Recently, more and more developers are focusing on the integration of AI and blockchain, but those who actually start building products will quickly encounter a practical problem. Blockchain architecture is not actually designed for AI.

AI requires continuous data streams, frequent computations, and large-scale storage, whereas traditional blockchains are better suited for handling discrete transactions. This contradiction has always existed, but it was not amplified until now.
The emergence of @0G_labs provides a systematic solution to this problem for the first time. By modularizing the architecture to separate the data layer, storage layer, and execution layer, the network can be optimized for AI scenarios rather than simply increasing TPS.
From an industry perspective, this change is very critical. Because the scale of AI applications far exceeds that of DeFi. Whether it’s model inference, data marketplaces, or autonomous agents, stable infrastructure support is needed.
More importantly, this design could change how developers think. Previously, when building on-chain applications, the first consideration was how to adapt to blockchain limitations. But in the future, if the underlying network itself supports AI workloads, the application design space will be greatly expanded.
From a user experience perspective, this upgrade might not be immediately noticeable to ordinary users. But as more AI tools start to appear as on-chain applications, everyone will gradually realize that the infrastructure has already changed.
Many technological revolutions start from the bottom. Projects like 0G may be at the very beginning of this stage.
@Galxe @GalxeQuest @easydotfunX @wallchain @TermMaxFi