A Munich court has ruled that AI music generator Suno violated copyrights by training on well-known musical works, following a lawsuit brought by German music rights organization GEMA. The court found that Suno's AI models effectively memorized songs and reproduced original elements from the source material, constituting copyright infringement. The court placed responsibility for the infringing outputs on Suno rather than its users, as the company chose the training data and model architecture. The case centered on six well-known songs, including 'Atemlos durch die Nacht' by Kristina Bach and 'Rasputin' by Frank Farian, Fred Jay, and George Reyam. Only the musical compositions were at issue, not the lyrics. The court determined that all six tracks are reproducibly contained in Suno's version 3.5 and 4 models. In AI research, this phenomenon is known as memorization: during training, a model may store specific content from its training data, which can later be pulled back out as outputs. Suno argued that its model doesn't store songs, only 'mathematically learned patterns and generalized features.' Any similarities in the outputs, the company claimed, were the result of user prompts and statistical correlations. GEMA entered each song's original lyrics, musical style, and title into Suno's generator without specifications about melody, harmony, rhythm, or arrangement. Suno still produced results in which the court recognized the original elements of the source tracks. Given the complexity and length of the songs, the court ruled out coincidence. Suno bears responsibility, not its users. The court explicitly held Suno responsible for the infringing outputs, not the users who typed the prompts. Suno had argued that deliberate user input broke the causal chain between the model and its output. The court disagreed. The prompts were 'simple and open-ended,' and Suno operates the models, selected the songs as training data, and is responsible for the architecture and the memorization. That means the models 'substantively determined' the outputs. Simply offering the generator for music creation already constitutes a legal violation, the court said. If this view holds up on appeal, it could affect other AI music services as well. Suno also invoked Germany's text and data mining exception, which allows automated analysis of content under certain conditions. The court ruled that this exception doesn't cover the memorization it found. US fair use doesn't protect Suno either. Under a special rule for collecting societies, the court also claimed jurisdiction over claims based on training activities that took place in the United States. It applied US law to those acts and concluded that fair use doesn't protect Suno either. The court drew a line between this case and two US proceedings, Bartz and Kadrey. In both of those cases, American courts had treated AI training as transformative use and thus fair use. The key difference, according to the Munich court, is that in those proceedings, the training data was 'not, or not substantially, made accessible to users in the outputs.' With Suno, simple inputs produced results that were 'substantially similar' to the originals. All factors of the fair use test laid out by the US Supreme Court in its Warhol decision weighed against Suno, the court found. The ruling is not yet final. Key questions remain unanswered. The distinction from the US cases sounds more sweeping than it actually is. The Munich court isn't saying that music models memorize works while text models don't. It's simply noting that reproduction was concretely proven with Suno, while that wasn't the case in the other proceedings. But research has confirmed this same effect with books, which suggests memorization isn't unique to music. How easily a work can be extracted from an AI model likely depends on how often it appeared in the training data and how targeted the prompt is. Popular works like 'Atemlos durch die Nacht' or 'Rasputin' are all over the internet, making their memorization more likely. Researchers have also been able to extract well-known books like Harry Potter from language models with high similarity and nearly unchanged. The Munich court's description of GEMA's prompts as 'simple and open-ended' looks particularly shaky. GEMA entered the complete lyrics, the musical style, and the title. It didn't specify musical elements, but the identity of the desired song was pretty well defined. A typical user creating original music with an AI generator would almost certainly not prompt that way. In the copyright battle between the New York Times and OpenAI, this is a central point of contention: whether targeted prompts designed to reproduce protected content reflect normal use of an AI system or represent a special case. Put simply, is it a bug when the system spits out originals, or a feature? Suno allegedly bypassed YouTube's protections. Nobody uses a music generator to recreate a song that already exists, so the entire focus on output similarity may be a dead end eventually. The more relevant question is whether Suno was allowed to use the copyrighted material for training without consent in the first place. The court's notes contain an interesting detail on that front. According to the court's press release, Suno used 'stream-ripping techniques' to extract the music from YouTube, and it bypassed YouTube's 'Rolling Cipher,' a technical safeguard designed to prevent downloading audio and video content. That raises a question that goes beyond this particular case: whether circumventing technical protections already amounts to piracy, regardless of what happens with the data afterward. US courts have taken a clear position on this front so far: fair use may apply to AI training, but not when it's based on pirated material. AI News Without the Hype – Curated by Humans to THE DECODER for ad-free reading, a weekly AI newsletter, our exclusive 'AI Radar' frontier report six times a year, full archive access, and access to our comment section. now
Source: thedecoder
