My mentor used to say, "Many teachers talk nonsense." This rings true right now.
The other day, I opened an app to binge-watch some shows, scrolling back and forth for nearly 10 minutes because there were too many choices, and in the end… I went back to my old favorites. That’s when I realized sometimes what drives us away isn’t a lack of options — it’s an overload of them.
@OpenGradient is building infrastructure so that more and more AI models, compute, and agents can coexist on an open network. It sounds like a solid trend. But I see a bigger question: what happens if the number of models on OpenGradient grows faster than the number of actual users?
That’s when the game changes.
As more models emerge, each one becomes harder to remember. Compute is no longer a rare asset. Attention has become the rare asset. OpenGradient can scale the supply of AI rapidly, but if demand doesn’t keep up, the value of each model will be diluted. It’s like adding more stalls in a mall while the foot traffic remains the same.
We’re seeing a form of Model Saturation Loop — the more new models there are, the more distribution mechanisms are needed to keep the old models in play.
This is where I find the role of the OPG token more interesting than just incentives.
If $OPG only rewards model deployment, OpenGradient will inadvertently create a supply race. But if the OPG token prioritizes rewards for repeated inference, retention, or the actual demand generated, then the token is helping the system filter real value.
OpenGradient shouldn’t just flaunt the number of models or builders.
Let’s showcase the number of models still alive after 30 days.
Because having many AIs doesn’t mean a rich ecosystem.
Sometimes it just means it’s crowded.
#opg $BEAT $ARX
The other day, I opened an app to binge-watch some shows, scrolling back and forth for nearly 10 minutes because there were too many choices, and in the end… I went back to my old favorites. That’s when I realized sometimes what drives us away isn’t a lack of options — it’s an overload of them.
@OpenGradient is building infrastructure so that more and more AI models, compute, and agents can coexist on an open network. It sounds like a solid trend. But I see a bigger question: what happens if the number of models on OpenGradient grows faster than the number of actual users?
That’s when the game changes.
As more models emerge, each one becomes harder to remember. Compute is no longer a rare asset. Attention has become the rare asset. OpenGradient can scale the supply of AI rapidly, but if demand doesn’t keep up, the value of each model will be diluted. It’s like adding more stalls in a mall while the foot traffic remains the same.
We’re seeing a form of Model Saturation Loop — the more new models there are, the more distribution mechanisms are needed to keep the old models in play.
This is where I find the role of the OPG token more interesting than just incentives.
If $OPG only rewards model deployment, OpenGradient will inadvertently create a supply race. But if the OPG token prioritizes rewards for repeated inference, retention, or the actual demand generated, then the token is helping the system filter real value.
OpenGradient shouldn’t just flaunt the number of models or builders.
Let’s showcase the number of models still alive after 30 days.
Because having many AIs doesn’t mean a rich ecosystem.
Sometimes it just means it’s crowded.
#opg $BEAT $ARX