Google’s Nobel-Winning AI Scientist Says Learning How To Learn Is The Key Skill in the AI Age September 16, 2025 by Ali Azhar 
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In Athens, where ancient ideas once shaped the foundations of logic and reason, one of today’s leading scientific minds returned to talk about intelligence of a different kind. Demis Hassabis — CEO of DeepMind, and fresh off a Nobel Prize win — wasn’t interested in looking back. His focus was firmly on what’s coming next: a world being reshaped by artificial intelligence, one that may force us to rethink not just what we learn, but how we learn it.
He spoke about the speed of change — how AI is already rewriting the rules in science, research, and engineering. Models now move faster than most institutions can keep up with. In his view, the most valuable skill won’t be deep technical training, but the ability to keep learning, over and over again, no matter your field.
And Hassabis isn’t just guessing. He’s been at the center of some of AI’s biggest moments — from AlphaGo’s shocking win over the world’s best Go player to AlphaFold’s leap in protein prediction, which changed the way biologists approach drug development and earned him the highest honor in chemistry.
He didn’t come from one discipline, either. His background blends neuroscience, computer science, and even competitive chess — a mix that puts him right where many of today’s breakthroughs are happening. So when he talks about what AI might become, people tend to listen.
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When asked about the future, Hassabis admitted that it’s hard to predict how things will shape up. “It’s very hard to predict the future, like 10 years from now, in normal cases,” he said. “It’s even harder today, given how fast AI is changing, even week by week. The only thing you can say for certain is that huge change is coming.”
That change, he explained, won’t just come in the form of new tools or faster machines — it will reshape how people approach knowledge itself. As AI takes on more of the heavy lifting in science and engineering, the real advantage for humans will lie in how quickly we can pivot, absorb unfamiliar concepts, and apply them in new ways.
According to Hassabis, the real skill going forward isn’t any one subject — it’s learning itself. Not just memorizing facts, but figuring out how to tackle new problems, how to get up to speed in unfamiliar areas, how to stay curious even when things keep changing.
He calls these “meta-skills,” and he made it clear they’re going to matter more than ever. When AI keeps shifting the ground under your feet, knowing how to keep learning might be the only thing that really sticks.
These meta skills are already in action. A scientist picking up basic coding so they can run their own models. An engineer figuring out how to work with a completely new simulation platform after theirs got phased out. A doctor learning to make sense of AI-generated diagnostics. These aren’t big career shifts or entirely new skills to learn. However, they’re signs of what’s becoming normal, which is stepping into something unfamiliar and figuring it out anyway. That’s exactly what Hassabis means when he says "learning how to learn.”
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Hassabis made one thing clear: the way we learn today won’t be enough for the world we’re heading into. Our education systems were built for a slower world, one where careers followed a straight line and knowledge stayed useful for decades. That’s not the case anymore, especially in science and engineering where AI is rapidly reshaping how problems get solved and how discoveries are made.
What matters most now isn’t knowing everything — it’s knowing how to keep learning and adapting. We might have to get used to learning not just once, but continuously as technologies continue to evolve.
Hassabis also spoke about where this might all be heading. He suggested that artificial general intelligence (AGI) — machines able to reason across different problems much like humans do — could be less than ten years away. If that happens, the pace of discovery could speed up in ways that are hard to picture. Medical research, climate science, energy systems — whole areas of work could move faster than ever before. He called it a future of “radical abundance,” where the limits aren’t about tools or data anymore, but about how ready we are to use them.
The Prime Minister of Greece, Kyriakos Mitsotakis, who joined Hassabis for the event, highlighted a different angle to what we can expect in the near future. He warned that none of it will matter if people don’t see personal benefit. If the wealth and progress created by AI pile up in a handful of companies, trust in the technology will collapse.
He said that when most people see vast fortunes made by a few and very little change in their own lives, resentment builds. And in his view, that path leads not to abundance, but to unrest.
