The OnlyFans Economy: Intellectual Property's Pivot from Scarcity to Authenticity

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Authors: Vincent Joralemon
Cited by: 1

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