The current development of large-scale models is facing two fundamental bottlenecks: one is the lack of closed loop commercial structures, and the other is the centralization of training paradigms. Today's AI systems are essentially platform dominated centralized production systems - data is provided by users, but value is captured by platforms, resulting in a serious mismatch between data producers and beneficiaries. Meanwhile, model training relies on closed datasets and offline processes, which artificially locks the path of intelligent evolution and makes it difficult to generate truly open growth. One possible breakthrough path is to reconstruct the large model into an open computing system similar to Bitcoin. The core idea is to introduce a "Proof of Work (PoW) like mechanism" to transform data and training processes into verifiable and incentivized computational contributions. In this structure, behaviors such as data provision and model optimization can be seen as "mining", where participants earn profits by contributing high-quality data or computing power, thereby achieving a value loop of "users as producers, consumers, and beneficiaries". The deeper transformation lies in the reconstruction of the training mechanism itself. Traditional models rely on static data and phased training, while PoW based large models evolve into a continuously running dynamic system: data flows in real-time, models are continuously updated, and the overall network approaches a better state in competition and collaboration. This process is similar to the transaction and block generation mechanism in blockchain, making the evolution of model parameters temporal and irreversible. When the 'data' itself is PoW transformed, AI systems will no longer be simple function approximators, but become complex evolutionary systems with self-organizing capabilities. The growth of intelligence is no longer completely predictable, but constantly emerges new structures and capabilities through nonlinear processes. This marks the transition of AI from "closed Turing machine computing" to "open evolutionary computing networks". From this perspective, future artificial intelligence is no longer just a trained model, but more like a computing ecosystem that continues to operate and generate new information - a truly intelligent network with vital features.