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.
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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?
Sarin: Yeah.
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.
Sarin: Yes.
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?
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 questions about exactly how much profit has to come in to justify the capex. Raising questions about exactly what it means that they’re getting pressured from China. And on some level, at a narrative level, it looks to me like this marks or punctuates a kind of reset where we’re now talking about the A.I. economy — who’s going to win, who’s going to navigate that bumpy road, how to allocate resources and capital, how to manage political challenges, and that’s a very real-world landscape.
Sarin: Yeah.
Wallace-Wells: Which is very different from the whiteboard in a conference room, the “We’re drawing a line on a board and saying that’s where we’re going to take off.” That’s at the narrative level. We’ve kind of left behind the big story that A.I. was selling us for several years, and we’re now trying to figure out where we are. Where are we?
Sarin: First of all, the last time we were here, we were talking about SpaceX and its valuation and its public offering. And since that moment, just very recently, SpaceX announced earnings, and it announced giant losses on its A.I. business — such that the stock came tumbling down and that valuation is somewhere like 50 percent of where it was when we were first having our conversation.
There are very fundamental questions about what A.I. is going to do to the economy writ large. What is it going to do to our capacity to work? What is it going to do to the labor market? Is it going to displace jobs? Is it going to make firms more productive? That’s one set of issues.
There’s another set of issues, which feels almost both more urgent and more complex to me: Even if you accept that A.I. is going to be transformational, it already has been transformational in lots of ways. If you look at these leading labs — and you are getting evidence right now in all different directions — you just heard recently that OpenAI and Anthropic are hitting these huge revenue targets, even exceeding them, that they had set for themselves, which means people are handing them dollars.
Wallace-Wells: Anthropic especially.
Sarin: Anthropic especially. People are handing them dollars in order to get access to Claude code. OK, that sounds really good.
But on the flip side, you’re also hearing about the fact that Chinese open-source models — which, by the way, do not require you to pay those dollars in order to get access to them — are likely to come in and compete away the capacity and the fundamental business model of these leading labs.
Wallace-Wells: And even the leading labs are now openly saying they need to compete on price, as opposed to quality — which is a sign that this is a huge threat to them.
Sarin: That they understand that it’s a huge threat to them and to their fundamental business model. But the valuations that they have, where they’ve been drawing in dollars from investors, have baked into them the idea that they are going to be the winners of this technology and their business models are going to stand.
And if that’s not true, I just wonder what that means for the economy. And again, I’m not saying that this is going to be the case; I’m just saying this is one of many plausible scenarios. Imagine you’re in a world where open source has competed away their business models and Anthropic isn’t worth that much anymore and OpenAI isn’t worth that much anymore.
Doesn’t that fundamentally cause a real decline in confidence in the American economy? And isn’t that going to have the type of systemic consequences that many who are wondering if we are in an A.I. bubble — are we not? You actually start to get those, even if A.I. in general is going to produce massive productivity gains and massive profits. But if those labs fail, what happens feels like a really fundamental question.
Wallace-Wells: Yeah, I think many Americans would also have big questions about how we found ourselves in a political economy that allocated so much capital to this project.
Sarin: And relatedly — and we were talking a little bit about leverage — you have these large technology companies that are called hyperscalers, that are really building up the data center infrastructure that these labs, agents and models are deploying. You have them, for the first time — for years, they have been sitting on piles of cash. Now you’re in a situation where they are taking on tons of debt in order to finance exactly these investments, and it’s sort of ——
Wallace-Wells: And there’s even some debt that’s off the books, right? That we don’t see.
Sarin: In these special purpose vehicles. That, again, starts to hark back to the financial crisis.
Wallace-Wells: That’s not a sign of economic health, to have huge amounts of off-book debt, right?
Sarin: I’m going to make the arguments both ways, in that off-book debt sounds bad, a harbinger of financial crises. Flip side, debt only loses value once equity — so the investors, who have handed dollars, are wiped out. So you have to think that there is a fundamental threat to the business model of Google in order to be superconcerned about this leverage building up.
And I am less convinced about that.
Wallace-Wells: Let’s think about this a little systematically, right? We’re talking about the risk that A.I. is overvalued in a sort of systemic way. Maybe that could lead to something like a bubble popping, maybe just a lesser correction, but some turbulence ahead. And when we think about that risk — I see the revenue for Anthropic, and to some lesser extent OpenAI, arguing that things are pretty good actually, that this is a good gravy train to be on as an economy, as a whole.
And then, on the other side, there’s a lot of stuff happening that suggests some concern. And I wonder if you could walk us through those worries. When you’re thinking about the risk that we’re heading toward — some adjustment, negative adjustment — what are the things that you’re focused on? What do you point to as signs of concern?
Sarin: The first we’ve started to touch on already, which is that it is true that OpenAI and Anthropic, especially, had banger Julys. They way exceeded their revenue expectations, even for themselves, which were quite high. And so they feel like they’re answering the question “How are we going to generate profits that justify these sky-high valuations?” by saying, “Look at the data. We, in fact, are generating those profits and then some.”
But I think this question, about how competitive this industry ends up being — and already is, frankly, with respect to not just Chinese open source models but open source models writ large — and the idea that, actually, you’re going to be quite content with a slightly less good model that you can get and then monetize, if you’re a firm, and deploy in a much cheaper way is ultimately going to win the day.
But getting from here, where these labs and these valuations and these dollars have been invested in such a dramatic way, to there, where they are not the main players, is going to cause some market disruption.
I think the second big risk, if you look at how profits are being allocated in the economy at the moment — the people who are doing super, super well are Nvidia. They are the ones making the infrastructure that powers this A.I. boom. They’re making the chips, the ones who are making the data centers. That’s great. They are actually doing better than the types of firms that are the deployers of this technology. And by the way, a lot more of it is being debt-financed now than it was before.
And that raises another concern, which is, if these bets don’t quite play out — if it turns out the data centers take longer to get online, if it turns out that actually we’ve overbuilt and we have too much capacity, if it turns out that there are political and regulatory barriers that we haven’t quite yet imagined — in those situations, you’re going to start to be in a dynamic where you very well might have dollars that have been borrowed that can’t quite be paid back, and that starts to get concerns about some sort of systemic crisis in the economy that spreads more generally. It starts to invoke those types of concerns.
Wallace-Wells: I wanted to drill down on two particular points: The first is about the arrival of open source threats to the frontier labs. And that is significant on a bunch of different fronts, some of which we’ve talked about, but one of them is the political economy part of it.
Now, if I think about why a lot of people in Silicon Valley are signing onto an open letter to say that we shouldn’t fight open source — that we shouldn’t fight Chinese models, that we should let them in — some of that is naturally just the competitive instinct of companies that are not Anthropic, which has a closed model and is doing extremely well.
But at the level of ideology and shaping the medium-term future of the economy, if it is the case or becomes the case that the Trump administration overall, the A.I. industry as a sector, if we see much more openness to this tech, where does that lead us?
I mean, we’ve been talking for years about this as an existential, Cold War-level threat — our race with China. And now we’re like: Maybe we should just let them in.
Sarin: There are many parts of your question that feel superimportant to me. One is that we haven’t really talked much about the nature of the security threats that A.I. potentially poses, and those have different flavors.
One, with respect to Chinese open source models. And there’s another type of security threat that’s also superimportant — and you’re also starting to see real fractures in — which is: We don’t quite have full control over what these models are actually doing. You’re watching these models fundamentally break out of enclosures that have been set for them by humans and do things and operate in ways that they’ve been explicitly told not to do.
And you know, there are real concerns about, like: Are these models building the capacity to build biochemical weapons? And bombs?
Wallace-Wells: Yeah, it’s like we used to talk about that, those risks, so much.
Sarin: So much. And they were ——
Wallace-Wells: The existential risks, the bioterrorists.
Sarin: They were at the forefront of the conversation, and they should, in fact, be at the forefront of the conversation. And part of what I find so interesting is that — and maybe this is somewhat hopeful about Chinese open source — if you talk to and hear from a lot of technologists in Silicon Valley, and you and I talk to a lot of them, they will tell you that there needs to be some globally coordinated regulatory framework because of these risks.
Wallace-Wells: Well, sometimes they’ll say that.
Sarin: Some of them. OK, sometimes they’ll say that.
Wallace-Wells: Sometimes they’ll say, “Don’t touch us.”
Sarin: And some of them will say that.
Wallace-Wells: Yeah.
Sarin: But it’s always struck me as kind of nuts, because we know how the political system works in the United States, which feels kind of dysfunctional. The idea that we’re going to be able to develop the political capital to do a whole global conversation about A.I. and what structures we should put around it hasn’t really seemed that likely to me.
But if you’re in a situation where the United States and China — and I actually give the Trump administration credit here — they’ve at least announced, ostensibly, a desire to have exactly these conversations about what it looks like to actually try to create some sort of agreement or structures about how the technology should ultimately be deployed and what guardrails should be put on it.
Wallace-Wells: It’s actually been a really interesting story of these 18 months of the Trump administration. They came in. They seemed like they were technological accelerationists. They wanted to rip off all the ——
Sarin: No bounds.
Wallace-Wells: And they’ve made a pretty serious evolution, even just in this year and a half that they’ve been in power, and they’re not on the safetyest end of the A.I. safety spectrum ——
Sarin: No, but they’re grappling with these questions. And I actually do think that there is some capacity. I mean, in some sense, we are all bringing to this our own interests, but China’s an authoritarian government that must also be concerned about the idea of technology developing capacities that it’s not able to control.
In some sense, a hopeful story is that there is some sort of motivation to come to the table, in a way, to deal with exactly these types of real existential concerns. And open source might actually give you an entry point and a necessary entry point into those discussions.
Wallace-Wells: One aspect of the China-U.S. contrast that’s always been interesting to me is that the U.S. is spending much more money here than any other country in the world in the build-out. But we’re also a country that’s really anxious about A.I. In China, they’re kind of in second place: The country’s much less anxious, they’re ——
Sarin: Jazzed about it.
Wallace-Wells: And there are a lot of things going into that, but one of them, I’ve always thought, is that they trust that their government is capable of taking control of their economy. And here in America, we basically ——
Sarin: We do not have that.
Wallace-Wells: But that raises the last thing I want to ask you, before we move on to the bleak part of the conversation ——
Sarin: Oh, good. We’re getting bleaker.
Wallace-Wells: The last question I wanted to ask you is about the political situation in the U.S. This has been something that I’ve been following, writing about now for a while, and it still astonishes me: Almost every month, there’s a new poll showing the incredible resistance to data center build-out in particular.
Sarin: Yeah, A.I. is less popular than ICE. And the booing of the grad speakers and all of this.
Wallace-Wells: I mean, I would go even further. We now have real policy implications here. In New York there’s a moratorium on data centers, and just this week they announced that in Texas, they’re putting a pause on attaching new data centers to the grid. Which is especially striking to me, because when I started thinking about, reporting on, writing about data center backlash, I would have said to you, “One of the things that’s driving this is that people are seeing an oligarchy building a new economy over which they have no democratic control.”
And they are expressing that anger in these town halls. But at core, it’s about the fact that the future is being built without their consent. And what we’ve watched, over the last few months, is actually an incredibly, on some level, inspiring populist backlash.
I have questions about exactly what the agenda there is. I think people are confused about some of the environmental issues. But nevertheless, we have seen a significant uprising against this technology and the economic future that it promises, despite the fact that not that long ago, many of us — maybe even you and I — were looking at it being like, “I don’t know if anybody’s going to be able to take control of this.”
And as a result, we now have, in state after state and community after community, really meaningful obstacles being put up to the build-out. Now, I don’t know how that looks heading into the midterms. I don’t know how it looks heading out of the midterms. It seems to me that one of the key stories in Abdul El-Sayed’s success is resistance to data centers. Certainly, it seems to be a big part of Francesca Hong’s apparent success in Wisconsin. We’re seeing politics being shaped by public resistance to data centers, and that is a big question mark.
But if they are actually able to take command of the levers of power, sufficient to block that development, that is a big, big challenge for the subject of this conversation, which is the medium-term future of the A.I. sector. How do you see that?
Sarin: I’m going to say two things: One about what this means for the labs and the other about what this means for the political project of A.I. in the United States — and why I am a little bit more worried than you about the data center moratorium enthusiasm.
If you go back to this conversation we were just having, about China and the U.S. and the nature of the importance of winning this A.I. race, which has categorized a lot of the conversations that people have been having about A.I. over the course of the last many years. The reason it is actually fundamentally good for the U.S. economy is that we are at the leading edge of this technological revolution. The investment, the development of the models that are driving A.I.’s growth, is happening here. The dollars are being deployed here. These are fundamentally good things.
Wallace-Wells: And a lot of the industrial capacity is being actually built here. And there are people who say that the models don’t even really matter; what really matters is just how much compute we’re building.
Sarin: And by the way, we might actually — regarding some of the environmental things — as we build out the power grid to support those investments, you might actually see in the medium-to-long term decreasing electricity costs because you’re building out capacity. And all of that is a good economic story.
I have been struck by the fact that — if you try to understand why it is the case that you have young people booing down commencement speakers who bring up A.I. or some of these polls that show that A.I. is less popular than pick your unpopular thing — I actually think there is a fundamental communications problem that exists among many of the leaders of these institutions and of this technological change, in that they have been saying things like, “A.I. is going to displace 50 percent of the work that you college graduates are doing.”
Who is going to be in favor of that thing? If, instead, they had been saying, “A.I. is going to deliver all of this growth. It’s going to deliver all of these profits. It’s going to fundamentally change the way that we think — and for the better — just like the internet changed the way that we live for the better,” that could be a message that people could buy into. It’s just not the message that’s been delivered.
Wallace-Wells: I think some of them are saying that. I mean, Demis Hassabis — who just stepped down from Google as part of this turmoil — said he thinks that the A.I. revolution is going to be 10 times as significant as the Industrial Revolution at 10 times the speed. Somebody ran the numbers and was like: This suggests 50 percent G.D.P. growth year on year.
Sarin: Which is nuts, right?
Wallace-Wells: Well, that’s what I mean. When they tell you that, you’re like, “That’s ridiculous.” And when they tell you, “We’re going to use this technology to find ways to downsize the work force,” you’re like, “That sounds credible, coming from billionaires.” Now, it’s not to say that what they’re saying — that we’re going to lose 50 percent of white-collar work — is all that plausible.
I’m personally skeptical. But if I put myself in the shoes of an open-minded, engaged normie American and I think, “Here’s this guy who’s telling me that A.I. is so great, it’s going to grow at 50 percent per year. And here’s this guy who’s telling me he’s going to use it to find a way to fire people,” I’m like, “I believe the guy who’s telling me he’s going to use it to fire people.”
Sarin: I agree with that. But I guess what I’m trying to say is that it’s pretty important, from the perspective of, like, how does A.I. realize the profits that are baked into these valuations that we started talking about, and when does the market correct?
The political economy question is superimportant. Because if this resistance builds — and you’ve seen versions of not just the data center moratoriums, but you’ve seen politicians propose things to essentially try to put a stop to the technology, whatever that means — if that enthusiasm builds, then we are not going to be able to reap the economic gains that A.I. promises, because this isn’t a closed economy.
This isn’t like the rest of the world isn’t going to move or China isn’t going to move; it is that we are going too slow and will no longer be at the forefront of all of the benefits that this technology can help us realize.
And what I think the fundamental challenge, for the political project right now, is articulating those benefits in a way that feels tangible to people — the potential job loss feels tangible to them — but also ensuring that we have a government in place that is capable of building guardrails, that’s capable of actually providing unemployment insurance and worker training. And no one trusts that we have that, which is why we are in this situation. I speculate that is why we are in a situation of seeing this much resistance to something that fundamentally we should be cheering as this great technological progress.
Wallace-Wells: I think it’s the reason that I’m skeptical that resistance will decline. I mean, I could be wrong. I don’t have a crystal ball — I’m going to ask you in a minute to look into your crystal ball — but what I think seems likely is that this becomes a bigger obstacle, imposing more costs and making the build-out of A.I. infrastructure slower. Which, at a local level, will probably make some people happy but at a systemic level is going to introduce an additional layer of risk.
Sarin: And then there’s a cyclical thing that could happen, right? Kind of like circular financing, where that resistance somehow impedes the build-out, such that you’re then in a situation where some of these investments start to sour, such that you’re then in a situation where the market starts to correct because of exactly those risks. But then that amplifies, because then the technology, which we don’t even like, is also leading to this negative economic impact that we definitely don’t like and potentially a downturn or a recession. So it all has this kind of self-amplifying effect that feels important.
But one of the things you said that I will be paying a lot of attention to is: What shape does this type of political push actually ultimately take? How real are these moratoriums, and what types of pathways are there to work around them? And ultimately, what capacity do we really have to constrain technology and technological progress, even if many would want to? That feels like an important question that I just don’t know the answer to right now.
Wallace-Wells: OK, so we’ll pull back from these unknowable questions about politics and focus on the incredibly knowable stuff about how the market is going to evolve and how we can all make a killing. If we’re thinking about the possibility of a market correction here — some meaningful change in the basic fate and valuation of these companies and the sector as a whole — what should we be looking for? How will that play out? If we’re imagining a scenario in which the problems that we’ve identified are serious and are really getting in the way of the economic promise of A.I. in the medium term, where are we going to see that show up, and how will it shake out for the rest of us?
Sarin: I think you should be looking at a couple of things: One of them is boring, in that you should be looking at solutions to a basic physical problem. Are we able to build power plants and plug in these computers at the speed that has been promised? And again, we’ll be able to, in real time, see the extent to which that is true.
And once you start seeing some of those targets miss and some of those delays occur, you should be a little nervous. The other thing is — and this is why I sort of started by talking about OpenAI and Anthropic in July looking very good from a revenue perspective — you have to start to look at the question of what types of revenue gains are you seeing and what types of revenue gains are you not seeing that lead you to some concern about the nature of whether there, in fact, is mispricing or overhyped asset valuations.
Wallace-Wells: It’s telling that you’re even saying revenue is not profits, right?
Sarin: Yeah, totally. Absolutely. And I think that we are in a situation where, for me, one of the bigger questions has been — and continues to be, but with more urgency now than we last spoke — whether the fundamental business model of a lot of these biggest labs kind of works and if it doesn’t.
In some sense, I speculate, I guess where I’m belying my biases as I talk to you, in that I actually think that we are very likely over the medium term, long term — whatever you want to call it — to see pretty significant economic growth on the heels of this technology. I think it is less clear who the ultimate winners and losers in the economy are likely to be as a result of that growth and whether at the end of all this some of the names that we talk about all the time are going to be the most significant players, particularly in a world where open source feels as important as it does.
And that strikes me as a place that will ultimately cause the market to correct, because they’re so important and so significant at the moment. And if they start to look less significant, those valuations just cannot be justified.
Wallace-Wells: It’s another way in which the story that I was telling at the top of our conversation, about the narrative reset, seems to hold.
It seems possible that in the relatively near future, Anthropic and OpenAI are not going to be as central to the way that we’re thinking about these things as has been the case for the last couple of years. But it also makes me wonder if we’re expecting something like that to play out, where A.I. is here to stay — it’s significant, meaningfully driving economic growth — but we see the decline or even collapse of a couple of these massively valued companies. What does that look like to the average American? What are the ripple effects to people who own stock but also just people who are looking at the unemployment rate and G.D.P. growth?
Sarin: A couple of things: One is, I wish I had a satisfactory answer for you, in that these are exactly the effects. But fundamentally, we have no idea.
And in part, we have no idea because of some of the things that we’ve been talking about — Where do the dollars sit that are financing these investments as these firms go south? Who loses, who wins? — we don’t really know the answer to those questions. But something that should give you a little bit of comfort is that in some sense, if you think about what happens when a firm fails, the people who stand to lose the most are the shareholders of that firm, who are going to watch its value deplete.
In some sense, that’s like, we’re worried about Elon Musk losing a lot as SpaceX’s valuation has declined. It’s true that we all have exposure to these companies in our portfolios, because of the nature of the fact that, especially as they go public, they’re listed on these indexes — but even as they’re private, by the way, our pension dollars, our insurance dollars are invested in them.
But whether that actually translates into the kind of systemic financial collapse that you get — when you’ve heard of some of these bubbles popping in the past or things like the Great Recession — there are a lot of reasons to be a little less concerned that this is likely to play out this time around, just because of the fact that we’ve talked about, which is that a lot of the investments are being driven by large technology companies that just feel fundamentally different and feel, fundamentally, on some level, more likely to be able to sustain their business models than maybe some of the labs whose work they’re ultimately funding.
Wallace-Wells: It’s like the self-dealing of these companies is actually a safeguard against it punishing us.
Sarin: Yeah, on some level.
Wallace-Wells: There’s also, I think, a possible cultural fallout — which is to say, if there’s a dramatic change in the A.I. outlook, even if it doesn’t produce a huge market correction at the level of 2008 or 1999 or whatever — people will still think, “What was that?” Like, those five years when all of our cultural capital, all of our literal capital was being dumped in this bucket.
Sarin: And these people and these characters that are driving these stories — it’s so weird, right?
Wallace-Wells: I think it could produce a significant cultural backlash, not unlike what happened after 2008, even if the consequences aren’t that bad, because people will just say: Why were we told that this was the future? Why did so many people bet on it when we could have been investing in other ways?
Now, like you, I have complicated feelings about this whole landscape and where it’s heading. There are probably meaningful benefits that are coming our way from this technology. I don’t want to sound like a Luddite on it or even a populist, but I do — taking the sense of the way the wind is blowing, it feels likely that even in a relatively good draw here, we still may be producing some meaningful public resentment and backlash.
Sarin: And you already are. And in fact, your view that we should have been directing all these dollars in these places, I mean, that’s very consistent with what happened in the dot-com bubble, right? Where it was that we underinvested in certain industries and sectors because dollars were flowing into Pets.com.
It’s not just that people will feel that way maybe; it might very well be that those are real and legitimate concerns about the allocation of resources in our economy. And it’s part of what really worries me about this moment. I think it’s going to ultimately be very hard to run the counterfactual and say: What would this time have looked like if we thought about the world slightly differently or ——
Wallace-Wells: If we had done a Green New Deal?
Sarin: Yeah. Or these characters in Silicon Valley weren’t commanding so much of our attention in so many of the podcasts that we’re doing. It seems striking to me, and it seems like a pretty weird time.
Wallace-Wells: Well, I think that’s a good place to leave it. Natasha Sarin, thanks so much for talking. It’s been great.
Sarin: Thanks so much for having me.
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This episode of “The Opinions” was produced by Derek Arthur, Vishakha Darbha and Rochelle Widdowson. It was edited by Kaari Pitkin. Mixing and original music by Isaac Jones. Video editing by Kristen Williamson. The postproduction manager is Mike Puretz. Fact-checking by Kim Freda and Kate Sinclair. Audience strategy by Shannon Busta and Kristina Samulewski. The director of Opinion Video is Jonah M. Kessel. The deputy director of Opinion Shows is Alison Bruzek. The director of Opinion Shows is Annie-Rose Strasser.
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