To The Moon

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In 2017, Beijing unveiled an ambitious roadmap to dominate artificial intelligence development, aiming to secure global leadership by 2030. By 2020, the plan called for “iconic advances” in AI to demonstrate its progress. Then in late 2022, OpenAI’s release of ChatGPT took the world by surprise—and caught China flat-footed.

At the time, leading Chinese technology companies were still reeling from an 18-month government crackdown that shaved around $1 trillion off China's tech sector. It was almost a year before a handful of Chinese AI chatbots received government approval for public release. Some questioned whether China’s stance on censorship might hobble the country’s AI ambitions. Meanwhile, the Biden administration’s export controls, unveiled just a month before ChatGPT’s debut, aimed to cut China off from the advanced semiconductors essential for training large-scale AI models. Without cutting-edge chips, Beijing’s goal of AI supremacy by 2030 appeared increasingly out of reach.

But fast forward to today, and a flurry of impressive Chinese releases suggests the U.S.’s AI lead has shrunk. In November, Alibaba and Chinese AI developer DeepSeek released reasoning models that, by some measures, rival OpenAI’s o1-preview. The same month, Chinese videogame juggernaut Tencent unveiled Hunyuan-Large, an open-source model that the company’s testing found outperformed top open-source models developed in the U.S. across several benchmarks. Then, in the final days of 2024, DeepSeek released DeepSeek-v3, which now ranks highest among open-source AI on a popular online leaderboard and holds its own against top performing closed systems from OpenAI and Anthropic.

Read more: How the Benefits—and Harms—of AI Grew in 2024

Before DeepSeek-v3 was released, the trend had already caught the attention of Eric Schmidt, Google’s former CEO and one of the most influential voices on U.S. AI policy. In May 2024, Schmidt had confidently asserted that the U.S. maintained a two-to-three year lead in AI, “which is an eternity in my books.” Yet by November, in a talk at the Harvard Kennedy School, Schmidt had changed his tune. He cited the advances from Alibaba, and Tencent as evidence that China was closing the gap. “This is shocking to me,” he said. “I thought the restrictions we placed on chips would keep them back.”

Beyond a source of national prestige, who leads on AI will likely have ramifications for the global balance of power. If AI agents can automate large parts of the workforce, they may provide a boost to nations’ economies. And future systems, capable of directing weapons or hacking adversaries, could provide a decisive military advantage. As nations caught between the two superpowers are forced to choose between Chinese or American AI systems, artificial intelligence could emerge as a powerful tool for global influence. China’s rapid advances raise questions about whether U.S. export controls on semiconductors will be enough to maintain America's edge.

Building more powerful AI depends on three essential ingredients: data, innovative algorithms, and raw computing power, or compute. Training data for large language models like GPT-4o is typically scrapped from the internet, meaning it’s available for developers across the world. Similarly, algorithms, or new ideas for how to improve AI systems, move across borders with ease, as new techniques are often shared in academic papers. Even if they weren’t, China has a wealth of AI talent, producing more top AI researchers than the U.S. By contrast, advanced chips are incredibly hard to make, and unlike algorithms or data, they are a physical good that can be stopped at the border.

The supply chain for advanced semiconductors is dominated by America and its allies. U.S. companies Nvidia and AMD have an effective duopoly on datacenter-GPUs used for AI. Their designs are so intricate—with transistors measured in single-digit nanometers—that currently, only the Taiwanese company TSMC manufactures these top-of-the-line chips. To do so, TSMC relies on multi-million dollar machines that only Dutch company ASML can build. 

The U.S. has sought to leverage this to its advantage. In 2022, the Biden administration introduced export controls, laws that prevent the sale of cutting-edge chips to China. The move followed a series of measures that began under Trump’s first administration, which sought to curb China’s access to chip-making technologies. These efforts have not only restricted the flow of advanced chips into China, but hampered the country’s domestic chip industry. China’s chips lag “years behind,” U.S. Secretary of Commerce Gina Raimondo told 60 minutes in April.

UPDATED: JANUARY 28, 2025 7:02 AM EST | ORIGINALLY PUBLISHED: JANUARY 8, 2025 12:46 PM EST

In 2017, Beijing unveiled an ambitious roadmap to dominate artificial intelligence development, aiming to secure global leadership by 2030. By 2020, the plan called for “iconic advances” in AI to demonstrate its progress. Then in late 2022, OpenAI’s release of ChatGPT took the world by surprise—and caught China flat-footed.

At the time, leading Chinese technology companies were still reeling from an 18-month government crackdown that shaved around $1 trillion off China's tech sector. It was almost a year before a handful of Chinese AI chatbots received government approval for public release. Some questioned whether China’s stance on censorship might hobble the country’s AI ambitions. Meanwhile, the Biden administration’s export controls, unveiled just a month before ChatGPT’s debut, aimed to cut China off from the advanced semiconductors essential for training large-scale AI models. Without cutting-edge chips, Beijing’s goal of AI supremacy by 2030 appeared increasingly out of reach.

But fast forward to today, and a flurry of impressive Chinese releases suggests the U.S.’s AI lead has shrunk. In November, Alibaba and Chinese AI developer DeepSeek released reasoning models that, by some measures, rival OpenAI’s o1-preview. The same month, Chinese videogame juggernaut Tencent unveiled Hunyuan-Large, an open-source model that the company’s testing found outperformed top open-source models developed in the U.S. across several benchmarks. Then, in the final days of 2024, DeepSeek released DeepSeek-v3, which now ranks highest among open-source AI on a popular online leaderboard and holds its own against top performing closed systems from OpenAI and Anthropic.

Read more: How the Benefits—and Harms—of AI Grew in 2024

Before DeepSeek-v3 was released, the trend had already caught the attention of Eric Schmidt, Google’s former CEO and one of the most influential voices on U.S. AI policy. In May 2024, Schmidt had confidently asserted that the U.S. maintained a two-to-three year lead in AI, “which is an eternity in my books.” Yet by November, in a talk at the Harvard Kennedy School, Schmidt had changed his tune. He cited the advances from Alibaba, and Tencent as evidence that China was closing the gap. “This is shocking to me,” he said. “I thought the restrictions we placed on chips would keep them back.” 

Beyond a source of national prestige, who leads on AI will likely have ramifications for the global balance of power. If AI agents can automate large parts of the workforce, they may provide a boost to nations’ economies. And future systems, capable of directing weapons or hacking adversaries, could provide a decisive military advantage. As nations caught between the two superpowers are forced to choose between Chinese or American AI systems, artificial intelligence could emerge as a powerful tool for global influence. China’s rapid advances raise questions about whether U.S. export controls on semiconductors will be enough to maintain America's edge.

Read more: How Israel Uses AI in Gaza—And What It Might Mean for the Future of Warfare

Building more powerful AI depends on three essential ingredients: data, innovative algorithms, and raw computing power, or compute. Training data for large language models like GPT-4o is typically scrapped from the internet, meaning it’s available for developers across the world. Similarly, algorithms, or new ideas for how to improve AI systems, move across borders with ease, as new techniques are often shared in academic papers. Even if they weren’t, China has a wealth of AI talent, producing more top AI researchers than the U.S. By contrast, advanced chips are incredibly hard to make, and unlike algorithms or data, they are a physical good that can be stopped at the border.

The supply chain for advanced semiconductors is dominated by America and its allies. U.S. companies Nvidia and AMD have an effective duopoly on datacenter-GPUs used for AI. Their designs are so intricate—with transistors measured in single-digit nanometers—that currently, only the Taiwanese company TSMC manufactures these top-of-the-line chips. To do so, TSMC relies on multi-million dollar machines that only Dutch company ASML can build. 

The U.S. has sought to leverage this to its advantage. In 2022, the Biden administration introduced export controls, laws that prevent the sale of cutting-edge chips to China. The move followed a series of measures that began under Trump’s first administration, which sought to curb China’s access to chip-making technologies. These efforts have not only restricted the flow of advanced chips into China, but hampered the country’s domestic chip industry. China’s chips lag “years behind,” U.S. Secretary of Commerce Gina Raimondo told 60 minutes in April.

Read more: Research Finds Stark Global Divide in Ownership of Powerful AI Chips

Yet, the 2022 export controls encountered their first hurdle before being announced, as developers in China reportedly stockpiled soon-to-be restricted chips. DeepSeek, the Chinese developer behind an AI reasoning model called R1, which rivals OpenAI’s O1-preview, assembled a cluster of 10,000 soon-to-be-banned Nvidia A100 GPUs a year before export controls were introduced.

Smuggling might also have undermined the export control’s effectiveness. In October, Reuters reported that restricted TSMC chips were found on a product made by Chinese company Huawei. Chinese companies have also reportedly acquired restricted chips using shell companies outside China. Others have skirted export controls by renting GPU access from offshore cloud providers. In December, The Wall Street Journal reported that the U.S. is preparing new measures that would limit China’s ability to access chips through other countries. 

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