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Companies like OpenAI and Google are running out of the data used to train artificial intelligence systems. Can new methods continue years of rapid progress?
Published Dec. 19, 2024Updated Dec. 25, 2024, 11:18 p.m. ET
Demis Hassabis, one of the most influential artificial intelligence experts in the world, has a warning for the rest of the tech industry: Don’t expect chatbots to continue to improve as quickly as they have over the last few years.
A.I. researchers have for some time been relying on a fairly simple concept to improve their systems: The more data culled from the internet that they pumped into large language models — the technology behind chatbots — the better those systems performed.
But Dr. Hassabis, who oversees Google DeepMind, the company’s primary A.I. lab, now says that method is running out of steam simply because tech companies are running out of data.
“Everyone in the industry is seeing diminishing returns,” Dr. Hassabis said this month in an interview with The New York Times as he prepared to accept a Nobel Prize for his work on artificial intelligence.
Dr. Hassabis is not the only A.I. expert warning of a slowdown. Interviews with 20 executives and researchers showed a widespread belief that the tech industry is running into a problem that to many was unthinkable just a few years ago: They have used up most of the digital text available on the internet.
That problem is starting to surface even as billions of dollars continue to be poured into A.I. development. On Tuesday, Databricks, an A.I. data company, said it was closing in on $10 billion in funding — the largest-ever private funding round for a start-up. And the biggest companies in tech are signaling that they have no plans to slow down their spending on the giant data centers that run A.I. systems.