Do AI Overviews Benefit Search Engines? An Ecosystem Perspective
Yihang Wu, Jiajun Tang, Jinfei Liu, Haifeng Xu, Fan Yao

TL;DR
This paper examines how AI Overviews impact search engine ecosystems, proposing game-theoretic models and mechanisms to balance user experience, creator incentives, and long-term profitability.
Contribution
It introduces a game-theoretic framework for creator competition with effort costs and designs incentive mechanisms to sustain search engine profits.
Findings
AI Overviews can harm long-term search engine profit.
Citation and monetary reward mechanisms can mitigate profit loss.
Proposed interventions improve long-term profitability in realistic scenarios.
Abstract
The integration of AI Overviews into search engines enhances user experience but diverts traffic from content creators, potentially discouraging high-quality content creation and causing user attrition that undermines long-term search engine profit. To address this issue, we propose a game-theoretic model of creator competition with costly effort, characterize equilibrium behavior, and design two incentive mechanisms: a citation mechanism that references sources within an AI Overview, and a compensation mechanism that offers monetary rewards to creators. For both cases, we provide structural insights and near-optimal profit-maximizing mechanisms. Evaluations on real click data show that although AI Overviews harm long-term search engine profit, interventions based on our proposed mechanisms can increase long-term profit across a range of realistic scenarios, pointing toward a more…
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Taxonomy
TopicsEthics and Social Impacts of AI · Expert finding and Q&A systems · Consumer Market Behavior and Pricing
