The AI bubble — a term used to describe the rapid rise in stock prices driven by enthusiasm for artificial intelligence — is showing signs of slowing, according to some economists. Wall Street analysts have begun to question whether the surge in U.S. stocks, fueled by optimism about AI’s future, is sustainable. John Higgins, chief economic adviser for financial markets at Capital Economics, stated in a recent report that the AI bubble may be entering its final phase. The firm predicts that the bubble could burst by 2027, with a potential correction — a sharp decline in stock prices — in the S&P 500 index, a key measure of the U.S. stock market, by next year. Economists point out that while AI is expected to be transformative, current expectations about its profitability may be overly optimistic. James Reilly, a senior markets economist at Capital Economics, compared the current situation to the dot-com bubble of the late 1990s, when tech stocks soared only to later crash. He noted that while AI will likely generate profits, they may not reach the high levels currently projected by analysts. Meanwhile, Goldman Sachs predicts that global spending on AI-related projects will reach $1 trillion by 2026, with nearly $581 billion of that in the U.S. This spending surge has driven stock prices higher, as investors chase anticipated future gains from AI-leading companies. Identifying investment bubbles — periods of excessive speculation — is notoriously difficult, and economists caution against using the term too loosely. Greg Daco, chief economist at EY-Parthenon, explained that technological revolutions often attract heavy investment early on, even if the full economic impact is unclear. He acknowledged that while there may be excesses in AI investment, the concerns are not necessarily the same as a classic bubble. Kenneth R. French, an investment strategist at Dartmouth College, is more cautious about the idea of an AI bubble bursting soon. He believes that AI may turn out to be even more impactful than currently anticipated, and that its influence on corporate earnings is already significant. As the public conversation around AI evolves, concerns about its risks are growing. Many AI researchers and corporate leaders are warning about the potential dangers of the technology, calling for a more measured approach to its development. While these concerns may affect public perception and investor sentiment, they are distinct from the economic question of whether AI will deliver the profits that investors are betting on. Daco emphasized that the fear is more about the need for proper safeguards to control the technology itself, rather than a straightforward bubble scenario. For now, the debate over whether the AI boom is overinflated remains unresolved.