My impulse to write this piece came from a question at a recent Conference, where I was speaking about AI training, fair use and EU text-and-data mining (TDM). During the Q&A, someone asked about the fresh decision of the Landgericht München I in GEMA v OpenAI (42 O 14139/24, 11 November 2025). I answered a bit too briskly that I did not think the case deserved the weight people were giving it: in my view, it misreads how machine learning works, mislabels memorisation as “reproduction”, and arrives at the wrong policy conclusion at exactly the wrong time.
Since then, media coverage, collecting-society press releases and early academic commentary have started to cast GEMA as a landmark for AI training in Europe. That, I think, is dangerous. So, this post tries to do what I have not had the time to do in the conference room: slow down, unpack what the Munich court actually did, and explain why it is a poor candidate for setting the legal frame for AI training in the EU.
Part 1 of this post will outline the decision and place it in the action workflow of large language models (LLMs), as well as explaining why treating training as “reproduction”, in the way GEMA suggests, is technically and doctrinally misguided. Part 2 will highlight the broader policy costs of that move - for innovation, for Europe’s position in AI, and for copyright’s own idea-expression architecture.