Why Paul Kedrosky Says AI Is Like Every Bubble All Rolled Into One

24:07And look, it's backed up by Meta and Google or whoever else. Well, so this actually brings an important point. I mentioned this great report out from the Center for Public Enterprise. One of the things that they pointed out is in this market environment where everyone is just, you know, there's this sort of AI pixie dust that but also just the reality of your revenues are surging, the market probably loves you. like talk to us about the unit economics here like is the incentive for all the players essentially to just grow the top line as much as possible even if these aren't whether we're talking about inference on a per token basis even if these aren't particularly profitable how are you thinking about the unit economics of some of these businesses and how that could eventually perhaps sort of um you know come home to roost so to speak yeah so the term of art obviously is these things have negative unit economics, which is a fancy way of saying that we lose money on every sale and try to make it up on volume, right?

25:05I mean, that's the problem here. But that's okay. I mean, we've had lots of things. Amazon in its early days had negative unit economics. You can get past that. And as an aside, I'll say right here, all of the things I'm saying isn't to say that AI is some kind of furry Tamagotchi thing that's just a fad. AI is an incredibly important technology. What we're talking about is how it's funded and the consequences of doing that in terms of what's going to happen with respect to the businesses and the return on those businesses, right? So the unit economics are dire for a bunch of reasons, mostly having to do with the more tokens you have to produce, the costs rise more or less linearly with the demand on the system, as opposed to an orthodox software business where the more people who use my service, the more people across which I can spread my relatively fixed costs.

25:52That's not the way that, for the most part, current generation large language models work. costs rise linearly or sublinearly with the number of users, which makes for really crappy unit economics. And that's a big part of the problem. So from there, you get to the question of, okay, so what does it have to look like in terms of making it look profitable? There's lots of ways to back into this. You can do bottoms up models that would suggest that like if every iPhone user on earth paid 50 bucks, that'd work. We could have around a$400 billion,$500 billion annual stream of revenue flowing. And well, that's not going to happen, but it's worth pointing out like that would do it but it gives you a sense of the kind of scale of what at a consumer level for example it might have to look like people come at it from the other end one of my favorite ways that people come out is to say well we could create a viable model here if you think this was in the jpm call last week i don't know if you guys saw the summary of it but it was huge fun for the whole family listening in um so one of the ways they backed into it was a top-down model where they said well the global tam for human labor i love this trillion dollars i love the global TAM.

26:57I said that was right up there with saying, like, if I reduce humans to their chemical components, here's what I can get for you. Well, this was Steve Eisman's line, which was like, beware of anyone that mentions TAM. Right, right, right. No, exactly. And so then they play the next step is, of course, to say, well, imagine we can get 10 percent of that, right, which is obviously one of the oldest cliches. It's like saying, you know, I'm going to get 5 percent of the Chinese market. No one ever gets 5 percent of the Chinese market. This doesn't happen. So the same thing won't happen with global labor.

27:26But if you were to do that, you do the math on that, that call those kinds of numbers get you to a weighted average cost of capital basis to a reasonable return on current and planned expenditures with respect to AI data centers. If you assume we're heading to about a three or four trillion dollar number, which is kind of the I think it's around the number that most people put out there, which I think is a completely wrong number. But nevertheless, that's the kind of number in which you'd have to do to get there. So you can get there from a bottoms up model by making some really unreasonable assumptions about the total numbers of subscribers and what they pay.

27:56You can get there from a top down model. You can also get there by thinking about it purely in terms of industrial users. Think about purely API users, let's pretend retail users of AI don't exist and say, Anthropics projecting$70 billion in revenue in 2028, something like 35 % of their current revenues, most of their revenues today are from their API. 35 % of that is from software developers. that split between two large users, Copilot and Cursor. And so, you know, we can model that out. Everybody has to become a software developer and we can make the math work. The problem is it's got huge fragility, right, and customer concentration risk.

28:34So a Cursor disappears as a user of Anthropix API and you just blew out 15 % of your revenues because they're gone and they've done something else. And as it turns out, Cursor two weeks ago announced that they were trading their own internal model that you could use for software development. and you wouldn't have to call the Anthropic API. So you can think about all these different ways to get there, but they all have a lot of built-in fragility with respect to either, so we all become software developers and we all subscribe to Cursor. Just going back to the used car analogy that you mentioned before, when we're thinking about all this financing of the AI CapEx spend, is it useful to think of GPUs essentially as the collateral?

29:14The problem, yes. Or what would you call the collateral in this case? So what ends up happening, the collateral in this case is the GP. There's no question it is the GP. The issue is this disconnect, this temporal mismatch that you alluded to earlier with respect to the duration of the underlying debt and the assets that are producing the income that allows me to pay for the debt, right? So we've got this probably unprecedented temporal mismatch with 30-year loans and two-year depreciation on the underlying collateral, which is essentially the GPUs that are the income-producing assets. And so that creates this constant refinancing risk because I'm going to continually have to turn over the base.

29:50And we've seen this many, many times. Right now, it's easy to turn it over. But in two years, it may not be possible. There's a wave of refinancings coming in 2028 in many of the more speculative data centers. Will they be able to turn over their debt and refinance all the GPUs? Today, they could. Today is in 2028. So that's the inherent problem is this structural temporal mismatch between the income producing assets and the duration of the loans. And it gets worse if you think about it in more holistic terms. Think about it in terms of one of the other gating factors here that's driving all of this is the scarcity of energy supply.

30:22It's really difficult. You can hook them up to the – well, it's actually kind of turned into a bit of a joke. I can hook you up to the grid, but I can't give you power. I don't know if you saw the recent episode with the Oregon Public Utilities Commission. Amazon had three data centers that they connected to the grid. And it was kind of like the Oregon PUC said, oh, you want power too. Oh, well, we can't help you with that. We can't help you with that. So now there's a complaint in the Oregon PUC from ADS, Amazon's the digital services group that runs AWS, complaining that we now have data centers, but we have no power.

30:51It sounds a little bit like a winter storm hazard or something, but it's a structural problem with respect to the inability. We can connect people, but we can't provide them with power. So the next stage is, and this takes us back to the collateral problem and the temporal mismatch, is that people are doing behind the meter power. They're building natural gas. Or if you're Fermi, you're saying wild things about nuclear power. And you're saying, OK, I'm coming with my own power. You don't need to connect me to the grid because I'm going to power this myself. That creates two or three different issues.

31:21But among the more important is think about how long lived an asset, a natural gas plant is. This is not something that's got a five-year lifespan and we just cheerily wave goodbye. This is going to be running probably 25 to 30 years. And the only thing, your ability to forecast, we know the cost of the natural gas plant, but in terms of the cost of the center and its ability to generate enough income to pay off the loan associated with the natural gas plant. God help you if you think you can sort that out, because what you've really got is a huge likelihood of a stranded asset out there, natural gas plants that are no longer useful for powering these things that they were built for.

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34:34Join over 500 happy families who've discovered the magic of Guardian. Visit GuardianBikes.com to shop now. That's GuardianBikes.com. The good news is that Daniel Juergen said this on the show. You know, the back orders for natural gas turbines, like you probably if you order one today, you would probably get it in 2030. So the good news, I suppose, is that at least you don't have to have the turbines sitting there for years. Like, I don't know. I don't know if that's good news at all. But there are you may never get it. You may never get the gas plant built anyway. Someone will be stuck with the bill.

35:08It kind of raises this goes back to Tracy's question earlier. This raises a really interesting thing. So like, like, honestly, what the F are all these people doing? who are announcing these giant funding transactions. I think of it like people all showing up at the OK Corral at once. And it's like, dude over there has one gun. I got two. Yeah. That guy's got, oh, two. That's not a knife. This is a knife. But it's this deterrence program that's going on. Don't even imagine spending 50 because I'm spending 100. There's no point in you doing any of this. So it's this game theoretic. Well, this also worries me because you hear so many people framing this as like an existential competition, right?

35:47And once you start calling something existential, the limit on spend, well, it becomes unlimited, right? It's about survival, so you'll spend anything. That's why the conversation has turned in recent weeks to the one entity that actually, at least in theory, can print as much money as possible. Right, that's the Sarah Friars accidental foot and mouth thing earlier in the week. But that's right, but that's again, it goes back to my original point about what makes this bubble unusual. individual it's the this element that not only is there a kind of bag stop but there's actually a notion of wrapping it in the flag we have to win this competition we have to do what it takes this is existential it's us versus china and it's not just the u.s doing this i was talking to some canadian policymakers just earlier this morning exact same thing going on there we have to build out a domestic in the same thing in the uk same thing in germany and so there's this idea around the world, that sovereign AI is something that's incredibly important.

36:43So this government backstop isn't just mythic, it's global. It's this idea that we all have to win. We all have to win, which obviously can't happen, but that the government's playing a role in it that can create this kind of limitless course of capital. You know, so one of the things that's going on, and maybe it's part of the same, this sort of maximalist strategy mentioned, Anthropic wants to get into data centers. So everyone's sort of looking at how they can expand vertically. Can I own the data centers. I think, you know, Sam Altman has talked about owning chips or owning a semiconductor fab at some point.

37:16Like maybe that'll be part of the story. Who knows? There's one thing that I don't, I'm sort of curious. I'd love to have your take on. There was at the end of September, Meta announced a deal to buy Compute from CoreWeave, one of these NeoClouds. I don't totally get that because Meta has its own data centers, et cetera. Do you have some intuitive sense about what an established hyperscaler needs a neocloud for in this arrangement? What core we can supply that Meta can't build on its own or buy on its own? Nothing. So that's the answer. So here's what's going on. This is what's going on, is that there's this form of hoarding going on.

37:56So what's happening is, is people saying, you have capacity, I can lock that up, I'll lock that up. And because I can't lock it up yet by building a data center quickly enough, I'll lock it up in the marketplace. So once you start thinking of compute as a hoardable commodity, and what people are doing is trying to hoard it, control it before someone else can do it, because until they bring on their own excess capacity, that's really what's going on in a lot of these transactions. This is a way of making sure that I may not need this, but you sure can't have it. And so there's an element of compute hoarding going on across the map because of this backlog in building data centers that may or may not ever get built.

38:34So that's the answer. The answer isn't that they care at all about whether or not they're going to run giant workloads on any particular neocloud provider. It's the idea of hoarding capacity and making sure that no one else can have it, like trying to have the Hunt brothers and getting a corner on the silver market. You know, I want to go back to China because it is true that the U.S. and China seem locked in this existential race for AI supremacy, but they seem to be taking very different approaches to it. And in the U.S., it's all about spending as much money as you can, developing these, you know, state of the art, mostly closed source models.

39:08Whereas in China, it seems to be much more about rapid adoption and creating open source models that just get out into the market much faster and much more cheaply. And so I'm curious, like, which of those approaches do you think is going to win here? Yeah, so that's a really good question. So I think it's going to be something closer to the Chinese approach, but not for the reasons they expect. So the reason is because I'll reframe what the Chinese are doing slightly. So I'll say that instead of it just being a sort of an example of open source, I don't think that's the right way to think about it is they're using this kind of distillation approach increasingly, where there's kind of a, you think about it like, okay, I'm a sales manager.

39:51I don't want to train all my salespeople. I'm going to train this dude, and they're going to train all the sales, but that's distillation, right? You train the trainer. I train somebody who trains something else, and the something else in this case are these smaller models. So that approach of kind of training the trainer really speeds up the process of creating new models because I distill them. I train them out of other models that are really compute intensive like Anthropics or OpenAIs or whomever else is right. So the notion is, so is there a huge efficiency gains to be had in training? And the Chinese are showing the huge efficiency gains to be had.

40:25And one way to think about it is that the transformer models that underlie large language models that are so computationally intensive went from the lab to the market faster than any product in technology history. So they're absolutely bloated and full of crap, right? So these things are wildly inefficient. There's all kinds of other ways to do the same sorts of things, one of which is distillation. So what you're really seeing is a kind of an accident of history that we came down, the US came down this path that led directly to the original transformer paper in 2017. And the Chinese have said, yeah, we're not going to be able to do that for a bunch of different reasons, but we don't have to do that because I can take this approach of distillation, which lets us get, you know, if you look at Kimmy, this sort of relatively recent open source model, these things are actually really effective in benchmark very well.

41:11And it's not surprising because they've been trained by really good trainers, which is to say some of the other models that are out there. But it's, these are about efficiency gains, which should then ask the next question is, whoa, wait a minute. If there's all these efficiency gains ahead from training and training is 70 % of the workload on data centers, hang on a second, And aren't we completely misforecasting the likely future, the arc of demand for compute? And the answer is yes. And this is rather than looking at it as an example of why China is doing something better for worse. Another way of looking at it is saying just just refuted the approach that we're taking to training altogether because it shows how bloated and inefficient the approach we're taking is.

41:49And yet we're projecting on that basis what future data center needs are. Part of the question, it seems to me, and this is where it gets a little bit philosophical, is what do these AI companies think they're building? Because one theory is like, well, maybe they're building business tools, right? Maybe they're building business tools of various sorts. And if they're building business tools of various sorts, that implies the possibility that eventually they get good enough. This does the job, right? This makes it easier for this website. You can use an agent to book your travel and the technology works and we don't have to keep building it because we got to the point where it works.

42:26And then there is this other question of like, well, maybe they want to build something called AGI or ASI that's so sci-fi, et cetera, in which case you could never get enough or simply having built the thing that allows you to book your travel or book a dinner reservation or translate a text or whatever, that's not nearly enough. You hear different things, but what do you think the builders at the cutting edge of these labs are going for? Is it really the sort of sci-fi building God cliche, or do they want to build profitable business tools? So it's the first thing until you challenge them, and then it's the second.

43:02So what happens is if you have the conversation internally, they'll say, yeah, no, no, no, we're building this really effective productivity-enhancing tools that will be used across a host of businesses. And these all sounds really good. But then when you walk through some of the math in terms of justifying the ROI on the spend, all of a sudden, then it turns into what I call faith based argumentation about AGI. And they say it's like the greatest call option ever. Like, what would you pay for a call option that could get you anything? And it's like, well, wait a minute. This isn't a way of justifying any particular expenditure.

43:33This is just faith based argumentation. implementation. We were saying, you know, with the Uber call option for anything, you should be willing to pay anything for it. And obviously that kind of justification doesn't get you anywhere. So in-house, they'll arm wave a lot about these different models that will emerge. Who knows? I had someone at NVIDIA tell me the other day that we really are just waiting for the Uber of AI to come along and show us the future. And I'm like, okay. But it's not an answer, right? Because in theory, if you're building a business productivity tool, then eventually you could solve your unit economics problem, right?

44:07If you're just trying to build a really great business opportunity, then it's simply, you know what, we don't have to build anymore. It works. And then the cash flow just starts pouring in and the cost per token goes down. You can. And there's a bunch of that already happening. It's really interesting. But what's increasingly happening is the problems they're solving are really mundane. So it's things like I'm trying to onboard a bunch of new suppliers. Right now, the people have weird zip codes and they sometimes don't match up. I have a dude in the back who fixes that. I'd rather have someone who could do it faster so they could onboard a lot more suppliers.

44:36Oh, it turns out these small language models are really good at that, these micro models like IBM's, Granite, and whatever else. But those things require a fraction of the training, are very cheap, are not going to justify anywhere near the economics needed to pay for the current spend. And yet those things are almost like very likely the future because it'll be profligate token use from micro models often hosted internally to do really mundane background tasks. Not very glamorous, onboarding new suppliers, matching records. Yeah. Great stuff. Just not really very exciting. But large language models are amazing at it.

45:12And small language models are amazing at it and almost free. And writing songs. Right, Joe? And writing songs. They can do that. I'm actually, I'm still annoyed that AI is like getting into art and music writing and all the fun stuff versus the stuff that I don't want to do, like folding laundry to your classic example. Or matching customer records. So going back to the beginning of this conversation, when we were just talking about the scale of AI investment and its impact on the U.S. economy, I'm pretty sure you are one of the ones who's described AI CapEx as like a private sector stimulus program for the U.S.

45:47economy. What are the actual consequences, either positive or negative, of having this massive private sector spend in the economy versus something, I guess, more typical, which would be a government stimulus or maybe growth driven by consumer spending or something like that? Yeah. So to an orthodox economist, the old line is like, it really doesn't matter what we pay people to do as long as we pay them. Right. It's the idea of I should be I should be you should be willing to pay people to dig holes in the ground and people over there to fill the holes back in again. It really doesn't matter as long as the money is out there and in circulation, right?

46:21It's all just stimulus, right? So to that way of thinking, it doesn't matter because the money is all finding its way back into the economy. But I think that's obviously hugely misleading because in this context, these are investments created with an expectation of a return. If they can't, then that flows backwards into all the entities that are built on that basis, whether it's private credit firms and their returns. The S &P 500, what is it, like 35 % now is AR-related, MAG-7, MAG-10, whatever. 40%, 50 % now the last two years return. So these are massive negative wealth effect when you unwind it, not just in terms of the direct spending, but in terms of the wealth effect with respect to what people's holdings are.

46:59So this is not as simple as saying this has just been a wonderful stimulus program. We're paying people to dig holes and filling them back in again. This is a wasting asset on something that's likely to be produced in quantities that we can never earn an economic return from, in part because of wildly flawed assumptions and projections about the future of demand for those units. And so that's the deep structural problem. And then you can get into this whole question of like, well, if it's just private equity guys get hurt, who cares? Screw those guys, right? And it's not, of course, because as we just talked about, it's in equity funds.

47:31It's firefighters and teachers money. Yeah, yeah, yeah. And it's in REITs now. Look at the larger holdings in REITs now, increasingly our data centers. And it's even in sort of sneaky backdoor ways, like we're seeing increases. I don't know if you guys are familiar with these new interval funds that are appearing there. It's all over now. Paul Kudrowski, I have a million more questions we could ask you, but much like the race towards AGI itself, that would imply that we'll ever actually get to the end of this conversation. So how about we wrap here and then just plan on, you know, revisiting the six months, maybe three years.

48:01We just keep revisiting down the line where we are in the cycle. As long as we haven't been turned into paperclips, I'm good. Yeah. That's the nightmare. Clippy. I feel like that was, no one talks about the old school paperclip maximizer stuff. Everyone's on to more esoteric fears. I know. People have moved on. Yeah. We need to worry. Did anyone, wait, did anyone ever try to securitize Clippy? They didn't, right? I don't think so. No. No, they never did. Thanks, Paul. Okay. Thanks guys.

48:39Paul's so good. That was a lot of fun. He's so good. Here's my highest form of praise for an Odd Lots guest. I am going to go back and read that transcript from beginning to end. That is a very good practice to do. Wait, you're not going to listen to it? You're only going to read it? No, I'm going to read it. Yeah, I can't listen to it. I just listened to it. I need to read it. I can't listen to our episodes. No, I just, you know, I think there's a lot more to do on all this topic. But the financing in particular and some of these arrangements, it's just incredible how the speed with which I guess I would say the financing has gotten interesting.

49:16Do you know what I'm saying? I think like a data center project 10 years ago, Microsoft AWS thing, just seemed like a fairly straightforward, it's probably more complicated than I appreciate at the time, but basically straightforward. We make this money and part of it is going to go to building more data centers to, you know, serve, you know, Amazon Prime streaming or whatever it is or some client thing or whatever. And then the degree of complexity with these SPVs and rollover risk and depreciation schedules and tranching of who it's gotten very interesting, very fast. Life finds a way. Life finds a way.

49:51Yeah. That was my terrible, terrible impression. I think that's absolutely right. One thing I would say is the fact that a lot of these big supposedly cash rich companies are doing this through SPVs that effectively preserve their balance sheet and their cash flow so they can do something else with it. I mean, a lot of companies use SPVs, sure. But I do think it says something about the scale. Yes. Right? Like, there's a scale problem here where if all your spending was appearing on balance sheet, investors might think very, very differently about your company. And then the other thing I would say is I still think the compare and contrast between the U.S.

50:29and China and their approaches to AI, both of them I think would agree that this is an existential problem of some sort or an existential competition. But they're following very different paths. And it does seem to me like the arc of history kind of leans towards stuff becoming cheaper. I think the arc of history bends towards China is what I thought you were going to say. That too. But it bends towards, you know, people generally want the cheaper thing and they want the thing that's like available now. And China seems to be going for that. The counter argument is that if you're going to use an open source model for some purposes, you have to supply your own electricity, right?

51:09You have to supply your own inference. You've got to host on your service. Like you still run into some constraints. And so rather than having it be on whatever, whoever else's data center, you got to find a way to run it yourself. Yeah, OK, but China has a leg up in electricity, too. Which was the point that Jensen Wong made. I mean, part of the reason, like, there's so much talk about this these days right now is that the industry insiders are saying a bunch of weird things. Paul mentioned the Sarah Fryer comment. Oh, yeah. And she sort of had to walk back, but then she said the same thing. Wasn't there that Sam Altman thing?

51:40Then there was the Sam Altman thing where he was asked, how are you going to pay for all this? And he said, look, you want to sell your shares or not? Which is like the interviewer probably thought he was a little defensive. Obviously, Jensen Wong talking recently about how China was going to win. Maybe he was saying that because he wanted to catalyze more action on solving some of the electricity problems in the U.S. But, you know, the very people at the center of this are saying things right now that, you know, what's interesting, too, is, you know, this bullwhip phenomenon. Everyone, as Paul described it, he didn't use the word bullwhip.

52:13But when everyone is trying to get their hands on the same gear, you got to wonder how sustained, what the other side of a bullwhip could look like. I don't know. We just got to do more episodes on this. Yeah, we have to. Shall we leave it there for now? Let's leave it there. All right. This has been another episode of the All Thoughts Podcast. I'm Tracy Allaway. You can follow me at Tracy Allaway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Check out Paul Kodroski's writing at paulkodroski.com. Follow our producers, Carmen Rodriguez at CarmenArmond, Dashiell Bennett at Dashpot, and Kale Brooks at Kale Brooks.

52:42And for more OddLots content, go to Bloomberg.com slash OddLots with the daily newsletter and all of our episodes. And you can chat about all of these topics 24-7 in our Discord, discord.gg slash OddLots. And if you enjoy OddLots, if you like it when we talk about the AI, private, credit, leverage, subprime, economy, nexus, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you have to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there.

53:16Thanks for listening.

53:56We'll be right back.

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