Michael Mauboussin Master Class — Moats, Skill, Luck, Decision Making and a Whole Lot More

Yeah, dude, we should see if any listeners want to create some cool animation for the intro music for the YouTube channel. Are we going to open source it to the fans? We got to do it. Who got the truth? Is it you? Is it you? Is it you? Who got the truth now? Is it you? Is it you? Is it you? Sit me down, say it straight. Another story on the way. We got the truth. Welcome to this special episode of Acquired, the podcast about great technology companies and the stories and playbooks behind them.

I'm Ben Gilbert, and I'm the co-founder and managing director of Seattle-based Pioneer Square Labs and our venture fund, PSL Ventures. And I'm David Rosenthal, and I am an angel investor based in San Francisco. And we are your hosts. Well, today we interview one of our heroes, Michael Mobison. We've referenced his work on many episodes before. He's given talks that have been my carve-outs. And as many of you know, Michael is the head of conciliant research at Counterpoint Global, which is part of Morgan Stanley Investment Management.

At mid-year 2021, Counterpoint Global had assets under management of approximately $180 billion. And for those who don't know Michael's work, boy, are you in for a treat. David, I think it's fair to say he's your favorite investor's favorite investor. I love that. Yeah, he's done mind-expanding research on a ton of topics. And today's show, of course, has a lens on how to interpret all of Michael's work over the years in the context of today's unprecedented macroeconomic environment.

I like that. Unprecedented. Good phrasing. All right, listeners. Now is a great time to talk about a new partner of ours here on Acquired, Lagora, the agentic operating system that is redefining how the world's best legal teams work. Yep. It's sort of obvious that AI is going to completely change the legal industry. I bet most of you listening have dropped a contract into some sort of AI chatbot out there. Lagora took that insight and asked the question, what if you really built something with that power from the ground up for the legal industry?

So the founders did exactly what great founders do, operate with obsessive customer focus. They embedded inside a massive law firm for months. They sat with the lawyers just watching how the work really gets done. And that's how you get features that customers love, like tabular review, where you drop in a folder of hundreds of contracts and it pulls every key term into a grid a lawyer can actually work with. Lagora's bet here is interesting. Since it lets each lawyer handle more complexity, any given person can increase the quality of their work and do higher value work.

And this means that the pie can grow even as each individual task takes less time. And they recently launched Lagora Agent, offering greater intelligence and performance. The agent lets lawyers set an objective. Then it can handle the planning and the execution and delivery of the final product. Legal teams get to maintain full control and transparency since they're still involved where judgment is required. And Lagora works where you already work. You can use it within Microsoft Word while redlining or drafting.

The early Lagora numbers essentially speak for themselves. When they have a head-to-head pilot with their top competitor, they win 70% of the time. Lagora now has over 100,000 lawyers on the platform from 1,200 legal teams in 50 countries. And crazily, they went from 1 million to 100 million in ARR in about 18 months. Truly insane numbers. And that is the real test. Plenty of things demo well, but the question is whether a busy associate actually reaches for it during crunch time, or whether a partner trusts it before going into a conversation with a major client.

If your legal team wants to check it out, whether you're a law firm or you're in-house at a company, you can learn more at lagora.com slash acquired and just tell them that Ben and David sent you. As always, this is not investment advice. It's advice about investing, but not any specific investment. Yes, no doubt it'll be educational, entertaining, and Michael's an absolute riot. So without further ado, we'll get into it. Well, Michael, when we all met at Capital Camp, hosted by Patrick and Brent the other week, we knew we needed to find some excuse to get you on the show and discuss all the big ideas that you've had over your career.

Untangling skill and luck, measuring moats, decision-making, complexity theory. But we thought maybe the best place to start would actually be expectations investing. One, because you and your co-author, Al Rappaport, just published a revised edition of the book. But also, too, it's kind of, you know, think about expectations. Probably a good frame for the current market. So let's dive in on that. Well, thank you, both David and Ben. Great to see you guys. The story is very quickly, as I was a liberal arts major in college, I went to Wall Street.

I had no idea what was going on. I took no business classes, by the way. My father, I take that back, my father made me take accounting for non-business majors. And I got like a C in the class out of the generosity of the professor's heart. But I was there just Wall Street, and I think even the venture world and even corporate world filled with sort of rules of thumb and sort of like old wives tales of how things work.

And I was sort of swimming in all this. And one of the guys in my training program handed me a copy of Al Rappaport's book called Creating Shoulder Value. That book came out in 1986. So I read it shortly after it came out. And for me, it was a professional epiphany. I'll just say almost everything I've done since then has been patterned on that work. There were three things he said, I think, remain the bedrock of everything I think about.

One is, it's not about earnings that matters. It's really about cash flow. So the ultimate driver value of a business is cash, not accounting earnings. And we can come back and deepen on that thought. The second is, and I also think really important, is that we tend to think about strategy. So what is our strategy and how do we position ourselves and so forth? And we think about valuation as two separate things. And he made the point, I think very correctly, that you have to combine these two things to understand a business and to do evaluation properly.

So in other words, the litmus test of a strategy is that it creates value. And you really can't understand or value a business until you understand the competitive situation, the competitor set, the growth of the market, and so on and so forth. And then the third and final thing was in chapter seven, he had this called stock market signals to managers. And the argument was, hey, executive, your stock price reflects a set of expectations about the future financial performance of your company.

And it behooves you to understand what's priced in. If you want to do really well from the point of view of the stock market, you have to not only meet, but exceed those expectations. I, of course, hadn't met him. He was like some awesome big guy. And I had the opportunity. I started using his work in my work as an analyst. And then in 1991, May of 1991, I had the opportunity to meet with him. And it was absolutely phenomenal.

So for me, a real great experience as someone who was trying to learn from the master. We maintained a relationship through the 1990s. And then toward the end of the 90s, 1998 or 1999, he said, you know, it might be fun for us to write a book using the same principles, but aimed at investors. So that was the birth of expectations investing. That former one was sort of aimed at executives, at CEOs. Yeah, Ben, it was.

And that particular idea of expectations was clearly useful for everybody. And so we write the book. And by the way, we signed it in the late 1990s, right? So the world's ripping and the stocks are doing great and everything. Oh boy, that sounds familiar. Exactly. You may have just jinxed me there, David. The book came out September 10th, 2001. Oh. So you can imagine a worse time preceding, obviously, a national tragedy, but really in the middle of a three-year, a brutal three-year bear market as we're coming off the dot-com boom into the dot-com bus.

So the timing was not great. So it was very well received. And we got, you know, there's a lot of people who are still have used some of the techniques, but the timing couldn't have been worse. So we put this back together. But I went to Al and said, would you like to work on that? And he agreed. He's now in his late 80s. He's amazing to talk to. I still find it every day I talk to him.

It's exhilarating and an awesome intellectual journey. So it was super fun working on it. And then the other interesting thing is how much the world has changed in 20 years, of course. So a lot of new stuff has come along. So anyway, that's the story of expectations investing. 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 and they haven't picked it up yet.

What's kind of the big seminal idea? I don't want to discourage anybody from buying it. 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 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 than right. But 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 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. This concept is really interesting and one that we ended up talking about in our conversation at Capital Camp, where you brought up the point that most of the time, the way people come up with a valuation or a price target or a share price that they're willing to buy the company at is they make their own model with sort of the bottoms up, bake in all the assumptions and then say, okay, here's what I'm willing to pay.

And you're sort of making the argument here that the market has set a price. And actually, what you should do is reverse engineer that and say, well, what are the assumptions that I need to believe to make that a good thing to purchase right now? Make this a buy instead of a sell or an ignore. And basically trying to come up with a probability distribution for each of those assumptions. That's right, Ben. And I'll nerd out for just a second that sort of the original framework for discounted cash full model was laid out by a guy named John Burr-Williams in 1938, so a very long time ago.

So he's laying out a DCF model and it's a little bit complicated. So he's got a chapter, chapter 15, called the chapter for skeptics. So he's like, okay, you guys are doing it a certain way and I'm showing you something new. You're going to be skeptical about it here. He tries to dress head on all those skepticisms. And actually, John Burr-Williams says, hey, if you think it's too complicated to forecast what you think the value is, use the tools to go backwards.

So he actually talked about reverse engineering in 1938, which you're exactly right. And I was trying to put my finger on why it is that people feel so compelled to project value and compare that to price versus reverse engineering price and what it means. And I'm not sure I have a good answer for that, but I think maybe you feel like you're more in control if you're dictating what the value is versus going backwards. So I don't know what it is, but it seems to me a much more reasonable task to say, what do I have to believe?

And by the way, again, like in investing a lot, you're going to pass on a lot of things because you're just not going to have a differential view. So you're like, all right. It makes me think so much. I literally just wrote down in my notebook. It's the famous Charlie Munger quote. Invert. Always invert. Right? Like, you know, what would Charlie do? The Buffett quote is price is what you pay, value is what you get. They're a pair for a reason.

100%. And the other thing I'll just mention, Ben, you alluded to, but I just want to also amplify on it, which is expectations investing. I should have been more explicit about it. It's very probabilistic, right? So what we're really trying to do is think through scenarios. So the if then kind of scenario. So we're getting knowledge that the price today is just one of many potential outcomes. It's actually a price reflecting a distribution of potential outcomes.

We want to really understand the richer distribution. And, you know, again, this all lends itself to good analysis. It lends itself to good strategic analysis and financial analysis. But it's not here is an answer. It's really trying to think about the world probabilistically, which is also very much a sort of Buffett and Munger type of thing. Okay. So this is great because the thing about today's world, probably even this was changing when you wrote the book the first time with technology, but in Buffett and Munger's original world, the expectations seem to me like they would have been so much more simple.

This company is going to perform in X way. Cash flow is going to be Y. Now, even if we're just talking about companies that are traded on public stock markets, the expectations built in seem to me like they're a lot more complex than just like Facebook or Amazon's cash flow next year will be Z. You know, how should folks think about that? Yeah. And David, I'll just build on this and, you know, this sort of now versus then 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, 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. 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 U.S. public companies was about the same amount, 630, 640 billion, something like that. So just think of a starting line for a race and they're both standing there at the same spot. Fast forward to 2021, 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 $2 trillion and CapEx is $1 trillion.

So going from the same starting point, intangible investments are 2x, the tangible investments. And can you for everybody just explain what you mean by intangibles? Yeah. So in intangible, intangible, the basic distinction is exactly 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 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, all that kind of stuff.

And so it's been a watershed change and this is called 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 just because the accounting changed means that they're much less relevant today than they used to be. Patrick O'Shaughnessy did a really interesting podcast a little over a year ago with John Collison from Stripe and just such a thoughtful guy, but Collison is spending a lot of time and he's like, I don't understand why the accounting works this way, right?

Because we're spending tons of money at Stripe to try to build our business, but these are mostly intangible investments and they're showing up on our income statement, right? So we're expensing everything. So our income doesn't look that great. They look unprofitable. Yeah, they look unprofitable, but we're building incredible value, right? Incredible wealth. And that's why this original message from Rappaport of cash flows, not earnings is so in my mind all the time, right? I think this is a really big change.

And what's exciting for me, and I think it's executives or even investors should be thinking about this, is that we're a little bit in the wild west of this. No one really knows how to think about and grapple with these intangibles from an accounting point of view. But if you're really trying to understand a business, what I always recommend doing is getting down to the basic unit of analysis. How does this company make money? And really focusing on that and really refining laser focus on that to understand it.

And again, that the numbers are becoming less insightful for giving us guidance on how to think about that. And then the other thing that's been interesting, I think the last 20 years has been true for a long time, but increasingly software-based companies can be much more global. They can grow much faster and they can be much more global than businesses in the past. And that's another thing, another feature. By the way, it helps some businesses, but when you have a lot of intangible assets or you're built on an intangible edifice, it also makes you vulnerable, right?

So if your product or service does not work, there's not much there left, right? Right. You're not going to sell for book value. Exactly. So if you think about sort of the tails, pushing out the tails relative to traditional businesses, that's the way I think the way I think about it. There are more extreme good things and more extreme bad things than what we had witnessed in the past. Maybe to go back and re-articulate something the way I understand it, venture capital investors have not had a financial investing fundamentals background.

They often come from being entrepreneurs. And so you have people that don't have a robust or certainly as robust as the people you work with, Michael, an understanding of financial statements. And so the idea that intangibles are investments is sort of like inherent. It's like, duh. And then it just feels weird that it doesn't show up in the right place in your financial statements. So it's almost like this hardheaded view that VCs have had is now being forced to be adopted by the broader investment community because, as Marc Andreessen puts it, software is eating the world.

And so more and more of the very valuable companies in the world sort of think about their investing internally the same way that the non-financial sector of venture capital has thought about them for 30, 40 years. Yeah. I mean, I agree with all that. And I do think that the market has sorted this out to some degree, even public companies, right? So I think we're close to a record number, if not a record number of public companies today that, in quotes, lose money.

You can lose money the old-fashioned way, where it's just your costs are bigger than your revenues. But you can lose money the way we're talking about, which is you're actually making very productive investments. And by the way, let's just take one step back, because the number, when I talk about cash flow, the number we really care about is so-called free cash flow, which is earnings minus investments. And some people think, oh, you want positive free cash flow.

Well, the answer is not really. I mean, what you want is if you can invest at a high return, you want to invest as much as you humanly possibly can, right? That you have access to. And I always like to point out that Walmart, for the first 15 years that it was public, had negative free cash flow for each of those years. Walmart was profitable on the income statement, but they were investing like crazy. And why was that good?

Because their stores had great economics. So knock yourself out. And so that's a little bit of the same mindset. So much easier for a Walmart to untangle because you can just look at the cash flow statement and be like, oh, I see. Your operating cash flow is excellent. Then you're investing in CapEx on the... That's on the investing cash flow portion of the cash flow statement. So you can disentangle that. But with these software companies, it all gets tied up in OpEx, right?

So you're investing in acquiring customers and hiring engineers, etc. That gets muddied. You can't just look at one number and be like, oh, I see your operating cash flow is excellent. So you're doing the right thing. I want to talk about company analysis. 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. And we thought maybe the right way to dissect this.

I think you teach Ben Graham's legendary security analysis course at Columbia Business School. So how do you think about this concept in the course and how's the course structured? Yeah. So the course structured, and we can dwell on the competitive strategy piece, but I usually like to think about it in four parts. The first is just thinking about markets. And the fundamental question is, are markets efficient? Are they inefficient? Whether I'm a venture capitalist or a 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? And then the last piece, which by the way, is 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, I got to ask, what's the story of how you came to teach this legendary course? Because this was so awesome. I mean, all this stuff is luck, right? So I joined what at the time was the first Boston corporations, now Credit Suisse, as a food industry analyst in 1992. So liberal arts major gone food industry analyst. Exactly. So think General Mills and Kellogg's and Campbell's Soup and all that kind of stuff.

That was my industry. So I'm a new guy and I'm like plugging away. And by the way, I'll just say that from the very beginning, I loved to hang out with the technology guys because I just thought they were the coolest guys. And they got to work on all the cool stuff, right? That's how I got to know like Bill Gurley very early in Bill's career when he was an analyst and just like a cool guy working on cool stuff.

So there was a guy there named Charlie Wolf, who just the greatest guy. And Charlie was actually a tenured professor at Columbia Business School who decided he had a sabbatical year, decided he wanted to do equity research of all things. And every firm turned him down except for first Boston. They gave him a job. This is like now the late 70s, early 80s. And they said, what industry would you like to follow? He's like, well, there's this new thing called personal computers.

Maybe I could do that. And they're like, personal computers? Yeah, nobody cares about that. Yeah, go ahead and take that industry. So Charlie was the PC analyst and like, you know, so there's like Apple coming in public. Oh, man. And he was an academic. He was an academic. He's a trained academic. Yeah, trained academic. So he walks into my office and he goes, hey, you know, I'm working on the PC stocks. And I wonder if I'm thinking about brands, you know, like so Dell and Compaq and all these.

He's like, what do you know about brands? You're a food guy. So I was like, I don't really know that much about brands, actually. But I'm like, here's some stuff I've done. And, you know, you can check it out. And of course, just to be clear, this is me coming right off working on the Rappaport stuff. Right. So I'm using an approach that, you know, you could argue is a little bit more academic than that was traditional on Wall Street at the time.

So he comes back the next day and he goes, yeah, there's not that much about brands in here, but you should teach at Columbia Business School. So I was like, wait, what? So how do you make this connection? And I think at the time, you know, he had had a connection to this school and they were looking for people to teach security analysis. Right. Which is this. I mean, this is the course that Warren Buffett, the whole reason he went to Columbia was to take this course.

Yeah. I mean, I don't want to overstate all this. I mean, it is called security analysis and Graham did teach a version of all this, but many people have taught it over a long period. So in other words, it's not, there's nothing, I'm not unique in any way in this way. But so then he asked me to teach it and I went up there and you can also, when you're in New York, you can bring in great guests.

And so it's a fun experience for the student. So I started doing that in the summer of 1993. So this year, 2022 will be my 30th year of doing this in a row, which is actually really cool. So that's the story on how I got there. And so let me now delve into Ben's question about competitive strategy. And I'll just say that, I don't know if people really recognize this, but the very first version of measuring the mode came out in 2002.

So nearly 20 years ago. And I'll just say that that was among probably the top three hardest things I've ever done professionally. And the reason was not so much that any of the ideas were that difficult, but it was an incredible exercise in synthesizing, right? So like many other people, I'd read Michael Porter, I'd read Clay Christensen, I'd read all the, I knew the Brian Arthur literature on increasing returns and so forth. But the question is, how do you bring this together in a way that's sort of cohesive, that allows an investor or an executive or somebody to understand?

Not to mention, these were abstract concepts. I mean, you read them and they click and you're like, oh yeah, competitive strategy by Michael Porter. This totally innately makes sense. But then that next level of literally measuring. Yeah. So the thing is, I mean, you can start with basic things like competitive advantage. Interestingly, by the way, and I have all the Porter books and I read many of them when I was very young and they're really rich, but they're difficult.

They're not fun. They're not easy books to read. And in fact, I usually recommend that people who are interested in understanding Porter read a book by a woman named Joan McGretta called Understanding Michael Porter, you know, because she's a journalist. She worked elbow to elbow with him for many years. And she actually explains the ideas, I think, more clearly than he does with a lot of examples. So here's an interesting question. What is the definition of a competitive advantage?

You know, if you say a moat and turns out that Porter himself never really defined it. And so we argue that a competitive advantage should have two features. One is an absolute one. One is a relative one. The absolute one is you should have returns today or returns that are promised to be above your cost of capital, right? So in other words, cost of capital is simply an opportunity cost concept. So if I'm taking a dollar here, it should earn above what that dollar could earn somewhere else in terms of opportunity costs.

And then the relative one is you should be better than your competitors, right? If we can define a competitive set, you should be better. That's a competitive advantage. So Ben, your point's exactly right. We wanted to start with something a little bit quantitative in the sense you could hang your hat on it. And we try to measure that by things like returns on invested capital. So we basically broke the strategy into three pieces. One is I call it lay of the land.

But basically, what am I dealing with here, right? So we do things like entry and exit in the industry, market share changes, pricing flexibility. So these are all sort of broader to get a sense of the field that you're dealing with, right? So for instance, if you have an industry where the market shares are whipping around all the time, it's really hard to be king of the hill for a long time if market shares are really transitioning a lot.

By contrast, you'll get like soft drinks. These guys slug it out for one market share point, right? So that's a really stable industry. Then we talk about industry dynamics. So this would be the classic Porter stuff for you. This is where you roll up your sleeve value chains and the five forces. I also put the Christensen stuff on disruptive innovation there. By the way, disruptive innovation is, I think, a very helpful theory. I think most people don't really understand exactly what he's talking about.

So it's worth understanding, like going back to his basic principles. And then the third piece is, what is the source of this company's competitive advantage if it has one? And the simplest way to say it is usually low cost producer or some sort of differentiation. And what's also neat about the low cost producer differentiation is we can tie that back to return on capital, right? So basically, the simple model is low cost producers tend to have low margins and high capital velocity.

And what's capital velocity? So capital velocity would just be margins are going to be profits divided by sales. And capital velocity is sales divided by invested capital, right? So low margins, high velocity, that means you're turning your capital fast. That's a low cost producer. High margins and low capital velocity, that's a differentiation. So you think about, here's a way to make it more concrete. Think about a supermarket. They don't make a lot of money on all the items they sell, but they sell a ton of stuff, right?

You think that versus Tiffany's, I don't really know Tiffany's business, but Tiffany probably make a lot of money when they sell stuff and they don't sell it that frequently. A jewelry store generically, right? So what happens is immediately, you just show me the income statement or even adjusted statement, financial statements, and I can tell you right away, like if they're going to have a competitive advantage, sort of how are they going after it, right? Which is interesting.

Yeah. So measuring the mode, I think, was an attempt to try to be structured and thinking through this stuff. And I was very specific about putting a checklist at the end. And I think checklists are interesting just because they force you to think about all the different issues. Not all the issues are going to be relevant for all the companies, but just to make sure that you're being systematic and thinking through the various issues. And it sounds a little bit trite to talk about, like David was saying before, sort of these markets are a little bit crazy.

So it sounds a little bit trite to do this kind of work, but I just feel so much better trying to really understand the economics of a business, right? Before I get involved with it. I'll tell you a funny story. This is the influence you had on me. I first discovered your work through Bill Gurley talking about it when I was a super young whippersnapper VC a decade ago. And I read Measuring the Moat. I actually pulled up my copy of it ahead of this.

Literally, the whole thing is highlighted. Why did I even bother highlighting this? Because it's only the words that aren't highlighted. But I took your checklist at the end. And I was like, I'm going to make this part of my early stage investing process. And I tried it for a couple of them. I was like, well, wow. Too much work. Applying this to a seed stage investment is... Yeah, it's hard. Yeah, it's hard. It requires a little bit of a mental leap, but it was so fun.

David, that is a great bridge to complexity investing. The future is so freaking unknown for early stage companies. Michael, I'm curious, 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? I mean, these are really hard questions. And there are sort of two pieces. One is, how would you value it? And then how do you just think about the business itself and how the world might unfold?

When I think of complex adaptive systems, I think about 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 the 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. So that said, Ben, I think that one thing that I often think about young companies is really options more than like a sort of bond or something boring like that. And an options theory has been around for a very long time. Obviously, Black-Scholes in the 1970s 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 1970s, early 80s, academics started saying, well, these ideas are interesting for financial options, but we can apply them to real businesses as well. And so how do we think about that? So where real options tend to be valuable is when you have sort of three or four characteristics in place. First is it's good to have volatility in the market, right?

So this is an interesting thought that's a little bit backwards, right? So typically, if you say for a financial asset, your discount rate is some sort of cost of capital, lower is better for value, right? So if I have a lower discount rate, I'm going to have a higher value, right? All things being equal. So I think everybody sort of gets the math of that. Look at the current market where the discount rate is zero to negative.

Yeah, look at the current market. But options are actually interesting because an option is the right, but not the obligation to do something, right? So you take out the downside. So in an option, what you want is lots of volatility. You want lots of volatility, right? Which is sort of counter. So the more volatile the world is, the more valuable the option is. And so that's, I think, an interesting thought for there. You also want, this is where there becomes a big premium on management.

So management's ability to understand options and exercise them intelligently is extremely valuable. And you could think about the history of corporate executives, some of whom have been amazing at identifying and exercising options. Too easy example would be, of course, Jeff Bezos, but he has been. He's just like, I'm just saying he's been great at it. And then the other thing is interesting is a feature is access to capital, right? Because if you, even if you decide to exercise an option, you need sometimes have to do things like you have to pay for them, right?

And I think that there was a lot of really interesting stuff intellectually going on in the early 2000s. So 20 years ago, right? But it was a huge bear market, a huge hangover from the dot-com. And there was just limited access to capital. And as a consequence, there are probably a lot of really interesting things that didn't happen. Just look at webvan and pets.com and look at Instacart and Chewy today. You know, like these weren't bad ideas.

It's just the access to capital went away. So that's all really interesting too. But even just strategically, I think that the key is still to go back to the basic formula, which is the basic unit of analysis is what we're doing makes sense. The only other thing I'll add is that in doing this work over the years, one of the things I've always found is underappreciated is sort of the role of entry and exit in industries.

And I recommend my students spend time understanding entry and exit. I think very few people are, by the way, are familiar with these statistics typically. But I think that one thing that's important to recognize is that as an industry starts, and by the way, the guy that did the main work on this and its beautiful work is a guy named Stephen Klepper from Carnegie Mellon. Klepper died a few years ago, but this is really cool stuff.

And so what Klepper showed was that almost every industry as it gets going, there's a huge upswing in the number of competitors. And again, think evolution, right? So the market's sorting out what it likes. And then once it's figured out kind of what it likes or what works, then there's a huge downswing, right? So that's consolidation or businesses going out of business, bankrupt or whatever it is. And so you get this pattern of up and down.

And that's another really interesting thing to think about when you're looking at early stage stuff, which is say, all right, where are we in this whole cycle? And by the way, when it rolls over, so in other words, the number of companies is declining, it's actually a really interesting time to invest because usually the industry itself is continuing to grow and it's a fewer number of companies that are capturing the spoils, right? So it's like a really interesting dynamic.

We wrote a little bit about this. I mean, Klepper is obviously the guy, but you can do this for industry after industry, certain automobiles would be classic example, radio, a lot of it in the internet for sure, disc drive. So there are lots of cool examples of this pattern playing out over time. So those are just some thoughts that might be fun to think about and play with. One thing to drill in on is, so you mentioned with the early stage investing, the idea is you could think about it more as optionality versus the same way you would think about investing in a late stage company.

Are you sort of making the argument that you can deploy a little bit of capital and it's effectively buying an option on the potential that the way the world shifts, that company becomes big, that that's sort of the way to think about an early stage investment? I think that's right, Ben. And I think the other interesting thing is, 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 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 periods for public companies. And so what you're looking at is the distribution of payoffs, right?

So I'm going to say what everybody already knows, right? Which is the median venture deal earns nothing, right? And many venture deals lose money, but the tails are super extreme. So that's a really interesting way to think about essentially an option payoff, right? And then buyouts were a little bit, like 25% lost money, but most of them kind of did okay, but a little bit more right, more skewed than the public markets. And then the public markets look much more like a bell-shaped distribution.

And it says, the interesting question is like, what is the best set of frameworks to map what we actually know empirically the payoffs look like? And that's why even in venture, it's like, you think about, especially early stage venture, I mean, whether the thing's worth 50 million or a hundred million, if it's going to be worth 10 billion in 10 years or three years, like it doesn't really matter that much what you pay for it today.

So that's why these sort of extreme outcomes obscure the sort of first day. And that's why you always, you know, these funny stories about people like, oh, we pass on Amazon because it was too expensive. Or it's like, you know, it made sense at the time, but in retrospect, obviously those things don't look like they make sense, but they do make sense actually. I mean, to the extent that something is in the pool where it could be the next day, Amazon, if it's truly early stage, then it's worth kind of any price at that early stage.

But the trick is determining if it is of the set of companies that truly could be the next Amazon. That's why you're also building a portfolio of these things, right? So you would, I mean, some, obviously if you're, for example, a founder or whatever, you're going to have most of your skin in that one game. But if you're a venture person, you're going to spread out your bets a little bit and hope that, you know, these are very familiar patterns that you just hope a couple of things in your fund are the ones that hit and sort of pull the wagon along for everything.

Yeah. Well, I think the reason why I had a tough time as a young BC applying your measuring the moat checklist to early stage investments is I didn't realize the paradigm of what the asset was that I was buying. It was an option. And so you should think of it as an option, this framework we were just talking about versus if you're buying a public security, you should think, well, I don't even know what the right word is of that type of asset that you're buying of an option versus a...

Yeah. It's just more of a cash flowing business that's clear and more almost like a fixed income, you know, where we have sort of visible and predictable to some degree cash flows. Yeah, no, exactly. I think that's not a bad way to think about it. Yeah. Okay. So before we move on from your class, and since we're in valuation land a little bit here, and we are in, as they say, unprecedented times, let's take the most extreme example of having to work backwards from price.

So for fun, let's look at Tesla 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, 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? Yeah. 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 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, sort of the head scratching component. Well, you know, you just have to 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, you know, we have a bit about this in the book, but the idea has been around for a very long time, this concept of reflexivity. So we tend to think that there's this thing called the value of the firm, and I'm sort of the observer.

And if the value is 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 thing itself. And that reflexivity basically says the very act of bidding up a stock changes the fundamental outlook for that company and so on and so forth. Especially if they can raise gobs of money at that new valuation.

Precisely. And I think that it was not too many years ago that Tesla was sort of skating on thin ice in terms of finances and so forth. And then as the stock took on a life of its own, the stock went up a lot that allowed them to raise capital and get themselves on a much stronger footing. And then that buys them time, buys them runway to do other stuff. So I think this idea of reflexivity is a really big one.

Now, and by the way, the idea, I mean, the idea has been around for a very long time, but the term reflexivity, I think it was coined by George Soros. So just to be clear where that intellectually comes from. Oh, I didn't know that. That's awesome. Again, a very old idea, but reflexivity. Now, the key is like when you get off this thing, right? Because reflexivity works in two directions. And you can think about another area where reflexivity has been historically a very big deal is in mergers and acquisitions and sort of conglomerate roll-ups.

So you think about businesses buying other businesses and their stock as well. And then they use their stock to buy another business and they keep doing this and so on and so forth. And often where the gig ends up is that they have to do deals that are so large to perpetuate their growth rate, to perpetuate, to fulfill the expectations that it just becomes like essentially an insurmountable task. So I think that's one way to think about some of these businesses.

Now, the meme stocks, we've had flavors of this. People think it's all new, but we've had flavors of all this stuff for a really long time. So there's really not that much new to that. I think maybe perhaps that people can organize themselves more efficiently because they can use online tools and that they can transact essentially free or very low cost that takes frictions out of the system that allows it to be perhaps a little bit easier.

But there have been basically versions of this for a long time. Now, again, some of these meme companies have been pretty smart about raising capital as well. So again, they bought runway and maybe bought some optionality through that. But most of these movies don't tend to end well, just to be clear. So we'll see how this unfolds and finishes, but they tend not to be good endings. And how do you reconcile most of these movies don't end well with Bill Gurley's comment of the only way to get through the downside is to enjoy every last minute of the upside?

In general, people should be fully invested. So what do we buy? Yeah. And I think that 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 take is a bit more to me like this idea of market timing. You think to yourself, 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 us are that clever and 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 throttling back a little bit. But we, in retrospect, have a hard time being good at doing that. So that would be my context there. But I think the idea of the movie doesn't end well, that is pretty easy to document, right?

We've seen that plenty of cases. And I just love, I mean, Matt Levine at Bloomberg is a genius. And 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. They had no sports to bet on. We put a little extra money in their pocket through stimulus. And they're like, all right, you know, here's something we can do to keep ourselves entertained and in some cases make some money.

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You said a minute ago that the difference that you've found between great investors and average investors is the quality and temperament of their decision-making. How should people think about that? This has been an area I've been fascinated by. And I think as a world, we avail ourselves of these tools too infrequently, right? We should be doing more of this. 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. So there are a number of tools, and I'll rattle them off very quickly. 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 for those that are not familiar with this, 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 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 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.

Let's just basically ask what happened when other people were in this situation before us. And it's a very unnatural way to think about the world, right? Because you have to leave aside your own views, you have to leave aside all the stuff you've gathered, and so on and so forth. I mean, psychologists have demonstrated this is a very, very robust component to your decision making. So understanding and thinking about base rates, I think, is a really powerful thing.

And if you asked me if I could go back to my 20-year-old self and say, whisper in the ear and say, there's one mental model to sort of put into your life, I would say base rates. Another idea is premortem, same idea. And there's an interesting psychological piece to this, but premortem just says, let's pretend we make an investment today, pretend, and then we launch ourselves into the future. So now it's a year from now, it's 2022, whatever.

And this investment has turned out sour. It's been really bad. And then each of us independently, and this is important, each of us independently writes down why this turned out badly. So in other words, each of us is going to write a 200-word Wall Street Journal article dated 2022 as to why this turned out badly. And it turns out that, again, you don't have the intellectual baggage of having made the investment and your mind is opened.

And there's some interesting reasons why future to present is better than present to future. But again, a mind-opening exercise. The third thing is this idea of red teaming. So again, people are very familiar with this. Probably the most cybersecurity is a good example now, right? So the blue team defends, the red team attacks. And you say, all right, we think we're secure, but we're going to hire hackers to try to hack our own system. They're the red teamers just to see how vulnerable we are.

So red teamers are people that are organized to challenge thinking, challenge the prevailing views of things. And it's really hard, right? Because even organizationally, we fall into these mindsets. We all start to believe the same thing. And you need someone to sort of to jar you into reality. And then the last one is journaling. And that's this idea of feedback. And I think it's just brutally hard in our world, whether it's venture, even as an executive, public markets, doesn't matter.

It's brutally hard to give yourself honest feedback about what's happened, right? So even if something turns out great, did it turn out great for the reasons you thought it would, right? Or were you just a lucky, did you just come up lucky? Or maybe sometimes you did all the right things and it turned out poorly, but it was the right decision, right? At the time, given the information you had. So this idea of journaling is just keeping a decision log and reviewing it periodically to make sure that you're thinking about things properly.

And then you're giving yourself honest feedback. And the ideal is to do it probabilistically. If you can write down, I think there's an X percent probability, this is going to happen by Y date that gives you the apparatus for a scoring system that can be super helpful. And again, it's not a ton of extra work because you're doing it already, right? You're just being overt about it and writing it down. And so that's another thing that I think people can do in terms of their decision-making to improve.

It takes a little bit of discipline. It's not like a ton of time, but it takes discipline to do that. And I think those that do it well certainly benefit from it massively. For sure. Just because you are relatively quick in your moment there on base rates, I want to take a quick break and read this passage from Kahneman and Tversky. Because for anyone who hasn't studied base rates and is like, oh, I should Google this after Michael talks about it.

This one little quip will be like the beginning of the rabbit hole for you. So the quote is, Okay, so everyone has some idea in their mind at this point. I was hanging there. Now, of course, your intuition says a librarian, but in fact, there's something like 10x or 20x the number of farmers in the world. So you should really just look at the base rate and go, I'm going to ignore everything you just told me and say farmer.

But of course, our brains trick us and we all say librarian. One of the things we might want to talk about is a little bit of the stuff on luck and skill. So can we dive into that a little bit? Oh, this is one of my favorite books. So look, I just think that one of the most fascinating topics out there is this idea of untangling skill and luck. So I was able to write a book about it about eight or nine years ago.

And what inspired you to write the book, by the way? It's so good. It's funny. I love this day when you ask, like, where do these ideas come from? Because 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 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 piece about it, like within a week or two of the book coming out. Because I was so fired up. And 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 I wrote a book called Think Twice in 2009. And Think Twice is about decision-making. It's really an homage to Kahneman, actually, to like the kinds of stuff that Ben just read about. And I had a chapter on luck and skill. And I'm like, this is the coolest thing ever, right? And I made it chapter two, right? So I'm like, oh, people are going to get this. The first one was on base rates, actually, right?

Then this is chapter two. So I'm like, we're going to get it right. And my editor reads this and she comes back. She goes, oh, I don't know. It was skill luck stuff. It was too complicated. You know, put it, you know, if you want to keep it, put it at the end, right? So I'm like, all right, all right. So it's like one of the last chapters. And so I would get friends that would read it.

And my friends would go, oh, I liked your book. But that chapter on skill and luck, now, that was cool. So I'm like, I knew it. I knew I should have put that at the beginning, right? And so I was like, so this is like a spinoff, like those TV shows. Like, oh, that, you know, like Mork and Mindy spun off from Happy Days or whatever. Like, this is like a spinoff. So I was like, okay, this luck skill thing, there's a lot more here.

I also read Fool by Randomness by Tellab, obviously, in 2001, as many people did. And obviously, the basic point hits you in the head like a two by four, that there's more randomness in the world than you anticipate. But I felt that it was lacking in the sense that it didn't really give you the tools to quantify any of that stuff, right? So I was like, okay, I'm loaded up now. I've got this idea that this is really important.

By the way, the subtitle of the book is Untangling Skill and Luck in Business, Sports and Investing, right? So it's all stuff I find interesting. So that encouraged me to go down the path. And so it actually may make sense just to very quickly define some terms, right? So skill, we're going to say, is the ability to apply one's knowledge readily in execution or performance, right? So you know how to do something. And when you're called on to do it, you can do it.

So you have to go play violin at Carnegie Hall, like snap your fingers, you're going to crank, right? You're going to be awesome. Luck is much more difficult to define. And by the way, it gets into philosophy very quickly. So you have to put a pole down to figure out where you want to stay. But I'm going to say it has three key attributes. One is it happens to an individual organization. So it happens to you or your company or your favorite sports team or whatever.

Second is it can be good or bad. And I don't mean to suggest that it's symmetrical because it's not, but there's a good positive side and a negative side. And third is, and this is the squishiest one, is it's reasonable to expect a different outcome could have occurred. So if we rewound the tape of time and we played it again, it would be reasonable to see a different outcome, right? So that's what I'm going to say is luck.

And so when you have that in your mind, there are a couple of things that come out of really, really interesting. One is what we call the luck skill continuum. So you could think about activities along a continuum. On the one extreme would be all skill, no luck, right? Nothing really over there, but you think about chess matches or running races, right? There's the fastest person's usually going to win, right? Then you think about the other extreme, which would be all luck, no skill.

So roulette wheels, lotteries, right? They're fair. Okay. So there's no element of skill in those whatsoever. Public market investing. Yeah. So it's actually interesting. Hold on to that thought because we want to come back to that in just a moment. And so then you have everything arrayed between those two extremes. And by the way, we did in the book, we did for fun, which was professional sports leagues based on a season. And you can see, for example, that basketball is a sport that's furthest away from randomness.

So the most essentially skilled dictates the outcomes. So Ben, you were sort of joking a little bit about that, about where public market investing is. But I actually want to build on this because this is actually probably the most popular concept that came out of the book and it's called the paradox of skill. Oh, so good. This is so mind blowing. Yeah. And this, again, none of these ideas are new with me. I got this idea from Stephen Jay Gould in his book called Full House from the mid-1990s.

And so the idea is that when you think about- The biologist? Yeah. Evolutionary biologist. Exactly. Good call. Yeah. So the paradox of skill says, and activities where both skill and luck contribute to outcomes, which is most stuff, as skill increases, luck becomes more important. And you're like, wait a second, how does this work exactly? Right? So we can think about skill in two dimensions. The first is absolute and the second is relative. So the first is absolute skill.

And I think that we agree, if we look around the world, whether it's sports or business or investing, the level of absolute skill has never been higher, right? If I gave you what is at your fingertips today and put you back in the 1960s as an investor, for instance, you could run circles around your competition, right? Because you just have better tools available to you. And certainly sports, we can see that, especially sports measure versus a clock, right?

Things where people are just faster and so on and so forth. The second dimension, though, is the really important one, which is relative skill. And what we've seen in domain after domain is relative skill gaps have narrowed. The difference between the very best and the average is less today than it was in the past. And you can think about all sorts of tons of reasons. For example, sports leagues are super easy, right? Because you think about the NBA used to be certain types of players from a certain part of the country.

And now it's a completely global market. The best players anywhere in the world will be found and they'll be drawn. Will Chamberlain could just totally dominate back in the day. But if Will were playing in the NBA today, he would have a lot more. Right? In fact, this is how the whole thing got going was Stephen Jay Gould wrote about Ted Williams, who hit 406 in 1941, that very magical year. And by the way, if Ted Williams, and he was almost exactly a three standard deviation event, I don't know what the 2020 numbers will prove to be.

But if you're a three standard deviation event and in the most recent full season, you hit like 385 or 390. So it's awesome, right? You win the batting title going away. But that's what the top one and a half percent or something of everyone. Yeah. Top one and a half percent. Right. So you're not breaching that 400 level, which is super interesting. So the point is, if you now you think about two people with absolutely wickedly high skill levels, but they're completely equal, then the outcome is going to be a coin toss.

It appears to be random, even though they're incredibly skillful. So it's funny because I still play like beer league hockey. And so the hockey guys are like, the hockey players are the most skillful guys. And it shows up as a very random sport in our system. And I'm like, I'm like, you're missing the point. It's not that they're not skillful players. They're amazing players. It's just that they're all equally skillful. Right. And so as a consequence, differentiating, just as you said a moment ago, David, differentiating yourself, it's extremely difficult to do.

And as a consequence, it all feels like a big coin toss. And so then just to come back on investing, I think that's what we see in investing, which is in public market investing, the numbers appear to be random or partially random in large part because markets are so good. It's not because markets are bad. The markets are actually really good. Now, the other thing I'll say about venture in particular is that there is persistence of performance.

One of the ways we measure ongoing skill is this notion of persistence. So if you do well in period one, you'll do well in period two. Right. So if you're really good at math tests, you take a math test today and you take one, two weeks, you'll do well both times. Right. So it indicates skill. And by the way, there's almost always this concept of regression toward the mean. If you do really well, you go, okay, so I want to come back to regression in just a second.

So persistence is an indication of skill. Right. And so it turns out if you look at venture capital in particular, by the way, in public equity markets, very limited persistence. Right. So if you did really well last year, your expected value is closer to the average the following year. Buyouts, they used to be persistent. Now it seems to be much more closer, not so much persistent, but venture, we still see a lot of persistence. And that's the top 10%, maybe top 20% do really well over time.

So if you can get access to one of those funds and invest with them, you tend to do very well. So the interesting question is, why is that? You guys might have better views on that. I have a pet theory as to why that is, but there is persistence in venture in particular, and that stands out relative to a lot of other asset classes. And then here's the last thing I want to say about this luck and skill thing, which is, and this goes back to base rates, right?

Which is- Wait, we can't let you get away with that. We want the pet theory. Okay. Yeah, yeah, yeah, for sure. All right. I mean, this is my pet theory. So you guys can tell me, you can shoot me down, but it actually came out of network theory, but it's this idea called preferential attachment. So website, for example, website traffic tends to follow power law and there are power laws all over the place, right? But we don't always know what the causal mechanisms are.

We can build mathematical models that generate power laws, but they may not be representative of the real world. But power laws and websites might be something like, if you're building a website, what you want to do is point to other ones that are popular, right? And if everybody's doing that, then that leads to this phenomenon of some becoming super, super popular, right? So the theory would be something like preferential attachment and venture, right? And there's a little bit of evidence to this.

This is a very academic way to say they get the best deal flow. They get the best deal flow, exactly. But there's a big caveat here, which is if you're a great startup, you have to know that you're great, right? And then you have to know to call Sequoia or Benchmark or Antresen Horowitz or wherever it is, right? There has to be identification on both sides. And then by the way, going to one of these leading firms, there's an imprimatur and so forth.

It gives you like a stamp of approval that also helps your future. And that goes back to like a reflexivity thing, right? So there's this sort of, like you said, best deal flow, perhaps best terms, but it's this reinforcing mechanism. And by the way, the process can be bootstrapped by something random, right? It could be just, we happened to get three lucky deals and we did well. And so now everybody thinks we're smart, right? One of the most testable hypotheses, and I've not really seen really robust work on this, but one of the testable hypotheses would be something like if a partner leaves a leading venture firm and starts his or her own shop, if it's like the person's genius and skill,

then that should port. And if it's the preferential attachment, that would not port, right? So that's an interesting way to test that. My feeling without having looking at the data is it does not port, or it does not port nearly as well as the individuals might hope it would. Right, exactly. And so let me talk a little bit about regression toward the mean. And this is interesting. I'll just try to close out this thought, which is base rates, right?

Is this idea of like just statistical, you know, base foundation. And then the inside view, which would be, let's just look at my own analysis and what I know about the world, right? So it turns out that on the all skill side, all you need is the inside view. So if you're running, you might be a good chess player, right? In your local club. But if you're playing Magnus Carlsen, it doesn't matter. Your win loss record does not matter, right?

So Magnus is going to beat you every single time he plays you. By contrast, if it's, so there's no regression, right? There's no regression. And then if you go to the complete luck side of the continuum, there's completely regression. So you won the lottery yesterday. That's awesome. But you're expected to probably win the lottery today is the same as it was yesterday. Which is my new race. It goes back to complete randomness. So you can actually figure out not just, we all know that regression toward the mean happens, but you can actually figure out the rate at which it happens by understanding sort of where you fall in this continuum, which is super cool.

It's a very powerful mental model. And by the way, if you're a sports fan, I mean, you could go on all day about this stuff, right? Because almost every sports statistic has these features and you can figure out how fast players will regress based on these statistical concepts. Well, this is interesting. This leads us to, there's a question that David and I have been like bickering about since the end of our Berkshire Hathaway 10 hour extravaganza.

And David was sort of asserting that in this world where investment returns happen so much faster than ever before with tech and especially in crypto. Yes, Warren Buffett was very impressive, but the next Warren Buffett will be even more impressive. And my pushback to David 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.

The paradox of skill has gotten so extreme. 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? 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.

You need both components to it. So what we're arguing here is the luck piece hasn't changed, right? So that's the world. Maybe some of the outcomes are more extreme, but luck is basically the same thing. Although we could talk about 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 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.

So there are certain statistical streaks that I think are going to be very difficult for people to match or exceed. Joe DiMaggio, 56 game hitting streak. And by the way, there are a bunch of books about DiMaggio's streak. Some of them are right over on that shelf over there. And there were a couple of kind things by scorers at the scorers table. There were a couple of random plays, you know? So there's a lot of luck, but again, amazing skill, right?

He was a 325 hitter. That's easy. He's an amazing player. We're going to talk about Ted Williams. You know, Bill Miller beat the S&P 500 for 15 years in a row. I think that's going to be very difficult for anyone to do again for the same reasons we talked about. So there are certain streaks. I think they're going to stand. Eventually they may get broken, but they're going to stand the test for a long time. So it's going to be difficult.

And I mean, Buffett is amazing and so forth. You know, obviously when you're moving as much capital around as they are today, it's just a much taller task. If you think about the Buffett partnership from the late 1950s to the late 1960s. Oh, amazing. Shot the lights out. Just shot the lights out. But again, much smaller fund and much more nimble and sort of a little under the radar and so on and so forth. Well, and the paradox of skill was much lower then.

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 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 there's going to be a lot of people losing.

Nets to zero, of course, right? So you want that to be fat because that means there's lots to gather. And then what has happened consistently is the bell-shaped distribution has gotten skinnier and skinnier and skinnier, right? Which is exactly what you'd expect from the paradox of skill. And 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 idea would predict, which is cool. So anyway, 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 better than others, but they're all really good and you can go buy- And really safe. It's crazy. And they're really safe, right?

And they're, you know, all things being equal. So, I mean, obviously there's status stuff related to it, but in terms of actual performance getting around, like they're pretty, they're all pretty good, right? And part of it is, it's just stuff on, you know, again, this is why this happens is best practices. You know, think about athletes, you know, best practices, best training, nutrition, all these techniques. So in the corporate world, you know, the best ideas get transferred from one organization to another very fast and so forth.

So it's, it stands to some reason that that would be, there'd be uniformity of excellence. Even in just in our world in venture, you know, when we do our sort of classic episodes, we talk about what things were like. The level of skill among venture capitalists, like back in the day was laughable. And today it is extremely competitive. So you're seeing this happen. Definitely. Yeah. All right, listeners. Now is a great time to thank our longtime friend of the show, ServiceNow.

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ServiceNow already runs more than 100 billion workflows annually and trillions of transactions for more than 85% of the Fortune 500. So when companies need a place to govern AI at enterprise scale, they're building on a platform at the center of how their business already operates. And in a future that isn't going to be one AI, it's going to be thousands of AI agents working across every function of the company. But the question is, who's managing them all?

So if you're trying to turn AI ambition into real business outcomes and make it work safely, securely, at scale, go check out ServiceNow.com slash acquired and tell them that Ben and David sent you. All right. Well, that's a great lead into a discussion on, Michael, your career path. So the world of investing today is so much more competitive in every asset class than it was even in the mid-late 80s. David, especially as you're alluding in startup investing.

I mean, it was shooting fish in a barrel at that point, and now 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? Yeah. Well, I don't know. 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, 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, I just want people to conjure up in their mind there was no internet, right? When I want to do financial statement analysis, I would request from our library a mimeograph of the 10K. You know, so like this is just a different world than what we're used to today, right? So just, you know, we used to fax our reports and mail our reports to clients, mail them.

Oh my God. So if you want, yeah, exactly. So you want to see my report on Kellogg, it would come in the mail. Wasn't this, this was one of Bill Gurley's like big distribution innovations when he was an analyst, right? Is he would fax a like newsletter, right? 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, 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's this young guy and he's obviously also very marketing oriented and very alert. And he realized, well, this guy's leaving, but everyone's used to getting this fax at, you know, like 8 PM on Tuesday or whatever it is.

So he's like, I'm going to launch above the crowd and it's going to come at the exact same time that Benton's thing used to come. And he's just going to be a pure substitution. It was completely brilliant. And I mean, obviously that's great marketing, but it was also great content, right? He had great stuff. So the combination of those two things really helped catapult him, you know, again, it's content, but it's content and good distribution. So yes, that's the point I do want to make it.

It doesn't seem that easy at the time, but going back to your question, I'm slightly dodging your question, Ben. No, but I'll go back to your question, which is as a broad concept, if I were to go into investing, the one thing you want to think about is this idea of looking for easy games, right? So the metaphor is poker, right? Which is if you like to play poker on Friday. So if I call you guys up and I go, Hey, David, Ben, I'm having a poker game at my house Friday night.

Would you like to come over and see me like to make money? You'd be like, Oh yeah, yeah, that's cool. Like who else will be there? Who else is playing? And I should be like very unimpressed by the list. Exactly. Be like, Oh, really rich guys who are really bad at poker. You'd be like, okay, I'll be over. That's cool. By contrast, if I said, Oh no, I've got these really good players that are as good or better than you are.

You'd be like, okay, I think I got better things to do. Right. So part of it is like thinking about who's going to play the game and, you know, and poker is an interesting metaphor and zero sum in the sense that, you know, a hundred dollars walks into the room, a hundred dollars will walk out, but who has it will change in the course of the, of the game. And so that's a little bit true about investing as well.

So part of it is thinking a lot about the game that you're playing. And so are there opportunities, whether those are niche or parts of the market, whether they're different geographies or something like that, where you feel like you can be the smartest person at the poker table. So now the challenge is often that it's difficult to scale those kinds of things. It's often easy to do that in a sort of niche way, but it's hard to do it in a very big, big way.

But that would be the first thing I would say. The other thing I'll just say is just in broadly speaking, is that I have three of my kids are out of college. I've got two in college, one's a senior. And, you know, so they're going into the world. Right. And I've always been ambivalent about finance because on the one hand, I think it's, it is an amazing, like what you guys do is super fun and you never cease learning.

And it's really interesting. On the other hand, there are a lot of big problems that need to be solved in this world. And I would love to see our best and brightest young people try to get after those problems or at least allocate some time and energy to doing those kinds of things. So I've always been a little bit ambivalent. So part of that might be, you know, if I were a younger person, I probably, by the way, I should have studied computer science.

And had I been born five years or 10 years later, I almost certainly would have been a computer science major instead of a government major. Um, cause I always, I always, and I actually did a little bit of tiny bit of programming back in the day, but so I think those kinds of skills and the CS thing is less the skills, actually programming skills that I find so attractive. What I really find attractive is it's sort of a way of thinking about the world, which I think is a pretty good way of thinking about the world for the, for the most part.

And, and, you know, so the question is, is there a really big issue out there that I'm passionate about? Could be climate, could be some sort of health mitigation, whatever it is. And are there ways that I can sort of make a dent at that problem? Um, that's the kind of stuff I also would think about, but, but if it's investing, the answer is try to find a game where you think you can be the smartest person or have the opportunity to be the smartest person in the room.

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 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 might exist in the world? 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.

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 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, are there segments of the markets or geography of the country, for example, where you think you could do something that's interesting.

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. 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 the other area, of course, which is now very much in its infancy is decentralized finance or crypto or so on and so forth. So there'll be many fortunes made and many fortunes lost in that area. But the question is, can you set yourself up in such a way to be, again, doing something ethically good and profitable? All right. One last question in our little fun wrap up round here.

It took us all of human history to see the first trillion dollar market cap company. And then in like 18 months, we had a couple more $2 trillion companies. Do you think we'll see a $10 trillion company? And how soon do you think we'll see that? I think the first one's easier to answer than the second one, right? So at some point, that seems very likely. I'll give you an infinite timeframe for something that generally increases.

What poker table are we playing at here? Exactly. Whether that's in my lifetime is another question. So part of it is, I think that David alluded to this before. I mean, it's hard to get your head wrapped around the impact on valuation of just declining interest rates, right? So I don't know where the 10-year treasury note today is around 1.4, 1.3%, something like that. If you told me 10 years ago, 20 years ago, 30 years ago, at some point, we're going to have a 130 tenure, I would say you're bonkers.

And I would have bet a lot of my money that that would not come to pass. And those things are real drivers of value, especially if you have some component of growth. We wrote a report last year called The Math of Value and Growth. And we just show how just theoretically, the mathematics really are crazy. If you have relatively rapid growth and high returns and a low discount rate, it just really cranks value substantially. And I think part of the $1 to $2 trillion sprint was a function of this, this sort of backdrop, right?

And by the way, it's not just equities, of course. It's across the board. I mean, you guys were talking about this. You know, credit, so bond spreads are let down. You know, venture, a lot of money flowing and valuations are up across the board. So that's common. So the answer has been, I don't know. The other thing I'll just say that I found fascinating is, as part of the thinking about the new version of expectations investing, I went back and looked at the top 10 companies today by market capitalization and the top 10 in 2001, right?

So 20 years ago. Oh, I don't know if you guys want to guess this. This is actually pretty interesting. So how many companies that were top 10 in 2001 or top 10 in 2021? What would you guess out of the 10? Let's see. Was Saudi Aramco in 2001? It wasn't public, right? And was Microsoft in there? Yes, it was. So I bet one. Yeah. I guess one. You guys are very good. So Microsoft is the only company that made the list both times.

And the estimate is something like excluding Microsoft. So if you just look at the other nine. And Microsoft is two different companies with the same name. That's right. So if you took Microsoft out, so you bought the other nine. Turns out their market capitalizations are down $460 billion in the 20-year period. Whoa. Right? So it's just an amazing. And then, of course, the wealth creation. Apple, by the way, Apple's market cap. I'm not going to get this right.

But Apple's market cap was less than $10 billion, I believe. And now it's $2.4 trillion, right? And Amazon was also $6.5 billion. Now they're $1.6 trillion or whatever it is. So there's just huge amounts of wealth sloshing around. Interestingly, by the way, three of the top 10 companies today were not public in 2001. And two of the top 10 were not founded, had not been founded yet. Facebook hadn't been founded. And I mean, I see you driving a car.

Is Tesla one of the top 10 most valuable companies in the world? In the United States. Oh, wow. My God. So that's interesting. So part of the answer, Ben, I think is that I don't know how long it'll take. You know, I think that we should have relatively muted expectations for returns in all asset classes. I know we're going to have, we're up for another great 2021. But because of where we are with risk-free rates and credit spreads and so on and so forth, people should have fairly muted, I think, expectations going forward.

So it's going to take, it could take a long time. But the other interesting question is if the next 20 years are like the past 20 years, is it conceivable that only one of the companies we see today as our leaders is going to be on the leaderboard in 20 years? Is it conceivable that 20% will be companies that have yet to be founded? Is it conceivable that 30% will be companies that are yet to be public?

Right. Super interesting. Right. And so it depends if you consider crypto companies public or not. Right. Yeah. Well, that's interesting. So the answer is, you know, we don't know, but it's when you take a 20-year snapshot of things, the change seems pretty extraordinary, right? Yeah. That's such a good point. Like, did we think if you reflect back to 2001, that the top 10 companies, you know, banks and oil companies 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 we think that 20 years ago? Yeah. But think about it. I mean, the number one company was General Electric. General Electric was considered to be sort of the case study in everything about innovation and management. And if you could draw a manager from GE, he's considered the best management training program in the world, right?

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 Google-esque type of moats is a different question. 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 excited as it is today. But yeah, I mean, 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, just to bear in mind 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 get a couple of marginal guys in healthcare, but might there be some sort of digital technology oriented healthcare company that becomes one of the top companies in the next 10, 15 years? Interesting questions anyway. It's certainly possible. Yeah. Well, Michael, we can't thank you enough.

Just such a fascinating last hour and a half. A couple things to point people to, if you want to dive deeper on any of these topics, of course, we'll link to all the papers that Michael has written, the books that he's written. And the first time I was introduced to your work, Michael, was the talk you gave at Google in 2012-ish. It's basically an hour-long talk taking the untangling skill and luck into slide form and walking through that visually totally blew my mind.

So, highly recommend that. And we'll link to it in the show notes. Where would you want to point people to? And if folks want to get in touch with you, what's the best way? Yeah. So, my email address, if you track down a report, my email address is on there. My Twitter handle's MJ Mobison. So, that's at MJ Mobison. So, that's not too hard. So, you can DM me if that's something of interest. And yeah, I'm a pretty easy guy to find.

Generally, you can go to Columbia Business School website. You can find my school email there, too. So, I'm an easy guy to find. Love it. All right, listeners. Now is a great time to talk about one of our favorite companies, Statsig. Yes. Long-time acquired partner. There is a reason why the best product teams at companies like OpenAI and Notion, Atlassian, Figma, Rippling, Brex, and more rely on Statsig, whether they are iterating on their core product features or shipping AI-powered experiences at scale.

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They're measuring what actually moved engagement, retention, and ultimately revenue. And as more teams build with AI, that learning loop becomes even more important. Building with LLMs introduces non-determinism into your product experience. The same input doesn't always produce the same output. And behavior can shift in subtle ways in real-world use. So doing offline evals will give you part of the picture, but you can really only understand the impact once your product is live with real users, and then you can measure how their behavior actually changes.

It's very different than the way that you would ship features in a pre-AI world where you knew exactly what the software was going to do in production. Yeah, exactly. So this is where Statsig comes in. It brings experimentation, feature flags, and product analytics into one unified system so teams can ship safely, test rigorously, and directly link what they changed to how users actually behaved. The result is a tighter feedback loop and learning that compounds over time so you don't just ship more, you ship better.

So if you want to make learning your competitive advantage, whether you're building new AI experiences or just evolving your existing core product, go to statsig.com slash acquired to get started. All right, listeners. That is all we have for you today, except we have an announcement. Some of you who are in the Slack at acquired.fm slash Slack already know this. Or if you follow us on Twitter at acquired.fm, you know this as well. But we just launched the acquired job board.

Woo! This is big news. Shout out to super intern Sandy Kim for putting this together and quarterbacking the whole project. But as many of you know, we have a jobs channel in the Slack. It's been a great way for listeners to share opportunities with each other forever. And now we've made a job board to basically start curating some of the opportunities we're super excited about out there in the startup ecosystem. And for those of you out there thinking, I kind of wonder what I'll do next.

We got a great set of jobs for you at acquired.fm slash jobs. If you're like, I love listening to this show and I wish I could work with like-minded people who also listen to this show. That is now possible. Acquired.fm slash jobs. David, anything else? It really is cool. A huge thank you to Sandy. Sandy is so awesome. If you're in Slack, you probably already know Sandy. And two, like it's so cool that like we can do this now and the infrastructure exists.

We use Palette. Like these are the companies in the community and the companies we care about. This isn't like monster.com. Like it's nothing against monster, but probably not where most of you would go to look for your next career. No. And speaking of the community, if you want to become an LP and dive deeper into the topics we cover here, you can do that at acquired.fm slash LP. There are over 50 episodes in the back catalog, plus new episodes coming out as well.

Got a few in the pipeline. We are super excited about both crypto stuff and non-crypto stuff. And you can join acquired.fm slash LP. And we encourage you, if you liked this episode and you were like, someone else out there should really hear the gospel that Michael Mobison is preaching, you should share this episode with them as well. And of course, while we love all the social media stuff, we really value the one-to-one relationship. So send it to a friend, send it to a coworker.

And thank you so much for listening. All right, listeners, we'll see you next time. See you next time. Who got the truth? Is it you? Is it you? Is it you? Who got the truth now? Huh. Is it you?