arXiv 论文分析 LLM 平台广告与订阅组合定价机制
Monetizing Intelligence: Advertising and Subscription Design on LLM Platforms
这论文回答了个实际问题：ChatGPT 们以后怎么靠广告赚钱、免费和付费档怎么设计，算力降价了广告收入反而可能更多，结论挺反直觉。
arXiv 论文 2610.32945 研究垄断性 LLM 平台在引入广告时如何同时决定回复质量、广告强度和订阅价格。研究发现广告补贴可以让免费档用户获得纯订阅制下没有的服务，但中档套餐质量下降反而支撑了高档免广告套餐的更高定价。设置中等广告上限能够最大化消费者剩余。论文还指出算力成本大幅下降时，服务变好会抬高剩余广告位的价值，使广告总收入不降反升。
Monetizing Intelligence: Advertising and Subscription Design on LLM Platforms
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.
