Experts across fields are experiencing AI solutions that are not just correct but elegant and human-like, solving problems they've worked on for decades. This 'Move 37' moment, named after the surprising Go move by AlphaGo, indicates AI is becoming a creative partner rather than just a productivity tool.

Historically, investment tech focused on speed. Modern AI, like AlphaGo, offers something new: inhuman intelligence that reveals novel insights and strategies humans miss. For investors, this means moving beyond automation to using AI as a tool for generating genuine alpha through superior inference.

The next major AI breakthrough will come from applying generative models to complex systems beyond human language, such as biology. By treating biological processes as a unique "language," AI could discover novel therapeutics or research paths, leading to a "Move 37" moment in science.

In domains like coding and math where correctness is automatically verifiable, AI can move beyond imitating humans (RLHF). Using pure reinforcement learning, or "experiential learning," models learn via self-play and can discover novel, superhuman strategies similar to AlphaGo's Move 37.

Drawing parallels to chess and Go, Demis Hassabis argues that AI's superiority doesn't kill human competition. Instead, it creates a new "knowledge pool" for humans to learn from. The current top Go player is stronger than any before him precisely because he grew up studying AlphaGo's strategies, suggesting AI tools will elevate, not replace, top human talent.

By removing all human game data and learning only from self-play, AlphaZero first rediscovered human strategies and then discarded them for superior, 'alien' ones. This showed that relying solely on human data can limit an AI's potential, anchoring it to existing knowledge and cognitive biases.

The key difference between modern AI and older tech like Google Search is its ability to reason about hypotheticals. It doesn't just retrieve existing information; it synthesizes knowledge to "think for itself" and generate entirely new content.

A core legacy of AlphaGo is turning complex search problems into 'games' for AI agents. AlphaTensor reframed the challenge of finding the fastest matrix multiplication algorithm as a game, allowing it to discover a more efficient method than any human had found in over 50 years, proving the approach's power for scientific discovery.

In the endgame, AlphaGo made moves that seemed suboptimal, even giving up points. This was because it wasn't optimizing for a large victory margin (a human heuristic) but purely for maximizing the probability of winning, even by a half-point. This reveals how literal AI objective functions can differ from human proxies for success.