浩云长盛彭轶峰：兼容并蓄和系统性思维是应对IDC行业急速迭代的生存法则

Core Viewpoint - The data center industry is undergoing a profound transformation, evolving from a mere physical space for data storage and computation to a "computing power center" essential for AI model training and algorithm iteration [1] Group 1: Industry Challenges and Opportunities - The primary challenge facing the IDC industry is not merely technical bottlenecks but a shift in cognitive understanding, moving from "what I can do" to "what I need to do in the future" [3][11] - The demand for computing power in the AI era is characterized by diversification, high elasticity, and rapid iteration, with hardware and algorithm cycles shortening from five to six years to two to three years, and potentially to six months in the future [5] Group 2: Strategic Directions - The future computing power centers must embody compatibility and foresight, as traditional data centers lacking these traits are at risk of obsolescence [4] - The company advocates for a "toolbox" approach, storing a series of compatible technologies that can be flexibly combined to meet varying needs and developmental stages [6] Group 3: Lifecycle and Sustainability - The industry is currently focused on PUE (Power Usage Effectiveness) and WUE (Water Usage Effectiveness), but a more comprehensive view of the full lifecycle resource consumption is necessary, from raw material extraction to end-of-life disposal [7][8] - The company has developed models to analyze the true value of energy-saving measures, emphasizing the importance of optimizing applications and reducing energy conversion losses [8] Group 4: Cooling Technologies - Liquid cooling technology is seen as a potential solution for high-density heat dissipation, but its current market share is only about 15%, with mainstream applications still concentrated in the 10-20 kW range [10] - The company acknowledges that the development of high-density liquid cooling is still in the exploratory phase, requiring extensive application cases and data to validate its effectiveness [11]