The AI boom has reached a point where investors are no longer asking only how high the market can climb. They are asking what happens when the spending stops matching demand. Warnings from Joachim Klement, Ray Dalio, and market analysts now place the possibility of an AI bubble bursting in 2027 or 2028.
Klement, managing director and research analyst at Panmure Liberum, gave the clearest timeline. “My core conviction is that the AI bubble will either burst in 2027 or in 2028,” he said. His warning focuses on the gap between the money flowing into AI infrastructure and the demand that supports those investments.
The spending problem behind the AI boom
Klement argues that the industry may be building toward the wrong version of AI. “I think the entire AI boom is investing in the wrong future of AI,” he said. In his view, the current rush centers on frontier large language models running in data centers, while the real future may belong to small language and open-weight models running on local desktop computers.
That distinction matters because data centers require huge investments, and those investments depend on sustained demand for AI computing power. Klement said, “When it comes to the underlying demand, if you strip out OpenAI and Anthropic’s demand, there’s hardly any demand there for AI compute that justifies the trillion-dollar investments that the hyperscalers alone are expected to do next year.”
The concern is not that AI has no use. OpenAI, the ChatGPT maker, remains part of the demand picture, as does Anthropic. The concern is that removing those two companies leaves too little demand to support the trillion-dollar investments expected from hyperscalers next year.
The pressure is also showing up in business expectations. OpenAI was set to miss its annual revenue goal by a cool $20 billion, a figure that adds to questions about how fast the biggest AI companies can turn huge infrastructure bills into revenue.
Why the risk reaches beyond AI companies
Klement said the bubble does not need a dramatic collapse to create trouble. “All I need for this bubble to burst is for growth plans to be revised downward, and for growth to slow down, because that already will change the earning’s outlook for basically the entire supply chain.” A change in expected growth could affect the companies that build, supply, and support the AI infrastructure boom.
Ray Dalio, founder of Bridgewater, described the market in similar terms. He saw a “classic bubble” market that was close to bursting. His warning points to a broader market problem: AI gains have overcome declines for financial, healthcare, and consumer-focused companies, leaving the market driven by AI stocks.
That concentration can make a market look stronger than the wider economy. If AI firms continue gaining while other sectors fall, the headline market picture may depend on a small group of companies. The Nasdaq index fell 1.25 percent, adding a clear reminder that market confidence can shift even during an AI-led boom.
The risk has drawn attention from investors and market watchers tied to the Oct 07, 2026, discussion of investor warnings and market highs, the Oct 08, 2026, discussion of AI and market warnings, and Joe Wilkins’s Oct 10, 2026, article about the AI bubble. Dani Burger is identified as a Bloomberg anchor, while Temasek is an investment company named in the discussion.
What a shift in AI could look like
The debate is not only about stock prices or data center budgets. It is also about which type of AI becomes useful at scale. The current boom favors frontier large language models hosted in large data centers, but Klement’s alternative points toward smaller models that run on local desktop computers.
Open-weight models could support that shift because they can run outside the biggest centralized systems. The argument favors a future where useful AI does not always require access to enormous data centers. It also challenges the assumption that bigger models and bigger infrastructure will remain the main path forward.
The physical buildout shows how much money is already committed to that assumption. The construction of an AI data center in Texas stands as one example of the infrastructure behind the boom. Yet Klement’s warning is that growth plans can change before all that capacity finds enough demand.
Other figures add complexity to the picture. Instinct handles $1 billion in annual transaction volume, showing that AI-related businesses can operate at a substantial scale. But the wider market question remains whether individual successes can support the trillion-dollar investment plans expected from hyperscalers.
For now, the central warning is simple: the AI bubble may be close to bursting, and 2027 is already being identified as the biggest risk to markets from an unwinding of the AI trade. If that unwinding arrives, the damage may not stop with AI firms. It could change earnings expectations across the entire supply chain and expose how much of the market now rests on AI stock gains.
Based on
