Why The AI Bubble Will Burst: The Most Logical Case — With Paul Kedrosky

[00:00:00] Speaker 1: If we are in an AI bubble, what could an unraveling look like? Let's talk about it with investor and analyst Paul Kudrosky right after this. Welcome to Big Technology Podcast, a show for cool-headed and nuanced conversation of the tech world and beyond. We have a great show for you today. We are going to tackle what I think is the strongest argument that all this AI investment is going to lead to, well, a collapse because our guest today, Paul Kudrosky, thinks that we are in the midst of an AI bubble. He's been making the case far and wide and has not backed off despite the fact that this technology has gotten much better over time. This will be a really fun discussion to ask. Basically, even if everything goes right, are the economics on the downside going to be so bad that it will still fall apart? Paul, it's great to have you on the show. Welcome. [00:00:49] Speaker 2: Sure. Great to be here. [00:00:51] Speaker 1: All right. Let's just start with the spending and the return necessary to make that investment pay off, right? If we're in an AI bubble, as you argue, there's going to have to be some level of overspend and then an inability to make those returns materialize. So first off, can you just talk about the magnitude of spending going into the AI build out today and how that compares to maybe previous infrastructure build outs and the rest of our economy right now? [00:01:20] Speaker 2: Sure. I mean, there's a thousand ways to kind of put it in context for people. But one of the ways I try to do it is to compare it to, as you say, prior infrastructure build out. So you can go back to the 19th century and canals and railroads or you come forward to the late 19th century and early 20th and talk about electrification and rural electrification or the interstates, World War II re-arminant, the fiber optic build out. These are all these moments in Western economic history, in particular US economic history, where we had this massive infrastructure investment that in some ways, not obviously the analogies are never perfect, but in some ways are analogous to what's happening today. So one way to think about the sizes of each of these moments is to think about their contribution to GDP, or you can think about them in terms of their contribution to GDP growth. You can think about them in terms of their contribution to non-residential fixed investment. There's lots of ways to back into this so you can kind of provide some context. And it doesn't really matter anymore which one of those you use, we're the winners. So we're now currently larger than everything except for, and this was an unfortunate analogy I made recently on a German interview, as I said, we're now larger than everything except for World War II re-arminant, which doesn't play as well in Germany as it does everywhere else. But nevertheless, the point being that as a percentage of GDP, as a percentage of non-residential fixed investment, as I've documented for like the last year or so, in terms of its contribution to GDP growth, in all of those metrics, we've now exceeded all of the largest capital expenditure kind of paroxysms, impulses in Western economic history. And again, you can say to yourself, well, so what, or anything else, but that's sort of a separate question. So the point to start off with is this is a really, really unusual moment in terms of the scale of capital expenditure, normalized against all of these other capex moments. And then we can get into whether or not any of those analogies matter, or whether, you know, this time is different, the favorite sort of responses to these kinds of things, or all sorts of other stuff. But the point is, we've now reached that moment. And it's now, you know, in lots of other measures, it's now the largest, tech is now the largest piece of the high yield bond market, it's now the largest piece outside of financial services of the, of the investment grade bond market. So in terms of new issuance, tech companies themselves are now at a point where for the last two years, people repeatedly told me that it really didn't matter, because they were doing it out of cash flows. And so it would only become worrisome if this was becoming out of debt. Well, guess what, as of the second quarter of 2026, this is now more than 50% of the funding for data centers is all is external financing, which is obviously the term of art for off balance sheet and out of your own cash flows. And now, of course, the same people who were saying that a year ago, we're saying that that wouldn't matter is now saying, well, that's perfectly fine now. So you know, by any of these metrics, GDP, non residential fixed investment, percentage of GDP, percentage of GDP growth, off balance sheet financing, we're now at a point where this is a remarkable historical moment, full stop. [00:04:29] Speaker 1: Yeah, and a way that I like to talk about this is, you know, first of all, it's not only a bigger magnitude than these previous build outs, but it is a bigger, bigger magnitude contracted into a fewer number of years. So just to put a fine point on it. That's a really important point. Yeah, yeah. [00:04:46] Speaker 2: And that's a really important point that, you know, for example, to put that in context, electrification took almost 30 years, the build out of the US railroad was a multi decade proposition, the interstates were a decadal proposition, even the build out of the fiber optic backbone was probably four and a half to six years, something like this. So this is a higher scale of spending happening at a much more rapid pace. So, and that matters in the context of capital markets, because you don't have time to slow down and consider exactly what's happening and where are the returns going to come from. But you know, that's a problem for another day. But just to put it in context, that's, that's an appropriate context. [00:05:23] Speaker 1: Right. And so if you have, let's say, a build out that goes over a couple of decades, or even five years, you have these, I think this is what you're talking about, you have these stop points where you put some investment in, you get some time to marinate in your projections. And then you say, should we put some more in, right? And of course, in many of these build us that we talked about, there were collapses. But what we're seeing now is this rush in to invest in the AI infrastructure build out without those natural stop points, and the numbers are bigger. So we're looking this year, it's looking like big tech alone will put something like 700 billion in towards capex this year. I think last year was something like 350 to 400 billion. And next year is projected to be 1.5 trillion in build. Yes. Now we're going to write, you know, you mentioned a lot of the different dynamics about like where this money is coming from. And that's important. But let me just put this to you to begin with, with the level of investment that we have coming in to this type of build out, what is the return that's going to be necessary to justify these investments? So let's just take like 700 billion, what is what for even investors to like, I don't know, not go under, or I guess a lot of this is big tech. But like, what are the numbers that we need to be looking for, for those numbers to be rational? [00:06:43] Speaker 2: So you have to turn it around and look at it from the standpoint of the providers of capital. So alternative uses of capital and what return I could get on the same capital in another context. So the way that I try to analogize this loosely, and this is very loose, is that data centers from the context of many capital providers are real estate, they're really just multi tenant apartment buildings, it just so happens, there's no humans in the apartment building, there's just GPUs. And so from the standpoint of providers of capital who look at these as project finance, and then by that measure, try to compare the returns they're getting on this to the returns they're getting from doing project finance. So think about it in the context of commercial real estate, a strip mall, a multi tenant apartment building, or whatever else. So increasingly, the providers of capital for these things look at it in that context and say, well, what's the yield in terms of I'm contributing, you know, $100 billion to some massive meta project, what's my reasonable cash flow expectation, very much analogous to what I might expect from the cap rate on a multi tenant apartment building, and is this competitive on that basis. So that's the short answer to your question is, it's very much a market based return that's required, the scale of the money is irrelevant in some weird context, because it's really all about, you know, what sort of return can I expect and how does that compare to comparable investments. So in again, in this context, CRE is the most comparable investment from the standpoint of external capital providers. So they say to themselves, you know, we're looking at cap rates around 6.8%, 6%. Is that reasonable? Well, that compares reasonably well to the following five projects, but not particularly well to this project. So what it's provided is a way of putting the returns from these things in context. So it's wrong to say, for better or worse, we're going to be putting in a trillion, therefore, I need 100 trillion out of this. That's not the way investors are looking at this. And it's and it will lead you down the wrong path if you take that approach, because now you're forced to say, well, I'm going to have to estimate what percentage of some giant number I'm going to earn over the next five years. And that's where you get into these loony arguments from some of the sell side analysts where they'll say things like, well, the TAM, the total available market for human labor is like $12 trillion. If I get 20% of the TAM, like this is ridiculous, right? This is just completely, you know, see to your pants speculative stuff. So it won't get you anywhere in terms of understanding the calculus that's driving people to provide the off balance sheet financing for these projects. So the right way to think about it, for better or worse, is to analogize it to commercial real estate and ask yourself what kind of cap rates they could get on comparable projects. And that is really the answer. Now that leads you into a trap. But nevertheless, that's the answer in terms of thinking about what kinds of returns are required to justify continuing providing a capital. Okay, this is really important table setting here. [00:09:30] Speaker 1: And I think this is sort of worth digging into a bit because the way that you're framing this is actually suggesting that we don't even need to hit the best case scenario, right? So like a way that I've thought about it is almost everything needs to go perfectly in order to return on these investments because they're so big. But if you're saying that this is just like being invested in the manner of a typical real estate investment, then that perfection is actually not necessary. And my level of concern goes down here. So let's take an example. I'm meta. I invested $100 billion in data centers. So let's say you're like looking at like, I don't know, you could help me with the math here, but you want to get this like a 6% return or 20% return on your investment, you might only need to get $120 billion back, you know, if you're going to compare this to a real estate investment. And now that I'm thinking about it, I'm like, well, meta makes what like 30, 40 billion a quarter, that might be eminently possible with, you know, the outlay. So where's the concern here? Well, the concern is [00:10:37] Speaker 2: that the nature of the investment is profoundly different from real estate. So what you're really entering into is a project that not only has current capital requirements, but has ongoing capital requirements. This isn't just now and then I'm going to have to replace a tenant's drywall. This is a project that would require wholesale replacement of most of the hardware and probably changes in the cooling system and probably changes in other aspects of these data centers continuously, and probably, you know, depending on the math, anywhere from a four to seven year period. So it's nothing like an apartment building in the sense that most of the capex occurs up front, and then it generates recurring annuity cash flow from which I would, that I bask in and generates compelling returns back to my investors. This is much more like a utility, a non-regulated utility who has continuing capital requirements, which continually dilute the returns because you're having to raise more capital all the way down the path. And this will continue for the lifespan of the project. So what you end up, from the standpoint of an investor, you end up with a duration mismatch problem, right? So I've got what looks like a long duration project, like an apartment building, that's actually a short duration project in the sense that most of the underlying assets need to be turned over relatively frequently, or at least upgraded. Now you can get into all kinds of traps, the Michael Burry thing with respect to like, well, what's the proper depreciation schedule for, you know, GPUs or whatever. But the point still stands that you not only have upfront capital requirements, but you have continuing capital requirements. So that's, that's problem number one in terms of thinking or making the direct analogy to commercial real estate back to data centers. And the second one is that, um, you need to also think in terms of, of the nature of, of, of why this replacement happens. Some of the replacement happens because of the MTBF, the meantime between failure of GPUs, which varies depending on what the GPUs are being used for and the generation of the GPUs. So we have some GPUs that are failing inside of modern data centers on an 18 month cycle, some that are failing on a much longer period. So we've got constant churn just from that standpoint. And then we have familial upgrades in terms of upgrading to new generations of GPUs that cause, uh, upgrades. And then we have the whole replacement cycle of maybe we won't have GPUs in some of the upcoming data centers. There'll be increasingly, you know, inference specific ASICs, and we're seeing lots of that going on. So that's problem. Number two, problem number three is for the, we've got, we're paying a fixed rate of return on a depreciating asset, not just a capital, but also in terms of the thing under the hood, that's generating the cashflow. So what's generating the cashflow data centers can be thought of as factories. And the thing that they produce, the widget that they produce is this thing we euphemistically call tokens. And these tokens are among the most rapidly depreciating assets we've ever seen in a modern economy that they've continually been falling 70 to 80% year over year on a constant performance basis for at least the last four years. And there's no reason to expect that to change. So we've got at least three different problems here in terms of making that naive comparison to commercial real estate and saying, okay, everything's going to be fine. Look, look, these guys are good for it. And we've got these long duration contracts. We have a rapidly, we have the depreciation of the data centers. We have the continuing capital requirements. And then we have this unprecedented problem of a hyper deflationary commodity at the core of the revenue generation engine of these so-called data centers. None of those existed in the context of any other cycle in the past. Railroads weren't going through hyper deflationary cycles and neither were rural electricity. So fiber certainly wasn't. Fiber was actually the reverse. It became more valuable over time. So all of this is really unusual. And makes the naive analogy to commercial real estate that brings in those kinds of investors who have showed up in huge numbers because they see this analogy incredibly fraught and probably [00:14:21] Speaker 1: perilous for them. Okay. So there's a lot here. And I don't have a dog in this fight, but I'm going to do my best to advance the counter arguments to your arguments here. Sure. And you tell me what you think of them. Okay. And maybe I can do, you know, two in one here. So the depreciation that you're talking about is because 50%, I think you've said 50% of the cost of data centers is in the GPU. And the GPU has a lifespan, you know, someone, some people say three years, right? This is the typical depreciation argument. You put, let's say, a NVIDIA H100 in there, three years later, it either fails, like you said, or you have to replace it with a black whale or a Rubin, whatever it might be. So, and so therefore these expenses in the data centers aren't just like you invest $100 billion in a data center and you get to live off the land for 20 years. The investment is, you know, you have to continually, you know, feed that data center with more money in order to make it work. [00:15:25] Speaker 2: Right. So, which doesn't work in the context of the NPV calculations that underlie a typical real estate project, obviously. That's completely different from the kind of math that we use to justify a multi-tenant apartment building, for example. [00:15:40] Speaker 1: And then, you know, further on, what you mentioned is that the tokens, right, with the things that these GPUs produce, they are depreciating, they are getting cheaper. No one will argue with that. [00:15:51] Speaker 2: I'll just say they're not really depreciating. They're actually staying the same value. They're just deflating. There is a difference. [00:15:58] Speaker 1: Okay. Right. The deflating, right. The, what you used to pay for a token is, is much cheaper than it was previously. Yeah. Okay. So here's, here's what the counter argument would be wrapped up into one. The counter argument would be, you know, as tokens have gotten cheaper, people have wanted more of them because the, the AI models that used to use them have become a more powerful and be more capable. So, therefore, you know, even if those tokens are cheaper, people, just the demand for the outputs of these factories have grown by a magnitude, sometimes, you know, 10, 20, 30 X than they were previously. And as you do that, you know, as that demand has grown, you know, people are willing to use even the older chips at rates that would be higher than when they initially came out with the less powerful models. So I was speaking with CoreWeave at the end of the year last year, beginning of the year this year, you know, around New Year time. And they said they were actually renting out H100s for higher prices than they had previously. So all of what you said is true. The counter argument that they would make is, yes, and their demand for the tokens is higher and the old hardware is working well beyond that chip, that typical three to five year estimate that people expected. So what is your thought when people say that? [00:17:21] Speaker 2: So there's a whole bunch of nested arguments in there. So let's take them on kind of one at a time. The lifespan of a GPU, in terms of just looking at it from an MTBF standpoint, I mean time between failure standpoint, depends very much on what it was used for in its adolescent years inside the data center. The analogy I often make is if you could buy a used car, you have two used cars, one of them both has, they both have like 5,000 miles on them. One was driven in a, you know, 72-hour nonstop race across the country. The other one was driven, that was the only, that's where all the 5,000 miles came from. And the other one was driven to church on Sunday per year. Which car would you buy? Well, I think we would all buy the car that was driven to church on Sundays. I want nothing to do with the one that was raced in some kind of, you know, bubblegum rally across the country. So in the context of GPUs, what we have is a generation of GPUs that were largely used for very intensive training purposes. And so the failure rates of GPUs used so intensively for training purposes are much higher than inference-specific usage. So yes, there's no question that if a chip is used exclusively for inference, which is to say token completion in response to prompts, then the lifespan will, all else being equal, likely be longer. And if I have a chip that didn't fail during training, then can I repurpose it potentially to be used for inference? Sure, there's no reason. But in aggregate, there is this problem that the failure rates of chips that were used for training is very different from the failure rates that were used for inference. So we have this kind of mixed population of chips inside of data centers with very different failure rates. And people have a tendency to conflate this and just pretend that it's all the same thing, and it's not. And that's not very helpful, because if you actually talk to people who are running data centers, they will say, this is exactly what we're seeing, is we see much higher failure rates. So there is this sort of blended problem that you have to understand the nature of what the chips were actually used for. And that's only going to become more profound in future, because increasingly, I often joke that the frontier model company that will, the most valuable frontier model company in future will be the one that stops pretending to train models and actually just moves on to harnesses and moves up the stack. Because what we're seeing increasingly, if you look at things like the Epic Composite Index and other things, is that while models are still improving, they're improving at a much slower rate. And I often do this kind of Pepsi Coke test where I'll put a couple of different models in front of people using some kind of a harness like open code and ask them to tell the difference. So now everyone thinks they can tell the difference, and the reality is no one can tell the difference. And so right at this point of convergence that we're increasingly, the thing that differentiates models outside of marketing is price, which is one of the reasons why on tables like Open Router or whatever else, it's now dominated by Chinese models. So we're rapidly seeing this move away from any kind of premium pricing in terms of the models themselves, which is, and I'm trying to get to the point about this kind of Jevons paradox, which is really what you're pointing to, this idea that as models get cheaper, we use, or as tokens get cheaper, we use more of them. And this is a common idea. And, you know, we've seen it repeatedly play out in different ways over the last 150 years. But I think this mostly speaks to the enumeracy of people that they don't understand what a compounding price decline of 80% means in terms of what you would have to see in terms of growth on the other side. You have to see around 100 million fold growth over the next six years in terms of tokens. Is it possible? Absolutely, it's possible. Is it likely? No, it's not likely, but it could happen. But let's not pretend that it's probably that it's one of the most probable outcomes to throw out to throw out this and say, But Jevons paradox, but people will use more, is to really dodge the core problem of the geometric decline in the price, which will only continue and get faster now that we've got increasingly price-based competition because of the convergence of models. So that problem not only doesn't go away, it gets even harder in future. And, you know, now you're competing with sovereigns who have state-subsidized token prices, as China is probably the canonical example. And so it just becomes increasingly difficult to make the kinds of returns that your investors expect given the comparable cap rates that they're comparing them to. So this idea that but it will work itself out because prices will continue to decline and magically will just use enough is both historically naive. This argument gets made all the time, has been made repeatedly in prior tech bubbles, and people wave their arms and say this, and it almost never works that way. And it's worse this time because at the core is this deflating commodity called tokens that is being used to pay a fixed cap rate in terms of what the expectation is from investors who have fronted capital for these instruments. So is it possible? Sure. But think about some of the carnage that it's already creating. You know, Alex Karp was complaining on CNBC the other day, I'm sure you saw it, that these companies are increasingly marching up market and trying to eat other. The reason why they're marching up market is because they see this coming and they're looking for higher return places to be because they see the collapse in the fundamental commodity that they're selling. No different than, you know, a gold miner deciding they need to start making jewelry. This is the same phenomenon playing out. So they're marching up, and so that's going to have collateral damage in terms of them being seen as fair and unbiased players, which will then play into the likelihood of companies going down the path of, you know, sovereign data centers and doing token inference generation inside their own organizations as that becomes increasingly possible. So I think there's no doubt that we'll see this continuing growth, but whether or not the growth will be large enough to compensate for what will essentially be an asymptotic collapse to zero in terms of the price of tokens is mathematically a very hard argument to make. [00:23:03] Speaker 1: Okay, so this is a great point to dig into as well. So Karp, of course, went on CNBC and talked about how you can't trust the Anthropics and the Open AIs of the world with your data because they'll take your data and they'll build their products that are, you know, sort of compete with yours. Obviously, they're competing with Palantir, right? Because they're going to go and they have these, you know, Palantir has forward deployed engineers. Now Open AI and Anthropic have forward deployed engineers and they have effectively the intelligence underlying a lot of what Palantir is doing, right? So if they sort of go up market, you know, you can all of a sudden, and this has sort of always been the fear about these AI companies is their AI would be smart enough that when they see companies building on top of it, they would just go in and take their business. So to me, seeing Karp on CNBC, yes, he was, he was sounding concerned about what Open AI and Anthropic might do to, you know, quote unquote, your business, but he's also talking about what they were doing. Oh, there's no question to his business. There's no question. Yeah. And then just, just from a like, because we're talking about the economics of these build outs and whether these companies will be successful from a pure, like, sort of ruthless business perspective. Is this the way that they can actually make these investments pay off is they say, all right, well, we have the intelligence because that we will agree that that technology is good in some areas. [00:24:33] Speaker 2: No, it's good in lots of areas. I mean, and I think, let me just jump in here for a second, because this is a common misconception is that I actually think this is probably the most. [00:24:40] Speaker 1: Yeah. [00:24:41] Speaker 2: Yeah, yeah, yeah. It's probably the most transformative technology of the last hundred years. And, and that's, I, you know, it's obviously a strong claim, but I genuinely believe that. But that's not the same thing as saying that, therefore, it's fundamentally justified in a by default justifies the investments being made on its behalf. These are two very different things. And as a matter of fact, the former is almost required for the latter to fail, right? Because if it wasn't a good story, who the hell would show up with lots of capital? [00:25:06] Speaker 1: Absolutely right. And, you know, on this show, all the time, we talk about how, like, we, we think that this technology is real. And, you know, you got to question the economics because of many of the things we're talking about. All right. But let's just go back to this argument. So they're coming at, let's say they're coming after Palantir. Anthropic is coming after Figma, right? And, you know, sort of the list goes on as, you know, there's always been this, like, is, does the entire economy effectively become like a wrapper on top of these AI models? And if so, what's to prevent the AI companies from going out and building into verticals that have been successfully captured by other companies? And so even though it, you know, it's, it's ruthless, et cetera, it's going to make Alex Karp jump out of his seat and, you know, various TV appearances. Is that the route to, you know, having the, the business, OpenAI and Anthropic having the business to pay back the investment? Because if you're able to do that and jump up market, you can, you know, potentially justify all this investment by creating these massive businesses. [00:26:11] Speaker 2: Yeah. I just think it's reversing the logic and I have no sympathy for Karp whatsoever. I mean, you know, Palantir can burn or not burn. It doesn't matter to me. But I think it's reversing the logic to say that if you're, if the idea is to say, I need to find a way to justify the investment. And therefore it's okay for frontier models to move up, where up market, eat the economy and become a logopolis, then I guess that's okay. It's, I think this is a cart before the horse problem is because we're not trying to, I'm not in the business of justifying what they're doing by coming up with societally toxic mechanisms that therefore make it work. That's not at all appealing to me any more than it would have been because we heard these same arguments way back in the go-go days of Microsoft. Long ago, whenever Microsoft first launched Windows and some of the early operating systems were coming out, one of the things that happened was people built applications on top of the operating system. Microsoft saw those applications were doing really well and guess what they did? They launched their own. Now, most of the ones they launched were garbage. Microsoft turns out for a long time, wasn't particularly good at launching applications, but they got better at it. And over time, eight, a host of different applications in spreadsheets, word processors, all over the place, they essentially removed the oxygen supply for all of those different markets and moved up market. So that's not unprecedented. So it's not surprising at all that we'd see these companies do that. The difference this time, obviously, is we have a generic, a general-purpose technology that has much broader applicability, so in theory, can do this across a host of other domains, which at the very least should be cautionary and can do it at a much faster rate. The only thing I will say in a weird sort of defense of the frontier companies is, and it's the same thing I say with my venture capitalist hat on, is we have startups show up all the time that say, I have this amazing technology that's really high alpha, should be bought by every hedge fund and so on, and I'm like, okay, fine. Why are you telling me then? And they're like, well, what do you mean? And I said, if your technology is so good and you can generate a competitive alpha with it, don't be an idiot. Go out there, raise some capital, and invest it directly. Don't tell other people. So the fact that they're telling other people about this alpha-generating technology is by default a refutation because if it actually worked, they wouldn't tell me. So the same logic applies to the frontier companies. So if the frontier company's technology is so amazing that it can eat the entire economy, why don't they just ingest the economy and stop selling it to us? Why are they even bothering to sell tokens at all? Why not just move up market immediately? So what that tells you is, in the same way that these companies launching hedge fund tools don't actually have things that can do that, the frontier model companies know perfectly well they can't do that. They know perfectly well they can't move all the way up market to generate those kinds of returns, and the proof is in their own behavior. [00:28:56] Speaker 1: That was basically Karp's argument, which is, why are you selling tokens if you can increase my sales by 2x? Why don't you just take 30% of that uplift? That's right. That's right. [00:29:06] Speaker 2: And it's compelling. It's a compelling argument. You could argue that it's because Dario at Anthropic is so darn ethical that he refuses to do that, and I guess it's possible. That's certainly not it. It just seems unlikely. Yeah. No, of course not. [00:29:18] Speaker 1: So just to, all right, this is good to go back and forth and talk through the arguments here. The argument would be that basically this technology is so new and it's moving so fast that it's going to take time to figure these things out. And so you can't just like, on day one, that fable comes out or you have mythos in-house, go ingest the entire economy. You have to do this step by step. And a case in point is a Claude design, where Anthropic has been watching the design, of course. Mike Krieger, he used to run product there, was on the board of Figma, and that's led to this whole issue and made like Dylan Field, like one of Anthropic's biggest critics, the Figma CEO. And so instead of like going out and saying, we're just going to do this wholesale, we'll do it step by step. We'll see what the technology is capable of, see how people are using it, see what other solutions are out there, and then go ahead and build. And it just goes to this whole concept of the deflation of tokens is like, you know, it seems to me that we're at this point where everybody agrees that these models underneath are commoditizing. And owning the model is valuable only in the way, only in your ability to customize your own products, to have that like deep sync between your products and what the models you build that nobody else could have. And that's where this is going. [00:30:42] Speaker 2: Yeah, I think that's broadly true. And I think, I don't necessarily agree with you that everyone believes these models are commoditizing. It still feels to me like we're in the, I don't know, iPhone version 4 era where people get all excited about a new release and then everyone whines because they say, well, this didn't change the world and it's not, you know, AGI. That's kind of like the fourth iteration of the iPhone where people want to believe that there's breakthroughs still coming. And there were huge breakthroughs in the early days of launching large language models. But now it's not just commoditizing, but I've, you know, some data that I often show people that shows a kind of convergence that's also happening. So it's not just that there's kind of a plateauing phenomenon going on, it's that the variance among models, the best practices across all of these different models has kind of, has converged to a large degree reducing the variance in terms of the composite performance of various models, which means that the opportunity cost for changing models is much lower. So if it's, if the opportunity cost is much lower, unless they can lock me in, there's a huge incentive for me to constantly arbitrage and play back and forth across them, which is hence the rise of Chinese models, why Deep Seek is doing so well all of a sudden and why, you know, Gwen is and others and why open code is emerging as a, you know, viable tool for many people. Because there's this sense that I don't, because there's this sense that I'm not really locked in at all and the convergence means that the model differences while there are so minimal as I can't tell the difference in a kind of Pepsi Coke phenomenon, which again, to cut to the investment chase, suggests that the competition then becomes much more about marketing expenditure, another form of costs and price. So both of those auger poorly in terms of the investment returns for this asset class. [00:32:14] Speaker 1: Correct. And so this is sort of, I think we're, we're both seeing it in a similar way, which is that the economics is going to force these companies to go up market. [00:32:23] Speaker 2: At 100%. [00:32:23] Speaker 1: And that's where things get interesting. [00:32:26] Speaker 2: Yeah. And I think that's going to accelerate with these, assuming the IPOs happen, that's going to accelerate with the IPOs because public markets investors will look at the underlying economics of this fund, of the commodity called tokens and say, so what else you got? Right. And say, what are we going to do next? What are we, what markets are you going to move into? And so that's going to increase the pressure to do this absorptive move up market. And then you get into this problem that, and this was my complaint early on about the SaaS apocalypse earlier this year is there's a deep misunderstanding about why companies buy software. It's not because they think ServiceNow or Salesforce or whoever is somehow, you know, bold innovators that could not be replaced. No, it's because they have a problem. They don't want to build it themselves and they want someone to sue or shout at. That's it. That's why people buy SaaS software. It's not. And so whenever you start building it for yourself, this notion that companies are going to increasingly use these frontier tools to build things for themselves or vice versa, that the frontier companies are willing to be sued and shouted at by everyone on earth for building vertical apps for them. This will rapidly be abused because it is a terrible business. You do not want to be in that position of continually having to service people whose main utility for your product is having someone to shout at or sue, which is, again, it's a gross exaggeration, but it's a misunderstanding of why verticalized software exists and why those companies exist to service the peoples in those verticals. And to just naively say the frontier models companies will blithely race up market in, you know, in service of their new public investors is to misunderstand why those markets exist in the first place. [00:34:00] Speaker 1: Right. I mean, maybe they'll have to, though. That's the thing. [00:34:04] Speaker 2: No, no, no. They'll have to. But that's my point. But it won't be easy. It won't be easy. And even more importantly, it'll probably be very painful and costly. And so be careful what you wish for, I think, is where you get to on that one. [00:34:16] Speaker 1: Okay. Let me make one more of the lab's arguments. And then I actually want to get into some more of the weaknesses that you see and that I see. Okay. The other, you know, you kind of winked at this, you know, the people that believe this time is different. This is not like a super technical argument. This is sort of like the general argument that you might hear. It's the Andre Sonian argument, yes. Would be somebody saying, you know what, Paul? This is different. You have these labs who've built magical, you know, thinking computer machines. Yes, they're investing a lot. But in tech, what you do is you build an asset, you find a way to scale it through computers in some way, and you mark it up and people will buy it because it beats any other alternative. And what you've seen recently is, like, even in the past, let's say, six, seven months, the capabilities have scaled dramatically. You've been able to, like, now leave these computers alone and they can code on their own and do a decent job to the point where, like, they're not just useful for engineering. They're useful for all types of work. And so over time, you know, that there will, despite the fact that so much has been invested, like we said, you know, maybe $2 trillion that are coming between this year and next, that will be so economically useful that the business is going to have to work out. And there's, this is sort of why people are rushing toward it. Your thoughts. [00:35:45] Speaker 2: Sure. But again, this is, this is, this is a classic logical fallacy of assuming what you're trying to prove, right? So you, you, you race ahead and say, it has to work out because I need it to work out. And, and I'll go more deeply into this whole question of, of the, this time is different thing. The corollary to the, this time is different thing is all of, there's always something useful left after these moments, right? Then the idea that there's always some important, some useful assets left after the fiber bubble years later, we could, you know, we could use the fiber for things. Even though half of railroads were eventually abandoned because of overbuilding, railroads are still hugely valuable. That's all true, but it's kind of an unsequitur. Well, of course it's true. We didn't build it because it was useless. We built it because it was useful. The issue is what are the, the, what are the consequences of massive overbuilding in terms of spiraling consequences in the broader economy? You know, increasingly some of the largest purchasers of, of data center related debt or insurance companies. We know what happens whenever insurance companies get in the middle of this stuff. We've seen it in the global financial crisis. We've seen it repeatedly. So that's the right question is not, you know, oh, you know, pat people on the head and say, this is all going to work out because it's always worked out in the past. One, while it's always worked out in the past, it's nearly taken out the global economy. At least four times. So that's worth noting. And, and the other, the other issue is, and this is, I think the more insidious one that people miss because you'll see people refer to there's this woman named Carlotta Perez who wrote a book called technological revolutions and financial capital, which in a sense is the Bible for many of the most, um, I don't know, bullish, uh, partisans pushing some of this stuff. And they'll say, well, this is what has to happen. It has to, we have to have this kind of huge moment of spending and waste and everything else, but then it works out. Here's the problem with that argument in prior episodes. People didn't know that. And that's really important distinction. We've created this reflexivity where now we justify overspending on the basis of prior overspending having worked out. Well, in prior episodes where that happened, people were not justifying the overspending by saying, say in rural electrification, you know, this may look bad, but it worked out in railroads. No, no, no, no, no, no. You don't get to play that game. We didn't have that. So now what's happened is, is it's become a hermetically sealed fly, almost a flywheel in a sense, because we're justifying things on the basis of information that we, that we didn't have in prior episodes and using that to justify an even larger overbuilt. Uh, and that's why the notion that this time is different, it is different, but it's different in a really dangerous way. [00:38:14] Speaker 1: Okay. So, so I, I, I hear that I accept that, um, the argument that I was trying to like put forth on your plate here is, you know, it's, it's a little bit different. I think like the argument that I'm trying to get you to respond to is the, Paul, it is AGI, man. Like this is, this is a, you know, so what is your response on that? [00:38:37] Speaker 2: Feel the AGI. So, yes. So the question then that turns into this one of what would you pay for a call option on AGI? This is essentially the, the, the argument is you cannot possibly overspend because the value is like saying, what would you spend for a call option on immortality? Well, the mathematically I should be willing to spend anything. Similarly, I, I call option on AGI is, is there's no discountable net present, no discountable net present value that is too large. So once you start down that path, once you, I accept that premise that this is, you know, feel the AGI or feel the, the immortality. Then again, I'm into this trap of, well, yeah, absolutely. But the problem is that I'm now, we're now going along with this cultish idea that we both now agree that, you know, what you should be trying to approve, I should assume. And therefore we should be willing to spend anything. And it's simply a, it's, that becomes a, like a toxic board game. It's tennis without a net, right? There's no, there's no way for us to have a reasonable conversation once the other side of the conversation is, what would you be willing to pay for a call option on immortality? What are you willing to pay for a call option on AGI? So that you don't need that argument. We should be able to make an argument and say that this technology is very powerful and very important and transformative. And here's the way it's going to change things without having to have, you know, it's, it's like in classic sort of agnostic theory, this idea of inserting God into every gap in an argument where you can't find a good argument. This is a God of the gaps argument and I'm inserting AGI because now that allows me to create an undiscountable call option that I can't price. Therefore, I should be willing to spend anything. And I reject that. [00:40:13] Speaker 1: Don't you think that all the money that's going towards this AGI or AI build out, the people writing the checks have been told the AGI argument. And therefore, despite all of the economic weaknesses that you've pointed out in our discussion, are, are basically writing that check for a call option on AGI. [00:40:39] Speaker 2: To a degree, um, investors that I've talked to are, are very cynical. So they're perfectly happy to use that in front of their own LPs, but they don't, they don't believe that in house. They look at this very cynically and with very cold calculating eyes and compare it to other similar real estate projects. It's really compared to other sorts of project financing from hydroelectric dams to long lived capital intensive projects. That's the hurdle that it has to clear in terms of promoting it. Sure. We can call these AGI factories. I was talking to a regional economic development official in New Mexico recently who had a hyperscaler show up and tell him, don't you want to be part of AGI factories? I was like, what, this is the picture being made because you're, you're, you're, you're signing up for the future because now you can help us build the factories that dictate the future of AGI. And so, you know, all of these objections you might raise in terms of the kinds of tax evayances that they want with respect to water and power and real estate and other things. It doesn't matter because think about the scale of the call option that I'm offering you. And it's a, it's a get out of jail free card. And it's, and it's really, I think, unfortunately offensive, but nevertheless, it's more marketing than anything else. And when I, when I talk to the largest investors who are putting capital into this, they'll use it with their own LPs, but they don't do it in partner meetings. [00:41:53] Speaker 1: Interesting. So they don't do it because they don't actually believe it. [00:41:57] Speaker 2: No, they don't believe it. Not that they don't believe it. I'll put it differently. It's not that they believe it or don't believe it. They just couldn't be bothered caring because it's non-material. It's non-material. [00:42:07] Speaker 1: So then why are they, okay. If you, you're speaking to these folks, you're, you're seeing the, the clear problems here. What is the justification that they make in their mind? Let's say they take everything that you say and they, they sort of, they, they give it credence, right? The fact that, okay, we've talked about, you have to replace the GPUs, uh, token prices are going down to make, uh, the, to make this work. The demand would have to be like a hundred X, uh, what it is, or like a million X, but okay. A million X. Okay. Let's just say that, um, a million X. Why are they still putting the checks? [00:42:43] Speaker 2: Because, yeah, no, it's, it's a bit crazy, but yeah. So why are they still writing checks? But you might say the same thing. It's back to the Harold Prince line back at the, during the financial crisis. As long as the music is playing, I keep dancing. This is that, right? This, as long as the music is playing, they're all going to keep dancing because there is absolutely no incentive as any of the largest capital providers on earth. From sovereigns down to private equity and private credit to walk away because you get pressure from the LPs saying, why aren't you participating in this? And then even worse as a sovereign, as a sovereign wealth fund. And I've been inside these folks is that once you're managing hundreds of billions of dollars, you start looking at opportunities, not in terms of their economic value, but in terms of check size. And you say, I need to write a check for, fill in the blank, $100 billion, because I do not want to write $101 billion checks. So this weird filter starts happening where you now, these projects are like, look at my friend, Saudi, Qatar, I have this project that's perfect for you. You want to write 50, $100 billion checks? Nowhere else on earth can you write it other than these giant data center campuses like the Meta Project in Louisiana or take your pick. And so once you become, develop a check size filter, the world starts to twist on its axes. And that's why these projects become even more interesting because there's just nothing else out there like them. [00:44:05] Speaker 1: So I will just respond by saying everything that you just said sounds crazy to me, that that is the way people operate. [00:44:12] Speaker 2: Oh, I was inside, I'll tell you a funny story. I was inside of a $700 million venture fund at one point that turned down five terrific projects. So this is at a very small scale. So think about it now as a sovereign. Because the entrepreneur, the entrepreneur wanted $4 million and the fund wanted to write a $20 million check and said, you know, I can't do a $4 million check. And they walked away from four terrific projects. And I thought that is absolutely to use the technical term, batshit. And then I thought, I'll never see that again. And I've now seen it repeatedly inside of some of the largest funds on earth. Looking at projects through the filter of, can I write a large enough check because I have this much capital burning all in my pocket. So it doesn't mean that I'll just give it to any random. I'll write checks to anybody for anything. But it does change the way that you filter the landscape of viable investments in a really and truly perverse way. So these point masses of capital worldwide are part of the issue. [00:45:05] Speaker 1: Right. So to basically sum it up, we've talked like for 40 minutes so far about the logic of investing in these AI projects. And we've gone through all the logic. And I've made all the arguments. You've made the counter arguments. And basically what you're saying it boils down to is this is just a groupthink and convenience thing, which is why all this money is going this direction. [00:45:27] Speaker 2: There's a huge component of that. But there's also this, and I make this argument all the time, that the largest bubbles in U.S. history usually had either to do with technology, loose credit, government policy, some combination of these things. The U.S. in particular is very good at ones that also include real estate, so we can add that to the mix. So technology, real estate, loose credit, government policy. This is the first moment in U.S. history that sits at the intersection of all four of those. So it shouldn't be particularly surprising that we have people who live in each of those bubbles who feel as if they can justify what's happening on their own basis. So I have real estate investors who see this as a real estate project. And they're like, look, I do long duration, high cap X projects all the time. Don't tell me what to do. I have technology people telling me this is the most important technology in history. People always tell us that these things are going to work. And they always work out. It's the Andre Sionian argument. And then the other piece that's pushing this to a real cliff is that it's also seen as an existential battle with some of our competitors around the world like China. So there's this government component where we must win. We must win because to not win is to somehow foreshadow some future decline. And so the notion that the idea of sitting at the intersection of those four forces is incredibly important because you can end up with four very powerful justifications, just one of which is the large funds with point masses of capital who need to write large checks. [00:46:53] Speaker 1: So the checks will keep coming until the music stops. On the other side of this brick, I want to talk about what would cause the music to stop and what happens when it stops playing. We'll be back right after this. Hi, everyone. Alex Kantrowitz here. I want to tell you about a documentary I've made with Gravity to explore the future of AI agent security. To find out if we're truly ready for autonomous agents, I sat down with MIT professor Ramesh Raskar, former White House CIO Teresa Payton, Michelin's Group Chief Data and AI officer Ambika Rajagopal, and Sharon Guy, a former executive at Alibaba. They each offer unique insights into this evolving landscape. We conclude with Rory Blundell, CEO of Gravity, to discuss the path forward. With Gravity leading the way, join us on this journey. You can watch the full documentary at the link in the show notes. And we're back here on Big Technology Podcast with investor and analyst Paul Kodrosky. You can go and sign up for his great newsletter at paulkodrosky.com. All right, Paul. You know, we talked a little bit on the other, on the first side of this break or before the break about the money will keep coming until the music stops. I mean, I imagine it would take something dramatic for the music to stop playing. What do you think could be the compelling event? [00:48:23] Speaker 2: So the argument I make is, you know, people fall into this trap of saying it's going to be this or it's going to be that. I think it's actually overdetermined in a statistical sense, meaning that there are so many different ways it can stop that the only thing you can say is that it's going to stop because it could stop because of a macro event that changes the hurdle rate that external capital providers are looking for. If I'm suddenly looking for, you know, high single digits and not six and a half anymore, well, then all of a sudden data center projects with their deflating underlying token pricing looks much less competitive. So that changes things dramatically, given that more than half of data center projects now or half of the capital for data center projects now are external financing. So that changes things dramatically. So the providers of capital pulling back is an obvious source. And then obviously the post IPO phenomenon of having these companies having to generate competitive returns on the back of a deflating commodity and then moving up market and discovering the returns aren't there as they move up market and they continue to spend aggressively on CapEx. Investors become unhappy about it very, very quickly, as we know from hanging around this stuff for a long time. So it wouldn't take very much to have people feel like this is a much less compelling investment opportunity than I felt like because they're having to immediately abandon the thing that I thought they were selling. And now they're having to move up market and chase applications. I'm not that excited about that anymore. So, you know, there's a host of these different pieces. Another one obviously is, and we're seeing rumblings of this already, is that as this becomes increasingly the state versus state existential battle that you can see export controls instituted. So we can't use Chinese models, Chinese models, Chinese companies can't use U.S. models. We start balkanizing the market. The balkanized market looks much slower and smaller. Well, I don't know what I'm willing to pay for that. That changes things. Government involvement. So we're talking already about Anthropic or I guess it was OpenAI having potentially a 5% U.S. share in it. What am I willing? How do I feel about that as an investor? Do I want the U.S. as a co-investor in my company? What does that change? What multiple should I be willing to pay? You know, you can go down all of these paths. And if these things are truly capex intensive, much like the, you know, the railroads or much like utilities, as I've argued, and we see this already in Microsoft and some of the other hyperscalers, then there's a re-rating required. I'm not willing to pay a 30 times price earnings multiple in a company that essentially has a utility class capex usage and, you know, sort of an asymptotic decline back towards a more utility-like hurdle, right? So there's so many ways this can break. And the way it doesn't break is if it's actually a call option on AGI. [00:50:57] Speaker 1: Right. That seems like the only way. Because, like, I'm looking at the, I made a list of, like, the different arguments that you can make for, like, the fact that anything these big AI labs are going to sell, the price will inevitably come down and won't support the investment. We've covered a number of them. But, like, the open source models out there can make, you know, the proprietary model costs come down. You've talked about this in the past. There are these small models. Okay. So you have small models out there that are doing as good of a job in some areas of, as the big models, and they can bring the cost down. Then another thing I wrote down is Zuck, right? Mark Zuckerberg probably sees it to his advantage to, like, not have open AI and Anthropic dominate this next paradigm. And he's already trying. First, he started with open source. Now he's starting with his new proprietary model. But the cost is, like, 25%. Then you add in the fact that all these super apps that are coming out, which is sort of, like, the prayer for these companies on the product side, which we just both, I think, agreed is going to be more important. Well, you're going to have a super app from OpenAI. You'll have a super app from Anthropic. You're going to have a super app from, you know, who knows what. And all of a sudden, you're just like, why am I, you know, paying all this money to use the super app if I could just use a different one for cheaper? [00:52:08] Speaker 2: Well, and even more, no, no, for sure, and even more fundamentally, these super apps or whatever you want to call it, I think of them as harnesses, right? They sit on top and kind of orchestrate what models are doing. So harnesses like, you know, Cloud Code, like Codex, which I think is being renamed. But anyways, Codex, like OpenCode or whatever, all of these increasingly are like, the analogy I use is it's kind of, you've got a bunch of bratty kids and the models are kind of bratty kids. And then the harnesses are kind of like really, really high functioning nannies. And so they take the bratty kid and they make them actually do useful stuff, right? So much of the improvement we've seen in the last 18 months has really been about the imposition of harnesses, effective nannies sitting on top of bratty kids and not about the actual structural improvements in the models themselves. And that's a sort of a huge misunderstanding, but it's a reflective of where we're going in the future that investors increasingly are going to look at this stuff and say, well, why am I continuing? Why are you continuing to spend a billion dollars on a huge training run for a model that may be out in 18 months? Because let's not kid ourselves, like GPT 5.6 was not a massive training run. This was a relatively modest enhancement on an existing foundational model that was pre-trained, pre-trained probably two years ago now. And most of the gains we're seeing are harnesses and post-training, things like what are called reinforcement learning with human feedback, RLHF, all of these other tools that are now coming in after the fact. Once investors look under the hood and see more and more of this, they'll be questioning, why are we spending so much on pre-training? Why are you doing billion-dollar training runs anymore if most of the gains and models are coming from post-training and RLHF and some of these other tools or even like quantization and whatever else? There's going to be immense pressure for the companies to cut back on that spending, which will have huge knock-on consequences across the hyperscalers and across the board because that's the food system, the ecosystem they live in. That's the food system they rely on. So it's another way that this can potentially fail is when the realization strikes that a lot of this or increasing fraction of this training expenditure could be wiped out with almost zero consequence for the future utility of what we will get from these models, given the increasing reliance on harnesses. [00:54:25] Speaker 1: Okay, and so then, you know, the obvious follow-up here is, well, what happens when the music stops playing, you know, if it does? Yeah, so then... Assuming you're right, because we're talking about, like, again, a compressed, massive investment cycle with, like, many companies, you know, betting, you could say their future on it, all this, like, off-balance sheet financing, which we really didn't get into today. But, like, it's not being financed in traditional ways, you know, we're seeing the return or the highlight of credit default swaps again. So talk a little bit about what happens if this goes under. [00:55:01] Speaker 2: Well, so it's in many ways analogous to what happened during the global financial crisis is that you find out how it has metastasized across the economy because there's an increasing fraction of the institutional investor population that mathematically, because this is such a large fraction in percentage terms, of issuance in both high yield and prime, both high yield and investment debt over the last 6 to 12 months, and it's a growing fraction of it going forward, mathematically, they must be holding this stuff. So you're in it whether you like it or not, and so people are realizing that they're in it even by holding an S&P 500 index fund because of the concentration of hyperscaler and AI-related names, which is something like 40-odd percent now. So even by trying to diversify, you're still in it, but it's even more insidious than that because it's sitting inside of what we, euphemistically, what we might think of as higher-grade investment bonds that are increasingly being taken up by hyperscaler-related debt, and then that in turn is rapidly, it shows up in a place like PIMCO first, and then they'll flip it, and it ends up inside of, you know, some insurance company. It'll be spreading across European banks, it'll be in all the same places that we were like startled to find U.S. real estate and CMBS debt, and then subsequently credit default swaps and credit CES squares back in the 2007, 2008, 2009 period. So we're going to, it's literally following the same playbook, just with a different asset class. [00:56:38] Speaker 1: That is scary. So how are you playing it? I mean, you are an investor, are you like shorting certain things, or what is your plan here? [00:56:45] Speaker 2: So I very much, so my day job in large part is in venture capital, and so for the most part, we just don't invest in it. We just, it's not obvious, it's not obvious how to invest around AI because one of the worst things you can do as a venture capitalist is get into a marathon where there's a thousand participants, they're all at the start line, they're all well-funded and well-trained, and it's like, oh my God, I'm going to have to outlast all these people to get to the finish. And so you really have to pick your spots and try to stay away from these sectors where people are concentrating capital and doing it in a way that leads to much poorer returns. So for the most part, you know, we've been, we're very active in a host of different areas, but not AI, which is perverse because it's not because we don't believe in AI, it's because we believe it's structurally a terrible place to be as an investor. And then on a, on a more personal level in terms of, you know, assets, I just very loathe to invent, to commit any, haven't committed new capital to any sort of broad index class, passive categories in over two years for that reason. Because you just, you, whether I like it or not, prior commitments now amount to a much larger commitment to this asset than I would like already. So I'm already over-invested in this stuff just by the fact of having a pulse and having some assets in the market. And so you have to be very careful about not having it grow in a really, you know, in a way that you wouldn't otherwise have noticed. So it's sort of personally and professionally, I take a different approach, but they're all kind of mirrors of the other, each of the other. [00:58:16] Speaker 1: Right. Now, this is not an investment advice podcast, I have to say that, but you're, you're not like, for someone with such conviction that this is, you know, all going to come down, you're not taking any like personal big short position where you could benefit. I mean, you even have a time when you think this goes down in like a year and a half. [00:58:34] Speaker 2: Sure, sure, sure. Yeah, yeah, yeah. So it's not my job, but I mean, you know, and I get more pleasure from sleeping well at night is the honest answer. But I will say, so I'm, I'm, I'm working with two very large hedge funds who put on some fairly complicated positions that I've been working with for close to a year now. So I am indirectly all biases on the table, very closely associated with a couple of very large trades related to this stuff. And, you know, if it works out, it works out for me and them. [00:58:58] Speaker 1: I see. Can I ask you two more before we go? Yeah, sure. Or do you have to run? Okay. I just want to ask you about, about China. So China has a very different approach here, right? It is effectively, it is the, you can tell me if I'm wrong here. From my understanding, the government is basically backing a lot of this. So if everything goes up, it's just like government allocation, you know, wasted, which like is, that's, you know, happens around the globe on a Wednesday, right? It's not like, you know, it's not like, you know, their whole system collapses. So do you think that they're insulated much more than the US system, which relies much more on the private investment in data centers and AI training, you know, given like, let's say this all starts to collapse. China will still have the technology and, you know, that's ultimately right. I think we agree what, you know, I mean, what matters in the end will be like, actually, I don't know. I don't want to put words in your mouth, but like, effectively, what we're left with is pretty important. So China might have no economic collapse, and, you know, a technology inside of this thing that they can keep investing in it and feel good about. [01:00:10] Speaker 2: To a degree, the problem that China has, and I've been doing a lot of work on China lately, is that much like what happened with battery manufacturing, much like what happened with solar, that there is this huge incentive across the country to impress the central government by building these things locally. And so the Chinese premier has been recently cautioning the provincial governors, stop building so many data centers because this is now the new thing. It was battery plants. It was solar cell manufacturing. And for a while, 40 years ago, it was hydroelectric dams. And so China has a history of consumers' underspending and regional and central governments' overspending, and which led to, you know, massive investment in real estate and ghost cities. I expect you'll see the same phenomenon, albeit with a little less social consequences once all of this turns out to be. It'll be much like what happened with their overbuilding in apartment buildings and residential and industrial space over the last decade. [01:01:04] Speaker 1: Which they, I mean, they definitely shook a little bit, but they didn't crumble from it, which is instructive. [01:01:09] Speaker 2: Which is instructive. And I think it'll look a little like that. And that's in large part because this is an economy not as reliant on consumer spending as Western economies in the U.S. in particular. [01:01:18] Speaker 1: So, Paul, can I summarize what your position is, which is basically we have a technology here that is commoditizing, that is effectively, you know, any move beyond just selling sort of like pure intelligence is not going to be easy. And alongside that, we have a data center build-out that is getting a ton of money based on the promise that it will pay off, but ultimately will be much more expensive than people anticipate. And that is going to lead, those two factors combined will lead to an inevitable collapse. [01:01:55] Speaker 2: Yeah, and the only piece I would add to that is that structurally one of the reasons why the data centers will become an even more fraught business, even with all of the other pieces working out, is that this technology came to market faster than any technology in modern history and reached a billion users faster than any. So, under the hood, there are vast inefficiencies. The technology industry is very good at wiping away vast inefficiencies, whether it's through the launching of new silicon or improving in software compilers or anything else. So, that's all a long way of saying that we should expect token deflation to continue and even accelerate in future because of how quickly this stuff came to market and how many opportunities there are to drive efficiencies. So, that creates just incredible pressure on the underlying economics. [01:02:40] Speaker 1: Okay, last one for you. What does AI look like after all this? Like, is there, you know, even though that there's a problem, there will be problems ahead, in your view, there will be a winner, the technology will continue to advance. Do you think, I mean, you know, I know that investing based off of a call option on AGI might be unwise, but do you think that there's a chance that that is where this technology goes? What does the future look like in your perspective? [01:03:05] Speaker 2: Not this generation of technologies. There's a deep structural problem with large language models that they can't easily update the model weights in real time. So, in that sense, these are not dynamic systems. And, you know, Jan LeCun and others, the former researcher at Meta, who's now off doing his own world model thing, there's lots of people who will say the same thing. And so, I doubt that this is the path, but I do think it's incredibly valuable technology that, in a sense, will disappear. In that, it will become like electricity. It's a utility. It will become underlying a host of other things that go on all the time. And I will no more know who provides my tokens than I do from which hydroelectric dam the power came from that's powering my MacBook right now. [01:03:46] Speaker 1: So, the open AI and anthropics of the world, their future is? [01:03:51] Speaker 2: Like a power dam. I don't know who, I don't know where they are or who they are, but I guess they exist and they'll earn utility-like rates of return. [01:04:01] Speaker 1: Okay. Paul, thank you so much. Really appreciate your time today. [01:04:05] Speaker 2: Yeah, sure. No problem. [01:04:07] Speaker 1: All right. Great. Well, folks, do sign up for Paul's newsletter. It's at paulkadroski.com. This has been great. I hope we can do this again. Thank you again to Paul. And we'll see you next time on Big Technology Podcast. [01:04:31] Speaker ?: you