As artificial intelligence and high-performance computing advance rapidly, computational power has become a core element of national technological competitiveness. However, the limiting factor for scaling up computing capacity is no longer chips or algorithms—it’s electricity supply. Massive data centers and AI training clusters consume enormous amounts of power; training a single large-scale AI model can use as much energy as thousands of households in a year. With computational demand growing exponentially, the stability, sustainability, and cost of power infrastructure now directly constrain the scale and efficiency of computing deployment. For instance, some U.S. states have paused approvals for new data centers due to grid constraints, while China is implementing its ‘East Data, West Computing’ initiative to relocate computing hubs to western regions rich in renewable energy. Going forward, the nation or region that builds an efficient, low-carbon, and reliable power support system will gain a decisive edge in the next global computing power race. Electricity is moving from the background to center stage—becoming the key variable that defines the ceiling of computational capacity.
在人工智能与高性能计算迅猛发展的今天，算力已成为国家科技竞争力的核心要素。然而，决定算力扩张上限的不再是芯片或算法，而是电力供应。大型数据中心和AI训练集群功耗惊人，单个超大规模AI模型训练的能耗可媲美数千户家庭年用电量。随着算力需求呈指数级增长，电力基础设施的稳定性、绿色化程度与成本，直接制约着算力部署的规模与效率。例如，美国部分州已因电网压力暂停新建数据中心审批；中国也在推动“东数西算”工程，将算力中心向西部可再生能源富集区转移。未来，谁能构建高效、低碳、可靠的电力支撑体系，谁就能在下一轮全球算力竞争中占据先机。电力，正从幕后走向台前，成为决定算力天花板的关键变量。
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