I used to think the value of an AI network could be measured by a simple number: how many nodes were online.
The more I learned, the more I realized how misleading that can be.
A network isn't tested when everything is running smoothly. It's tested when demand suddenly spikes, a region goes offline, or operators start questioning whether rewards are worth the cost of staying active.
That's why I've become interested in OpenGradient.
What they're building goes beyond AI inference. The goal is Open Intelligence: a decentralized system where AI models can be hosted, executed, and verified at scale.
The interesting part is that every new operator doesn't automatically make the network stronger. Real strength comes from filling coverage gaps, improving reliability, and increasing the probability that a request can be served and verified when it matters most.
That's also how I think about $OPG.
Its long-term value won't come from speculation alone. It comes from coordinating incentives, attracting useful infrastructure, and helping the network remain resilient as adoption grows.
Growth gets attention.
Reliable, verifiable AI infrastructure is what creates lasting value.