Mindy Jensen: Before we get into today’s show, we have a fun update for you. Maybe you’ve noticed, maybe you haven’t, but over the past couple of months, we’ve had a few bonus episodes with Evan Lawler from The Financial Foundation. Well, we are excited to officially announce that Evan is going to be co-hosting a new special bonus episode every single week. These episodes will drop on Wednesdays, and we’ll take a deeper dive into Coast FI, with Evan sharing his own experience pursuing it and explaining what Coast FI can look like for all of us.
Aswath Damodaran: Yeah.
Scott Trench: And I’ll also chime in there that we’re super excited about Evan, because we believe that Evan embodies a lot of the values that we have here at BiggerPockets Money. He’s focused on the fundamentals. He’s in the thick of it, living frugally, working hard, saving money, and learning as much as he can about personal finance, which is clearly a passion of his. And so we look forward to watching his journey grow and having him ask questions. As someone who’s in the seat right now — you know, Mindy and I have both been there, but it’s been a couple years. Our process has resulted in significant wealth for us. And we thought it was time to bring in somebody who is in the thick of it, fighting for it. So we hope to see a lot more of Evan and are looking forward to working with him going forward.
Mindy Jensen: AI is everywhere, and a handful of mega-cap companies are spending and investing enormous amounts of money to build the future of it. But how much of that future is already priced into today’s stock market?
Scott Trench: Today we’ve got perhaps one of the best researchers in the world in Professor Aswath Damodaran from New York University to come on and talk about valuing the AI complex. What’s going on, everybody? I’m Scott Trench, host of the BiggerPockets Money Podcast and also host of the BiggerPockets Money Podcast. And with me today is Mindy Jensen. I’m so happy to be here today with you.
Mindy Jensen: That was good, Scott.
Scott Trench: Today we’re going to be joined by, like I said, Professor Aswath Damodaran to break down the mega-cap AI complex. And he’s going to give me a little bit of feedback on my reverse discounted cash flow analysis I’ve published at biggerpocketsmoney.com/megacap. And we’re going to talk about what these companies need to do to justify their valuations. I love his framing. I think you’re going to love this episode and really get a lot out of it. As a reminder, this episode, as always, is not investment advice and is for entertainment purposes only. Professor Damodaran, welcome to the BiggerPockets Money Podcast.
Aswath Damodaran: Thank you for having me.
Scott Trench: Awesome. You know, just to frame the issue here, you are perhaps the world’s leading expert on valuing a lot of these AI companies and valuation principles in a general sense. Your class at NYU is, I think, really well regarded. When we think about the AI complex in aggregate, not each individual company, but boiling them all together, including the Magnificent 7 and other supporting players like Broadcom, like TSMC, like Oracle and others, when we add them all together, we get to an enormous enterprise value, if you will, with a lot of interconnected pieces. And the ratio of enterprise value to free cash flow, whether or not you’re using operating cash flow and trying to segregate CapEx, or if you’re including free cash flow net of all of those expenditures, you get to a really, really large number, and you’ve got to believe a really, really big number for future cash flow growth, fundamentally. I’d love to hear you just opine on the situation and give us your thoughts on how you feel about the situation.
Aswath Damodaran: I think the first thing we need to do is to start to disaggregate some of the things you’ve said. I mean, let’s take the companies you talked about. Take Nvidia. Nvidia makes its money from the AI business by selling the chips that make up the architecture. In fact, let’s think of an analogy that I find useful — think of a factory being built. So think of this collective factory being built to make AI products and services. You have all these companies that are feeding into the factory. You got Nvidia selling the chips, you got the power companies supplying the power, you have the data centers being built, the real estate. So there’s this architecture — companies that make their money in building the factory. But included in your mix are those companies that are spending the money on this architecture because they want to make products and services. I would include Meta and Alphabet and Amazon in that mix, ’cause they’re not interested in the architecture. They want to use the architecture to make money. The first thing to do is step back instead of adding up all of those market caps, because some of these companies make their money from building the architecture and some hope to make their money from the products and services that come out of the architecture — is to separate the companies and look at it differently. The building up of the architecture — and forget the market cap, because that can get contaminated or affected by other things — the actual amount being invested in the architecture, building of the data centers, by my estimate is in excess of $2 to $2.5 trillion. This is the largest buildup in business history for any new business.
Scott Trench: Over what period is that investment going to be?
Aswath Damodaran: I dated back to November 30th, 2022. That’s the day ChatGPT went public, ’cause that’s the day AI came into our public consciousness. This is really over four years. So this is bigger than what the railroads spent in the 1800s in terms of dollar value. It puts the dot-com investment into shade, because that was a tiny amount. This is $2+ trillion. So you built a factory that cost $2 trillion, and the companies that supplied the inputs to build the factory have clearly made money. Nvidia has already made its money, right? It hopes to continue to make money. The electrical equipment companies, the power companies, the real estate — all of those components have already made money building the architecture. But we’ve also built the largest factory in history without a sense of what that factory will produce as products and services and whether people will pay for the product. It’s the largest leap into the unknown as well that we’ve made in history. We have no sense of what it will deliver, but we’re investing upfront. So the debate about AI is: what will come out of these factories? What are the products and services? What will people pay for them? And collectively, is there enough money to be made from those products and services to justify the $2 trillion plus in CapEx? So that reframes the discussion, allows you to separate companies that are architecture companies from the companies that hope to make money on products and services.
Scott Trench: Fantastic. Do you have answers to those questions, or speculation?
Aswath Damodaran: I think we’re starting to see clues, right? I mean, the companies that probably make the most money from AI products and services right now are perhaps Anthropic and OpenAI, because they sell through their subscriptions and usage models the AI products and services. Collectively, even the best-case estimates of the products and services that come out of AI right now — the revenues, not earnings, the revenues from those products and services, even at the most optimistic numbers — was about $250 billion over the last 12 months. If you remember, the architecture, the factory you’ve built, was a $2 trillion factory. You have $250 billion in revenues. Let’s play the worst-case scenario. If revenues level off at $250 billion, that $2 trillion is almost entirely going to be written off. Your revenues will have to climb from $250 billion to a much, much, much, much higher number. How much higher? Well, it depends on what kind of margins and profits you can make. So let’s say there’s a steady state 10 years from now, 15 years from now, where the AI business is well established. We’re looking at the collective revenues from that business, asking how much would those revenues need to be to justify the $2 trillion invested upfront. The revenues will have to be maybe $8, $10 trillion, because if you think about the expenses to produce the revenues, and the profits, and the tax on the profits, you’re very quickly going to start to scale the ladder up to $10 trillion. And that becomes then the core question you’re asking: can the AI product and service market be large enough to generate $10 trillion in revenues with decent profit margins along the way? It can’t be mass market products. So that’s, I think, the big unknown we’re jumping into. I cannot rule out the possibility that it can be there, but I use what I call the 3P test. Is it possible? Is it plausible? Is it probable? Right now it’s possible. You can have $10 trillion in revenue, and I’ll explain why that possibility exists. Is it plausible? You’re probably pushing the limits of what’s plausible with $10 trillion. And is it probable? Right now I’d attach a low probability to it. The reason I’d be cautious about using the words “impossible” or “this is a bubble” is you’re making a judgment that I don’t think you can make with the data we have right now.
Scott Trench: One of the other components — with these companies attached to this factory — are a number of other very profitable, durable revenue streams, like Google’s, or Alphabet’s, like Microsoft’s.
Aswath Damodaran: And those should not be part of the AI discussion. They are ways in which these companies are coming up with the cash flows to fund them. Bringing them in will just contaminate the questions you’re trying to answer. So in the case of Alphabet and Meta, they have an incredibly successful advertising business. The only way they enter the discussion is if the factory turns out to be not profitable in hindsight and we have to write it down and there are losses to be taken. The question is, where will the losses go? In the case of Meta and Alphabet, it’ll be their shareholders who will lose, because the money from the advertising business that could have been used for dividends and buybacks was instead funneled into the business. There’ll be write-offs, those shareholders will lose money, and they will have regrets about what was done, but the damage will be limited there. With CoreWeave, it gets messier. The reason it gets messier is if the factory has to be written down, CoreWeave has enough debt that you worry about not being able to make debt payments. And when you’re unable to make debt payments, that pain gets spread to the rest of us. That is pain that damages not just the companies involved and the players involved, but everybody else. So I think that’s where I would draw the line. The fact that some of these companies have incredibly profitable businesses means that the damage from AI is going to be more contained in those companies. But the companies that don’t have the side businesses are going to be much more exposed if or when there’s a write-off of the AI investment.
Scott Trench: I think the question that millions of Americans are asking to some degree is, if I’m an S&P 500 investor, I’ve got something like 39 to 40% of my position in this complex, in companies attached to this factory. What I was attempting to get at there is: if the factory does not generate the returns necessary — if that possible-but-improbable outcome does not happen, right — then my investment in Alphabet is not going to go to zero, most likely. How can I correctly separate out the factory from what I’m doing with my money in today’s market, and how I think about allocating to an index fund or any of these companies individually or in aggregate?
Aswath Damodaran: My question is, what’s your alternative? I mean, you are exposed to AI risk, whether it is as an investor, as a human being, as a worker — this is an incredibly large disruption. There are two choices you can make. One is you can say, I want to get out of this AI space because it’s likely overpriced and I don’t want to see the pain, which means you pull your money out of companies that are AI related. That’ll include most of the Mag 7, a lot of the AI companies, and you put your money in the rest of the index. The good news for you then is when the correction comes, you’re going to be hurt less. But the bad news is it could take three years for the correction to happen. And what you lose while you wait might vastly exceed what you benefit. It’s a market timing question. It’s a question as old as time. And I know in hindsight, we crown the people who ride out these bubbles that burst as heroes. We did it with the dot-com. We did it with 2008. But we don’t follow through and look at what happened to them in the after years. I mean, take somebody who managed to get out of the market in 2008 because of the crisis that was coming. That’s good news, and we view them, we look back, and we made movies about them. We make them stars. If you track it, what most of them did in the decade after — you know what they ended up doing? They ended up staying out of the market for much of the last decade, because once you get your money out of the market, especially if it’s all of your money, it gets very difficult to decide to get back in, because it’s too much at play. You get too cautious. So what you gain by being not in the storm, you lost by staying out of the market for too long. The facile advice you could give is stay away from AI companies, invest in the rest of the market, you’ll be more protected. But the long-term answer might be, hey, ride the wave through. You will lose money when a correction happens, but if you have a long enough time horizon, you’re going to be okay. And a long enough time horizon doesn’t have to be 50 years. It could be 6, 7, 8, 9 years. If you’re two years from retirement, for God’s sakes, get your money out of stocks then, put it into something — and bonds right now deliver 5.5%. Don’t get greedy. But if you’re 35, I don’t think it should alter the trajectory of your investing in any significant way, even if you believe there’s an AI bubble.
Scott Trench: So you’re going to be mad at me. I moved out of the market-cap-weighted index into the equally weighted index, and then also remained in some factor tilts. I also own real estate there. That was my answer to it. It sounds like you do not agree.
Aswath Damodaran: That’s a mild course shift, right? It’s not a major one. The bigger concern I have is people pulling their money out of these stocks and essentially going into utilities or consumer products, thinking that’ll protect you when AI collapses. Or worse still, taking your money out of stocks and leaving it in cash because you want to wait for a good time to get back in. An equally weighted versus a value-weighted index, I can live with as a choice you make because you feel too exposed. I mean, in investing, I have what I call the sleep test. And the sleep test is: if you lie awake at night wondering what your portfolio is doing, you fail the sleep test, and you have to do whatever you need to do to pass the sleep test. And if your concern is, I’m overexposed in a value-weighted index in the S&P 500 because of these big cap companies, I’m going to sleep better if I have an equally weighted index — I think that’s worth an insane amount, if not of money, of wellbeing, to be able to do that. So that, I think, is a reasonable course correction to make if that is your biggest concern.
Scott Trench: So if I understand correctly, you have an opinion about each or many of the companies that we’ve just discussed in this complex, and you make personal investment decisions based on that analysis. Is that correct? What are you doing in response to today’s environment?
Aswath Damodaran: I used to own all of the Mag 7, but I bought them at very different points in time. I bought Microsoft when Satya Nadella became CEO in 2013 to ’14. I bought Facebook after the Half of it after the fiasco they had in 2017 with Cambridge Analytica, and the other half after the metaverse fiasco where people decided that Mark Zuckerberg could not be trusted to run a company. I bought Tesla just ahead of the COVID correction. So I bought them at different points in time because at that point in time, I wasn’t buying them for AI. I was buying them because they looked undervalued to me given what I thought about their future. And I make that point because people always look at the Mag 7 and say, I could never have bought the Mag 7 because they’re such expensive expensive companies. In the last 20 years, each of the Mag 7, including Nvidia, has had at least 3 or 4 points in time where they were incredibly cheap because of something that happened to them. I bought Nvidia in 2018. I’d love to tell you I saw AI coming. Wasn’t the case. I bought Nvidia because it had a pricing collapse that made it look cheap to me on a valuation basis. And coming into 2023, I owned all 7. I now own 5 of the 7. And there’s a story behind the 2 that I sold. I sold Tesla right after the election because I don’t like to own companies that become political as well as business plays. And whatever you think of the politics of it, politics is now part of the Tesla story for better or worse, and I don’t feel comfortable with it. I sold Nvidia reluctantly because it’s been an insanely big winner for me. My split-adjusted price per share for Nvidia is $1.80. So when it hits $100, you know, you’re, you’re looking at an insanely big payoff. I sold a quarter of it in late 2023, a quarter in 2024, and the rest over the last 6 months. Have I left money on the table? Absolutely. Do I have any regrets? Absolutely not, because I think Nvidia is an awesome company, but it’s being priced as the greatest company ever. And to me, that’s not a good investment. You can have amazing companies that are not good investments. If I look at the remaining 5, My guess is all 5 are either fairly valued or overvalued by a little bit. You’re saying, why aren’t you selling? Because just as there’s a margin of safety when you buy, there’s a margin of safety when you sell. Taxes especially, I think, contaminate the investment process because they can affect when you sell. Because when you sell, you have to pay taxes, and that’s going to be larger on your biggest winners. And to me, that means that something has to get overvalued by about 25 or 30% before I sell because Living in California, by the time I add state and local taxes on top of my federal taxes, I’m looking at a 26%, 27% capital gains tax on my winners. So I’ve sold all of my Nvidia, I’ve sold all of my Tesla. I still have the remaining 5, and I watch the pricing. If they get overvalued enough, I will start to shed them. The fact that I will leave money on the table when that happens doesn’t bother me. I mean, I also have simple rules on my portfolio where I will not let an investment get above 15% of my overall portfolio. It’s on autopilot. I sell it once it hits 15%. which means none of these stocks are in double-digit levels now on my portfolio. On any given day, I could have a really bad day with Meta or a really bad day with Alphabet. I don’t notice it at the portfolio level, which is what I want in my portfolio. So I think it requires kind of monitoring what you have. It’s one of the prices you pay as an active investor is you can’t buy something and forget about it, especially in your biggest winners, because then those companies can very quickly become 30%, 40%, 50% of your portfolio. I don’t care how great you are as an investor, that’s taking a risk you should not be taking.
Mindy Jensen: Well, I’m feeling a little bit seen here right now. My husband and I have been investing since the late ’90s, very tech heavy. Scott, I don’t remember exactly what the money guy said. Is it 70% of our portfolio is in companies that are headed by Elon Musk?
Scott Trench: Yep.
Mindy Jensen: And then 85% are these like 7 tech stocks. At what point Do you start to get rid of stocks that are such an outlying weight in your portfolio? Like when you still believe in the viability of the company?
Aswath Damodaran: Mine is 15%. It’s an absolute cap. So no individual company can be 15%. If you say 70% of your portfolio is in Musk stocks, then you have a lot of Tesla in your portfolio, well above 15%. But you gotta do it with open eyes, which is you can’t have regrets. What I mean by that is there will be stocks you sell that will double after you sell them, and you’re saying, I wish I hadn’t done that. So here’s my suggestion. Selling all of it is going to be too much. It’s going to be too much on a tax basis. It’s going to be too much emotionally. Do it in stages. So sell a quarter of your Tesla stock, you know, put it on autopilot. Every 6 months, I’m going to sell a quarter of my Tesla stock no matter what the price is. Because if you have to think through whether this is the right time to sell, you will find a reason not to sell. well, stocks that have done well for you. It’s human nature. You hold onto your winners ’cause they’ve been so good for you. And we have the same kind of relationship with our losers. We hold onto our losers because we hope things will turn around. So sometimes you got to take these decisions out of your hands and put a limit sell. Or so one of the things I use is limit buys and sells when I know I will not have the stomach to make the decision myself. There are stocks I love, but I don’t like the price they’re selling And 3 of the stocks that I used to, that I track are BYD, Palantir, and MercadoLibre. 3 companies that I like for very different reasons. But when I first looked at them, they were all overpriced. I valued them, which required that I kind of get comfortable with what they are as companies. And I put limit buys at prices well below today’s price without an expiration date. You’re saying, why would you do that? Because if they drop by 30, 40, 50%, they’re dropping for a reason. There’s going to be a lot of bad news around them. You will not feel ready to buy because you’re surrounded by bad news, you’re surrounded by selling. So sometimes you almost have to take decisions out of your hand because you know emotionally you will not be ready to make those decisions. So do it in stages. Don’t try to do it all at one go because, you know, trying to do it all one go, it’s too much. You will not pull the trigger. It’s just too much to do.
Mindy Jensen: Yeah, we had this same advice with the Mad Fientist right at the beginning of COVID when the market was crashing and he was putting more money in. He’s like, I’ve got this. this, this plan, but it required me to execute it every single time. So when the market comes down 10%, I’m putting in more. When it comes down another 10%, I’m putting in more.
Aswath Damodaran: March 23rd, 2020 was the absolute low. That was the day when if you looked at the news stories, your first reaction is, I’m going to sell everything and head for the caves. And that’s, I think, part of the problem is the times when you should be buying are often the times where it’s most difficult to make that buy decision. In the abstract, intellectually, you can say yes, but emotionally you won’t be ready. So sometimes when you see a company that you really like and you got to do a decent, your homework, understand the company, because most companies, when you see prices fall, they fall for good reasons, right? So you want to separate out the companies who you want to be buying when the price falls from the companies where you’re catching a falling knife, where you’re buying them and they look cheap, but they get cheaper and cheaper and cheaper because there’s something fundamentally broken. in the business. So I think doing your homework is critical as well when you put in these limit buys and limit sells.
Mindy Jensen: Do you invest in any index funds or do you just do individual stocks?
Aswath Damodaran: I do individual companies because my portfolio now is 50 companies built up over a period of 40-something years. They’re all over the globe, different sectors. So one of the advantages of my job, which is teaching, is I have to go to other parts of the world. I have to value companies I’ve never heard of. And once in a while I look at a company and say, this would be an amazing company. company for my portfolio. It’s a company that’s on nobody’s radar because it’s a Turkish company making infrastructure investments. I’ve never heard of this company, but then you take a deeper look and say, this is a great company. So my portfolio is diversified enough that I don’t gain much by going to an index fund in terms of diversification. And to the extent that these companies are even slightly better than an average company, I’m going to be okay. in the long term. I mean, one of the first rules in investing is do no damage. It’s like the Hippocratic oath. Don’t do something that can hurt you big time. Those are the kinds of things that hurt you as an investor. That’s why you should never have 80% of your money in one stock, no matter how well it’s doing. You should never have, you know, get overinvested in a particular macro story, no matter how strongly you feel about it. Because it’s not that You won’t get a big payoff if you’re right, but if you’re wrong, it takes you down and it changes your lifestyle. Your kids might not be able to go to the college they were planning to go to because you screwed up. So avoiding those big screw-ups is as part of designing an investment portfolio as it is finding the big winners.
Scott Trench: I would love to go back to, for a minute here, the AI compost. And this is wonderful advice. And Mindy, I’m going to call you out here. Your SpaceX and Tesla stock are in your retirement accounts, so you don’t even have to worry about the tax hit. that he’s talking about here when you actually make those moves. I hope you guys act on it before, you know, that changes things for you.
Mindy Jensen: We’re in the middle of a big move. Once we’re finished moving, which should be the end of this week, then we have a lot of time to have conversations about it.
Aswath Damodaran: And I think especially getting more balance in, especially if it’s in your pension, it’s in your 401 or somewhere where it’s tax protected, then I would park it for the moment into an index fund. Even if you can’t find something specific to invest in. If you don’t like index funds, put them in, you know, I use the, I go directly into the Treasury. You can invest in Treasury bills directly, put into a 6-month bill, you’re going to get close to 4%. And while you wait, start looking around because once it goes into cash, it gets sticky. It’s very difficult to get it outta cash because it, you know, it just, it feels safer staying there. So you want to kind of do your homework while it sits around.
Scott Trench: I’d love to go back to the AI complex for a minute here, and I’d love to talk about Alphabet specifically, because I think it’s a wonderful illustration of what confuses me as an amateur relative to someone like yourself by a long shot. But when I look at Alphabet, I see revenue of $120 billion, price-to-earnings ratio of like 15. I think we had $90 billion somewhere in that range on their income statement, or $77 billion unrealized gain in the last quarter’s income statement on what was likely Anthropic and SpaceX.
Aswath Damodaran: Yeah. Mark to market, which you shouldn’t even count as part of earnings, but no.
Scott Trench: Perfect. We have that. We also have an enormous, I think it’s $200 billion in deferred revenue, revenue that will be realized in the coming years coming from Anthropic, I believe, right? And Microsoft has its parallel universe over there with OpenAI.
Aswath Damodaran: And I think that’s what I think troubles me most about the AI story, which is the companies that claim to be companies that want to make their money on AI products and services. Let’s take SpaceX, You know where xAI’s primary revenues came from last year?
Scott Trench: xAI? No, I do not.
Aswath Damodaran: It didn’t come from selling subscriptions to Groq or usage of Groq. It came from taking data centers they built and leasing it back out to Anthropic. And the problem with that story is if you are telling me as a banker that SpaceX is worth a lot because the AI business is going to be big and Groq is going to be a key player in that business, The catch in that story is you’re now leasing out the factory you built to your biggest competitor. And if you truly believed your story, that would never happen. So either you don’t buy into your own story and you want to make your money by selling to other people who might buy into the story more, or there’s something I’m not seeing in here. So you’re right, a lot of those deferred revenues come from renting out data centers they’ve already invested into other people and collecting. There’s nothing wrong with doing that. You’re getting money while you do it. But it cuts in the way of your bigger story, why you’re making the investment in the first place, which is you think there’s money to be made from selling AI products and services. And if you believe that’s so, how come you’re leasing your factory out to competitors who can do that? What is it that you hope to do? There is this component of— there’s a circularity here that comes from many of these companies reporting revenues, but they’re basically leasing the factory space to each other. That’s why I would keep my eye on the product and service part, because without that, All of that intracompany leasing is all going to fall apart because none of the data centers will be valuable if nobody buys product services that use them.
Mindy Jensen: You had said earlier they’re building data centers for $2, $2.5 trillion. And right now Anthropic’s biggest revenue is $250 billion.
Aswath Damodaran: No, that’s not just Anthropic. Anthropic is less than— their annualized run rate is about $70 billion.
Mindy Jensen: Oh, that’s not just Anthropic. That’s everybody is $250 billion.
Aswath Damodaran: Everybody, Palantir, Anthropic, OpenAI, the collective revenues from selling products and services. So no contamination from leasing. So that’s not fair, right? Because that’s just the factory being leased across people building. This is the actual stuff being sold from the factory. There’s only $250 billion collectively. That’s the bad news. The good news is it could be huge. So now let’s ask a question. How huge can it get? And that requires dealing with an existential question about AI. What is AI going to do? Let’s take a business. Let’s take McKinsey. Let’s assume Anthropic comes up with Claude consulting agents, which will help McKinsey consultants. It’s a tool that makes them more productive. It’s a cost to McKinsey, right? McKinsey will pay for these tools. But because it’s a cost on top of their existing consultant pay, it can’t be huge, right? It can’t be billions because if you’re paying $800 million in consultants, you can’t pay another $800 million because then where are your revenues going to do? So it’s going to be, if it’s a tool, it’s a relatively small number. So already if AI is just a tool, the potential revenues you’re talking about is much smaller. I’ll give you a sense of what that collective number, the maximum it can look like. Last year across the globe, the collective amount paid to employees in salaries, wages, compensation, all the good stuff collectively was $26 trillion. Every employee in this private and public companies across the world. So let’s say AI’s pipe dream is it can replace every employee at every company. I’ll tell you in a minute why this is going to be a nightmare for the rest of the world, right? But potentially revenues could be up to $26 trillion. They’re probably going to be smaller because why would you replace an employee with an agent if it costs the same? Potentially up to $26 trillion. But here’s why the story crashes and burns almost instantaneously. If your end story is AI will replace every employee, it’s true, that’s a lot of money. But then all these employees who are now displaced have lost their jobs, right? And their incomes and their consumption power. So what are all these companies that have replaced their employees with agents? Who are they going to sell this stuff to? So that story kind of crashes and burns because the macroeconomics don’t work out. So when people talk about total addressable markets, $26 trillion is the absolute limit, but it’s got to be much lower than that because you have to have a sustaining economy where people really have jobs. That’s why I think if you’re talking about total addressable markets more than $10 trillion, you’re already reaching the limits of what’s plausible, because you’re talking about AI then displacing a fairly large percentage of the workforce. It can’t be just a tool anymore. If it’s a tool, it’s a $2 trillion total addressable market. We can already start writing off big chunks of the factory. If it’s potentially replacing a lot of white-collar employees, it could be $8 billion, $9 billion, $10 billion. You could get to some of AI architecture being justified, but $2 trillion is probably still going to be too rich a CapEx. The problem is $2 trillion is right now, and these companies are not done, right? They’re continuing to build data centers. They’re continuing to add to architecture. It could be $3 trillion very quickly. We’re adding about $700 to $800 billion every year to this CapEx. So unless you stop, this is very quickly going to become a number that is $3 trillion, $4 trillion, $4.5 trillion without the And this brings me back to the Alphabet item here.
Scott Trench: So I’m going to use numbers from a quarter out of date because I wrote this last quarter here, but their TTM operating free cash flow last quarter was about $174 billion, and they spent about $110 billion on CapEx, which gives them net free cash flow of $64 billion. That was through about June of this year. This year they’re projected to do $193 billion in operating cash flow, and that’s real business. That’s their core business, the advertising, 93% advertising revenue we talked about. They’re going to spend $185 billion, give or take. That probably has moved a little bit since this update, which gives them net free cash flow of $8 billion this year. Their CFO said next year CapEx is going to significantly increase. So you can put another number on there. I put $215 billion against that number, a small, about $20 billion increase over this year’s projected CapEx. And that means that for the next 2 years, for 2026 and 2027, investors are expecting zero free cash flow from Alphabet in an aggregate sense. And that same thing is effectively true at Microsoft. It’s effectively true at Amazon. It’s effectively true at Meta. Nvidia is generating real enormous cash flows.
Aswath Damodaran: Nvidia is the beneficiary of all of this overreaching, right? So in many ways, Nvidia wants this circus to continue. So if you’re thinking of this as musical chairs, Nvidia doesn’t want the music to stop. That actually explains why Nvidia is doing a lot of the things they’re doing to keep the musical chairs going, like financing the purchase of their own chips by companies that can’t afford to pay for the chips themselves, and providing that by investing in those companies. So in many ways, Nvidia wants to make this a fait accompli, where people are already committed to investing more and more, because they benefit. So that’s why I would separate Nvidia from the rest, because in many ways they’re playing a different game than Alphabet or Meta. You’re right, Alphabet and Meta are making big, loaded bets on AI. There are two ways to look at this. One is that they know more than us, and maybe they’re seeing things that we don’t see, and that’s why they’re making these bets. That’s the upbeat, optimistic view of this. These are not dumb people. The other is that they’re caught up in a different kind of race that’s leading them to invest, not because it’s the right thing to do, but because they don’t want to be left out. Now, I have a book called The Corporate Life Cycle, where I talk about companies aging, and how difficult it is for companies to age. Growth companies want to stay growth companies, just like human beings don’t want to get middle-aged. They want to be in their 20s for the rest of their lives. Growth companies want to be growth companies, and the more glorious your history, the more you want to hold on to what used to be true. It’s always been the case. Let’s face it, these Mag Seven companies have had glorious histories in the market. They’ve had an amazing run as growth companies, and they can sense middle age creeping up on them. Meta sensed it even six years ago with the metaverse thing, which is a badly thought through smaller version of what you’re seeing with AI. But I think there are two ways to explain what’s going on. One is these companies want to stay growth companies, and they think that this is a business where they can stay growth companies. This can be another disruption where they can succeed. And they’re all driven by the same forces that brought them here, which is their managers have succeeded at what they did. So they say, we’re going to succeed at this next great market. Let’s assume the AI market turns out to be $5 trillion, half of what you need it to be to justify the $3 trillion. But it turns out to be a market where there are two big winners, which I think is going to be the case. And let’s say it’s Alphabet, and you think you can be one of those winners. You know what? That explains $194 billion this year, $220 billion, because if you’re going to be one of the two winners, you can justify the CapEx upfront. I call this the big market delusion. It happens every time there’s a big market and you have overconfident businesses looking at that big market. Collectively, they overinvest. It’s a feature, not a bug. And I have a feeling right now, one thing that’s happening is what’s being driven at these companies is everybody’s looking at the big market, making judgments based on it. Nobody’s looking sideways at what other companies are doing. They’re asking, how do we all collectively live in that big market? Because their overconfidence leads them to believe that they’re going to be the winners, and the other guys are the ones who are investing too much money. So psychologically, we can see what’s going on, but as investors, it does mean that if you have all these companies in your portfolio, there might be one winner and three losers, and collectively you might end up losing money. But it’s the nature of how this will play out.
Scott Trench: That’s exactly my conclusion with this. And I’m, again, an amateur here. I put these all together and I say, I can make a case for Alphabet winning. I can make a case for Microsoft winning. It’s actually quite easy and quite believable. The story is not very complicated in many of these areas, and the revenue is easy to flow through, right? I mean, Microsoft has the bookings from OpenAI. It’s going to collect those unless OpenAI can’t pay it. But when you boil it all together, that’s where the trouble starts, because so much of the factory that you described is circular. Alphabet has invested in Anthropic, and then Anthropic is using those dollars, or some portion of them, to then buy or lease data centers back from Alphabet. And that’s going on all over this. And that’s where I have a lot of trouble building up that valuation of Alphabet and saying, what’s real here in terms of external demand that is flowing into the factory and then going into Alphabet’s share, and how’s that going to be long-term? How do you do that when you evaluate the companies that you hold personally?
Aswath Damodaran: The first is, if you can make a judgment on a winner and you are right, it’s going to be an insane payoff. Let’s take the dot-com boom, right? Let’s say you made the decision to invest in Amazon and five other dot-com companies. Even with the dot-com boom busting, you’d still be a winner in this space, because Amazon would’ve carried you. So my suggestion to you is, if you can get the winner in your portfolio, you’re going to be okay. Your problem is if you pick five companies and you say one of these is going to be the winner, and you turn out to be wrong and none of them happens to be the winner, then you are in trouble, because an outsider has come in and essentially stolen the big market away from you. And then you end up with a portfolio that’ll really feel the pain. So you have two choices here. One is to stay out of AI altogether, which is going to give you a portfolio composed of less tech, because almost every tech company has an AI component now. You’re going to end up with less tech, which I think is perfectly okay. The other is that, look, I can find winners. And if I find a winner, this is that hundred-bagger that I talked about, that I can boast about for the rest of my life. But that comes with a whole set of consequences that you got to be willing to live with. One of which might be that none of the companies you picked, that you thought were players in the field, end up being the winner. And you end up with a portfolio of companies that all get hurt. I mean, if you think there was an argument for index funds before all of this craziness, I think the argument just got stronger rather than weaker for index funds, because the more uncertain you feel about how processes play out and winners and losers, the better off you are letting your money ride with a bunch of all companies in the market, hoping that you catch some of them. The investors who’ve been most hurt over the last 20 years are old-time value investors who’ve never owned any of the Mag Seven ever, because they’ve always looked too expensive for them. There’s too much uncertainty. The models are more— they don’t fit into their definition of a cheap company. I can’t imagine an investor being able to beat the market over the last 20 years without having any of them, because the Mag Seven alone accounted for about 25% of the increase in market cap of all publicly traded US companies in the last decade. It’s tough to pick a portfolio without those Mag Seven that actually matches or beats the index. So it actually makes the case stronger for passive investing, where rather than wrangle with these individual questions that you find overwhelming, you basically spread your bets and you move on to living the rest of your life.
Scott Trench: That’s a very sage point there, and a practical takeaway is the index. And in that context, the question I think that I keep coming back to is, which index then?
Aswath Damodaran: Why does it have to be one index? Pick a big mix of indices. In fact, I don’t invest in individual stocks, but all of my children’s money I’ve increasingly moved into index funds, because it’s not fair to them for me to leave them with the individual companies in their portfolio.
Scott Trench: Why not fair?
Aswath Damodaran: Because it requires more day-to-day attention. They have lives to live. They shouldn’t be checking, should I be selling Apple? Should I be holding Adobe? So I increasingly moved them to index funds, but not a single one. I have the S&P 500, and it’s going to be usually the largest of the holdings, because in market cap terms, the S&P 500 is such a— but I also have a small cap index, an emerging market index. The advantage of index funds and ETFs is you can create as diversified a portfolio as you want to, with no upfront flotation or transaction costs. I can just use Vanguard— I mean, I’m not pushing Vanguard, but I can just use Vanguard’s website to add a bunch of index funds that meet my requirements, that give me coverage across the entire market. That’ll mean that the S&P 500 doesn’t become 90% of your portfolio, it’ll become 30% of your portfolio, and you have these added index funds that give you the spread. But you will have your moments of regret, where the S&P 500 will outperform the entire market by 6%, and you’ll think, I wish I’d done that. You have to be okay with that. That’s the nature of index fund investing, is you’re going to match whatever market you were aiming to match, and not much more than that.
Scott Trench: Sounds great. Well, I thought I would wrap up here by showing you my research project, inspired by your work. So this is my mega-cap valuation workbook here, and what I’ve done here is I’ve said, let’s aggregate the roster of mega-cap tech companies: Nvidia, Alphabet, Apple, Microsoft, Amazon, Taiwan Semiconductor Manufacturing Company, Broadcom, SpaceX, Meta, Tesla, and Oracle. I’m missing a few, and I will add Anthropic and OpenAI when they go public. I’ve also factored out— you know, TSMC and SpaceX are not in the S&P 500, right? And some people, some of these are debatable whether you should include or not, so you can change these. And what I’ve done is I’ve said, here’s a discount rate associated with these— that’s the first thing you’re going to poke a hole in here. I’ve just used 10% as a plug.
Aswath Damodaran: Probably closer to 8.5% to 9%, but that’s okay.
Scott Trench: You can use 9% here. Then I took a terminal growth rate, and I have a very simple way to articulate what the cash flows need to do for this complex in order for them to justify their current valuation with this reverse discount.
Aswath Damodaran: And you’re looking at the collective cash flows, or individual company cash flows?
Scott Trench: This is the collective cash flow.
Aswath Damodaran: Okay, so you’re valuing them collectively as a group.
Scott Trench: Yeah. If you model it based on, you know, Wall Street consensus, you get to some very crazy stuff. And then I have to defend each one of those. So I just said, what has to happen for this valuation to be justified at today’s valuation as an aggregate? That was the question I was asking here. Aggregate together to about a $30 trillion market cap. And after net cash and debt, we get to about $29.9 trillion in enterprise value. And the free cash flow over the last 12 months is about $450 billion, giving us a 66 times enterprise value to free cash flow across the collective here, which contains the factory and, confusingly, the other income. So how am I doing so far as a student in your class?
Aswath Damodaran: Good.
Scott Trench: Great. From there, I see that if you started today, you’d need a 32.6% per year growth rate on the free cash flow.
Aswath Damodaran: One thing about free cash flow is it’s a very badly defined word in practice. So people are going to take different— are you talking about free cash flow to equity? This is after debt payments. So is it net income-based free cash flow, or is it free cash— this is operating cash flow adding back CapEx? That’s actually free cash flow to equity, and you have enterprise value. So it should be free cash flow to the firm. And even if you did free cash flow to the firm, it’s a tough number for people to relate to, right? Even if they know the definition, because it’s the end number. It’s a number that comes from revenues, margins, earnings, CapEx, working capital, et cetera. So my breakeven, I framed in terms of revenues, because the big debate here is, is there enough revenue here to justify? Because the pushback you’d get is, you’re missing the fact that there’s a big market out there and we can make a lot of money in that market. This captures the free cash flow today, which doesn’t include the free cash flow you will get from AI. So it will just require a couple of tweaks. Your free cash flow is an end product that comes from your CapEx, your investing, and your margins, which give you your earnings. So if you can put those into your equation as kind of inputs, I can then estimate the revenues I would need. So I asked, what’s your target margin going to be in the AI business? Is it a high-margin business, a low-margin business? Because if you can give me that, then I can take your existing market cap and reverse engineer from it, not the free cash flow, which is the end product, but the revenues that would give me that free cash flow. Sounds like I’m playing a play on words, but it creates a more healthy conversation, because now when you chat with somebody in the AI space who has no idea what free cash flow is— they’re a tech person, listen, they can still talk about revenues. And when they talk about revenues, you can ask them, what’s your vision of AI? It creates conversations across areas, which I think is healthier, because finance people talking to finance people, we can’t resolve this. We don’t understand enough of the technology. AI people talking to AI people can’t resolve this, because they don’t understand enough of the business of AI. We need to create more of a conversation between the people who look at the bottom line, the EBITDA, the free cash flow, all of that stuff, and the people who are asking the questions about AI. Is it a tool or is it a replacement? What is it going to do? I need to be able to talk to somebody at Anthropic who talks about Claude and be able to get them to relate to my judgments on revenues. And I think that gap can be bridged. So that’s one suggestion I would make, is reframe this not in terms of free cash flow, but in terms of revenues, because I think that’s really what this big debate is. What will the revenues look like in this business? And are they large enough to justify what we’re investing in, what we are building right now? And if the collective answer is no, then what do I do as an investor? Do I avoid all of these companies, or do I start asking this for individual companies? I’ll take one company: Apple. The breakeven revenues you need to justify the market cap today are not that much higher— it’s like 5% a year growth. Why? Because they’ve not had this big CapEx boom in AI. So there might be individual companies you might still choose to invest in, even though collectively this space is a mess. You might believe that it’s overpriced, and that might be a way in which you can bridge the gaps between people who like individual companies and dislike the group, as well as people who like AI as a technology and people who think about it as a business. I mean, one reason I wrote that post on AI as a business is I think we have two camps here. You have the optimists on AI who talk about how big it can be and how powerful it is, and they’re right on both, but they talk about the size of the market, the revenues. On the other side, you have people who talk about the bottom line: where are the earnings, where are the cash flows? And they look at it and say, there’s no way. And right now, there is no crossing across these groups. We’re talking different languages. We need to find a way to make the language something that we can both talk about. I’m an AI novice. As I wrote, I don’t own the pro version of ChatGPT. I don’t think I’ve ever used ChatGPT on my own writing. I’m an extremely ill-positioned person to talk about AI as a technology. So I need to talk to people who develop Claude to understand what it can do. And I need to keep that conversation open. And the only way to do this is to stay away from finance terminology. I’m not talking cost of capital, I’m not talking free cash flow. I’m talking, what are you going to do, where are the revenues? And I’ll impose macro constraints: this is how much you can afford to spend, if you’re McKinsey, on replacing employees. You can’t get any higher than that. Tell me where else you’re going.
Scott Trench: So just some reactions to this. I think you’re completely right. I started with free cash flow because I can’t build a reverse discounted cash flow without the cash flow. So that was the point of the engine here. I did build the model to dynamically respond— so, for example, to the 9% discount rate, and that implies that revenue needs to grow from $2.8 trillion for this collective to about— is that keeping margins constant as all of this shifts?
Aswath Damodaran: If you take current free cash flow, there’s an existing margin, existing reinvestment that gave you the free cash flow, but both those numbers are in motion, right? Because you’re talking about these companies changing their business models. You don’t have to change what you’re doing— you’re doing a reverse DCF, but a reverse DCF, even though it might be based on cash flows, those cash flows come from revenue. So what I’m doing, my reverse engineering, is doing a DCF, but rather than framing it in terms of the free cash flow, I’m making the revenue— it’s an algebra equation, I can make any variable the unknown. Rather than making the free cash flow the unknown, I’m making the revenue the unknown. So I’m reversing the process to get to those revenues. So it stays within the DCF approach, but it builds up to a number where more people can have a conversation about what that number is.
Scott Trench: What do you think— I assumed in my model that it starts at 15% today for free cash flow margin and it ramps to 25%.
Aswath Damodaran: Again, I can’t even relate to free cash flow margin because free cash flow is already after reinvestment. The margin is kind of meaningless. It mixes up two things, right? The profitability of your business model and the reinvestment you need. The reason a free cash flow margin is going to rise in any company as it matures is your reinvestment as a percentage of your revenues will decrease as your growth decreases. That’s why the operating margin is a number I can relate to, because it comes from business economics, unit economics, economies of scale. The more you can move this conversation away from financial statement numbers to business models and market size, the healthier the conversation becomes. Because the way you push back at somebody who’s being overoptimistic on AI is not telling them that the free cash flows are negative right now or not there, it’s to show them that the revenues they claim they will have are just not there. That’s a tough constraint to get over, that you can’t get to the revenues you need to justify even your story. Forget about the margins, the reinvestment, all the rest of the stuff. You’re just in the realm of fairy tales now when you’ve created a revenue number that can’t be reached.
Scott Trench: And what’s fun about that is the revenue, it overlaps with some of these, right? Like Google, Apple.
Aswath Damodaran: This is a collective revenue, so you can’t count revenues you get from each other, right? It’s got to be collective revenues. And that’s why I think you need to go outside these companies and look at, hey, what do companies collectively spend on employees? I need a story about what AI is going to do. And it also lets you connect with that part of the story, the dystopian stories of AI, is going to leave us in a society with much greater inequalities of wealth and 70% of people either unemployed or underemployed sitting at home doing what?
Scott Trench: Nothing.
Aswath Damodaran: Even if you guaranteed them a universal basic income, work is not just about generating a paycheck. It’s about a sense of self. What are you going to do to— I mean, it’s a strange scenario to unfold because 33 years ago, 35 years ago, the early ’90s, we saw this unfold in a different part of the economy. The disruption there was Chinese manufacturing. The disrupted were factory workers and miners. They lost their jobs. And they were told pretty casually, learn to code, which I thought was the most insulting thing you can tell a 55-year-old factory worker, learn to code. To code what? But we kind of let that pass because the people who were making the decision sat in New York and London and Tokyo. And to them, this was an abstraction. A small percent of people lost their jobs, blue-collar workers. And it took a while for the economy to adjust even to that loss of work. And that was a small loss. Can you imagine how much it’s going to take the economy to absorb loss of bankers and consultants, white-collar workers with large incomes? And now strangely, the people who are most protected are the electricians, the plumbers, the blue-collar workers, because AI can’t replace them. But I think in a sense, we’ve got to think through this fully. So when I talk to optimists, I let them play it up, give me their optimistic story. And then I say, okay, let’s take your optimistic story and let’s see what the rest of the world will look like if your story comes true. And it’s a scary thought, because their most optimistic stories are dystopian ones for the rest of the world.
Scott Trench: That’s where I come down to. Like, if you steelman the case for the AI complex, you get to a very large revenue number, ten to twenty trillion dollars, depending on what you want to plug in for the other assumptions that are debatable. And I walked through there, and now you get to something like ten thousand dollars per every affluent person on Earth spending that on AI to some degree. That’s how I framed it. It’s worse than the way that you just articulated, a pool of corporate profits. But I think then you have to either confront— either that means that these companies, as a collective, have dominated the global economy to yet another, almost a tenfold increase in scale compared to where they’re at today, or one company has done that and carried the rest of them in order to produce investment returns. And now you’ve made a political bet. Now you’re making a bet that the world population, or the American population, will accept that still very large increase in concentration of power.
Aswath Damodaran: There’s a much higher top line, which is global GDP can’t grow at more than 2 or 3% a year in real terms. There’s a cap. So all of this stuff is happening under the surface. Collectively, revenues at all companies can’t grow 20% a year. There’s not enough income for it. So it’s going to grow 2, 3%. So if revenues are not growing, all of this stuff is happening in the expense part of collective businesses, right? So if AI is getting bigger, something else is getting smaller. It— you know, you can’t have your cake and eat it too. The most obvious item getting smaller is what you’re paying employees. But there’s an unfortunate problem. That is now the income that is used to buy, to generate the revenues seen as the top line. That’s the part of the cycle where I think you need to kind of bridge the gap, because I don’t see an easy way to tell a really big AI story that doesn’t cause the kind of disruption that damages your top revenues. But you end up in a global depression because so many people have lost their jobs and income. So with the AI optimist, that’s my pushback. Tell me what happens if your story comes true. Tell me what the rest of the world looks like. And you don’t have the option of saying that’s not my problem. It will be a problem politically. It’ll be a problem economically. It’ll be a problem business-wise. Because if that happens, you’re going to get a backlash that makes the backlash we’ve seen in the last 20 years look like child’s play. You’re going to see entire systems get overthrown, businesses get shut down. So I think this is a problem when you let 25- to 30-year-olds, without adult supervision, build trillion-dollar companies without somebody pushing back and saying, this makes no sense. I mean, AI has been— the people who are spokespeople for AI have been among the worst tellers of a story that I’ve ever seen. They’ve taken a story that started right after ChatGPT came out in 2022, as this positive story, nice tool, look at all the neat stuff, into a story where 60% of not just Americans, of the global population, is looking and saying, that’s terrifying. I don’t want that to happen. They should talk to tobacco company CEOs as to what happens when you become a company in a space where people think you’re evil. Everything you do becomes the equivalent of pulling teeth. Everything is ten times harder than you thought it was going to be. And if AI doesn’t get this storyline going… one of the suggestions I made was maybe they should follow AT&T’s path from the last century, when AT&T, when it was a regulated monopoly and everybody complained about their phone service, invested in Bell Labs. Something where they made no money but generated social goodwill. And they did it because it allowed them this freedom to operate as a regular money-making company, that maybe the AI companies collectively need to think of their own version of Bell Labs. But I’ve heard these stories about how AI can cure cancer, and things that could give you the kind of good storyline you need. But the way they’re going about it, they’re going to make lives more difficult for themselves. So even though that $20 trillion market is potentially there, they might never get even a fraction of that if governments and politics start— I mean, look at how difficult it is to build a data center relative to two years ago, five years ago. It’s only going to get more difficult rather than less so. And that’s going to play out in every single dimension of the AI architecture if they don’t start to fix the storyline.
Scott Trench: I think that’s a wonderful place to end, and a note to end on— I think that’s right. There’s a political or storytelling problem in the bull case for AI here. I have a question here. Is there any chance that I could take your feedback and your wonderful wisdom you shared on today’s show and re-architect my model here and submit it to you for a grade?
Aswath Damodaran: Sure, I’d be glad to.
Scott Trench: Could I get the rubric for your final exam, or the project of this type that you provide to students in your class?
Aswath Damodaran: Yeah, absolutely. It’d be easy enough to do.
Scott Trench: Yeah, I’d love to do that. I have some tweaks I need to make based on this, and some tweaks that I was hoping to make before today’s show but did not quite get to. But anyways, yes, I would love to do that. And thank you so much for sharing your wisdom here. Any parting thoughts on the AI story here?
Aswath Damodaran: I mean, the only thing I’d say, don’t be an absolutist, where you’re on one side or the other. You know, “I’ll never buy an AI company” or “I’ll buy every single AI company.” There’s room to learn here, because there’s so much we don’t know about this space that I think we need to keep talking. We need to keep the conversations going, because that’s the only way we get to a healthier place than we are right now.
Scott Trench: Love it. For what it’s worth, I’ll say I think AI is a great opportunity for those pursuing financial independence. It’s going to bring the cost of executing a lot of ideas out there way down for you at a very small cost.
Aswath Damodaran: As long as it doesn’t replace their job, in which case they will have nothing to do financial independence with. I mean, I think that’s a scary thought, right? This is not a benign technological shift. This is a technological shift which will have major consequences at the personal level. This is not a business tool, at least the way it’s being sold. It is something that’s going to change the way we live and change the way we invest and finance our lives. So I think it’s something that’s going to affect— that’s a really interesting thing.
Scott Trench: Now, I know we just said we’re going to get out of here, but I have a pessimism, I think, about the ability for the AI complex to generate strong returns for investors over the next ten years, and a huge optimism for the effect of AI on the broader economy. I agree with the real risk— people are going to get their lives disrupted. There’s going to be terrible outcomes in certain specific areas. And I also think there are broad gains, right? We should see better distribution, more efficient distribution and logistics in many companies. I think that many companies out there who suffer from weak functions in various departments, they should have their floors rise from AI.
Aswath Damodaran: But the one problem is if everybody has it, nobody has it. So let’s say grocers are able to get more efficient, but everybody is sold an AI tool that makes them all efficient. We benefit as consumers— that’s the consumer surplus— but the businesses don’t gain. The one thing about net effects of any of these technologies is, I’m old enough to remember when PCs were a new technology and we were promised how this would relieve us of the tedium of work because we now have computers to let do that. And how did that work out, right? And then we were told the internet would be this world of information. We’d have this amazing info, we’d all be more informed, and we would have fewer bad arguments. How did that work out? And when social media was introduced, it was, how we all connected across the… you know, my experience with these technologies is there’s a lot of social stuff that they create and changes in the way we operate that we look back and say, I wish we hadn’t done that. But the problem is these are like genies out of a bottle. Once the technology… you can’t put it back. You can’t regulate it away. The EU keeps trying, but it keeps— so, twenty years from now, I might not be around, but if I’m around, I’d love to come back and talk about the net effects of AI. But I’m worried. I’m worried that the net effect might actually be a negative for all of us rather than a positive, because it might take away things we do as human beings, and over time, we might lose them. I look at my grandchildren, I wonder what the world will look like and how they will learn in a world full of AI. I’m not particularly happy about the kinds of things AI will do to their learning processes. But, as I said, that might be just because I’m old and cantankerous. But it’s also, I think, a reflection of having lived through technological changes that in hindsight always leave more negative debris than we thought they would when they were introduced.
Scott Trench: Well, that’s very sobering. Like I said, this has been an absolute privilege to get a chance to meet you and talk with you. Thank you so much for coming on and sharing your wisdom about valuation principles, about the AI complex, and a warning, I think, that hey, this— it may not be another, you know, a few decades of wonderful gains and booms. There could be real risks associated with this that could disrupt your life.
Mindy Jensen: Yes. Thank you so much for your time today, and we’ll talk to you again soon.
Aswath Damodaran: Take care. Bye-bye.
Mindy Jensen: All right. That was Aswath Damodaran, and that was a really, really fascinating conversation. Scott, I am coming at AI from two kind of opposing— first off, I saw Terminator 2, so that is really, really, really scary to me. But on the other hand, I don’t want to be one of those “no, horses are great, we don’t need cars” people that were anti the Industrial Revolution way back when, and not want to embrace AI. Aswath makes a really great, compelling argument that this is going to have a massive impact on the stock market over the next few years. And I kind of wonder if maybe these tech companies are setting us up for a really big crash. And I know you have been there already, ahead of me, saying, I’m pulling my money out of the stock market and I’m going to put it in a different direction ’cause I think it’s going to crash. And we say, oh, this time is different, and this time is never different. It’s always the same. But do you feel like this time is different?
Scott Trench: I don’t know if I’m saying AI will crash. I’m saying I find it very hard to personally believe that the 40% of the S&P 500 that comprises these companies, many of which we discussed today, can produce a 10% return over the next 10 to 15 years. Very few forecasters or analysts are saying that that will happen. Professor Damodaran seems to be pretty pessimistic on the aggregation’s ability to generate very strong revenue growth that’s required to justify the bet on the factory here. But that’s my bet. It’s not that it’s going to crash, it’s that it’ll be very challenging for this group of companies to produce satisfactory returns over the next ten years. And I have to believe much less for the equal cap-weighted index, or my factor tilts, or my real estate, to produce a 10% return. There’s not really robust assumptions that have to go in there. So I could certainly be wrong. I’m certainly losing badly in the last 18 months. We’ll certainly pay attention to this over the next couple of years, and we can laugh at me, you know, in hindsight, but that’s how I feel. And that’s what my analysis says. And I think, you know, Professor Damodaran seems to be directionally aligned with some of that.
Mindy Jensen: Yeah, it was difficult to listen to this episode and not agree with what you guys were saying. I mean, just the spending $2 trillion on building out the infrastructure and these data centers and whatever, and right now the income is $250 billion. I mean, these numbers seem ridiculous, but—
Scott Trench: Well, and it’s really hard to get those numbers, right? I tried to aggregate those in some way, and I would assume that even Professor Damodaran is going to say that’s a fuzzy number. That’s the best estimate that you can cobble together right now. It’s really hard to understand how revenue is moving in and out and investment dollars are moving in and out of this complex, because you have to separate out the factory, which I really struggled to do when I was analyzing this, and he seemed to have a very clear framework for it. That’s the biggest challenge there. And yes, then you have to have enormous cash flow growth to justify just that factory bet, but you’ve got to have even bigger growth going on in order to justify the valuations of the companies that make this up. There’s a $2.5 trillion factory investment that has been made, as he put it, but these companies combined are $30 trillion in enterprise value. I believe fundamentally someday cash flow has to materialize from that complex to justify the valuation. So maybe that’s wrong. Maybe the world has changed and you no longer need that. It’s no longer about that. Something else drives the price in perpetuity. But I’m not putting my FIRE plan on that particular assumption, that dependence ultimately on cash flow has evaporated going forward.
Mindy Jensen: I mean, you both make a really compelling argument. Now it’s time to dive into a rabbit hole and go into a bunch of different research projects.
Scott Trench: So Mindy, you know, I think the ten million dollar question here is, are you going to draw a line in the sand about how much of your portfolio one stock is allowed to be at this point? Or are you going to keep riding the SpaceX and Tesla wave?
Mindy Jensen: Well, I pulled up our spreadsheet, and SpaceX specifically is about 43%. I am starting to get a little uncomfortable with that much in one stock, and it has skewed all the rest of the percentages because so much is in that one stock. It’s time to have a conversation with Carl about, you know, how much do we really need? And maybe we carve off a portion of that, take it out completely, and okay, this is the money that we are going to be able to live off of for the rest of our lives. And then everything else we can continue to experiment with, because we do have far more than we need and we want to leave a legacy to our kids. But it’s going to sound kind of snotty, but it’s hard to walk away from a position that you believe in. And we’re not investing in SpaceX for today. We’re investing in SpaceX for five years from now.
Scott Trench: Mindy, what would you tell a listener who came in and said, here’s my position and here’s where I’m at? What would you tell them to do?
Mindy Jensen: You have too much money in one stock and you need to diversify.
Scott Trench: It’s funny, it’s very hard to move off, for example, in my position from the buy VOO or buy VTI in those areas. I can defend, I think, the— hey, if you’re comfortable with mega-cap complex, equal weight and factor tilts are well-researched areas to go into there. But I think that’s something that’s a struggle for you guys: this is not a position you could justify ever saying to somebody else to hold. It’s interesting in this particular context how that works. I really like, for what it’s worth, his rule of— there’s a number, it doesn’t have to be 15%, but there’s a number beyond which I will prune this and move into other plays here. Because even if your confidence— let’s say that your next level of confidence, SpaceX is a 10 out of 10 confidence. That’s what you think is going to be the home run over the next 10 years. And then there’s something else that’s a 7 out of 10. It’s a big gap. That’s not SpaceX or Tesla. Just moving your money into that probably gives your portfolio a much better risk-adjusted— I need to explore this mathematical concept, but I think that will give your portfolio a much better risk-adjusted set of returns than just having this large of a single bet on your highest conviction investment.
Mindy Jensen: Yeah, we are in that position though. We believe it’s going to go up, so you don’t pull your money out of a company that you believe is going to go up. I think that’s what he said you do. Well, that’s what he said he does. Yeah, so Scott, Carl, and I, we’re literally wrapping up this week the move from the old house to the new house. I’m in my new studio, you can see if you’re watching on YouTube. And then next week we’re going to FinCon. So the trip to FinCon— while we’re there, the trip home is going to be a lot of conversations about this. So I’m going to play this conversation for Carl so he can hear what Professor Damodaran has to say about all of these different things and get his opinion on, you know, what he wants us to do with our money, because it is getting to the point where it’s hard to justify. So, ooh, more money conversations. Yay, my favorite.
Scott Trench: Some last parting thoughts on AI, because I am an AI power user, unlike everybody else in the discussion. I’m like the guy who’s like, hey, I’ve used it all the time and I’m kind of bearish on the investment profile for the factory here, but I love the product. What I observe as a user is a continuous leapfrogging of one model to the next. Grok is great. GPT-5, the new version that just came out, is phenomenal in terms of what I use it for. Opus 4.5 from Claude was an incredible leap forward. And all I can project is better and better use cases for this stuff. A couple months ago, I was saying I spent $169 and got all the AI I wanted from these things. I think that has since increased to like $250 a month. Again, I’m a power user here at BiggerPockets Money, but I shifted it. I shifted it from Claude to ChatGPT, basically, for those who are using the products, because GPT-5 just got better. And so that’s the game, I think, here: I’m building everything I’m doing, everything I’m working on, to be portable, because I’m betting on the next AI model coming out of nowhere from the next provider. Maybe Grok leapfrogs it in a few months. Maybe Gemini does in a couple of months. Maybe Perplexity. Or maybe all these guys get arrows in their backs, as is common in technology pioneering, and a new one that doesn’t exist yet takes over in two years. I’m not going to build a business or any of my workstreams in such a way that they’re dependent on any one model. I’m going to build them so that as soon as the next one leaps forward, I can port it all over to the new one. And I don’t think that should scare the investor here, because I’m very price-sensitive and usage-sensitive to this stuff. And I think a lot of people are going to— there’s no reason why the market, in a general sense, can’t be as well.
Mindy Jensen: Yeah, I do think price sensitivity is going to come into play, but I have to think that somebody is just going to knock it out of the park and it’s going to be so good that people are going to go use that one. But I am not a power user of AI.
Scott Trench: Let’s get out of here, Mindy, and see what the world looks like, and we’ll invite it back in 20 years, see how it all turned out.
Mindy Jensen: That sounds great. I would love to talk to him in 20 years. All right, would you like more financial independence information? Follow us on Instagram, Facebook, and YouTube @BiggerPocketsMoney, or you can head on over to biggerpocketsmoney.com to sign up for our newsletter, and you can also find free resources, calculators, and templates to help you accelerate your FI journey, and that is free. That wraps up this episode of the BiggerPockets Money Podcast. He is Scott Trench, I am Mindy Jensen, saying see ya, chia. Meet a flat fee or hourly financial advisor who actually understands Scott and I built a list of FI-friendly professionals to help you on your FI journey, and we’re constantly vetting and adding new pros to the list. Find yours at biggerpocketsmoney.com/FIpro. That’s biggerpocketsmoney.com/FIpro.
