The Exchanges

Q Well, we want to ask you for a spoiler in case people haven't read the book from the first time around, in case it sort of slipped through the cracks in anything else they were doing in 2001, um, and they haven't picked it up yet. What's kind of the, the big seminal idea?

A I don't want to discourage anybody from buying it, but here it is in 30 seconds. Here it is in 30 seconds. So the idea is to say a stock price, or it could really be any asset price, a price of an asset, but let's say a stock price reflects a set of expectations about future financial performance. So the first step is to say, what do I have to believe for this to make sense? And you can apply that broadly. The second thing is, the second step is to say, let's introduce strategic and financial analysis to judge whether that set of expectations is too optimistic, too pessimistic, or about right. And by the way, more times than not, you're not going to have a view that that's different. But if it's, if your views are more optimistic, then you should buy the stock. If your view is more pessimistic, you should sell the stock. And then the third and final thing is as, as a result of those things, take action, right? So buy, sell, or hold, or do nothing. But the core idea is just basically saying, what do I have to believe? Is the company going to do what the market believes it's going to do? And then let me make decisions as a consequence.

AI assessment note: “the idea is to say a stock price reflects a set of expectations”

Q to perform in X way. Cashflow is going to be Y. Um, And now, um, you know, uh, even if we're just talking about companies that are traded on public stock markets, like the, the expectations built in seem to me like they're a lot more complex than just like, uh, uh, Facebook or Amazon's cashflow next year will be Z, you know, um, how should folks think about that?

A Yeah. And David, I'll just build on this and, and, you know, the sort of noun versus that is an interesting way to frame it. If you go back way to Ben Graham and so forth, you know, they focus a lot on things like book value, which was, you know, where the accounting was actually probably a reasonable representation because most of your assets were things that truly showed up on your balance sheet. But as you pointed out correctly, the world has changed a ton and now more of our investments are intangible versus tangible. So as a consequence, what's going on, the income statement and the balance sheet and so forth, cashflow statements is getting a little bit mixed up. So let me just give you one little stat I found interesting that we've just recently ran, um, back in 2001. So the year the first book came out capital expenditures and intangible investments. And this is for like called the Russell 3000. So basically us public companies Was about the same amount, 636 hundred and forty billion, something like that. So just think about their, think of a starting line for a race and they're both standing there at the same spot. Fast forward to 20, 21, obviously we don't have all the full numbers, but if the projections sort of hold out, it'll be the case that intangible investments now are two trillion dollars and CapEx is one trillion dollars. So going from the same starting point,…

AI assessment note: “now more of our investments are intangible versus tangible”

Q Can you for everybody just explain what you mean by intangibles?

A Yeah. So I mean, tangible, intangible, the basic distinction is exactly what you, what it sounds like. So tangible are things you can touch and feel and kick and so forth. And intangible are things that are not physical. Obviously canonical examples would be software code, but it could be anything. It could be marketing, branding, all that kind of stuff, training your employees and so forth. So what accountants try to do now is to look at the income statement and say, which Of those items that are spent on, on selling general administrative expenses, which are necessary to maintain the current business and which are discretionary investments, right? An investment defined as an outlay today with an expectation for a future return that are in this case that are intangible. So the big buckets classically are research and development, branding, but today you think a lot about customer acquisition costs, you know, all that kind of stuff. And so it's been a watershed change and this is You know, call it even maybe not even a generation of investors. And so a lot of those tools that were developed incredibly useful and thoughtful at the time, but, um, that just because the accounting changed means that they're much less relevant today than they used to be. And so this, you know, I was listening to, um, You know, Patrick O'Shaughnessy did a really interesting podcast a little over a ye…

AI assessment note: “intangible are things that are not physical. Obviously canonical examples would be software code”

Q And the paradox of skill was much lower then, right?

A Like it was, and I, just to be, again, I'll nerd out for just a second. One of the ways we can measure that is to look at the standard deviation of excess returns, right? So alpha, right? So excess returns. So, so you'd imagine, uh, and if you'd imagine If you're an active manager, what you want is a big fat bell shaped distribution, right? So lots of positive alpha that's on the right and lots of negative alpha. So you're going to be the winner and they're going to be a lot of people losing nets to zero, of course, but you're going to be right. So you want that to be fat because that means there's lots, lots to gather. Um, and then what has happened consistently is the bell shaped distribution has gotten skinnier and skinnier and skinnier, right? Which is exactly what you expect from the paradox of skill. And that's actually what That's how I picked up on the Gould thing. So Gould showed that the reason there have been no 400 hitters is precisely because the standard deviation of batting average has gone down over time, right? Which is all, these are all the things that are symptomatic of what this, this idea would predict, which is cool. So anyway, um, and, and again, it's not just, you just think about If you bought an automobile in 1970 or something, there was a huge variation in the quality of automobiles today. They're all really good, right? I mean, you know, some are be…

AI assessment note: “Like it was, and I, just to be, again, I'll nerd out”

Q Wasn't this, uh, this was one of Bill Gurley's like big, um, Distribution innovations when he was an analyst, right? Is he would fax a like newsletter, right?

A Do you know the story on that? It's a great story because there was a guy, yeah, there was a great analyst at Goldman Sachs named Dan Benton, who ended up being a great investor on the, uh, he had a hedge fund, a great investor as well. Dan, super talented guy, probably top ranked guy in his sector, had a really loyal following and decided one day to, to go to the buy side. So he was leaving his job and he had a very popular newsletter that was sent out at a very specific time slot. And so Gurley is this young guy and he's obviously kind of, you know, like also very marketing oriented and very alert. And he realizes, well, this guy's leaving, but everyone's used to getting this fax at, you know, like eight PM on Tuesday or whatever it is. So he's like, I'm going to launch above the crowd and it's going to, it's going to come at the exact same time that Benton's thing used to come.

AI assessment note: “So he's like, I'm going to launch above the crowd”

Q analysis. So, so Michael, you published the awesome measuring the moat paper a few years back that has become basically the Bible for how to do this. Uh, and we thought maybe the right way to dissect this, uh, I think you teach Ben Graham's legendary security analysis course at Columbia Business School. So like, how do you think about this concept in the course and how's the course structured?

A Yeah. So the core structure and we can dwell on the competitive strategy piece, but I, I usually like to think about it in four parts. The first is just thinking about markets and, you know, the fundamental question is, are markets efficient? Are they inefficient? If I'm a, whether I'm a venture capitalist or public market investor, if I have hopes to generate sort of attractive returns, how do I go about that? So how do I differentiate myself to do that? So that's a whole thing on markets and that ties a lot of back to the Santa Fe Institute stuff. So we'll come back. We'll, we'll put that to the side for just a moment. A lot of work on valuation. So we've talked a bit about that. And then the third module is competitive strategy. So, so Ben, that's what we'll dive into in just a second. And then the last piece, which by the way is the most, the newest part of the course is on decision-making. And what I came to realize, you know, probably 15 or 20 years ago was what differentiates good to great investors has little to do with their sort of technical skills, like their ability to build spreadsheets or whatever, and much more about their temperament. And in particular, their ability to make decisions under some sort of stress or tension. So we'll come back to decision-making real quick.

AI assessment note: “I usually like to think about it in four parts.”

Q And what, what inspired you to write the book, by the way? It's so good.

A It's funny. I love this day when you ask, like, where did these ideas come from? Because the, um, I, I was not a big, I I'm a big huge sports fan. I played lacrosse in college actually, but I was kind of an anti-baseball guy. So I didn't, I didn't mind baseball, but I didn't really like baseball that much. But then I read Moneyball and I was like, this is awesome. This is like so interesting. Right. And I think I was the first person on wall street to write about Moneyball. So I wrote a, I wrote a piece about it, like within a week or two of the book coming out. Cause I was so fired up and, and, um, you know, part of what they're trying to do is figure out, Like forget about what the person looks like, whatever, let's figure out what wins. And so these are things that are skill contribution. So that got me thinking a lot about this in terms of, and then it got me focused on the analytics community where this thing is really important. And then, um, I wrote a book called think twice in 2009 and think twice is about decision-making. It's really an homage to Kahneman actually. So like the kinds of stuff that Ben just read about. And I had, I had a chapter on luck and skill and I'm like, this is the coolest thing ever.

AI assessment note: “then I read Moneyball and I was like, this is awesome.”

Q You said a minute ago that the difference that you've found between great investors and average investors is the quality and, uh, temperament of their decision-making. How should people think about that?

A This has been an area I've been fascinated by, and I think that as a, as a world, we avail ourselves of these tools too infrequently, right? We should be doing more of this. Um, you know, Ben brought up a point early on, which I just want to reiterate, which is sort of thinking about different scenarios for how the world might unfold. And I think that one of the biggest mistakes we tend to make is that we tend to think we know the future better than we actually do, right? So the idea is to maintain sort of an open-ended understanding of how things might unfold. Um, so there are a number of tools and I'll, I'll rattle them off very quickly. Um, most of them are about opening up your mind and one of them is about feedback. So the first one on opening up your mind is this idea of base rates. And, and for those that are not familiar with this, you know, when we, when we are faced with problems, the typical way we solve a problem is to gather a bunch of information, right? Combine it with your own analysis and your, in your experience and your own input, and then you project into the future, right? And it feels very natural because you've gathered the information and you're, you're obviously, Using your own devices to figure things out. Base rates are actually a very different exercise, which is, it says, hey, let's think about this problem as an instance of a larger reference class…

AI assessment note: “So the first one on opening up your mind is this idea of base rates.”

Q Do you have any inklings about, uh, and it's okay if you don't right now, but I think everyone listening can sort of muse for themselves. Where do I feel like, uh, there's not enough smart people running and I can go be king of the hill over here. Do you have any inklings about where that, that might exist in the world?

A No, I mean, investing, I don't like, I would just try to stick to investing where I, I think it's the most clear. Um, there are a couple of things that are interesting. One, certainly I would just, I would go geographically, right? So are there markets where, I can land on the ground, you know, whether they're frontier markets or what we would call smaller emerging markets where we're really the due diligence and shoe leather will get you ahead of the game. In the U S it might be, uh, for example, in private equity, a lot of people talk about this, but if you're doing buyouts, are there other segments of the markets or geography of the country, for example, where you think you could do something that's interesting. Um, the other thing in public markets, one of the interesting ideas is that most public companies are now in index funds or ETFs or something like that. And so they're, they're fairly well trafficked and studied. The question is, can you develop a list of companies that are not followed by analysts, that are not in indexes, that are not in ETFs, right? That might be a little bit neglected. Um, so that might be an area where again, you show up and you're the only person playing at that poker table, something like that. So, so there might be some creative ways to think about that. And, um, The, the other area of course, which is now very much in its infancy is decentra…

AI assessment note: “frontier markets or what we would call smaller emerging markets”

Q Ooh, David, that is a great bridge to, to complexity investing. Like, the future is so freaking unknown for early stage companies. Michael, I'm curious, uh, how, how do you apply this in an early stage type company where the world could change so much between what the nascent company is now and what it will become?

A I mean, these are really hard questions and there are sort of two pieces. One is, you know, how would you value it? And then how do you just think about the business itself and how the world might unfold? Um, and we should come back to, you know, when I think of complex adaptive systems, you know, I think about a certain features. I mean, to break that term down, complex just means the interactions of lots of agents, right? Adaptive means that those agents learn They try to anticipate their environment and react to it, but the environment changing itself changes how they learn and changes their behaviors, right? So it's, it never, the city system never settles down. And then system is the whole is greater than the sum of the parts. So when you think about the world that way, there's a very big evolutionary component to it, which means that's why we can't, I think have a difficult time anticipating where the world's going to go. That said, um, Ben, I think the one thing that I often think about young companies is really options more than, you know, like a sort of bond or something boring like that. And, you know, an option, you know, options theory has been around for a very long time. Obviously Black-Scholes in the 19 seventies sort of defined mathematically some of the key principles. It's not a perfect mapping to the real world, but not too bad. And then in the late 19 sevent…

AI assessment note: “the one thing that I often think about young companies is really options”

Q more as, uh, um, optionality versus the same way you would think about investing in a late stage company. Are you sort of making the argument that, um, and you can deploy a little bit of capital and it's effectively buying an option on the potential that the, the way the world shifts that company becomes big, that that's sort of the way to think about an early stage investment?

A I think that's right, Ben. I think the other interesting thing is, you know, we wrote a big piece on public to private equity probably a year, a little over a year ago. And one of the things that I thought was really cool in that report was an analysis done by a few academics on the return profiles for three sets of investments, asset classes. The first were venture, right? So I think they looked at, um, look at the number, right? I hope. 30,000 venture deals. Some gargantuan number of venture deals. And then they looked at 15,000 buyouts. And then we looked at 30,000, uh, periods for public companies. And so what you're looking at is the distribution of payoffs, right? So, and I'll, I'm going to say what everybody already knows, right? Which is the, the, the median Venture deal earns nothing.

AI assessment note: “I think that's right, Ben.”

Q is, well, no, everyone is competing on this global playing field now. And so, it's so much harder to get the type of returns, especially at the amount of capital that Warren was investing. Yeah, so Michael, my question for you is, will we ever see someone who has the 60 plus year track record that Warren did ever again, or will no one ever be able to match that?

A It's a fascinating question. And this is another Stephen Jay Gould from the same book where he says extraordinary streaks are a combination of skill and luck, right? If you think about it, you can't have a streak without having a lot of skill and a lot of luck. They, they need both components to it. Um, so what we're arguing here is the luck piece hasn't changed, right? So that's, that's the world. Maybe, maybe some of the outcomes are more extreme, but luck is basically the same thing. Although we could talk about, you know, luck has their, their sort of independent event luck, like rolling dice or whatever. And then there's sort of social phenomenon where we get these power law outcomes, but basically that whole thing is, is roughly the same. And then I think if we're arguing that skill has become more uniform, then it would say that it'd be very difficult for people to replicate that. There are certain statistical streaks that I think are going to be very difficult for people to match or exceed Joe DiMaggio, a 56 game hitting streak. And by the way, there are a bunch of books about DiMaggio streaks. Some of them are right over on that shelf over there. And, um, he, you know, there are, there are a couple kind things by scores at the scores table. There are a couple of random plays, you know, so he, there's a lot of luck, but again, amazing skill, right? He was a three, 25 hi…

AI assessment note: “it would say that it'd be very difficult for people to replicate that.”

Q at Tesla and, and say, like, when the margin of safety is as narrow as it's ever been in making an investment in any asset, because multiples based on any aspect of a business are at all-time highs, uh, how, How are you sort of walking through an exercise with your students of working backwards from some ungodly valuations of companies and where it still may make sense to invest?

A Yeah. And by the way, not surprisingly, Tesla has been a company we've analyzed in our class a bunch of times. Usually, by the way, at the end of the class, I bring in, uh, portfolio managers who assign stocks for the students to work on. And Tesla has been one that's been sort of a perennial one for many of the reasons you just described, Ben, um, sort of the head scratching component. Well, um, yeah, I, I, you know, you, you just have to sit, sit down and pencil it out and think to yourself. And by the way, Tesla is another example of sort of this optionality, you know, are there things that they're doing that are not visible? That could be a value in the future. So you have to pencil all that stuff out. The other thing I'll say about Tesla, which is, um, you know, we have a bit about this in the book, but the idea has been around for a very long time is this concept of reflexivity. So we tend to think that, you know, there's this thing called the value of the firm and I'm sort of the observer. And if the value is, you know, higher than the price, I'm going to buy it and make money and so on and so forth. And we forget that this goes back to complex systems, that there's an interaction between the observer and the actual A thing itself. And, um, that reflexivity basically says the very act of bidding up a stock changes the fundamental outlook for that company and so on and so…

AI assessment note: “you just have to sit, sit down and pencil it out and think to yourself”

Q point. Like, did we think if you reflect back to 2001, That the top 10 companies, you know, banks and oil companies were, had as many defensible business model characteristics as the big five tech companies today. Like everyone is obsessed with these network effects and the value that they derive from being platforms and their staying power is just unbelievable. Did, did, did we think that 20 years ago?

A Yeah, but think about it. I mean, the number one company was General Electric. General Electric was considered to be sort of the You know, case study and everything about innovation and management. And I mean, if you could draw a manager from GE's considered the best management training program in the world, right? Um, the banks, interestingly, look, most of these banks have been around for Decades, if not centuries, right? You think about these leading banks. So, you know, whether they were considered to have, you know, like Google-esque type of moats is a different question, but, but, you know, they had decent returns on equity and so on and so forth. So, yeah, I mean, I don't know if that was quite as, quite as excited as it is, um, today, but, uh, but yeah, I mean, I, I, and by the way, you can go back in time, you know, General Motors, Seemed like it was untouchable in 1970, right? Untouchable. And so that just, just to bear in mind that, that, that worlds change and things show up and, You know, interestingly, one area that seems to be substantially underrepresented, but a huge sector is healthcare, right? So you, you get a couple of marginal guys in the healthcare, but might there be some sort of digital technology oriented healthcare company that becomes one of the top companies in the next 1015 years? Interesting questions anyway.

AI assessment note: “I don't know if that was quite as, quite as excited as it is”

Q And how do you reconcile most of these movies don't end well with, uh, Uh, the Bill Gurley's comment of the only way to, uh, um, get through the downside is to enjoy every last minute of the upside. Like in general, people should be fully invested. So what do we buy?

A Yeah. And I think that, and I, I don't, I, I, the context may be slightly different and I want to put words in anybody's mouth, but I think Bill's attitude was, Bill's, Bill's take is a bit more to me, like this idea of market timing. Rich is, you know, you, you think yourself, you're, I'm really clever and the market seems really expensive. So I'm going to sell it. And then when it gets cheap, I'm going to buy it back and so on and so forth. And what history tells us in that is that none of our, none of us are that clever. We just don't know. And I think that's a little bit of what Bill was saying with the venture thing is that things feel a little bit rich and gee, we should be, you know, we should be throttling back a little bit, but we, we, in retrospect have a hard time being good at doing that. So that would be my context there. Uh, the context I would take that in, but, um, Yeah, no, I, I, but I think, I think the idea of the movie doesn't end well, that, that is pretty easy to document, right? We can, we've seen that plenty of cases and, you know, I just love, I mean, Matt Levine at Bloomberg is, is a genius and, you know, he's got this thing called the boring market hypothesis, which I've always loved. And I think there's something to that, right? Which is, you know, 18 months ago, we sort of locked people up. They had nothing to do. Uh, they had no sports to bet on. W…

AI assessment note: “Bill's take is a bit more to me, like this idea of market timing”

Q in the, you know, mid late eighties, David, especially as you're alluding in startup investing. I mean, it was a shooting fish in a barrel at that point. And now it's, you know, you, you, you, uh, you need to do a lot of things to be the best. And Michael, I'm curious if you were 18 years old today, what do you think you would do with your career?

A Yeah. Well, I don't know. That's a, that's a little bit too hard to answer, but if you're talking about investing, the first thing I should just say is that at any point, you know, it doesn't seem like it's easy, right? Like it only seems easy when you think back on it, you know? So when I started, I mentioned when I started teaching at Columbia Business School, You know, I, I just want people to conjure up in their mind. There was no internet. Right. When I want to do financial, like I, when I, when I want to do financial statement analysis, I would request from our library, a mimeograph of the 10 K, you know? So like, this is just a different world than what we're used to today. Right. So, so just, and you know, we used to fax our reports and mail our reports to clients, mail them.

AI assessment note: “That's a, that's a little bit too hard to answer, but if you're talking”