Things Paul Kedrosky Has Said on Podcasts

“This is as good as it looks from the standpoint of the business as currently structured. And now you go through this inflection that makes it much more capex intensive. … I think this was probably their best. And they know this. This is the best time to do it because the future business is going to be very different.” — Paul Kedrosky, Prof G Markets

Ed Elson floated a "conspiracy theory" that Anthropic chose to go public at the one moment its operating profitability looks good. Kedrosky agrees: the S-1 signals a shift from renting compute through rivals to building its own, which will make the business far more capital intensive.

Prof G Markets · 2026-10-27 Permalink → Listen →

Prof G Markets Around 11:48 into the episode

Speaker 2

…to maintain these very expensive facilities. But they're in this Hobbesian choice. They have no choice but to do that because otherwise it's to be reliant on their largest competitors who could, you know, like Microsoft did back in its heyday, you know, cut off their air supply at any moment. So

Ed Elson

…wanted to go public now? Is it possible that they wanted to take this one moment in time where the operational, the operating profitability looks pretty good right before it's about to look quite bad? Because that seems like that is what is about to happen. They're at this inflection point. No,

Speaker 2

no, no, no, no. Listen, I was arguing that earlier today multiple times with people. So I think that's exactly what's going on. This is as good as it looks from the standpoint of the business as currently structured. And now you go through this inflection that makes it much more capex intensive. So you're essentially changing jockeys and changing the underlying structure of what's happening. And so I think this was probably their best. And they know this. This is the best time to do it because the future business is going to be very different. They're essentially reduplicating AWS, Amazon Web Services, in both directions, building up and building down in terms of both the client customer facing as well as the cloud-facing stuff. It doesn't mean it's impossible. It could work, but it does mean that the way the business is characterized in this current statement is not representative of the future. So

Ed Elson

what would it working look like in your view? Well,

Speaker 2

I think what have to look like you'd see, for example, one of the statistics they want to tout in the document is the declining compute intensity of token production, that it's been coming down something like it's fallen like 50% or something like that. And so you want to, what the business would…

Ed Elson

Final question. This is set to go public. The report of valuation is going to be $2 trillion. There has been some debate as to whether that will actually materialize. Yeah,

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

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“In a weird way, societies require friction. And when you take away frictions, even frictions that you didn't realize existed, all of a sudden it has consequences. … blithely removing all of them is kind of creating in tech parlance almost a denial of service attack on modernity.” — Paul Kedrosky, The Ezra Klein Show

Kedrosky was responding to examples of AI agents robocalling restaurants and people using language models to file small-claims suits. His point is that the backlash against AI is partly a reaction to everyday frictions disappearing all at once.

The Ezra Klein Show · 2026-10-16 Permalink → Listen →

The Ezra Klein Show Around 59:03 into the episode

Ezra Klein

this. I love, sorry, I love this stat in one of your newsletters recently that more people pay for GLP1s. A larger number of Americans paid for a GLP1 than for AI.

Azeem Azhar

…system and we're going to have to figure out how to deal with it. I now pay, it's not a lot. I pay about a buck a month to my phone operator to screen out spoof phone calls that are essentially robocalls. And that's what's going to happen. And we're going to call that GDP. Those additional costs.

Paul Kedrosky

I think one of the things that's your comment, Ezra, really reminded me of this is that we have this idea in sort of Western capitalism that it's always about efficiency. In a weird way, societies require friction. And when you take away frictions, even frictions that you didn't realize existed, all of a sudden it has consequences. You know, there have been examples of people using large language models to launch claims in small claims court, for example. Do we want that to be more to have less friction? Do we want more of that? Do we want this, you know, your example of this of the restaurants? And we have all of these frictions that exist in society, and blithely removing all of them is kind of creating in tech parlance…

Ezra Klein

And what's going to happen is that it's going to be these two AIs bouncing things back and forth. And it's just going to be a more and more and more unpleasant economy, which is why I do think there are going to be large areas where people just say maybe not. So I want to end on this question…

Azeem Azhar

Well, there are so many risks that we have talked about, and there's all the complexity and fragility in the financing. The thing that keeps this on the other side of healthy is whether real customers are spending real money and getting real results. So the thing that would make me uneasy would be…

Paul Kedrosky

There's so many. I mean, I'm convinced I'm wrong about most things. So I'm always happy to find more things I'm wrong about. So I just look at economic data. So if we began to see, I don't know, productivity growth go well beyond trend, that would be striking, right? So far, we're sitting at around…

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

“The Article of Faith, the West Coast ideology on this stuff is technology always creates more jobs than it destroys. And so it becomes just a thing that people repeat, like a mantra that they hope will become true. And the reality is we've never seen a general purpose technology that had such broad applicability, was adopted so quickly, and was repricing labor at the same time.” — Paul Kedrosky, The Ezra Klein Show

In an Ezra Klein debate on whether AI is a bubble, Azeem Azhar had argued that new kinds of jobs will appear over five to ten years. Kedrosky disagrees: AI is making cognitive work like call centers far cheaper, and he thinks the disruption will move faster than new jobs can be created.

The Ezra Klein Show · 2026-10-16 Permalink → Listen →

The Ezra Klein Show Around 49:55 into the episode

Azeem Azhar

…to absorb what it is they have to do with this technology. And it's just not the case that you get the best model from OpenAI and say, fix my company and it fixes it. I mean, these things take time. And that's what I see when I talk to bosses. However fast they want to move, it just takes time.

Ezra Klein

Paul, how do you see this?

Paul Kedrosky

We're basically repricing labor, right? I mean, this is the fastest adopting technology in history. And its fundamental function is to reprice cognitive labor. And I mean, this is the deep struggle because the Article of Faith, the West Coast ideology on this stuff is technology always creates more jobs than it destroys. And so it becomes just a thing that people repeat, like a mantra that they hope will become true. And the reality is we've never seen a general purpose technology that had such broad applicability, was adopted so quickly, and was repricing labor at the same time. Because when I say repricing labor, I mean it's making it vastly cheaper to do things like call centers, for example. And as a result, these things, this change is going to be destabilizing long before we figure out how to turn them all into, I don't know what the equivalent of webmasters is. And so this is the problem, right? Is that it's happening so much faster than any prior GPT that these…

Ezra Klein

This has also struck me as another form of economic risk for not just workers, but actually AI and for some form of this bubble popping or deflating if it is a bubble. Because you can imagine a world where actually the problem is not whether or not companies are willing to adopt AI, that actually…

Paul Kedrosky

This has been my complaint from the very beginning of the process by which these companies have launched models. That they've essentially been conducting unlicensed drug trials on the public in the sense that these are hugely consequential technologies that are launched in a way that were they a…

Ezra Klein

Zeem, how do you see this political risk dimension?

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

“The valuation of OpenAI and Anthropic alone combined in the private markets and what we're likely to see as they come public is already more than the peak NASDAQ valuation when it crashed in 2000. So the problem is, is people are looking in public markets for a thing that's sitting in plain sight in private ones.” — Paul Kedrosky, Prof G Markets

Kedrosky answers the common pushback to his bubble warnings: public-market price-earnings multiples look nothing like the dot-com era, so where's the bubble? He says it's in the private markets, and expects a significant correction by around the first half of next year, after the trouble at CoreWeave.

Prof G Markets · 2026-10-15 Permalink → Listen →

Prof G Markets Around 15:26 into the episode

Speaker 1

once it starts here, how far do you go with this? It wouldn't surprise me to see someone try to staunch the bleeding as the

Speaker 2

…of something similar. So he's much smarter than I am. So I'll go with Paul and say the first half of the year. But I think it could be sooner. The thing is, is that one way to think about what's going on, and I get this question all the time, is like the timing question. And they say, well, you

Speaker 1

can't be soon because market price earnings multiples are nothing like they were back in the dot-com era. Where's the bubble, Paul? Where's the bubble? And of course, the answer is the valuation of OpenEye and Anthropic alone combined in the private markets and what we're likely to see as they come public is already more than the peak NASDAQ valuation when it crashed in 2000. So the problem is, is people are looking in public markets for a thing that's sitting in plain sight in private ones. And so we're already there and the triggers

Speaker 2

are everywhere. But it's your view then that the bubble exists, but it's not pervasive to the entire market. It's in pockets of the market. It's in places like private markets or the neo clouds or certain chip manufacturers. There are some stocks which are way over their skis. Yes.

Speaker 1

I mean, it's widespread. It's just that private markets are the new public markets, right? That's the term we've been hearing now for five years because of the disappearance of public equities from the marketplace. And so as a result, unsurprisingly, if private markets are the new public, Public…

Speaker 2

China. And those two are rippling and going to like waves coinciding. Whenever they go down, they'll bring each other down. And so that's, say, $6 trillion in nonsensical market capitalization. As we record this, Anthropic is just wrapping up its private investor day. They are reportedly disclosing…

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

“I call it earnings before bad things. So if I cut out all of the things that are costing me a lot of money and then I calculate EBBT — not EBIT, but EBBT, earnings before bad things — my cash flow is tremendous.” — Paul Kedrosky, Better Offline

Kedrosky and Ed Zitron were arguing about whether AI inference is actually profitable for the hyperscalers, against claims that pure inference margins are very high. His point is that those margins only look good once you strip out frontier-model training spend. His test for the argument: show him a hyperscaler that has stopped spending on frontier models.

Better Offline · 2026-09-23 Permalink → Listen →

Better Offline Around 38:39 into the episode

Paul Kedrosky

rise, but on that order of things. That's right.

Speaker 3

And that's what I'm saying. Which people will say is, oh,

Paul Kedrosky

that's really straightforward. You know, Gavin Baker's out there telling you that, right? That the margins on these things in terms of pure inference are so high. But of course, the problem is that this is the classic phenomenon of I call it like earnings before bad things. So if I cut out all of the things that are costing me a lot of money and then I calculate EBBT, not EBIT, but EBBT, earnings before bad things, my cash flow is tremendous. Well, then fine. Show me the hyperscalers that have stopped spending on frontier models and I'll buy the argument that the economics now make sense purely in terms of commodity inference. But if you go to the world of saying, okay, fine, industrial inference is actually a relatively high cash flow business. Let's just grant them that. Then the question becomes, well, who's going to win that? Well, if it's just industrial inference, so then meaning that I'm just doing token completion sequences as low cost as possible and at the highest scale possible…

Speaker 3

So, but that's the thing. Even with standing up inference, because is inference profitable? It's like the Wario is a libertarian conversation online when it comes to this stuff. It's like, and no one can really, I think it's unprofitable. But the reason I do is because inference isn't just, oh, I…

Paul Kedrosky

Oh, absolutely. Which a classic scale problem, right? That either I'm, it's kind of like running an airline, right? Either I'm a very, very high fixed cost business. So either I'm fantastically profitable or I'm aft, right? And And if you and very few people are able to run it at a scale requisite…

Ed Zitron

Yeah. It just, it feels so inherently doomed. But now, you know what? You said Anthropic. And before we got on, you were mentioning you'd heard some strange things. And I am now curious about how strange those are.

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

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