The American stock market is booming, thanks to artificial intelligence. Tech giants are borrowing billions to acquire AI talent, purchase chips and hardware, and construct data centers. And market watchers are starting to get worried. They see financiers bulldozing giant piles of money to private AI start-ups with no realistic path to profitability, tech companies reliant on other tech companies for revenue growth, and non-tech businesses without a lot to show for their AI investments. The value of AI-linked firms has climbed $27 trillion in the past three years—an astonishing amount, equivalent to 36 percent of the value of the entire U.S. stock market today. Although future earnings could justify those valuations, as Dominic Wilson and Vickie Chang of Goldman Sachs argued in a note to clients, the profit expectations require Panglossian optimism.

No less an authority than Sam Altman is arguing that we are in an AI bubble. The International Monetary Fund is citing it as a significant risk to financial stability and warning about what might happen when it bursts: diminished investment, tighter credit, reduced consumption, disrupted trade flows.

That’s pretty much what happens when any bubble pops, as a Dutch tulip obsessive could have told you in 1637 or a bitcoin evangelist could have told you in 2011, 2013, 2014, 2018, or 2022. Yet the AI bubble is no ordinary bubble. Hyper-rich corporations are stoking it, rather than kitchen-table investors. They’re blowing it up when credit is fairly expensive, not dirt cheap. That might make the bubble less fragile and longer lasting than those of the past. But it won’t make it any less painful when it pops.

The dot-com bubble of the late 1990s and the housing bubble of the late aughts were remarkably broad-based compared with the AI bubble today. Uncle Ted got a hulking desktop computer, opened a newfangled E*TRADE account, and started day-trading shares in Apple and Pets.com. The share of American households owning equities climbed 13 percentage points from 1995 to 2001, during which time more than 2,752 firms went public (far more than the 730 operating businesses that have IPOed in the last six years). A half decade later, Aunt Linda bought a condo with nothing down, flipped it, and mortgaged three new investment properties in the Phoenix suburbs, sight unseen. The homeownership rate rose 5 percentage points as the housing bubble inflated; 40 percent of mortgages issued at its height went to investment or vacation properties.

In both cases, regular people were staking their savings on what seemed like a winning bet: the internet changing everything, housing prices never going down. In both cases, cheap credit fueled the irrational exuberance. Low interest rates let venture capitalists fund nonsense web businesses and let banks provide junk loans to borrowers with terrible credit. In both cases, rising interest rates popped the bubble.

Today, Uncle Ted and Aunt Linda aren’t really getting in on the AI frenzy. The share of Americans who own stocks has held steady. Household debt has grown, but it has fallen relative to disposable income and GDP. Everybody seems to know someone who was personally burned by the dot-com collapse and the housing crisis. How many people know someone who’s staking it all on OpenAI and Anthropic today? (The companies aren’t public, after all.) Indeed, how many people know someone whose livelihood has been directly affected by the AI frenzy at all?

The insularity of the AI bubble isn’t the only thing that makes it unusual, and we should probably think about it as two overlapping bubbles instead of just one. AI is driving tremendous spending on capital expenditures—physical infrastructure, software development. And it is driving a tremendous run-up in company valuations.

Digital innovations don’t tend to require a ton of labor or heavy equipment. The companies offering them tend to be capital-light, like marketers and insurers, rather than capital-intensive, like airlines and hotel chains. But AI is different. A start-up might need thousands of times as much computing power to train an algorithm as it would need to develop a conventional software product. To get that power, Silicon Valley is building 1,500 data centers in the United States and counting, and purchasing a mammoth quantity of semiconductor chips. (The Wall Street Journal calls these chips “the 21st century’s most important market.”) Amazon, Microsoft, Alphabet, and Meta alone are spending more than $700 billion on the build-out this year. AI-infrastructure investment is responsible for essentially all American GDP growth at the moment. Without it, in other words, we might be in a recession.

The boom is lifting the value of farmland, driving up the cost of construction employment, and flushing huge sums to water and electrical utilities. Still, tech companies are the main beneficiaries: NVIDIA is selling chips to Meta; Amazon is selling cloud-computing capacity to OpenAI. This surge in revenue, and the excitement about the money to be made when AI starts bolstering productivity and profits, is driving up valuations. The Magnificent Seven—Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla—now account for one-third of the value of the Standard & Poor’s 500. OpenAI is worth more than Eli Lilly, JPMorgan Chase, Visa, Costco, Exxon Mobil, Wells Fargo, CVS Health, McDonald’s, and Boeing.

Tech companies are going to need to start generating huge revenues and huge profits to justify these valuations. OpenAI needs to spin up roughly $100 billion in free cash flow by 2030, according to calculations by Harrison Rolfes of PitchBook. Analysts expect it will lose $10 billion to $30 billion that year. If it does—or if more communities ban data centers, or if non-tech companies prove reticent about purchasing AI software, or if China develops AI models that do not require so much computing power—we could be in for a massive correction. The AI economy is a trillion-dollar ouroboros of buying and selling, investment and equity staking, all happening between San Francisco and San Jose. Big Tech is advancing money to AI start-ups to buy cloud services from Big Tech, which is using the revenue to run new AI models … you get the idea. What happens to one firm could happen to all of them.

Tech companies are also going to need to start generating even bigger revenues and profits to pay off their debts. In the early days of AI, venture capitalists, wealthy individuals, and Big Tech firms used hard cash to invest in the new technology. But the build-out has proven so expensive that Silicon Valley has turned to corporate bonds and private credit, meaning loans made by entities other than banks. Thus, though the AI boom has happened while interest rates are fairly high, it still involves a lot of leverage. The deals are complicated, opaque, and structured so as to be invisible on traditional balance sheets, making the “ultimate distribution of risk less transparent,” Stijn Van Nieuwerburgh of Columbia Business School has found. Already, lenders are getting queasy—“buy-side indigestion,” as Morningstar describes the market’s recent reticence to meet Silicon Valley’s demands.

When the bubble bursts, Uncle Ted and Aunt Linda will be affected, even if they were never the ones stoking the frenzy. Their retirement plans and pensions are invested in Silicon Valley stocks that could drop; their small businesses are reliant on access to credit that might get throttled. But, hey, there’s always the possibility that AI might steal their job before any of that happens.