The development speed of AI has far exceeded many people's imagination, but the real problem isn't actually model capability—it's where the computing power comes from.

Large-scale model training requires massive GPU resources, yet computing power in the real world is highly concentrated in the hands of a few institutions.

This structure looked normal in the Web2 era, but it seems somewhat misaligned in the Web3 context.

It's against this background that I started paying attention to @dgrid_ai and its token $DGAI .

What this project is trying to do is quite straightforward: connect GPU resources scattered around the world to form a decentralized AI computing network, allowing developers to access computing power on demand, while computing power providers can earn incentives by contributing resources.

Once you truly understand this logic, you get an intuitive feeling: idle computing power can be utilized, AI developers don't need to depend on a single cloud provider, and the entire system is more like an open computing power marketplace.

From a user perspective, the changes brought by this model are quite obvious—AI is no longer an exclusive tool for large companies but can be accessed by more developers through an open network.

Web3 over the past few years solved financial infrastructure, while the truly scarce resource in the AI era has become computing power.

If AI really becomes as ubiquitous as the internet in the future, then networks like dgrid_ai might become that invisible but critically important underlying power grid.

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