The enormous compute budget for the original AlphaGo was not about finding the most efficient training method, but about proving a method could work at all. Once a breakthrough is made and the path is clear, subsequent efforts can focus on optimization and achieve similar results with far less compute.

According to Demis Hassabis, LLMs feel uncreative because they only perform pattern matching. To achieve true, extrapolative creativity like AlphaGo's famous 'Move 37,' models must be paired with a search component that actively explores new parts of the knowledge space beyond the training data.

Go's search space is larger than the number of atoms in the universe, making exhaustive search impossible. AlphaGo's core breakthrough was using neural networks to intelligently guide its search, evaluating only the most promising moves and making an intractable problem solvable.

AlphaGo's architecture mimicked human cognition by pairing a 'fast thinking' neural network for intuition with a 'slow thinking' search algorithm for explicit planning. This hybrid model, combining pattern recognition with calculation, proved more powerful for tackling complex problems than either approach alone.

An AI model disproved a mathematical conjecture not through a flash of creative genius, but by methodically applying a known technique from a different math subfield. This highlights AI's current strength: synthesizing vast, disparate human knowledge rather than generating truly novel, alien ideas. It's an exhaustive librarian, not an intuitive genius.

OpenAI's president predicts that AI will soon produce creative breakthroughs comparable to AlphaGo's Move 37, which redefined Go strategy. This will not be limited to science and math but will extend to domains like literature and poetry, unlocking novel forms of human creative understanding and ideation.

True creative mastery emerges from an unpredictable human process. AI can generate options quickly but bypasses this journey, losing the potential for inexplicable, last-minute genius that defines truly great work. It optimizes for speed at the cost of brilliance.

AlphaGo's infamous 'Move 37' was a play no human expert would have made, initially dismissed as an error. Its eventual success demonstrated that AI can discover novel, superior strategies beyond the existing corpus of human knowledge, fundamentally expanding a field of study rather than just mastering it.

AI models operate in a 'probability space,' making predictions by interpolating from past data. True human creativity operates in a 'possibility space,' generating novel ideas that have no precedent and cannot be probabilistically calculated. This is why AI can't invent something truly new.