Sony’s researchers made headlines recently with a bold claim: they’ve cooked up tech that might tell you exactly which songs fed into a generative AI model to make a new music track. Some headlines went further, suggesting you could end up with a breakdown like “30% influence from The Beatles and 10% from Queen.” That makes for a catchy soundbite but here’s the thing: the reality is more nuanced, more complex, and a long way from becoming a tool record labels can put to work.
This isn’t about dismissing innovation. It’s about unpacking what the research actually says, what it could lead to, and where the big gaps still lie. For the music industry from songwriters to publishers the question isn’t academic: it’s about how creators get paid and protected in an age when generative AI models consume oceans of copyrighted material.
From Press Headlines to Research Labs
The story kicked off with coverage particularly in Nikkei, which reported that Sony’s technology can “analyse which musicians’ songs were used in learning and generating music” and even quantify their contribution. That kind of framing spread quickly through tech and music media.
But what Sony’s AI R&D division actually published is a research paper titled Large-Scale Training Data Attribution For Music Generative Models Via Unlearning, presented at the NeurIPS 2025 conference. It’s an academic exercise showing a new way to evaluate how training data might influence a generative music model’s output not a plug‑and‑play industry solution.
The key method involved is known as machine unlearning basically, observing how a generative model’s internal understanding shifts when you remove or “unlearn” specific portions of its output to infer which training data points were most influential. But this isn’t as simple as deleting a track from a playlist and seeing what happens. It involves tweaking a model’s internal parameters and then statistically re‑evaluating a training dataset, which at scale is massively complex.
How the Attribution Process Works Without the Hype
Let’s break down the unlearning idea in practical terms:
- A generative music model, like a text‑to‑music diffusion network, learns patterns from thousands of tracks in Sony’s case, a dataset of around 115,000 pieces.
- If you could remove a specific generated output from the model’s internal parameters and measure how that affects its ability to reconstruct each training track, you get a signal about which pieces of training data mattered most.
- In essence, you pull on the model’s inner wiring and see which parts of that wiring change a proxy for “influence.”
It’s clever, but it’s a far cry from summing up “percentage influence” by artist or genre. And the math gets hairy fast. Researchers note that computing this for every track in a large dataset would require impractical amounts of computational power and the more tracks you have, the worse it gets.
How It Fits Into Industry Needs And Legal Battles
Why does any of this matter to record labels and publishers? Because today many generative models are trained on massive, often unlicensed datasets of copyrighted material. Rights holders are pushing back legally arguing that using copyrighted works without permission should trigger licensing obligations or even infringement liability.
In parallel, startups and technology ventures are building systems to trace and monitor where copyrighted content appears in training workflows, or to provide attribution frameworks that could underpin licensing deals. A recent example is Musical AI, a startup that raised $4.5 million to develop attribution systems that identify what percentage of a generated output came from which source though the methods and claims differ from Sony’s academic work.
The music business is already hashing out broader licensing frameworks with AI music platforms like Suno and Udio, signaling that the industry is ready to move beyond litigation toward structured deals if the technology and legal standards can keep up.
Limits and Practical Challenges
There are several critical limitations standing between an academic proof‑of‑concept and a useful industry tool:
- Scalability: Even in research settings, attributing influence across tens of thousands of tracks is computationally heavy requiring clusters of high‑end GPUs and hours of processing time per track analysis.
- Accuracy vs. Interpretation: A score indicating that certain tracks were influential doesn’t automatically translate into clear, legally defensible artist or publisher attribution percentages. The leap from “this generative output was affected by these training examples” to “this artist contributed X%” involves further modeling assumptions and normative choices.
- Black‑Box vs White‑Box Methods: When you don’t have access to an AI model’s internal parameters the so‑called “black box” scenario attribution becomes even more speculative. Sony’s method depends on “white box” access, which many commercial AI services won’t allow.
- Legal Uncertainty: Copyright law hasn’t settled on how generative AI should treat training data, enforcement, or licensing obligations. Some jurisdictions treat any use of copyrighted material as potentially infringing, while others hinge on fair use or other exceptions.
Where We Go From Here
Sony’s work is valuable in that it pushes the conversation about traceability in generative AI a discussion that intersects tech, law, and creative labour. But the more cautious framing is that it’s a research milestone, not a commercial product or a definitive solution to attribution or royalty allocation.
What the industry really needs next are standards and interoperable frameworks that can work across multiple systems, multiple legal regimes, and at real‑world scales. If the music business hopes to build reliable compensation systems for creators in the age of generative AI, technology like Sony’s unlearning‑based attribution will be part of the puzzle but not the whole picture.