When AI fails, it’s usually the data. In Aug 2025 a healthcare model flagged a stroke in the “basilar ganglia”, a place that doesn’t exist. @JoinSapien puts the focus back on data quality. Proof of Quality verifies where data came from and whether it can be trusted. Staking enforces accountability. Peer validation raises accuracy. On-chain reputation tracks trust. Slashing penalizes bad work. Pipelines become a live feedback loop: quality by design, not by luck. 185M+ tasks, 1.93M contributors and counting. Simple rule: before data hits a model, ask two things who created it, and can it be trusted?