I recently read 먼저 온 미래 (roughly, “The Future That Arrived Early”) by Chang Kang-myoung. The book traces how the world of Go changed in the decade after AlphaGo defeated Lee Sedol in 2016. AlphaGo was the Go-playing AI developed by Google DeepMind, and Lee, a South Korean professional player, was one of the best players in the world at the time.
There has already been plenty of coverage of AlphaGo and the team behind it at Google DeepMind in documentaries, articles, and interviews. But much less attention has been paid to how the Go world changed afterwards. That is what makes this book so interesting. More than the match itself, what stayed with me was everything that followed.
Here are a few of the changes in the book that stood out most.
AI became the final authority
Go has too many possible variations for humans to fully comprehend. That is why professional players have long relied on more abstract language to explain positions: influence, thickness, momentum, board sense, awkward shape, poor aftertaste, and so on. Some players went even further than that. They talked not just about winning and losing, but about balance, elegance, and beauty. For them, Go was something close to art.
AlphaGo broke that world open. As AI began showing better answers through win rates and optimal sequences, the old language of intuition and interpretation quickly lost its authority. Long-established game records and joseki, the standard opening sequences of Go, no longer carried the same weight. Once AI could show deeper Go than any human record or human theory, it stopped being a reference and became the closest thing to a right answer.
Before AlphaGo, the Go world was a place where people proposed new ideas, argued over them, and let different styles and interpretations coexist. After AlphaGo, that order changed almost overnight. Game records, joseki, commentary, and intuition were no longer the final word. AI was.
For professional players, it was more than a loss
For professional players, AlphaGo’s victory did not feel like an ordinary defeat. Go was not just their job. It was the system of meaning they had built their lives around: their instincts, their discipline, their pride. That is why so many of their reactions sounded less like disappointment and more like collapse. “It felt like everything was falling apart.” “It felt like the world I knew had collapsed.” “What was the value of all the effort I had put in?” Those are not the words of someone who simply lost to a stronger opponent.
Lee Sedol eventually retired after the AlphaGo match and said this: “When I was young, I was taught that Go was like art. I think of Go as something like a work of art made with stones, but what kind of art can this be now? The very art I learned has collapsed. I felt it would be hard to keep going.” What broke after AlphaGo was not just a few patterns or ideas. It was the meaning many professional players had attached to the game for their entire lives.
At first, some people tried to frame the loss differently. Since AlphaGo had been trained on human game records and centuries of accumulated knowledge, you could still say this was not a machine simply surpassing humans, but human knowledge being extended in a different form.
Then AlphaGo Zero arrived in 2017 and even that reading stopped working. AlphaGo Zero was not trained on human games. It was given only the rules of Go and improved by playing against itself over and over. In just 36 hours, it surpassed the version of AlphaGo that had beaten Lee Sedol. After 72 hours, it went 100–0 against that earlier version. For many professional players, that may have landed even harder than Lee Sedol’s defeat itself. It suggested that the game knowledge built up over thousands of years were not essential building blocks for perfect Go after all. They may even have looked more like human limitations that had to be left behind.
Players started training to play more like AI
Before AlphaGo, professional study was mostly about reviewing the games of stronger players, discussing them with other professionals, and absorbing them in your own way. Even when two players studied the same game record, what they took from it could be completely different. After AlphaGo, that changed fast. Study became much more about checking where your judgement diverged from AI’s recommendation and trying to close that gap. It became less important to deepen your own interpretation and more important to play closer to AI.
Professional players began reviewing games with AI analysis tools. These programs calculate win probability move by move and mark the best point with a blue dot, which players call the “blue spot”. The problem is that AI does not really tell you why that move is best. So studying became less about expanding your own understanding and more about trying to understand the move AI chose, then correcting yourself in that direction. Players who resisted AI-style play quickly fell behind. If you wanted to stay competitive, you had to train yourself towards AI.
The clearest example of this shift is Shin Jinseo. He matters not just because he became the world number one in the AI era, but because his move choices were known to line up with AI recommendations more closely than most top players, and because he pushed harder than anyone to reduce the gap between AI’s judgement and his own. Shin himself said that this kind of study was not enjoyable and was mentally draining, but also that he had worked harder than anyone. After AlphaGo, the standard for being the best changed. It was no longer about pushing your own style to the limit. It was about endlessly correcting the distance between yourself and AI. Shin Jinseo became the number one player in that new world.
The authority of players, teachers, and commentators all weakened
What changed after AlphaGo was not just the confidence of individual players. The authority that professional players had long held also started to erode. Professionals used to be seen as people who could read deeper and judge more accurately than everyone else. Once AI arrived, that information gap narrowed very quickly. The shift showed up early in the teaching market. Go schools, private lessons, and teaching games became less valuable, and the old structure where being a professional automatically meant being closer to the right answer started to weaken.
Teachers were affected too. In the past, if a teacher said, “This is how you should play here,” that was often the end of it. Now even students can check with AI straight away. Teaching started to move away from simply declaring the right answer and towards comparing one’s own thinking with AI’s and explaining the difference. The teacher became less of an authority figure and more of an interpreter.
Commentators changed as well. AI win-rate graphs made judging the flow of a game easier, but they also weakened the commentator’s role. The commentator was no longer the person best placed to say who was ahead. More often, they were explaining the judgement that was already visible on the screen. Watching Go also changed. It became less about interpretation and appreciation, and more about live evaluation. A professional move was no longer just something to admire or discuss. It was also something anyone could instantly see as a mistake.
In the end, the same thing happened to players, teachers, and commentators alike. They were no longer the people who possessed the answer. In the AI era, expertise in Go started to look less like owning the truth and more like explaining a truth that had already been surfaced by AI.
In some ways, the world of Go became more open
After AlphaGo, the world of Go became colder and more standardised. But in another sense, it also became more open. The Go world had always been more closed than it looked from the outside. As Lee Sedol once recalled, there was even a time when you had to physically go to the Korea Baduk Association and photocopy game records. The people who had quicker access to strong game records, better teachers, or stronger training environments had a real advantage. Players outside the major Go countries were often at a disadvantage in that system.
AI broke some of that open. In the past, even when you learned from a stronger player, the explanation could be vague, and it was often hard to tell whether you were really on the right track. AI, at least, could show you more directly which move was better and which direction looked more promising. Studying may have become more sterile, but the path also became shorter and clearer. You no longer had to rely as heavily on instinct, connections, or environment to improve quickly.
One of the most interesting examples was the rise of women players. Go had long been a male-dominated world, and that bias ran through training schools, institutions, mentorship structures, and personal networks as well. Seen in that light, the narrowing gap between male and female players after AI became part of training feels especially significant. It suggests that the old gaps in Go were not just about talent. They were also shaped by access to information, access to training, and informal structures around the game. AI made Go harsher in some ways, but it also opened up parts of the game that had once been more closed off.
So the post-AlphaGo world became more uniform and less romantic, but also more open in some important ways. AI took some of the mystique out of Go, but it also widened access to knowledge and learning.
The past ten years of Go were, in many ways, an extreme case. The standard changed almost overnight. There was barely any time to prepare. Before the AlphaGo match, very few people really felt that shift coming, and Lee Sedol himself had been confident of a clear win. Then after that event, Go was no longer a game humans could beat AI at.
That said, I do not think this maps neatly onto the changes we are starting to see now. What we are living through is fast enough already. But there is still a real difference between a world like Go, where the rules and the goal are clear, being reorganised around a single dominant standard, and the real world, where work is shaped by competing interests and much fuzzier criteria.
Go did not disappear. But it stopped being the same game in the same world. Authority changed. Study changed. Even the meaning of being the best changed. That is what I found most interesting in the book. Not simply that humans lost, but what the loss reorganised afterwards.