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Do AI Overviews Benefit Search Engines? An Ecosystem Perspective

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

TL;DR

The paper analyzes the long-run impact of AI Overviews in search engines by modeling creator competition under a position-based model with multiplicative payoffs. It develops a PBM-based game-theoretic framework, derives unique symmetric and binary-type asymmetric equilibria, and designs two incentive mechanisms—citation and compensation—to sustain creator incentives and improve profit. It provides structural results showing that AI Overviews can harm long-term profit absent incentives but that simple, near-optimal mechanisms (UBL and TPBL) can restore profitability across realistic regimes, with empirical validation from real click data. The work yields a practical blueprint for ecosystem-friendly AI-enhanced search, balancing user experience, content creation incentives, and platform profitability, and points to nuanced, topic-aware deployment guidance.

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.

Do AI Overviews Benefit Search Engines? An Ecosystem Perspective

TL;DR

The paper analyzes the long-run impact of AI Overviews in search engines by modeling creator competition under a position-based model with multiplicative payoffs. It develops a PBM-based game-theoretic framework, derives unique symmetric and binary-type asymmetric equilibria, and designs two incentive mechanisms—citation and compensation—to sustain creator incentives and improve profit. It provides structural results showing that AI Overviews can harm long-term profit absent incentives but that simple, near-optimal mechanisms (UBL and TPBL) can restore profitability across realistic regimes, with empirical validation from real click data. The work yields a practical blueprint for ecosystem-friendly AI-enhanced search, balancing user experience, content creation incentives, and platform profitability, and points to nuanced, topic-aware deployment guidance.

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.
Paper Structure (43 sections, 41 theorems, 145 equations, 36 figures, 13 tables, 3 algorithms)

This paper contains 43 sections, 41 theorems, 145 equations, 36 figures, 13 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Model
  4. Mechanisms for Symmetric Creators
  5. Equilibrium Properties
  6. Profit-Maximizing Mechanisms
  7. Mechanisms for Asymmetric Creators with Binary Distinct Types
  8. Equilibrium Properties
  9. Profit-Maximizing Mechanisms
  10. Experiments
  11. Data Collection and Position Bias Estimation
  12. Analysis of Search Engine Profit
  13. Conclusion and Discussion
  14. Existence of Mixed Nash Equilibrium
  15. Properties of Symmetric Equilibrium
  16. ...and 28 more sections

Key Result

Theorem 4.1

Game $\mathcal{G}^{(1)}$ admits a unique symmetric mixed Nash equilibrium.

Figures (36)

  • Figure 1: Support structure of equilibrium in the binary-type setting
  • Figure 2: Short- and long-term effects of AI Overview on search engine profit
  • Figure 3: Long-term search engine profit under mechanism design
  • Figure 4: Plot of $\max \ W(\bm{c})$ against $c_n$ in Example \ref{['ex:cn-increasing']}
  • Figure 5: Plots of $\max W(\bm{c})$ against $c_n$ with different $g$
  • ...and 31 more figures

Theorems & Definitions (57)

  • Definition 3.1: mixed Nash equilibrium
  • Theorem 4.1
  • Theorem 4.2
  • Proposition 4.3
  • Definition 5.1: pseudo strategy
  • Theorem 5.2
  • Proposition 5.3
  • Remark 5.4
  • Proposition 5.5
  • Conjecture 5.6
  • ...and 47 more