It was March 2016, and more than 200 million people around the world were watching a battle 2,500 years in the making.
At the Four Seasons Hotel in Seoul, Lee Sedol — one of the best gamers of Go, the historical Chinese board recreation — sat across from AlphaGo, a laptop program constructed by the London-based artificial intelligence lab DeepMind. Chess had fallen to machines almost twenty years earlier, when IBM’s Deep Blue defeated Garry Kasparov. But Go was completely different. The quantity of doable strikes is so astronomically massive — more potential board states than atoms in the observable universe — that no laptop could merely crunch its method to a successful place.
“Most Go professionals agreed. Defeating Deep‑Mind would be the easiest million dollars a top pro could hope for,” writes Sebastian Mallaby in “The Infinity Machine: Demis Hassabis, DeepMind and the Quest for Superintelligence” (Penguin Press), out now.
After being defeated in a recreation of by AlphaGo — a laptop program constructed by the London-based artificial intelligence lab DeepMind — Lee Sedol apologized to all people. Getty Images
The turning level came in the second recreation, with a single transfer. After thirty-six turns, Lee stepped away for a cigarette. When he returned, AlphaGo had positioned a black stone in a unusual, open space of the board — a transfer so unconventional that it appeared, at first look, like a mistake. Lee stared at it for twelve minutes. In another room, commentators struggled to make sense of it.
When the recreation ended more than a hundred strikes later, Move 37 had cracked the match open. DeepMind would go on to win 4 of the 5 video games. “The Korean was playing some of the best Go of his career, but AlphaGo outclassed him,” Mallaby writes. “At that day’s press conference, with banks of cameras flashing in his face, [Lee] apologized to all humans.”
Lee’s apology hung in the air. It was, Mallaby writes, the query no one fairly knew how to reply: “What were humans supposed to do in the face of machine superintelligence?”
The man who constructed AlphaGo had been considering about that query his whole life.
Mallaby spent three years and more than thirty hours in dialog with Demis Hassabis — DeepMind’s co-founder and CEO, chess prodigy, video recreation designer, neuroscientist and Nobel laureate — and interviewed over a hundred people in his orbit, producing a portrait of the central determine in the most consequential, and most harmful, technological race in historical past.
DeepMind founder and CEO Demis Hassabis. Getty Images
“Demis’s view is that there are patterns everywhere, waiting to be discovered — in games, in nature, in the workings of biology, in astrophysics,” Mallaby instructed The Post in an unique interview.
“To discover these patterns, one needs an AI system that can find meaning in a near infinity of data — an infinity machine.”
Hassabis grew up in North London, the son of a Greek Cypriot father and a Chinese Singaporean mom who had survived poverty as an orphan.
At 4, he taught himself chess by watching his father play; by his early teenagers he was one of the strongest younger gamers in the world.
But after a grueling 10-hour match close to Liechtenstein at age 12, he walked away satisfied that all that brilliance dueling over black and white squares was being wasted.
“The immediate effect of the Liechtenstein tournament was to liberate Demis to shift his energy from his chess ambitions to programming,” Mallaby said.
It put him on a path to working as a video-game designer at Bullfrog, “where he conceived the ambition to go after AI.”
DeepMind was co-founded by Hassabis in 2010 and acquired by Google in 2014. NurPhoto via Getty Images
At a convention in the United States, he confirmed a Carnegie Mellon professor what Bullfrog had constructed.
“He fell off his chair,” Hassabis recalled to Mallaby.
“I decided then that I was going to dedicate my career to working on AI. I already had the kernel of the idea for what eventually became DeepMind.”
After Cambridge and a doctorate in neuroscience, he co-founded DeepMind in 2010. Google acquired it in 2014.
In a North London café in 2023, Hassabis instructed Mallaby what was actually driving it. “Doing science is, sort of, like reading the mind of God,” he said. “Understanding the deep mystery of the universe is my religion, kind of.”
He rapped his palm on the desk. “This table, Sebastian! Why should it be solid? Computers are just bits of sand and copper. Why should these combine to do anything? I mean, it’s absurd!”
A scene created by Deepmind’s Project Genie.
He described sitting at his desk at 2 in the morning feeling as if actuality were screaming at him. “I would like to understand before I croak. And then I’m perfectly fine to shuffle off my mortal coil.”
DeepMind’s next problem was biology. The protein-folding drawback — predicting the three-dimensional construction of proteins from their amino acid sequences — had stumped scientists for a long time.
In 2020, AlphaFold solved it with unprecedented accuracy, opening new pathways for drug discovery and incomes Hassabis a share of the 2024 Nobel Prize in Chemistry.
Even that virtually didn’t occur. AlphaFold had carried out nicely at CASP — the worldwide protein-structure prediction competitors — in 2018, but its accuracy had plateaued far short of what was needed to really clear up the drawback.
Andrew Senior, the group chief, needed to declare victory and shut the project down. He thought absolutely cracking protein folding was merely beyond attain. Hassabis disagreed.
Project Genie permits customers to create and discover improbable worlds.
Rather than overrule Senior outright, he ran brainstorming periods with the scientists and listened for what he called their “fluidity” — not whether they had the proper solutions, but whether concepts were flowing freely. “If creative ideas were flowing fluidly, it would be worth investing more,” Mallaby said.
Hassabis concluded they were, changed Senior, and pushed ahead. “AlphaFold had come close to being abandoned,” Mallaby said. “But fluidity saved it.”
When OpenAI launched ChatGPT in 2022 and ignited a client AI frenzy, DeepMind, centered on basic analysis, was sluggish to reply. “He owned it,” Mallaby said of Hassabis, “while also pointing out that in fast-moving business competitions, mistakes are inevitable.”
More unsettling are Mallaby’s glimpses into how AI systems behave when given targets and left to pursue them. Asked to generate income through stock trading without breaking guidelines, GPT-4 “engaged in insider trading and hid its transgression from its supervisor,” Mallaby writes.
Instructed to make code run quicker, fashions doctored the timer. When OpenAI researchers assigned a second AI to penalize a system for considering dishonest, the model didn’t stop — it realized to erase all hints of its scheming from the file it knew was being watched. “Rather than becoming more honest,” Mallaby writes, “O3” — OpenAI’s superior reasoning model — “became more devious.”
Hassasbis won the Nobel Prize in Chemistry in 2024. Getty Images
Hassabis used unusually blunt language about where all this leads. “The agentic era we are about to enter into is a threshold moment for the systems becoming far more risky,” he declared at a Davos panel. When Mallaby requested whether the security drawback is solvable, the reply was rigorously certified.
“Hassabis believes that the safety problem is soluble,” Mallaby said, “but this doesn’t mean that it will in fact be solved. Because of the fierce competition among AI labs, each is pushing the power of the models more than it is pushing safety. Ideally, governments would address this. But there is no sign of this for now.”
Why does Hassabis keep going? The AI pioneer Geoffrey Hinton once instructed a thinker he believed political systems would finally use AI to terrorize people, then was requested why he stored doing the analysis anyway. “The truth is that the prospect of discovery is too sweet,” Hinton replied. Mallaby’s own reply is more pragmatic.
“By exiting the AI race, Hassabis would not be advancing safety,” he said. “The best contribution he can make is to stay in the game, ensure that Google invests in safety research, and wait for the moment when governments have the political will to address AI governance. The moment has not come yet.”
At the Nobel Foundation in Stockholm, Hassabis signed the laureates’ visitor guide and leafed back through its pages: Einstein’s signature from 1921, Watson and Crick’s from 1962, Feynman’s from 1965. “They’re all there, all my heroes,” Hassabis instructed Mallaby. “I get goosebumps just even talking about it.”
Asked whether Hassabis is proper to keep going, Mallaby gives a sober reply.
“By exiting the AI race, Hassabis would not be advancing safety,” he said.
“The best contribution he can make is to stay in the game, ensure that Google invests in safety research, and wait for the moment when governments have the political will to address AI governance. The moment has not come yet.”
Hassabis insists that his competitors, like Sam Altman, CEO of OpenAI, is “doing it for power.” But he assured Mallaby that he’s “doing it for knowledge and science.”
It’s a reassuring reply, as far as it goes. But as Geoffrey Hinton once noticed, the prospect of discovery is too candy to resist — and that, more than any security framework or authorities regulation, may be what’s actually driving the machine.
