Interview

Suno launches V6 model and announces music industry partnerships with Warner, BMG, and Believe

Sep 9, 2026 with Mikey Shulman

Key Points

  • Suno launches V6 model and inks licensing deals with Warner Music Group, BMG, and Believe, establishing the first revenue-sharing framework between an AI music platform and major rights holders.
  • V6 Wild, a separately trained variant optimized for unusual outputs, reflects Suno's approach to a hard measurement problem: music lacks benchmarks equivalent to those for large language models.
  • Suno founder Mikey Shulman argues AI music expands the total addressable market by changing how people interact with media, not by compressing existing music industry economics.

Suno V6 and music industry partnerships

Suno is launching its V6 model alongside licensing and partnership agreements with Warner Music Group, BMG, and Believe — the first structured attempt to establish a revenue-sharing blueprint between an AI music platform and major rights holders. Artists who opt in receive a portion of revenues attributable to their content when users interact with it.

Shulman frames the partnerships less as a settlement and more as a foundation for building products together. The economic models aren't locked in; the agreements create alignment to iterate on them as the technology evolves. His view is that trying to get the economics right in a single moment would be a mistake given how fast AI is progressing.

Way better than the last one. It's actually way faster. And so you'll see the way that you can use it when the songs come back so faster is just very different, much more fun, way more expressive... It's actually a big day for Suno and actually for the whole music industry where this is done in concert in partnership with some of the best music companies in the world.

V6 and V6 Wild

V6 is faster and more expressive than its predecessor. Alongside it, Suno is releasing V6 Wild, a separately post-trained variant optimized for unusual, more interesting outputs at the cost of some polish in full-song structure. Shulman says he's having the most fun with Wild. The dual-model approach is Suno's answer to a hard measurement problem — music has no benchmarks equivalent to those used for LLMs, so separating "interesting" from "good" requires deliberate post-training choices and a lot of judgment.

The TAM argument

Shulman pushes back on the idea that AI music is competing in a small market. The pitch is that Suno isn't compressing the existing music industry's economics — it's changing how people interact with media entirely, which opens a much larger addressable market. The "Oreo gate" example he cites captures the logic: a woman turned her family's text message argument about Oreos into a song. Without Suno, expressing that moment publicly would have required becoming a stand-up comic. With it, it took a few words.

Pro tools and consumer crossover

Suno's product range spans from iMessage integration at the casual end to Suno Studio, a full digital audio workstation for professional workflows. Shulman says professional tooling is drawing in average users rather than staying siloed among experts — people fall in love with using the same tools the greats use, even if they don't expect to become great themselves.

Where AI music heads

Shulman doesn't expect charts full of one-shot AI-generated hits. A song is a point of view, and one-shotting a point of view that resonates with the world is rare regardless of the tools. His year-ahead prediction is mundane by design: AI becomes embedded across every part of the music production chain, used by many artists sometimes, but not replacing the iterative process through which a distinctive artistic voice develops. That's a more defensible commercial position than the "AGI for music" framing would suggest — AI as infrastructure, not as the artist.

On the video side, he expects speed and affordability of iteration to drive the next cultural moment in AI video, without being able to predict which creator or point of view will break through first.

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