Demis Hassabis is now one of the most important figures in the AI revolution, but things could’ve gone very differently had he not lost a chess match all the way back in 1988.
The Google DeepMind CEO in the Google DeepMind documentary ‘The Thinking Game’ shared a striking story about the moment that redirected his path from professional chess to artificial intelligence. At just 12 years old, Hassabis was the second-highest rated chess player in the world for his age, and was seemingly destined for a career at the board. But a grueling 10-hour match at an international tournament in Liechtenstein would change everything—not because he lost, but because of what that loss made him realize about how human intelligence should be spent.
The pressure on the young prodigy was immense. “Although I was on track to be a professional chess player, I thought that was what I was gonna do, no matter how much I loved the game, it was incredibly stressful,” Hassabis recalled. “Definitely was not fun and games for me. My parents would get very upset when I lost a game and angry if I forgot something, because it was quite high stakes for them. It cost a lot of money to go to these tournaments and my parents didn’t have much money.”
The pivotal moment came during that tournament in the mountains of Liechtenstein in 1988. “I was at this international chess tournament up in the mountains, and we were in this huge church hall with hundreds of international chess players,” he said. “I was playing the ex-Danish champion. He must have been in his thirties.”
What followed was a marathon match that tested not just his chess skills but his mental endurance. “There was a long time limit. The games could literally last on deck. We were into our 10th hour, and we were in this incredibly unusual ending. I think it should be a draw, but he kept on trying to win for hours.”
Then came the mistake that would paradoxically become one of the most consequential decisions of his life. “Finally, he tried one last cheap trick. All I had to do was give away my queen, then it would be stalemate. But I was so tired. I thought it was inevitable I was gonna be checkmated, and so I resigned.”
His opponent’s reaction was immediate and crushing. “He jumped up, he just started laughing and he went, ‘Why have you resigned? It’s a draw.’ And he immediately, with a flourish, showed me the drawing move. I felt so sick to my stomach.”
But it wasn’t just the embarrassment of the blunder that affected young Hassabis. “It made me think the rest of that tournament, are we wasting our minds? Is this the best use of all this brain power? Everybody’s collectively in that building. If you could somehow plug in those 300 brains into a system, you might have solved cancer with that level of brain power. This intuitive feeling came over me that although I love chess, it’s not the right thing to spend my whole life on.”
That epiphany in a church hall in Liechtenstein planted the seed for what would eventually become DeepMind, the AI research company that Hassabis co-founded in 2010 and sold to Google in 2014 for over $500 million. The company has since developed groundbreaking AI systems including AlphaGo, which defeated the world champion Go player in 2016, and AlphaFold, which solved the protein-folding problem that had stumped scientists for decades. In 2024, Hassabis was awarded the Nobel Prize in Chemistry for his work on AlphaFold. Google DeepMind is now at the cutting edge of AI, having released its Gemini 3 Pro model, which has topped most benchmarks.
The irony is that Hassabis’s question—could we “plug in those 300 brains into a system”—is essentially what he’s spent his career trying to answer. Rather than abandoning chess entirely, he redirected that competitive intensity and pattern-recognition ability toward building machines that could augment and amplify human intelligence. His story reflects a broader trend among AI pioneers who came from competitive gaming backgrounds: the skills honed in games like chess and Go—strategic thinking, pattern recognition, handling high-pressure decisions—turned out to be remarkably transferable to designing intelligent systems. The lost game wasn’t a defeat at all. It was the beginning of something far larger.