WSJ. MAGAZINE
YOU CAN LEARN a lot about the people racing to build the world's most powerful technology by asking what keeps them up at night.
In the case of Demis Hassabis, it's not just the complexities of AI. He's wide awake when he should be asleep because the middle of the night also happens to be his favorite time to work. In the small hours of the morning, his brain is loose, the world is silent, and he can read, think deeply and really focus on the problems he's spent a lifetime trying to solve.
Ever since he was a boy, Hassabis dreamed of building a form of artificial intelligence that would be smart enough to crack the mysteries of the universe. When he started the research lab that became Google DeepMind, he turned his life's obsession into the mission of his company.
"The ultimate goal was to build AI and understand the mind, then use it to understand science," Hassabis tells me. "Step one, solve intelligence. Step two, solve everything else."
AI can already write code and rewrite emails and summarize meetings and prove abstract math conjectures and figure out why your washing machine keeps making that strange beeping noise. But in Hassabis's mind, the whole point of intelligence is to do something with it. What he wants to do with it is make the kind of discoveries that actually change our lives.
The clarity of his ambition makes him an outlier among the titans of his industry. Then again, Hassabis has always stood apart. Now 50 years old, he lives and works in England, where he was born, raised and knighted, and the distance between Sir Demis Hassabis and Silicon Valley gives him a different perspective altogether. "I feel like it helps me think original thoughts," he says from London.
In this moment of AI anxiety, his most original idea is offering a vision of the future that doesn't make us want to pull the plug on every data center. He understands the profound risks of this technology, but he feels it's possible to address them without overhyping them -- "It's not all disaster, and it's not all nirvana" -- and he finds it irresponsible to bring up the problems without trying to solve them.
"I'm very much in the solutions business," he says. "If there's a problem, how do we solve that with collective human ingenuity? And if you don't have a solution, then you probably need to work harder."
These days and nights, he's taking on a new role after a recent leadership shuffle at Google DeepMind, but he's working as hard as ever on artificial general intelligence. He thinks of AGI as a system with the full cognitive range of the human brain -- not just some of our capabilities but all of them. Earlier this year, he felt AGI was still three to five years away. Now he expects AGI within two to three years.
"It's soon," he tells me. "I'm updating nearer and nearer."
The people who know him best tend to use the same words to describe Hassabis: brilliant, relentless, so ferociously competitive that he can't stand losing a game of foosball. So as it gets nearer and nearer, I ask someone who has spent almost his entire life preparing for this moment what it will take to win.
"Winning," he says, "means helping humanity steward AGI into the world safely and securely for the benefit of everyone. I think that's the most important thing I can do over the next few years."
HASSABIS FIRST started thinking about thinking as a 4-year-old chess prodigy who needed to sit on a phone book just to see the board. By the time he was 12, he was one of the best chess players in the world for his age. But he suspected there was more to life than moving "inanimate lumps of plastic." With earnings from chess tournaments, he bought his first computer.
He turned out to be just as talented at programming games as he was at playing them. When he was 16, he took a gap year before college to work at a videogame production company and experimented with primitive forms of AI. But he already had reason to believe AI would reshape the world.
"Not to ascribe too much to my childhood," he says, "but one of the reasons I got into this whole field is that I read way too much sci-fi."
His boss offered Hassabis a million pounds to skip college, but he turned down the money and went to Cambridge. At the university that produced Newton and Turing, he studied computer science -- and more games. He taught himself Go, a board game older and vastly more complex than chess. He also went to the pub and mastered a simpler game: foosball. "I was Cambridge champion," he tells me. He later set up a foosball table at work and dared colleagues to beat him, according to The Infinity Machine, Sebastian Mallaby's biography of Hassabis. When he finally lost, he stood up and declared: "It's like my soul is on fire!"
In 2010, after earning a Ph.D. in neuroscience and deepening his study of the mind, he started DeepMind. When he set out to apply his knowledge of human intelligence to the artificial kind, it was only natural to start with games.
At a company that he envisioned as a cross between Bell Labs and the Manhattan Project, the research began by teaching AI to play Pong. For months that felt more like an eternity, the AI didn't win a point. But once it started winning, it couldn't stop. The same thing happened in Breakout. After 100 games, the AI wasn't very good. After 300, it was as good as any human. After 500, it was superhuman. When their study of AI conquering Atari games was published, Hassabis stood up at a DeepMind meeting and toasted what they all thought was a huge accomplishment.
By the way, he added, one day we're going to win a Nobel Prize with this technology.
Google bought DeepMind in a $650 million deal in 2014, and the lab had its breakthrough in 2016, when its revolutionary system AlphaGo beat a top-ranked Go player in a series of matches that stunned humans around the world. The highlight of AlphaGo's victory was a move so counterintuitive and terrifyingly creative that Move 37 became a watershed moment in AI history.
When it came time for his next challenge, a scientific puzzle that had fascinated Hassabis ever since he learned about it from a biologist friend at a Cambridge foosball table was at the top of his list: the protein-folding problem.
In this one problem, Hassabis saw an opportunity to find solutions to a whole lot more, since predicting the shapes of proteins -- "unbelievably exquisite things," he calls them -- held the promise of understanding diseases and curing them. When DeepMind's AlphaFold system effectively solved this problem in 2020, it was AI's most impressive feat yet. But it wasn't enough to satisfy Hassabis. In a meeting to discuss what else they could do, his colleague John Jumper mentioned that they could predict the structures of millions and millions of proteins in a month, and --
"Why don't we just do that?" Hassabis said. "We should just do that."
So they did. Before long, they had predicted the structure of nearly all known proteins -- more than 200 million in total -- and released them to scientists free of charge. And in 2024, he nailed one more prediction.
For their pioneering work on AlphaFold, Hassabis and Jumper won the Nobel Prize in chemistry.
A DECADE AGO, before the Nobel, before his lifetime of interests came together like a protein taking shape, so long ago that he still had a full head of hair, Hassabis took a long walk with a researcher he was trying to hire.
"I was expecting to talk to a CEO," says Pushmeet Kohli, now DeepMind's vice president of science. "I left with the impression that I was talking to a scientist."
From the company's early days, Hassabis's priority was using AI to advance science and improve human health. If step one is solving intelligence, he was always more interested in step two. "You have to have the tool first, and building that tool is fascinating," he says, "but it's even more fascinating what you can do with those tools."
But in America, the tools are deeply unpopular. This bothers Hassabis, who sees it as another problem to solve. For years, AI leaders have teased the possibility of curing cancer. To change people's minds about AI, he says it's time to actually cure cancer.
"Then," Hassabis says, "I think people could really start feeling the benefits of AI directly in their lives."
Does he feel that's possible?
"I don't see any insurmountable obstacles currently," he says. "I see hard things, but not impossible things."
To overcome them, he's in the process of reorienting his own life. After running Google DeepMind, he recently took on the job of Google parent Alphabet's chief scientist and DeepMind's chair. For his next act, he's shifting his attention to accelerating scientific progress even more than he already has.
A few years ago, after the AlphaFold milestone, Hassabis launched a company inside Google called Isomorphic Labs, a nod to the mathematical term describing objects with the same form, shape or structure.
As the CEO of Isomorphic, he isn't satisfied by simple progress. When the DeepMind team entered a competition for predicting the shape of proteins, for example, he didn't just want to win. He wanted to make the entire premise of the competition obsolete. He wanted to beat the game. He still does.
"We're not just trying to solve one disease," he says. "We're trying to create many models, and then a whole process, that can revolutionize the drug discovery process itself."
To him, the difficulty of the problem is precisely what makes it worth tackling. When I ask if he expected AI-designed drugs to take so long to be discovered, tested and turned into medicine, he says of course he did. Then he sounds less like a CEO than a scientist in awe of his subject.
"It's just sort of mind-blowing that it works at all," he says. "That's what draws me to biology -- and life. It's the most complex system we know of."