Many people understand the combination of AI and Web3 as a traffic narrative, but if you broaden your perspective, you’ll find that what truly has value is whether the underlying architecture undergoes migration.

That’s also why I continue to pay close attention to @0G_labs.
What they’re doing can actually be summarized in one sentence: enabling AI to have native decentralized infrastructure.
But if you break it down, you’ll see that its design is layered—data availability is responsible for high-throughput data publishing, the storage layer handles long-term preservation, and the computation layer supports AI-related execution.
This structure isn’t just a simple assembly; it’s redefined around AI needs.
Especially at the data layer: AI’s core isn’t code but data. If the data sources can’t be verified, there will always be issues with the trustworthiness of model outputs.
0G is trying to provide a verifiable data stream, and this is the part I believe has the most potential.
From the user perspective, the short term may not feel obvious, but once AI Agents run at scale on-chain, these underlying capabilities will become a foundational infrastructure requirement.
Projects of this type don’t show up every cycle, but once they do, they often determine the technical direction of the next phase.
@Galxe @GalxeQuest @easydotfunX @wallchain @TermMaxFi