On November 18, 2025, the Dow was down nearly 500 points in morning trading as investors became increasingly concerned about AI stocks and the possibility of a bubble. Image: Spencer Platt/Getty Images
By Jeremy Hsu
Silicon Valley and its backers have placed a trillion-dollar bet on the idea that generative AI can transform the global economy and possibly pave the way for artificial general intelligence, systems that can exceed human capabilities. But multiple warning signs indicate that the marketing hype surrounding these investments has vastly overrated what current AI technology can achieve, creating an AI bubble with growing societal costs that everyone will pay for regardless of when and how the bubble bursts.
The history of AI development has been punctuated by boom-and-bust cycles (with the busts called AI winters) in the 1970s and 1980s. But there has never been an AI bubble like the one that began inflating around corporate and investor expectations since OpenAI released ChatGPT in November 2022. Tech companies are now spending between $72 billion and $125 billion per year each on purchasing vast arrays of AI computing chips and constructing massive data centers that can consume as much electricity as entire cities—and private investors continue to pour more money into the tech industry’s AI pursuits, sometimes at the expense of other sectors of the economy.
“What I see as a labor economist is we have starved everything to feed one mouth,” says Ron Hetrick, Principal Economist at Lightcast, a labor market analytics company. “These are now three years that we have foregone development in so many industries as we shove food into a mouth that’s already so full.”
That huge AI bet is increasingly looking like a bubble; it has buoyed both the stock market and a US economy otherwise struggling with rising unemployment, inflation, and the longest government shutdown in history. In September, Deutsche Bank warned that the United States could already be in an economic recession without the tech industry’s AI spending spree and cautioned that such spending cannot continue indefinitely. However it ends, the AI bubble’s most enduring legacy may be the global disruptions from any financial crisis that follows—and the societal costs already incurred from betting so heavily on energy-hungry data centers and AI chips that may suddenly become stranded assets.
Warning signs. Silicon Valley’s focus on developing ever-larger AI models has spurred a buildout of bigger data centers crammed with computing power. The staggering growth in AI compute demand would require tech companies to build $500 billion worth of data centers packed with chips each year—and companies would need to rake in $2 trillion in combined annual revenue to fund that buildout, according to a Bain & Company report. The report also estimates that the tech industry is likely to fall $800 billion short of the required revenue.
That shortfall is less surprising than it might seem. US Census Bureau data show that AI adoption by companies with more than 250 employees may have already peaked and began declining or flattening out this year. Most businesses still don’t see a significant return on their investment when trying to use the latest generative AI tools: Software company Atlassian found that 96 percent of companies didn’t achieve significant productivity gains, and MIT researchers showed that 95 percent of companies get zero return from their pilot programs with generative AI. Separately, a paper by Nobel laureate Daron Acemoglu, an MIT professor in economics, estimates AI-driven productivity will produce only a “modest increase” in gross domestic product (GDP) of between 1.1 and 1.6 percent over the next 10 years.
Claims that AI can replace human workers on a large scale also appear overblown, or at least premature. When evaluating AI’s impact on employment, the Yale Budget Lab found that the “broader labor market has not experienced a discernible disruption since ChatGPT’s release 33 months ago,” according to the group’s analysis published in October 2025. Coincidentally, the largest tech companies spending billions of dollars on data centers have also led recent private-sector job cuts by laying off tens of thousands of their own workers—but observers such as Hetrick suggest that the tech companies are actually reducing workforces to help pay for their ongoing AI investments.
Another bubble warning sign: Silicon Valley’s accelerating spending spree on data centers and chips has outpaced what even the largest tech companies can afford. Companies such as Amazon, Google, Microsoft, Meta, and Oracle have already spent a record 60 percent of operating cash flow on capital expenditures like data centers and chips as of June 2025.
The financing ouroboros. Now, tech companies are increasingly resorting to “creative finance” such as circular financing deals to continue raising money for data centers and chips, says Andrew Odlyzko, professor emeritus of mathematics at the University of Minnesota, who has studied the history of financial manias and previous bubbles around technologies like railroads. Such creative finance “generates these very complex structures which nobody fully understands until things blow up and then various people are left to pick up the pieces,” he says. “I’m seeing more of that, and that’s what is getting me concerned; this part is typical of bubbles.”
For example, Meta sold $30 billion of corporate bonds in late October and also secured another $30 billion in off-balance-sheet debt through a joint venture structured by Morgan Stanley, arrangements that can hide the risks and liabilities of such deals. The swift accumulation of $100 billion in AI-related debt per quarter among various companies “raises eyebrows for anyone that has seen credit cycles,” said Matthew Mish, head of credit strategy at UBS Investment Bank, in a Bloomberg interview.
Business journalists and analysts have also called attention to the growing number of “circular finance” deals fueling the AI bubble, with one example being AI chipmaker NVIDIA investing $100 billion in OpenAI even as the latter plans to buy more NVIDIA chips. A recent Morgan Stanley presentation showing the messy entanglement of AI-related deals, illustrated by arrows connecting various tech companies, “resembled a plate of spaghetti,” according to the Wall Street Journal.
As a result, a growing number of business leaders and institutions have voiced alarm about the stock market bubble building around AI, including the Bank of England and the International Monetary Fund. Even bullish tech and financial CEOs such as Amazon’s Jeff Bezos, JPMorgan Chase’s Jamie Dimon, Google’s Sundar Pichai, and OpenAI’s Sam Altman have acknowledged the existence of an AI bubble, despite their optimism about the advance of AI generally.
RELATED: AI is polluting truth in journalism. Here’s how to disrupt the misinformation feedback loop.
After the crash. If the stock market craters after a bursting of the AI bubble, it won’t just be financial institutions and venture capitalists losing money. Some 62 percent of Americans who reported owning stocks in 2025, according to a Gallup survey, could also be affected. Another national survey by The BlackRock Foundation and Commonwealth nonprofits also shows that approximately 54 percent of people with incomes between $30,000 and $80,000 have investment accounts. All these people stand to lose much of their investments if the AI bubble pops and market exuberance evaporates. The Economist has highlighted how the top 20 companies on the S&P 500 stock market index currently account for 52 percent of total market value, with the top 10 especially dominated by AI-related companies.
The market mayhem brought on by a deflation of the AI bubble could also mean economic disruption worldwide. Writing for The Economist, Gita Gopinath, former chief economist for the International Monetary Fund, warned that a bursting of the AI bubble on the magnitude of the dot-com bubble collapse in 2000 could have “severe global consequences,” including the wipeout of more than $20 trillion in wealth for American households and $15 trillion in wealth for foreign investors.
Similarly, the International Monetary Fund’s latest World Economic Outlook report described how “an abrupt repricing of tech stocks could be triggered by disappointing results on earnings and productivity gains related to artificial intelligence, marking an end to the AI investment boom and the associated exuberance of financial markets, with the possibility of broader implications for macrofinancial stability.”
If the AI bubble pops, the US government will likely turn to its central bank, the Federal Reserve, to stabilize the wider economy by injecting huge amounts of cash into the financial system, as it did after the 2008 financial crisis, Odlyzko says. But he warned that a new government bailout of the financial system would mean another significant jump in the national debt and increased wealth inequality, because taxpayer dollars would be once again focused on stabilizing a sector in which the wealthiest individuals will benefit disproportionately from recovering corporate profits and rebounding share prices. A repeat of the financial bailout cycle that privatizes the gains of wealthy risk-takers while socializing the losses to everyone else is “likely to lead to even more [political] polarization and perhaps true populist movements,” Odlyzko says.
The United States is less well equipped to handle the AI bubble if it were to burst today because of the weakened US dollar, political pressure on the Federal Reserve’s institutional independence, limitations on economic growth due to President Trump’s sweeping tariffs and trade wars, and record levels of government debt that could constrain attempts to use fiscal stimulus to right-size a sinking economy, Gopinath wrote in The Economist.
Not every major country pursuing lofty AI ambitions is equally vulnerable to such a crash. For example, China faces less risk than Silicon Valley should the AI boom goes bust, even if the event would likely “deflate some froth on both sides,” says Lizzi C. Lee, a fellow on Chinese Economy at the Center for China Analysis at the Asia Society Policy Institute in New York. That is because China’s pragmatic approach to deploying AI more closely resembles “industrial electrification” rather than “funding the dot-com boom,” she says.
“China is structurally less exposed to the ‘AI-for-AI’ bubble because its policy and investment logic centers on integrating AI into the real economy—manufacturing, logistics, public services—rather than frontier models for their own sake,” says Lee. “So, the focus is more on measurable productivity gains, not speculative valuations.”
Tech skeletons. Previous financial bubbles around technologies such as railroads and the internet left behind some functional physical infrastructure, which proved useful for newcomers that swooped in afterward to establish new businesses. But the sudden end of the AI bubble may not yield that kind of silver lining; should the AI boom not produce the requisite income to support continued investment in it, tech companies could find themselves with plenty of underutilized data centers and chips.
Some AI-focused data centers could be repurposed to run less intensive computing workloads. But AI chips usually lose their economic value within just a few years as they fall behind the latest chip technologies, and they also experience rapid physical deterioration from running typical AI workloads, writes Mihir Kshirsagar, Technology Policy Clinic director at Princeton University. Shipments of AI-related chips for data centers reached 3.85 million units in 2023 alone, and that number could grow to 7.9 million by 2030.
A sudden glut of “three-year-old chips at firesale prices” won’t enable newcomers to challenge the entrenched market dominance of existing tech giants, given that the latter can easily afford to run the latest AI hardware, according to Kshirsagar. By comparison, the dot-com bubble collapse allowed newcomers to buy internet fiber at bargain-bin prices, and those physical assets lasted for decades.
Stranded energy infrastructure assets associated with the end of the AI bubble could pose an even bigger complication: Historic electricity demand from the rapid buildout of data centers has spurred utility companies to commit billions of dollars to building new power transmission lines, natural gas pipelines, and power plants, but someone will need to pay the costs of all that energy infrastructure buildout if AI demand fizzles.
Paying for power. Data centers currently represent the fastest-rising source of power demand for the United States, and the electricity needs of individual data center campuses are also growing to gargantuan proportions. Tech companies have rushed to build new gigawatt-scale data centers such as Meta’s “Hyperion” data center in Louisiana, which would consume twice as much electricity as the entire city of New Orleans. Meanwhile, a new Amazon data center campus in Indiana will require as much electricity as half of all homes in the state, or approximately 1.5 million households.
RELATED: How Trump’s new H-1B fee will hurt Silicon Valley and AI startups
To meet that demand, utility companies are making a projected $1.4 trillion investment in electricity infrastructure between 2025 and 2030. And they are paying for it by raising electricity rates for all ratepayers, including households and small businesses. Utility companies have already gotten approval to increase rates by approximately $29 billion nationwide in the first half of 2025, compared to just $12 billion requested and approved in the first half of 2024. This is taking place while US residential electricity costs have jumped by almost 30 percent on average since 2021, according to a report by the nonprofit PowerLines.
“If you believe the projections here, we’re in the early stages of this buildout for industrial-scale computing, and therefore the infrastructure needed to power these facilities is just starting to get built, which means we’re all just starting to pay for it,” says Ari Peskoe, director of the Electricity Law Initiative at Harvard Law School. “So, there’s a possibility that the consumer impacts get a lot worse, unless there’s significant reforms of how utilities spread the costs of infrastructure.”
There is already some evidence showing that data center demand for power is driving up local electricity costs. A Bloomberg investigation found that areas of the country with “significant data center activity” saw wholescale electricity prices soar by as much as 267 percent for a single month compared to five years ago. More than 70 percent of regions that saw price increases were located within 50 miles of such data center clusters.
Tech companies are helping to finance some of the energy infrastructure expansion, especially by using power purchase agreements to bring more power generation into the mix. For example, Microsoft and Google have signed power purchase agreements with energy companies to reopen the nuclear power plants at Three Mile Island in Pennsylvania and the Duane Arnold Energy Center in Iowa, respectively.
But utility companies and their other ratepayers still bear the brunt of expenses for building new power plants, local power lines and transformers, and transmission lines to carry electricity across longer distances. Peskoe and Eliza Martin, a Legal Fellow at Harvard’s Environmental & Energy Law Program, reviewed almost 50 regulatory proceedings related to data center utility rates to show how those processes can shift the utility costs to the public.
If the AI bubble pops and many data centers shut down, much of the local energy infrastructure “is probably mostly unnecessary,” says Peskoe. But new power plants could potentially be repurposed for other energy customers if the demand is there, and new transmission lines that carry electricity across long distances are particularly helpful under a variety of future conditions, he says.
Some state public utility commissions have begun working with utility companies to implement measures that would make data centers pay higher electricity rates and interconnection costs for using the electricity grid, along with exit fees that data center owners would have to pay if they cease operations before contracts expire. Nearly 30 states have proposed or approved new electricity rates for large customers such as data centers, according to a database maintained by the Smart Electric Power Alliance and North Carolina Clean Energy Technology Center.
But energy infrastructure development costs associated with data centers could still be “socialized” and borne by ordinary utility customers if projects don’t have those protections in place, Peskoe says. “I’m sure there would be some utilities that, if there were a burst of the bubble, would probably go to regulators and say, ‘Hey, we want to recover the cost of these facilities from everyone,’” he says.
The unsustainable bubble. The timing of an AI bubble implosion could also shape its long-term impact on the US power grid’s energy mix and carbon emissions. Data center developers have purchased power from, and sometimes made direct investments in, a diverse array of sources, including renewable power, along with nuclear power and energy storage solutions—and yet half of the rising power demand from US data centers is still likely to be supplied through natural gas generation by 2030, according to an S&P Global report.
This is especially problematic for attempts to mitigate climate change, given that natural gas power plants produce carbon dioxide while natural gas wells and pipelines can leak methane as an especially potent greenhouse gas. But that has not stopped some tech companies from rushing to install dozens of gas turbines as on-site power plants for data centers, including Elon Musk’s xAI company and the Project Stargate joint venture involving OpenAI and Oracle. This AI-driven demand for natural gas generation is occurring even as the United Nations’ Emissions Gap Report 2025 warns that the world already faces escalating climate risks from its failures to limit global warming.
Like financial crises of the past, an abrupt end to the AI bubble could inflict considerable economic pain on millions of people worldwide. But the alternative is the prolonging of an AI bubble that is increasingly unsustainable in both the financial and environmental senses, with the winners mainly being some of the wealthiest companies on the planet and their investors.
“Ultimately, for society’s sake, it would be a wonderful thing the faster this thing goes, because very few people are benefiting from it,” says Hetrick, the labor economist at Lightcast. “Had we spread the wealth and invested in various industries, who knows how many innovations we could have come up with by now while we’ve been incinerating this money.”
Jeremy Hsu covers science and technology trends as a NYC-based journalist. He is especially keen on understanding how the development and deployment of technologies such as artificial intelligence impact the wellbeing of human societies and the environment. His reporting has also examined how government, industry, academia and civil society shape the conversations and decision-making around science and technology. (More about Jeremy Hsu here.)
This article was published on December 5, 2025 at the Bulletin of the Atomic Sciences.