SZA denounces ‘Suno’ and musicians who use AI

Suno’s approach to platform integrity and music copyright protection

Last week, we shared that we are now testing a new music model developed in partnership with the music industry. We’re really excited about this and it’s a good opportunity to share more about how we think about the integrity of the platform and protecting the people behind the music. At Suno, preserving art and human creativity is as important as product innovation itself. We know it’s easy to be skeptical, especially if you haven’t immersed yourself in our product. But most of the people who build Suno are musicians. Empowering human creativity is why we are here, not just for corporate positioning. We believe that companies building AI have a responsibility to invest in safeguards with the same level of ambition and rigor that they bring to building products. Since our earliest days, we’ve worked to build protections directly into the foundation of our platform. That includes clear rules prohibiting users from uploading or distributing content they don’t own or have the right to use, meaningful enforcement when those rules are violated, and partnerships with industry-standard providers like Audible Magic, Musixmatch, and ACRCloud to help us identify and prevent misuse. It has also determined how we have approached model development itself. One of the most common questions people ask about AI is whether models can reproduce material from their training data. Our answer is simple: no, that shouldn’t happen. Our philosophy has always been that AI should help people create new music, not replicate someone else’s. That’s why we build our models around what we call “Original Creation, by Design” – training strategies aimed at reducing the risk of generating unauthorized reproductions. For example, we don’t use artist names as a training metadata category, which means we made an intentional decision not to teach artist names to models because our goal is to help people create new songs, not music that sounds like existing artists. In addition to those efforts, we are actively developing additional approaches for audio fingerprinting, watermarking, and spam and phishing detection. Additionally, we are engaging in discussions about AI labeling with our users and creatives at all levels, to understand how to best empower them while respecting privacy. We do not treat these issues as a compliance exercise. It’s a core part of how we build and has led us to implement more copyright safeguards than traditional music software, not just compared to AI music tools, but compared to all music tools that came before them. We are in active dialogue with artists, songwriters, producers, labels, distributors and partners across the music industry, and those conversations will continue to shape the safeguards we build and the investments we make going forward. We will launch our associated models in the coming months. We will soon have much more to share 31 comments on LinkedIn.