Spotify has taken a flamethrower to the digital landfill, revealing that it removed more than 75 million “spammy” tracks from its platform during a single 12-month period.

That number is absolutely staggering.

Seventy-five. Million. Tracks.

That is not a dodgy EP uploaded by someone’s cousin after discovering an AI music generator at three in the morning. That is an industrial-scale avalanche of mass-produced audio clogging up one of the world’s biggest streaming platforms.

And while Spotify’s enormous clean-up sounds like a victory for genuine artists, songwriters and listeners, the uncomfortable truth is that the battle against AI-generated musical sludge may only just be getting started. 🤖🗑️

🚨 First Things First: Were All 75 Million Tracks AI-Generated?

Not necessarily.

The viral headline suggesting Spotify deleted 75 million AI songs is slightly more dramatic than Spotify’s own wording.

The company said it had removed more than 75 million “spammy tracks” during a period marked by the rapid growth of generative AI. That category can include mass uploads, duplicate recordings, misleading metadata, manipulated content and other material designed to exploit streaming systems.

So this was not simply Spotify pressing a giant red button marked DELETE ALL ROBOT MUSIC.

It was a broader attack on content created or distributed primarily to manipulate algorithms, impersonate musicians or squeeze money from the streaming royalty system.

Still, AI has undoubtedly made this behaviour easier, faster and cheaper than ever before.

One person can now generate albums’ worth of music before a real drummer has finished arguing with the sound engineer about the snare monitor.

🤖 Welcome to the Age of Musical Spam

Generative AI tools have made it possible to create a complete track from a handful of written instructions.

Type in a genre, mood, lyrical theme and a few musical references, and software can produce something resembling a finished song within minutes.

Used responsibly, that technology could become another creative tool. Musicians already use software to experiment with arrangements, generate ideas, clean recordings and assist with production.

The real problem begins when creativity is replaced by quantity.

Some users are generating huge numbers of barely distinguishable tracks, attaching generic artwork and uploading them under endless fictional artist names. The music is then aimed at playlists built around sleep, studying, meditation, relaxation, background ambience or other passive listening habits.

You have probably seen the sort of titles involved:

Deep Focus Dreams.

Peaceful Rain Frequency.

Midnight Coffee Beats.

Ultimate Productivity Soundscape Volume 48.

It is music designed less to inspire a listener and more to sit quietly in the background while accumulating streams.

Musical wallpaper, mun — except the wallpaper has discovered metadata manipulation.

💰 Why Upload So Much AI Music?

Because even tiny royalty payments can become significant when multiplied across enormous catalogues.

A single track earning very little money is hardly worth celebrating. Thousands of tracks generating small amounts of revenue, however, could begin to add up — especially when automated listening, artificial streaming or playlist manipulation enters the picture.

This creates an ecosystem where the goal is not necessarily to write a brilliant song.

The goal is to upload as much content as possible, dominate search terms, appear in mood playlists and collect whatever revenue slips through the cracks.

That is the sludge problem.

It is not simply that AI-generated music exists. It is that generative tools can be used to flood platforms at a speed no traditional musician could possibly match.

A human band might spend months writing an album.

An automated operation could potentially create hundreds or thousands of recordings during the same period.

Good luck competing with that while also rehearsing, touring, selling merch, answering emails and trying to remember which member of the band booked the van.

🎤 Spotify Tightens Its Rules

Spotify announced several measures intended to tackle the growing problem.

These included stronger action against unauthorised vocal impersonation, improved systems for detecting musical spam and support for clearer disclosures showing when AI has been involved in the creation of a recording.

The platform has also said it wants to stop deceptive content from being pushed through its recommendation systems.

That last part matters enormously.

Removing fraudulent tracks is one thing. Preventing them from appearing in playlists, recommendations and search results is another.

A mass uploader does not necessarily need every track to become successful. They only need enough of them to slip through the net.

Spotify’s challenge is therefore not just identifying individual suspicious songs. It must recognise the behaviour surrounding them: repeated uploads, duplicate recordings, unusual metadata, artificial listening patterns and networks of fictional artists.

It is digital whack-a-mole, except the moles can generate another 400 albums while you are lifting the hammer.

🗣️ The Deepfake Problem

The rise of AI music has also created a far more personal threat: unauthorised vocal cloning.

AI systems can now create recordings that imitate the voices of recognisable performers. That raises serious questions surrounding consent, identity, ownership and reputation.

Imagine discovering a new song supposedly featuring your voice — except you never wrote it, recorded it or approved it.

Worse still, imagine that fake recording contains offensive lyrics, political messages or content completely at odds with everything you represent.

Spotify has stated that vocal impersonation should only be permitted when the artist being imitated has authorised it.

That sounds straightforward, but enforcing it across millions of uploads will be anything but simple.

The technology is improving rapidly, and fake voices are becoming more convincing. Streaming platforms will need reliable ways to distinguish authorised experimentation from outright impersonation.

🎸 Why Independent Artists Should Be Worried

For established stars, AI-generated spam is frustrating.

For independent musicians, it could be devastating.

Smaller bands already face an uphill battle for attention. They are competing against major-label campaigns, endless social media content, changing algorithms and a streaming economy where enormous play counts may still produce modest income.

Now add millions of mass-generated tracks to the pile.

Every fake artist occupying a playlist position is potentially taking visibility away from a genuine musician. Every fraudulent stream risks pulling money from a royalty pool that real bands depend upon.

This is particularly worrying for artists creating instrumental, ambient, electronic or soundtrack-style music, where AI-generated material may be harder for casual listeners to recognise.

But rock and metal should not assume they are immune.

AI can already generate convincing guitar riffs, drum arrangements, orchestral sections and vocals. The results may not yet capture the chemistry of a brilliant band, but they are becoming polished enough to pass as background listening.

The concern is not that an algorithm will suddenly write the next Master of Puppets.

The concern is that thousands of generic “metal” tracks could be uploaded every day, burying young bands beneath a mountain of synthetic chugging.

🧠 AI Is Not Automatically the Enemy

There is an important distinction between AI-assisted music and AI-generated spam.

A real artist might use AI during the creative process while still making meaningful decisions about songwriting, performance and production.

They might use software to explore harmonies, restore damaged recordings, isolate instruments or test arrangement ideas.

That is very different from generating thousands of tracks with minimal involvement and uploading them purely to exploit recommendation systems.

The technology itself is not necessarily the villain.

The problem is deception, impersonation, copyright abuse and industrial-scale flooding.

A synthesiser did not destroy music. Drum machines did not destroy music. Digital recording did not destroy music.

Each new technology caused arguments before becoming part of the creative toolbox.

AI may follow a similar path, but only if musicians retain control and listeners are given honest information about what they are hearing.

🏷️ Should AI Music Be Clearly Labelled?

Absolutely.

Listeners should be able to see whether a track was entirely generated by AI, partly created with AI tools or made conventionally by human performers.

That does not mean AI-assisted songs must automatically be treated as inferior. It simply gives audiences the information needed to make their own decision.

Transparency would also protect musicians who use AI responsibly from being lumped together with anonymous spam operations.

The difficulty will be deciding where the line sits.

Does an AI noise-removal tool count?

What about AI mastering?

What if a songwriter creates the lyrics and melody but generates the instruments?

What if a band records everything themselves but uses software to create backing vocals?

The music industry will need clear definitions rather than one enormous label slapped across every piece of modern production software.

Otherwise, the whole thing will become messier than a festival toilet after three days of rain.

🔥 The Sludge Machine Never Sleeps

Spotify removing 75 million spam tracks is an enormous action, but it also exposes the terrifying scale of the challenge.

Those recordings were removed during just one 12-month period.

Generative tools are continuing to improve. Creation costs are falling. Distribution remains accessible, and detecting AI-generated audio is far from perfect.

Recent academic research into AI music spam has suggested that mass production could develop into a self-sustaining shadow industry unless platforms, distributors and rights holders introduce stronger safeguards.

That means Spotify cannot fight this alone.

Music distributors must improve their checks. Streaming services need stronger verification. Rights holders require effective reporting systems, and artists need meaningful control over how their names, voices and recordings are used.

Most importantly, platforms must stop rewarding quantity over quality.

Because when the system encourages endless uploads, somebody will always build a machine capable of feeding it.

🤘 The Riff Report Says…

AI might be able to generate a riff.

It might recreate a vocal style, imitate a production technique or churn out seventeen albums of suspiciously similar meditation music before breakfast.

But music is bigger than sound files.

It is the nervous excitement before a band walks onstage. It is the guitar string snapping halfway through a solo. It is a sweaty crowd screaming the chorus louder than the singer. It is four mates crammed into a van, travelling six hours to play for fifty people and a confused bloke waiting for bingo to start.

That human connection is what turns noise into culture.

Spotify removing more than 75 million spammy tracks is a necessary step, but it cannot be the final one. The industry needs proper transparency, meaningful artist protections and consequences for those attempting to drown streaming platforms in fraudulent content.

Otherwise, genuine musicians will keep pouring their lives into songs while automated upload farms pour sludge into the system by the tanker-load.

And that is not progress.

That is pollution with a playlist. 🎧🤖🔥