Recently, I read the analysis of "Crypto Theses for 2026" published by Messari, and there is a particularly interesting point: current large models are all trained by stacking and synthesizing data, but the ceiling of this approach is quite obvious—the real bottleneck is still the authentic interaction data from the physical world.

It makes sense when you think about it. Without enough frontline data inputs like sensors, location information, and environmental variables, models are prone to issues in real-world applications. This is not an algorithm problem; it's a problem with the data sources.

This observation directly points to a direction: why has the path of decentralized data networks (DePAI) suddenly become so critical? Instead of letting a centralized organization monopolize data collection and annotation, it's better to involve sensor nodes, IoT devices, and ordinary users worldwide to contribute real data. This not only solves the pain point of AI models lacking authentic data but also provides reasonable incentives and returns to data owners.