Here's How The AI Bubble Bursts — With Paul Kedrosky

If we are in an AI bubble, what could an unraveling look like? Let's talk about it with investor and analyst, Paul Kydroski, 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. You're gonna 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 Kydroski thinks that we are in the midst of an AI bubble. He has been making the case far and wide and has not backed off despite the fact that this technology has gotten much better over time. And so this will be a really fun discussion to ask, basically, even if everything goes right, are there economics on the downside gonna be so bad that it will still fall apart? So, Paul, it's great to have you on the show. Welcome. - Sure, great to be here. - 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 gonna have to be some level of overspend and then an inability to make those returns materialized. So first off, can you just talk about the magnitude of spending going into the AI buildout today and how that compares to maybe previous infrastructure buildouts and the rest of our economy right now? - 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 buildouts, so you can go back to the 19th century and canals and railroads or you come forward to the 19, well, the late 19th century and early 20th and talk about electrification and rail electrification or the interstates, World War II re-armenant, the fiber-optic buildout, these are all these moments in Western economic history, in particular, US economic history where we had these massive infrastructure investment. Then 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 were 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-armenant, 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 some of the largest capital expenditure kind of proxisms, 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 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, in lots of other measures, it's now the largest tech. It's now the largest piece of the high yield bond market. It's now the largest piece of the financial services 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 a lot of cash flows. And so we'd 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 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 would matter is now say, well, that's perfectly fine now. So 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. - Yeah, and a way that I like to talk about this is, first of all, it's not only a bigger magnitude than these previous build-outs, but it is a bigger magnitude contracted into a fewer number of years. So just to put a fine-- - That's a really important point. - Yeah, yeah, that's a really important point. 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 that's a problem for another day, but just to put it in context, that's an appropriate context. - 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-outs 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. We'll put something like 700 billion in towards CapEx this year. I think last year was something like 350 to 400 billion, and next year was projected to be 1.5 trillion in build. Now, you mentioned a lot of the different dynamics about where this money's 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 gonna be necessary to justify these investments? So let's just take 700 billion. What is, even investors to, I don't know, not go under, or I guess a lot of this is big tech. Like, what are the numbers that we need to be looking for for those numbers to be rational? - 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 building. 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 a hundred billion dollars 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 the 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, 'cause it's really all about 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, 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 a hundred trillion out of this. That's not the way investors are looking at this, 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 sales 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, this is ridiculous, right? This is just completely, you know, CD or PAN 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, 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 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 say let's take an example. I'm meta. I invested a hundred billion dollars 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. You want to get us like a six percent return or 20 percent return on your investment. You might not might only need to get 120 billion back. You know if you're going to compare this to a to a real estate investment and now that I'm thinking about it and like well meta makes what like 30 40 billion a quarter. That might be imminently possible with you know the outlay. So where's your concern here? Well the concern is that the nature of the investment is profoundly different from real estate. So what you're really entering into 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 tenants drywall. This is a project 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 a new eddy 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 with 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 at 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 up front capital requirements but do 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 you need to also think in terms of the nature of why those replacement happens. Some of the replacement happens because of the MTBF the means behind 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 upgrades. And then we have the whole replacement cycle of maybe we won't have GPUs in some of the upcoming data centers. They'll be increasingly you know inference specific A6 and we're seeing lots of that going on. So that's problem number two. Problem number three is for the you 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 the thing under the hood that's generating the cash flow. So what's generating the cash flow data centers can be thought out of as factories and the thing that they produce the widget that they produce is this thing where you domestically call tokens and these tokens are among the most rapidly depreciating assets we have ever seen in a modern economy that they've continually been following 70 to 80 percent 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-generation contracts. We have a rapidly we have 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 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 real electricity so fiber certainly wasn't fiber was actually the reverse 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 perilous for them. Okay so there's a lot here and I you know 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 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 yeah it either fails like you said or you have to replace it with a black whale or a rubit whatever it might be. So and so therefore these these expenses in the data centers aren't just like you invest a hundred billion dollars in a data center and you get to live off the land for 20 years investment is you know you have to continually feed that data center with more money in order to make it work. 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. And then you know further on what you 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. The I'll just say they're not really depreciating they're actually staying the same value they're just deflating there is a difference. Okay right deflating right that what you used to pay for token 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 then 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 Corrie at the end of the year last year or beginning of the year this year you know around the New Year time and they said they were actually renting out H 100s 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 they're 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? So there's a whole bunch of nested arguments in there. So let's take them on kind of one at a time. The life span 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 the in its in its in its adolescent years inside the data center. The analogy I often make is if you could buy a used car both two you two use cars one of them both as 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 with all the 5,000 miles came from and the other one was driven to church on Sunday for a 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 raised in some kind of you know bubble gun rally across the country. So the 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 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 life span will all else being equal likely be longer and if I have if I have a chip that didn't fail during training then I 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 as 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've often I often joke that the 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 well 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 and everyone thinks they can tell the difference and the reality is no one can tell the difference. And so right 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 Jevin's paradox, which is really what you're pointing to this idea that as models get cheaper, we use, we use, our tokens get cheaper, we use more of them. And this is a, 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 enumercy 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 a hundred 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 Jevin's 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 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 it's China, probably the canonical example. And so all it just becomes increasingly difficult to make the ricans of returns that your investors expect given the comparable cap rates that they're comparing them to. So this idea that, but it will swork itself out because prices will continue to climb and magically will just use enough. Is his 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 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 Carp was complaining on CNBC the other day. I'm sure you saw it. Yes. That these companies are increasingly marching up market and trying to eat other, but 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, goldmine are deciding they need to start making jewelry. This is the same phenomenon playing out. So they're marching out 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 of 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. Okay, so this is a great point to dig into as well. So Carp, of course, went on CNBC and talked about how you can't trust the Anthropics and the OpenAI's 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 in, they have these, you know, Palantir has forward deployed engineers now OpenAI 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 Carp on CNBC, yes, he was he was sounding concerned about what OpenAI 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 like, because we're talking about the economics of these buildouts 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. No, it's good. And 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, yeah, yeah, yeah, it's probably the most transformative technology for the last 100 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 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? Absolutely right. And you know, on this show, we're all the time, we talked 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. And Thropic is coming after Figma, right? And you know, sort of the list goes on. And 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 ruthless, et cetera, it's going to make Alex Carp jump out of his seat and, you know, various TV appearances. Is that the route to, you know, having the, the business opening in and Thropic, 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. Yeah, I just think it's reversing the logic. And I have no sympathy for Carp whatsoever. May, 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. Therefore, it's okay for Frontier models to move up where up market, eat the economy and become a legopolis. 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 anymore 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, we have a generic, a general purpose technology that has much broader applicability. So in theory, you 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 defense of, you know, weird sort of defense of the frontier companies is, and it's the same thing I say with my venture capitalist hat on, as we have companies or start up show up all the time that say I have this amazing technology that's really high alpha, we should be bought by every hedge fund and so on. I'm like, okay, fine. Why are you telling me that? 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 reputation because if it actually worked, they wouldn't tell me. So the same logic applies to the frontier companies. So if the frontier companies 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 they can do, they're frontier model companies. No, 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. That was basically Carp's argument, which is, why are you selling tokens? If you can increase my sales by 2x, what do you take? No, no, exactly, we're sending that up. That's right. And it's compelling. It's a compelling argument. You can argue that it's because Dario and Tropic is so darn ethical that he refuses to do that. And I guess it's possible to see him as unlikely. No, of course. 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 like step by step. And a case in point is a clawed design where like, and Tropic has been watching the design, of course, like my Krieger, he's to run product there was on the board of Figma. And that's led to this whole issue and made Dylan Field like one of the Anthropics biggest critics, the FedMCO. And so instead of 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 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. Sure. And that's where this is going. 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. That's true. You know, iPhone version 4 era, when people get all excited about a new release, and then everyone winds 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, as converged to a large degree, reducing the variance in terms of the composite performance of various models, which means that the opportunity costs for changing models is much lower. So 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, again, is in 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 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, and other form of costs, and price. So both of those algorithms, really, in terms of the investment returns for this asset class. Correct. And so this is sort of, I think we're both seeing it in a similar way, which is that the economics is going to force these companies to go up market and that's where things get interesting. 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 do we, what markets are you going to move into? And so that's going to increase the pressure to do this absorbent of move up market. And then you get into this problem that, and this was my complaint early on about the SaaS, SaaS apocalypse earlier this year is, there's a deep misunderstanding about why companies buy software. It's not because they think service now or sales force or whoever is somehow, you know, bold innovators that cannot be replaced. No, it's because they have a problem. They don't want to build themselves and they want someone to sue or shout at. That's it. And so when 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 going to be sued and shouted at by everyone on earth for building vertical apps for them. This will rapidly be disabused 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 verticalize software exists and why those companies exist to service the peoples in those verticals. And to just naively say, the frontier models companies will blively race up market in service of their new public investors is to misunderstand why those markets exist in the first place. Right. I mean, maybe they'll have to though. That's the same. No, no, no, they'll have to, but that's what my point is is that 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. Okay. Let me make one more of the labs arguments and then I actually want to get into some more of the weaknesses that you see in that I see. Okay. The other, you know, you kind of winked at this time, you know, the people that believe this time is different. This is not like super, a super technical argument. This is sort of like the general argument that you might hear. It would be- It's the only in 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, 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? Sure. But again, this is this is this is a plastic logical fallacy of assuming what you're trying to prove, right? So 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 this time is different thing. The the corollary to the this time is different thing is all that 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 are eventually abandoned because of over building railroads are still hugely valuable. That's all true. But it's kind of an on-sequiter. Well, of course, it's true. We didn't build it because it was useless. We build it because it was useful. The issue is what are the what are the consequences of massive over building 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 when ever 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, well, 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 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 on Financial Capital, which in a sense is the Bible for many of the most, I don't know, bullish 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. That's a really important distinction. We've created this reflexivity. We're now, we justify overspending on the basis of prior overspending haven't worked out. Well, in prior episodes where that happened, people were not justifying the overspending by saying, say, in real electrification, you know, this may look bad, but it worked out in railroads. No, no, no, no. You don't get to play that game. We didn't have that. So now what's happened is it's become a hermetically sealed, almost a flywheel, in a sense, because we're justifying things on the basis of information that we didn't have in prior episodes and using that to justify an even larger overbuild. And that's why the notion that this time is different. It is different, but it's different in a really dangerous way. Okay, so I hear that, except that the argument that I was trying to, like, put forth on your plate here is, you know, is 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 a, you know, so what is your response on that for? 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? Essentially, 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 call option on AGI is, there's no discountable net present, no discountable net present value that is too large. So when should you start down that path? Once you, I accept that premise that this is, you know, feel the AGI or feel 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, 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 play for a call option on immortality? 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 an argument where you can't find a good argument. This is a God of the gaps argument. 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. 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 they're far just by all of the economic weaknesses that you've pointed out in our discussion are basically writing that check for a call option on AGI. To a degree, investors that I've talked to are very cynical. So they're perfectly happy to use that in front of their own LPs, but 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 document development official in New Mexico recently who had a hyperskiller 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 signing up for the future because now you can help us build the factories that dictate the future of AGI. All of these objections you might raise in terms of the kinds of tax evidences that they want with respect to water and power and real estate and other things doesn't matter because think about the scale of the call option I'm offering you and it's a get out of jail free card and it's really, I think, unfortunately offensive. But nevertheless, it's more marketing than anything else and 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. Interesting. So they don't do it because they don't actually believe it. 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 they think they're material. It's non-material. So then why are they? Okay, if you're speaking to these folks, you're seeing 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 give it credence. The fact that we've talked about, you have to replace the GPUs. Token prices are going down to make this work, the demand would have to be like a hundred X. What it is. I think it's more like a million X but okay. A million X. Okay, let's just say that. A million X. Why aren't these in the checks? No, it's it's a bit crazy. But yeah, so why are they so many checks, but you might say the same thing. It's back to the Harold Purse line back at the during the financial crisis. As long as the music is playing, I keep dancing. This is that, right? This is 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 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, a hundred billion dollars because I do not want to write a hundred one billion dollar 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, a hundred billion dollar checks. No one 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 a 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. So I will just respond by saying everything that you just said sounds crazy to me that that is the way people operate. Oh, I was inside. I'll tell you a funny story. I was inside of a 700 million dollar 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 because the entrepreneur we want the entrepreneur wanted four million dollars. And the fund wanted to write a 20 million dollar check and said, you know, I can't do a four million dollar check. And they walked away from four terrific projects. And I thought that is absolutely to use the technical term that shit. And then I thought I'll never see that again. And I've now seen it repeatedly inside of the 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 out. 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 truly perverse way. So these point masses of capital worldwide are part of the issue. Right. So to basically sum it up, we've talked like for 40 minutes so far about the logic of investing in these in these AI projects. And we've gone through all the logic and I've made all the arguments. You made the count arguments. And basically what you're saying it boils down to is this is just a group think and convenience thing, which is why all this money is going this direction. There's a huge component of that. There's also this and I've made this I make this argument all the time that the largest bubbles in US history usually had either to do with technology, loose credit, government policy, right. Some combination of these things. The US 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 US history that sits at the intersection of all four of those. So it isn't 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 at it. I do long duration high-capics 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 win. They always work out. It's the Andrew Salney and argument. So and then the other are the other piece that's pushing this to a real cliff is that it's also seen as a scene as an existential battle with some of our competitors around would as well 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, you know, foreshadow some to free to decline. And so the notion, the idea of sitting at the intersection of those four forces is incredibly important because you get into it with four very powerful justifications, just one of which is the large funds with point masses of capital who need to write large checks. So the checks will keep coming until the music stops. 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It's a single platform instead of a pile of tools, bringing security, governance, and resilience all together. AppPoint, the unifying trust layer for AI. Learn more at AVPT.co/BigTechnologyPodcast. That's AVPT.co/BigTechnologyPodcast. We're back here on Big Technology Podcast with Investor and Analyst Paul Kodroski. You can go and sign up for his great newsletter at PaulKodroski.com. All right, Paul. We talked a little bit on the first side of this break or before the break about the money will keep coming until the music stops. I imagine it would take something dramatic for the music to stop playing. What do you think could be the compelling event? So the argument I make is 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 over-determined 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 high single digits and not six and a half anymore, well, then all of a sudden data center projects with their deflating underlying token comprising looks much less competitive so that changes things dramatically given than 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, if 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 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 US models. We started 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 open AI, having potentially a 5% US sharing it. How do I feel about that as an investor? Do I want the US as a co-investor in my company? What does that change? What multiple should I be willing to pay? You can go down all of these paths. And if these things are truly capex intensive, much like the railroads or much like utilities, as I've argued. And we see this already. And in Microsoft and some of the other hyperscalers, then there's a rewriting 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 it's 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-up show on AGI. Right. That seems like the only way. Because I'm looking at the-- I made a list of the different arguments that you can make for the fact that anything these big AI labs are going to sell with the price will inevitably come down and won't support the investment. We've covered a number of them. But the open source models out there can make the proprietary model cost come down. You've talked about this in the past. There are these small models. So you have small models out there that are doing as good of a job in some areas as the big models and they can bring the cost down. Then another thing I wrote down is Zuck. Mark Zuckerberg probably sees it to his advantage to not have open AI and anthropic dominate this next paradigm. And he's already trying. Well, first, he started with open source. Now he's starting with his new proprietary model, but the cost is like 25%. Then you add that 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 Open AI. You love a super app from Anthropic. You're going to have a super app from who knows what. And all of a sudden, you're just like, why am I paying all this money to use the super app? If I could just use a different one for cheaper-- Well, and even more-- No, no, for sure. And even more fundamentally, these super apps are whatever you want to call-- I think of them as harnesses, right? They sit on top and work to straight what models they're doing. So harnesses like cloud code, like codex. But I think it's being renamed. But anyways, codex, like open code, 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 the harnesses are really, really high functioning nannies. And so they take the bratty kid and they make them actually do useful stuff. 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 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 $1 billion 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, which 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 in RLHF and some of these other tools that are even 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 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. OK, and so then the obvious follow-up here is, well, what happens when the music stops playing, if it does. Yeah, so then-- Assuming you're right, because we're talking about, again, a compressed, massive investment cycle with many companies betting, as you could say, their future on it, all this off-balance sheet financing, which we really didn't get into today. But it's not being financed in traditional ways. We're seeing the return or the highlight of credit to false swaps again. So talk a little bit about what happens if this goes under. Well, so it's in many ways. And now it goes to what happened during the global financial crisis, is that we find out how it is metastasized across the economy, because there's an increasing fraction of the institutional investor population that mathematically-- because this is such a large fraction as a percentage terms of issuance in both high yield and prime. So both high yield and investment debt over the last six, 12 months, and it's a growing fraction of it going forward, mathematically, they must beholden 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 hyper-scaler 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 will-- euphemistically, might think of this higher-grade investment bonds that are increasingly being taken up by hyper-scaler-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 some insurance company. It'll be spreading across European banks. It'll be in all the same places that we were like to startle defined US real estate and CNBS debt. And then subsequently, quite a default swaps and could see S2 back in the 2007-2008-2009 period. So it's literally following the same playbook just with a different asset class. That is scary. So how are you playing it? I mean, you are an investor. Are you shorting certain things? Or what is your plan here? So my day job in large parties and venture capital, and so for the most part, we just don't invest in it. 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 kind of have to outlast all these people to get to the finish and so you 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 poor return so for the most part you know we've been we're very active in a host of different areas but not 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 more personal level in terms of you know assets I just very low 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 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 but 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 each of the other 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 you think this goes down right right you're in a half sure sure yeah yeah so it's not my job but I mean you know I and I get more pleasure from sleeping well at night as 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 I see class you two more before we go yeah sure already 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 could tell me if I'm wrong here for 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 it happens around the globe on a Wednesday right 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 pull towards 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 and feel good about 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 so manufacturing and for a while 40 years ago was hydroelectric dams so China has a history of under of consumers understanding and regional and central governments over spending in which led to you know massive investment real estate and go cities I expect to see the same phenomenon I'll be it 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 which they I mean they definitely shook a little bit but they didn't crumble from it which is destructive 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 US in particular yeah 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 this prompt 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 yeah and the only 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 reach to 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 okay last one for you what does AI look like after all this like is there you know even though that there's a prop there 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 not this generation of technologies there's a deep structural problem with large language models that they can't they can't easily update the model weights in real time so in that sense these are not dynamic systems and you know you am LaCoon and others the former researcher at Metta who's now off doing his own world model thing sort of there's lots of people who will point we'll we'll say the same thing and so that I doubt that this is the this is the path but I do think it's incredibly valuable technology that in a sense will disappear and 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 the power in my MacBook right now so the open AI and Anthropics of the world their future is like a power dam I don't know who I don't know where they are who they are but I guess they exist and they'll earn utility like rates of return okay Paul thank you so much really appreciate your time today yeah sure no problem all right great well folks do sign up for Paul's newsletter it's at Paul Kadroski dot 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