Alphabet Inc.’s GOOG GOOGL Google DeepMind CEO Demis Hassabis has offered a more cautious outlook on the arrival of artificial general intelligence (AGI) than the tech giant’s co-founder, Sergey Brin.
What Happened: In a conversation on the Hard Fork podcast by the New York Times this week, the interviewer pointed out that Brin thinks AGI will come before 2030, while Hassabis says just after 2030.
This raised an intriguing question: given that both individuals likely have access to similar data, roadmaps, and insights into the state of AI development, what accounts for their differing forecasts on AGI’s arrival? What is one seeing that the other is not?
In response, Hassabis said that he is sticking to a timeline he’s maintained since DeepMind’s founding in 2010.
“We thought it was roughly a 20-year mission, and amazingly, we’re on track,” Hassabis said. “It’s somewhere around there, I would think.”
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Earlier, Brin jokingly accused Hassabis of “sandbagging”—intentionally downplaying timelines to later overdeliver. However, during the interview, Hassabis stood by his reasoning, pointing to the complexity of defining AGI itself.
“I have quite a high bar,” he said. “It should be able to do all of the things that the human brain can do, even theoretically. And so that’s a that’s a higher bar than, say, what the typical individual human could do, which is obviously very economically important.”
When asked whether AGI would emerge through steady improvements or sudden breakthroughs, Hassabis said both are likely necessary. “We push unbelievably hard on the scaling,” he said, while also investing in “blue sky” research like AlphaEvolve.
Why It’s Important: Last year, Anthropic CEO Dario Amodei predicted that AGI could become a reality as early as 2026 or 2027, though he cautioned that unforeseen external factors might delay its development.
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OpenAI CEO Sam Altman has previously said that AGI could arrive during Donald Trump’s presidency.
In 2022, Ark Invest CEO Cathie Wood also suggested that AGI could become a major driver of economic growth.
The definition of AGI itself remains fluid. As noted by Tom’s Guide, while some define AGI as human-level competence across all domains, others focus on its capacity to learn, adapt, and perform tasks in ways that mirror human cognition, requiring the system to go beyond its initial training data and generate truly autonomous output.
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