Is China pulling ahead of the US in AI? Not quite, argues Dieter Ernst of CIGI in a recent report entitled “Competing in artificial intelligence chips: China’s challenge amid technology war.” His deep dive into the dynamics behind China’s recent progress on AI chips manufacturing merits closer attention.
In addition to the hard engineering, Ernst reveals a social story of a global AI community on the verge of fracture. These new restrictions will likely bring the best out of some Chinese firms, while putting others out to pasture. All the while, basic research is likely to suffer worldwide as ties that bound the Chinese and western academic communities fray.
Jordan Schneider is the host of the ChinaTalk podcast, and a regular contributor to TechNode’s China Voices translation column, available to members.
Most western coverage of western AI firms focuses on those that operate in the application layer of the AI stack. But in order for Bytedance to instantly recommend tailored TikTok videos, or for JD to optimize delivery orders, they need to run their applications on hardware.
What AI chips do is optimize performance for specific tasks further down the AI stack. For instance, an AI chip can be tailored specifically for facial recognition, autonomous driving, or cloud computing. The best of these chips represent the bleeding edge of global semiconductor technology, and have grabbed the attention of Washington and Beijing. The Trump administration sees Chinese semiconductor progress as a grave economic and national security threat, and hopes to use a combination of sanctions and incentives to slow down China’s work on AI chips. Beijing officials hope to create a self-sufficient industry capable of withstanding American sanctions and ultimately competing on the world stage.
Basic research lies at the heart of AI development. American researchers invented the field, and have been at its forefront ever since. Ernst contends that America’s “informal, flexible, and undogmatic approach to innovation is, arguably, the root cause for the resilience of the United States’ AI development trajectory.” He argues that “technology diffusion through knowledge networks, combined with intense contests among competing ideas” throughout academia, DARPA projects, and the private sector have made the American AI and semiconductor ecosystem the world’s most vibrant.
In contrast, China has struggled to marry basic research with industry. China’s electrical engineering community was practically wiped out by the Cultural Revolution, forcing researchers in the 80s to start decades behind global best practice. It has to contend with dramatic disconnects between academia and industry as well as, Ernst writes, “the institutional heritage of the Soviet planning system,” which assumed enterprises’ purpose was to meet production targets and not conduct research themselves (that work was reserved for national academies and institutes).
Until recently, the commercial and academic Chinese AI communities rarely interacted. While two of Ernst’s contacts in Chinese consumer-oriented AI companies noted that they had some researchers from public organizations take on moonlight consulting work, these sorts of arrangements pale in comparison to the public-private ecosystem America has created.
Many western analysts have pointed to China’s share of global AI publications as evidence of increasingly successful basic research efforts. The global AI research community is notable for its openness, with academics commonly posting their research in open platforms like Github and arXiv online. “If you don’t share your work, it’s meaningless,” said Yunji Chen, a researcher at the Institute of Computing Technology in Beijing, in a 2019 interview with Nature.
The global AI community is, by and large, not happy about politics intruding. A Huawei researcher who due to a US State Department travel ban was forced to deliver his presentation remotely received a rousing round of applause at a normally staid conference.
But politics is likely already reshaping the academic community. As Western universities reject Huawei’s money and face increasing scrutiny for connections with the Chinese government’s Thousand Talents plan, western researchers are forced to reconsider their Chinese connections. Co-authorships develop out of connections made through global conferences and academic fellowships, which are less and less likely to be accessible to Chinese nationals.
I’d be interested to see research that asks whether the global community is splitting into Chinese and western halves—perhaps measured by how often researchers on each side of the divide co-author with the other? This disconnect is likely to harm upstart Chinese researchers more than established western ones.
This comes at a time when basic research is only growing more important in the field. As AI chips are called upon to process massive datasets, companies around the world need to innovate with new architectures. This “paradigm shift” as “the focus of semiconductor innovation shifts from process technology and fabrication to architecture and design at the front end, and post-fabrication packaging at the back end” leaves an opening for a Chinese upstart to contend with American giants like Intel, AMD, and NVIDIA.
But despite the PR bluster, Chinese firms are all to various degrees behind the cutting edge and vulnerable to American actions. Chinese AI chip startups mostly focus on inference, as opposed to training algorithms, a much less technically demanding task. The fact that American capital markets are seemingly closed off to Chinese AI firms is another significant hurdle. For the time being, going public on Chinese stock markets requires three years of profitability, a rule that discourages investment in R&D.
Industry experts agree that China only really has one player capable of competing with American giants on an even footing in any AI chip vertical: Huawei’s HiSilicon. And even HiSilicon is severely vulnerable to US sanctions. Since its most advanced chips are manufactured in Taiwan at TSMC’s 7nm foundries, American pressure could force Huawei to cut ties with one of its most important AI chip partners. As this week’s recent Department of Commerce regulation release attests, it looks like the US is preparing to drop this hammer. Given that Huawei has been preparing for years for the US government to come after it, the fact that they still have foreign parts in their flagship phones means that this is much easier said than done.
American firms also stand to lose out from increasing restrictions on their ability to sell to Chinese companies. “A staggering 67% of Qualcomm’s revenue comes from China, for Micron this is 57%, and for Broadcom 49%.” With nearly one fifth of US semiconductor firms’ revenue reinvested into R&D, any big hit to their top line, if not paired with substantial US industrial policy to make up for this gap, will have long term consequences for
US competitiveness relative to Chinese, European, and other Asian competitors.
Chinese companies have made some real progress, particularly in areas where China has a natural comparative advantage. For instance, China currently leads the world in a handful of narrowly defined AI chips related to surveillance. Further, some argue that China has an edge in access to cheap, structured data sources thanks to a large pool of affordable college-educated labor. However, data-labeling is eminently outsourceable, and nowadays Chinese labor really isn’t cheaper than comparably educated Indians or Filipinos. After all, average hourly rates on Mechanical Turk are just $2.
However, American sanctions are forcing Chinese players out of their comfort zone in ways that will help the ecosystem over the long term. As anonymous blogger Youshu writes,
Some will no doubt say that “Yeah, we knew China wanted to develop its own semi industry, so what’s the rumpus?” This observation misses the mark. Before private firms were happy with the Americans, and state firms would just tell their bosses there were no good alternatives. But now orders are being pushed towards domestic rivals, even where they are not very good, providing them with revenues today, and confidence about future revenues, with which to fund R&D.
Ernst is more pessimistic. He expects “islands of technological excellence [to] continue to coexist with deeply entrenched structural weaknesses in China’s emerging AI chip industry.” The Chinese AI industry will doubtless continue developing, but is unlikely to challenge America for global preeminence any time soon.