The Recording Industry Association of America (RIAA) has recently filed copyright infringement lawsuits against two AI music generation companies, Udio and Suno. While these lawsuits raise understandable concerns about the impact of AI on the music industry, their legal arguments are fundamentally flawed and unlikely to succeed in court. This essay will explore the key reasons why the RIAA's lawsuits against both Udio and Suno are likely to fail.
The core of the RIAA's argument against both Udio and Suno is that they have committed copyright infringement by training their AI models on vast datasets of copyrighted songs without permission. However, this argument ignores the well-established legal doctrine of fair use, which allows for the use of copyrighted material without permission for transformative purposes.
Training AI models on copyrighted music is a quintessential example of fair use. The AI systems are not simply copying or distributing the original songs, but using them as raw material to learn patterns, styles, and techniques which they then use to generate novel compositions. This is a highly transformative use that does not substitute for the original works in the market.
The lawsuits claim that Udio and Suno's use cannot be fair use because it is commercial and creates directly competitive digital music files. However, the commercial nature of a use does not automatically disqualify it from being fair use. Courts have repeatedly found commercial uses to be fair when they are sufficiently transformative, as in the Google Books case.
Applying the four statutory fair use factors further strengthens this argument:
Both lawsuits heavily emphasize the ability of Udio and Suno's systems to generate outputs that resemble specific copyrighted recordings. However, this focus on outputs misses the mark legally. The fair use analysis for AI training focuses on the nature of the use in the training process, not on the outputs generated later.
Even if some outputs bear similarities to copyrighted works, this does not negate the fair use of the works for training purposes. The ability to generate similar-sounding outputs is a byproduct of learning general musical patterns and styles, not a result of storing and reproducing specific copyrighted expression.
The Suno lawsuit introduces the concept of "overfitting" in machine learning. However, this is a technical issue related to model training, not a legal standard for copyright infringement. An AI model producing outputs that sound similar to its training data is not equivalent to copying or reproducing copyrighted works.
Both lawsuits argue that Udio and Suno copy entire sound recordings into their training data. However, in the process of AI training, the original works undergo substantial transformation. They are broken down, analyzed, and converted into abstract statistical representations. The original recordings are not preserved in a recognizable form within the AI model. This level of transformation strongly favors a finding of fair use.
The RIAA claims that both Udio and Suno's services pose a significant threat to the market for and value of the copyrighted recordings. However, these claims are largely speculative at this stage. Courts generally require concrete evidence of market harm, not just theoretical possibilities.
Moreover, the potential for AI-generated music to compete with human-created music does not necessarily constitute the kind of market harm that weighs against fair use. Copyright law does not protect copyright holders from all forms of competition, only from unauthorized copying of their specific expression.