Reinforcement learning incentivizes AIs to find the right answer, not just mimic human text. This leads to them developing their own internal "dialect" for reasoning—a chain of thought that is effective but increasingly incomprehensible and alien to human observers.

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.

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.

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.

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.

China's intense national focus on AI was sparked by a 'Sputnik Moment.' During a live match, as DeepMind's AlphaGo was defeating their top Go player, Chinese authorities cut the broadcast feed to avoid losing face. This event served as a wake-up call, igniting the country's massive investment in AI.

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.

Google DeepMind CEO Demis Hassabis argues that today's large models are insufficient for AGI. He believes progress requires reintroducing algorithmic techniques from systems like AlphaGo, specifically planning and search, to enable more robust reasoning and problem-solving capabilities beyond simple pattern matching.

While GenAI continues the "learn by example" paradigm of machine learning, its ability to create novel content like images and language is a fundamental step-change. It moves beyond simply predicting patterns to generating entirely new outputs, representing a significant evolution in computing.