You might think that the problem with AI is that the models are not strong enough, but actually it's the high costs—training is expensive, inference is costly, and scaling is even more so.

This is also why AI has been monopolized by giants for so long. @0G_labs's approach is very straightforward: reduce costs.
Using a distributed network to handle computation and storage eliminates the reliance on AWS or centralized clouds.
Developers can call resources directly on the chain instead of renting servers, which is a huge step forward.
The supply side is liberated; control of computing power is no longer limited to a few companies—any node can participate.
Looking at the structural design, 0G features a modular architecture with data, computation, and execution layers that are independent and infinitely scalable.
This is true scalability—not just TPS, but the ability to support AI.
Once costs decrease, AI applications will explode like DeFi did.
But this time, the explosion won't just be in finance—it will encompass the entire computing market.
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