Understanding AI Provider Recommendations in Local Service Markets

Abstract

When someone asks an AI assistant which doctor to see or which firm to trust with their savings, the answer is a referral. We audit AI provider recommendations in four registry-backed service domains across the 100 largest U.S. metropolitan areas, matching every recommendation against the official registry for its domain (Medicare clinician and facility records, and SEC adviser disclosures), under three conditions: an open-weight model, a proprietary model without web search, and the same proprietary model with search. Without search, both models largely fabricate recommendations in the domains the web covers thinly. Only 4% of the open-weight model's recommended doctors and 11% of the proprietary model's match a clinician in the queried city, and the open-weight matches are name coincidences: its matched clinicians are no likelier to be primary-care doctors than names drawn at random from the registry. With search, 64-71% of recommendations in the same domains match a real provider. Search also changes who is recommended. Without it, recommended advisory firms carry SEC misconduct disclosures at 3.6 times the registry base rate, even after adjusting for firm size; with search, significantly below it. Restaurants, where quality and visibility are separately measurable, show a 3-5x review-count premium but a rating premium of at most a tenth of a star. Finally, search largely removes the metro-size penalty: without it, real recommendations concentrate in the largest metros; with it, match rates are similar across metro-size terciles. Whether an AI referral is trustworthy depends strongly on its retrieval configuration rather than on the underlying model alone, yet an answer produced without retrieval often carries no sign that its recommendations were never verified.