In 2016, a luxury hotel in Seoul became the unlikely ground zero for an existential crisis.
Lee Sedol sat there. He was (and is) a legend. A 9-dan professional with 18 international titles, he represented the absolute peak of human intuition in the ancient game of Go. Across from him sat a computer program called AlphaGo, developed by DeepMind. Before the first stone touched the board, Lee was confident. He predicted a landslide victory, maybe 5-0 or 4-1. Most of the Go world agreed. Go was supposed to be the "Holy Grail" of AI—a game so complex, with more possible positions than atoms in the observable universe, that a machine couldn't possibly master it for decades.
Then the games started. And everything changed.
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The Shock of Game One
Honestly, the first game felt like a glitch in reality. Lee Sedol played aggressively, testing the machine with unconventional moves to see how it would react. AlphaGo didn't blink. It responded with a level of precision that felt... weirdly human, yet entirely alien. When Lee eventually resigned after 186 moves, the room went silent. This wasn't supposed to happen.
The "Strong Stone," as Lee is nicknamed, looked visibly shaken. He had spent his entire life mastering a game that defines human creativity, and suddenly, a bunch of code was outthinking him.
Move 37: The Ghost in the Machine
If Game 1 was a shock, Game 2 was a revelation. This is where AlphaGo played "Move 37."
It was a shoulder hit on the fifth line. In the 2,500-year history of Go, no human would ever play that move in that situation. It was "wrong" according to every textbook ever written. Even the commentators, including 9-dan pro Michael Redmond, were confused. They thought it was a mistake.
Lee Sedol literally got up and walked out of the room to smoke a cigarette. He needed to process what he just saw.
Later, the DeepMind team revealed that AlphaGo calculated the probability of a human playing that move at 1 in 10,000. It played it anyway because its internal "value network" saw a long-term advantage that no human brain could grasp. It wasn't just calculating; it was creating a new way to play. Lee lost Game 2. Then he lost Game 3. The match was over, technically. AlphaGo had won the series.
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Game 4 and the "Divine Move"
This is where the story gets truly incredible. Lee Sedol was down 0-3. Most people would have crumbled. Instead, Lee went back to his hotel and stayed up all night with his "brain trust" of fellow pros. He decided that if he couldn't beat the machine at its own game, he had to find a "bug" in its logic.
In Game 4, move 78 happened.
It was a wedge in the center of the board. It’s often called the "Divine Move" or "God's Touch." Just like AlphaGo's Move 37, this move was something the machine didn't see coming. AlphaGo’s algorithm, which uses Monte Carlo Tree Search, had completely dismissed the possibility of move 78. It calculated the odds of a human finding that move at—you guessed it—1 in 10,000.
After Lee played move 78, AlphaGo "broke." It started playing nonsensical moves, losing points left and right. For the first and only time, Lee Sedol saw the machine falter. He won the game. It remains the only time a human has ever beaten the full-strength version of AlphaGo in a formal match.
Why Lee Sedol Walked Away
You’d think a victory like that would keep him going, but it actually did the opposite. In 2019, Lee Sedol announced his retirement from professional Go. His reason was heartbreakingly simple: "With the debut of AI in Go games, I've realized that I'm not at the top even if I become the number one."
Basically, he felt the soul of the game had been compromised. Go used to be an abstract art form where two humans searched for "the truth" together. Now, players just memorize moves suggested by AI. In his 2025 memoir, The Art of Reading Moves in Life, Lee admitted he regrets agreeing to the release of the AlphaGo source code because it turned a game of infinite mystery into a game with "correct answers."
Actionable Insights from the AlphaGo Match
The AlphaGo vs Lee Sedol match wasn't just about a board game. It was a preview of the world we live in now.
- Intuition vs. Calculation: We often think our "gut feeling" is superior, but Lee found that in the opening moves (where he thought AI would be weak), the machine was actually strongest because it could calculate broader patterns faster.
- The "Move 78" Mentality: When facing an automated system or a rigid environment, the only way to "win" is to introduce complexity that the system hasn't been trained to handle. Innovation often lives in the 1-in-10,000 probability.
- Accepting the Tool: Today’s top Go players don't fight the AI; they use it to train. The lesson for us is that while AI might be "better" at a specific task, it lacks the human narrative and the emotional weight of the struggle.
To understand the full technical breakdown of the match, you should look into the DeepMind research papers published in Nature. They explain how the "policy networks" and "value networks" actually functioned during those five days in Seoul. If you're interested in the human side, the AlphaGo documentary is genuinely one of the best films about the intersection of technology and spirit.
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Go is still the greatest game humanity ever made. Even if we aren't the best at it anymore.
Next Steps for You
- Study the "God Move": Look up the specific board coordinates for Move 78 (L11) and try to visualize why it caused a "meltdown" in the AlphaGo algorithm.
- Watch the Documentary: Find the AlphaGo film on streaming platforms to see the raw emotion in Lee Sedol's face after Game 4.
- Explore Modern Go: Check out the "3-3 invasion" strategy, an AI-discovered move that has completely revolutionized how professionals play the game today.
The match changed AI research forever, leading directly to the development of AlphaZero and eventually influencing the architecture of the LLMs we use today. Understanding Lee Sedol's struggle is understanding our own future with AI.