We tested every venue in 2 Bali regions against 4 AI assistants

A new audit of AI restaurant recommendations found that 85.6% of 4,776 cafes, restaurants, and bars in Canggu and Ubud, Bali, were never recommended by any of four AI systems (ChatGPT, Claude, Gemini, Perplexity) across 2,208 responses to 96 persona-conditioned queries. The study, submitted to arXiv on 7 Aug 2026, also found that 72.6% of established venues with 50 or more ratings were invisible, and that systems recommended permanently closed venues 93 times, indicating staleness rather than hallucination as the primary failure mode.

Computer Science Information Retrieval Submitted on 7 Aug 2026 Title:Invisible to the Machine: Auditing AI Restaurant, Cafe, and Bar Recommendation Against a Complete Market Census View PDF /pdf/2608.07069 HTML experimental https://arxiv.org/html/2608.07069v1 Abstract:AI assistants are becoming a primary interface for local discovery, yet almost nothing is known about which venues they surface -- especially in food and drink, where recommendations carry direct revenue consequences. We present the first census-denominated audit of AI venue recommendation: a complete enumeration of 4,776 cafes, restaurants, and bars across two bounded markets Canggu and Ubud, Bali , against which we evaluate 2,208 search-grounded responses from four production AI systems ChatGPT, Claude, Gemini, Perplexity to 96 persona-conditioned queries, collected over seven days under a pre-registered protocol. Because we observe the full market, we can measure what sampled audits cannot: 85.6% of venues were never recommended by any system -- 72.6% even among established venues with fifty or more ratings. Visibility follows a two-margin structure. Entry into answers is associated with documentation: review volume OR 1.64 , an own website OR 1.92 , listed price information OR 1.54 , and third-party web mentions OR 1.44 -- while star rating is null at this margin OR 0.89 . Rank within answers reverses the pattern: among recommended venues, rating significantly predicts first position OR 1.17 . Presence in an open POI dataset Foursquare , a folk-theorized visibility factor, shows no positive effect at either margin. Outright fabrication is rare 0.08% of mentions , but systems recommended permanently closed venues 93 times -- staleness, not hallucination, is the practical failure mode. Cross-system agreement is low top-20 Jaccard 0.33-0.54 . A two-week test-retest shows cross-period answer similarity comparable to same-day rerun similarity: the churn is sampling stochasticity, not temporal drift. We release our protocol, registry construction method, and derived data. Additional Features References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .