HOW AI TRAINING ATTRIBUTION WORKS UNDER EGO
A Plain-Language Guide for Musicians, Performers & Unions
AI is already learning from music and performances.
The real question is whether it does so ethically.
Right now, most AI systems:
- train on massive piles of music
- don’t track who they learned from
- can’t explain where a sound or style came from
- don’t pay the people whose work made the AI possible
This creates fear, confusion, and justified resistance.
EGO (Extracted Genetic Ownership) exists to solve that problem by making AI training trackable, attributable, and payable—at the artist level.
The Core Idea (In Simple Terms)
Instead of AI being trained on “anonymous music,”
EGO requires that AI be trained on licensed artist datasets, where:
- every musician is identified
- permission is clearly granted (or not)
- influence can be measured
- usage can be tracked
- and money can be paid correctly
Think of it like this:
If an AI learns from you, it must know it learned from you—and pay you when that learning is used.
What AI Is Actually Learning (This Is Important)
AI does not learn music the way people do.
It doesn’t “listen to songs.”
It breaks performances down into patterns like:
- tone and timbre
- timing and groove
- phrasing and feel
- dynamics and expression
Those patterns are what make you sound like you.
EGO calls this your creative DNA or expressive identity.
That identity is what needs protection.
How EGO Changes AI Training (Step by Step)
Step 1: Artists Are Registered as Individuals, Not Just Tracks
Under EGO:
- each artist or session player gets an EGO ID
- that ID represents you, not just a song
- your performances are tied to your identity and consent
This is similar to how:
- PROs track writers
- unions track members
But now it applies to AI training.
Step 2: AI Can Only Train on Music It Has Permission to Use
Instead of scraping the internet, EGO-compliant AI models train on:
- licensed datasets
- with clear rules:
- what the AI can learn
- how close it can sound to you
- whether it can generate in your style at all
If permission isn’t granted, training is not allowed.
This alone answers the question:
“Was this AI trained ethically?”
Under EGO, the answer is provable.
Step 3: Training Is Logged Like a Ledger
When AI is trained under EGO:
- the system keeps a record of:
- which artists were used
- how much each artist contributed
- which model version used them
Think of it like studio session logs—but for AI.
This means:
- no mystery training
- no “we don’t know what’s in the model”
- no plausible deniability
Step 4: When AI Generates Music, Influence Is Measured
When AI creates something:
- it checks:
- which artists’ identities influenced the output
- how strong that influence was
- the result is a percentage-based breakdown
Example (simplified):
This output was influenced by:
- 20% Artist A
- 5% Artist B
- 75% general training
This doesn’t mean copying a song.
It means stylistic influence at a measurable level.
Step 5: Influence Triggers Payment
If the AI output is used commercially:
- revenue is split based on influence
- artists are paid automatically
- payments can flow through:
- unions
- PROs
- labels
- publishers
- or directly to the artist
This is similar to:
- streaming royalties
- reuse fees
- residuals
But for AI-generated work.
Why This Is a Big Deal for Unions
EGO gives unions something they’ve never had before with AI:
Proof
- proof of consent
- proof of training
- proof of influence
- proof of misuse
No more guessing. No more arguing in the dark.
Enforcement Power
If a model:
- trains without permission
- exceeds allowed similarity
- generates unlicensed “soundalikes”
Unions can point to hard data, not opinions.
Fair Compensation
Instead of “AI replaces musicians,” EGO enables:
- musicians being paid because AI exists
- identity becoming a revenue stream
- new licensing categories negotiated by unions
This turns AI from a threat into a bargaining opportunity.
Why This Solves the “Ethical Training” Question
Most AI companies today say:
“Our models are trained ethically.”
But they can’t prove it.
Under EGO, ethical training means:
- consent is documented
- training data is known
- influence is traceable
- royalties are paid
- audits are possible
If a system can’t do that, it’s not ethical.
Simple as that.
What This Changes Long-Term
With EGO:
- AI training becomes licensed, not scraped
- artists become stakeholders, not victims
- unions gain real oversight
- AI companies gain legal certainty
- the industry gains standards instead of chaos
This is the same shift that happened when:
- radio led to performance royalties
- recordings led to neighboring rights
- streaming led to digital accounting
AI is just the next step—and EGO makes sure artists aren’t left behind.
If an AI learns from you, EGO makes sure it knows it learned from you—and pays you when that learning is used.
Why AI-Ego Matters
AI-Ego isn’t an AI company trying to replace artists.
It’s infrastructure designed to:
- protect identity
- enable consent
- track influence
- route payments
- and give unions tools they’ve never had before
It exists so artists can participate in the AI future instead of being erased by it.
Our identity. Our future.
This only works if we do.
Let's create a sustainable future built with artists, not around them.
ai-ego.ai
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