By Samantha Sawyer, General Manager, Licensing and Technology Solutions, MassiveMusic
Anyone reading music industry news in recent weeks will have noticed a flurry of deals forged between AI companies and major labels. There’s likely to be many more to come, and it points towards a future where AI companies will build much stronger ties with the music industry.
Why are these deals happening? As AI systems become more sophisticated, they’re developing an appetite for high-quality, properly licensed music data to train their models. To do that effectively and responsibly, developers need music that is clean, rights-cleared, traceable, and structured – and not scraped, incomplete or ambiguous.
Why AI Companies Need Music (and Why it’s Complicated)
The scale and variety of AI applications using music continues to proliferate. From streaming platforms building smarter recommendation and discovery systems to social platforms matching audio to content. From gaming companies developing adaptive soundtracks to generative AI firms creating tools for everything from stem separation to voice synthesis to full composition.
The use cases are wide but they align on similar but hard-to-achieve needs: they need to understand the complexities of musical structure, genre, tempo, harmony, and emotional resonance. They need to enable precise music recognition and matching at scale. They need to support adaptive sound design in interactive experiences. And, of course, they need to power creative tools that artists, brands and platforms can rely on.
The quality of the training data determines the quality of the model, and as the AI landscape becomes increasingly competitive, that quality will continue to grow in importance in 2026. That means permissioned music that’s traceable, licensed, and auditable, alongside clarity across every layer of rights: composition, master, performance, publishing, territories, formats, usage, and duration.
The Problem: a Rights Maze Without a Map
The reality is that most of the music industry wasn’t built for AI training at scale. There’s no unified global standard for AI training rights in music, so most models end up being trained on large catalogues of instrumentals.
The datasets that are available typically lack robust metadata, making clean chain-of-rights validation nearly impossible without sophisticated technical solutions. AI companies often want to train models responsibly, but they’re faced with navigating a complex web of overlapping rights, incomplete information, and uncertain legal frameworks.
The result? Some companies take shortcuts and train on copyrighted material without proper licensing. We’ve watched the consequences unfold as a series of high profile AI companies have faced serious legal action for copyright violations. These cases are now defining the legal precedents that will guide the next decade of AI development.
The Opportunity: Building AI Systems the Right Way
While some AI firms scramble to sort out their legal exposure after the fact, there is a better route forward.
The industry needs a blueprint for ethical, scalable AI training. It needs partners that can bridge the gap between cutting-edge AI development and the established music rights ecosystem. It needs infrastructure that makes responsible AI training not just legally sound, but practically achievable. MassiveMusic’s approach has been to position itself at this intersection – and here is what we’ve learnt.
Making It Work: Licensing, Infrastructure and Compliance
If AI training is to work responsibly at scale, it requires three core pillars, each addressing a different layer of the challenge.
1. The Licensing Foundation
Successful AI training licensing starts with deep relationships across the rights holder ecosystem: labels, publishers, and independent rights holders globally. The negotiation challenge is unique because traditional licensing frameworks weren’t designed for AI training use cases.
The companies that get this right can secure rights cleanly and quickly, protecting AI platforms from the kind of liability that’s now making the headlines. The ones that get it wrong face years of litigation together with the associated financial and reputational damage.
But even perfect licensing only solves the permissions layer, not the data quality or delivery layer that follows.
2. The Infrastructure Reality
AI companies don’t just need permission to use music; they need data that’s ready for ingestion and model training. This means verified, structured music data with enriched metadata that ensures accurate ownership and rights information. It means enhanced contextual tags that provide meaningful information about the music beyond basic genre classifications. And it means delivery infrastructure that can support everything from bulk ingest to API access to format-ready exports.
Perhaps most critically, it requires expert human curation. While AI can assist with initial tagging and organisation, human expertise is what ensures quality datasets that will produce better-trained models. Without this blend of human and machine analysis, datasets become noisy, inconsistent, and unreliable which limits model performance.
3. The Compliance Imperative
The compliance layer is often overlooked until it becomes a crisis. Transparent reporting and compliance frameworks for rights validation aren’t optional extras; they’re essential for long-term governance and trust-building with creators, regulators, and partners.
This is where the industry separates companies building for the long term from those taking shortcuts. The platforms investing in proper compliance infrastructure today are the ones that will survive regulatory scrutiny tomorrow. Clear lineage, audit trails, and usage visibility are no longer “nice to have,” they are becoming regulatory expectations.
What this Means for the Ecosystem
The emergence of AI training as a significant use case for music licensing represents a new revenue stream for rights holders, a new technical challenge for music tech companies, and a new frontier for innovation in rights management.
For the UK music tech sector, it’s a reminder that our strength lies not just in consumer-facing applications, but in solving the complex infrastructure challenges that underpin the entire ecosystem. The companies that can bridge traditional music industry expertise with cutting-edge technical capabilities are the ones positioned to make the most of an AI-driven future.
For instance, today’s AI training licensing typically operates on a flat fee per track model. But as AI systems grow more sophisticated and their outputs become more commercially valuable, the industry will increasingly look to attribution-based licensing. But how do we identify which source tracks contributed to specific AI outputs? And how do we value each track’s contribution to a generated composition or audio?
These questions don’t have clear answers yet, but they’re coming. The companies and rights holders thinking about these frameworks now will be the ones shaping the next generation of AI music licensing.
The question isn’t whether AI systems will be trained on music data. The question is whether that training happens in a way that respects creators’ rights, provides fair compensation, and builds toward a more transparent and equitable industry.
The UK music tech sector has an opportunity to lead in answering that question correctly – and MassiveMusic is committed to helping the AI industry get this right by building a future that’s ethical, transparent, and built around the people who make the music.