Mecha-nudges for Machines

Abstract

As AI agents make decisions in the same environments as humans, the environments themselves can change to influence them. We call this mecha-nudging: subtle changes to how choices are presented that systematically influence AI agents without materially degrading the decision environment for humans. It can arise from both intentional and unintentional adaptation to the rise of agents. To measure this phenomenon, we combine two frameworks—Bayesian persuasion from economics and V-usable information from computer science—to get a common unit (bits) for quantifying how environments change across a wide range of interventions, contexts, and models. Applying our framework to over six million product listings on Etsy—a global marketplace for independent sellers—we find that after ChatGPT’s release, listings contain significantly more machine-usable information about agentic curation, rising by over 40% of the maximum possible increase. This shift is robust across prompts, token choices, and models; absent in a regulated-text placebo; and cannot be explained by the rise of LLM-assisted writing. In contrast, a human study finds little to no change in human-usable information. Our results provide the first large-scale evidence that mecha-nudging is already happening in the wild, but going unnoticed.