Economics of AI
Competition, pricing, product differentiation, and demand in markets for artificial intelligence.
Nadav Tadelis · Senior Economist at Microsoft
Studying AI markets, mechanism design, and organizational learning.
My work combines microeconomic theory, causal inference, and large-scale data to study and improve how compute and model services are allocated, priced, and differentiated. My broader research includes digital markets, matching, principal-agent incentive alignment, organizational learning, and applied econometrics.
Featured research
The emerging market for intelligence
API usage data reveal a fast-growing LLM market with rapid entry, falling prices, persistent differentiation, and frequent turnover among leading models.
Questions I keep returning to
Four threads across my work
Digital markets and market design
Online marketplaces, auctions, matching, and mechanisms for markets with congestion or asymmetric information.
Organizations and learning
How workers and firms build, retain, and use knowledge.
Applied econometrics
Causal inference and machine-learning methods for high-dimensional settings.