preprint · arXiv (Cornell University) · 2026

Do AI Overviews Benefit Search Engines? An Ecosystem Perspective

Yihang Wu, Jiajun Tang, Jinfei Liu, Haifeng Xu, Yao Fan

Cited by 0Open access

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 sustainable trajectory for AI-enhanced search ecosystems.

Cite this paper

APA 7

Wu, Y., Tang, J., Liu, J., Xu, H., & Fan, Y. (2026). Do AI Overviews Benefit Search Engines? An Ecosystem Perspective. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2601.22493

MLA 9

Wu, Yihang, et al. “Do AI Overviews Benefit Search Engines? An Ecosystem Perspective.” arXiv (Cornell University), 2026, https://doi.org/10.48550/arxiv.2601.22493.

Chicago

Wu, Yihang, Jiajun Tang, Jinfei Liu, Haifeng Xu, and Yao Fan. 2026. “Do AI Overviews Benefit Search Engines? An Ecosystem Perspective.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.2601.22493.

Harvard

Wu, Y. et al. (2026) ‘Do AI Overviews Benefit Search Engines? An Ecosystem Perspective’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.2601.22493.

BibTeX

@misc{wu2026overviews,
  title = {{Do AI Overviews Benefit Search Engines? An Ecosystem Perspective}},
  author = {Wu, Yihang and Tang, Jiajun and Liu, Jinfei and Xu, Haifeng and Fan, Yao},
  journal = {arXiv (Cornell University)},
  year = {2026},
  doi = {10.48550/arxiv.2601.22493}
}

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