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New Google paper finds that when AI agents write the descriptions other AI agents use to choose software tools, they start exaggerating what the tools can do.
when Gemini 3.8 Flash wrote tool listings, buyer agents' success on Amazon fell from 89% to about 30%.
more and more often, an AI decides which app, plugin, data source or product gets used for you, and it often decides by reading a short description the provider wrote about itself.
The paper shows that if providers let their own AI write those descriptions with the goal “get chosen more,” the descriptions slowly stop being true, and the AI doing the choosing ends up picking worse options. Nobody has to tell the AI to lie; the goal alone is enough. You can think of it as AI-to-AI advertising where the ads go bad.
The researchers built markets of 10 real Amazon or Instagram data tools and ran every tool on every question. Sellers on 5 production models rewrote listings to get picked, and Claude Code and Codex buyers chose from listings alone.
All 5 models inflated claims, including fields their tools return for under 50% of targets. Under Gemini 3.8 Flash sellers, listings converged and Amazon buyer success fell from 89% to about 30%, near random choice at 28%.
Test a model as a seller before it writes listings, tell it to claim only real fields, and have marketplaces check claims against tool outputs.