quote: Enterprise AI is moving towards domain-specific models fine-tuned on a company’s own data. Coinbase has done it in-house. Most organisations don’t have that luxury. Compute, tooling and ML talent are expensive, and the best training data usually sits across teams, departments, partners, and jurisdictions that can’t easily share it. Federated learning and decentralised AI close that gap. With FLock’s decentralised training infrastructure, teams and institutions can collaboratively train domain-specific models across regions, without exposing their raw data. This will be the new standard for enterprise AI. | We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case). I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves. We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them: "/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started" I imagine we'll have some backend services that could be replaced entirely by this new architecture.