The AI bubble is a concept asserting that a stock market bubble has been growing since 2025 amid the AI boom, a period of rapid increase in investment in artificial intelligence (AI) that The Wall Street Journal reported as "the biggest economic bet in U.S. history." Early AI investment was funded primarily with operating cash flow, but by 2026 the bond market came to dominate the financing, alongside $3 trillion in off-balance-sheet commitments. Speculation about a bubble also stems from concerns that leading AI tech firms are involved in a circular flow of investments that artificially inflate the value of their stocks and the broader market. The Wall Street Journal reported that this circularity, reminiscent of the dotcom build-out of the 1990s, "can be virtuous on the upside, and vicious on the downside." Some, like hedge fund manager Ray Dalio, see other similarities with the dot-com bubble.
History
In January 2025, the Chinese-made chatbot DeepSeek demonstrated performance on par with AI models developed at far greater expense. The stock prices of many AI companies dropped - for example, Nvidia's dropped 17%, or approximately US$600 billion in market value in a single day, though it recovered 8.8% the following day. In late 2025, spending from US mega caps was expected to reach $1.1 trillion between 2026 and 2029, while total AI spending was expected to surpass $1.6 trillion.
Due to the growing demand for semiconductors to sustain AI technologies, Nvidia in July 2025 became the first company in the world to reach a market value of $4 trillion. The figure had quadrupled since 2023, when it surpassed $1 trillion. The company's value made up roughly 7.3% of the S&P 500, which hit an all-time high. In October 2025, the company's value grew beyond $5 trillion, rising higher than the GDP of every country except for the United States and China, according to data from the World Bank. Over the year 2025, AI-related enterprises accounted for roughly 80% of gains in the American stock market. Some sceptics warned that the rapid rise of AI tech firms may be the result of excessive financial engineering.
Microsoft disclosed that it had spent almost $35 billion on AI infrastructure in the three months leading up to the end of September. In October, it became the second-most-valuable company in the world, largely due to its 27% stake in OpenAI. While seeing increases in revenue by 18% and in net income by 12%, share values dropped by 4% in after-hours trading amid investors' concerns about the possible costs of sustaining the AI boom.
In late 2025, 30% of the US S&P 500 and 20% of the MSCI World index was solely held up by the five largest companies (Nvidia, Microsoft, Apple, Alphabet, and Amazon), which was the greatest concentration in half a century, and share valuations were reportedly the most stretched since the dot-com bubble. Experts warned that AI companies were extremely overvalued, with the S&P 500 trading at 23 times forward earnings, and the FTSE Index trading at 14 times, showing how expensive the US market had become. The Shiller price-to-earnings ratio for the US market also exceeded 40 for the first time since the dot-com crash.
In late June and across July 2026, South Korea's stock market had a historic crash, with the KOSPI index dropping by 44% in 40 days, erasing $2.18 trillion in market value due to a massive sell-off in Samsung Electronics and SK Hynix stocks (which together held more than half of the index value), triggered by Big Tech's lack of short-term return on investment in AI infrastructure, and fears of a subsequent slowdown in demand for High Bandwidth Memory chips. In October, the Chinese index STAR50 had dropped 40% since July due to limitations on Chinese exports and limited demand, described by several organizations as the bursting of China's AI bubble.
By October 2026, the demand for AI computing power was exceeding supply, leading to increasing prices for capacity and what one start-up executive described as a "gold rush" mentality.
Speculation
In early 2025, Bridgewater Associates co-chief investment officer Ray Dalio said that the current levels of investment in AI are "very similar" to the dot-com bubble. Sam Altman, CEO of OpenAI, which created ChatGPT, said in August 2025 that he believed that an AI bubble exists. In September 2025, the Australian Financial Review said that "If we really are in another share-market bubble, it's surely the most anticipated example in history."
In October 2025, Jamie Dimon, chief executive of JPMorgan Chase, the largest bank in the US, said he thinks "AI is real" but said he believes some money invested now will be wasted. He also said there is a higher chance of a meaningful drop in stocks over the following two years than the market was reflecting. Dimon warned that an AI-driven stock crash could result in substantial losses, although he acknowledged that AI would pay off "just like cars in total paid off, and TVs in total paid off, but most people involved in them didn't do well." However, he further stated on AI that "the level of uncertainty should be higher in most people's minds."
Lack of profitability
Critics argue that the values of technology company stocks have been inflated based on AI hype regardless of market fundamentals or the financial reality behind monetizing AI products. A National Bureau of Economic Research study published in February 2026 found that despite 90% of firms reporting no impact of AI on workplace and productivity, executives projected AI to increase productivity by 1.4% and increase output by 0.8%, leading to comparison with productivity paradox.
OpenAI committed to spending US$1.4 trillion over 8 years in building new data centers, partnering with Nvidia to deliver 10 gigawatts of data center computation, with just US$13 billion in revenue. This long-term spending is funded by debt. An estimate from Morgan Stanley put global spending on datacenters between 2025 and 2028 at US$3 trillion, half of which is covered by private credit. In November 2025, OpenAI said it expected to report annual losses through 2028, including US$74 billion in operating losses in 2028 alone. The Wall Street Journal obtained financial documents where OpenAI projects significant profits in 2030 despite preceding years of deep losses. Deutsche Bank analyst Jim Reid estimated OpenAI's losses amounting to US$140 billion between 2024 and 2029.
Former Fidelity manager George Noble said that OpenAI is "burning US$15 million per day on Sora alone." He also highlighted that AI companies will face diminishing returns in model improvements paired with rising costs, saying that "It's going to cost 5x the energy and money to make these models 2x better." OpenAI has been projected to run out of money by mid-2027.
AI build-out financing
Concerns were raised, notably by critic Ed Zitron, that leading AI tech firms were using circular financing and investment to artificially inflate their stock values. This involves companies investing in each other's AI ventures, creating a feedback loop that boosts market valuations without corresponding underlying economic value. The Wall Street Journal noted that this circularity, reminiscent of the dotcom build-out, "can be virtuous on the upside, and vicious on the downside." The financing of the AI build-out has shifted from operating cash flow to bond markets and off-balance-sheet commitments, with $3 trillion in such commitments by 2026, raising concerns about systemic risk.
Comparisons to historical bubbles
Many analysts draw parallels between the AI bubble and the dot-com bubble of the late 1990s. During the dot-com era, excessive investment in internet companies led to a market crash in 2000. Similar to the current situation, there was a focus on speculative growth over profitability, and a concentration of market value in a few tech giants. Ray Dalio has noted that the current investment levels are "very similar" to the dot-com period. However, some argue that AI has more tangible applications and revenue streams, potentially making the bubble less severe if it bursts. The Shiller price-to-earnings ratio exceeding 40 for the first time since the dot-com crash is a key indicator of overvaluation.
Impact on the semiconductor industry
The AI bubble has had a profound impact on the semiconductor industry, particularly for companies like Nvidia, AMD, and Intel, as well as memory chip makers like Samsung Electronics and SK Hynix. The demand for High Bandwidth Memory chips has surged due to AI workloads, leading to record revenues and market valuations. However, the 2026 crash in South Korea's stock market, driven by a sell-off in these chip stocks, highlighted the vulnerability of the industry to shifts in AI investment sentiment. The AI boom has also spurred investments in TSMC and Broadcom, which manufacture and design chips for AI applications, but any slowdown in AI spending could have cascading effects on their revenues.
Future outlook
As of late 2026, the AI bubble remains a topic of intense debate. While some experts believe that the rapid growth in AI investment is unsustainable and will lead to a market correction, others argue that AI's transformative potential justifies the high valuations. The demand for AI computing power continues to exceed supply, driving up prices and fostering a "gold rush" mentality among startups. However, the lack of profitability at major AI companies like OpenAI, coupled with massive debt-funded spending, raises questions about the long-term viability of the current investment trajectory. The outcome may depend on whether AI can deliver tangible productivity gains and revenue growth in the coming years, or whether the bubble will burst as many predict.