Why Paul Kedrosky Says AI Is Like Every Bubble All Roll… — Transcript

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Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Weisenthal.

And I'm Tracy Alloway.

Tracy, covering the AI boom is actually reminding me a little bit of the tariff boom in April, simply because every day there are new headlines. Like just today, we're recording this November 12th, Anthropic commits 50 billion dollars to build AI data centers in the US. So the advanced model companies are vertically integrating more to build their own data centers. Every day some new development.

Yeah, it's becoming pretty hard to keep up. So I think we're probably just going to talk in terms of billions and trillions. We're just going to say lots and lots of money is going into this space.

But the way I've been thinking about it is, okay, at this point, everyone agrees that the AI build out is super expensive. And all these companies are spending massive amounts of CAPEX to do this. And I'm starting to think that AI CAPEX is kind of like the Schrodinger's cat of markets, in the sense that it could either be a massive strength for these companies because the CAPEX is so expensive and it takes so much money to build out. And so anyone who manages to do it kind of builds a moat around their business, or it could be a massive weakness, right? If you're spending all this money and then that doesn't end up generating the revenues that you actually need to justify it. And going back to the Schrodinger's analogy, it seems like we just don't know what's going to come out of the box, right? Like it's simultaneously a strength and a weakness, and until we build out AGI or whatever, like we're just not going to know.

I totally right. There's so much at stake here and obviously, we know the numbers are absolutely enormous, they're staggering, and we can talk about them too. The financing structures are also very interesting. You know, it's one thing if you just have Meta or Alphabet and they make a ton of money already and they're spending money on data centers, whatever, that's one thing. It's another thing when you start seeing these SPVs where the hyperscaler puts in this amount of money and then the private credit puts in this equity and then they borrow a bunch and then there's all these questions about the payback and we think of tech as from years and years as basically being this equity story. And when it becomes a credit story, Yeah. And when, you know, people are talking about quoting Oracle CDS. I always forget these company even have CDS because I'm so unused to thinking of big tech companies as credits. So when I see people starting to tweet Oracle CDS charts or Coreweave CDS charts, it's like, okay, we are in a different level of capital intensity here.

Right. And some of those swaps have been going up lately.

I'm going to say one more thing. Thinking back to the 2008 financial crisis. I remember the economist at Raymond James, I think it was Jeff Saut who went on to become a very big name. Yeah, we should we should have him on the podcast. But he made the point that historically, when you had real estate crashes, property crashes, it was usually because of a problem in the economy. But then what happened in the run up to 2007, 2008 is the housing market crash became the proximate cause of the troubles in the economy. And if you think about how much money is being spent on AI right now, again, billions, trillions, possibly, of dollars, it's very easy to see how AI could morph into a problem for the wider economy.

For the real economy, totally. Just on this note, and then we'll get into our conversation. The Center for Public Enterprise is out with a great report today called Bubble or Nothing by Ed Vate Arun, pointing out one of the things that makes data centers interesting is how they sit at this intersection of essentially industrial spending and real estate. It's an interesting asset class for its own right. So much to talk about, we could never do it justice in one episode, but that means we got to do more. Anyway, I'm very excited for today's episode. We really do have the perfect guest. Someone who's been writing about this for a long time. Someone who's just been writing about the internet and all things for longer than any of us, someone who's been blogging and investing for far longer than um either of us or anything like that. Way more knowledgeable about how these businesses work than most. Very focused on the data center build out. We're going to be speaking with Paul Kadrosky. He is a fellow at the MIT Institute for the Digital Economy, also a partner at SK Ventures, and long time internet blogger, writer, newsletter, yapper, et cetera. Someone we've never never had on the podcast before. So Paul, thank you so much for uh joining us.

Hey guys, thanks.

Good to be here other than the blogging part, but yeah.

No, it's all it's all you're you're a true pioneer in that and it's impressive that you still write with the output that you do. At some point in the last year, I feel like you really got laser focused or maybe in the last two years, really got laser focused on the data center story is this is where the action is.

Yeah, I did. And in part just because I caught myself by surprise with it. It was weird. I was looking at first half GDP data, actually first quarter GDP data earlier in the year. And you know, this has become now a common place that people know this, but I hadn't realized what a large fraction of GDP growth in the first quarter data centers were. It was on the order of 50%, much larger if you included all sort of externalities, all the other things that data center spending in turn kind of accelerates. And then obviously the same thing was true in the second quarter. And it was I got back to thinking about my dog and I was my analogy is that

As one does.

As one does. I got thinking like, my dog barks when the mailman comes to the house and keeps barking and then the mailman goes away. And I'm convinced he thinks he makes the mailman go away, right? He has this really screwed up causality. And it's like, dude, if you don't bark, it goes away anyway. This is part of the job. They just go away. And I think about macro policy in the same way, that if you don't understand the drivers of GDP growth, you're likely to think that whatever it is you would most like to be causing GDP growth is doing that. So in the case of the US in the first half of the year, you know, it was this puzzle was, well, maybe it's tariffs. Maybe tariffs are actually contributing to it. Maybe consumers are much more resilient than we expected. And as it turns out, a huge factor, probably the largest factor was this sort of unintentional private sector stimulus program, otherwise known as data centers.

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