# Study prices sellers' hidden influence on AI shopping agents

Published: 2026-10-20T03:48:36.801Z · Source: arXiv GEO (https://arxiv.org/abs/2610.25843v1)
Source date: 2026-10-19

A study of 113,472 trials across seven AI models finds that seller-controlled content — badges, endorsements, framing — shifts AI shopping agents' product picks by amounts that vary widely even when two cues look equally persuasive by simple win-rate. The authors introduce "price-equivalent auditing," which raises a cued product's price until the model picks it only half the time, to measure that gap in dollar terms. Tested against a decision-native model, Jev, which outputs a choice probability rather than text, the authors found it resisted most cues but remained susceptible to the strongest social-proof claims — a profile unlike any text model in the study.

Why it matters: Choice-rate audits saturate once a cue wins almost every time, hiding how much stronger one seller tactic is than another; pricing the cue in dollars gives GEO auditors a way to actually rank seller tactics by strength.

## What this answers

**Can sellers manipulate what an AI shopping agent recommends?**

Yes — a new study finds seller-controlled cues like badges and endorsements measurably shift which product an AI shopping agent picks, with the effect large enough to express as a dollar premium.

**Are all AI shopping models equally swayed by seller cues?**

No. Jev, a decision-native model tested in the study, resisted most seller cues but still gave in to the strongest social-proof claims — a different profile than any text-based model tested.


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