Mecha-nudges for Machines

As AI agents make decisions in the same environments as humans, the environments themselves may change to influence them. We call this $\textit{mecha-nudging}$: subtle changes to how choices are presented that systematically influence AI agents without materially degrading the decision environment for humans. To measure this phenomenon, we combine two frameworks---Bayesian persuasion from economics and $\mathcal{V}$-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 agent curation, increasing by $0.143$ bits (over 40\% of the maximum possible increase). This shift is robust across prompts, token choices, labeling models, and fine-tuning architectures; absent in a regulated-text placebo; and far larger than the effect of generic LLM rewriting. In contrast, a human study finds little to no change in human-usable information. Our results provide the first large-scale evidence that systematic mecha-nudging is already occurring in the wild, but going unnoticed.