AlphaGo anniversary sparks AI reality check

The post lands on a tension AI developers feel every day: model capability has moved absurdly fast, but real-world deployment has moved much slower than the hype cycle promised.

• –It frames AlphaGo’s Move 37 as a symbolic starting point for the modern AI era, then contrasts that breakthrough with the still-limited impact of robotics, autonomy, and consumer hardware in 2026.
• –The strongest insight is that the path to today’s AI boom came through scaled language modeling, not through the game-playing approaches that made AlphaGo feel like a preview of general intelligence.
• –DeepMind’s own 10-year retrospective strengthens the argument by explicitly connecting AlphaGo’s search and reinforcement-learning ideas to later systems like AlphaFold, AlphaProof, and Gemini’s reasoning stack.
• –For AI developers, the takeaway is less “AI stalled” than “capabilities outran productization” — the bottleneck is no longer proving models can do impressive things, but getting those abilities into science, robotics, and daily workflows.