Ex-Google DeepMind researcher resigns, warns AI could ‘kill all humans’

Bilal Chughtai, a former Google DeepMind employee based in London, became the latest AI researcher to warn that the technology could “kill all humans”, saying time ​could be running out to avoid the outcome.

Public alarm about the ‌potential danger posed by AI is growing, after Anthropic researcher Jacob Coxon said last week he had resigned, in part because the “people building AI earnestly believe that it could ​kill us all by the end of the decade.”

Anthropic scientist ​Evan Hubinger then responded to Coxon’s comments, saying he was correct ⁠and that he believed there was a greater than 10 per cent chance ​that AI could kill all humans within the next decade.

“I recently resigned from ​Google DeepMind, where I worked on AGI safety and alignment research,” Bilal Chughtai wrote on X.

“I earnestly believe that AI has the potential to kill us all, and ​that we might be running out of time to avoid this outcome.”

Chughtai, ​who co-authored research papers while at Google DeepMind, worked there as a research engineer ‌and ⁠left the company in July 2026, according to his LinkedIn profile.

Google did not respond to a request for comment outside regular business hours.

Anthropic CEO Dario Amodei called for a slowdown in the pace of advanced ​AI development over ​the weekend, winning ⁠rare backing from SpaceXAI’s Elon Musk and OpenAI CEO Sam Altman and sparking a debate over AI “doomerism.”

Chughtai also said ​that navigating AI safely was possible but would require ​coordination “to avoid ⁠this manic race between AI companies.”

“We need to pace AI development to a speed that society can handle, where emerging risks can be addressed before ⁠extreme ​harm is realised,” he added.

President Donald Trump, however, ​has dismissed the warnings, saying on social media the AI industry’s call for regulation is a “hoax.”

Warnings about AI’s risks are not new. But they took on added urgency this month after researchers in leading AI labs attached both a timeline and a probability to those concerns.

The concern is that AI could become capable of improving itself before researchers ⁠have developed reliable methods to align, monitor and control increasingly powerful systems.