#opg $OPG @OpenGradient
I used to think verifed execution was the hard part, but now I’m less sure.
My thesis is simple: OpenGradient can prove a MODEL ran correctly, yet that dont prove the model learned enough.
OpenGradient reports 2,000+ hosted AI models; that signals choice, but also a wider selection surface where weak evidance can hide.
It also reports 2M+ inferences. Thats real useage, not 2M independent labels, so the sample behind generalization may still be alot smaller.
OPG Token has roughly 190M circulating from a 1B maximum supply, meaning only 19% is in circulation today; the active float is smaller, but future dilution pressure cant be ignored.
VC dimension sounds academic—it measures how flexible a model class is—but underneath, it asks how much data is needed before confidence becomes statstical rather than cosmetic. 🧠
So OPG Token demand may price compute activity faster then OpenGradient can prove learning quality.
Usage is visible. Evidence shoud be too.
I used to think verifed execution was the hard part, but now I’m less sure.
My thesis is simple: OpenGradient can prove a MODEL ran correctly, yet that dont prove the model learned enough.
OpenGradient reports 2,000+ hosted AI models; that signals choice, but also a wider selection surface where weak evidance can hide.
It also reports 2M+ inferences. Thats real useage, not 2M independent labels, so the sample behind generalization may still be alot smaller.
OPG Token has roughly 190M circulating from a 1B maximum supply, meaning only 19% is in circulation today; the active float is smaller, but future dilution pressure cant be ignored.
VC dimension sounds academic—it measures how flexible a model class is—but underneath, it asks how much data is needed before confidence becomes statstical rather than cosmetic. 🧠
So OPG Token demand may price compute activity faster then OpenGradient can prove learning quality.
Usage is visible. Evidence shoud be too.