Google DeepMind CEO Demis Hassabis explains how governments could deliver incredible good for citizens by applying AI at scale: Asked what he'd hope governments would use AI for if he could wave a magic wand, Hassabis points to the foundations of public life—health, education, and the basic machinery of administration. "I think governments should be using AI," he says, adding that he wants "to support all sort of democratically elected governments." His priorities are clear: "The things I would love to see them use it for and what we're trying to build our systems to be good for is things like improving public health, education. I mean, all of these things need to be rethought." For @demishassabis, the upside is transformative: "The efficiency gains and the amount of good we can do with it, governments could do with it for their citizens could be incredible." He notes that this isn't hypothetical. Some governments are already moving in this direction: "some countries are doing it like Singapore and UAE I think are leaning into these types of use cases." Energy is another area he highlights, and here he draws on DeepMind's own experience: "I would love to see it being used for things like energy like optimizing energy grids. We did that with our data centers and save 30% of the energy used for the cooling systems." The throughline of his argument is scale: "I think there's enormous societal gain from applying AI at scale to these types of areas." But Hassabis doesn't end on pure optimism. He acknowledges the harder reality surrounding the technology: "The geopolitics of the world is very complicated right now and these are dual-purpose technologies."

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Google DeepMind CEO Demis Hassabis on why young people should make themselves "superpowered" with AI tools: When Hassabis gives talks at universities and schools, his core piece of advice is simple: Lean all the way into where technology is heading. "You've got to just go with the flow of the direction," he says. "I would immerse myself in every tool available and just become almost like superpowered with those tools and those capabilities." His reasoning comes from what he sees inside the frontier labs themselves. So much effort goes into simply building the next versions of these models that even the people making them can't keep up with everything those models could do. "Even at the frontier labs, there's so much work that has to go into just making the next versions of these frontier models and then all the adjacent models. So for us, like Veo and Nano Banana and Gemini, even we can only explore a fraction of the applied things you could do with it, the applications you could make with it." And that gap, @demishassabis explains, is widening: "I think that gap's getting bigger and bigger, in terms of the overhang of the capabilities, all the cool stuff on the latest models. And the release schedules are getting faster and faster." This is where Hassabis sees the opportunity. The people positioned to win aren't necessarily the ones building the models, they're the ones who master the tools and point them at something new. "The opportunity space is getting huge for people who are really expert at using those tools and then apply it to some new domain." He puts it in stark terms: "A kid these days could probably start a multi-billion dollar business in some ways, using these tools in some new way that no one had thought about."

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Google DeepMind CEO Demis Hassabis on the central question of his life: what can humans do that AI never will? Asked about the limits of artificial intelligence, Hassabis doesn't hesitate to say this is the question that drives him: "Yes, it is." His starting point is one of his heroes, Alan Turing. He explains that Turing described theoretical constructs—Turing machines—that underpin all modern computers, machines "that are able to compute anything that's computable… anything that can be described as an algorithm." The provocative leap comes when Hassabis turns that lens on biology. The AI systems his teams build are Turing machines, and he suspects the brain may be one too: "A lot of neuroscientists including me think that maybe the brain… a good model for the brain is an approximate Turing machine." He's careful to note this isn't settled. Some of his peers see it differently, including physicist Roger Penrose: "Friends of mine like Roger Penrose… believe there might be some quantum effect in the brain." But Hassabis points to where the evidence currently stands: "So far neuroscience hasn't found any quantum effects in the brain… people have looked quite carefully and we haven't found any." His conclusion from that absence is striking. If the brain runs on ordinary computation, then there may be no hard ceiling on what AI can eventually match: "It looks like most of what's going on in the brain is kind of classical computation… so therefore it's not clear what the limit would be in terms of eventually what an AI system could do and could mimic." For @demishassabis, building intelligence is a mirror held up to ourselves, not only a feat of engineering. He describes the project as a kind of experiment that reveals what we are: "I think we'll have almost like a control study comparison to the human mind. And then I think we'll see in this journey what are the differences and what's unique about the mind." He stays genuinely open about what that comparison might expose. Some things, he suspects, may never transfer: "There could be unique things and certainly unique connections between humans that will never be replicated by these AI systems." But the capabilities we tend to treat as distinctly human, he believes, are within reach: "A lot of things that we currently are not in reach, like long-term planning and reasoning and maybe some forms of creativity... I think eventually AI systems will be able to do."

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Sam Altman on what to tell a 25-year-old trying to figure out what to build their life around: Asked what advice he'd give to someone in their mid-twenties today, Sam splits his answer into the obvious tactical layer and the deeper one most people miss. He starts with the tactical: "Learn how to use AI tools." Then he points out how quickly that advice itself has shifted: "It's funny how quickly the world went from telling the average 20 year old to 25 year old, learn to program, to programming doesn't matter. Learn to use AI tools. I wonder what will be next. But of course there will be something next." That's the catch. Whatever specific tool you learn today will get replaced. Which is why he doesn't stop there. On the broader front, @sama argues the real edge comes from a set of skills people usually treat as innate personality traits: "I believe that skills like resilience, adaptability, creativity, figuring out what other people want — I think these are all surprisingly learnable. And it's not as easy as, say, like go practice using ChatGPT, but it is doable. And those are the kind of skills that I think will pay off a lot in the next couple of decades." The framing is worth sitting with: Most people assume resilience and creativity are things you either have or you don't. Sam is saying the opposite: they're trainable, they just don't come with a tutorial. There's no ChatGPT prompt for getting better at reading what other people actually need. Asked whether the same advice applies to a 45-year-old, his answer is short — learn to use it in your role now. And when pressed on whether OpenAI will employ more or fewer people once AGI arrives, Sam doesn't hesitate: "More." Then he expands: "There will be more people, but each of them will do vastly more than what one person did in the pre-AGI times."