Most AI projects focus on answering the question of how to make the model stronger.

@0G_labs addresses another, more fundamental question: how data flows.
This entry point determines its upper limit.
The core of AI has never been computing power itself, but data input and output.
If data cannot be written to and read from efficiently, even the strongest model can only stay local.
0G's approach is very straightforward.
First solve the data layer, then discuss execution.
This sequence actually aligns more closely with the technological evolution in the real world.
From a design perspective, it treats the chain as a data network, not a settlement network.
This means the entire system's priorities have shifted.
It's no longer transaction-first, but data-first.
From a user experience perspective, you'll notice a change.
In the future, users may not perceive transactions, but they will definitely perceive data responses.
The smoothness of AI applications fundamentally depends on the data layer, not consensus time.
That's also why 0G's positioning is closer to "AI infrastructure" rather than "AI application chains."
Looking at it over a longer timeframe, the value of such projects won't be fully realized in the early stages.
But once AI scales into on-chain scenarios, the data layer will become the first part to be validated.
What 0G is doing now is likely about preemptively securing this layer.
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