I used to believe that scaling up centralized AI systems was the straightforward answer—bigger infrastructure, better results. Reality proved otherwise.

Here's what actually happens: as centralized AI expands, vulnerabilities multiply. Single points of failure become critical. Coordination costs explode. The whole thing gets fragile.

Distributed intelligence sidesteps this entirely. By spreading computational load and decision-making across a network, you eliminate bottlenecks. Each node operates independently yet harmonizes with others. The system becomes antifragile—more participants means stronger resilience, not weaker.

This is why the decentralized AI model matters. It's not overthinking. It's learning from how complex systems actually break.