The potential impact of an AI crash
A crash could occur in two primary ways, each with a potentially "epic" economic impact.
A. The AI bubble bursts
This scenario is similar to the dot-com bust of the early 2000s, but with potentially more severe consequences.
Stock market collapse: The concentrated stock market power of AI-related companies means a bubble bursting could trigger a broad stock market crash and wipe out trillions of dollars in household wealth.
Sudden stop of stimulus: If the bubble bursts, the massive spending on AI infrastructure would abruptly halt. This would remove a key source of economic growth and could plunge the country into a recession.
Credit crisis: The expansion of the AI industry is heavily funded by debt. A market crash could trigger a wave of defaults, leading to a financial crisis.
B. AI is "too successful" and causes mass job displacement
This outcome involves the actual technology succeeding but creating a different kind of economic disaster.
Consumer spending collapse: If AI automates millions of white-collar jobs, mass unemployment could cause consumer spending, which makes up about 70% of the U.S. economy, to plummet.
Shift in the workforce: While AI is creating jobs in some areas, pessimists predict it will displace many more, particularly among higher-paid, college-educated workers.
Long-term recession: With less consumer demand, corporate profits would fall, potentially causing a protracted and severe economic downturn.
A more nuanced reality
Most analysts do not believe the U.S. economy is supported only by AI. Instead, they believe the situation is defined by a significant, and potentially risky, overreliance on the AI sector for growth. Experts also highlight that AI could fail for more mundane reasons than a bubble or job displacement:
Governance failures: A lack of standardized governance could erode trust in AI, causing the market for the technology to stall or crash.
Implementation issues: Studies show that many companies are failing to realize a return on their AI investments due to poor implementation, a problem that could cause a more gradual but still damaging correction.
The current economic landscape reflects a high-stakes gamble on the promise of AI. Its potential failure, for various reasons, carries significant risks to the U.S. and global economies.
“If artificial-intelligence models have a hometown, it is probably Ashburn, northern Virginia, just outside Washington, DC. Attentive window-seaters flying into Dulles airport might notice a clutch of white-roofed boxes jutting out next to rows of suburban culs-de-sacs. Those data centres are part of a cluster—the world’s biggest—which last year guzzled more than a quarter of the power produced by Virginia’s main electrical utility.
Fears of a slowdown abound in America, with high interest rates and tariff chaos weighing on most of the economy. But they are doing little to reduce the breakneck pace at which firms are building the infrastructure needed for AI.
Something like a sixth of the 2% rise in American GDP over the past year has come from investments in computer and communications equipment, including chips, and data centres.
Add in the grid upgrades to power AI models, plus the intellectual-property value of the software itself, and one estimate puts the boom’s contribution to GDP growth at 40%. It is an astonishing figure for a sector that accounts for just a few per cent of America’s total GDP.
Tech support
The AI build-out is not a normal investment boom. Until recently, big tech firms paid for most of it from their earnings and cash piles. Now the scale of construction is too great even for these giants, so they are turning to borrowing. They are building data centres in the belief that AI will drive explosive economic growth, and hence demand for computing power, within a matter of years. This is not like building houses or factories. It is a high-reward, winner-takes-all market, in which ordinary concerns such as the cost of borrowing are easy, and tempting, to wave away.
That is just what the big tech firms are doing. In the face of their determination to build AI infrastructure at any cost, higher interest rates offer little deterrence. Neither does the cost of electricity: schemes for gigawatt-scale data centres, demanding as much power as a small city, are increasingly in vogue. Grids across the country are bracing for a squeeze.
If the history of the dotcom boom in the late 1990s is anything to go by, the mania could have much further to run. Then, the roll-out of costly technology required to build the internet continued for many years, with a much sharper impact on GDP than America has experienced so far as a consequence of AI. And the early enthusiasm for AI has probably been even greater than that in the internet’s youth.
For all the Y2K-era excitement, few expected the web to lead to mass automation or unprecedentedly fast economic growth. Both are now fairly mainstream predictions about AI among the Silicon Valley set, even if the incremental-seeming progress of OpenAI’s GPT-5 model has dampened the frenzy a touch.
The trouble is that the very industry powering so much of America’s economic growth is squeezing the rest of its output. Housebuilders, for instance, cannot afford to be blithe about higher borrowing costs. Nor can plenty of regular businesses. Data centres have also constrained the rest of the economy by keeping energy prices high. Average American electricity bills have risen by 7% so far in 2025, at least in part because of the extra strain that data centres have put on the country’s grids.
Sure enough, look beyond AI and much of the economy appears sluggish. Real consumption has flatlined since December. Jobs growth is weak. Housebuilding has slumped, as has business investment in non-AI parts of the economy. Both activities are highly sensitive to interest rates, and so act as bellwethers for output more broadly.
In other words, an economy-wide reallocation is under way: interest-rate- and energy-sensitive sectors are contributing less to growth, while AI investment contributes more. And if it is to continue to do so, big tech firms must continue to increase spending. Any slowdown in capital expenditure—say, if constraints on power or chip availability bite—would mean less support for overall economic growth.
Should that happen, there would be the silver lining that interest rates and energy prices would probably fall, too, which would ease the pressure on the rest of the economy. History carries a warning, though. After the dotcom boom came a ferocious bust. A similar drop in AI investment would remove a sizeable source of America’s growth just as the rest of the economy has begun to look fragile. If desire for data centres cools, it is not just Ashburn that might be in trouble.” [1]
1. Everything’s computer. The Economist; London Vol. 456, Iss. 9462, (Aug 23, 2025): 67, 68.
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