HONG KONG, Aug 23, 2026: Alibaba's proposed investment comes days after Trump declared America “way ahead” of China — but the competition is increasingly about chips, electricity, critical minerals and the industrial capacity to power AI.
Alibaba says 100 percent of the net proceeds will be used to strengthen its full-stack AI capabilities, including AI infrastructure. Earlier, Trump made his claim at a White House gathering of technology and financial leaders on Aug. 19.
Trump says the U.S. is “way ahead” of China in the artificial intelligence race, while highlighting America’s technological leadership in AI, crypto, prediction markets and other emerging industries.#WashingtonEye pic.twitter.com/65MHlHZKHi
— Washington Eye (@washington_EY) August 20, 2026
He said the United States was “way ahead of number two” in AI, identified China as number two, and added that he did not want to underestimate China. Trump also said AI would probably be discussed when Chinese President Xi Jinping visits Washington on Sept. 24.
How Close Is the AI Race?
The available evidence points to a much closer contest than Trump's statement suggests. Stanford University's 2026 AI Index says the performance gap between leading US and Chinese AI models has effectively closed. The report says US and Chinese models have exchanged the lead several times since early 2025, with the leading US model ahead by only 2.7 percent in March 2026.
The United States does, however, have a major financial advantage in private AI investment. Stanford says US private AI investment reached $285.9 billion in 2025, compared with $12.4 billion in China. But the researchers caution that private investment figures probably understate China's total AI spending because Beijing also directs money through government guidance funds.
China's AI effort is also becoming a major industrial sector. China's Ministry of Industry and Information Technology says the country's core AI industry exceeded 1.2 trillion yuan, about $177 billion, in 2025, up 40 percent from the previous year. The ministry said China had more than 6,600 AI companies by June 2026.
Chinese officials say the country's AI development is moving beyond laboratories and into the wider economy. The Ministry of Industry and Information Technology has reported that AI is being used across manufacturing and other traditional industries, while China's government is continuing its “AI Plus” programme to expand the technology across the economy and society.
Alibaba's fund-raising therefore comes at a time when China is building a broad AI ecosystem rather than relying on a few individual companies. The company's own announcement says the new money will be used to extend its global AI leadership by expanding and enhancing its full-stack AI capabilities.
🇨🇳Alibaba Group today announced that it proposes to place newly issued ordinary shares of the Company (the “Placement Shares”) to non-U.S. persons outside the United States with an aggregate placing consideration of HK$80 billion, subject to market and other conditions.
— CN Wire (@Sino_Market) August 23, 2026
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The Power Behind AI
There is, however, a part of the AI race that receives much less public attention: electricity. The International Energy Agency says data centres are becoming a major source of new electricity demand because AI systems require increasingly powerful computing equipment.
The International Energy Agency estimates that global electricity consumption by data centres will roughly double to about 945 terawatt-hours by 2030. It expects data-centre electricity use to grow by around 15 percent a year from 2024 to 2030, more than four times faster than electricity demand from all other sectors combined.
The International Energy Agency says the reason is straightforward: AI models require large numbers of powerful computer chips working together in data centres. Those machines consume electricity while processing information, whereas additional power is needed for cooling and other supporting systems. The IEA identifies the rapid expansion of AI as a major driver of future data-centre electricity demand.
The United States already has the world's largest number of data centres. Stanford's 2026 AI Index says the country has 5,427 data centres, more than ten times the number in any other country, while also consuming more data-centre energy than any other country.
China is approaching the energy challenge through national computing and infrastructure planning. Chinese policy has promoted the “Eastern Data, Western Computing” strategy, which seeks to place computing resources in regions with suitable energy and other infrastructure while improving the efficiency of the country's computing network.
Brookings researchers say energy supply is becoming an important part of the US-China AI competition. They note that China produces more than twice as much electricity as the United States and has expanded its power generation rapidly, giving energy availability a growing strategic role in the technology contest.
The Materials Behind the Machines
Electricity alone cannot build an AI system. Data centres, power networks, cooling equipment and semiconductor facilities also require large amounts of industrial materials. Copper is especially important because of its extensive use in power networks and electrical equipment. The International Energy Agency identifies copper as a critical material for the expansion of electricity infrastructure.
The IEA's latest critical-minerals assessment says China has accounted for more than 90 percent of the growth in global copper-smelting capacity since 2005, raising China's share of global smelting capacity from about 15 percent to 50 percent by 2025. This is important because AI expansion will require more electricity networks as well as more computing infrastructure.
Copper is only one part of the picture. Lithium, nickel, cobalt, graphite and rare earth elements are also important to modern energy and technology systems. The IEA says the average share of the three largest refining countries for these key minerals rose to 86 percent in 2024, up from about 82 percent in 2020.
China has a particularly strong position in the processing of several of these minerals. The IEA says China is the leading refiner for many critical minerals and warns that high concentration in processing creates risks for countries that depend on international supplies.
This makes critical minerals part of the wider AI competition. The IEA's 2026 assessment warns that supply concentration, export restrictions and weaker investment are creating new risks for critical-mineral security. Those risks extend beyond energy technologies to other industries that depend on secure supplies of processed minerals.
From Technology to Supply Chains
The United States is already responding to the mineral challenge. The White House has moved to address imports of processed critical minerals, while US officials have argued that heavy dependence on foreign processing creates economic and security risks.
The result is that the US-China AI competition is no longer only a contest between technology companies. It increasingly involves investment, semiconductor capacity, electricity generation, power grids, data centres, mining, mineral processing and international supply chains. The IEA and Stanford's AI research both point to infrastructure and resources as important parts of the rapidly expanding AI economy.
The two countries also have different strengths. Stanford says the United States leads by a wide margin in private AI investment and produces more top-tier models, while China leads in AI publication volume, citations and patent grants. Stanford also says the US-China model-performance gap has become very small.
Who Can Build the Complete AI System?
These facts suggest that the future AI contest may not be decided simply by which country produces the best individual model. Stanford's research shows that model performance is already a close contest, while the IEA's work shows that electricity and critical-mineral supply chains can become major constraints on technological expansion.
The larger question is therefore whether a country can build and maintain the complete AI system: advanced chips, computing centres, reliable electricity, cooling systems, skilled workers, capital, manufacturing capacity and secure supplies of critical minerals. This is an analytical conclusion drawn from the investment, infrastructure and supply-chain evidence cited by Stanford and the IEA.
Trump says America is already “way ahead.” China is responding with state policy, a rapidly growing AI industry and major corporate investment. Alibaba's proposed US$10.2 billion share placement is the latest and perhaps most visible sign that Chinese technology companies are preparing to invest heavily in the next stage of the competition.
The AI race may therefore be fought not only in laboratories and on computer screens. It may also be fought in power plants, mines, refineries, semiconductor factories and financial markets. The competition between Washington and Beijing is becoming a contest not just over artificial intelligence itself, but over the physical resources and industrial capacity needed to power the technology of the next decade.
