After Napster, Generative AI Demands a New Deal for Music and Its Makers

AI, Copyright, and the Next Test for the Music Industry

The music business has been here before. Peer-to-peer sharing gutted recorded revenue by roughly 40% over 15 years. Streaming restored growth, using adaptive AI to recommend music and track rights without replacing creators. Now, a new wave is different: generative AI doesn't just distribute; it produces audio, lyrics, melodies and vocal likenesses trained on vast catalogs.

A recent paper on the Social Science Research Network argues that unless copyright, case law and policy evolve, human authorship risks being pushed into a murky, unregulated system. The warning is clear: resist the future and lose control, or shape it and keep value with the people who create it.

What the industry veteran is proposing

He argues that AI shouldn't be blocked. It should be integrated within a multi-stakeholder framework that protects creative labor, enables innovation, and scales with the speed of change. His stance: "AI services and human content creators must coexist and both be allowed to thrive."

With over 50 major U.S. lawsuits in progress, the likely outcome won't be sweeping wins in court; it will be settlements and new licensing structures. Courts appear inclined to balance the transformative potential of new tech against traditional copyright monopolies, and litigation takes years and millions. Negotiation is the only practical path that benefits both sides.

Why this wave feels different from streaming

Adaptive AI helped audiences find human-made music and paid rightsholders. Generative models are trained on large-scale ingestion of copyrighted works to produce new outputs. That's a structural shift.

Combine that with near-unlimited cloud compute, mainstream access to language and audio models, and aggressive commercialization, and you get a force the industry hasn't faced. Major AI music platforms aligned with tech giants can outspend and out-litigate rightsholders, putting the core legal and creative pillars of the music economy at risk.

What creatives can do now

• Insist on clear training permissions: define whether your works, stems, and likenesses (voice, style, image) can be used to train models. Price these uses distinctly.
• Add AI clauses to all contracts: usage scope, credit, revenue shares, disclosure requirements, and prohibitions on unauthorized voice cloning and vocal likeness.
• Upgrade metadata hygiene: complete credits, ISRCs/ISWCs, splits, and contributor roles so future AI-disclosure and licensing systems can pay the right people.
• Prepare for AI disclosures: keep a creation log showing where AI tools were used (vocals, instrumentation, post-production). Expect distributors to request it.
• Work with your PRO, label, or distributor to align on new licensing models for AI platforms, including training, synthesis, and derivative uses.
• Register works early and keep session files and timestamps. A clean audit trail strengthens claims and accelerates settlements.
• Set policies for voice and style: formal consent for voice models, no "soundalike" releases that confuse audiences or dilute your brand.
• Join collective efforts: creator alliances and trade bodies will have more leverage in negotiations with AI platforms.
• Track U.S. case outcomes and settlements. Expect their terms to spread to other markets.
• Diversify income: live, fan clubs, limited editions, and direct licensing reduce dependence on any single platform's policy swing.

Africa: build a regional stance before the wave hits

African languages and sounds are not a priority for most generative platforms yet. That will change. Watching U.S. settlements is wise, but waiting is risky.

Local rightsholders should align now on licensing principles, disclosure standards, and enforcement strategies that reflect regional realities. The first movers will set the baseline others get offered.

Standards and transparency are coming

Spotify plans to support a new industry standard for AI disclosures in music credits through standards body DDEX. This is about trust and clarity, not punishment. The aim is nuance: indicating where AI contributed, instead of forcing a binary label.

For creators, that means better credit rails, cleaner data, and clearer paths to compensation. Start building your disclosure workflow now; it will save time and disputes later.

The bottom line

Generative AI is here. The question is whether human authorship gets sidelined or set up to thrive alongside it. The practical path is negotiated licensing, clear disclosures, and contracts that reflect the new reality.

If the industry moves together-artists, labels, distributors, platforms-creative labor keeps its value. If it doesn't, the market will default to whoever has the most capital and compute.

Resources

• Social Science Research Network (background paper and related research)
• DDEX (metadata and credit standards, including AI disclosures)
• Complete AI Training: Courses by Job (practical AI skills for creative roles)

Key stats to keep in mind

• Recorded music revenues fell about 40% after the Napster era before streaming drove recovery.
• Over 100,000 tracks are uploaded to Spotify daily-noise rises, so provenance and disclosure matter.
• Copyright suits can take years and cost tens of millions; settlements and licensing will likely set the playbook.