The idea of running AI infrastructure on edge devices sounds ambitious until you remember that your laptop is sitting idle for most of the day.
OpenLedger's community node architecture distributes computational work across devices owned by ordinary participants rather than centralizing it in data centers. You run a node, you contribute processing capacity, you earn rewards. The pitch is familiar from other distributed computing projects. The AI data market context makes it more interesting than most.
What I wanted to understand was what the node actually does. Processing data locally before it hits the chain reduces costs and latency in ways that matter for real AI workloads.
My hesitation is about consistency. Distributed edge infrastructure is only as reliable as its least reliable participant. That's a lot of variables to trust with serious AI computation.