Ben Thompson on Big Tech, China, and the AI Boom Running Out of Money - [Invest Like the Best, EP.487]

The U.S. and the AI Race

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2:19So Ben, if you can believe it, how long it's been since we last did this. The world was very different. No AI at the time. We talked about aggregation theory mostly. I thought a fun place to begin since the world has changed so much is to hear what you think it would mean for the U.S. to win the AI race. I think it would be very problematic for the U.S. to win. Let's say we take the most sort of fantastical scenario where if you control AI, you basically, your military is better than anyone else. Somehow it fixes their manufacturing, all these things that I don't think AI is necessarily going to do because they sort of deal with the real world.

2:53But in this world, what is the game theory optimal response of China? To blow up TSMC. Game theory can get very sort of convoluted and complex. To me, this one actually isn't that complicated. There is a fundamental disconnect that I have with a lot of the rhetoric coming out of Silicon Valley, coming out, I think, of one of the labs in particular, where if we get to a place where we have a meaningful superiority in terms of a military national security perspective, I think that's very dangerous for the world.

3:25But in that state, how much does it extend beyond TSMC being blown up? Because in that state, I would assume we figured out how to build fabs here in the U.S., you know, to some degree and are less reliant on that one choke point. I think there's a little bit of magical thinking, which I just invoked in terms of manufacturing and whether it be fabs, whether that be actuators, all these precursors. I think the degree to which we are dependent on China is underappreciated and is not something that is going to be fixed outside of a conflict.

4:01Just because fixing so many of these things is going to be dramatically dumb. If your competitor is sourcing from China and you're going to start sourcing or getting things from the U.S., you're going to be at such a disadvantage, relatively speaking, that you're just not going to do it. So you do it when you have literally no choice. And that works for very big headline items. You can browbeat Apple to move some of their iPhone manufacturing to India, for example. But even that is a good example because Apple is not truly moving out of China.

4:31They're diversifying to an extent, but it would just cost so much. It's like paying an insurance policy that if you don't have to pay it and it's astronomically expensive, you're just not going to pay it. It's one of those sort of hypotheses that I just have a hard time even grokking because the only world I see where we truly pull out and have no dependency on China, such that if they want to blow up Taiwan, who cares? There's no impact on us. It seems pretty fantastical to me.

5:02And I think there's a bit of facing reality in this regard that is not present in these conversations. Put yourself in their shoes. What do you think the motivations are? Everyone can use a good bogeyman. I think from the AI trade perspective, nothing works better than we have to beat China. And I do think we need to beat China. We need to be competitive. I despair at the extent to which over the last few years in particular, so many of our responses, particularly from a political perspective, has been to try to be like China.

5:37I think we should be going the other direction, more openness, more innovation, less top-down control, less restrictions on speech and things along those lines. America succeeds by being on the leading edge and by leading into that.

Global AI Equilibrium

5:51You said probably the U.S. being purely dominant in AI is not the right end state for the world. What is your ideal equilibrium for how this goes worldwide? There's a bit where AI right now is kind of like the Taiwan situation in that the current status quo actually doesn't seem so bad. The question is, how sustainable is it? But maybe it's sustainable for longer than we think. The way I think about it right now is I think OpenAI and Anthropic are clearly on the frontier. Who knows what's happening with Google and then Grok and Meta are chasing them.

6:25Meanwhile, the Chinese are very capable, very smart, and also definitely distilling these models to sort of stay about six to nine months behind. And it feels like a pretty good equilibrium that I think is generally favorable to the U.S. Now, the question is, how long can it stay this way? And there's lots of questions on there, like, can the Chinese actually pull ahead? I'm still a little skeptical for various reasons, whether it be from chips. Getting to the leading edge in that last six to nine months is very difficult.

6:57It's going to be instructive how Meta and Grok do in terms of actually catching up, especially as we get to the world of AI improving itself, using AI to make the AI better, which I think is definitely a real thing. I think you see a real acceleration from both OpenAI and Anthropic recently, which was sort of theorized and it seems to be coming true. And to the extent that's true, can you actually catch up? And I think the other question about this, by the way, is to what extent does that apply to cost to serve, to marginal costs? If you can apply AI to optimizing your stack, to figuring things out, to analyzing all the data, is your cost to serve structurally lower than anyone else?

7:33This is the thing about the open source models. The talk about them being free is bizarre to me because it's marginal costs. You still have to run inference like GLM or Kimmy. Kimmy is very expensive to serve. The cost per answer is significantly higher. Everyone referring to these as free, it feels like in the narrative, it's in people's head that free is free. Now I can use AI for free. No, you can't use AI for free. You're not paying necessarily the R&D to create the AI, but you're definitely paying the inference to sort of run it. So right now, I kind of like where we are and the pushback would be that's right now.

8:08It's not going to stay that way, which I think is fair pushback, but I don't know this way longer than we think. If you can know anything about the future of how this will go to be more confident in like where the equilibrium will end up, what is it? Is it like the length of the S curve, like how far up the S curve we are? At some point, these things presumably will level out, maybe not. What would be the thing you'd want to know that would give you a better sense of what the future might look like? I am concerned that with the scare around people freaking out about Mythos and this hugging face incident, that the actual implication of that is not that we reduce these dangers, but we just stop releasing stuff.

8:50We on the outside start to lose any sense of like where exactly what is actually the frontier and where it is. There becomes sort of a false sense of security because right now everyone's basing their understanding of Mythos on Fable, but how good is Fable actually relative to Mythos? That sort of gap is only going to, I think, increase over time. So I think that's a real question that I'm not sure about. This question of the recursiveness and AI sort of making itself better, does that lead to some sort of takeoff?

9:24And at the end of the day, there's timing questions in lots of different ways. I'm worried about the timing mismatch in terms of the actual return on investment producing enough revenue to fuel investment. We're working our way down the capital curve. We started with free cash flow. The speed with which the tech companies blew through the debt markets is kind of incredible. It took like a year. And now Google's issuing equity. NVIDIA's putting together these. This $500 billion thing. This $500 billion thing to tap into like pension funds and insurance floats and things like that.

9:55What's after that? Where's the money come after that? Well, ideally, we actually flip back to free cash flow funding this. But if there's a gap there, if we don't get there soon enough, then we could have a big blow up. But at the same time, even if we have this blow up, the AI is not going away. It's not going to stop improving. It's going to keep sort of progressing in a way that we look back on the dot-com era. We look back on the railroad era. We look back on whatever bubbles through history, ultimately immaterial in terms of the broad scope of humanity, even if they were very devastating.

Railroads, Bubbles, and Capital

10:32What can the railroads teach us to think? It's not the last bigger build out, right, in terms of percent of GDP or getting there? I think we might be bigger at this point. Or it's like it was the biggest. In the ballpark. The railroads had a real duration mismatch. To build a railroad and make money off it was a decade or multiple decades long endeavor. Whereas you had to issue money to pay for it in the short term. And the world ran out of money, right? And I think that is probably the aspect. I think that's why people reach for the railroads.

11:04Because everyone talks about, are we going to have enough compute? Are we going to have enough electricity? Maybe the nearest term questions, are we going to have enough money? Which is kind of a bizarre thing to think about. That's what happened in the 1870s. The world just ran out of money. The funny thing is the railroads kept operating and they expanded the West. Their contributions to GDP was astronomical. They're still contributing to GDP. Railroad money is what's going into Google right now for Berkshire Hathaway.

11:34It's very funny. It's quite literal. Berkshire Hathaway has this problem. To me, this NVIDIA deal is very much paired with the Google equity issuance, which I thought was shocking when it happened. Why was it shocking? Because it's Google. They can't raise money. Like, why are they issuing equity? Why are they reducing their upside if they believe so strongly in this? But the Berkshire comparison is interesting because, to a rough approximation, they have seized candies famously, right? Tremendously high margin business. The problem with a lot of high margin businesses is the percentage profit you can make is very high,

12:09but the absolute profit you can make is cash. There's no reinvestment runway. That's right. You're just accumulating cash. The brilliance of the BNSF railway thing was basically they took the seized candy profits and said, here's another industry whose margins are way worse, but the absolute dollar amounts are so large that those way worse margins result in absolute profits that are much larger. BNSF in 2025 or something, the amount of free cash they threw off in one year was more than seized candies

12:40that threw off its entire lifetime, even though you're talking about a low margin business compared to a very high margin business. I think there's an aspect from Berkshire Hathaway where once your capital gets so large, you start operating in a world of like absolute numbers as opposed to percentage numbers. And the reason why I thought that was so interesting, that story, is it seems to capture where Google itself might be going. And so there is very symbolic for them to invest in Google. Google has this unbelievable high margin business of search,

13:11one of the most perfect, beautiful business models of all time, and the purest aggregator of them all, like scales in every direction, doesn't have to invest any money to do it. Everything's zero marginal cost. It's amazing. Meanwhile, there's this AI opportunity, which requires just astronomical, it's just incinerating cash. But you can imagine if AI is intelligence and its TAM is basically all white collar work and eventually with robotics, everything potentially, the absolute profits available here, even if the margins are lower,

13:43is so much larger that will we look back and Google search what sees candies? It feels like that's what's happening. In that world, you use all your free cash flow. They've done that. You tap the debt markets to the tune of hundreds of billions of dollars. They've done that. You issue equity. What does an equity issue do? It dilutes your interest and your interest of your shareholders. So you have a smaller percentage of the pie. Well, you have a smaller percentage of an astronomically larger pie. At the end of the day, no one's going to be complaining.

14:14It was very symbolic. Berkshire being the symbol of that equity issuance in that are they actually not just an investor in Google, but a model for Google and where they're going?

Verifiable Domains and AI Utility

14:26I'm curious, setting aside the commercial and competitive components of this, like you're describing, how AI pilled on the pure technology would you say you are relative to other people thinking about this space? I have a view that is both super bullish and less bullish in some respects. Okay. So I am not fully convinced about the generalizable argument. AI is clearly incredible at coding. It kind of blows my mind that people were doing this a year ago, like actually writing out code.

15:00It's very good at math, obviously, but the obvious riposte is that these are sort of verifiable domains. What is the evidence or where is the compelling evidence of being very good at verifiable domains queenly translates to being very good at sort of unverifiable domains or domains that take have a very long sort of verification loops? I think that's still a little bit to be determined. And it's interesting because I raised this question and there are some people at the labs that were on a panel and I was kind of annoyed at the answer because the answer took me for an AI bear.

15:35Oh, well, people thought we couldn't solve chess or we couldn't solve Go and we solved those easy enough. And I'm like, I thought we could solve chess. I thought we could solve Go because they're knowable domains. Scale was the answer to both of those, but also both of those were bounded. What is the go-to example that's not chess, that's not go, that is genuinely in a new space that's sort of an unknowable space where it's doing things that were not possible? That is sort of the I'm not fully convinced sense. However, AI trained at a rough approximation, trained on all the data of the Internet, all the

16:11data of the Internet, that's distillation. It distilled all of the end state of human thought, the actual typing on Reddit. It doesn't have the traces. It doesn't actually have the thought, the emotion or whatever that went into typing that comment or typing, writing that essay, say Neuralink, whatever. What if the actual payoff from Neuralink is actually capturing the traces of human thought that actually dramatically expands the capabilities of these models? In this world, my concerns about verifiability is like, well, we solve verifiability by getting

16:46more data. My sense is that a huge number of jobs, a huge amount of economic activity does not exist in these domains that I'm not convinced that AI is good at. Actually, there's a lot of people in the world who are kind of like sentient AIs to a certain extent. They operate very well in verifiable domains. They're given jobs. They do them. And it's almost like a somewhat pessimistic view of humanity to a certain extent. But I think that market is so huge and so large that if the models did not improve at all from where they are right now, the economic opportunity is actually massive.

17:20I wrote an article a while ago. There's the whole like accelerationist movement. What I call myself was a reluctant accelerationist. I think we need to push forward because we can't go back. And the worst thing we can do is get stuck where we are. So I'm very AI piled in terms of its impact on the economy. It's sort of upside in terms of monetization. I'm not sure about the timing. What would be like the gradient towards it? Imagine law or medicine where I don't know whether or not you would consider those verifiable

17:53like law is like a code of some sort. Medicine, we have a certain state understanding of things. I mean, I think medicine is by far one of the biggest opportunities. It's both of the biggest opportunities and also one of the most challenging ones because of all the regulations and all the access. Like if you could turn an AI, turn machine learning onto all the medical records, I think the number of discoveries and improved treatments we could come up with in a very rapid amount of time would be unbelievable. So that is a very optimistic view.

18:23On the flip side, like what is that going to happen, right? I think the optimistic frame I put on humans is our capacity to create needs is sort of unlimited. So I think we'll do a very good job of creating new opportunities and job serving the fullness of time. The sort of more pessimistic way to put it is our ability to create red tape and muck is also fairly unlimited. How much of our economy is actually we've managed to create more and more jobs that is just make busy and make slow to a certain extent.

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Aggregation Theory in the AI Era

20:23If I go back to the early 2010s, maybe the aggregation theory was stewing in your brain and then you published it in 2015. I think it's fair to say like that theory, that idea, maybe you can just quickly remind people what it is, defined the winners and losers of that era of technology. I'm really curious how you're thinking about what theory or principles will define this era of winners from like a financial perspective and market cap perspective. I go back and forth even just on the question of aggregation theory itself. How much does that apply in the current era?

20:54Just the same thing. Yeah, yeah. Yeah, because like a pushback that people have is one of the key components of aggregation theory is zero marginal costs and zero marginal costs shows in lots of ways. The one that I focused on the beginning was distribution and people say, oh, I don't have distribution. I have to pay Google for ads. Like, oh, no, you have a website. Your problem isn't that you have distribution. Your problem is you don't have demand and you're paying for demand when you're paying for ads and things on those because the aggregators control demand and they control demand because in a world of abundance, the hard problem is not distribution, it's discovery.

21:25How do you actually find what you're interested in? So the companies that solve discovery in their domain come to dominate that markets, they get a virtuous feedback loop, that sort of aggregation theory in a nutshell. And the other thing is transaction costs. There's no transaction costs. Google can scale to the whole world and they can scale to the whole world, not just on the user side, but also on the monetization side. The vast, vast, vast majority of advertisers on Google or Meta never talk to someone at Google or Meta. They just go up and they buy ads. It's all done by computers. The perfect business. And those computers, from a business perspective, cost zero dollars.

21:56AI, obviously, that changes significantly. Inference costs are real. But then again, how real are they? They're real right now. I don't know. Are they? Depends on the company. But they're way more real than those prior examples. Well, like if you look at gross margins. For sure. But you have this incredible spread. So you have people, I think the vast majority of people who are using ad today are using it as basically a Google substitute or like a recipe maker or whatever it might be. And my suspicion is that the cost to serve those people is extremely low.

22:28And low in the basically similar to serving them a web page. I would imagine it's marginally higher, but not that much higher. Then you have on the other extreme, people who are actually leveraging test time scaling. It used to be we just scaled by making the models bigger and bigger. Now you can scale as far as time. How long do you think about the answer? Well, you could think about the answer for days or weeks or months. That is directly marginal cost. Every second longer you're thinking is costing more money, which speaks to like we think

22:59about AI and inference as this one question. That's I was pushing back on you. But actually, the marginal cost question for the different user, the user using free chat GPT and the user trying to solve a math theorem. They're not even remotely in the same universe. I think you see this challenge actually in the enterprise in a very interesting way. Microsoft recently, they are shifting their enterprise plan. So they come out with like an E7 plan, $100 per user per month. That includes some amount of usage. But then they also are charging for usage on top of that.

23:34I think this is a kind of a fraught position for Microsoft to an extent, because the positive way to think about Microsoft is they do everything you need as a business. Every individual component might not be the best, but you get it all for one price and they all mostly work together. And if you're particularly a small or medium sized business or even a large enterprise, there's real value in that. That's right. It makes life easy. The moment you start having to think about how much you're paying, it's not just that

24:05that's a new decision, number one, that is untethered from headcount. Microsoft got the benefit is when you were hiring a new employee, you would think about the cost of that employee and baked in the cost of that employee is $100 a month or $50 a month for their license. It was kind of a thoughtless revenue stream from Microsoft. Now, if you think about usage, you have to think every single month, how much do I want to spend? That introduces two problems. Number one, most companies aren't set up to do this.

24:36They make budgets like once a year. This idea we're going to be thinking about through our budgetary allotment on like a monthly basis doesn't compute. There's an aspect where they're used to thinking about CapEx decisions or one time cost. And there's a bit where when I'm talking about this employee, like the loaded cost of employee, it's not CapEx, but it's kind of like CapEx. It's like you make the decision up front and you don't think about it anymore. The decision is sort of already made. But if you're thinking about usage, you're doing it again. The final thing is, if you're every month looking at your Microsoft bill and how much

25:08did I use, you start thinking about what am I paying for? How good is each of these products? Should I actually just start thinking about and spraying this out? And I think they had to do it because that extreme of user who uses a ton of tokens and is actually leveraging AI costs way more to Microsoft than $100 a month. They can't support them, but they want to hold on to this set cost for the vast majority of employees who can fit in that because they need to ask their customers to think a little bit for those extreme employees.

25:40But they don't want them to think too much because that breaks the model in very surprising ways. Are you surprised at all that the recipe builder user that is very low cost to serve, that there hasn't been a great business model that's emerged around them just yet? Google and Facebook are sort of business perfected in this prior era. They haven't seemed to figure this out at all. I am frustrated, but not surprised. This is obviously a market that should be supported by advertising. That is why advertising is always the consumer business model. Consumers don't want to pay.

26:10There's two things to understand about consumers that Silicon Valley has to relearn about every 10 years. Number one, consumers do not want to pay for software. And number two, consumers do not care about being productive. We went through this in early SaaS. The canonical company for this, in my mind, is Dropbox. So Dropbox, unbelievable product, like especially when it first came out in business school, I was one of the first people who used Dropbox. And that went off like crazy. I have so much storage still, like my free Dropbox, because I gave out my code to like so

26:40many people. So Drew Houston makes this amazing product, so easy to use. It's just absolutely seamless. He was very clear about this. He wanted to build a consumer company. And there's that famous story of him meeting with Steve Jobs. Apple was interested in acquiring Dropbox. And they're like, oh, we want to build a company. And Steve's, you know, you're a feature, not a company. Which that plain Jane just files sync. Apple did make a feature as far as like sort of iCloud Drive. And with Dropbox, they grew very fast. And then they had like a two-year lull.

27:11And in that two-year lull, what they had to do was basically completely rebuild the app from the bottoms up. Because not enough consumers are going to pay for it. Enterprises could see the value they would pay. But if you want an enterprise, you need permissions. You need control. You need someone else to be able to set all these sorts of things. And their app wasn't even created to do that at all. So they had to rebuild the whole thing and realize the only way we're going to make money is by selling to companies. Why do companies pay? Because companies are paying employees. To the extent they can make their employees more productive,

27:44they're getting a greater return on their investment. It's the complete inverse of a consumer. A consumer is like, I spent all day working. Why do I want to come home and be more productive? I want to sit on the couch and watch reels. But you see that with AI. And you also have this overarching just skepticism of advertising. I've gotten so much traction on Chatechery by being an advertising appreciator. And I go back and read my early articles about advertising that were kind of directionally correct, but also like were not very good at all.

28:16But I got so much traction doing it because I was the only person writing about advertising. In a world of everyone who want to have a blog, in Twitter, no one wants to talk about advertising. But even now, there's in Silicon Valley, this sort of embarrassment about the fact that the Valleys, in many respects, monetized by advertising. And particularly during the last sort of eight years, there was a Facebook's icky. The best engineers don't want to go work on this problem. And so you literally had OpenAI replaying the Dropbox story, but at like 100x the size,

28:46being like, no, we're going to sell subscriptions to consumers. They did. They sold a lot, but they didn't sell enough. If you're going to be in the consumer market, you have to be doing advertising. They're doing advertising now. It's a little weird. They finally pivoted to doing advertising at the same time. They're like, oh, crap, we need to go for the enterprise because Anthropic is kicking our end. So I'm not quite sure what they're doing there. They have been rolling out ad features very rapidly. Things like copy and the connections with retailers. So you know if a purchase went through, so you can do all the tracking and things like that.

29:19I'm very interested to see how that goes. There's a bit where had they leaned into advertising immediately as soon as ChatGPT was a hit, I think they would have a killer ad product right now. I think that Google would be in much bigger trouble. I think Meta would be in much bigger trouble because if you have this flywheel, the thing about advertising with consumers is your ability to monetize the consumer goes up as infinite because the advertiser is bearing the price increase. So there's zero elasticity issues.

29:51If you're charging consumers a price, if you want to raise the price like Netflix, this is their problem with the subscription plan. How much can they raise prices before consumers rebel and drop a tear or give up the service entirely? Charging people money is hard. Giving people things for free is easy. And it's very frustrating that OpenAI did not pursue this sooner.

Compute, Shortages, and Commodity Markets

30:11I know you've been spending time with some of the big money firms and sources of capital. What is your sense of their appetite right now and how they're thinking about the future? Because I think this year it's going to be $800 billion or something that we're going to spend in CapEx. Next year is supposed to be $1.3 trillion, I think, is the current estimate. It's going to keep going up from there. We're burning through all the compute that gets installed basically immediately. It's such a strange circumstance that we can use the capacity right away as soon as it's online. Well, that's the thing, though. So there's a few timing mismatches that are happening right now.

30:43We can't use it right away. All the bulls on Twitter is always like, we don't have enough compute. We don't have enough compute. Well, we don't have enough compute because there was insufficient investment made in 2023 and 2024, which, yes, absolutely. And by the way, if you think there's not enough compute, TSMC decreased their rate of growth in 2023 and 2024 and 2025. Our shortage of compute is going to get worse in the next few years because a fab, the lead time is even greater than a data center. Today, when we say there's not enough compute, it's not like all the money that the companies

31:17are putting in today manifests in compute short of your computer. No, it all manifests in compute in 2028 and 2029. On the calls, you have both Andy Jassy and Cyanadella are out there saying, look, we're just building data centers. Like, these are the shells. We might not use them now. Maybe we'll use them in the future. And we only buy GPUs when we know there's demand for them. That is a great story to tell. I'm not sure that I think is a lot of BS because the reality is if you've built the shell, that

31:49money is sitting there. You're not going to let it just sit there. If you invested a fixed cost, and this is the whole logic of commodity markets. I think tech in general doesn't understand commodity markets. Tech is, by and large, focused on if I produce a highly differentiated product, and that differentiation could be like software. It could be a network in terms of developers. It could be a social network sort of thing where peer-to-peer, where I'm highly differentiated, then my ability to charge higher prices provides sort of my profit margin. So the classic example is like Apple.

32:19They have their ecosystem, and they have their software, and they have third party, and all those sorts of things. And so they can charge 50% margins on their iPhone. Everyone looks at Apple as like the ideal business model. That's how you run a business. But in a commodity market, the price is set by the marginal supplier. Cost of service is all that matters. That's right. I had a good friend in Taiwan who was in shipping. Fascinating industry. It's kind of like the airlines, too, another industry that I love to look at. You buy a ship, and the cost of that ship is depreciation. Your marginal cost is actually quite low.

32:50It's the fuel to run the ship, and the cost of the crew, and like your port fees. Not that much. What that means is you are going to run that ship. As full as you really thought. No, you're going to run it no matter what. And you're going to bring down the price of a container as low as it needs to be to cover your marginal costs. Now, your paper losses in this situation might be very large because your accounting loss includes depreciation. But the depreciation is an accounting figment. You already paid the money. You're going to run that ship at whatever the market will bear.

33:22And the beauty of the container, it is a pure commodity. The cost of the market is going to be the marginal cost. Now, if it gets low enough, at some point, people will exit because their marginal costs, they're actually losing money on a shipment. Not just paper money, but like actual real money. They will exit, but then the supply is diminished. So then the price will go back up and you get this interplay of sort of coming in and off. But then let's say the market's very high, like it was during COVID. It's like, wow, we're making so much money right now because there's not enough supply.

33:53There wasn't enough supply of ships. So containers went from usually being like $3,000, $4,000 to $17,000, $18,000. The amount of money that these shipping companies made in a very short amount of time was insane. What happens though? Well, imagine if we had more ships, right? The problem is it takes two years to build a ship. If everyone makes this decision simultaneously, you suddenly have a lot of ships, price plummets, etc. Where we see this is in components, in memory in particular. Memory, very famous for boom and bust cycles, people entering the market late.

34:24But to what extent are data centers going to be memory makers? Where right now everyone can see we don't have enough compute. So everyone's like, we absolutely have to be investing because there's so much money to be made. And look at our payback period. The problem is you're measuring your payback period in a time of scarcity. Is that payback period going to hold in a time of abundance? And the sort of the bulls would say there's never going to be a time of abundance. AI, we're going to be short forever. We're going to be short forever, which maybe we will be. My concern is even if that's right, we could still have an air gap in that there's so much money going into it right now.

35:01And not enough has come online to actually make sufficient revenues to handle the situation where we run out of capital. I believe in AI. I think it's a real thing. I think the economic impact is going to be astronomical. I think all the concerns about societal impact are very real and are going to come to bear in a major way. You can believe all that and still be worried about, are we going to make the bridge to this actually generating the level of returns necessary to continue to feel this sort of going forward? Can you zoom in on TSMC and the component makers where fabs are involved?

35:33And so far, at least my understanding is that they've been quite conservative in their willingness to expand capacity, build new fabs, meet the market's demand with similar growth, which they have not done. If that just really limits this whole thing and prevents us from getting one of these giant overbuilds. We can talk about a few different ones. Like, we'll start with memory. Memory used to have tons and tons of memory makers. Every time there'd be a boom, memory makers would sort of reenter the market. New countries would come in, like Taiwan used to have like a memory market.

36:04But you would get these exact dynamics. If there's a shortage of memory, there's so much money to be made, you can't bring capacity on immediately. It's the same as shipping. It's the same as what we're seeing right now. That would spur people to come in the market. You get too much capacity. Prices would plunge. And people would just get blown out. Because the issue is the upfront cost for these is so large. Just like buying a ship, like building a fab is even more so. And memory now, like the leading edges of memory are using things like EV machine. So the costs are getting into the billions of dollars for these lines. What happens is every time with these boom and bust cycles, some people would enter, more people get washed out.

36:37You go through these famous historical moments for these memory cycles. Companies just get blown out. One of the most interesting, actually, memory stories is how Samsung sort of took over memory was they saw it as an opportunity. And they had studied history. And they realized that actually the way to take over the market is to invest into downturns so that you're ready when the next cycle comes around, which requires a ton of guts and a ton of discipline and a ton of money. But they did that. It basically wiped out the Japanese. That's when the South Koreans generally took over the market in a major way. But it got down to three.

37:09And the problem is three, it's not a monopoly, but it's kind of an oligopoly. And they all got a lot more discipline about let's not make the mistakes of the past. And we're not colluding, but we all are on the same page about let's not do that. And I think that dynamic sort of ran head on to the current moment where it just took a while for them to realize, no, there is a secular shift in memory demand that didn't exist for a very long time. I think the memory solution will be solved eventually.

37:43The other risk they run is Apple's lobbying to get Chinese memory. What is the number one focus of like algorithmic changes? How can we use less memory? I think the memory makers probably screw themselves in the long run by creating such a massive target on their back. I've analogized memory makers to Iran. The issue with the Strait of Hormuz is it's very effective. It's more effective if you don't use it. Because then it's always hanging out there as something you could do. Now they did it. Turns out it worked. But the UAE, Saudi Arabia, they're going to build pipelines.

38:16They're going to build new ports. They're not going to let this happen again. It's very painful right now. But say Iran wants to close the Strait of Hormuz in 2035. It's not going to have any effect because it will have been built around. My concern for the memory makers is they might have done the same thing. No one's going to let themselves get in this situation again as far as memory goes. TSMCs are going to be worse because there's only one. There's one company on the leading edge. Obviously, Intel and Samsung are trying to get there. It's the same thing. All markets carry risk. And a lot of the question is, who ends up holding the risk?

38:48What I think the law that tech companies didn't fully appreciate is the extent to which TSMC has offloaded risk onto the big tech companies. And the way they've done that is the risk that TSMC is worried about is overcapacity. If we build too much, it's not just that we built too much and we have all these fixed costs that are not being fully utilized. But if we build a fab, we expect that fab to run for 30 years. We've like baked in too much capacity into the system for years and years and years.

39:19So they are very biased towards being much more conservative. There's a little bit of a culture component to this, too. One of the most interesting TSMC stories, it's kind of a Nellist, that Samsung story, was Morris Chang retired in like the late 2000s. New leadership took over. There was the Great Recession. And so they pulled back their planned spending. He comes in, fires everyone. And he's like, the iPhone just launched. This is the biggest opportunity we've ever seen. We need to be investing, not cutting. And they invested through the Great Recession and through that downturn.

39:52That's what laid the foundation for them taking over sort of leading edge semiconductors in that time. Morris Chang is a one of one. On the Mount Rushmore, in my mind, of the greatest and most impactful tech executives of all time, the entire fabless model is so critical to what tech is and what it does. And also just the guts to do that at that time, particularly in someone who lived there, a culture that doesn't necessarily tend to make those sorts of bets. TSMC, they were pretty conservative, to be totally honest. So what happens, though?

40:23Where'd the risk go? TSMC's like, we don't want to take the risk. Risk doesn't disappear. It just moves. The risk is right now where you have every single big tech company realizes if we had more compute, we could be making more money. So there's lots of foregone revenue and foregone profits that is the manifestation of the risk that TSMC handed off to them. Risk doesn't disappear. It just gets handed off. And sometimes that risk doesn't manifest in losing money.

40:53It manifests in not making money. And there is money not being made right now because what happened was they were very excited about 5G. They did a big wave of like investment, expanding their fabs in around 2020, 2021, 22. And they're like, oh, yeah, we're good. Like I said, 2024, Chattel B. Big thing in tech in 2023, in 2024, their growth rate went down. In 2025, their growth rate went down. In 2026, it's up now. It was very funny because I was writing about this a while ago. And then I think it was like one or two earnings calls ago.

41:26Suddenly, CCUA, the CEO and chairman, is talking about like use cases for AI, the whole earnings call in a way he never had before. This is why the merrymakers are scared. Usually there's like a bullwhip. And they're worried about being at the end of the bullwhip where the demand happens and it works its way down the chain. And they're at the end. And then they double down. It's already too late. They're wasting all their money. And I think the thing with AI is if it's a bullwhip, it's like the longest bullwhip of all time. There's still so much to be built. And it just took a while for Asia to get the message where these sort of companies are.

41:59I think they've by and large gotten it. But them getting the message, it then takes several years for that to actually materialize. Do you have a sense for how long you think it will take given the extreme shortage of compute? The interesting thing is what this means for Intel and Samsung's sort of logic business. I've been writing about the problem of this dependency on TSMC for years. One of my first articles in 2013 was exhorting Intel. Well, I say you have to build a fab business. You're not going to be a designer anymore.

42:30There's a huge business in manufacturing chips. I thought I was late writing it then. Their stock goes to the moon throughout the 2010s as they're riding the sort of cloud wave. And it wasn't until 2020 where they finally realize we fell behind. By the way, there's this huge opportunity. We're totally unprepared for it. We don't have a customer service mindset or culture organization or all the IP building blocks and all these things that TSMC has. And they need a customer. They need customers to help them actually build a real foundry business.

43:00So I would write about this as a problem. And I write about the China issue. You're dependent on a company that is 60 miles offshore of our greatest geopolitical opponent who thinks it's theirs. So these are big problems. That's where I came to appreciate this insurance issue. For a big tech company to go to Intel and say, Intel, you make our chip. And by the way, the biggest benefactor of this is going to be you because you're going to learn how to work with a partner. And the biggest pain is going to be us because we're going to have to figure out how to work with you.

43:31We could just go to TSMC. They are awesome. They are so great to work with. We know they're going to do a good job. It just never made rational sense for anyone to go work with Intel. That was their fundamental problem. In an unchanging world, TSMC would just win forever. But this is where TSMC, in some respects, made the same mistake as the money makers, made the same mistakes as I ever am, if I can continue the analogy. Because they didn't invest the last few years, the shortages are going to be so acute. Big 10 companies that were foregoing so much revenue and so many profits because we don't have enough compute.

44:05We will go through the pain of getting Intel up to speed, of getting Samsung's logic up to speed. The scarcity is what ultimately saved Intel. I expect at some point that they're going to announce some major partner for the first time. It's going to be a big deal. But ultimately, TSMC brought it on themselves. It's the cure for high prices is high prices thing. We're going to route around them. There's all these things as like an analyst sitting on the side. You can write these things. And it's one of those things I sort of warned. No one's going to pay insurance that they don't need to pay when that insurance expected value is negative.

44:38The way to solve the geopolitical problem of dependence on TSMC is to come up with a compute use case that is so massive that everyone is economically incentivized to bring other people up to speed. And then we get the sort of geopolitical insurance for free. If you think about the, let's say, top 10 or 15 technology companies, which ones do you think have the most interesting setups today for their business?

Amazon and Big Tech Strategies

45:02The answer is always Amazon. The reason Amazon is so compelling is the extent to which they build for them. They are their first best customer. They provide the scale to get basically anything off the ground, which they then sell to other people. AWS is the most obvious example. AWS, contrary to sort of popular thought, was not spare Amazon capacity. Actually, it took a long time to get Amazon.com onto AWS. What it drove was the understanding that we can't be having so many meetings like we need to have just compute that you can plug in purely API surface.

45:36You don't need to talk to anyone. It's just there. And oh, by the way, if we do that for our internal retail teams, we could do that for anyone. Turns out the retail is so big. We have to start with everyone else. AWS actually started serving external customers before it served internal ones. But now it serves them all. You got other products like, say, the logistics, where it was the opposite. Right now we're using external providers for logistics, UPS and FedEx and USPS. We need to build this up ourselves. And now they built it up themselves. They're offering to third parties.

46:06Other people can use their delivery services. You see this in market after market. They're talking about some of their AI products or their chip products. What's the beauty of the Graviton or the Trainium, particularly the early versions? The early versions were terrible. But if you're on Amazon and you're using some of their managed services, like, say, the Redshift database service, they don't tell you what the processor is underneath that. You're just buying a managed service. So they can put all their crappy processors underneath the services they're selling, and that gives them the volume and the capacity to iterate them and get better.

46:39And they get to the point where they can actually sell them externally because they were the first best customer for Graviton. Graviton got better because they were the first best customer for Trainium. Trainium got better. And now Trainium is obviously running Anthropic and AI products. We'll see if any of them take off. They have call center software. Their call center or their customer experience is going through AI. By the way, it's pretty good. I haven't tried it. Moving back to America, I've been buying lots of stuff. Every summer I'd buy lots of stuff in a very brief amount of time. Sometime in, like, the last year or so, you can go on and you're clearly talking to a chatbot, but the chatbot does a great job, and it actually does take care of the problem.

47:13So you can see that actually starting to work in that regard. But they're building up these AI services for their own business that they're going to make broadly available. And some of them will work, some of them won't. It's such an elegant approach given they have so many investments in the real world. Their core business feels so impervious to AI for the model version of AI. It will benefit from AI, but their moat feels deeper than anyone as far as their core business. And their ability to just sort of generate new business lines organically is very compelling.

47:48What about Apple? They've sat this whole thing out, it seems. It feels like it might be a situation of better be lucky than good to a certain extent. Apple has their whole ecosystem. At the end of the day, they do own access to customers, so they can get suppliers. This is the classic aerator play. If you own access to customers, suppliers come to you, not the other way around. So they can get suppliers for their AI as needed. And by the way, to the extent it's true that people don't want to be productive, they just want to sort of a chatbot.

48:19Not only can they serve them a chatbot and finally getting a Siri that works, but you can see a future where this absolutely can work on device. And they actually don't even need to pay for inference costs either because they're using the customer's electricity. I don't think we're quite there. There's a reason they're using Google Cloud and NVIDIA chips. But you can certainly imagine a future where that's the case. And they're in physical goods. Actually making phones is hard. Having retail, having distribution for physical goods, they're more insulated.

48:49The smartphone is so perfect. It's small to fit in your pocket. It's big enough to watch basically anything on it. You can run your whole life on it. All your entertainment is there. When we talk about customers who want to be entertained, the TV is now an accessory. It's all on your phone. I don't see anyone taking over the phone. The question is, is the phone always going to be the center? Or is there a bit where, particularly in the home, this is where OpenAI's efforts here are very interesting, where you want sort of an ambient AI, where you just talk to the AI and it tells you what you need.

49:20Apple is the best position to provide that. But can they provide that without having leading edge models? Can they provide that if they're so phone-centric? Or is it like a Microsoft situation? Microsoft didn't miss mobile. They were very early to mobile. The problem is their mobile was a small PC. They assumed the PC would always be the center and their phones were going to be something that was off that. Apple realized, no, we need to reset. The phone is not going to be accessory to the Mac. The phone is going to be the phone.

49:51The iPod helped them realize that and going with Windows and all that. But will they fall into a Microsoft-like trap? Assuming the phone's so good, it's always going to be the center. And then let's figure out around it. Or is this finally the time when actually ambient, the cloud, just in general, AI being everywhere, it can manifest through your phone. It can manifest through a device. It can manifest on your computer. Is actually better and is actually disruptive to them. I think it's possible. I also think it's totally valid for Apple to double down on what they do.

50:24The other thing about the AI stuff is, on what basis should we expect Apple to be good at this? At the most crude level, AI is this probabilistic endeavor. Apple is the king of deterministic products. If it's a physical product, you ship that iPhone, you ship it once, and it's got to be good. If it's bad, it costs you billions and billions and billions of dollars. Apple's never had an iPhone recall. It's amazing. That care and decision-making and diligence and fierceness in terms of your supply chain and making hard decisions is very, very different than everything that goes into making great AI.

51:03I generally prefer companies to do what they're good at. So, from my perspective, I'm fine with Apple not doing AI. I want them to keep making great devices. Of the five potential frontier AI winners, so OpenAI, Anthropic, Gemini, SpaceX AI, Grok, and Meta, which of those firms do you think has the most interesting setup? OpenAI and Anthropic obviously are the riskiest but also have the biggest upside. Never discount, number one, the power of belief. They think they're creating God. The most impactful things in history have usually been fueled by religion.

51:37The two religious organizations in Silicon Valley are OpenAI's kind of like mainline. They go to church every Sunday. They're sort of like evangelicals. That's Anthropic. They're all in. It is core of their belief. That goes a long way. The fact you need to make a business work for you to survive goes a very long way. Google just needs search to not die too quickly. Meta has the huge advertising business. In a world where Meta was run by anyone other than Mark Zuckerberg, they would not be on the weaning edge. That is one of the purest manifestations of founder energy for better or for worse.

52:11Their business is so amazing. You see them just easily sort of doubling down on that. Google, there's a bit where they had Google Cloud. They have TPUs. They've been doing research in this. It makes sense why they're pursuing this. Meta being like, actually, we're going to hire a completely new team. We're going to start from scratch. This all, again, is pretty insane. Credit to Mark Zuckerberg in that regard. Again, you could decide whether that's a good idea or not. And then SpaceX AI, data centers in space, the theory is there. Do they have to own their own model, though, to do that? They'd get better margins if they do. Then again, if we actually run out, whether through political opposition or power or whatever it might be,

52:46if we run out of data centers on Earth, they can run whatever model they want, as we're seeing with selling their capacity to Anthropic right now. They're all pretty interesting.

Microsoft, Meta, and Middleware

52:54Probably the case for SpaceX AI is probably the weakest because the data center in space play is so highly differentiated. If that plays out, I'm not sure to what extent they need to even have their own model. So why are you wasting billions and billions of dollars in the meantime? That's a fair question. From a tactical perspective, I love the cursor acquisition. That makes so much sense for both companies. And so I've been intrigued to see what they do. Meta is probably the most interesting.

53:24You've written a lot about this recently. I think there's a very good case to make that it is more reckless to not be on the frontier if you're a digital company. The counter to Meta is actually Microsoft. Microsoft is not on the frontier. The reason why Microsoft has $20 million of free cash flow last quarter, Microsoft paid a $10 billion dividend last quarter. There's some money, but their play is, oh, we're going to play all these off each other. We're going to provide middleware. We're going to provide the platform that enterprises will build on us, and we're going to sort of disintermediate the models. I think it's a rational play.

53:55It's the IBM play of the 90s. History echoes. Everyone talks about Google. Google, like, following Microsoft. But Microsoft follows IBM. And you can see that to an extent. What did IBM do? What's the analogy? Well, so IBM had this dominant, we talked about it in the 70s. And then you fast forward to the 90s, and IBM is this very distressed asset. And the thought was IBM needed to break up into all these different pieces they had. So Lou Gerstner comes in and he takes it over. Gerstner's real key insight to IBM is we're pretty mediocre at everything.

54:27It's kind of like when I talked about Microsoft before. And that's the price of monopoly. Once you've been a monopoly, you kind of lose your capacity to be good because you didn't need to compete anymore. And I think a lot of tech incumbent companies have this problem. It didn't matter what they did. They were going to rake in money. And if you don't have the pressure, if you don't have the incentive, if you don't have the fear of death or the fear of God, as we talk about these model companies, then you don't do your best work. And the problem is that once you lose that muscle, it's gone.

54:58You're just sort of fat and flabby. So what Gerstner realized is actually the worst thing IBM could do would be to break it up into component pieces because all those component pieces are actually not very good. Our biggest asset is that we're big. It's like, what? No, what does it mean we're big? It's the 90s. This internet thing's coming along. There's all these companies that kind of know they have to figure out the internet and they don't know what to do. They need someone who can come in, understand their business, and help them get online. That's basically what IBM did. So they built out, and this is an echo of what's happening now, huge consultant force, and they put all their time into building, basically it was middleware, where they would go in and they'd put this layer between a company's old school mainframe, which all these companies had, and then modern web services.

55:43On the other hand, they could have websites and e-commerce sites and all these sorts of things, and it gave IBM a 30-year lease on life. Yes, in theory, you could go get point solutions from all these hot Silicon Valley startups, but you don't understand that. You don't know how to do that. You know us. We'll come in. We'll create all this middleware, build this big consulting force to help you implement it, and you'll get online. And IBM basically brought all of corporate America online. That's Microsoft's playbook. Microsoft will help you figure out AI. It will help you figure out in a way where you're not giving away the crown jewels to these companies. We're going to build this platform, this harness, this sort of middle layer.

56:17We're dependable. We're stable. You know us. We have backwards compatibility to the 80s. You can build on us, and then we'll manage all the changing models and what's updating and do all those sorts of things. And does that mean you'll get the absolute best experience? No. Middleware saws off the sharp edges. You sort of get a lowest common denominator capacity. But if you value, and this is the oldest enterprise sales motion, how did Oracle go to market? Oracle went to market in the 1980s, Larry Allison, with another technology taken from IBM, or just IBM didn't want it, relational databases.

56:50And they're like, you don't want to be locked into IBM. Relational database, you could run anywhere. Come with us. The reason this is a joke is because Oracle locks you in more than anyone, right? But all of enterprise sales is companies whose long-term goal is to lock you in, getting you on board by trying to make you scared of being locked into somebody else. All the cloud companies are like, oh, portability, whatever, you'd be whatever. They're like, oh, just use our service that only runs in our cloud, and now you're locked in. That's Microsoft's playbook. It's a very rational playbook. And I think it makes sense.

57:22That's why they have extra money, because they're not on the frontier. They are building massive data centers, but they're building data centers for inference. They're not building it for training. And their story about we're investing in time in response to customer demand is more believable in that regard. They're not having to tell a fungibility story where we're building big data centers for training that will be used for inference down the road, maybe. Go back to this notion that it's reckless to not be in the front. The reason why that's concerning, though, is at the end of the day, why are we using Microsoft products again? Because we did before. Like, to what extent does it actually make sense to have all these artifacts, all these documents, all these email inboxes?

57:57Can't AI just do that? There's a real threat here where to Microsoft's software business, the whole systems of record thing is funny because one reason why systems of records are so powerful is it's so hard to move them to somewhere else. Because it's a very tedious, repetitive job. Oh, AI is actually surprisingly good at that. I'm not sure how good the systems of record. Microsoft isn't so much systems of record. They do have some of the dynamics business. It's user interface. It's like where you actually interact with the computer. That's the part when you see Codex, Quad, Cowork, or whatever.

58:30It is aimed like an arrow to the heart of what Microsoft has. In the long run, all digital companies are, but Microsoft is very much. Their strategy is sound. It's also desperate in an existential way and also in a they might pull it off because they're desperate sort of way. Meta is not threatened immediately, but this is where my bullish view of AI comes in. I think all digital companies are threatened, and Meta is a digital company. They have software. Now, one worry is AI takes up more and more time.

59:02Time ultimately is Meta's currency. We saw, opening, I tried the Sora thing, didn't really take off. Social networks are actually pretty hard. Also cost a lot of money. It's kind of really interesting. So this came up with the creator payment stuff. So YouTube, very famously as paid creators kind of from the beginning. And that's a much bigger drag on the business than people appreciate because YouTube has marginal costs to their content. Now, unlike a Netflix, they don't have to pay that cost up front. They'll pay it after the fact. So their sharing revenue is a better model than a Netflix model.

59:35Netflix has to pay up front for content and then ideally make more money. YouTube pays along the way. But Facebook or Meta. Pays nothing. They pay nothing. Instagram is this unbelievable product that generates all this money for which Facebook pays zero dollars for content. It's unbelievable. It's funny because you could see a world where for YouTube, AI-generated content could theoretically be a positive because the inference cost of generating content could be less than what they're sharing with creators. For Meta, AI-generated content, to the extent they're the ones generating it, is actually a worse margin profile than what they have today because what they have today is free.

1:00:11So they have attention. There is a bullish world where Meta is actually very well placed because in a world where we interact with the AI all the time, the desire for a human connection becomes greater. And it's sort of like a Meta going back to their roots. Meta, one of their biggest mistakes, actually, Meta was always a social network company. They killed Snapchat or stopped Snapchat's growth by realizing Snapchat is a great product. Let's layer it onto our network. They brought their network to bear to kill Snapchat. But the reason why TikTok was a blind spot for them is TikTok is classified as a social network, and it's not a social network at all.

1:00:44TikTok is an entertainment product. It doesn't matter who you follow on TikTok. What you see on TikTok is a function of what you watched, and you're going to get more of the same. It's a user-generated content network. And the insight from TikTok was the way to get the best content to limit it to your social network is an artificial constraint. We're going to give you the best content from across the whole network. And the vast majority of content is going to be crap, but this is like the absolute question before. You don't think about margins. You think about absolute numbers. The absolute amount of great content, even if the margin for great content is infinitesimal, if we have a ton of content, the absolute amount of great content is going to be very large.

1:01:22Then Meta is like, where's a social network? Meta is serving you content from your network of people you know, and TikTok is serving you the best content from around the world. That's why they took a huge chunk out of them. Meta had to shift. But that's what's happened with Instagram, with Reels, is it's not really a social network. It is an entertainment product that pulls from the entire network. And social networking is like the group chat. It's possible in AI. Actually, social network is important again, because we actually want humans. We want to have some sort of connection to them.

1:01:52That will be interesting to see how that plays out. But the other thing with the models is they're so impactful on advertising. The biggest impact of the models, the biggest monetization right now is probably not anthropic opening. It's the incremental gain that is happening for Google and Meta. Most of the stuff is pre-LLM, but we're getting to LMs, whether it be generating advertising content. What do we want? We want verifiable domains. How do you verify if a generated image is good for an ad? Does the ad sell or not? They actually can validate their image creation and their text creation in a way no one else can.

1:02:27And their validation is the ad marketplace. Running a gazillion A-B tests on all these different things, see what works, see what doesn't. Most ads are a throwaway. It's fine. The vast number of ads don't convert. They have this massive advantage, this huge liquid market that is a verification machine where the verifiers are humans deciding whether they click on that ad and make a purchase or not, but they're doing it at global scale. That can actually have a feedback loop to make their products better. You're also going to get a world where ad matching is actually still fairly crude. Here's the qualities of the person. Here's the qualities of the ad.

1:02:58And it's like you create an embedding, like a vector calculation and see what numbers match. And then you sort of match an ad to the person. What do LMs do? LMs predict. We're going to move to this world where Meta is going to look at people and say, this person probably wants to see this next. And they're going to go find that thing and show it to them. The potential upside in terms of showing people better ads that are more relevant to them, they only need to increase a few percentage points for the returns to be billions and billions of dollars. This alone is worth them investing in being on the leading edge, in having these amazing models.

1:03:32I think a big problem Meta has is they don't tell this story. It's weird, but Mark Zuckerberg has the same problem Sam Altman does. He doesn't love ads. They have the best ad business in the world. They have an ad business that I think is a societal positive. You and I have set up these little content businesses that make great money. Content is you get a ride on social media. I grew up on Twitter, people sharing my links. It was amazing. If you're selling some product, the beauty of the internet is there is a niche out there that wants that product.

1:04:04The question is, how do you find the niche? Facebook advertising. That's what it does. It helps products find the people who didn't even know they wanted that product, but when they get it, they're so happy they got it. That's a huge societal positive. You have new business from a new entrepreneur making a new product. You have customers who are happy they got something that they didn't know they would get otherwise. Those customers, by the way, got lots of free entertainment and they'd have to pay for it along the way. And Meta made a bunch of money for themselves and their shareholders, which is basically everywhere in the world. This is why advertising is great and Meta's advertising in particular is awesome.

1:04:35And I get frustrated that Meta doesn't talk about that. Mark Sanberg has never really talked about the societal benefits of advertising except in passing in 20 years. He's handed it off to other people to take care of. And maybe there's a bit where him not paying attention is why there is a certain grit and grind that goes into building an advertising business. People get frustrated or have questions about it as far as data and all those sorts of things. And maybe there was a bit where he didn't want to be involved in it and wipe his hands of it. But you saw this when Apple passed ATT.

1:05:07App tracking transparency was one of the worst antitrust violations in the history of technology. Apple unilaterally obliterating all these business models while they're simultaneously building their own as far as advertising goes and doing all this tracking. Why? Trust us. And meanwhile, they're running these advertisements. I remember that advertisement of people on the bus, like overhearing everyone around them, what they're saying. That was such a dishonest representation of how advertising works on the Internet. You had Tim Cook in Congress talking about companies selling data. Facebook's not selling your data. That's value to them. Why would they sell the data?

1:05:38Meta was not prepared to respond. I think you got this to Sheryl Sandberg back in the day. She, in every call, would talk about advertising, how great it is, and have a bunch of case studies. People who are benefiting from advertising and these new entrepreneurs. And then she left, and it's kind of like that hole never got filled. It feels like it's a company that's kind of like embarrassed. We make a lot of money from ads, but we got glasses, and we're doing AI. It's like you have ads, and ads are awesome. I think if they had communicated that more consistently, they would be in a better place generally from a PR perspective. They would have been in a better place relative to Apple.

1:06:10And I think they would have an easier time right now convincing Wall Street that let us invest. The other problem is they've spent cumulative hundred-some billion dollars on Oculus, which I dated all along. And so there's a bit where, why should we let you spend money again?

Nvidia, Chips, and Energy Constraints

1:06:27The one major player and company that we haven't talked about much is Jensen and NVIDIA. And I'm curious how you would tie this back to the notion of not understanding commodity markets in Silicon Valley, whether or not you think compute ultimately is a commodity. I'm curious whether or not you think intelligence will ultimately be a commodity. It's interesting that intelligence and compute, which seem to be by far the most interesting and important topics in tech, both might be commodities and less differentiated than tech-spired products. Well, that's always the case, though. The most interesting thing about the internet is free distribution. Bandwidth is a commodity. The fact that I can pull out my phone right now and connect to any information source in the world for free, free on a marginal cost basis, is because it's a commodity.

1:07:03It changes the world. Commodities change the world. There is an aspect of differentiated products, by definition, have lower TAMs because there's an elasticity aspect to it. Not everyone can afford to pay for it. People's willingness to pay is going to differ. Your market is going to be constrained. Apple is never going to serve the whole world by selling a device, whereas a Google can because it's free. That matters. You're paying for a commodity, but to the extent it is available to everyone is the extent it is impactful. The internet is a commodity. It changed the world.

1:07:33So I don't think it'd be weird that intelligence ends up a commodity and changes the world. Commodities often are not thought of as good of businesses as these differentiated high margin products. I'm curious for your thoughts on Jensen and NVIDIA specifically. NVIDIA's position is, I think, definitely unnatural. You look at NVIDIA, they've maintained all their margins. Isn't that amazing? It's 2026 and everyone's coming for them and they're still charging however much money for a chip. But they're actually not maintaining their margins because this whole question of circular financing is people talk about Lucent and things like that.

1:08:05And, you know, this whole deal and NVIDIA is providing a 25% backstop. But if you actually ascribe a value to that, to NVIDIA's taking equity in the new clouds or whatever, they guarantee they're going to buy all their compute to 2030. Why do they do that? So that the entity in question can get a lower cost of capital, so they can buy over GPUs, etc. But implicit in that, why do they get a lower cost of capital? They get a lower cost of capital because NVIDIA assumed risk. This is my point before.

1:08:36Risk never disappears. It just appears somewhere else. Taking on risk has a price. There is a world where AI takes off, it never stops, and everything is fine. And NVIDIA captured all the upside of their risk. But there's also a world where, say, there's this new cloud they backed up. A ton of compute comes to market. The hyperscalers have plenty of compute. They don't have enough compute. NVIDIA is paying for a compute that no one wants. They just lost a bunch of money. If you think about it, there's an expected value of that investment. That expected value, it's not zero.

1:09:08It's not 100%. It's somewhere in the middle. But that is a diminution of NVIDIA's profitability. If you actually look at their business holistically, what that is, is a price cut. Now, the price cut didn't show up in margins, didn't show up in what they're offering. But a lot of what NVIDIA is doing is, how can we maintain our margins, even if the wide view, sort of discounted cash flow, expected value, holistic view of our company. People do discounted cash flows, but are you actually considering all these pieces? The reality is, is that moving stuff off the balance sheet, by and large, works.

1:09:40But they're doing all these deals to maintain what feels somewhat unnatural. We have seen price cuts. They're just manifesting in these very bizarre sort of ways. Now, in the long run, I think the challenge is their ultimate competitors are the hyperscalers, particularly Google and Amazon. So Google and Amazon aren't just building their own chips, but they're also looking to sell those chips externally. Google already made a deal to sell, like, 20% of their TPUs to Anthropic. On the last earnings call, Andy Jassy practically confirmed that they'll be selling Trinium 3s,

1:10:12or maybe Trinium 4s, or they'll be selling Trinium chips sort of eventually externally, which makes sense. That gives them a long-term buy-in to these companies. There's a huge amount of R&D that goes into developing chips. They get more leverage on their spend. It all makes sense. And by the way, they're not selling their chips on differentiation. They're selling their chips as commodities. NVIDIA's the one selling on differentiation. People aren't going to Amazon to use Trinium. So they're not cannibalizing the attractiveness of their cloud by selling Trinium outside. So they're NVIDIA's biggest problem, because what's the number one advantage that the hyperscalers have?

1:10:44Scale. Lower cost of capital. It's a capital fight. They have a lower cost of capital than the neoclouds do. The neoclouds are, they'll buy NVIDIA left, right, and center. And by the way, it also makes total sense that why SpaceX, I like Elon's out there, we will always buy NVIDIA because they're the best. No, you'll buy NVIDIA because they're the most fungible. NVIDIA is true. It is the most fungible. Kuda's moat is dramatically diminished, because the models don't care what they run on, and that's what actually matters, what's built on top of the models. But it still matters. It's still something of a moat. If you want to play the game SpaceX is doing,

1:11:16where we're going to build a lot and rent it out, but reserve the right to pull it back, of course you're going to be on NVIDIA, because the easiest way to rent it out is to be on NVIDIA. You saw this very early, by the way. You go back to 2024, 2023. NVIDIA starts talking about all these sovereign clouds. They start talking about, they tried to call these Neotron models. They had this thing in 2024, I remember. It was the first one where it was like the rock star GTC at San Jose, and like the huge Coliseum, and just no one comes out. It was a very boring GTC. The old ones used to be NVIDIA, demonstrating like 50 gazillion things, because they're throwing stuff at the wall. They knew they had something with GPUs,

1:11:47and they're trying to like find the use case. Yeah, people are excited about them, yeah. Once LM showed up, it's like, oh, we have the use case. But they were coming up with all these enterprise offerings. I can't remember what they were called, but they were like these modules, basically, that, of course, they were free, but they only ran on NVIDIA. And you could see what they were doing, is they were trying to lock people in. Intel is a good example here. AMD cleaned them out in hyperscaler sales, because the hyperscalers were put in the effort to get stuff working on AMD versus Intel. There are still small differences, even though they're x86, because they're buying at such scale, the investment to do it is worth it

1:12:18to get a better chip or lower price or whatever it might be. The part of Intel's business that never floundered was selling to government and selling to enterprises. They don't have the resources of a hyperscaler. They're not buying at that scale. They're just going to keep buying what they had before. That's why NVIDIA talks about selling to sovereign clouds. That's why they talk about selling to enterprises, because they want to get in these markets where they're not going to be balancing this chip versus that chip. The hyperscalers have always been the threat to NVIDIA for that reason. They're actually bigger.

1:12:49So you have this issue where the hyperscalers are the threat. The hyperscalers have a better cost of capital than the other companies NVIDIA wants to buy them. That's how you get this deal this week. I see this deal as a response. That's why it goes with the Google deal. Google can just issue equity. The shareholders don't love it, but their monetization capacity is much higher than NVIDIA or NVIDIA's customers are. I think what NVIDIA is hoping for, maybe they wouldn't say this in so many words, but if we get to a world where we actually run out of power, that's probably good for NVIDIA.

1:13:21Because in a world where we're totally constrained on power, we have to get the best efficiency, the best token efficiency. And I think NVIDIA is still the most token efficient. So that is a good world for them. Probably the biggest problem for NVIDIA over the last couple of years is I think the U.S. has actually brought a lot more power online than expected. It surprised me. Whether it be what Elon did sort of behind the meter, which has been replicated, or West Texas and natural gas, but even like restarting nuclear plants. You love how the U.S. responds to these things.

1:13:52That's awesome. It's actually one of the biggest encouraging signals about the U.S. is I was writing early on, assume this is a bubble. You want there to be a long-term payout. The dot-com, we got fiber in the ground. And by the way, Google's played this game before. Google built its business by buying up dark fiber. They had the killer search engine, but so much of the power of what they do is because they bought up all this dark fiber that was basically free after the dot-com era. Our core internet still runs on world-com fiber. That was a lasting benefit.

1:14:24The railroads, BNSF is throwing off money that's going to Google from Northern Pacific and Jay Cook selling bonds to retail investors. You want a bubble that produces something that lasts. And very long, it's like, what's going to last from AI? The GPUs don't last that long. Data centers, okay, fine. But what is it going to be? Power, it has to be power. If we're in a world where this all blows up and we have way too much power, that is an amazing world to be. We've always been energy constrained. Energy undergirds everything. What would it be like to live in a world of energy abundance?

1:14:56It's hard to even imagine because our minds are so constrained by the fact we've actually always been in energy scarcity. I think we've done an unbelievable job. Power for sure is a constraint. It's going to be a constraint. But I think it has taken longer to become a constraint than anyone expected. And I wouldn't be surprised if that includes Jensen Huang. I think he thought insufficient power was going to be NVIDIA's moat sooner than it happened. It turns out that the longer we have enough power,

1:15:27the more time Amazon has to make Trinium better. The more time Google has to make TPUs competitive from an efficiency standpoint. And if we get in a world where, this is a world where those margins seem very hard to sustain. I love hearing your takes on just everything going on. It's the most interesting time I've ever observed in this world that you love so much. So thank you so much for your time. Thank you very much.

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