China’s Ministry of Industry and Information Technology has laid out an ambitious five-year plan to expand the country’s AI computing capacity to 9,800 eflops by 2030 — more than four times its current level. The plan calls for roughly 3.8 trillion yuan, or about $532 billion, in cumulative investment in information infrastructure between 2026 and 2030.
China had already reached 2,185 eflops of intelligent computing capacity by the end of June, a jump of 177 percent from a year earlier, with capacity climbing further to roughly 2,450 eflops by the end of July. The plan envisions AI computing clusters containing tens of thousands of accelerator cards, with some clusters exceeding 100,000 cards, while emphasizing the use of domestically produced chips rather than relying on foreign suppliers.
The buildout comes as China faces continued restrictions on access to the most advanced foreign-made AI chips, pushing the country to invest heavily in domestic semiconductor capacity even as it scales up raw computing power. The scale of the planned investment underscores how AI infrastructure has become a central pillar of industrial policy in China, comparable in ambition to the country’s historic investments in areas like high-speed rail and telecommunications.
The announcement adds to a broader global pattern in which major economies are treating AI compute capacity as strategically important infrastructure, with the United States, European Union, and Gulf states all pursuing their own large-scale data-center and chip investment plans in parallel.
Google is securing nuclear power capacity to supply new AI data centers being built in Finland, part of a broader push by major tech companies to lock down reliable, low-carbon electricity as AI workloads drive an unprecedented spike in power demand. The deal reflects a growing recognition across the industry that energy availability — not just chip supply — has become one of the central bottlenecks constraining how fast AI infrastructure can scale.
Data centers built to train and run large AI models consume enormous, constant amounts of electricity, and traditional grid expansion has struggled to keep pace with the sudden surge in demand from hyperscale computing campuses. Nuclear power’s steady, round-the-clock output makes it an attractive option for tech companies trying to guarantee reliable supply without leaning entirely on fossil fuels or waiting years for new transmission infrastructure.
Google joins a growing list of tech giants pursuing nuclear deals to power AI ambitions, following similar moves elsewhere in the industry toward both existing reactor capacity and next-generation small modular reactor projects still under development. The strategy also reflects mounting local pushback in some regions where utilities and regulators have grown wary of data centers straining local power grids — a tension that has already led some U.S. jurisdictions to pause new data-center power hookups.
As AI companies continue racing to build ever-larger training clusters, expect energy procurement — not just GPU supply — to remain one of the defining constraints on the pace of the industry’s growth.