Copyright Challenges in AI-Generated Music

Think that AI track is "Royalty-Free"? Think again folks! Last week I spoke on a panel and someone asked me, can they just make a song on Suno/Udio and licence it to me for an advert? My quick answer was no. Here is why... Zero Ownership: Current copyright laws are clear, if a human didn't create it, you don't own it. If you're a brand that uses an AI created song, you’re on rented land that could be reclaimed at any moment. Major labels are moving quickly and if an AI model was trained on copyrighted hits to generate your "original" track, you aren't just using a tool, you’re potentially infringing on a massive scale. The Indemnification Gap: Most AI terms of service including Suno have shifted from "You own this" to “You generally are not considered the owner of the songs, since the output was generated by Suno” If a record label / publisher sues your brand, the AI company isn't responsible, you are. The Bottom Line: Saving a few hundred pounds/euros/dollars on a sync license today isn't worth a six-figure copyright infringement lawsuit tomorrow. Despite the legal minefields, we are entering an era where technology can amplify our vision like never before, yet, it’s crucial to remember that while AI can mimic a melody, it cannot replicate the lived experience, soul and intentionality that define true human artistry. This is what connects us and I believe connects brands with their audience. #MusicSupervision #Advertising #CopyrightLaw #AIMusic #IntellectualProperty #BrandSafety

AI generated songs puts music supervisors in an impossible position. For years, music supervisors have played a critical role in film, television, advertising, and streaming content. Their job isn’t simply finding “good music.” It’s finding music that is emotionally right, legally safe, culturally appropriate, budget-friendly, and ethically usable. Now AI-generated music is complicating every one of those responsibilities. At first glance, AI tracks may seem attractive for productions under pressure: • Faster turnaround • Lower costs • Endless customization • Instant revisions But beneath that convenience lies a growing legal and ethical gray area. Many AI music models have been trained on enormous amounts of copyrighted recordings and compositions without clear consent from artists, producers, songwriters, or rights holders. That means music supervisors are increasingly being asked to place tracks whose origins may be impossible to fully verify. And that creates enormous risk. If a supervisor licenses an AI-generated track that later becomes the subject of copyright litigation, who is responsible? • The AI company? • The production company? • The supervisor? • The advertiser? • The streaming platform? Nobody seems to fully know yet. Music supervisors often build long-term relationships with composers, artists, producers, and labels. They know the people behind the music. They understand their stories, reliability, professionalism, and artistic identity. AI-generated music removes the human chain of accountability that the sync world has traditionally relied on. And there’s another issue that deserves more attention: AI music could unintentionally devalue original composers and independent artists who depend on licensing income to sustain their careers. For many musicians, sync placements are not a side business — they are survival income. A film placement, ad campaign, or TV cue can financially support an artist long enough to continue creating music. If productions increasingly replace human creators with instantly generated tracks trained on existing music, the long-term economic consequences for working musicians could be severe. This puts music supervisors in an almost impossible position. They are being asked to move faster, cut budgets, and deliver more content than ever before, while simultaneously navigating legal uncertainty that may not be resolved for years. The technology is advancing much faster than the policies surrounding it. But the industry urgently needs clearer standards around: • Training data transparency • Copyright accountability • Consent from creators • Licensing protections • Disclosure requirements Without those protections, music supervisors may end up carrying enormous professional and legal risk for decisions they were never properly equipped to make. Technology may change how music is made, but the industry still depends on trust, transparency, and the value of human creativity.

I just published a new, members-only analysis on Water & Music on one of the hottest and most complex topics in the music business right now: Music AI content and copyright detection. When it comes to the value of music AI, data is like oil — and rights holders are determined to control the pumps. Over 350 music industry organizations have signed ethics statements on music AI, emphasizing the importance of data transparency and artist consent in the model training process. Meanwhile, lawsuits and cease-and-desist letters against music AI startups are piling up, involving every major rights holder. The next six months will define the future of the music business, as we move beyond philosophical and ethical debates to practical solutions for IP protection in an AI-led market. In my analysis, I break down: - How each step of the music AI detection supply chain works, from auditing training data for copyrights to detecting AI-generated deepfakes. - Who the key players are at each stage, and how their detection models work. - Why these developments matter for the future of music, and have a direct tie to the latest lawsuits against Suno and Udio. This piece was several weeks in the making — involving background conversations with several kind people at Audible Magic, BMAT Music Innovators, Pex, Deezer Research, MatchTune, Ircam amplify, and more, along with meticulous fact-checking and research help from my team (Yung Spielburg & Alex Flores). While my resulting analysis only scratches the surface on the topic from a technical and legal standpoint, I'm quite proud of how it turned out, and I hope it helps you make sense of an otherwise quite complex and noisy landscape. Let me know what you think in the comments! 😊 #music #musicindustry #musicai #copyright #legal #ai #futureofmusic https://lnkd.in/eaMB4AQm

The music industry is finally having a real conversation about AI, and it's encouraging to see more players joining it. At Deezer, we were among the first to confront the challenges of generative AI head-on. With that experience, I want to clarify a few things. AI is not the real threat to music. Used creatively, AI can generate value and open the door for more people to make art, something fundamental to human well-being. But AI-generated music also has negative effects the industry must tackle, faster than we currently are. ISSUE #1 : IP and royalty theft The numbers speak for themselves: → More than 50% of songs uploaded to Deezer daily are now 100% AI-generated, created with models facing lawsuits from most rightsholders. → Up to 85% of streams on those songs are fraudulent, attempting to game royalty distribution. What we've done: Deezer built a detection tool to identify fully AI-generated tracks. Combined with our fraud detection systems, it lets us exclude them from royalty distribution, protecting the revenue pool for real artists. What needs to happen next: → We welcome the deals between labels and AI music apps. They clarify remuneration for catalog used in training and, by limiting downloads per user per day, can help stop the flood of AI slop. → Distributors should also act. We welcome TuneCore's recent commitments and continue working with other distributors to flag fraudulent accounts. ISSUE #2: TRANSPARENCY FOR FANS → More than 80% of music lovers want to know when they're listening to AI-generated music. It's simply logical: fans deserve to know whether there's a human behind the song they love, someone they could meet, follow, and see in concert. What we've done: Deezer labels AI tracks from predominantly AI-generated discographies and has removed fully AI-generated songs from algorithmic recommendations. An imperfect compromise, giving fans visibility on "artists" that are mostly AI without penalizing real artists who use AI as one tool among others. What needs to happen next: → As an industry, we must agree on a standard for what gets flagged as AI. Not for its own sake, but to provide clear value to artists and fans. Deezer's ambition is to build consensus on what content should be flagged and how AI songs should be monetized, distinguishing licensed from unlicensed AI. The music industry has weathered every technological shift by adapting, together, always the first cultural industry to do so. AI will be no different, but only if we act as an industry, not as isolated players. That's why Deezer is proposing a pragmatic way forward achieving both goals: safeguarding royalties for human artists and giving transparency to music lovers.

AI cloned her voice. Then claimed her songs. Then took her money. Meet Murphy Campbell, a North Carolina singer-songwriter with 7,800 monthly Spotify listeners. She spent weeks in what she called "a weird limbo telling robots to take down music robots made." An entity called Timeless Sounds IR scraped her YouTube performances, ran them through AI voice cloning tools, and uploaded the results to every major platform. Then filed copyright claims against her originals. The underlying compositions are all public domain, "In the Pines" dates to the 1870s. But her specific recordings aren't. That's the gap they exploited. Here's the structural problem. Content ID processes millions of claims. Vydia, the distributor used in the fraud, reports 0.02% invalid, by industry standards, excellent. At 6 million claims, that's still 1,200 fraudulent cases. No human reviews the initial match. No platform verifies that an upload came from the credited artist. And customer support ... that's AI now as well And ACR fingerprinting database? Well, ACR protects your exact recordings from being re-uploaded verbatim, but does nothing to detect AI voice clones, which are new files, new fingerprints, invisible to the system entirely. Another case makes the scale clear. Michael Smith used AI to generate hundreds of thousands of songs and bot-streamed them billions of times. He collected $8M in fraudulent royalties before his arrest. He pleaded guilty in March 2026, the first criminal prosecution for AI-assisted streaming fraud in U.S. history. Deezer estimates 60,000 AI-generated tracks are uploaded to its platform daily. Up to 85% of streams on those tracks are fraudulent. Two cases. Same broken infrastructure. Different attack vectors. AI has invalidated three assumptions the entire rights stack was built on: that creating music requires human effort, that uploading implies authorship, and that a stream represents a person choosing to listen. Remove those three and every platform's fraud architecture collapses. For anyone managing a catalog or building a distribution strategy: Register your recordings. Audit your distributors. Monitor your profiles. That closes the gap Campbell fell through. It doesn't solve the clone problem. Nobody has solved that yet. Platforms optimized for scale will always sacrifice individual protection. That's not a bug. 💬 How long before a major label's catalog gets hit the same way?

The US Copyright Office has just released its Part 3 Report on Generative AI Training, and it addresses the elephant in the dataset: Can AI companies use copyrighted content to train their models without permission or payment? The report says this is not a grey area. Training on copyrighted works is not automatically protected under fair use, particularly when conducted at scale and for commercial use. The report outlines multiple stages that can raise infringement claims from scraping and dataset curation to model training and the generation of outputs. The Office explicitly rejects the idea that “publicly available” content online is free for use in AI training. That position, often relied on by developers, does not hold up under copyright scrutiny. The fair use analysis is direct: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: The use is commercial, high-volume, and systemic, not limited or research-driven. 𝐀𝐦𝐨𝐮𝐧𝐭 𝐮𝐬𝐞𝐝: Full works and large repositories are routinely copied. 𝐌𝐚𝐫𝐤𝐞𝐭 𝐢𝐦𝐩𝐚𝐜𝐭: AI outputs often compete with the original works and may displace licensed content. 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐯𝐞𝐧𝐞𝐬𝐬: Using expressive content to generate similar expressive content is unlikely to qualify. The Office states: 𝘛𝘩𝘦 𝘤𝘰𝘱𝘺𝘪𝘯𝘨 𝘪𝘯𝘷𝘰𝘭𝘷𝘦𝘥 𝘪𝘯 𝘈𝘐 𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘵𝘩𝘳𝘦𝘢𝘵𝘦𝘯𝘴 𝘴𝘪𝘨𝘯𝘪𝘧𝘪𝘤𝘢𝘯𝘵 𝘱𝘰𝘵𝘦𝘯𝘵𝘪𝘢𝘭 𝘩𝘢𝘳𝘮 𝘵𝘰 𝘵𝘩𝘦 𝘮𝘢𝘳𝘬𝘦𝘵 𝘧𝘰𝘳 𝘰𝘳 𝘷𝘢𝘭𝘶𝘦 𝘰𝘧 𝘤𝘰𝘱𝘺𝘳𝘪𝘨𝘩𝘵𝘦𝘥 𝘸𝘰𝘳𝘬𝘴. This is a key clarification for the industry. Developers relying on generic fair use claims will have to prove that their specific training methods and outputs meet the legal threshold but most won’t. The report also addresses and rejects common defenses: 📌AI training is not a “non-expressive” use. 📌Public access is not the same as permission. 📌Training on infringing datasets attracts stricter scrutiny. While the report stops short of policy prescriptions, it identifies extended collective licensing as a possible solution where voluntary markets fall short. It also notes legal and operational barriers that would need to be addressed for such a system to work. The report can be accessed at: https://lnkd.in/gD8fn-jA #copyright

An AI artist just signed a $3 million deal. Revolution or just a marketing trick? At first glance, it looks like the future. But in reality, it’s a lesson on how technology, money, and law are colliding inside the music business. Let’s break it down. What we know: 1. An “artist” named Xania Monet went viral. 2. The lyrics were written by a real poet, Telicia Jones, but the voice and music were created by artificial intelligence using the Suno platform. 3. An independent label called Hallwood Media signed a deal valued at up to $3 million. But there’s a problem. The current legal reality: 1. Works created 100% by AI are not protected under copyright law. 2. Works created with AI can also face issues: ↳ Suno and Udio are being sued — including by Universal, Warner, and Sony — for training their systems with copyrighted material. ↳ Anthropic (Claude) recently paid $1.5 billion for using authors’ books without permission. 3. AI voices that imitate real artists without consent may violate Name, Image & Likeness (NIL) rights. Why this matters to you: 1. Publishing: Neither BMI nor ASCAP accepts AI-created works. 2. Copyright: You can’t register AI-generated songs with the U.S. Copyright Office. 3. Fingerprinting: When you use Suno, your music is tagged as AI-Created, even if you only used it to finish your song. Here’s what no one’s telling you: 1. The deal is actually with Telicia Jones, the real person behind the AI. 2. That “multi-million dollar deal” is probably not a direct payout. It’s a mix of budgets, options, and services. 3. Even if Jones owns the lyrics, how will they recoup the advance if the publishing royalties can’t be collected? The truth behind the hype: Technology can make music. But it can’t make purpose. And in this industry, that’s still your most valuable asset. Lesson for artists and managers: 1. Don’t fall for the hype. Keep perspective. 2. Read every platform’s policy before using AI tools. 3. Never give away your rights without talking to a music industry lawyer first. Follow me for more legal insights about the music industry.

The Wild West of AI music is coming to an end. For the past year, a lot of AI music platforms positioned themselves as tools for “anyone to make songs.” But recent changes around rights and ownership are making one thing very clear: commercial music still runs on ownership, authorship, and chain of title. If a platform can’t give creators: Copyright ownership Authorial control A clean, defensible chain of title the way labels, publishers, and supervisors require …then you’re not creating IP. You’re generating licensed content. Commercial use rights without ownership don’t survive contact with the real music business. They don’t work for sync, catalog building, publisher pitching, exclusivity, or any deal where you have to warranty rights. Legal shuts that down immediately. This isn’t anti-AI. AI is powerful for ideation, speed, and exploration. But as lawsuits, label partnerships, and policy rewrites roll in, the industry is drawing lines again. The era of “generate first, figure out ownership later” is over. AI music is moving out of the Wild West phase and into a regulated one and creators who care about long-term value need to pay close attention to who actually owns the output. https://lnkd.in/ebT2XBhY

I Was There When Napster Set Fire to the Music Industry — This Time, We Can’t Be That Slow I was new to the music industry when Napster flipped the table and set fire to our business model. I saw firsthand how unprepared we were — how slow the industry was to adapt. And I saw how that delay devastated independent artists, songwriters, and publishers struggling to make a living. Now, we're staring down another massive transformation. AI-generated music tools like Suno and Udio are flooding the market with cheap, copyright-skirting content — trained on work they never licensed, never credited, and never paid for. This isn't just a legal debate. This is an existential threat to independents — the creators of the background scores, sync tracks, custom compositions, and indie gems that power so much of the listening experience. They're already underpaid and undervalued. Now they’re being replaced, one AI prompt at a time. But here’s the difference: this time, the industry is not standing still. Lawsuits are already underway. Licensing conversations are happening. There’s movement — and we must make sure that independent creators are not left behind in this next wave. What can we do? - Demand clear, enforceable licensing for training data - Support opt-in frameworks where artists retain control - Ensure that royalties and rights apply even in the AI era We live in a world where everything is licensed. Let’s make sure that principle holds — whether the music comes from a human or a machine. If we move fast, we can make this another turning point — not a repeat of the burn Napster left behind, but a path to fairness, sustainability, and respect for creators. #IndependentMusic #ArtistRights #AIMusic #MusicPublishing #FairPay #CopyrightMatters #MusicIndustry #EthicalAI #ProtectCreators