The Turing Valley: How AI Capabilities Shape Labor Income

There is concern that progress toward AI systems with strong capabilities across domains will reduce the importance of human input in production and thus wages. We show that when knowledge is tacit and multidimensional, making AI less jagged can instead raise labor's marginal product. Tacit knowledge makes sequential problem solving efficient because problems cannot be assigned ex ante to the agent best equipped to solve them. When organizations cannot fully integrate human and AI knowledge across dimensions, improving AI where humans initially have an advantage can remove from the referral pool problems that would otherwise consume human time and remain unsolved. By concentrating scarce human time on problems humans can solve, better screening can raise labor's marginal product even as fewer problems require human input. Our results imply that the human-AI versus AI-only performance gap used to measure human contribution to output need not track the marginal product of labor.