LLM platforms balance ads and subscriptions to optimize user experience
Monetizing Intelligence: Advertising and Subscription Design on LLM Platforms
Artificial Intelligence
Summary
The paper looks at how big AI chat services that use large language models can make money from ads and subscriptions. The authors find that mixing free, partially ad-supported, and paid ad-free plans can lead to different levels of user happiness and prices. Ads can help pay for better service, but too many ads hurt users. Also, cheaper computing power can improve quality but might increase total ad revenue because better service makes ads more valuable. The study shows that designing the right mix of plans and ad levels is key for these platforms.
What this means in practice
- •For platform product teams: Set subscription tiers and ad levels on LLM services to balance revenue and user satisfaction considering how ads affect payments and service quality.$Commercial implications: Enables commercial LLM providers to optimize pricing plans that integrate ads while maintaining or improving service attractiveness.
- •For cloud infrastructure operators: Adjust hardware cost management to influence LLM response quality and advertising dynamics, potentially increasing overall ad revenue through improved service.
Authors
Yuhang Ji, Fasheng Xu, Tianjun Feng
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.