The most common pitfall for AI project developers: when encountering subpar model performance, the first reaction is to blame the algorithm or the model itself. But those who have truly experienced this know that the problem often lies elsewhere.

A careful look at the current Web3 technology stack makes this clear. Data sources are scattered and chaotic, with various protocols and dApps operating independently, and there is no unified data standard. Even more frustrating, signals that have already been generated are hard to reuse, requiring reprocessing every time, which is extremely inefficient.

This is the fundamental reason why many AI applications perform only moderately on-chain. Intelligent agents need to reason based on the same set of facts, but the current infrastructure simply cannot support this.

The key breakthrough lies at the data layer. If behavioral data can be standardized, allowing various intelligent agents, dApps, and protocols to operate based on the same data baseline, everything will change — iteration speeds will increase, execution logic will become clearer, and the system will truly become scalable. This is not a minor optimization; it’s a game-changer.