The real bottleneck in AI training isn't computational power—it's data. Quality examples. Once the model exhausts good training data, learning plateaus. No amount of processing can fix that gap.

What if instead of centralized data collection, we distributed it? Thousands of contributors simultaneously feeding examples into a shared learning network. Each node trains locally, the system evolves globally.

That's where decentralized AI protocols come in. They're rewiring how intelligence gets built—turning data collection from a top-down problem into a collaborative, incentive-aligned process. The network learns everywhere at once, never bottlenecked by a single source.