Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Master's in
Speaker 1: Business with Barry Ridholts on Bloomberg.
Speaker 2: Radio this week on the podcast, Boy Do I have
Speaker 2: an extra special guest.
Speaker 1: I know Jim O'Shaughnessy.
Speaker 2: For I don't know, maybe twenty plus years something like that.
Speaker 2: We actually first met in the green room at CMBC
Speaker 2: like early two thousands and found we shared some similar
Speaker 2: likes and philosophies. And I've been a fan of his
Speaker 2: book What Works on Wall Street pretty much from when
Speaker 2: it came out. This is a fascinating conversation about a
Speaker 2: person who has worked through multiple locales and seats in finance,
Speaker 2: not just running a systematic investing at bear Stearn's, but
Speaker 2: creating O'Shaughnessy asset management, creating a unique custom index product
Speaker 2: that ended up attracting the attention of Franklin Templeton, who
Speaker 2: paid some undisclosed and ungodly amount of money for the
Speaker 2: whole firm, and now in a later phase of his
Speaker 2: career doing O'shaughnessee ventures and the O'shaughnessee Fellowship. I first
Speaker 2: know him from really the first pant book What Works
Speaker 2: on Wall Street? That was a half a century of
Speaker 2: data analysis. Really was never accessible to.
Speaker 1: The public before.
Speaker 2: I found the conversation to be fascinating and I think
Speaker 2: you will also. And at this point I am obligated
Speaker 2: to do a disclosure. My firm Retults Wealth Management has
Speaker 2: been working with O'Shaughnessey on their direct Index platform. Really
Speaker 2: we were one of the first beta testers. We now
Speaker 2: have over a billion dollars on that platform, maybe coming
Speaker 2: even closer to another big round number with no further ado.
Speaker 2: My discussion with O'Shaughnessy ventures Jim O'Shaughnessy.
Speaker 1: It's great to see you, Berry and congratulations. Wow, that's a.
Speaker 2: Well congratulations to you. I'm still My firm just had
Speaker 2: its tenth anniversary. You guys, anytime I see the phrase
Speaker 2: for an undisclosed amount, my brain automatically says.
Speaker 1: Wow, that has to be a lot of money. If
Speaker 1: it's if.
Speaker 2: They're not disclosing it, it's material, but undisclosed, that's a
Speaker 2: lot of casts.
Speaker 1: Or it could be like trading places and the normal
Speaker 1: bet of a dollar.
Speaker 2: The usual bet Mortimer one dollar. So we know each
Speaker 2: other from way back when you first came into my
Speaker 2: orbit from the book What Works on Wall Street. I
Speaker 2: read it from cover to cover. I was on a
Speaker 2: trading desk when that came out, and I'm like, huh,
Speaker 2: so there's some science and math behind this. It's not
Speaker 2: just rumors and whatever happens to cross TV that day.
Speaker 1: I'm intrigued.
Speaker 2: Before we get there, let's talk a little bit about
Speaker 2: what you were doing prior. Tell us about the early
Speaker 2: Jim O'Shaughnessy.
Speaker 1: Well, I was always fascinated about the markets in general,
Speaker 1: which stemmed from a very angry conversation between my uncle
Speaker 1: and father about IBM. And I had just been allowed
Speaker 1: to go to the adult table, and I was sitting
Speaker 1: next to my dad and he and my uncle John
Speaker 1: were going hammer and tong about whether IBM was a
Speaker 1: good company or not. And I was listening, and it
Speaker 1: was all about the chairman. It was all about, you know,
Speaker 1: things that I looked at as kind of soft intelligence,
Speaker 1: squishy squishy, and so I just thought I asked at
Speaker 1: the dinner, I said, well, would it make more sense
Speaker 1: to like look at how much money they're making and
Speaker 1: what their earnings are and how much you have to
Speaker 1: pay for that? And they both just literally glared at me.
Speaker 1: That's hilarious kids. They don't know anything exactly exactly. It's
Speaker 1: the chairman. How tall is he? I like to cut
Speaker 1: his gips. It's almost as if you were there that
Speaker 1: bug got implanted, that mind worm got implanted in my brain.
Speaker 1: How old were you when that? I was seventeen. Oh
Speaker 1: so you're just going into college? Yeah?
Speaker 2: Absolutely, And you were a Minnesota kid, Is that right?
Speaker 2: I grew up in Saint Paul, Minnesota and beautiful country.
Speaker 2: Certainly in the summer anyway, gorgeous. The winter's tough.
Speaker 1: Yeah. Yeah. Well, if this were the old USSR, that
Speaker 1: is where all the political prisoners would be of Minnesota.
Speaker 1: But so I started doing research on essentially the Dow
Speaker 1: thirty because it was manageable, thirty stocks I could list
Speaker 1: by hand showing how old I am, because you literally
Speaker 1: there were no computers that we could use at the time.
Speaker 1: Simple things like like what's the price, what's the dividend,
Speaker 1: what's the price to earnings, book value, etc. And I
Speaker 1: found a definite trend, right. I found that buying the
Speaker 1: ten stocks and the diw with the lowest pees from
Speaker 1: nineteen like thirty five. I think I started through when
Speaker 1: I was doing it, and this would have been about
Speaker 1: nineteen eighty, absolutely decimated the ten highest PE stocks. So wow,
Speaker 1: I love this. In the meantime, I had computers, and
Speaker 1: the only reason I actually got to write What Works
Speaker 1: on Wall Street was because Ben Graham didn't have computers.
Speaker 1: If he had had them, I would have had no
Speaker 1: chance because he would have done it. Basically, what I
Speaker 1: wanted to see was, is there any rhyme or reason
Speaker 1: to all of these reasons people say they like or
Speaker 1: hate a stock? Right? Where is the proof? Where is
Speaker 1: the empirical evidence that say, buying the low PE stocks
Speaker 1: from the die works very well over many market cycles.
Speaker 1: So I wrote a first book called invest Like the Best,
Speaker 1: in which I basically showed you how you could clone
Speaker 1: your favorite portfolio manager by taking his or her stocks,
Speaker 1: putting them on a big database like Compustat, seeing how
Speaker 1: they differed from the overall market, and then using those
Speaker 1: as factor screens to get down to a portfolio that looked, acted,
Speaker 1: and most importantly, performed like your favorite manager.
Speaker 2: Now, the average investor typically didn't have access to Compustat,
Speaker 2: to big data, to big computers, and so they relied
Speaker 2: on you who did.
Speaker 1: And if I recall what Works on Wall Street.
Speaker 2: You back tested like half a century worth of data
Speaker 2: something like that, and it was the full market, not
Speaker 2: just the thirty dove stocks.
Speaker 1: Yeah. Absolutely. And also not just the full market, it
Speaker 1: was also any company that had been but went bankrupt
Speaker 1: or got taken over, the very very needed research database
Speaker 1: on compustat. So no survivorship bias none, You back that out, Yeah, great, Yeah,
Speaker 1: because some of the early academic studies were they had
Speaker 1: a lot of survivorship bias. They didn't properly lag for
Speaker 1: when you actually knew a number, so they just assumed, right, well,
Speaker 1: there's the number on March thirty first, I'm going to
Speaker 1: use that number. Well, you didn't really know that for
Speaker 1: most of history until maybe May or June. Really interesting,
Speaker 1: so you run these numbers. What sort of strategies do
Speaker 1: you find perform best. Well, we found that on the
Speaker 1: value side, smaller value stocks that had some catalysts and
Speaker 1: had turned a corner and their prices had started to
Speaker 1: go up. A beautiful strategy.
Speaker 2: Small cap value with a touch of momentum.
Speaker 1: Yes, okay. On the growth side, we found momentum works
Speaker 1: really really well. As we continued the research, we found okay,
Speaker 1: there's all sorts of caveats. So, for example, we learned
Speaker 1: after a severe bear market i e. One in which
Speaker 1: the market had declined by forty or more percent, not
Speaker 1: a lot of those, not a lot, thank god, but
Speaker 1: momentum inverted, and the stocks with the worst six or
Speaker 1: twelve month momentum actually did vastly better than the ones
Speaker 1: with the best. And if you think about it even
Speaker 1: for a minute, it makes sense, right deepest value. But
Speaker 1: what happened was a lot of really great stocks during
Speaker 1: the bear market got pushed way low in price, and
Speaker 1: so people when the market was recovering jumped on those stocks.
Speaker 1: They were like, I can't believe I'm getting you know,
Speaker 1: these earnings at six times earnings for an IBM or
Speaker 1: a you know Qualcom, Right, that's the baby with the
Speaker 1: bathwater strategy exactly. And so but we found, you know,
Speaker 1: that value actually works now. It hasn't for a long time.
Speaker 1: But we also found that large stocks with high shareholder
Speaker 1: yield i e. Dividend yield plus buyback yield was an
Speaker 1: excellent way to identify big stocks that are obviously much
Speaker 1: more conservative than the smaller fry in the small cap world.
Speaker 2: Interesting, So let's talk a little bit about your work
Speaker 2: at bear Stearns. Really, where I first met you in
Speaker 2: the two thousands, you were head of systematic Equity at
Speaker 2: bear Stearn's asset management. I'm assuming you were applying a
Speaker 2: lot of the lessons you learned in what works on
Speaker 2: Wall Street to the Bear institutional and tel investing strategies.
Speaker 1: Absolutely, and you know, let me just say Bear was
Speaker 1: really a great company, very unfortunate what happened to it
Speaker 1: during the financial crisis. But the reason I love Bear is,
Speaker 1: you know, a lot of big banks talk about being entrepreneurial.
Speaker 1: Bear Stearns really was. And essentially, if you were doing
Speaker 1: your thing and playing by the rules and doing well,
Speaker 1: they let you alone. Which was pretty important for me
Speaker 1: because when I got there, it was right after the
Speaker 1: dot bomb, and a lot of the brokers had done
Speaker 1: pretty poorly because they were in a lot of those names.
Speaker 1: And so I convinced Steve Dantis, who was then head
Speaker 1: of Private Client Services, that wouldn't it be better if
Speaker 1: we did a packaged portfolio, a separately managed to count
Speaker 1: and we offered at one time I think we were
Speaker 1: all the way up to the brokers so that they
Speaker 1: could use a more systematic time tested way of investing
Speaker 1: for their clients.
Speaker 2: Bringing a little discipline into what had been, at least
Speaker 2: in the nineties very much a cowboy type of environment.
Speaker 2: And I'm not just for fron of Bear. The entire
Speaker 2: retail stock brokerage was wild totally.
Speaker 1: He was very open to it. We ended up putting
Speaker 1: together a separately managed to count platform that they brokers embraced.
Speaker 1: They loved it because literally they did what they did well,
Speaker 1: which was calm the client during bad times, try to
Speaker 1: keep them from getting too excited during great times. But
Speaker 1: they also loved the idea that it had a very
Speaker 1: explicit explanation for why they were putting that client in
Speaker 1: that portfolio. So that was a lot of fun. By
Speaker 1: the time I left Bear, my group controlled about seventy
Speaker 1: percent of Bear Stearn's asset management long only, and that
Speaker 1: was a lot of money, wasn't it. It was It
Speaker 1: was about fourteen billion dollars.
Speaker 2: Okay, so you mentioned you left Bear. Let's put a
Speaker 2: little flesh on those bones. Your timing was perfect.
Speaker 1: You exit Bear in two thousand and seven, Is that right?
Speaker 2: To set up O'Shaughnessy's asset management was the thinking, Hey,
Speaker 2: I want to do this out on my own shop,
Speaker 2: or were you sniffing something out in O seven that's like, hey,
Speaker 2: maybe I don't want to be attached to a giant
Speaker 2: ocean liner taking on water.
Speaker 1: You know, that's funny. I spent the next two years
Speaker 1: after that trying to convince reporters that I really didn't
Speaker 1: know anything. Why I left Bear was because I felt
Speaker 1: that I really wanted to be on my own again.
Speaker 1: I really wanted to be able to just talk about
Speaker 1: quantitative investing. Bear was a boutique, so there were a
Speaker 1: lot of different managers. Liked them all. I thought they
Speaker 1: all were great, but I really really wanted to focus
Speaker 1: just exclusively on quant And secondly, we had upgraded a
Speaker 1: lot of our systems to the idea that would become canvas, right,
Speaker 1: because remember Netfolio was our first try at that. That
Speaker 1: was nineties or ninety nine. Yeah, really, well, of course,
Speaker 1: you know the really funny story here is in April
Speaker 1: of nineteen ninety nine, I wrote a piece called the
Speaker 1: Internet Contrarian, and in that piece I said, eighty five
Speaker 1: percent of the companies currently extant in the Internet space
Speaker 1: are going to be carried out of the market. Feet First,
Speaker 1: I've never seen a bubble like this in my history
Speaker 1: of investing. And what did I do next? Berry? I
Speaker 1: started an Internet company.
Speaker 2: Well just because the stocks or a bubble, does I
Speaker 2: mean this internet thing isn't going to catch on?
Speaker 1: That's true, right, it's there are you know?
Speaker 2: It's funny we forget in the thirties, forties, fifties there
Speaker 2: was only ma bel Every company used telephones. Yep, the
Speaker 2: way we describe internet companies, if you use the Internet
Speaker 2: as a core part of your platform. There's difference between
Speaker 2: the dot.
Speaker 1: Coms and the nineties and people.
Speaker 2: Who have just really integrated the technology into their business. Right,
Speaker 2: So I think Netfolio is not a dot com but
Speaker 2: a comm that used the net as a way to
Speaker 2: reach more people and give them access to data.
Speaker 1: Well, it's really funny because I made a couple well
Speaker 1: I made more than a couple of mistakes, but one
Speaker 1: of the big ones I made was we designed Netfolio
Speaker 1: as a B two C company, so we called we
Speaker 1: were taking on at the time mutual funds, which were dominant.
Speaker 1: We didn't have ETFs while we had them, but they
Speaker 1: were there very early day, very very early days.
Speaker 2: And so what did the spiders just turned twenty five recently, Yeah,
Speaker 2: I think of something like that. Yeah, So ninety nine
Speaker 2: is like it was really the beginning.
Speaker 1: Oh totally. And basically the idea was it was the
Speaker 1: first online investment advisor. And the reason that we thought
Speaker 1: it would work so well was personalization, tax management, all
Speaker 1: of those things. So, for example, we would they were
Speaker 1: all run by quant models that we had developed, right,
Speaker 1: but it gave the user the ability to say, let's
Speaker 1: say they're anti smoking, right, and Philip Morris is one
Speaker 1: of these selections they could just check nope, don't want it.
Speaker 1: Up comes the next stock that meets the criteria, and
Speaker 1: so it had a lot of really great features, but
Speaker 1: the tech was not quite there yet.
Speaker 2: You were twenty years ahead of where you would end
Speaker 2: up in the late tens, right.
Speaker 1: I.
Speaker 2: Was.
Speaker 1: I really do have to give my son Patrick the
Speaker 1: credit for resurrecting the idea because when we were at OSAM,
Speaker 1: I said, listen, we left Bear right into the Great
Speaker 1: Financial Crisis, and I put the team together and I'm like,
Speaker 1: I don't think that we're going to be able to
Speaker 1: sell many long only portfolios after the market has collapsed
Speaker 1: by nearly fifty percent, So let's spend our time developing
Speaker 1: internal technology that works the way we work. The office
Speaker 1: shelf stuff really wasn't cutting it, and so the project
Speaker 1: to get there was multi year and Patrick oversaw that,
Speaker 1: and then he walked into my office one day and
Speaker 1: he goes, you know, Dad, we've been using the desk
Speaker 1: star to kill a mouse. And I'm like, okay, I
Speaker 1: like the metaphor, but what do you mean? And he
Speaker 1: started talking about AWS, talking about netfolio and he's like,
Speaker 1: we have the perfect tech now that our clients results
Speaker 1: being one of them, could use and I'm like, brilliant,
Speaker 1: let's go with it.
Speaker 2: So we're going to talk a little more about Canvas,
Speaker 2: but I want to stay with the launch of O
Speaker 2: SM and O seven. So A, you don't need to
Speaker 2: disclose this, but I'm going to assume you had a
Speaker 2: lot of bear Stern stock options that you had a
Speaker 2: vest on your exit, so you probably had a pretty
Speaker 2: good sale, pretty good print on those when you first
Speaker 2: set up O'Shaughnessy, you're running your traditional models, things like
Speaker 2: Cornerstone value and Cornerstone growth, and I'm a big fan
Speaker 2: of your microcap sleeve, which really operates parallel to venture
Speaker 2: capital returns, only using public stocks.
Speaker 1: Am I getting that more or less right? Actually we
Speaker 1: use that also. Yeah, we wrote a paper saying that
Speaker 1: it was the poor man's way to get exposure to
Speaker 1: private equity. Private equity or venture capital are both both
Speaker 1: really private equity closer because the the microcap I love
Speaker 1: microcap investing. The only real reason that we offered that
Speaker 1: was because I loved it so much. Well, and the
Speaker 1: data backs it up, Oh, totally, totally it is. Microcap
Speaker 1: is an amazing place if you've got the right tools
Speaker 1: to sort through the thousands of names in the microcap universe,
Speaker 1: because you would not want to buy an index of
Speaker 1: microcap stocks. For the most part, there are microcaps because
Speaker 1: they kind of suck. However, there are so many diamonds
Speaker 1: in the rough in microcap that if you have a strategy,
Speaker 1: like a quant strategy that can sort through these thousands
Speaker 1: of names, you can do extraordinarily well. I love the
Speaker 1: strategy and I know.
Speaker 2: Oh the OSAM microcap sleeve is what I call. It
Speaker 2: has just really shot the lights out, especially last year
Speaker 2: when the market was having.
Speaker 1: A pretty good year. They did pretty well, didn't they
Speaker 1: They did? They did. Now, remember you introduced me as
Speaker 1: chairman of o SAM, I'm no longer no longer. Yeah,
Speaker 1: they let me retire and actually Patrick is now chairman
Speaker 1: emeritus over at OSAM. Let's talk a little bit about Canvas,
Speaker 1: and again full disclosure, we're a client.
Speaker 2: We were a beta test. Do we love the product?
Speaker 2: Which is kind of ironic because I used to hate
Speaker 2: direct indexing every time I would demo or see a product.
Speaker 2: It was clunky, it was cludgy. You would get these
Speaker 2: statements that were like hundreds of pages long.
Speaker 1: You guys kind.
Speaker 2: Of figured out the secret sauce for how do we
Speaker 2: make this clean, usable and easier to understand. Tell us
Speaker 2: a little bit about the genesis of Canvas.
Speaker 1: Well, first of all, we call it custom indexing as
Speaker 1: opposed to direct and the reason I make that distinction
Speaker 1: is because, as you point out, the direct indexing products
Speaker 1: of that time were clunky, they were difficult. You got
Speaker 1: reams and reams of paper reports, and they were really
Speaker 1: only focusing on tax benefits. Right. What we wanted to
Speaker 1: do with Canvas, which is custom indexing, is, as the
Speaker 1: name implies, give you, as the advisor, full control over
Speaker 1: what your client portfolio wanted to look like. You got
Speaker 1: the advantages of tax harvesting. You got the advantages of
Speaker 1: being able to mix indexes in with active strategies, but
Speaker 1: you could also do a social investing fund if you
Speaker 1: want it. But the way we did it was we
Speaker 1: didn't presume what your client was going to think of
Speaker 1: as good social investing. So often when you see some
Speaker 1: of the ESG portfolios, they've been predetermined as to what
Speaker 1: is going to be included. We give you the tools
Speaker 1: to turn a dial up or down on whatever you want.
Speaker 1: I think last I looked, there were over fifty eight
Speaker 1: separate things that you could find tune around on the
Speaker 1: idea of ESG. We wanted to give the tools to
Speaker 1: you because you knew your client vastly better than we did,
Speaker 1: and we thought, let's try. As you mentioned you were
Speaker 1: one of the beta testers. That was actually one of
Speaker 1: the smartest things we did, I think, because we had
Speaker 1: really good advice from a lot of people that we
Speaker 1: knew in both venture and other places. The first thing
Speaker 1: that many of them said to us was do not
Speaker 1: try to go big with this. Originally, find advisors who
Speaker 1: you trust who will give you you real feedback. In
Speaker 1: other words, they won't shine you on if they didn't
Speaker 1: like you. Guys were very good at times.
Speaker 2: And Michael batt nicking my office, one of my partners,
Speaker 2: who was over the moon when he first saw this.
Speaker 2: Every time another product came in, it would take me
Speaker 2: thirty seconds to poke holes in it. And he came
Speaker 2: breathless into my office, Dude, you got to see this.
Speaker 2: And I'm like, yeah, yeah, okay, another garbage right, tee
Speaker 2: it up. And it took about thirty seconds to go,
Speaker 2: Oh my god, how do we get a piece of this?
Speaker 1: This is fantastic.
Speaker 2: The interface, the design, all of the bullet points that
Speaker 2: all the boxes checked were great. Let's stick with what
Speaker 2: we no longer call esg and Meyer Statman famously called
Speaker 2: values based investing. Some people have called it woke investing,
Speaker 2: but that's really the wrong phrase. I'm fascinated, for example,
Speaker 2: by the Catholic bishops whose endowment says, look, we don't
Speaker 2: want any aboard of any drugs that do that. We
Speaker 2: can't invest in those companies. We can invest in hospital
Speaker 2: chains that perform these sort of surgeries, or insurers. You
Speaker 2: have the ability to say, whatever your personal preferences are,
Speaker 2: you could just tune those out of pick an index,
Speaker 2: the S and P five hundred the Vanguard Total Market.
Speaker 2: You could say, I don't want X or Y or Z,
Speaker 2: and how it comes tell us a little bit about that.
Speaker 1: I felt that that was really really important because everybody
Speaker 1: has different ideas. As you point out, the Catholic bishops
Speaker 1: wanted to exclude certain things. Others might want to include
Speaker 1: certain things. Actually felt it would be very arrogant of
Speaker 1: us to determine what good social investing was because we
Speaker 1: had managed money for a variety of religious institutions, and
Speaker 1: guess what, they all have different takes on what they
Speaker 1: want to see. We did one where, for example, well
Speaker 1: you couldn't buy any company that did anything with animals
Speaker 1: with eyes. That was an interesting one. But then on
Speaker 1: the other hand, we had a client who wanted to
Speaker 1: see more female board members and females in the C suite,
Speaker 1: and you could you could screen for that.
Speaker 2: You can screen and there's a bunch of research that
Speaker 2: shows those companies. Now you don't know if it's posative
Speaker 2: or just merely correlated, but those companies tend to outperform.
Speaker 2: The request we probably hear the most is no gun stocks,
Speaker 2: no tobacco stocks.
Speaker 1: Yeah, kind of interesting. Yeah, the tobacco guns, those are
Speaker 1: pretty large groups where majority of investors want nothing to
Speaker 1: do with them. But the other thing that's cool about
Speaker 1: our dials on canvas you Let's say that Ritholtz has
Speaker 1: a wild eyed libertarian walk in who happens to have
Speaker 1: a billion dollars and he says, you know what I
Speaker 1: want the gun manufacturers. I want I'm a big like
Speaker 1: an amendment guy, right, Or I want the pharmaceuticals, or
Speaker 1: I mean the sinstock, I mean gambling and alcohol. Well,
Speaker 1: and you know the joke there was that my first company,
Speaker 1: O'Shaughnessy Capital Management, we used to keep a joke portfolio
Speaker 1: which was called the Eat, Drink and Be Merry for
Speaker 1: tomorrow you die Berry. It killed me sure.
Speaker 2: So what ends up happening very often is when there's
Speaker 2: a non financial reason for kicking a stock out out
Speaker 2: of a lot of portfolios. Eventually a company with still
Speaker 2: having decent financial prospects, it becomes cheap.
Speaker 1: Yep. Absolutely, But the thing with the social style investing,
Speaker 1: we wanted you to be able to reflect your client's
Speaker 1: unique needs. And there really wasn't anything like that. I
Speaker 1: don't know if there is now, but I haven't seen
Speaker 1: anything like that.
Speaker 2: Well, certainly not to this degree of granularity. By the way,
Speaker 2: when we first we're beta testing Canvas. Internally, my view was, Hey,
Speaker 2: people are going to want to use this for value
Speaker 2: based investing. Then they're going to want to deconcentrate. If
Speaker 2: I work for Google, do I really need all this
Speaker 2: tech exposure My income is coming from there, Let me
Speaker 2: diversify that way. And then tax loss harvesting was going
Speaker 2: to bring up the rear. I had it exactly backwards,
Speaker 2: in large part because I don't know, maybe a year
Speaker 2: into it we had the COVID crash market falls thirty
Speaker 2: four percent and coincidentally bottoms just near the end of
Speaker 2: the quarter. That rebalance. You know, typical tax loss harvesting
Speaker 2: own a dozen mutual funds. You pick up ten twenty
Speaker 2: basis points against the portfolio of losses to offset gains.
Speaker 2: The hope with this was it would be fifty sixty.
Speaker 2: We had clients getting two hundred, three hundred, four hundred
Speaker 2: basis points. And I've talked to some of your staff
Speaker 2: or former staff, and they've told us some unique use
Speaker 2: cases where the numbers are bonkers. First off, explain to
Speaker 2: the audience who may not be familiar with this what
Speaker 2: is tax loss harvesting.
Speaker 1: So essentially what it does is we had to build
Speaker 1: a non trivial algorithm that could monitor every portfolio we
Speaker 1: were managing on behalf of clients and as you know,
Speaker 1: they can go all the way up get maximized tax
Speaker 1: losses or all the way down don't worry about them. So,
Speaker 1: for example, you wouldn't care about it in an IRA
Speaker 1: right right. But the purpose was that we found through
Speaker 1: our research that a tremendous amount of alpha was being
Speaker 1: left on the table, and that was the alpha from
Speaker 1: tax lost harvesting. When you're in a market like the
Speaker 1: market we had when we went into COVID and the
Speaker 1: bear market ensued, and under other circumstances, well kind of
Speaker 1: you're out of luck. But in this particular case, that
Speaker 1: creates the kick in for harvesting the losses, reducing the
Speaker 1: overall tax needs for the portfolio, and you could really
Speaker 1: look at that as that's money in your pocket. By
Speaker 1: the way, we had the benefits completely backward too. A
Speaker 1: tax loss harvesting was at the bottom of our list
Speaker 1: as well.
Speaker 2: It's arcane and technical and you don't really think about it,
Speaker 2: but we have clients who were either you know startup
Speaker 2: founders that cashed out, or they inherited or or just
Speaker 2: owned stock with a very low cost basis. You know,
Speaker 2: it's always funny when you see a five million dollar
Speaker 2: portfolio and some stock has blown up where it's eighty
Speaker 2: percent of the holdings. Hey, if you have five million
Speaker 2: dollars and four million of it is Apple or Amazon
Speaker 2: or some combination of big stocks, that's a lot of
Speaker 2: angle stock risk. And to a man, every person says, hey,
Speaker 2: you should diversify. The answer as always, I'm gonna get
Speaker 2: killed in capital gains taxes. This worked out to be
Speaker 2: a really good way to say we're gonna work out
Speaker 2: of your concentrated position over three, four or five years,
Speaker 2: and then twenty twenty comes along and what should have
Speaker 2: been a five year process took half as long because
Speaker 2: you had so many losses. So for those people who
Speaker 2: may not be familiar with this, let's say you own
Speaker 2: ten mutual funds, right and some are up, one or
Speaker 2: two are down. You sell the ones that are down,
Speaker 2: you replace it with something very similar. Hey, now I
Speaker 2: got a little bit of loss even and my portfolio
Speaker 2: looks the same, but I have an actual realized loss
Speaker 2: that I could use to offset my real gains. But
Speaker 2: those losses are three five ten percent.
Speaker 1: They're nothing.
Speaker 2: On the other hand, if you have a direct index
Speaker 2: or a custom index that has a couple of hundred stocks, well,
Speaker 2: the worst stocks in those portfolios, they're not down three
Speaker 2: four five percent, they're down forty sixty seventy five percent.
Speaker 2: You sell the ones that are down, you replace them.
Speaker 2: And this is one of the things I like about canvas.
Speaker 2: You identify the replacement stocks that are is it fair
Speaker 2: to say mathematically similar?
Speaker 1: They look, well, they come from the same strategy, so yeah,
Speaker 1: you could say they were mathematically similar. So the overall
Speaker 1: portfolio more or less retains the same characteristics. You're just
Speaker 1: realizing losses deep losses on some stocks and replacing them
Speaker 1: with something relatively similar exactly. And you know, we're just
Speaker 1: basically making math work for us. And because the entire
Speaker 1: thing is operated within the canvas architecture. After getting the algorithm,
Speaker 1: which was non trivial what do you mean by non
Speaker 1: trivial outbum, it took a hell of a lot of work,
Speaker 1: okay to be able to make that function properly, And
Speaker 1: as we worked with firms like yours, it became very
Speaker 1: very clear to us that that was going to be
Speaker 1: a big deal in canvas, So we wanted that algorithm
Speaker 1: to work perfectly. But as you also note, we wanted
Speaker 1: the nearest neighbor, if you will, that would replace that
Speaker 1: stock to not affect the overall metrics of your portfolio.
Speaker 1: So it's going to look, act and perform very much
Speaker 1: like the earlier portfolio, but you've already taken that wonderful
Speaker 1: tex loss so that you can offset the gains from elsewhere.
Speaker 1: The other use case that we thought would be number
Speaker 1: one was, you know you have a concentrated position. Let's
Speaker 1: say Google right, don't give me any tech exposure, or
Speaker 1: give me tech exposure only in this tech which is
Speaker 1: like hardware for example, right that I can do and
Speaker 1: that type of use case would work hand in hand
Speaker 1: with the tax loss, making it a much much more efficient,
Speaker 1: more money in the investors pocket. In terms of final
Speaker 1: outcomes with the portfolios.
Speaker 2: What was the uptake on that approach? People enthusiastic about.
Speaker 1: It, they were, but they were not nearly as enthusiastic
Speaker 1: as we anticipated they would be. There were a few
Speaker 1: advisors that we were working with who worked specifically with
Speaker 1: founders and early employees who had a lot of options
Speaker 1: in that particular and usually tech, but we also did
Speaker 1: work and do work with a lot of people who
Speaker 1: just amassed through employment a huge position in their particular company,
Speaker 1: and they wanted to have the rest of the portfolio
Speaker 1: be built to complement and offset, if you will, any
Speaker 1: further invent's over there. So it's worked actually quite nicely.
Speaker 2: And then in twenty twenty one, Franklin Templeton comes knocking
Speaker 2: at the door. They're an investment giant with a trillion
Speaker 2: plus dollars on their books and they've been pretty acquisitive
Speaker 2: over the past few years. Tell us a little bit
Speaker 2: about how that transaction began. If I recall correctly, you
Speaker 2: guys weren't out shopping the firm to.
Speaker 1: Be sold, were you not? At all? We were. It's
Speaker 1: a funny story. We almost got kind of a cold
Speaker 1: call from a gentleman at Franklin Templeton. I was sort
Speaker 1: of like, give it to Chris Lovelace or you know
Speaker 1: who's the president of the firm, And ultimately Patrick spoke
Speaker 1: with him and came into my office and he's like, hey,
Speaker 1: Franklin Templeton is really interested in canvas. I'm like, okay,
Speaker 1: they want to use it. No, no, they want to
Speaker 1: buy it. And I'm like, okay, well, let's do a
Speaker 1: due diligence on Franklin Templeton. They're massive, as you know,
Speaker 1: I think trillion and a half in assets under management,
Speaker 1: and we were really having great results, as you know,
Speaker 1: with Canvas on our own. We thought about it for
Speaker 1: a long time, and you know, we really wanted custom
Speaker 1: indexing to be a new category of asset management, and
Speaker 1: we felt really proud about that because it isn't too
Speaker 1: often that you're able to invent kind of a new
Speaker 1: category of investing. And as we chatted about it and
Speaker 1: talked it out, we're like, you know, we're at an
Speaker 1: inflection point here. We are relatively small boutique, even though
Speaker 1: this is working really really well. If we want custom indexing,
Speaker 1: custom portfolio creation to really make the big time, it
Speaker 1: probably makes sense for a much larger asset manager with
Speaker 1: all sorts of advantages that we did not have to
Speaker 1: take it and run with it. So we let that
Speaker 1: be our guide, and after doing quite a bit of
Speaker 1: due diligence on the people at Franklin, we were like, okay,
Speaker 1: let's negotiate about selling the firm to them.
Speaker 2: Talk about good timing. Morgan Stanley bought one of your
Speaker 2: competitors in that space. Vanguard rolled out their own product,
Speaker 2: which quickly amassed you know, billions and billions of dollars
Speaker 2: on it. So this has worked its way into the mainstream,
Speaker 2: even though it's still relatively I don't want to call
Speaker 2: it a niche product because it's bigger than that.
Speaker 1: But it's not ETFs. It's not giant yet, but it's
Speaker 1: still growing at a pretty rapid clip, isn't it Totally?
Speaker 1: And I think that ultimately we might look back ten
Speaker 1: years from now and have the thought, can you imagine
Speaker 1: that people just bought packaged products? I mean, like, my god,
Speaker 1: no tax advantage, none of the customization, none of the
Speaker 1: immunization for concentrated positions that I have. And so we
Speaker 1: definitely think that this is a way of investing that well,
Speaker 1: you know, once a client sees their portfolio under canvas
Speaker 1: and with the customization, it's really really hard to go
Speaker 1: back to thinking, ah, you know what, I think I'll
Speaker 1: just go with five mutual funds or five ETFs. I
Speaker 1: don't really care about much of the other I think
Speaker 1: that you know, these things take time. But I mean again,
Speaker 1: your firm is a classic example here. You were able
Speaker 1: to use custom in a way that was good for
Speaker 1: your firm, good for your clients. And you know, the
Speaker 1: clients that we speak with love it. You know, they
Speaker 1: all love it.
Speaker 2: That's been our experience. It's really Mark Andriesen's software is
Speaker 2: the world writ large. Because there are two aspects to this,
Speaker 2: and I'm going to circle back to the database part
Speaker 2: of it in a bit. But the front end, the
Speaker 2: user interface and the software that allows a very simple
Speaker 2: set of choices and that you could go increasingly down
Speaker 2: the rabbit hole and find more and more and more
Speaker 2: issues certainly is a big factor a lot of what
Speaker 2: is done. The technology just wasn't quite mature enough fifteen
Speaker 2: twenty years beforehand. And when you look at it, it's
Speaker 2: just well, this is just software. It's just a user
Speaker 2: interface and a way of organizing it. But now let's
Speaker 2: circle back to the database, which I recall you saying
Speaker 2: was the secret sauce. Tell us a little bit about
Speaker 2: the database that you've been working on for a quarter century.
Speaker 2: That drives canvas.
Speaker 1: So we use the copystat universe. They cover virtually every
Speaker 1: company that trades both here on American exchanges and elsewhere,
Speaker 1: and it is kind of the gold standard really in
Speaker 1: terms of databases.
Speaker 2: How does it compare to something like CRISPER or some
Speaker 2: of the other.
Speaker 1: Well, so, CRISP. It comes to us from the University
Speaker 1: of Chicago Center for Research and Security Pricing. The downside
Speaker 1: of CRISP is it's, first off, I love chris We
Speaker 1: used it in the most recent edition of What Works,
Speaker 1: but it doesn't have enough of the fundamental factors attached
Speaker 1: to it. In other words, it's mostly price history rice history.
Speaker 1: And it also tries and generally succeeds to include all
Speaker 1: of the names that might have been around trading on
Speaker 1: the AMEX or the New York Stock Exchange or NASDAC.
Speaker 1: But the challenge is a guy by the name of
Speaker 1: Macquarie wrote a really compelling paper talking about how a
Speaker 1: lot of the historical data not compustat, but further back
Speaker 1: right in the twenties and thirties, come from the papers. Yeah,
Speaker 1: and also wasn't nearly as thorough as say the compustat is.
Speaker 1: In fact, one of the things that we were doing
Speaker 1: before Franklin Templeton approached us is we were literally digitizing
Speaker 1: old Moody's manuals. They go back to nineteen hundred, and
Speaker 1: what we wanted to do was marry into the CRISP
Speaker 1: data all of the fundamental factors that would have given
Speaker 1: us the ability to run a nineteen hundred through nineteen
Speaker 1: fifty five when Compustat begins test We ran some test runs.
Speaker 1: We did price to book, and we did a couple others.
Speaker 1: And what we were finding and won't surprise you, generally speaking,
Speaker 1: same kind of results, right with the exceptional price to book,
Speaker 1: we actually took price to book of our composites. You
Speaker 1: know how we have the composits for value and momentum
Speaker 1: and all of those things, and we took price to
Speaker 1: book out because of the research that we did that
Speaker 1: covered the great depression from the thirties. You know, and
Speaker 1: I know if you've taken any finance courses, price to
Speaker 1: book previously had been used as a proxy for likelihood
Speaker 1: of bankruptcy. Right, Well, guess what during the thirties, a
Speaker 1: lot of those low price to book companies went bankrupt. Well,
Speaker 1: when your book value collapses, exactly, it's the book isn't
Speaker 1: much value exactly exactly. So we did find some learnings
Speaker 1: where we jiggered with the composites that we use. That's
Speaker 1: another thing we do. We don't use a single factor.
Speaker 1: In my first version of What Works on Wall Street,
Speaker 1: we would sort down for the final portfolio on a
Speaker 1: single factor, and we found that that wasn't nearly as
Speaker 1: effective as a composite of factors. Again, a lot of
Speaker 1: people the old joke about quants, right, what do you
Speaker 1: guys do golf all day? You know you're just running
Speaker 1: your models. Well, we don't golf all day. But what
Speaker 1: we do do all day is research the underlying models.
Speaker 1: What we're always trying to do is improve them. But
Speaker 1: it's evolutionary, not revolutionary. Listen, the foundations are very, very similar.
Speaker 1: By the way. They make a lot of sense too.
Speaker 1: I say, if we changed it and walked out onto
Speaker 1: Lexington Avenue here and we found a food truck, right,
Speaker 1: and we went up and long line. Everything looks good.
Speaker 1: And we talked to the owner and we said, how
Speaker 1: about you clearing a year And he says, well, I'm
Speaker 1: clearing one hundred thousand. And we're like, well, would you
Speaker 1: take a buy offer from us? And he goes, yeah,
Speaker 1: you can buy it for ten million. You and I
Speaker 1: are going to go get out of here. There's no
Speaker 1: way we're going to buy this right, Well, change it
Speaker 1: to a stock ticker. There's a lot of stocks trading
Speaker 1: at that kind of multiple, and so when you look
Speaker 1: at the underlying strategies, they make intuitive economic sense, and
Speaker 1: so the data set that you're using becomes of paramount importance.
Speaker 1: The other thing I found was that, and this one
Speaker 1: disturbed me a little. I haven't looked at this recently,
Speaker 1: but when I was doing it several years ago, you
Speaker 1: could get really different numbers if you went to Bloomberg,
Speaker 1: or if you went to Reuter's, or if you went
Speaker 1: to Dow Jones or any other innumerable providers of data.
Speaker 1: And so that was another huge project for us, and
Speaker 1: also part of the data set that we're talking about.
Speaker 1: One of the other things that I was widely hated
Speaker 1: for by my research team was we went on a
Speaker 1: multi year data cleansing exercise because we found that a
Speaker 1: lot of it had a lot of hair on it.
Speaker 1: And so I'd made no friends on the research desk
Speaker 1: when I said, listen, we've got to get this pristine.
Speaker 1: And so our data cleansing of the universe also is
Speaker 1: another real important distinction between just generally available data and
Speaker 1: that which we are using.
Speaker 2: Huh, really really interesting. Let's stay with price to book
Speaker 2: because I want to ask your opinion on something, and
Speaker 2: you're the perfect quant to bring this up to. Which
Speaker 2: is all right? So we're talking about price to book.
Speaker 2: Back in the day when manufacturing required a lot of
Speaker 2: men and material and capital, and you had big factories
Speaker 2: and railroads were laying thousands of miles of steel, and
Speaker 2: you know, you were building these forges and foundries to
Speaker 2: make cars. The modern era, especially with technology, there are
Speaker 2: a lot of intangibles that don't seem to find their
Speaker 2: way to book value. Things like patents and copyrights and
Speaker 2: algorithms and processes that are prepared rietary that really are
Speaker 2: the whole value of the company, but somehow never show
Speaker 2: up in metrics like price to book, which has led
Speaker 2: to some people and I'm not positive who to name.
Speaker 2: I don't want to mischaracterize anybody, but some folks have
Speaker 2: said we're mispricing companies that operate in the tech space
Speaker 2: because we're not giving them the appropriate credit for all
Speaker 2: of this intellectual property. Is that an overstatement or is
Speaker 2: there some truth there?
Speaker 1: I think there's more than some truth to that. We
Speaker 1: published a papers called the Veiled Value, and it looked
Speaker 1: at the idea that brand value, that all of the
Speaker 1: items that you just delineated, we're not being captured in
Speaker 1: Trademark's research and development straight across the board. When we
Speaker 1: took a look at that, we found that you could
Speaker 1: figure out a way to price that into the model.
Speaker 1: So you are absolutely right. This is one of my bugaboos,
Speaker 1: things like GDP, all of the metrics that we continue
Speaker 1: to report and get obsessed about. Basically they've lost a
Speaker 1: lot of their meaning because they were designed for the
Speaker 1: world you just articulated. They were designed for manufacturing. They
Speaker 1: were designed for physical things, and we moved off that
Speaker 1: for many many decades. Atoms to bitts was a big transition,
Speaker 1: huge transition, and so we think that we another aspect
Speaker 1: of research right when when we got the idea, you know,
Speaker 1: we think we're missing something here. That's what resulted in
Speaker 1: the paper about brand value and goodwill and all those
Speaker 1: things not being taken into account by investors at all.
Speaker 1: And so we found ways we could do that with
Speaker 1: factors and improved the efficacy of the underlying modelsificantly.
Speaker 2: I think one of the greatest quotes ever issued by
Speaker 2: a statistics professor is George Bachs All models are wrong,
Speaker 2: but some.
Speaker 1: Are useful, exactly. I quote him all the time because
Speaker 1: he's absolutely right. The idea that you're going to get
Speaker 1: anything to perfection is a fool's errand right. I have
Speaker 1: a writer that we're working with under O'Shaughnessy Adventures, one
Speaker 1: of our new verticals, which is Infinite Books, and he's
Speaker 1: got a great quote, which is perfection is a one
Speaker 1: hundred percent tax.
Speaker 2: Really interesting. Let's talk a little about O'Shaughnessy Adventures starting
Speaker 2: with your mission statement. Osv's mission is to fuel creators
Speaker 2: in the worlds of art, science and technology with the advice,
Speaker 2: data and resources they need to stay focused and get
Speaker 2: great ideas out of their heads, off of their whiteboards,
Speaker 2: and out into the world discuss I had.
Speaker 1: A thesis that started to develop around twenty seventeen twenty
Speaker 1: eighteen as I watched old playbooks that used to work
Speaker 1: beautifully stop working, and so I came up with this
Speaker 1: idea that we were in a great reshuffle where all
Speaker 1: of the old models were collapsing and people were kind
Speaker 1: of freaked out. They were like, this has worked for decades,
Speaker 1: Why doesn't it work anymore? And I think that one
Speaker 1: of the reasons it didn't work anymore was because the tools,
Speaker 1: the tech tools, and the platforms and the Internet and
Speaker 1: all of that put together allowed for much more innovative
Speaker 1: business models in a variety of industries. Right, So if
Speaker 1: you look at the verticals of O'shaughness adventures, you'll see
Speaker 1: what we think. Right, So we have what we call
Speaker 1: infinite adventures. That's venture capital. But I love in the
Speaker 1: old days they used to call venture capital adventure capital.
Speaker 1: And the one I really loved liberation capital. Uh, well,
Speaker 1: to find that what is what is liberation? And I've
Speaker 1: heard the phrase, yeah in the old days, the so
Speaker 1: called hateful eight that wanted to leave shockly.
Speaker 2: Right, the early days of semiconductors and the pre in
Speaker 2: Fairchild semi conductor.
Speaker 1: Exactly exactly right, good call. And back then, the idea
Speaker 1: that a group of engineers or even you know, regular
Speaker 1: business people would leave a big company that was well
Speaker 1: funded by a bank or a series of other investors
Speaker 1: was almost unthinkable. And so what came to be known
Speaker 1: as the hateful eight who created Fairchild got pitched by
Speaker 1: a variety of investors, external investors saying why don't you
Speaker 1: guys to start your own company. He finally talked them
Speaker 1: into it, and that's when we use the term this
Speaker 1: is your liberation capital, where you can focus on just
Speaker 1: what you want to focus on, making better semiconductors. You
Speaker 1: don't have to play any of the politics of the
Speaker 1: big company, you don't have to answer to people who
Speaker 1: don't really understand what you're doing. Right, the people in
Speaker 1: New York that might have owned it or financed it
Speaker 1: had very little understanding of what semiconductors were all about
Speaker 1: in the fifties and sixties, and so I like that
Speaker 1: part very very much.
Speaker 2: That's the genesis of Intel right, as well as the
Speaker 2: whole run of other semiconductors, can trace its roots back
Speaker 2: to fair Child right exactly.
Speaker 1: And so there we're looking for companies that we think
Speaker 1: will expand the opportunity set for very clever entrepreneurs and creators.
Speaker 1: Another vertical is infinite films. Why that, well, I think
Speaker 1: we're approaching a period where you can make films, documentaries,
Speaker 1: You can use AI to augment your filmmaking in such
Speaker 1: a way that the people who couldn't make movies in
Speaker 1: the past are going to be able to make them
Speaker 1: in the future. You could legitimately make.
Speaker 2: A film with an iPhone. Now that you can you
Speaker 2: couldn't do even five years ago. Is kind of on
Speaker 2: the border.
Speaker 1: Barry. Some of the things that I've seen as submissions
Speaker 1: to infinite films, Oh my god, Like literally, I'm sixty three.
Speaker 1: If I had seen that as a trailer for a
Speaker 1: movie at a movie theater like ten years ago, I
Speaker 1: would have thought, Wow, this is amazing, this is cool.
Speaker 1: And then the guy at the bottom says, by the
Speaker 1: way I made this on my iPhone, that's crazy. That
Speaker 1: really is great, And so that Unlock's tremendous talent that
Speaker 1: never had access to the Hollywood infrastructure. So our thesis
Speaker 1: is there are tons of really creative people out there
Speaker 1: who now have the tools to make great movies. Another
Speaker 1: thing I wanted to do was where are the Rudies
Speaker 1: of movies today? Now? Rudy's, of course, is about the
Speaker 1: kid who goes to Notre Dame and he's five foot
Speaker 1: nothing and weighs a buck nothing and he gets on
Speaker 1: the team, the Notre Dame team. Why was that such
Speaker 1: a great movie? Because it's incredibly inspirational, It gives the
Speaker 1: viewer like, you know what, I can take a shot
Speaker 1: at it, I can do it. Hollywood seems to have
Speaker 1: completely forgotten about making these types of movies.
Speaker 2: And just for people who might not remember the movie Rudy,
Speaker 2: it's the story that drives the whole thing and the characters.
Speaker 2: There's not a whole lot of expensive special effects or
Speaker 2: you know, they're not flying out to Nepal. It's all
Speaker 2: done pretty much on the cheap. And that's the area
Speaker 2: of film you're looking to explore, or narrative driven accessible story.
Speaker 1: Narrative driven accessible stories that we're also changing the underlying
Speaker 1: economics on. So here's how we're gonna do that. Everyone
Speaker 1: who comes and works on one of our films is
Speaker 1: going to own a piece of that film and back
Speaker 1: end points, back end points. But for everybody, we're not
Speaker 1: going to use Hollywood accounting. Our accounting is very, very straightforward.
Speaker 1: Here's what it cost us to make it. What happens
Speaker 1: after we recover those costs, You own x percent. If
Speaker 1: we manage to sell it or generate revenue from it
Speaker 1: through the multiple platforms you can put it out on,
Speaker 1: you're going to benefit from that. The other thing that
Speaker 1: we're going to do is we're going to give young
Speaker 1: people a shot right now, if you want to try
Speaker 1: to beat Let's say you graduate from NYU Film School
Speaker 1: and you decide you're gonna go out to Hollywood and
Speaker 1: you're going to pitch all of these student video its,
Speaker 1: say you want to get good luck, because it ain't
Speaker 1: gonna happen. Right There is almost a guild like system
Speaker 1: out in Hollywood where you know, it's kind of the
Speaker 1: idea that, yeah, I want to get in the Screen
Speaker 1: Actors Guild. How do I do that? Well, to get
Speaker 1: in the Screen Actors Guild, you have to be in
Speaker 1: three movies. Well, wait a minute, how do I get
Speaker 1: in the movie if I'm not in the Screen Actors Guild.
Speaker 1: So there are a lot of really old fashioned rules.
Speaker 1: And it's not just Hollywood, by the way, it's much
Speaker 1: of media. It's much of all of the things that
Speaker 1: we consume every day. And so basically what I did
Speaker 1: was say, what industries that I find fascinating that I'm
Speaker 1: interested in have the greatest arbitrage ability. Huh.
Speaker 2: I love that concept. And you know it's funny you
Speaker 2: mentioned films because that dynamic tension of indie films. Look
Speaker 2: at how great A twenty four has been doing amazing
Speaker 2: as a as an independent studio. The timing is really good,
Speaker 2: and the technology tools, the ability to film on a phone,
Speaker 2: edit on your laptop and then distribute it by uploading
Speaker 2: to YouTube or wherever.
Speaker 1: Barry, that's the key. There's always cultural lag, right, you
Speaker 1: know the S curve tech adoption, right, it's real, And
Speaker 1: let's change industries and let's look at publishing. Right, So
Speaker 1: we are launching Infinite Books. Why well, because the current
Speaker 1: publishing industry is still playing under nineteen twenty rules, not
Speaker 1: twenty twenty rules. We no longer have to have minuscule
Speaker 1: amounts going to the author. We can, because of the tech,
Speaker 1: because of our ability to produce that book, give the
Speaker 1: author much more of the upside. So, for example, we're
Speaker 1: going to give any where between depending on what the
Speaker 1: author wants us to do for them. It's going to
Speaker 1: always be above fifty percent. Mostly it's going to be
Speaker 1: seventy percent. But that's just the start. Imagine Berry, you
Speaker 1: write a book, you bring it to Infinite Books, and
Speaker 1: I say, hey, Barry, what other languages do you want
Speaker 1: this published in? And You're like, I don't know, maybe Spanish,
Speaker 1: maybe French. Maybe done. Because of AI, we can translate
Speaker 1: the entire book and have it available for the French
Speaker 1: or Spanish speaking markets. Even better, let's say you want
Speaker 1: to do an audiobook and you want to read it
Speaker 1: because you've got a great voice. I say, Berry, do
Speaker 1: a minute on this for me, say Express Surprise or
Speaker 1: Anger or whatever. It will model your voice and you
Speaker 1: can read your book on all the audiobooks. But what's
Speaker 1: really cool is we can translate your voice into French,
Speaker 1: into Spanish, in to Russian, into anything. Wow. And so
Speaker 1: all of these tech advantages are being left just lying
Speaker 1: around on the floor, and we think that's crazy.
Speaker 2: We're still early days of the transition, oh very early,
Speaker 2: to technology, to AI, to all these changes in platforms.
Speaker 2: It's amazing how slowly it takes place. I think our
Speaker 2: mutual friend Morgan Housel described how long it took from
Speaker 2: the Wright Brothers doing the first test flight in Kitty
Speaker 2: Hawk before it even made its way into newspapers.
Speaker 1: Exactly takes forever, and it does. And this lag, even
Speaker 1: in our twenty four to seven always online environment remains right.
Speaker 1: It's like, if you think about it, it makes tons of sense.
Speaker 1: People are habitual, right, they get into habits, they do
Speaker 1: all of these things. Now, I think that the pandemic
Speaker 1: really sped up a lot of these trends, things like
Speaker 1: work from Anywhere. O'shaughness Adventures is a work from anywhere enterprise.
Speaker 1: We have people in Singapore, India, UK, all over the
Speaker 1: world because we can, and the idea that we have
Speaker 1: to have a traditional office, the idea that we have
Speaker 1: to do any of those traditional things goes right out
Speaker 1: the window. It becomes a much less costly enterprise when
Speaker 1: you can do it this way. But we back to
Speaker 1: infinite books like we also are going to at the
Speaker 1: author's decision. Right, We're not going to force anything on
Speaker 1: our authors. But if the author wants an AI agent
Speaker 1: to Let's say, for example, your new book, Let's say
Speaker 1: if it were an Infinite Books publication and you noted
Speaker 1: that it quadrupled sales in Omaha, Nebraska, how about having
Speaker 1: an AI agent find out what podcasts in Omaha are
Speaker 1: interested in the subject Berry's written about. How about sending
Speaker 1: them a query letter. How about setting them a clip
Speaker 1: from the book and saying you really ought to have
Speaker 1: him on your show or podcast or write about them.
Speaker 1: In your substack. All of the tools that are available
Speaker 1: to us work today, and people aren't using them, and
Speaker 1: so we suspect that this is going to really I
Speaker 1: hate the word revolutionize because that's, you know, come on,
Speaker 1: but it's certainly going to accelerate. That's a better ride.
Speaker 2: So I want to talk about another aspect of O'shaughnessee ventures,
Speaker 2: which is the fellowship program, which I find to be
Speaker 2: absolutely fascinating. How does this work tell us a little
Speaker 2: bit about the O'shaughnessee Fellowship.
Speaker 1: For most of history, a genius could be born, live
Speaker 1: and die without even knowing they were a genius, far
Speaker 1: less other people knowing it. Right, We were really bound
Speaker 1: by our geography and by our networks, and those networks
Speaker 1: were pretty small. Like who'd you grow up with, who'd
Speaker 1: you go to school with, who'd you mary? Where are
Speaker 1: your kids going to school? What church do you go to?
Speaker 1: That kind of stuff pretty random. You're just random where
Speaker 1: you were born. I was just dumb. Luck was kind
Speaker 1: of dumb. Luck. You could move, of course, but changing
Speaker 1: your digital zip code is a hell of a lot
Speaker 1: easier than changing your physical zip code. But more importantly,
Speaker 1: we now are interconnected. I can find somebody who's a
Speaker 1: genius who happens to live in Bangladesh. I would have
Speaker 1: never under the old system ever known about that person.
Speaker 1: Now I have the ability to know about that person
Speaker 1: and find and fund them. The whole idea behind the
Speaker 1: fellowships was we wanted to come up with something that
Speaker 1: highlighted the fact that there are tons millions of brilliant
Speaker 1: people who in the past just didn't have the right connections,
Speaker 1: didn't have the right credentials, you'd name it, to get
Speaker 1: into a place where they could get funding, they could
Speaker 1: make their idea come to life. And so the idea
Speaker 1: is quite simple. We're gonna find and fund them and
Speaker 1: see what comes from that. I think that it allows
Speaker 1: for so many things, Like it allows we have a
Speaker 1: guy who got one of our grants, which is the
Speaker 1: smaller amount. It's ten thousand. The fellowships are one hundred
Speaker 1: thousand over a year, no strings.
Speaker 2: No strings attached. He has a check for one hundred k.
Speaker 2: Go do something interesting. We don't care what it is exactly.
Speaker 1: And we wanted to do no strings because like we
Speaker 1: don't want Gotcha's we don't want. But you've got to
Speaker 1: do You've got to give us right of first refusal.
Speaker 1: The way I look at it is if if we
Speaker 1: got somebody so wrong that they're going to take one
Speaker 1: hundred thousand fellowship from us, develop something really cool, decide
Speaker 1: to start a company around it, and then take it
Speaker 1: to a different person for funding, well, we made the
Speaker 1: mistake right, because generally speaking, what we're finding is they
Speaker 1: love being part of the community. Because I'm also a
Speaker 1: huge believer in cognitive diversity, right, there's a great quote
Speaker 1: that is like, no matter how smart somebody is, no
Speaker 1: matter how insightful, no matter how brilliant, you can't ask
Speaker 1: them to make a list of things that would never
Speaker 1: occur to them. And so essentially what happens when you
Speaker 1: get all of these really bright people in our fellowship
Speaker 1: and grant community communicating with each other. Wow, the ideas
Speaker 1: that come out of those cross pollenization of ideas are
Speaker 1: really extraordinary.
Speaker 2: But this sounds like this is really an incubator of sorts.
Speaker 1: It can be, but it needn't be. Here's a great example.
Speaker 1: One of the guys that we gave a great aunt too,
Speaker 1: his name's just that's his stage name, was an accountant
Speaker 1: in India who decided he really had music in him
Speaker 1: and he really wanted to do a musical video using
Speaker 1: traditional Indian songs and singing in Hindi and other Indian dialects.
Speaker 1: He went super viral, tens of millions of downloads of
Speaker 1: his song. He's being put on all of their Good
Speaker 1: Morning India. You know, we have Good Morning America being
Speaker 1: written about in all of their newspapers. And essentially that
Speaker 1: was because we thought, Wow, this guy's got talent. Let's
Speaker 1: see what happens. We're not incubating him for anything. Right,
Speaker 1: if he goes off and signs a deal with a
Speaker 1: music company, we don't do music, so God bless.
Speaker 2: This sounds a little bit like the MacArthur Genius Awards,
Speaker 2: where here's a chunk of money, go be a genius.
Speaker 1: There's just so much potential around the world, Barry, that
Speaker 1: I feel compelled to amplify. Everybody loves to bag on
Speaker 1: the generation before or after them, Right, Listen, the kids today,
Speaker 1: young people today are digital natives. They know how to
Speaker 1: use these tools in ways that we boomers probably are
Speaker 1: never going to get to. And I say, let's empower them.
Speaker 1: Let's demonstrate to the world that this makes real practical sense. Right,
Speaker 1: Now let's take somebody else who is turning his grant
Speaker 1: into a company. It's a guy in Africa who faced
Speaker 1: a problem I knew nothing about, which was the cost
Speaker 1: of sanitary napkins for women who are menstruating is out
Speaker 1: of reach. They are all imported from the West and
Speaker 1: they can't buy them because they don't have enough money. Well,
Speaker 1: he came up with an idea where his mostly female
Speaker 1: staff and researchers use banana leaves and other biodegradable products
Speaker 1: that they can make on the ground in Africa sell
Speaker 1: for a fraction of the cost that the important ones
Speaker 1: work just as well. Now I believe he is turning
Speaker 1: that into an enterprise. He's founding a company. Will take
Speaker 1: a look at investing in it, because of course he's
Speaker 1: asked us to. It can be on the business side,
Speaker 1: definitely an incubator, but on the social side, on the
Speaker 1: music side, on the art side. So for example, this year,
Speaker 1: I really want to have a fine artist get one
Speaker 1: of these grants, because again I want really people to
Speaker 1: be able to see there is so much talent in
Speaker 1: the world, and I always try to look for things
Speaker 1: to root for as opposed to against. They're so easy
Speaker 1: to root against something thing, right, you don't have to
Speaker 1: be terribly bright to say that sucks. That sucks. Here's why.
Speaker 1: How about doing things the other way around? How about
Speaker 1: finding things you can root for? And then the results
Speaker 1: have been kind of like the coolest things we've ever seen,
Speaker 1: like the guy going viral in India, like we have.
Speaker 1: We funded a guy trying to advance open source quantum computing.
Speaker 1: He now is a big deal in quantum computing. It's
Speaker 1: a great thing to do in general.
Speaker 2: Tell us about some of the first few you tried.
Speaker 2: Who were the people that were the first couple of
Speaker 2: recipients of.
Speaker 1: The guy I just mentioned with the quantum computing. He
Speaker 1: had me at hello because I love that stuff.
Speaker 2: What about people who are looking at markets in the economy,
Speaker 2: I know that that's a pieve of yours. Oh.
Speaker 1: Absolutely, the thing there is we wanted it to be
Speaker 1: significantly different than our traditional quant One of the reasons
Speaker 1: I became so interested in machine learning and AI was
Speaker 1: I viewed that as the next frontier for quant The
Speaker 1: dirty little secret of week quants is if you really
Speaker 1: press us and ask us to really explain your model
Speaker 1: like you would to a five year old we're using
Speaker 1: pretty much the same stuff, right, So what we wanted
Speaker 1: to do there was push the needle as far as
Speaker 1: we possibly could. But then one of the first people
Speaker 1: to get one of the fellowships was a married couple,
Speaker 1: Matt and Martha Sharp, and what they wanted to do
Speaker 1: was make a documentary about non traditional schools for their kids.
Speaker 1: They have a bunch of young kids below the age
Speaker 1: of seven, and they put out a great documentary about
Speaker 1: a particular school, which was really novel. And so we
Speaker 1: really are all over the map in the type of
Speaker 1: per groups that were willing to consider. Yet another was
Speaker 1: a refugee in Ireland who found that she couldn't figure
Speaker 1: out a way in her native language to work her
Speaker 1: way through the halls of the bureaucracy, to figure out
Speaker 1: how do I get a place to live? How do
Speaker 1: I do all of these things? So we funded her
Speaker 1: to make an app. And then finally another one that
Speaker 1: I just love is we have a doctor who came
Speaker 1: to us and said what he wanted to do was
Speaker 1: make an app for an iPhone or an Android where
Speaker 1: you could completely non invasively, I could point the phone
Speaker 1: at you, get your vitals on the phone, just by
Speaker 1: the camera on the phone. Really yeah, And what was
Speaker 1: cool for us was we really pushed him. We're like, why, why,
Speaker 1: why why? And finally at the end of our interview
Speaker 1: with him, he was near tears and he went the
Speaker 1: real reason for this is my dad died of a
Speaker 1: stroke and I was in medical school and I didn't
Speaker 1: save him. I didn't even know that he had a problem.
Speaker 1: And so this is why I'm so passionate about this.
Speaker 1: To get a life saving thing in the hands of
Speaker 1: an on something that we all carry with us, these smartphones,
Speaker 1: is what motivated him. And on top of that, looks
Speaker 1: like it could also be a great business.
Speaker 2: Well that's really interesting. Let's stay with AI and talk
Speaker 2: about medicine in particular. I'm fascinated by the concept of
Speaker 2: AI running through billions or even trillions of molecular combinations
Speaker 2: to identify promising drugs, some of which are already out there,
Speaker 2: some of which haven't been created. But it really gives
Speaker 2: us the ability to take millennial worth of experimentation and
Speaker 2: do it in a really very short period of time.
Speaker 1: It's a world changer. The ability to, as you mentioned,
Speaker 1: take different molecules where there isn't a drug addressing a
Speaker 1: certain problem and or taking existing research from drugs and
Speaker 1: repurposing it. AI can go into all of those spaces
Speaker 1: that we humans simply can't do and find the connections
Speaker 1: on an existing drug. You know what, this drug was
Speaker 1: originally done for malaria, Well it doesn't work for malaria,
Speaker 1: but it works really well for this disease over here,
Speaker 1: and then new drugs that the discovery is going to
Speaker 1: be amazing. And you've got to remember a lot of
Speaker 1: this stuff can be done what they call in silico.
Speaker 1: You don't have to test it on humans or animals.
Speaker 1: You can test it on the clone of we humans
Speaker 1: that you set up in the computer. And so these
Speaker 1: types of thing things like I honestly don't think it's
Speaker 1: an overstatement to say, like this, this AI and it's
Speaker 1: many use cases belong up there with the wheel and
Speaker 1: fire and the printing press, because it is a multi
Speaker 1: use technology that's going to affect everything from drug discovery
Speaker 1: to financial analysis. What about we had trained an AI
Speaker 1: to generate nothing but null sets? Right, Like, if you're
Speaker 1: a medical researcher and you're trying to get funding, what
Speaker 1: do you want to do? You want to prove something new? Right,
Speaker 1: you don't. You're not going to get funded to prove
Speaker 1: you know that aspirin works, but you want to find
Speaker 1: something new and you also want it to be a
Speaker 1: positive finding. So what happens is the incentives preclude a
Speaker 1: lot of brilliant scientists from looking for things that don't work.
Speaker 1: And yet, like the dog that didn't bark in Sherlock Holmes,
Speaker 1: there's a lot of really cool information, useful information via negativia.
Speaker 1: And so one of the things that we want to
Speaker 1: do is just have a large language model, churnout hypothesis
Speaker 1: after hypothesis that is going to generate a null set,
Speaker 1: publish them to a database that all scientists can have
Speaker 1: access to. Because there's a wealth of information in the
Speaker 1: stuff that doesn't work. Here are things you don't want
Speaker 1: to waste your time exactly. Let's talk a bit about
Speaker 1: stability AI. You're on the board of directors, you're the
Speaker 1: executive chair, and you started back in September twenty twenty two.
Speaker 1: Pretty good timing. Tell us a little bit about what
Speaker 1: stability AI does and how does this relate to the
Speaker 1: rest of O'Shaughnessy ventures. So stability AI builds foundational open
Speaker 1: source models. I had a very pointed point of view
Speaker 1: that with a technology this powerful, I did not want
Speaker 1: it controlled by a panopticon controlled by a few, and
Speaker 1: I saw that with that kind of power could come
Speaker 1: some pretty negative externalities. And so Stability AI was the
Speaker 1: one that really caught my eye because they really were
Speaker 1: the ones who shot the gun. Back in the summer
Speaker 1: of August of twenty two, they released a stable diffusion
Speaker 1: model which generates images, right, but they did something that
Speaker 1: no one had done before. They released that model with
Speaker 1: all of its weights. Now, not to get too geeky here,
Speaker 1: but the only way people can build on that type
Speaker 1: of model is to know what the weights are. And
Speaker 1: so what they did was show it all. They released
Speaker 1: the whole thing, full open source, driven source, fully transparent,
Speaker 1: and bury the Cambrian like explosion of creativity that happened
Speaker 1: almost immediately really proved to me. Yeah, back to cognitive diversity, right,
Speaker 1: when you allow all of these clever people the ability
Speaker 1: to play with it, to tinker with it, you get
Speaker 1: a much better model. For example, that's why Linux runs
Speaker 1: the web. Linux is open source, right, and it does
Speaker 1: so because a bunch of different people work on different problems.
Speaker 1: And so my point of view was I'm all for
Speaker 1: the open I use open AI. I use all of
Speaker 1: the commercial Uh.
Speaker 2: What are some of the commercial apps? So perplexity, I
Speaker 2: love perplexity.
Speaker 1: It's on my phone's open AI. I'm looking at Claude,
Speaker 1: the new Claude that you know.
Speaker 2: Perplexity can be driven by either Claude or there's like
Speaker 2: four different engines.
Speaker 1: That which really interesting. One of the things I love
Speaker 1: about perplexity.
Speaker 2: It's just so great and it's cheap and it's so useful.
Speaker 2: Exactly every interview I do, I don't start with perplexity,
Speaker 2: I finished with perplexity and what did I miss?
Speaker 1: What did I get wrong?
Speaker 2: Although you still have to be careful because every now
Speaker 2: and then, like O'Shaughnessy is not the rarest of names,
Speaker 2: you know. I had Bill Dudley, former New York Fed chair,
Speaker 2: and I learned that he was a running back in
Speaker 2: the NFL in the forties, which is kind of interesting
Speaker 2: because he wasn't born till the fifties. But every now
Speaker 2: and then something will pop up that is a little off.
Speaker 2: I love the phrase hallucination for that. What else do
Speaker 2: you use besides perplexity and chatching.
Speaker 1: The stability AIS for his models. Are they available? Are
Speaker 1: they accessible to the lay person? Like, that's the beauty
Speaker 1: of they are, but through different APIs. We really wanted
Speaker 1: to focus on being the builder, right, so we did
Speaker 1: not want to try to compete in the direct to
Speaker 1: consumer space. And so what we're focusing on is multimodals,
Speaker 1: including generative models, including specific models for medical research, obviously,
Speaker 1: generative art models, movie models, et cetera. The thing I
Speaker 1: wanted to mention when you were talking about perplexity in
Speaker 1: it coming up with I also passionately believe that the
Speaker 1: models that are going to win, or not the models,
Speaker 1: the approach that's going to win is human plus machine,
Speaker 1: so called Senator model. I think that you're going to see,
Speaker 1: you know, we're going to see a deluge of AI
Speaker 1: only generated stuff, content, movies, et cetera. And to be honest,
Speaker 1: most of it's gonna suck. Right. The magic comes when
Speaker 1: you add a human in the loop. The magic comes
Speaker 1: by being able to partner with that and co create
Speaker 1: and sometimes iterate on your own stuff. Like you said,
Speaker 1: the ideas that you can generate through putting your own
Speaker 1: stuff into the various models is really cool. We invest
Speaker 1: in a startup called wand and what they do is
Speaker 1: it's for graphic artists and it's an AI, but it
Speaker 1: has an actual tool, thus the name wand And. What
Speaker 1: the artist is able to do is feed their own
Speaker 1: work into the model and then ask it hey, spin
Speaker 1: out variations on it, and then the artists will look
Speaker 1: at it and say, wow, I never thought about it
Speaker 1: that way. That's really cool. And then he or she
Speaker 1: will iterate, iterate, send it back and this is an
Speaker 1: iterative process. But what's really cool is they end up
Speaker 1: in places. We had one artists say to me, I
Speaker 1: would never have thought to do it this way, but
Speaker 1: I absolutely love it. It's his work. He's iterating on his
Speaker 1: own work, but he's using a tool, the WAND, that
Speaker 1: makes it infinitely easier for him to get these great ideas. Huh.
Speaker 2: Really interesting. Last question before we jump to our favorites,
Speaker 2: we ask all our guests, which is I want to
Speaker 2: bring this back to stocks. I know thanks to perplexity
Speaker 2: as an example, but there are lots of other tools.
Speaker 2: I find myself going to Google a whole lot less
Speaker 2: than I used to, and in fact, the Google search
Speaker 2: results like, suddenly you realize these are crude. They're much
Speaker 2: less useful than they used to be. They're festooned with
Speaker 2: a lot of advertising and a lot of Google in
Speaker 2: internal products dominate that first page.
Speaker 1: What else is AI? What other companies?
Speaker 2: What other sectors might AI affect, either positively or negatively?
Speaker 1: Well, honestly, how much time do you have it? I
Speaker 1: think that AI is going to transform virtually every industry.
Speaker 1: And one of the things that people they get afraid
Speaker 1: when they hear that, and my view is quite different.
Speaker 1: It's going to transform for a lot of industries, the
Speaker 1: pure drudge work, the pure copy and paste stuff. What
Speaker 1: do you want? Do you like copying and pasting? I
Speaker 1: hate it? And so it also is going to be
Speaker 1: able to create jobs that we can't even conceive of
Speaker 1: right now. Right like two years ago, would you have
Speaker 1: known what a prompt engineer was? No, I certainly wouldn't have, right,
Speaker 1: and yet there's a lot of people doing really well
Speaker 1: pursuing that is a career. And so I think that
Speaker 1: entertainment is going to be materially affected media, materially affected search,
Speaker 1: as you well point out, like you can do a
Speaker 1: customized search just for Barry and it, you know, depending
Speaker 1: on how much information you want to give that AI
Speaker 1: about yourself. You're going to be at a place where
Speaker 1: you're going to be able to say, hey, what was
Speaker 1: that place that I had lunch with Jim last time?
Speaker 1: We both really really liked it. I'd like to go
Speaker 1: there again and guess what, it's going to give you
Speaker 1: the name and address of that restaurant. Because it has
Speaker 1: access to your calendar, It has access to all of
Speaker 1: that type of stuff.
Speaker 2: It feels like I'll never forget. I tweeted out this
Speaker 2: really interesting Roman Pizza place, and Roman Pizza is a
Speaker 2: different type of and I just you know, I used
Speaker 2: Sarah to speak into the iPhone. Hey we had a fend.
Speaker 2: This is really different than your usual pizza. And some
Speaker 2: how it showed up on Twitter as woman Pizza and like, wait,
Speaker 2: I'm standing right in front of the place. Any correlation
Speaker 2: between my geotag and business I'm in front of it
Speaker 2: just felt like technology should have figured that out. Yeah,
Speaker 2: what you're saying is that sort of access to your contacts,
Speaker 2: access to your where you are, access to your calendar,
Speaker 2: once there's an intelligent agent running all of that. A
Speaker 2: lot of these sort of silly why can't Siri talk
Speaker 2: with this person, Why can't Alexa? It just seems like
Speaker 2: the pre AI era was filled with a lot.
Speaker 1: Of pretty dombai. It's starting to get smarter. Yeah, And
Speaker 1: that's the thing going back to your Right Brothers example.
Speaker 1: You know when the Right Brothers did that very brief flight,
Speaker 1: it was only a matter of eight yeah, I think
Speaker 1: it was twelve seconds, and I think they went like
Speaker 1: one hundred and odd feet. Like you could see why
Speaker 1: a lot of people would going, Eh, they didn't accomplish much,
Speaker 1: but I like the person who was watching and said,
Speaker 1: this changes everything, And so that's kind of how I
Speaker 1: see AI. Of course, we're in the early innings of this,
Speaker 1: and of course it's going to this is the worst
Speaker 1: you're ever going to see it. It's going to improve, improve, improve.
Speaker 1: But the other thing I want to really underline here
Speaker 1: is it's the quality of the data that you train
Speaker 1: your AI on that determines its value to you. And
Speaker 1: one of the big reasons I'm a huge believer in
Speaker 1: private AIS is that you will feel if you know
Speaker 1: that no one else can have access to that right,
Speaker 1: you're going to give it a lot more access to
Speaker 1: things than you might otherwise. That's happening right now. Wow.
Speaker 1: And so one of the things, you know, a lot
Speaker 1: of people see this as, you know, like the great
Speaker 1: model that will figure everything out. I don't see it
Speaker 1: that way at all. I see it as a lot
Speaker 1: of smaller but incredibly useful AI agents doing specific things
Speaker 1: for each of us. Again, Canvas fits in beautifully. Here.
Speaker 1: We are now in an era of mass customization. We
Speaker 1: are in an era where it's going to be able
Speaker 1: to design it just for you and your likes and dislikes.
Speaker 1: That's really profound when you think about.
Speaker 2: It, really fascinating. So let's jump to our speed round,
Speaker 2: our favorite questions. We ask all of our guests, starting
Speaker 2: with what has been keeping you entertained these days? What
Speaker 2: are you either watching or listening to?
Speaker 1: So we rewatched True Detective, my wife and I I
Speaker 1: would highly recommend rewatching the first season of that. It
Speaker 1: was brilliant. It led us into a reward of the
Speaker 1: entire series. And now we're on number three, the second one.
Speaker 1: Here's one of the funny things, like in memory, I
Speaker 1: kind of my wife and I were both kind of like, yeah,
Speaker 1: that second one wasn't very good. It was good, and
Speaker 1: so we're doing that Masters of the air that's on. Yeah, great,
Speaker 1: really loving that. I loved Band of Brothers, so we're
Speaker 1: both really really liking that. And then we are also
Speaker 1: watching a series, or I guess I should say rewatching
Speaker 1: a series which kind of kicked off the idea of
Speaker 1: the Golden Age of television. It was one of the
Speaker 1: earlier ones. I'm not the Sopranos, but The Wire.
Speaker 2: Now, I recall The Wire being very brutal and difficult.
Speaker 1: It watch it is, But what's so cool if you
Speaker 1: choose to watch it again, you see that the reason
Speaker 1: it kicked off that kind of tea was because it
Speaker 1: was brutally honest about things. It wasn't trying to lie
Speaker 1: to you about anything, and the characters are incredibly complex,
Speaker 1: even though even the evil guys are incredibly complex, And
Speaker 1: so watching it now from the vantage point of like
Speaker 1: twenty years or more, it's really amazing.
Speaker 2: Really interesting. Tell us about your mentors who helped to
Speaker 2: shape your career.
Speaker 1: Primarily, I would list my grandfather. I was lucky enough,
Speaker 1: he was very successful in the oil industry, and I
Speaker 1: am the youngest of the third generation at least the males.
Speaker 1: I have one younger female cousin and she's just a
Speaker 1: few months behind me. But I lived in the same
Speaker 1: town my grandfather did, and after my grandmother died, he
Speaker 1: would come to our house twice a week for dinner
Speaker 1: and literally I would literally sit at his knee. And
Speaker 1: he was a wonderful storyteller. He was a wonderful teacher,
Speaker 1: and he taught me this idea of premeditating that I
Speaker 1: have written a lot about and use all the time.
Speaker 1: Another was a wonderful man, not related to me at all,
Speaker 1: by the name of Jim Myers. Any entrepreneur, you hit
Speaker 1: some rough spots, sure, and I had hit a really
Speaker 1: rough spot and was basically broke and trying to pay
Speaker 1: for a house because we'd moved to Greenwich and keep
Speaker 1: my business afloat and all of that, and the banks
Speaker 1: are like, dude, like, you're an entrepreneur. This is back
Speaker 1: in the nineties. Yeah, sorry, we're not going to give
Speaker 1: you a mortgage. He stepped in and he's like, Jim,
Speaker 1: I believe you're going to be tremendously successful and gave
Speaker 1: me one on handshake, which I was able to repay rapidly.
Speaker 1: But more than that, just being a super high quality man,
Speaker 1: he taught me more about real business than any textbook
Speaker 1: because I was young, right, and I started with him
Speaker 1: when I was in my early twenties and just an
Speaker 1: amazing man. And then finally the other mentors that I
Speaker 1: would say are like the greatest minds of history. I
Speaker 1: love to read. I particularly like to read biographies about
Speaker 1: people I admire. And you know what, Barry life was
Speaker 1: not easy. We remember them now, right, like, oh, they
Speaker 1: were this huge success. When you read their biographies, you
Speaker 1: see they went through a lot of muck to get
Speaker 1: where they got and so kind of universal lessons.
Speaker 2: So perfect segue. Let's talk about some of your favorite
Speaker 2: books and what are you reading right now?
Speaker 1: So right now I am reading about four different books
Speaker 1: and I which.
Speaker 2: By the way, is an occupational hazard for folks like us,
Speaker 2: because there's always a book I'm prepping for a podcast,
Speaker 2: there's a book I'm reading for work, and then there's
Speaker 2: a book I'm just like, I'm going to relax and
Speaker 2: read this.
Speaker 1: Yeah, so for fun. Right now I'm reading Burned book
Speaker 1: by Karra What's her last Swisher? Which I find very interesting.
Speaker 1: She's always fascinating, yeah, kind of an inside look. My
Speaker 1: only comment there was she might be a little guilty
Speaker 1: of the things that she accuses the people she doesn't
Speaker 1: like are. But other than that, it's a fun and
Speaker 1: kind of a rollicking read. I am reading or rereading
Speaker 1: several of the books from Will Durant's Story of Civilization,
Speaker 1: which I read as a kid or a young man,
Speaker 1: loved and thought, you know what, we moved recently, and
Speaker 1: so I was going through all my books and I
Speaker 1: found that I'm like, I should reread some of these
Speaker 1: just to see if it still stands up. Barry, It's
Speaker 1: still stuff, really really stands up. And then just finished
Speaker 1: an additional biography about Teddy Roosevelt Teddy Rex And then
Speaker 1: finally I'm reading a lot about AI and scientific development.
Speaker 1: The book I'd recommend there is written by a pair
Speaker 1: of authors, one an AI expert, the other a great storyteller,
Speaker 1: and it's called AI twenty forty one, Ten Visions of
Speaker 1: Our AI Future. Huh, highly recommend.
Speaker 2: I'm going to check that out. We've been talking about
Speaker 2: the Ripe Brothers. Did you ever read the David McCullough
Speaker 2: biography of the Ripe Brothers?
Speaker 1: I did.
Speaker 2: Fascinating, right, really really really fascinating. And our final two questions,
Speaker 2: what sort of advice would you give to a recent
Speaker 2: college graduate interested in a career in either quantitative analysis,
Speaker 2: finance as management.
Speaker 1: What's your advice for them? My advice is to focus
Speaker 1: on the parts of learning that might not be included
Speaker 1: in a business or finance degree. My line is that
Speaker 1: markets change second by second, but human nature barely budgees
Speaker 1: millennia by millennia. Arbitraging. Human nature is the last sustainable
Speaker 1: edge in investing. And so if you read about evolutionary
Speaker 1: psychology and biology, regular psychology and biology and history, what
Speaker 1: you're going to see is no, history doesn't repeat, but
Speaker 1: it rhymes, and you can see in you know, all
Speaker 1: you got to do is go read a book about
Speaker 1: the south Sea Scandal where Isaac Newton, one of the
Speaker 1: most brilliant guys of his era, lost a fortune, causing
Speaker 1: him to lament that he could measure the motion of
Speaker 1: heavenly bodies but not the madness of men. And guess
Speaker 1: what we're not changing. So you can read it in
Speaker 1: a market related way, or just understand human nature better,
Speaker 1: you're going to be miles ahead of the people who
Speaker 1: are just studying math or finance or economics.
Speaker 2: Really interesting, and our final question, what do you know
Speaker 2: about the world of investing today you wish you knew
Speaker 2: forty or so years ago when you were first getting started.
Speaker 1: I think maybe just the advice that I just gave,
Speaker 1: I wish that I would have known forty years ago
Speaker 1: that markets are market prices are determined by human beings,
Speaker 1: and if you are ignorant of all of the ways
Speaker 1: that we let things affect us, from whether we're hungry
Speaker 1: or not, or whether we're angry, or whether we're calm,
Speaker 1: I would have understood that it was not just numbers
Speaker 1: on a page, that markets are full blooded, almost human
Speaker 1: like things because they're driven and created by humans. If
Speaker 1: I could have told Jim of age twenty three that
Speaker 1: it would have hastened but also improved the pretty circuitous
Speaker 1: path that I took to becoming a quat really interesting.
Speaker 2: Thank you, Jim for being so generous with your time.
Speaker 2: We have been speaking with Jim O'Shaughnessy, founder of OSAM
Speaker 2: Asset Management and currently CEO and founder of O'Shaughnessy Ventures
Speaker 2: and host of the Infinite Loops podcast. If you enjoy
Speaker 2: this conversation, well.
Speaker 1: Be sure and check out any of.
Speaker 2: The five hundred previous discussions we've had over the past
Speaker 2: ten years.
Speaker 1: You can find those at.
Speaker 2: iTunes, Spotify, YouTube, wherever you find your favorite podcast. Be
Speaker 2: sure and sign up for my new podcast At the Money,
Speaker 2: where we speak with an X and give you information
Speaker 2: on a topic relative to your money in short eight
Speaker 2: to twelve minute batches. You can find those in the
Speaker 2: Masters in Business podcast feed, or wherever you get your
Speaker 2: favorite podcasts. I would be remiss if I did not
Speaker 2: thank the Cracked team that helps us put these conversations
Speaker 2: together each week. My audio engineer is Sebastian Escobar, My
Speaker 2: producer is Anna Luke. Sean Russo is my head of research.
Speaker 2: Attika Valbrun is my project manager. Sage Bauman is the
Speaker 2: head of podcasts.
Speaker 1: I'm Barry Riddolts. You've been listening to Masters in Business
Speaker 1: on Bloomberg Radio.