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- [00:02]
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Michael Mauboussin
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Podcast Host (Intro/Outro)
excited to announce the launch of a new podcast, the Intangible Economy with Kai Woo. AI and the broader technology revolution are changing how we live, work and create value. In each episode, Kai will sit down with investors, researchers and other experts to discuss how innovation and other intangible forces such as brands, human capital and network effects are transforming markets and investment outcomes. And I can't think of a better guest to start with than Michael Maubison. Kai and Michael discuss his excellent paper Bayes and Base Rates, How Intangibles have Changed Distributions, why Asset Light Is Misleading, and a lot more. You can subscribe to the Intangible Economy on all major podcast platforms using the links in this episode. Description thank you for listening. We hope you enjoy the new show.
- [01:13]
Michael Mauboussin
Just to give you a sense of the OpenAI numbers, specifically in 2024 they did revenues at $3.7 billion and they are forecasting for 2029145 billion billion. Right? So that's a 108% compound annual growth rate. It turns out when you do that 75 years of data, you have about 18,900, you know, firm years or like, you know, things you can examine. And the answer is no company had ever done it before. When you think about intangible intensive businesses versus non intangible intensive businesses, it turns out the average, the means and medians are not that different for those distributions returns on capital or growth. But what is very substantial is that it's just a much larger standard deviation. So you have more really great businesses and more businesses that go bust, right? So the on average you're seeing about the same thing, but you get more much fatter tails.
- [02:09]
Kai Wu
Welcome to the first episode of the Intangible Economy with Kai Wu where we explore how intangible assets, innovation and technological change are reshaping investing. I can't think of a better guest to kick this off than Michael Mauboussin. Michael, welcome.
- [02:24]
Michael Mauboussin
Thanks Kai. And by the way, I'm just super honored to be one of your first guests and I'm A huge fan of the work that you do. So I'm really, really looking forward to our conversation today.
- [02:34]
Kai Wu
Me too. A lot of my research has been inspired by the work you've done over the years. I think this will be a lot of fun. Okay, so let's kick it off. Dive right in. One of the biggest questions today, of course, is how investors should be thinking about navigating the current AI boom. Companies are spending trillions of dollars on AI infrastructure, presumably with this expectation of huge returns at some point in the future. You recently published a piece called Bayes and Base Rates, assessing the plausibility of these expectations. Talk me through what the main question you were trying to answer is and why you felt base rates provided a useful framework for doing this.
- [03:14]
Michael Mauboussin
Yeah, I mean, maybe just to take one step back to make sure all the listeners are on the same page. You know, usually when you think about how do you make forecasts? The common way to do that and probably resonates with most people, even when I say it is, you kind of do a lot of work. You do grounds up work, you gather lots of information, you combine it with your own experience and inputs, and then you project into the future. So usually when you see an analyst forecast or even a company forecast, that's typically what they're doing. Another way to think about how to make forecasts is to use so called base rates. Right? It's called the, also called the outside view. And now rather than building it up from the bottom, you're saying, let's think about this problem as an instance of a larger reference class. So you're basically saying like in history, what happened when other people or organizations were in these situations before, how did that all turn out? So in the fall, to your point, in the fall, I was, you know, looking at all these numbers, two of them that really popped off the page. One was some of the stuff OpenAI was saying and the other was Oracle within their cloud business. But just to give you a sense of the, of the OpenAI numbers, specifically in 2024 they did revenues at $3.7 billion and they are forecasting for 2029, $145 billion. Right? So that's a 108% compound annual growth rate. And you know, the question then becomes like, how many companies of that size have ever grown? You know, 108% compounded annually for five years. So for our reference class, we went back to every U.S. public company since 1950. So this is basically the compust. Our initial, our reference class was companies with initial revenues between 2 and $5 billion. So you want to start at the right kind of level. And we asked, you know, and by the way, it turns out when you do that 75 years of data, you have about 18,900, you know, firm years or like, you know, things you can examine. And the answer is no company had ever done it before, right? And it turns out the average growth rate was around 7%. The standard deviation around 10.6%. By the way, you know, the feedback I got, people call me up and they're like, oh no, you should be looking at just technology or you should be looking at just software. And of course all those numbers are in there. So if you remove the slower growing things, it does move the mean up and does move the standard deviation up, but it still obviously doesn't, doesn't change the basic fact that no company's ever done it before. So with the, with the standard numbers, it's like a nine and a half standard deviation event, right? Seems like something that seems implausible. So my, my point was less that they can't do it. It's not like a physical impossibility. To state the obvious. Base rates are not things that are handed down on tablets from on high, right? These are living and breathing distributions. But when you're considering the pos, the possibility of something like this, you want to, you want to bear that in mind. You're talking about something that's never been achieved before. That's probably not going to be your base case, like your most likely scenario of unfolding. It may be a scenario where you place some probability on it, but unlikely to be, to be that. Now what's interesting, by the way, amazingly, Even last month, OpenAI actually revised up their estimates for their 2029 revenues. So they now have it at $185 billion or $184 billion. So actually, if you stay with that 2024 base, they're now looking at 118% compound annual growth. Which is, which is actually pretty extraordinary. So now the next question would be something like, all right, well, is that possible? Is it, is it plausible for them to do this? And if you want to, you know, revise your views up and be bullish on it, there are a couple ways to think about that. One is in 2025, they actually did $13 billion of revenue. So that's 250% growth. So they, so they're actually, their growth rate in the first year of the, of the five years was well ahead of expectations. The second is to look at basic diffusion, right? So you say like, how fast is this technology diffused versus other technologies? As we know, sort of famously ChatGPT got to a hundred million users in two months. It took for example, TikTok, which was also extraordinary, nine months, took Instagram, 28 months, took Facebook, four and a half years. So they really are really fast out of the blocks in terms of diffusion. I'll just say, Kai, that we, you know, like to think, we think about total addressable market. We kind of come at that with three different lenses. The first lens is sort of that bottoms up buildup. So you know, how many customers could possibly use this, how much are they going to pay, and so on and so forth. So that'd be a very standard way to do this. The second is to look at diffusion models. And diffusion models are often underutilized in the financial community, but they're super interesting to look at to see how diffusion works. And there are simple models, even things like the Bass diffusion model that give you a multi, it's a three parameter model, but that can give you some sense of that. And then the last is base rate. So we try to triangulate these three different things to think about what the potential is. So again, I'm not saying any of this should be ruled out. In fact, again, they're running ahead of pace on this, but by the same token, just acknowledging that it's, it's a tall order to get to those kinds of growth rates they're talking about.
- [08:04]
Kai Wu
That's right, you're saying that, look, this is not saying that this couldn't happen, but if it did, it would be unprecedented.
- [08:10]
Michael Mauboussin
Unprecedented, yeah. And then, you know, for OpenAI specifically, just sort of say like, why is this difficult? You know, one is you have to have a great product and we could talk more about that, but you know, they have to have property, obviously it's very competitive and these things are all related to one another. The second is you have to have great people, right? So you have to create people to great that problem. And there's obviously a very dynamic market for talent right now. And then the third is you have, in their particular case, as you mentioned in your intro, they have to raise an enormous amount of capital. So the latest number I've seen is they're going to burn soup through something like $218 billion of cash before they go to free cash flow neutral. And if you go back to even recently, the big Ubers and so on and so forth, you know, those guys, none of those guys burned anything Close to that, that much money. So that means you have to raise a lot of capital. So you have to have a great product, have great people and raise a lot of capital. All, you know, those are the three sort of like legs of the stool. All three of them are kind of hard to do, but doing all of them simultaneously might is a trick. And by the way, they're on pace to do it all. But, but that's, that's sort of the trick.
- [09:06]
Kai Wu
Yeah, lots of hopes to jump through. So yeah, that's a really interesting framing of the question which, you know, I completely agree with you with how much money is at stake. You know, we should be, you know, thoughtful as investors as to what is the likelihood that, you know, they managed to nail again all these difficult things in succession or together. You did another interesting part analysis in your paper which drew on this work studying large scale projects. I grew up in Boston, so Big Dig, of course. So when you think about the, I'm not going to give it away, the base rates implied by this study and in particular tying it to how the AI boom has become so capital intensive and there's so much complexity to the build out and the rollout of these data centers. You know, what is the lesson from this?
- [09:55]
Michael Mauboussin
Yeah, and this is another, you know, this is another great example of base rates, by the way. Kai, you probably, you know, we've all seen this experience. You say like you want to remodel your kitchen or something, right? So you go pick out all the appliances and you, you got a contractor and then you mentioned it to a friend and you say, here's what I think it's going to cost and here's what, how long I think it's going to take. And what does your friend say immediately, without knowing about the details, like whatever it is, double the cost and double the time rate. So people have a reflective reaction to that. So there's a, there's a economist named Ben Fluvia who wrote a book called How Big Things Get Done along with Dan Gardner. It's really, it's a book I would highly recommend. I think it's a terrific book. And Fluvia is famous for building a database of 16,000 projects. And so this is, you know, 20 different sectors, 130 plus countries, as you pointed out. You know, the Big Dig is of course on that. But think of Sydney Opera House, you're hosting the Olympics, you know, the Chunnel, all these kinds of things. And so the question is in the, with these 16,000. And by the way, he's the guy that coined the term reference class forecasting. So the question is, of these 16,000 projects, you know, how many were done on time, how many were done on time on budget, and how many done on a time budget delivering what they're supposed to? And the answer is on budget is less than 1/2 of them on budget and on time is less than 9% of them. And on budget, on time and actually delivering what they promised at the outset is 1/2 of 1%. Right? So here again, just acknowledging that these big projects are very difficult to do. Now on the one hand, these, as you point out, are complex. These AI data centers are tricky things. On the other hand, they are some degree modular. So modularization actually helps you a little bit. So that would be the bullish case. The other complexity with permitting and energy and cooling and all that, that would be the, the, the, the downside. Now there's an article in the Economist. Again, I don't know if this is right or wrong, but it said about 25% of AI data centers in delayed for some reason and the estimate for 2026 is somewhere around 30 to 50%. So the way to think about this is just stuff happens along the way, right? So very rarely does anybody lay out a plan and that plan get executed perfectly. There's now again 16,000 projects that have been studied to demonstrate that that's, that's not something you should expect. And you should expect some problems along the way.
- [12:11]
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- [13:10]
Kai Wu
Interesting. So, yeah, you know another let's go back to an earlier piece you did on the impact of intangible assets on base rates. We'll get into intangible assets in depth later in the conversation, but that was a really interesting piece because at the time investors were quite skeptical of the ability of companies like Amazon to put up sustained growth at such high rates for a long period of time. And your argument was, hey, look, the economy is transformed. We now have more intangible assets which have unique properties of, say, scalability and suck in this that potentially reshape the distribution of what's possible. You know, both, you know, more faster growth on the upside, but also, you know, more risk on the downside. You know, as we think about AI as almost being an extension of the technological frontier beyond, you know, cloud computing and many of the other, you know, intangible assets that Amazon levered on its way to glory, you know, should we be like, when we think about, you know, you mentioned some, some folks kind of pushed back on your base rate saying, hey, you should just focus on tech companies only. Right? And yes, while the forecasts baked into these capital investments are unprecedented, you know, maybe AI is the next technology, right? The next frontier of, you know, human intellect and one that perhaps benefits even more from, from intangible assets in a way that does shift the distribution further. What do you think of this argument? You know, I'm comparing yourself to yourself in the past.
- [14:35]
Michael Mauboussin
Exactly. No, I think it's fabulous. It's a great argument. And just to summarize kind of what we found, and I think how your work is consistent with this is that when you think about intangible intensive businesses versus non intangible intensive businesses, it turns out the average, the means and medians are not that different for those distributions, returns on capital or growth. But what is, what is very substantial is that it's just a much larger standard deviation. So you have more really great businesses and more businesses that go bust. Right? So the, on average you're seeing about the same thing, but you get more, much fatter tails. I want to unpack. There are a couple things to say here, but one on, on the Amazon, you know, totally agree that, that, you know, that's a beneficiary. Now in retrospect, that's a right tail event. So they're growing much faster than what people anticipated. There was another company in the 1990s, I don't know if anybody remembers this, that was actually talking about their asset light business and how intangibles were going to really save them. And a company that had actually extraordinary growth rates. This company, by the way, from 1995 to 2000 grew 61% compounding annually. So that's not, it's not 100% but 61% compound annually. And by the way, they ended up 2000 with revenues of a hundred billion dollars. Right. So they went really, they went, grew really fast. The name of that company is Enron and within, you know, nine months that company filed for bankruptcy. So we sort of forget about the other sort of asset lights slash intangible stories where they basically go completely belly up. So to me that's, you know, you're, that's an illustration of both sides of that and you're, you're absolutely correct to point out that both of those things are likely to happen and perhaps AI is going to accelerate those things to some degree. Now the other thing I'll mention, and Kai, I think this shows up in your work as well, but it's, I, I, I, I find it to be completely fascinating is that really in this century. So let's say in the last 25 years ago or so, large companies, and these are large like you, you think about, these are the top 10 companies and magnificent seven. These companies are just growing faster than what we have seen big companies grow in the past. And that's a really interesting phenomenon. You know, one of the, one of the inspirations I take is from work of Jim Bessen who's an economist at Boston University where he basically argues, hey, you know, the catalyst of this is these guys spending enormous amount of money on proprietary software that allows them to gain both economies of scale, old school economies of scale and differentiation, a way that companies couldn't do in the past. And then finally that technology doesn't really diffuse. So like the secret sauce at Amazon or the secret sauce at Meta or the secret sauce at Google does not get spread to the world as a consequence, it's allowing these companies to both build these moats but also grow at rates faster than what we've seen historically. So as I like to joke, usually when we point out a trend like this, it's about on the verge of reversing. So maybe it's going to reverse. But, but that, that does sort of explain a little bit of the whole Magnificent Seven thing and why these big companies have done really well. And you know, as, as a little reality check on this, one of the things we like to look at is, you know, the top 10 companies in the US public equity markets, you know, what share of the market capitalization are they? And the answer is about a third right at year end. But they're by our reckoning about 2/3 of the economic profit. Right. So if you say economic profit, return on capital as cost Capital spread times the invested capital. You could do that for the. For the whole market. It's a positive number. But two thirds of that is gathered by this one, these 10 companies, just 10 companies. So in fact, their market cap is actually punching under the weight of their actual economic profit contributions, which tells you the market probably doesn't believe they're sustainable forever and so forth. So these are all sort of interesting things just to. It's almost a counterbalance to this argument that, you know, we're overweighted. These big companies, they actually, at least to date, have had fundamentals to justify, or at least you could argue they could justify their positions.
- [18:25]
Kai Wu
Right. And to tie this back into your base rates analysis. Right. Perhaps what you're. Another way of saying, what you're saying is due to the Jim Besson argument that, you know, we have more scalability due to the rise of intangible assets companies today, maybe the correct reference class isn't, you know, historical companies with 1 or even 10 billions of revenue, maybe it's 10 to 100 billion. Right. Because, you know, we were all people were saying back then, oh, wow, Amazon's $100 billion company, there's no way it gets to 1 trillion, you know, and then it did, Right. So relative to the past, due to the presence of intangibles, you're now seeing these mega companies form on the back of the scalability of these assets. And perhaps AI is just the next in line. And it'd be interesting to see because we all know that if you look at like venture and private market returns. Yeah, there are certainly examples of companies that do manage to grow at rates like, similar to what elephant AI thinks they can do, but at much smaller scale? Historically, I guess the question is, can OpenAI take that same growth rate and apply it at much larger scale due to the fact that maybe the TAM of AI is bigger. And, you know, this technology has certain properties that we can get into as well that are almost an extension of many of the features we've seen in kind of the Web 2.0 or, you know, Internet businesses of the past couple of.
- [19:43]
Michael Mauboussin
One thing, I chip in on this, and I agree with you 100% that there's sort of, if we think about sort of the big LLMs, right? So the frontier models, you could sort of put them into roughly two different camps, right? One are sort of the new companies coming along, starting off by themselves. So this is OpenAI and this is anthropic. And then the other camp would be, you know, sort of the Legacy guys building their own models. And that would obviously be sort of Gemini and Google as an, as an example. You know, these guys have very different financial resources right behind these things. And so this goes back to the way I might think about this is the Clay Christensen argument about is it sustaining or disruptive innovation. Right. So if AI comes along, it's actually a sustaining innovation for Google or Alphabet. Right. So it actually makes a strong player, actually stronger. And by the way, I think there, there were a lot of concerns and I think those don't evaporate. That AI itself could undermine the goose that lays the golden eggs for Google, which is their search business. That doesn't seem to have completely happened, at least not yet, versus these new guys that really seem like they're disruptors. So it's a really interesting question, like, is this, is this going to be, is AI just going to be a wind in the sails of these big guys that helps Amazons and Microsoft's and so forth, or is it something that, that unseats them? So that'll, that'll be an interesting to see. And by the way, the dichotomy and financial resource is also very market. Right. So, you know, Meta and Google and Amazon, Microsoft have a lot of money they can spend on stuff, whereas these other guys really have their, you know, tin cup in hand and they have to raise a lot of capital. Xai solved that by merging with SpaceX. But those guys, they still have enormous capital needs just to sort of stay in the game. Which is. Yeah, which is, which is really interesting and be fun to watch.
- [21:20]
Kai Wu
Yeah, I mean, this is like, this is a central question of, you know, markets today. And I guess it's a perfect bridge into my next question. You know, one of the big, the big question, I guess, with these major technological shifts is less about whether or not they create value, but more who captures that value. We have seen many historical cases where a new technology comes around and it's transformative for society, but the progenitors of that idea don't actually make any money and perhaps they even just go bankrupt. So as we think about the AI value chain, you mentioned, of course, the hyperscalers and certain model labs, but we can even expand that further to include chip makers like Nvidia, data center operators, the model providers, applications, and then ultimately their customers. Right. If you're an enterprise company that's using, say, Claude code in order to enhanced productivity, that would be, you know, in the stack as well. So as we think about the, the way that value. Let's Assume that we do. Let's assume that technology, this technology is indeed transformative and it does create a pie of value. How do we think about that being distributed across these things? What are the factors that ultimately will determine where this profit pool accrues?
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Michael Mauboussin
So, you know, one of the frameworks I really like a lot on this is the sort of well known Brandenburger and Stewart framework. And just to make sure everyone's on the same page, it sort of lays out these four different dimensions. Willingness to pay is what the consumer's maximum price at which they're indifferent to buying the good and their money. Price for the company, cost for the company, and then willingness to sell the suppliers. Right. So you have these four, these, these four different markers. The difference between willingness to pay in price is consumer surplus. Difference between price and cost is the customer, the company surplus, or economic profit. And there'd be cost and willingness to sell is the supplier surplus. Okay, so to your point, Guy, I think historically what you would have to argue is that almost all this ends up going to the consumer ultimately. And that's because of competition, basically. Right. So you and I both have LLMs or we have some sort of a product and we're competing with one another and we want to gain market share. Obviously we try to have the lowest, you know, like our. We want to focus on our marginal costs, but basically we lower our prices in order to do that. So I think, I think the high level, my assumption is a bunch of that value should probably accrue to. We should ultimately assume it's going to be consumer surplus. Right. And there's already some really interesting research about the magnitude of the consumer surplus that's already been generated by all this stuff. Now that said, I think there are a couple interesting things to consider as well. One is if you go back to Michael Porter, sort of classic Michael Porter work, he talks about, you know, sources of competitive advantage, you know, the big ones being cost leadership and differentiation. But he's also got this concept called operational effectiveness, which is, you know, the stuff that you do that everybody else has to do, we all have to do to compete. And he basically argues that's not a source of comparison. Everybody eventually will do the same thing. It'll get commoditized and so forth. However, if you look at microeconomics, it turns out that actually doesn't seem to hold empirically. In other words, some companies are just better at doing the same thing as other companies are. So, for example, really high quality managers can run a manufacturing facility almost twice as productively as a less skilled manager. And my guess is, and I think this should be a really big focal point of investors, my guess is that companies themselves will embrace AI with differing degrees of effectiveness and that operational effectiveness is actually going to be really important as well. So I would be, when you're talking to companies, you know, one of my colleagues likes to joke that when you're talking to a company about their AI strategy, ask them if it's, you figure out if it's PowerPoint or Python. Right? And I think there's still a lot of PowerPoint AI strategies versus Python AI. I guess we don't know Claude code, whatever. But, but that I think that that basic argument I think is a really, really important one to, to bear in mind. So I think ultimately this stuff now that all said we, we also know the other thing to think about is sort of first order and second order effects. This is, you know, there are now suppliers and we obviously know the semiconductors and so forth have done very well. Nvidia is obviously the most valuable company in the world as a, as a first order provider. Now what's interesting of course is Nvidia is relatively concentrated in their customers and almost every one of their customers themselves are trying to develop their own technology to weed themselves off of the reliance on Nvidia. So, so they're, you know, we'll see. It's obviously that's probably all priced in. I'm not saying anything that anybody else doesn't know but, but that's also something to bear in mind. Now Nvidia by the way, has had like, it's, it's been the performance of that company financially in the last three or four years is, is really up there in the hall of fame of great financial performance.
- [25:59]
Kai Wu
So I want to go back to something you said, right, which is in the kind of economic theory would suggest that competition should drive excess returns to zero. And we certainly, we were talking with this before the call have seen this in the LLM space, right where at one point OpenAI had a huge lead and that was eventually eroded by the progress of their spin out anthropic and then Gemini and we're seeing open source competition as well. But to take the other side and going back to your Michael Porter framework, barriers to entry is kind of an important concept here. To what extent if you were to make the reverse case and say Nvidia currently they have Cuda, they have a big lead in chip making. Of course people see their margins, that's an opportunity they want to go after it, you know, where would you see potentially barriers to entry and you know, in various points along the AI stack.
- [26:55]
Michael Mauboussin
Yeah, well, the first thing to say is, you know, the LLMs, as far as I can tell, they're, you know, they're now a handful. You, you pointed out that ChatGPT was really ahead of everybody else. By the way, ironically is the technology was developed at Google, which is interesting, but you know it. But they're now, you know, whatever you, however you want to call, count it but somewhere between three and six models that are roughly doing similar stuff that's similar. So that doesn't really feel like right now anybody's really distinguished themselves. I also don't. It doesn't feel like a network effect business to me. Right. The network effect is the value of a good or service increases as more people use that good or service. You know, whether you use, you know, anthropic or, or OpenAI or whatever, it doesn't affect me that much. Now that was also true of Google and Google search. Right. There was no one there really. I don't think search was about network effects. Search was about economies of scale. And it's a slightly distinct concept. So there's an economies of scale thing I think is also important. Now to go back to your point on like, where are the barriers to entry? We like to say in finance that financial capital is not a barrier to entry. But it feels like this is probably right now, you know, just the ability to raise capital to fund all these operations seems extraordinary. Now when you think about the big hyperscalers again, and these are like the hyperscalers are doing the traditional cloud stuff as well as AI stuff. But Amazon ridiculous resources, Microsoft ridiculous resources, Google ridiculous resources. Right. So trying to, trying to pony up, you know, and obviously Oracle's like number four in that business and you know, they're, they're in a much weaker financial position. They have very large aspirations, they have very large goals actually specifically about their revenue growth. But whether they can pull that off financially I think remains to be seen. So I don't have a good answer to this, but I think it's a, it's a really interesting. But I think Barry Century is part of, part of it is. And then the other one is just talent. You know, a lot of this is about talent. You think about, you know, how did XAI get going quickly? The answer is you hire a bunch of people from OpenAI. They basically have the secret sauce or some components of the secret sauce in their mind. They go back and replicate what they're doing right. So this, you're seeing a lot of talent sloshing around. That'll be another interesting component to all this. Who can preserve and maintain the talent. And by the way, it goes back to capital raising. A lot of the compensation of these people is in the form of equity. Many of these companies, some of the companies are private. So of course when they're, when they're up rounds in private, it looks like at least your, your wealth feels like it's going up. If and when a bunch of these companies go public, if by whatever, for whatever reason their stocks go down versus up, that just makes the market for talent that much more difficult. Right? So you're, you're, you're what you've promised in terms of compensation. What people expectations are in compensation aren't met. Maybe that means you give out more equity which is diluted for the other shareholders. So it'll, that'll be this. All this stuff will be fascinating to see how it all unfolds over time. AI is incredible. They can teach you how to fry an egg and even write a poem pirate style. But it knows nothing about your work. Slackbot is different. It doesn't just know the facts, it knows your schedule. It can turn a brainstorm into a brief. And it doesn't need to be taught because Slackbot isn't just another AI. It's AI that knows your work as well as you do. Visit slack.com meetslackbot to learn more.
- [30:14]
Kai Wu
Hablas espanol?
- [30:15]
Michael Mauboussin
Spriest du Dzoitsk?
- [30:17]
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- [30:46]
Kai Wu
Interesting. So you're saying of the various, you know, moats that that Porter talks about. Capital requirements and economies of scale are almost in your mind. The most important thing here it's less of a technical moat. You mentioned human capital, but not brand interestingly. And when it comes to capital raising and stability to your Sam Altman. Take a trip to the Middle east and come back with a trillion dollars or whatever. It's really Interesting. And I think it gets to the next question I have, which is around the game theory of this whole thing and you've written about this, that in some ways one barrier of entry, one barrier to entry, is just the threat of a massive war by which the competitors, especially in this case the smaller ones, get bled dry. If you're a Google, this is the hand you might consider playing. So let's talk about that. The competitive dynamics around. Especially last summer when we saw a series of escalating announcements of firms saying, hey, I'm going to commit X. Okay, the next that goes over the top, 2x, 4x 8x. Right. This whole dynamic, and you pointed this out as well, in past capital cycles, I think, about the telecom bubble, the railroads. We've seen a consistent pattern of overinvestment into infrastructure which has led to poor returns, bankruptcies for the builders. And I'm sure that all these CEOs have seen the data and they know that this historical pattern and yet they persist in doing this. So you mentioned something really interesting in your Bayes paper around a kind of preemptive strategy of deterrence. Maybe you could walk me through that. That's like a really interesting, you know, potential take on what this actually could potentially be.
- [32:30]
Michael Mauboussin
Yeah, no, Kai, you're exactly right. And I, you know, encouraged me to go back and reread Porter. And by the way, this is like from the 1980 Porter book and it's actually pretty good stuff and idea of preemption, right? Which is you want to try to lock up the market, the resources of the market, essentially to discourage competition. Right. And so by our tally, I might be off by a bit, but OpenAI did 15 deals in 2025. Right. And you remember in the fall there was just a flurry of them. It felt like multiple deals per week. And these were very large deals coming out. And basically if you're a competitor looking at this, you might be saying like, gee, how am I going to keep up with that? Now what's interesting already this year, Oracle, the deal OpenAI, Oracle deal expanded and they're not going to expand Stargate, which is one of their biggest AI data center projects. In a way they were expecting this. Just this week we had Sora AI, they, they shut that down. So they had a billion dollar deal with Disney, a bunch of copyright stuff as well with Disney, that's gone. So you're seeing some of these things being walked back. And by the way, even many of these, these headline, staggering headline numbers were not really financial commitments. They were sort of like at more aspirational to some degree. So, but, but it is, it's like, you know, this is just the peacock flip, you know, showing its feathers or any animal becoming large to try to scare everybody else, scare off everybody else. Now the other thing is again, just to echo this point again, you know, the challenge is that both anthropic and OpenAI have to raise a lot of capital. And so you know, that's, that's, that's another issue that's going to be important for them. So you can, you can sort of signal all you want to the world but at some point you have to be able to back it up with having money, you know, some sort of money to do that. I think the other thing is, and this goes back to point on like historically this has led to some difficulties and bankruptcies is that the challenge here really is you're making huge, you have to make huge commitments before you learn about the economics of the business. So we don't really know what the economics are. And so that's, I think that's the thing that's giving pause to. Even with the hyperscalers. Right. I think that's what's giving pause to everybody, which is you're seeing these Camp Capex numbers ramp up a lot of. And I think the companies themselves believe that they're going to do fine. But again they often almost think internally. They don't think game theoretically. They're not thinking themselves like what everybody else is doing, they're thinking about what they're doing. And it remains to be seen what those economics actually turn out to be. So that's what the risk is, that you have to basically put up a big ante before, you know, the hand and time will tell, I guess. Yep.
- [35:02]
Kai Wu
Yeah. And I think for the average investor the big risk is that, you know, most of us are in the S&P 500 which is a cap weighted index of which a third of the index is in mag 7, these hyperscaler companies and almost a half is in the collection of AI infrastructure companies, that includes Oracle and some of these other names. So it's like Game of Thrones, right? These guys are playing a high stakes game of poker and we as the investors are kind of sitting here perhaps unwittingly unclear that half of money is being bet on this gambit paying out. So as you say, it remains to be seen but certainly something of concern to investors today. So I'd like to switch gears now to think kind of more high level, not just about AI, but what drives modern businesses today? And we touched upon this a few times, this idea of intangible assets. I think it would be probably helpful for the listeners just to kind of maybe start from the basics. Can you maybe just define what intangible assets are and why do they matter? Why do investors even care about them?
- [36:06]
Michael Mauboussin
So there are two aspects of this, by the way. Again, Kai, you know, I think your listeners probably know this, but you've done some of the best work on this that's out there. So I would also recommend that everybody go. If they haven't, I'm sure they've read all your stuff, but if they haven't, they can, they can go back and revisit it and it'll be to the, to their benefit. Well, an intangible is basically something that's not tangible, right? So a tangible asset is something you can look, look, touch or feel. An intangible doesn't have those characteristics. So the kinds of things that would typically be in that bucket would include things like software code, research and development, advertising, training of your employees. The key issue is that intangibles typically show up on the income statement as an expense in the SG and a line selling, general administrative line. So rather than a physical, like if you buy a machine, if you're a company or a factory, it goes on the balance sheet and gets depreciated over time. In these cases, the intangible is expensed immediately. Now just to give a little bit of level set, these are our data. So other people might have slightly different data. The first thing I would say, this is actually not our work going back that tangible investments, these are by macroeconomists, tangible investments were about 1.4 times intangible investments in the late 1970s. So call that about 50 years ago, something like that. By our numbers, intangible investments now are, it's almost the flip, about 1.5 times tangible. Right? So, so if you're just taking a 50 year point of view, things have really changed quite markedly. By the way, Kai, I think you know this, like we may have talked about this, but if you look at the, the crossover line, the point where intangibles crossed over intangibles, I mean intangibles, crossover tangibles was right when the original, the big Fama French paper came out in 92, 93, something like that. So right when they were talking about price to book is when price to book started to lose some of its relevance. We run these numbers for the US public equity markets. So these are our estimates. We Estimate that investment SG and A, XR and D, so this is the component of SG and E that would be considered an intangible, was $2.2 trillion last year. To give people some calibration, our estimate is that capex was $1.7 trillion investment R& D. So again, not all of our, our argument is not all of R and D is in, is, is investment. Some of its maintenance, that investment piece was $700 billion. And so, you know, you take $700 billion plus 2.2 trillion, that's $2.9 trillion. And that gets you back to that ratio that I was mentioning a few moments ago. So these are very, very large numbers. And not to say it snuck up on anybody, because it really didn't. But you have to take a step back and understand what these trends have looked like. And by the way, you can see it if you just look at the composition of companies by sector or industry in The S&P 500, you've seen that mixed shift, you know, toward technology, toward healthcare, away from materials, away from industrials and so forth. Now the second point, Kai, and you've, you've already touched on this a couple points, but maybe times, but I'm gonna just, I'll amplify it. There's a wonderful book which I'd recommend, I think both of us are fans of this called Capitalism Without Capital by Haskell and Westlake. So for folks that want to get initiated, this is a really nice book. And I'll say they, they, it was a nice piece of marketing where they talk about intangible assets specifically and they call it the four S's, right? So, and they, they, they had to, they had to wiggle some stuff around to make it work. But it's a nice way, it's a nice way to remember this. The first of the, and, and the key is that, look, none of the laws of economics have been repealed. There's nothing, nothing magical here in any way, shape or form. It's just that intangible assets have different characteristics. Intangible assets that you just need to be aware of, right? And as we'll see, there are kind of pros and cons to intangibles. Just we saw that distribution, we're seeing faster growth and faster decline than we saw in the past. So the first is something you've mentioned a couple times is scalability, right? Which is, it's typical that an intangible asset has a high upfront cost, but once it's established, replicating and distributing it tends to Be relatively cheap, right. So writing software is just the classic example of that. Things like network effects also would fit into that bin as well. So you can grow much faster than what we've seen before because you don't have those physical constraints as you used to have. Again, that's the good news. The bad news, there can be obsolescence and that sort of leads to the second asset and that's sunkenness, which is once you've invested in something, if it doesn't work out, that asset tends to be not very valuable. By contrast, if you invest in a physical asset, you know, you start a restaurant and you, you know, you buy a building, you beat tables and cash registers and all that stuff. Well, if it doesn't work out, those assets still have value because they're, they can just be sold to someone else who's doing basically the same thing. So you could think about recovery values might be better with tangible assets than they are with intangible assets. So sunkenness and this, I almost put it in the obsolescence be sort of in that bucket as well. The third one is what they call spillovers. And the idea here is that it's really difficult to protect intangible assets. It's easy for people to take them by the way. You can get into big issues about intellectual property, people stealing this stuff and different countries and so forth. But basically the idea is best practices disseminate very quickly with intangibles. So you know, the example I think I often like to talk about is the, is the iPhone. You know, it was launched by Apple in 2007. It was a very different form function by the way, Nokia had more than 50% share of the smartphone market at that time. And within, you know, and they had patents and all this stuff, but within very short order, everybody basically had a phone that had the same basic function and features of the iPhone. Right? And so, so that was just. That's a classic example of a spillover. The other example I always like to give is shooting in the NBA. There are these great charts on shooting. Turns out that, you know, since the late 1970s there's been a three point line. But it turns out that it was last 15 or 20 years that front offices in the NBA recognized that three was actually more than two and took a, the expected value of a three point shot, even though the probability of making it was lower, that it was a much higher expected value than taking a long range two point shot. And as a consequence, you've seen a complete migration in the spots from where the players take their shots. So now they're around the rim and they're three point shots. They really have gravitated to the higher expected value places. Interestingly, you're able, you can actually track the expected value of two and three point shots. And at the peak, three point shots were 14% more attractive than two point shots because of mid range shots. And that gap is almost completely gone away. So it's been arbitraged away by NBA teams, which is super cool. So put that in the spillover bucket as well. And then the last is synergies. And I think this is a really exciting, it can be a very exciting concept. If you want to be really bullish about the world, this is what you'd focus on. By the way, this is in part why Paul Romer won the Nobel Prize, I think 2017 for his work on adages growth theory. So the idea is innovation basically is recombination of building blocks. And the more building blocks you have and the degree to which they're digital means that you can actually innovate even faster than you did before. And I think, you know, Kai, that's one of the areas that's, you know, I don't know much about this area, but one of the areas that seems super exciting. For example, the application of AI in healthcare or medicine. Right. So the question is, can we search the space and recombine building blocks in a way that's vastly faster? We saw this when RNA folding, you know, faster than protein folding, much faster than we did before. And I think that's, that's, so that's this idea of synergies. Recombination of building blocks is something that's, that is, that is defined by intangibles to some degree. So, so yeah, you both have, you know, the fact that they've risen, broadly speaking, and the fact that they have different characteristics and that is really important for how you think about, you know, everything, you know, all our metrics for valuation, our metrics for growth, our metrics for distributions of return on capital, all these things to some degree get affected by those, those basic observations.
- [43:49]
Kai Wu
Right. And you mentioned earlier, of course the fact is that the best performing companies of the past few decades happen to also be the ones kind of most leveraging intangible assets, whether it's brand network effects, human capital or ip. And in a part can kind of explain why what seemed implausible as a trajectory for these firms was actually something that they were able to achieve due to their investments in this area, which you know, at the time, of course, was, well, less studied than it is today. You know, one question I have now is that we're starting to see a shift in terms of these magnificent seven stocks, you know, away from this capital light business model that led to so much success over this period towards a kind of more capital intensive one, right? Their free cash flow has basically been close to eroded. Given how much money they're now pouring into the build out of AI data centers via capex. How should investors be thinking about these companies? Obviously they still trade at reasonable premiums on the back of their historical success. But as they shift to more utility like capital intensity, should that be an area of concern for investors?
- [45:04]
Michael Mauboussin
Yeah, I mean, you framed it so well. I would just take one step back and say, look, what is an investment, right? An investment is an outlay today in the anticipation of future benefits, right? So the cash flows that I'm a generate over time discounted today's values more than what I'm investing today, right? So it's going to be NPV positive. That is the level set for all this, right? So whether it's intangible or tangible doesn't make any difference, right? Is this a good investment or not a good investment? One of things I'd like to remind people of is that, you know, Walmart, which is one of the great companies of all time, had negative free cash flow for the first 15 years that it was a listed company. First 15 years. Now it turns out it was profitable, right? Had net income, but it was investing more than it earned. So it had negative free cash flow. Do you think it, you know, was buying, buying Walmart when it first got listed? Was that a good investment? The answer is a fabulous investment, right? Because the return on investment was really high and when the return investment's really high, you want to do as much of that as you possibly can while you can do it, right? So, so that's, I think a really important illustration that even though Walmart was profitable because of the way the accounting works, it still is, you have to assess it. So that gets down to the basic point is you have to assess the return on investment and kind of you put your finger, you set up just perfectly right, which is, that's, I think what people are worried about. If you have a view that all this spending is going to deliver, you mentioned sort of utility like returns. Do you think it's going to be something better than utility like returns? Then there's an enormous opportunity in front of you if you think it's going to be because of all the spending and the commoditization of the goods or services that it's going to be utility like and we'll, I'll use the proxy sort of cost of capital type returns. Then it's going to be value neutral, right? May not hurt you, but it's going to certainly not going to be value creating. I always thought this idea of asset light was a bit of a, bit of a myth because if you're just, you're laser focused on investment, you're just realizing the investment was not in the balance sheet as it was historically, it's now on the income statement. So that, that to me was, you know, if you're, if you're, you know, and this is what I always like to say, and I say this to my students quite, quite directly. Like at the end of the day as a financial analyst, your job is to figure out how much money is the company investing and what's the return on investment. And if you understand those two things, that's the whole gig, right? That's the whole gig because you'll understand growth rates, you'll understand economic, you know, understand profitability, understand return on capital and then the degree which you can figure out how long they can do this trick, you know, invested returns, that strategy, that's the whole gig. That's the whole gig, right? That's, you know, multiples will follow that and so on and so forth. So I think we, you know, we're get, we just want to not lose sight of what we're ultimately here to do, which is figure out how much, how much is being invested, doesn't matter where it's going on the income statement, balance sheet, what the return is going to be. And I think to your point, I think you really. That's correct. I think there's just enormous amounts of uncertainty about what those returns are likely to look at. And you can paint a very positive picture, you can paint a very negative picture and I think it remains to be seen. Now the other thing I'll just say is, you know, for my, this is the work I've done with Rapaport on expectations. You know, the thing I find to be very useful is to sort of go backwards and say if the stock price is at X, you know, what do I have to believe about the future states of the world to, to, to solve for X stock price? And then you're doing an over under. So it's like, oh, I think, I think we should be more optimistic than that. Which means you buy it. I think no, we should be more pessimistic, which means you should sell it. So rather than pinpointing what the future is going to be, maybe an easier way to do it is to say what do I have to believe about X, Y, Z stock? And then say I think they're going to do better or worse than that.
- [48:35]
Kai Wu
Hence the base rates.
- [48:36]
Michael Mauboussin
Hence the base rates. Yeah.
- [48:38]
Kai Wu
So you said something really interesting to me, which is around the idea that the term asset light is kind of a not value neutral term. Effectively, when someone says asset light, what they mean is that, oh, we're not going to count, we're going to count physical capex as an asset, but intangible investment, whether it's through marketing or R and D, is not considered an asset. And then you said something in addition to that where you said, look, all that matters as an investor is what kind of investments are they making and what's the ROI on those investments. Now, you've done a lot of work on the kind of issue about how accounting obscures intangible investments, how it perhaps elevates physical capex but doesn't quite give proper treatment to intangible investment and thus intangible assets that are formed on the back of this investment. Maybe walk me through this because I think this is quite important and I think this is what leads to the misconception that has birthed this idea that asset light businesses don't have assets, they do have assets. The assets just don't show up in the balance sheet because of accounting quirks.
- [49:41]
Michael Mauboussin
It's exactly right. And by the way, you know, even before I get into this, I will say, and I think Kai, you and I both live through this, the devil's in the details here. So there's a lot of, there are a lot of judgments that go along with this. But let's just say that there are sort of two or three steps. Number one is you have to break down SG and a so selling general administrative costs into basically two components. One is a maintenance component, which is how much money does the company need to spend to sustain current revenues or perhaps market share, however you want to think that. So maintenance component and then the other component's an investment component. Right. So that comp. The investment component, we can think of deem it to be a discretionary investment in pursuit of value, creating growth. Right. So that segregation is the first big thing we need to do. The second thing is once we have that investment piece, as you point out, sort of this asset that we're building internally asset, then you need to determine or estimate an asset life for it, right? So just like if you put a machine on your factory, in your, on your balance sheet has a five year life or a three year life or seven year life, you're making some sort of a judgment. We have to do the same thing for an intangible investment. And then of course all you're doing is you're putting that investment on the balance sheet. So what we'd want to do is ultimately put that on the balance sheet just like we would something else and then amortize it, right? So you depreciate physical assets, you amortize intangible assets. So maybe I could try to give a really trivial example to try to make this a little bit clear. Let's say there you're, you can buy a machine that cost 500 with a 505-5-year life and let's say it's NPV positive, right? The cash flows are great. We want to make this investment. So if it's a machine, what do we do? We put it 500 on the balance sheet and then for five years in a row we depreciate $100. It's a great investment. And that's how it shows up. So you see $100 on the income statement, but the, the, and then your, your gross property equipment goes to zero net. Okay, now, now say you're going to acquire a customer, right? Let's pretend the customer's going to be around for five years. So they're going to churn after five years. They have the same exact cash flows as the machine. Well, what are we doing now? The answer is we're expensing $500 on the income statement, right? So we're all the cost upfront and there's none of the benefits showed up. In fact, it's an, we're saying it's an NPV thing. We should bring on as many of these customers as we possibly can. Right? But the more that we do that, the more money we're going to lose, right? So on the one hand, one looks like really attractive and the other looks really unattractive from an, in just a pure income statement standpoint. Okay? So that when I say the devil's in the details, the, the, the key issue is, you know, how do we think about this maintenance versus investment component and then how do we think about asset lives? Now this, as you know, is a very, very active area of research in academia, by the way. Strategy professors are taken on, you know, finance professors taking it on, accounting professors taken on the paper we've used the most is a paper called the Better Estimate. I love this title, by the way. A better estimate, no matter what you have, this is better, a better estimate of internally generated intangible capital. So this is Iqbal, Rajkapal Shrivastava and jal, and that paper's management science came out in the last year or so. And they go through the fama, French industries and give you their estimates of the intangible component of sga, the intangible component investment component of R and D. And then they give you asset live estimates. So you can tailor this by industry, fama, French industry, which is really good. Now the other thing I'll just say is if you make all these adjustments and it's a lot of work to do this right, it turns out that free cash flow doesn't change, right? Because free cash flow is just net operating profit after tax minus investment equals free cash flow. Well, what we're doing when we go through this, this exercise is we actually are increasing notepad, we're increasing earnings, right? Because we're taking away an expense and we are adding amortization. But typically if it's a growing company, that net will increase earnings and we're increasing investment by the same exact amount, right? So you're increasing one, increasing your free cash flow doesn't change. So the question is, why are we going through all this effort, right. If the free cash flow doesn't change? And I think that's. That to some degree is a fair argument. But I go back to the basic core, which is as an investor, as a business person, right. If you're trading, it doesn't make any difference. But if you're a business person trying to understand a business, I would pose the question, is it relevant to you to understand how much money the company's truly investing and how much money they're truly earning? And if those things are important to you, and I would argue they should be, then I think this exercise is actually a worthy exercise to go through. Now that said, you can use proxies, you can do things like free cash flow yield. That's not going to change as a consequence of this, but you're going to be one step removed. You're going to be a little bit more blind about understanding how you got to the free cash flow, the path of the free cash flow. And I think in this case understanding that path is actually a pretty valuable exercise. So that's why we, we go through this whole, we go through this whole thing. And I just, I Just have to believe this is a step toward the truth. This is a step toward understanding economic reality. By the way, I think it leads to much more quality conversations with management teams for instance. By the way, even managements, I don't think they understand, they don't know these numbers. If you, if you can walk in there say hey, is your SGA is $100? How much is maintenance? How much is discretionary investment? They don't, they just don't know, they don't think about it that way. So in some ways we're actually doing something that's distinct from what management themselves, they're using a lot of inertia, they're just doing what they did last year, plus or minus a little bit. So that's another really interesting point to think about that this is not, this is not like in the day to day language people don't do this normally. I think the market sniffs it out by the way, but I don't think people do this normally.
- [55:10]
Kai Wu
So this distinction between maintenance spending as opposed to investment is actually quite important because sure, free cash flow is what it is, it's unchanged. But it doesn't tell you like the future growth prospects for a business.
- [55:22]
Michael Mauboussin
Right?
- [55:23]
Kai Wu
There could be two businesses with the same free cash flow yields that have very different investment profiles. And I think you've done some interesting work on this actually. Like where you looked at first the tangible investments on the capex side against depreciation and you kind of found that actually like companies are, the depreciation kind of understates how much is maintenance as opposed to growth. Because most of the companies that are very asset heavy to use or fine, we'll say tangible capital heavy are older businesses, more mature businesses for whom the obsolescence and the wear and tear on their physical machinery is actually faster than estimated by depreciation. And then that leads to systematic issues with investing in these businesses perhaps explains part of the reason why they've underperformed historically. And then on the flip side, intangible intensive businesses that have been doing kind of the same thing on the intangible side, investors have actually been giving them not enough credit for how much they've been investing in future growth, whether it's R and D, developing a new drug or some new software. And that could potentially explain part of the reason why these intangible intensive companies have outperformed historically. Is that what you're saying? And kind of ties back into why free cash flow is, is helpful as a way of solving limitations.
- [56:40]
Michael Mauboussin
But enough, no 100% Kai, let me play this back to you just to make sure everybody's on the same page. Right. So a company has capex of a hundred dollars and their depreciation is fifty dollars. Right. So usually what we argue in finance is depreciation is a proxy for maintenance capex. Right. So we need to spend that depreciation just to maintain our organization. So, so in that case you say $50 of the spending is maintenance, $50 is investment. To your point, if the maintenance is actually higher than $50, let's say it's $60 or $70. That means there's less money going to an investment and more money to maintenance and as a consequence less money to investment. You know, all things being equal, same return on capital assumption means slower future earnings growth. Right? So that's why this is, you know, again this may feel like accounting, you know, splitting accounting hairs, but this is actually really important because if you don't have a good, good grasp on that, you may be overestimating future earnings growth again, all things being equal because you've miscalibrated the, the maintenance component. So yeah, I think that's another really important thing to think through that most people don't. And by the way, that depreciation is a proxy for maintenance capex, you know, when there are two reasons we can miscalibrate this one is technological obsolescence and the other is inflation. Those, you know, there's always been technological obsolescence but you know they, that comes in, in cycles but you know, inflation flaring up a little bit and ops to obviously pointed out throughout our thread, you know, AI and so forth, technological obsolescence risk, those things are much more tangible today than they were before. And so like you really do need to think this through very carefully to understand what's going on. So I think you, you stated it really well. But just to be clear, that's the key thing is you may, you may think more money's going to investment than is. And again, with your whatever return on capital substance you have, that means future earnings are less than what you think they're going to be. And that's not going to be good.
- [58:33]
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- [58:47]
Michael Mauboussin
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Kai Wu
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Michael Mauboussin
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- [59:33]
Kai Wu
and just to kind of drive this home. Right. Like so it's. So it's. People don't just take away the fact that. Okay, this is an interesting accounting exercise.
- [59:40]
Michael Mauboussin
Right.
- [59:40]
Kai Wu
You mentioned, you know, Fama French in 92. Right. The idea that as an investor, there's a premium associated with buying low prices book stocks and avoiding or shorting high price to book stocks. It turns out that hundreds of billions, if not trillions of dollars are tied to indices and strategies on the basis of this idea. Price to book, price to earnings, price to cash flow. Just to put a bow on this discussion which has been so fascinating over the past hour, talk to me a bit more about how you think that the rise of intangible assets and some of these adjustments you've discussed could potentially guide investors as they think about addressing some of the limitations of these kind of more traditional approaches, allowing us to kind of keep our value discipline while without simply excluding some of the most important modern businesses.
- [60:31]
Michael Mauboussin
Yeah. So, Kai, I do want to make a distinction. I think everybody will agree with this, but just to make sure that it's really clear between value investing and value factors. And these are somewhat different things. So value investing is buying something for less than what it's worth. Right. I think we all. That's. That's mom and apple pie.
- [60:46]
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- [60:46]
Michael Mauboussin
I don't think anybody's going to disagree that that's a good idea. The value factor is one of the ways we try to get to that. Right. So they're saying like, let's buy statistically cheap things, let's avoid statistically expensive things and on average we're going to generate some, some premium to do that. Right. And by the way, again, that's very, very sensible. In the, in the Fama French model, you know, one of the things they relied on was price to book, or they use book to price, but basically price to Book. And the challenge is if book value is less reliable as a measure of value than it used to be, then we could run our, we could run into problems. And this is precisely the conversation we're having now, right, which is if you're expensing all your investments, you're not adding, putting on to invested capital, hence you're not building up your book value. So as a consequence, the punchline is that book value is probably understated. In fact, I'm sure it's understated for businesses, broadly speaking. The adjustments we just talked about or talked through will increase your invested capital, hence increase your book value, hence lower your price to book. And that may reshuffle companies. So there's a really nice paper by Shrivastava, whose name's come up a couple times, and Baruch Lev, and the paper's called Explain the Recent Failure of Value Investing. It's in Critical Finance Review a couple years ago. And they basically go through and make these adjustments. And what they find is it gives a huge boost to the value factor in terms of explaining performance. And so, you know, these adjustments are not, you know, again, like you said, it's not just accounting, you know, fun. This actually does improve the quality of the signal. And this, as you point out, the value factor is a very important signal used certainly in quantitative work. So I think this gets us again closer to what we were trying to do before, which is buy what's cheap, sell what's dear, earn some sort of a premium over time. So again, getting it and boosting our signal basically to, by doing this.
- [62:37]
Kai Wu
Thank you. So I, I could, I'm sure we could talk for four more hours, but since our time is winding to a close, I want to just ask one closing question of you, Michael, which is, what is the one thing you believe about investing that the majority of your peers would disagree with?
- [62:52]
Michael Mauboussin
Yeah, I don't know. I don't know if they, if why they would disagree. But there are a couple topics that come to mind. The first one is that this idea that dividends contribute to total holder returns, and you often see people like market historians saying, oh, dividends are, you know, a third or two thirds of the total returns over time. The total share of return technically is a capital accumulation rate, and which is, which assumes, and a capital accumulation rate assumes 100% dividend reinvestment with no friction, so no taxes, no transaction costs or so forth. And so on. And if you accept that, you know, so you get, you have a hundred dollar stock, it pays a $4 dividend. So you now have $96 and $4 in dividend. Then you take your $4 and buy stock to get you back to 100. It becomes very obvious the price appreciation is the only thing that drives capital accumulation over time. So dividends actually play no role whatsoever in capital accumulation. Capital accumulation. So I think that would be one. The other one I'll just say, which is related, is I think both dividends and buybacks are just wildly misunderstood topics. I don't understand why they are so flummoxing for people, but they seem to be so. You often hear things like buybacks in quotes, create value or destroy value for the company, which is just mathematically nonsensical. What buybacks do is cause wealth transfers. So there can be a wealth transfer from the sellers to the buyers or the buyers to the sellers based on whether stocks are over undervalued. But there's no wealth creation, right? So again, the company's worth a hundred. They buy back $4 worth of stock. Now the name, the value of the company is 96, right? It's just 100 minus 4. There's no wealth creation or destruction. They could pay a dividend, they could buy back stock, they could burn the cash in a parking lot. Doesn't make any difference, right? So those would be 2 of the dividend thing on, on the role of dividends in, in terms of capital accumulation. And by the way, for most people, capital accumulation is what they're after and as a consequence, they misunderstand the role of dividends in, in. In achieving that.
- [64:47]
Kai Wu
Great. Well, thanks Michael. I really appreciate you taking the time to chat with me.
- [64:51]
Michael Mauboussin
My pleasure, Kai. And again, I look forward to your future work. It's always. It's the best stuff out there.
- [64:56]
Podcast Host (Intro/Outro)
Thank you for tuning in to this episode. If you found this discussion interesting and valuable, please subscribe on your favorite audio platform or on YouTube. You can also follow all the podcasts in the Excess Returns network@excessreturnspod.com if you have any feedback or questions, you can contact us at xs returns podmail.com no
- [65:15]
Kai Wu
information on this podcast should be construed as investment advice.
- [65:18]
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- [65:21]
Kai Wu
holdings of the firms of the hosts pet owner.
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- [66:02]
Kai Wu
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