Imagine this.

You open an AI music platform and type:

"Create an emotional melodic song with cinematic production, Punjabi-style vocal delivery, and global pop influence."

Thirty seconds later, AI generates a hit.

You upload it.

The song explodes.

Streams start coming in.

Money starts flowing.

But then a question appears:

Who actually made this song?

You?

The AI?

Or the artists whose styles the AI quietly learned from?

And if AI borrowed heavily from existing artists, should they receive a share of the earnings too?

This question is no longer science fiction.

Researchers, labels, and technology companies are already discussing systems that could work like Content ID for AI music systems that analyze AI-generated songs, identify artistic influence, and automatically allocate royalties.

At first glance, it sounds like the perfect solution.

Until you realize one major problem:

Music style is not math.

And the entire system could collapse because of it.

The Dream: AI Music That Pays Everyone Fairly

The proposed idea sounds simple:

AI creates a song → technology analyzes influence → royalties get split among contributors.

Let's say an AI song sounds similar to:

• Drake's delivery

• Pritam's composition style

• Arijit Singh's emotional vocals

The system could theoretically say:

Drake influenced this song.
Pritam influenced this song.
Arijit Singh influenced this song.

Then everyone receives a percentage.

Fair.

Transparent.

Problem solved.

Right?

Not exactly.

There Is One Huge Question Nobody Can Answer

How do you actually prove artistic influence?

Copying is easy.

Platforms already detect:

• reused samples

• duplicated recordings

• matching melodies

• identical stems

Technology already does this.

But style is completely different.

Suppose an AI song contains:

• dark synths

• emotional singing

• melodic rap

• Punjabi cadence

• trap percussion

Who owns that?

Drake?

Arijit Singh?

An entire genre?

Five artists?

Twenty years of music evolution?

Even humans disagree about inspiration.

So how can software decide:

"This song is 34% Artist A and 22% Artist B."

No universal formula exists.

Because music evolves through influence.

Artists inspire artists.

Genres inspire genres.

Style itself is shared.

There Is an Even Bigger Problem — Trust

Most proposed AI royalty systems quietly assume something:

AI companies will tell the truth.

Until you imagine this:

An unknown AI platform trains on:

• downloaded streaming catalogs

• YouTube rips

• leaked stems

• commercial releases

Then later says:

"We trained independently."

How do you verify that?

You can't.

And suddenly a black market appears.

If attribution relies entirely on AI companies honestly reporting their data, bad actors can simply avoid the rules.

So Are We Solving the Wrong Problem?

Most discussions today focus on Spotify.

Or Apple Music.

Or YouTube.

But maybe the industry is looking at the wrong layer entirely.

Most people ask:

How do we detect copied influence after the song reaches stores?

Maybe the better question is:

Why wait until after upload?

DNM View: Fix It Before Distribution

At DNM, we believe accountability should begin before a song reaches streaming platforms.

Not after.

Imagine this:

Aditya creates a song using Suno.

During generation, Suno already knows which references were used internally.

Suppose AI relied heavily on:

• Arijit Singh

• Pritam

• Drake

Suno already possesses this information.

Instead of hiding it, Suno embeds it directly into song metadata:

Creator: Aditya
AI Influences: Arijit Singh, Pritam, Drake

Now Aditya uploads the track to DNM.

Instead of guessing influence through forensic analysis, DNM immediately sees:

Contributors:

1. Aditya

2. Arijit Singh

3. Pritam

4. Drake

No reverse engineering.

No speculation.

No post-release detective work.

The Next Step: Trust Only Verified AI Platforms

This system only works if AI generators participate.

So DNM maintains an approved AI registry.

Approved:

✓ Suno
✓ Licensed AI platforms
✓ Verified systems with attribution support

Rejected:

✗ Anonymous generators
✗ Unsupported AI systems
✗ Platforms without attribution records

Simple rule:

No attribution data → no distribution

But Where Does The Money Go?

Now imagine the song earns:

₹100

Revenue reaches DNM.

Aditya receives his creator share.

But the influence shares for Arijit Singh, Pritam, and Drake move differently.

DNM sends:

• payment amount

• artist identifiers

• influence allocation data

• metadata records

to a centralized AI rights society.

Artists register there.

The society collects and redistributes influence royalties.

Almost like traditional royalty societies — but redesigned for AI-generated music.

The Future Question Might Change Completely

Today everyone asks:

Who did this AI song copy?

But perhaps the better question is:

Where did this song come from?

Because the companies that solve provenance before distribution not after release may ultimately reshape the future of AI music.