There is a problem that has actually started to become unavoidable: when AI becomes the productivity itself, where does it run?

Today’s answer is simple: in the cloud, in the hands of a few companies, but that is precisely the biggest source of uncertainty.
@0G_labs is trying to offer an alternative path.
It is building a modular Layer1 designed for AI, splitting storage, computation, and data availability into independent components, then reorganizing them through on-chain systems.
The key to this design is not performance, but control.
As AI’s operating environment shifts from centralized clouds to distributed networks, data, models, and execution logic begin to become verifiable.
This signifies a deeper change: AI is no longer just a tool, but starts to become part of on-chain systems.
From a developer’s perspective, this is more like a foundational infrastructure—you no longer rely on a single API but call computation, storage, and models within an open network.
When capabilities are modularized, innovation is no longer limited by platforms, which is why these projects are more like a foundation than an application layer narrative.
True competition has never been about whose model is stronger, but about who provides a freer operating environment.
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