just to give you a sense of the open AI number specifically. In 2024, they did revenues of $3.7 billion, and they are forecasting for $2,029, $145 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 types of businesses, it turns out the average, the means and mediums 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. Welcome to the first episode of the intangible economy with Kaibu. Where we explore how intangible assets, innovation and technological change are shaping investing. I can't think of a better guess to kick this off than Michael Moboson. Michael, welcome. Thanks, Kaib. 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. Meet me too. It's 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. I'm 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 Bays and Bays Baserates, assessing the plausibility of these expectations. Talk me through what the main question you were trying to answer is and why you felt Bays rates provided a useful framework for doing this. Yeah, maybe just to take one step back to make sure all the listeners are on the same page. Usually when you think about how do you make forecast, 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 forecast is to use so-called Bayserates, right? It's also called the outside view. And now rather than building it up from the bottom, you're saying, let's think about this problems in 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, you're looking at all these numbers, a tomb that really popped off the page. One was some of the stuff open AI was saying and the other was Oracle within their cloud business. But just to give you a sense of the open AI number specifically, in 2024, they did revenues of $3.7 billion and they are forecasting for $2,029, 145 billion. And the question then becomes how many companies of that size have ever grown 108% compound annually for five years. So for our reference class, we went back to every US public company since 1950. So this is basically the CompuStat database. Our initial reference class was companies with initial revenues between $2.5 billion. So you want to start at the right kind of level. And then we asked, in a by the way, turns out when you do that 75 years of data, you have about 18,900 firm years or things you can examine. And it turns out the average growth rate was around 7%, the standard deviation around 10.6%. By the way, the feedback I got people call me, I'm like, oh no, you should be looking at just technology or 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's still obviously doesn't doesn't change the basic fact that no companies have ever done it before. So with the standard numbers, it's like a nine and a half standard deviation event, right? It seems like something that seems implausible. So my point is less that they can't do it. It's not like a physical impossibility to state the obvious on base rates or not. Things that are handed down on tablets from on high, right? These are living and breathing distributions. But when you're considering 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 play some probability on it, but unlikely to be to be that. Now, what's interesting, by the way, amazingly, even last month, open AI 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 percent compound annual growth, which is actually pretty extraordinary. So now, the next question would be something like, all right, well, is that possible? Is it plausible for them to do this? And if you want to revise your views up and be bullish on it, there are a couple of ways to think about that. One is in 2025, they actually did $13 billion revenue. So that's 250 percent growth. So they're actually their growth rate in the first year of the five years was well ahead of expectations. The second is to look at basic diffusion, right? So you say, how fast is this technology diffused versus other technologies? As we know, sort of famously, chat GBT got to 100 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, a guy that we like to think we think about total dress will market, we kind of come at that with three different lenses. The first lens is sort of that bottoms up build up. So how many customers could possibly use this? How much are they going to pay and so on so forth? So that would be a very standard way to do this. The second is to look at diffusion models. And diffusion models are often underutilized, infinite in the financial community, but they're super interesting to look at to see how diffusion works. And there's simple models, even things like the best diffusion model that give you a multi, it's a three parameter model 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, this should be ruled out. In fact, they're, again, they're running ahead of pace on this. But by the same token, just acknowledging that it's a tall order to get to those kinds of growth rates they're talking about. 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 unprecedented. Yeah. And then for opening, I specifically just sort of say like, why is this difficult? You know, one is you have to have a great product. We could talk more about that. But you know, they have to have properties. It's very competitive. And these things are all related to one another. Second is you have to have great people, right? So you have to create people to create that product. And there's obviously a very dynamic market for talent right now. And then 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, they're going to burn soup through something like $218 billion a cash before they go to free cashful neutral. And if you go back to even recently, you know, the big new birds and so on and so forth, you know, those guys, none of those guys burn anything close to that, huh? That much money. So I mean, 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 of the, you know, those are the three, it's like legs of the stool, all three of them are kind of hard to do, but doing all of them simultaneously is a trick. And by the way, they're on pace to do it all, but that's sort of a thing. 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 manage 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. Yeah, I grew up in Boston. So you know, when you think about 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 is become so capital intensive and it's there's so much complexity to the build out in the role of these data centers. You know, what is the lesson from this? 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 get 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 is your friend's a immediately without knowing about the details like, whatever it is, double the cost and double the time rates of people have a reflective reaction to that. So there's a there's a economist named Bentfluvia 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, your hosting Olympics, you know, the channel, all these kinds of things. And so the question is in that 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 time budget, delivering what they're supposed to? And the answer is on budget is less than one half 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 one.
half of 1%. Here again, just acknowledging that these big projects are very difficult to do. Now on the one hand, as you point out, our complex, these AI data centers are tricky things. On the other hand, they are some degree modular. 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 downside. Now, there's an article in the economist. Just again, I don't know if this is right or wrong, but it's had about 25% of AI data centers in 2025 were delayed for some reason. The estimate for 2026 is somewhere around 30 to 50%. A way to think about this is just stuff happens along the way. 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 not something you should expect and you should expect some problems along the way. Interesting. So yet, yet, let's go back to an earlier piece you did in the back of intangible assets on base rates. We'll get into the 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. Your argument was, hey, look, like the economy is transformed. We now have more intangible assets which have unique properties of, say, scalability and secondness that potentially appreciate the distribution of what's possible, both more faster growth on the upside but also more risk on the downside. As we think about AI as almost being an extension of the technological frontier beyond cloud computing and many of the other intangible assets that Amazon leveraged on its way to glory, should we be like, when we think about, you mentioned some folks push back on your base rates and say, hey, you should just focus on tech companies only. And yes, while the forecast baked into these capital investments are unprecedented, maybe AI is the next technology, the next frontier of human intellect and one that perhaps benefits even more from intangible assets in a way that does shift the distribution further. What do you think of this argument? I'm comparing yourself to yourself in the past. Exactly. No, I think it's fabulous. It's a great argument. And just to summarize kind of what we found and I think, I hear work is consistent with this is that when you think about intangible intensive businesses versus non-intangible types of businesses, it turns out the average, the means and mediums are not that different for those distributions returns on capital or growth. So on average, you're seeing about the same thing, but you get more much fatter tails. I want to unpack. There are a couple of things to say here, but one on the Amazon, you totally agree that that's a beneficiary now in retrospect, that's a right tail event. So they're growing much faster than what people anticipated. There's another company in the 1990s. I don't know if anyone remembers this that was actually talking about their asset-like 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, well, it's not 100%, but 61% compounding annually. And by the way, they ended 2000 with revenues of $100 billion, right? So they went really, they went really fast. The name of that company is Endland. And within nine months, that company filed for bankruptcy. So we sort of forget about the other sort of asset lights at slash intangible stories where they basically go completely belly up. So to me, that's an illustration of both sides of that. And 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 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 think about, these are the top 10 companies and magnifs. And these companies are just growing faster than what we have seen big companies grow in the past. And that's a really interesting phenomenon. One of one of inspirations I take us from work of Jim Besson, 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, or way the 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 that's allowing these companies to both build these modes, but also grow 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 emerge. But 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 a little reality check on this, one of 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 your end. But they're by our reckoning about two thirds of the economic profit, right? So if you say economic profit, return on capital, us 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 that 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 so that 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. 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, right? Maybe the correct reference class isn't, you know, historical companies with one or even 10 billions of revenue. Maybe it's 10 to 100 billion, right? Because, you know, we were all saying when people were saying back then, oh wow, Amazon's $100 billion company is no way gets it one trillion, you know, and then it did, right? So what 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 would be interesting to see because we all know that if you look at like venture or private market returns, yeah, there are certainly examples of companies that do manage to grow at rates like similar to what elephant eye thinks they can do. At much smaller scale historically, I think the question is, can open an eye take that same growth rate and apply it at much larger scale due to the fact that maybe the tan of AI's 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 as fast as companies. One thing I chip in on this and I agree with you 100 percent, there's sort of, if we think about sort of the big LLM's, right, so the frontier models, you could sort of put them into roughly two different camps, right, one or sort of the new companies coming along starting off by themselves. So this is open AI and then the topic. And then the other camp would be sort of the legacy guys building their own models. And that would obviously be sort of Gemini and Google as an example. Now, 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 Christiansen argument about is this 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 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 go completely happened at least not yet versus these new guys that really seem like their disruptors. So it's a really interesting question. Like, is this going to be, is AI just going to be a wind in the sales of these big guys that helps Amazon's and Microsoft's and so forth? Or is it something that unseats them? So that'll be an issue. And by the way, the dichotomy and financial resource also very market, right? So you know, Metta and Google and Amazon, Microsoft have a lot of money they can spend on stuff. Whereas these other guys really have their tin cup in hand and they have to raise a lot of capital. X AI, X AI solved that by merging with SpaceX. But those guys, they still have, he norms capital needs just to sort of stay in the game, which is, yeah, which is, which is really interesting and it'll be fun to watch. Yeah, I mean, this is like, this is a central question of, you know, markets today. And I guess it's a perfect version to 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 transformed into a different society, but the progenitors of that idea don't actually make any money and perhaps even just go bankrupt. So as we think about the AI value chain, you mentioned, of course, you know, the hyperscalers and certain model labs, but we can even expand that further to include chip maker like Nvidia, data center operators, the model providers, applications, and then ultimately they're customers, right? If you're enterprise company that's using say, Claude code in order to enhance productivity, that would be, you know, in the stack as well. as we think about the way that value, let's assume that we do.
That's assuming that technology is indeed transformative and it does create a pie of value. How do you think about that being distributed across these things? What are the factors that ultimately will determine where this profit pool will cruise? So, you know, one of the frameworks that I really like a lot on this is the sort of well-known Brandon Berger 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, which is what the consumers maximum price of which they're indifferent to buying the good and their money, price for the company costs for the company and then willingness to sell the suppliers. So, you have these four different markers. The difference between willingness to pay and price is consumer surplus. Difference between price and cost is the company surplus or economic profit. And those would be costs and willingness to sell is the supplier surplus. Okay, so to your point, 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. So, you and I both have LLMs or we have some sort of 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 we want to focus on our marginal cost, but basically we lower our prices in order to do that. So, I think the high level, my assumption is a bunch of that value should probably accrue to, we should all mean, there'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 of 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, and the big ones being cost leadership in 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 competitive advantage, right? Because everybody eventually will do the same thing, it'll get commoditized and so 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 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, I think there's still a lot of PowerPoint AI strategies versus Python, AI, I guess we don't know, cloud code, whatever. But I think that that basic argument I think is really really important one to bear in mind. So I think ultimately this now that all said, 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. Nvidia, by the way, has had like it's been the performs that company financially in the last three or four years is is really up there in the hall of fame of great financial performance. So I want to go back to something you said, right, which is you know, in the like the kind of economic theory would suggest that competition or drive um, access 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 open AI had a huge lead and that was eventually eroded by the, you know, progress of their spin out and the topic and then Gemini and we're seeing open source competition as well. But you know, to take the other side and go back here Michael Porter framework, you know, barriers to entry is kind of a an important concept here, right? To what extent, you know, if you were to make the reverse case and say, you know, Nvidia, they're currently they have you know, kuda, they have a big lead in chipmaking. Of course, they, you know, 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. Yeah. Well, the first thing to say is, you know, the LLM's as far as I can tell they're, you know, they're now a handful. You pointed out the chat GPT was really ahead of everybody else. Oh, he ironically is the technology was developed at Google, which is interesting. But you know, but they're now, you know, whatever you however you want to call it, but somewhere between three and six models that are roughly doing some 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, and Thropbick or, or opening or whatever doesn't affect me that much now. That was also true of Google and Google search, right? There was no, there really, I don't think search was about network effects. Search was about economies of scale. And that's a, it's a slightly distinct concept. So there's an economies of scale thing. I think it's all important. Now, to go back to your point, I'm like, where are the barriers to entry? We like to say in finance, the financial capital is not a barrier to entry, but I feel like this is probably right now, you know, just the ability to raise capital to fund all these operations seems extraordinary. Now, we think about the big hyper scalers again, and these are like, the hyper scalers 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, it workles like number four in that business, and you know, they're at 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 very century 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 a higher bunch of people from open AI. 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, and 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's in the form of equity. Many of these companies, some of the companies are private. So of course, when they're, when they're upgrounds in private, it looks like at least your, your wealth feels like it's going up. If in one 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, well, 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 saluted for the other shareholder. So it'll, that'll be, this, all of this stuff will be fascinating to see how it all unfolds over time. Interesting. So you're saying of the various, you know, modes that that Porter talks about, capital requirements and economies of scale are almost in your mind, the most important thing here. It's less than like a technical mode. You mentioned human capital, but not for end, interestingly. And when it comes to, you know, capital raising and, you know, stability to your Sam Altman, you know, take a trip to the Middle East and come back with, you know, trillion dollars or whatever. You know, it's, 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, right? Because, and you've written about this, that, that in some ways, you know, one way, one barrier of entry, then barrier to entry, is just the threat of, you know, a massive war by which, you know, if you compare to, especially in this case, the smaller ones, get blood dry, right? You know, if you're a Google, this thing, you might consider playing. So let's talk about that, the competitive dynamics around, you know, the, especially last summer, right? When we saw like a series of escalating announcements, affirms saying, "I'm going to commit X." Okay, the next thing goes over the top, you know, 2X, 4X, 8X, right? This whole dynamic, you know, and you pointed this out as well. In past capital cycles, they think about the telecom bubble, the railroads. We've seen, you know, a consistent pattern of over-investment into infrastructure, which has led to, you know, poor, or, poor returns, bankruptcies for the, the builders, you know, and I'm sure that all these CEOs I've seen the data, and they know that this, the historical pattern, and yet they persist in doing this. So, you know, you mentioned something really interesting in your base 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. Yeah, no, Kai, you're exactly right. And it 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 this 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, it's interesting already this year, you know, Oracle, the deal opening, I Oracle deal, looks
They're not going to expand Stargate, which is one of the big AI data center projects in a way they were expecting this, just this week we had Sora AI. 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 headline, staggering headline numbers, we're not really financial commitments. They were sort of like at more aspiration, all to some degree. But it is. It's like this is just the peak-ock flip showing its feathers or any animal becoming large to try to scare everybody else. Scary off everybody else. Now the other thing is again, just to echo this point again, the challenge is that both and Throp again, open AI have to raise a lot of capital. So that's another issue that's going to be important for them. So 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 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 in 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 cap-ex numbers ramp up a lot. 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, 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 like us. Yep. Yeah. And I think for the average investor, the big risk is that, you know, most of us are in the S&B 500, which is a cap-weight index of which a third of the index is in Mag 7, these hyperscaler companies. And almost a half is in the collection of infrastructure companies that includes, you know, Oracle and some of these other names, right? So, you know, 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, you know, unclear on that, that, you know, half the money, half of the money's being bet, you know, on this, on this gambit paying out. So it, I do say it remains to be seen, but you know, certainly something of concern to investors today. So I'd like to switch gears now to think, you know, kind of more high level, not just about AI, but you know, what drives, you know, modern businesses today. And we touched upon this a few times this idea of intangible assets. You know, I think what 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 don't you invest in you and care about them? 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 have it, they can, they can go back and read it and it'll be to their benefit. Well, an intangible is basically something that's not tangible, right? So a tangible asset, something you can look, look, talk to your 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 SGNA line, selling general administrative line. So rather than a physical, like 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 expense immediately. Now just to give a little bit of a little 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 Macrocona. This 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 almost the flip, about 1.5 times tangible. 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 crossover line, the point where intangibles crossed over intangibles, I mean, intangibles crossed over intangibles, it was right when the origin, the big FOMA French paper came out 92, 93 something like that. So right when they were talking about price to book, as 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 SGNA XR&D, so this is the component of SGNA that would be considering intangible investment, was $2.2 trillion last year to give people some calibration. Our estimate is that cap-backs was $1.7 trillion. Investment R&D, so again, not all of our arguments, not all of R&D is investment, some of its maintenance. That investment piece was $700 billion. And so you take 700 billion plus 2.2 trillion, it'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, not to say it's knock-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 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 already touched on this a couple of points, but maybe times, but I'm going to just 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 West Lake. So for folks who want to get initiated, this is a really nice book. And I'll say 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 they had to wiggle some stuff around to make it work, but it's a nice way to remember this. The first of the key is that look, none of the laws of economics have been repealed. There's nothing, nothing magically here in any way, shape or form. It's just that intangible assets have different characteristics and tangible assets that you just need to be aware of, right? And as we'll see, there are kind of pros and cons to intangible, just so we saw that distribution. We're seeing faster growth and faster decline than we saw in the past. So the first is some of you've mentioned a couple of times of scalability, right? Which is, it's typical that an intangible asset has has a high upfront cost, but once it's established, replicating and distributing, it's tends to be relatively cheap, right? So writing software is just a classic example 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's can be obsolescence and that sort of leads to the second S and that 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 start a restaurant and you buy a building, you bid tables and cash, registered and all that stuff, well, if it doesn't work out, those assets still have value because 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. They are with intangible assets. So sunkenness, and this, I almost put it in the obsolescence to 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. On, by the way, this is even getting into big issues about intellectual property, people stealing the stuff and different countries and so forth. But basically, the idea is best practices disseminate very quickly with intangible. So you have the example I think I often like to talk about is the iPhone. 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, really, 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 that was just, that's a classic example of a spillover. The other example I always like to give is shooting in the NBA. They're these great charts on shooting. It 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 recognize that three was actually more than two. And if you took a took a, the expected value of a three point shot, even though the probability 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 they're around the rim and there are 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 of 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 arvaged, charged 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 Adage's growth theory. So the idea is innovation, basically, is recombination of building blocks. And the more building blocks you have and the agree to which their 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 is, you know, I don't know much about this area, but one of the areas that seems super exciting, for example, is the application of AI and healthcare or medicine, right? So the question is, can we search the space and recombine building blocks in a way that's vastly faster?
this one RNA folding faster than protein folding much faster than we did before. And I think that's, so that's this idea of synergies recombination of building blocks is something that's, that is defined by intangibles to some degree. 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, 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. Right. And, you know, you mentioned earlier, of course, the, the fact is that the, the, you know, the best perform companies of the past few decades, you know, happened to also be the ones got a most leveraging intangible assets, whether it's brand network effects, human capital or, or, or IP and, you know, in a park and kind of explain, you know, why, what seemed implausible as a trajectory for these firms was actually, you know, 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 study 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-like business model that led to so much success over this period, towards a kind of more capital intensive one, right? They're, they're free cash flow has basically been, you know, close to over, given how much money they're now pouring into the, the, the build out of iData centers via CapEx. How should investors be thinking about these companies? You know, obviously, they still treat at regional premiums, you know, on the back of their historical success. But as they shift to more kind of utility like capital intensity, should that be an area of concern for investors? Yeah, I mean, you framed it so well. I would just take one step back and say, look, what, 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 thing 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 it, buying Walmart when it first got listed? Was that a good investment? But the answer was a fabulous investment, right? Because the return on investment was really high. And when the return of investments really high, you want to do as much of that as you possibly can while you can do it, right? 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 said it 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 I'll, I'll use the proxy of sort of cost to capital type returns, then it's going to be value neutral, right? I mean, I 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, your laser focus on investment, you're just realizing the investment was not in the balance sheet as it was historically, it's now in the income statement. So that, to me, was, you know, 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, if the other day is a financial analyst, your job is to figure out how much money is it coming 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, I can, you know, understand profitability, understand return on capital. And then to agree which you can figure out how long they can do this trick, you know, invested recruiters, that's 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, we just want to not lose sight of what we're ultimately here to do, which is figure out how much, how much has been invested, doesn't matter where it's going on the income statement, balance sheet, what the return's going to be. And I think to your point, I think you're really, it'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 Rappaport on expectations, you know, the other 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 overrun or 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 XYZ stock? And then say, I think they're going to do better worse than that. Hence the base rates. Yeah. So you said something really interesting to me, which is around the, the idea that the term asset light is kind of a not value neutral term. Right. Because effectively when someone says asset light, what they mean is that, oh, we're not going to count, we're going to count physical capital assets, but intangible investment, whether it's your marketing or, you know, R&D is not considered an asset. Right. And then you said something in addition to that, where you said, look, all that matters as investor is, you know, what kind of investment are they making and what's the ROI on those investments? Now, you've done a lot of work on the, kind of issue of about how accounting obscures intangible investments, how it perhaps elevates you know, physical cap X, but doesn't quite give proper treatment to intangible investment and and thus intentional assets that are formed on the back of this investment. You know, maybe walk me through this, but I think this is quite important. And I think this is what leads to the misconception that, you know, has birth this idea that asset light businesses don't have assets. They, they do have assets. They assets just don't show up in the balance sheet because of accounting quirks. It's exactly right. And by the way, you know, even before I get into this, I will say, and I think I unite both live through this, the devils and the details here. So there's a lot of, there are a lot of judgments that go along with this. But we'll just say that there are sort of two or three steps. Number one is you have to break down SGNA, so selling general administrative costs into some 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 markets or however you want to think that. So maintenance component and then the other components and investment component. All right, so that the investment component we can think of, a, 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'd appreciate physical assets, you're amortized intangible assets. So maybe I can try to give a really trivial example to try to make this a little bit clearer. Let's say you're, you can buy a machine that costs $500 with a $500 five 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'd appreciate $100. It's a great investment. And that's how it shows up. So you see $100 on the income statement with the, and then your your gross property plan equipment goes to zero net. Okay. Now now say you're going to acquire a customer, right. Let's pretend the customer is going to be around for five years. So they're going to turn after five years. They have the same as that cash flows as the machine. Well, what are we doing now? The answers were expensive, $500 on the income statement, right. So it works 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 for them. And then just a pure income statement standpoint. Okay. So that when I say the devil's in the details, 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 is you know, it's a very, very active area of research and academia. By the way, strategy professors are taken on, you know, finance professors taking it on, accounting professors are taking 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 why you have this is better, a better estimate of internally generated and tangible capital. So this is Ickball, Raj Kapol, Sri Vastava and Jowl. And that paper's management science came out the last year or so. And they go through the Fama French industries and give you their estimates of the intangible component of S.G.N.A. The intangible component investment component of R&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? Turns out that free cash flow doesn't change, right? Because free cash flow is just not operating profit after tax, minus investment, free equal free cash flow. Well, what we're doing when we go
through this this exercises we actually are increasing no pad increasing earnings right because we're taking away an expense 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 increase your free cash flow doesn't change so the question is why we go through all this effort right if the free cash flow doesn't change and I think that's that to some degrees of fair argument but I go back to the basic core which is as an investor as a business person right if you're trading doesn't make any difference but if you're a business person trying to share a business I would pose the question is irrelevant to you to understand how much money the company is 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 you know 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 towards the truth this is a step toward understanding the economic reality by the way I think it leads to much more quality conversations with management teams for instance by the way even management I don't think they understand they don't know these numbers if you walk in there say hey is your S&A $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 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'm like people do this normally so this distinction between maintenance spending as opposed to investment is actually quite important because sure free cash flow is what it is unchanged but it doesn't tell you like future growth prospects for a business right 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 the you know the first the tangible investments on the cat backside against depreciation and you kind of found that actually like you know companies are you know the depreciation kind of understates you know how much is maintenance as opposed to growth right because you know most of the companies that are very asset heavy to use that we'll find we'll say tangible capital heavy are you know older businesses more mature businesses for whom the obsolescence and aware and tear on their physical machinery is actually faster than than estimated by depreciation right and so and then that that leads to systematic issues with investing in these businesses and perhaps explains part part of the reason why they've underperform historically and then on the flip side intangible intensive businesses right that I've been you know doing kind of the same thing with on the on the intangible side investors have actually been given them not enough credit for how much they've been investing in future growth whether it's you know are indeed 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 is that kind of what you're saying and kind of ties back into why pre cash flow is is helpful as a way of solving one of the limitations but enough enough no 100 percent kind let me play this back to just to make sure everybody's on the same page right so company has catbacks of a hundred dollars and their depreciation's fifty dollars right so usually what we argue in finances is depreciation is a proxy for maintenance catbacks right so we need to spend that depreciation just to maintain our audience so so in that case you say fifty dollars of the spending is maintenance fifty dollars is investment to your point if the maintenance is actually higher than fifty dollars let's say it's sixty dollars or seventy dollars that means there's less money going to 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 overest many future earnings growth again all things being equal because you've miscalibrated 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 catbacks you know when there there are two reasons we can miscalibrate this one is technological obsolescence and the other is inflation um those you know there's always been technological obsolescence but you know they they comes in cycles but you know inflation flaring up a little bit and upstab you pointed out throughout our thread you know AI and so for 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 since 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 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 right you mentioned you know pharma French in 92 right the idea that as an investor the there's a premium associated with buying low price book stocks and avoiding or shorting high price book stocks you know it turns out that hundreds of billions of not trillions of dollars are you know tied indices and strategies on the basis of this idea price book price earnings price the cash flow you know just to kind of put a bow on this discussion which has been so fascinating over the past you know hour you talk to me a bit more about how you think that the rise of intangible assets and some of these adjustments you've discussed um a contingently 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 yeah so I do want to make a distinction I think that I think everybody will agree with this just to make sure that it's really clear between value investing and value factors and these are so much 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 right I don't want anybody to disagree 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 um in the in the farmer 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 um the challenge is if book value is less reliable is a measure of value than it used to be then we could run art 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 invest the capital and you're not building up your book value so as a consequence the punchline is that book value is probably understated in fact it I'm sure it's understated for businesses broadly speaking the adjustments we just talked about or talk through will increase your invest the 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 sure vast of uh whose names come up a couple times in brook lev and the papers called explain the recent failure 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 um you know these adjustments are not you know again like you said this not just accounting you know fun this actually does improve the quality of the signal and this is you point out the value factor is a very important signal you 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 thank you um so I could I'm sure we could talk for four more hours but since um our time is winding to a close um 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 yeah I don't know I don't think if why they would disagree but there are a couple topics to come to mind the first one is that this idea that dividends contribute to total shoulder 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 um the total shoulder return technically is a capital accumulation rate and uh which is which assumes and a capital accumulation rate assumes 100% dividend reinvestment with no friction so no taxes no transaction costs are so forth and so on and if you accept that you know so you get you have a hundred dollar stock it pays a four dollar dividend so you have now have ninety six dollars and four dollars and dividend and then you take your four dollars and buy stock to get you back to 100 it becomes very obvious the price appreciations the only thing that drives capital accumulation over time so dividends actually play no role whatsoever in capital accumulation a capital accumulation so I think that would be one um the other one I'll just say which is related is uh I think both dividends and buybacks are just wildly misunderstood topics I don't understand why they are so flumoxing for people but they seem to be um 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 of the buyers to the sellers based on whether stocks over undervalued. But there's no wealth creation, right? So again, the company's worth 100 day buyback four dollars worth of stock. Now the name of the value of the company is 96, right? It's just 100 minus four. 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 two of the dividend thing on the role dividends in terms of the capital accumulation. And by the way, for most people, capital accumulations, what they're after and as a consequence, they misunderstand the role of dividends in achieving that. Great. Well, thanks, Michael. I really appreciate you taking the time to chat with me. My pleasure, Kai. And again, I look forward to your future work. It's always the best stuff out there. Thank you for tuning into 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 at excess returns pod.com. If you have any feedback or questions, you can contact us at excess returns
[email protected]. No information on this podcast should be construed as investment advice. Securities discussed in the podcast may be holdings of the firms of the hosts or their clients.
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