DeepMind Paper: First-Price Auctions in Agentic Markets Reach 1-1/e^2 Efficiency
dair_ai · x · 2026-10-16
A Google DeepMind paper studies agentic markets where AI agents bid (expected reward minus inference cost) to answer each user prompt via first-price auctions. Despite bid shading, welfare loss stays small when multiple agents value prompts similarly (e.g., distilled cheaper models) and approaches 1-1/e^2 of optimal when values hinge mostly on prompt difficulty — far above the general 1-1/e bound. Simulations on the EmbedLLM routing dataset match the theory.
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