mlsanityData to 14 Oct 2026

The End of Rented Discovery: How AI Search Redistributes Power Between Hotels and Intermediaries

arXiv 2603.20062 ↗First posted 20 Mar 2026cs.IR cs.AIcited by 1

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Abstract (arXiv)

When a traveler asks an AI search engine to recommend a hotel, which sources get cited -- and does query framing matter? We audit 1,357 grounding citations from Google Gemini across 156 hotel queries in Tokyo and document a systematic pattern we call the Intent-Source Divide. Experiential queries draw 55.9% of their citations from non-OTA sources, compared to 30.8% for transactional queries -- a 25.1 percentage-point gap (p < 5 × 10-20). The effect is amplified in Japanese, where experiential queries draw 62.1% non-OTA citations compared to 50.0% in English -- consistent with a more diverse Japanese non-OTA content ecosystem. For an industry in which hotels have long paid OTAs for demand acquisition, this pattern matters because it suggests that AI search may make hotel discovery less exclusively controlled by commission-based intermediaries.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 20 Mar 2026.

Builds on

7 of 18 references.

References · 7

  1. 2023GEO: Generative Engine Optimizationcited by 56
  2. 2017Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Mediacited by 18
  3. 2025Generative Engine Optimization: How to Dominate AI Searchcited by 18
  4. 2024Generative AI Search Engines as Arbiters of Public Knowledge: An Audit of Bias and Authoritycited by 13
  5. 2025News Source Citing Patterns in AI Search Systemscited by 10
  6. 2025Role-Augmented Intent-Driven Generative Search Engine Optimizationcited by 5
  7. 2026The Discovery Gap: How Product Hunt Startups Vanish in LLM Organic Discovery Queriescited by 2

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