Gema v. OpenAI (Munich I Regional Court, 2025)
Doctrinal, Comparative, and International Perspectives on Copyright and Generative AI
Bibliographic Data
| ID | 24049231 |
|---|---|
| Authors | Tim W Dornis (0000-0003-0103-1047), Jane C Ginsburg (0000-0001-9718-8932), Nicola Lucchi (0000-0001-8611-0072) |
| Year | 2026 |
| Volume | 81 |
| Issue | 6 |
| Pages | 235-246 |
| Publication date | 2026-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | JuristenZeitung (JOURNAL) |
| Journal identifiers | ISSN: 0022-6882 • E-ISSN: 1868-7067 |
| Publisher | Mohr Siebeck (PUBLISHER) |
| DOI | 10.1628/jz-2026-0084 |
| OpenAlex | W7139977745 |
| Language | DE |
This article analyzes the Regional Court of Munich I's decision in the GEMA v. OpenAI case, examining whether training generative AI models on copyrighted works constitutes infringement. The Court held OpenAI liable, finding that AI training creates copyright-relevant reproductions inside generative models not sheltered by text and data mining exceptions under EU and German law. The authors explore the ruling's implications, compare current US fair use litigation, and assess both against the background of international copyright law, particularly the Berne Convention's three-step test.
Copyright law · Generative grammar · Generative model · German · International law · Copyright and Intellectual Property · Intellectual Property and Patents · Law, AI, and Intellectual Property
| Citation velocity | historical |
|---|---|
| Highly cited | No |