Music Industry

Spotify’s Label Partnerships Accelerate the End of Unlicensed AI Music Training

Credit: Outlever

Key Points

  • Spotify’s shift to licensed, artist first AI tools shuts down unlicensed training and pushes the AI music industry toward compliance as the new baseline.
  • Wei Hsueh, Co-Founder of AI licensing platform IntentBridges, explains that this pressure lets incumbents use licensed data as a competitive moat while forcing startups to rethink their approach.
  • He says the path forward is either to build inside an incumbent’s ecosystem or use emerging data marketplaces to license only what’s needed and create a focused, defensible niche.
Wei Hsueh - Co-Founder | IntentBridges
Now, once the models cross the baseline quality standard, it’s not about being better. It’s about being compliant enough to serve enterprise-grade clients.Wei Hsueh - Co-Founder | IntentBridges

Spotify’s move to build licensed, artist-first AI tools with the major labels didn’t just make headlines. It snapped the AI music world into a moment of reckoning. The era of unregulated data scraping is ending fast, and emerging AI companies are feeling the heat. Compliance has become the new entry fee, and anyone without licensed data is suddenly playing a much harder game.

Watching this shift unfold is Wei Hsueh, Co-Founder of IntentBridges. Today, his company is building the next generation of AI content licensing. But before that, he helped grow Criteo’s APAC app supply business from the ground up and later led Equativ’s APAC operations. That mix of monetization work and market building gives him a clear view of how GTM strategy is colliding with the rights holder economy now reshaping the AI music world.

"Previously, AI generation itself was a breakthrough, but the quality wasn't there yet. Now, once the models cross the baseline quality standard, it’s not about being better. It’s about being compliant enough to serve enterprise-grade clients," Hsueh says. Eventually, once AI models reach a certain level of quality, the defensibility of a company's licensed data emerges as the key strategic battleground.

  • The Adobe playbook: Today, that pressure is driven largely by enterprise clients who prioritize legal safety, Hsueh explains. But it also creates a new competitive moat, raising the cost of entry for new competitors. "This is exactly what Adobe is doing. Because they have everything trained on licensed data, they can now use public earnings calls to differentiate themselves. They can highlight that those startups are not copyright-cleared, while they are, and ask enterprises which they would rather choose."

    Wei Hsueh - Co-Founder | IntentBridges
    What Spotify did had a ripple effect on the other AI startups. After that peer pressure moment, there was no room for anyone to use unlicensed data anymore.Wei Hsueh - Co-Founder | IntentBridges

    The market pressure Hsueh identifies is a force with immediate consequences. He points to a "ripple effect" where, within weeks of Spotify's announcements, other AI startups began falling in line.

  • Domino effect: For example, Udio quickly settled its copyright dispute with UMG, and Stability AI announced its own deal with the music giant, Hsueh says. "What Spotify did had a ripple effect on the other AI startups. After that peer pressure moment, there was no room for a company like Udio to use unlicensed data anymore. They all began saying the same thing, announcing that they are now cooperating on new music generation tools based on licensed data."

  • Rise of the data market: The immediate consequence of this pressure is a fundamental challenge: How can a cash-strapped startup possibly afford the "huge bottom line" of licensing costs? Hsueh predicts a new infrastructure will emerge as the solution. “Ecosystems like Spotify will probably create a marketplace where AI developers can buy specific licensed data. Instead of trying to license everything, you’d pick only what you really need."

For many startups, the new reality forces a strategic decision. Here, Hsueh sees two paths forward.

  • Join the giant: One is to fold into an incumbent’s ecosystem: “That innovation will be absorbed into the existing players. Startups become more like an enabler, allowing them to come to an incumbent’s platform and build new things on their infrastructure."
  • Vertical vision: The other is to specialize and own a smaller slice of the market: “It’s going to be more verticalized. If I want to train on K-pop music, then you’d license most of the K-pop content and make your tool more K-pop-centric. You control your cost and don’t go crazy trying to get everything.”

Ultimately, the opportunity for broad, general-purpose models is closing for anyone trying to break in. The shift is global, shaped by actions from the EU AI Act, the US Copyright Office, and markets like South Korea, where the largest copyright collective has already banned AI-generated songs. The pressure is the same everywhere, and the direction of travel is clear. "The window for general-purpose things is shrinking fast," Hsueh concludes. "It’s narrowing by the day, and it’s basically going back to the incumbents."