If you review the development of blockchain, you'll find that much of its infrastructure was designed around financial needs. High-frequency trading, asset transfers, and smart contract execution were the primary objectives, but AI applications have completely different requirements for networks.

AI requires greater data bandwidth, lower costs, and verifiable compute environments—precisely the areas where traditional blockchain struggles most.

@0G_labs's approach is to redesign the architecture from the ground up. 0G adopts a modular structure that decouples data availability, computation, and storage capabilities, enabling the entire network to scale according to AI workloads rather than being constrained by a single chain structure.

The advantage of this structure lies in its flexibility. Developers can select different components based on application requirements, building AI-suitable application environments rather than being bound by rigid blockchain structures.

From an industry perspective, the emergence of such infrastructure represents an important shift. Blockchain is no longer merely a financial system, but is beginning to become a computational network supporting AI application execution.

When AI starts running on-chain, data, models, and execution processes can all be verified. This transparency itself is one of Web3 technology's most important values.

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