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

TL;DR

This paper investigates how AI search engines influence hotel source citations, revealing a significant shift away from traditional OTAs towards diverse non-OTA sources, especially in experiential queries and Japanese language contexts.

Contribution

It uncovers the systematic pattern of the Intent-Source Divide in AI hotel recommendations and highlights its implications for industry power dynamics.

Findings

Experiential queries cite non-OTA sources 55.9% of the time, versus 30.8% for transactional queries.

Japanese queries cite non-OTA sources 62.1%, compared to 50.0% in English.

AI search may reduce hotel industry reliance on commission-based intermediaries.

Abstract

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 ($p < 5 \times 10^{-20}$). 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.