IP Attribution System

IP Attribution System

Attribution is a conversation between creators about contribution and intention.

Overview

A human-centered system for attributing creative contributions across any medium, where containers provide structure and conversations determine value.

The Deepest Why: Mattering Without Money

While this system enables fair payment, its deeper function is making creative contribution visible. A teenager making their first beat can see it travel the world, even if it never earns a cent. A grandmother sharing a family song can watch it inspire remixes across continents.

The system says: "Your creativity matters, here's the proof" - not through dollars but through visible connection and continuation.

Core Beliefs

• Humans declare contribution - not algorithms
• Anyone can be a contributor - people, places, AI, communities, vibes
• Attribution ≠ Payment - credit everyone, pay who you can
• Intentions guide remixing - licensing as creative conversation
• Communities shape the system - not vice versa

Essential Concepts

Work

Something created that carries its story of creation.

Attribution

A declaration: "This contributed to this work."

• WHO: Any entity (person, place, thing, idea)
• WHAT: Their contribution (decided by creators)
• HOW MUCH: Their share (human judgment)

TBD Attribution

Crediting someone not yet present in the system.

• Reserve their attribution
• Hold their share if any
• They can claim later
• Or it remains as historical record

Intentions (Creative Licensing)

How creators hope their work lives on.

• "Free for all uses"
• "Sacred - please contact first"
• "No commercial use without discussion"
• "AI training OK with attribution"
• "Would love to hear what you make!"

These are conversations, not legal barriers.

Inheritance

How new works relate to previous works.

• Default: 50/​50 between original and new
• Always negotiable by humans
• Creates attribution chains/​trees
• Respects original intentions

The Mechanical Layer

The 50/​50 Split Principle

Every content type has two halves:

• IDEA (Composition/​Concept) = 100% of idea pie
• IMPLEMENTATION (Recording/​Execution) = 100% of implementation pie
• Each pie = 50% of that content type's total value

Content Type Modularity

Works can contain multiple separable content types:

• Music (melody/​lyrics + recording)
• Video (concept + filming/​editing)
• Dance (choreography + performance)

Like TikTok - someone can take just your audio, or just your dance.

The Two Economies: Creation vs Curation

Economy 1: Transformation (Remixes)

• Sources contribute to new work's IP
• Creator gets commission (20%), not ownership
• IP inheritance flows through
• Pays license fee upfront

Example: Producer remixes two loops

• Original creators: 80% (40% each)
• Remix producer: 20% commission

Economy 2: Curation (DJ Mixes, Playlists)

• NO IP inheritance
• Curator gets discovery commission (20%)
• Original creators keep 80%
• Can earn from ongoing discovery

Example: DJ creates fire mix

• When mix sells: DJ 20%, originals 80%
• When originals sell via discovery: DJ gets referral

Why This Distinction Matters

• Creation = Making something new (deserves commission)
• Curation = Finding gems (deserves discovery fee)
• Both valuable, different contributions

Emergent Business Models

The Instant Record Label

Anyone can become a label through curation:

• Create account with label identity
• Curate playlists and artists
• Feature in your store
• Earn 20% on discoveries
• No contracts needed

The Artist Strategy Flip

Old: Seek ONE label deal New: Appear in MANY curator stores

• 10 stores = 10 audiences
• 10 revenue streams
• Keep all rights
• Cooperation over competition

AI Collaboration

AI as Contributor

Like any contributor, AI can be credited:

• Declare AI's contribution percentage
• Humans decide, not calculated
• Revenue typically stays with humans (who pay for AI)
• Attribution for transparency, not payment

AI Training Permissions

Artists set preferences:

• "No AI training"
• "Training OK with attribution"
• "Training OK with fee"
• "Academic only"

Why Attribute AI?

• Transparency (audiences deserve to know)
• Cultural documentation
• Legal protection
• Community norms emerge

Templates (Conversation Starters)

Creation

1. Solo Creator - "I made this"
2. Equal Partners - "We're all in this together"
3. Weighted - "We contributed differently"
4. Community + Individual - "Traditional song, modern recording"
5. AI Collaboration - "Me and Claude made this"

Intentions

1. Open Garden - "Plant seeds freely"
2. Sacred Ground - "Tread respectfully"
3. Fair Trade - "Share alike"
4. Gift Economy - "Keep creating, forget money"

Real Examples

The Restaurant

"Midnight Masala"

• Asha: 40% (melody, lyrics)
• Dev: 40% (production)
• Indian Kitchen: 20% (where every hook was born)

Sacred Remix

Traditional Lakota song:

• Marked: "Sacred - contact first"
• Remixer reaches out
• Agreement: 40% to Pine Ridge Foundation
• New work honors tradition

The Vibe Credit

"Summer Sessions EP"

• Band: 80%
• Jamie: 10% (kept everyone laughing)
• Beach House: 10% (where we recorded)

Platform Context

This attribution system is Layer 1 of a three-layer architecture:

• Layer 1: Attribution (this system)
• Layer 2: Visualization (see your impact)
• Layer 3: Creative toys (DJ mixer, cultural interfaces)

The attribution must be solid because everything builds on it.

Key Principles

1. Simplicity Over Complexity - Basic containers, human decisions
2. Attribution Over Calculation - Declare, don't measure
3. Conversation Over Contract - Negotiate, don't dictate
4. Evolution Over Perfection - Let communities shape the system
5. Everyone Matters - From Navajo grandmother to 14-year-old in Canada

Implementation Status

✅ Built: Gen 1 Remix System

• 50/​50 IP inheritance
• Smart contract payment distribution
• Up to 7 contributors per side

📝 Planned: Curation Economy

• Playlists with 20% commission
• Discovery referral tracking
• Creator stores as labels

Further Reading

• See references/​philosophy.md for deeper exploration of human judgment vs algorithmic calculation
• See references/​examples.md for detailed case studies
• See references/​ai-attribution.md for AI collaboration patterns