At the last Democratic presidential debate, the technologist candidate Andrew Yang emphatically declared that “we’re in the process of potentially losing the AI arms race to China right now.” As evidence, he cited Beijing’s access to vast amounts of data and its substantial investment in research and development for artificial intelligence. Yang and others—most notably the National Security Commission on Artificial Intelligence, which released its interim report to Congress last month—are right about China’s current strengths in developing AI and the serious concerns this should raise in the United States. But framing advances in the field as an “arms race” is both wrong and counterproductive. Instead, while being clear-eyed about China’s aggressive pursuit of AI for military use and human rights-abusing technological surveillance, the United States and China must find their way to dialogue and cooperation on AI. A practical, nuanced mix of competition and cooperation would better serve U.S. interests than an arms race approach.
AI is one of the great collective Rorschach tests of our times. Like any topic that captures the popular imagination but is poorly understood, it soaks up the zeitgeist like a sponge.
It’s no surprise, then, that as the idea of great-power competition has reengulfed the halls of power, AI has gotten caught up in the “race” narrative. China—Americans are told—is barreling ahead on AI, so much so that the United States will soon be lagging far behind. Like the fears that surrounded Japan’s economic rise in the 1980s or the Soviet Union in the 1950s and 1960s, anxiety around technological dominance are really proxies for U.S. insecurity about its own economic, military, and political prowess.
Yet as technology, AI does not naturally lend itself to this framework and is not a strategic weapon. Despite claims that AI will change nearly everything about warfare, and notwithstanding its ultimate potential, for the foreseeable future AI will likely only incrementally improve existing platforms, unmanned systems such as drones, and battlefield awareness. Ensuring that the United States outpaces its rivals and adversaries in the military and intelligence applications of AI is important and worth the investment. But such applications are just one element of AI development and should not dominate the United States’ entire approach.
The arms race framework raises the question of what one is racing toward. Machine learning, the AI subfield of greatest recent promise, is a vast toolbox of capabilities and statistical methods—a bundle of technologies that do everything from recognizing objects in images to generating symphonies. It is far from clear what exactly would constitute “winning” in AI or even being “better” at a national level.
The National Security Commission is absolutely right that “developments in AI cannot be separated from the emerging strategic competition with China and developments in the broader geopolitical landscape.” U.S. leadership in AI is imperative. Leading, however, does not mean winning. Maintaining superiority in the field of AI is necessary but not sufficient. True global leadership requires proactively shaping the rules and norms for AI applications, ensuring that the benefits of AI are distributed worldwide—broadly and equitably—and stabilizing great-power competition that could lead to catastrophic conflict.
That requires U.S. cooperation with friends and even rivals such as China. Here, we believe that important aspects of the National Security Commission on AI’s recent report have gotten too little attention.
First, as the commission notes, official U.S. dialogue with China and Russia on the use of AI in nuclear command and control, AI’s military applications, and AI safety could enhance strategic stability, like arms control talks during the Cold War. Second, collaboration on AI applications by Chinese and American researchers, engineers, and companies, as well as bilateral dialogue on rules and standards for AI development, could help buffer the competitive elements of an increasingly tense U.S.-Chinese relationship.
Finally, there is a much higher bar to sharing core AI inputs such as data and software and building AI for shared global challenges if the United States sees AI as an arms race. Although commercial and military applications for AI are increasing, applications for societal good (addressing climate change, improving disaster response, boosting resilience, preventing the emergence of pandemics, managing armed conflict, and assisting in human development) are lagging. These would benefit from multilateral collaboration and investment, led by the United States and China.
The AI “arms race” narrative makes for great headlines, but the unbridled U.S.-Chinese competition it implies risks pushing the United States and the world down a dangerous path. Washington and Beijing should recognize the fallacy of a generalized AI arms race in which there are no winners. Instead, both should lead by leveraging the technology to spur dialogue between them and foster practical collaboration to counter the many forces driving them apart—benefiting the whole world in the process.