Study models advertising and subscription menus for LLM platforms
Original title: Monetizing Intelligence: Advertising and Subscription Design on LLM Platforms
An intermediate ad cap can raise consumer surplus, and large compute-cost cuts can increase total ad revenue even as ad intensity falls.
The paper studies a monopolist that jointly sets response quality, advertising intensity, and subscription prices. When mixed and ad-free plans coexist, ads can fund service for users who get none under pure subscription, while weaker middle plans let the platform charge more for unchanged ad-free service. Cheaper compute can raise quality and cut ad intensity, but large cost drops may lift aggregate ad revenue because better answers make remaining ads more valuable. An extension finds ad-funded upgrades can equalize capability and ad exposure, removing the basis for different prices.
Unconfirmed: Results come from the authors' theoretical model, not measurements from a live platform.
- Some fully ad-supported users gain utility; partly ad-supported and ad-free users lose.
- Broader access can coexist with utility polarization across plan tiers.
- Advertising is modeled as reducing user value while better service raises ad returns.
From the original article
1 min read at arxiv.orgLarge 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.
By Yuhang Ji · arxiv.org
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