Will artificial intelligence ever make big profits? Experts weigh in as bubble fears loom

Fears of an artificial intelligence bubble have rattled markets as analysts question whether the technology can generate the profits needed to justify massive infrastructure investment. Experts are divided on timing and scale of returns.

Fears of an artificial intelligence bubble have shaken the stock market and raised concerns about broader risks to the U.S. economy. JPMorgan Asset Management found a surge of Artificial Intelligence spending accounted for roughly two-thirds of gross domestic product growth over the first half of 2025, with many large companies investing heavily in the chips and data centers required to run models. That scale of spending prompts a central question for investors and policymakers: will Artificial Intelligence deliver profits commensurate with the trillions being poured into infrastructure and development?

Evidence of profits is mixed. Chip maker Nvidia has delivered major profits selling semiconductors and became the most valuable company in the world by market capitalization, signaling strong demand for building blocks rather than end uses. Consumer adoption shows promise: OpenAI’s ChatGPT has about 800 million weekly active users and a user base that amounts to about a quarter of the 3 billion monthly active users combined on the array of apps offered by Meta. Still, revenue lags adoption. An MIT study in July found roughly 95% of businesses invested in Artificial Intelligence have failed to make money off the technology, estimating the combined amount spent by the firms is around $40 billion. OpenAI’s CFO told CNBC the company is on pace to earn about $13 billion in revenue over the course of 2025, which amounts to $3.25 billion per quarter, and CEO Sam Altman said the firm is generating “well more revenue than that.”

Experts remain split on the outlook. Some, like Ethan Mollick, point to the fastest consumer adoption of any technology and say “There is a path to making money.” Arun Sundararajan and Vasant Dhar argue a delay in productivity gains is typical for paradigm-shifting technologies and that experimentation precedes large-scale value creation. Skeptics highlight structural challenges, noting energy and computational costs rise with use and that investments in data centers “in the trillions of dollars” require revenue figures that can exceed the total revenues of major incumbents. The debate centers on whether the current spending pace is sustainable and whether significant profit pools will emerge within years rather than decades.

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