The development strategy of Web3AI should avoid simply imitating the Web2 model and instead adopt a differentiated path. The core challenge currently faced by Web3AI is the semantic alignment issue of multimodal models, specifically how to map different forms of data such as images and text into a unified semantic space. Compared to the mature cross-modal conversion mechanisms of Web2AI, the semantic alignment efficiency under the flat architecture of Web3 still needs improvement, which directly affects system performance. Experts recommend that Web3AI adopt a gradual development strategy of 'surrounding the city from the countryside', focusing on breaking through key technical bottlenecks such as high-dimensional space semantic alignment, optimization of attention mechanisms, and coordination of heterogeneous computing power.