AI bubble

The AI bubble is the contested debate, most intense across 2025 and into 2026, over whether the surge in artificial intelligence investment, company valuations, and infrastructure spending that began around 2023 amounts to a speculative financial bubble. In essence: some investors, economists, and even AI executives argue that prices and capital commitments have run well ahead of the revenue AI products actually generate, the classic signature of a bubble, while others argue that AI demand, usage, and revenue are real and growing fast, that the leading companies are highly profitable, and that the infrastructure being built retains lasting value even if individual bets fail. The single most-quoted line of the debate came in August 2025, when OpenAI chief executive Sam Altman conceded, "Are we in a phase where investors as a whole are overexcited about AI? My opinion is yes," while also calling AI "the most important thing to happen in a very long time."^[15] As of mid-2026 the debate had not been settled, and the same facts were read in opposite directions by serious people on both sides.

This article lays out the bubble thesis, the evidence its proponents cite, the counterarguments, the specific flashpoints of 2025 and 2026, and the historical analogies that get invoked. It attributes claims to their sources and does not take a position on whether a bubble exists or what will happen next.

What is the AI bubble?

In finance, a bubble is usually described as a situation in which the price of an asset rises far above any reasonable estimate of its underlying value, driven by speculation, optimism, and fear of missing out, until sentiment reverses and prices fall sharply. The AI bubble thesis applies this idea to the cluster of assets tied to artificial intelligence. That cluster includes the shares of chipmakers and large technology firms, the private valuations of AI model developers, and the enormous sums being spent to build data centers.

Proponents of the thesis generally do not claim that AI is useless or fake. The more careful versions of the argument hold that AI can be a genuinely important technology and still be the subject of a financial bubble at the same time. The claim is about prices and capital flows, not about whether the technology works.

What evidence do bubble proponents cite?

How large is AI capital spending?

The most concrete piece of evidence is the size of capital expenditure by the large cloud companies, often called hyperscalers. Analysts estimated that combined 2025 capital spending by Microsoft, Alphabet (Google), Amazon, and Meta would land somewhere around 350 to 410 billion dollars, with many estimates clustering near 380 billion after the firms raised their guidance during third-quarter 2025 earnings calls.^[1][2] Alphabet lifted its 2025 capex guidance to roughly 91 to 93 billion dollars, Meta guided to about 70 to 72 billion and signaled notably larger spending in 2026, and Amazon guided to well over 100 billion, much of it for its cloud and AI work.^[2] By early 2026 the trajectory had steepened sharply: analysts projected that combined capex for the four firms would reach roughly 600 to 725 billion dollars in 2026, an increase on the order of 55 to 77 percent over 2025, with individual 2026 guidance near 190 billion for Microsoft, 200 billion for Amazon, 175 to 185 billion for Alphabet, and 115 to 135 billion for Meta.^[1] Morgan Stanley estimated that cumulative data center spending could approach 3 trillion dollars by 2028.^[1][3]

The consulting firm Bain and Company put the challenge in revenue terms. In its 2025 Global Technology Report, its sixth annual edition, Bain estimated that AI companies would need to generate about 2 trillion dollars in combined annual revenue by 2030 to fund the computing power required to meet projected demand, and it projected an annual funding gap of roughly 800 billion dollars that would remain even if firms shifted all on-premise IT budgets to the cloud and reinvested every dollar of AI-driven savings.^[4] The estimate assumed something like 200 gigawatts of new data center capacity coming online globally by 2030, with the United States accounting for about half of the required power.^[4] Bain framed this as a financing challenge rather than a prediction of collapse.

An earlier and influential version of the revenue-versus-spending argument came from David Cahn, a partner at the venture firm Sequoia Capital, who in 2024 published an essay called "AI's $600 Billion Question." Cahn estimated the gap between the capital being poured into AI infrastructure and the revenue AI applications were actually producing, and asked whether the revenue would materialize to justify the spending.^[5] He remained optimistic about AI's long-term potential while urging realism about near-term returns.^[5]

What is circular financing?

A second strand of the bubble argument concerns the structure of the deals that move money among AI companies. Skeptics point to arrangements they describe as circular or round-tripping, in which a small group of interconnected firms invest in and buy from one another. Frequently cited examples include Nvidia's announced plan to invest in OpenAI while OpenAI buys Nvidia chips, OpenAI's large compute deals with Oracle and CoreWeave, and an agreement between OpenAI and AMD that included warrants letting OpenAI acquire AMD shares.^[6][7]

The worry is that such deals can make demand look stronger than it is, because the same dollars circulate within the ecosystem and can show up as revenue for more than one party. Critics drew an explicit comparison to the vendor financing of the telecom bubble around 2000, when equipment suppliers such as Lucent and Nortel lent money to customers so those customers could buy the suppliers' gear.^[7][8] Coverage in the Financial Times, Wall Street Journal, Bloomberg, and The Economist examined the web of interlocking commitments among Nvidia, OpenAI, Oracle, CoreWeave, AMD, and Microsoft, and the phrase circular financing became common shorthand for it.^[6][7]

Why does market concentration worry critics?

A third concern is concentration. A historically large share of US stock market value and of recent gains came to rest in a handful of large technology companies, the group often called the Magnificent Seven, which includes Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta, and Tesla.^[9] By 2025 these firms represented roughly 35 percent or more of the S&P 500 by market value, among the highest concentrations in decades, and the five largest names alone accounted for about 30 percent of the index, the greatest such concentration in 50 years.^[9][19] Nvidia became the first company to cross a 4 trillion dollar and then, on October 29, 2025, a 5 trillion dollar market capitalization, a level larger than the combined value of AMD, Intel, Broadcom, TSMC, Micron, ASML, Lam Research, Qualcomm, and Arm.^[9] Critics argued that this concentration created a systemic risk, because a reversal in AI sentiment could drag down the broad index funds held by ordinary savers.^[9]

Are private AI valuations stretched?

The private market drew similar scrutiny. OpenAI reached a valuation around 500 billion dollars in late 2025 through a secondary share sale, after raising a primary round earlier in the year at about 300 billion led by SoftBank.^[10] Anthropic raised at valuations that climbed through the year, with reports of rounds around 183 billion and later figures near 350 billion, and it continued to climb in 2026.^[10] Elon Musk's xAI also raised at sharply rising valuations, reported in late 2025 to be in talks around a 230 billion dollar level.^[10] Skeptics noted that these numbers sit far ahead of the companies' current revenue. Private valuations are negotiated in funding rounds and can move quickly, and figures vary by source and date.

How weak are near-term enterprise returns?

A fourth line of evidence concerns whether companies adopting AI are getting their money back. In August 2025 a report from MIT's NANDA initiative, titled "The GenAI Divide: State of AI in Business 2025," drew wide attention for finding that about 95 percent of the organizations it studied were getting zero return on their generative AI investments, while only about 5 percent of custom enterprise pilots reached production and produced measurable value.^[11] The report, whose lead author was Aditya Challapally, was based on more than 150 interviews, a survey of around 350 employees, and analysis of roughly 300 public deployments.^[11] It distinguished between general tools such as ChatGPT that individuals found useful and bespoke enterprise systems that often failed, attributed the failures to a learning gap in how the tools were integrated rather than to the quality of the models, and found that buying tools from specialized vendors succeeded about 67 percent of the time versus roughly one-third as often for internal builds.^[11]

The finding was cited heavily in bubble coverage and was credited with helping trigger an AI stock pullback in mid to late August 2025.^[11] Some analysts cautioned that the 95 percent figure came from a specific sample and reflected the early stage of enterprise adoption rather than a permanent verdict on the technology.

What did Michael Burry bet against?

The bear case also gained a prominent public face. On November 3, 2025, a quarterly regulatory filing for Michael Burry's Scion Asset Management disclosed put option positions against Nvidia and Palantir, covering roughly 1 million Nvidia shares and about 5 million Palantir shares, with notional values of about 187 million and 912 million dollars respectively, together roughly 80 percent of the fund's reported portfolio.^[12] Put positions in such filings are reported at notional value, which can overstate the actual capital at risk.^[12] Burry, who is known for betting against the housing market before the 2008 crash and was portrayed in the film The Big Short, then began posting publicly to criticize what he characterized as aggressive accounting at AI infrastructure companies.^[12] His central argument concerned depreciation. He contended that hyperscalers were extending the assumed useful life of AI chips and servers, which lowers annual depreciation expense and thereby flatters reported earnings, and he estimated the effect could understate depreciation by tens of billions of dollars across the industry over the following years.^[12] Companies and some analysts pushed back, arguing that longer useful lives reflected genuine hardware improvements and that the accounting was appropriate and audited.^[12]

What are the counterarguments?

Those who doubt that AI is a bubble, or who think the bubble label is misleading, make several points.

The first is that the revenue is real and growing. Nvidia's data center business grew to an annualized run rate measured in the tens of billions of dollars per quarter, OpenAI's annualized revenue was reported to have climbed into the range of roughly 12 to 13 billion dollars by mid-2025, and consumer products such as ChatGPT reached hundreds of millions of weekly users.^[7][13] Defenders argue this is a different situation from many dot-com companies that had little or no revenue.

The second is profitability and balance-sheet strength. The companies leading the AI buildout are among the most profitable in the world and fund much of their capital spending from operating cash flow rather than from speculative equity or debt.^[13] Defenders also note that the price-to-earnings multiples of the leading firms in 2025, while elevated, were generally well below the extreme levels seen at the dot-com peak in 2000, when many technology stocks traded at triple-digit multiples or had no earnings at all.^[9][13]

The third is that infrastructure has lasting value. On this view, even if some capacity is overbuilt, data centers, power, and chips are durable assets that retain usefulness, much as railways and fiber-optic cable did after earlier booms.^[8] The fourth is the productivity argument, that AI may eventually deliver large efficiency gains across the economy that companies will pay for, which would close part of the revenue gap that Bain and Cahn described.^[4][5]

Industry figures who pushed back hardest included Alex Karp, the chief executive of Palantir, who repeatedly and forcefully rejected the bubble label through 2025 and argued that his company's accelerating results demonstrated real demand, even as Palantir traded at one of the highest price-to-sales multiples in the S&P 500.^[14] On the sell side, analysts such as Dan Ives of Wedbush remained bullish, framing the buildout as a genuine technology shift comparable to the early internet.^[7]

Which leaders acknowledged bubble dynamics?

A striking feature of the 2025 debate is that several central figures acknowledged bubble-like conditions while continuing to invest. In August 2025, OpenAI chief executive Sam Altman told reporters that investors as a whole were, in his words, overexcited about AI, and he compared the moment to the dot-com era, saying "when bubbles happen, smart people get overexcited about a kernel of truth" that is real.^[15] He also said, in substance, that someone was going to lose a great deal of money and someone was going to make a great deal, while maintaining that AI was the most important thing to happen in a long time.^[15]

In October 2025, speaking at Italian Tech Week on October 3, Amazon founder Jeff Bezos described the environment as an industrial bubble and argued that such a bubble can be good, because the underlying technology is real and society benefits from the resulting investment even when many individual investors lose money. "This is a kind of industrial bubble, as opposed to financial bubbles," he said, drawing comparisons to the biotech bubble of the 1990s, and predicting that "the benefits to society from AI are going to be gigantic" even if the bubble bursts.^[16] These remarks were widely reported as examples of leaders conceding bubble dynamics while defending continued spending.

What were the major 2025 and 2026 flashpoints?

How did the Nvidia and OpenAI deal evolve?

On September 22, 2025, Nvidia and OpenAI announced a letter of intent for a strategic partnership. Under the announcement, Nvidia intended to invest up to 100 billion dollars in OpenAI progressively as capacity was deployed, supporting at least 10 gigawatts of Nvidia systems for OpenAI's next-generation infrastructure, with the first gigawatt targeted for the second half of 2026 on Nvidia's Vera Rubin platform.^[17] The arrangement is described in more detail in the article on the Nvidia OpenAI partnership. Sam Altman framed compute as the basis of the future economy, and Nvidia's Jensen Huang described the project's scale in expansive terms.^[17] The deal drew immediate scrutiny precisely because of its circular structure, with Nvidia investing cash that OpenAI would in part use to buy Nvidia chips, and it became the most cited single example in the circular financing discussion.^[6][7] The arrangement was later restructured. On February 27, 2026, OpenAI confirmed that Nvidia would take a roughly 30 billion dollar direct equity stake as part of a funding round of more than 100 billion dollars that valued the company at around 830 billion dollars, alongside Amazon and SoftBank, and Jensen Huang said in March 2026 that the full 100 billion dollars was "probably not in the cards."^[21]

How was the data center buildout financed?

The physical buildout itself became a flashpoint. Some spending moved off balance sheets through debt issuance and special purpose vehicles, drawing scrutiny about how the buildout was being financed.^[3] Meta raised a large bond offering and used special purpose vehicle structures for data center projects, and Oracle issued substantial debt to fund its expansion.^[3] OpenAI's Stargate effort, a multi-year plan to build out large amounts of AI infrastructure, was repeatedly cited in discussions of the sheer scale of committed spending.

How volatile were AI stocks?

AI-linked stocks swung sharply during the autumn of 2025. There was a sell-off in August around the MIT report and Altman's comments, and further declines in October and November amid renewed bubble fears, with Nvidia, Palantir, and Oracle among the volatile names.^[18] Palantir fell after strong November earnings because of concern about its valuation, and Oracle was volatile after disclosing both a large AI-related backlog and thin cloud margins alongside heavy debt.^[18] Despite the swings, major indices remained near record highs for much of late 2025, and many AI stocks recovered from individual drops, so the overall picture into early 2026 was one of nervousness and sharp swings rather than a sustained crash.^[18]

What did central banks and the IMF warn?

Official bodies weighed in during October 2025. The Bank of England's Financial Policy Committee warned that the risk of a sharp market correction had increased and stated that equity valuations appeared stretched, particularly for technology companies focused on artificial intelligence, a warning it reiterated in its December 2025 Financial Stability Report.^[19] The International Monetary Fund, in its Global Financial Stability Report and in comments from Managing Director Kristalina Georgieva, warned of stretched valuations and the possibility of a sharp correction, with Georgieva saying valuations were "heading toward levels we saw during the bullishness about the internet 25 years ago."^[20] Federal Reserve Chair Jerome Powell separately described equity prices as fairly highly valued.^[20] These bodies generally framed their remarks as financial-stability risk assessments rather than predictions of imminent collapse.^[19][20]

How does the AI boom compare to the dot-com bubble?

The debate leans heavily on comparison to past episodes. The most common is the dot-com bubble of the late 1990s, when speculative investment in internet companies, many without viable business models, crashed in 2000 to 2002. Commentators also point to the telecom and fiber-optic bubble of roughly the same period, when overinvestment in network capacity, based on overestimated traffic growth, left large amounts of unused dark fiber that sat idle for years before demand caught up, and which featured the vendor financing that critics liken to AI circular financing.^[7][8] Older still is the British railway mania of the 1840s, a share bubble that ruined many investors yet left behind a rail network that powered the economy for decades, often cited as the archetype of a bubble that destroys capital while building lasting infrastructure.^[8]

The economist Carlota Perez, in her 2002 work Technological Revolutions and Financial Capital, described a recurring pattern in which major technologies pass through an installation period marked by speculation and a bubble, followed by a crash, and then a deployment period of broader productive use.^[8] Her framework was invoked in 2025 to argue that even a crash would not negate AI's long-term importance.

Those who resist the analogies stress the differences. The companies leading the AI boom are large, established, and highly profitable, with strong cash flows, unlike many dot-com startups, and their valuation multiples, while high, are generally below the 2000 extremes.^[9][13] Real revenue and adoption exist and are growing.^[7][13] Where the analogies are said to hold are in the concentration of gains, the narrative-driven and fear-of-missing-out character of the investing, the heavy spending on infrastructure that might be overbuilt, and the circular financing reminiscent of the telecom era.^[7][8][9] A recurring point made by economists is that a technology can be genuinely revolutionary and still be the subject of a financial bubble, because the railways and the internet were both transformative and the subjects of bubbles that wiped out investors while leaving useful infrastructure behind.^[8]

Bubble arguments and counterarguments

The table below summarizes the main claims on each side as they were presented during 2025 and 2026. It is a map of the argument, not a scorecard.

Scroll sideways for more →

Is AI a bubble? The unresolved state of the debate

As of mid-2026 the question of whether AI investment constitutes a bubble remained open. The factual building blocks were largely agreed upon. Capital spending was very large and rising, the deals among the main players were genuinely interlocked, valuations were high by historical standards, official institutions had flagged stretched prices, at least one widely cited study reported weak near-term enterprise returns, and at the same time AI revenue and usage were growing quickly and the leading firms were profitable. What divided observers was the interpretation. The bubble case read the spending and the deal structures as a speculative excess likely to correct, while the other side read the same figures as a rational, if aggressive, buildout of a real and durable technology. Several prominent figures, including Sam Altman and Jeff Bezos, occupied a middle position, acknowledging froth while continuing to invest. No outcome had been determined, and this article does not forecast one. Related debates over earlier cycles of AI optimism and disappointment are covered in the article on the AI winter.

See also

• Nvidia OpenAI partnership
• Stargate
• AI infrastructure
• Dot-com bubble
• AI winter
• Hyperscaler
• Generative AI

References

1. Morgan Stanley Research and analyst coverage of hyperscaler capital expenditure and data center spending projections, 2025 to 2026, including estimates that combined Microsoft, Alphabet, Amazon, and Meta capex would rise from about 380 billion dollars in 2025 toward 600 to 725 billion dollars in 2026; summarized in financial press reporting (CNBC, Tom's Hardware). ↩
2. Reuters, reporting on third-quarter 2025 earnings and raised 2025 capital expenditure guidance from Alphabet, Meta, Amazon, and Microsoft, October 2025. ↩
3. Reuters and Financial Times, reporting on debt issuance and special purpose vehicle financing of data center buildouts by Meta and Oracle, 2025. ↩
4. Bain and Company, "$2 trillion in new revenue needed to fund AI's scaling trend," 6th annual Global Technology Report, September 2025; Reuters and Tom's Hardware coverage of the roughly 800 billion dollar funding gap and 200 gigawatt 2030 compute estimate. ↩
5. David Cahn, "AI's $600B Question," Sequoia Capital, 2024. ↩
6. Financial Times and Bloomberg, reporting on circular financing and interlocking deals among Nvidia, OpenAI, Oracle, CoreWeave, and AMD, 2025. ↩
7. The Economist and Wall Street Journal, analysis of the AI investment cycle, vendor financing, and revenue growth at Nvidia and OpenAI, 2025. ↩
8. Carlota Perez, Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages, Edward Elgar, 2002; and press analyses applying railway, telecom, and dot-com analogies to AI, 2025. ↩
9. Reuters and Financial Times, reporting on Magnificent Seven market concentration, the S&P 500 share of those firms, and Nvidia crossing a 5 trillion dollar market capitalization on October 29, 2025; CNBC and NBC News coverage. ↩
10. Reuters and Bloomberg, reporting on private valuations of OpenAI (around 300 billion and later about 500 billion dollars), Anthropic (around 183 billion rising toward 350 billion), and xAI (reported talks near 230 billion), 2025. ↩
11. Aditya Challapally et al., "The GenAI Divide: State of AI in Business 2025," MIT NANDA initiative; and Fortune coverage, "MIT report: 95% of generative AI pilots at companies are failing," August 2025. ↩
12. Reuters, CNBC, and Sherwood News, reporting on Michael Burry's Scion Asset Management 13F disclosure (filed November 3, 2025) of put options against Nvidia (about 1 million shares, 187 million dollar notional) and Palantir (about 5 million shares, 912 million dollar notional) and his commentary on AI depreciation accounting, November 2025. ↩
13. Reuters and CNBC, reporting on Nvidia data center revenue, OpenAI revenue growth, ChatGPT usage, and comparisons of AI-leader profitability and valuation multiples with the dot-com peak, 2025. ↩
14. Reuters and CNBC, reporting on Palantir's November 2025 earnings, its valuation multiple, and Alex Karp's rejection of bubble claims, 2025. ↩
15. CNBC and The Verge, reporting on Sam Altman's August 2025 comments that investors were overexcited about AI and his dot-com comparison, including the quotes "Are we in a phase where investors as a whole are overexcited about AI? My opinion is yes" and "when bubbles happen, smart people get overexcited about a kernel of truth," August 2025. ↩
16. CNBC and Fortune, reporting on Jeff Bezos at Italian Tech Week on October 3, 2025, describing AI as "a kind of industrial bubble, as opposed to financial bubbles" and saying "the benefits to society from AI are going to be gigantic," October 2025. ↩
17. NVIDIA Newsroom and OpenAI, "OpenAI and NVIDIA Announce Strategic Partnership to Deploy 10 Gigawatts of NVIDIA Systems," stating intent to invest up to 100 billion dollars and deploy at least 10 gigawatts of systems on the Vera Rubin platform, September 22, 2025. ↩
18. Reuters and CNBC, reporting on AI stock volatility in October and November 2025, including swings in Nvidia, Palantir, and Oracle, 2025. ↩
19. Bank of England, Financial Policy Committee statement (October 2025) and Financial Stability Report (December 2025) warning of stretched valuations and a sharp correction risk, particularly for AI-focused technology companies; Reuters and CNBC coverage. ↩
20. International Monetary Fund, Global Financial Stability Report and comments from Managing Director Kristalina Georgieva that valuations were "heading toward levels we saw during the bullishness about the internet 25 years ago," October 2025; and remarks from Federal Reserve Chair Jerome Powell that equity prices were fairly highly valued; Reuters and CNBC coverage. ↩
21. CNBC and Crowdfund Insider, reporting that OpenAI confirmed Nvidia's roughly 30 billion dollar equity stake on February 27, 2026 as part of a funding round of more than 100 billion dollars valuing the company near 830 billion dollars, restructuring the September 2025 letter of intent, with Jensen Huang saying in March 2026 that the full 100 billion dollars was "probably not in the cards," 2026. ↩