“AI arms race” rhetoric has zero solvency -- experts concludes US-Sino interpendence is KEY to development – triggers all aff impacts

Interpreting U.S.-China AI development as an “arms race” or a winner-takes-all competition fundamentally misunderstands the transnational nature of AI development and technological interdependence. Policy prescriptions drawn from this “race” concept will thus be ill-fit to the goals they attempt to serve. This bad fit could result in, among other undesirable outcomes, damage to AI development, missed opportunities for AI development, and mishandling of real AI risks.

Yoshua Bengio, an early pioneer of modern AI techniques, has publicly disapproved of framing AI development as a race. “We could collectively participate in a race,” he told MIT Technology Review, “but as a scientist and somebody who wants to think about the common good, I think we’re better off thinking about how to build smarter machines and make sure AI is used for the well-being of as many people as possible.”7 Possible idealism aside, his point strikes at a fundamental flaw with this arms race analogy: AI is developed by a vast community of scientists, developers, and researchers who are not isolated within their respective countries. As such, the development of AI is not siloed within Chinese or American borders, and the benefits of artificial intelligence are not exclusive to either of those nations. Public and private entities in both countries can benefit from developments of artificial intelligence in areas that have wide reach between nations and positive impacts on economic growth and public well-being (e.g., skin cancer detection). In other words, the development of artificial intelligence is not winner-takes-all. And contrary to what some commentators seem to believe (or at least constantly and singularly discuss), not every significant application of AI will be a weapon.

Graham Webster eloquently provides context on this fact through his analysis of winner-takes-all U.S.-China rhetoric. “Unlike the US and USSR, in which science and technology developed on largely independent tracks, the US and China are part of a globally intertwined ecosystem,” he explains. As a result, “companies and innovators in both countries would suffer if international research, development, and manufacturing were to shut down,” and “even if the US and China cut off trade with each other, both countries would still have to worry about security risks from components, since risks along the supply chain exist everywhere.”8 There is far more interconnection and interdependence than may otherwise be apparent in an “arms race” framing.

American firms rely heavily on Chinese manufacturing technologies (e.g., in Shenzhen),9 and that reliance is likely to grow as artificial intelligence applications made in the United States are increasingly deployed in drones, robots, and the like which may depend upon Chinese-made hardware. Trade and supply-chain links aid both countries in further developing artificial intelligence.10 China is a major market for U.S. AI hardware, and researchers from around the world—including between China and the United States—might work on similar AI problems, share data used to develop AI systems, or coauthor research papers.11 All of these factors further bolster the interconnections between the two countries’ AI sectors.

As is perhaps expected, money, too, has an impact. In 2013, Chinese investment in Silicon Valley was at $1.17 billion. By 2015, it was $11.52 billion.12 From 2012 to 2016, American firms had invested $2.6 billion in the other direction.13 While Chinese venture capitalists have invested heavily in U.S. tech companies like Uber and Airbnb,14 Tencent and Alibaba—both tech giants in China—are themselves multinational public corporations with significant ownership by international stakeholders.15 Worth noting is that both companies have extensive ties to the Communist Party of China (CCP) and the Chinese government, and are currently subject to Chinese laws mandating information-sharing with the government (e.g., via the 2017 Cybersecurity Law).16 But even this considered, there is notable economic interconnection and interdependence between AI development in the two states.

Interconnections and interdependencies between AI development in the United States and AI development in China don’t end there. China’s Tsinghua University opened an Institute for Artificial Intelligence in June 2018, for which Google’s AI Chief Jeff Dean is an advisor.17 Alibaba, another Chinese tech giant, has multiple research labs located in the United States as part of its global AI research efforts.18 China’s largest retailer has a research partnership with Stanford University’s Artificial Intelligence Lab to fund such research areas as computer vision, robotics, machine learning, forecasting, and natural language processing.19 Kai-Fu Lee, former head of Google’s operations in China, runs an AI training institution in the country that leverages the expertise of Chinese government personnel and some of North America’s “leading” computer scientists.20 And Baidu, the Chinese search company, belongs to the U.S.-based Partnership for AI, which aims to develop best practices for AI technology.21 It is worth noting potential risks associated with these kinds of relationships, including the Chinese government’s intentions to bring Western technology talent from the United States into its own country22 and previous instances of intellectual property theft. But that still doesn’t change the existence of collaborative relationships and the value garnered from at least some of them. The United States and China have many interconnections and interdependencies between their AI sectors—and so the key is in managing the risks that result therefrom, not denying the interconnectivity and interdependence in the first place.

The “AI arms race” framing implies isolated competition between two global powers, which is clearly inaccurate when it comes to the United States and China; their AI development is anything but isolated from one another. Ideally, the “openness that is so integral to American innovation should be sustained and safeguarded”23 rather than building walls between American and Chinese AI development.24 But as a result of this winner-takes-all framing, a growing emphasis on a winner-takes-all race threatens to push the two countries’ tech sectors apart, potentially damaging AI development in ways we can’t foresee. It doesn’t matter what exactly initialized this way of thinking—for instance, some would argue this is a reaction to China’s aggressive push for AI supremacy in many areas25—because this is the reality.

As a result of this winner-takes-all view of AI development, mutually damaging U.S. policies towards China—like reckless trade policies or trying to hamper all collaboration on AI whatsoever—may very well hurt American AI development,26 not to mention play into Beijing’s vision of “science as a tool of national greatness and scientists as servants to the state.”27 The Trump administration’s recent talk of limiting the “export of artificial intelligence”28 is just one recent example. This winner-takes-all, race-to-the-bottom approach, which Remco Zwetsloot, Helen Toner, and Jeffrey Ding argue is guided heavily by fear and speculation,29 could further compromise the likelihood of developing sound policies that can advance mutual interests—while not lending too much advantage to Chinese AI capabilities.30

In addition to crafting hurtful policies and missing opportunities to advance mutual interests, United States policymakers, as a result of the AI arms race framing, may mishandle AI risks. An increasingly common refrain, for instance, is that any privacy regulation in the United States is going to doom AI development because Chinese competitors face no restrictions on their data collection. This argument risks exploiting individuals’ information, by which American data privacy legislation—which is desperately needed—is guided by what the Chinese government has or has not done in the same vein.31 American companies can still remain competitive in the AI sector while working under the guidance of some form of national privacy regulation.