By Samantha Sawyer, General Manager, Licensing and Technology Solutions, MassiveMusic
Music was the first industry disrupted by digital technology. From Napster’s peer-to-peer MP3 sharing to the streaming revolution that followed, the sector has weathered waves of technological change.
The licensing challenges that emerged during those transformations are now facing every AI startup building today. But today’s builders have the advantage of hindsight – if they’re willing to learn from these mistakes.
Three Lessons from Music Licensing’s Painful History
“Move Fast and Break Things” Breaks Your Business
When music streaming launched in the early 2000s, it did so without proper licensing frameworks in place. The promise was simple: figure out the technology first, sort out the rights later. The result was anything but simple.
Years of litigation followed. Billions in unpaid royalties. Platforms couldn’t scale because they were tied up in legal battles rather than product development. Some companies survived by eventually securing licenses; many didn’t survive at all. Building without licensing doesn’t just create legal risk – it creates exponential problems that compound over time.
“We’ll Figure Out Licensing Later” Means We Won’t Figure It Out
The catch-22 facing music tech startups has become painfully familiar: you need licenses to validate your product, but you need validation to secure investment that allows you to afford licensing. Licensing timelines often exceed startup financial runways, leaving founders stuck between investor pressure to grow and rights holder requirements to prove value.
MTUK’s Sound Investments report revealed a stark reality: there’s a 35% drop-off in UK music tech companies progressing from seed to Series A funding. Licensing uncertainty is a significant driver of this “missing middle”, because investors see it as a huge risk.
Rights frameworks must be built in from day one, not bolted on once you’ve achieved product-market fit. By then, you’ve already made architectural decisions that are expensive or impossible to reverse, and you’ve established business practices that may be fundamentally incompatible with proper licensing.
“Fair Use” Isn’t a Business Model
Quite a few music tech startups have bet their futures on fair use arguments. This is a risky strategy, not least because the fair use defence is only applicable in certain jurisdictions, the US being the main one. Even in those jurisdictions where the defence is available, a determination of fair use must still be established and isn’t guanteed. Legal ambiguity doesn’t just create potential liability; it creates investor flight risk. Fair use uncertainty kills features, partnerships, and fundraising opportunities long before it gets tested in court.
Three Pillars of Ethical AI That Create Competitive Advantage
Transparency: Build Trust Through Clarity
Dataset provenance tracking means building systems that can answer critical questions: Where did this data come from? Who owns it? What permissions do we have? How is it being used? When you can demonstrate clear documentation of training data sources and provide audit trails that rights holders can access, you’re building trust with the partners and investors who determine your future. This is about having the infrastructure to prove what you’re doing.
Licensed Data Use: Access Markets Less Scrupulous Competitors Can’t Touch
But documentation systems alone aren’t enough – you also need actual legal rights to your training data. Major corporations understand that using AI tools trained on unlicensed data creates downstream liability for them. Your customers’ legal teams want to know if you have licenses for your sources, and “we think it’s probably fine” isn’t an acceptable answer.
Rights-Holder Control: Win Long-Term Partnerships
Opt-in models, granular permissions, and attribution mechanisms aren’t barriers to innovation – they’re the foundation for sustainable growth. Products that respect creator control attract premium partnerships and avoid regulatory risk. When rights holders have visibility and control over how their content is used, they become partners rather than adversaries. This shifts the relationship from combative licensing negotiations to collaborative product development.
Why Ethical AI Accelerates Startups
De-Risk Investment
Investors are increasingly sophisticated about AI risks. They’ve watched the headline-grabbing lawsuits against companies that trained on copyrighted material without permission. They’ve seen valuations collapse when legal challenges emerge. Companies with clean licensing histories and robust compliance frameworks simply raise capital more easily.
Open Enterprise Doors
Government contracts increasingly mandate data provenance. B2B enterprise customers want to de-risk their supply chain by ensuring their vendors operate within clear legal boundaries. Ethical AI credentials aren’t just about avoiding lawsuits – they’re about accessing markets that less scrupulous competitors can’t touch. When a Fortune 500 company runs its vendor due diligence process, licensing clarity becomes a competitive advantage.
Future-Proof Against Regulation
The EU AI Act, UK proposals, and emerging US regulations are coming. Rights frameworks get stricter over time, never looser. The companies building compliant systems today won’t need to retrofit their entire stack when regulations tighten tomorrow. Build right from the start, and you’re positioned to expand into new markets and use cases without fundamental architecture changes.
Practical Framework: Four Phases to Build Ethically
Phase 1: Product Design (Before You Write Code)
Conduct a rights clearance assessment before you commit to specific architectures or datasets. Budget licensing costs upfront as part of your technical infrastructure, not as an afterthought. Identify potential licensing partners early and work to realistic timelines.
Phase 2: Development
Build consent mechanisms into your architecture, not as a layer you add later. For approved repertoire, implement dataset tracking from day one – provide transparency as to which repertoire is actually deployed for training and for what purposes.
Phase 3: Go-to-Market
Lead with transparency in your pitches to investors, customers, and partners. Highlight licensing as a competitive moat, not a cost centre. Frame ethical AI practices as de-risking rather than idealism.
Phase 4: Scale
Regular compliance audits ensure you don’t drift from your standards as you grow. Proactive rights-holder engagement builds partnerships before issues arise. Automated compliance systems which manage updates, takedowns and any other relevant status changes of data within training data sets, reduce the operational burden as your data and usage scale.
Three Fatal Assumptions to Avoid
“Public Internet = Fair Game”
Courts are increasingly siding with creators in cases involving scraping and unauthorised use of public content. Just because something is accessible on the internet doesn’t mean it’s available for commercial AI training. The legal precedents being set right now consistently reject this assumption.
“We’re Too Small to Sue”
Once you raise money, you’re big enough. Legal fees kill companies regardless of merit. Even if you eventually win a legal battle, the cost of defending yourself can exhaust your runway and destroy your valuation.
“Everyone Else Does It”
This doesn’t hold up in court. It doesn’t protect investor capital. And it doesn’t build sustainable businesses. The fact that competitors are taking shortcuts doesn’t make those shortcuts less risky – it just means more companies will face consequences when the legal landscape catches up.
The Opportunity
Music tech founders understand both innovation and rights complexity better than almost anyone. This positions the UK music tech sector as leaders in the development of global standards for ethical AI development. The competitive advantage doesn’t go to those who ignore licensing challenges – it goes to those who solve them.
The question facing AI development isn’t whether to engage with licensing and rights management. The question is whether AI music tools will be built on foundations of consent, transparency, and fair compensation – or whether the AI industry will repeat the mistakes of digital music’s first days. Only time will tell.