The OnlyFans Economy: Intellectual Property's Pivot from Scarcity to Authenticity
Abstract
Generative AI is destabilizing the foundational assumption of intellectual property law: that creation is difficult, expensive, and requires legal inducement. When machines produce text, images, and code at near- zero marginal cost, the utilitarian justification for copyright and patent protection begins to collapse. This Essay argues that what emerges in its place is a regime organized not around the scarcity of creation, but around the scarcity of verification – a shift from an incentive paradigm to a source identification paradigm dominated by trademark, rights of publicity, and platform-controlled authentication infrastructure. OnlyFans provides a revealing case study. Despite AI-generated pornography flooding the market, the platform paid over $5.8 billion to human creators in 2024. Consumers pay not for content, which AI can approximate, but for provenance – the verified knowledge that they are interacting with a specific, authenticated person. This Essay identifies a "triple-lock" structure underlying this economy: verified identity draws consumers in, proprietary infrastructure ensures access requires staying inside the walled garden, and aggressive copyright enforcement destroys unauthorized copies that would otherwise undermine authenticity's value.<br><br>Applying a Law and Political Economy lens, the Essay argues that this emerging regime systematically advantages those who already possess recognized brands, legal departments, and capital for proprietary infrastructure – while offering little to individual creators who lack pre- existing fame. It concludes by proposing that the Library of Congress serve as a public "digital notary," preventing the infrastructure of authenticity from becoming a private toll road.
Fields of Study & Subjects
Copyright and Intellectual Property
Law
AI
and Intellectual Property
Legal and Constitutional Studies
Publication details
- Journal / source
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Publication date
- 1 January 2026
- Document type
- Preprint