Traditional royalty collection is built on two bedrock assumptions. First: there is a composition, a song with an identified songwriter or group of songwriters registered with a performing rights organization. Second: there is a recording, performed by an identified artist, with a specific label and publisher chain attached to it. Every PRO licensing framework, from ASCAP performance reports to the MLC's mechanical data intake, was designed around these assumptions. When both hold, the path from a public performance to a royalty payment is well-defined, even if the paperwork takes time.
Generative AI breaks both simultaneously. The implications for collection infrastructure go deeper than most industry discussions have acknowledged, and they require a structural response, not a procedural workaround.
What Happens When the Creator Is a Model
An AI music model that generates an original-sounding track is not a songwriter in any recognized legal sense. There is no composition registration. There is no PRO membership. There is frequently no human author attached to the output at all, or the only human involved is someone who entered a text prompt.
What there is, though, is a training corpus. The model learned to generate music by processing thousands of recorded works. Those works had composers, performers, publishers, and labels. The model absorbed statistical patterns from each of them. What exactly that absorption means for rights is the legal and technical question the industry is now trying to define.
This is not the same as sampling. A sample is a direct, identifiable copy of a specific recording, and the legal framework, while imperfect, exists to address it. Training influence is subtler. A model trained heavily on a particular catalog will produce outputs that carry the statistical signature of those inputs. That signature is measurable. It is not accidental. But no existing royalty framework captures it.
Where the Collection Machinery Stalls
Royalty collection today starts at the point of distribution or performance. A track goes on a streaming platform, gets reported as a play, and flows into a matching system that tries to identify it against a registered work. The operative question is: which known composition and recording is this?
For AI-generated music, that question has no answer in the usual sense. The track was not derived from any single composition directly. The better question is: which recordings contributed to the training run that produced the model that generated this track?
That is a different type of query entirely. It requires working backward through a model's training history rather than forward through a catalog match. It requires provenance data about the model itself: what recordings were in the dataset, in what proportion, and with what measurable influence on specific outputs. None of the current collection infrastructure was designed to receive, process, or act on that type of data.
A Concrete Picture of the Gap
Consider a hypothetical scenario that illustrates the mechanics. A music supervisor for an independent film production licenses a two-minute background score generated by a commercial AI music tool. The platform charges a flat licensing fee and represents that the output is "rights-clear." The music supervisor asks, reasonably, whether any underlying training data claims could surface after the fact.
Under current infrastructure, no one in that transaction can give a confident answer. The AI music platform may have licensed training data from some publishers but not others. The degree to which any individual recording influenced that specific two-minute output is not disclosed. There is no audit trail linking the output to the training inputs at the level of individual recordings.
The PRO cannot resolve this question because it has no intake pathway for training provenance data. It cannot receive, validate, or act on a provenance claim because no workflow exists to process one. The result is that a licensing transaction completes, usage accumulates, and rights holders whose catalog contributed to the model's capability receive nothing.
The Input Layer That Collection Infrastructure Is Missing
Collection infrastructure needs a new front end: a mechanism to receive and validate training provenance data for AI-generated tracks, translate that data into rights holder identifiers, and route it into existing royalty calculation workflows.
This is technically tractable. Spectral signatures from training recordings persist in model outputs in measurable ways. Source separation and fingerprinting methods can identify those signatures at scale. The data formats to express attribution results can be designed around JSON structures that PROs already use for mechanical and performance data. The integration problem is significant, but it is an engineering problem, not a fundamental impossibility.
What PROs need to add to their intake workflows is a layer that precedes the current matching step. Before asking "which registered work is this?", the system needs to ask "which training recordings contributed to this model, and in what proportion?" That attribution layer is the missing input.
The Boundary Worth Stating Clearly
We are not arguing that every AI-generated track represents a clear rights violation. The legal framework is genuinely unsettled, and legitimate arguments exist about whether and to what degree statistical influence on a model constitutes actionable use of underlying recordings. Different courts in different jurisdictions will reach different conclusions over years of litigation and, eventually, legislation.
What we are saying is distinct from the legal question: the collection infrastructure is not even positioned to evaluate the question. PROs cannot assess a training provenance claim because they have no mechanism to receive, parse, or verify one. That is a systems gap that exists independently of the law. Even if the law eventually provides a clear answer about what rights holders are owed, the infrastructure to deliver those royalties would still need to be built.
Building Toward a Solution
The practical path forward starts with data infrastructure alongside legal strategy. Rights holders need a technical mechanism to identify which recordings were included in training datasets, at what scale, and to connect that provenance data to specific generated outputs. PROs need intake formats that can accept, validate, and process attribution claims. Publishers need catalog data clean enough to be matched against attribution results when those results start arriving.
The collection infrastructure will not solve this problem by incremental adjustment of existing workflows. Generative AI changes the fundamental input to the royalty equation. The first step is acknowledging that the equation has new terms, and then building the systems to compute them.
At Musical AI, this is the problem we are working on directly: an attribution API that accepts a generated track and returns training provenance data in a format that existing royalty workflows can actually process. The collection machinery needs a new input layer. The sooner rights organizations start building toward it, the smaller the gap between AI music usage and rights holder compensation will become.
Stay Informed
