In the past few years, everyone has been talking about decentralized computing, but most solutions remain at the conceptual stage.

Either performance is insufficient, costs are too high, or they cannot support real AI workloads.
@0G_labs's approach is more aggressive; it doesn't try to optimize a single link but directly reconstructs the entire chain. Data availability, storage, and computation are separated and then combined through modular methods.
The change brought by this design is that the system is no longer limited by a single point bottleneck. Computation can be scaled horizontally, data can be read efficiently, and storage can grow at low cost.
For AI, this means a very realistic possibility. Training and inference no longer have to rely on centralized clouds but can be completed within an open network.
This is not just a cost issue but also a power structure issue.
Who controls the computing power, who defines the rules. And what @0G is doing is decentralizing this control across the network.
From a developer's perspective, the significance of this infrastructure lies in transforming the originally complex and closed AI production process into modular, callable components.
When computation becomes a public resource, AI can truly become part of an open ecosystem.
If we extend the timeline, the integration of AI and blockchain will not happen at the application layer but at the infrastructure layer.
@0G is occupying exactly this position.
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