More money is now being invested in artificial intelligence than it took to land on the moon. Spending on the technology this year is projected to reach up to $700 billion, nearly double last year’s outlay, fueled by a conviction among investors and policymakers in the United States that it must “beat China” in the field.
For years, headlines have framed AI development as a zero-sum rivalry between the U.S. And China, casting the technology’s advance as an arms race with a defined finish line. But a closer examination reveals the two countries aren’t racing toward the same goal. “The U.S. And China are running in very different lanes,” says Selina Xu, who leads China and AI policy research for Eric Schmidt, the tech investor and former Google chief executive, in Fresh York City.
While the U.S. Is focused on scaling its frontier AI models in pursuit of artificial general intelligence (AGI), China is prioritizing boosting economic productivity and real-world impact, Xu explains. This divergence in approach challenges the prevailing narrative of a singular “AI race” and suggests that lumping the two countries onto a single scoreboard is not only inaccurate but potentially harmful.
“An arms race can grow a self-fulfilling prophecy,” Xu warns. “If companies and governments all embrace a ‘race to the bottom’ mentality, they will eschew necessary security and safety guardrails for the sake of being ahead. That increases the odds of AI-related crises.”
The framing of AI competition as an arms race echoes Cold War-era strategic competition, with prominent figures like Stephen Hawking and Elon Musk previously warning of the inseparable link between AI’s potential and its military and economic implications. But, Karson Elmgren, a China researcher at the Institute for AI Policy and Strategy, notes that the arms race framing benefits frontier labs, investors, and media, who favor easily comparable metrics like model size and computing power.
The implied “finish line” in this arms race is often artificial general intelligence – a machine capable of surpassing human cognitive abilities. But the very nature of AGI raises concerns about control. “If superintelligence were to emerge in a particular country, there’s no guarantee that that country’s interests are going to win,” says Graham Webster, a China researcher at Stanford University.
the AGI finish line assumes both the U.S. And China are prioritizing its development. China’s economic realities suggest otherwise. After decades of rapid growth, the country is facing an economic slowdown, prompting leaders to seek a new engine for sustained growth. “China has been suffering through an economic slowdown for a mixture of reasons, from real estate to credit to consumption and youth unemployment,” Xu says.
Rather than investing heavily in speculative frontier models, Beijing is focused on leveraging AI to improve existing industries – healthcare, energy, agriculture – and boost overall productivity. “In China we define AI as an enabler to improve existing industry,” says Liang Zheng, an AI policy researcher at Tsinghua University in Beijing. “The first priority is to use it to benefit ordinary people.”
This approach is reflected in China’s AI Plus initiative, which encourages the integration of AI into manufacturing, logistics, energy, finance, and public services. As of 2024, China had around five times more factory robots in use than the U.S., with automakers embracing “dark factories” with minimal human intervention. Computer vision systems are now used for quality control and predictive maintenance, while agricultural models advise farmers on optimal crop selection and pest control.
In healthcare, AI tools are being piloted to triage patients, interpret medical images, and assist diagnoses, including an AI “Agent Hospital” at Tsinghua University where physicians operate alongside virtual clinical assistants. These applications often rely on “narrow AI” designed for specific tasks.
While AI is also increasingly embedded across industries in the U.S., the focus tends toward service-oriented and data-driven applications, leveraging large language models (LLMs) to automate communication and handle unstructured data. Banks are using LLM-based assistants to aid users manage accounts, and healthcare professionals are using them to extract information from medical records.
“LLMs as a technology naturally fit the U.S. Service-sector-based economy more so than the Chinese manufacturing economy,” Elmgren says.
Competition between the U.S. And China is particularly acute in areas like semiconductor supply chains, with both countries seeking to secure independent capabilities in design, manufacturing, and packaging. Recent tariff and export control disputes underscore this struggle. Military applications of AI are also a significant arena of competition, with both governments aiming to improve decision-making, intelligence gathering, and weapon system autonomy. The U.S. Department of Defense launched its AI Acceleration Strategy last month, and China has integrated AI into its military modernization strategy.
Despite China’s focus on military and industrial applications, it has not designated a single “national champion” in AI. “After Deepseek in early 2025 the government could have easily said, ‘You guys are the winners, I’ll give you all the money, please build AGI,’ but they didn’t,” says Kristy Loke, a fellow at MATS Research who focuses on China’s AI innovation and governance strategies. “They see being ‘close enough’ to the technological frontier as important, but putting all eggs in the AGI basket as a gamble.”
American companies continue to work with Chinese technology and workers, despite a gradual decoupling of the two economies. Xu argues that increased cooperation – and less emphasis on cutthroat competition – could yield better results for all. “For building more secure, trustworthy AI, you need both U.S. And Chinese labs and policymakers to talk to each other, to reach consensus on what’s off limits, then compete within those boundaries,” she says. “The arms race narrative also just misses the actual on-the-ground reality of companies co-opting each other’s approaches, the amount of research that gets exchanged in academic communities, the supply chains and talent that permeates across borders, and just how intertwined the two ecosystems are.”