While most people are still using AI as a tool, a few have already begun to realize that the way data and computing power are organized is the core of the next round of competition.

Recently, I revisited the @0G_labs architectural design, and it gave me a real sense of being awakened.
It isn’t simply about stacking AI on-chain; it’s trying to rebuild a native data availability layer and a modular execution environment for AI services.
If public chains in the past were focused on solving transaction problems, then 0G is more like working to solve the underlying supply issue for intelligent generation.
How data can be stored efficiently, how it can be called quickly, and how consistency can be maintained in a distributed environment—these are the real keys to getting AI implemented in practice.
More importantly, this design doesn’t stay at the narrative level; it moves forward along the direction of modularization and high-performance DA, which is in sync with the current overall expansion roadmap.
When you shift your perspective down one layer from the application layer, you’ll find that what’s truly scarce isn’t the model, but the infrastructure that supports the model’s operation.
What 0G is trying to do is essentially to preemptively fill this structural gap.
Many people haven’t yet realized that this could be the next cycle’s most undervalued category of assets.
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