The future direction of AI computing has become clear. The previous approach of offline pre-training static models without considering power consumption costs will gradually shift towards real-time, continuous learning system architectures. This change is not just a technical issue—it involves a complete redesign of the entire computing paradigm. Future AI systems need to operate at the edge and on a global scale, with energy efficiency upgraded from "negligible" to a core design metric. In other words, those who can reduce energy consumption while maintaining performance will hold the competitive advantage of the next generation. This will have a profound impact on hardware architecture, algorithm optimization, and even the entire ecosystem.