Why Paul Kedrosky Says AI Is Like Every Bubble All Rolled Into One
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What is the current state of the AI boom and why are investors worried?
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Hello and welcome to another episode of the Oddlots podcast. I'm Joe Wisenthal. 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 there's just today, we're recording this November twelfth. Anthropic commits fifty 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 gonna talk in terms of billions and trillions. We're just gonna say lots and lots of money is going into the 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. Yes. 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 Schrdinger'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.
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 Schrdinger's analogy. It seems like we just don't know what's gonna 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 gonna 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 could talk about them too. The financing structures are also very interesting. Yeah. 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 companies 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 core weave CDS charts, it's like, okay, we are in a different level of capital intensity.
Right. And some of those swaps have been going up lately. I'm gonna say one more thing. Thinking back to the 2008 financial crisis, I remember the economist at Raymond James, I think it was Jeff Sout who went on to um 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.
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Chapters
8 chapters
1
What is the current state of the AI boom and why are investors worried?
0:00–6:18
2
How does AI capital‑expenditure compare to past technology bubbles?
6:18–12:27
3
Why are special purpose vehicles (SPVs) being used to finance AI data centers?
12:27–19:16
4
What are the risks of the temporal mismatch between long‑term loans and short‑life GPU assets?
19:16–25:42
5
How does the depreciation schedule of GPU‑driven data centers affect their economics?
25:42–31:41
6
Why might the United States and China take opposite approaches to AI development?
31:41–38:16
7
What are the potential macro‑economic consequences of massive private‑sector AI spending?
38:16–43:55
8
How does Paul Kedrosky summarize why the AI bubble is a ‘meta‑bubble’?
43:55–49:56
Speakers
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