Small multi-model test shows how AI consensus could become a discovery gatekeeper
A small ranking experiment points to the commercial stakes when AI systems repeat and reinforce one another’s recommendations.
An experiment sent the first few hundred user-generated ranking questions to 12 models and scored each against the other 11, finding average agreement of 0.47 among five Chinese models versus 0.38 among six U.S. models. The author cautions that the panel and sample were arbitrary, English-language questions dominated, and agreement does not measure accuracy. If model preferences are copied and recycled, visibility in one answer engine could propagate across others, raising the commercial value of influencing the consensus that steers product and source discovery.