We sit down with the one & only Michael Mauboussin to dive deep into his incredible body of work: untangling skill and luck, measuring moats, persistence of returns in venture capital, decision making and — particularly timely — expectations investing and how to think about valuations in the current 2021 market environment. (!!) Michael's work is maybe our most frequent carve out on Acquired, so we're pumped to finally have a chance to interview the man himself. Big thank you to Patrick O'Shaughnessy and Brent Beshore for introducing us all at Capital Camp this year!
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Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.
We finally did it. After five years and over 100 episodes, we decided to formalize the answer to Acquired’s most frequently asked question: “what are the best acquisitions of all time?” Here it is: The Acquired Top Ten. You can listen to the full episode (above, which includes honorable mentions), or read our quick blog post below.
Note: we ranked the list by our estimate of absolute dollar return to the acquirer. We could have used ROI multiple or annualized return, but we decided the ultimate yardstick of success should be the absolute dollar amount added to the parent company’s enterprise value. Afterall, you can’t eat IRR! For more on our methodology, please see the notes at the end of this post. And for all our trademark Acquired editorial and discussion tune in to the full episode above!
Purchase Price: $4.2 billion, 2009
Estimated Current Contribution to Market Cap: $20.5 billion
Absolute Dollar Return: $16.3 billion
Back in 2009, Marvel Studios was recently formed, most of its movie rights were leased out, and the prevailing wisdom was that Marvel was just some old comic book IP company that only nerds cared about. Since then, Marvel Cinematic Universe films have grossed $22.5b in total box office receipts (including the single biggest movie of all-time), for an average of $2.2b annually. Disney earns about two dollars in parks and merchandise revenue for every one dollar earned from films (discussed on our Disney, Plus episode). Therefore we estimate Marvel generates about $6.75b in annual revenue for Disney, or nearly 10% of all the company’s revenue. Not bad for a set of nerdy comic book franchises…
Total Purchase Price: $70 million (estimated), 2004
Estimated Current Contribution to Market Cap: $16.9 billion
Absolute Dollar Return: $16.8 billion
Morgan Stanley estimated that Google Maps generated $2.95b in revenue in 2019. Although that’s small compared to Google’s overall revenue of $160b+, it still accounts for over $16b in market cap by our calculations. Ironically the majority of Maps’ usage (and presumably revenue) comes from mobile, which grew out of by far the smallest of the 3 acquisitions, ZipDash. Tiny yet mighty!
Total Purchase Price: $188 million (by ABC), 1984
Estimated Current Contribution to Market Cap: $31.2 billion
Absolute Dollar Return: $31.0 billion
ABC’s 1984 acquisition of ESPN is heavyweight champion and still undisputed G.O.A.T. of media acquisitions.With an estimated $10.3B in 2018 revenue, ESPN’s value has compounded annually within ABC/Disney at >15% for an astounding THIRTY-FIVE YEARS. Single-handedly responsible for one of the greatest business model innovations in history with the advent of cable carriage fees, ESPN proves Albert Einstein’s famous statement that “Compound interest is the eighth wonder of the world.”
Total Purchase Price: $1.5 billion, 2002
Value Realized at Spinoff: $47.1 billion
Absolute Dollar Return: $45.6 billion
Who would have thought facilitating payments for Beanie Baby trades could be so lucrative? The only acquisition on our list whose value we can precisely measure, eBay spun off PayPal into a stand-alone public company in July 2015. Its value at the time? A cool 31x what eBay paid in 2002.
Total Purchase Price: $135 million, 2005
Estimated Current Contribution to Market Cap: $49.9 billion
Absolute Dollar Return: $49.8 billion
Remember the Priceline Negotiator? Boy did he get himself a screaming deal on this one. This purchase might have ranked even higher if Booking Holdings’ stock (Priceline even renamed the whole company after this acquisition!) weren’t down ~20% due to COVID-19 fears when we did the analysis. We also took a conservative approach, using only the (massive) $10.8b in annual revenue from the company’s “Agency Revenues” segment as Booking.com’s contribution — there is likely more revenue in other segments that’s also attributable to Booking.com, though we can’t be sure how much.
Total Purchase Price: $429 million, 1997
Estimated Current Contribution to Market Cap: $63.0 billion
Absolute Dollar Return: $62.6 billion
How do you put a value on Steve Jobs? Turns out we didn’t have to! NeXTSTEP, NeXT’s operating system, underpins all of Apple’s modern operating systems today: MacOS, iOS, WatchOS, and beyond. Literally every dollar of Apple’s $260b in annual revenue comes from NeXT roots, and from Steve wiping the product slate clean upon his return. With the acquisition being necessary but not sufficient to create Apple’s $1.4 trillion market cap today, we conservatively attributed 5% of Apple to this purchase.
Total Purchase Price: $50 million, 2005
Estimated Current Contribution to Market Cap: $72 billion
Absolute Dollar Return: $72 billion
Speaking of operating system acquisitions, NeXT was great, but on a pure value basis Android beats it. We took Google Play Store revenues (where Google’s 30% cut is worth about $7.7b) and added the dollar amount we estimate Google saves in Traffic Acquisition Costs by owning default search on Android ($4.8b), to reach an estimated annual revenue contribution to Google of $12.5b from the diminutive robot OS. Android also takes the award for largest ROI multiple: >1400x. Yep, you can’t eat IRR, but that’s a figure VCs only dream of.
Total Purchase Price: $1.65 billion, 2006
Estimated Current Contribution to Market Cap: $86.2 billion
Absolute Dollar Return: $84.5 billion
We admit it, we screwed up on our first episode covering YouTube: there’s no way this deal was a “C”. With Google recently reporting YouTube revenues for the first time ($15b — almost 10% of Google’s revenue!), it’s clear this acquisition was a juggernaut. It’s past-time for an Acquired revisit.
That said, while YouTube as the world’s second-highest-traffic search engine (second-only to their parent company!) grosses $15b, much of that revenue (over 50%?) gets paid out to creators, and YouTube’s hosting and bandwidth costs are significant. But we’ll leave the debate over the division’s profitability to the podcast.
Total Purchase Price: $3.1 billion, 2007
Estimated Current Contribution to Market Cap: $126.4 billion
Absolute Dollar Return: $123.3 billion
A dark horse rides into second place! The only acquisition on this list not-yet covered on Acquired (to be remedied very soon), this deal was far, far more important than most people realize. Effectively extending Google’s advertising reach from just its own properties to the entire internet, DoubleClick and its associated products generated over $20b in revenue within Google last year. Given what we now know about the nature of competition in internet advertising services, it’s unlikely governments and antitrust authorities would allow another deal like this again, much like #1 on our list...
Purchase Price: $1 billion, 2012
Estimated Current Contribution to Market Cap: $153 billion
Absolute Dollar Return: $152 billion
When it comes to G.O.A.T. status, if ESPN is M&A’s Lebron, Insta is its MJ. No offense to ESPN/Lebron, but we’ll probably never see another acquisition that’s so unquestionably dominant across every dimension of the M&A game as Facebook’s 2012 purchase of Instagram. Reported by Bloomberg to be doing $20B of revenue annually now within Facebook (up from ~$0 just eight years ago), Instagram takes the Acquired crown by a mile. And unlike YouTube, Facebook keeps nearly all of that $20b for itself! At risk of stretching the MJ analogy too far, given the circumstances at the time of the deal — Facebook’s “missing” of mobile and existential questions surrounding its ill-fated IPO — buying Instagram was Facebook’s equivalent of Jordan’s Game 6. Whether this deal was ultimately good or bad for the world at-large is another question, but there’s no doubt Instagram goes down in history as the greatest acquisition of all-time.
Methodology and Notes:
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Transcript: (disclaimer: may contain unintentionally confusing, inaccurate and/or amusing transcription errors)
Ben: 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.
David: And I'm David Rosenthal and I am an angel investor based in San Francisco.
Ben: We are your hosts. Today we interview one of our heroes, Michael Mauboussin. We've referenced his work on many episodes before. He's given talks that have been my carve outs. As many of you know, Michael is the head of consilient 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. 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.
David: I love that.
Ben: He's done mind-expanding research on a ton of topics. 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 macro economic environment.
David: I like that, unprecedented. Good phrasing.
Ben: For the presenting sponsorship on this episode, we have the SoftBank Latin America Fund back again. As many of you know from previous specials, SoftBank LatAm is deploying capital into the Latin America startup ecosystem and it's absolutely fascinating. They just announced they have another $3 billion to invest in addition to their initial $5 billion. So clearly, it is working.
When we asked Paulo and Shu, two of the partners in the fund, if we could grab some voices from the founders themselves, they were like of course. Today we are joined by Gabriel Braga, the co-founder and CEO of QuintoAndar, the $5 billion real estate tech company founded in 2012 in Brazil. Can you explain how the platform works and what your journey to start and grow the company has been like?
Gabriel: Definitely. We enable a seamless housing experience from searching for a home towards the transaction and after the transaction. We started back in 2012, focused on long term rentals. We chose that segment because it was the most neglected part of the market. It was particularly painful in Brazil.
In addition to all the inefficiencies in finding a home, I made duplicate listings, full photos, and complete info online. Tenants were required in Brazil to provide a very cumbersome and expansive rent guarantee, while the landlords were afraid of not receiving the rent on time and having headaches with delinquent tenants and evictions. We fixed the transaction by eliminating the need for those rent guarantees from the tenant side but guaranteeing the rent on time for the landlord no matter what happens.
Right now, we are about 10 times bigger than our closest competitor. We are the largest platform in Brazil, one of the largest in the world. We have more than 120,000 owned running rentals that we manage on a monthly basis. Just like we did in rentals where we kind of reinvented the transaction itself, how it's done, we intend to do this in the home buying segment. A bit more than a year of operation, we have more than a 10,000 per sale transactions rent rate right now.
Ben: Wow, just so impressive. One thing I've been wondering as we've learned more and more about the LatAm ecosystem, can you give us a sense of how it's evolved since you started the company?
Gabriel: We launched QuintoAndar in 2013. It was hard to attract talent to work in a small company. There weren't many tech company startups that had scaled. Fast forward, we've experienced a major shift, especially since SoftBank launched the LatAm fund. They basically invested in many of this earlier cohort of startups. Some of them became unicorns. Investors are coming and looking for new opportunities, founders coming from all over the world and trying to address problems here.
I think SoftBank specifically was pivotal in this process because they were very deliberate in investing in these companies and showing the confidence in the region.
Ben: Our thanks to the SoftBank Latin America Fund, to Gabriel, and QuintoAndar. If you want to get in touch with SoftBank, you can do so at latinamericafund.com or click the link in the show notes. If you're interested in working at QuintoAndar, there's a link in the show notes for that too. As always, this is not investment advice.
David: It's advice about investing, but not any specific investment.
Ben: Yes, no doubt it'll be educational, entertaining, and Michael's an absolute riot. So without further ado, we'll get into it.
David: 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 modes, 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 two, think about expectations, probably a good frame for the current market. Let's dive in on that.
Michael: Thank you both, David and Ben. Great to see you guys. The story is very quickly, I was a liberal arts major in college. I went to Wall Street, I had no idea what going on. I took no business classes, by the way. My father made me take accounting for non-business majors. I got like a C in the class out of the generosity of the professor's heart.
Wall Street, I think even the venture world, and even the corporate world is filled with rules of thumb and old wives’ tales of how things work. I was swimming in all these. One of the guys in my training program handed me a copy of Al Rappaport's book called Creating Shareholder Value. That book came out in 1986. I read it shortly after it came out. 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 that remain the bedrock of everything I think about. One is it's not about earnings that matters, it's really about cash flow. The ultimate driver of value of business is cash, not accounting earnings. We can come back and deepen on that thought.
The second is, and I also think it is really important, that we tend to think about strategy. What is our strategy and how do we position ourselves and so forth? We think about valuation as two separate things. He made the point, I think, very correctly that, do you have to combine these two things to understand a business and to do evaluation properly?
In other words, the litmus test of a strategy is that it creates value. You really can't understand or value a business until you understand the competitive situation that competitors set, the growth of the market, and so on and so forth.
Then the third and final thing was in chapter seven, it's called stock market signals to managers. The argument was, hey, executive, your stock price reflects a set of expectations about the future financial performance of your company. 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, had met him. He was an awesome good guy. I had the opportunity. I started using his work in my work as an analyst. Then in May of 1991, I had the opportunity to meet with him. It was absolutely phenomenal. For me, a really great experience as someone who's trying to learn from the master. We maintained a relationship through the 1990s. 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. That was the birth of Expectations Investing.
Ben: That former one was aimed at executives, at CEOs?
Michael: Yeah, Ben, it was. And that particular idea of Expectations was clearly useful for everybody. We wrote the book. By the way, we signed it in the late 1990s. The world's ripping and the stocks are doing great and everything.
David: Boy, that sounds familiar.
Michael: Exactly. You may have just jinxed me there, David. The book came out on September 10th, 2001. If you can imagine the worst time preceding obviously a national tragedy. But really, the middle of a brutal three-year bear market is we're coming off the dot-com boom into the dot-com bust, so the timing was not great.
It was very well received. A lot of people 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? He agreed. He's now in his late 80s. He's amazing to talk to. Every day I talk to him, it's an exhilarating and awesome intellectual journey. It's super fun working on it. The other interesting thing is how much the world has changed in 20 years, of course. A lot of new stuff has come along. Anyway, that's the story of Expectations Investing.
Ben: We want to ask you for a spoiler, in case people haven't read the book from the first time around, in case it slipped through the cracks in anything else they were doing in 2001 and they haven't picked it up yet. What's the big seminal idea?
Michael: I don't want to discourage anybody from buying. The idea is to say a stock price or could really be any asset price, a price of an asset, but to say stock price reflects a set of expectations about future financial performance. The first step is to say, what do I have to believe for this to make sense? 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. By the way, more times than not, you're not going to have a view that that's different. But if your views are more optimistic, then you should buy the stock. If your view is more pessimistic, you should sell the stock.
Then the third and final thing is as a result of those things, take action. So buy, sell, hold, or do nothing. The core idea is just basically say, what do I have to believe? Is a company going to do what the market believes it's going to do? Then let me make decisions as a consequence.
Ben: 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, a price target, or a share price that they're willing to buy the company at, is they make their own model with the bottoms up. Bake in all the assumptions and then say, okay, here's what I'm willing to pay. You're making the argument here that the market has set a price.
Actually, what you should do is reverse engineer that and say, what are the assumptions that I need to believe to make that a good thing to purchase right now, or make this a buy instead of a sell or an ignore. Basically trying to come up with a probability distribution for each of those assumptions.
Michael: That's right, Ben. I'll nerd out for just a second that the original framework for discounted cash flow model was laid out by a guy named John Burr Williams in 1938, so a very long time ago. He's laying out a DCF model and it's a little bit complicated. He's got a chapter, chapter 15 called the chapter for skeptics. He's like, okay, you guys do it in a certain way, and I'm showing you something new. You're going to be skeptical about it here. He tries to address head-on all those skepticisms.
Actually, John Burr Williams says, hey, you know, if you think it's too complicated to forecast what you think the value is, use the tools to go backward. He actually talked about reverse engineering in 1938, which you're exactly right. 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. 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 backward.
I don't know what it is, but it seems to be a much more reasonable task to say, what do I have to believe? By the way, again, in investing a lot, you're going to pass on a lot of things so you’re just not going to have a differential view. So you're like, all right.
David: It makes me think so much. I literally just wrote it down in my notebook. It's the famous Charlie Munger quote, "Invert, always invert." What would Charlie do?
Ben: The Buffett quote is "Price is what you pay, value is what you get." They're a pair for a reason.
Michael: One hundred percent. 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, is very probabilistic. What we're really trying to do is think through a scenario—the if-then kind of scenario.
We're getting knowledge at the price today is just one of many potential outcomes. It's actually a price reflecting the distribution of potential outcomes. We want to really understand the richer distribution. Again, this all lends itself to good analysis. It lends itself to good strategic analysis and financial analysis. But it's not here as an answer, it's really trying to think about the world probabilistically, which is also very much a Buffett and Munger type of thing.
David: This is great because think about today's world. Probably even this was changing when you read 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 Facebook or Amazon's cash flow next year will be Z. How should folks think about that?
Michael: David, I'll just build on this. This now versus then is an interesting way to frame it. If you go back way to Ben Graham and so forth, they focus a lot on things like book value, which 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. Now, more of our investments are intangible versus tangible.
As a consequence, what's going on in the income statement, the balance sheet, and so forth is getting a little bit mixed up. Let me just give you one little stat I found interesting that we've just recently run. Back in 2001, the year the first book came out, capital expenditures and intangible investments—this is called the Russell 3000, basically, US public companies—was about the same amount. $630 billion, $640 billion, or something like that.
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 hold out, it'll be the case that intangible investments now are $2 trillion and CapEx is $1 trillion. Going from the same starting point, intangible investment or 2X the tangible investment.
David: Can you, for everybody, just explain what you mean by intangibles?
Michael: Yeah. An intangible and tangible, the basic distinction is exactly what it sounds like. Tangibles are things you can touch, feel, kick, and so forth. Intangibles 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.
What accountants try to do now is to look at the income statement and say, of those items that are spent on selling general administrative expenses, which are necessary to maintain the current business and which are discretionary investments—an investment defined as an outlay today with an expectation for future return—that in this case are intangible. The big buckets classically are research and development, branding. Today, you think a lot about customer acquisition costs, all that kind of stuff.
It's been a watershed change. Call it maybe not even a generation of investors. A lot of those tools that were developed were 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.
Just such a thoughtful guy, but Collison is spending a lot of time like, I don't understand why the accounting works this way 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. We're expensing everything so our income doesn't look that great.
David: They look unprofitable.
Michael: Yeah, they look unprofitable, but we're building incredible value, incredible wealth. That's why this original message from Rappaport of cash flows not earnings is so in my mind all the time. This is a really big change. What's exciting for me, and I think 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. Again, the numbers are becoming less insightful for giving us guidance on how to think about that.
Then the other thing that's been interesting, I think, for the last 20 years has—it 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. 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. If your product or service does not work, there's not much there left.
David: Right. You're not going to sell for book value.
Michael: Exactly. If you think about pushing out the tails relative to traditional businesses, that's the way I think about it. There are more extreme good things and more extreme bad things than what we'd witnessed in the past.
Ben: Maybe I'd go back and rearticulate something the way I understand it. Venture capital investors have not had a financial investing fundamentals background. They often come from being entrepreneurs. 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. The idea that intangibles are investments is like inherent is like duh, and then it just feels weird that it doesn't show up in the right place in your financial statements.
It's almost like this hard-headed 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. So more and more of the very valuable companies in the world think about their investing internally the same way that the non-financial sector of venture capital has thought about them for 30, 40 years.
Michael: Yeah, I agree with all that. I do think that the market has sorted this out to some degree, even public companies. I think we're close to a record number, if not a record number of public companies today that "lose money." You can lose money the old-fashioned way, it’s just your costs are bigger than your revenues. But you can lose money that way we're talking about, which is you're actually making very productive investments.
By the way, just to take it one step back, when I tell you about cash flow, the number we really care about is so-called free cash flow, which is earnings minus investments. Some people think, oh, you want positive free cash flow. The answer is not really. What you want is, if you can invest at a high return, you want to invest as much as you humanly possibly can that you have access to.
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're investing like crazy. Why was that good? Because their stores had great economics. So knock yourself out. That's a little bit of the same mindset.
David: It's 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—what is that on the investing cash flow portion of the cash flow statement? You can disentangle that, but with the software companies, it all gets tied up in Opex.
You're investing in acquiring customers and hiring engineers, et cetera. 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.
Ben: I want to talk about company analysis. Michael, you published the awesome Measuring the Moat paper a few years back that has become basically the Bible for how to do this. We thought maybe the right way to dissect this, I think you teach Ben Graham's legendary security analysis course at Columbia Business School. How do you think about this concept in the course and how's the course structured?
Michael: The course is 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. The fundamental question is, are markets efficient? Are they inefficient? Whether I'm a venture capitalist or public market investor, if I have hopes to generate attractive returns, how do I go about that? How do I differentiate myself to do that?
Then the last piece, which by the way, is the newest part of the course is on decision making. What I came to realize probably 15 or 20 years ago was, what differentiates good to great investors has little to do with their technical skills—their [...] built spreadsheets or whatever—and much more about their temperament and in particular, their ability to make decisions under some sort of stress or tension. It will come back to decision-making.
David: Real quick, I got to ask. What's the story of how you came to teach this legendary course? Because this is so awesome.
Michael: All this stuff is luck. I joined—at the time the First Boston Corporation which is now—Credit Suisse as a food industry analyst in 1992.
Ben: So liberal arts major gone food industry analyst?
Michael: Exactly. Think General Mills, Kellogg's, Campbell Soup, and all that kind of stuff, that was my industry. I'm a new guy and I'm like plugging away. By the way, I'll just say that from the very beginning, I love 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. That's how I got to know Bill Gurley very early in Bill's career when he was an analyst. He was just like a cool guy working on cool stuff.
There's a guy there named Charlie Wolf, who is just the greatest guy. Charlie was actually a tenured professor at Columbia Business School who decided to have a sabbatical year, he decided he wanted to do equity research of all things. Every firm turned him down except for First Boston and they gave him a job. This is now the late ‘70s, early ‘80s. 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. They're like, personal computers? Nobody cares about that. Charlie was the PC analyst and there's Apple coming in public.
David: Oh, man. He was an academic.
Michael: He was an academic. He's a trained academic. He walks in my office and he goes, hey, you know, I'm working on PC stocks and I'm thinking about brands like Dell, Compaq, and all these. He's like, what do you know about brands? You're a food guy. I was like, I don't really know that much about brands actually, but here's some stuff I've done and you could check it out.
Of course, just to be clear, this is me coming right off working on the Rappaport stuff. I'm using an approach that you could argue is a little bit more academic than it was traditional on Wall Street at the time. He comes back the next day and he goes, yeah, there's not that much about brands here, but you should teach at Columbia Business School. I was like, wait, what? How do we make this connection? I think at the time, he had had a connection to the school. They were looking for people to teach security analysis, which is—
David: Warren Buffett, the whole reason he went to Columbia was to take this course.
Michael: Yeah. I don't want to overstate all this. It is called security analysis. Graham did teach a version of all this, but many people have taught it over a long period. In other words, I'm not unique in any way in this way. Then he asked me to teach it and I went up there. When you're in New York, you can bring in great guests. So it's a fun experience for the students.
I started doing that in the summer of 1993. This year, 2022, will be my 30th year of doing this in a row, which is actually really cool. That's a story of how I got there. Let me now delve into Ben's question about competitive strategy. I'll just say that I don't know if people really recognize this, but the very first version of Measuring the Moat came out in 2002, nearly 20 years ago.
I'll just say that that was among probably the top three hardest things I've ever done professionally. The reason was not so much that any of the ideas were that difficult, but it was an incredible exercise in synthesizing. Many other people, I had read Michael Porter, I had read Clay Christensen. 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 cohesive that allows an investor or an executive for somebody to understand?
Ben: Not to mention, these were abstract concepts. You read them and they click. You're like, oh, yeah, competitive strategy by Michael Porter. This totally innately makes sense, but then that next level of literally measuring.
Michael: The thing is you can start with basic things like competitive advantage. Interestingly, by the way, I have all the Porter books, and I read many of them when I was very young. They're really rich, but they're difficult. They're not fun. They're not easy books to read.
In fact, I usually recommend that people who are interested in understanding Porter, read a book by a woman named Joan Magretta called Understanding Michael Porter because she's a journalist. She worked elbow to elbow with him for many years. She actually explains the ideas, I think, more clearly than he does with a lot of examples.
Here's an interesting question, what is the definition of a competitive advantage? If you say a moat and it turns out that Porter himself never really defined it. 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. In other words, the cost of capital is simply an opportunity cost concept. If I'm taking $1 here, it should earn above what that dollar could earn somewhere else in terms of opportunity cost.
Then the relative one is you should be better than your competitors. If we can define a competitive set, you should be better. That's a competitive edge. Then your point is exactly right. We want to start with something a little bit quantitative in the sense, you could hang your hat on it. We try to measure that by things like returns on invested capital.
We basically broke the strategy into three pieces. One, I call it lay of the land, but basically, what am I dealing with here? So we do things like entry and exit in the industry, market share changes, pricing flexibility. These are to get a sense of the field that you're dealing with.
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 soft drinks. These guys slug it out for one market share point. So that's a really stable industry.
Then we talk about industry dynamics. This would be the classic Porter stuff, where 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 innovations 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 going back to his basic principles.
Then the third piece is, what is the source of this company's competitive advantage if it has one? The simplest way to say it is usually low-cost producer or some sort of differentiation. What's also neat about the low-cost producer differentiation is we can tie that back to return on capital. Basically, the simple model is low-cost producers tend to have low margins and high capital velocity.
Ben: What's capital velocity?
Michael: Capital velocity would just be margins are going to be profits divided by sales and capital velocity sales divided by invested capital. Low margins, high velocity, that means you return your capital fast. That's a low-cost producer. High margins and low capital velocity, that's a differentiation.
Here’s where 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. You think that versus Tiffany's. I don't really know Tiffany's business, but Tiffany products make a lot of money when they sell stuff and they don't sell that frequently, jewelry store generically.
What happens is immediately, you show me the income statement or even adjusted financial statements and I can tell you right away if they're going to have a competitive advantage, how are they going after it, which is interesting. So Measuring the Moat was an attempt to try to be structured and thinking through this stuff. I was very specific about putting a checklist at the end. 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. It sounds a little bit trite to talk about, like David was saying before, some of these markets are a little bit crazy. 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 before I get involved with it.
David: I'll tell you, when we started, 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. 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. I'm trying to break up. I don't know, it was like, well, wow.
Michael: Too much work.
David: Applying this to a seed stage investment requires a little bit of a mental leap, but it was so fun.
Ben: 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?
Michael: These are really hard questions and there are 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. To break that term down, complex just means the interactions of lots of agents. 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 change their behaviors. The system never settles down. Then system is the whole. It's greater than the sum of the parts. 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 I have a difficult time anticipating where the world's going to go.
That said, Ben, I think that one thing that I often think about young companies is really options more than a bond or something boring like that. An options theory has been around for a very long time. Obviously, Black-Scholes in the 1970s defined mathematically some of the key principles. It's not a perfect mapping to the real world, but not too bad.
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. How do we think about that? Where real options tend to be valuable is when you have three or four characteristics in place. First is it's good to have volatility in the market. This is an interesting thought that's a little bit backward.
Typically, if you say for financial assets, your discount rate is some sort of cost of capital. Lower is better for value. So if I have a lower discount rate, I'm going to have a higher value, all things being equal. I think everybody gets the math of that.
David: The current market where the discount rate is zero to negative.
Michael: Yeah, look at the current market. But options are actually interesting because an option is the right but not the obligation to do something. You take out the downside. In an option, what you want is lots of volatility. You want lots of volatility, which is counter. The more volatile the world is, the more valuable the option is. That's, I think, an interesting thought there.
This is where there becomes a big premium on management. A management's ability to understand options and exercise them intelligently is extremely valuable. You can 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. I'm just saying he's been great at it.
Then the other thing that's interesting is a feature is access to capital. Because even if you decide to exercise an option, you need some time to do things, like you have to pay for them. I think that there was a lot of really interesting stuff intellectually going on in the early 2000s, 20 years ago, but it was a huge bear market, a huge hangover from dot-com. There was just limited access to capital. As a consequence, there are probably a lot of really interesting things that didn't happen.
David: Just look at Webvan and pets.com, and look at Instacart and Chewy today. These weren't bad ideas. That's just the access to capital went away.
Michael: Yeah. 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 to the role of entry and exit in industries. I recommend my students to spend time understanding entry and exit. I think very few people, by the way, are familiar with these statistics typically.
By the way, the guy that did the main work on this and it's a beautiful work is a guy named Steven Klepper from Carnegie Mellon. Klepper died a few years ago, but this is really cool stuff. What Klepper showed was that in almost every industry as it gets going, there's a huge upswing in a number of competitors. Again, think evolution.
The market is sorting out what it likes and then once it's figured out what it likes or what works, then there's a huge downswing. That's consolidation or businesses going out of business, bankrupt, or whatever it is. You get this pattern of up and down. That's another really interesting thing to think about when you're looking at early stage stuff, which is to say, all right, where are we in this whole cycle?
By the way, when it rolls over, 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. It's a fewer number of companies that are capturing the spoils. It's like a really interesting dynamic. We wrote a little bit about this.
Klepper is obviously the guy, but you can do this for industry after industry. Certainly automobiles would be a classic example, radio, a lot of it on the internet for sure, disk drives. There are lots of cool examples of this pattern playing out over time. Those are just some thoughts that might be fun to think about and play with.
Ben: One thing to drill in on is 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 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 the way to think about an early stage investment?
Michael: I think that's right, Ben. 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. 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 was a venture. I think they looked at 30,000 venture deals—some gargantuan number of venture deals—then they looked at 15,000 buyouts, and then we looked at 30,000 periods for public companies. What you're looking at is the distribution of payoffs. I'm going to say what everybody already knows, which is the median venture deal earns nothing. Many venture deals lose money, but the tails are super extreme. That's a really interesting way to think about essentially an option payoff.
Then buyouts were a little bit about 25% lost money, but most of them kind of did okay, but a little bit right more skewed than the public markets. Then the public markets look much more like a bell-shaped distribution. 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? That's why even in ventures, especially early stage ventures, whether the thing's worth $50 million or $100 million, if it's going to be worth $10 billion in 10 years or 3 years, it doesn't really matter that much what you pay for it today.
That's why these extreme outcomes obscure the first day. These funny stories about people like, oh, we pass on Amazon because it was too expensive. 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.
Ben: To the extent that something is in the pool, where it could be the next Amazon, if it's truly early stage, then it's worth 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.
Michael: That's why you're also building a portfolio of these things. 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 these are very familiar patterns that you just hope a couple of things in your fund are the ones that [...], pull the wagon along for everything.
David: I think the reason why I had a tough time as a young VC 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. You should think of it as an option—this framework we're just talking about—versus if you're buying a public security, I don't even know what the right word is of that type of asset that you're buying of an option versus...
Michael: Yeah, it's just more of a cash-flowing business that's clear and more almost like a fixed income where we have visible and predictable to some degree cash flows. Exactly, that's not a bad way to think about it.
Ben: 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 backward from price. For fun, let's look at Tesla and say, when the margin of safety is as narrow as it's ever been in making an investment in any asset because multiple is based on any aspect of a business are at all-time highs, how are you walking through an exercise with your students of working backward from some ungodly valuations of companies and where it's still may make sense to invest?
Michael: By the way, not surprisingly, Tesla's 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. Tesla has been one that's been a perennial one, for many of the reasons you just described, Ben, the head-scratching component. You just have to sit down, pencil it out, and think to yourself. By the way, Tesla is another example of this optionality. Are there things that they're doing that are not visible that could be of value in the future? You have to pencil all that stuff out.
The other thing I'll say about Tesla, which is we have a bit about this in the book, but the idea has been around for a very long time is this concept of reflexivity. We tend to think that there's this thing called the value of the firm and I'm the observer. If the value is higher than the price, I'm going to buy it, make money, and so on and so forth.
We forget that—this goes back to complex systems—there's an interaction between the observer and the actual thing itself. 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.
Ben: Especially if they can raise gobs of money at that new valuation.
Michael: Precisely, and I think that it was not too many years ago that Tesla was skating on thin ice in terms of finances and so forth. Then as the stock took on a life of its own, stock went up a lot and that allowed them to raise capital and get themselves on much stronger footing. 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. By the way, the idea has been around for a very long time, but the term reflexivity, I think, was coined by George Soros, just to be clear where that intellectually comes from.
David: I didn't know that. That's awesome.
Michael: Again, a very old idea, but reflexivity. The key is when you get off this thing because reflexivity works in two directions. One other area where reflectivity's been historically a very big deal is in mergers, acquisitions, and conglomerate roll-ups. You think about businesses buying other businesses, their stock as well, then they use their stock to buy another business, and they keep doing this and so on and so forth.
Often where the gig ends up is that they have to do deals that are so large to perpetuate their growth rate, to fulfill the expectations that it just becomes essentially an insurmountable task. 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 is all new. We’ve had flavors of all this stuff for a really long time. There's really not that much new to that. I think 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 the friction out of the system. That allows it to be perhaps a little bit easier, but, they've been basically versions of this for a long time.
Again, some of these meme companies have been pretty smart about raising capital as well. 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. We'll see how this unfolds and finishes, but they tend not to be good endings.
Ben: 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?
Michael: I think that the context may be slightly different. I want to put words in anybody's mouth, but I think 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. Then when it gets cheap, I'm going to buy it back, and so on and so forth. What history tells us is that none of us are that clever, we just don't know.
I think that's a little bit of what Bill was saying. With a venture thing, things feel a little bit rich and we should be throttling back a little bit. But we, in retrospect, have a hard time being good at doing that. That would be my context there, but I think the idea that the movie doesn't end well, that's pretty easy to document. We've seen it in plenty of cases.
Matt Levine at Bloomberg is a genius. He's got this thing called the boring market hypothesis, which I've always loved. I think there's something to that, which is 18 months ago, we 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, here's something we can do to keep ourselves entertained and in some cases, make some money. That's sparked the thing, but we'll see how it unfolds.
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David: You said a minute ago, 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?
Michael: This has been an area I've been fascinated by. I think as a world, we avail ourselves of these tools too infrequently. We should be doing more of this. Ben brought up a point early on which I just want to reiterate, which is thinking about different scenarios for how the world might unfold. 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.
The idea is to maintain an open-ended understanding of how things might unfold. 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. The first one on opening up your mind is this idea of base race.
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, combine it with your own analysis, your experience, and your own input, and then you project into the future. 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 the situation before us?
It's a very unnatural way to think about the world 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. Psychologists have demonstrated, this is a very, very robust component to your decision making. Understanding and thinking about base rates, I think, is a really powerful thing. If you ask me if I could go back to my 20-year-old self and whisper in the ear and say, there's one mental model to put into your life, I would say base rates.
Another idea is pre-mortem, same idea. There's an interesting psychological piece to this, but pre-mortem just says, let's pretend we make an investment today. Pretend, then we launch ourselves into a future. A year from now, it's 2022. This investment has turned out sour, it has been really bad. This is important, each of us independently writes down why this turned out badly.
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. It turns out that again, you don't have the intellectual baggage of having made the investment and your mind is opened. There are some interesting reasons why the future to present is better than present to future, but again, a mind-opening exercise.
The third thing is this idea of red teaming. Again, people are very familiar with this. Cybersecurity is a good example now. The blue team defends, ted team attacks. 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.
Red teamers are people that are organized to challenge thinking, challenge the prevailing views of things. It's really hard because even organizationally, we fall into these mindsets. We all start to believe the same thing. You need someone to jar you into reality.
Then the last one is journaling and that's this idea of feedback. I think it's just brutally hard in our world. Whether it's venture, even as an executive, or public markets, it doesn't matter. It's brutally hard to give yourself honest feedback about what's happened. So even if something turns out great, it did turn out great for the reasons you thought it would, or you're just unlucky, you just come up lucky. Maybe sometimes you did all the right things and it turned out poorly, but it was the right decision at the time given the information you had.
The idea of journaling is just keeping a decision log, reviewing it periodically to make sure that you're thinking about things properly, and then you're giving yourself honest feedback. 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, it gives you the apparatus for a scoring system that can be super helpful. Again, it's not a ton of extra work because you're doing it already. You're just being overt about it and writing it down.
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. I think those that do it well certainly benefited from it massively.
Ben: 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 the beginning of the rabbit hole for you.
The quote is, "An individual has been described by a neighbor as follows. "Steve is very shy and withdrawn, invariably helpful but with little interest in people or in the world of reality. A meek and tidy soul, Steve has a need for order and structure, and a passion for detail." Is Steve more likely to be a librarian or a father?"
Everyone has some idea on their mind at this point?
Michael: I was hanging there.
Ben: Of course, your intuition says a librarian, but in fact, there's something like 10X or 20X the number of farmers in the world. 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.
Michael: 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?
David: Please. This is one of my favorite books.
Michael: Look, I just think that one of the most fascinating topics out there is this idea of untangling skill and luck. I was able to write a book about it about eight or nine years ago.
David: What inspired you to write the book, by the way? It’s so good.
Michael: It's funny. I love this, David, when you ask where did 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. I didn't mind baseball but didn't really like baseball that much. Then I read Moneyball and I was like, this is awesome. This is so interesting. I think I was the first person on Wall Street to write about Moneyball. I wrote a piece about it within a week or two of the book coming out so I was so fired up.
Part of what they're trying to do is forget about what the person looks like, whatever, let's figure out what wins, so these are things that are skill contributions. That got me thinking a lot about this and then it got me focused on the analytics community where this thing is really important. Then I wrote a book called Think Twice in 2009. Think Twice is about decision-making. It's really a homage to Kahneman actually, like the kinds of stuff that Ben just read about.
I had a chapter on luck and skill and I was like, this is the coolest thing ever. I made it chapter two. I'm like, people are going to get this written. The first one was on base rates actually then this is chapter two. My editor reads this and she comes by. She goes, I don't know the skill and luck stuff, it’s too complicated. If you want to keep it, put it at the end. I'm like, all right, all right. It’s one of the last chapters.
I get friends that would read it and my friends would go, I liked your book. That chapter on skill and luck, that was cool. I knew it. I knew I should have put that in the beginning. So this is like a spin-off like those TV shows like Mork and Mindy spin-off from Happy Days or whatever. This is like a spin-off. This luck and skill thing, there's a lot more here.
I also read Fooled by Randomness by Taleb, obviously in 2001, as many people did. Obviously, the basic point hits you in the head like a 2x4. That there’s randomness in the world than you anticipate. I felt that it was lacking in the sense that it didn't really give you the tools to quantify any of that stuff. 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. It's all stuff I find interesting. That encouraged me to go down the path. It actually makes sense, just to very quickly define some terms. Skill, we're going to say, is the ability to apply one's knowledge readily in execution or performance. So you know how to do something, and when you're called on to do it, you can do it. You have to go play the violin at Carnegie Hall, like snap your fingers, you're going to crank. You're going to be awesome.
Luck is much more difficult to define. By the way, it gets into philosophy very quickly. You have to put a pole down to figure out where you want to stay. I'm going to say it has three key attributes. One is it happens to an individual organization. It happens to you, your company, your favorite sports team, or whatever. Second is, it can be good or bad. I don't mean to suggest it as symmetrical because it's not, but there's a good positive side and negative side.
Third is, and this is the squishiest one, it's reasonable to expect a different outcome could have occurred. If we rewind the tape of time and we played it again, it would be reasonable to see a different outcome. That, I'm going to say, is luck.
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 can think about activities along a continuum on the one extreme would be all skill, no luck. Nothing’s really over there. You think about chess matches or running races. The fastest person is usually going to win. Then you think about the other extreme, which would be all luck, no skill, so roulette wheels, lotteries are right there. There's no element of skill in this whatsoever.
Ben: Public market investing?
Michael: Yeah. That's actually interesting. Hold on to that thought because we want to come back to that in just a moment. Then you have everything arrayed between those two extremes.
By the way, in the book, we did it for fun, which was professional sports leagues based on a season. You can see, for example, that basketball is a sport that's furthest away from randomness. The most essentially skilled dictates the outcomes. Ben, you're sort of joking a little bit about more public market investing, but I actually want to build on this because it's probably the most popular concept that came out of the book. It's called the paradox of skill.
David: This is so mind-blowing.
Michael: 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. The idea is that when you think about—
David: Was he the biologist?
Michael: Yeah. The visionary biologist, exactly. Good call. So the paradox of skill says activities where both skill and luck contribute to outcomes, which is most stuff. As skill increases, luck becomes more important. You're like, wait a second, how does this work exactly? We can think about skill in two dimensions. The first is absolute and the second is relative.
The first is absolute skill. I think that we agree, if we look around the world—whether it's sports, business, or investing—the level of absolute skill has never been higher. 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 because you just have better tools available to you. We can see that especially in sports measure versus a clock, 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. What we've seen in domain after domain is that relative skill gaps have narrowed. The difference between the very best and the average is less today than it was in the past. You can think about all sorts of tons of reasons. For example, sports leagues are super easy. The NBA used to be certain types of players from certain parts of the country. Now it's a completely global market. The best player anywhere in the world will be found and they'll be drawn.
David: Wilt Chamberlain could just totally dominate back in the day. If Wilt was playing in the NBA today, he would have a lot more.
Michael: Right. In fact, this is how the whole thing got going. Stephen Jay Gould wrote about Ted Williams who hit 406 in 1941, that very magical year. By the way, Ted Williams, 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 in the most recent full season, you hit like 385 or 390. So it's awesome. You win the batting total going away.
Ben: That's what, the top 1.5% or something of everyone?
Michael: Yeah, top 1.5%, right. You're not breaching that 400 level, which is super interesting. The point is if you think about two people with absolutely wickedly high skill levels but they're completely equal, then the outcomes can be a coin toss. It appears to be random, even though they're incredibly skillful.
It's funny because I still play beer league hockey. The hockey guys go, the hockey players are the most skillful guys. It shows up as a very random sport in our system. 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. As you said a moment ago, David, differentiating yourself is extremely difficult to do. As a consequence, it all feels like a big coin toss.
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. Markets are actually really good.
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 the notion of persistence. If you do well in period one, you'll do well in period two. If you're really good at math tests, you take a math test today, you take one after two weeks, you'll do well in both times. That indicates skill.
By the way, there's almost always this concept or regression toward the mean, if you do really well. I want to come back to regression in just a second. Persistence is an indication of skill. It turns out that if you look at venture capital, in particular, by the way in public equity markets, very limited persistence. If you did really well last year, your expected value is closer to the average. The following year. Buyouts 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.
If you can get access to one of those funds and invest with them, you tend to do very well. 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. There is persistence in venture in particular, and that stands out relative to a lot of other asset classes. Here's the last thing I want to say about this luck and skill thing, which is, and this goes back to base rates, which—
David: We can't really get away with that. We want the pet theory.
Michael: All right, this is my pet theory. You guys can tell me, you can shoot me down. It actually came out of network theory, but it's this idea called preferential attachment. For example, website traffic tends to follow a power law. There are power laws over the place, 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.
Power laws in websites might be something like if you're building a website, what you want to do is point to other ones that are popular. If everybody's doing that, then that leads to this phenomenon of some becoming super, super popular. The theory would be something like preferential attachment and venture right, and there's some there's a little bit of evidence to this.
Ben: This is a very academic way to say they get the best deal flow.
Michael: They get the best deal flow. Exactly. There's a big caveat here, which is, if you're a great startup, you have to know that you're great. You have to know to call Sequoia, Benchmark, Andreessen Horowitz, or wherever it is. There has to be identification on both sides. By the way, going to one of these leading firms, there's no [...] and so forth. It gives you a stamp of approval that also helps your future. That goes back to a reflexivity thing. Like you said, best deal flow, there has the best terms, but it's this reinforcing mechanism.
By the way, that process can be bootstrapped by something random. We happen to get three lucky deals and we did well, so now everything thinks we're smart. I've not really seen robust work on this, but one of the testable hypotheses would be if a partner leaves a leading venture firm and starts his or her own shop. If it's the person’s genius skill, then that should port. If it's the preferential attachment, that would not port. That's an interesting way to test that.
David: My feeling, without having to look at the data, is it does not port or it does not port nearly as well as the individuals might hope it would.
Michael: Right, exactly. Let me talk a little bit about regression toward the mean. This is interesting. I'll just try to close out this thought, which is base rates is this idea of just statistical base foundation. Then the inside view, which would be, let's just look at my own analysis and what I know about the world.
It turns out that on the all-skill side, all you need is the inside view. You might be a good chess player in your local club, but if you're playing Magnus Carlsen, it doesn't matter. Your win-loss record does not matter. Magnus is going to beat you every single time he plays you. By contrast, there's no regression.
Then if you go to the complete luck side of the continuum, there's complete regression. You won the lottery yesterday, that's awesome. Do you expect your probability to win the lottery today is the same as it was yesterday, which is my new race? It goes back to complete randomness.
We all know that regression toward the mean happens, but you can actually figure out the rate at which it happens by understanding where you fall in this continuum, which is super cool. A very powerful mental model. If you're a sports fan, you could go on all day about this stuff. Almost every sports statistic has these features, and you can figure out how fast players will regress based on these statistical concepts.
Ben: Well, this is interesting. This leads us to a question that David and I have been bickering about since the end of our Berkshire Hathaway 10-hour extravaganza. 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.
My pushback to David is, well, no, everyone is competing on this global playing field now. It's so much harder to get the type of returns, especially at the amount of capital that Warren was investing.
David: The paradox of skill has gotten so extreme.
Ben: Yeah. So, Michael, my question for you is, will we ever see someone who has the 60+ year track record that Warren did ever again, or will no one ever be able to match that?
Michael: It's a fascinating question. This is another Stephen Jay Gould from the same book where he says extraordinary streaks are a combination of skill and luck. 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. What we're arguing here is that the luck piece hasn't changed. Maybe some of the outcomes are more extreme, but luck is basically the same thing.
Although we could talk about there are independent event luck like rolling dice or whatever, then there's a social phenomenon where we get this power law outcome. But basically, that whole thing is roughly the same. I think if we're arguing that skill has become more uniform, then I would say that it'd be very difficult for people to replicate that. There are certain statistical streaks that I think are going to be very difficult for people to match or exceed. Joe DiMaggio’s 56-game hitting streak. By the way, there are a bunch of books about DiMaggio’s streaks, some of them are right over on that shelf over there.
There are a couple of kinds of things by scores at the scores table. There are a couple of random players. There's a lot of luck, but again, amazing skill. He was a 325 hitter. He's an amazing player. We're talking about Ted Williams. 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. 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. It's going to be difficult.
Buffett is amazing and so forth. 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. Just shot the lights out, but again, much smaller fund, much more nimble, sort of a little under the radar, and so on and so forth.
David: The paradox of skill was much lower then.
Michael: 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, so alpha. Excess return. If you're an active manager, what you want is a big fat bell-shaped distribution. Lots of positive alpha that's on the right and left on negative alpha. You're going to be the winner and there are going to be a lot of people losing, nets to zero, of course. You want that to be fast because that means there's lots to gather.
What has happened consistently is that the bell-shaped distributions have gotten skinnier and skinnier and skinnier, which is exactly what you expect from the paradox of skill. That's how I picked up on the Gould thing. Gould shows there’s a reason there have been no 400 hitters is precisely because the standard deviation of batting average has gone down over time. These are all the things that are symptomatic of what this idea would predict, which is cool.
Anyway, you think about if you bought an automobile in 1970, there was a huge variation in the quality of automobiles. Today, they're all really good. Some are better than others, but they're all really good.
Ben: And really safe. It's crazy.
Michael: They're really safe. All things being equal. Obviously, there is status stuff related to it, but in terms of actual performance getting around, they're all pretty good. Again, this is why this happens. It’s best practice. Think about athletes—best practices, best training, nutrition, and all these techniques. In the corporate world, the best ideas get transferred from one organization to another very fast and so forth. It stands to some reason that there would be a uniformity index of excellence.
David: Even in our world in venture, when we do our sort of classic episodes, we talk about what things are like. The level of skill among venture capitalists back in the day was laughable. Today, it is extremely competitive. So you're seeing this happen.
Michael: Definitely, yeah.
David: All right, for our final sponsor of the episode. Listeners of the show, if you've been listening to our specials so far this season, you know that Ben and I collaborate, not just on Acquired, but in quite a bit more in our lives. In particular, we are constantly sending back and forth versions of this massive spreadsheet that we both use to track our personal investing portfolios.
Ben: Hopefully, we'll be making Michael proud.
David: Yes, indeed. One thing though, that until this year was not in our personal investing portfolios was real estate outside of our primary residences. We've been wanting to add it because as we've been talking about on this whole episode, tech prices and the markets are a little unprecedented.
Ben: I feel like I'm taking crazy pills, David. Is that the correct term to say?
David: That is one way to say it.
Ben: I wish I could diversify my assets and invest in something more predictable and more stable, really to counterbalance a lot of the rest of my portfolio right now.
David: Indeed, indeed. Now, we have the perfect way to do that, which is Fundrise. Fundrise is one of the only platforms that lets anyone, not just accredited investors, buy fractional ownership in custom portfolios of private real estate assets. This is the same type of thing that University endowments do and sophisticated pools of capital. You can access cash flowing real estate properties to invest directly without paying the management fees of a REIT, and also having fine-grained control over what you actually are investing in.
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Ben: All right. Well, that's a great lead into a discussion on your career path, Michael. 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. It was shooting fish in a barrel at that point. Now, you need to do a lot of things to be the best. Michael, I'm curious if you were 18 years old today, what do you think you would do with your career?
Michael: That's a little bit too hard to answer. 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. It only seems easy when you think back on it. I mentioned when I started teaching at Columbia Business School, I just want people to conjure up in their mind that there was no internet.
When I want to do financial statement analysis, I would request from our library a mimeograph of the 10k. This is just a different world than what we're used to today. We used to fax our reports and mail our reports to clients—mail them.
David: Oh my god.
Michael: Yeah, exactly. If you want to see my report on Kellogg, it would come in the mail.
David: Wasn't this one of Bill Gurley’s big distribution innovations when he was an analyst is he would fax a newsletter, right?
Michael: Do you know the story on that? It's a great story, because there was a guy—
David: I want Bill’s story.
Michael: Yeah, there's a great analyst at Goldman Sachs named Dan Benton, who ended up being a great investor. He had a hedge fund and a great investor as well. Dan’s a super talented guy. Probably top rank guy in his sector, had a really loyal following, and decided one day to go to the buy side. He was leaving his job. He had a very popular newsletter that was sent out in a very specific time slot.
Gurley’s this young guy. He's obviously also very marketing oriented and very alert, and he realizes, well, this guy is leaving. Everyone's used to getting this fax at 8:00 PM on Tuesday. He says, I'm going to launch above the crowd. It's going to come at the exact same time that Benton's thing used to come. It’s just going to be a pure substitution.
David: That’s brilliant.
Michael: It was completely brilliant. Obviously, that's great marketing, but it's also great content. He had great stuff. The combination of those two things really helped catapult him. Again, it's content, but it’s content and good distribution. That's the point I want to make. It doesn't seem that easy at the time.
I'm slightly dodging your question, Ben, but going 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. So the metaphor is Poker. If I call you guys up and go, hey, David, Ben, I'm having a poker game at my house on Friday night. Would you like to come over? I assume you like to make money? You'd be like, oh, yeah, yeah. That's cool. Who else would be there?
David: Who else is playing?
Ben: I should be very unimpressed by the list.
Michael: Exactly. 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. They're as good or better than you are, you'd be like okay, I think I got better things to do. Part of it is thinking about who's going to play the game. Poker is an interesting metaphor and zero-sum in the sense that $100 walks into the room $100 will walk out, but who has it will change in the course of the game. That's a little bit true about investing as well.
Part of it is thinking a lot about the game that you're playing. Are there opportunities, whether those are nichier 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. Now, the challenge is often that it's difficult to scale those kinds of things. It's often easy to do that in a nichey way, but it's hard to do it in a very big, big way. That would be the first thing I would say.
The other thing I'll just say is just broadly speaking, three of my kids are out of college. I've got two in college, one to senior, so they're going into the world. I've always been ambivalent about finance. On the one hand, it is amazing. What you guys do is super fun. 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. 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. I've always been a little bit ambivalent. By the way, I should have studied computer science. Had I been born 5 years or 10 years later, I almost certainly would have been a computer science major instead of a government major. I actually did a tiny bit of programming back in the day.
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 most part. The question is, is there a really big issue out there that I'm passionate about? It could be climate, it could be some sort of health mitigation, whatever it is. Are there ways that I can make a dent at that problem? That's the kind of stuff I'd also think about. If it's investing, the answer is try to find a game where you think you can be the smartest person. [...] smartest person in the room.
Ben: Do you have any inklings? It's okay if you don't have right now, but I think everyone listening can sort of muse for themselves. Where do I feel like there are 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?
Michael: No, I would just try to stick to investing where I think it's the most clear. There are a couple of things that are interesting. Certainly, I would just go geographically. Are there markets where I can land on the ground? 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 US, it might be, for example, in private equity. A lot of people talk about this, but if you're doing buyouts, are there other segments of the markets or geography of the country, for example, where you think you could do something that's interesting?
The other thing in public markets, one of the interesting ideas is that most public companies are now in index funds, ETFs, or something like that. 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 indexes, and that are not in ETFs that might be a little bit neglected? That might be an area where again, you show up and you're the only person playing at that poker game. There might be some creative ways to think about that.
The other area, of course, which is now very much in its infancy, is decentralized finance, crypto, or so and so forth. 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?
Ben: 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. Then in 18 months, we had a couple more $2 trillion companies. Do you think we'll see a $10 trillion company? How soon do you think we'll see that?
Michael: The first one's easier to answer than the second. At some point, that seems very likely.
Ben: I'll give you an infinite timeframe for something that generally increases.
David: What poker table are we playing?
Michael: Exactly. Whether that's in my lifetime is another question. I think David alluded to this before. It's hard to get your head wrapped around the impact on valuation of just declining interest rates. I know the 10 year treasury note today is around 1.4 or 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. I would have bet a lot of my money that that would not come to pass. 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. We just show how, just theoretically, the mathematics really is crazy. If you have relatively rapid growth and high returns and a low discount rate, it just really cranks value substantially. I think part of the $1–2 trillion sprint was a function of this sort of backdrop. By the way, it's not just equities, of course. It's across the board. You guys were talking about this, credit, the bond spreads are let down. Venture, a lot of money flowing. Valuations are up across the board, so that's common.
The answer, Ben, is I don't know. The other thing I'll just say that I found fascinating is, 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, so 20 years ago. I don't know if you guys want to guess this. This is actually pretty interesting. How many companies that were top 10 in 2001 are top 10 in 2021? What would you guess out of the 10?
Ben: Let's see. Was Saudi Aramco in 2001?
Michael: No, it wasn’t public, right?
Ben: It was Microsoft in there.
Michael: Yes, it was.
Ben: So I bet one.
David: Yeah, I guess one.
Michael: You guys are very good. Microsoft is the only company that made the list both times. The estimate is something like excluding Microsoft. If you take just look at the other nine—
David: Microsoft is two different companies with the same name.
Michael: That's right. So if you took Microsoft out, so you bought the other nine, it turns out their market capitalizations are down $460 billion in the 20-year period. So it's just amazing. Of course, the wealth creation. By the way, Apple's market cap, I'm not going to get this right. Apple's market cap was less than $10 billion, I believe. Now it's $2.4 trillion. Amazon was also $6.5 or $7 billion. Now they're $1.6 trillion or whatever.
There's just huge amounts of wealth sloshing around. Interestingly, 3 of the top 10 companies today were not public in 2001. Two of the top 10 had not been founded yet.
Ben: Facebook hadn't been founded. I see you driving a car. Is Tesla one of the top 10 most valuable companies in the world?
Michael: In the United States.
David: Oh, wow.
Ben: Oh my God.
Michael: That's interesting. Part of the answer, Ben, I think is that I don't know how long it'll take. I think that we should have relatively muted expectations for returns in all asset classes. I know we're up for another great 2021. But because of where we are with risk-free rates, credit spreads, and so on and so forth, people should have fairly muted expectations going forward. So it could take a long time.
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 is our leader 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. Super interesting.
David: It depends if you consider crypto companies public or not.
Michael: Yeah, right. Well, that's interesting. So the answer is we don't know. When you take a 20-year snapshot of things, the change seems pretty extraordinary, right?
Ben: That's such a good point. Did we think if you reflect back to 2001, that the top 10 companies—banks and oil companies—had as many defensible business model characteristics as the big five tech companies today? Everyone is obsessed with these network effects and the value that they derive from being platforms. Their staying power is just unbelievable. Did we think that 20 years ago?
Michael: Think about it. The number one company was General Electric. General Electric was considered to be the case study in everything about innovation and management. If you could draw a manager, GE i’s considered the best management training program in the world.
The banks, interestingly, most of these banks have been around for decades, if not centuries. You think about these leading banks Whether they are considered to have Google-esque type of moats is a different question. They've had decent returns on equity, and so on and so forth.
I don't know if I was quite as excited as it is today. By the way, you can go back in time, General Motors seemed like it was untouchable in 1970. Untouchable. Just bear in mind that the world is changing and things show up. Interestingly, one area that seems to be substantially underrepresented, but a huge sector, is healthcare. You get a couple of marginal guys in healthcare. 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.
David: Certainly possible.
Ben: Well, Michael, we can't thank you enough. Such a fascinating last hour and a half. A couple of 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 and the books that he's written.
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. I 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?
Michael: If you track down a report, my email addresses would be on there. My Twitter handle is @mjmauboussin. That's not too hard. You can DM me if that's something of interest. I'm pretty easy 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.
David: Love it.
Ben: 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/slack, already know this, or if you follow us on Twitter at @acquiredFM, you know this as well. We just launched the Acquired job board.
Ben: This is big news. Shout out to super intern, Sandy Kim, for putting this together and quarterbacking the whole project. As many of you know, we have a jobs channel on the Slack. It's been a great way for our listeners to share opportunities with each other forever. 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.
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/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 the show. That is now possible. Acquired.fm/jobs. David, anything else?
David: It really is cool. A huge thank you to Sandy. Sandy is so awesome. If you're in Slack, you probably already know Sandy. It's so cool that we can do this now. The infrastructure exists. We use pallets. These are the companies in the community and the companies we care about. This isn't like monster.com. Nothing against Monster, but probably not where most of you would go to look for your next career.
Ben: 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/lp. There are over 50 episodes in the back catalog plus new episodes coming out as well. We got a few in the pipeline we are super excited about, both crypto stuff and non crypto stuff. You can join acquired.fm/lp.
With that, we want to say thank you to the SoftBank Latin America Fund, to Modern Treasury, and to Fundrise. We encourage you if you like this episode and you were like, someone else out there should really hear the gospel that Michael Mauboussin is preaching, you should share this episode with them as well. Of course, while we love all the social media stuff. We really value the one-to-one relationship. Send it to a friend. Send it to a coworker. Thank you so much for listening. All right, listeners, we'll see you next time.
David: See you next time.
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