A.I. Hype Is Running Into Reality

And what a hedge fund’s $35 billion loss reveals about the state of the industry.

By David Wallace-Wells and Natasha Sarin

Produced by Derek ArthurVishakha Darbha and Rochelle Widdowson

Billions of dollars are flooding into — and out of — artificial intelligence, sparking concerns of a 2008-style economic bubble. The Opinion writer David Wallace-Wells is joined by the Yale economics professor and contributing Opinion writer Natasha Sarin to talk about whether the A.I. hype is exaggerated and if the fears of an impending economic crash-out are valid.

Why the U.S. Economy Needs A.I. — Bubble or Not

Below is a transcript of an episode of “The Opinions.” We recommend listening to it in its original form for the full effect. You can do so using the player above or on the NYTimes app, Apple, Spotify, Amazon Music, YouTube, iHeartRadio or wherever you get your podcasts.

The transcript has been lightly edited for length and clarity.

David Wallace-Wells: I’m David Wallace-Wells. I’m a writer for New York Times Opinion and a columnist for The Times Magazine.

Natasha Sarin: And I’m Natasha Sarin. I’m a contributor to Times Opinion, a law professor and an economist at Yale Law School and the founder of the Budget Lab.

Wallace-Wells: We’re here today to talk about something very, very big that just happened in the A.I. economy.

We’re talking about Situational Awareness, a hedge fund run by a guy named Leopold Aschenbrenner, which made a huge bet on the future of A.I.

Sarin: Fantastic name. Leopold Aschenbrenner, 24-year-old with no actual finance background who ended up running one of the most significant A.I. hedge funds in the country and then watched it almost collapse.

Wallace-Wells: I first became aware of this guy because of an essay he wrote and ——

Sarin: Called “Situational Awareness.”

Wallace-Wells: The hedge fund kind of grew out of a blog post, which is a remarkable thing, given that he also ended up raising tons of money to start this hedge fund.

Sarin: Tons of money from lots of names that we know, like Goldman Sachs and JPMorgan, right?

Wallace-Wells: And all these people were reading this blog post, this essay, and thinking: The person who wrote this has unique insight into the future of the A.I. economy, such that we’re going to entrust huge amounts of money to his care.

What was in that essay? What did it say about A.I.?

Sarin: “Situational Awareness” seems quite prescient. It was written in 2024, and it kind of predicted that we would be at a moment when, first of all — by 2027, he thought — very close to artificial general intelligence, or the idea that we are going to have some sort of superintelligence in these models. He also thought and kind of understood before many did that in order to get from where we were in 2024 to that moment, you were going to need massive capital investments in things like data centers and that was really going to be imperative to power this boom.

And we have seen exactly that since then. And the nature of the hedge fund’s bet, once he eventually started Situational Awareness, was about understanding that he was essentially long A.I. — making a lot of investments in A.I., in the types of capital expenditures that are likely to profit as we’re powering this new technological revolution. And by that I mean things like heavy infrastructure of data center build-out, power investment and the like.

Wallace-Wells: And the way that people talk about this is, they use the term “capex.”

Sarin: Correct. And the nature of the hedge fund’s bet, once he eventually started Situational Awareness, was about understanding that he was essentially long A.I., so making a lot of investments in the types of things that are likely to either profit as we are building out A.I. capital expenditure, or ultimately profit as we are deploying this technology, and short companies like Adobe, where you are worried that the nature of enterprise software is going to be fundamentally disrupted by the fact that artificial intelligence is here.

What happened at the hedge fund — it’s actually interesting to try to understand whether it’s really a dramatic collapse of recent. Is it telling us something about A.I., or is it telling us a tale as old as time, with respect to how hedge funds like this collapse?

Wallace-Wells: Well, my view is that it’s both, right?

Wallace-Wells: He was incredibly overleveraged.

Sarin: Four times leveraged, right? For every dollar that he raised from investors, he borrowed four times that from public markets and from private markets.

Wallace-Wells: And that meant that he was really exposed to any short-term fluctuations in these patterns that he was projecting, so when there were such fluctuations, he was in a really tight spot and ended up having to sell, depending on the reporting, almost all or all of his public portfolio in order to cover himself in relatively short order. Also, this happened, like, three days before his wedding.

Wallace-Wells: Extra drama. The fact that he’s 24 years old.

Sarin: Wedding in Carmel, I think, to the chief of staff at Anthropic. So it’s all this, like, tangled web of really interesting things.

Wallace-Wells: And incredibly rich people. And it’s a kind of an old Wall Street story, especially when you think that he is this young gun who had come in — was not that long ago being talked about as one of the great success stories of the recent hedge fund world.

Sarin: Yeah, 1,000 percent returns, you know? And what’s interesting about it is that in some sense, he might very well end up being right. And what I mean by that is, it very well might be true, and in fact, we are watching and have been talking about and will continue to talk about these massive artificial intelligence expenditures — the idea that you’re going to start to see productivity gains from automation of certain types of tasks and that it might very well disrupt legacy software.

The problem is — and this is, again, why I say, “tale as old as time” — there’s a quote that’s attributed to the economist John Maynard Keynes that says, “The markets can remain irrational longer than you can stay solvent.”

What ultimately happened here is that the same banks that were happy to lend him money on the way up and say, “Keep making those trades” and “They’re so profitable. That’s great,” immediately, as it started to look a little shaky — as it started to look like the banks themselves were going to lose money — they made what is called a margin call, where they essentially said: Either you have to give us cash right now in order to protect these positions or you have to be in a situation where you start to liquidate or sell your assets in order to be able to hand us dollars.

And that creates this perpetuating cycle on the way down, right? Because if you sell the stuff, well, then it actually pushes the price further down, such that you have to sell more of it, and that’s ultimately what happened. And when Aschenbrenner described this, he said it was like a traditional bank run type of dynamic and was caused by leverage. We’ve seen this story before. We saw it in Long Term Capital Management in the late ’90s. That was kind of a harbinger of a financial crisis, which is what people are worried about right now.

But we shouldn’t mistake the fact that this hedge fund was overlevered and many others might be that are making these trades. What do we know right now about the fundamentals of artificial intelligence, and how has that changed over the course of the last few months?

Natasha SarinOpinion contributing writerThere’s been no shortage of talk about an AI bubble. But recently we saw more than talk: A $45 billion hedge fund that bet big on chips, data centers, and the potential of AI combusted. I’ve been thinking a lot about what, if anything, this blowup tells us about risks in AI more generally. I suspect there are important lessons to be learned about the possibility of broader disruptions — and what to do about them.

There’s been no shortage of talk about an AI bubble. But recently we saw more than talk: A $45 billion hedge fund that bet big on chips, data centers, and the potential of AI combusted. I’ve been thinking a lot about what, if anything, this blowup tells us about risks in AI more generally. I suspect there are important lessons to be learned about the possibility of broader disruptions — and what to do about them.

Wallace-Wells: Well, the thing that I would say, the reason that I think that it does tell us something about those dynamics — which is not to say that everybody’s going to go bust or that we’re heading for an immediate crash. But the reason that this does raise some serious questions for me is that the story that Aschenbrenner was telling in “Situational Awareness” matches the story that all of the A.I. companies have been telling all of their investors and all Americans for several years.

And in broad strokes — you summarized it, but I just want to give a compressed version — the story here is that A.I. is completely transformative. It’s getting much better, much faster than anyone understands or appreciates. That means that very soon we’re going to see a dramatic takeoff in capability, and beyond that point, the economy will be so transformed that the first companies to cross that finish line are going to be reaping immense profits of a scale like we have never seen before.

And when investors hear that, they get excited. When Americans hear that, they may get scared about what it means for their jobs, etc.

But it’s basically a story of such overwhelming narrative propulsion that all the little considerations — the question of leverage, the question of whether this is going to happen in nine months or 10 months or 12 months or 15 months — all of those things seem kind of secondary.

And here we had someone who made an enormous bet not just that A.I. is going to be a big deal but that it was going to be such a big deal that none of the conventional guardrails were necessary. And that, to me, is a big observation, because two years ago, three years ago, A.I. boosters were often telling some version of this story, and we’re now in a place where I hear many more people and read many more people raising questions about those little things.

Raising........

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