In these aspects, the integration of Web3 and AI is mainly reflected in four aspects: 1. Computing power layer: In the past two years, the computing power used for training large-scale AI models has grown exponentially, doubling almost every quarter, far exceeding Moore’s Law. This situation has led to a long-term imbalance between supply and demand for AI computing power, causing rapid price increases in hardware such as GPUs and raising the cost of computing power. However, at the same time, there is also a large amount of idle mid-to-low-end computing hardware in the market. It is possible that the individual computing power of these mid-to-low-end hardware cannot meet high-performance requirements. However, if a distributed computing power network is built through Web3, and a decentralized computing resource network is created through computing power leasing and sharing, it can still meet the needs of many AI applications. By using distributed idle computing power, the cost of AI computing power can be significantly reduced. The computing power layer is subdivided into:
