Enabling AI innovation is critical to global AI leadership and chance for peaceful collaboration with China. Now is the time the use US tech advantages to promote peaceful, ethical AI development

The US and China are the world’s leading AI competitors, but they are also the most important AI collaborators. According to Stanford University’s 2023 Artificial Intelligence Index Report, despite increased geopolitical competition, the number of AI research collaborations between the two countries quadrupled between 2010 and 2021, though the rate of collaboration has slowed significantly since then, and will likely continue to do so. More so every day, AI is a critical domain in which the US and China cooperate, compete, and confront one another economically, technologically, politically, and militarily. The great power AI competition focuses on hardware, data, software, and talent. Each country is pushing the technology further, developing AI champions, and finding the most relevant use cases and advantageous areas for AI adoption. The US and China are, in different ways, seeking to advance their absolute and relative positions, protect their interests, and secure leverage as they each follow distinct strategies. The US is the world’s preeminent AI power, thanks to its world-leading universities and companies. Working alongside a global network of allies and partners on everything from research to export controls, Washington is concerned with keeping its advantages and accelerating the pace of domestic AI innovation. American industry leaders warn of the potential perils if the US were to “slow down American industry in such a way that China or somebody else makes faster progress.” Meanwhile, China will continue executing its long-term, state-led policies, including initiatives like “Made in China 2025” to increase self-reliance, bolster its domestic AI industry, and increase its leverage over competitors. Today, large language models (LLMs) are the primary arena of AI innovation and competition, and countries are racing to develop more advanced and capable LLMs. In general, LLM performance improves with scale – more parameters, more and better training data, more training runs and more computation. While GPT2 boasted some 1.5 billion parameters, subsequent models have grown by orders of magnitude, with GPT4 reportedly incorporating 1 trillion such variables. The massive open data ecosystem of the internet has played a fundamental role in the development of these models. The amount of compute dedicated to training these models has also increased sharply, driven by advances in silicon innovation. Finally, breakthroughs in software and model architecture design drive and accelerate improvements. While China had matched or exceeded the US’s historic lead in many fields of traditional AI research, the overwhelming majority of innovations in generative AI today are coming from the US, and China faces an uphill battle in training LLMs, for now. As with past technological revolutions, the nature of different governance systems can enhance or impede progress. Open societies like the US worry about AI risks, including the accuracy of LLM outputs and hallucinations, a phenomenon wherein an LLM provides inaccurate information based on the perception of patterns that do not exist or are otherwise faulty. But they do not let those concerns halt progress. Meanwhile, closed societies, including China, have different worries, particularly about their ability to control domestic speech and content that may not be state-approved, and their capacity to advance what many analysts describe as Beijing’s “discourse power,” a way to shape global narratives. AI systems are unpredictable, and their black-box structure features inputs that are often invisible and outputs that cannot be determined by government officials and censors. These are particular concerns for closed societies, and have led the Chinese state to impose restrictions that may limit innovation, hold back incumbents, and deter new entrants and entrepreneurs from engaging with this technology. For example, Beijing proposed new rules in April 2023 for developers and deployers of generative AI, including a requirement that “content generated using generative AI shall embody the Core Socialist Values and must not incite subversion of national sovereignty or the overturn of the socialist system.” The Party’s concern was therefore not about inaccurate information or hallucinations, but about potentially truthful outputs that may not conform to a Beijing-endorsed position. While the Chinese government has since issued somewhat less restrictive regulations, pushes for political control over technological developments highlight self-imposed challenges to AI development. As a result, Chinese LLMs are often trained on smaller, more restricted data sets than their Western counterparts. Censorship regimes may also compromise or bias access to and the production of data. And they pose even greater engineering challenges regarding moderation of non-deterministic outputs vs. democratic counterparts. In addition to models, in recent years, the AI competition between the US and China has focused on hardware, as the clearest path to greater compute is via access to high-performance graphics processing units (GPUs). While China has a robust domestic semiconductor industry, China’s vulnerability in the “chip wars,” the competition over the microchip technologies that power AI, was exposed on October 7, 2022, by a set of US-led export controls on high-end semiconductors and related technologies, which the US coordinated with the Netherlands and Japan. These export controls follow a strategy described as “small yard, high fence,” wherein a limited number of national-security sensitive technologies are protected through strict measures. While these US-led export controls have marked a clear turning point in US-China technology competition, there have been challenges to enforcement, including due to the need to coordinate with international partners over complex, global supply chains. To tighten enforcement, on October 17, 2023, the US released updated export controls on advanced semiconductor technology, specifically targeting chips and equipment essential for artificial intelligence development, including restrictions on high-performance chips and advanced lithography tools. Beijing has responded to the US-led controls with its own export controls, including on critical minerals such as germanium, gallium, and more recently graphite, areas where China has a historic supply-chain advantage. More significantly, Beijing increased its focus on domestic technological development and self-reliance. As the US-China Economic and Security Review Commission reported, Chinese companies “surged their orders for foreign semiconductor manufacturing technology in 2023, capitalizing on the roughly eight-month lag between when the Dutch government announced its intent to place controls on exports to China and its implementation.” Despite US-led export controls, China’s progress in hardware has continued, if at a slower pace. In September, the Chinese state-owned Semiconductor Manufacturing International Company (SMIC) and a Huawei subsidiary named HiSilicon demonstrated the capability to produce hardware comparable to Nvidia’s A100 chip, with the fabrication of a 7-nm chip using foreign-made equipment already present in-country. This chip is shipping in Huawei’s latest smartphone, the Mate 60 Pro, which scholar Chris Miller has described as “the most ‘Chinese’ advanced smartphone ever made” because “the phone’s primary 7-nm processor [and] many of the phone’s auxiliary chips are homegrown, including the Bluetooth, WiFi and power management chips.” The Mate 60 Pro was a significant breakthrough that showed the shortfalls of existing export controls and the capabilities of China’s semiconductor sector. While China remains two generations behind the world’s most cutting-edge chips, its technology ecosystem is a world leader in many fields, partially due to its robust domestic industries, strengths in data, and significant domestic talent pipeline. However, serious questions remain about the depth of China’s technological ecosystem, its ability to achieve comparable results without further international imports, and its capacity to scale production at acceptable costs, given the challenges that export control impose. The chip wars have significance not only for geopolitics, but also for global markets. They have reshaped the movement of goods and intellectual capital, and major chipmakers like Nvidia have attempted to operate within the new paradigm by releasing versions of their products targeted for Chinese markets that are as advanced as possible, given the restrictions. The results have been clear: US semiconductor exports to China dropped by 51%, from $6.4 billion to $3.1 billion, in the first eight months of 2023 compared to the same period in 2022. Meanwhile, the US is working to friendshore and onshore semiconductor supply chains. The CHIPS and Science Act introduced $39 billion in incentives for domestic chip production, and this funding includes national-security conditions, such as restricting recipients of federal dollars from involvement in the “material expansion” of “semiconductor manufacturing capacity” in a “foreign country of concern” (e.g., China, Russia, Iran, or North Korea). Such programs aim to give the US a relative edge and, as White House National Security Advisor Jake Sullivan stated, “as large of a lead as possible” in critical technologies. But leadership in AI does not come primarily from state-led initiatives. For countries to lead in AI, they need national strategies that foster and direct innovation, as well as world-leading AI companies and research institutions. The generative AI ecosystem will empower incumbent enterprises and also likely define the next generation of Big Tech companies. Private AI investment globally is considerable and growing, and is forecast to increase to more than $160 billion by 2025, according to Goldman Sachs Research. However, sustained growth and innovation requires a system that promotes property rights and entrepreneurship, and that provides predictable rules of the road to startups and mega-cap technology companies alike. China’s crackdown on technology companies, dating back to at least 2020, and dramatic shifts like the cancellation of a leading Chinese technology company’s IPO, increases uncertainty in the non-state sector, decreasing the dynamism of China’s technology ecosystem. The US is the preeminent AI power today. But the US-China technology competition is far from settled. We will see evolving strategies of techno-economic statecraft from both sides. In the past, China has shown a remarkable ability to overcome economic, geopolitical, and structural challenges to its position, and Beijing will continue to strive do so, as the Mate 60 Pro demonstrated. There are also potential paths of chip research and development, including advanced packaging, that provide new trajectories for improvement. For AI models, it is possible that entirely novel architectures could emerge that are not as dependent on the chips the West dominates today. Even if China remains the world’s number-two AI power, China will remain a formidable competitor to the US and to the US-led technology ecosystem. For the foreseeable future, we expect geopolitics to increasingly driving economic decision-making in Washington and Beijing, and in capitals around the world. The US, China, and other countries are using new and old tools to advance political objectives through economic means. The table below outlines a few of the most prominent techno-economic tools shaping technology competition.