AI’s Profitability Dream: A Nobel Economist Warns of ‘Very Very Long Shot’ Success

Nobel Prize-winning economist Daron Acemoglu cautioned on Tuesday that artificial intelligence’s path to profitability remains increasingly unlikely due to the staggering capital required to build and maintain the infrastructure behind the technology. Speaking in an interview, Acemoglu highlighted that companies investing heavily in AI are betting on productivity gains that have yet to materialize at scale.

Acemoglu, a professor at the Massachusetts Institute of Technology, noted that the numbers for AI data centers are “astronomical.” He referenced IBM CEO Arvind Krishna’s earlier statement that constructing a single data center using just 1 gigawatt of power would cost an estimated $80 billion. If one company were to commit to building out 20 to 30 gigawatts, the capital expenditure could reach $1.5 trillion—roughly equivalent to Tesla’s current market cap.

“The only way you would rationalize this,” Acemoglu said, “is if one or two companies dominate multiple industries and generate trillions of dollars in profits. It’s a very, very long shot.”

Even the largest technology firms have never generated profits on the scale required to justify trillions of dollars in capital expenditures, particularly when AI hardware becomes obsolete every three to five years and must be replaced. While some consumers are willing to pay modest subscription fees for AI tools, Acemoglu said businesses remain reluctant to spend heavily because real-world productivity gains have been limited.

“There aren’t that many applications that have proven to be very productive in the wild,” he added. “Some of them work fine. But in the wild, where they have to deal with real-world problems, that’s a different ballgame.”

Acemoglu also highlighted an emerging cost complicating the AI business case: Companies increasingly need to hire additional employees to monitor, verify, and correct AI-generated output. “Integrating AI is very difficult,” he said. “You need to understand your organization, what your employees really add, and then bring AI to help them. Rote automation is not going to work.”

He noted that many businesses feel pressure from consultants, boards, and public narratives to show they are adopting AI—even when returns remain uncertain. “Businesses aren’t spending all that much,” Acemoglu said. “And when they do, they’re not getting all the returns.”

Acemoglu stated AI’s long-term success will ultimately be decided by whether the economics can deliver sustainable profits, not technological promise alone.