Ten years ago, Google DeepMind's AlphaGo defeated world champion Lee Sae Dol in Seoul — a milestone experts predicted would take decades. The match captivated over 200 million viewers and featured "Move 37," an unconventional play so unexpected that commentators initially thought it was an error. AlphaGo demonstrated AI could move beyond mimicking human expertise to discovering genuinely novel strategies.

The system combined deep neural networks with reinforcement learning and advanced search, learning first from human games then refining through millions of self-played matches. This approach spawned AlphaZero, which mastered Go, chess, and shogi from scratch without any prior knowledge. The techniques proved AI was ready for real-world scientific challenges.

That promise materialized quickly. AlphaFold 2 solved the 50-year protein folding problem in 2020, earning John Jumper and Demis Hassabis the 2024 Nobel Prize in Chemistry. Over 3 million researchers now use the AlphaFold database. AlphaProof achieved silver-medal performance at the International Mathematical Olympiad, while AlphaEvolve discovered more efficient matrix multiplication algorithms.

The work points toward artificial general intelligence. Gemini now uses AlphaGo's search techniques alongside multimodal understanding, and an AI co-scientist at Imperial College London independently arrived at the same antimicrobial resistance hypothesis researchers spent years validating experimentally.