Bloomberg Audio Studios, Podcasts, radio News. This is Master's in Business with Barry Ridholts on Bloomberg.
Radio this week on the podcast, Boy Do I have an extra special guest.
I know Jim O'Shaughnessy.
For I don't know, maybe twenty plus years something like that. We actually first met in the green room at CMBC like early two thousands and found we shared some similar likes and philosophies. And I've been a fan of his book What Works on Wall Street pretty much from when
it came out. This is a fascinating conversation about a person who has worked through multiple locales and seats in finance, not just running a systematic investing at bear Stearn's, but creating O'Shaughnessy asset management, creating a unique custom index product that ended up attracting the attention of Franklin Templeton, who paid some undisclosed and ungodly amount of money for the whole firm, and now in a later phase of his
career doing O'shaughnessee ventures and the O'shaughnessee Fellowship. I first know him from really the first pant book What Works on Wall Street? That was a half a century of data analysis. Really was never accessible to.
The public before.
I found the conversation to be fascinating and I think you will also. And at this point I am obligated to do a disclosure. My firm Retults Wealth Management has been working with O'Shaughnessey on their direct Index platform. Really we were one of the first beta testers. We now have over a billion dollars on that platform, maybe coming even closer to another big round number with no further ado. My discussion with O'Shaughnessy ventures Jim O'Shaughnessy.
It's great to see you, Berry and congratulations. Wow, that's a.
Well congratulations to you. I'm still My firm just had its tenth anniversary. You guys, anytime I see the phrase for an undisclosed amount, my brain automatically says.
Wow, that has to be a lot of money. If it's if.
They're not disclosing it, it's material, but undisclosed, that's a lot of casts.
Or it could be like trading places and the normal bet of a dollar.
The usual bet Mortimer one dollar. So we know each other from way back when you first came into my orbit from the book What Works on Wall Street. I read it from cover to cover. I was on a trading desk when that came out, and I'm like, huh, so there's some science and math behind this. It's not just rumors and whatever happens to cross TV that day.
I'm intrigued.
Before we get there, let's talk a little bit about what you were doing prior. Tell us about the early Jim O'Shaughnessy.
Well, I was always fascinated about the markets in general, which stemmed from a very angry conversation between my uncle and father about IBM. And I had just been allowed to go to the adult table, and I was sitting next to my dad and he and my uncle John were going hammer and tong about whether IBM was a good company or not. And I was listening, and it
was all about the chairman. It was all about, you know, things that I looked at as kind of soft intelligence, squishy squishy, and so I just thought I asked at the dinner, I said, well, would it make more sense to like look at how much money they're making and what their earnings are and how much you have to pay for that? And they both just literally glared at me. That's hilarious kids. They don't know anything exactly exactly. It's the chairman. How tall is he? I like to cut
his gips. It's almost as if you were there that bug got implanted, that mind worm got implanted in my brain. How old were you when that? I was seventeen. Oh so you're just going into college? Yeah?
Absolutely, And you were a Minnesota kid, Is that right? I grew up in Saint Paul, Minnesota and beautiful country. Certainly in the summer anyway, gorgeous. The winter's tough.
Yeah. Yeah. Well, if this were the old USSR, that is where all the political prisoners would be of Minnesota. But so I started doing research on essentially the Dow thirty because it was manageable, thirty stocks I could list by hand showing how old I am, because you literally there were no computers that we could use at the time. Simple things like like what's the price, what's the dividend, what's the price to earnings, book value, etc. And I
found a definite trend, right. I found that buying the ten stocks and the diw with the lowest pees from nineteen like thirty five. I think I started through when I was doing it, and this would have been about nineteen eighty, absolutely decimated the ten highest PE stocks. So wow, I love this. In the meantime, I had computers, and the only reason I actually got to write What Works on Wall Street was because Ben Graham didn't have computers. If he had had them, I would have had no
chance because he would have done it. Basically, what I wanted to see was, is there any rhyme or reason to all of these reasons people say they like or hate a stock? Right? Where is the proof? Where is the empirical evidence that say, buying the low PE stocks from the die works very well over many market cycles.
So I wrote a first book called invest Like the Best, in which I basically showed you how you could clone your favorite portfolio manager by taking his or her stocks, putting them on a big database like Compustat, seeing how they differed from the overall market, and then using those as factor screens to get down to a portfolio that looked, acted, and most importantly, performed like your favorite manager.
Now, the average investor typically didn't have access to Compustat, to big data, to big computers, and so they relied on you who did.
And if I recall what Works on Wall Street.
You back tested like half a century worth of data something like that, and it was the full market, not just the thirty dove stocks.
Yeah. Absolutely. And also not just the full market, it was also any company that had been but went bankrupt or got taken over, the very very needed research database on compustat. So no survivorship bias none, You back that out, Yeah, great, Yeah, because some of the early academic studies were they had a lot of survivorship bias. They didn't properly lag for when you actually knew a number, so they just assumed, right, well, there's the number on March thirty first, I'm going to
use that number. Well, you didn't really know that for most of history until maybe May or June. Really interesting, so you run these numbers. What sort of strategies do you find perform best. Well, we found that on the value side, smaller value stocks that had some catalysts and had turned a corner and their prices had started to go up. A beautiful strategy.
Small cap value with a touch of momentum.
Yes, okay. On the growth side, we found momentum works really really well. As we continued the research, we found okay, there's all sorts of caveats. So, for example, we learned after a severe bear market i e. One in which the market had declined by forty or more percent, not a lot of those, not a lot, thank god, but momentum inverted, and the stocks with the worst six or twelve month momentum actually did vastly better than the ones with the best. And if you think about it even
for a minute, it makes sense, right deepest value. But what happened was a lot of really great stocks during the bear market got pushed way low in price, and so people when the market was recovering jumped on those stocks. They were like, I can't believe I'm getting you know, these earnings at six times earnings for an IBM or a you know Qualcom, Right, that's the baby with the bathwater strategy exactly. And so but we found, you know, that value actually works now. It hasn't for a long time.
But we also found that large stocks with high shareholder yield i e. Dividend yield plus buyback yield was an excellent way to identify big stocks that are obviously much more conservative than the smaller fry in the small cap world.
Interesting, So let's talk a little bit about your work at bear Stearns. Really, where I first met you in the two thousands, you were head of systematic Equity at bear Stearn's asset management. I'm assuming you were applying a lot of the lessons you learned in what works on Wall Street to the Bear institutional and tel investing strategies.
Absolutely, and you know, let me just say Bear was really a great company, very unfortunate what happened to it during the financial crisis. But the reason I love Bear is, you know, a lot of big banks talk about being entrepreneurial. Bear Stearns really was. And essentially, if you were doing your thing and playing by the rules and doing well,
they let you alone. Which was pretty important for me because when I got there, it was right after the dot bomb, and a lot of the brokers had done pretty poorly because they were in a lot of those names.
And so I convinced Steve Dantis, who was then head of Private Client Services, that wouldn't it be better if we did a packaged portfolio, a separately managed to count and we offered at one time I think we were all the way up to the brokers so that they could use a more systematic time tested way of investing for their clients.
Bringing a little discipline into what had been, at least in the nineties very much a cowboy type of environment. And I'm not just for fron of Bear. The entire retail stock brokerage was wild totally.
He was very open to it. We ended up putting together a separately managed to count platform that they brokers embraced. They loved it because literally they did what they did well, which was calm the client during bad times, try to keep them from getting too excited during great times. But they also loved the idea that it had a very explicit explanation for why they were putting that client in
that portfolio. So that was a lot of fun. By the time I left Bear, my group controlled about seventy percent of Bear Stearn's asset management long only, and that was a lot of money, wasn't it. It was It was about fourteen billion dollars.
Okay, so you mentioned you left Bear. Let's put a little flesh on those bones. Your timing was perfect.
You exit Bear in two thousand and seven, Is that right?
To set up O'Shaughnessy's asset management was the thinking, Hey, I want to do this out on my own shop, or were you sniffing something out in O seven that's like, hey, maybe I don't want to be attached to a giant ocean liner taking on water.
You know, that's funny. I spent the next two years after that trying to convince reporters that I really didn't know anything. Why I left Bear was because I felt that I really wanted to be on my own again. I really wanted to be able to just talk about quantitative investing. Bear was a boutique, so there were a
lot of different managers. Liked them all. I thought they all were great, but I really really wanted to focus just exclusively on quant And secondly, we had upgraded a lot of our systems to the idea that would become canvas, right, because remember Netfolio was our first try at that. That
was nineties or ninety nine. Yeah, really, well, of course, you know the really funny story here is in April of nineteen ninety nine, I wrote a piece called the Internet Contrarian, and in that piece I said, eighty five percent of the companies currently extant in the Internet space are going to be carried out of the market. Feet First, I've never seen a bubble like this in my history of investing. And what did I do next? Berry? I started an Internet company.
Well just because the stocks or a bubble, does I mean this internet thing isn't going to catch on?
That's true, right, it's there are you know?
It's funny we forget in the thirties, forties, fifties there was only ma bel Every company used telephones. Yep, the way we describe internet companies, if you use the Internet as a core part of your platform. There's difference between the dot.
Coms and the nineties and people.
Who have just really integrated the technology into their business. Right, So I think Netfolio is not a dot com but a comm that used the net as a way to reach more people and give them access to data.
Well, it's really funny because I made a couple well I made more than a couple of mistakes, but one of the big ones I made was we designed Netfolio as a B two C company, so we called we were taking on at the time mutual funds, which were dominant. We didn't have ETFs while we had them, but they were there very early day, very very early days.
And so what did the spiders just turned twenty five recently, Yeah, I think of something like that. Yeah, So ninety nine is like it was really the beginning.
Oh totally. And basically the idea was it was the first online investment advisor. And the reason that we thought it would work so well was personalization, tax management, all of those things. So, for example, we would they were all run by quant models that we had developed, right, but it gave the user the ability to say, let's say they're anti smoking, right, and Philip Morris is one of these selections they could just check nope, don't want it.
Up comes the next stock that meets the criteria, and so it had a lot of really great features, but the tech was not quite there yet.
You were twenty years ahead of where you would end up in the late tens, right.
I.
Was.
I really do have to give my son Patrick the credit for resurrecting the idea because when we were at OSAM, I said, listen, we left Bear right into the Great Financial Crisis, and I put the team together and I'm like, I don't think that we're going to be able to sell many long only portfolios after the market has collapsed by nearly fifty percent, So let's spend our time developing
internal technology that works the way we work. The office shelf stuff really wasn't cutting it, and so the project to get there was multi year and Patrick oversaw that, and then he walked into my office one day and he goes, you know, Dad, we've been using the desk star to kill a mouse. And I'm like, okay, I
like the metaphor, but what do you mean? And he started talking about AWS, talking about netfolio and he's like, we have the perfect tech now that our clients results being one of them, could use and I'm like, brilliant, let's go with it.
So we're going to talk a little more about Canvas, but I want to stay with the launch of O
SM and O seven. So A, you don't need to disclose this, but I'm going to assume you had a lot of bear Stern stock options that you had a vest on your exit, so you probably had a pretty good sale, pretty good print on those when you first set up O'Shaughnessy, you're running your traditional models, things like Cornerstone value and Cornerstone growth, and I'm a big fan of your microcap sleeve, which really operates parallel to venture capital returns, only using public stocks.
Am I getting that more or less right? Actually we use that also. Yeah, we wrote a paper saying that it was the poor man's way to get exposure to private equity. Private equity or venture capital are both both really private equity closer because the the microcap I love microcap investing. The only real reason that we offered that was because I loved it so much. Well, and the
data backs it up, Oh, totally, totally it is. Microcap is an amazing place if you've got the right tools to sort through the thousands of names in the microcap universe, because you would not want to buy an index of microcap stocks. For the most part, there are microcaps because they kind of suck. However, there are so many diamonds in the rough in microcap that if you have a strategy, like a quant strategy that can sort through these thousands
of names, you can do extraordinarily well. I love the strategy and I know.
Oh the OSAM microcap sleeve is what I call. It has just really shot the lights out, especially last year when the market was having.
A pretty good year. They did pretty well, didn't they They did? They did. Now, remember you introduced me as chairman of o SAM, I'm no longer no longer. Yeah, they let me retire and actually Patrick is now chairman emeritus over at OSAM. Let's talk a little bit about Canvas, and again full disclosure, we're a client.
We were a beta test. Do we love the product? Which is kind of ironic because I used to hate direct indexing every time I would demo or see a product. It was clunky, it was cludgy. You would get these statements that were like hundreds of pages long.
You guys kind.
Of figured out the secret sauce for how do we make this clean, usable and easier to understand. Tell us a little bit about the genesis of Canvas.
Well, first of all, we call it custom indexing as opposed to direct and the reason I make that distinction is because, as you point out, the direct indexing products of that time were clunky, they were difficult. You got reams and reams of paper reports, and they were really only focusing on tax benefits. Right. What we wanted to do with Canvas, which is custom indexing, is, as the name implies, give you, as the advisor, full control over what your client portfolio wanted to look like. You got
the advantages of tax harvesting. You got the advantages of being able to mix indexes in with active strategies, but you could also do a social investing fund if you want it. But the way we did it was we didn't presume what your client was going to think of as good social investing. So often when you see some of the ESG portfolios, they've been predetermined as to what is going to be included. We give you the tools to turn a dial up or down on whatever you want.
I think last I looked, there were over fifty eight separate things that you could find tune around on the idea of ESG. We wanted to give the tools to you because you knew your client vastly better than we did, and we thought, let's try. As you mentioned you were one of the beta testers. That was actually one of the smartest things we did, I think, because we had really good advice from a lot of people that we
knew in both venture and other places. The first thing that many of them said to us was do not try to go big with this. Originally, find advisors who you trust who will give you you real feedback. In other words, they won't shine you on if they didn't like you. Guys were very good at times.
And Michael batt nicking my office, one of my partners, who was over the moon when he first saw this. Every time another product came in, it would take me thirty seconds to poke holes in it. And he came breathless into my office, Dude, you got to see this. And I'm like, yeah, yeah, okay, another garbage right, tee it up. And it took about thirty seconds to go, Oh my god, how do we get a piece of this?
This is fantastic.
The interface, the design, all of the bullet points that all the boxes checked were great. Let's stick with what we no longer call esg and Meyer Statman famously called values based investing. Some people have called it woke investing, but that's really the wrong phrase. I'm fascinated, for example, by the Catholic bishops whose endowment says, look, we don't want any aboard of any drugs that do that. We can't invest in those companies. We can invest in hospital
chains that perform these sort of surgeries, or insurers. You have the ability to say, whatever your personal preferences are, you could just tune those out of pick an index, the S and P five hundred the Vanguard Total Market. You could say, I don't want X or Y or Z, and how it comes tell us a little bit about that.
I felt that that was really really important because everybody has different ideas. As you point out, the Catholic bishops wanted to exclude certain things. Others might want to include certain things. Actually felt it would be very arrogant of us to determine what good social investing was because we had managed money for a variety of religious institutions, and guess what, they all have different takes on what they
want to see. We did one where, for example, well you couldn't buy any company that did anything with animals with eyes. That was an interesting one. But then on the other hand, we had a client who wanted to see more female board members and females in the C suite, and you could you could screen for that.
You can screen and there's a bunch of research that shows those companies. Now you don't know if it's posative or just merely correlated, but those companies tend to outperform. The request we probably hear the most is no gun stocks, no tobacco stocks.
Yeah, kind of interesting. Yeah, the tobacco guns, those are pretty large groups where majority of investors want nothing to do with them. But the other thing that's cool about our dials on canvas you Let's say that Ritholtz has a wild eyed libertarian walk in who happens to have a billion dollars and he says, you know what I want the gun manufacturers. I want I'm a big like an amendment guy, right, Or I want the pharmaceuticals, or
I mean the sinstock, I mean gambling and alcohol. Well, and you know the joke there was that my first company, O'Shaughnessy Capital Management, we used to keep a joke portfolio which was called the Eat, Drink and Be Merry for tomorrow you die Berry. It killed me sure.
So what ends up happening very often is when there's a non financial reason for kicking a stock out out of a lot of portfolios. Eventually a company with still having decent financial prospects, it becomes cheap.
Yep. Absolutely, But the thing with the social style investing, we wanted you to be able to reflect your client's unique needs. And there really wasn't anything like that. I don't know if there is now, but I haven't seen anything like that.
Well, certainly not to this degree of granularity. By the way, when we first we're beta testing Canvas. Internally, my view was, Hey, people are going to want to use this for value based investing. Then they're going to want to deconcentrate. If I work for Google, do I really need all this tech exposure My income is coming from there, Let me diversify that way. And then tax loss harvesting was going
to bring up the rear. I had it exactly backwards, in large part because I don't know, maybe a year into it we had the COVID crash market falls thirty four percent and coincidentally bottoms just near the end of the quarter. That rebalance. You know, typical tax loss harvesting own a dozen mutual funds. You pick up ten twenty basis points against the portfolio of losses to offset gains. The hope with this was it would be fifty sixty. We had clients getting two hundred, three hundred, four hundred
basis points. And I've talked to some of your staff or former staff, and they've told us some unique use cases where the numbers are bonkers. First off, explain to the audience who may not be familiar with this what is tax loss harvesting.
So essentially what it does is we had to build a non trivial algorithm that could monitor every portfolio we were managing on behalf of clients and as you know, they can go all the way up get maximized tax losses or all the way down don't worry about them. So, for example, you wouldn't care about it in an IRA right right. But the purpose was that we found through our research that a tremendous amount of alpha was being left on the table, and that was the alpha from
tax lost harvesting. When you're in a market like the market we had when we went into COVID and the bear market ensued, and under other circumstances, well kind of you're out of luck. But in this particular case, that creates the kick in for harvesting the losses, reducing the overall tax needs for the portfolio, and you could really look at that as that's money in your pocket. By the way, we had the benefits completely backward too. A tax loss harvesting was at the bottom of our list as well.
It's arcane and technical and you don't really think about it, but we have clients who were either you know startup founders that cashed out, or they inherited or or just owned stock with a very low cost basis. You know, it's always funny when you see a five million dollar portfolio and some stock has blown up where it's eighty percent of the holdings. Hey, if you have five million dollars and four million of it is Apple or Amazon or some combination of big stocks, that's a lot of
angle stock risk. And to a man, every person says, hey, you should diversify. The answer as always, I'm gonna get killed in capital gains taxes. This worked out to be a really good way to say we're gonna work out of your concentrated position over three, four or five years, and then twenty twenty comes along and what should have been a five year process took half as long because
you had so many losses. So for those people who may not be familiar with this, let's say you own ten mutual funds, right and some are up, one or two are down. You sell the ones that are down, you replace it with something very similar. Hey, now I got a little bit of loss even and my portfolio looks the same, but I have an actual realized loss that I could use to offset my real gains. But those losses are three five ten percent.
They're nothing.
On the other hand, if you have a direct index or a custom index that has a couple of hundred stocks, well, the worst stocks in those portfolios, they're not down three four five percent, they're down forty sixty seventy five percent. You sell the ones that are down, you replace them. And this is one of the things I like about canvas. You identify the replacement stocks that are is it fair to say mathematically similar?
They look, well, they come from the same strategy, so yeah, you could say they were mathematically similar. So the overall portfolio more or less retains the same characteristics. You're just realizing losses deep losses on some stocks and replacing them with something relatively similar exactly. And you know, we're just basically making math work for us. And because the entire
thing is operated within the canvas architecture. After getting the algorithm, which was non trivial what do you mean by non trivial outbum, it took a hell of a lot of work, okay to be able to make that function properly, And as we worked with firms like yours, it became very very clear to us that that was going to be a big deal in canvas, So we wanted that algorithm
to work perfectly. But as you also note, we wanted the nearest neighbor, if you will, that would replace that stock to not affect the overall metrics of your portfolio. So it's going to look, act and perform very much like the earlier portfolio, but you've already taken that wonderful tex loss so that you can offset the gains from elsewhere. The other use case that we thought would be number
one was, you know you have a concentrated position. Let's say Google right, don't give me any tech exposure, or give me tech exposure only in this tech which is like hardware for example, right that I can do and that type of use case would work hand in hand with the tax loss, making it a much much more efficient, more money in the investors pocket. In terms of final outcomes with the portfolios.
What was the uptake on that approach? People enthusiastic about.
It, they were, but they were not nearly as enthusiastic as we anticipated they would be. There were a few advisors that we were working with who worked specifically with founders and early employees who had a lot of options in that particular and usually tech, but we also did work and do work with a lot of people who just amassed through employment a huge position in their particular company, and they wanted to have the rest of the portfolio be built to complement and offset, if you will, any
further invent's over there. So it's worked actually quite nicely.
And then in twenty twenty one, Franklin Templeton comes knocking at the door. They're an investment giant with a trillion plus dollars on their books and they've been pretty acquisitive over the past few years. Tell us a little bit about how that transaction began. If I recall correctly, you guys weren't out shopping the firm to.
Be sold, were you not? At all? We were. It's a funny story. We almost got kind of a cold call from a gentleman at Franklin Templeton. I was sort of like, give it to Chris Lovelace or you know who's the president of the firm, And ultimately Patrick spoke with him and came into my office and he's like, hey, Franklin Templeton is really interested in canvas. I'm like, okay, they want to use it. No, no, they want to buy it. And I'm like, okay, well, let's do a
due diligence on Franklin Templeton. They're massive, as you know, I think trillion and a half in assets under management, and we were really having great results, as you know, with Canvas on our own. We thought about it for a long time, and you know, we really wanted custom indexing to be a new category of asset management, and we felt really proud about that because it isn't too often that you're able to invent kind of a new
category of investing. And as we chatted about it and talked it out, we're like, you know, we're at an inflection point here. We are relatively small boutique, even though this is working really really well. If we want custom indexing, custom portfolio creation to really make the big time, it probably makes sense for a much larger asset manager with all sorts of advantages that we did not have to
take it and run with it. So we let that be our guide, and after doing quite a bit of due diligence on the people at Franklin, we were like, okay, let's negotiate about selling the firm to them.
Talk about good timing. Morgan Stanley bought one of your competitors in that space. Vanguard rolled out their own product, which quickly amassed you know, billions and billions of dollars on it. So this has worked its way into the mainstream, even though it's still relatively I don't want to call it a niche product because it's bigger than that.
But it's not ETFs. It's not giant yet, but it's still growing at a pretty rapid clip, isn't it Totally? And I think that ultimately we might look back ten years from now and have the thought, can you imagine that people just bought packaged products? I mean, like, my god, no tax advantage, none of the customization, none of the
immunization for concentrated positions that I have. And so we definitely think that this is a way of investing that well, you know, once a client sees their portfolio under canvas and with the customization, it's really really hard to go back to thinking, ah, you know what, I think I'll just go with five mutual funds or five ETFs. I don't really care about much of the other I think that you know, these things take time. But I mean again,
your firm is a classic example here. You were able to use custom in a way that was good for your firm, good for your clients. And you know, the clients that we speak with love it. You know, they all love it.
That's been our experience. It's really Mark Andriesen's software is the world writ large. Because there are two aspects to this, and I'm going to circle back to the database part of it in a bit. But the front end, the user interface and the software that allows a very simple set of choices and that you could go increasingly down the rabbit hole and find more and more and more issues certainly is a big factor a lot of what is done. The technology just wasn't quite mature enough fifteen
twenty years beforehand. And when you look at it, it's just well, this is just software. It's just a user interface and a way of organizing it. But now let's circle back to the database, which I recall you saying was the secret sauce. Tell us a little bit about the database that you've been working on for a quarter century. That drives canvas.
So we use the copystat universe. They cover virtually every company that trades both here on American exchanges and elsewhere, and it is kind of the gold standard really in terms of databases.
How does it compare to something like CRISPER or some of the other.
Well, so, CRISP. It comes to us from the University of Chicago Center for Research and Security Pricing. The downside of CRISP is it's, first off, I love chris We used it in the most recent edition of What Works, but it doesn't have enough of the fundamental factors attached to it. In other words, it's mostly price history rice history. And it also tries and generally succeeds to include all of the names that might have been around trading on the AMEX or the New York Stock Exchange or NASDAC.
But the challenge is a guy by the name of Macquarie wrote a really compelling paper talking about how a lot of the historical data not compustat, but further back right in the twenties and thirties, come from the papers. Yeah, and also wasn't nearly as thorough as say the compustat is. In fact, one of the things that we were doing before Franklin Templeton approached us is we were literally digitizing
old Moody's manuals. They go back to nineteen hundred, and what we wanted to do was marry into the CRISP data all of the fundamental factors that would have given us the ability to run a nineteen hundred through nineteen fifty five when Compustat begins test We ran some test runs. We did price to book, and we did a couple others. And what we were finding and won't surprise you, generally speaking, same kind of results, right with the exceptional price to book,
we actually took price to book of our composites. You know how we have the composits for value and momentum and all of those things, and we took price to book out because of the research that we did that covered the great depression from the thirties. You know, and I know if you've taken any finance courses, price to book previously had been used as a proxy for likelihood of bankruptcy. Right, Well, guess what during the thirties, a
lot of those low price to book companies went bankrupt. Well, when your book value collapses, exactly, it's the book isn't much value exactly exactly. So we did find some learnings where we jiggered with the composites that we use. That's another thing we do. We don't use a single factor. In my first version of What Works on Wall Street, we would sort down for the final portfolio on a single factor, and we found that that wasn't nearly as
effective as a composite of factors. Again, a lot of people the old joke about quants, right, what do you guys do golf all day? You know you're just running your models. Well, we don't golf all day. But what we do do all day is research the underlying models. What we're always trying to do is improve them. But it's evolutionary, not revolutionary. Listen, the foundations are very, very similar. By the way. They make a lot of sense too.
I say, if we changed it and walked out onto Lexington Avenue here and we found a food truck, right, and we went up and long line. Everything looks good. And we talked to the owner and we said, how about you clearing a year And he says, well, I'm clearing one hundred thousand. And we're like, well, would you take a buy offer from us? And he goes, yeah, you can buy it for ten million. You and I are going to go get out of here. There's no way we're going to buy this right, Well, change it
to a stock ticker. There's a lot of stocks trading at that kind of multiple, and so when you look at the underlying strategies, they make intuitive economic sense, and so the data set that you're using becomes of paramount importance. The other thing I found was that, and this one
disturbed me a little. I haven't looked at this recently, but when I was doing it several years ago, you could get really different numbers if you went to Bloomberg, or if you went to Reuter's, or if you went to Dow Jones or any other innumerable providers of data. And so that was another huge project for us, and also part of the data set that we're talking about.
One of the other things that I was widely hated for by my research team was we went on a multi year data cleansing exercise because we found that a lot of it had a lot of hair on it. And so I'd made no friends on the research desk when I said, listen, we've got to get this pristine. And so our data cleansing of the universe also is another real important distinction between just generally available data and that which we are using.
Huh, really really interesting. Let's stay with price to book because I want to ask your opinion on something, and you're the perfect quant to bring this up to. Which is all right? So we're talking about price to book. Back in the day when manufacturing required a lot of men and material and capital, and you had big factories and railroads were laying thousands of miles of steel, and you know, you were building these forges and foundries to
make cars. The modern era, especially with technology, there are a lot of intangibles that don't seem to find their way to book value. Things like patents and copyrights and algorithms and processes that are prepared rietary that really are the whole value of the company, but somehow never show up in metrics like price to book, which has led to some people and I'm not positive who to name.
I don't want to mischaracterize anybody, but some folks have said we're mispricing companies that operate in the tech space because we're not giving them the appropriate credit for all of this intellectual property. Is that an overstatement or is there some truth there?
I think there's more than some truth to that. We published a papers called the Veiled Value, and it looked at the idea that brand value, that all of the items that you just delineated, we're not being captured in Trademark's research and development straight across the board. When we took a look at that, we found that you could figure out a way to price that into the model. So you are absolutely right. This is one of my bugaboos, things like GDP, all of the metrics that we continue
to report and get obsessed about. Basically they've lost a lot of their meaning because they were designed for the world you just articulated. They were designed for manufacturing. They were designed for physical things, and we moved off that for many many decades. Atoms to bitts was a big transition, huge transition, and so we think that we another aspect of research right when when we got the idea, you know,
we think we're missing something here. That's what resulted in the paper about brand value and goodwill and all those things not being taken into account by investors at all. And so we found ways we could do that with factors and improved the efficacy of the underlying modelsificantly.
I think one of the greatest quotes ever issued by a statistics professor is George Bachs All models are wrong, but some.
Are useful, exactly. I quote him all the time because he's absolutely right. The idea that you're going to get anything to perfection is a fool's errand right. I have a writer that we're working with under O'Shaughnessy Adventures, one of our new verticals, which is Infinite Books, and he's got a great quote, which is perfection is a one hundred percent tax.
Really interesting. Let's talk a little about O'Shaughnessy Adventures starting with your mission statement. Osv's mission is to fuel creators in the worlds of art, science and technology with the advice, data and resources they need to stay focused and get great ideas out of their heads, off of their whiteboards, and out into the world discuss I had.
A thesis that started to develop around twenty seventeen twenty eighteen as I watched old playbooks that used to work beautifully stop working, and so I came up with this idea that we were in a great reshuffle where all of the old models were collapsing and people were kind of freaked out. They were like, this has worked for decades,
Why doesn't it work anymore? And I think that one of the reasons it didn't work anymore was because the tools, the tech tools, and the platforms and the Internet and all of that put together allowed for much more innovative business models in a variety of industries. Right, So if you look at the verticals of O'shaughness adventures, you'll see what we think. Right, So we have what we call
infinite adventures. That's venture capital. But I love in the old days they used to call venture capital adventure capital. And the one I really loved liberation capital. Uh, well, to find that what is what is liberation? And I've heard the phrase, yeah in the old days, the so called hateful eight that wanted to leave shockly.
Right, the early days of semiconductors and the pre in Fairchild semi conductor.
Exactly exactly right, good call. And back then, the idea that a group of engineers or even you know, regular business people would leave a big company that was well funded by a bank or a series of other investors was almost unthinkable. And so what came to be known as the hateful eight who created Fairchild got pitched by a variety of investors, external investors saying why don't you
guys to start your own company. He finally talked them into it, and that's when we use the term this is your liberation capital, where you can focus on just what you want to focus on, making better semiconductors. You don't have to play any of the politics of the big company, you don't have to answer to people who
don't really understand what you're doing. Right, the people in New York that might have owned it or financed it had very little understanding of what semiconductors were all about in the fifties and sixties, and so I like that part very very much.
That's the genesis of Intel right, as well as the whole run of other semiconductors, can trace its roots back to fair Child right exactly.
And so there we're looking for companies that we think will expand the opportunity set for very clever entrepreneurs and creators. Another vertical is infinite films. Why that, well, I think we're approaching a period where you can make films, documentaries, You can use AI to augment your filmmaking in such a way that the people who couldn't make movies in the past are going to be able to make them in the future. You could legitimately make.
A film with an iPhone. Now that you can you couldn't do even five years ago. Is kind of on the border.
Barry. Some of the things that I've seen as submissions to infinite films, Oh my god, Like literally, I'm sixty three. If I had seen that as a trailer for a movie at a movie theater like ten years ago, I would have thought, Wow, this is amazing, this is cool. And then the guy at the bottom says, by the way I made this on my iPhone, that's crazy. That really is great, And so that Unlock's tremendous talent that
never had access to the Hollywood infrastructure. So our thesis is there are tons of really creative people out there who now have the tools to make great movies. Another thing I wanted to do was where are the Rudies of movies today? Now? Rudy's, of course, is about the kid who goes to Notre Dame and he's five foot nothing and weighs a buck nothing and he gets on the team, the Notre Dame team. Why was that such
a great movie? Because it's incredibly inspirational, It gives the viewer like, you know what, I can take a shot at it, I can do it. Hollywood seems to have completely forgotten about making these types of movies.
And just for people who might not remember the movie Rudy, it's the story that drives the whole thing and the characters. There's not a whole lot of expensive special effects or you know, they're not flying out to Nepal. It's all done pretty much on the cheap. And that's the area of film you're looking to explore, or narrative driven accessible story.
Narrative driven accessible stories that we're also changing the underlying economics on. So here's how we're gonna do that. Everyone who comes and works on one of our films is going to own a piece of that film and back end points, back end points. But for everybody, we're not going to use Hollywood accounting. Our accounting is very, very straightforward. Here's what it cost us to make it. What happens
after we recover those costs, You own x percent. If we manage to sell it or generate revenue from it through the multiple platforms you can put it out on,
you're going to benefit from that. The other thing that we're going to do is we're going to give young people a shot right now, if you want to try to beat Let's say you graduate from NYU Film School and you decide you're gonna go out to Hollywood and you're going to pitch all of these student video its, say you want to get good luck, because it ain't
gonna happen. Right There is almost a guild like system out in Hollywood where you know, it's kind of the idea that, yeah, I want to get in the Screen Actors Guild. How do I do that? Well, to get in the Screen Actors Guild, you have to be in three movies. Well, wait a minute, how do I get in the movie if I'm not in the Screen Actors Guild. So there are a lot of really old fashioned rules. And it's not just Hollywood, by the way, it's much of media. It's much of all of the things that
we consume every day. And so basically what I did was say, what industries that I find fascinating that I'm interested in have the greatest arbitrage ability. Huh.
I love that concept. And you know it's funny you mentioned films because that dynamic tension of indie films. Look at how great A twenty four has been doing amazing as a as an independent studio. The timing is really good, and the technology tools, the ability to film on a phone, edit on your laptop and then distribute it by uploading to YouTube or wherever.
Barry, that's the key. There's always cultural lag, right, you know the S curve tech adoption, right, it's real, And let's change industries and let's look at publishing. Right, So we are launching Infinite Books. Why well, because the current publishing industry is still playing under nineteen twenty rules, not twenty twenty rules. We no longer have to have minuscule amounts going to the author. We can, because of the tech, because of our ability to produce that book, give the
author much more of the upside. So, for example, we're going to give any where between depending on what the author wants us to do for them. It's going to always be above fifty percent. Mostly it's going to be seventy percent. But that's just the start. Imagine Berry, you write a book, you bring it to Infinite Books, and I say, hey, Barry, what other languages do you want this published in? And You're like, I don't know, maybe Spanish,
maybe French. Maybe done. Because of AI, we can translate the entire book and have it available for the French or Spanish speaking markets. Even better, let's say you want to do an audiobook and you want to read it because you've got a great voice. I say, Berry, do a minute on this for me, say Express Surprise or Anger or whatever. It will model your voice and you can read your book on all the audiobooks. But what's really cool is we can translate your voice into French,
into Spanish, in to Russian, into anything. Wow. And so all of these tech advantages are being left just lying around on the floor, and we think that's crazy.
We're still early days of the transition, oh very early, to technology, to AI, to all these changes in platforms. It's amazing how slowly it takes place. I think our mutual friend Morgan Housel described how long it took from the Wright Brothers doing the first test flight in Kitty Hawk before it even made its way into newspapers.
Exactly takes forever, and it does. And this lag, even in our twenty four to seven always online environment remains right. It's like, if you think about it, it makes tons of sense. People are habitual, right, they get into habits, they do all of these things. Now, I think that the pandemic really sped up a lot of these trends, things like work from Anywhere. O'shaughness Adventures is a work from anywhere enterprise.
We have people in Singapore, India, UK, all over the world because we can, and the idea that we have to have a traditional office, the idea that we have to do any of those traditional things goes right out the window. It becomes a much less costly enterprise when you can do it this way. But we back to infinite books like we also are going to at the author's decision. Right, We're not going to force anything on
our authors. But if the author wants an AI agent to Let's say, for example, your new book, Let's say if it were an Infinite Books publication and you noted that it quadrupled sales in Omaha, Nebraska, how about having an AI agent find out what podcasts in Omaha are interested in the subject Berry's written about. How about sending them a query letter. How about setting them a clip from the book and saying you really ought to have him on your show or podcast or write about them.
In your substack. All of the tools that are available to us work today, and people aren't using them, and so we suspect that this is going to really I hate the word revolutionize because that's, you know, come on, but it's certainly going to accelerate. That's a better ride.
So I want to talk about another aspect of O'shaughnessee ventures, which is the fellowship program, which I find to be absolutely fascinating. How does this work tell us a little bit about the O'shaughnessee Fellowship.
For most of history, a genius could be born, live and die without even knowing they were a genius, far less other people knowing it. Right, We were really bound by our geography and by our networks, and those networks were pretty small. Like who'd you grow up with, who'd you go to school with, who'd you mary? Where are your kids going to school? What church do you go to? That kind of stuff pretty random. You're just random where you were born. I was just dumb. Luck was kind
of dumb. Luck. You could move, of course, but changing your digital zip code is a hell of a lot easier than changing your physical zip code. But more importantly, we now are interconnected. I can find somebody who's a genius who happens to live in Bangladesh. I would have never under the old system ever known about that person. Now I have the ability to know about that person
and find and fund them. The whole idea behind the fellowships was we wanted to come up with something that highlighted the fact that there are tons millions of brilliant people who in the past just didn't have the right connections, didn't have the right credentials, you'd name it, to get into a place where they could get funding, they could make their idea come to life. And so the idea is quite simple. We're gonna find and fund them and
see what comes from that. I think that it allows for so many things, Like it allows we have a guy who got one of our grants, which is the smaller amount. It's ten thousand. The fellowships are one hundred thousand over a year, no strings.
No strings attached. He has a check for one hundred k. Go do something interesting. We don't care what it is exactly.
And we wanted to do no strings because like we don't want Gotcha's we don't want. But you've got to do You've got to give us right of first refusal. The way I look at it is if if we got somebody so wrong that they're going to take one hundred thousand fellowship from us, develop something really cool, decide to start a company around it, and then take it to a different person for funding, well, we made the mistake right, because generally speaking, what we're finding is they
love being part of the community. Because I'm also a huge believer in cognitive diversity, right, there's a great quote that is like, no matter how smart somebody is, no matter how insightful, no matter how brilliant, you can't ask them to make a list of things that would never occur to them. And so essentially what happens when you get all of these really bright people in our fellowship
and grant community communicating with each other. Wow, the ideas that come out of those cross pollenization of ideas are really extraordinary.
But this sounds like this is really an incubator of sorts.
It can be, but it needn't be. Here's a great example. One of the guys that we gave a great aunt too, his name's just that's his stage name, was an accountant in India who decided he really had music in him and he really wanted to do a musical video using traditional Indian songs and singing in Hindi and other Indian dialects. He went super viral, tens of millions of downloads of his song. He's being put on all of their Good Morning India. You know, we have Good Morning America being
written about in all of their newspapers. And essentially that was because we thought, Wow, this guy's got talent. Let's see what happens. We're not incubating him for anything. Right, if he goes off and signs a deal with a music company, we don't do music, so God bless.
This sounds a little bit like the MacArthur Genius Awards, where here's a chunk of money, go be a genius.
There's just so much potential around the world, Barry, that I feel compelled to amplify. Everybody loves to bag on the generation before or after them, Right, Listen, the kids today, young people today are digital natives. They know how to use these tools in ways that we boomers probably are never going to get to. And I say, let's empower them. Let's demonstrate to the world that this makes real practical sense. Right, Now let's take somebody else who is turning his grant
into a company. It's a guy in Africa who faced a problem I knew nothing about, which was the cost of sanitary napkins for women who are menstruating is out of reach. They are all imported from the West and they can't buy them because they don't have enough money. Well, he came up with an idea where his mostly female staff and researchers use banana leaves and other biodegradable products that they can make on the ground in Africa sell for a fraction of the cost that the important ones
work just as well. Now I believe he is turning that into an enterprise. He's founding a company. Will take a look at investing in it, because of course he's asked us to. It can be on the business side, definitely an incubator, but on the social side, on the
music side, on the art side. So for example, this year, I really want to have a fine artist get one of these grants, because again I want really people to be able to see there is so much talent in the world, and I always try to look for things to root for as opposed to against. They're so easy to root against something thing, right, you don't have to be terribly bright to say that sucks. That sucks. Here's why. How about doing things the other way around? How about
finding things you can root for? And then the results have been kind of like the coolest things we've ever seen, like the guy going viral in India, like we have. We funded a guy trying to advance open source quantum computing. He now is a big deal in quantum computing. It's a great thing to do in general.
Tell us about some of the first few you tried. Who were the people that were the first couple of recipients of.
The guy I just mentioned with the quantum computing. He had me at hello because I love that stuff.
What about people who are looking at markets in the economy, I know that that's a pieve of yours. Oh.
Absolutely, the thing there is we wanted it to be significantly different than our traditional quant One of the reasons I became so interested in machine learning and AI was I viewed that as the next frontier for quant The dirty little secret of week quants is if you really press us and ask us to really explain your model like you would to a five year old we're using pretty much the same stuff, right, So what we wanted to do there was push the needle as far as
we possibly could. But then one of the first people to get one of the fellowships was a married couple, Matt and Martha Sharp, and what they wanted to do was make a documentary about non traditional schools for their kids. They have a bunch of young kids below the age of seven, and they put out a great documentary about a particular school, which was really novel. And so we really are all over the map in the type of
per groups that were willing to consider. Yet another was a refugee in Ireland who found that she couldn't figure out a way in her native language to work her way through the halls of the bureaucracy, to figure out how do I get a place to live? How do I do all of these things? So we funded her
to make an app. And then finally another one that I just love is we have a doctor who came to us and said what he wanted to do was make an app for an iPhone or an Android where you could completely non invasively, I could point the phone at you, get your vitals on the phone, just by the camera on the phone. Really yeah, And what was cool for us was we really pushed him. We're like, why, why,
why why? And finally at the end of our interview with him, he was near tears and he went the real reason for this is my dad died of a stroke and I was in medical school and I didn't save him. I didn't even know that he had a problem. And so this is why I'm so passionate about this. To get a life saving thing in the hands of an on something that we all carry with us, these smartphones, is what motivated him. And on top of that, looks like it could also be a great business.
Well that's really interesting. Let's stay with AI and talk about medicine in particular. I'm fascinated by the concept of AI running through billions or even trillions of molecular combinations to identify promising drugs, some of which are already out there, some of which haven't been created. But it really gives us the ability to take millennial worth of experimentation and do it in a really very short period of time.
It's a world changer. The ability to, as you mentioned, take different molecules where there isn't a drug addressing a certain problem and or taking existing research from drugs and repurposing it. AI can go into all of those spaces that we humans simply can't do and find the connections
on an existing drug. You know what, this drug was originally done for malaria, Well it doesn't work for malaria, but it works really well for this disease over here, and then new drugs that the discovery is going to be amazing. And you've got to remember a lot of this stuff can be done what they call in silico. You don't have to test it on humans or animals. You can test it on the clone of we humans
that you set up in the computer. And so these types of thing things like I honestly don't think it's an overstatement to say, like this, this AI and it's many use cases belong up there with the wheel and fire and the printing press, because it is a multi use technology that's going to affect everything from drug discovery to financial analysis. What about we had trained an AI to generate nothing but null sets? Right, Like, if you're a medical researcher and you're trying to get funding, what
do you want to do? You want to prove something new? Right, you don't. You're not going to get funded to prove you know that aspirin works, but you want to find something new and you also want it to be a positive finding. So what happens is the incentives preclude a lot of brilliant scientists from looking for things that don't work. And yet, like the dog that didn't bark in Sherlock Holmes, there's a lot of really cool information, useful information via negativia.
And so one of the things that we want to do is just have a large language model, churnout hypothesis after hypothesis that is going to generate a null set, publish them to a database that all scientists can have access to. Because there's a wealth of information in the stuff that doesn't work. Here are things you don't want to waste your time exactly. Let's talk a bit about
stability AI. You're on the board of directors, you're the executive chair, and you started back in September twenty twenty two. Pretty good timing. Tell us a little bit about what stability AI does and how does this relate to the rest of O'Shaughnessy ventures. So stability AI builds foundational open
source models. I had a very pointed point of view that with a technology this powerful, I did not want it controlled by a panopticon controlled by a few, and I saw that with that kind of power could come some pretty negative externalities. And so Stability AI was the one that really caught my eye because they really were the ones who shot the gun. Back in the summer of August of twenty two, they released a stable diffusion model which generates images, right, but they did something that
no one had done before. They released that model with all of its weights. Now, not to get too geeky here, but the only way people can build on that type of model is to know what the weights are. And so what they did was show it all. They released the whole thing, full open source, driven source, fully transparent, and bury the Cambrian like explosion of creativity that happened
almost immediately really proved to me. Yeah, back to cognitive diversity, right, when you allow all of these clever people the ability to play with it, to tinker with it, you get a much better model. For example, that's why Linux runs the web. Linux is open source, right, and it does so because a bunch of different people work on different problems. And so my point of view was I'm all for the open I use open AI. I use all of the commercial Uh.
What are some of the commercial apps? So perplexity, I love perplexity.
It's on my phone's open AI. I'm looking at Claude, the new Claude that you know.
Perplexity can be driven by either Claude or there's like four different engines.
That which really interesting. One of the things I love about perplexity.
It's just so great and it's cheap and it's so useful. Exactly every interview I do, I don't start with perplexity, I finished with perplexity and what did I miss?
What did I get wrong?
Although you still have to be careful because every now and then, like O'Shaughnessy is not the rarest of names, you know. I had Bill Dudley, former New York Fed chair, and I learned that he was a running back in the NFL in the forties, which is kind of interesting because he wasn't born till the fifties. But every now and then something will pop up that is a little off. I love the phrase hallucination for that. What else do you use besides perplexity and chatching.
The stability AIS for his models. Are they available? Are they accessible to the lay person? Like, that's the beauty of they are, but through different APIs. We really wanted to focus on being the builder, right, so we did not want to try to compete in the direct to consumer space. And so what we're focusing on is multimodals, including generative models, including specific models for medical research, obviously,
generative art models, movie models, et cetera. The thing I wanted to mention when you were talking about perplexity in it coming up with I also passionately believe that the models that are going to win, or not the models, the approach that's going to win is human plus machine, so called Senator model. I think that you're going to see, you know, we're going to see a deluge of AI only generated stuff, content, movies, et cetera. And to be honest,
most of it's gonna suck. Right. The magic comes when you add a human in the loop. The magic comes by being able to partner with that and co create and sometimes iterate on your own stuff. Like you said, the ideas that you can generate through putting your own stuff into the various models is really cool. We invest in a startup called wand and what they do is it's for graphic artists and it's an AI, but it
has an actual tool, thus the name wand And. What the artist is able to do is feed their own work into the model and then ask it hey, spin out variations on it, and then the artists will look at it and say, wow, I never thought about it that way. That's really cool. And then he or she will iterate, iterate, send it back and this is an iterative process. But what's really cool is they end up in places. We had one artists say to me, I would never have thought to do it this way, but
I absolutely love it. It's his work. He's iterating on his own work, but he's using a tool, the WAND, that makes it infinitely easier for him to get these great ideas. Huh.
Really interesting. Last question before we jump to our favorites, we ask all our guests, which is I want to bring this back to stocks. I know thanks to perplexity as an example, but there are lots of other tools. I find myself going to Google a whole lot less than I used to, and in fact, the Google search results like, suddenly you realize these are crude. They're much
less useful than they used to be. They're festooned with a lot of advertising and a lot of Google in internal products dominate that first page.
What else is AI? What other companies?
What other sectors might AI affect, either positively or negatively?
Well, honestly, how much time do you have it? I think that AI is going to transform virtually every industry. And one of the things that people they get afraid when they hear that, and my view is quite different. It's going to transform for a lot of industries, the pure drudge work, the pure copy and paste stuff. What do you want? Do you like copying and pasting? I hate it? And so it also is going to be able to create jobs that we can't even conceive of
right now. Right like two years ago, would you have known what a prompt engineer was? No, I certainly wouldn't have, right, and yet there's a lot of people doing really well pursuing that is a career. And so I think that entertainment is going to be materially affected media, materially affected search, as you well point out, like you can do a customized search just for Barry and it, you know, depending on how much information you want to give that AI
about yourself. You're going to be at a place where you're going to be able to say, hey, what was that place that I had lunch with Jim last time? We both really really liked it. I'd like to go there again and guess what, it's going to give you the name and address of that restaurant. Because it has access to your calendar, It has access to all of that type of stuff.
It feels like I'll never forget. I tweeted out this really interesting Roman Pizza place, and Roman Pizza is a different type of and I just you know, I used Sarah to speak into the iPhone. Hey we had a fend. This is really different than your usual pizza. And some how it showed up on Twitter as woman Pizza and like, wait, I'm standing right in front of the place. Any correlation between my geotag and business I'm in front of it
just felt like technology should have figured that out. Yeah, what you're saying is that sort of access to your contacts, access to your where you are, access to your calendar, once there's an intelligent agent running all of that. A lot of these sort of silly why can't Siri talk with this person, Why can't Alexa? It just seems like the pre AI era was filled with a lot.
Of pretty dombai. It's starting to get smarter. Yeah, And that's the thing going back to your Right Brothers example. You know when the Right Brothers did that very brief flight, it was only a matter of eight yeah, I think it was twelve seconds, and I think they went like one hundred and odd feet. Like you could see why a lot of people would going, Eh, they didn't accomplish much, but I like the person who was watching and said, this changes everything, And so that's kind of how I
see AI. Of course, we're in the early innings of this, and of course it's going to this is the worst you're ever going to see it. It's going to improve, improve, improve. But the other thing I want to really underline here is it's the quality of the data that you train
your AI on that determines its value to you. And one of the big reasons I'm a huge believer in private AIS is that you will feel if you know that no one else can have access to that right, you're going to give it a lot more access to things than you might otherwise. That's happening right now. Wow. And so one of the things, you know, a lot of people see this as, you know, like the great model that will figure everything out. I don't see it
that way at all. I see it as a lot of smaller but incredibly useful AI agents doing specific things for each of us. Again, Canvas fits in beautifully. Here. We are now in an era of mass customization. We are in an era where it's going to be able to design it just for you and your likes and dislikes. That's really profound when you think about.
It, really fascinating. So let's jump to our speed round, our favorite questions. We ask all of our guests, starting with what has been keeping you entertained these days? What are you either watching or listening to?
So we rewatched True Detective, my wife and I I would highly recommend rewatching the first season of that. It was brilliant. It led us into a reward of the entire series. And now we're on number three, the second one. Here's one of the funny things, like in memory, I kind of my wife and I were both kind of like, yeah, that second one wasn't very good. It was good, and so we're doing that Masters of the air that's on. Yeah, great, really loving that. I loved Band of Brothers, so we're
both really really liking that. And then we are also watching a series, or I guess I should say rewatching a series which kind of kicked off the idea of the Golden Age of television. It was one of the earlier ones. I'm not the Sopranos, but The Wire.
Now, I recall The Wire being very brutal and difficult.
It watch it is, But what's so cool if you choose to watch it again, you see that the reason it kicked off that kind of tea was because it was brutally honest about things. It wasn't trying to lie to you about anything, and the characters are incredibly complex, even though even the evil guys are incredibly complex, And so watching it now from the vantage point of like twenty years or more, it's really amazing.
Really interesting. Tell us about your mentors who helped to shape your career.
Primarily, I would list my grandfather. I was lucky enough, he was very successful in the oil industry, and I am the youngest of the third generation at least the males. I have one younger female cousin and she's just a few months behind me. But I lived in the same town my grandfather did, and after my grandmother died, he would come to our house twice a week for dinner and literally I would literally sit at his knee. And
he was a wonderful storyteller. He was a wonderful teacher, and he taught me this idea of premeditating that I have written a lot about and use all the time. Another was a wonderful man, not related to me at all, by the name of Jim Myers. Any entrepreneur, you hit some rough spots, sure, and I had hit a really rough spot and was basically broke and trying to pay for a house because we'd moved to Greenwich and keep my business afloat and all of that, and the banks
are like, dude, like, you're an entrepreneur. This is back in the nineties. Yeah, sorry, we're not going to give you a mortgage. He stepped in and he's like, Jim, I believe you're going to be tremendously successful and gave me one on handshake, which I was able to repay rapidly. But more than that, just being a super high quality man, he taught me more about real business than any textbook because I was young, right, and I started with him when I was in my early twenties and just an
amazing man. And then finally the other mentors that I would say are like the greatest minds of history. I love to read. I particularly like to read biographies about people I admire. And you know what, Barry life was not easy. We remember them now, right, like, oh, they were this huge success. When you read their biographies, you see they went through a lot of muck to get where they got and so kind of universal lessons.
So perfect segue. Let's talk about some of your favorite books and what are you reading right now?
So right now I am reading about four different books and I which.
By the way, is an occupational hazard for folks like us, because there's always a book I'm prepping for a podcast, there's a book I'm reading for work, and then there's a book I'm just like, I'm going to relax and read this.
Yeah, so for fun. Right now I'm reading Burned book by Karra What's her last Swisher? Which I find very interesting. She's always fascinating, yeah, kind of an inside look. My only comment there was she might be a little guilty of the things that she accuses the people she doesn't like are. But other than that, it's a fun and
kind of a rollicking read. I am reading or rereading several of the books from Will Durant's Story of Civilization, which I read as a kid or a young man, loved and thought, you know what, we moved recently, and so I was going through all my books and I found that I'm like, I should reread some of these just to see if it still stands up. Barry, It's
still stuff, really really stands up. And then just finished an additional biography about Teddy Roosevelt Teddy Rex And then finally I'm reading a lot about AI and scientific development. The book I'd recommend there is written by a pair of authors, one an AI expert, the other a great storyteller, and it's called AI twenty forty one, Ten Visions of Our AI Future. Huh, highly recommend.
I'm going to check that out. We've been talking about the Ripe Brothers. Did you ever read the David McCullough biography of the Ripe Brothers?
I did.
Fascinating, right, really really really fascinating. And our final two questions, what sort of advice would you give to a recent college graduate interested in a career in either quantitative analysis, finance as management.
What's your advice for them? My advice is to focus on the parts of learning that might not be included in a business or finance degree. My line is that markets change second by second, but human nature barely budgees millennia by millennia. Arbitraging. Human nature is the last sustainable
edge in investing. And so if you read about evolutionary psychology and biology, regular psychology and biology and history, what you're going to see is no, history doesn't repeat, but it rhymes, and you can see in you know, all you got to do is go read a book about the south Sea Scandal where Isaac Newton, one of the most brilliant guys of his era, lost a fortune, causing him to lament that he could measure the motion of heavenly bodies but not the madness of men. And guess
what we're not changing. So you can read it in a market related way, or just understand human nature better, you're going to be miles ahead of the people who are just studying math or finance or economics.
Really interesting, and our final question, what do you know about the world of investing today you wish you knew forty or so years ago when you were first getting started.
I think maybe just the advice that I just gave, I wish that I would have known forty years ago that markets are market prices are determined by human beings, and if you are ignorant of all of the ways that we let things affect us, from whether we're hungry or not, or whether we're angry, or whether we're calm, I would have understood that it was not just numbers on a page, that markets are full blooded, almost human
like things because they're driven and created by humans. If I could have told Jim of age twenty three that it would have hastened but also improved the pretty circuitous path that I took to becoming a quat really interesting.
Thank you, Jim for being so generous with your time. We have been speaking with Jim O'Shaughnessy, founder of OSAM Asset Management and currently CEO and founder of O'Shaughnessy Ventures and host of the Infinite Loops podcast. If you enjoy this conversation, well.
Be sure and check out any of.
The five hundred previous discussions we've had over the past ten years.
You can find those at.
iTunes, Spotify, YouTube, wherever you find your favorite podcast. Be sure and sign up for my new podcast At the Money, where we speak with an X and give you information on a topic relative to your money in short eight to twelve minute batches. You can find those in the Masters in Business podcast feed, or wherever you get your favorite podcasts. I would be remiss if I did not thank the Cracked team that helps us put these conversations
together each week. My audio engineer is Sebastian Escobar, My producer is Anna Luke. Sean Russo is my head of research. Attika Valbrun is my project manager. Sage Bauman is the head of podcasts.
I'm Barry Riddolts. You've been listening to Masters in Business on Bloomberg Radio.