Decentralized storage just changed the economics of distributing AI models. Centralized model hosting costs scale linearly with usage. Every inference request hits the same servers, bandwidth costs compound, and providers pass those costs to users through API pricing. That's why GPT-4 API calls cost what they do: someone's paying for compute AND bandwidth at scale. With decentralized storage like 0G's DA layer, model weights get distributed across nodes. Users pull from the nearest node instead of hammering central servers. Bandwidth costs distribute across the network. Suddenly hosting a 70B parameter model doesn't require infrastructure that costs six figures monthly. This doesn't just make AI cheaper. It makes completely new distribution models viable: models that update frequently, models with regional variants, models that users can run locally after initial download. The constraint was never model quality, it was distribution economics.