Power Law Divergence
AI doesn't make everyone equally better at their jobs. Instead, it acts like a multiplier: most people get a small, standard boost, but a small group of people who know how to guide the AI with their own expertise pull significantly further ahead than everyone else.
Power Law Divergence describes the shift from a bell-curve distribution of productivity to a power-law distribution when AI is introduced. Rather than raising the average, AI creates a performance gap where a small percentage of users—those who combine domain judgment with high-level AI-direction skills—capture the vast majority of the value, while the rest of the workforce converges toward a baseline of automated output.
The finding that AI's effect is not a uniform productivity bump but a divergence: value concentrates in those who hold the conjunction of substrate, AI-direction skill, and motivation ('what the 5% do differently'), while the rest are equalised to a borrowed-output floor. Bell-curve to power-law. (C4AIL.)