The project positioning is widely misunderstood: this is not about building an AI application or a simple AI Agent product. The core logic is to establish a coordination layer for AI resources.

The real pain points of market mechanisms are often overlooked. The main bottleneck in the current AI industry is not only limited to the evolution of model algorithms or the supply of GPU computing power, but more critically, how resources are efficiently coordinated and scheduled.

Analogous to the development path of Web2: Scale AI systematically handles data annotation, data cleaning, data set optimization, and other processes to establish an end-to-end data optimization and coordination system, becoming the infrastructure layer of training data pipelines. The same logic applies to the current supply side of AI computing resources—who can better coordinate heterogeneous AI resources and improve their utilization efficiency will stand out from this wave.