Thinking On Paper AI Stole the Beatles: Who Gets Paid When Music Is Training Data?
You're listening to AI-generated music and don't realize it. The musicians whose work trained those models know. They check their empty bank accounts daily.
99,000 new songs upload to streaming platforms every day. One in five are AI-generated (Deezer). You wouldn't play a single one at your funeral.
59% of musicians use AI in some aspect of their music (Ditto Music). The question: How do real musicians get paid when AI uses their work?
We break down Water & Music's research on AI music attribution. Cherie Hu, Yung Spielburg, and Alexander Flores investigated what's at stake and which companies are trying to solve it.
The problems:
- Session musicians, producers, songwriters—how do they get paid when AI uses their beats?
- Record labels can't track which training data influenced which outputs
- Proving a musician's input in a model's output is nearly impossible
- Copyright law wasn't built for this
- Most AI music companies scraped without permission or payment
We talk about:
- How attribution technology could work (vector matching, contribution tracking)
- Which companies are building payment systems
- Why Suno and Udio's approach creates legal chaos
- How ethical companies like Overtune license training data and split royalties
- Whether streaming platforms can detect AI-generated music
- What happens when the Beatles' catalog trains a model
The stakes: If attribution fails, AI becomes theft at industrial scale. Music becomes disposable content optimized for algorithms, not humans.
Based on Water & Music's research—the best reporting on AI music economics.
Share with a music lover.
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Research: Water & Music (Cherie Hu, Yung Spielburg, Alexander Flores)
Topics: AI music, copyright, attribution, streaming, royalties, training data
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(00:00) The Intersection of Music and AI
(03:26) Understanding Music Attribution
(03:51) Sonic Characteristics and AI Influence
(06:39) The Complexity of AI Music Generation
(07:36) The Value Equation in AI Music Creation
(08:08) Understanding Influence Functions in Music AI
(09:44) Challenges of Attribution in AI-Generated Music
(11:38) Exploring Embeddings and Their Role in Music AI
(14:17) Watermarking and Its Limitations in Music Attribution
(15:30) Synthetic Data and Its Implications for Music AI
(17:48) Innovative Solutions for Music Rights Attribution
(18:01) Distinguishing Compositional vs. Recording Contributions
(19:59) The Impact of AI on the Music Industry's Inequities
(23:03) Trust and Technology in Music Attribution
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