Music copyright in 2025 sits at an inflection point that resembles, in structural terms, the late 1990s moment when Napster made digital distribution of copyrighted recordings technically trivial and legally murky simultaneously. Then as now, the technology moved faster than the legal framework. Then as now, the question of who would capture the value of the new capability was contested from the first day. And then as now, the rights holders who adapted earliest to the new technical reality ended up with more control over the outcome than those who waited for the law to settle before building their positions.
The digital streaming era eventually produced royalty frameworks, however imperfect, that created revenue for rights holders. The training data era will produce something analogous: a framework for attribution and compensation of rights holders whose recordings contributed to AI model training. The shape of that framework will be determined partly by litigation, partly by legislation, and partly by the technical infrastructure that gets built in the meantime. This article is about the technical part.
What the Cases Pending in US Courts Are Actually Deciding
Several copyright infringement suits involving AI training data are currently moving through federal courts. The central question in most of them is whether the use of copyrighted works in model training constitutes actionable reproduction under 17 U.S.C. 106(1) and, if so, whether that use qualifies for the fair use defense under 17 U.S.C. 107.
The fair use analysis in AI training cases turns primarily on the first and fourth factors: whether the use is transformative in nature, and whether it has an adverse effect on the market for the original work. The AI defendants argue that training is transformative because the model output is statistically derived abstraction rather than reproduction of the training inputs. The plaintiff rights holders argue that training use displaces demand for licensing, both because the AI company would have had to license the material if it could not use it for free and because the AI outputs compete with and substitute for the original works in end markets.
Courts will reach different conclusions on these questions across different jurisdictions and fact patterns, and the outcomes will likely take years to propagate through the appeals process. We are not arguing that any particular outcome is likely or correct. What matters for this discussion is what the legal framework requires of rights holders regardless of how the cases resolve.
Why Provenance Records Matter in Every Scenario
Consider three plausible outcomes in the legal landscape over the next decade, all distinct, and what they require of rights holders technically.
Scenario one: courts in major jurisdictions hold that AI training constitutes infringement and fair use does not apply. In this scenario, rights holders with established provenance records are positioned to assert specific claims against specific AI developers for use of specific recordings. Rights holders without provenance documentation can assert general claims that are harder to quantify and easier to contest. The rights holder with a recording-level provenance record wins a specific, calculable damages award; the one without it wins a vague equitable remedy that may or may not reflect actual value.
Scenario two: courts hold that training use qualifies as fair use, but a legislative framework mandates licensing and compensation for ongoing training use going forward. This is the digital audio recording act analog: usage is legal but compensation is required. The compensation formula will be based on some measure of how much a given recording contributed to a model's capability. That measure requires provenance data. Rights holders who have provenance documentation participate in the compensation formula; those who do not receive nothing beyond the base rate, if a base rate exists.
Scenario three: the major AI companies negotiate blanket training licenses directly with major PROs and label groups, similar to the mechanical licensing blanket. The license fee pool is distributed proportionally among rights holders based on documentation of whose catalog was included in training datasets. The distribution methodology requires attribution data. Rights holders who cannot demonstrate catalog inclusion in a licensing-relevant training dataset do not receive distributions from the pool.
In each scenario, provenance documentation is the prerequisite for meaningful participation. The form of the legal framework changes across scenarios, but the requirement for recording-level training data documentation does not.
What Building the Provenance Record Requires Now
The core technical challenge is that provenance data about training datasets is held primarily by the AI companies that assembled them, not by the rights holders whose material was used. This is the fundamental asymmetry of the training data problem: the entity with the most to gain from opacity (the AI developer) is also the entity with the most detailed knowledge of what was in the training set.
Rights holders can approach this asymmetry from two directions. The first is the regulatory and legal compulsion direction: using EU AI Act transparency requirements, discovery in litigation, and legislative advocacy to require AI developers to disclose training dataset contents. This is a legitimate approach and the regulatory developments described elsewhere in this blog are part of it.
The second is the technical detection direction: using audio analysis to identify which recordings were likely included in training datasets based on the measurable influence of those recordings on model outputs. Source separation and spectral fingerprinting methods can identify training corpus signatures in generated outputs with measurable confidence. This is the approach that an attribution API enables, and it is the one that produces provenance records that rights holders control rather than provenance records that AI developers disclose voluntarily.
The two directions are complementary, not competing. Legal compulsion can produce disclosure of what AI companies say they used. Technical detection can produce evidence of what can be measured as having been used. The combination produces a more complete picture than either alone.
The Infrastructure Window
There is a practical urgency to building provenance infrastructure that goes beyond the legal strategy argument. AI music models are being trained right now, and each training run that completes without provenance documentation creates a larger retroactive gap. The recordings that contribute to models trained in 2025 and 2026 are generating statistical influence that will persist in AI-generated outputs for years. The window to create contemporaneous documentation of that training influence is the current moment, not after litigation settles or legislation passes.
The analogy to the streaming era is instructive here too. Rights holders who waited for legal clarity on digital performance rights before building royalty collection infrastructure for streaming missed years of untracked usage. The technical infrastructure that eventually captured streaming royalties was built by organizations that anticipated the legal framework rather than waiting for it.
We are building Musical AI's attribution API because the provenance infrastructure needs to exist before the legal framework is settled, not after. The rights holders who will have the clearest position when AI training cases resolve or legislation passes are the ones who started building their provenance records in 2025, not the ones who waited for clarity. The decade ahead will reward those who treat training data documentation as an active operational priority rather than a passive legal problem.
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