arXiv cs.AI· Yuhang Ji, Fasheng Xu, Tianjun Feng·· 12 小时前AI 评分20
LLM 平台如何设计广告与订阅：一项理论经济学研究
Monetizing Intelligence: Advertising and Subscription Design on LLM Platforms
AI 导读
一项理论经济学研究分析了垄断性 LLM 平台在引入广告时如何联合选择回复质量、广告强度和订阅价格。研究发现，部分广告支持与无广告套餐共存时，广告可为纯订阅下得不到服务的用户提供有用服务，但会造成效用分化；适度的广告上限可最大化消费者剩余。算力成本下降能提升质量并降低广告强度，但足够大的降幅反而可能推高广告总收入。
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