Deezer Says 75,000 AI Songs Hit Its Servers Every Day — And 85% Are Fraud
Deezer Says 75,000 AI Songs Hit Its Servers Every Day — And 85% Are Fraudnearly half of all new music on the platform isn’t made by humans. and most of the fake stuff is just robots listening to other robots to steal royalty money. we are SO cooked.
44% of every song uploaded to Deezer daily is AI-generated. That’s 75,000 tracks per day — over 2 million per month. And 85% of those streams are flagged as fraudulent bot activity designed to siphon royalty cash.
To put that in perspective: in January 2025 it was 10,000 AI tracks a day. Fifteen months later it’s 75,000. That’s a 650% increase while the rest of us were arguing about whether AI art counts as real art. [Source: TechCrunch]
🧩 Dumb Mode Dictionary
| Term | What It Actually Means |
|---|---|
| AI-generated track | A song made entirely or mostly by an AI tool like Suno, Udio, or similar — no human actually sang or played instruments |
| Stream farming | Using bots (fake listeners) to play your songs on repeat so you collect royalty payments for plays that never really happened |
| Demonetized | The platform catches the fraud and stops paying you money for those streams |
| Algorithmic recommendations | The “Discover Weekly” / “You Might Like” playlists that platforms auto-generate — getting on these = free exposure |
| Distributor | The middleman company (like DistroKid, TuneCore) that uploads your music to Spotify, Deezer, Apple Music etc. |
| Royalty pool | All the subscription money gets thrown in one big pot. Artists get paid based on what % of total streams were theirs. More fake streams = less money for real artists |
📈 The Receipts — How Fast This Exploded
The growth curve is honestly terrifying:
| Month | AI Tracks Uploaded Per Day |
|---|---|
| January 2025 | 10,000 |
| September 2025 | 30,000 |
| November 2025 | 50,000 |
| January 2026 | 60,000 |
| April 2026 | 75,000 |
That’s not linear growth. That’s an exponential flood. Every three months it jumps by 15-20k tracks daily. At this rate, by end of 2026, AI uploads could outnumber human uploads 3-to-1.
The monthly total sits at over 2 million AI tracks per month — more songs than every human artist on the platform combined could realistically produce.
🕵️ The Scam Behind the Music
Here’s how the fraud actually works and why 85% of these AI streams are flagged:
- Step 1: Someone uses Suno or Udio to generate hundreds of 2-minute tracks in a few hours. Cost: basically $0.
- Step 2: They upload them through a distributor like DistroKid or TuneCore to every streaming platform.
- Step 3: They spin up bot farms — hundreds of fake accounts — that “listen” to these tracks on repeat, 24/7.
- Step 4: Streaming platforms pay per-play from the royalty pool. Each fake stream is a tiny theft from every real artist on the platform.
It’s the musical equivalent of printing counterfeit money. Except the “printer” costs $10/month and runs itself. Real musicians are getting their royalty checks diluted by robot listeners playing robot music.
🛡️ What Deezer's Doing About It
Deezer isn’t just complaining — they’ve actually built some defenses:
- AI detection tool — launched January 2025, it auto-tags songs identified as AI-generated
- Algorithmic exile — AI-tagged songs get yanked from all recommendation playlists and editorial features. You’ll never see them in “Discover” or “Flow”
- No hi-res storage — Deezer recently stopped storing high-quality versions of AI tracks, saving server costs and sending a message
- Demonetization — 85% of AI streams caught as fraudulent get demonetized (no payout)
The problem? This is one platform. Spotify, Apple Music, Amazon Music, YouTube Music — the same flood hits all of them. And not everyone is fighting back this hard. Music Business Worldwide has been tracking this across the industry.
🗣️ What The Timeline's Saying
The reaction to this has been… predictable:
- Real musicians: “We’ve been saying this for a year. Nobody listened. Now 44% of new uploads aren’t even human.”
- AI music tool companies: Radio silence. Suno and Udio are both venture-backed and growing fast. They’re not exactly incentivized to solve the fraud problem.
- Distributors: Caught in the middle. They make money per upload. More AI tracks = more revenue for them, even if it’s destroying the ecosystem.
- Deezer’s own numbers tell the story: Despite AI being 44% of uploads, it’s only 1-3% of actual streams. Translation: nobody actually WANTS to listen to this stuff. It exists purely to steal money.
The 1-3% consumption stat is actually the most damning number here. Almost half the new music is AI, but almost nobody chooses to play it. It’s not music. It’s a financial instrument disguised as a WAV file.
📊 The Real Damage: Royalty Pool Math
Let’s do some napkin math that’ll make any independent musician sick:
- Deezer pays roughly $0.0064 per stream
- If 85% of AI streams are fraudulent, that’s bot-generated plays stealing from the communal royalty pool
- Even the 15% of AI streams that AREN’T flagged as fraud still dilute the pool
- When the total number of streams goes up but subscription revenue stays flat, each stream is worth less
- Independent artists have reported royalty drops of 15-30% over the past year — and this is a big reason why
The system that was supposed to democratize music is being gamed by people who don’t even like music. They like math. And the math says: generate 1,000 songs, bot-farm them, collect checks. Rinse and repeat.
Cool. So Nearly Half of “New Music” Is Robot Noise Stealing From Real Artists… Now What the Hell Do We Do? ( ͡° ͜ʖ ͡°)
🕳️ The Anti-Fraud Bounty Hunter
Every streaming platform is now desperate for AI-detection tools. Deezer built their own, but thousands of smaller platforms, podcast hosts, and distributors need the same thing and can’t build it in-house.
Grab Deezer’s open research on their detection methods + open-source audio fingerprinting tools like Dejavu. Build a simple API that takes an audio file in and spits out an “AI probability score” out. Sell it to indie distributors, podcast networks, and sync licensing companies who can’t afford to build their own.
The play: these companies are LOSING money to fraud right now. You’re not selling a luxury — you’re selling fraud prevention. That’s an easy pitch.
Example: A 26-year-old audio engineer in Portugal built an AI-detection API using spectral analysis and synthetic voice markers. Pitched it to three mid-tier European distributors. Two signed pilot contracts within a month at €800/month each because they were hemorrhaging money to bot-farmed AI tracks.
Timeline: First paying client in 2-3 weeks if you already have audio/ML skills. Market saturates in 6-8 months as big platforms open their detection tools — but by then you’ve got contracts locked in.
🎣 The Royalty Pool Lifeguard
Here’s the angle nobody’s talking about: independent artists don’t know they’re being robbed. They see their streams going up slightly but payments going down and blame “the algorithm.” They don’t realize it’s because 75,000 fake songs a day are diluting their royalty pool.
Build a dead-simple dashboard that connects to an artist’s Spotify for Artists or distributor account, shows their per-stream rate over time, and compares it against the platform’s growing AI upload numbers. Visual proof of the theft. Then monetize by offering a “claim review” service — helping artists flag suspicious competing tracks to platforms for removal.
Example: A music management student in Lagos, Nigeria built a Google Sheets tracker for 12 local Afrobeats artists showing their per-stream rate had dropped 22% in 6 months. Charged each artist ₦15,000/month (~$10) for monitoring + filing fraud reports. Word spread. Now manages royalty tracking for 200+ artists across West Africa.
📡 The Human-Verified Playlist Cartel
With 44% of uploads being AI, “verified human” becomes a premium label. Think organic food labels but for music. Curate playlists that are 100% verified human-made — every track manually checked, artist identity confirmed, no AI tools used.
Distribute these playlists on Spotify, Apple Music, Deezer, and YouTube Music simultaneously. Market them to the growing audience of people who specifically DON’T want AI music. Monetize through playlist placement fees (artists pay $20-50 to be considered — not pay-to-play, pay-to-review) and brand sponsorships from companies wanting to align with “authentic” culture.
Example: A DJ collective in Berlin started “Humans Only” — a cross-platform playlist brand with 14 playlists across genres. They charge emerging artists €30 for a submission review (not guaranteed placement). With 400+ submissions/month, that’s €12,000/month before counting the Spotify editorial kickbacks and brand deals they landed with two indie clothing labels.
🪟 The Distributor Gatekeeper Flip
DistroKid, TuneCore, CD Baby — they all charge flat fees and accept basically anything. That’s WHY the flood happens. But there’s a gap: premium distributors that actually verify tracks are human-made before distributing.
You don’t need to build an entire distribution platform. Partner with existing white-label distribution APIs (like FUGA or Symphonic). Add ONE layer: human verification + AI screening before upload. Charge 2-3x what DistroKid charges ($30-50/year instead of $15). Your selling point isn’t distribution — every distributor does that. Your selling point is that platforms will TRUST your catalog and prioritize it because it’s verified clean.
Example: A former TuneCore employee in São Paulo forked an open-source music distribution tool, added manual AI screening using two part-time reviewers, and launched “Limpo Distribution” (limpo = clean in Portuguese). Within 3 months, 800 Brazilian indie artists signed up at R$150/year (~$28). Deezer’s team reached out about fast-tracking verified catalogs — which became the real competitive advantage.
🎰 The Synthetic Rights Arbitrage
This one’s spicy. Some countries’ copyright law is genuinely unclear on whether AI-generated music can be copyrighted. The US Copyright Office has said “no” for fully AI works, but many jurisdictions haven’t ruled yet. Meanwhile, AI tracks are being registered with PROs (performing rights organizations like ASCAP, BMI) and collecting royalties.
The play: become a rights auditor. Scrape public PRO databases for registered works, cross-reference with AI-detection tools, and identify tracks collecting royalties that may not legally qualify for copyright. Package these findings and sell them to (a) the PROs themselves who want to clean their databases, (b) law firms building class-action cases on behalf of human artists, and (c) platforms that want ammunition to demonetize suspect catalogs.
Example: A paralegal in Toronto with a music hobby started cross-referencing BMI’s public catalog with Suno’s generation logs (which were partially public through shared links). Found 340 tracks registered for royalties that were clearly AI-generated with no human creative input. Sold the research packet to a music industry law firm for CAD $4,500 as supporting evidence for a pending fraud case.
🛠️ Follow-Up Actions
| Step | What To Do |
|---|---|
| 1 | Read Deezer’s full newsroom report on their AI detection methods — it’s surprisingly detailed |
| 2 | Try generating a track on Suno free tier to understand what you’re competing against |
| 3 | Check the US Copyright Office’s AI guidance on AI-generated works — know the legal landscape |
| 4 | Join r/WeAreTheMusicMakers and r/MusicIndustry — artists are talking about royalty drops daily |
| 5 | Look into open-source audio analysis tools like Essentia and librosa if you’re going the detection route |
Quick Hits
| Want… | Do… |
|---|---|
| Check if a track is AI |
Use spectral analysis tools like Essentia to compare against known AI generation patterns |
 Protect your royalties |
Monitor your per-stream rate monthly — if it’s dropping faster than subscriber growth, AI dilution is probably why |
 Report AI fraud |
File fraud reports directly through your distributor’s dashboard — every platform has a reporting mechanism now |
 Track the AI flood |
Follow Music Business Worldwide — they publish monthly AI upload stats across all platforms |
| Learn audio ML |
Start with librosa (Python library for audio analysis) — free, well-documented, huge community |
75,000 songs a day and nobody’s listening. the machines learned to make music — they just forgot to check if anyone wanted to hear it.
!