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

25:45Big Technology Podcast:And so even though it's ruthless, et cetera, it's going to make Alex Karp jump out of his seat in various TV appearances. Is that the route to having the business, OpenAI and Anthropic, having the business to pay back the investment? Because if you're able to do that and jump up market, you can 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. Palantir can burn or not burn. It doesn't matter to me. But I think it's reversing the logic to say that. 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 market, eat the economy and become a logopolies, then I guess that's okay.

26:33I think this is a cart before the horse problem because we're not trying to, I'm not in the business of justifying what they're doing by coming up with societally toxic mechanisms that therefore make it work. That's not at all appealing to me any more than it would have been because we heard these same arguments way back in the go-go days of Microsoft. Long ago, whenever Microsoft first launched Windows and some of the early operating systems were coming out, one of the things that happened was people built applications on top of the operating system. Microsoft saw those applications were doing really well, and guess what they did?

27:04They launched their own. Now, most of the ones they launched were garbage. Microsoft, it turns out, for a long time, wasn't particularly good at launching applications, but they got better at it. And over time, eight, a host of different applications in spreadsheets, word processors, all over the place, they essentially removed the oxygen supply for all of those different markets and moved up market. So that's not unprecedented. So it's not surprising at all that we'd see these companies do that. The difference this time, obviously, is we have a generic, a general purpose technology that has much broader applicability.

27:35So in theory, we can do this across a host of other domains, which at the very least should be cautionary and can do it at a much faster rate. The only thing I will say in a weird sort of defense of the frontier companies is, and it's the same thing I say with my venture capitalist hat on, And we have companies or startups 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. And I'm like, OK, fine. Why are you telling me then? And they're like, well, what do you mean? And I said, if your technology is so good and you can generate a competitive alpha with it, don't be an idiot.

28:11Go out there, raise some capital and invest it directly. Don't tell other people. So the fact that they're telling other people about this alpha generating technology is by default a refutation because if it actually worked, they wouldn't tell me. So the same logic applies to the frontier companies. So if the frontier company's technology is so amazing that it can eat the entire economy, why don't they just ingest the economy and stop selling it to us? Why are they even bothering to sell tokens at all? Why not just move up market immediately? So what that tells you is, in the same way that these companies launching hedge fund tools don't actually have things that can do that, the frontier model companies know perfectly well they can't do that.

28:49They 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.

28:56Big Technology Podcast:That was basically CARP's argument, which is, why are you selling tokens if you can increase my sales by 2x? Why don't you just take 30 % of that uplift? That's right. And it's compelling. It's a compelling argument. You could argue that, you know, it's because Dario at Anthropic is so darn ethical that he refuses to do that. And I guess it's possible. That's certainly not it. It just seems unlikely. Yeah. No, of course not. So just to, all right, I mean, let's, this is good to go back and forth and talk through the arguments here. The argument would be that, you know, basically this technology is so new and it's moving so fast that it's going to take time to figure these things out.

29:31Big Technology Podcast:And so you can't just like, you know, on day one, that fable comes out or you have mythos in house. you know, go ingest the entire economy. You have to do this like step by step. And a case in point is a Claude design where like Anthropic has been watching the design. Of course, like Mike Krieger used to run product there, was on the board of Figma. And that's led to this whole issue and made like Dylan Field, like one of Anthropic's biggest critics, the Figma CEO. And so instead of like going out and saying, we're just going to do this wholesale, we'll do it step by step. We'll see what the technology is capable of, see how people are using it, see what other solutions are out there and then go ahead and build and it just goes to this whole concept of the um the the deflation of of tokens is like you know it seems to me that we're at this point where everybody agrees that these models underneath are commoditizing and owning the model is valuable only in the way only in your ability to um customize your own products to have that like deep sink between your products and what the and the models you build that nobody else could have and that's where this is going yeah i think that i think that's broadly true and i think i don't necessarily agree with you that everyone believes these models are commoditizing it still feels to me like we're in the okay i don't know iphone version 4 era where people get all excited about a new release and then everyone whines because they say well this didn't change the world and it's not you know agi that's kind of like the fourth iteration of the iphone where people want to believe that there's breakthroughs still coming.

31:05And there were huge breakthroughs in the early days of launching large language models. But now it's not just commoditizing, but I've, you know, some data that I often show people that shows a kind of convergence that's also happening. So it's not just that there's kind of a plateauing phenomenon going on. It's that the variance among models, the best practices across all of these different models has kind of, has converged to a large degree, reducing the variance in terms of the composite performance of various models, which means that the opportunity cost for changing models is much lower. So if 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 DeepSeq is doing so well all of a sudden, and why, you know, Quen is and others, and why OpenCode is emerging as a, you know, viable tool for many people, because there's this sense that I'm not really locked in at all, and the convergence means that the model differences, while there, are so minimal as I can't tell the difference in a kind of Pepsi Coke phenomenon, which again, to cut to the investment chase, suggests that the competition then becomes much more about marketing expenditure, another form of costs, and price.

32:10So both of those auger poorly in terms of the investment returns for this asset class. Correct.

32:14Big Technology Podcast: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. At 100%. 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? And say, what are we going to do next? What markets are you going to move into? And so that's going to increase the pressure to do this absorptive move up market.

32:46And then you get into this problem that, and this was my complaint early on about the SaaSpocalypse earlier this year, is there's a deep misunderstanding about why companies buy software. It's not because they think ServiceNow or Salesforce or whoever is somehow, you know, bold innovators that could not be replaced. No, it's because they have a problem. They don't want to build it themselves, and they want someone to sue or shout at. That's it. That's why people buy SaaS software. And so whenever you start building it for yourself, this notion that companies are going to increasingly use these frontier tools to build things for themselves, or vice versa, that the frontier companies are willing to be sued and shouted at by everyone on earth for building vertical apps for them, this will rapidly be disabused because it is a terrible business.

33:30You do not want to be in that position of continually having to service people whose main utility for your product is having someone to shout at or sue, which is, again, it's a gross exaggeration. But it's a misunderstanding of why verticalized software exists and why those companies exist to service the peoples in those verticals. And to just naively say the frontier models companies will blithely race up market in service of their new public investors is to misunderstand why those markets exist in the first place.

34:00Big Technology Podcast:Right. I mean, maybe they'll have to, though. That's the thing. No, no, no. They'll have to. But my point is that 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 lab's arguments. And then I actually want to get into some more of the weaknesses that you see and that I see. Okay. You kind of winked at the people that believe this time is different. This is not like a super technical argument. This is sort of like the general argument that you might hear.

34:38Big Technology Podcast:It's the Andre Sonian argument, yes. Would be somebody saying, you know what, Paul? This is different. You have these labs who've built magical, you know, thinking computer machines. Yes, they're investing a lot. But in tech, what you do is you build an asset. You find a way to scale it through computers in some way. and you mark it up and people will buy it because it beats any other alternative and what you've seen recently is like even in the past let's say six seven months the capabilities have scaled dramatically you've been able to like now leave these computers alone and they can code on their own uh and and 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, 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.

35:40Big Technology Podcast:And this is sort of why people are rushing toward it. Your thoughts? Sure. But again, this is a classic logical fallacy of assuming what you're trying to prove, right? So you race ahead and say it has to work out because I need it to work out. And I'll go more deeply into this whole question of the this time is different thing. The corollary to the this time is different thing is there's always something useful left after these moments, right? And the idea that there's always some useful assets left after the fiber bubble years later, we could use the fiber for things. Even though half of railroads were eventually abandoned because of overbuilding, railroads are still hugely valuable.

36:22That's all true, but it's kind of an unsequitur. Well, of course it's true. We didn't build it because it was useless. We built it because it was useful. The issue is what are the consequences of massive overbuilding in terms of spiraling consequences in the broader economy? Increasingly, some of the largest purchasers of data center related debt or insurance companies, we know what happens whenever insurance companies get in the middle of this stuff. We've seen it in the global financial crisis. We've seen it repeatedly. So the right question is not, 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.

36:54One, while it's always worked out in the past, it's nearly taken out the global economy at least four times. So that's worth noting. And the other issue is, and this is, I think, the more insidious one that people miss because you'll see people refer to this woman named Carlotta Perez who wrote a book called Technological Revolutions and Financial Capital, which in a sense is the Bible for many of the most, I don't know, bullish partisans pushing some of this stuff. And they'll say, well, this is what has to happen. We have to have this kind of huge moment of spending and waste and everything else, but then it works out.

37:26Here'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 where now we justify justify overspending on the basis of prior overspending having worked out. Well, in prior episodes where that happened, people were not justifying the overspending by saying, say, in rural electrification, you know, this may look bad, but it worked out in railroads. No, no, no, no, no, no. You don't get to play that game. We didn't have that. So now what's happened is 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 overbuilt.

38:08And that's why the notion that this time is different, it is different, but it's different in a really dangerous way.

38:14Big Technology Podcast:Okay, so I hear that, I accept that. The argument that I was trying to put forth on your plate here is a little bit different, I think. The argument that I'm trying to get you to respond to is, Paul, it is AGI, man. like this is, you know, so what is your response on that? Feel the AGI. So yes. So the question then that turns into this one of what would you pay for a call option on AGI? This is essentially the argument is you cannot possibly overspend because the value, it's like saying, what would you spend for a call option on immortality? Well, mathematically, I should be willing to spend anything.

38:55Similarly, a call option on AGI is, there's no discountable net present, no discountable net present value that is too large. So once you start down that path, once I accept that premise, 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 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, that becomes like a toxic board game. It's tennis without a net, right?

39:31There's no way for us to have a reasonable conversation once the other side of the conversation is, what would you be willing to pay for a call option on immortality? What are you willing to pay for a call option on AGI? So 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 like in classic sort of agnostic theory, this idea of inserting God into every gap in an argument where you can't find a good argument. This is a God of the gaps argument.

40:03I'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.

40:13Big Technology Podcast:Don't you think that all the money that's going towards this AGI or AI build out, the people writing the checks have been told the AGI argument. And therefore, despite all of the economic weaknesses that you've pointed out in our discussion, are 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.

41:03That's the hurdle that it has to clear. In terms of promoting it, sure. We can call these AGI factories. I was talking to a regional economic development official in New Mexico recently who had a hyperscaler show up and tell him, don't you want to be part of AGI factories? I was like, what? This is the picture being made? because you're signing up for the future because now you can help us build the factories that dictate the future of AGI. And so all of these objections you might raise in terms of the kinds of tax evayances that they want with respect to water and power and real estate and other things, it doesn't matter because think about the scale of the call option that I'm offering you.

41:37And 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.

41:53Big Technology Podcast: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 can clear. 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 sort of, they give it credence, right? The fact that, okay, we've talked about you have to replace the GPUs.

42:28Big Technology Podcast:Token prices are going down. To make this work, the demand would have to be like 100x what it is. More like a millionx, but okay. A millionx, okay. Let's just say that. A millionx. Why are they still putting the checks?

42:45Because, yeah, no, it's a bit crazy. But yeah, so why are they still writing checks? But you might say the same thing. It's back to the Harold Prince line back during the financial crisis. As long as the music is playing, I keep dancing. This is that, right? 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 ELPs saying, why aren't you participating in this? And then even worse, as a sovereign, as a sovereign wealth fund.

43:15and I've been inside these folks, is that once you're managing hundreds of billions of dollars, you start looking at opportunities not in terms of their economic value, but in terms of check size. And you say, I need to write a check for fill in the blank,$100 billion, because I do not want to write$101 billion checks. So this weird filter starts happening where you now, these projects are like, look at my friend, Saudi, Qatar, I have this project that's perfect for you. You want to write 50,$100 billion checks? Nowhere else on earth can you write it other than these giant data center campuses like the Meta Project in Louisiana or take your pick.

43:54And so once you 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.

44:05Big Technology Podcast: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 venture fund at one point that turned down five terrific projects. So this is at a very small scale. So think about it now as a sovereign. Because the entrepreneur wanted$4 million and the fund wanted to write a$20 million check and said, you know, I can't do a$4 million check. And they walked away from four terrific projects. And I thought that is absolutely, to use the technical term, batshit. And then I thought, I'll never see that again.

44:41And I've now seen it repeatedly inside of some of the largest funds on earth, looking at projects through the filter of, can I write a large enough check because I have this much capital burning all in my pocket? So it doesn't mean that I'll just give it to any random. I'll write checks to anybody for anything. But it does change the way that you filter the landscape of viable investments in a really and truly perverse way. So these point masses of capital worldwide are part of the issue.

45:05Big Technology Podcast:Right. So to basically sum it up, we've talked like for 40 minutes. so far about the logic of investing in these AI projects. And we've gone through all the logic, and I've made all the arguments, you've made the counter arguments. And basically what you're saying it boils down to is, this is just a groupthink and convenience thing, which is why all this money is going this direction. There's a huge component of that. There's also this, and 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, some combination of these things.

45:42The 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 shouldn't be particularly surprising that we have people who live in each of those bubbles who feel as if they can justify what's happening on their own basis. So I have real estate investors who see this as a real estate project. And they're like, look, I do long-duration, high-capx 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.

46:15People always tell us that these things are going to work. And they always work out. It's the Andresianian argument. And then the other piece that's pushing this to a real cliff is that it's also seen as an existential battle with some of our competitors around the world like China. So there's this government component where we must win. We must win because to not win is to somehow foreshadow some future decline. And so the idea of sitting at the intersection of those four forces is incredibly important because you end up with four very powerful justifications, just one of which is the large funds with point masses of capital who need to write large checks.

46:53Big Technology Podcast:So the checks will keep coming until the music stops. On the other side of this brick, I want to talk about what would cause the music to stop and what happens when it stops playing. We'll be back right after this. Hi, everyone. Alex Kantrowitz here. I want to tell you about a documentary I've made with Gravity to explore the future of AI agent security. To find out if we're truly ready for autonomous agents, I sat down with MIT professor Ramesh Raskar, former White House CIO Teresa Payton, Michelin's Group Chief Data and AI officer Ambika Rajagopal, and Sharon Guy, a former executive at Alibaba.

47:29Big Technology Podcast:They each offer unique insights into this evolving landscape. We conclude with Rory Blundell, CEO of Gravity, to discuss the path forward. With Gravity leading the way, join us on this journey. You can watch the full documentary at the link in the show notes.

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50:14Big Technology Podcast:That's exactly what AvePoint does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended across your entire AI estate. Your data, your cloud, and the agents acting on your behalf. It's how more than 28 ,000 organizations deploy AI with confidence, so innovation scales without scaling risk. It's a single platform instead of a pile of tools, bringing security, governance, and resilience all together. AvePoint, the unifying trust layer for AI. Learn more at avpt.co slash bigtechnologypodcast. That's avpt.co slash bigtechnologypodcast. And we're back here on Big Technology Podcast with investor and analyst Paul Kodrosky.

51:02Big Technology Podcast:You can go and sign up for his great newsletter at paulkodrosky.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 mean, 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 overdetermined in a statistical sense, meaning that there are so many different ways it can stop that the only thing you can say is that it's going to stop because it could stop because of a macro event that changes the hurdle rate that external capital providers are looking for.

51:43If 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 pricing looks much less competitive. So that changes things dramatically, given that more than half of data center projects now, or half of the capital for data center projects now are external financing. So that changes things dramatically. So the providers of capital pulling back is an obvious source. and then obviously the post-IPO phenomenon of having these companies having to generate competitive returns on the back of a deflating commodity and then moving up market and discovering the returns aren't there as they move up market and they continue to spend aggressively on CapEx.

52:26Investors 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.

52:58So we can't use Chinese models. Chinese companies can't use U.S. models. We start balkanizing the market. The balkanized market looks much slower and smaller. Well, I don't know what I'm willing to pay for that. that changes things. Government involvement. So we're talking already about Anthropic, or I guess it was OpenAI having potentially a 5 % U.S. share in it. How do I feel about that as an investor? Do I want the U.S. as a co-investor in my company? What does that change? What multiple should I be willing to pay? You 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 in Microsoft and some of the other hyperscalers, then there's a re-rating required.

53:41I'm not willing to pay a 30 times price earnings multiple in a company that essentially has a utility class capex usage and sort of an asymptotic decline back towards a more utility-like hurdle, right? So there's so many ways this can break. And the way it doesn't break is if it's actually a call option on AGI.

54:00Big Technology Podcast: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, 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 costs 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 to zuck right mark zuckerberg probably sees it to his advantage to like not have open ai and anthropic dominate this next paradigm and he's already trying first he started with open source now he's starting with his new proprietary model but the cost is like 25 then you add 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'll have a super app from anthropic you're gonna have a super app from you know who knows what uh and all of a sudden you're just like uh why am i you know paying all this money to use the super app if i could just use a different one for cheaper well and and even and even more no no for sure and even more fundamentally these super apps or whatever you want to call it i think of them as harnesses right they sit on top and kind of orchestrate what models are doing so harnesses like you know cloud code like codex which I think is being renamed, but anyways, codex, like OpenCode or whatever, all of these increasingly are like, the analogy I use is it's kind of, you've got a bunch of bratty kids, and the models are kind of bratty kids, and the harnesses are kind of like really, really high functioning nannies, and so they take the bratty kid and they make them actually do useful stuff.

55:45Right? 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 a billion dollars on a huge training run for a model that may be out in 18 months? Because let's not kid ourselves, like GPT 5.6 was not a massive training run.

56:19This was a relatively modest enhancement on an existing foundational model that was pre-trained, pre-trained probably two years ago now. And most of the gains we're seeing are harnesses and post-training, things like what are called reinforcement learning with human feedback, RLHF, all of these other tools that are now coming in after the fact. Once investors look under the hood and see more and more of this, they'll be questioning, why are we spending so much on pre-training? Why are you doing billion dollar training runs anymore? If most of the gains and models are coming from post-training in RLHF and some of these other tools, or even like quantization and whatever else, there's going to be immense pressure for the companies to cut back on that spending, which will have huge knock-on consequences across the hyperscalers and across the board, because that's the food system, the ecosystem they live in.

57:08That'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 could be wiped out with almost zero consequence for the future utility of what we see what we will get from these models

57:25Big Technology Podcast:given the increasing reliance on harnesses okay and so then you know the obvious follow-up here is well what happens when the music stops playing you know if it does yeah so then assuming you're right, because we're talking about, again, a compressed, massive investment cycle with many companies betting, 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 default swaps again. So talk a little bit about what happens if this goes under. Well, so it's in many ways analogous to what happened during the global financial crisis is that you find out how it has metastasized across the economy because there's an increasing fraction of the institutional investor population that mathematically had because this is such a large fraction as a person in percentage terms of issuance in both high yield and prime uh it's for high uh both high yield and uh investment debt over the last six to twelve months and it's a growing fraction of it going forward mathematically they must be holding this stuff so you're you're you're in that 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 the concentration of hyperscaler and and ai related names which is something like 40 odd percent now um so you even by trying to diversify you're still that you're still in it but it's even more insidious than that because it's sitting inside of what we euphemistically what might think of as higher grade investment bonds that are increasingly being taken up by hyperscaler related debt.

59:09And then that in turn is rapidly, it shows up at 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 startle to find real U.S. real estate and CMBS debt and then subsequently credit default swaps and credit CS squares back in the back in the 2007, 2008, 2009 period. So we're going to it's literally following the same playbook just with a different asset class.

59:40Big Technology Podcast:That is scary. So how are you playing it? I mean, you are an investor. Are you like shorting certain things or what is your plan here? So I very much, so my day job in large part is in venture capital. And so for the most part, we just don't invest in it. We just, it's, it's not obvious. It's not obvious how to invest around AI because one of the worst things you can do as a venture capitalist is get into a marathon where there's a thousand participants. They're all at the start line. They're all well-funded and well-trained. And it's like, oh my God, I'm going to have to outlast all these people to get to the finish.

1:00:15And so you really have to pick your spots and try to stay away from these sectors where people are concentrating capital and doing it in a way that leads to much poorer returns. So for the most part, we're very active in a host of different areas, but not AI, which is perverse because it's not because we don't believe in AI, it's because we believe it's structurally a terrible place to be as an investor. And on a more personal level in terms of assets, I just very loathe to commit any, haven't committed new capital to any sort of broad index class passive categories in over two years for that reason.

1:00:52Because 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. it. And so you have to be very careful about not having it grow in a really, you know, in a way that you wouldn't otherwise have noticed. So it's sort of personally and professionally, I take a different approach, but they're all kind of mirrors of the each of the other.

1:01:19Big Technology Podcast:Right now, this is not an investment advice podcast, I have to say that, but you're not like for someone with such conviction that this is, you know, all going to come down. You're not taking any like personal, big short position where you could benefit. I mean, you even have a time when you think this goes down in like a year and a half. Sure, sure, sure. Yeah, yeah, yeah. So it's not my job, but I mean, you know, and I get more pleasure from sleeping well at night is the honest answer. But I will say, so I'm 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.

1:01:50So 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.

1:02:01Big Technology Podcast:I see. Can I ask you two more before we go? Yeah, sure. Or do you have to run? Okay. I just want to ask you about China. So China has a very different approach here, right? It is effectively, it is, you can tell me if I'm wrong here, from my understanding, the government is basically backing a lot of this. So if everything goes up, it's just like government allocation, you know, wasted, which like is that's, you know, happens around the globe on a Wednesday, right? It's not like, you know, their whole system collapses. so do you think that they're insulated much more than the u.s system which relies much more on the private investment uh 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 uh you know that's ultimately right i think we agree what you know i mean what matters in the end will be like actually i don't know i don't want to put words in your mouth but like effectively what we're left with is pretty important.

1:03:02Big Technology Podcast:So China might have no economic collapse and a technology inside of this thing that they can keep investing in it 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.

1:03:39It was solar cell manufacturing. And for a while, 40 years ago, it was hydroelectric dams. So China has a history of consumers underspending and regional and central governments overspending, and which led to massive investment in real estate and ghost cities. I expect you'll see the same phenomenon, albeit with a little less social consequences once all of this turns out to be. It'll be much like what happened with their overbuilding in apartment buildings and residential and industrial space over the last decade.

1:04:07Big Technology Podcast:Which they, I mean, they definitely shook a little bit, but they didn't crumble from it, which is instructive. Which is instructive. And I think it'll look a little like that. And that's in large part because this is an economy not as reliant on consumer spending as Western economies in the U.S. in particular. 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 the promise that it will pay off.

1:04:48Big Technology Podcast:But ultimately, we 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 piece I would add to that is that structurally, one of the reasons why the data centers will become an even more fraught business, even with all of the other pieces working out, is that this technology came to market faster than any technology in modern history and reached a billion users faster than any. So under the hood, there are vast inefficiencies. The technology industry is very good at wiping away vast inefficiencies, whether it's through the launching of new silicon or improving in software compilers or anything else.

1:05:27So 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.

1:05:43Big Technology Podcast:Okay, last one for you. What does AI look like after all this? Like, is there, you know, even though 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 technology. There's a deep structural problem with large language models that they can't easily update the model weights in real time.

1:06:16So in that sense, these are not dynamic systems. And, you know, Jan LeCun and others, the former researcher at Meta, who's now off doing his own world model thing. There's lots of people who will say the same thing. And so I doubt that this is the path. But I do think it's incredibly valuable technology that in a sense will disappear. In that, it will become like electricity. It's a utility. It will become underlying a host of other things that go on all the time. And I will no more know who provides my tokens than I do from which hydroelectric dam the power came from that's powering my MacBook right now.

1:06:49Big Technology Podcast:So the open AI and anthropics of the world, their future is? Like a power dam. I don't know where they are or who they are, but I guess they exist and they'll earn utility-like rates of return. 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 paulkadrosky.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.

1:07:26Thank you.