Carl-Christian Groh
Abstract
What determines the speed and direction of AI learning? I analyse this question from a microeconomic perspective by studying a model in which AI providers sell access to users who must complete tasks using either AI or labor. Users differ in the share of complex tasks they face. When complex tasks are delegated to AI, it learns to perform them better. In an initial technological state where AI has a comparative advantage at easy tasks, users with many complex tasks have low willingness to pay for access. This gives rise to a tension between profit maximization and complex-task learning. I characterize when this tension gives rise to a convex path of labor displacement or a learning trap in a monopoly benchmark. Competition between AI providers increases the speed of labor displacement if it is intense. Asymmetric competition can increase the speed of labor displacement by promoting endogenous specialization of AI providers.