We know that AI systems are powered by data; behavioral signals, identity footprints, social interactions, and digital activity generated by real people. But today’s AI stack is built on data flows that are largely opaque, fragmented, and often detached from explicit user consent.

As demand for high-quality training and inference data accelerates, scrutiny around provenance and authorization intensifies. Large-scale scraping, dataset aggregation, and off-chain data extraction have exposed a structural flaw in the current paradigm: intelligence is being built on data that users neither control nor transparently license.

This raises deeper structural questions:

🔹Who truly owns onchain and offchain behavioral data?

🔹What defines verifiable consent in an AI-native world?

🔹How can AI systems prove the origin, permission, and integrity of the data they rely on?

Without programmable identity, cryptographic consent, and verifiable data receipts, AI autonomy remains incomplete.

What do you think?