How PROs Can Prepare for AI-Generated Music Royalty Claims Today
The technology to trace which recordings trained an AI model exists. What performing rights organizations need now is a workflow for acting on that data.
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AI Music Attribution: Perspectives and Analysis
Insights on training data provenance, rights infrastructure, and the regulatory landscape shaping how the music industry responds to generative AI.
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Ghost in the Machine: Who Owns the Songs That Taught AI to Compose?
When a generative AI outputs a song, dozens of copyrighted recordings may have shaped its style and structure. The rights framework for those training contributions is still being written.
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Inside the Attribution Trace: How Training Data Provenance Works for Generated Tracks
Source separation and spectral fingerprinting can identify the recordings a model was trained on, even after generation and processing.
The MLC, PROs, and the Emerging AI Attribution Gap
The Mechanical Licensing Collective was built for a streaming world. As AI-generated music becomes mainstream, a new gap is forming.
Why Music Publishers Need a Data Strategy for AI Licensing Before It Is Too Late
Publishers who know exactly what is in their catalog are in a fundamentally different negotiating position when an AI company comes calling for a training license.
Sound to Signature: Audio Fingerprinting Is Not the Same as Training Attribution
Fingerprinting identifies a specific recording. Attribution asks which recordings shaped the model. Understanding the distinction matters for choosing the right tool.
How Generative AI Changes the Royalty Collection Equation
Traditional royalty collection assumes a known composition performed by a known artist. AI-generated music breaks both assumptions simultaneously.
The Metadata Problem: AI Music Attribution Starts with Clean Catalogues
Publishers and PROs with well-structured ISRC and ownership data are positioned to act on AI attribution results far faster than those still cleaning metadata.
Building a Provenance API for Recorded Music
A walkthrough of the engineering choices behind a provenance API designed to work inside existing rights-management workflows.
The EU AI Act and What It Means for Music Rights Holders
The EU AI Act includes transparency requirements for training data. Rights holders should understand what that means for provenance documentation and future enforcement.
The Question ASCAP and BMI Are Being Asked About AI Training Consent
As AI developers begin approaching rights organizations to license catalog for training, the question of consent is pushing into territory existing license structures were not designed to address.
Training Data Provenance and the Next Decade of Music Copyright
The winners in music rights enforcement over the next decade will be those who start building the provenance record now.
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