I used to think AI infrastructure would win by building the biggest network.
Now I think the biggest network doesn't matter if value can't move through it.
That shift happened after I spent time looking at @OpenGradient . It's easy to point to encouraging signals: decentralized compute supporting AI workloads, developers deploying models, applications requesting inference, verification creating trust, and payment mechanisms connecting participants across the network. Those are all signs of an ecosystem taking shape.
But they're only signs.
History has shown that infrastructure can grow much faster than actual demand. More models don't guarantee more users. More inference requests don't necessarily mean more economic value. Even verification is only useful when people depend on trustworthy outputs for real decisions.
What caught my attention is the dependency chain behind the network.
Compute enables models. Models power applications. Applications solve problems. Users create transactions. Transactions reward participants. Rewards attract more compute.
The system isn't strengthened by its strongest layer. It's constrained by its weakest one.
For me, the hardest question isn't whether @OpenGradient can scale AI infrastructure it's whether enough applications become indispensable to keep the entire loop alive.
That's why I no longer evaluate @OpenGradient as an infrastructure project. I evaluate it as an economic system where demand has to successfully pass through every layer before the network becomes truly valuable.
Which layer do you think is most likely to limit that flywheel over the long term?
#opg $OPG @OpenGradient
Now I think the biggest network doesn't matter if value can't move through it.
That shift happened after I spent time looking at @OpenGradient . It's easy to point to encouraging signals: decentralized compute supporting AI workloads, developers deploying models, applications requesting inference, verification creating trust, and payment mechanisms connecting participants across the network. Those are all signs of an ecosystem taking shape.
But they're only signs.
History has shown that infrastructure can grow much faster than actual demand. More models don't guarantee more users. More inference requests don't necessarily mean more economic value. Even verification is only useful when people depend on trustworthy outputs for real decisions.
What caught my attention is the dependency chain behind the network.
Compute enables models. Models power applications. Applications solve problems. Users create transactions. Transactions reward participants. Rewards attract more compute.
The system isn't strengthened by its strongest layer. It's constrained by its weakest one.
For me, the hardest question isn't whether @OpenGradient can scale AI infrastructure it's whether enough applications become indispensable to keep the entire loop alive.
That's why I no longer evaluate @OpenGradient as an infrastructure project. I evaluate it as an economic system where demand has to successfully pass through every layer before the network becomes truly valuable.
Which layer do you think is most likely to limit that flywheel over the long term?
#opg $OPG @OpenGradient