Google’s Nobel

Google’s Nobel-Winning AI Expert Departing for Anthropic

John Jumper, a Nobel Prize-winning Google DeepMind artificial intelligence scientist, is leaving the company for Anthropic.

“After nearly 9 years, I have decided to leave Google DeepMind and join Anthropic,” Jumper posted on the social media platform X Friday (June 19).

“Demis Hassabis took a real chance letting me lead the AlphaFold team just six months after finishing my PhD,” he added, referring to DeepMind’s CEO.

A report by Bloomberg News said that Google has confirmed Jumper’s departure. That report also noted that the move could hinder Google’s campaign to beat Anthropic, OpenAI and SpaceX in the race to create the most powerful AI models.

Former Google employees told Bloomberg the company has struggled to sell AI coding tools to businesses.

The report added that workers and executives at DeepMind — the Google AI division where Jumper had been vice president — have recently raised concerns that the company has no obvious solution for businesses in search of AI coding tools.

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Tools like that, the report added, have become a major focus for both Anthropic and OpenAI, helping fuel both companies’ momentum in the last few months.

Jumper and Hassabis shared the 2024 Nobel Prize for Chemistry for developing AlphaFold, an AI system which can accurately predict the three-dimensional structure of proteins from their amino acid sequences. As covered here at the time, this achievement solved a long-standing challenge in molecular biology.

“Thanks John for an extraordinary partnership and wonderful collaboration over the past 9 years,” Hassabis wrote in his own X post. “What we achieved with AlphaFold changed the world, and showed the field what was possible with AI for science and medicine, lighting the way for how AI can benefit humanity.”

In other Google AI news, PYMNTS wrote earlier this month about the company’s decision to slash the cost of its entry-level AI subscription from $7.99 to $4.99 a month as AI companies shift from competing on model performance to competing on price.

“The divergence between consumer and enterprise AI pricing points to the same underlying problem: Usage is growing faster than the economics are improving,” PYMNTS wrote.

“On the consumer side, companies are cutting prices to add subscribers while absorbing the cost of heavy users. On the enterprise side, companies that treated AI spend like flat-rate software are discovering it bills more like utilities.”

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