By Greg Allen
I currently serve as the Senior Advisor for the Wadhwani AI Center at CSIS, where I conduct policy research at the intersection of technology, economics, and national security. Prior to CSIS, I spent three years working at the U.S. Department of Defense Joint Artificial Intelligence Center, where I served as the director for strategy and policy, and participated directly in government-to-government conversations with China and the People’s Liberation Army related to AI. My primary professional background is corporate strategy roles in technology-driven industries, including artificial intelligence, semiconductors, robotics, and space systems. Over the past ten years, I have focused intensely on the geopolitical and national security implications of Artificial Intelligence technology with a special emphasis on U.S.-China competition. My remarks today reflect my conclusions from the research I have conducted while at CSIS as well as my prior professional experiences.
To begin, both the current administration of President Donald Trump and the prior administration of President Biden have likened the current race in AI between the United States and China to the Cold War-era space race between the United States and the Soviet Union. This is a helpful analogy in terms of capturing the strategic importance of this competition. However, the current AI race is bigger in terms of its absolute scale and different in its competitive dynamics. Between 1960 and 1973, the United States government spent $28 billion on the Apollo program, which is the equivalent of $326 billion in inflation-adjusted dollars. In 2026, just five U.S. companies—Meta, Alphabet, Microsoft, Amazon and Oracle—are expected to spend more than $450 billion in aggregate AI-specific capital expenditures. Other U.S. companies, such as OpenAI, Anthropic, and xAI, will add hundreds of billions of additional investment to that total. This reflects the fact that far more of the activity of the AI race is taking place in the commercial private sector than was the case with the space race. Overall, U.S. companies will invest far more in AI in a single year than the entire U.S. government spent over the entire 13-year life of the Apollo program. AI investment is not only bigger than Apollo in inflation adjusted dollars, but it is also bigger as a share of U.S. gross domestic product. The investment firm Goldman Sachs estimates that total AI-related investment in the United States already represents 1 percent of U.S. GDP, whereas during its peak years the Apollo program comprised only 0.4% of GDP.
The United States government is not directing U.S. companies to invest such astonishing sums of money, so why are they doing it? The clearest answer comes from Google DeepMind CEO Demis Hassabis, who claimed earlier this year that leading AI systems “will exhibit all the cognitive capabilities humans have, maybe in the next five to 10 years” and that the AI revolution is “going to be 10 times bigger than the Industrial Revolution, and maybe 10 times faster.” Leadership in a revolution that transformative provides a free market incentive sufficiently large to justify such investments, and the current demand for AI services is already enormous. Google reports that even the eight-year old versions of its AI chips are 100 percent utilized.
The most obvious product of these investments is the computing hardware infrastructure—data centers full of advanced AI chips—that powers AI software. The largest single AI datacenter cluster as of March 2025 included more than 200,000 AI chips and is planned to grow to more than one million AI chips in the near future. The current market price for the leading AI chips is in the range of $25,000-$60,000, implying chip costs alone in the tens of billions of dollars for a leading AI facility.
Experts in China have also paid close attention to the strategic importance of AI chips. A 2018 report by Tsinghua University in Beijing put it in stark terms: “Whether it is the realization of algorithms, the acquisition and a massive database, or the computing capability, the secret behind the rapid development of the AI industry lies in the one and only physical basis, that is, the chips. Therefore, it is no exaggeration to say, ‘No chip, no AI’ given the irreplaceable role of AI chip as the cornerstone for AI development and its strategic significance.”
Thus, the most strategically significant move that the U.S. government has taken in the AI race with China to date is working to deny China access to the most advanced AI chips and also to deny China the means to make its own chips. Key aspects of this strategy began during the first Trump administration and then were greatly expanded during the Biden administration. While the implementation of this strategy has been flawed, it has nevertheless contributed significantly to continued overall U.S. leadership in AI.
If the Biden administration had not blocked exports of advanced AI chips, it is possible, perhaps even likely, that the first million chip AI cluster would be built in China, rather than the United States. China has many advantages in the AI race and the disparity in computing resources stands out as almost certainly the largest single advantage that the United States enjoys over China. Two senior executives in key Chinese AI firms have explicitly stated that lack of access to advanced AI chips is the most significant challenge that they face. In May 2025, Wang Qi, still vice-president at Tencent’s cloud computing unit, said that “The most severe problem is the [limited] resources of [graphics] cards and computing resources,” and that tighter U.S. export controls would, “widen the gap [regarding AI adoption] between China and the US in the short term.” Similarly, in July 2024, DeepSeek CEO Liang Wenfeng said, “We do not have financing plans in the short term. Money has never been the problem for us; bans on shipments of advanced chips are the problem.” Liang has also said in an interview with a Chinese media outlet that U.S. restrictions on AI chips mean that Chinese companies must use two to four times the computing power to achieve the same results, referring to the penalty of using Nvidia H800 chips instead of H100 chips for large model training.
Similarly, if the first Trump administration had not blocked Chinese chipmakers from acquiring advanced semiconductor manufacturing equipment, such as Extreme Ultraviolet Lithography (EUV) tools it is possible that Chinese chip-making firms would be at or near parity with the global leaders. Without access to such equipment, China’s most advanced producer of logic chips, SMIC, has been stuck at the 7 nanometer (nm) technology node for years and may continue to be stuck there for more years to come. According to a report, SMIC had placed an order for an EUV lithography tool in April 2018 prior to being blocked.
The first Trump administration also blocked leading Chinese chip designers, such as Huawei’s HiSilicon subsidiary, from accessing U.S. semiconductor manufacturing equipment via contract manufacturers such as Taiwan’s TSMC. In August 2018, Huawei was the second company to launch a 7 nm smartphone application processor, the Kirin 980, trailing Apple’s A12 by mere weeks. Huawei launched an HiSilicon-designed, TSMC-manufactured 7 nm AI accelerator chip, the first Ascend 910, in mid-2019. Huawei deployed it with commercial partners later that year. Nvidia did not begin selling its first 7 nm AI accelerator, the A100, until 2020. At the time, Huawei was preparing to capitalize on the so-called 3-5-2 policy, in which the CCP’s Central Office ordered all government agencies and many state-owned enterprises to eliminate the use of all non-Chinese technology within three years. However, Huawei was unable to fulfill demand because of the first Trump administration’s updated 2020 entity listing, which temporarily cut Huawei’s Ascend and Kirin lines off from TSMC. Apple, TSMC, and Nvidia were all major beneficiaries of these early U.S. export controls, which delivered a setback to Chinese efforts to eliminate dependence on U.S. AI and semiconductor technology.
It is worth stressing that the competitive dynamics in the Chinese market are not the same as those outside of it. The competitive advantages that have resulted in Nvidia leading in the AI chip market around the world are not enough to guarantee a leading position in the Chinese market. This is because Chinese firms benefit from dramatic government support (including forced technology transfer, industrial espionage, and intellectual property violations) and because the Chinese domestic market is in and of itself large enough to reach globally-relevant economies of scale, which was not the case for the export-dependent growth strategies of South Korea or Taiwan.
https://www.csis.org/analysis/countering-chinas-challenge-american-ai-leadership