Last night while scrolling through X, I saw someone say, “AI public chains only need to move the model onto the chain and they’ll succeed by halfway.” After reading it, I chuckled. If it were really that simple, this track would have already been crowded into several leading players.
In my view, the real problem with many AI projects today isn’t the model—it’s the cost.
The resource consumption gap between on-chain AI tasks is enormous. A simple data analysis and a complex large-model inference don’t demand compute in the same order of magnitude. If you still use the traditional Gas-style approach to charge everything under one pricing scheme, then either ordinary users find it too expensive, or high-compute tasks won’t have any nodes willing to take them—ultimately, nobody benefits.
So I recently took a serious look at @OpenGradient .
What interests me most is that it doesn’t put all inference tasks under a single billing framework. Instead, it breaks down resources at a finer granularity based on model size, computational complexity, and verification method. In simple terms: different tasks match different costs, not one-size-fits-all pricing.
The biggest significance of this is not only making the network run more efficiently, but also helping developers more easily forecast costs. For future AI applications that become increasingly complex, this kind of resource scheduling may be even more important than simply improving model capability.
Of course, the role $OPG plays here isn’t just paying transaction fees. It’s more like a network-wide resource pricing tool—creating a dynamic balance among compute supply, node rewards, and user demand—rather than relying on fixed rules to allocate resources.
That said, I’m still keeping an eye on it.
The theoretical design sounds good, but what really tests it is a high-concurrency environment. If, in the future, many agents and AI applications connect at the same time, whether this dynamic pricing mechanism can remain stable will ultimately determine the project’s long-term value.
At least compared with projects that only know how to tell AI stories, OpenGradient has made me see some infrastructure-level thinking—and that’s why I continue to pay attention. #OPG
In my view, the real problem with many AI projects today isn’t the model—it’s the cost.
The resource consumption gap between on-chain AI tasks is enormous. A simple data analysis and a complex large-model inference don’t demand compute in the same order of magnitude. If you still use the traditional Gas-style approach to charge everything under one pricing scheme, then either ordinary users find it too expensive, or high-compute tasks won’t have any nodes willing to take them—ultimately, nobody benefits.
So I recently took a serious look at @OpenGradient .
What interests me most is that it doesn’t put all inference tasks under a single billing framework. Instead, it breaks down resources at a finer granularity based on model size, computational complexity, and verification method. In simple terms: different tasks match different costs, not one-size-fits-all pricing.
The biggest significance of this is not only making the network run more efficiently, but also helping developers more easily forecast costs. For future AI applications that become increasingly complex, this kind of resource scheduling may be even more important than simply improving model capability.
Of course, the role $OPG plays here isn’t just paying transaction fees. It’s more like a network-wide resource pricing tool—creating a dynamic balance among compute supply, node rewards, and user demand—rather than relying on fixed rules to allocate resources.
That said, I’m still keeping an eye on it.
The theoretical design sounds good, but what really tests it is a high-concurrency environment. If, in the future, many agents and AI applications connect at the same time, whether this dynamic pricing mechanism can remain stable will ultimately determine the project’s long-term value.
At least compared with projects that only know how to tell AI stories, OpenGradient has made me see some infrastructure-level thinking—and that’s why I continue to pay attention. #OPG