We are excited to announce the launch of a new podcast, the Intangible Economy with Kai Wu. 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. In this episode, Kai is joined by the Dean of valuation, Aswath Dhammadaran. They discuss AI, SpaceX, Value Investing, and a lot more. If you would like to continue receiving new episodes of the Intangible Economy, you can subscribe on all major podcast platforms using the links in this episode description. Thank you for listening. We hope you enjoy the new show. Any company can be a good investment at the right price. Conversely, any company can be a bad company at the wrong price. So there's no ocean of good companies are good and let that go. We make this mistake with human growth is always good, but growth, when it's accompanied by huge amounts of reinvestment, and substandard growth margins, which unfortunately the state of the AI market now is insane amounts and capex. Might not just be neutral to value, but actually be value destructed. What SpaceX saying, we're going to be players in this space. We're going to compete. We're going to win a significant market share of the AI market. But in the same breath, they're also saying, we're renting out space from our data centers to our biggest competitors. The AI is a tool. It's going to be a much smaller market than we are places people. So the stories, but telling about 10, 50, 20, 25 trillion markets are actually terrifying stories for the rest of the world. Our guest today is the legendary Aswath DeModern, who has been teaching corporate finance at NYU for over four decades, earning him the moniker, the dean evaluation. He has published a dozen books on finance investing and posts regularly at his blog, musings on markets. I am particularly excited to have Professor DeModern on as a guest on the intangible economy, as he has been a longstanding advocate for the importance of intangible assets in company valuation. In my opinion, this lens has helped him apply classical valuation techniques to a much broader set of companies, reaching from asset like tech companies to consumer brands to younger firms earlier in their corporate life cycle. Professor Aswath DeModern, thank you for taking the time and welcome to the intangible economy. Thank you for having me. So let's start with the news of the day, SpaceX. SpaceX, of course, went public last week. And the company brings together almost every hard problem and valuation today. High and uncertain growth, negative profits, limited financial history, few company peers, huge risk world market, massive capital requirements, disruptive narratives and key person risk. You first value the company in April and then updated your valuation once the perspective dropped a couple weeks ago. We now know what happened. SpaceX IPO'd at a valuation of $1.8 trillion and has been met with unbridled investor enthusiasm. As of this recording, it now trades at around $2.7 trillion, making it the world's fifth largest company with a market cra-- greater than that of Amazon and slightly below Microsoft. So obviously the narratives around SpaceX are super upbeat. However, you've argued that the narratives must be supported by the numbers, in this case, the valuations. Can you walk me through your SpaceX valuation? What was your target valuation and how did you arrive at this figure? If you look at SpaceX, it started as a space launch business. In fact, people don't seem to quite know this. It's older than Tesla. It was founded by Elon Musk with the money that he cashed out with on PayPal about a few months before Tesla was founded. So it's been around. In terms of chronological age, the company's been around a long time, 25 years. When it started, I think the Musk vision was people need to look to space, go to Mars. For many people, when you think about SpaceX, they think about like, blow origin. It's another company trying to get people out to Mars, but over time, that's not how that part of the business is involved. What SpaceX essentially has done is it's reinvented the space launch business. Let me step back. Pre-SpaceX. If you wanted to launch a satellite into space, you were dependent on either a government agency or an established defense company. And they later on the side, this was not their primary business. Little one in done, you launch something into space on a rocket, the rocket blew up. And you were done was an expensive process. And when SpaceX came along, the response from many experts in the field, and that's something to remember, Elon Musk has consistently proven his experts wrong. So you can't do this. This is a very different business. You don't have the expertise to do it. And he changed their minds. He changed the business by creating relaunchable rockets. That's a revolutionary product. And it gives that part of the business an incredible cost advantage over competition. Recently, there are some young companies that have tried to do what SpaceX has done, but essentially they've lowered the cost of launching things into space. And they've been lucky in a sense that we need more and more stuff in space to support what we do, need on earth, from GPS to our phones, satellites have become an indispensable part of what we do. So at its core, that is SpaceX's original business. And for about 15 years, it built that business. It was slow to take off because it's a very technological business of all, I mean, it's an engineering model. This is an app company or software company. This is a physical infrastructure company. And it took them a while to get their feet on the ground, but even after they got their feet on the ground, it's not a big business. It's not like you got thousands of people lined up to launch stuff into space. It's a small niche market and there were a huge market share of that market because of the cost advantage. But about 10 years ago, they realized that they could launch satellites into space cheaper than everybody else. They could create a second business. And that's of course the connectivity business. Starlink. Broadband internet using satellites. Now, the advantage there that SpaceX has over the competition again is since they can get satellites into space far cheaper than everybody else. They've far more satellites in space, 10,000, which allows Starlink to have coverage that no other satellite based internet service company can offer. And it allows Starlink to offer service in parts of the world where traditional internet is broken down. That business is now at the core of the revenue SpaceX. If you look at what's delivering the money for SpaceX right now, it's a connectivity business. It's a Starlink business. But if you stop right there, this would be a very good business, but it would be a niche business. Basically neither of those markets are big enough to sustain a trillion dollar company. What changed SpaceX was of course the acquisition of XAI parent company for GROC and also started by Musk in February of 2026 where the companies came together. And what that opens up is a third business which is the AI business. How big is the AI business? According to the prospect is it's 26 trillion dollars. It's the largest ever total addressable market I've ever seen. We can debate that. Is that really 26 trillion? But I think after the debate even if you are not as optimistic, you're going to come up with that really big business. What that opens up is a huge business where they're competing against this point two other private companies, open AI and then topic in the main. There are other side players, but a bunch of established tech companies including Google which has Gemini and these are all LLMs trying to go after that really big market. That market is huge promise, huge potential. It's a big market. That's where growth is going to come from. That's what's driving the trillion, two trillion, two trillion, two and a half trillion, two point seven trillion dollar pricing. But the business is really not a business yet. People are struggling on how to what business models work. If you look at Anthropic which is further still long in trying to make this a business. This is struggling with this subscription versus usage costs and I think increasingly they discovered that if you give subscriptions to people to use AI products and you don't constrain them, you're very quickly going to lose money. So they've increasingly shifted away to usage based models. But even if those usage based models, you run it to a second challenge which is how do you make enough money? The unity economics right now are not great. Let's take Claude Fable for instance which is the AI version that was pulled off the market last week because of the US government. I've read and I don't use Claude Fable but I've read that it costs $6,000 an hour to use Claude Fable whether it's $6,000 it's a really expensive product to use. And if your reaction is Anthropic must be making a lot of money on Claude Fable, you wouldn't be right because it actually costs to deliver the high-end AI products. Where did the costs come from? They come from the data center, the power, the water, all of the stuff and those don't easily lend themselves to economies of scale. So there's a business problem at the heart of the LLM business that nobody saw but somebody might solve it and that player is going to have insanely high value because it's a big market and you've solved the problem. You know, the XAI as part of SpaceX is part market and a. big, and a large amount of the numbers that you see, a revenue, I think Elon put out a number of a trillion dollars in revenues in 2030. The only way you can get there is if AI takes on. And if GROC happens to be one of the lead winners of that AI market. So this stage of three businesses loosely connected. Why loosely connected? Because the connectivity business is built off the space-large business. And even the AI business, there's an argument that being the best player in the space-long business might make give you an advantage in the AI market. It's right now almost science fiction of putting data centers in space. But let's face it, putting data centers in traditional, putting it on the ground, summer is becoming more and more difficult because it changes the landscape wherever you put it. So there's naturally political pushback from people living in the area saying, "We don't want a huge data center sucking up power and water here." So we've got to put them somewhere that people don't see them. I mean, I've read stories about you know, data centers in the middle of the ocean, data centers in space. Right now it's more science fiction, but you could argue, so there's a loose connection across the three businesses. Now let me get to the the brass text of how I valued each. I took each one and let's face it, there's not much there yet in any of the business. The connectivity business has about 15 billion dollars in revenues. It accounts for 60, 70% of the total revenues to the company, but collectively space-six is losing money. But to cut it some slack, much of that loss. In fact, all of that loss comes from the R&D expense they had in the most recent year. And this cuts the heart of this intensible question is as long as it contains mystery, R&D, as an operating expense, any young high growth tech company even after it starts to make money will look like it's losing money. If you correct for that R&D, I mean you might turn that negative into a positive number, but it's still a company with small revenues and at the margin very small profits. So the entire value of the company comes from what's going to happen in the future. So I told three separate stories, the keyword error storage is why you're using storage and you're doing valuation, because it's all about the future. I can't just extrapolate the past. And with each one I thought I told the story that for me was not just reasonable, but perhaps a bit. So I assume that the space-launch business would continue to grow as companies increasingly want to put things into space. And I assume that space-six would continue to dominate that business because they're cost-invented. 60% market share 10 years from now. Of a business that's going to be eight, 10 times larger than what you see today. On the connectivity business, the business itself is big. If you define it as internet services, right? Because all of us need the internet for our life, for our work, for enough, for even for our homes. And right now most of us get our internet either through fiber optic or phone lines. And at the moment, for most of us starlings is a suboptimal option because it cannot deliver the speeds that fiber optic and cable can do. So in my story, I assume that the technology for satellite-based broadband would improve, but it still remains only a portion of the total market. If you live in a city or in a well-service urban area, you're not going to switch to starling. But the reach will expand into parts of the country where traditional internet is not delivering good service, as well as on, on from companies that need to provide internet service when they put. Nairlines, classic example, if you've used traditional internet on an airline, it's so insanely slow that at the end of it, you get one email coming through every hour is pointless. So United has talked about converting 80% of their of their fleet to starling. And that would be a dramatic improvement. So I did assume that the market would grow and that because of it's again the fact that it has the most satellites in space, that starling would win that battle as well. So big stories. On AI, here's the challenge. The market is big, but the unit economics don't look great yet. And as far as I can see, I'll accept the big market. I'm willing to go five trillion, six trillion, not to the 26 trillion. But I don't see an easy way for this to become a market where by unit economics, I'm talking about how much you make on the next unit yourself, it's going to stay in my view a low gross margin business because of the nature of the business. In fact, there's an internal tension here, which is at the high end of the spectrum, AI can replace a person, but it's so expensive to deliver that AI that you've got to be a McKinsey consultant that I'm paying a million dollars for AI to actually pay off for me. So if you want to build the market being big story, it has to go with bringing down the cost of high power AI. And that's going to be tough to do. This business is still very much information. We're going to run incidents again when open AI goes public and then throttico's public. The total addressable market is what bankers and the company are going to use to dazzle us. Look at how big the market is. I'm looking to see what they tell me about the unit economics. And my guess is they'll underplay it because it doesn't look good now and there's no e. This is not like unity economics in a software business where, hey, you know what, as I scale up my margins are going to go up because it cost me almost nothing to produce that extra unit. Be interested to see what the stories are for these AI companies as they go public. In the case of SpaceX, I did assume that Grock would actually become a legitimate contender for the enterprise AI market. Which right now it's actually third in line. It does or even fourth in line after because it hasn't really focused that much on that market. I assume that they would find a way to focus the recently, I mean, they just announced cursors acquisition. And that's really an enterprise AI solution. You're providing coding solutions to companies that need coding. So I took them series. I took them at the word, well, because my margins are lower in this business than in the other two, it turns out that the net effect of going after the bigger enterprise AI market, which is what I allowed after reading the prospect is kind of cancelled out. We make this mistake with human growth is always good, but growth when it's accompanied by huge amounts of reinvestment and substandard growth margins, which unfortunately the state of the AI market now is insane amounts of capex might not just be neutral to value, but actually be a value destructive. I did assume that'd be value creation, but be far less than people will get at first impression by looking at the total addressable market world. Now after I did the valuation, people pointed out that SpaceX is actually generating or will be generating almost two billion in revenues from Google and Anthropic by renting our data centers. They said, how come you're not counting that? Now I am, but it's actually going head to head against your big AI story, right? In the big AI story, what SpaceX saying, we're going to be players in this space, we're going to compete, we're going to win a significant market share of the AI market. That's like a manufacturing company claiming that they're going to get a big market share, but they built a big factory and they've rent out two thirds of their factory to their two biggest competitors. Something in this story will have to gel. Either SpaceX decides that it's not going to go after the AI market and says, our competitive advantage is building data centers. We're really good at that. Maybe that's where the space launch data centers will come in. We're going to make our money charging open AI and Anthropic and Google for using our data centers. It's a viable model, but the trade office is a much smaller, don't it, in the dressable market? You might have higher margins. It'll be interesting to see how SpaceX plays it because right now you're looking at unformed companies still struggling. I don't think any of them know how this is going to play out that trying. You can see them almost throwing stuff in the wall and hoping something sticks. But my end result is I value the three companies and I think ultimately the companies will figure out something to happen. If your reaction is you look at my story and valuation, is that a lot of assumptions you're making? Absolutely. What choice do you have? If you're number bound, I'll tell you upfront SpaceX looks awful as an investment, right? If all you can focus on is what they have on their books. And I think the title of your session is intense. When people think about intense, when they think about brand name, it's
stuff that you don't, do you know the biggest intensiveness? Future growth. Even for a company like SpaceX, which is not a traditional intangible company, right? It's not a software company, it's not a brand name company. If everything from, for your new value is coming from what they will do in the future, you can't see it yet. And if my definition of intangible is you can't see it. That is the ultimate intangible. And from that perspective, the bulk of my $1.3 trillion value. And now at the same time, this is my story. It's not the story for SpaceX. And one of the things I did in my early evaluation that I should, I will probably follow up and do on the post prospectus valuation is I introduced the uncertain day of feel about the estimates. That the total address of the market for AI could be much bigger. That they will find a way for growth margins to improve. The way this will play out instead of having a point estimate valuation, which is what we're taught to do in valuation classes. This makes your big inputs into distributions. And what will create is a distribution of value. And distribution of values are good to counter hubris. Because what that will show you is, this is my estimate of value. This is how wrong I can be. That's one piece of output you should get from this is, you know, you have an estimate, but you could be wrong. And the other is when somebody else comes with a different number. If somebody says, is it possible that SpaceX is worth two trillion? Of course it is given the uncertainty about the future. And if someone else insisted that they think SpaceX is worth two trillion, who am I to contest them? They have a story. They want to put their money behind it. So I value for an audience of one, which is me. And for me, this is a company that is great promise. I love, I mean, I think it's an engineering marvel, an amazing company, a company that perhaps only Elon Musk could have created, no. By thinking out of the box and hiring incredible engineers. But at two trillion, two point two trillion, two point seven trillion, that's a bridge too far for me to cross. And I, you know, for me, it's not, it's a great company, but at the wrong press. I think what's so interesting about your framework is, you're trying to bridge two competing camps, right? There's your kind of numerically driven value investors who say, "Praying sales ratio 100, negative income, like no things pass, like no way." And then you have your kind of more story driven narrative investors who, they hear the story you told, and perhaps in a way that even further, and they're like, yeah, two point seven trillion, I mean, that's with the market trading. So obviously, someone believes that that's the correct evaluation. And so what you're trying to do, what's so interesting with your framework is, you know, through the decomposition into some of parts and then kind of interrogating these assumptions around, say, "Tam, unit economics, and then reinvestment requirements, putting together a framework by which now anyone who has your spreadsheet can download it and ask the question of, you know, if I'm at two points, that's trillion, like, where am I differing from, from you? Well, what is the growth margin F2B? Or you can, remember, an Excel spreadsheet, you can solve for any input. How big does the market have to be? You know, what does the growth margin F2B for this to play out? And then decide for yourself, is that something? No, it's, you know, when I teach my valuation classes, one of the first questions I ask my students is, what are you more comfortable with? Working with numbers or telling stories. And because I have MBAs who come from very different backgrounds, of course, that are quant oriented people, is I like working with numbers and storytelling people, who is sentry. And in the investing world, this plays out in the form of, if you go to bankers, especially old-time bankers, the valued investors, they're very focused and abit-dying, revenues and multiples, and trying to make sense of that. And if you go to VCs and founders, they're big storytellers. They say, "My problem with both sides, if you stain your camp as you're being, you're missing big pods of what you do." The number of crunches are so focused on what's there now that they forget that this is not a company. It's the analogy I would offer is, this is like having a kindergarteners, report guard, and extrapolating from that what they will be doing in college, which is essentially what you're getting with the SpaceX financials. There's not much there. And there's not much there because financial statements reflect your history and you don't have much in your history. My problem with the storytelling that I hear is it stops with the total addressable market. That seems, I mean, I've watched CNBC as to why SpaceX should be worth two trillion. And your story does it. It's a big market. And then I wait for the rest of the story and it doesn't come. And here's my problem. This is a business. I don't get value from having a big market. I've got to take that big market. I've got to monetize it as revenues for me. So I need to know, get your stories to why I am going to be a beneficiary of that big market, which requires that you talk about the relative advantages of GROC versus OpenAI versus Cloud versus Gemini. You need to get at least a working acquaintance of those. And there you got to talk about Unity Economics, which is how much are you going to charge for your product? There are a lot of-- these stories are incomplete. Incomplete stories are basically a recipe for essentially doing what everybody else is doing. In other words, these are not stories that tell you whether you want to be buying the stock. Their stories use because you already decided you want to own SpaceX. And you're looking for a justification for why that was OK. We all do it. Numbers people do it. Story people do it. We make decisions first. And then we look for rationalizations later. It's human nature. What I'm trying to do is slow the process down. Because like everybody else, I have instincts and lead me to do what to do something. I want to slow the process down and give my rational side a chance to at least mount an argument. Right. I think what's-- so it stands out to me about the point you made over the years is that intangible assets, narratives, they're real. But ultimately, you need them to flow into the financials. Like at some point, they need to show up in the higher margins, higher growth, or some lower cost of capital, something like that. And so that's one of the interesting things, too, which is, for example, we know that SpaceX has built a lot of organizational know-how and IP around reusable rockets and satellite launches. That has to show up somewhere in the financials. You can't just say, well, this is great. And then you double count the value. So I think that in addition to the point you made about capitalizing R&D, which makes a small difference in this case, in terms of the future value of the company, you really have to believe, for instance, that they will continue to have a commanding cost of the engine and launch to believe that that value materializes. And then, of course, to the AI point as well. I think that's-- it's a reality we tend to forget. Ultimately, you're valuing businesses. You're not valuing promises. Converting a promise into potentially do a business takes work. Right. Where the problem, though, is we have selective memories. We look at success. We use hindsight bias. And we act like we would have picked the successful company at the start. Amazon. Classic example, right? People assume they look back at Amazon. They say, how could you have missed it? Especially younger people. How could you have missed it? What do you buy Amazon in 1999? It's obvious internet was going to take off. It's obvious that Amazon was-- was it? I was there in 1999. Neither of those things were obvious. So I think there's a lesson from previous big buzzword disruptions that you can bring to the AI space. Like the internet, it is going to be huge. It's probably going to change. It's already changing the way we live and we work. This is not the Metaverse, right? Which I was never excited about. What the heck is the Metaverse? Why should I care? Remember that brief-brewed word. Facebook was throwing in billions of dollars in the Metaverse. This is real. I see it in my wife who teaches fifth grade dealing with chat GPT-generated solutions. I see it with my son who works at Disney, who's had interactions with OpenAI because Disney and OpenAI create a partnership with them since dissolved to bring AI into Disney. So this is showing up in our daily lives. It's showing up when I open up my iPhone in the form of AI-generated responses that I'm getting to things that I do on a daily basis. This is real. I think we need to accept that. So the people who hide behind this is all entirely fluff and buzzed, they're missing the point. They're not in the word. They need to be it. I think though converting that into a successful business is going to be tough to do. And of every 10 AI companies that have started, there's going to be one that looks like Amazon 30 years from now. It might not even be a name you recognize today. And people then are going to ask you, if you're 25 years now and you missed it, how come you didn't see that company on? I think there's a huge amount of hindsight bias that leads then to Rommel and then to Formel. And he's saying, what's Rommel regret over missing out? You regret the fact that it, so what do you do? You are afraid.
of missing out now. So you buy SpaceX at two trillion. It's like, I kind of forward to miss the next Amazon, the next Facebook. So I think that that hindsight bias and the combination of the regret over missing out and fear over missing out are kind of playing out in what you see. I mean, let's face it. We haven't had a high profile big IPO in a long time. Monkets came back after the 2022 drawback, but IPOs did not. So this will be a year with three huge IPOs come out. And there's a lot of bent up fomo out there that's going to go into these IPOs. So I'm not surprised. I'd be quite honest. I wasn't surprised at that first day. I'm surprised at the second day, but that's kind of mixed up with the war ending and oil prices dropping. So a lot of other stuff happening as well capital coming off the sidelines. But I'm not surprised that the money is coming in to SpaceX, but it's based on pricing. It's based on fear of missing out rather than people telling much bigger stories than I am and assessing a higher value for SpaceX as a company. Yeah, I mean, I think what you just said is interesting because it ties to your work with Brad Cornell on the big market delusion, right? The idea of a huge market, whether it's 26 trillion or even some fraction of that, combining with overconfidence in the point, oh yeah, you know, of course we're going to win, right? Overconsum entrepreneurs plus overconfident venture capitalists, which is kind of the standard description I would do for any set of entrepreneurs. You have to be overconfident to be an entrepreneur. You have to be overconfident to be a venture capitalist I can pick winners. It's a it's a feature not above of the people who self select to do that. And you put overconfidence in a big market together, you can see the big market delusion play out in the sense that you overreach, then there's a correction. You wish I and a big chunk of the the overreach is I wish I hadn't done that. But it's a feature not above a big market. So I'm not surprised that it's happening with AI. And there will be an overreach that would be a correction. But that doesn't mean every single one of these companies is overvalue. The correction will be in the aggregate, but there'll be a few winners that come out of this space. Right. So the problem is collectively if you were to invest in open AI, I'm thropic and X I have a space. If you were to invest in AI, yeah, and you know what they would be people to do there are ETFs. I'm sure that'll be created which would be I ETFs and you'd be investing in the I space. You are in a sense investing and hoping the big market delusion is not a delusion. I wouldn't go there, but there would be people who would be tempted to do. Yeah. I mean, there's another aspect of this collective dynamic around the capital cycle. So I had Edward Chancellor on last last month. And so that's another interesting question because in your space x valuation, you say that the AI segment of space x has like the lowest economics, the most fierce competition and the unit economics of AI due to the high cost of goods sold is just not as favorable as the other businesses and it's unclear whether they'll get scale economies. But at the same time, it is the biggest area of reinvestment for the company. Right. So like the so space x and Elon, they're pouring all the resources into the company. It is driving the economy. I mean, that's the real big difference between the dot com boom and bust and the AI boom. I don't know whether there'll be a bust for history. Yes, there will be a bust. The dot com boom bust, there was no huge capital expenditure in that cycle. In fact, it was that there was very little traditional cap ex or even R&D driving it. People started apps. They started basically going on. This has been the biggest, I think, infrastructure run up. I've ever seen of a business. You can go back and compare to the automobile business a hundred years ago. This is, you know, the amount of money that's being put into AI cap ex is immense, which means that when the correction comes, the pain will be more intense. And here in Liza's second problem, the dot com boom in bust was almost entirely equity funded. You're saying, so what? Well, when the bust came, there were shareholders who lost 60, 70, 80 or even 90% of money. You felt sorry for them, but the loss was restricted to shareholders. The problem with the AI cap ex boom is not only is it immense, but a big chunk of it is funded with debt. The debt coming from private capital rather than banks. And there's a very real chance that if there's a correction and companies start having problems, that problem is going to show up as distress and default. And that pain doesn't stay restricted. It spills over into the rest of society. I'm not saying it's going to be 2008, but 2000 inches in example of what happens when vendors overreach when they lend money at too low a rate. And the correction comes, the pain spills over. So that is my concern with this big market delusion is the potential societal cost of having to deal with debt coming due that you're unable to pay is much more painful than your share price dropping 90% and you feeling the pain. And what do you think about the big tech companies, the so-called Magnificent 7, who's businesses have transitioned for more asset light, more capital intensive utility like as a result of the AI build out? I think they're the least exposed to default risk. I'm not worried about default, the distress there, but they're changing their entire character. It's like, used to hang out with this 150 pound you know, lightweight and all of a sudden he disappears, he goes and he comes out as a 250 pound muscle bound. Very different character you're going to be hanging out with, right? I mean, it's he's got to eat protein six times a day. I mean, that's what's happened. These companies is they're changing their characteristics of the company. If you in a long term investor in these companies as I have, I've invested in Amazon since 97 off and on. And I own five of the mag seven companies. I'm coming to terms that these are different companies. The way I have to read their earnings reports has to change. It's not just about looking at margins and new businesses. I'm looking at CapEx. Where's the CapEx going? How is it depreciated? Things I didn't think about with these companies. Seven, eight, nine years ago, I've got to think about it. It doesn't mean that I can't value them, but I've got to value them differently than I did because they're changing their characteristics as companies. And my worry is these are not companies used to these traditional CapEx driven investment companies that got sloppy and lazy because they could grow with very little reinvestment. And they're now doing something they've never done before, build huge factories, infrastructure investments, which they 10 years to depreciate but could become obsolete in five. It's a very different game and I'm not sure they really know what they're getting themselves into. Which means that I have more acceptance of what Apple is doing as opposed to the Apple. Because Apple is saying, look, we've never done this. If we decided that tens of billions of dollars in CapEx, we're playing a game we never came to play. We're not very good at. We want to stay in our lane. So I think what Apple is doing is a very different way of approaching the market. And while there are more restrained in the CapEx. Absolutely. And I think a lot of analysts and investors are down an Apple for that reason, which is how come you're not jumping in with both feet. And I think they have a basis for their strategy reflective of Dimcook's personality, which is, no, when when he announced that he was going to step down in September, a lot of a lot of blog posts in the title kind of gets away and out to restraint. Because I think we undervalue restraint in business. And this might be one of those markets where restraint is a good feature. Let other people make the big mistakes. And then step in and say, hey, now that we've learned from Google's big screw up, which cost them 15 billion, I know what not to do. And I think that the worry of being first there and being able to provide their product and services and driving these companies to over invest and perhaps jump into spaces, then aren't equipped to be very good players. Right. So on the note of changing characters of businesses or potentially in that vein, you know, one of the big themes you've seen has been the rise of companies like Micron. So first Nvidia and then Micron. semiconductor stocks have historically been commodity like cyclical, like kind of lower quality businesses. And of course, they've, you know, risen tremendously. Microns of a trillion dollar company now. You've done some work actually. I was one of your older papers on evaluating commodity businesses and cyclical businesses. So kind of the question I have here is like should obviously not at this point in the cycle, these companies are being driven by these macro theme is namely the AI cab X and they're downstrew that. So the question is like, do we think that AI is a structural tailwind that has changed the characters of these businesses? Or do we think that, you know, we have now the risk of valuing them at a cyclical high peak earnings and they maybe do for a crash? How do we kind of think about where we sit now with this sector of the economy that has become I think a quarter of the stock market is now
chip stocks in the US, which is a surprise. >> We've talked about three groups of companies, right? We've talked about LLMs, we've talked about big tech, and we've talked about chip companies. You know what the common theme that's emerging in all of this, it all depends on what that final AI product and service market is going to look like. If it's going to be 10 trillion, with margins of 40%, all of these companies, all three groups are going to be much better off, right? They might not all win, but they're going to be in a much better space. If the final market turns out to be three trillion with gross margins of 20%, there's a heck of a lot of cleaning up to do with all three groups. There's nowhere around it. It's almost like with these groups, you have to take a stand on what you think the AI product. So if you're older and you've never used ChadGPT, and this is a completely foreign space for you, you need to get acquainted with this space. We're a portfolio manager. Of course, if you're an average person, go live your life, you say, you don't have to value space, sex or invest in a micron. But if you want to play in this sandbox, and after an as an active investor, you have no choice but to play in this sandbox, then you got to be part of this conversation. And some of that conversation is playing out in the social slash political realm. the best case scenario for AI that $10,000,000 billion dollar market will happen if it replaces people. Here's the bottom line--the VI is a tool. It's going to be a much smaller market than variader places people. So, the stories, but telling about $10,000,000,000,000 to $20,000,000,000 25 trillion markets are actually terrifying stories for the rest of the world. Why? Because if that story comes true, half of all white-collar people are going to lose their jobs. And what are they going to do instead? Who's going to come up with the income to buy the products and services? I mean, there's an entire debate going on on if AI works as well as it's supposed to replace us people. How do we deal with that as a society? Because people lose their jobs. Not only do you lose your income, you lose your likes, meaning, there's a whole set of-- I mean, back to the '90s, when factory workers lost their jobs and we were blasé about--I mean, I remember listening to people saying, "Learn to code." And that's--I can't even believe you would say something like that, "We're 55-year-old steel worker." And one of my favorite German words is "Shardinfreude." And I think that there's an element of "Shardinfreude," because what happened in the 1990s to factory workers is essentially threatening to revisit us, but this time it's going to be bankers, consultants, white collar workers who are targeted. And to the extent that those factory workers are still around, you know what they're going to turn around and say, "Learn to plumber." Nice. AI kind of replace your plumber because you have a leak. You can't call AI and say, "Fix my plumbing." But the scary thing is the big stories you tell that can justify AI if they come true are going to create some insane cost for society that we better start thinking about right now, because you can't wait till half of white collar workers lose their jobs and then say, "What now?" This is something we need to be thinking through. I don't think anybody is because it's almost like you don't want to open the lid of that box and look to see what's inside. But all of these groups are being driven by this AI fever dream at the moment. And we have to think about, "What if that fever dream is right and think through the consequence?" And ask, "What if the fever dream is wrong and think through the consequences?" Is everything on the table right now? Yes. I think you wrote a blog post on this in March on the Dune's A scenarios. Yeah. What was interesting in that piece was you put together a framework with two dimensions. It wasn't just the magnitude of how successful AI was and how big the markets were. The speed also mattered. I mean, the path for pointing to point B matters a lot. Does it happen overnight or does it take 10, 20 years for the S curve to four, but for diffusion to occur? And in that piece, you said, "Not only is the labor market impact going to be different in those two scenarios, but also the effect for investors since there's an investment podcast on the companies." Obviously, it was these software stocks selling off in the wake of AI disruption fears. So, my question here is around, as we think about the speed of the adjustment from the standpoint of an investor in, say, the non-AI stock, so the rest of the economy, what companies are truly being disrupted, which are maybe oversold, and how would that play out as you dial up and down the potential speed of a transformation? Would I get my data from capital IQ for individual companies? I would love it if capital IQ also had columns on what percentage of the workforce in these companies is white collar and what percentage is blue collar, right? So, you take a GM or even a graph. So, let's say GM, high percentage is blue collar, small. You take a McKinsey. It's almost all white collar. And so, one way to build a truly meaningful, total addressable market is to take the collective salaries you pay all of these white collar workers and say, "My limiting case is I replace all of them, right?" I mean, I know it's a nightmare scenario for those people that are replaced now. I think the wildcard in that post that I did not bring in that I should have is the cost of delivering these products and services. That's why I brought in the fact that the high end AI stuff, which is what you need to replace your white collar worker. I kind of ignored the cost of that because I did not fact-read that if it costs you 600,000 replace a consultant, the AI product you create, then only consultants you pay more than 600,000 are going to get replaced. So, that's going to be a crimp on how much that story can over eat. The Citrini story has to be revisited by saying, "Unless the cost of delivering AI product and services becomes much lower as we go through." And the pathway to a lower cost is not obvious to me because we pay what's driving up the cost. A lot of it is physical cost like power. So, something has to break in this, so change for inter-model efficiency. I have an idea of how we can get at that. I did a paper a little while ago where I looked at job postings for different companies. So, 4PM, which you mentioned. And you can look at over, say, a trailing 12-month period, what the composition of those things are. They're all mapped to ONET codes based on the BLS database. Each ONET code is then collapsible or expanded, I guess, into the specific tasks that those workers do. Each of which you can assign a score as to how much LLMs can automate each one. So, you can come up with a score. You also have average median and percentile salary ranges for each of these employees. So you can say, "What percentage of GD's workforce or GM's workforce is auto-workers' technicians, software engineers, so-and-so-forth, white collar, blue collar?" And then, which of these people are highly compensated versus more low-middle tier. And then you could certainly quantify it actually. What percentage for each company is in this quadruple here of highly paid white collar work? Because that seems to be, at least based on this framework here, the part at most. I think that's always going to be easier to come up with than the cost part. You know what I'd love to see? I'd love to see how much it costs and the topic to deliver that hour of clawed. In terms of how much comes from power, how much comes from water, how much comes from paying for the data. Because some of the high-end stuff, because in the topic is now big enough, I mean, they started, they just stole stuff, right? They just took stuff out of that friend. Acted like everything was in the public domain. In fact, about three months ago, I got a letter from anthropic lawyers, or the lawyers in what an lawsuit saying that a lawsuit had been settled and that anthropic had used 12 of my books in there in creating these bots that obviously they hadn't paid from. And that I was entitled to a payment and to put in, you know, side to fill out the paperwork. I usually don't fill out the paperwork for these, these, but I did anyway because, you know, I think that those days are done. So one of the things is if 80% of your cost is coming from having to pay for this high-end data because now the data will not just be numerical data. It'll be data on what does a consultant do? And so as a McKinsey consultant, what do you do? I think that that's going to be an issue which will determine as you get bigger, will those costs decrease? Will there be any economies of scale? Or will this cost keep increasing? It'll be like Spotify, right, Nissan and you scriber. The music has, you pay by stream. You never get the benefits of scaling up a Spotify that Netflix does because of the way you pay for content. I'd love to be able to delve into the inside. The odds are as you, these companies start to report financials with more detail, the kind of data I'd like them to report will be very different than the kind of data that I was
looking for in a traditional software company or manufacturing company. It's about the cost and how they're changing as you scale up. Because that's going to be a big part of how this story evolves is what percentage of that white collar high-end workforce gets replaced will very much depend on how this cost structure changes over time. And it's interesting that OpenAI recently announced price cuts. That suggests that there's a different business you can go after, right? Which was the deep seek on which was what the deep seek on tray kind of opened up, which is 85% of AI stuff doesn't require Nvidia chips and insane amounts of data. It's really machine learning carried an extra step. It's been around with us for a long time. And the reaction that deep seek was saying, why are you guys paying for these expensive chips and insane amounts of data? OpenAI might be saying, look, maybe that's a much better market to go after. A low-end AI market where we don't charge these premium prices. The products we offer are less powerful. But if you're shopping in a grocery store, you don't need high-end AI. You need very low-end AI to assist you on your grocery shopping. So that's why I think this business is still evolving. We don't know how it will play out. But that's a market. Yeah, there's totally different markets. There's markets for cancer research and markets for making a recipe on your phone. There's also the question around IP monetization, right? And last week's government decision on Clawd actually opens up another question. Are these LLMs better off? Getting individual niche companies create bots, AI bots for their niches in medicine. Because then, when there's a problem, it's just that one niche. Right now, the problem is you are the LLM providing these products for hundreds of businesses. You could after a day like Friday say, we've essentially cut off our entire lifeline. And these businesses are not even going to adopt you anymore because they're scared of the risk of adopting you and not having anything to do. So there's another tension that's going to play out here where AI companies, the LLM companies have to decide whether they will provide the engine for other people to create bots, which outsources that issue entirely, right? Because then if you create a bot that has problems, the government will come and shut that niche bot down rather than coyote. So I think there are a whole host of hundreds of issues here that are going to be played out in the public space like you saw last week. Yeah, I mean, super interesting. And obviously it impacts all the world's largest companies. So it's as an investor kind of something you can't ignore. So I want to make sure you leave some time for the final section here in Value Investing. This is kind of my pet topic, I guess. I was raised the Value Investor worked at GMO, a value shop in Boston. And so you've been generally critical of traditional value investors arguing that they've lost their edge over the past couple of decades due to becoming too, and I quote, rigid, ritualistic and righteous, I quote, can you maybe explain what you meant by this? Now actually let's step back. The commercial wisdom is value investing used to work. What's at base? What is that conventional wisdom based on? There's anecdotal evidence we offer. Look at Warren Buffett, Ben Graham, etc. But the overwhelming evidence for value investing, and this is ironic, came from academics. By doing what? Those graphs that form of French in 92 show that low price to book stocks did better than high price to book stocks, low PE stocks, better than said people said, look, value investing works low PE and low price to book stocks, do better than. But the problem is that's not value investing. I could create a need TF with low PE stocks and low price to book stocks. We should have delivered the same accessory terms with none of the additional baggage that value investing brought in, right? You've got to know the management, you've got to look at modes. Let's go back to the 20th century. If you look at active value investors, basically in value investors like Brandeis, they remember all the offshoots that went out around the country and created value investing as you heard and analysts, they did research. The average value investor in the 20th century actually underperformed a value index fund in the 20th. So this legend of it used to work, we used to make a lot of money, is based either on anecdotal evidence or by looking at these generic studies of low PE, low price to book stocks. And I think the promise value investors drank too much of their own cool aid. They started to believe the fact that we won for a century, we must be doing something right. And I think that led them to where the chosen ones, we do all the right things, we're going to continue to be rewarded for doing the right things. That's the right tier spot. You think you're entitled to an excessive turn to earning more than other people because you do your homework, you done your research, you read annual reports like you were taught to do a page, you know, page one to page 400 if you're looking at the prospect is, I do my homework. I buy companies that trade at less than what you generate from assets and place. That's a key. Additional value investing was not about buying stocks which traded less than value, but trading at less than the value of assets and place. Sounds like a weird side story, but it's basically what do you have? Traditional value investors were very untrustworthy of value from future growth. And you know, you can see it all the way back to Ben Graham, which is, you know, it's assets in place. You can buy something for 50 cents on the dollar if you can find the dollar. So the righteousness came from the history. We'll then let them to a period where they want to perform and the promise when you're right is in your underperform, you know, you know who you blame. You never blame yourself. You don't take responsibility. You blame the rest of the world. And for 20 years, and I'm not saying every value investor has many old time value investors have blamed the world, right? This is entire discourse around passive investing that I'm writing up blog posts called the indexology, emerging from this question of should these big trillion dollar new companies be part of the S&P 500? And that's become part of a post on how people have essentially, especially a lot of active investors, many of the value investors have made passive investing the villain of the pieces to why they've underperformed over the last 20 years. And you have the story, which is when you have 55, 60% of the money being going into index funds in ETFs, fund flow will basically mean that the largest cap companies will always have momentum behind them and the momentum will then carry them further even if the fundamentals don't support it. So their argument is this is all passive investing's fault because they've kind of broken the relationship between fundamentals and price. There are a whole set of errors in that logic, but the right in this is basically means you never take responsibility. It's always somebody else's fault. And if it's always somebody else's fault, you can never look inward to say, what are we doing that is not working? For instance, should this focus on book value be abandoned? A historic focus, right? Traditional value investing is very book value focus on the false belief that book value somehow proxy for liquidation value. I mean, I would wage at a 98% of companies book value has almost no relationship with what you get if you liquidate the company. That's the right instance mark. The rigidity comes from the sets of rules value investing is very rule driven. Why to stop human beings from using judgment to override what the numbers and that has two problems. One is because you're rigid, it essentially means you can never see nuance. And the second is you've set yourself up for disruption, right? What GPT can take that Ben Graham book and do what used to take value investors weeks or months to do it instantaneously. Such a rigid part and essentially it's becoming a less and less attractive. The ritualistic part basically comes from the things that you're supposed to do as a value investor. Like what? You're supposed to have read Ben Graham security analysis. It's like being a Christian and you're supposed to have read the Bible even though you haven't read it, you claim to have. And when pushed, you do little pieces, you've memorized the Bible even though you haven't read the rest of it. You're supposed to go to Omaha once a year and listen to two octogenarians at that
time sitting on, I mean, I am immense amount of admiration for Warren Buffett because he has a philosophy, he's very clear about his philosophy and he stays consistent with that philosophy. Hey, I love Charlie Munger even more because the guy spoke his mind on everything he was asked. But I think the notion that you cannot be a successful investor unless you've read the book, you're at the way annual reports every of the last 30 years and you read, "Bend, grant security analysis," you've gone, I mean, those are rituals. They don't make you a better, I mean, I think you can be a great value investor without ever having grad security analysis. And without believing that every word that comes out of Warren Buffett's mouth is gospel, it's not. He's human. He makes, he has his own blind spots and being aware of the blind spots doesn't take away from his greatness. In fact, it adds to his greatness that he's been able to overcome those blind spots. So the rigid ritualistic and right is my sound like harsh description but unfortunately for a vast majority of investors who call them so value investors, it's a fairly, it's a fair description of how they, they've operated and it's an explanation for why they have no way out of the hole there unless they start to break bad habits. So on the positive note then, you have actually given some prescriptions for how these investors can break these bad habits and perhaps evolve their philosophy and strategies in a way that is more conformist better with the new economy and the way the world works today and maybe does away with some over lines on absolute accounting rules. So I have a quote here from you and from the same paper, you say, quote, "terrestrious gregis cover itself, value investing needs to get over its discomfort with uncertainty and be more willing to define value broadly to include not just countable and physical assets in place, but also investments in intangible and growth assets." So I think that kind of summarizes a lot of the themes that we discussed today around trying to evaluate companies like SpaceX, right? They might be earlier on their life cycle. They may be, you know, have more of its value in future growth as opposed to assets in place that may be driven largely by stories and by narratives and being intangible intensive say around the IP, around reusable rockets. You know, what would you add to this in terms of like how the value investing in school, and I know it's a very kind of heterogeneous group of individuals, but how they should be, you know, what lessons should they be kind of keeping and not just thrown away? What rules should be retired as they kind of think about how they can revitalize or reinvigorate the class of investing in its value? The first is never say never, right? Too many value investors, I will never buy SpaceX, I will never buy Tesla, I will, at the right price, if you're a true value investor, you should be willing to buy companies' bad corporate governance, you know, companies' different voting shares, because it depends on the price. So first is recognize that any company can be a good investment at the right price. Firstly, any company can be a bad company at the wrong price. So there's no such a good company, so let that go, that connection that you make between companies and investments has to be more nuanced. Second, stop looking for conspiracies. Especially account, I mean, I find so much of the value investing discourse kind of focused in on some accounting choice the company made and then blowing it up. The amount of, you know, I've seen a hundred posts on how depreciation of AI-capics is the big world. Come on guys, if that's what you're focused on, you're missing the big picture. Depreciation for a young growth company is not even making my top 20 lists of worries. Could it affect my cash flows? Yes, it's going to affect earnings absolutely. But this is not the big driver, whether AI is going to make it or not. And if you don't trust people, that's a perfectly appropriate factor to come in, to bring into your assessment. Because when we invest, there's an article of faith, right, that the management can be trusted not to cook the books, not. So I think to distrust everybody because you've been cheated once means that you're going to be looking for accounting scandals so they don't exist. You know, I won't name names, but there are people who uncover the end-run, if you ask go in every company they look at. He's becoming another end-run. They focus in on a footnote that makes them uncomfortable and they spin it in this entire company's scan. So I would say never, say never and don't fall down that conspiracy hole because once you do that, then, you're going to lose sight of the things that matter. Thank you. So you've been super generous of your time. I have just one more question, the standard closing question for you, which is, what's one thing you believe about investing that most of your peers would disagree with? I think what the biggest problem in investing are the big mistakes you make that you refuse to acknowledge. I think that we're all going to make the nature of investing. He's going to be wrong the lot of the time. What gets you into trouble is when you're wrong and you dig in and you double down, you triple down, you quadruple down investing. A lot of other people might believe that as well, but I think that that's one fact of the other is, I think the least persuasive evidence you can show me that your successful active investor is your historical returns. Because I know how much luck and standard error are instant. I mean, I think that one of my favorite books is Mike Mabousen's book on separating luck from skill and how difficult it is to do in investing as opposed to basketball. You can't be a lucky basketball player and make 15 out of 23 pointers. It's not going to happen. But you can be a lucky investor and beat the market 15 years out of 20 all the time. So I think that people think that they've back tested something over the track record, they've got a conclusive way of making money. Now, it doesn't work. In fact, I'll close with a story from yesterday about this veteran. I think Kalshi or Draftkings are where I bet he bet a million dollars on Spain beating Cape Verde, which is this tiny five. The payoff was only like eight percent. It's like you invested in it because it was such a slam dunk. Spain was expected to win. And this guy had made a lot of these high probability winning bets. He'd pick a team which had a 90 percent chance of winning. He'd put a lot of money on it and walk away the signal. No. Until yesterday where they tied and he lost the million dollars and it cuts the heart of there's a subset of investment strategies where you will win most of the time. But when you lose, you wipe out 10 years of returns. And the problem with historical track record is you might be at one class in the investment strategy where this happens, for instance, is to sell Kals out of the money calls. Most of the time you're going to sell the calls, collect the money and you're going to keep it, right? It's an out of the money call. You're saying, "What's wrong with it?" The problem is all you need is a couple of stocks to explode out of the box and there goes 10 years of excessive damage. So be wary of historical track records and investors claiming to beat the market because eliminating luck is the reason for that is really tough to do. Thank you. That's really wise. Much appreciated. Well, thanks so much for coming on the podcast. I really appreciate it. Thank you, having me. 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 Access Returns Network at access-returnspod.com. If you have any feedback or questions, you can contact us at
[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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