How AI Is Transforming the Music Industry

🎵 An AI-generated song just hit #1 on Billboard. What does that mean for the future of music? “Walk My Walk” — by Breaking Rust — became the first fully AI-created track to top Billboard’s Country Digital Song Sales chart. No human singer. No studio sessions. No lived experience. Just an algorithm. This isn’t just a music story — it’s a business story. If AI can write, sing, produce and release a chart-topper: 💡 What happens to the economics of the music industry? 💡 How do record labels justify million-dollar artist investments when an AI can create infinite “artists” at near-zero cost? 💡 Who owns the royalties — the coder, the model, or no one? 💡 Will audiences care if the song emotionally moves them anyway? For the industry, this signals 3 major shifts: 1️⃣ Cost model disruption – AI can produce music faster and cheaper than human creators. 2️⃣ New talent definition – The next “hit maker” may be a machine, not a musician. 3️⃣ Ethical + legal grey zones – Copyright, transparency, and authenticity will become battlegrounds. But there’s opportunity too: ✔ Human-AI co-creation will unlock entirely new genres ✔ Independent artists can produce studio-quality music without big budgets ✔ Labels can build virtual artists and micro-target audiences at scale The big questions are: * What does it mean to be an “artist” in an age where identity can be generated, not lived? • How should the music industry balance innovation (AI art) with protection of human artists’ livelihoods? • What role should platforms (Spotify, YouTube) play in labeling or regulating AI-generated music? • How do we ensure that AI-generated music remains diverse and culturally inclusive, rather than homogenized? #AIMusic #FutureOfMusic #MusicIndustry #ArtificialIntelligence #CreativeEconomy Devraj Sanyal Jay Mehta Would love your views and inputs 🙏 Check out the song if you haven’t already : https://lnkd.in/dsegspCg

How are artists using AI to make music? We collected 337 music artworks and categorize them based on AI usage: - AI composition - Co-composition - Sound design - Lyrics generation - Translation And we study how musicians use AI across formats like: - Singles - Albums - Performances - Installations - AI voices - Operas - Soundtracks Our key insights are… 🎨 Artistic Agency 🎨 - Many musicians use AI for co-composition or sound design, maintaining creative control. - Some explore the absence of agency as part of their creative process. ✨ Aesthetics ✨ - AI music often embraces AI's flaws and aesthetics, but at times it can be nearly indistinguishable from non-AI music. - Some explored AI's imperfections and artifacts to evoke the uncanny. Others used AI's multi-genre generation capabilities to produce music in multiple (and potentially new) genres. 🎛️ Sound Design 🎛️ - Artists curate datasets to train their own models, similar to how producers program synthesizers to create their unique sounds. - This example reveals the common technical challenges AI musicians face, a shared difficulty among those in the field. 🎄 Innovative Uses 🎄 - Multilanguage song releases. - Exploring AI’s potential in live performances and installations. - Bringing AI into formats like opera, soundtracks, ballets, and online installations. 🥘 AI as an Artistic Medium 🥘 - Generative AI albums are a new artistic medium that can evolve dynamically with each listen. - AI voice releases are also an artistic medium that enables new interactions between artists and fans, raising interesting questions around authorship and digital identity. 🌍 Broader Implications 🌍 - Casual creators are contributing to the commodification of music, with millions of tracks generated weekly. - Parallels with past narratives showcase that current trends and cultural pushback are not new. We hope our work contributes to understanding how early adopters have used AI so far and serves as inspiration for future AI musicians, encouraging the creation of music that leaves a lasting, positive impact on music history. Links to the paper (arXiv), full database of AI music (GitHub), and video: https://lnkd.in/e8qVyxW2

Look closely at these Spotify credits. No songwriter. No producer. No mixing engineer. Just Claude, ChatGPT, Suno AI, and LANDR. Four AI tools built a song from scratch and placed it on the same platform where real artists pour their souls. This is not the future. This is already happening in 2026. And as someone who works in music and actually cares about it, we need to be honest about what this is doing to the industry. The numbers are brutal. Over 120,000 AI-generated tracks are uploaded to Spotify every single day. Independent artists already earn fractions of a cent per stream. When AI floods that same pool at infinite scale and near zero cost, the math becomes devastating for real musicians trying to survive. Why real artists are furious. This is not really about technology. It is about authenticity. When a real artist writes a song, it comes from years of struggle, sacrifice, late nights, and rejection. That struggle is part of the art. You cannot automate it. AI music does not just compete with human artists. It disrupts the ecosystem they rely on. Playlists get diluted. Discovery gets harder. Artists who invested years into their craft lose ground to systems that never felt anything. The question nobody is asking. We keep debating whether AI music is good. The real question is whether it is honest. Music moves people because we believe another human felt something first. That is the unspoken contract between artist and listener. You cannot truly connect with something that was never experienced. I am not calling for a ban. I am calling for transparency. Label AI music clearly, everywhere, always. Let listeners choose with full awareness instead of confusion. Real music can compete. The real question is whether the system will allow a fair fight. 💬 Musicians, listeners, and industry people. Does AI-generated music belong on the same platform as human art? #MusicIndustry #AIMusic #IndependentArtists #MusicMarketing #SpotifyArtists #SupportRealArtists #HumanArt #MusicBusiness

The music AI landscape is too complex for a single map — so instead, I made three. 🤓 As part of my presentation at last week's Boston Music AI Meetup, I developed a brand-new set of market map visualizations, capturing the rapidly evolving state of music AI from multiple angles. If you've been following Water & Music for a while, you may remember our first music AI market map from November 2022, focused on how AI slots into the creative process. Since then, the landscape has expanded dramatically, with dozens of new tools, industry partnerships, and legal challenges emerging. I've realized that one single map is simply insufficient to capture what's happening. So, I opted for three new ones, each showcasing a different part on the market: 🎯 Map 1: The Use Case Lens breaks down who's using what tools and why. The market has naturally organized into distinct segments serving specific needs — from consumer-facing, full-stack generation platforms like Suno and Udio, to specialized professional tools for audio processing and vocal editing. The "Rights & Protection" category in particular did not exist in our 2022 map, reflecting the music industry's growing focus on copyright and attribution for AI. 💰 Map 2: The VC Rollercoaster lens traces how the money trail of VC funding in music AI startups has shifted over the past few years. Ever since Suno's massive $125M raise, the investment focus has actually shifted away from full-stack consumer moonshots toward B2B solutions with clearer revenue paths, particularly those addressing rights management and professional workflows. 🔄 Map 3: The Industry Incumbent lens reveals how established players are responding, leveraging existing user bases and distribution channels to maintain relevance without starting from scratch. While some companies like Splice, Output, and YouTube are building their own capabilities in-house, we're also seeing strategic partnerships and integrations becoming the norm, led by SoundCloud's integrations with nearly 10 different AI tools. I'll be publishing a comprehensive analysis for Water & Music members next week that explores these patterns and their implications for creators, rights holders, and tech companies. Not a member yet? Sign up for our free newsletter to be notified when this analysis drops. Link in comments 👇 #MusicAI #MusicTech #AIStrategy #MusicIndustry #MusicBusiness

Securing rights to music catalogs has been a consistent business model in the industry: Buy up the hits, collect royalties for life. And it’s still a viable option. Catalog music is the most streamed online, accounting for 72.6% of total album-equivalent music consumption in 2023. But AI poses a unique threat. A lot of money made off these catalogs comes from sync licensing (background music, B-roll, or international campaigns). Using even a short snippet of a popular song can be costly. So, if you're a brand, and want something like “Bohemian Rhapsody” but don’t want to pay a fortune, AI may be able to get you 90% of the way there for next to nothing. Especially for music that’s only getting 10 seconds of air time, brands may think twice before they invest in the real deal. Of course, there will always be a difference between “sounds like” and “is.” The original work carries weight, and as long as that still matters to people, catalogs will hold value. But the economics around music are shifting. If you're investing in catalogs—or building a business around them—don't just look at historical returns. Factor in where technology is headed.

𝗪𝗵𝗲𝗻 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗰𝗮𝗻 𝗰𝗼𝗺𝗽𝗼𝘀𝗲 𝗺𝘂𝘀𝗶𝗰, 𝘁𝗮𝘀𝘁𝗲 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘁𝗵𝗲 𝗻𝗲𝘄 𝘃𝗶𝗿𝘁𝘂𝗼𝘀𝗶𝘁𝘆! I wanted to become a concert pianist, despite my piano teacher having thrown me out of piano classes several times. Life took a different turn but I kept playing, composing and performing. 30 years and 5x original Spotify albums later, AI has entered the music arena. Naturally, I get a lot of questions on the role of AI in music. Here my response, which took a year to mature :). AI has in essence collapsed the distance between an idea and a production-ready draft. The center of gravity in music is thus shifting from technique to taste, and from scarcity of production to scarcity of attention: 1) 𝘊𝘰𝘮𝘱𝘭𝘦𝘵𝘦 𝘥𝘦𝘮𝘰𝘤𝘳𝘢𝘵𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘵𝘩𝘦 𝘤𝘳𝘦𝘢𝘵𝘪𝘷𝘦 𝘱𝘳𝘰𝘤𝘦𝘴𝘴: AI is not a rival; it’s the next instrument! Like the piano or the music software, it lowers the skill floor and raises the taste bar. Creation is easy – editing & discovery are now the hard parts. Our craft moves from “can I play this?” to “should this exist?” Call this the era of the editor-producer, i.e. the person whose judgment shapes abundance into identity. 2) 𝘌𝘮𝘦𝘳𝘨𝘦𝘯𝘤𝘦 𝘰𝘧 𝘩𝘺𝘱𝘦𝘳-𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭𝘪𝘻𝘦𝘥 𝘮𝘶𝘴𝘪𝘤: Ironically, abundance is what enables personalization. Stems could turn songs into modular systems: mix, mood and structure can adapt to the listener, the room and even your heartbeat. That raises a cultural paradox: if every version is mine, what do we share? The practical answer is a canonical mix for the commons with personal variants for context. If done well, such an approach could transform tracks from finished products into living, responsive works. 3) 𝘛𝘩𝘦 𝘳𝘦𝘵𝘶𝘳𝘯 𝘰𝘧 𝘵𝘩𝘦 𝘶𝘭𝘵𝘳𝘢-𝘩𝘶𝘮𝘢𝘯: When the generated is trivial to make, the provenly human becomes precious. Think of an inverse Turing Test for music. Expect cryptographic provenance, phone-free concrthalls and one-take sessions as quintessentially human truth signals. In a world saturated with AI audio, the rarest thing is a moment you can’t replay. Musical identity migrates from infinite outputs to unrepeatable presence. As creation scales, value concentrates in taste, context and (!) community. New roles emerge: editor-producers who sculpt AI drafts; stem-architects who design catalogs to be remixed; music sommeliers who program for moments; provenance verifiers who certify the human. 𝗪𝗵𝗲𝗻 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗰𝗮𝗻 𝗰𝗿𝗲𝗮𝘁𝗲, 𝘁𝗵𝗲 𝗿𝗮𝗿𝗲 𝘀𝗸𝗶𝗹𝗹 𝗶𝘀 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗮𝘁’𝘀 𝘄𝗼𝗿𝘁𝗵 𝗳𝗶𝗻𝗶𝘀𝗵𝗶𝗻𝗴! PS: So, what should the new “unit of value” in music be - the track, the moment or the community that forms around it? Oh, and, feel free to share your music! PPS: Below a photo with Rob del Naja, founder and artist-in-chief of British band Massive Attack. Not only is he a wonderful person but also one of the most interesting and progressive thinkers of our generation. Proud to call him a friend!

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.

My takeaways on AI and music from my talk with AudioShake CEO Jessica Powell: 1. Too many AI music conversations start with “will AI replace artists, labels, or similar?” A different question to consider: if AI can automate more of the creative process, what becomes more valuable? 2 Music has a stronger human moat than many cognitive tasks. Code may be more about getting from point A to B. Music has more connection to the body through performance, instruments, and fandom. 3. AI can make creation easier, faster, and cheaper. But will the output matter? A song can be technically "good" and still fail to connect. Even if AI could generate the perfect pop song, everyone else would have access to the same machine. The advantage shifts back to taste, differentiation, story, and distribution. 4. Discovery was already the hardest part of music. AI hasn't solved that yet. At the moment, it makes it more difficult. More supply of music may mean more noise to break through. The bottleneck moves from creation to attention. 5. Most artists won’t live at the extremes that get the most media attenion. The debate often frames AI as either “real artists never touch it” or “push button music flooding the market.” The future is probably the middle: artists using AI as one tool among many. 6. The hard part will still be the transition. It’s easy to say new jobs will be created. That may be true. But for the people and families disrupted along the way, the shift can still be painful! 7. Autotune was once treated like the tool that would ruin music. But it soon became part of modern music. Generative AI has bigger rights and training-data challenges in the near term, but some of today’s moral panic may look different in hindsight. 8. AI may reward experts more than beginners, especially early on. The people who get the most out of these tools often understand the craft already. Taste, judgment, and clear direction will matter, which the experts are more likely to have. You can watch the full conversation here: https://lnkd.in/gwrNWA28 Or listen here: https://lnkd.in/gEWpDXfm

AI is one of the most discussed topics in my artist management and music entrepreneurship courses at Long Island University’s Roc Nation School of Music Sports & Entertainment—and for good reason. Across the creative and corporate landscapes, AI is transforming how music professionals operate and how creators bring their art to life. The numbers tell a compelling story. A recent Ditto Music study revealed that 60% of independent musicians in 2023 are already integrating AI into their music projects: 🎵 77% use it for album artwork. 🎵 66% for mixing and mastering. 🎵 62% for music production. 🎵 47% for songwriting On the corporate side, the adoption is just as significant. According to Gallup, 93% of Fortune 500 organizations have incorporated AI to enhance business practices. However, the rise of AI comes with its own set of challenges for the music industry. For example, AI startups Suno and Udio recently admitted to using copyrighted recordings from major labels without permission, defending their practices under the "fair use" doctrine for training AI models. This debate underscores the legal and ethical complexities AI brings to the table, especially in industries like music that deeply value creativity and ownership. These very issues were highlighted in my recent fireside chat with EMPIRE President Tina Davis as part of the Roc Nation Speaker Series. We explored two critical questions shaping the future of AI in music: 1️⃣ Will AI serve as a catalyst for artists to expand creative boundaries, or might it contribute to a decline in artistic innovation? 2️⃣ How can AI systems establish equitable compensation frameworks to ensure creators are fairly remunerated for their contributions, particularly when their work is utilized in training these technologies? As I prepare to join my former professor NYU Steinhardt School of Culture, Education, and Human Development Howie Singer's Music Data and Analytics class today to discuss the latest developments in AI and music, I’d love to hear your perspective. How do you think AI will reshape the music industry in the years ahead? Any predictions for 2025 that I can share with students? Drop your thoughts below ⤵️

The conversation around AI in music often focuses on fear—automation replacing artistry. But there’s another, more empowering path: AI as a tool that strengthens both creators and the industry. In Episode 39 of AI Snacks with Romy & Roby, we discussed how composers can now train their own AI with as few as 5–10 songs. Unlike traditional deep learning models requiring massive datasets, this focused approach allows individual artists to own and control their AI, using it for creative inspiration or as part of their marketing strategies. Labels are equally intrigued. Imagine a new album launch where fans are given the ability to remix songs through AI, with every remix tied back to the original work so royalties are still collected. This transforms AI into a bridge between creators, fans, and revenue models—not a barrier. Of course, with innovation comes complexity. Issues of copyright, ownership, and royalties must be carefully addressed. But if handled with foresight, AI offers musicians a way to expand their catalog, amplify their reach, and engage audiences in entirely new ways. AI isn’t the end of human creativity—it’s a chance to redefine it on our own terms. #AIandMusic #AIliteracy #MusicIndustry #AIethics #AIcreativity #DigitalTransformation #ArtificialIntelligence #FutureofMusic #Innovation