Renaissance Technologies
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I always used to miss spell rn issuance as I was type IT out at R E. And and then I was of like, not really know I came from there, but I learned a demonic to make sure get IT right.

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

there's that too. But you're ready for this. You can't spell renaissance without A I.

To say.

to say, let's .

do IT. Easy, you wait, you wait, you who got? Easy you, easy you with you see me down, say state.

Welcome to season and fourteen. Episode three are acquired, the podcast about great companies and the stories and playbooks behind them.

I'm then girl bert David rental.

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

When I was in college, I actually took an economics class with burton moko, who, of course, you know, was involved in starting vanguard and is a big proponent of all that. And that is what I learned to be.

Well, David IT, turns out they were wrong. Today, I listeners, we tell the story of the best performing investment firm in history, renaissance technologies, or rent tech. There, thirty year track record, managing billions of dollars, has Better returns than anyone you have ever heard of, including burker hathaway, bridge water, George sorrow, Peter linch, 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 eyes popping performance is matched only by their extreme secrecy, and they are unusual in almost every way.

Their founder, jim Simons, worked for the U. S. Government in the cold war as a code breaker.

Before starting, none of the founders or early employees had any investing background, and they built the entire thing by hiring P. H. D.

Physicists, astronomers and speech recognition researchers. They are located in the middle of nowhere and a tiny town on lung island. They don't pay attention to revenues, profits or even who the CEO are of the companies that they invest in.

And in 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, rent tax, flagship medallion fund that 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 oh.

we are going to talk a lot about that towards the end of the episode because I think it's kind of the key to the whole thing.

Cliff, hang your David. I'm excited. So what exactly does unison to do? Why does IT work? And how did IT evolve to be the way IT is today? And while the resources are out, there are scarce.

Because for one employees signing 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 at all. This episode was selected by our acquired limited partners. And to be honest, I didn't think enough people knew what in tech was to pick IT.

But when we put IT out for a vote, the people have spoke IT. So if you want to become a limited partner and pick one episode each season enjoyed quarterly zoom calls with us, you can join acquired slash lp if you want to know. Every time a new episode rop sign up at acquire data A M slash email.

These emails also contained hints at what the next episode will be and follow up facts from previous episodes. For example, we had a listener, Nicholas collin, email us this time who found the actual document with the bylaws of air mass control family shareholder h fifty one, which we linked to in this most recent email. Come talk about this episode with us after listening at a quired data at slash slack.

If you want more from David night, check out A Q 2， our most recent episodes with lotus c. Newson, who LED the team that created the first G L P ones at novo nor disk. So awesome.

Follow up to the novo episode, if you like that one. So with that, the show is not investment advice. Dave and I may have investments in the companies we discuss or perhaps wish we did. And the show is for informational and entertainment purposes only. David, where do we start our story today?

Um will we start in nineteen and thirty eight in newton master huett, which is a fairly wealthy suburb just outside of boston where when James Simons is born and both of James parents were very, very smart, especially his mother, maria, his dad was a salesman for twenty century fox, the movie company. His job was he went around the theaters in the northeast, sold packages of movies to them. Super cool.

By the way. We know all this because we have to think greg oker, man, author of the man who solved the market, which is the only book out there that is solely dedicated to rent tech. And jim Simonson, we actually got to talk to greg in our research. He helped us out of about thank you .

going and help fact, a few of our assumptions of what happened after the book came out.

So that was James parents, but really a major influence on him growing up with his grandfather, marsh's dad. There's already kind of echoes of the baza story here with the grandfather, the mother's father, and spending a unch of time with him and rubbing off on Young jeff for Young j. In this case and basis, of course, would get his start in his career at 地下 a quite .

fund coming up at the same time as erano。

But back to jim here in the nineties, his grandfather Peter, owned a shoe factory that made women's dashes. Jim spends a tone of time. They're growing up at the factory.

So jims 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. As greg puts IT in the book, Peter revelled intelligence m and his cousins stories of the motherland involving wolves, women, caviar and va. And he teaches Young gym when he's a child here in the factory to say russian phrases like give me a cigarette and kiss my ass.

which I think he really would say that thousands of times the rest of his life.

I think so if you watch the interviews with you, his hand are always twitching because he has change smoked his entire life, probably going back to like eight ten in the factory.

three packs of merit a day unavailable, although I think he's quit later in life. But he definitely change smoked the Better part of the first call IT seventy five .

years or something of these famous stories of the conference room at the war rooms when the market is going through like a crazy generation and they just filled with secret smoke. And it's all gym, different time, different time. So back to James childhood though, here in the boston suburbs, he goes up certainly not uber wealthy or uber rich, but very, very solidly upper middle class, and especially keys and 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, if he gets to rub shoulders in the boston area with people who are really rich, and he says later, I observed, that is very nice to be rich. I had no interesting in business, which is not to say I had no interest in money.

Yes, important to teeth out the difference between those two things.

Yes, very, very important. And what he means, what 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 zenos famous paradoxes is from aging greek times.

Yep, this is great. The basic gist of zeno s paradoxes. If you are always taking a quantity and dividing IT by two, you will never hit zero.

You will automatically approach zero, but you will never actually touch zero. You need to do addition or attraction to do that. Division will 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, rows 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 the half the gas in the tank. And you know, the funny thing that doesn't occur to before you all this, well, then we're just not onna.

get very far. So jim stream is to go to M, I, T down the street in cambridge and study math. He graduate high school in three years, and doing the second semester of jim's freshman year there, he enrolled a graduate math seminar on abstract algebra. So pretty, you know, heady stuff.

yeah. And jim would go on to finish undergraded M I, T. In three years and get a masters in one year.

Yeah, pretty, pretty smart. But IT turns out that that freshman year grad seminar he took actually has a big impact on him because he doesn't do well in the class. He can't keep up and jims pretty self aware here.

There are other people at M. I, T, who never run into problems. They never hit a limit.

They never struggle understanding any concept. And he realizes that, oh, i'm smart. I'm very, very smart. I'm smarter than most other people here, but i'm not one of those people.

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

So jim's own words on this are, I was a good mathematician. I wasn't the greatest in the world, but I was pretty good. But he recognizes, like his event, that he has a different advantage that most of the super genius has 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 going to care about the answer to anyway, that taste. And I think I have good taste.

By the way, this is exactly the same thing as jeff BIOS in college, realizing he wanted to be a theoretical physicist, he met some of the extreme brain power people that would go on to become the best theoretical physicists in the world. And he said, i'm smart, but i'm not that smart. And so switched to .

computer science. I think the analogy here is like sports, there are all star players, there are holo famous and then there's lebron and empty h and jim ends up being a hole famous mathematician, but he's not to brady. I mean.

he's got a pretty important theory named after that .

goes on to become a foundation of string theory and physics, which is even James field crazy. So this realization the 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.

The ends are getting built at reteach, 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 to M.

I, T. And the super smart people, they look up to him because you aren't like the kid in the corner at the high school dance. cool.

He's the extrovert of the redial mathematical.

yes. So he was elected class president in high school. You know, he smoke cigarettes.

He's popular with the ladies. He kind of looks like come free bogard. He's a popular dude, especially at this point in time.

We're now in the late fifties when jim at M. I, T. You know, this is kind of James d in rebel without a cause era. yeah. So after graduation, jim leads his buddies on a road trip with motor scooters.

You can make this stuff up from boston, down to bogota, where one of his classmates is from the ideas that they're going to do something so epic that the newspapers are gonna have to write about IT. So they all load up on scooters and drive down to bogota. They get in to all sorts of adventures. There's knives, guns, and they get thrown in jail.

Crazy that this group of people took this type of risk.

Totally crazy. So after he stand in MIT and after the road trip, jim heads out to berkeley in california so that he could do is P. H.

D. With the professor shin hern. And much later in life, jim would collaborate with turn for the turn Simons theory that we talked about earlier.

That becomes one of the foundational parts of string theory and physics. But before jim leaves for the west coast, he meets a girl in boston, and they decide to get engaged in four days. I.

Mean, this is, this is in back that these were the times. And when they get to california and they get married, jim takes the five thousand dollar wedding gift that they believe they got from her parents. And he decides, I won a multiply this. So he started driving from berkeley in the same from eco every morning to go hang out at the marial linch brokers office and just be a rat hanging around the brokers and find ways to trade and turn this money into something more.

which is so interesting to think about, because at that point in time, there was such an advantage to just being there. This wasn't even the trading floor. But information is also manual and also relationship driven in the markets that there is basically no way to be in on the action unless you were physically there in .

on the action. exactly. Yeah, you couldn't just log in the yahoo finance. If something are open, the stocks APP on your iphone, which even the information they were getting was god knows how long delayed from the york ker from chicago for the futures and commodities that are being traded.

The gym gets into, he's as close to the action as he can possibly be, but he's a long, long way from the action. Yep, none's. When he starts out doing this, jim is a hot streak, and he goes up fifty percent in a few days.

Trading is easy.

Trading is easy. He says, I was hooked. I was kind of a rush, I bet. Except he ends up losing all of his profits just as quickly.

Yeah, important to learn that lesson, really.

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 safety to go every morning at gambling our future like this.

right?

Effectively play in the ponies. Yeah, exactly. So seems like OK OK, i'll stop.

I'll focus on for now. So he finishes his P. H. D in two years, they come back to boston, and he joins MIT as a junior professor, ity twenty three. So they stay one year in boston.

But jim, even though he's got a family, even though his super successfully, a Young academic, here you do get kids. He's restless. So one of his bodies from the scar trip to bogota is from bogota, lives there, his families there.

He has an idea to start a flooring tile manufacturing company. He's like, you know, the flying A M I T. In event so much nicer than a bogota, we should start to make the same kind of flouring here.

When I read this, I couldn't believe that this was jim Simons first business venture like it's so randomly, but I really is emblematic of just how much he was thrill seeking and just looking for anything that was unexpected, different, exciting. He just gets bored fast.

totally. Not just is this the start of his entrepreneur career, the seeds of this financial are what go on to start rendez wild, totally wild. So jim takes a year off down the bogota.

This is a guy with an MIT undergrad and masters and a berkeley P. H. D. In theoretical .

math who is now a professor at M. I. T.

Who is taking a year off to go work on a Floral company in bogut. A.

yes, accurate. So he does that for a year. They get to set up.

They get bored. I don't want to run this company. I've helps set IT up by an ownership taking IT.

Now he bounces back to boston, this time to harvard as a professor there for a year. He's really racking up up, but he spent a year there. He's like, get the each again and you know the junior professor salary isn't that much.

And like we said about him back from his childhood days, he sees the appeal in being rich. He's like this is not a path to being. So he's like, I am going to go put my skills out on the open market.

He gets a job in princeton, new jersey, not at princeton university, but at the institute for defense analysis, which is a nonprofit organization that consults exclusively for the U. S. Government, specifically the defense department and specifically the nsa. These are the civilian code breakers. Yes.

I 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 too, they're going to be A A lot of people who don't work for the government that we're gonna anna hire to do some pretty secret work.

Yep, so the I, D, A therein, princeton, china functions like the institute for advanced study, which is also in princeton. That's where instead went when he came to america, kind of an independent think tank research group. Except it's solely focused on code breaking and signal intelligence with the russians .

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

Yes, this is so, so key. And this culture ends up getting translated whole cloth right. International tic, so the way I D A worked, and I assume still works to this day, is the recruit top mathematicians and academics to come be code breakers there. They would double their salaries.

And importantly, IT couldn't have been a government division if they were going to be doing that, because there is very specific congressionally approved .

budgets for payroll. exactly. They figured out that they needed to attract the smartest people in the world who weren't going to come to go work for the department of defense. This was the way to do IT.

So like you said then, 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 want IT 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 M I, T. Or prints in her harvard, whatever, except you're doing code breaking instead of teaching and there's no beer accuracy to worry about.

There's no politics. It's just like, hey, you do your code AK and work and then you publish that. You can collaborate with .

your colleagues there you now.

this is pretty crazy. Very quickly after jim arrives at I D A. Remember, he's in money making mode at this pointing dam. He recruits a bunch of his very brilliant colleagues to come work with him in there, fifty percent free time on an idea to apply the same work in 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 suggested. This paper really is rented just twenty years before a rental is crazy.

One thousand and sixty four. This was published.

yes. Now at this point in time, fundamental analysis was then, as in most of the world today, still is the primary way of investing in things of, hey, I know this company. I'm going to analyze their revenues, their Price multiple or i'm gna think about what's happening in the currency markets or in the commodity markets, why copper is moving here, the british pound is moving there and i'm to invest on those insights.

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

Yes, fundamental investing. They are also existed in the sixties, technical adJusting, which kind of his video. This is like i'm looking at a stock chart and i've got a feeling that it's gonna go up like i'm tracing this pattern and like it's going up, baby or no no, no, this pattern is going .

and down yeah using phrase technical might be little generous, but what they're looking for, basically trying to mind 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 think IT will do in the future.

right? And what gym in his colleagues here is suggesting is that, but just not really done by humans, is that with a lot more data and a lot more sophisticated signal processing.

and importantly, you might say, why is that 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 mind signal from something that otherwise kind of looks random totally.

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

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

yes, there's all this noise in this data. And IT is impossible for a human to sit here, look at this data and say, oh, I know what the soviet s. Are saying. No, no. You have to use mathematical models and statistical analysis to extract the patterns.

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

Yes, what they are really doing here at I D. A. And then soon in red tech is early machine learning. And jim just had this incredibly brilliant insight that you can use these techniques and this technology for making investments.

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Yep, so learn how you can put A I agents to work for your people by clicking the link in the shower notes or going to service now, docs, ash, A I dash. Agents, okay, David, so this paper is published. They're gna trade and make a home bunch of money in the stock market by applying this code breaking, signal processing, data analysis approach to investing.

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 idea at this time, and one of the members of this sort of rebel group, shall we say within the organization, is a guy named lenning bom. And lenny just happens to be the world expert in a mathematical concept called a markov model, specifically a version of mark ov model called a hidden a mark of model. Now, a mark of model is a statistical concept that used to model su du 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 observer able 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.

So the great example that greg OK gives in the .

book is just a baseball game.

There is three balls and two strikes. That state has a narrow set of states after IT, it's gonna a strike out, they're gonna get on base, it's gonna a walk or maybe they fall 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 is 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. Can I get pretty good at, at least if not predicting, understanding the probability distribution of the outcomes for any given state in the game?

So we brought up machine learning in A I A minute ago. This is a foundational concept to modern day A I. 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 pixel or frame said uh and obviously they're .

much fancy er than that but that is kind of the underpinning of at all. I mean, I remember in my soft moe of college computer science class, I had a markoff chain assignment and IT was basically write a java program to injust this public domain book. And then I would give you a seed word, you know, the first word of each sentence, and press return, return, return, return, return.

And I would scan through the probability tree and give me the most probable word based on the corpus of the book that I just read to create some sentence. And IT feels like magic. And of course, in these early rita entree, mark of chain, things like the one I did in college IT kind of spits out nonsense. But that would evolve to be the elms that we know of today.

Yes, totally. And that is what they were using an idea to do, code breaking. And that's what they proposed in this paper that they could use in the stock market too.

exactly. And the way that this applies to investing is. Just like you might not know the rules of baseball, but if you've watched enough baseball, you can kind of guess at what the probabilities of the next thing to happen are based on the state investors kind of the same thing, at least the stock market movements are where you don't know the future.

You don't know what's gona 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 the state today of what could happen next.

Yes, exactly. So jim and lenny and this whole little crew, they're pretty fired up. They're like, oh, great, let's go raise a fund and invest in the markets using this strategy. Certainly, we're going to be .

successful or raising that fund, and certainly, we're going to be very profitable because we've this great idea .

totally what could go wrong? Well, in the midst sixties, the idea that some monkey academics at some random secretive agency in print, new jersey, could go raise money was nonviable mean, I was hard enough for warn buffer to raise money at this point time for his fun. And he was Benjamin grams, annointed, appointed disciple.

And here are these academics who are working at some random, unknown, non profit, saying, give us money. We don't know anything about these companies that we're going to invest in. We don't know anything about fundamental, but we got a really good algorithm. People probably like, what is an algorithm? They just have no access to capital.

right? This was decades before IT became high pedigree to come from a technical computer science backgrounds in the world of investing.

Yes, so a bunch china keystone cop style fund raising happens here. They're go out around in secret. The trend keep the I. D. A bosses from knowing what they are doing. One of the group ends up leaving a copy of the investment perspectives on the copy machine work one night, and the boss discovers IT and calls them all into his office and is like, guys, what do you do in here? right?

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

Yes, so they end up abandoning the effort, both because they can't raise money and because I D A is found out about this, they're not too pleased. Shortly after all this though, gym ends up moving on anyway, because the vietnam war starts, and he is, you can imagine, from his background, is not a supporter of the vietnam or at this point time, jim writes an odd in the new york times denouncing the vietnam war and say, yeah, he's, you know, sort of part of the defense department, but like, not everybody in the defense department is for the world.

which is so naive thinking you can write up and in the new york freking times. And that's not going to create issues for you in your job.

Even more than that, amazingly, nobody really paid attention to IT except a reporter at news week who then comes to interview jim and ask you some more questions and he just doubles down on this. And when the news week peace comes out, that's when the department are defensively, are you got fired this? I yep.

So jim gets fired in one thousand nine hundred and sixty seven, even though is a star code break ker. He made supposedly huge contributions to the group, which are still classified. But at eight thirty, with a wife and three kids, he's out on the street.

And even though he's super smart, his colleagues love him. Clearly, he's now bounced out. M.

I, T. He's bounced out a harvard. He's gone to this seemingly final home for him.

Great place at idea. He gets bounced out there too. His job prospects are not great. yeah.

So he takes pretty much the only halfway decent paying job that he could get, which is to be the chair of a newly establish, or maybe reestablished math department at the state university of new york, stony broke, which is the long island campus of the state university of new york. This is not harvard. This is not M.

I. D. No, IT is not. But I did have one very important thing going for IT, which is why jim ended up there.

And that is that Nelson rock feller, who was then the governor of new york, had launched a campaign, 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 started thought mi t was the berkeley of the east already, but rockfeller is way to get, give him that. He wants stony brook to become a math and sciences power house, and jim is the key. He wouldn't be able to recruit somebody like jim otherwise. But because he's now kind of tarnish ed his career, here's a like, very talented mathematician that they can convince to capture .

of the department up.

So they basically give jim an unlimited budget and a way to go trying and poach math professors from departments all over the country in the world, and bring them there, the long island and part of how jim goes, and recruit foxes, money alike. W hey.

W your salary line but the other part of IT too is he's given such ley and stony broke is so different from the politics of an M I T or a harvard or a printer he says, hey, come here, i'll pay you more. But even more importantly, you can just focus on your research. You're not gona have to deal with committees.

You're not gna 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.

Rockferry doesn't want this necessarily become a great teaching institution. He just wants to assemble talent there. yeah. And amazing IT works. Jim starts getting a bunch of great talent, including James ask, who is a super star in algebra number theory from cornell. And he ends up at stony book recruiting and building one of the best math departments in the world.

Amazing.

told amazing. But in true jim fashion, after a couple years of this, and also his marriage with Barbara falling apart, he starts getting restless again. He sides that he wants to go honesty beatle and go back to berkeley and reunite with his old adviser there.

Go spend some time out on the coastal california. And this is where turn and Simons end up, collaborating and developing the turn Simons theory that ends up winning the highest award in geometric e and really kind is James personal mark on mathematics. Now, also right around the same time, remember, the colombian florian company, IT, gets acquired, and german is bodies who are partners in IT come into a good amount of money.

And jim is newly divorce his restless in academia. He has these ideas back from when he was idea 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 dissolution again and restless the academy. And in one thousand nine hundred and seventy eight, he leaves to focus full time on trading, which is a huge shock to the academic community. Remember, he's assembled the super star team.

There is doni brk. There's a quote and gregg 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.

Yeah, I mean, he 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 is sort of today.

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

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

right? Yeah, I don't think IT was that the rest of the math world was skeptical that they could work. They probably really like, yeah, this could work. But they were like, you 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 the higher calling in my currency is the papers I publish in the awards that I win.

And that's what I want. Yep, now stony break. We should say too, like it's a very nice place, yes, but it's in the middle of lung island on the north shore. This is not the hamptons. It's like the LG island suburbs.

Yeah, the wooded log island suburbs.

Yes, the wooded lung island suburbs. Here's jim in a strip mall next to a pizza join, setting up his trading Operation that he decides very cleverly to call monem rics a combination of money and metrics, or econometrics s and he recruits his old idea body, original partnering crime on the trading idea, lenny bam, to come and join him. And this time, though, they have some capital from the sale, the flowing company.

And how much did he make on that flooring sale?

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

capital in one hundred seventy eight. Yeah.

now jim also has another advantage at this point time, which is he's right down the street from stony brook and he's just recruit all of these superstar mathematicians.

The table has been set.

yes, and those folks are more loyal to gym than they are a stony broke.

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

yes, in general, but some of them in, in particular, the superstar James acx, jim convinces to come join him, illustrating Operations.

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

yes. Beyond credible.

right? All the themes that a lot of mathematicians are using everyday are all named after these three guys who are now at the .

same firm trading. yes. And it's LED by jim, whose somebody that they respect as an academic, but even more important is somebody they want to work for and they look up to and they think is cool and he's out there being like, hey, I think we can make money right now. At this point, they're primarily treating 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, are really even slightly more complex commodities like I don't know soybeans or whatever.

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

And this is super important. At the end of the day, they built some models there. They're getting the early versions and infrastructure and scaffolding of this quantity approach set up. But in terms of the actual trades they're putting on, they're still doing all 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 is interesting what they spit out, but they're not onna act on anything unless they can be like, oh yeah, I see what is going on here. I have a hypotheses, right?

The computers are by no means running loose .

at this point, by no means at all. They're just suggesting patterns and ideas. And jim and lending and James, they have to then decide, here, we gna do this or not, or we going to do something just totally different that we think is what's going to happen.

yeah. And this actually does make sense really for two reasons. One, computers in computing power just wasn't sophisticated enough yet to really build A I in a way that's powerful enough that I 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, right? And they're really, really good at building models, decoding signals, obviously, but they're much more from this realm of theory.

And I actually spoke with Howard Morgan, who's gonna a come up here in a second, and he made this point to me. He's like in math, there's this concept of trace ability that's a really, really important cultural tenant. It's like proving a proof for proving a thereon 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 suggest this. It's like, no, you need proof and that's the world that these guys are coming from the like, oh, we can use data, the sort, to help us here. But ultimately we want to have a rock solidity of what is fundamentally happening here.

Fascinating, which is very different. That will cram a huge amount of data.

And and then whatever the data suggest, we know it's true because the data suggest, which was 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 chop boards.

yes. And so much so that even jima's ring leader here, he's far from convinced, ed, that he should put all of his wealth into this thing. He's like, yeah, this is interesting. We're building were experimenting like great, but I also wanted put my money somewhere else to for some diversification. So this is where how where Morgan comes in.

Now we used to talk about this on all acquired episodes, that in the early days of silicon valley, there are only ten people 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 in early V. C. In these days, Howard Morgan would go on to be one of the cofounder .

of first on capital, which was essentially span out of renaissance. IT was kind of the venture capital work that they were doing at renaissance didn't fit with the rest of yes.

So here's how IT all went down. And this is so poorly. I .

understood out there. Yes.

Howard was a computer science and business school professor at the university of pensylvania, so we taught C. S. P. And business at warton. And he had been involved in bringing urban net to pen and was catholic, early, early internet pioneer.

And so as a result, he was super plugged in to tech and early startups and really early, early proto internet stuff. And jim gets excited about investing together with Howard. So they say, like, hey, maybe we should partner together.

And in one thousand nine hundred and eighty two, jim actually winds down mono metrics. And he and Howard o. Found a new firm together that's gonna reflect both of their backgrounds to be a great diversification. Jim and his group are onna. Bring in the quantity trading thing and again.

trading on currencies and commodities at this point .

and how it's going. Na bring in private company technology investing and they pick a name for a firm that is good, select this rena's ance technologies. It's crazy. And that is why rent tech is called rent tech.

I could not only figure this out 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 in liquid thing like venture capital, highly speculative versus know we're going to trade, whether we think the french Frank is gonna a go up or down tomorrow based on the wim of some government leader, it's unbelievable. These were under the same roof totally.

But when you know the whole back on in history, and kind of make sense, because this is their personal money, this is jim and his buddies at lenny and James and Howard. This is not an institutional capital here. They're not out pitching L P S. Of like, oh, you should invest in my diversified strategy of currency trading and private technology .

startups when they say multi strategy, this is really multi strategy.

We get into what multistate testing today means later. But in these early days of frantick, fifty percent of the portfolio was venture capital and fifty percent was currency trading. And in fact, a couple of years after they get started, the currency trading side of the firm almost blows up when leni goes super long on government bombs, and the market goes against him, and the whole portfolio drops forty percent, which is wild.

That ends up triggers a clause in lenny agreement with jim, and they sell off lenny entire portfolio and he leaves the firm. This is crazy, and blow up risk is always an issue in the markets. But this happened to rental because we .

quickly got to this point in the story. IT would be easy to say, well, that's a laws that has a lot of teeth. There were many sort of rumbles of something like this potentially happening. Simons going to any and saying, hey, maybe we should cut some of our losses and it's OK to trade out of these positions and landing, which is very dug in on, i'm a true believer and that saying, get into a situation where you trigger a ant like this totally.

And again, also shows they weren't doing model based quantitative trading really at .

this point in time. Now so much got .

so as a result of that for a while, red tech is truly almost entirely of venture capital firm. One point on the venture side, just one investment, Franklin dictionary. Do you remember the Franklin electronic dictionary that was one of their biggest investments? That one investment is half of jim's networks. What at this low point for the trading side? Yes.

I had no idea. That's crazy.

yeah. So in the book greg talks about, oh, jim was focused on venture capital and that kind of the story out there. Well, he was focused on ventures capital because I was the only thing worked in .

making money or I might see anything where they actually had an edge from Howards access to deal flow because they certainly didn't have an edge in the global currency markets.

So I think perhaps in part because of the trading losses, James acts starts to get a little dissolution too, and he tells jim that he wants to move out to california with sandor stress, who started working with them at this point. Sandor was another stonie brick alarm to join them, and the two of them want to move out to california. And due trading out there, jim says, sir, fine, i'm here with Howard.

I'm doing venture capital stuff. Why don't you go move out to california? You can start your own firm, which they do. It's called x com A X C O M, and will contract with x com to run what's left to the trading Operations here for rented.

So it's this interesting arms length thing where jim strikes a deal where he's going to own a part of in exchange for this very favorable contractual al relationship, where they gonna hire them to be the manager for this part of money that runs on has raised. But you know, it's technically not renaissance.

Its ex come right. It's another company that is now doing quantity trading.

yep. And I think jim owned a quarter of IT.

Is that right?

Yes, that's right. And importantly, I don't think anyone had any idea what x com would become or how unbelievably profitable .

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

Yes.

wouldn't have spent IT out. no. So once x and strings get out to california strong, he's kind on the computing data infrastructure side that what he was doing, a stony book, and that's what he came into reiss's to build.

He starts getting really into data and he starts collecting pricing movements on security. 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 security pricing. Stals finds a way to get take data like every twenty minute data on the securities throughout the day.

Not only that, he's getting historical data that predates what your traditional data providers will give you and then ingesting IT into computers and cleaning the data to get IT into the same format as the tech data. So he's getting early one hundred, even eight hundred stuff to try to just say at some point, hopefully will be able to make use of this. And I want to have this just really, really clean data set about the way that these markets interact.

Yeah I mean, he's doing etl on the data. Yes, I think before anybody knew what etl was, again.

no one told them to do that. That was just a self motivated, almost like obsession of like we're gonna have data IT should be well formatted, understood at label and all that.

So that's one thing that happens. The other thing is, jim says, oh, you're going out to california. Let me hook you up with my buddy who's a berkeley professor out there.

Elwin berlin camp. And berlin camp had studied with folks like john nash. And clutchin and MIT, I love that .

clott shining is coming in again. I know we talked to win a lot on the qualm episode father of information theory, really the center of gravity for attracting tons of talent MIT and kind of paving the way for what would become phone technology intellect unica broadly in the future. But the fact that burly campus is crossing pads at M. I, T. With club chin, so cool.

so cool. And most importantly, for this specific use case, burly camp had worked with john Kelly, who developed the Kelly criterion on bet sizing, which poker players will likely be well familiar with you.

So with accommodation now of much, much, much Better and deeper data from stress and barely camp coming in and working with acts on the models, and hey, we should be smart about the bet sizing that we're doing in the trades that are coming out of these models first, I don't know what they were doing before. Maybe I was naive of like every trade was the same. Or just like we should actually be systematic about this, the model start really working.

Yeah, this is the turning point.

Yeah, in these kind of mid eighty years, x com is generating I R S of like twenty plus percent on the trading side. You know not necessarily gonna a beat venture capital I R S, but liquid, yes, reliable.

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

Yes, but the early results are really good, and gym and berley camp especially are very encouraged by this. So in one thousand eighty eight, jim and hold Morgan decided to spin out the venture investments and how IT goes to manage those with basically their own money. Fun code on this when how IT starts first around the number of years later with josh, couple man, jim of courses.

A large L. P. Howard, of course, remains an investor in an tech.

The first institutional fund the first round ended up raising was a fifty x on one hundred and twenty five million dollar fund. IT had robo, ks, uber and square. So I believe this is right.

I think jim made as much money from his investments in first round as hower did from his L. P. Stake in reteach.

That wild is IT. That amazing? Oh, that is a untold story about jim Simons. I think I read basically every primary source thing on jm or rena sons on the whole internet. But I seem you ve got that from Howard.

Yeah, I was super fun talk in the Howard about this and just the history of how first around started early, super Angel investing and everything that became.

I also didn't realize that first rounds fund one was a fifty x on one hundred and twenty five million .

dollars und first institutional fund, which I believe they called fun too.

I mean, wild, wild stuff.

totally wild. So when how IT spends out the venture activities, jim then decides to set up a new fund as a joint venture between red tech and acts come, and they decide to name IT after all of the collective mathematical awards, gym and James and berlin camp and all these prestigious mathematicians have won in careers, they named IT, the medallion fund. And listeners.

we've arrived.

This is the .

part of the story that matters. The median fund is the crown jeel. Or you might even say actually the the interesting thing about renaissance and IT is born out of this observation that, oh my god, what they are doing over there at x come is really interesting.

Maybe they shouldn't be doing IT all the way over there. Maybe that should be a deeper part of the fold here at rn tech. And we shouldn't let that get away or freely given up on the quantitative trading strategies too early. And again, still just currencies, still just commodities, futures not play in the stock market at all, but the seeds and the ideas, the huge amount of clean data, the robust engineering infrastructure to process all that data, the mining of signals from data to figure out what trading strategies to execute, that is really starting to form here in this new joint venture, this medallion fund.

Those ideas had all existed before. This is the first time that it's all brought together. You can actually working and Operationalize ed and Frankly.

that computer has got good enough to actually do IT too.

That's another big piece of this. Yeah I don't know that draws could have done his data engineering too much earlier in time. Yeah all right.

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So they've got this grand new plan and vision with the medallion fund. Unfortunately, right out of the day, the fund stumbles a bit and act ends up getting burned out. Burly camp, though, is like, no, no, no, no.

This is an anomaly. Like we're going to fix this. I've really, really believe you that what we're doing with these models is going to be extremely profitable. So he buys out most of access stake in the summer of nineteen eighty nine, and he moves the offices up to berkely. And there he comes up with the idea that, hey, we should treat 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 best we're making so that we can then size them up or size them down.

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 ten dollars right now, what you know five minutes from now is, is probably to be worth about ten dollars.

The most likely situation is, is 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 continued out 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 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 am right about this particular rule wheel, which of course, you're not fifty point two five percent of the time, and I decided to play once, or play twice or play five times, there is a chance I could lose all my money. Or if I have tiny little bit sizes, then i'm just not going to make that much money. But if I walk up to set game with a little bit of edge and I use small bet sizes and I played ten thousand times, i'm going to walk out with a lot of money.

There is a great bob merger quote about this later. He says we're rate fifty point seven five percent at the time.

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

But where one hundred percent right, fifty point seven five percent at the time, you can make billions that way.

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

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

yep. Now of course, this is all great in the abstract of it's that you're literally sitting at a casino when 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 a real transaction costs, especially at this point in history before some of these mora innovative trading business models with pay for order flow and a zero transaction fees on all this stuff. There's real transaction costs to putting on these trades. And of course, you're going to move the market when you put on these trades.

Yes, this is slipped.

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 beats, are going to have a hundred thousand tiny bets.

yes. And as time goes on and the whole count industry emerges and becomes much more sophy, I think it's particularly the slippage there that becomes the governor on how high velocity you can actually be on this. And the slippage is that once you are at a certain scale, you are going to move the market with your trades.

So the deeper you get into the order. But like, let's say, you want to buy five million dollars or 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 dollars by the Price might have .

gotten pretty different already. Yeah, we're going to come back to this in just a minute, but this certainly for early rent tech and then even now still for all of quantitative finance, is a really, really, really important thing.

yep. And David, in a very crude way, calls back to last episode on a mess, the idea that the Price would be highest for the family member that is willing to sell now and sort of goes down over time if the family was going to sell to burner or no IT would be of view to be first in the order book, not last in the order book. Yes.

I feel like there's this metal lesson that i've been learning through required in my own personal investing over the past couple years. Every market is dependent on supply and demand. You can see quoted valuations and quoted Price streams. But often times that's like the mistake of just looking .

at averages exactly. Yes, looking at the quoted Price of an asset is wrong. You actually should be looking at what is the volume that is willing to buy and what is the volume that is willing to sell.

And for all of those buyers and all of those cellars, what are the Price at which they are willing to transact? And the way that tends to manifest on stock chart is here's the Price of the share right now. But that's not actually what's going on to the surface. It's a whole bunch of buyers and sellers who have different willingness to pay and have different amounts. They're trying to buy ycl now at .

this point time when the median fund is first starting to work and say late one thousand and eighty nine, early one thousand nine hundred ninety, it's small enough that this isn't a big consideration. The middle an was about twenty seven million dollars under management when berley can't bought out eggs in one nineteen ninety, 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 and Carry?

I been 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 actual repeatedly strategy that produces the numbers. You just said, unbelievable world changing here.

Yeah, let's go. yes. And indeed, IT was a hEllier let's go situation.

So the number is you quote to me that grows on the net. Sounds ded quite different.

Talk to me about the fees and kerry 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, which is crazy. The top venture capital firms in the world charge of three percent management feet.

Even that is like everybody holds their nose. And this like this is ridiculous. How on earth where these nobody is charging a five percent management fee out the gate to their investors? Well, a couple things. When their investors were not sophistic, ted IT was mostly their own money and their body's money.

So they said that president.

they said that president, but too, though they actually needed the money. Yes, because strs infrastructure costs were about eight hundred thousand dollars a year, so they just backed into the management fee based on my k, we need eight hundred thousand dollars a year to run the infrastructure, plus we need some money to, you know, pay folks and what not like great five percent management v and so .

the pitches are making to the investor basis, like if you believe that we should be able to massively outperform the market, doing quantity trading, what we're gna lead 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 lish performance fee fund at this point in time?

yes. High management fee, Normal list Carrier performance element? yes. So at the end of one thousand nine hundred ninety, Simons is so jazz about what's going on that he tells bert kim, hey, you should move here along island. Let's recently.

Ze, is everything here? I want to go all in on this. I think with some two weeks, we can be up eighty percent after fees.

Next year, barely camp is a little more circumspect. A, he wants to stay in berklee. He doesn't have any desire to move to log island. N, 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 gym and he says, well, if you're so optimistic, why don't you buy me out? So jim does at six x, the basis the burly camp had paid acts a year earlier.

On the one hand, making the success in one year sounds great.

On the other hand, this is the equivalent of when don valentine sold scores apple stick before the IPO to lock in a great game but miss out on all the upside to come.

David, I think we should throw this out so people understand the volume of this they've generated on the order of sixty billion dollars of performance fees for the owners of the fund over the entire lifetime. So on the one hand, six x and a year bed, on the other hand, you own a giant part of something that has dividends, sixty billion dollars in cash out to its owners move yeah that's just .

on the Carry side that if the owners are the principles. So it's 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 medalia over the last thirty five years. So jim buys up brail camp. He rolls everything in the medallion fund back into red tech itself, moves everything back to stony brook stress, moves to stony broke.

So it's now the jimsie s show in new york with straws building the curing systems and act, I think, still had a small stick.

Yes, that's right. And trials had a stake as well. So once gym takes control and moves everything back, he basically decides that he's gonna turn red tech into an even Better, even more idealized version of I D.

A. And the math department stony broke. He's gonna make this an academics paradise, where, if you are one of the absolute smartish mathematicians or systems engineers in the world, this is where you want to be.

So of course, he starts reading the stony y broke department itself again. And this is when Henry love for joins full time. Lofer had been consulting with medalia in the early days.

I'm working with berlet campus. They're doing Better sizing as they're making more frequent trades. But now once the whole Operation is moved back to long island, love is like, I, okay, great.

I'll come full time. I'm here. It's going any work anyway. This is way more fun than teaching and listeners.

I imagine this is, by the point, we are starting to get confused and saying, there are so many people in the story, I think we're on eight or nine. We keep introducing more people. And that is the story of 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 errors, where the firm was trying different things and eventually became phenomenally successful with a very particular approach. But while they were figuring that out along the way, I took a lot of people.

a lot of people, and just a lot of time to, this is twenty five years. This is a quarter century from the time that bow and Simons write the paper at I D. A. Until medalia really starts to work. IT takes a long time.

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

yes. Well, let's get to that. So jim moves everything back along. Island sets IT up, as this academic paradise is recruiting the smartest people in the world in one thousand nine hundred and ninety one.

The next year, the firm does fifty four point three percent growth returns and thirty nine point four percent net returns after fees. So not jims bogie of eighty percent, but still pretty frequent. great.

And we should say the years of modest performance are behind them from every single year forward. They shoot the lights out from ninety ninety onward. They never lose money.

And on a grow spaces, they never even due 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 wear on the train.

The historic run has begun, let's to say so. One hundred and ninety two grass returns are forty seven percent. Ninety three there, fifty four percent.

At the end of one thousand nine hundred ninety three, Simons decides to close the fund and not allow new L. P. S. So if you're an existing lp, you can stay in. But there no longer open for new inflows.

He has so much confidence in what they're doing that he thinks they're all going to make more money without accepting new capital by just keeping IT to the existing investor base. One thousand and ninety four gross returns are ninety three freaking percent. Medalia at this point is stacking up cash. IT is a meeting full fund. It's about two hundred and fifty million dollars total at this point time, which is but we're talking about nineteen ninety four with a bunch outsiders and academics that have managed to a mass a quarter billion dollars here.

People started to pay attention. 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.

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. yes.

And that sort of in the midst nineties.

yep, as they're hiding this two hundred or and fifty million a half .

a billion dollars scale, right? The computer model spits out we should go by this huge amount of something at this place 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 Prices is very different.

Yeah up to this point, the vast majority of what medalia is doing is trading currencies and commodities, not equities because you might be thinking. okay. Yeah, hear you.

The nineties was a different era, but half a billion dollar fun doesn't sound that big. How are they moving markets with half a billion dollars? It's not the equity markets.

It's because they're in the winner markets. It's not that commodities and futures are small markets, the large but they're thin compared to equities. There's just not that much volume. You just can't trade that much without slipping becoming a huge issue. And medan 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 equity pricing that we can feed into our models. And the signal processing that we can do in the signals that we can find are going na be even Better.

right? There are so many buyers and sellers every day showing up to trade so many different companies at such high velocity. It's almost this honeypot for renaissance stems.

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 ly and the whole time with these sydney traded markets with minimal data. yes.

And this brings us to Peter Brown and bob mercer. And in one thousand nine hundred ninety three, one of the mathematicians that jim had recruit, a rantin a gun. Nick patterson gets especially passionate about going out and recruiting new talent along with gym.

And this is, I think, one of the key's terrene c and the culture there. People want other smart people to come be there too. Mixing there like this is a joy I want to go find other best people in the world to hanging 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 A I machine learning research, specifically IBM deep blue chest project of the time, had come out of this group. And Peter Brown, there was the one that actually spearheaded the project.

Yeah 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 doing to analyze the market.

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

right? Speech recognition is a hidden mark of 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 he 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 greggs, booky greatly points out this perfect example. When I say apple, you might say, I, the probability that pie is going to be the next word following apple is significantly higher. And so these people who expect their careers not only doing the math and the theoretical computer science behind speech recognition to help figure out and predict the next words, that you have a narrow set of likely words to choose from. So when you're listening to those frequencies, you can say it's probably going to be one of these three, rather than searched the entire dictionary for anywhere that I could be to narrow the processing power. It's not only the theoretical side, but it's also people who built those systems at I, B M, like a real Operational computer company.

yes, at Operational scale. And this is what so important and why the two of them become probably the most critical higher in rent tax history and 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 gona move in the 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 talents rented coming from academia has just never experienced that are built anything like IT.

And the world that they're entering is just exploding in complexity and dimensionality. And I when I say that here's what I mean, the data that they are mining that they are looking for is this inter day tech data between every stock trading.

So there in this sort of trying to map the relationship between one stock in every other stock, not just at that moment in time, but every time before in, every time after IT, there are also, once they do identify patterns, which this is key, the algorithms identify the patterns is not a human with a hunch saying, I think when oil Prices go up, the airline Prices are going to get hit. Its computor is doing machine learning to discover the patterns in the data. Then there's the second piece of all, 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, one hundred thousands of cy multi ese trades, both two 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 multi dimensional problem, both on the data injustice side and on the how do I actually react to its side. And all of this computation can't take a long time because you must act, you know, not in small seconds.

It's not a high frequency trading the front running the market. That's not actually what they do. A lot of people think IT is but will get to that later, but they do need to act with reasonable quickness, probably all in the order of minutes. So these need to be really efficient computer systems, too.

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

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

So patterson and Simons go RAID IBM. They they like Steve jobs rating zero x park. They bring Peter and bob and one of their programme colleagues, David magnan, over from my B M.

International tech. And they get started on building the equity model. But IT turns out a there obviously very successful of that.

But the impact that they have and what they build is even bigger because bobin Peter realized that, hey, actually, we should just have one model for everything here, for currencies, for commodities, for equities. Everything is correlated. Everything is signal.

It's not like the equalities market is holly 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 midd nineties.

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 of the learnings from the other thing on to this brand new thing that we're looking out with little data for the first time. And because we're putting at all in one model and no one else in the world is we can discover patterns that no one else knows about.

IT turns out that this was actually the second most important innovation that bob on Peter bring to rented 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 thus modest people in the entire world, all in one building. Before this, there were separate models within reteach. So insights and innovations at work that one team was doing on one model wouldn't get applied or translate over to work that was happening by another team on another model.

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

get implemented. This is holy, unique and revolutionary, no other at scale. Investment from period, and especially quante firm Operates this way today with just one model. Their portfolio managers in teams and multistate testing people are culturally competitive with one another.

But even if they're not, the work that you're doing on this side of cid del is not impacting the work you're doing on that side of syddall, right? What bob and Peter do is they unify everything at an tex. So all the wood is going behind one arrow.

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Yeah, vana is the perfect example of the quote that we talk about all the time here and acquired jeff bases his idea that the company should only focus on what actually makes your beer taste Better. I E spend your time and resources only on what's actually going to move the needle figure product and your customers and outsource everything else that doesn't. 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 add zero flavor to your actual product that IT .

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So whether you are start up or a large enterprise es and your company is ready to automate complaints and streamline security use like vana seven thousand customers around the globe, i'd go back to making your beer taste Better, head on over to vantage accomba required and just tell them that ban and David sent you. And thanks to friend of the show, Christina anta CEO, all acquired listeners get a thousand dollars of free credit venta com slash acquired. So David, the equities machine.

yes, and indeed a machine that is. So Peter and bob come in one thousand nine hundred ninety three and one thousand nine hundred and ninety four. One thousand nine hundred ninety five, their building, this rantum, is getting into equities.

And yet, just imagine the computers that you were using during one thousand ninety four and one thousand ninety five IT is astonishing, the level of computational complexity and coordination and results that they are pulling off again in real time anal zing. These markets with the technology that was available during those years.

yes. And here's what's amazing. Returns go down. Maybe slavery, certainly a bit from the blow year, the one thousand nine hundred ninety four was, but they're still above thirty percent every single year, most years above forty percent.

This is unbelievable that they are 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 one hundred and ninety, medalia has almost two billion dollars in assets under management while maintaining roughly the same performance by getting into equities. This is huge.

yep. And David, if you just kind of look at this and do the math, okay, so ninety four, their A U M was two hundred and seventy six million, and they grew ninety three percent. And then their A U M, the next year was four hundred and sixty two million, and then they grew fifty two percent.

And their A M. The next year was six hundred and thirty seven million. You can quickly get where i'm going here, which is, oh, they're scaling A M, not by bringing in new investors. It's close to new investors. It's all just compounding. This is the same capital that they had in one thousand nine hundred ninety three that has gone from one hundred and twenty two million at the beginning of that year to one thousand nine hundred ninety nine, being one point .

five billion, yes. And then in the year two thousand, they just totally blow the doors off a hundred and twenty eight percent gross returns, net returns after fees of ninety eight point five percent. This is banana.

They grow the fund from one point nine billion to three point eight billion of assets management, again, purely by investing gains, not vie, getting any new investors. The year the tech bubble burst.

yes, while the whole rest of the market is down big time median is up hundred and twenty eight percent girls on the air. And this becomes the theme. High volatility is when medalia really shines.

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

And David, as you said, volatility actually makes their algorithms work even Better because what are doing, they are looking for scenarios where the markets gonna erratically and they can take advantage of people making decisions that they should. And any time many investors are under pressure, there's a little bit of edge that's going to accrete to a madan that's saying, okay, you're fear selling right now. Well, I can determine if you should be for selling or not. And if I determine that you shouldn't be dumping that asset, i'm buying IT from you.

So there's a really fun story around this that really illustrate James 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 bubble bursting medalia actually takes a bunch of large losses. Then part of IT might be that the model wasn't tuned right yet because nobody at renta had seen this type of behavior in the market before.

Part of IT might also be, too, that I didn't perform well for those couple days. It's a really stressful time for everybody. Everybody's in jims office.

Jim smoke in his cigarettes. 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.

Were not very far removed at this point from long term capital management. H, the model may be saying we should stay long here, but let's not blow up the firm. Yep, after this goes down, Peter Brown comes to jim and offers to resign, given the losses that they encouraged 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 live through this, and you now know not to one hundred percent trust the model in all situations.

it's fascinating. It's such a good insight that illustrates gm as a leader right there.

IT totally does. There is a parallel story when jim ultimately does retire two thousand and nine, and Peter and bob take over as co ceos, where a year so before the quantity ake had happened, where similar to the tech buff bursting, there was all of a sudden very large drawdowns about all quantitative firms in the market, and rent tech gets hit. And during that period, Peter argued very strenuously that we should trust the model, stay risk on.

This is going to be an incredibly profitable time for us. And jim pumped the breaks and stepped in, intervened and took a risk off. And Peter goes to jim again around the C. E. O.

Transition and says, hey, jim, aren't you worried that with me running the place now, i'm going to be too aggressive and blow IT up one of these days? And jim says, no, i'm not worried at all. I know you were only so aggressive in that moment because I was there pushing back on you. And when you're in the sea, you're gonna a great. He's just such a master insight in the human behavior.

IT is so true, though, even find this about myself, that I will naturally take the position of the foil to the person across from me. So if somebody y's being pushy in somewhere, i'll find myself taking a position or if I posted, reflect them 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.

yep. So back to the year two thousand and this incredible performance then to what you are 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 ve got to introduce this concept of a sharper issue now, which for all of you listeners that are in the finance world, you'll know this, but for everybody else, this is a really important concept.

And I think people grasped intuitively. We've mentioned this concept a couple times this episode where, okay, great. It's amazing to have a fund that twenty five aces 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 intuite vely know.

No, IT doesn't because maybe that was a fluke. Maybe you're taking on an extreme amt 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 rent tech is both you can be uncorrelated and massively outperform, which is effectively the holy grail of money management.

yes. And so the sharp ratio is a measurement combining these two concepts.

exactly. So it's named after the economist William of sharp. IT was pioneer in one thousand nine hundred and sixty six. IT is effectively the measure of a funds performance relative to the risk free rate.

So if you performed at fifteen percent that year in the risk free rate was three percent, then you know your newberry or is gonna twelve percent and IT is compared against the volatility where the standard deviation is technically what IT is, but effectively, how volatile have you been the last six years? And typically, it's looked at as a three year sharper or five years sharp, a ten years sharp. The sharp ratio represents the additional amount of return that an investor receives per unit of an increase in risk.

And so David, you start to throw out numbers. Low sharp ratios are bad, negative sharp y ratios are worse because that means you're under performing the risk rate rate. High sharp ratios are good because that means that you're producing lots of returns and your very answer, your standard deviation or your ort of risk is low.

So in ninety, they had a sharp of two point o which was twice that of the S M P five hundred benchmark. awesome. good. Nineteen ninety five to two thousand sharp ratio of two point five really started to hum. Pretty unbelievable.

good. Where do I sign up to invest?

At some point? They added foreign markets and achieved a sharp ratio of six point three, which is double the best quant firms. This is a firm that has almost no chance of losing money, at least historically, and massively outperforming the .

market on an uncorrelated basis. And I believe if I have my researcher in two thousand four, they actually achieved a sharp ratio of seven point five.

astonishing.

You do. I get back to our sports analogy here. These aren't hall of fame numbers. These are like, make tom brady look like a third stringer.

Yes, exactly.

So on the back of two thousand and this rise the next year, in two thousand, one, they raise the Carried interest on the fund to thirty six percent, up from other or twenty five percent whatever IT was before. Now remember, they ve already closed the fund to new investors, so they're still outside investors in the fund, but no new investors are coming in. And then the next year in two thousand two, they raise the Carrier of forty four percent. I mean, great work if you can get IT for context. The scores, the benchMarks out there, they have a seen Carrier of thirty percent forty four is unprecedented.

There's two interesting ways to look at this one, they're just trying to jack IT up so high that they just urge their existing investors out where they are saying we're not going to kick anyone out yet. We've been close 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 investors, are arbi treasures. They see a mispricing, they come into the market, they fix that mispricing.

So any time that there is 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 profit as a thank you, and then sort of fixing the market to make the market a true wing 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 renison is doing here to themselves or to their investors. They're coming in and saying, look, this is a scene we so clearly outperform the market.

You're still gonna take this deal even if we take more of this because there are just a mispricing here. This product should not be Priced at twenty twenty five percent Carried. This product should be Priced at a much higher Carried interest. And you're still going to love IT.

You should pay twenty percent Carry for a firm that delivers you fifty percent annual returns. We're delivering you fifty percent annual returns totally.

So I have to imagine IT didn't overall with the existing investors, but they just have so much leverage that .

what's gona happen. okay. Once again, i'm sorry, audience. I have to say hold on one more minute for another perspective that I have to offer on the Carry element, but I want to finish the story first.

Okay, so two thousand one, they raise the Carrier to thirty six percent. Two thousand two, they raise IT to forty four percent. And then in two thousand and thirty, they actually say, hey, we can't incentivize you out of the fund outside investors. We are going to kick you out. So starting in two thousand and three, everybody who's an outside investor who's not part of the red tech family, you know current employee or alumina of the firm gets kicked out and .

not all lami get to stay. There's select alumina that get grandfather.

Then yes. Now why did we do this? I want to talk about one reason in a minute, but one reason is super obvious.

The median fund is now at five billion dollars in assets under management. They're trading even in the equities market. They are now hitting up, up and slippage. Yeah, and so if they want to maintain this crazy, crazy performance, they just can't get that much bigger.

This is the problem that warn buffer t talks about all the time and why he has to basically just increase his position in apple rather than going and buying the next greet 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. And the strategy that red tech is employee right now, they're just deming doesn't work at north of five billion dollars.

So in two thousand three, they start kicking all the outside investors out of medalia. But clearly, they're still lots of institutional demand to invest with renaissance. What do they do?

Well, time to start another fund. So they start the rena sant institutional equities fund. And there is a couple things to add a lot of a context to really why they decide to do this. Well, the first one is sometimes there is just more profitable strategies than they had the capital to take advantage of in medalia, but they weren't sure that would be on a durable basis. If they were sure that they could manage ten, fifteen, twenty, twenty five billion in medalia all the time, then they would grow to that.

But if just sometimes there's these strategies that appear or we don't want to commit to a much higher fun size and not always have the available the other thing is that a lot of times, those strategies aren't really what medalia is set up to do. They require a 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 button with the short term stuff, we should be able to make some .

money doing IT. Yeah, there's a fun story around this that Peter Brown tells of. Jim came into his office one day and said, Peter, I got a exercise for you.

If you married a rocket fellow, would you advise the family that they should invest a large portion of their wealth in the S. M. P.

Five hundred? And Peter says, no. Of course not. That's not a great risk. Adjust to return.

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

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

right? And so that's basically what they come up with, is can we create something that's like an S M P.

Five hundred with a higher sharp ratio? Can we beat the market by a few percentage points or Frankly, even matched 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 draw dows or Frankly, just big volatility in general, should they need to pull the capital earlier.

And the nice thing about being investing in a hedgehog versus of venture fund is you can do redemptions, like if you look at the thirteen fs, the S C, C documents that the renaissance stimulates quality fund files over time. IT changes every core because there's new people put money in, there's people doing redemption. So it's a pretty good product, or at least the theory behind IT is a pretty good product of a lower risk, similar return thing to the S M P. Five hundred.

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

right? It's really fun, ty. There's only 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, were not charging for IT like the median fund of David. You said that the fees in carian medan went up to what? Five and forty four. Well, on the institutional fund, the fees are one in ten. They're only taking one percent annual fee and ten percent of the performance.

Clearly, this is a very different product.

but people did not perceive that people were very excited as a renaissance product as the same analia they use in other fancy computers. I'm sure we're going to get this crazy performance. And at the end of the IT is an extremely different .

vehicle that has not performed anywhere near how medalia has performed. correct.

Has IT served its purpose? yeah. But is that medalia? No, it's not special in the way the medalia is special. yes. A couple other funny things on the institutional fund. So I spent a bunch of times growing through thirteen fs over the last decade from the medalia filings, and they're all from I think they have two institutional funds.

Yep, there's institutional equities and diversified alpha.

So the honest thing is they find these thirteen youths and David and I are very used to looking at thirteen hours of in friends of the show who run hedge funds who we've had on his guests or perhaps really just any investor where you want to see like or what are they buying inside this corner. And usually you see fifteen, twenty five, maybe fifty different names on there.

Well, the thirteen f for resident has four, three hundred stocks in these tiny little chunks. And there's a little bit persistence corner a quarter, for example. Weirdly, novo notice has been one of their biggest holdings. Biggest, I say, like one to two percent. That's the biggest position for several quarters in a row.

Hey, they ve been listening to a car. That's right. That's one of the signals .

in the you kind of get the sense from looking at these filings that these things were flying all over the place. And this was just a moment in time where they decided to take a snapp shot and put IT on a piece of paper. And even though this is the end of quarter filing of what their ownership was, if you had taken IT a day or a week earlier, IT could look completely different.

Yes, the way that some folks we talk to described the difference between the institutional funds and medalia to us is that medalia average hold time for their trades and positions is call IT like a day, maybe a day and a half, whether the average hold time for the institutional funds is like a couple months.

So across forty three hundred stocks in the portfolio, there is a lot of trading activity that happens on any given day, but it's a lot slower in any given name. Then medan would be yeah which makes sense again, IT gets back to this slipped 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 games are onna slip away? yep.

And Frankly, I just looks a lot like the S P5 front。 Like when you look at as of november twenty three. So eleven to 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 debt from the pandemic, they were down in ten point four percent less than the broader market, but they still were sort of mirror of the broader market. So I think the R I E F institutional fund, yes, IT works as expected. No, it's not medalia. And if IT we're standing on its own, there is zero chance that we will be covering the organization behind IT on acquired zero percent.

Speaking of the fund, that is the reason why we are covering this company on this show we set up during the tech bubble crash. The volatility is when medalia really shines. Well, there is no more valid periods than two thousand and seven and two thousand eight.

yeah. Two thousand seven media does one hundred and thirty six percent gross. Two thousand and eight media does one hundred and fifty two percent gross. Like, get out of here. This is two thousand and eight, while the rest of the financial world is melting down.

And so this really does illustrate where do they make their money from, who is on the other side of these trades. It's people acting emotionally. They have effectively these really robust models that are highly unemotional that are making these super. Multi security bets. And they are putting on exactly the right side 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 like, oh, crap, we ve got to 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 verses 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 fight over a fixed pie here, i'm trading against someone else. I win, they lose.

yes. Well, there's one slight nuance to that, but I don't know how much to hold the water and the apologist nuance would be. Well, warn buffer could be on the other side of the trade and median could make money on that trade with Warren over its time horizon of a day in half. And warn could make money over his time horizon of veto fifty years.

Super fair.

So I think the argument against that though is that median 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 uploaded to somebody. The direct counter party of middle an and the quin industry red large might not take the loss, but somebody is gonna take the loss along the way. IT is, as you say, as zero some game.

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

So after the historic performance during the financial crisis, as I did to earlier, gym retires at the end of two thousand and nine, and Peter and bob become coc. E. S.

Coheirs of the firm in twenty ten. They take the portfolio size up to ten billion dollars when they take over. IT had been at five for the last few years of jim ten year. They take IT up to ten and really with no impact, which I assume means that red tech was getting Better and the models were getting Better because others SE, they would have gone to ten before.

right? They gained confidence that they had enough profitable trades they could make that they could raise the capacity without dampening returns. yes. And perhaps they could have done that earlier and they just didn't have the confidence that I would work at larger size. But I bet they are very good knowing how large can our strategy work up to before IT starts having diminishing returns.

yep. And importantly, during periods of peak volatility like say, twenty twenty medalia continues to shoot the lights out. So from at least the data that we were able to fine on medalia performance over the past few years, twenty twenty, they were up one hundred and forty nine percent growth and seventy six percent. So the magic is still there.

And one way to look at IT, which may not be the ball and and all, but I think is a good way to compare James era at medalia versus Peter and bob era during James ten year medians total egg get I R R from one thousand and eighty eight when the fund was formed, to two thousand nine when he retired, with sixty three point five percent gross annual returns and forty point one percent net annual returns, which of course did include many periods of lower Carries. Twenty percent verses the forty four percent during the post gym era. The Peter in babara from twenty ten to twenty twenty two was when we were able to get the latest data.

I R, R, seventy seven point three percent growth and forty point three percent. Det, so Better on both fronts, even with my tire average fees. So yeah, I think madeleine .

is doing fine. It's amazing. And we weren't able to tell there some sources that report that they've grown from ten billion dollars in the last few years to be uncomfortable at a fifteen billion dollar fun size. And if so, that just means that they continue to find bar profitable strategies within medalia to keep those same unbelievable returns at larger sizes.

yeah. And at the end of the day, this is all just the same. So as far as we can tell benue eluted to this a bit at the beginning of the episode.

And as far as anybody else can tell, medan has by far the best investing track record of any single investment vehicle in history. So give me those net numbers. So during the entire lifetime so far of medalia, from one hundred eighty to twenty twenty two, that's thirty four years. The total net annual return number is forty percent four zero over thirty four years after fees is sixty eight percent before fees, which equate to total lifetime Carry dollars for the whole firm of sixty billion dollars just in Carry by our calculations.

astonishing.

That is a lot of .

money also. David rosen, so good spread sheet work on this. You have not done a spread sheet an episode in a while. So I admire your your work on this one.

Yeah, I still know how to use excel. barely. It's going to be a dying art now with copy and gp.

that's right. OK, so sixty billion and total?

Kerry, so sixty billion and total Carry is a lot of money. And well, speaking of a lot of money, we do need to mention before we finished the story here that, that red tech 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 great bar and camera galatia and one of the major financial backers of both the twenty sixteen trump campaign and the brakes ic campaign in great britain. Now less you think that rent tech dollars are solely being funded into one side of the political spectrum. Jim Simons is a major democratic donor, as are many other folks at an .

tech yeah had really low. And other folks are also huge donors, approximately to the same tune as what bob merce is on the right.

Yeah, tens of millions of dollars, many tens of millions of dollars on all sides through many campaign cycles here from rented employees and alumni. This did become a flash point for the firm in the week of the twenty sixteen election. Mercer obviously became a controversial figure, both externally and internally within the firm.

especially once people realized he was the three line through bright bar camera gena the trump l election hand brakes.

Yes, ultimately, jim asked bob to step down as o CEO in twenty seventeen, 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 time point any longer.

Ultimately, the thing that surprised me the most is how these people all still work together, despite having about the most opposite political belief you could possibly have .

yeah under statement of .

the century and all being extremely influential and active in those political systems. Yes, bob matter is no longer the CEO of rena's ance technologies, the coc, E, O. He still works there.

He still associated. They all still speak highly of each other. It's unexpected.

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

like everything with renaissance works, a little bit different than the rest of the world.

yes. okay. Speaking of let's transition to analysis, and I have a fun little monogue, I want to go on if there with me.

I think this qualifies as the red tech playbook. But I really kind of think of IT is the reteach tapestry. I was inspired by costco here because we are talking to focus on the research. And everybody said, you know, red tech IT just has these puzzle pieces that fit together IT on the surface.

Rent tech does the same thing that C L D shah, two sigma gene street, others that set up a deal, they 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 sited, i'll can say the exact same things just the same as like walmart. Amazon can say we too have large scale supplier relationships that we leverage to provide low Prices to customers just like cosine pe.

But it's underneath that where I think thematically ze, there are three very interrelated things that make rent tech unique. 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, cause I compete with one another yeah I mean.

typically in kind of a friendly way. But yeah.

let's take like any 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. And I think that's the most collegial way that this happens in finance. Yeah then you've got multi strategy hedge funds out there where literally firms are being kilted against one another to be waited in the ultimate ating model for .

the front yeah at rantin though.

because of the one model architecture, everyone works together on the same investment strategy and the same investment infrastructure. That means everyone sees everybody else is work. Everybody who works at a tech, on the research team, on the infrastructure team, they have access to the whole model. That's not true anywhere else. Yeah.

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

And that also means because it's just one model, just one strategy when somebody else improves that models performance that directly impacts you as much as IT impacts them. This is really different than any other heads fund out there.

So why is that different than if I rule on my compensation into a multi strategy hedged fun than I work at? Don't I love other teams creating high performance also?

sure. But you don't love IT as much as your team because either compensation or career wise, you are much more dependent on your performance than you are other people's performance.

Oh yes, this is a big thing. You intend to have a job after that job at most places, most of the time. So you care about credit and you care about smashing the pyotr than going elsewhere or building reputation than going elsewhere. Most of people at retch are not gonna have another job.

What did you find on linked in at least the media and tenure of employees is like sixteen years.

I just got linked in premium and you can see median tenure and it's crazy. There's only like three, four hundred employees at renison, ance and the media and ten year at least as reported by lindis, like fourteen years?

yes. okay. This brings me the point number two, which he said, this is an absurd small team. There are less than four hundred employees that work errante c, only half of which work in research and engineering, and the other half or either back office or institutional sales for the open funds. So let's call IT, I don't know, one hundred and fifty, two hundred people max, who are like hands on the wheel here for middle you every other peer firm of antique ma, all you lump James street, you know, jump the high frequency guys in here. Minimum two to five thousand people work at those places.

Oh, I realized was that big .

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

unless you think that that's like a capital base thing. No, the institution al funds have gotten big. They peaked at over one 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 median fund.

yeah. So A U M is like the same yeah as these big funds. This has all sorts of benefits.

Number one, there's like the armies at a workshop benefit. Everyone knows each other by name. You know your colleagues kids, you know your colleagues families.

Yes, they put right on their website. There are ninety P. H, D. In mathematic physics, computer science and related fields. The about page has these ten kind of random bullets points and that's one of them. yes.

Then there's the related aspect, told this the firm is in the middle of nowhere on lung island. You actually know your colleague s 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 no soil measuring parts of your anatomy with someone else you like hanging .

out of the swimming. Totally sense renison 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 to talk long island which has its like ten thousand people or something. There are 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 N D, A, you're very unlikely to be in the social circles.

You're just not getting exposed exactly. And in text, hiring established scientists and P, H, D, they're not hiring kids out of undergrad like gee street or bridge water. Is my sense is that the places like a college campus without any students.

Have you seen the pictures online? yeah. If you look up redon technologies at google and you go and look at the photos on campus, court yard and winding walking path and woods doll around the tennis courts. Yp.

so then there is the last piece of the small team element, which is just the magnitude of the financial impact, which I don't think is true. But let's say that there were another quant fund that made the same number of dollars of performance returns that red tech does a red tech. You're split that a couple hundred ways at city del splitting that five thousand ways is IT just doesn't make sense to go anywhere else.

We are chatting with someone to press this episode, and they told us you can ever compete with them, but theyll pay you enough that you won't want to.

yes. okay. So this brings me to what i've been kind of tee zee. I'm super excited about. I think the third puzzle piece of what makes rent tech so unique, indefensible, is medians structure itself that IT is A L P G P fund with five percent management fee and forty four percent Carry.

So it's not like a prop shop or like propriety is just one part of money. It's literally A G P L P, even though the g GPS in the L P, S are the same people.

So here's my thinking on this. I don't know how IT is actually structured, but there was something about this whole crazy forty forty percent Carried that just wasn't sitting with me right throughout the research because I kept asking myself, why, right? We've already .

kicked out most of the L, P. S, if not all. So why are they are raising the Carry?

right? It's all themselves. It's all insiders. Why do they charge themselves forty four percent, Carry in five percent management fees? I think jim talks about this. Oh, I pay the fees just like everybody else.

Yes, it's always a funny argument. It's like, who are you paying the fees too.

right? It's always like what is happening here. So okay, here's my hypothesis. This is not about having crazy performance. This is not about having the highest Carrier in the industry.

This is a value transfer mechanism within the firm from the ten year base to the current people who are working on media in any given year. So here's how I think IT works. When people come in to rent tech, they obviously have way less wealth than the people who've been there for a long time.

Vote 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, who's either the state of new york or the federal government to court to be able to have the foo in k plan at rantin be the medallion fund? no. yeah. So like if you worked there, you're four one k is the medallion .

fund that's crazy. So really doesn't take more than a few years before you'll set for life.

So depending on definition of set for life, I think IT ens, very, very. yes. 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 median fund, right.

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

right? So I think it's the forty four percent every structure that does because 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 medan 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 ten year base. I was like, what is the equivalent here? I think it's kind of like a academic ten kind of think the longer ten you you are the firm, the more your baLance shifts to the lp side of things.

And the Younger you are, the firm, the more year baLances on the G P 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 incentivise. And as you stay there longer, you are paying your Younger colleagues to work for you.

right? It's funny. I think it's a good insight that is structured like a university department ten year.

Well, I just kept asking myself, why, why? Why do they have this if there's no outside lp. S and this was the best thing I could come up with. And I actually think it's kind of genius.

yet more elegant than it's all one person's money. And they're deciding to bone us out the current team every year and just give enough money to make sure you retain them.

right, which is how I think most prop shops work like gene street is mostly a prop shop. I think IT is mostly the principles money, but that's a static situation. It's not like, you know, if that we're true, then jim would just own this thing forever. And I don't think that's true here.

antic. yes. So essentially, David, the real magic is they've got one fund. It's ever Green. 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 in the new sort of shift to that fifty one percent, you're kind of the L P. And then over time, you eventually graduate out entirely and you're all the lp and see, 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 verses just doing on the owner. And i'm giving everyone arbitrator uses you.

And at the end of the day, I think these three pieces, to me are the core of the sort of tap history of the antec one model that everybody collaborates on together, a super small team where we all know each other in the financial impact that any of us make to that one model is great to all of us. And three, this L, P, G, P model with very high Carry performance fees that creates the right set of incentive, both for new talent on the way in and old talent on the way out.

Yes, 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 two thousand and two. Red tech has thirteen years of knowing that they basically have a machine that prints money.

So what should you do when you have a machine that prints money? Lover, now there are all sorts of restrictions around firms like this and how much leverages they can take on. You can't just go and say i'm going to borrow.

We know one hundred dollars for every dollar of equity capital that I haven't here. So you need to work to get clever to borrow a whole bunch of money from banks or from any lender. You basically juicer returns.

If again, you have a money printing machine that's reliable, most people don't. Most people probably shouldn't take leverage because they're just as likely to blow the whole thing up as they are to be successful. So basket options, I am going to read directly from the man who solve the market because greg are common.

Just put IT perfectly. Basket options are financial instruments whose values are peg to the performance of a specific basket of stocks. While most options are based on an individual stock or 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 loan 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 medalia property. So this is very clever medallion saying, well, the way we're going to lever up is there is 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 medalia computers and automated instructions to the banks, sometimes in order a minute or or even a second, the options gave medalia the ability to borrow significantly more than that otherwise would be allowed to. Competitors generally had about seven dollars of financial instruments for every dollar of cash. By contrast, medians option strategy allowed to have twelve dollars and fifty cents worth of financial instruments for every dollar of cash, making IT easier to trance rivals, assuming they could keep finding profitable trades when medan spited especially juicy opportunity could boost leverage, holding close to twenty dollars of asset for every dollar of cash. In two thousand and two, medalia managed over five billion, but IT controlled over sixty billion dollars of investment positions. David, this exposes something we haven't shared the on the episode, which is, is not just that they could find five billion dollars worth of profitable trades, is that they wanted deliver the crap out of five billion dollars and find sixty billion dollars of profitable trades to make and basket options gave them illegal way to have an incredible amount of leverage in a way that they .

felt safe about a the unlevel return n if you are running, this strategy would be much lower. yeah. So I big. He's this playbook .

that we didn't talk about as leverage, but every quant fund does the leverage. And so renaissance was just more clever than everyone else.

Yep, it's an important point though. Nine out of every ten companies that we cover on acquired leverages zero part of the story, right? And for us coming from the world we come from in tech and venture capital, leverage is like a dirty word like i'm scared of IT.

right? I mean, you could imagine, let's say IT wasn't. They were right fifty point two five percent of the time, but they were right fifty point one percent of the time. They would need to do eight ten 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 yeah 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 other pieces.

They thought this was a remarkably tax efficient vehicle. The way that they were filing their taxes said, oh, sure, they're 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 yourself.

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 own nap, asked 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 billion dollars in taxes that you didn't pay. You're going to need to pay that with interest, with penalties. And by the way, jim Simons, we're gonna 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 I O. S. And back taxes for this basket option strategy that turned out not to be a long term capital again.

yes.

All right. So numbers on the business today, and then we will dive into power and playbook. So today we've talked about medalia ten or fifteen billion, depending on who you asked history.

Ally was more like vibor ten billion. The institutional fund is about sixty to seventy billion, and at one point was a hundred billion. The total Carry generated David inside of sixty billion dollars, forbes estimates.

The gym 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 runaways sance the returns. Obviously, the medallion fund generated approximately sixty six percent annualized from one hundred and eighty eight to twenty twenty, after those fees was about thirty nine percent wild. So an interesting thing to understand.

I ran 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 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 because I always asked myself the question, does that actually matter? They did always work to stand up the institutional side.

Who cares? Well, let's say adele does their average sixty six percent growth on fifteen billion. That is seven hundred and fifty million in fees and four point three billion on performance.

So a total of five billion from a dalian and one point two billion from the institutional side of the business. Now of course, the employees are the investors in midi alien. 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.

right? Because that's not including the L P. Return on ma a hundred percent. It's not wait again, as we spent the last time talking about, it's all the same thing, yes.

but it's kind of interesting just to compare IT against other companies to have this in the back of your head. This is seven, eight billion dolla year revenue business.

Now I think there are a lot of expenses on the infrastructure side totally.

That was another thing I want to talk about, the fact that they do, let's say, medalia alone. So they have seven hundred and fifty million dollars in fees. I don't think they come close to seven hundred and fifty million dollars year and expenses, but they are running who knows what infrastructure, some kind of supercomputing cluster, what is IT cost to run one amazon data center? I mean, it's I think much smaller scale.

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

Yeah, IT says right on their website. They have fifty thousand computer cores with a hundred and fifty gigabytes per second of global connectivity and a research data base that grows by more than forty terabytes a day.

That's a lot of data. Is that seven hundred and fifty million a year? I don't know, but it's not zero.

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

I wonder to if the fee element of median basically pays the base salaries for the current team that feels .

like it's right. If you're someone who has done a data center build out before or has any way to sort back into what the costs of medians Operating expenses are on the computer and data and network side, we would love to hear from you hello at acquired df m.

Okay, power, power. This is a good one. yeah.

So listeners who are new to the show, this is hamilton helmer er'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 r counter positioning scale economies, switching costs, network economies, process power, branding, encountered resource.

And David, my question to you to open this section is specifically about rent tax, lifelong non compete. 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? Well, I think it's lifelong g in non compete as long as the state of new york legally allows for. But that is not lifetime.

I've heard various figures, six years, five years, something like that yeah. I mean, at the end of the day, non compete are more or like what is one side willing to go accord over. But the reality is people don't leave. People don't leave period. And people especially don't leave and start their own firms.

Yep, I was thinking .

about this in the middle night, and I think there's three layers. Two, the effective non compete that happens with an tech, there is the legal layer. The base layer that you're talking about is like the agreements you sign.

Then there's the economic layer of what we spend a long time talking about in history of IT would just be done to leave. You are Better off staying there as part of that team with a smaller number of people than going to sign ma with a lot more people. Yeah, I think that's the next level, love. And then the highest levels is just probably the social layer. You're there with the smartest people in the world in the collegial atmosphere where you're all working hard on something that has direct impact on you.

right?

It's your community. It's your community totally. You're not new york city. You're not in the hampton, you're not in silicon valley. You are selecting into that. And I think if that's what you want to like, what Better place in the world?

Alright, so classify IT, what power does that fall under? Well.

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

Yeah, I think that's right. I also had my biggest one being process power. You actually can develop intricate knowledge of how a system works and then build processes around that, that are hard to replicate elsewhere. I think these systems have been layer 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 gotten 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 trade, what trade is putting on, why the speed at which they need to happen. I'd actually don't think anyone holds the whole model in their head. And so I think there is process power just because it's thirty plus years of complexity that's been built up.

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

I am going to argue that the data is a corner resource. I don't know for sure about the model. maybe. I mean, I guess that's the same thing.

I think the knowledge of what the ten million lines of code does that the model, but I actually think the fact that they have clean data and they've been creating systems like they have the best PHD in the world thinking about data cleaning, that's not a sexy job. And yet they have probably the treasure trove of market data in the best format that nobody else has. That's an actual cornered resource. I have a .

couple of things on this. So one, I think IT probably is true that they have Better data than any other firm. Thanks to send s in the work that he started doing in the eighties before anybody else was really doing this.

yeah. So they have that, and other firms don't. That said, certainly all the other quant firms are throwing untold resources at all. This too.

right? They want to do this, and money is not the issue.

So in chatting with a few folks about this episode, I had more than one person said to me, there's two ways that red tech could work. And one version of how that 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.

right? There's one particular relationship, Green types of equities that they're just been exploited and no one can figure out except them.

right? And that may entirely be possible. Is that crazy right now? Rent tech will say they will all say that is a hundred percent not the way that the works. It's not that at all, if that worth the way that they were. The court still say that because they don't want even right.

Don't look at the relationship between soybean futures and G M, just don't do IT right.

So let's accept that there are easy possibility that, that might be true. More likely, though, is that what rent tech does say is true, which is no, there is no holy grail. What we do here is we completely reinvent the whole system continuously on a two year cycle.

Two years is kind of what I heard. The model is fully restructured every two years is not like on the date every years is being restructured day. But collectively, it's about a two years cycle.

So that would be an argument then that the people actually could, with five people left, they I could go create IT. And all they would need is the data.

It's also an argument that there is no actual cornet resource here in terms of other the model itself and maybe not the data either.

I bet that data is. So let's say you've been working there for ten years. You don't know how the one thousand nine hundred and fifty five soybean futures data ended up in the database. Even if you're used to using that data and you're able to go recreate the model elsewhere, you don't know how IT originally found its way.

I think that's fair. I think there might also be some argument to the data that, that older data is helpful, but its valued decays over time as markets of lw.

Definitely.

the brother point I want to make curious that every other major quant firm out there is also spending hundreds of millions, if not billions, on this stuff too.

And people are looking for all data everywhere. The bridges, waters 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 billions, or tens of millions or hundreds .

of millions dollars for IT. yep. So I think we can rule out scale economies for sure. If anything, there are anti scale economies here.

Oh yes, there's totally there's this economies of scale. Your strategy, stop working. When you get to my J, U.

M. Yes, you get slippage. I don't think there's any network economies here. I mean, they literally don't talk to anybody, although.

well, they do have some very well established relationships with electronic brocades and different players in the trade execution chain. I think they have very good trade execution and very fast market data. Their ability to pull data out of the market is very high quality. Do you think it's .

actually Better than their competitors?

So I don't know that's probably not the secret OSS.

Yeah, I don't think so.

It's the table sticks.

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

The feed stream of the institutional fund may entirely belong to branding, yes, but I think there's a lot of public equity firms and a lot of hedge funds that have a lot of branding power that have, on average market returns with decent sharp issues and are able to raise because theyve built a brand, yep, venture firms the same way.

totally. So for me, this kind of leaves counter positioning. I actually think there's some counter positioning here, and I think we're going to have two episodes in a row of counter positioning at scale.

Tell me about your counter positioning, who is being counter position in what way 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 .

like their counter parties in trades.

I don't think they are counterparties. I think they are all seeking to exploit similar types of trades. I think the counterparties are the people there, the denis that they're taking advantage of.

well, but quant funds are often in counterparties to each other that too.

But I think, yes, adversary is in finding the similar types of trades. And I think the counter positioning for or rent tech or for malian specifically is one, I do think the single model approach versus the multi I model, multi strategy approach that most others have does have benefits like I was talking about in the tap of trees.

But I think also, and maybe bigger, is every incentive at reteach is fully aligned to optimize fun size for performance in a way that is not true just about everywhere else. 嗯， i think they have the most incentive of anybody to truly maximize performance. Were able to achieve.

right? Even though the dollars would continue to rise because they get few dollars from more money in the door, they are incentivized in a unique way that makes IT. So they're not willing to trade theed damp in our own performance to get those dollars.

Yes, particularly because it's all the same people on the G P and lp side.

Um we keep gone round and around that X I loosely ly by the counter positioning thing. I just think the answer is disgustingly simple and kind of annoying here, which is they're just Better than everyone else at this particular type of math and machine learning and they're doing IT for longer so they're just gna keep beating you oh.

that's another argument I heard from people in that reteach basically is a math department in a way that one of these other firms are IT. Could be culture? Yeah, be culture.

I mean, onest to god. I 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 fatal truism. 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 six ital.

Yep, I think that could be. So maybe that feeds in the process power. Yep, OK for me, IT is some combination of process power in counter persisting. And I don't think it's any the other powers for me.

IT is processed, wer and ordered resource.

Okay, I about that.

And a thing that's not captured in seven powers is tactical like execution. The whole point of seven powers is strategy is different than tactics. And I think legitimately in tech may just have persistently been able to out execute their competitors. There's part of IT that's just like they're smarter than you.

yeah. Well, if you buy the the whole thing gets reinvented continuously every two years, then yes, and there's .

remnant knowledge. Like if you started building a machine learning system in nineteen, 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 systems are also going to a be Better a machine learning.

Then 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 learnings compound is my answer. great. Okay, playbook. So in addition to the three part David s insult tapestry that you have.

women have nothing more.

There are a handful of things that I think are worth hitting. So the first one is signal processing. As signal processing is signal processing, they buy not caring about the underlying assets. They literally don't trade on fundamental, I accept in the institutional fund.

When they trade on fundamentals a little bit, they use pray, earnings ratio 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 madan, 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 lining a regression or any either fani yourself 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 d noise, a quote, quote, photo of a star super far away, or whether theyve tried to do like natural language processing, it's just signal.

This is this really funny line that jim and Peter and others will say when I asked about why they only hire academic enough from wall street, not well, we found is easier. Teach smart people the investing business, then teach investing people how to be smart, right? That's ridiculous.

They don't teach anybody anything about investing. They're just doing signal processing. I bet at least half the people at rent tech on the research side could not read baLancer.

It's so funny. It's a whole bunch of people who are in the investment business, none of which are investors. Yes, another one that you can decide if this fits or not.

I was thinking a lot about complex adaptive systems. It's always been on my mind since we have the N Z S capital guys on a few years ago read their work in the nf institutes. Work on this in a complex adaptive system is really difficult to actually understand how one thing affects everything else.

Because the ideas, the relationships are so commonest, orally complex that you can't deterministic ally nail down. This one thing is the cause of that. Other thing is the butterfly flapping its wings.

But there are relationships between entities that. You can't understand or see on the surface, do you? Member, way back, we did our second and video episode.

I opened with the idea that when I was a kid, I was used to look at fire and think like, if you actually knew the composition of the Adams in the wood, and you actually knew the way the window is 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 red tech has basically, they haven't figured that out for the market. They can't predict the future.

But if they have a fifty point o one percent chance of being correct, then they can sort of take a complex adaptive system and say, we really care that as a complex adaptive system, our models understand enough about the relationships between all these entities that we're just going to run the simulation of bunch of times and we're going going to be profitable enough from all of a little pennies that we're collecting on, all the little coin flips where we have a slight edge over over and over over again. Now there 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. yes. And I think when i've heard people from rent tech 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 I can't tell you why, right? But that's okay.

But IT doesn't IT has a slight edge and so IT a trade on IT even though I can explain why. yes. Or a speaking of models, i've been trying to nail down and answered to this question, do you think red tech was the birth place of machine learning?

This is such a tough answer to tell. We actually mailed some friends who are very prominent, A I researchers, AI historians, and sort of asked this question. And the end we got back is I D surprising. They said, we don't know because they don't share anything.

right? It's like the principle certainly came out of the same math community that spd machine learning, but is what red tech has figured out over the last couple decades in google geri model in chat. No, it's not because they don't contribute any research back.

IT may be the case that actually rent tech has been everyone else to the punch and they have a strong A I or something that is actually much more so histin ted than all the a we have out in the world today. And they've just chosen that they'd rather keep IT locked up and captive and make money. I mean, IT could just be the case that renaissance is just taking in as much on structure data as IT possibly can. And they sort of we're just a decade or two ahead of everyone else and 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, these trades be right more than fifty percent of the time.

right? Make trades that sound right.

right? They figured out this big, unsupervised learning thing before anybody else, all the way up until last year, when the I men happened.

I ve ever the case. We should have very different answer to powers to illustrate this points.

It's quite interesting. Peter Brown's academic advisor was Jeffery hinton. Yes.

I so glad we brought this up. Yes, IT was the exact same due and the exact same cohort of people and social group and academic groups that renter came out of that a guy came out of .

the other person, just for people who are like, why are you saying that to make a super explicit? The other person whose academic advisor was Jeffery hinton is illegal skiver, who is the cofounder of OpenAI. I mean, many years later, but still .

yeah I mean, it's like we're talking about with mark of models and hidden mark of models, that is the foundation of antec, that is one of the foundations of A I and generate A I today. yep.

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

But I said a couple of words there, which is, of course, I should come up with. And there in 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 land tech 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 I ever saying to IT end things, you know, ten things, twenty things, one hundred things in in various different ways, at various different time skills, that is a killer recipe to exploit a secret that no one else knows and be able to beat other people in the market.

Such a good point. And many of that 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 alligators that pull the trigger or don't pull the trigger.

yes. And to be super illustrative because I think their 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 weed futures. And you might try to, because humans or story's tellers can cock some story in your head of why those move together. And if you believe IT, then you might decide there 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 rent text sort of admit is we have no idea why anything is actually connected, but IT doesn't matter.

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 one tech and maybe a couple other people.

okay? My next one is brought to you by a friend of the show, bret Harrison, who has worked in the contrary 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 access, fast and slow in terms of trade execution. And on the y access you have smart vers, obvious. Yeah.

the way he phrased to to us was smart vers dumb. But dumb doesn't mean dumb.

right? It's the obvious trades. And the high level point is all quanta funds are not high frequency trading firms. Advice first, 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 class of high frequency trader, the front running trades they're locating in a data center that's really near the you know, this is flash boys or theyve got a microwave line between new jersey in chicago. And they are trying to arb the difference between two markets. You need to have the fastest connectivity in .

the world to pull this off. Yes, this is jee street.

Yes, there's fast and smart, which you kind of don't need to be both. You don't need the fastest connectivity in the world and the most clever trade to put on. So people kind of tend to pica lane that they are either a high frequency traders or they're trying to make the smartish, you know, most non obvious trades possible.

And that, of course, leads us to medalia, which is in the slow and smart quadri. All of the machine learning system discovered the relationships in the data. So there's a huge amount .

of compute the nominal trades.

exactly that goes into finding the non obvious trades. But they're actually made reasonably slowly. They still have to happen within seconds or minutes. But the advantage isn't that their high frequency. The way that all the flash boys stuff is.

my sense, is red tech is not a hy frequency trading shop. They're not front running things. They're not flash boys compared to you and me. They still Operate incredibly fast, but it's more about the smartness and less about the fastest drag .

has a quote book. They hold thousands of long and short positions at any given time, and they're holding period ranges from one to two days or one to two weeks. They make between one 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. Oh, this is another thing that we heard in tech is world class at disguising their trades.

Yeah they can make IT so that they don't move the market. And you don't know who is acting or when. And this is because in the early days, they weren't good at this and people basically intercepted the trades that they were making and word 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 and what quantities and you don't know are going to keep buying. Yeah, my last one before we get in the 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 at all? You know, that wasn't possible in the old world where you're calling your broker. That totally is possible here.

And IT happened yet.

And well, it's never happened to rented. There was a company called night capital in twenty twelve that lost four hundred and sixty million in a single day. There was a bug in their process to deploy the new code.

And basically what happened? IT was a simple flag air, a misinterpret 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. If flip a different bit, systems were not looking at the same location and memory for the same bit. And so I basic thought I was never flipped, is 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 at all.

There are just drained out and there was nothing they could do.

Uh, well, I don't know, is the whole portfolio. But IT was enough that they lost a huge amount of the L. P.

capital. And then they were republican traded firm. Overnight, their equity traded down seventy five percent. And then someone stepped in and about them when .

they probably ly got margin called by all their counterparties.

So whoever is in charge of the financial controls and safety systems at red tech, that's a huge job for someone in this industry.

totally.

all right, to kick off value creation, value capture. I have a provocative statement, which is David reiss's technologies is actually not in the investment business. They are in the gambling business. And in particular, they were the house.

Why I would tell, I thought where you thought you were going with this. I was like, guess, I would totally agree, they're not in the investment business. They have no idea how to invest. The model does.

I'll say at this, they're not investors and they are not the investment business. There is investment going on all around them in the markets that they trade them. But the fact that they're in those markets, they are not there as investors there.

They are setting up shop as scissors palace, letting everyone come in and do business with them while they have a slight edge. And we'll lose sometimes, but most of the time, they're gna come out slightly ahead. And I think, let's say, they do have a fifty point o 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 treated with.

Now I think where you're going with this is perhaps similarly along the lines to scissors palace or casino, they are not in the investment business, but they are providing a service. sure. Is this where you go with this? Well.

I mean, the investment business IT sort of depends how you define investor. If you, anna, be like a hot today about IT, which I know in this illustrative example, I being one and saying an investor is someone who provides capital in a risk capital to a business for that business to create value in some way in the future, are you lend money to some intrinsic underlying asset so that I can be productive with the capital and produce a return for us and 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 vacuum IT out of right right?

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

correct? But is literally the same business model as a casino. You have a slight edge and you let a whole bunch of patrons come in and lose money to you in your slide.

Well, where I was going with the service provider, I think casino s our 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. I think this is probably quite accurate, that rent tech and all other quante firms like them are providing a service to the market in that they are allowing trades that people want to make happen faster and much lower spreads.

absolutely. That is the undeniable, yes, quana funds create value in the world thing, which I think it's very easy to say. Quant funds provide no value because it's like it's zero.

Some they're not actually providing the capital, the businesses to do something with. They're purely looking to do an arbitration of this. We 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 rena sons was started, there's no chance that retail is able to function like IT does today with europe transaction fees and people able to invest in all these different companies near real time.

And any single one of us can go buy a security and just about any market at just about any time of day pretty much instantaneously and get a very, very, very granular Price on IT. Yeah, none of what he used to be true.

No, the fact that there is a whole bunch of quant funds, hedge funds out there that are ready to be willing counterparties to anyone who wants to trade, that is a service. You're right. They're also not all medalia. They actually don't all have an IT, even though they might proper to lots of them gonna lose money to you.

right? Lots of them lose money. You to listeners could beat the market, not investment office. Please don't try.

right? On average, medalia will not lose money to you. But you know, there are plenty of other hedge funds out there and high frequency shops and counterparties for you where you could take them is just not gym signals.

Um there's this great, great video at the end of great book who was he was doing one of the like sell offs in the mid twenty teens in the market where jim calls the head of his family office, these longer retired from rent ticket. This place calls the head of his family office, says, what should we do with all the cell off in the mark? U. M. signs. U signs.

What should we do?

What should we do?

All humans are fallible.

totally.

A couple of other are sequentially. The value creation exists. It's easy to knock that all these smart people are going in the finance and you wish they were doing something more.

Or for the world, at the end of the day, humans are going to do what they are incentive to do. And so absent a larger global concern, that is incredibly motivating to people. I mean, you look at world, world two people's level of patriotism and wanting to go save the world from evil was a huge, unbelievable motivating factor to move mountains.

When that is absent or when people feel that there is some existence al thing that is absent, they're going to go do what's best for them and their family. And if they are an empire builder, go build empires and if their fierce capitalists go make about your money. And so the system is set up the way that, that is.

So like you can be mad about that given that, okay, people are going to go engage in quantitative finance as a lucrative profession. Fortunately, there is a bunch of valuable stuff that comes out of that. And I think that is often missed, is that these really lucrative professions and businesses can often produce R N D that becomes valuable elsewhere. For example, which did this big in video a series, what do you think milano x was used for before a large language models?

Oh yes, this is such a really mind blowing point here in value creation, value capture. Go for take you away.

There's not much to IT other than a huge amount of infinitive and was used by a high frequency trading firms. And I don't know for sure, but I kind of think melanotan to build their business on quana finance. Yes, that's one of many examples.

But now you know that has limits, but I think that goes over. IT looked that there is a lot of technology innovation here. Yeah.

these are all great points. They all came up in the research. I totally agree with all of them. IT is, in my opinion, false to say the quantitative finance does not create value for the world. IT definitely does, in my opinion.

but does IT create anywhere in here as much as IT capture?

That said, they're really, really good of value capture. Yes, this is not wikipedia here. This is about as far a way on the spectrum as you can get.

There is a great, always Sunny in phildee pha, where Frank dandy David sort IT goes back to his whatever business he founded in the eighties. And he's like dressing in his pin and stuff again and he's taken back over. He brings charlie with him and charly, you know, he's like, so Frank, what is the business what we do here? What does the business make? And Denny divide looks and we because we mean we make money.

He like, no, no, like what do you build? Because we build wealth. I think that's a pretty good mean for kind. I was gone on here.

Yeah totally, very, very good of value capture to, yes.

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

Bar versus ball is uncalled, and IT is back, resurrected like resurrected. However, this is about the lamest episode to resurrected on what's the bull case for rent tech. Past performance .

is an indicator of future success, right?

Like they're onna, keep attracting all the most people in the world. There's going to have the ability keep their incredibly unique culture. They're not going to get tempted to let the business of institutional funds become the dominant business. You know keep on keeping on is basically the bull case maybe that they're actually still ahead.

The bull case for the G P N L P stakeholders in medalia, which is, I don't know, five hundred people in the world and none of the rest of us can get any exposure to IT.

Yeah, the bare case is things are changing. And I think things are changing basically on any access is the bare case for them. So if things are changing where competitors are catching up, maybe maybe the fact that the tech industry has figured out these large language models, maybe that trickles into making IT here to compete with an tech, it's a blurry line.

But IT is plausible, like maybe in tech actually was here a decade before everyone else and now everyone else has arrived to the party. There's things that are changing maybe about their culture like jim has been gone for a long time. Bob mercer is no longer a co CEO, Peter Brown is a co C E.

O. And they just announced that they are making the guy who was in charge of the institutional funds. David lippy, he is becoming a co CEO as well. So maybe there's a bare case around that, that someone from the institutional side of the house is becoming the current coast C E O. And maybe eventually CEO.

If you believe the medi is the special thing in the institutional funds are sort of a limited on the business, there are the airm s apple watch strap and David parLance, maybe that's a bare case. Maybe there's a bare case that their talent is becoming kind of the same as everyone else's talent. When you look on linked in, i've recognize a lot of the companies that people worked at are more junior at the tech.

And in the past, I think that would have been all people just out of university research shops. So I think if it's true that they're are starting to see the same talent flow as everyone else, that would be concerning. These things are all sort of narratives you can concoct and really no way to enough.

They are true or not, right? There's no way for us to know anything because there's .

no way to know any this, right?

It's all the secret. Yep, OK our new ending section, the splinter in our minds that take away the .

one thing you can stop thinking about.

What is the one thing for each of us personally from doing this work over the past months on red tech that sticks with us? For me, perhaps this is obvious from my little diatribe on the tapestry. I just think this is such a powerful example of the power of incentives and getting demo and setting them up right.

And culture, too. I don't want to short change that. I think the culture of managing an academic environment in a fashion like a lab, but without letting IT spin into the privity of a lab that jim Simon s set up right .

in other worlds, early google.

Yeah, this is like early google. exactly. There historically has not from our research and as best as we can tell currently, is not anything going on at in tech that is driver less. They are all very focused, which is an to meet them to speak back to the power of incentives when you're there with less than four hundred people.

And on the research and engineering side, less than two hundred people and those colleagues who you work with are the sole pervs supervisors and beneficiaries of all of this that you're doing like that is so powerful. Yes, I can think of anywhere else like that in the world of me. Maybe some Better funds are other investment firms, but not on a data day, fully liquid with returns like this. There's nothing like .

IT no pure gasoline right to the vans ah.

which is not to say I would necessarily want to work there. I think I but IT is truly unique.

But the one thing I can stop thinking about is the idea of the complex adaptive system that always talking about earlier.

I think from what everything we can tel from the outside, rena's ance actually has built a large scale computer system that discovers relationships between different entities in the world, stocks, commodities, bond Prices and whether I can explain them or not, IT is correct most of the time, and I might be a small most, but all you need is most. And then you can Operate a casino business. That is, my take away is that they are the house and they have an edge. And that edge is predicated on a graph of all the relationships between these entities that we think are just noise.

And they know the signal IT does make you wonder to what you are talking about with the tech industry catching up, quote, quote, in recent years, how much IT is IT to build this now given the technology open source, otherwise that's available for sale out there? That's the bad case. I don't know.

yeah. And then what's gonna en by nature, given that IT to complex adaptive system, if you can now buy and build this, well, that returns. Look at arbitrary you.

All right. Should we have some fun car? fouts? What's have some fun? sweet.

So I have one T, V show. And IT is actually acquired related. IT is called the new look on apple T V plus.

Yes, but Christian is such a new luck.

exactly. So for anyone who listened the L V M H episode, remember, we are talking about the ground breaking thing that Christian dior was his collection, the new look that was a post world war to explosion under the scene.

celebration of life.

Yes, gone are the days of the military tic, boxy clothing. And now we are in with these seductive, and there I say .

some suas materials. War rationing is over.

exactly. Yes, provocative dresses. The apple T V show is this incredible drama of kind of flashbacks to the war time experiences, hero wing, war time experiences of Christian dior, of benza aga, of cocoa l and everything they went through, and how all their paths crossed.

Oh, cocos in IT, yes. Oh, wow. How do they treat that?

IT will be very interesting if a lot of people watch the show to see if that affects product cells of channel. I am also very curious for people who are watching, feel free to put a thing in the slack, in carve outs. Do you think she's a sympathetic figure? Do you think she's villinous figure? And curious how you think of hero trail versus reality?

So well, this is the old crazy thing. We want to know where the company ends up getting bought by chanel, the perfume division, which is the two jeos brothers in new york.

the world fm's. Indeed, a got we get to do is chanel episode at some point, but the new look on apple TV plus, I promise you whether or not fashion luxury is your thing. It's a beautiful and hero wing story.

As you and listeners know. I'm not A T V guy, but this is so up. My ali.

the whole thing. IT takes place in war time.

Paris, I gotta watch.

IT, you got to watch. IT, are I David? Your car vets.

My carve out is related to the new look, a very different way, but both video consumption and fashion and luxury and style. IT is the class of palm beach, instagram and tiktok account. This is so great.

David. You and I got a pomp ach for two days, and you get hooked .

on this is amazing. So but I went to pump beach for a couple days for speaking event recently, which amazing. I've never been the pump ge before.

who IT is nice.

So great. We didn't knowingly spot any reteach people there.

but I we may have, we did knowingly spots and .

birkin bags though. Yes, the style in pombo, we had just recorded that armas episode. And oh, man, I was so pleased.

Be there. And then I got home and jelly. My wife was like, do you not know the class of mp beach tiktok account?

And David, like, i'm a thousand. I have no idea you're talking about Jenny.

yeah, I live on A K. My dad and he showed to me this is a woman who lives in on peach and SHE goes around to post on instagram, anon, tec, toc and SHE just interviews people on the street about what they're wearing, what brands they are wearing. The style IT is magnificent. My favorite is, well, super incited and linked to red in the showed te. There is a video of one woman who is being interviewed who has a mini Kelly inside her burden access.

truly access.

And that's all I was hooked. I was just like, this is the greatest st thing I ve ever watched. Uh, i'm obsessed.

right? If I used to tiktok, I would subscribe. No way you can .

get on instagram, right?

good. I actually .

subscribe the acquired account on instagram. Class of pm. Peach, I don't know how many people we are following. It's not many, but we are following classic of on peach.

Look at David opening of our instagram account. You're so youthful. H. O. David, I know you've got some thank you from folks you talked with and a few of them we did together yes.

for sources for this episode de, who were so generous with their time and thoughts first huge thank you to great exact man, author of the man who solved the market, the onic book out there about rent tech and jim Simons. Ah greg was super generous, spending time talking to us, emAiling with making sure we're getting things right. He also he and the book is the canonical source of medalia investment returns. And I know he worked so hard to get that table together that is now all over the internet as IT should be.

IT is crazy everywhere you hear that sixty six percent number quoted and that is from greg analysis. Yes.

truly a service to us and to corporate historians, infinitive historians everywhere that he did that research ching out their returns.

And there's a few other primary sources. There's really not much. So we can actually list all of them here.

There is, uh, congressional testimony of Peter Brown about the basket options thing. There is Peter Brown doing interview at G. S. Exchanges which again, many of the questions were straight out of greggs book and the stories told.

yeah there is a funny moment where Peters, like, where do you get these questions? How do you know all this stuff .

like come are in the book clearly yeah, there's a great book called the quotes, which is a little bit I think it's twenty eleven, so it's not as updated as the man who sold the market. There's only a sort of a couple chapters about rent tech, but some good stuff in there. And then there's a good bloomberg piece from twenty sixteen that will linked to that. I think between that and the quanta was sort of the first time there was really anything at all that was published about rent tech. So all those will be in the show notes, other people who think David.

other people who think Howard Morgan, who we spoke to, which was so fun to get a one of the first round history from him. And that, of course, to be a founding of rent tag in partner ing with gym and investing in each other's funds and all that. So fun.

Bret Harrison, who you mentioned, ben, bret is now building architect, which I love this. This is so needed in the world, the interactive brokers for the twenty first century. Well, anybody who uses interactive brokers knows exactly the opportunity there.

So thank you. bread. And then Matthew grenade, who I spoke with, matt is the cofounder of domino data lab, which great enterprise AI ops platform back by sqa many others.

IT allows model driven businesses and products to accelerate research, increase collaboration, rapidly delivered new machine learning models. All of the sorts of things that we were talking about here with rendered man before starting domino data came out of the quantity. He was a point seven two. And bridge water IT doesn't really quana sort of I think he was a long time of senior employee, both of those firms, and he gave us great, great perspective on. The landscape of everybody out there and where rent .

tech fits in awesome. Well, if you like this episode, you should check out a burger healthy episodes from a few years ago for a very different style to investing. You can sign up for new episode emails at acquire data. M slash email will be including little tidbits that we learn after releasing each episode, including listener corrections. If you can listen to A C, Q, two surgeons subscribed in any podcast player and listen for our most recent epo de with the well really creator, or a person who LED the team that created a ler glue tide, which were on to become summer glue tide. Wh, of course, as effect we go .

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Beer nudi from novo nordisk. Awesome to have her on the show. And after you finished the episode, come talk about IT with other smart members of the acquired community.

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will see you next time. The truth. 哼。