LLM 平台的广告与订阅定价设计如何变现智能
Monetizing Intelligence: Advertising and Subscription Design on LLM Platforms
AI 导读
一项 arXiv 理论经济学研究分析垄断性 LLM 平台如何联合选择响应质量、广告强度和订阅价格。研究发现：广告与无广告方案并存时，广告可资助免费用户获得服务，但中间档质量下降推高无广告档价格，造成效用极化；适度的广告上限可最大化消费者剩余；算力成本大幅下降时，服务质量提升反而可能使广告总收益上升。
正文 · 原文
Abstract:Large language model (LLM) platforms introducing advertising must decide how much capability ad revenue should fund and what consumers should pay to avoid ads. We study a monopolistic platform that jointly chooses costly response quality, advertising intensity, and subscription prices when advertising reduces user value but better service raises advertising returns. Three findings follow. First, when partly ad-supported and ad-free plans coexist, advertising can finance useful service for consumers who receive none under pure subscription. Lower effective quality in middle plans weakens the alternatives available to premium users, allowing higher prices for unchanged ad-free service. Some fully ad-supported users gain utility, while partly ad-supported and ad-free users lose: broader access can coexist with utility polarization. Second, an intermediate advertising cap can maximize consumer surplus by preserving some revenue that finances service while limiting degradation. Third, cheaper compute can raise quality and reduce ad intensity, yet sufficiently large cost reductions can increase aggregate advertising revenue above its initial level because better service raises the value of the remaining advertised interactions. An extension shows how advertising-funded upgrades can bring plans to the same maximum capability and advertising exposure, eliminating the basis for different prices. These results make menu design central to evaluating LLM advertising: the consequences extend beyond users who see ads to the alternatives and prices faced by those who do not.
| Subjects: | Theoretical Economics (econ.TH); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32945 [econ.TH] |
| (or arXiv:2610.32945v1 [econ.TH] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32945 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Fasheng Xu [view email]
[v1]
Fri, 23 Oct 2026 17:39:42 UTC (846 KB)
来源：arXiv cs.AI · arxiv.org