Many people underestimate @0G_labs because they are used to understanding it through the logic of public blockchains, but fundamentally, 0G has already diverged from the traditional L1 competition framework.

$0G 's core is an AI-native execution environment that packages GPU computation, data storage, model invocation, and on-chain settlement into a unified system, allowing AI Agents to complete their full lifecycle on the chain.
The biggest difference from traditional blockchains is that it does not record state but runs intelligent tasks.
I increasingly feel that its significance is not in creating a "faster chain," but in attempting to answer a deeper question: what should infrastructure look like when AI becomes the main computing entity.
From this perspective, 0G is not about scaling blockchain but about standardizing AI infrastructure. It is even closer to a cloud computing protocol than a financial protocol.
Of course, the risks along this path are also clear, including whether the computational cost model is sustainable, whether decentralized GPU supply is sufficient, and the speed of genuine developer migration. But if it succeeds, its impact will not be limited to crypto but will influence the entire AI application layer structure.
What it is doing essentially is transforming AI from a corporate asset into a public computing layer.
@Galxe @GalxeQuest @wallchain @TermMaxFi