The data black hole at the center of AI
Dwarkesh Patel
Jun 19, 2026
Current AI progress relies on brute-force data ingestion rather than human-like sample efficiency. While humans learn complex tasks with minimal exposure, frontier models require trillions of tokens and bespoke expert data to function. This massive computational overhead suggests AI operates on a fundamentally different learning curve than biological intelligence, prioritizing raw scale over cognitive optimization.
Key insight: Humans learn to drive with about 20 hours of practice, whereas self-driving models require three to four orders of magnitude more data, highlighting a massive gap in learning efficiency.
