← Plain English with Derek Thompson
Plain English with Derek Thompson
Plain English BEST OF: This Is How the AI Bubble Could Burst
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- [00:00]
A
If you're a fan of the inner workings of Hollywood, then check out my podcast, the Town on the Ringer Podcast Network. My name is Matt Bellany. I'm founding partner at Puck and the writer of the what I'm Hearing newsletter. And with my show the Town, I bring you the inside conversation about money and power in Hollywood. Every week we've got three short episodes featuring real Hollywood insiders to tell you what people in town are actually talking about. We'll cover everything from why your favorite show was canceled overnight, which streamer is on the brink of collapse, and which executive is on the hot seat. Disney, Netflix, who's up, down, and who'll eat lunch in this town again? Follow the Town on Spotify or wherever you get your podcasts.
- [00:41]
B
This episode is brought to you by Ninja 1. Ninja 1 understands that it teams today are stretched thin, trying to manage too many disconnected tools and rising security demands keep raising the stakes. Ninja 1 unifies everything in a single intelligent platform, from endpoint management and autonomous patching to to backup and remote access. Fewer tools, lower costs, higher efficiency Trusted by more than 35,000 customers in over 140 countries. Unify it to simplify work with NinjaOne. Learn more at ninjaone.com this episode is brought to you by Indeed. If I had to hire someone for this show, I wouldn't want to pick up just anyone off the street. They need to have the right skill set and background. If I wanted to hire an editor, I'd probably want someone who knew how to use editing software. If I needed a writer, it'd be nice to have someone with experience in journalism who closely follows the political and tech world. When you're running a business, you shouldn't settle for anyone, but the best Indeed can help you find the best. With Indeed sponsored Jobs, you can stand out from the crowd, reach qualified candidates faster, and increase the amount of people who see your job listing. By the end of this ad, companies like yours will have made 27 hires, according to Indeed data. And that's just in one minute. Think of how many hires are made per day. Get the results you want with Indeed sponsored Jobs. Listeners of this show will get a $75 sponsored job credit to help get your job the premium status it deserves@inn Indeed.com plane that's Indeed.com plan plane right now and support the show by saying you heard about Indeed on this podcast. Indeed.com plane terms and conditions apply. Hiring do it the Right way with Indeed. Hi everybody, Derek here. In December, my wife and I welcomed our second baby girl into the world. I'm going to be taking some time off, but we wanted to keep the pod going through the holidays. So we're going to be re airing some of our favorite episodes from the last 12 months, a kind of best of compendium. And this list includes interviews that really stuck with me and others that really stuck with you. And you had lots of feedback and thoughts on including this one. I'll be back in the new year with fresh content, but until then, happy Holidays and Happy New year. Today, the AI bubble this year, American tech companies will spend about 300 to $400 billion on artificial intelligence. That's more in nominal dollars than any group of companies has ever spent to do just about anything. And notably, these companies are not anywhere close to earning back that $400 billion that they're about to spend. This is why you're starting to hear some people wonder if the AI buildout is turning into the mother of all economic bubbles. Sometimes you'll hear this case from critics of the technology. Critics will sometimes point out that we're on track to spend trillions of dollars this decade building something that might be all smoke and mir. I'm more interested, though, in the boosters of artificial intelligence. They'll sometimes argue that we are living through a transformative tech akin to the creation of the Internet or the railroads or the telegraph. I think they might be right. I also think they don't realize what being right would imply. The infrastructure buildout of the Internet created an enormous bubble in the late 1990s and early 2000s. The infrastructure built out of the telegraph created another bubble in the 19th century. The construction of the transcontinental railroad system, as we explained in a previous episode, created several bubbles, ending in the panic of 1857, the panic of 1873, and the panic of 1893, a half century of panics in the 20th century, radio was a bubble, the dawn of automobiles and aviation companies also quite bubblicious. In short, if AI's boosters are right with their comparison of AI to the greatest technology of the last 150 years, their own analogy anticipates that their product too will pass through a calamitous crash on the way to changing the world. This should absolutely scare you. If you care about the US economy. Half of GDP growth comes from infrastructure spending on AI on data centers, chips, and energy. More than half of stock market appreciation in the last few years comes from companies associated with AI. If you open up the hood of these biggest companies meta, Microsoft, Alphabet, Amazon, AI infrastructure spending or CapEx accounts for, you guessed it, nearly half of their revenue. If the AI spending project blows up in the next few years, as our next guest says it might, the implications for technology, the economy, and politics would be immense. Paul Kudrowski is an investor and writer. Today we talk about the AI boom, how it works, who's paying for it, and how they're financing it. We put the AI build out in historical context, and then we spend a great deal of time walking through what could go wrong and when it might go wrong. Hi, I'm Derek Thompson. This is plain English. Paul Kudrowski, welcome to the show.
- [06:29]
C
Hey, Derek, good to be here.
- [06:31]
B
Before we start, who are you? What do you do?
- [06:34]
C
Yeah, that's a good question. So I have a couple of day jobs. One day job is I'm a partner with a venture capital firm called SK Ventures, where we're mostly doing early stage investing, which is to say high failure rate, low capital, most things break. And then I also sit in as a fellow at the MIT center for the Digital Economy. So this is sort of we're closer to the spirit of some of the things we're working on. And then I also have a newsletter that goes out to a bunch of hedge funds and generally to hedge funds and buy side firms and things like that, just because my background way back when was I was on the sell side, I worked for a brokerage firm and I've just never been able to shake that. So I can't help myself. Sometimes I just provide, I want to give them advice and whether they like it or not. And so I still do a lot of work with a bunch of hedge funds and buy side firms, which takes us back to data centers and AI and blah, blah, blah.
- [07:24]
B
Well, you should know your newsletter doesn't just go out to hedge funds, it also goes out to podcast hosts, which is one reason why you're on this show.
- [07:31]
C
Yes.
- [07:32]
B
One thing I find so interesting about your analysis is that artificial intelligence is sometimes talked about as being the technology of the future. And I'm trying to ring the bell very loudly that AI is the most important economic phenomenon of the present. It is here, it's happening right now, and you've been sounding the alarm maybe more than just about anybody or more effectively than just about anybody on just how massive US investment in artificial intelligence is by historical standards. So why don't you just start with your thesis statement, how big is this?
- [08:05]
C
So, yeah, let's maybe go. Can I go back and tell a quick back story here first? Just because what got Me interested was what you're describing, which is there's a huge amount of money being deployed. It's going to a very narrow set of recipients, some of these chip firms and others, and it's going to some really small geographies like Northern Virginia. So it's an incredibly concentrated pool of capital, and yet it's so large that when you do the aggregating of the math and do the math, it seems to be large enough to affect gdp. So I was saying, okay, fine, this is crazy, I should do the math. So I did the math and it's found out that in the first half of this year, the data center related spending, so spending on these giant buildings full of GPUs and racks and servers and what have you that are then used in turn by the large AI firms to generate responses and train models that, that probably accounted for something like half of GDP growth in the first half of the year, which was absolutely bananas. And I was like, I did the math four or five different ways trying to prove myself wrong. And then I said, okay, fine, this feels like something I should mention. And so I said it. And I think it's a startling figure for a whole bunch of reasons, one of which you alluded to, which is that even compared to historical spending, whether you pick the telecom bubble or railroads or whatever else, and we can dive into those, it's unprecedented. It's also unprecedented because of the nature of the spending, which I think is incredibly important, because railroads are very different from GPUs, not just in the trivial sense, but in some very deep and important ways. And all of this gets missed. But the upshot is spending is huge, it's driving the economy. People are very confused about this. And as a result you end up making bad policy decisions because you think policy decision A is driving the economy when it's this wacky stuff over here on the left.
- [09:56]
B
So we're talking about an infrastructure boom that is on par with the broadband build out of the 1990s, early 2000s. Still behind it seems like the railroad boom of the 19th century. But we're talking essentially about an amount of spending on one emerging technology that is without precedent in at least 60 to 100 years. How does AI capex break down? Right, we're talking about capital expenditures, so money that's being spent on essentially machines rather than people. How much of this is chips versus energy versus building the actual data centers themselves? Is there a good way to think about where all this money is going?
- [10:36]
C
So a little more than half the cost of a data center are the chips that are going in. So say 60%. It varies depending on the model of the data center, because there's a whole bunch of different styles of data centers, if you will. There are some that are built almost on spec. Think of companies like Corweave, where they're buying it, and it's almost like they're hoping to tenant a building. Think about it as commercial real estate. And I'm hoping to get people to move in. I'm building a shell and people are moving in and I'm hoping to get tenants. And then the tenants pay rent, right? So think of it in those terms. And then there's the Metas and the Googles and the Amazons where they're using a huge amount of what they're building, which again, roughly 50 to 60% of it is the GPU cost. The rest is a combination of cooling and energy. And then a relatively small component is the actual manufacturer, the actual construction. So think about the frame of the building, the concrete pad and purchasing the real estate so you can break it out that way. So it depends a little bit on what you're planning to use it for. If I'm trying to build something that's for training, well, I'm going to buy more expensive GPUs, right? I need the latest products from Nvidia. If I'm building something that's more for inference, meaning that I'm just going to have using it largely for people that are trying to generate responses, I'm hoping, well, then I don't need the latest GPUs and I can cut costs and cut some corners in there. So you can think about it as that continuum.
- [11:54]
B
You compared the infrastructure boom several times to the railroads, the fiber build out. You also indicated that there's ways in which that analogy does not hold up. So I want to get into that, right? What the analogy misses. Like the rail that we laid in say the 1860s was still around in the 1890s. Maybe some of it's still around the 1950s, right? The fiber optic cable that was laid still works for years. But I keep seeing news headlines about GPUs getting better all the time, right? So I wonder, like, are companies buying something they're going to have to replace in like three years? Talk a little bit about how AI is just fundamentally different than steel rail or fiber optic cable in a way that's really important in understanding what it is that we're building here.
- [12:43]
C
So I'll start at a high level. This is my Complaint about economic statistics in general. You know this is plain English, right? So capital expenditures is such a misnomer in such a. I'll say misleading, but it's not consciously misleading. It's just that it's so aggregated up. It assumes that everything I'm spending money on, that's capital expenditure. And AI data centers are lumped into that loosely, as were railroads. As are. That's was the dot com construction. But they're all very different for exactly the reason you allude to, which is the lifespan of the thing you're creating is wildly different. So when you think about comparing railroads, which, let's say I built something during the railroad bubble in the 19th century, say 1855, I only got around five years later to running traffic down the line, what were the biggest issues I potentially had? Weeds. I don't know weeds. Maybe some cows had settled in. It's not clear to me what the forces I was pushing against were. So in all likelihood I could quote light that line very, very quickly. I could put it into use relatively easily. Similarly, with the fiber boom back in the late 1990s, if I was putting in some gear from Siena and JDS Uniface and all the great names of that era and building out fiber, and it was like, wait a minute, nobody wants me to light the fiber. Nobody wants to send data down. Netflix isn't doing that yet. Okay, so I wait five years and I light the fiber. What are the things that then are pushing against me? Nothing. Maybe someone accidentally put a backhoe through a fiber line, but I can very quickly put that back together. So it's not as if the fiber optic cables themselves are a depreciating asset that they're becoming less useful over time. Because I didn't write it to send some streaming stuff for Netflix down it, it has no bearing. Now we turn to the current wave of quote capital expenditures. And that's why this is very different. The lifespan of a GPU is on the order of two and a half to three and a half years. This is nothing like the spending that's being done on railroads where five years later, completely the same value. Or in the case of fiber, a couple of years later, light it for Netflix, no problem. If I build a data center today and populate it with Blackwell GPUs from Nvidia and hope in three years I can get the same rental prices that I could have gotten today. I'm dreaming. There's likelihood I'll get a fraction of that, if anything at all. So these assets decline Exponentially, which is completely different from all these historical bubbles that we were spending on the same level. So you have this problem that you need to recoup your investment if you are doing this very, very quickly because you're sitting on a depreciating asset, which creates this perverse problem.
- [15:15]
B
So when people look at the fact that these Hyperscalers are spending 200, 300, almost $400 billion a year on, we can call it Capex, we can call it just AI infrastructure, there's a way in which, if you're rooting against a bubble, you could say, well, it's like building a railroad. You can use it forever. So the $300 billion that's being spent right now can be made useful 10 years from now, 20 years from now. That's the railroad analogy. On the opposite side of railroads, there's like, bananas, right? If you spend $300 billion on bananas today, your capex isn't worth shit in like, two weeks because all the bananas are brown. And like, I don't think GPUs are like, entirely like bananas, but they're also not entirely like railroads.
- [16:03]
C
They're closer to bananas than anything that.
- [16:04]
B
They'Re closer to bananas than steel. Deal. And so what does this tell us at a high level about the value of this kind of spending and the threat that these companies are just not going to be able to return capital from all this upfront investment?
- [16:24]
C
And I hate to say this, but it's reminiscent in some ways of Bitcoin, treasury for companies. So this idea that it made no sense that companies were like strategy, like MicroStrategy, Michael Saylor's firm, were being rewarded for putting Bitcoin into Treasury. Their market value was increasing by $2, roughly for every dollar of Bitcoin they put into, quote, Treasury. So we have the same perverse phenomenon happening here, which is that the market's rewarding you for doing this, even though it makes no economic sense to spend at this level, because there's no way I can recoup the value of the capital spending I'm making over the next three years. So then you're forced to do these kind of wacky shell, say, well, it's okay because the shell itself, not to use shell for two different purposes here, but the shell, the building itself will actually be valuable in five years. It'll still have energy, it'll still have water. I'll still be able to cool things. The walls will still be standing. I'll just swap out the GPUs. But as we talked about at the outset the problem is the GPUs are the majority of the cost. So the shell, to a first approximation, is the thing I'd like to write off. I don't want to have to write off GPUs every three years because they're the most of the cost of the thing that we call center. So you can't play the game of saying, well, there actually is capital here, money that I'm spending that will be valuable in a couple of years. Again, unlike telecom, unlike the fiber boom, unlike in railroads. There is actually two assets here, one that's long lived, a building, which is essentially a small fraction of the cost of the center. And then there's one that's very short lived, which is the GPUs, which is the thing we'd like to have last and doesn't, but yet represents as much as 60% of the cost of the data center. So there's the perversity and that's the.
- [18:11]
B
And before we talk really deeply about how this could lead to a bubble or a crash, I do want to talk about how this is affecting the economy. Right now. It's eating tech jobs. There was a University of Maryland study that found that if you subtract out AI jobs from all IT jobs, basically all it is declining. When you subtract out AI tech is just in large part becoming an enormous employment bet in the future of artificial intelligence. I'm also looking at the fact that construction jobs are declining, mining jobs are declining, manufacturing jobs are declining in America, despite the fact that the tariffs are nominally about re industrialization. It almost feels to me, Paul, like AI is like this star that is pulling in all of these resources gravitationally from throughout the economy. In your own words, and hopefully I got you started along the right track. How do you see AI spending warping the 2025 economy?
- [19:10]
C
Yeah, so the analogy I draw is looking back, you can see how a similar effect happened. That is massive capital spending in one narrow slice of the economy during the 1990s caused a diversion of capital away from manufacturing and away from small manufacturing in the United States. There's been some good studies on this showing exactly the effect and it's not surprising because people were rewarded for accepting that money to build out telecom and they were rewarded for spending that money because look, I'm spending in an area with high returns. But what that did was it starved small manufacturers of capital, which made it very difficult for them to raise money cheaply, which raised their cost of capital. Mean their margins had to be Higher. Now, let's follow that along. During that same time China had entered the World Trade Organization, tariffs were dropping. We've made it very difficult for domestic manufacturers to compete against China. In large part, not entirely, but because of the rising cost of capital, because it all got sucked, to use your Death star term, it all got pulled into this death star of telecom. So in a weird way, we can trace some of the loss of manufacturing jobs in the 90s to what happened in telecom, because it was the great sucking sound that sucked all the capital out of everywhere else in the economy. The exact same thing is happening now. There is no reward for spending money if I'm a large private equity firm, if I'm any kind of large capital allocator anywhere else but in data centers. Which is why if you watch the announcements from places like blackrock or from Blue Owl or from any of the large private equity firms or private debt providers, the thing that they're making the most noise about and are most excited about are these giant multibillion dollar checks. They're riding towards data centers. And so again, the same phenomenon. If I'm a small manufacturer and I'm hoping to benefit from the onshoring of manufacturing as a result of tariffs, it's leaving aside whether they're good or bad economic policy, but I want to benefit from it. So I go out trying to raise money with that as my thesis. The hurdle rate just got a lot higher, meaning that I have to generate much higher returns because they're comparing me to this other part of the economy that will accept giant amounts of money, huge checks I can write for this, to data centers. And it looks like the returns are going to be tremendous because look at what's happening in AI and the massive uptake of OpenAI. So I end up inadvertently starving a huge slice of the economy yet again, much like what we did in the 1990s.
- [21:38]
B
It's such an interesting interpretation because the story that we tell is that trade with China took our jobs. And the China shock, as economists like David Auter call it, moved manufacturing to China. And that is what's hollowed out the Rust Belt. You're saying, yes, trade with China might have been a factor at the margins, but also the telecom buildout took capital once allocated manufacturing and moved it to tech. And what's so interesting about that is if you fast forward to the2020s, Trump is trying to reverse the China shock with the high tariffs, but we're recreating the capital shock with AI serving as the new telecom. So rather than reverse the conditions that led to the decline of manufacturing. The Trump administration is ironically recreating those conditions in a way that's hurting manufacturing even more, with all of this money moving toward AI and away from traditional manufacturing. It's such an interesting idea.
- [22:36]
C
Yeah. And it's even more insidious than that. And this requires some inside baseball. And it's insidious because. Let's say you're Derek's giant private equity firm and you control, I don't know, let's say you've got $500 billion burning a hole in your pocket. What do you not want to do? I do not want to allocate that money, one $5 million check at a time, to a bunch of manufacturers, because all I see is a nightmare of having to keep track of all of these little companies doing who knows what and everything else. What would I like to do? I'd like to write 30 $50 billion checks, or 30, you know, I'd like to write a small number of huge checks. And this is a dynamic in private equity that people don't understand that capital can be allocated in lots of different ways. And. But the partners at these firms do not want to write a bunch of small checks to a bunch of small manufacturers, even if the hurdle rate is competitive, even if they're operating at a level where they can compete against what the perceived return is on data centers. Because I'm a human, I don't want to sit on 40 boards. And so you have this other perverse dynamic that even if everything else is equal, it's not equal. So we've put manufacturers who might otherwise benefit from the onshoring phenomenon at an even worse position, in part because of the internal dynamics of capital.
- [23:55]
B
What about the energy piece of this? So electricity prices are already rising. This revolution, in a way, is just getting started. And these data centers are incredibly energy thirsty. How much do you think this is going to result in? Energy. Energy inflation that becomes an economic, consumer, and even political problem. That these data centers are essentially seen as a lever on electricity inflation, such that you've got your average Joe saying, why is my economy essentially a temple to AI? And all it does is make it harder for me to keep my child's room 69 degrees while she's sleeping. How is that going to play out?
- [24:33]
C
So that's already beginning to play out in the strangest possible ways. The most obvious way is that we're already seeing energy inflation in part, part driven by, again, consumers being outbid, if you will, by data centers, because it's almost like the private equity phenomenon. If I'm a utility, I would love to have a bunch of large people I can put on large buyers I can put on the grid because I can manage them. They're good for payment. They're not going to go away. But a bunch of people in some exurb in rural Virginia, Yeah, I don't know. Maybe they'll pay their bills, maybe they won't. So again, it's the same phenomenon is happening at the larger scale in terms of the capital allocation. I'm perfectly happy to put these large buyers onto my network, meaning the power grid, because I feel like there's some security of payment. But there is this perverse thing happening at the same time, and we saw this in the PJM recent regulatory filing that got kind of ditched. But they were proposing to add people to their network. They're an interconnect provider in the Northeast, one of the largest, and they were proposing to add data centers to the grid with the proviso that anytime the utility, the grid is under stress and I have to cut back because of a heat wave or because of heating or whatever else, I can disconnect the data centers because it was fine for a few hours if I disconnect a data center. So they're trying to have their energy cake and eat it too by connecting these large buyers, but saying it's okay, don't worry consumers, because we have a provision in our agreement with these data centers to disconnect them if things become really difficult. Well, of course, if you're a data center, you're like, yeah, no, that doesn't work for me. That's not going to work. And so you can see if you look underneath the hood, how the tensions are beginning to play out in ways that cannot be resolved straightforwardly. We have energy inflation on the one side and we have these somewhat dodgy interconnection agreements being proposed where we propose that someone will actually be cut off from the grid, meaning a data center will be cut off from the grid. And that's just not. It's not going to fly. Even if I sign on to that now, rest assured, I will sue you in two years. If you do it to me, me 100%, the lawsuits will be just massive. Even if I agreed to it today, I will sue you in two years.
- [26:43]
B
So what's a reasonable prediction here? If consumers are going to, I think, start to be much more explicitly nimby about the construction of local data centers if they see these data centers as Essentially not just, you can either say it, stealing energy or basically a very clear leverage on raising energy prices. The data centers have enormous political power of their own, enormous economic power of their own. These are large rich companies that can spend a lot, pay a lot maybe for that energy. I mean, how is this going to play out, do you think, in two years?
- [27:20]
C
So I think you're going to rapidly see an offshoring of data centers. So that will be the response. It'll be increasingly be that it's happening in India, it's happening in the Middle east, for example, with massive allocations are being made to new data centers. And it's happening all over the world in China, where to the point that there was recently a warning from the Chinese government that every city does not need its own data center because what they're trying to do obviously is create a massive oversupply at the local, regional level. There's an incentive to create these things. And so you're seeing lags aside because it's nice to have a data center located locally to you in terms of actually providing services. Nevertheless, the focus will increasingly move offshore for exactly this sort of NIMBY esque reason. And there's been some great. Bloomberg had a great story the other day about an exurb in Northern Virginia is essentially surrounded now by data centers. This was a, was previously a rural area and everything around them, all the farms sold out and people in this area were like, wait a minute, who do I sue? I never signed up for this. I never signed up. At night they would go outside their houses and they hear hum and it's like I didn't sign up for. I didn't. This is the beginnings of the NIMBY phenomenon because it's become, you know, visceral and emotional for people. It's not just about prices, it's also about this isn't having this, this six acre building beside me that's making this noise all the time. This is not what I signed up for. So I think the pushback has already begun and it'll become much larger within two years. And increasingly the largest construction will move elsewhere.
- [28:51]
B
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- [29:24]
D
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- [29:57]
B
Brought to you by Workday. When you're a forward thinker, you don't just bring your A game, you bring your AI game. Workday is the AI platform that transforms the way you manage your people, money and agents so you can transform tomorrow Workday, moving business forever forward. I want to talk about how some of this might go badly in the next few years, and I want to preface that discussion by saying that when I talk about AI as a bubble, I think some people see me as being pessimistic about the technology. The railroads were a bubble. There was a panic of 1857, of 1873, 1891, I think. There were constant railroad depressions. And also the railroads changed the world. Broadband was a bubble. It also changed the world. Big infrastructure buildouts that change the world often pass through a bubble phase. So it's not particularly pessimistic to say that AI is currently in a bubble. You could say it's actually incredibly historically in tune to say that we are very likely in the middle of a bubble, because every industrial revolution like this passes through bubble phases. So let's start here. How close are the hyperscalers, Meta and Google, Microsoft, the big boys? How close are they to aligning spending and revenue in the AI space? Or how far, I guess you could say. On the other hand, how far are they from seeing what could be plausibly called AI revenue catching up with AI spending?
- [31:28]
C
Nowhere near, I'll say. First, I agree with you about the bubbles. I mean, my general argument is you never know if you've spent enough on capital till you've spent way too much. So it's like Michael Kinsley used to say. This sort of had a similar wording, but the notion being that you're never going to have a rational expenditure of capital on new equipment and do it in a way that makes economic sense all the way up. You will eventually spend too much and then pull back. So let's take that as a given. As you said, it's just part of the process of building out. But the deeper issue is, are you going to get to a point where it's obvious that the companies are stretched in terms of. Let's take for example, most of the publics that we're familiar with, the hyperscalers are spending as much as 50% of income on CapEx, which is unprecedented. This doesn't happen normally. If I did that as a Microsoft or an Amazon, I would absolutely be taken to the woodshed and beaten by investors because that's such an incredible investment, not just in terms of capital expenditures, but on one narrow slice of capex that you're going to be punished for that. So they're not being punished for that. So what are they doing instead? And this goes to your point about what we should be watching for. In a sense, there's a way of thinking about it. It goes back to economists like Hyman Minsky and others that what you start looking for are whenever the mechanisms that they use to raise money to do this become increasingly opaque. So what I'm watching is how they're moving the financing off balance sheet because that for me is a reflection of I don't want the credit rating agencies to look at what I'm spending, I don't want investors to roll it up into my income statement. So what we're seeing increasingly are these SPVs, these special purpose vehicles being created where I have a stake in it as meta, some giant private debt provider, credit provider has a stake in it. And yeah, okay, fine, the data center at the end is under my control, but hey, hey, hey, I don't own it, right? And so you don't get to roll it back into my balance sheet in terms of assessing my credit worthiness, it doesn't change my credit rating, it doesn't change my income. So we're seeing for the first time over the last six, seven months the beginnings of a wave of these special purpose vehicles and other more exotic financing structures. We're seeing the equivalence of some of the old collateralized debt obligations emerge where you're this tradable debt, interests in data centers, these are all for me, the beginning of the sign that the bubble is becoming tired because the market is beginning to punish. At least there's perception that the market will punish me if I continue to keep this on my income statement. So I won't, I'll move it somewhere else. And that makes the entire process much more opaque. It's almost obfuscatory in terms of preventing people from understanding it. How do I go through all these? The footnotes of all of these statements. And so that for me is that's the thing to watch people get hung up on. I think a lot of the wrong things in terms of trying to assess what's going on. For example, is the rental rate of GPUs now competitive in terms of the actual costs of running the center? These are good things to look at, comparing your cost to your rental rates. But look at it from the company standpoint, how hard are they trying to hide the expenditure? And for me, that's the factor to watch. And it's just begun accelerating.
- [34:41]
B
I feel like people who remember 2006, 2007 are feeling their eyes start to twitch as you talk about this general law that I love. The way you put it, it's kind of like, you know, your behavior is unethical if you try to keep it a secret. You know, your economic activity is bubblicious if you try to dress it up in financial opacity. So let's talk about just exactly how this works. I've seen you talk about this in other interviews. I think it's really important to understand how these data centers are being built and specifically how it's not as simple as, oh, Meta just has a line item in their overall spending that says, and then we bought a bunch of land near Ashburn, Virginia and built a data center there. What's happening is the hyperscalers like Meta are getting together with the private equity firms like Apollo, and they're, they're both putting money into this box, right, These special purpose vehicles. And that box is the thing that's investing in these data centers. Just take me through exactly how this works.
- [35:46]
C
So if I don't. So the idea obviously is you can look at it from a couple of different ways. One is that the private credit firms, the Apollos and others want a stake in data centers, but they also, they want their stake in data centers to be in a data center that has built in customers. So from their standpoint, I can write a large check and if I partner with a Meta or a Google or whoever, there's a high likelihood that immediately people will be using, it'll be populated, there'll be rental income flowing back from it, because that's what they want. Think of it like interest on a debt. Note that there'll be interest flowing back because people are paying for hourly usage of these GPUs. And that flows back to me as a partial owner of this data center. And that's what I'm looking for, a stake in that Rental income, no different than having a stake in a note that I've extended to someone else in the form of debt. So now I have multiple people participating in part because their interests are aligned, but also from the standpoint of these individual public companies, because I don't have to roll it up into my income statement if I control less than 50% of it. So that's a really important proviso that I want de facto control because I'm actually using it and benefiting from it. But from a legal standpoint, I don't want legal control because then that flows back and I have to deal with it from the standpoint of my credit rating in terms of the leverage, the amount of debt I have on my balance sheet against my equity, and all of these things that are really mundane and boring, but matter immensely to CFOs. So from their standpoint, the idea of partnering with a large private credit firm to create these bespoke special purpose vehicles that are these one off vehicles that we all sign up for and we join in and the data center gets created, they're great because now I get what I wanted, which is a new data center, and I don't get what I didn't want, which is a hit to my credit rating. And so there's a huge incentive to create these and create more, especially given that we're already at these historical limits in terms of the amount of spending we're already making. So there's a huge incentive to put it somewhere else.
- [37:45]
B
Let me try to restate this so I understand it. So Meta wants to build these gigantic AI data centers. These projects cost tens of billions of dollars altogether. Even though Meta's rich, they don't want to just borrow all the money the normal way. They don't want the spending necessarily on their balance sheets. So they solve the problem by creating this special box. As I put it, Meta puts some assets into the box, another private investor puts some money in the box, and now that box, that special purpose vehicle, is going out, borrowing money, paying for construction and owning the data center, right? On the surface, I guess you could say everyone's happy, right? Meta gets money without messing up its balance sheet. The investors get high returns, I guess without obvious risk because they're basically working with Meta. But what happens if we build too many data centers? Meta's exposed. The private equity firms, maybe more importantly, are exposed, maybe some of these REITs are exposed. And that means the limited partners, the LPs, whoever's putting the money into those private equity firms, they're exposed as well. So the same way that like if you were going back to 2006, 2007, we were thinking if this whole house of cards comes down, who's hurt? You could say, oh, it's Bear Stearns, oh it's aig. Like, give me a sense of the kind of companies that would be most exposed if we saw a significant slowdown in the AI capex world or some kind of significant pullback here.
- [39:11]
C
Yeah. So not to go full hedge fund, but if you think about it in terms of the companies that have really benefited, they're in construction, they're in air conditioning. So for like carrier, for example, think about them as being, if I'm building out industrial class air conditioning for giant data centers at an unprecedented scale, what business would I really like to be in? I want to be selling them air conditioning because cooling those buildings is a huge problem. And it's great if I'm an industrial, a provider of industrial air conditioning, for example. So these are the kinds of companies that are, that aren't as obvious but are huge beneficiaries of this build out. So leaving aside you and I as beneficiaries from AI, think about the construction providers, architectural providers, think about the carriers of the world and the air conditioning providers and all of these people who have to whose products end up helping turn a piece of real estate into a functioning shell into which I can insert GPUs. And all of them are delighted to be participating in this. But then there's the perverse fact that you as an individual investor might say, well, you know what, let them all burn. If this goes bad, it's their problem carrier or these private equity firms or the private credit firms or even Meta and whoever else. But yet it's not going to work out that way. The reason why it's not going to work out that way is in part because it's driving economic growth. And we've talked about that a little bit. But it's also because increasingly these investments in special purpose vehicles and other related data center spending is showing up inside of things like REITs. So real estate income trusts. So if I'm creating a, if you look inside of any large REIT in the United States Today, somewhere between 10 and 22% of it is already directly data center related. So if you're a conservative investor with a REIT in your portfolio, because you're saying, you know what, I don't care about any of that crazy tech stuff, I'm going to be over here safe as houses, commercial real Estate or whatever else getting real estate income. Go have a look inside your reit, see what's actually in there today. Two years ago, there was nothing in there that was related to data centers in some of the largest ones. Today we're up to 21 little 21, 22% is directly data center related. So you're soaking in it. You're already in there, my friend.
- [41:25]
B
And then putting this all together, right? Let's say you're a typical, you're an older investor, you're a conservative investor, you've got some money in REITs. You think this is just sort of your meat and potatoes investment vehicle. And now you said between 10 and 20% of these REITs are directly or indirectly tied to data centers.
- [41:42]
C
10 or 20% of their assets under management. So all REIT, pretty much all the REITs, but 10 or 20% of their assets under management, just to asset management.
- [41:49]
B
Okay? So you're right. So 10 to 20% of these REITs assets under management is in data centers. You told me 25 minutes ago or whatever that data center costs are like 70% GPUs. Which means, in effect that these REITs are basically just like significantly in Nvidia. Right? Like, I mean, these grandparents who like don't even know that they're like Nvidia investors are like significant investors in Nvidia. Which means as Nvidia goes, so do their investment portfolios. I mean, that also seems like a significant part of this, which is that like, you know, you've got this enormous US economy, $35 trillion, and it's like a significant amount of its growth on a quarter to quarter basis, whether it's equities or GDP balances on the narrow read of like, how's Nvidia doing really? It just seems like an enormous share of economic growth right now is like, basically, how are we doing with chip sales?
- [42:48]
C
Yeah, absolutely. And it's not funny, but it is kind of funny that imagine you got scared. I'm a risk averse investor. And I said, you know what, I'm only in index funds. And so a year or two ago someone told you, you know what, you may think you're being risk averse, but 30% of the S&P 500 is now tied to what's euphemistically called the MAG7 stocks. You're actually long Nvidia. You're long Nvidia in a huge way. You're like, oh, I'm getting out of the S&P 500. I'm going only into really safe stuff like REITs. So now it's sort of this problem of there's nowhere to run. It's increasingly the case that you've got nowhere to run. And in a backdoor kind of way, private credit now is now allowed inside of retirement funds. You're seeing increasingly these showing up in other ways, not just as REITs, but let's say I'm an investor in private credit, thinking that as a retail investor, I'm now investing in, I don't know, take private operations for a manufacturer in Iowa. No, you're not. You're in data centers. And by proxy, by being in data centers, you're also in Nvidia. So this notion of have, it's a complex system, but there is a single point of failure. And in this single point of failure is a couple of semiconductor stocks who are highly leveraged to everything that's going on and yet have kind of metastasized across each of these pieces, from the S&P 500 to REITs, to private credit, to backdooring their way into new private credit ETFs. It's incredibly insidious and important, and yet most people haven't even realized how deeply it's insinuated itself.
- [44:18]
B
So going further along this particular train of thought, what does a bubble look like to you? What are the news headlines?
- [44:25]
C
So I think the news headlines are, for starters, it would be the largest share of future building in terms of data centers is all through SPVs. So for me, it's people saying, oh, look, it's now all being done in partnerships. It's not as risky for meta, it's not as risky for Amazon. Look, they're partnering. For me, that would be the hallmark of a bubble that's hitting the point of, okay, we need to really be paying close attention because the companies themselves are stepping away so aggressively because they see the effect this might have on them. And the other thing to watch for is delays in terms of the provision of air conditioning and other of these ancillary equipment. That's incredibly important. Interconnect gear for interconnecting racks and GPUs. Inside of these centers, delays at one point were going out to four and five months. If that continues to come back, I'd be watching that because now it's the reverse phenomenon where it's like, oh, wait, if I can actually get stuff, that must mean things are slowing down. So these are things to watch for. If you suddenly hear about any industrial class air conditioner suddenly missing their numbers. Well, the only reason they're going to miss their numbers today is because they don't have data centers to sell to. That's the only reason. Because otherwise they're going to blow the doors off from now into eternity. So it's these things at the edges that you need to watch, as opposed to saying, my brother in law tried OpenAI and doesn't like it anymore. Or even to go back to your original point, yes, AI is stealing jobs probably, and people are increasingly feel threatened, especially in white collar jobs, especially in areas like software, maybe in law and so else. But the bigger risk remains this great sucking sound of capital being pulled out of small manufacturers who might otherwise be on shoring and employing people and are now forced to say, wait a minute, I can't compete against this. So this goes back to a point I make all the time about this stuff, which is that this is how you end up making bad policy decisions. So if you say to yourself, oh, wait, people say the economy's weak, it grew 3% in the second quarter, it's not weak at all. Well, yeah, but you're not factoring in how much of that was tied to data centers and how transient that spending is. And I make this stupid joke all the time, but I'll make it one more time, which is that having messed up causality in terms of understanding the causal nature of what's going on is a little bit like my dog. He barks every time the mailman comes to the house and then he keeps barking and the mailman goes away. And he's like, dude, I totally have this. If I bark long enough, the mailman goes away. No, no, no, the mailman goes away every time. It doesn't matter how long you bark. So the dog's notion of causality is completely wrong. We're like that barking dog in terms of understanding the drivers of economic growth right now. We think it's because of tariffs. We think it's because of all of these other factors, and it's not. So there's this perverse incentive to keep doing the wrong things because look, they're working. And they only are working because no one's going down deep enough to understand. Wait a minute, it's being driven by completely different things.
- [47:24]
B
The economic commentator Noah Smith wrote a piece about what it would look like if a data center slowed down became a true financial crisis. And he put it this way, and I would just love to hear you evaluate this particular logic. He said, number one, we've got this big story about how this time is different, that AI is going to be the technology to overtake all technologies. Number two, we've got a large and increasing amount of debt being used to fund one single sector. And that means that the loan's probability of default is highly correlated. If one loan defaults, it means there's probably others that are going to default as well. We've got an opaque corner, as you've said of the financial system, with private credit that's grown a lot. And finally, we have systemically important lenders, banks, and even insurance companies. I believe life insurance companies in particular are significant LPs to some of these private credit firms you talked about, and they're enmeshed in this new sector that might see a drawback in the near future. To what extent do you think that this represents the ingredients for an actual financial crisis?
- [48:25]
C
Oh, it absolutely does. It has all the pieces. So I'll pick on just one that you mentioned, and I'll just flip it slightly, which is that the connection, for example, to insurance is very poorly understood. So what's happened over the last few years, it's not so much that insurance Companies are large LPs in these private credit providers, meaning that they're large investors in them, it's the other way around. So what's happened is private equity and private credit have purchased insurance companies because. And they call it, the term of art is they called it a captive source of capital, which is to say the premiums get reinvested in what the private equity firm is doing. And I can use those assets in turn to invest in data centers or whatever else I choose to do. But what we have, and this goes back to the time of Bear Stearns and the financial crisis, we have a classic temporal mismatch, a timing mismatch in terms of when the debt comes due and when I have to make my payments and when I provide things, you can see how the data centers are relatively transient. But on the other side, I've got these obligations to my insurance policyholders, which are longer term. So I have mismatched assets and liabilities, wildly mismatched on a temporal, on a time horizon no different than what happened way back during the financial crisis, which did in Bear Stearns, which was they had lent long and owed short. Right. And so they ended up blowing up on that basis. So you can see how the same thing would happen through a back door that doesn't look like it has anything to do with data centers. And it's because the nature of the funding of the providers of this debt, private credit firms, is increasingly tied to a sector whose obligations don't match the returns from the data centers. And that specifically is insurance firms, which are increasingly owned by private equity firms. And that's not nearly well understood enough that the nature of the capital structure in the economy that's driving this has changed, and that's created a new source of risk because of this temporal mismatch.
- [50:22]
B
What's the most likely way that you're wrong or that we're wrong, that the case for the bubble has some error in it? I could imagine maybe if Michael Sembalis was on this call, he'd say, look, these companies have more free cash flow than any group of companies in the history of modern capitalism. They can withstand enormous, enormous amounts of infrastructure spending for years and years, and they'll still be highly profitable because they're fundamental, fundamental business models, whether it's ad sales or whatever collection of businesses you could say Microsoft is in, they can withstand an enormous hit to their balance sheet. They are withstanding it right now. That's number one. And number two, maybe this technology is closer to a breakthrough that will yield significant income than you. And I think at the moment, right now, when I think about how OpenAI makes money, they've made money from subscriptions, they make money from their business relationships. But maybe they're on the cusp of something that's about to become a $100 billion annual business, in which case, of course, that's going to pay for all of their investment in training and inference. What's the most likely way that you're wrong?
- [51:32]
C
I've had this discussion with Michael Semblis, so I'll tell you.
- [51:37]
B
Tell me how I mischaracterized his argument.
- [51:40]
C
So here's the nut of the discussion we had was about this difference between what we're earning right now, what a data center earns on renting out GPUs versus what its costs are. So let's say I can rent for $35 an hour, and it's costing me $12 an hour. The combination of air conditioning power and the net of my debt on this, that's a $23 per hour gap. So let's say that gets halved over the next two years, that's still a huge premium over the costs, the rental cost of these data centers. So as much as you might say the capital flowing into this is going to cause a big hit, it's still very competitive as a commercial real estate play, if you will, in terms of the amount of premium I'm earning on top of my Costs. And that's a perfectly sound argument. You could make that argument and say that, that, yeah, we've earned, we used to earn, I don't know, 25 or $30 an hour of straight margin on top of the cost of these data centers from a rental standpoint. And that's going to get cut in half. But bro, that's still a lot of money, right? So that's the argument you will get from many on the other side of this, that even a sharp decline in the margins on the rentals of these GPU assets still doesn't affect the amount that I'm going to get back. Now the problem with that is it doesn't get to the question of, okay, fine, where's that money coming from? Where's the money coming from? That's the rental. The rental price is coming from somewhere. Most people I talk to are not. Most people probably, you know as well, typical consumers are not paying for ChatGPT, right? Some enterprises are and others. And ChatGPT has built a nice little business on it, but they're still going to burn. What was it? I lost like 100 billion over the next two years, I think was the number. The deeper problem is there's a great subsidy going on right here. So the data center rental income is coming from people whose economic models currently don't work and they show no sign of it working in the near future. So yeah, they're continuing to earn margin as a data center provider because of the monies that they're being spent, but that still reflects a massive subsidy to the people who are paying the data centers, even at half off prices. And so for me, that's the way I'd be wrong. The way I'd be wrong is that margin doesn't continue to decline. That even though it declines, it doesn't decline back to the point where it's no longer economically viable given rising costs of operating a data center and declining costs or declining prices of being able to rent them, that those two don't come into line. I think they're going to come into line and it's going to become a deadweight loss business. The argument from the other side is no, it won't. Costs will continue to improve. The providers of these services will find high margin businesses that will support those rental prices. The de facto subsidy from private credit and venture capital and others that allow these prices to stay so people continue to pay. That's not going to go away. That's the argument from the other side.
- [54:39]
B
Based on the math that you're describing when is it reasonable to think that some kind of break could appear in the system? You can't look at it right now and say, oh, things are starting to break. It seems like it's basically status quo. But when you look at the amount of free cash flow that these biggest companies have and their spending levels on infrastructure, and the fact that they're going to have to buy essentially each generation of GPUs every two to three years in order to stay up to the frontier when. When does the math stop making sense for you?
- [55:14]
C
So I'll start with a naive projection. So a naive projection given current decline rates would put you into about two and a half years out where they're no longer earning a risk adjusted return commensurate with the cost of doing business as a data center, meaning that it no longer becomes a productive investment because I'm not earning enough in rental prices to compensate for the cost of doing all of the things you just described. Not just building the data center, but having built the data center, continuing to refurbish, continue to rebuild the GPU constellation inside of this data center footprint. So an IE project will put you out like two and a half years. It's hard to imagine absent something changing materially with respect to the amount that companies are earning from selling AI services that it doesn't happen even faster than from that. So my guess is two to two and a half years. We'll suddenly see these two numbers come very close into alignment. And that's when you really have to be saying to yourself, I don't see how this can continue. At that point the subsidy goes away. Data center construction, stop. Something in these moving pieces has to stop at that point because if you can't earn on that a competitive rate of return, it's no different than having an office building whose costs are higher than the amount I'm able to rent it out to to my favorite loft them.
- [56:32]
B
And not to blend subject matter here, but two years from now you're going to have the debates, the Democratic primary for president, and two and a half years from now you are in the middle of the 2028 election cycle. So I think the interaction effect between an AI bubble beginning to pop and the 2028 election could certainly put us in line for quite a chaotic news cycle in 2028. I want to end on a positive note because I think you like I do see this technology as akin to the railroads and broadband in that it's almost certainly in a bubble dynamic now. But predicting that something is A bubble is not the same as predicting that it will have no effect in the world. I think that this is probably going to end up being conclusively and definitively the most important technology of let's say, the 2000 and 30s. Where are you most bullish on or interested in the application of AI at the moment?
- [57:32]
C
So I wrote a piece recently related to this where I said, I think we went. ChatGPT is kind of the original sin, meaning that we as humans get sucked into things that sound like us, dating back to Eliza, the original fake psychologist online, where you could ask it questions and it would say, why do you feel that way? ChatGPT is that to a different level of verisimilitude. It sounds so much like us that we want it to be wanted to be our friends. So the most interesting applications of these, of these tools and of this whole idea of large language models and predictive next tokens and all the technical gobbledygook is at a deeper level. Think about, I'm a small manufacturer and I'm trying to bring on a bunch of new suppliers. All of them have different systems. I got a person who sits in the back office and tries to say, whenever they say zip code, they don't always have a dash. What do I do with that? These are all things that people do that not only is it a job, but it prevents a more competitive landscape emerging. I don't want to have 20 providers because it's too hard to bring on new providers. If you think about the ways that companies communicate with each other at a very low and boring level, it's kind of like English, French, Spanish. It's the things that large language models are good at. They know the grammar, they can predict the next token. They say, yeah, yeah, yeah, that's a zip code. I know it doesn't look like a zip code, but that's a zip code. All these kinds of very mundane. So think about that as one of the areas that I think there's huge promise. I actually think that this is an area where we've got a company that we've been looking at in this area as an investment. But it's just broadly makes the most sense that these are all languages that these models can handle really effectively. And it's at that level we got sucked into the idea of they sound so much like us. This must be super important. That's kind of a dead end because you can only. As you watch public companies increasingly pulling back and saying, oh, we added a bunch of chat stuff to all of our products last quarter. And then analysts asked how that's doing. And yeah, not so much. People don't want chat added to everything. That's just some crazy bipedal ape thing where we want to talk to everything around us and then realize we don't actually want to talk to everything around around us. The interesting stuff is all deep under the hood, messy, boring, the language of business, talking to each other in this kind of mundane business of is that a zip code or not? And that's the stuff that this stuff is tremendous at. And it is really transformational. I can now have 20 different suppliers of that widget, not just two. And that's great for me as a provider of widgets. It's great for the economy. It's great for individuals. That's it's super important and no one talks about it.
- [60:02]
B
Paul Kudrowski, thank you very much.
- [60:04]
C
Yeah, thanks, Eric.