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What Happens When the AI Boom Runs Out of Money

Ben Thompson joins Invest Like the Best for a wide-ranging conversation about the economics, geopolitics, and business models shaping the AI era.

Ben ThompsonguestPatrick O'Shaughnessyhost
Aug 18, 20261h 25mWatch on YouTube ↗

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Transcript

  1. 0:00 – 0:59

    Intro

    1. BT

      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, your military is better than anyone else. In this world, what is the game theory optimal response of China? To blow up TSMC. If we get to a place where we have a meaningful superiority, particularly from, like, i- in terms of a military national security perspective, I think that's very dangerous for the world. [upbeat music]

    2. PO

      So 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. Uh, we talked about aggregation theory mostly, which I'm sure we'll, we'll hit at some point today. 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.

  2. 0:59 – 8:26

    America and the AI Race

    1. BT

      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. You can, like, somehow it fixes our manufacturing, all these, like, things that I don't think AI is necessarily going to do because they sort of deal with the real world. But in this world, what is the game theory optimal response of China? To blow up TSMC. Like, it- it, to me, this is like game theory can get very sort of convoluted and complex. To me, this one actually isn't that complicated. Uh, so I, there's a, just a fundamental disconnect that I have with a lot of the rhetoric coming out of Silicon Valley, coming out of, I think, one of the labs in particular, where if we get to a place where we have a meaningful superiority, particularly from like i- in terms of a military national security perspective, I think that's very dangerous for the world.

    2. PO

      But in that state, how, how, 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.

    3. BT

      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. Like, all these sort of precursors. Like, the, 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. Just because fixing so many of these things is going to be dramatically, like, dumb. Like, if your competitor is sourcing from China and you're gonna start sourcing or getting things from the U.S., you're gonna be at such a disadvantage, relatively speaking, that you're just not gonna do it. So you do it when you have literally no choice, and that works for, like, very big headline items. Like, 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 moving, truly moving out of China. Like, they're diversifying to an extent, but the prob- It would just cost so much, and 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 in what, 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, is seems pretty fantastical to me. And I think there's a bit of facing reality in this regard that is not present in these conversations.

    4. PO

      So put yourself, put yourself in their shoes. Like, what do you think the motivations are?

    5. BT

      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 for, particularly from a political perspective, has been to, like, try to be like China. I think we should be going the other direction, uh, 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 leaning into that.

    6. PO

      You 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?

    7. BT

      There's a bit where AI right now is kind of like the Taiwan situation in that it feels the current status quo actually doesn't seem so bad, and the question is how sustainable is it? But maybe it's sustainable for longer than we think. So the, 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. Meanwhile, 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, right? There's lots of questions out there, like can the Chinese actually pull ahead? I'm still a little skeptical by, you know, for various reasons. Getting to the leading edge, things that last six to nine months, is very difficult. I think we'll see how... It's gonna be in- instructive how Meta and Grok do in terms of actually, actually catching up. Is that sort of-- 'Cause 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, uh, OpenAI and Anthropic recently, which, and that 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 b- 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, can- is your cost to serve sort of structurally lower than anyone else? This is the thing about the, the open source models. The talk about them being free is bizarre to me b- because it's marginal costs.

    8. PO

      Yeah.

    9. BT

      Right? You still have to run inference, like GLM or Kimi. Kimi's very expensive to serve.

    10. PO

      Yeah.

    11. BT

      It- the cost per answer is significantly higher. So the, the- 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 kinda like where we are, and the pushback would be, "Oh, yeah, w- that's right now. It's not gonna stay that way." Um, which I think is fair pushback, but I don't know.

    12. PO

      So, so if, if-

    13. BT

      Maybe it will stay this way

    14. PO

      ... if you could know anything about the future of how this will go, to be more confident in, like, where, 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 of at some point these things presumably will level out, maybe not. What, what would be the thing you'd wanna know that would give you a better sense of what the future might look like?

    15. BT

      I am concerned that with, like, the scare around, like, people freaking out about Mythos and, like, this Hugging Face incident, that the actual implication of that is not that we reduce these dangers, but we just stop releasing stuff.

    16. PO

      Mm.

    17. BT

      And we on the outside start to lose any sense of, like, where exactly-

    18. PO

      What's actually the frontier

    19. BT

      ... what is actually the frontier and where it is. And it, and there becomes sort of a false sense of security because, like, right now everyone's basing their understanding of Mythos on Fable, but how good is Fable actually relative to Mythos, right? Like, that sort of gap is only going to, I think, increase over time. And so I think that that's, that's a real question that, that I'm not sure about. The, this question of the AI, the recursiveness in AI sort of making itself better, like, does that lead to sort of some sort of takeoff? And a- at the end of the day, there's timing questions in lots of different ways.

  3. 8:26 – 15:31

    AI’s Funding Problem

    1. BT

      I'm worried about the timing mismatch in terms of the actual return on investment producing enough revenue to fuel investment. Like, we're, we, we're, we're working our way down the capital curve. Like, we, we started with free cashflow, then, like, the speed with which the tech companies blew through the debt markets is kind of incredible. Like, it took, like, a year, and now Google's issuing equity. Nvidia's putting together the, the, oh, the, the, the, these-

    2. PO

      This $500 billion thing

    3. BT

      ... this $500 billion thing to tap into, like, pension funds and insurance floats and things like that. And the what's after that? Where's the money come after that? Well, ideally we actually flip back to free cashflow 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, right? But at the same time, even if we have this blow up, the AI is not going away. It's not gonna stop improving. It's going to keep sort of progressing, uh, in a way that we look back on the dot-com era, or we look back on the railroad era, or we look back on whatever bubbles through history, ultimately immaterial in terms of the broad scope of humanity, even if they were very devastating to lots of people at the time.

    4. PO

      What, what can the railroads teach us, do you think? It's now the last bigger build-out, right? In terms of percent of GDP, we're getting there.

    5. BT

      I think we might be bigger at this point, or it's like it was the biggest.

    6. PO

      It's in the ballpark, yeah.

    7. BT

      Yeah. You know, the railroads had a real duration mismatch. It, like, to build a railroad and make money off it was a decade or multiple-decades-long endeavor. And the so- whereas you had to issue money to pay for it in the short term, and the world ran out of money [laughs] , right? And I think that is, that's probably the, the aspect. I think that's why people reach for the railroads, because everyone talks about are we gonna have enough compute? Are we gonna have enough electricity? Maybe the nearest term question is, are we gonna have enough money, which is kind of a bizarre thing to think about. Like, that's what happened in the, in the 1870s. Like, we, the, the world just ran out of money. But the, the, the funny thing is, is the railroads kept operating, and they expanded the West. And they, the, the, their contributions to GDP was astronomical. They're still contributing to GDP. Railroad money is what's going into Google right now from Berkshire Hathaway.

    8. PO

      [laughs]

    9. BT

      Like, like, th- th- they-

    10. PO

      That's very funny. [laughs]

    11. BT

      No, it's, it's, it's literally-

    12. PO

      It's quite literal. [laughs]

    13. BT

      It's literally Berkshire Hathaway has this problem. To me, this Nvidia deal is very much paired with the Google equity issuance, which I thought was, was that w- I mean, that one was shocking when it happened.

    14. PO

      Why was it shocking?

    15. BT

      Because it's Google. They can't raise money? [laughs] Like, why are they issuing equity, right? Like, the, the, why are they giving away the, their, you know, reducing their upside if they believe so strongly in this. But the Berkshire, the Berkshire Hathaway comparison is interesting because in, to a rough approximation, they make, they have See's Candies famously, right? Tremendously high, high-margin business. The problem with a lot of high-margin businesses is you can, your, your, the percentage profit you can make is very high, but the absolute profit you can make is capped.

    16. PO

      There's no reinvestment runway.

    17. BT

      That's right. Like, you just, you're just accumulating cash. And so the brilliance of the BNSF Railway thing was basically they took the See's 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." Like BNSF in 2025 or something, their- The amount of free cash they threw off in one year was more than See's Candies had thrown off its entire lifetime, even though you're talking about a low-margin business compared to a very high-margin business. And there's a, I think there's an aspect from Berkshire Hathaway where if your, once your capital gets so large, you start operating 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 it was very symbolic for them to invest in Google. Google has this unbelievable high-margin business of search, one 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. And meanwhile, there's this AI opportunity which requires just astronomical... It's just a cash, incinerating cash. But you can imagine if AI is intelligence and its TAM is basically all white-collar work, and eventually with robotics-

    18. PO

      A lot more

    19. BT

      ... everything potentially. Like, why, like, the absolute profits available here, even if the margins are lower, is so much larger that will we look back and Google Search was See's Candies? And I, it feels like that's what's happening. And, and in that world, even, yeah, 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 because, like, the, it, what does, what does an equity issuance do? It dilutes your interest, your interest of your shareholders, so you have a smaller percentage of the pie. Well, if you have the smaller percentage of an astronomically larger pie, at the end of the day, no one's gonna be complaining. And I, I just thought it 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?

    20. PO

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  4. 15:31 – 20:30

    AI’s Capabilities and Limits

    1. PO

      I'm, I'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?

    2. BT

      I have a view that is both super bullish and less bullish in some respects.

    3. PO

      Right.

    4. BT

      So I am not fully convinced about the generalizable argument. Like, AI is clearly incredible at coding. It kind of blows my mind that people were doing this a year ago, [laughs] like, actually, like, writing out code. It's very good at math, obviously. But the obvious an- you know, riposte is that these are sort of verifiable do- domains. And what is the evidence or where is the compelling evidence of being very good at verifiable domains cleanly translates to being very good at sort of unverifiable domains, or domains that take, have very long sort of verification loops? And I think that's still a little bit to be determined. And it's interesting because I raised this que- I raised this question and there were some people at the labs that were o- on a panel, and I was kind of annoyed at the answer 'cause the answer took me for an AI bear. And they're like, "Oh, 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." And, you know, scale was the answer to both of those, but also both of those were bounded, right? What, what is the go-to example that's not chess, that's not, 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, not possible? So 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 data of the internet, that is like, that's dis- distillation. It d- distilled all of human thought.

    5. PO

      Human mind, yeah. [laughs]

    6. BT

      Uh, no, it didn't. It distilled all of the end state of human thought. The actual-

    7. PO

      No traces. [laughs]

    8. BT

      ... typing on, on Reddit. It doesn't have the traces, right? It doesn't actually have the thought, the emotion, or whatever that went into typing that comment or typing, writing that essay. What if we, like, say Neuralink, whatever, what if the actual payoff from Neuralink is actually capturing the traces-

    9. PO

      Training data. [laughs]

    10. BT

      ... of, 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 more 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 that, it's almost like a somewhat pessimistic view of humanity, uh, 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. I wrote an article a while ago. You know, there's the whole, like, accelerationist movement, and I, what I call myself was a re-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 pilled in terms of its impact on the economy, its sort of upside in terms of monetization. I'm not sure about the timing.

    11. PO

      What would be, like, the gradient towards it? Like, imagine law or medicine, where I don't know whether or not you would consider those verifiable. Like, law is a, like a code of some sort. Medicine, we have a certain state understanding of, of things.

    12. BT

      I mean, I think medicine is, like, by far one of the biggest opportunities.

    13. PO

      Yeah.

    14. BT

      Like, it's both one of the biggest opportunities and also one of the most challenging ones i- because of all the regulations and all the access. Like, if you could turn an AI, turn machine learning onto all the medical records, 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. On the flip side-

    15. PO

      It's not happening [laughs]

    16. BT

      ... like, when is that gonna happen, right?

    17. PO

      Yeah.

    18. BT

      I think the, the, the optimistic f-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 jobs sort of in 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, and, you know, I, how much of our economy is actually we've managed to create more and more jobs that is just sort of like make busy and make slow to a certain

  5. 20:30 – 31:10

    Aggregation Theory, AI, and Ads

    1. BT

      extent.

    2. PO

      If I go back to the early 2010s or, you know, 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 could 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, or principles will define this era of winners from a, like, a financial perspective and market cap perspective.

    3. BT

      It's a good question. I go back and forth even just on the question of, of aggregation theory itself. How much does that ex- you know, apply in the current era?

    4. PO

      Just same thing. Yeah, yeah.

    5. BT

      Yeah, 'cause like, like a pushback that people have is one of the key components of aggregation theory is zero marginal costs, and zero marginal costs, uh, shows in lots of ways. The one that I f- sort of focused on the beginning was distribution. Like, and people say, "Oh, I don't have distribution. I have to pay Google for ads." Like, well, 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 wh- 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. How do you actually find what you're interested in? So the companies that solve discovery in their domain come to dominate that market. They get a virtuous feedback loop. That's sort of aggregation theory i-i-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.

    6. PO

      Yeah.

    7. BT

      The vast, vast, vast majority of advertisers on Google or Meta never inter- never talk to someone at Google or Meta. They just go up and they buy ads. It's all done by computers.

    8. PO

      It's like the perfect business [laughs] .

    9. BT

      And those computers, from a business perspective, cost $0. AI, obviously that changes significantly, like be- their inference costs are real. Uh, but then again, sort of how real are they?

    10. PO

      They're real right now.

    11. BT

      They... Well, how real, I don't know, are they?

    12. PO

      Depends on the company, but they're, they're way more real than the, those prior examples.

    13. BT

      Well, we-

    14. PO

      Like, if you look at gross margins or something.

    15. BT

      For sure, but, but you have this incredible spread. So you have people, I think the vast majority of people who are using AI 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 and low in the basically similar to serving them a web page. Like, I would imagine it's, it's marginally higher, but not, not that much higher. Then you have on the other extreme people who are actually leveraging test time scaling, right? So u- it used to be we just scale by making the models bigger and bigger. Now you can scale as far as time, how long do you think about the answer?

    16. PO

      Yeah.

    17. BT

      Well, you could think about the answer for days or weeks-

    18. PO

      Seconds

    19. BT

      ... or months, and that is a di- that is directly marginal cost. Like, every second longer you're thinking is costing more money, which speaks to, like, we think about AI and inference as this one question. That's why I was sort of being a bit, you know, pushing back on you, but actually the marginal cost question for the different user, the user using free ChatGPT and the user trying to solve a, a math theorem, they're not even remotely in the same universe. And I think you see this challenge actually in the enterprise in a very interesting way. So Microsoft recently, you know, they, they are shifting their enterprise plan. Like, 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, and this is kind of a really... I 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, you know, particularly a small or medium-sized business or even a large enterprise, there's real value in that.

    20. PO

      It makes life easy. Yeah.

    21. BT

      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 that's a new decision, number one, that is untethered from headcount, right? 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, their license. It was kind of a thoughtless revenue stream for Microsoft. Now, if you think about usage, you have to think every single month, "How much do I want to spend?" And that pr- introduces two problems. Number one, most companies aren't set up to do this. They 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 costs, and there's a bit where, where 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 then you don't think about it anymore. It- the decision's sort of already made. But if you're thinking about usage, you have to do it again. But the, the final thing is if you're, every month you're looking at your Microsoft bill and, "How much did I use?" you start thinking about, "What am I paying for? Like, how good is each of these products? Should I actually just start thinking about and spreading 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 onto the 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, but they don't want them to think too much because that sort of breaks the model in very sort of surprising ways.

    22. PO

      Are you surprised at all that the, 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? Like, you know, business, Google and Facebook are sort of business perfected in this p- in this prior era. They haven't seemed to figure this out at all.

    23. BT

      I am frustrated but not surprised. This is obviously a market that should be supported by advertising.

    24. PO

      Yeah.

    25. BT

      Like, that, that is why advertising is always the consumer business model. Consumers don't wanna pay. There, so there's two things to understand about consumers that Silicon Valley has to relearn about every 10 years. Number one, consumers do not wanna pay for software, and number two, consumers do not care about being productive. And this is like we went through this in early SaaS. Like, the, the canonical company for this, in my mind, is Dropbox.

    26. PO

      Right.

    27. BT

      So Dropbox, unbelievable product. Like, especially when it first came out. I- in business school, I was one of the first people to use Dropbox, and that was, went off like crazy. I have so much storage s- uh, still, like my free Dropbox 'cause I gave out my code to, like, so many people. So Drew Houston makes this amazing product, so easy to use, just absolutely seamless, and I think very clear about this. He wanted to build a consumer company, and there's that famous story of him meeting with Steve Jobs, and I think, you know, s- Apple was interested in acquiring Dropbox. And they're like, "Oh, we want to build a company," and Steve's, you know, "Your feature, not n- your feature, not, not, not a company." And, you know, which that plain Jane just file sync, Apple did make a feature as far as like sort of iCloud drive. And Dropbox, they grew very fast, and then they had like a two-year lull. And in that two-year lull, what they had to do was basically completely rebuild the app from the bottoms up because the people- not enough consumers are gonna pay for it. They needed- enterprises could see the value, they would pay, but if you want 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 created to do that at all.

    28. PO

      Yeah.

    29. BT

      So they had to rebuild the whole thing and realize the only way we're gonna make money is by selling to companies. Why do companies pay? Because companies are paying employees. So to the extent they can make their employees more productive, they're getting a greater return on their investment. It's the complete inverse of a consumer.

    30. PO

      Yeah.

  6. 31:10 – 47:31

    Compute, TSMC, and Intel

    1. BT

      pursue this sooner.

    2. PO

      I know you've been spending time with, you know, some of the big money firms and sources of capital. What, what is your sense of their appetite right now and how they're thinking about the future? Because I think this year it's gonna be 800 billion or something that we're gonna spend in CapEx. Next year it's supposed to be 1.3 trillion, I think is the current estimate. It's gonna keep, you know, keep going up from there. We're burning through all the compute that gets installed-

    3. BT

      Yeah

    4. PO

      ... like basically immediately. W- it's, it's such a strange circumstance that we can use the capacity right away as soon as it's online.

    5. BT

      Well, that's the thing though. So there's a few timing mismatches that are happening right now. All the bulls on Twitter is always like, "We're s- 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. So, like w- we're- 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. So on today when we say there's not enough compute, it's not like all the money that the companies are putting in today manifests in compute tomorrow.

    6. PO

      Equals short-term compute, yeah.

    7. BT

      No. It all manifests in compute in 2028 and 2029. So you have, like, on the calls, you have both Andy Jassy and Satya Nadella 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 how much that, I think, is a lot of BS [laughs] because the reality is, is if you've built the shell, that money is sitting there. You're not gonna let it just sit there. Like, if you have, 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, you know, software. It could be, uh, a net- a network in terms of developers. It could be a social network sort of thing. We're peer-to-peer. Where I'm highly differentiated, then my ability to charge higher prices provides sort of my profit margin. So the, the classic example is like Apple, right? Th- they have their ecosystem, and they have their software, and they have third party and all those sorts of things. And so they can charge, they have 50% margins on their iPhone. Everyone looks at Apple as like the ideal business model.

    8. PO

      Yeah.

    9. BT

      That, that's how you run a business. But in a commodity market, the price is set by the marginal supplier.

    10. PO

      Cost to serve is all that matters.

    11. BT

      That's right. And so, uh, I had a good friend in Taiwan who was in shipping, um, fascinating industry. It's kinda like the airlines too, another industry that I love to look at, but like you, you, you buy a ship, and the cost of that ship is depreciation, and your marginal cost is actually quite low. It'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-

    12. PO

      As full as you humanly possibly can

    13. BT

      ... basically no, no, you're gonna run it no matter what.

    14. PO

      Yeah.

    15. BT

      And you're gonna 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 'cause your accounting loss includes depreciation. But the depreciation is an accounting figment. You already paid the money.

    16. PO

      Yeah.

    17. BT

      And so you're, you're going to run that ship at whatever the market will bear, and the container, the beauty of the container is it is a pure commodity. And so the cost of the market is gonna be the marginal cost. Now, if it gets low enough, at some point people will exit 'cause their marginal costs actually can't, like, they're actually losing money on a shipment, right? Uh, not just paper money, but like actual real money. They will exit, but then the supply's diminished, so then the price, the price will go back up, and you get this interplay of sort of coming in and off. But then if, 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 'cause there's not enough supply. There wasn't enough supply of ships. So containers went from like usually being like 3,000, $4,000 to 17,000, $18,000. Like, the, the amount of money that these shipping companies made in a very short amount of time was insane. And so what happens though? Well-

    18. PO

      More ships [laughs]

    19. BT

      ... imagine if we had more ships, right? The problem is it takes two years to build a ship. So by the t- ev- if everyone makes this decision simultaneously, then the ship, you suddenly have a lot of ships, price plummets, et cetera. Where we see this is in components, in memory in particular. Memory very famous for boom and bust cycles, uh, people entering the market late. But 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 gonna hold in a time, in a time of abundance? Uh, a- and, and the sort of the bulls will say, "There's never gonna be a time of abundance." [laughs] AI-

    20. PO

      We're gonna be short forever

    21. BT

      ... test-time scaling-

    22. PO

      Yeah

    23. BT

      ... we're gonna be short forever, which maybe we will be. My concern is even if that's right, we m- could still have an air gap in that there's so much money going into it right now- And not enough has come online to actually make sufficient revenues to c- to handle the situation where we run out of capital. That like, I, like, again, I believe in AI. I think it's a real thing. I think the economic impact is gonna 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 gonna make the bridge to this actually generating the level of returns necessary to continue to fuel this sort of going forward.

    24. PO

      Can, can you zoom in on TSMC and the, and the, maybe the, some of the component makers where fabs are involved? And so far, at least my understanding is that they've been quite conservative in their willingness to expand capacity-

    25. BT

      Yeah

    26. PO

      ... build new fabs, meet the market's demand with similar growth, which they have not done. And if that, if that just rate limits this whole thing and prevents us from getting one of these giant overbuilds.

    27. BT

      Well, we can talk about a few different ones. Like, we'll start with memory. Memory used to have tons and tons of memory makers, and every time there'd be sort of a boom, memory makers would sort of like reenter the market, um, or like new countries would come in. Like Taiwan used to have like a memory market. It, but you would get these exact dynamics. If there's a shortage of memory, there's so much money to be made 'cause no one, you can't bring capacity on immediately. We're, like it's the same as shipping, it's the same as what we're seeing right now. And so what would that, that would do is that would spur sort of people to come in the market, you get too much capacity, prices would plunge, and people would just get blown out. 'Cause 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 EUV machines, so the costs are getting into the billions of dollars for these lines. And what happens is every time with these boom and bust cycles, some people would enter, more people get washed out. You go through, there's like these, these famous historical moments for these memory cycles, and like 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 and basically wiped out the Japanese. That's when the sort of, the South Koreans generally took over the market i- in a major way. But it got down to three, and 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, for a very long time. And so I, I think m- the memory solution will be solved eventually. The other thing they, the risk they run is Apple, like Apple's lobbying to get Chinese memory, right? Uh, and what is the number one focus of like al- 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. Like the issue with the Strait of Hormuz is it's very effective. It's more effective if you don't use it, 'cause then it's always hanging out there as something you could do. Now they did it, turns out it worked, but like the UAE and Saudi Arabia, they're gonna build pipelines, they're gonna build new ports. They're not gonna 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 gonna have any effect 'cause it will have been built around. My concern f- for the memory makers is they might have done the same thing. Like, no one's gonna let themselves get in this situation again as far as memory goes. TSMC is arguably worse 'cause there's only one. Uh, there's one company on the leading edge. Um, obviously Intel and Samsung are trying to get there, and it's the same thing. Like, like the, all markets carry risk, and a lot of the question is who ends up holding the risk? And what I think a lot of the 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. So they are very biased towards being much more conservative. And there's a little bit of a culture component to this too. One of the most interesting TSMC stories, it's kind of analogous to that Samsung story, was Morris Chang retired in like the late 2000s. And new leadership took over, and 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. That's what laid the foundation for them taking over sort of leading edge semiconductors in that time. Morris Chang is what? A one of one. Like on the Mount Rushmore in my mind of the greatest sort of and most impactful tech executives of all time. The entire fabless model is so critical to, to what tech is and what it does, and also just the guts to do that, right, at that time, particularly in, you know, as 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. And so what happens though? Where'd the risk go? TSMC's like, "Well, we, we don't wanna 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. It manifests in not making money.

    28. PO

      Yeah.

    29. BT

      And there's money not being made right now because what happened was they were very excited about 5G. They did a big, like, wave of, like, investment, um, in expanding their fabs in around 2020, 2021, '22. And they're like, "Okay, we're good." And like I said, 2024, like, G- ChatGPT comes out 2022, 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, you know, I was writing about this a while ago, and then I think it was like one or two earnings calls ago. Suddenly, uh, CC Wei, the, the CEO and chairman, is talking about, like, the use cases for AI, like s- the whole earnings call in a way he never had before. This is the problem with the, why the memory makers 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, and they're, 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. Like, 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. Uh, but I think, I think they've by and large gotten it. But them getting the message, it then takes several years for that to actually materialize.

    30. PO

      Do you have a sense for, like, how long you think it will take given the extreme shortage of compute?

  7. 47:31 – 54:43

    Amazon and Apple’s AI Moats

    1. PO

      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?

    2. BT

      The answer's always Amazon. Um, and the reason, because what Amazon is so compelling is the extent to which they build for them. They are their first best customer. Like, 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. But the re- what it drove was the understanding that we need to have a scalable, we can't be having so many meetings. [chuckles] Like, we need to have just compute that you can plug in, purely API surface. You 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 can do that for anyone. And 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, right? Where It was the opposite. Like right now we're using external providers for our logistics, UPS and FedEx and USPS. We need to build this up ourselves. And now they built it up themselves, they're offering it to third parties, right? Now other people can use their, use their delivery services. And you see this in market after market. They're, like, they're talking about things like some of their AI products that they're... Or their chip products, right? 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 their 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-

    3. PO

      It gets better, yeah

    4. BT

      ... iterate them and get better, and they get to the point where they can actually sell them externally. And so because they were the first best customer for Graviton, Graviton got better. 'Cause they were the first best customer for Trainium, Trainium got better. And now Trainium is obviously, you know, running Anthropic. They're doing the same thing. They're doing the same with the 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 don't, I don't know if, like-

    5. PO

      I haven't tried it.

    6. BT

      Well, because I, you know, moving back to, to America, I've been buying lots of stuff. And e- well, and 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 j- and it actually does take care of the problem. So you can see that actually starting to work in that, in that regard. But they're gonna, 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. But this is, it's such an elegant sort of approach. And given they have so many investments in the real world, their core business feels so impervious to AI for, like, the model version of AI. It will benefit from AI, but their, 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, is very compelling.

    7. PO

      What about Apple? They've sat this whole thing out it seems.

    8. BT

      It feels like it might be a situation of it's better be lucky than good to a certain extent. I mean, Apple has their whole, has their whole ecosystem, and at the end of the day, they do own access to customers, so they can sort of get suppliers. This is the classic aggregator play. If you own access to customers, suppliers come to you, not the other way around. And, a- and so they can get suppliers for their AI sort of as, as needed. And by the way, you know, to the extent it's true that people don't wanna be productive, they just want a sort of a chatbot, not only can they serve them a chatbot and be with, you know, 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, you know, they're using the customer's e- electricity. I mean, we're, I, I don't think we're quite there. There's a reason they're using Google Cloud and, and Nvidia chips. But you can certainly imagine a future where, where that's the case. And they're in physical goods. Like, actually making phones is, is hard, right? And having retail, having distribution for physical goods is, is good. So they're, they're more insulated. The smartphone is per- so perfect. It's small to fit in your pocket. It's big enough to watch basically anything on it. You can run your, your whole life on it. All your entertainment is there. Like, when we talk about customers just wanna be entertained, the TV is now an accessory. It's all on your phone. And, you know, I don't see anyone taking over the phone. The question is, is the phone always gonna be the center? Or is there a bit where, particularly in the home, this is where I'm very, you know, OpenAI's efforts here are very interesting, uh, where you want sort of an ambient AI, where you can just talk to the AI and it tells you what you need. Apple is the best positioned 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 gonna be something that was off that. Apple realized, no, we, the pho- we need to reset. The phone is not gonna be accessory to the Mac. The phone is gonna be the phone. The iPod helped them realize that and going with Windows and all that, but will they fall into a Microsoft-like trap? Like assuming the phone's so good it's always gonna 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. The other thing about the AI stuff is on what basis should we expect Apple to be good at this? Like, just in, like, at the most crude level, AI is this probabilistic endeavor. Apple is the king of deterministic products. Like a physical product, you ship that iPhone, you ship it once, and it's gotta be, it's gotta be good. If it's bad, it costs you billions and billions and billions of dollars. Apple's never had a, an iPhone recall, which if you think about it is actually, it's amazing. And that, the sort of care and decision-making and diligence and, you know, fierceness in terms of your supply chain and, like, making hard decisions is very, very different than everything that goes into, like, making great AI. And I'm generally prefer companies to do what they're good at. So from my perspective, I'm fine with Apple not doing AI. I, I, I want, I want them to keep making great

  8. 54:43 – 1:14:07

    The Frontier AI Players

    1. BT

      devices.

    2. PO

      Of the fi- let's call it five potential frontier AI winners, so OpenAI, Anthropic, Gemini, let's put SpaceX AI, you know, Groq, and Meta in that pile, which of those firms do you think has the most interesting setup?

    3. BT

      OpenAI and Anthropic obviously are the riskiest but also have the biggest upside. You know, they're just the never discount, number one, the power of belief. They think they're creating God. Like, like, the, the most impactful things in history have usually been fueled by religion, and the two religious organizations in Silicon Valley are ... The sort of, like, OpenAI's kinda like mainline. Like, they go to church every Sunday.

    4. PO

      [laughs]

    5. BT

      The sort of, like, evangelicals [laughs] are, is like that's Anthropic. Like, they're, they're all in. Uh, the, the, it is, it is core to 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, like, search to not die too quickly, right? Meta has, uh, the huge advertising business. W- in, in a world where Meta was run by anyone other than Mark Zuckerberg, they would not be on the leading edge. That is the, one of the purest manifestations of founder sort of energy, for better or for worse. Their business is so amazing. You see them just easily sort of doubling down on that. Like Google, there's a bit where it, it made sen- ... They had Google Cloud. They have TPUs. They've been doing research in this. It's like, it makes sense why they're pursuing this. Meta being like, "Actually, we're gonna hire a completely new team and we're gonna start from scratch," is all, again, is pretty insane. So credit to Mark Zuckerberg in that regard. Again, you can decide whether that's a good idea or not. And then SpaceX AI, I mean, the data centers in space is, like, that is, the theory is there. Like, do they have to own their own model though to do that? They'd get better margins if they do. Um, then again, if we actually run out, whether through political opposition or power or whatever it might be, if we run out of data centers on Earth, like, they can run whatever model they want, uh, as we're seeing with their sort of, you know, selling their capacity to Anthropic right now. So they're all pretty interesting. I think, um, probably the case for SpaceX AI is probably the weakest because the data center and space play is so highly differentiated. Like, if that plays out, it, 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? Um, that's a fair question. From a tactical perspective, I love the Cursor acquisition. Like, that makes so much sense for both companies, and so I'm intrigued to see what they do. Um, Meta's probably the most interesting just because-

    6. PO

      You've written a lot about this recently

    7. BT

      ... the, I think there's a very good case to make that it is actually more reckless to not be on the frontier if you're a digital company.

    8. PO

      Mm.

    9. BT

      Right? So the, the counter to, the counter to Meta is actually Microsoft. Microsoft is not on the frontier. The reason why Microsoft has $20 billion of free cash flow last quarter, Microsoft paid a $10 billion dividend last quarter, right?

    10. PO

      [laughs] Crazy.

    11. BT

      Like, the, there's mon- there's some money. But their play is, "Oh, we're gonna play all these off each other. We're gonna provide middleware. We're gonna provide the platform. The enterprises will build on us, and we're gonna sort of interme- you know, disintermediate, disintermediate the models." And I think it's a, I think it's a rational play. It's the IBM play of the '90s. Like, there's, you know, history sort of echoes. Everyone talks about Google, Google, like, following Microsoft. Microsoft follows IBM, and you can see that, uh, to an extent, uh-

    12. PO

      What did IBM do? What's the f- what, what's the analogy?

    13. BT

      Well, so IBM, um, so IBM had this dominant, you know, we talked about it, in the '70s. Uh, and then you fast-forward to the '90s, and IBM is this very sort of 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, and I think L- Gerstner's real key insight to IBM is actually everything, we're pretty mediocre at everything. It's kinda like what I talked about with Microsoft before, and that's the price of monopoly. Once you've been a monopoly, you kinda lose your capacity to be good because you're, you didn't need to compete anymore. And I think a lot of tech incumbent companies have this problem. They, it didn't matter what they did, they were gonna 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-

    14. PO

      [laughs]

    15. BT

      ... as we talk about these mono companies, then you don't do your best work. And the problem is that you, once you lose that muscle, it's gone. You're just sort of fat and flabby. And so what Gerstner realizes, actually the worst thing IBM could do would be to break it up into component pieces 'cause all those component pieces are actually not very good. W- our biggest asset is that we're big. It's like, what? No, what does it mean we're big? We can, it's the '90s. This internet thing's coming along. There's all these companies that kinda 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 in echo of what's happening now, huge consultant force, and they s- put all their time into building basically what was middleware, where they would go in and they'd put this layer between a company's old-school mainframe, like, which all these companies had, and then modern web services on the other end so they could have websites and e-commerce sites and all these sorts of things. 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, 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. We'll give, build this big consulting force to help you implement it, and you'll get online. And IBM basically brought all of corporate America online. And that's M- what, Microsoft's playbook. Microsoft is, um, we'll help you figure out AI, and we'll help you figure out a way where you're not giving away the crown jewels to these companies. We're gonna build this platform, this harness, this sort of middle layer where you can, we're dependable. We're stable. Like, w- you know us, we've, we have backwards compatibility to the '80s. Like, 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 sort of saws off the sharp edges, right? Like, you're, 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 Ellison, with this, you know, another technology taken from IBM, or just IBM didn't want it, relational databases. And they're like, "You don't wanna be locked into IBM. You want to be able to e- a re- relational database you can run anywhere. Come, come with us." The, the reason this is a joke is 'cause Oracle locks-

    16. PO

      Everyone's locked in [laughs]

    17. BT

      ... you in more than anyone, right? But the, the, the, 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, right? Like, all the cloud companies are like, "Oh, portability and whatever. You can do whatever." And then they're like, "Oh, just use our service that only runs on our cloud," and now you're locked in. Um, so that, that, that's Microsoft's playbook. And it, it's a very rational playbook, and I think it makes sense. And that is the opposite... That's why they have extra money, 'cause 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.

    18. PO

      But go back to this notion that it's reckless to not be in the frontier as a digital-

    19. BT

      Well, so, well, so, so the reason why that's concerning though is at the end of the day, why are we using Microsoft products again?

    20. PO

      'Cause we did before. [laughs]

    21. BT

      Like, to what extent does it actually make sense to have all these artifacts, all these documents, all these, like, email inboxes? Can't AI just do that? Like, there, there's a real threat here where to Microsoft software business, the whole systems of record thing is, it's kinda funny because one reason why systems of records are so powerful is it's so hard to move them to somewhere else, 'cause it's a very tedious, repetitive job.

    22. PO

      [laughs]

    23. BT

      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 that is, like, when you see Codex or when you see, uh, you know, Claude Coworker or whatever, it is, like, aimed like an arrow to the heart of what Microsoft ha- of what Microsoft has. And in the long run, all digital companies are, but Microsoft is very much. Like, there, there's a re- their strategy is sound. It's also desperate in a existential way and also in a, they might pull it off because they're desperate sort of way. Meta is not threatened immediately. But if this is where my bullish view of AI comes in, I think all digital companies are threatened, and Meta is a digital company. Like, they, they, they have software. Now, the sort of one worry is AI takes up more and more time, and that, like, time ultimately is fa- is Meta's currency. We saw OpenAI tried the Sora thing. Didn't really take off. Social networks are actually pretty hard, also costs a lot of money. Like, it's kind of really interesting. This came up with the creator payment stuff. So YouTube very famously has, has paid creators kind of from the beginning, and that's a much bigger drag on the business than people appreciate because th- YouTube has marginal costs to their content. Now, unlike a Netflix, they don't have to put, pay that cost upfront. They pay it after the fact, so they're sharing revenue. So it's a much, it's a better model than a N- Netflix model. Netflix has to pay upfront for content and then ideally make more money. YouTube pays along the way. But, but Facebook or Meta-

    24. PO

      Pays, pays nothing

    25. BT

      ... they pay nothing. People are like, Instagram is this unbelievable product that generates all this money for which Facebook pays $0 for content. It's unbelievable. And so 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, 'cause what they have today is free. So they have attention. Um, there's a bullish world where Meta is actually very well-placed because in a world where we're interacting with 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, they took their, they brought their network to bear to kill Snapchat. Uh, where TikTok, the reason why TikTok was just a blind spot for them is TikTok is classified as a social network, and it's not a social network at all. TikTok is an entertainment product. You, it doesn't matter who you follow on TikTok. What you see on TikTok is a function of what you watched, and you're gonna get more, more of the same, right? And the, i- it's a user-generated network, content network, and the, 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 gonna give you the best content from across the whole network, and the vast majority of content is gonna be crap, but this is like the absolute question before. Like, 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 an in- a ton of content, the absolute amount of great content is gonna be very large. And so the, then Meta's like, "We're a social network." And so Meta's serving you content from your network of people you know, and TikTok's serving you the best content from around the world. That's why they took a huge chunk out of them. Meta had to shift. That's what's happened with, with Instagram and with Reels is it's not really a social network. It is a 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, like, we actually want humans. We want to have some sort of connection to them. That'll be interesting to see how that plays out. Then the other thing with, with the models is they're so impactful on advertising. Biggest impact of the models, the biggest monetization right now is probably not Anthropic or OpenAI. It's, it's the incremental gain that is happening for Google and Meta. And most of that, most of, most of the stuff is pre-LLM, but we're getting to LLMs, whether it be generating advertising content. Like, they are the be- Like, 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? Like, they can, they actually can validate their image creation and their text creation in a way no one else can, and their validation is the ad marketplace. Like, 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. Um, like, the vast majority of ads don't convert, so they have this, they have this a- 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 gonna get a world where ad matching is actually still fairly crude. It's like, "Here's the qualities of the person. Here's the qualities of the ad," and it's like you create an embedding, like a me- a vector calculation, uh, and see what numbers match, and then you sort of match an ad to the person. What do LLMs do? LLMs predict. Like, we're gonna move to this world where Meta's gonna look at people and say, "This person probably wants to see this next," and they're gonna go find that thing and show it to them. The potential upside in terms of just showing people better ads that are more relevant to them, they only need to increase, like, 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, in having these amazing models. I think a big problem Meta has is they don't tell this story. Like, 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. Like, you and I have set up these little content businesses that make great money, but we're... Content is kind of you get a ride on social media, right? I grew up on Twitter, people sharing my links. It was amazing. If you're selling some product, like, the beauty of the internet is there is a niche out there that wants that product. The question is, how do you find the niche? Facebook advertising. That's what it does. It, it connects. 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. And that is treme- 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 didn't have to pay for it along the way. And Meta made a bunch of money for themselves and their shareholders, which is basically everyone in the world. Like, th- this is why advertising is great, and Meta's advertising in particular is awesome, and I get frustrated that Meta doesn't talk about that. Mark Zuckerberg has never really talked about the societal benefits of advertising except in passing-

    26. PO

      I see

    27. BT

      ... in 20 years. Like, 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, like, grit and grind that goes into building an advertising business. Like, and, like, you know, all the Facebook people get frustrated or have questions about as far as data and all those sorts of things, and maybe there was a bit where he didn't wanna be involved in it and wipe his hands of it. But you saw this, like, when, when Apple passed ATT. App Tracking Transparency was one of the most, one of the worst antitrust violations in the history of technology. Like, just Apple unilaterally obliterating all these business models while they're simultaneously building their own as far as advertising goes and doing, like, doing all this tracking. Why? Trust us. And meanwhile, they're running these advertisements. Like, 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 da- Like, and Meta was not prepared to respond because they... I think you got this with Sheryl Sandberg back in the day. She would, in every call, would talk about advertising and how great it is and have a bunch of case studies of, like, people who are benefiting from advertising and these new entrepreneurs. And then she left, and it's kinda like that never, hole never got filled, and you, you... It feels like it's a company that's kind of, like, embarrassed. "Yeah, we make a lot of money from ads, but we got glasses and, uh, we're doing AI." It's like, you have ads, and ads are awesome. And I think if they had made that, communicated that more consistently, they would be a, in a better place generally from a PR perspective. They would be a be- better place relative to Apple, and 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 hated all along. Uh, and so there's a bit where, "Why?" Like, "Why should we let you spend money again?"

    28. PO

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  9. 1:14:07 – 1:22:52

    Nvidia and Commoditized Intelligence

    1. PO

      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, like, 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's prior products.

    2. BT

      Well, that's always the case though. The most interesting thing about the internet is free distribution. Like, bandwidth is a commodity. The, the fact that I can pull out my phone right now and connect to any information source in the world for free-

    3. PO

      Yeah

    4. BT

      ... um, free on a marginal cost basis, it's because it's a commodity.

    5. PO

      Yeah.

    6. BT

      It changed the world.

    7. PO

      Yeah.

    8. BT

      Commodities changed the world.

    9. PO

      Yeah.

    10. BT

      Like, the, there's a aspect of differentiated products by definition have lower TAMs because you're, like, that, there's a elasticity aspect to it. Not everyone can afford to pay for it. People's willingness to pay is going to differ. Your market is gonna be constrained. Apple's never gonna serve the whole world by having, by selling a device, whereas a Google can because it's free, right? They, that matters. And commodities, you know, you're paying for a commodity, but to the extent it is available to everyone is the extent it is impactful. Yeah, the internet is a commodity, I would say, and it changed the world, so I don't think it'd be weird that intelligence ends up a commodity and changes the world.

    11. PO

      But, but still, so s- so commodities often are not thought of as, as good of businesses as these differentiated high m- high-margin products. So curious for your thoughts on, yeah, the, uh, Jensen's and, and Nvidia specifically.

    12. BT

      Nvidia's position is, I think, definitely unnatural. It's like they've maintained all their margins. Isn't that amazing? It's twenty twenty-six and everyone's coming for them and they're still charging, you know, however much money for, for, for a chip. Um, but they're actually not maintaining their margins because who is buying... Like, this whole question of circular financing is people talk about Lucent and things like that and, you know, this whole deal and, and Nvidia's providing a twenty-five percent backstop. But if you actually, uh, ascribe a value to that, to Nvidia's taking equity in the neo clouds or whatever, like they guarantee they're gonna buy all their compute to twenty thirty, right? And why do they do that? So that the entity in question can get a lower cost of capital so they can buy more GPUs, et cetera. But implicit in that, why do they get a lower cost of capital? They get a lower cost of capital because Nvidia assumed risk, right? This is my point before. Risk never disappears. It just sort of appears somewhere else. Taking on risk has a price. Like, so Nvidia, like, now 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, that there's this neo cloud they backed up, a ton of compute comes to market, the hyperscalers have plenty of compute. They don't have enough compute. Nvidia's paying for a compute that no one wants. They just lost a bunch of money. So if you think about it, there's an expected value of that investment. That expected value has... It's not zero, and it's not n- one hundred percent. 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.

    13. PO

      Yeah.

    14. BT

      Right? Like, they, it, now the price cut didn't show up in margins. It 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, are you actually considering all these pieces, right? The, uh, the reality is, is that moving stuff off the balance sheet by and large works, right? [laughs] And so, uh, but they're doing all these, all these deals to maintain what feels somewhat unnatural. And so then I would say, like, we have seen dis- price cuts.

    15. PO

      Mm.

    16. BT

      They're just manifesting in these very bizarre sort of ways. Now, in the long run, I think the challenge is, the, the challenge Nvidia faces 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, sell like twenty percent of their GPUs to Anthropic. On the last earnings call, Andy Jassy practically confirmed that they'll be selling Trainium 3s or maybe Trainium 4 or those Trainium 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, it, it, 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 a differentiation. People aren't going to Amazon to use Trainium, so they're not cannibalizing, like, the attractiveness of their cloud by selling Trainium outside. So they're Nvidia's biggest problem. It, uh... Because what's the number one advantage that the hyperscalers have?

    17. PO

      Scale.

    18. BT

      Lower cost of capital.

    19. PO

      Yeah.

    20. BT

      It's a capital fight. They have a lower cost of capital than the neo clouds do. The neo clouds are They'll buy Nvidia left, right, left, right, and center. And by the way, it also makes total sense that, like, why SpaceX, like Elon, 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. Like, Nvidia is true. It is the most fungible. CUDA'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, it's still something of a moat. It's-- So if you're going to be-- If you want to play the game SpaceX is doing, where 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. And 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 come up with these Ne- or they had the Neutron models, but they had all these, they had this thing in 2024, I remember. It was the first one where it was like the rockstar GTC at San Jose and like the huge coliseum and Jensen Huang comes out. And it was a very boring GTC. The old ones used to be Nvidia demonstrating like 50 gazillion things as they're throwing stuff at the wall. They knew they had something with GPUs, and they're trying to like find a use case.

    21. PO

      Get people excited about them, yeah.

    22. BT

      Once Elon showed up, it's like, "Oh, we have the use case."

    23. PO

      Yeah.

    24. BT

      But they were coming up with all these enterprise offerings. I can't remember what they were called. 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. They were-- And, and Intel is a good example here. Intel got-- AMD cleaned them out in hyperscaler sales because the hyperscalers have put in the effort to get stuff working on AMD versus Intel. There are still small differences, even though they're, they're, they're x86. Because they're buying at such scale, the investment to do it is worth it to get a better chip or a lower price or whatever it might be. Where Intel, the part of Intel's business that never floundered was selling to government and selling to enterprises because you're sel- Like, 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, 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. Just to be like, because they're the, they're actually, they're actually bigger. So, so you have this issue where they, the hyperscalers are the threat. The hyperscalers have a better cost of capital than the other companies if anyone 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. Like it's not, shareholders don't love it, but their, their monetization capacity is, at the end of the day, like it's, it's much higher than, than, 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. Because in a world where we're totally constrained on power-

    25. PO

      Everyone will want the best stuff

    26. BT

      ... we have to get the best efficiency, the best token efficiency. And I think Nvidia is still the most token efficient, um, and so that is a good world for them. I think it's been probably the biggest problem for Nvidia over the last couple of years is I think the US has actually brought a lot more power online than expected. It, it surprised me.

    27. PO

      Yeah.

    28. BT

      Like whether it be what Elon did sort of behind the meter, which has been, been replicated, West Texas and natural gas and, but even to like restarting nuclear plants, like the extent to which we've-

    29. PO

      I love how the US responds to these things.

    30. BT

      It's a- it's awesome. It's actually one of the biggest like encouraging signals about the US, is I was writing early on like, what's going to be the

  10. 1:22:52 – 1:25:52

    What Survives an AI Bubble?

    1. BT

      long-term-- Like assume this is a bubble. You want there to be a long-term payoff, right? The dot com, we got fiber in the ground. Google like, 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 they, so much of the, the power of what they do is because they bought up all this dark fiber that was basically free after the dot com era. Like our core internet still runs on Worldcom fiber, right? Like, uh, like the, and so that was a lasting benefit. The railroads, BNSF is, is throwing off money that's going to Google from Northern Pacific and Jay Cook selling bonds to retail investors. Like the, the, you, you want a bubble that produces something that lasts. And very honestly, what's going to last from, from AI? The GPUs don't last that long. Like data centers, eh, okay, fine. But what is it going to be? It's like power. It has to be power. If we have, 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? Like it's, it'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. Like power for sure is a constraint. It's going to be a constraint. But I think it has taken longer to bec-come a constraint than anyone expected, and I wouldn't be surprised if that includes Jensen Huang. Like I think he thought a power, insufficient power was going to be Nvidia's moat sooner than it, than, than, than, than it happened. And it turns out that the longer we have enough power, the more time Amazon has to make Trainium better, the more time Google has to, to make TPUs competitive from a, an efficiency standpoint. And if we get in a world where, this is a world where those margins seem very hard to sustain.

    2. PO

      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.

    3. BT

      Thank you very much. [upbeat music]

    4. PO

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Episode duration: 1:25:54

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