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Things couldn’t be going much better for Nvidia, which is one of the few large companies making serious profits that are primarily and unambiguously attributable to AI. The response from investors, though, was strange. The next morning, the stock popped a few percent but remained below recent highs, and ended the day slightly down. For many analysts and industry watchers, this wasn’t a story about the greatest quarter for the greatest company of all time. It was merely a relief. The “AI trade” was still alive, and the party could continue; more broadly, the anomalous sector propping up economic indicators would, for at least another quarter, and maybe even a bunch of quarters, continue to do so. It was, above all, an assurance and occasion to talk about it. You know. The bubble.

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In late 2025, AI bubble talk isn’t just for outsiders, skeptics, and short-sellers. Increasingly, it’s the frame through which the industry’s most important figures, and biggest boosters, talk about their technology, their companies, and the industry around them. “When bubbles happen, smart people get overexcited about a kernel of truth,” OpenAI’s Sam Altman told a group of reporters in August. “Are we in a phase where investors as a whole are overexcited about AI? My opinion is yes.” Mark Zuckerberg, while suggesting there were “compelling arguments” that AI could be an “outlier,” drew parallels to bubbles past. “I do think there’s definitely a possibility, at least empirically, based on past large infrastructure buildouts and how they led to bubbles, that something like that would happen here,” he said on the ACCESS podcast in September.

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There’s some transparent positioning here, of course — both Altman and Zuckerberg were implying that their companies were unique and would be fine either way — but inside-the-bubble bubble talk has since morphed into an odd strain of conventional wisdom, a premise from which high-level conversations about AI now proceed, or at least a possibility that has to be acknowledged. Google CEO Sundar Pichai invoked the dotcom crash. “I expect AI to be the same. So I think it’s both rational and there are elements of irrationality through a moment like this,” he said this month. In the event of a major correction, he said, “I think no company is going to be immune, including us.” The CEO of Google DeepMind, Demis Hassabis, emphasized Google’s particular strength but conceded on Hard Fork that there are “some parts of the AI industry that are probably in a bubble.” Jeff Bezos has said that while AI is “real,” and “is going to change every industry,” it’s also showing signs of an “industrial bubble.”

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Against the backdrop of all this hedging and narrower speculation about markets, the remaining practitioners of wide-open AI CEO futurism — that is, tech leaders still speaking the way most of them did as recently as last year – suddenly sound like outliers. At the Saudi Investment Forum, onstage with Huang, Elon Musk confidently stated that AI, with humanoid robots, will “eliminate poverty” and “make everyone wealthy.” In the future, he added on X, the “most likely outcome is that AI and robots make everyone wealthy. In fact, far wealthier than the richest person on Earth.” For the last few years, the public has been left to interpret competitively extreme visions of the future floated by strangely cavalier tech executives, who agreed on little but the inevitability of total change: mass unemployment; luxurious post-scarcity; human obsolescence; hyper-accelerated scientific progress; and, perhaps, total annihilation. Now, markets are concerned with narrower questions, with more specific answers, and more immediate consequences: How many GPUs has Nvidia sold? How many can it make? (Or, rather, how many can Taiwan Semiconductor Manufacturing Company manufacture for it?) There are plenty of theories about how generative AI might diffuse into the economy and change the world, and as more people use it, and companies start to deploy it, a few of them are snapping into focus (buy a drink for any young programmers in your life). But after years of boosterish warnings about the extraordinary and esoteric risks posed by mysterious and profound technology — we’re creating software so powerful even we can’t control it — tech executives are instead trying to get out in front of a profound non-technological risk that may be manifesting much sooner: that if they lose even a little bit of momentum, they might end up tanking the American economy.

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If Huang’s everything, everywhere, “all at once” line was a reference to the 2022 absurdist multiverse movie, it’s a funny one: the film opens with its protagonist shuffling through a pile of papers, anxiously preparing for a financial audit (and features a villain who “got bored one day” and decided to collapse the entirety of creation into a bagel-shaped singularity). As the AI boom has sprawled into a larger and more complicated financial story, scrutiny of the businesses behind the models has become as intense as scrutiny of the models themselves. To raise money and finance data center deals, OpenAI, which is both the leading consumer AI company and one of the industry’s most aggressive and, let’s say, inventive dealmakers, has manifested some truly dizzying arrangements, many of which involve Nvidia, a circular deal innovator in its own right. Take CoreWeave, a crypto-mining company that pivoted to AI data centers in 2022. CoreWeave rents access to Nvidia chips to firms that need them for AI inference and training. OpenAI is a CoreWeave customer, but also a Coreweave investor. Nvidia is a CoreWeave vendor — it supplies the GPUs – but also an investor and, somehow, a customer. Coreweave also loses a lot of money, and its stock price has, after peaking in July, collapsed.

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Lately, the deals are getting more brazen and less convoluted. In September, Nvidia announced it would invest $100 billion in OpenAI, which OpenAI said it would use to build data centers full of Nvidia hardware. This month, alongside Microsoft — OpenAI’s biggest early investor and primary partner — Nvidia announced the companies would invest up to $15 billion in OpenAI competitor Anthropic in exchange for a $30 billion commitment from the company to buy computing capacity from Microsoft, powered, naturally, by Nvidia hardware. Altman’s moments of candor about a possible bubble have been scattered between more defensive messaging from the company, which may be losing as much as $12 billion per quarter. In a recent podcast interview, investor Brad Gerstner asked Altman, “How can a company with $13 billion in revenues make $1.4 trillion of spend commitments?” Altman shot back: “If you want to sell your shares, I’ll find you a buyer. Enough.”

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That insiders seem to agree that we could be in a massive bubble is, counterintuitively, not very useful: whether or not they mean it, and whether or not they’re right, their incentives as leaders of mega-scale startups and public tech companies are such that raising, spending, and committing as much money as possible for as long as possible is probably the rational, self-interested choice either way. Anxious, skeptical, or merely satisfied investors looking for excuses to pull back or harvest gains don’t have to look hard, and there’s evidence some are; before its earnings report, Peter Thiel’s investment firm unloaded its position in Nvidia, and Softbank cashed out of the chipmaker at around the same time. Similarly, OpenAI’s ability to send public companies’ stocks soaring by announcing massive “commitments” seems to be fading — Oracle’s recent $300 billion valuation bump, based on some shockingly optimistic guidance it offered investors in September, has since gone negative.

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But focusing on the flagrant circularity of AI financing can feed the impression that the risks are contained within Silicon Valley. The bigger problem is the ways in which they’re already not. If it exists, you might call it a load-bearing bubble. In the first half of 2025, “investment in information processing equipment and software” — a sort of informal, private stimulus package — accounted for 92 percent of GDP growth for the United States, while AI-related tech stocks account for nearly all recent growth in the S&P 500. Early funding for companies like OpenAI came from venture capitalists and incumbent tech giants, while Google and Meta pushed into AI with their own massive revenue and cash, but multi-hundred-billion-dollar commitments mean they’re getting more creative, both in how they raise money and how they distribute risk. Companies like Meta are funding data centers with “special purpose vehicles,” which may sound familiar if you were reading the financial news in 2008, and with massive corporate bond sales. As the investor Paul Kedrosky has argued, the AI boom has traits, at least, of every major financial bubble in modern history: a narrative-driven tech bubble, a credit bubble, a real estate bubble, and an infrastructure bubble. To tie it all together, you’ve got OpenAI’s CFO floating, then frantically backtracking on, the idea of a government backstop for financing AI expansion, almost instantly elevating the prospect of an AI bailout into fodder for conservative and progressive lawmakers.

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Huang has two typical responses to all this. One speaks for itself: look at all those GPUs we’re selling. The other is more direct. “There’s been a lot of talk about an AI bubble. From our vantage point,” he said after earnings, “we see something very different.” In other words: No it’s not. The “virtuous cycle” is just beginning, and the accelerating potential of the most versatile technology the world has ever seen will one day expose complaints about incremental model updates and hand-wringing about data center deals as short-sighted and insignificant. Huang is still able to speak with authority and tell a story that, for investors, still has juice.

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For everyone else, though, neither side of this wildly polarized, high-stakes bet sounds ideal. If this really is a bubble, and it deflates even a little, it could send the American economy into a serious slump, with consequences for almost everyone, getting rid of plenty of jobs the old-fashioned way. If it doesn’t — and Huang’s sanitized visions of mass automation rapidly start to spread across the economy, justifying all that CapEx, and all those strange deals, and then some — well, aren’t we getting laid off anyway?

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