An Economic Framework for Generative Engines: Advertising or Subscription?
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arXiv
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| Format: | Preprint |
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2026
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| author | Zhang, Luyang Jiao, Cathy Li, Beibei Xiong, Chenyan |
| author_facet | Zhang, Luyang Jiao, Cathy Li, Beibei Xiong, Chenyan |
| contents | Generative Engines (GEs) such as ChatGPT and Google's AI Overviews are rapidly reshaping search economics by delivering synthesized responses that allow users to bypass third-party websites, cutting those sites' advertising revenue. Yet this shift also leaves GEs facing their own monetization problem: whether to insert ads into synthesized responses or keep them ad-free to drive subscription conversions. In this paper, we introduce a dynamic framework to study this problem, which captures how query-level design choices shape user engagement, retention, and subscription conversion over time. Using this framework, we show that the optimal policy follows a cutoff rule: ads should only be shown to users only when the immediate ad payoff exceeds the long-term value of providing ad-free responses. This cutoff shifts toward with-ad responses when i) ad revenue is high or ii) users are less sensitive to ads, and toward ad-free responses when iii) subscription conversion becomes relatively more valuable. In addition, the presence of rival GEs shifts the optimal policy further toward ad-free responses, as ad-heavy monetization becomes less sustainable when users can freely switch to alternatives. Our findings reveal incentives for real-life generative engine providers to adopt designs that enhance user experience and long-term sustainability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29071 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Economic Framework for Generative Engines: Advertising or Subscription? Zhang, Luyang Jiao, Cathy Li, Beibei Xiong, Chenyan Computer Science and Game Theory Generative Engines (GEs) such as ChatGPT and Google's AI Overviews are rapidly reshaping search economics by delivering synthesized responses that allow users to bypass third-party websites, cutting those sites' advertising revenue. Yet this shift also leaves GEs facing their own monetization problem: whether to insert ads into synthesized responses or keep them ad-free to drive subscription conversions. In this paper, we introduce a dynamic framework to study this problem, which captures how query-level design choices shape user engagement, retention, and subscription conversion over time. Using this framework, we show that the optimal policy follows a cutoff rule: ads should only be shown to users only when the immediate ad payoff exceeds the long-term value of providing ad-free responses. This cutoff shifts toward with-ad responses when i) ad revenue is high or ii) users are less sensitive to ads, and toward ad-free responses when iii) subscription conversion becomes relatively more valuable. In addition, the presence of rival GEs shifts the optimal policy further toward ad-free responses, as ad-heavy monetization becomes less sustainable when users can freely switch to alternatives. Our findings reveal incentives for real-life generative engine providers to adopt designs that enhance user experience and long-term sustainability. |
| title | An Economic Framework for Generative Engines: Advertising or Subscription? |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2603.29071 |