Geoffrey Hinton says AI will boost corporate profits while driving 'massive' unemployment

Geoffrey Hinton, a Nobel laureate and one of the pioneers of modern machine learning, warned that artificial intelligence will make large companies more profitable while likely producing a surge in unemployment.

In an interview with the Financial Times published Sept. 6, Hinton said the gains from AI will be concentrated among companies and their owners, with broad negative effects for employment. "What's actually going to happen is rich people are going to use AI to replace workers," he told the newspaper. "It's going to create massive unemployment and a huge rise in profits. It will make a few people much richer and most people poorer. That's not AI's fault, that is the capitalist system."

Hinton, who this account says won the Nobel prize last year for work on machine learning and artificial neural networks, has long been a prominent voice on the social and economic implications of AI. Dubbed the "Godfather of AI," he previously worked as a computer scientist at Google and has publicly urged caution about rapid deployment of advanced models.

In the interview Hinton said firms such as OpenAI, Anthropic and Google — among the companies he singled out — are primarily developing AI models to chase short‑term profits, a strategy he said will accelerate the replacement of human labor. He told the Financial Times that while AI is a powerful tool, its likely economic trajectory will depend on corporate choices and broader policy responses.

The current labor market picture is mixed. Hinton acknowledged that, to date, mass layoffs have not materialized specifically as a result of AI deployment. Still, he warned that the underlying economic forces could lead to far higher unemployment if firms continue to substitute machines for workers without offsetting measures such as new forms of employment, retraining or redistribution.

Hinton's comments come amid a wider public and policy debate over the pace of AI development and its possible social consequences. Governments, industry groups and academics have variously called for stepped‑up regulation, stronger worker protections, investment in skills training and research into the long-term effects of automation. Proponents of accelerated AI deployment argue that the technology can raise productivity and create new types of jobs, while critics point to the risk of concentrated gains and worker displacement.

Analysts and economists differ on timing and scale. Some studies suggest certain occupations and tasks are more susceptible to automation, while other research points to complementary effects in which AI augments human labor and changes job content rather than eliminating positions outright. Hinton's assessment focuses on the risk that market incentives will favor profit extraction over broader societal distribution of AI benefits.

Hinton has repeatedly sounded similar alarms in recent years, even as he acknowledges the field's technical progress and potential. His latest remarks are likely to intensify scrutiny of business strategies for deploying generative models and to renew calls among policymakers for measures to address potential labor‑market disruptions.

The Financial Times interview adds to a string of public interventions by leading AI researchers and executives about the need to balance innovation with social safeguards. As companies continue to integrate AI into products and operations, the debate over how to manage the economic fallout — including whether and how to tax, regulate or otherwise share AI's gains — is expected to shape policy discussions in the months and years ahead.