While setting up a basic Datanet contribution in the CreatorPad task, what struck me was how the on-chain attribution for data uploads worked seamlessly in the default flow but revealed friction once I tried fine-tuning a small specialized model. The Proof of Attribution logged my modest dataset contribution instantly with transparent provenance, yet the actual compute step for even a lightweight training run pushed me toward advanced node setup or waiting queues that felt more gated than the "community-owned" promise suggested.

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It highlighted how early participants like me end up providing the raw data liquidity first, while smoother model ownership and rewards seem to favor those who scale up infrastructure. This isn't a flaw so much as a quiet reminder of where the real bottlenecks sit in decentralizing AI. What does that mean for who truly steers these community datasets long-term?