@OpenGradient #opg $OPG
Lately I've been thinking about how markets tend to price ownership before they price utility.
Every cycle seems to have its favorite asset. At one point it was blockspace. Then liquidity became the obsession. Data followed. Now AI models sit at the center of the conversation, as if owning the model itself is where all the value lives.
I'm not convinced that's the full story.
What caught my attention while exploring OpenGradient wasn't simply the AI angle. It was a different question: what happens if the real economic value comes from inference rather than the model?
Because a model sitting on a server isn't doing much on its own.
The moment value is created is when someone actually requests intelligence. An agent needs an answer. Compute providers generate it. The network verifies the work. Fees are paid. Then the process repeats again and again.
Viewed that way, AI starts looking less like software and more like a utility layer that powers activity across a network.
That's where things become interesting to me.
Of course, not every network with impressive numbers is creating real demand. Incentives can inflate activity, and artificial usage is nothing new in crypto. We've all seen projects where metrics looked strong until rewards disappeared.
So when I watch OpenGradient, I'm focused on one simple signal:
When incentives fade, does usage remain?
Because sustainable demand is usually what separates a compelling narrative from a durable asset.