This talk will apply insights from classic economic incentive theory to problems in machine learning and artificial intelligence. In particular, it will apply multi-task theory to the design of A/B testing platforms and bandit objective functions, and it will analyze human-AI interaction as a problem of optimal delegation. We will also consider how informational asymmetries and observability problems can lead to systematic challenges for learning by bidders at auctions.