The global artificial intelligence boom is reshaping energy needs across every continent, and it has carved out an unexpected new space for cooperation between the world’s two largest economies: the United States and China. Industry leaders and clean energy experts argue that a partnership between the two nations on clean energy development and grid management could not only ease the growing energy crunch holding back AI expansion but also deliver widespread benefits for both countries and the entire global economy.
As AI adoption accelerates, data center footprints are expanding rapidly and computing power requirements are skyrocketing, pushing energy infrastructure to its breaking point. This crisis has emerged as one of the most urgent bottlenecks for sustained AI development worldwide. Against this backdrop, industry observers note that the complementary strengths of the US and China make cross-border collaboration not just a feasible path forward, but a logically necessary one to address the shared challenge.
Ramkumar Krishnan, a cleantech entrepreneur and technologist with over two decades of experience in clean energy and advanced technology, shared his perspective with China Daily during an interview in San Francisco. “Technologies and products that are used, whether it’s in the US or in China or in other parts of Asia, they’re all very similar,” Krishnan explained. “There are lots of ways that I think technology is a connecting piece that brings all the nations together, because we all need technologies to solve the problem.”
China has secured its position as a global leader in large-scale clean energy production. Official data from China’s National Energy Administration shows the country’s annual clean power output has already surpassed 10 trillion kilowatt-hours — more than double the comparable figure for the United States, and exceeding the combined total power consumption of the European Union, Russia, India and Japan. This massive scale has allowed China to build unmatched operational experience and deploy extensive infrastructure that other nations can draw from, especially when it comes to developing intelligent grid management systems capable of handling the high, consistent energy demands of AI data centers, Krishnan said.
Krishnan pointed out that renewable energy technologies hold a major advantage over traditional large-scale power projects in their speed of deployment. A utility-scale solar farm, for example, can be completed and connected to the grid in 18 months or less, while conventional nuclear or hydropower projects often require 10 to 15 years to reach operation. “Bringing the right portfolio of solutions that can help to accelerate the adoption of energy, and bringing new energy, that could be another area that we can solve the demand that we have from AI,” he added.
For the United States, the key challenge lies in unlocking greater efficiency from existing energy infrastructure, Krishnan noted. The country holds a large amount of untapped excess grid capacity, but much of this capacity remains underutilized due to inconsistent availability and geographically fragmented distribution. “How do we actually intelligently manage that? That’s an area of pretty strong interest — how do we model how energy is used, so that we can utilize that excess capacity in lots of different places, whether it’s industries, EV charging stations,” he said. Notably, Krishnan suggested that AI itself could be part of the solution to the energy crisis it has created: advanced AI systems can enable far more dynamic, intelligent energy management that matches variable renewable energy supply with constantly shifting demand across grids.
One major Chinese clean energy enterprise is already moving to turn this collaborative vision into concrete action. GCL Group, a leading Chinese clean energy service provider that has already developed utility-scale power projects across California, Colorado and New York, is actively pursuing partnerships with United States AI companies to deliver integrated energy solutions for global AI infrastructure expansion.
During a recent visit to Silicon Valley, GCL Group chairman Zhu Gongshan outlined the scope of the growing energy challenge. “Computing demands of generative AI are driving explosive growth in the global AI data center market, while putting mounting pressure on power supplies worldwide,” Zhu said. “As demand for computing capacity surges, energy is emerging as one of the biggest bottlenecks to AI development.”
Zhu highlighted Southeast Asia as a particularly high-potential emerging market for this cross-border collaboration model. The region is seeing a rapid surge in demand for AI computing capacity, and it could be best served by integrated platforms that combine US AI technological expertise with China’s scalable clean energy solutions, he added.