China’s New AI Strategy Explained

Early in October 2025, China’s National Development and Reform Commission and the National Energy Administration jointly released a statement announcing plans to accelerate the integration of AI into the energy sector. With a goal of widespread application by 2027, China’s new AI strategy aims to secure itself as the global leader in AI applications in energy by 2030. This is merely one aspect of Chinese AI’s push to lead in the energy sectors, emphasizing cutting-edge renewable technology for both domestic use and for export to secure a position of leadership in the energy transition. For China, AI’s energy consumption thus provides an opportunity for long term dominance in both the input and output of this cutting edge technology.

Where China Stands On Energy

Since taking office in 2013, Chinese President Xi Jinping has consistently prioritized energy security in China’s development strategy. Since the outbreak of the Russo-Ukrainian war in 2022, China has accelerated its push for energy independence. Russia, having lost the EU as a major consumer base, has turned to Beijing to offset its declining oil and gas revenue. This has brought China short-term benefits, boosting its oil and gas security through discounted imports. In August 2025, China remained the largest global buyer of Russian fossil fuels, accounting for 40% of Russia’s export revenue. Of China’s purchases, 58% were comprised of crude oil imports, followed by 15% coal, 12% pipeline gas, and 10% oil products.

Despite the short-term advantages of cheaper energy from Russia, China remains cautious about dependence on Moscow and is equally eager to deny Moscow strategic leverage. The Russo-Ukrainian war and instability in the Middle East have underscored the vulnerability of China’s oil and gas transit routes to geopolitical conflict and external interference. In response, Beijing has placed renewable energy at the center of its long-term energy security strategy, linking it to both economic growth and emissions reduction goals.

Beijing stands as a global leader in green technology innovation and has only continued to accelerate its adoption, seeing a 25% growth in wind and solar electricity generation between 2024 and 2025. Despite this, renewable energy remains insufficient in satisfying the country’s growing demands. China’s gargantuan manufacturing sector, coupled with increased household energy consumption, suggests that demands are not projected to plateau anytime soon. Critically, China continues to rely heavily on foreign energy from a diverse set of state suppliers

What Can AI Bring In The Chinese Energy Sector?

In their joint statement, NDRC and the NEA outlined their implementation goals for promoting the high-quality development and utilization of artificial intelligence in the energy sector. AI with Chinese Characteristics clearly means utilizing it to resolve baseload problems to act as a force multiplier across Chinese industry. The report outlines ambitions for integrating AI across the hydropower, nuclear, thermal, oil, gas, and coal sectors.

In hydropower, the NDRC and NEA emphasize construction of projects in cold, high-altitude regions and complex river basins. AI is expected to enhance the accuracy of coupled meteorological and hydrological forecasts, optimize decision-making, and support station maintenance. Similarly, in the thermal power industry, AI applications are focused on enhancing fuel management and operational control, supporting equipment maintenance, and accelerating plant construction to achieve more efficient and reliable outcomes.

For nuclear power, AI implementation will focus on strengthening safety support systems. This includes early warning mechanisms, operational traceability and analysis, and automated startup and shutdown processes. AI is also envisioned as a technical advisor, aiding in plasma predictive control and advancing controlled nuclear fusion research.

The US VS China AI Race

AI innovation stands as one of the most crucial and competitive arenas for economic development and national security, and China’s intention to apply it to renewables has highlighted a new arena for competition. China has already demonstrated aggressive efforts in selling its critical green technology, including solar panels and wind turbines. Countries such as Mexico, Bangladesh, South Africa, and Nigeria have eagerly bought up Chinese surpluses of solar technology. China’s efforts to broadly integrate AI as a force multiplier for its energy technologies, with goals of widespread integration by 2027 and global leadership by 2030, pose the risk of wresting the future of energy from Washington’s hands, attracting an increasingly large customer base of energy-hungry states.

The United States remains a global leader in AI chips and model development, but widespread implementation of these technologies has lagged, especially in utilities and infrastructure, the areas China is focusing on. Renewable energy must be recognized as a critical front in the worldwide AI race, one that is not only about prestige and scientific innovation, but also a matter of national security. Currently, U.S. renewable energy capacity lags significantly behind that of its Chinese competitors. This gap is further concerning, given the Trump administration’s opposition to renewable development, which serves as a vector of Sino-American competition.

Greater integration of AI into American renewable energy could help close this gap by optimizing production schedules, refining equipment design, reducing costs, and improving overall performance. Several American renewable energy firms, such as Constellation Energy,Google, and Duke Energy, have started integration processes. While these initiatives are promising, most remain far from achieving AI maturity. High upfront costs, limited technical expertise, and fragmented investment strategies, have led to underwhelming pilot programs, discouraging further adoption.

A recent report by the Boston Consulting Group highlighted that these setbacks often stem less from the technology itself than from inadequate and poorly directed investment. Private firms will remain the driving force during this critical inflection point, functioning as key partners to integrate AI across generation methods and into the grid. The path forward lies in treating AI not as an experimental add-on or as a panacea to resolve structural issues. This requires tempering expectations. AI in energy is increasingly emerging as a force multiplier, a strategic tool that maximizes revenue, streamlines production, and achieves energy dominance.

Paradoxically, America may be best served by toning down its expectations of AI before seeing it applied more broadly. America’s AI strategy ultimately revolves around the belief that massive investments can boost productivity and reduce expenses in the form of wages, waste, and other overhead. China’s AI strategy involves around reinvestment into the energy inputs that AI rapidly consumes and a distribution across systems with less promise for revolutionary change. While America’s over-investment into AI risks over-hyping a technological panacea and creating a bubble where productivity gains don’t justify investments, China’s model risks difficulties from a lack of short to medium term monetization and a dependence on optimization and technological breakthroughs which are not guaranteed. The current leader in AI is unclear, let alone the which is the superior model, but American stakeholders should be conscious at the very least not to measure China’s success through their own metrics of success. To do so will leave American AI overconfident of victory.