I used to think inference was the expensive part of AI.
Now I think uncertainty is.
The model can generate an answer in seconds.
But if I spend the next ten minutes checking where it came from, which version produced it, whether I can reproduce it, or whether I should trust it...
Then the real cost wasn't compute.
It was uncertainty.
That changed how I look at OpenGradient.
Maybe AI infrastructure shouldn't only be measured by inference speed.
Maybe it should also be measured by uncertainty reduction.
Every proof, every execution record, every reproducible result removes a little more doubt from the system.
Over time, that might become more valuable than making models slightly faster.
I don't think the future AI economy will reward whoever computes the cheapest.
I think it'll reward whoever leaves the smallest amount of uncertainty behind every computation.
Maybe I'm overthinking it...
But uncertainty feels like AI's hidden transaction fee.
#OPG @OpenGradient $OPG
Now I think uncertainty is.
The model can generate an answer in seconds.
But if I spend the next ten minutes checking where it came from, which version produced it, whether I can reproduce it, or whether I should trust it...
Then the real cost wasn't compute.
It was uncertainty.
That changed how I look at OpenGradient.
Maybe AI infrastructure shouldn't only be measured by inference speed.
Maybe it should also be measured by uncertainty reduction.
Every proof, every execution record, every reproducible result removes a little more doubt from the system.
Over time, that might become more valuable than making models slightly faster.
I don't think the future AI economy will reward whoever computes the cheapest.
I think it'll reward whoever leaves the smallest amount of uncertainty behind every computation.
Maybe I'm overthinking it...
But uncertainty feels like AI's hidden transaction fee.
#OPG @OpenGradient $OPG