| name | ip-attribution |
| description | A human-centered system for attributing creative contributions across any medium, where containers provide structure and conversations determine value |
| metadata | [object Object] |
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
- Solo Creator - "I made this"
- Equal Partners - "We're all in this together"
- Weighted - "We contributed differently"
- Community + Individual - "Traditional song, modern recording"
- AI Collaboration - "Me and Claude made this"
Intentions
- Open Garden - "Plant seeds freely"
- Sacred Ground - "Tread respectfully"
- Fair Trade - "Share alike"
- 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
- Simplicity Over Complexity - Basic containers, human decisions
- Attribution Over Calculation - Declare, don't measure
- Conversation Over Contract - Negotiate, don't dictate
- Evolution Over Perfection - Let communities shape the system
- 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.mdfor deeper exploration of human judgment vs algorithmic calculation - See
references/examples.mdfor detailed case studies - See
references/ai-attribution.mdfor AI collaboration patterns