom is generating IRRs of, like, twenty-plus percent on the trading side. You know, not necessarily gonna beat venture capital IRRs, but liquid-

Ben Gilbert 55:48 Yes.

David Rosenthal 55:49 Reliable.

Ben Gilbert 55:50 Well, that's the thing. They don't know how reliable yet. They know they've done it kind of a few years in a row here, but the question is, how uncorrelated to the stock market over a long period of time, and how predictable are these returns? Or is it just super high variance?

David Rosenthal 56:05 Yes, but the early results are really good, and Jim and Berlekamp especially are very encouraged by this. So in 1988, Jim and Howard Morgan decide to spin out the venture investments, and Howard goes to manage those with basically their own money. Fun coda on this: when Howard starts First Round a number of years later with Josh Kopelman, Jim, of course, is a large LP.

Ben Gilbert 56:33 Ah.

David Rosenthal 56:35 And Howard, of course, remains an investor in RenTech. The first institutional fund that First Round ended up raising was a fifty X on a hundred and twenty-five million dollar fund. It had Roblox, Uber, and Square. So I believe this is right, I think Jim made as much money from his investments in First Round as Howard did from his LP stake-

Ben Gilbert 57:03 No

David Rosenthal 57:03 ... in RenTech.

Ben Gilbert 57:04 That's wild!

David Rosenthal 57:06 Isn't that amazing?

Ben Gilbert 57:07 Wow, that is a untold story about Jim Simons. I think I read basically every primary source thing on Jim or Renaissance on the whole internet, but I assume you got that from Howard.

David Rosenthal 57:19 Yeah, it was super fun talking to Howard about this, and just the history of how First Round started and early super angel investing and everything that became.

Ben Gilbert 57:27 I also didn't realize that First Round's Fund One was a fifty X on a hundred and twenty-five million dollar fund.

David Rosenthal 57:33 First institutional fund, which I believe they called Fund Two.

Ben Gilbert 57:38 I mean, wild. Wild stuff.

David Rosenthal 57:41 Totally wild. So when Howard spins out the venture activities, Jim then decides to set up a new fund as a joint venture between RenTech and Axcom, and they decide to name it after all of the collective mathematical awards that Jim and James and Berlekamp and all these prestigious mathematicians have won in their careers. They name it the Medallion Fund.

Ben Gilbert 58:10 Ba na na!

David Rosenthal 58:11 Ba na na.

Ben Gilbert 58:12 And listeners, we've arrived. This is the part of the story that matters. The Medallion Fund is the crown jewel, or you might even say, actually, the only interesting thing about Renaissance, and it is born out of this observation that, "Oh, my God, what they're doing over there at Axcom is really interesting. Maybe they shouldn't be doing it all the way over there. [chuckles] 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 Rosenthal 59:08 Those ideas had all existed before. This is the first time that it's all brought together-

Ben Gilbert 59:14 Yeah

David Rosenthal 59:14 ... and actually working and operationalized.

Ben Gilbert 59:17 And frankly, that computers got good enough to actually do it, too. That's another big piece of this.

David Rosenthal 59:22 Yeah, I don't know that Strauss could have done his data engineering too much earlier in time.

Ben Gilbert 59:29 Yeah.

David Rosenthal 59:30 But before we get into the just absolutely insane run [chuckles] 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 fourteen and is just an incredible company.

Ben Gilbert 59:49 Yep. ServiceNow digitally transforms your enterprise, helping automate processes, improve service delivery, and increase operational efficiency, all in one intelligent platform. Over eighty-five percent of the Fortune five hundred 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 Rosenthal 1:00:09 So we talked on our Novo Nordisk episode about how ServiceNow founder Fred Luddy discovered this core insight that software can transform and eliminate manual tasks. And on Hermes, we told the story of how current CEO Bill McDermott came in and turbocharged that into an absolute monster, hundred and fifty 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 Gilbert 1:00:53 Yep, 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 Rosenthal 1:01:06 Totally.

Ben Gilbert 1:01:07 Well, this is what's so cool and where I think the playbook lesson really is for listeners. Because you can't just pick any use case, you have to be strategic about it, and IT was the perfect vertical because every other department has to interface with them, from the CEO on down. So they're gonna notice when IT service management rapidly improves. All of those support tickets that used to take forever are now just magically resolved.... and 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 Rosenthal 1:01:38 Yep. 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- [chuckles] ... who are already true ServiceNow believers. I'm honestly not sure that there's a better enterprise software playbook in history than ServiceNow's. So once they established the beachhead in IT, they then took the same platform to HR with employee experience, they took it to CSM with customer service requests, 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 Gilbert 1:02:13 Yep. So if you want to learn more about the ServiceNow platform and playbook and hear how it can transform your business, head on over to servicenow.com/acquired, and when you get in touch, just tell them that Ben and David sent you.

David Rosenthal 1:02:26 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 gonna fix this. I really, really believe that what we're doing with these models is gonna be extremely profitable." So he buys out most of Ax's stake in the summer of 1989, and he moves the offices up to Berkeley, and there he comes up with the idea that, hey, we should trade more frequently, a lot more frequently. Because if what we're trying to do is understand the state of the market from the data we have and then predict the future state of the market and then combine that with figuring out the right bet sizing to make, we actually want to make a lot more trades to get a lot more data points and learn a lot more about the bets we're making so that we can then size them up or size them down.

Ben Gilbert 1:03:31 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 gonna be worth about $10. The most likely situation is it's within five percent of that. If you ask me three years from now, I have almost no intuition about that, and a state machine is the same way. If you flash forward a whole bunch of states, you sort of lose predictability as you sort of continue down that chain. The second thing is, if your models are showing that you're gonna be right, call it something like fifty point two five percent of the time, then the amount of money you can make 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, fifty point two five percent of the time, and I decide to play once or play twice or play five times, there's a chance I could lose all my money, or if I have tiny little bet sizes, then I'm just not going to make that much money. But if I walk up to said game with a little bit of edge, and I use small bet sizes, and I play ten thousand times, I'm gonna walk out with a lot of money.

David Rosenthal 1:04:43 There is a great Bob Mercer quote about this later. He says, "We're right fifty point seven five percent of the time."

Ben Gilbert 1:04:50 And I do think he's making up that number. I think it's illustrative.

David Rosenthal 1:04:53 Right. "But we're one hundred percent right- [chuckles] ... fifty point seven five percent of the time." You can make billions that way. [chuckles]

Ben Gilbert 1:05:03 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 Rosenthal 1:05:15 A lot, yes. And then back to the Kelly Criterion, adjust your bet sizes over time as you're making those bets.

Ben Gilbert 1:05:21 Yep. Now, of course, this is all great in the abstract if it's that you're literally sitting at a casino, and you're somehow perfectly making these bets, and you're just sitting right there at the table, and then you can walk over to the cashier. It gets a little bit different in the market. For example, there are real transaction costs, especially at this point in history, before some of these more, uh, innovative trading business models with pay for order flow and zero transaction fees and all this stuff. There's real transaction costs to putting on these trades, and of course, you're gonna move the market when you put on these trades.

David Rosenthal 1:05:50 Yes, this is slippage.

Ben Gilbert 1:05:52 There's all sorts of practical consideration. You could get front-run by other people. It's not just a computer program that gets executed. You actually have to meet the constraints of the real world when you're deciding, instead of a few big bets, we're gonna have a hundred thousand tiny bets.

David Rosenthal 1:06:08 Yes, and as time goes on, and the whole quant industry emerges and becomes much more sophisticated, I think it's particularly the slippage there that becomes the governor on how high velocity you can actually be on this, and the slippage is that once you are at a certain scale, you are gonna move the market with your trades.

Ben Gilbert 1:06:26 So the deeper you get into the order book, like, let's say you want to buy five million dollars of something, maybe your first hundred thousand dollars, you're pretty sure you can get the quoted price. But buy your last hundred thousand dollars of that five million dollar buy, the price might have gotten pretty different already.

David Rosenthal 1:06:40 Yeah. We're gonna 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 Gilbert 1:06:52 Yep, and David, in a very crude way, calls back to last episode on Hermes, the idea that the price would be highest for the family member that is willing to sell now and sort of goes down over time. If the family was gonna sell to Bernard Arnault, it would behoove you to be first in the order book, not last in the order book.

David Rosenthal 1:07:11 Yes. 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, like, the mistake of just looking at averages.

Ben Gilbert 1:07:30 Exactly. Yes, looking at the quoted price of an asset-... is wrong. You actually should be looking at what is the volume that is willing to buy, and what is the volume that is willing to sell? And for all of those buyers and all of those sellers, what are the price at which they are willing to transact? And well, the way that tends to manifest on a stock chart is, here's the price of a share right now, but that's not actually what's going on under the surface. It's a whole bunch of buyers and sellers who have different willingness to pay and have different amounts that they're trying to buy or sell.

David Rosenthal 1:08:01 Yes. Now, at this point in time, when the Medallion Fund is first starting to work in, say, late 1989, early 1990, it's small enough that this isn't a big consideration yet.

Ben Gilbert 1:08:12 Yeah, right.

David Rosenthal 1:08:13 Medallion was about twenty-seven million dollars under management when Berlekamp bought out Ax. In 1990, the first full year after that, the fund gains seventy-seven point eight percent gross, which after fees and carry, was fifty-five percent net. Now, what were the fees in carry?

Ben Gilbert 1:08:34 I mean, either one of those numbers is shooting the freaking lights out. Assuming that this is not a crazy high-risk strategy that they executed and will completely fall apart under different market conditions, like, if this is an actual repeatable strategy that produces the numbers you just said, unbelievable, world-changing.

David Rosenthal 1:08:54 Hell yeah, let's go. [chuckles]

Ben Gilbert 1:08:56 Yes.

David Rosenthal 1:08:57 And indeed, it was a hell yeah, let's go situation. [chuckles]

Ben Gilbert 1:09:01 So the numbers you quoted me, the gross and the net, sounded quite different. Talk to me about the fees in carry.

David Rosenthal 1:09:06 So carry, I've seen different sources of whether it was twenty or twenty-five percent in the early days, but the management fee on the fund was five percent-

Ben Gilbert 1:09:14 Whew!

David Rosenthal 1:09:14 ... which is crazy. The top venture capital firms in the world charge a three percent management fee, and even that is like, everybody holds their nose and is like, "This is ridiculous." How on earth were these nobodies charging a five percent management fee out the gate to their investors? Well, a couple things. One, their investors were not sophisticated. [chuckles] It was mostly their own money and their buddies' money.

Ben Gilbert 1:09:40 So they set that precedent.

David Rosenthal 1:09:41 They set that precedent. But two, though, they actually needed the money-

Ben Gilbert 1:09:46 Yes

David Rosenthal 1:09:46 ... because Strauss's infrastructure costs were about eight hundred thousand dollars a year. So they just backed into the management fee based on like, "Hey, we need eight hundred thousand dollars a year to run the infrastructure, plus we need some money to, you know, pay folks and whatnot." Like, great, five percent management fee.

Ben Gilbert 1:10:03 And so the pitch they're making to the investor base is like, "If you believe that we should be able to massively outperform the market doing quantitative trading, well, we're gonna need a lot of fees to do that." And so the investors basically took the deal, if they thought about it enough. Okay, so that's the fees. On the performance, that twenty or twenty-five percent, it's just not actually that far above market, if it's above market at all. What you're seeing is a high-fee, normal-ish performance fee fund at this point in time.

David Rosenthal 1:10:30 Yes, high management fee, normal-ish carrier performance element.

Ben Gilbert 1:10:34 Yep.

David Rosenthal 1:10:35 So 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 wanna go all in on this. I think with some tweaks, we can be up eighty percent 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 six X the basis that Berlekamp had paid Ax a year earlier.

Ben Gilbert 1:11:28 On the one hand, making a six X in one year sounds great.

David Rosenthal 1:11:32 On the other hand, [chuckles] 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 Gilbert 1:11:47 David, I think we should throw this out so people understand the volume of this. They've generated on the order of sixty billion dollars of performance fees for the owners of the fund over their entire lifetime. So on the one hand, six X in a year ain't bad. On the other hand, you owned a giant part of something that has dividended sixty billion dollars in cash out to its owners. Oof!

David Rosenthal 1:12:12 Yeah. That's just on the carry side. I mean, the owners are the principals, so just like dollars out of the firm, it's probably twice that. I would estimate probably a hundred and fifty, two hundred billion dollars that have come out of Medallion over the last thirty-five years. So Jim buys out Berlekamp. He rolls everything in the Medallion Fund back into RenTech itself, moves everything back to Stony Brook. Strauss moves to Stony Brook.

Ben Gilbert 1:12:43 So it's now the Jim Simons Show in New York, with Strauss building the engineering systems, and Ax, I think, still had a small stake.

David Rosenthal 1:12:51 Yes, that's right, and Strauss had a stake as well. So once Jim takes control and moves everything back, he basically decides that he's gonna turn RenTech into an even better, even more idealized version of IDA and the math department at Stony Brook. He's gonna make this an academics paradise, where if you are one of the absolute smartest mathematicians or systems engineers in the world, this is where you wanna be. So of course, he starts raiding the Stony Brook [chuckles] department itself again, and 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 Gilbert 1:14:01 And listeners, I imagine this is probably the point where you're starting to get confused and saying, "There are so many people in this story." I think we're on eight or nine. We just keep introducing more people, and that is the story of Renaissance. It is not this singular, clean narrative. 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 Rosenthal 1:14:36 A lot of people, and just a lot of time, too. This is twenty-five 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 Gilbert 1:14:55 And we haven't even introduced the two people who would become the co-CEOs of this company-

David Rosenthal 1:15:00 [laughing]

Ben Gilbert 1:15:00 -for twenty years.

David Rosenthal 1:15:02 Yes. Well, let's get to that. [chuckles] So Jim moves everything back to Long Island, sets it up as this academic paradise. He's recruiting the smartest people in the world. In 1991, the next year, the firm does fifty-four point three percent gross returns and thirty-nine point four percent net returns after fees. So not Jim's bogey of eighty percent, but still pretty freaking great.

Ben Gilbert 1:15:29 And we should say, the years of modest performance are behind them. From every single year forward, they shoot the lights out. From 1990 onward, they never lose money, and on a gross basis, they never even do less than thirty percent. 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 Rosenthal 1:15:54 The historic run has begun, [chuckles] let's just say.

Ben Gilbert 1:15:58 Yep.

David Rosenthal 1:15:58 So 1992, gross returns are forty-seven percent. 'Ninety-three, they're fifty-four percent. At the end of 1993, Simons decides to close the fund and not allow new LPs in. So if you're an existing LP, you can stay in, but they're no longer open for new inflows. He has so much confidence in what they're doing, that he thinks they're all gonna make more money without accepting new capital, by just keeping it to the existing investor base. 1994, gross returns are ninety-three freaking percent. Medallion, at this point, is stacking up cash. It is a meaningful fund. It's about two hundred and fifty million dollars 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 Gilbert 1:16:58 And the performance fees on this are seven million dollars, thirteen million dollars, fifty-two million dollars. The free cash flow flowing to partners here is certainly becoming real, too.

David Rosenthal 1:17:10 Yes. But as they get into that, call it on the order of magnitude of a billion dollar scale, they start bumping into the moving markets problem and the slippage that we were talking about earlier.

Ben Gilbert 1:17:24 Yep, and that's sort of in the mid-nineties?

David Rosenthal 1:17:26 Yep. As they're hitting this two hundred and fifty million, half a billion dollar scale.

Ben Gilbert 1:17:30 Right, the computer model spits out, "We should go buy this huge amount of something at this price." They go to do it, they can only buy ten, twenty, thirty percent of the amount they want at that price, and then suddenly, the price is very different.

David Rosenthal 1:17:42 Yep. Up to this point, the vast majority of what Medallion is doing is trading currencies and commodities, not equities. 'Cause you might be thinking, "Okay, yeah, I hear you, the nineties was a different era, but half a billion dollar fund doesn't sound that big. How are they moving markets with half a billion dollars?"

Ben Gilbert 1:18:04 It's not the equity markets.

David Rosenthal 1:18:05 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. So Simons decides, "The only thing we can do here to expand, which I'm such a believer in what we're doing, we need to expand, is we need to move into equities. Equities are the holy grail. 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 and the signals that we can find are gonna be even better."

Ben Gilbert 1:18:55 Right. There's so many buyers and sellers every day showing up to trade so many different companies at such high velocity, it's almost this honeypot for Renaissance's systems. This is sort of their moment. This is what they were built for, and it's kind of funny that they've just been in kid glove land the whole time with these thinly traded markets with minimal data.

David Rosenthal 1:19:16 Yes, and this brings us to Peter Brown and Bob Mercer. And in 1993, one of the mathematicians that Jim had recruited to RenTech, a guy named Nick Patterson, gets especially passionate about going out and recruiting new talent, along with Jim. 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's sitting there like, "This is a joy. I want to go find other best people in the world to hang out with."... And he had read in the newspaper that IBM was going through cost-cutting and was about to do layoffs. And he also knew that the speech recognition group at IBM had some absolutely fantastic mathematical talent. And really, what they were doing was, again, another vector in the early AI machine learning research. Specifically, IBM's Deep Blue chess project [chuckles] of the time had come out of this group, and Peter Brown there was the one that actually spearheaded the project.

Ben Gilbert 1:20:25 Yep. And it's interesting that you talk about speech recognition as the perfect fit for what they were doing, and you might say: Why is that? Well, the actual work that goes into speech recognition, natural language processing, is kind of the same signal processing that Renaissance is doing to analyze the market.

David Rosenthal 1:20:45 It's not just kind of, it's exactly the same signal processing. [chuckles]

Ben Gilbert 1:20:48 Right. Speech recognition is a hidden Markov process, where the computer that's listening to the sounds to try to turn it into language doesn't actually know English, right? Obviously. But what it does know is, when I hear this set of frequencies and tonalities and sounds, there's a limited set of likely things that could come after it. And in Greg's book, he greatly points out this perfect example: when I say apple, you might say pie. The probability that pie is gonna be the next word following apple is significantly higher. And so these people who have spent their careers not only doing the math and the theoretical computer science behind speech recognition to help figure out and predict the next words, that you have a narrow set of likely words to choose from, so when you're listening to those frequencies, you can say, "It's probably gonna be one of these three," rather than search the entire dictionary for any word that it could be, to narrow the processing power. It's not only the theoretical side, but it's also people who have built those systems at IBM, like a real operational computer company.

David Rosenthal 1:21:51 Yes, at operational scale, and 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. And Jim and Nick know that if they're gonna 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 Gilbert 1:22:29 And the world that they're entering is just exploding in complexity and dimensionality. And when I say that, here's what I mean: the data that they are mining, that they're looking for, is this intraday tick data between every stock trading. So they're in this sort of trying to map the relationship between one stock and every other stock, not just at that moment in time, but every time before it and every time after it. They're also, once they do identify patterns, which this is key, the algorithms identify the patterns. It's not a human with a hunch saying, "I think when oil prices go up, the airline prices are gonna get hit." It's computers doing machine learning to discover the patterns in the data. Then there's the second piece of, well, what trades do you actually put on to be profitable 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 ten, a hundred, 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. So you're looking for a super multivariate, multidimensional problem, both on the data ingestion side and on the how do I actually react to it side. And all of this computation can't take a long time because you must act, you know, not in milliseconds. It's not a high-frequency trading that's front-running the market. That's not actually what they do. A lot of people think it is, but we'll get to that later. But they do need to act with reasonable quickness, probably on the order of minutes, so these need to be really efficient computer systems, too.

David Rosenthal 1:24:14 Yeah. 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 Gilbert 1:24:40 And just keep adding layers of complexity here, keep adding new things to multiply by. Many of these are traded on multiple exchanges, so you might also be looking for pricing disparities on the same equity on different markets at different points in time. So there's just dimensions upon dimensions of things to analyze, correlate, and act upon.

David Rosenthal 1:25:01 So Patterson and Simons go raid IBM. [chuckles] They're like Steve Jobs raiding Xerox PARC. They bring Peter and Bob and one of their programming colleagues, David Magerman, over from IBM into RenTech, and they get started on building the equities model. But it turns out, A, they're obviously very successful at that, but the impact that they have and what they build is even bigger because Bob and Peter realize 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 Gilbert 1:26:07 Right. But if you can nail it, it means that you can do interesting things like, "Hey, we don't have a lot of data on this particular market, but it looks a lot like something we do have data on. So if it's all part of the same model, we can kind of just apply all the learnings from this other thing onto this brand-new thing that we're out 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 Rosenthal 1:26:36 It turns out that this was actually the second most important innovation that Bob and Peter bring to RenTech, the actual product and performance of having one model. The most important thing is that if you have only one model, one infrastructure, everybody in the firm is working on that same model. You can all collaborate all together, which is especially important when you have the smartest people in the entire world all in one building. Before this, there were separate models within RenTech, so insights and innovations and work that one team was doing on one model wouldn't get applied or translate over to work that was happening by another team on another model.

Ben Gilbert 1:27:24 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 Rosenthal 1:27:36 Yeah. This is wholly unique and revolutionary. No other at-scale investment firm, period, and especially quant firm, operates this way today with just one model. There are portfolio managers and teams and multi-strategy. People are culturally competitive with one another, but even if they're not, the work that you're doing on this side of Citadel is not impacting the work that you're doing on that side of Citadel.

Ben Gilbert 1:28:05 Right.

David Rosenthal 1:28:06 What Bob and Peter do is they unify everything at RenTech, so all the wood is going behind one arrow.

Ben Gilbert 1:28:13 Yes. And before we talk about the impact of that, we wanna 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-draining efforts for your organization and makes them fast and simple.

David Rosenthal 1:28:46 Yeah, 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 gonna 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 Gilbert 1:29:19 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 three hundred external tools. Plus, they let customers build private integrations with their internal systems.

David Rosenthal 1:29:46 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 Gilbert 1:29:55 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 seven thousand 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's CEO, all Acquired listeners get a thousand dollars of free credit, vanta.com/acquired. So David, the equities machine.

David Rosenthal 1:30:23 Yes, and indeed, a machine it is. So Peter and Bob come in in 1993. In 1994, 1995, they're building this. RenTech is getting into equities.

Ben Gilbert 1:30:34 And yet, just imagine the computers that you were using during 1994 and 1995. It is astonishing, the level of computational complexity and coordination and results that they are pulling off, again, in real time, analyzing these markets with the technology that was available during those years.

David Rosenthal 1:30:53 Yes, and here's what's amazing. Returns go down maybe slightly, certainly a bit from the blowout year that 1994 was, but they're still above thirty percent every single year, most years above forty percent. This is unbelievable that they're maintaining this performance as they're going into this hugely more complex market, and they're scaling assets under management. So by the end of the 1990s, Medallion has almost two billion dollars in assets under management while maintaining roughly the same performance by getting into equities. This is huge.

Ben Gilbert 1:31:33 Yep. And David, if you just kind of look at this and do the math, okay, so '94, their AUM was two hundred and seventy-six million, and they grew ninety-three percent. And then their AUM the next year was four hundred and sixty-two million, and then they grew fifty-two percent. And their AUM the next year was six hundred and thirty-seven million. You kinda quickly get where I'm going here, which is: Oh, they're scaling AUM, not by bringing in new investors-

David Rosenthal 1:31:57 Right, it's closed to new investors.

Ben Gilbert 1:31:59 It's all just compounding. This is the same capital that they had in 1993 that has gone from a hundred and twenty-two million at the beginning of that year to 1999 being one point five billion.

David Rosenthal 1:32:12 Yes, and then in the year 2000-... they just totally blow the doors off. One hundred and twenty-eight percent gross returns, net returns after fees of ninety-eight point five percent. This is bananas!

Ben Gilbert 1:32:32 They grow the fund from one point nine billion to three point eight billion of assets under management, again, purely by investing gains, not by getting any new investors, the year the tech bubble burst.

David Rosenthal 1:32:46 Yes. While the whole rest of the market is down big time, Medallion is up [chuckles] a hundred and twenty-eight percent gross on the year. And this becomes a theme: high volatility is when Medallion really shines.

Ben Gilbert 1:33:00 And here you go, uncorrelated. They have their final stamp of approval right here of, not only are we a money-printing machine, we are a money-printing machine in all environments, regardless of the state of the broad market. And David, as you said, volatility actually makes their algorithms work even better, 'cause what are they doing? They're looking for scenarios where the market's gonna act erratically, and they can take advantage of people making decisions that they shouldn't. And any time any investors are under pressure, there's a little bit of edge that's gonna accrue to a Medallion that's saying, "Oh, okay, you're fear-selling right now? Well, I can determine if you should be fear-selling or not, and if I determine that you shouldn't be dumping that asset, I'm buying it from you."

David Rosenthal 1:33:44 So there's a really fun story around this that really illustrates Jim's genius in managing the firm and the people, and how this year was when they really figured this out. So the first couple days of the tech bubble bursting, Medallion actually takes a bunch of large losses, and part of it might be that the model wasn't tuned right yet because nobody at RenTec had seen this type of behavior in the market before. Part of it might also be, too, that it didn't perform well for those couple days. It's a really stressful time for everybody. You know, everybody's in Jim's office. Jim's smoking his cigarettes.

Ben Gilbert 1:34:25 [laughs]

David Rosenthal 1:34:25 It's a cloud of smoke, and they're debating what to do, and Jim makes the call to take some risk off. He's worried about blowing up. We're not very far removed at this point from Long-Term Capital Management. Uh, the model may be saying we should stay long here, but let's not blow up the firm.

Ben Gilbert 1:34:41 Yep.

David Rosenthal 1:34:42 After this goes down, Peter Brown comes to Jim and offers to resign, given the losses that they incurred over these couple days, and Jim says, "What are you talking about? Of course, you shouldn't resign. You are way more valuable to the firm now that you've lived through this, and you now know not to one hundred percent trust the model in all situations."

Ben Gilbert 1:35:04 It's fascinating. It's such a good insight. That illustrates Jim as a leader right there.

David Rosenthal 1:35:09 It totally does. There's a parallel story when Jim ultimately does retire in 2009, and Peter and Bob take over as co-CEOs, where a year or so before, the, quote-unquote, "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 RenTec gets hit. And during that period, Peter argued very strenuously that we should trust the model, stay risk on. This is gonna be an incredibly profitable time for us. And Jim pumped the brakes and stepped in, intervened, and took risk off. And Peter goes to Jim again around the CEO transition and says, "Hey, Jim, aren't you worried that with me running the place now, I'm gonna be too aggressive and blow it up one of these days?" And Jim says, "No, I'm not worried at all. I know you were only so aggressive in that moment because I was there pushing back on you, and when you're in the seat, you're gonna be less aggressive." [chuckles] He's just such a master at insight into human behavior.

Ben Gilbert 1:36:15 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 r- pause and reflect, I'm like, "I don't think I expected to take this position coming into this conversation." But, you know, you naturally want to sort of play the other side to balance out the person sitting across from you.

David Rosenthal 1:36:38 Yep. So back to the year 2000 and this incredible performance. Ben, to what you were saying earlier about uncorrelated returns, not only do they shoot the lights out that year, they're doing it when the market is down. We gotta 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 Gilbert 1:37:00 And I think people grasp it intuitively. We've mentioned this concept a couple times this episode, where, okay, great, it's amazing to have a fund that twenty-five X's or a year where you have a hundred percent investment return, or, "I bought Bitcoin yesterday, and it doubled overnight." Does that make you one of the best investors in the world? We all intuitively know, no, it doesn't, because 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? And so you basically can provide value to investors as a fund manager in two ways: you can outperform the market, or you can be entirely uncorrelated with the market and get market returns. Or what you can do, as RenTec, is both. You can [chuckles] be uncorrelated and massively outperform, which is effectively the holy grail of money management.

David Rosenthal 1:37:56 Yes, and so the Sharpe ratio is a measurement combining these two concepts.

Ben Gilbert 1:38:01 Exactly. So it's named after the economist William F. Sharpe. It was pioneered in 1966. It is effectively the measure of a fund's performance relative to the risk-free rate. So if you performed at fifteen percent that year and the risk-free rate was three percent, then, you know, your numerator is gonna be twelve percent, and it is compared against the volatility, or the standard deviation is technically what it is, but effectively...... how volatile have you been the last X years? And typically, it's looked at as a three-year Sharpe or a five-year Sharpe or a ten-year Sharpe. The Sharpe ratio represents the additional amount of return that an investor receives per unit of an increase in risk. And so David, you're starting to throw out numbers. Low Sharpe ratios are bad, negative Sharpe ratios are worse, 'cause that means you're underperforming the risk-free rate. High Sharpe ratios are good, 'cause it means that you're producing lots of returns, and your variance or your standard deviation or your sort of risk is low. So in 1990, they had a Sharpe of two point oh, which was twice that of the S&P 500 benchmark. Awesome.

David Rosenthal 1:39:07 Yep. Good.

Ben Gilbert 1:39:08 Nineteen ninety-five to two thousand, Sharpe ratio of two point five, really starting to hum. Pretty unbelievable.

David Rosenthal 1:39:14 Good. Where do I sign up to invest?

Ben Gilbert 1:39:16 At some point, they added foreign markets and achieved a Sharpe ratio of six point three, 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 Rosenthal 1:39:34 And I believe, if I have my research right, in 2004, they actually achieved a Sharpe ratio of seven point five.

Ben Gilbert 1:39:44 Astonishing.

David Rosenthal 1:39:45 You know, again, back to our sports analogy here, these aren't Hall of Fame numbers. These are like, I don't know, make Tom Brady look like a third stringer.

Ben Gilbert 1:39:52 Yes, exactly.

David Rosenthal 1:39:54 So on the back of 2000 and this rise, the next year in 2001, they raise the carried interest on the fund to thirty-six percent, up from either twenty or twenty-five percent, whatever it was before. Now, remember, they've already closed the fund to new investors, so there's still outside investors in the fund, but no new investors are coming in. And then the next year, in 2002, they raise the carry to forty-four percent. I mean, great work if you can get it, but for context, the Sequoias, the benchmarks out there, they have obscene carry of thirty percent. Forty-four is unprecedented.

Ben Gilbert 1:40:35 There's two interesting ways to look at this. One, they're just trying to jack it up so high that they just purge their existing investors out, where they're saying, "We're not gonna kick anyone out yet, but we've been 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. So 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 sort of fixing the market to make the market a true weighing machine, not a voting machine, but making it so that all prices reflect the value of what something is actually worth. And in some ways, 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 gonna take this deal even if we take more of this, because there's just a mispricing here. This product should not be priced at twenty, twenty-five percent carry. This product should be priced at a much higher carried interest, and you're still gonna love it."

David Rosenthal 1:41:49 You should pay twenty percent carry for a firm that delivers you fifteen percent annual returns. We're delivering you fifty percent annual returns [chuckles] .

Ben Gilbert 1:41:58 Totally. So I have to imagine it didn't go over well with the existing investors, but they just have so much leverage that what's gonna happen?

David Rosenthal 1:42:06 Okay. Once again, I'm sorry, audience, I have to say hold on one more minute for another perspective that I have to offer on the carry element, but I want to finish the story first. Okay, so 2001, they raised the carry to thirty-six percent. 2002, they raise it to forty-four percent. And then in 2003, they actually say, "Hey, we can't incentivize you out of the fund, outside investors. We are gonna kick you out." So starting in 2003, everybody who's an outside investor, who's not part of the RenTech family, you know, current employee or alumni of the firm, gets kicked out.

Ben Gilbert 1:42:40 And not all alumni get to stay. There's select alumni that get grandfathered in.

David Rosenthal 1:42:45 Yes. Now, why did we do this? I'm gonna talk about one reason in a minute, but one reason is super obvious. The Medallion Fund is now at five billion dollars in assets under management that they're trading. Even in the equities market, they are now hitting up against slippage.

Ben Gilbert 1:43:00 Yep.

David Rosenthal 1:43:01 And so if they wanna maintain this crazy, crazy performance, they just can't get that much bigger.

Ben Gilbert 1:43:08 This is the problem that Warren Buffett talks about all the time, and why he has to basically just increase his position in Apple rather than going and buying the next great family-owned business. The things that move the needle for them are so big that that's really all they can do. And when you are big, you're gonna move any market that you enter into.

David Rosenthal 1:43:26 Yeah.

Ben Gilbert 1:43:26 And the strategy that RenTech is employing right now, they're just deeming doesn't work at north of five billion dollars.

David Rosenthal 1:43:34 So in 2003, they start kicking all the outside investors out of Medallion, but clearly, there's still lots of institutional demand to invest with Renaissance. So what do they do?

Ben Gilbert 1:43:46 Well, time to start another fund. So they start the Renaissance Institutional Equities Fund, and there's a couple things to add a little bit of context to really why they decide to do this. Well, the first one is, sometimes there's just more profitable strategies than they had the capital to take advantage of in Medallion, but they weren't sure it would be on a durable basis. If they were sure that they could manage ten, fifteen, twenty, twenty-five billion in Medallion all the time, then they would grow to that. But if just sometimes there's these strategies that appear, well, we don't wanna 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. And so there's a little bit of downside to that because these new strategies, the predictive abilities are less because they have to predict further into the future to understand what the exit prices will be on these longer term holds. 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 Rosenthal 1:44:52 Yeah, there's a fun story around this that Peter Brown tells of Jim came into his office one day and said, "Peter, I got a thought exercise for you. If you married a Rockefeller, would you advise the family that they should invest a large portion of their wealth in the S&P 500?" And Peter says, "No, of course not. That's not a great risk-adjusted return."

Ben Gilbert 1:45:15 And these guys are very used to Sharpe ratios that are far better than the S&P. [chuckles]

David Rosenthal 1:45:20 Right. And so Jim says, "Yes, exactly. Now get to work on designing the product that they should invest in." [chuckles]

Ben Gilbert 1:45:27 Right. And so that's basically what they come up with, is, "Can we create something that's like an S&P 500 with a higher Sharpe ratio? Can we beat the market by a few percentage points, or frankly, even match the market each year with lower volatility than if they were buying an index fund?" And you can see who this would be very attractive to: pensions, large institutions, firms that want to compound at market or slightly above market rate, but don't want to risk these massive drawdowns or frankly, just big volatility in general, should they need to pull the capital earlier. And the nice thing about being invested in a hedge fund versus a venture fund is you can do redemptions. Like, if you look at the 13Fs, the SEC documents that the Renaissance Institutional Equities Fund files over time, it changes every quarter because there's new people putting money in, there's people doing redemptions. So it's a pretty good product, 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 Rosenthal 1:46:24 And the marketing is built in. [chuckles] It's not like there's any lack of demand of outside capital that wants to invest with RenTech.

Ben Gilbert 1:46:32 Right. It's really funny, there's all these stories about how the marketing documents literally say, "This is not the Medallion Fund. We don't promise returns like the Medallion Fund. In fact, we're not charging for it like the Medallion Fund." You know, David, you said that the fees and carry-on medallion went up to, what? Five and forty-four. Well, on the institutional fund, the fees are one in ten. You're only taking one percent annual fee and ten percent of the performance.

David Rosenthal 1:46:54 Clearly, this is a very different product.

Ben Gilbert 1:46:56 But people did not perceive that. People were very excited. It's a Renaissance product. It's the same analysts. They're using all their fancy computers. I'm sure we're gonna get this crazy outperformance. And at the end of the day, it is an extremely different vehicle.

David Rosenthal 1:47:08 Yeah. That has not performed anywhere near how Medallion has performed.

Ben Gilbert 1:47:13 Correct. Has it served its purpose? Yeah. But is it Medallion? No, it's not special in the way that Medallion is special.

David Rosenthal 1:47:21 Yes.

Ben Gilbert 1:47:22 A couple other funny things on the institutional fund. So I spent a bunch of time scrolling through 13Fs over the last decade from the Medallion filings, and they're all from... I think they have two institutional funds.

David Rosenthal 1:47:34 Yeah. There's Institutional Equities and Diversified Alpha.

Ben Gilbert 1:47:38 So the funniest thing is they file these 13Fs, and 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, like, or what are they buying and selling this quarter. And usually, you see fifteen, twenty-five, maybe fifty different names on there.

David Rosenthal 1:47:56 [laughing]

Ben Gilbert 1:47:56 Well, the 13F [chuckles] for Renaissance has forty-three hundred stocks in these tiny little chunks, and there's a little bit of persistence quarter to quarter. For example, weirdly, Novo Nordisk has been one of their biggest holdings. Biggest, I say, at, like, one to two percent. That's their biggest position for several quarters in a row.

David Rosenthal 1:48:15 Hey, they've been listening to Acquired.

Ben Gilbert 1:48:16 That's right.

David Rosenthal 1:48:17 That's one of the signals in the model.

Ben Gilbert 1:48:18 [laughing] You kind of get the sense from looking at these filings that these things were flying all over the place, and this was just the moment in time where they decided to take a snapshot and put it on a piece of paper. And even though this is the end-of-quarter filing of what their ownership was, if you had taken it a day or a week earlier, it could look completely different.

David Rosenthal 1:48:40 Yes. The way that some folks we talked to described the difference between the institutional funds and Medallion to us is that Medallion's average hold time for their trades and positions is, call it, like, a day, maybe a day and a half, whereas the average hold time for the institutional funds' positions is, like, a couple months. So across forty-three hundred 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.

Ben Gilbert 1:49:17 Yep.

David Rosenthal 1:49:18 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 gonna slip away.

Ben Gilbert 1:49:30 Yep. And frankly, it just looks a lot like the S&P 500. Like, when you look at as of November twenty-three, so eleven of the twelve months of the year had happened, they were up eight point six percent. Okay, that sounds like an index-type return. You look at the first four months of twenty-twenty, right after the crazy dip from the pandemic, they were down ten point four percent. Less than the broader market, but they still were sort of a mirror of the broader market. So I think the RIEF, their institutional fund, yes, it works as expected. No, it's not Medallion, and if it were standing on its own, there's zero chance that we would be covering the organization behind it on Acquired.

David Rosenthal 1:50:08 Zero percent chance. Speaking of the fund, that is the reason why [chuckles]

Ben Gilbert 1:50:11 [chuckles]

David Rosenthal 1:50:11 ... we are covering this company on this show. We set up during the tech bubble crash that volatility is when Medallion really shines. Well, there's no more volatile periods than 2007 and 2008.

Ben Gilbert 1:50:26 Yep.

David Rosenthal 1:50:27 ... 2007, Medallion does 136% gross. 2008, Medallion does 152% gross. Like, get out of here! [laughing]

Ben Gilbert 1:50:39 Crazy.

David Rosenthal 1:50:41 This is 2008, while the rest of the financial world is melting down.

Ben Gilbert 1:50:46 And so this really does illustrate where do they make their money from? Who is on the other side of these trades? It's people acting emotionally. They have effectively these really robust models that are highly unemotional, that are making these super intricate multi-security bets, and they are putting on exactly the right set of trades to achieve the risk and exposure that the system wants them to have. And who is on the other side of those trades? It's panic sellers, it's dentists, it's hedge funds who don't trust their computer systems and are like, "Ah, crap, we gotta just take risk off, even though it's a negative expected value move for us." They're basically trading against human nature, and importantly, in this business versus every other business that we cover here on Acquired, or most other businesses, this is truly zero-sum. It's not like they're here in an industry that's a growth industry, and lots of competitors can take different approaches, but the whole pie is growing so much that I don't care if... No, you're fighting over a fixed pie here. I'm trading against someone else. I win, they lose.

David Rosenthal 1:51:45 Yes. Well, there's one slight nuance to that, but I don't know how much it holds water, and the apologist nuance would be, well, Warren Buffett could be on the other side of the trade, and Medallion could make money on that trade with Warren over its time horizon of a day and a half, and Warren could make money over his time horizon of, you know, 50 years.

Ben Gilbert 1:52:09 Super fair.

David Rosenthal 1:52:10 So I think the argument against that, though, is that Medallion sold after a day and a half to somebody else who bought at that lower price. And so somewhere along the chain, that loss is getting offloaded to somebody. The direct counterparty of Medallion and the quant industry writ large might not take the loss, but somebody is gonna take the loss along the way. It is, as you say, a zero-sum game.

Ben Gilbert 1:52:39 Yeah, but I think the important thing is, can you and your adversary both benefit? And I think in this case, you and your counterparty, the person you're trading against, yes, you have two different objective outcomes. Like, can I get a penny over on Warren Buffett by managing to take him on this one trade? Sure, but his strategy is such that that is irrelevant.

David Rosenthal 1:52:57 So after the historic performance during the financial crisis, as I alluded to earlier, Jim retires at the end of 2009, and Peter and Bob become co-CEOs, co-heads of the firm in 2010. They take the portfolio size up to $10 billion when they take over. It had been at five for the last few years of Jim's tenure. They take it up to 10, and really with no impact, which I assume means that RenTech was getting better and the models were getting better, 'cause otherwise they would've gone to 10 before.

Ben Gilbert 1:53:33 Right. They gained confidence that they had enough profitable trades they could make, that they could raise the capacity without dampening returns.

David Rosenthal 1:53:43 Yes.

Ben Gilbert 1:53:43 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 Rosenthal 1:53:55 Yeah, and importantly, during periods of peak volatility, like, say, 2020, [chuckles] Medallion continues to shoot the lights out. So from at least the data that we were able to find on Medallion's performance over the past few years, 2020, they were up 149% gross [chuckles] and 76% net. So the magic is still there. And one way to look at it, which may not be the be-all and end-all, but I think is a good way to compare Jim's era at Medallion versus Peter and Bob's era, during Jim's tenure, Medallion's total aggregate IRR from 1988, when the fund was formed, to 2009, when he retired, was 63.5% gross annual returns and 40.1% net annual returns, which of course, did include many periods of lower carry, 20% versus the 44%. 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% [chuckles] gross and 40.3% net, so better on both fronts, even with much higher average fees. So yeah, I think Medallion is doing fine.

Ben Gilbert 1:55:23 It's amazing. And we weren't able to tell, there's some sources that report that they've grown from $10 billion in the last few years to being comfortable at a $15 billion fund size, and if so, that just means that they continue to find more profitable strategies within Medallion to keep those same unbelievable returns at larger sizes.

David Rosenthal 1:55:43 Yeah, and at the end of the day, this is all just insane. So as far as we can tell, Ben, you alluded to this a bit at the beginning of the episode, and as far as anybody else can tell, Medallion has, by far, the best investing track record of any single investment vehicle in history.

Ben Gilbert 1:56:04 So give me those net numbers.

David Rosenthal 1:56:05 So during the entire lifetime so far of Medallion, from 1988 to 2022, that's 34 years, the total net annual return number is 40%, four zero-

Ben Gilbert 1:56:22 Oof!

David Rosenthal 1:56:22 ... over 34 years after fees. It's 68%- [laughing] ... before fees, which equates to-... total lifetime carry dollars for the whole firm of $60 billion just in carry, by our calculations. [chuckles]

Ben Gilbert 1:56:37 Astonishing.

David Rosenthal 1:56:38 That is a lot of money.

Ben Gilbert 1:56:41 Also, David Rosenthal, good spreadsheet work on this. You have not done a spreadsheet for an episode in a while, so I admire your, uh, your work on this one.

David Rosenthal 1:56:48 Yeah. I still know how to use Excel. [laughing]

Ben Gilbert 1:56:53 [laughing]

David Rosenthal 1:56:53 Barely. It's gonna be a dying art now with Copilot and GPTs.

Ben Gilbert 1:56:58 That's right. Okay, so 60 billion in total carry.

David Rosenthal 1:57:02 So 60 billion in total carry is a lot of money. And, well, speaking of a lot of money, we do need to mention, before we finish the story here, that that RenTech money has bought a lot of influence in society. So Bob Mercer, that name may have sounded familiar to many of you along the way. Bob was the primary funder of Breitbart and Cambridge Analytica, and one of the major financial backers of both the 2016 Trump campaign and the Brexit campaign in Great Britain. Now, lest you think that RenTech dollars are solely being funneled into one side of the political spectrum, Jim Simons is a major Democratic donor, as are many other folks at RenTech.

Ben Gilbert 1:57:51 Yeah, Henry Laufer and other folks are also huge donors, approximately to the same tune as what Bob Mercer is on the right.

David Rosenthal 1:57:58 Yeah, tens of millions of dollars, many tens of millions of dollars on all sides and through many campaign cycles here from RenTech employees and alumni. This did become a flashpoint for the firm in the wake of the 2016 election. Mercer obviously became a, uh, controversial figure, both externally and internally within the firm.

Ben Gilbert 1:58:21 Especially once people realized he was the through line through Breitbart, Cambridge Analytica, the Trump election, and Brexit.

David Rosenthal 1:58:28 Yes. Ultimately, Jim asked Bob to step down as co-CEO in 2017, which he did, but he did remain a scientist at the firm and a contributor to the models, even though he wasn't leading the organization with Peter from a leadership standpoint any longer.

Ben Gilbert 1:58:45 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 Rosenthal 1:58:56 [chuckles] Yeah, understatement of the century.

Ben Gilbert 1:58:58 And all being extremely influential and active in those political systems. Yes, Bob Mercer is no longer the CEO of Renaissance Technologies or the co-CEO. He still works there. He's still associated. They all still speak highly of each other. It's unexpected.

David Rosenthal 1:59:15 Yeah. I think unexpected is the best way to put it.

Ben Gilbert 1:59:19 Like everything with Renaissance, it works a little bit different than the rest of the world.

David Rosenthal 1:59:23 Yes. Okay, speaking of, let's transition to analysis, and I have a fun little monologue I want to go on, if you will-

Ben Gilbert 1:59:33 Ooh!

David Rosenthal 1:59:33 ... bear with me, Ben. I think this qualifies as the RenTech playbook, but I really kind of think of it as the RenTech tapestry. And I was inspired by Costco here because we were talking to folks in the research, and everybody said, you know, RenTech, it just has these puzzle pieces that fit together. 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, and they give them the best data and infrastructure in the world to work on, and they say, "Go to town and make profitable trades." Those are very expensive commodities, those two things, the smartest people in the world and the best data and infrastructure, but they are commodities. Like, Citadel can say the exact same things, just the same as, like, Walmart and Amazon can say, "We, too, have large-scale supplier relationships that we leverage to provide low prices to customers," just like Costco. But it's underneath that where I think the magic lies. There are three very interrelated things that make RenTech unique.

Ben Gilbert 2:00:43 Hmm.

David Rosenthal 2:00:44 So 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. [chuckles]

Ben Gilbert 2:01:02 Yeah. I mean, typically in kind of a friendly way, but yeah.

David Rosenthal 2:01:06 Let's take, like, in a venture firm, you've got your lead partner on a deal or a deal team. They're working that deal, and maybe some of the other partners help a little bit, but mostly they're off prosecuting their own deals. [chuckles]

Ben Gilbert 2:01:19 Yep.

David Rosenthal 2:01:20 And I think that's the most collegial way that this happens in finance.

Ben Gilbert 2:01:23 Yeah.

David Rosenthal 2:01:24 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.

Ben Gilbert 2:01:32 Yep.

David Rosenthal 2:01:33 At RenTech, though, because of the one-model architecture, everyone works together on the same investment strategy [chuckles] and the same investment infrastructure. That means everyone sees everybody else's work. Everybody who works at RenTech, on the research team, on the infrastructure team, they have access to the whole model. That's not true [chuckles] anywhere else.

Ben Gilbert 2:01:58 Yeah, that's a good point. The whole codebase is completely visible.

David Rosenthal 2:02:01 And that also means because it's just one model, just one strategy, when somebody else improves that model's performance, that directly impacts you as much as it impacts them. This is really different than any other hedge fund out there.

Ben Gilbert 2:02:19 So why is that different than if I roll some of my compensation into a multi-strategy hedge fund that I work at? Don't I love other teams creating high performance also?

David Rosenthal 2:02:27 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 Gilbert 2:02:39 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 piñata and then going elsewhere, or building reputation and then going elsewhere. Most of the people at RenTech are not gonna have another job.

David Rosenthal 2:02:55 What did you find on LinkedIn, at least the median tenure of employees is, like, sixteen years?

Ben Gilbert 2:03:01 Yeah, I just got LinkedIn Premium, and you can see median tenure, and it's crazy. There's only, like, three, four hundred employees at Renaissance, and the median tenure, at least as reported by LinkedIn, is, like, fourteen years.

David Rosenthal 2:03:12 Yes. Okay, this brings me to point number two, which you said, this is an absurdly small team. There are less than four hundred employees that work at RenTech, only half of which work in research and engineering, and the other half are either back office or institutional sales for the open funds. So let's call it, I don't know, a hundred and fifty, two hundred people max, who are, like, hands on the wheel here for Medallion. [chuckles]

Ben Gilbert 2:03:39 Yep.

David Rosenthal 2:03:39 Every other peer firm of RenTech, you know, Citadel, D. E. Shaw, Two Sigma, et cetera, all of them, you lump Jane Street, you know, jump the high-frequency guys in here, minimum two to five thousand people work at those places.

Ben Gilbert 2:03:55 Wow, I didn't realize it was that big!

David Rosenthal 2:03:57 It is an order of magnitude more people who are working at the other firms versus who are working at RenTech.

Ben Gilbert 2:04:04 And lest you think that it's, like, a capital-based thing, no, the institutional funds have gotten big. They peaked at over a hundred billion, but they're currently between sixty and seventy billion that they manage on top of the ten or fifteen that's in the Medallion Fund.

David Rosenthal 2:04:18 Yeah, so AUM is, like, the same-

Ben Gilbert 2:04:20 Yep

David Rosenthal 2:04:20 ... as these big funds. This has all sorts of benefits. Number one, there's, like, the Hermès Atelier workshop benefit. Everyone knows each other by name. You know your colleagues' kids, you know your colleagues' families.

Ben Gilbert 2:04:33 Yep, they put right on their website, "There are ninety PhDs in mathematics, physics, computer science, and related fields." The About page has these ten kind of random bullet points, and that's one of them.

David Rosenthal 2:04:43 Yes. Then there's the related aspect to all this. The firm is in the middle of nowhere on Long Island. [chuckles] 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.

Ben Gilbert 2:05:01 [laughing]

David Rosenthal 2:05:01 You're, like, hanging out at the swimming pool.

Ben Gilbert 2:05:03 Totally, and since Renaissance doesn't recruit from finance jobs, it's kind of unlikely that you know someone else in finance. You came out of a science-related field. You now work in East Setauket, Long Island, which has, it's like ten thousand people or something, or less, that live there. So you're in this little town. You're not actually going into the city that often, and if you are, it's, again, not to grab drinks with other finance people. So 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 Rosenthal 2:05:38 You're just not getting exposed.

Ben Gilbert 2:05:39 Exactly.

David Rosenthal 2:05:40 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 Gilbert 2:05:53 Have you seen the pictures online?

David Rosenthal 2:05:55 Yeah.

Ben Gilbert 2:05:56 If you look up Renaissance Technologies at Google, and you go and look at the photos on campus, it's a little courtyard, and winding walking path, and woods all around it, tennis courts.

David Rosenthal 2:06:07 Yep. So then there's the last piece of the small team element, which is just the magnitude of the financial impact, which I don't think is true, but let's say that there were another quant fund that made the same number of dollars of performance returns [chuckles] that RenTech does. At RenTech, you're splitting that a couple hundred ways. At Citadel, you're splitting that five thousand ways. [chuckles] It just doesn't make sense to go anywhere else.

Ben Gilbert 2:06:34 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 Rosenthal 2:06:40 [chuckles] Yes. Okay, so this brings me to what I've been kind of 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 a LP/GP fund with five percent management fee and forty-four percent carry.

Ben Gilbert 2:07:07 So it's not like a prop shop or like proprietary, it's just one pot of money. It's literally a GP/LP, even though the GPs and the LPs are the same people.

David Rosenthal 2:07:16 So here's my thinking on this. Now, I don't know how it is actually structured, but there was something about this whole crazy forty-four percent carry that just wasn't sitting with me right throughout the research because I kept asking myself, "Why?"

Ben Gilbert 2:07:31 Right. They've already kicked out most of the LPs, if not all, so why are they raising the carry?

David Rosenthal 2:07:36 Right. It's all themselves. It's all insiders. Why do they charge themselves forty-four percent carry and five percent management fees? I think Jim talks about this, that, "Oh, I pay the fees just like everybody else."

Ben Gilbert 2:07:47 Yes. It's always a funny argument. It's like, who are you paying the fees to?

David Rosenthal 2:07:50 Right. So I was like, "What is happening here?" [chuckles] So, okay, here's my hypothesis. This is not about having crazy performance fees. This is not about having the highest carry in the industry. This is a value transfer mechanism within the firm from the tenure base to the current people who are working on Medallion in any given year. So 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 Gilbert 2:08:47 No way!

David Rosenthal 2:08:48 Yeah, yeah. So, like, if you work there, your 401 (k) is the Medallion Fund.

Ben Gilbert 2:08:52 That's crazy. So it really doesn't take more than a few years before you're set for life.

David Rosenthal 2:08:56 Totally. I mean, depending on your definition of set for life, I think it happens very, very quickly.

Ben Gilbert 2:09:01 Yep.

David Rosenthal 2:09:01 Okay, so given that, though, how do you avoid the incentive for a group of talented younger folks to split off and go start their own Medallion Fund?

Ben Gilbert 2:09:12 Right, especially when they all have access to the whole codebase. The whole thing is meant to function like a university math department, where everyone's constantly knowledge-sharing because we're gonna create better peer-reviewed research when we all share all the knowledge all the time. You would think that's a super risky thing to give everyone all the keys.

David Rosenthal 2:09:31 Right. So I think it's the forty-four percent carry structure that does it, 'cause basically what you're saying is every year, five percent management fee, so five percent off the top, and then forty-four percent of performance. So let's say Medallion is on the order of, call it doubling every year. Let's round that up and just add them and say forty-nine percent of the economic returns in any given year go to the current team, and fifty-one percent of the economic returns go to the tenure base. I was like, "What is the equivalent here?" I think it's kinda like a academic tenure kinda thing. The longer tenure you are-

Ben Gilbert 2:10:11 Ah

David Rosenthal 2:10:11 ... at the firm, the more your balance shifts to the LP side of things.

Ben Gilbert 2:10:16 Interesting.

David Rosenthal 2:10:17 And the younger you are at the firm, the more your balance is on the GP side of things. But at the end of the day, it's fifty-one forty-nine. So there's this very natural value transfer mechanism to keep the people that are working in any given year super incentivized, and as you stay there longer, you are paying your younger colleagues to work for you. [chuckles]

Ben Gilbert 2:10:41 Right. It's funny, I think it's a good insight that it's structured like a university department tenure.

David Rosenthal 2:10:47 Well, I just kept asking myself, "Why? Why? Why do they have this if there's no outside LPs?" And this was the best thing I could come up with, and I actually think it's kinda genius.

Ben Gilbert 2:10:59 Yeah, it's more elegant than it's all one person's money, and they're deciding to bonus out the current team every year and just give them enough money to make sure you retain them.

David Rosenthal 2:11:07 Right, which is how I think most prop shops work. Like Jane Street is mostly a prop shop. I think it is mostly the principal's money, but that's a static situation. It's not like... You know, if that were true, then Jim would just own this thing forever, and I don't think that's true here at RenTech.

Ben Gilbert 2:11:24 Yeah, so essentially, David, the real magic is they've got one fund, it's evergreen, and when you start at the firm, you're only getting sort of paid the carry amount, but over time, you become a meaningful investor in the firm, and you sort of shift to that fifty-one percent. You're kind of the LP, and then over time, you eventually graduate out entirely, and you're only an LP. And so you're right. It's a value transfer mechanism from the old guard to the new guard in a way that is clear, well understood, probably tax-advantaged, versus just doing, "I'm the owner, and I'm giving everyone arbitrary bonuses."

David Rosenthal 2:11:59 Yep. And at the end of the day, I think these three pieces, to me, are the core of this sort of tapestry of RenTech: one model that everybody collaborates on together, a super small team where we all know each other, and the financial impact that any of us make to that one model is great to all of us, and three, this 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 Gilbert 2:12:32 Yep, I think that's right. Okay, there's a few other parts of the story that we skipped along the way because there was no real good place to put them in, but these are objectively fascinating historical events that are totally worth knowing about, and the first one is called basket options. So the year is 2002. RenTech has thirteen years of knowing that they basically have a machine that prints money. So what should you do when you have a machine that prints money? Leverage. Now, there are all sorts of restrictions around firms like this and how much leverage they can take on. You can't just go and say, "I'm gonna borrow, you know, a hundred dollars for every dollar of equity capital that I have in here." So you need to sort of get clever to borrow a whole bunch of money from banks or from any lender to basically juice your returns if, again, you have a money-printing machine that's reliable. Most people don't. Most people probably shouldn't take leverage because they're just as likely to blow the whole thing up as they are to be successful. So basket options. I am gonna read directly from The Man Who Solved the Market because Greg Zuckerman just put it perfectly: "Basket options are financial instruments whose values are pegged to the performance of a specific basket of stocks. While most options are based on an individual stock or a financial instrument, basket options are linked to a group of shares. If these underlying stocks rise, the value of the option goes up. 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." So this is very clever. Medallion's saying, "Well, the way we're gonna lever up is there's a basket. We have an option to purchase that basket. Most of the capital in that basket is actually the bank's capital, but the bank has hired us to trade the options in the basket, and then after a year, when long-term capital gains tax kicks in, we have the option to buy that basket." So anyway, all day, Medallion's computer sent 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 seven dollars 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 spied an especially juicy opportunity, it could boost leverage, holding close to twenty dollars of asset for every dollar of cash. In 2002, Medallion managed over five billion, but it controlled over sixty billion dollars of investment positions.... David, this exposes something we haven't shared yet on the episode, which is it's not just that they could find five billion dollars worth of profitable trades, it's that they wanted to lever the crap out of five billion dollars and find sixty billion dollars of profitable trades to make. And basket options gave them a legal way to have an incredible amount of leverage in a way that they felt safe about.

David Rosenthal 2:15:33 Yeah, the unlevered returns, if you were running this strategy, would be much lower.

Ben Gilbert 2:15:40 Yeah. So a big piece of this playbook that we didn't talk about is leverage, but every quant fund does leverage, and so Renaissance was just more clever than everyone else.

David Rosenthal 2:15:48 Yeah. It's an important point, though. Nine out of every ten companies that we cover on Acquired, leverage is zero part of the story.

Ben Gilbert 2:15:56 Right.

David Rosenthal 2:15:57 And for us, coming from the world we come from, in tech and venture capital, leverage is like a dirty word, like, I'm scared of it.

Ben Gilbert 2:16:03 Right. I mean, you could imagine, let's say it wasn't they were right fifty point two five percent of the time, but they were right fifty point zero zero zero one percent of the time, they would need to do a ton of trades in order to generate enough profits. So that's why you need, you know, sixty billion dollars of cash to actually execute the strategy to produce the returns that they were looking for-

David Rosenthal 2:16:23 Yep

Ben Gilbert 2:16:24 - on five billion dollars of equity. Anyway, there's a second chapter to this, which is it's all well and good that this is how they get a bunch of leverage. That's one piece of it. The other piece is they thought this was a remarkably tax-efficient vehicle. The way that they were filing their taxes said, "Oh, sure, there's stuff in that basket, but the thing that we actually own is an option to buy that basket or sell that basket, and we only exercise that once every thirteen months or so." I don't know the exact number, but something like that, over a year. "And so therefore, we're buying something, we're holding it for a year, we're selling it. Oh, of course, there's millions and millions of trades going on inside the basket, but we don't own that basket. The banks do. We're just advising them." You can kind of see the logic here. Over time, eventually, in twenty twenty-one, the IRS said, "No, you made all those trades. That was not a completely separate entity, and so you guys owed six point eight billion dollars in taxes that you didn't pay. You're gonna need to pay that with interest, with penalties. And by the way, Jim Simons, we're gonna want you and the other few partners to really bear the load of that." And they did. So for Simons alone, he paid six hundred and seventy million dollars to the IRS in back taxes for this basket option strategy that turned out not to be a long-term capital gain.

David Rosenthal 2:17:38 Yep.

Ben Gilbert 2:17:39 All right, so numbers on the business today, and then we will dive into power and playbook. So today, we've talked about Medallion, ten or fifteen billion, depending on who you ask. Historically, it was more like five or ten billion. The institutional fund is about sixty to seventy billion, and at one point was a hundred billion. The total carry generated, David, you said is sixty billion dollars. Forbes estimates that Jim Simons alone is worth about thirty billion dollars today, which kind of pencils with a bunch of other stats over the years that he owned about half of Renaissance. The returns, obviously, the Medallion Fund generated approximately sixty-six percent annualized from nineteen eighty-eight to twenty twenty. After those fees, was about thirty-nine percent. Wild. So an interesting thing to understand, I ran a hypothetical scenario of how much money do you think Renaissance the business makes a year in revenue? And so the institutional fund, let's call it ten percent on sixty billion of assets, so that's six hundred million from fees and six hundred million from performance, so one point two billion a year in revenue to the firm from the institutional side of the business. 'Cause I always ask myself the question: Does that actually matter? They did all this work to stand up the institutional side. Who cares? Well, let's say Medallion does their average sixty-six percent gross on fifteen billion. That is seven hundred and fifty million in fees and four point three billion on performance.

David Rosenthal 2:19:07 [laughing]

Ben Gilbert 2:19:07 So a total of five billion from Medallion and one point two billion from the institutional side of the business. Now, of course, the employees are the investors in Medallion, so you could just argue it's actually silly to cut them up, but I don't know, it's a seven, eight, nine billion dollar revenue business.

David Rosenthal 2:19:24 Right, 'cause that's not including the LP return on Medallion.

Ben Gilbert 2:19:27 A hundred percent, it's not.

David Rosenthal 2:19:29 Which, again, as we spent a long time talking about, it's all the same thing.

Ben Gilbert 2:19:32 Yes. But it's kind of interesting just to compare it against other companies, to have this in the back of your head. This is a seven, eight billion dollar a year revenue business.

David Rosenthal 2:19:41 Now, I think there are a lot of expenses on the infrastructure side.

Ben Gilbert 2:19:45 Totally. That was another thing I wanted to talk about, the fact that they do... Let's say Medallion alone, so they'd have seven hundred and fifty million dollars in fees. I don't think they come close to seven hundred and fifty million dollars a year in expenses, but they are running who knows what infrastructure, some kind of supercomputing cluster. What does it cost to run one Amazon data center? I mean, it's, I think, much smaller scale.

David Rosenthal 2:20:06 I don't know. I mean, you're talking about a lot of data here.

Ben Gilbert 2:20:10 Yeah, it says right on their website, they have fifty thousand computer cores with a hundred and fifty gigabits per second of global connectivity and a research database that grows by more than forty terabytes a day. That's a lot of data.

David Rosenthal 2:20:24 Right. Is that seven hundred and fifty million a year? I don't know, but it's not zero. [chuckles]

Ben Gilbert 2:20:29 I don't think so. They're certainly not losing money [chuckles] on the fees, but there are actual hard costs to this business.

David Rosenthal 2:20:36 Right. I wonder, too, if the fee element of Medallion basically pays the base salaries for the current team.

Ben Gilbert 2:20:46 That feels like it's right. If you're, uh, someone who has done a data center build-out before or has any way to sort of back into what the costs of Medallion's operating expenses are on the compute and data and network side, we would love to hear from you, hello@acquired.fm. Okay, power?

David Rosenthal 2:21:05 Power. [sighs] This is a fun one.

Ben Gilbert 2:21:09 Yeah, so listeners who are new to the show, this is Hamilton Helmer's framework from the book Seven Powers. What is it that enables a business to achieve persistent differential returns to be more profitable than their closest competitor on a sustainable basis? And the seven are counter-positioning, scale economies, switching costs, network economies, pro-... process power, branding, and cornered resource. And David, my question to you to open this section is specifically about RenTec's lifelong non-competes. That feels like a big reason that they maintain their competitive advantage, and I'm curious if you agree with that. What would you put that under?

David Rosenthal 2:21:48 Well, I think it's lifelong NDAs and non-competes as long as the state of New York legally allows for- [chuckles]

Ben Gilbert 2:21:55 Hmm.

David Rosenthal 2:21:56 -but that it's not lifetime. I've heard various figures, six years, five years, something like that.

Ben Gilbert 2:22:02 Yep.

David Rosenthal 2:22:02 I mean, at the end of the day, non-competes are more like, what is one side willing to go to court over? [chuckles]

Ben Gilbert 2:22:08 Right.

David Rosenthal 2:22:08 But the reality is, people don't leave. People don't leave, period, and people especially don't leave and start their own firms.

Ben Gilbert 2:22:15 Yep.

David Rosenthal 2:22:16 I was thinking about this in the middle of the night, and I think there's three layers to the effective non-compete that happens with RenTec. There's the legal layer, the base layer that you're talking about. It's like the agreements you sign. Then there's the economic layer of what we spent a long time talking about in Tapestry of it would just be dumb to leave. You are better off staying there as part of that team with a smaller number of people than going to Two Sigma with a lot more people.

Ben Gilbert 2:22:48 Yep.

David Rosenthal 2:22:49 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 Gilbert 2:23:01 Right, it's your community.

David Rosenthal 2:23:02 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, and I think if that's what you want, then, like, what better place in the world?

Ben Gilbert 2:23:15 All right, so classify it. What power does that fall under?

David Rosenthal 2:23:19 Well, I mean, I think the people specifically, you would put into cornered resource, but I'm not actually sure that fully captures it here. I was thinking more process power, 'cause I think it is the combination of the people, and the model, and the incentive structures. [chuckles]

Ben Gilbert 2:23:37 Yep. I think that's right. I also had my biggest one being process power. You actually can develop intricate knowledge of how a system works and then build processes around that that are hard to replicate elsewhere. I think these systems have been layered over time also, where anyone who's come into the firm in the last five years doesn't know how it works start to finish. I didn't ask anyone to verify that, but it's over ten million lines of code, and the level of complexity of the system of when it's putting on trades, what trades it's putting on, why, the speed at which they need to happen, I actually don't think anyone holds the whole model in their head. And so I think there's process power just because it's thirty-plus years of complexity that's been built up.

David Rosenthal 2:24:25 Yep. I totally agree with that, particularly in the model itself. I mean, maybe you could argue the model is a cornered resource.

Ben Gilbert 2:24:33 I am going to argue that the data-

David Rosenthal 2:24:35 Oh, okay.

Ben Gilbert 2:24:35 -is a cornered resource. I don't know for sure about the model. Maybe? I mean, I guess that's the same thing as saying the knowledge of what the ten million lines of code does, that's the model. But I actually think the fact that they have clean data and they've been creating systems... Like, they have the best PhDs in the world thinking about data cleaning. 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 Rosenthal 2:25:07 I have a couple nuances on this. So one, I think it probably is true that they have better data than any other firm, thanks to Sandor Straus and the work that he started doing in the '80s before anybody else was really doing this.

Ben Gilbert 2:25:20 Yep.

David Rosenthal 2:25:21 So they have that, and other firms don't. That said, certainly, all the other quant firms are throwing untold resources [chuckles] at all this, too.

Ben Gilbert 2:25:32 Right, they wanna do this, and money is not the issue.

David Rosenthal 2:25:36 So in chatting with a few folks about this episode, I had more than one person say to me: "There's two ways that RenTec could work, and one version of how it works is they discovered something twenty-plus years ago that is a timeless secret, and they've been trading on that for twenty-plus years."

Ben Gilbert 2:25:59 Right. There's one particular relationship between types of equities that they've just been exploiting, and no one can figure out except them.

David Rosenthal 2:26:05 Right, and that may entirely be possible.

Ben Gilbert 2:26:07 Isn't that crazy?

David Rosenthal 2:26:09 Right. Now, RenTec will say, they will all say, that is a hundred percent not the way that it works. It's not that at all. If that were the way that it works, they would, of course, still say that, 'cause they don't want anybody to know.

Ben Gilbert 2:26:18 Right. Don't look at the relationship between soybean futures and GM. Just don't do it.

David Rosenthal 2:26:25 Right. So let's accept that there is a possibility that that might be true. More likely, though, is that what RenTec 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 kinda 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 Gilbert 2:26:55 So that would be an argument then that the people actually could... If five people left, they probably could go recreate it, and all they would need is the data.

David Rosenthal 2:27:03 It's also an argument that there is no actual cornered resource [chuckles] here in terms of either the model itself and maybe not the data either.

Ben Gilbert 2:27:10 I bet the data is, though. Let's say you've been working there for ten 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 Rosenthal 2:27:28 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 Gilbert 2:27:38 Definitely.

David Rosenthal 2:27:39 The broader point I want to make here is just that every other major quant firm out there is also spending hundreds of millions, if not billions, on this stuff, too.

Ben Gilbert 2:27:46 And people are looking for alt data everywhere. The Bridgewaters of the world are paying gobs of money for things that you would never dream could possibly have an effect on the stock market, and yet they're paying millions or tens of millions or hundreds of millions of dollars for it.

David Rosenthal 2:27:59 Yep. So I think we can rule out scale economies for sure. If anything, there are anti-scale economies here. [chuckles]

Ben Gilbert 2:28:06 Oh, yes! There's totally, there's diseconomies of scale. Your strategies stop working when you get too much AUM.

David Rosenthal 2:28:13 Yep, you get slippage. I don't think there's any network economies here. I mean, they literally don't talk to anybody. [laughing]

Ben Gilbert 2:28:21 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 Rosenthal 2:28:40 Do you think it's actually better than their competitors, though?

Ben Gilbert 2:28:42 I don't know. That's probably not the secret sauce.

David Rosenthal 2:28:44 Yeah, I don't think so.

Ben Gilbert 2:28:45 It's the table stakes.

David Rosenthal 2:28:47 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 Gilbert 2:28:55 The fee stream on the institutional fund may entirely belong to branding.

David Rosenthal 2:28:59 Yes.

Ben Gilbert 2:28:59 But I think there's a lot of public equity firms and a lot of hedge funds that have a lot of branding power, that have, on average, market returns with decent Sharpe ratios and are able to raise because they've built a brand.

David Rosenthal 2:29:12 Yep.

Ben Gilbert 2:29:12 Venture firms, the same way.

David Rosenthal 2:29:14 Totally. So for me, this kind of leaves counterpositioning. I actually think there's some counterpositioning here, and I think we're gonna have two episodes in a row of counterpositioning at scale.

Ben Gilbert 2:29:24 Tell me about your counterpositioning. Who is being counterpositioned in, in what way?

David Rosenthal 2:29:28 Their direct competitors in the market, the other quant firms. And when I say direct competitors, I obviously don't mean for LP dollars; I mean for, like, the same type of trading activity.

Ben Gilbert 2:29:38 Like their counterparties in trades.

David Rosenthal 2:29:40 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 they're... the dentists that they're taking advantage of.

Ben Gilbert 2:29:50 Well, but quant funds are often counterparties to each other.

David Rosenthal 2:29:53 That's true, but I think, yes, adversaries in finding the similar types of trades, and I think the counterpositioning for RenTech, or for Medallion specifically, is, one, I do think the single model approach versus the multi-model, multi-strategy approach that most others have, does have benefits, like I was talking about in the tapestries. But I think also, and maybe bigger, is every incentive at RenTech is fully aligned to optimize fund size for performance in a way that is not true just about everywhere else.

Ben Gilbert 2:30:29 Hmm.

David Rosenthal 2:30:31 I think they have the most incentive of anybody to truly maximize performance we're able to achieve.

Ben Gilbert 2:30:38 Right. Even though the dollars would continue to rise because they get fee dollars from more money in the door, they are incentivized in a unique way that makes it so they're not willing to trade the dampener on performance to get those dollars.

David Rosenthal 2:30:54 Yes, particularly because it's all [chuckles] the same people on the GP and LP side. [chuckles]

Ben Gilbert 2:31:00 Oh, we keep going round and round that axle.

David Rosenthal 2:31:02 [chuckles]

Ben Gilbert 2:31:03 I loosely buy the counterpositioning thing. I just think the answer is disgustingly simple and kind of annoying here, which is they're just better than everyone else at this particular type of math and machine learning, and they've been doing it for longer, so they're just gonna keep beating you.

David Rosenthal 2:31:17 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 Gilbert 2:31:27 It could be culture.

David Rosenthal 2:31:28 Yeah, it could be culture.

Ben Gilbert 2:31:29 I mean, honest to God, it could just be that the culture is set up in a way that continues to attract the right people and incentivize them in a sort of fake altruistic way. Like, "This is just a fun place to do my work, and yeah, the outcome is getting really rich, but I wouldn't go work at Citadel."

David Rosenthal 2:31:45 Yep, I think that could be. So maybe that feeds into process power.

Ben Gilbert 2:31:48 Yep.

David Rosenthal 2:31:49 Okay. For me, it is some combination of process power and counterpositioning, and I don't think it's any of the other powers.

Ben Gilbert 2:31:55 For me, it is process power and cornered resource.

David Rosenthal 2:31:58 Yep. Okay, I buy that.

Ben Gilbert 2:32:00 And a thing that's not captured in Seven Powers is tactical, like execution. The whole point of Seven Powers is strategy is different than tactics, and I think legitimately, RenTech may just have persistently been able to out execute their competitors. There's part of it that's just, like, they're smarter than you. [chuckles]

David Rosenthal 2:32:19 Yeah. [chuckles] Well, if you buy the, the whole thing gets reinvented continuously every two years, then yes.

Ben Gilbert 2:32:26 And there's remnant knowledge. Like, if you started building a machine learning system in nineteen, whatever it was, sixty-four, you're gonna be really good at machine learning today, and the people that you've been spending time with for the last fifteen years, learning all of your historical knowledge and working in your systems, are also gonna be better at machine learning than probably the other people who are out in the world learning it from people that just got inspired to start learning machine learning based on the new hotness. So learning is compound, is my answer.

David Rosenthal 2:33:00 Great.

Ben Gilbert 2:33:01 Okay, playbook. So in addition to the three-part David Rosenthal tapestry- [laughing] ... that you have woven-

David Rosenthal 2:33:07 I have nothing more to add.

Ben Gilbert 2:33:08 [chuckles] There are a handful of things that I think are worth hitting. So the first one is signal processing, is signal processing, is signal processing. They, by not caring about the underlying assets, they literally don't trade on fundamentals, except in the institutional fund, when they trade on fundamentals a little bit. They use price-to-earnings ratios and stuff like that in the institutional fund, which is kind of funny, because that's a completely different skill set. But if you just look at Medallion-... It's all just abstract numbers. 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. And once you reduce it to that, it is so brilliant that they can just recruit from any field. It's not relevant how someone has done sophisticated signal processing in the past, whether it's being an astronomer and trying to denoise a, quote-unquote, "photo of a star super far away," or whether they've tried to do, like, natural language processing. It's just signal.

David Rosenthal 2:34:17 There's this really funny line that Jim and Peter and others will say [chuckles] when asked about why they only hire academics and not from, you know, Wall Street and whatnot, and they're like: "Well, we found it's easier to teach smart people the investing business than teach investing people-

Ben Gilbert 2:34:33 How to be smart. [chuckles]

David Rosenthal 2:34:34 Right. That's ridiculous. They don't teach anybody anything about investing. They're just doing signal processing. [chuckles] I bet at least half the people at RenTech on the research side could not read a balance sheet.

Ben Gilbert 2:34:45 It's so funny. It's a whole bunch of people who are in the investment business, none of which are investors.

David Rosenthal 2:34:49 Yes.

Ben Gilbert 2:34:50 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, 'cause 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-

David Rosenthal 2:35:35 Oh, yes

Ben Gilbert 2:35:35 ... like, if you actually knew the composition of the atoms in the wood, and you actually knew the way the wind was blowing, and you actually knew that-- like, all the... Could you actually model the fire? And when I was a kid, and you always just assume no, but actually, the answer is yes. This is a known thing of what will happen when you light this log on fire for the next three hours, and can you see exactly the flames? I think RenTech has basically... They haven't figured that out for the market. They can't predict the future, but if they have a fifty point oh one percent chance of being correct, then they can sort of take a complex adaptive system and say, "We don't really care that it's a complex adaptive system. Our models understand enough about the relationships between all these entities, that we're just gonna run the simulation a bunch of times, and we're gonna be profitable enough from all the little pennies that we're collecting on all the little coin flips where we have a slight edge over and over and over and over again," that they're sort of the closest in the world to being able to actually 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 Rosenthal 2:36:44 Yes, and I think when I've heard people from RenTech talk about this, they will all say, "The model does not actually understand the market, but it can predict, and we can be so confident in its predictions about what the market will do, that we rely on it. Whether it understands or doesn't understand doesn't actually matter." Like, it can't tell you why-

Ben Gilbert 2:37:11 Right.

David Rosenthal 2:37:12 -but that's okay.

Ben Gilbert 2:37:13 But it does know it has a slight edge, and so it should trade on it, even though it can't explain why.

David Rosenthal 2:37:17 Yes.

Ben Gilbert 2:37:18 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 Rosenthal 2:37:25 This is such a tough answer to tell. We actually emailed some friends who are very prominent AI researchers and AI historians and sort of asked this question, and the answer [chuckles] we got back is unsurprising. They said, "We don't know, 'cause they don't share anything." [laughing]

Ben Gilbert 2:37:43 [laughing] Right. It's like the principles certainly came out of the same math community that spawned machine learning, but is what RenTech has figured out over the last couple decades in Google's Gemini model and in ChatGPT? No, it's not, 'cause 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 and captive and make a bunch of money. I mean, it could just be the case that Renaissance is just taking in as much unstructured data as it possibly can, and they sort of were just a decade or two ahead of everyone else 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 fifty percent of the time.

David Rosenthal 2:38:42 Right. Make trades that sound right.

Ben Gilbert 2:38:44 Right. They figured out this big unsupervised learning thing before anybody else, all the way up until last year when the AI moment happened.

David Rosenthal 2:38:52 If that were the case, we should have a very different answer to Powers.

Ben Gilbert 2:38:55 To illustrate this point, it's quite interesting, Peter Brown's academic advisor was Geoffrey Hinton.

David Rosenthal 2:39:01 Yes. Oh, I'm so glad we brought this up. Yeah, it was the exact same stew and the exact same cohort of people and social group and academic groups that RenTech came out of, that AI came out of.

Ben Gilbert 2:39:13 The other person, just for people who are like, "Why are you saying that?" To make it super explicit, the other person whose academic advisor was Geoffrey Hinton is Ilya Sutskever, who is the co-founder of OpenAI. I mean, many years later, but still.

David Rosenthal 2:39:28 Yeah. I mean, it's like we were talking about with Markov models and hidden Markov models. That is the foundation of RenTech. That is one of the foundations of AI and generative AI today.

Ben Gilbert 2:39:38 Yep.... Okay, another big one is this concept that you should trade on a secret that others are not trading on. So on the face of it, it seems obvious. Of course, I should come up with some strategy to trade on that other people aren't trading on. But I said a couple of words there, which is, of course, I should come up with, and 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, and so therefore, you are going to put trades on that make no intuitive sense. And so when you're putting trades on that are profitable and make no intuitive sense, 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, and we're saying, too, N things, you know, ten things, twenty things, a hundred things, and in various different weights at various different time scales, that is a killer recipe to exploit a secret that no one else knows and be able to beat other people in the market.

David Rosenthal 2:40:57 Such a good point, and many, if not most, of the other quant firms are not doing that. Some of them maybe, but I think most of them are, the model is suggesting things, and there is a person or persons who are the master portfolio allocators that pull the trigger or don't pull the trigger. [chuckles]

Ben Gilbert 2:41:16 Yes. And to be super illustrative, 'cause I think your natural tendency is like, oh, I can understand why these two things would be related. The relationship may not be what you figure. For example, there could be two things that always move together: Tesla stock and wheat futures. And you might try to, because humans are storytellers, concoct some story in your head of why those move together. And if you believe it, then you might decide there's some date where they should stop moving together. Well, it could very well be that some other big hedge fund just owns both of those things, and when they rebalance, it causes those assets to move together. But you would never think of that. You would think these things have a direct relationship with each other, not just that there's liquidity in the market from both of them at the same time because someone else owns both of them. So I think what RenTech sort of admitted is, we have no idea why anything is actually connected, but it doesn't matter.

David Rosenthal 2:42:09 Yep, totally. And that was surprising for me in the research. Like, I sort of assumed that was the whole quant industry, and it was very surprising to me to discover that I believe, no, it is pretty much only RenTech and maybe a couple other people.

Ben Gilbert 2:42:25 Okay, my next one is brought to you by a friend of the show, Brett Harrison, who has worked in the quant trading industry for a long time and shared an idea that he has with us, which is that there's basically this two-by-two matrix. You have, on the one axis, fast and slow in terms of trade execution, and on the Y-axis, you have smart versus obvious.

David Rosenthal 2:42:46 Yeah, the way he phrased it to us was smart versus dumb, but dumb doesn't mean dumb. [chuckles]

Ben Gilbert 2:42:50 Right. It's the obvious trades. And the high-level point is all quant funds are not high-frequency trading firms and vice versa. And this is something that I didn't know, not coming from this industry, and now makes total sense to me. I think I thought they were the same thing. But fast and obvious is your classic high-frequency trader. They're front-running trades, they're locating in a data center that's really near the... You know, this is flash boys. Or they've got a microwave line between New Jersey and Chicago, and they're trying to arb the difference between two markets. You need to have the fastest connectivity in the world to pull this off.

David Rosenthal 2:43:24 Yep, this is Jane Street.

Ben Gilbert 2:43:25 Yes. There's fast and smart, which you kind of don't need to be both. You don't need the fastest connectivity in the world and the most clever trades to put on. So people kinda tend to pick a lane, that they're either a high-frequency trader or they're trying to make the smartest, you know, most non-obvious trades possible. And that, of course, leads us to Medallion, which is in the slow and smart quadrant. All the machine learning system discovered the relationships in the data, so there's a huge amount of compute-

David Rosenthal 2:43:55 The non-obvious trades.

Ben Gilbert 2:43:57 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 Rosenthal 2:44:11 My sense is RenTech is not a high-frequency trading shop. They are not front-running things. You know, they are not flash boys. Compared to you and me, they still operate incredibly fast, but it's more about the smartness and less about the fastness.

Ben Gilbert 2:44:28 Greg has a quote in his book: "They hold thousands of long and short positions at any given time, and their holding period ranges from one to two days or one to two weeks. They make between a hundred and fifty thousand and three hundred thousand 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 Rosenthal 2:44:48 Oh, this is another thing that we heard. RenTech is world-class at disguising their trades. [chuckles]

Ben Gilbert 2:44:54 Yeah, they can make it so that they don't move the market, and you don't know who is acting or when. And this is because in the early days, they weren't good at this, and people basically intercepted the trades that they were making and were front-running them, and they had to adapt and develop these clever systems to make it so you don't know who's buying, and you don't know in what quantities, and you don't know if they're gonna keep buying.

David Rosenthal 2:45:16 Yep.

Ben Gilbert 2:45:17 ... My last one before we get into value creation, value capture, is that this is a terrifying business to be in. The amount of controls and risk models that you need and kill switches are just so important. What if the software has a bug? Is it possible to make a ton of unprofitable trades in a matter of minutes and lose it all? You know, that wasn't possible in the old world where you're calling your broker. That totally is possible here.

David Rosenthal 2:45:39 And it happened.

Ben Gilbert 2:45:40 Yeah, and while it's never happened to RenTech, there was a company called Knight Capital in 2012 that lost four hundred and sixty million in a single day. There was a bug in their process to deploy the new code, and basically what happened, it was a simple flag error, a misinterpretation of setting a bit from zero to one, that caused this infinite loop to run, where once a certain trade happened, it was supposed to flip the bit. It flipped a different bit. The systems were not looking at the same location and memory for the same bit, and so it basically thought it was never flipped. This infinite loop ran four million trade executions in forty-five minutes, and there wasn't the appropriate kill switches built in, and they basically watched it all just drain out, and there was nothing they could do.

David Rosenthal 2:46:24 Yeah, so, like, the whole portfolio gone, right?

Ben Gilbert 2:46:27 Yes. Uh, well, I don't know if it's the whole portfolio, but it was enough that they lost a huge amount of the LP capital, and then they were a publicly traded firm. Overnight, their equity traded down seventy-five percent, and then someone stepped in and bought them.

David Rosenthal 2:46:41 Well, they probably got margin called by all their counterparties.

Ben Gilbert 2:46:44 So whoever is in charge of the financial controls and safety systems at RenTech, that's a huge job for someone in this industry.

David Rosenthal 2:46:52 Totally.

Ben Gilbert 2:46:54 All right. [sighs] To kick off value creation, value capture, I have a provocative statement, which is David, Renaissance Technologies is actually not in the investment business. They are in the gambling business. And in particular, they're the house.

David Rosenthal 2:47:09 Well, I would totally-- I thought where you thought you were going with this, I was like, "Yes, I would totally agree they're not in the investment business." [chuckles] They have no idea how to invest. The model does.

Ben Gilbert 2:47:17 I'll say this: they're not investors, and they're not in the investment business. There is investment going on all around them in the markets that they trade in, but the fact that they're in those markets, they're not there as investors. They're there setting up shop as Caesars Palace, letting everyone come in and do business with them while they have a slight edge, and they'll lose sometimes, but most of the time, they're gonna come out slightly ahead. And I think, let's say they do have a fifty point oh one percent chance of being right. They're just there to collect their vig on everyone who is willing to trade with them over all these years. And at scale, it really worked. Jim Simons managed to drain thirty billion dollars into his own pocket out of everybody that he ever traded with.

David Rosenthal 2:48:03 Now, I think where you're going with this is perhaps similarly along the lines to Caesars Palace or a casino. They are not in the investment business, but they are providing a service.

Ben Gilbert 2:48:16 Sure.

David Rosenthal 2:48:17 Is this where you're going with this?

Ben Gilbert 2:48:18 Well, I mean, the investment business, it sort of depends how you define investor. If you wanna be, like, all hoity-toity about it, which I'm... You know, in this illustrative example, I'm kind of being one and saying an investor is someone who provides capital, you know, risk capital to a business for that business to create value in some way in the future. Or you lend money to some intrinsic underlying asset so that it can be productive with that capital and produce a return for you as an investor. And of course, lots of things are called investing that are not that. Is it investment if I put money to work and then I get more money back later, and I don't actually care how the money got made, and it's actually zero-sum, I'm just vacuuming it out of-

David Rosenthal 2:48:59 Right, right. Yeah, the money is not being invested in anything to produce.

Ben Gilbert 2:49:03 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 and your slight edge.

David Rosenthal 2:49:12 Well, where I was going with the service provider, I think casinos are service providers. They are providing entertainment to their customers. Everybody knows that the games are stacked in the casino's favor. Similarly, I think you could make an argument, and I think this is probably quite accurate, that 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 Gilbert 2:49:42 Absolutely. That is the undeniable, yes, quant funds create value in the world thing, which I think is very easy to say quant funds provide no value because it's like it's zero-sum. They're not actually providing the capital to businesses to do something with. They're purely looking to do an arbitrage or any of the strategies we've talked about this episode. 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 Rosenthal 2:50:24 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.

Ben Gilbert 2:50:39 Yep.

David Rosenthal 2:50:40 None of which used to be true.

Ben Gilbert 2:50:42 Nope. The fact that there is a whole bunch of quant funds, hedge funds out there that are ready to be willing counterparties to anyone who wants to trade, that is a