PATHWELL CONNECT™ • Genesis EO §3(a)(v) & §5(c)(ii)
Attribution Lineage
Simulated training provenance & royalty distribution
Who Contributed What? Who Gets Paid?
When AI models train on data from multiple sources, attribution collapses—no one knows who contributed what. When those models commercialize, economic injustice follows. This demo shows how cryptographic provenance and automatic royalty distribution solve both problems.
Select Training Scenario
1Data Sources (Provenance)
NOAA Weather Data
National Oceanic & Atmospheric Administration
35%
DOE Energy Grid Data
Department of Energy
25%
NASA Satellite Imagery
National Aeronautics & Space Administration
20%
University Research Corpus
Academic Consortium (12 institutions)
20%
Total Training Contribution
100%
2Training Lineage
Cryptographic Binding
Provenance: 0x7a3f...e91d
3Commercialization (Royalty Engine)
Revenue to Distribute
$100,000
How Attribution + Economics Work Together
Provenance Objects
Cryptographically binds each data source to training lineage with immutable contribution weights
Immutable Ledger
Stores lineage receipts in tamper-evident ledger accessible to all parties
Royalty Engine
Automatically calculates and distributes royalties when commercialization events occur
GENESIS EO ALIGNMENT
§3(a)(v) IP protections§5(c)(ii) Commercialization§5(c)(ii) Trade secret protections§5(d) International collaboration