Tokenized Data Markets
Why data markets need provenance, evaluation loops, and protocol-level incentives instead of simple upload funnels.
Data markets fail when they optimize for volume before they optimize for accountability. A large pile of uploads does not become a useful asset unless the network can trace provenance, evaluate effect size, and route rewards back to the contributors who actually improved outcomes.
That is why Hokusai treats tokenized data markets as part of the model feedback loop, not a separate warehouse. Data should enter the protocol with enough structure to be scored, replayed, and compared against new policy or model versions.
The result is a market where supply is shaped by measurable usefulness. That is a better foundation for long-term contributor trust than any one-off bounty program.