AI training pipelines consume creator-produced content at scale with no mechanism to track data provenance, attribute contributions to specific creators, or trigger downstream compensation. The absence of an attribution and royalty infrastructure is not incidental but architectural, making retroactive remediation legally complex and technically intractable. A coordination layer that logs training data lineage and routes micropayments to contributors does not yet exist.

Creators whose content trains AI models have zero visibility into usage and receive no compensation, while AI companies face growing legal and reputational risk from unattributed training data.

Digital content creators (writers, artists, photographers, musicians) and AI companies seeking licensed, provenance-clear training data to reduce litigation risk.

AI companies are already paying billions for licensed data (Reddit, Shutterstock, AP deals); creators are already suing (NYT, Getty). A standardized attribution layer lets both sides transact efficiently instead of through bespoke legal deals or courtrooms.

MVP is a creator-facing registry where content is fingerprinted (perceptual hashing + embeddings), paired with a lightweight API that AI training pipelines call to check provenance and trigger usage-based micropayments via Stripe Connect; start with text/image, one model provider partnership.

AI training data licensing is projected at $10B+ by 2028; the creator economy is 50M+ people, and even capturing 1-2% intermediation fees on data licensing deals yields a $100M+ revenue opportunity.