https://jamestown.org/prc-elites-voice-ai-skepticism/ Skip to content Jamestown * China Brief * Eurasia Daily Monitor * Terrorism Monitor Subscribe Account Donate * About Us + Who We Are + Our Team + History + Careers + Funding * Publications + China Brief + China Brief Notes + Eurasia Daily Monitor + Terrorism Monitor + Militant Leadership Monitor + Jamestown Perspectives * Projects + Black Sea + Commentaries + Guardians of the Belt + New Versus Old * Topics + Military & Security + Foreign Policy + Information Warfare + Economics & Energy + Politics & Society + Technology * Media + Foundation Updates + Mentions + Books + Events [ ] * Technology * Politics & Society * Economics & Energy * Information Warfare * Foreign Policy * Military & Security 1754288876200464 PRC Elites Voice AI-Skepticism Technology Publication China Brief China Volume 25 Issue 21 11.14.2025 Daniel Fu Download * * * * Top Stories * Comac's Homegrown Aircraft Goes Global C919 * PRC Influence Operations in the Philippines: Can Beijing Flip the South China Sea Script? CCP * Beijing's Aggression Behind Emerging India-Philippines Defense Relationship Diplomacy PRC Elites Voice AI-Skepticism Executive Summary: * Alongside continued investment in artificial intelligence (AI) technology and applications, a growing body of skeptics has emerged within media, policy, academic, and scientific circles in the People's Republic of China (PRC). * AI skeptics voice concerns over a lack of coordinated deployment, overhyped technology that may not produce the economic development many expect, effects on labor, and general social and safety issues. * Analyses of the U.S.-China AI race often overlook national-level debates and local implementation, where some skeptics see wasted resources and inefficiencies. --------------------------------------------------------------------- Rapid advancement in artificial intelligence (AI) has become a point of national pride in the People's Republic of China (PRC), driven in part by a cohort of accelerationist advisors who view technology as a transformative solution to the country's economic ills. Lauding it as a "new productive force" (Xin Zhi Sheng Chan Li ), these experts view AI as a new "engine" (Yin Qing ) for economic growth that will cause "lead goose" (Tou Yan ) spillover and driving effects that will benefit every industry ( Xinhua, October 30, 2024). Some experts, such as Chief China Economist at DBS Bank Ji Mo (Ji Mo ), have asserted that AI's contribution to GDP can "partially offset" (Yi Ding Cheng Du Shang Mi Bu ) challenges like population ageing (China News, May 2). Chinese academics, engineers, and media commentators are increasingly questioning this premise. They highlight additional fears related to the rise of AI and warn that overreliance on the technology could exacerbate structural problems rather than resolve them. The Chinese Communist Party (CCP) leadership is increasingly taking their concerns into account. Deployment Lacks Coordination Chinese experts recognize that Beijing's accelerationist strategy has led to fragmented provincial competition in AI development. As one media article wrote, "no locality wants to miss the opportunity of the AI industry" (Mei You Di Fang Xi Wang Cuo Guo Ren Gong Zhi Neng Chan Ye De Ji Yu ) (China Newsweek , March 20). Wang Yong (Wang Yong ), Vice Dean of the Institute of New Structural Economics at Peking University, observed that some local governments believe that "continuing to develop traditional industries is a sign of being outdated" (Zai Fa Zhan Chuan Tong Chan Ye Jiu Luo Hou Liao ) ( Lianhe Zaobao, August 29). A clear example can be seen in Guangxi, where the Party Secretary declared that the province "cannot be absent" (Bu Neng Que Xi ) from the AI sector, despite its limited relevance to the national AI landscape (China Newsweek, March 20). The provincial sprint to seize the AI opportunity has led to duplicated efforts and wasted resources. This mirrors patterns seen in other strategic industries, such as solar panels, electric vehicles (EVs), and semiconductors, in which fragmented investment, redundant projects, and overcapacity represent increasingly acute challenges. Pan Helin (Pan He Lin ), a prominent Chinese economist and member of the Expert Committee on Information and Communications Economy under the Ministry of Industry Information Technology (MIIT), has warned that "local governments blindly supporting emerging industries through tax breaks or direct investment risk creating significant overcapacity" (Di Fang Zheng Fu Tong Guo Shui Shou You Hui Huo Zhi Jie Tou Zi De Fang Shi ,Mang Mu Zhi Chi Xin Xing Chan Ye ,Dao Zhi Chan Neng Guo Sheng ) (Lianhe Zaobao, August 29). Tan Tieniu (Tan Tie Niu ), a Chinese Academy of Sciences (CAS) professor, similarly cautioned at the recent Two Sessions against "blindly rushing" (Yi Hong Er Shang He Yi Hong Er San ) into AI. He asserted that there is "no need for every province and city to duplicate efforts" (Bing Bu Xu Yao Mei Ge Sheng Shi Du Yao Zhong Fu Jian She ), as it could "lead to overcapacity and a tangle of bad debts" (Chan Neng Guo Sheng ,Che Bu Qing De Yi Pi Gu Pi Zhang ) (Sina Finance, September 30). Beijing has become increasingly attentive to problems arising from uncoordinated AI development across regions. A People's Daily commentary urged localities to "play to their unique strengths, rely on local methods, and pursue differentiated paths" (Da Hao "Te Se Pai ",Duo Yong "Tu Ban Fa ",Zou Hao "Chai Yi Lu "), arguing against homogenization. The commentary cited Zhejiang as a model (People's Daily, August 4). Government officials have also underscored the importance of avoiding "disorderly competition and blind expansion" (Wu Xu Jing Zheng He Yi Yong Er Shang ) with regard to Beijing's recent "AI+" initiative (Wall Street CN, August 29; China Brief, September 21). AI May Fail to Deliver Technological Progress Chinese policy elites have sounded the alarm about excessive hype surrounding large language models (LLMs) in the domestic AI sector. Mei Hong (Mei Hong ), a professor at Peking University and former president of the China Computer Federation, explained that with AI, "isolated successes are exaggerated and generalized without regard to context, leading to overpromises" (Yi Pian Gai Quan ,Dui Cheng Gong Ge An Bu Gu Qian Ti Di Fang Da , Fan Hua ,Guo Du Cheng Nuo ) (Aisixiang, December 11). Song-Chun Zhu (Zhu Song Chun ), dean of the Beijing Institute for General Artificial Intelligence (BIGAI) and director of a state-backed program to develop artificial general intelligence (AGI), has similarly warned that the field is "exciting on the surface, but chaotic when it comes to substance" (Biao Mian Re Nao ,Shi Zhi Hun Luan ) (The Paper, April 5). He argues that public opinion has marginalized foundational research while focusing on large models. Others have echoed these concerns. Sun Weimin (Sun Wei Min ), Chief Engineer of the Cyberspace Administration of China, has said that large models are overhyped and that there is still a "significant gap before they can truly serve as production tools" (Chi Cheng Wei Sheng Chan Gong Ju Huan Cun Zai Bu Xiao De Chai Ju ) (QQ News, April 16). Baidu CEO Robin Li (Li Yan Hong ) has offered a similar diagnosis. Explaining that developers will "rely on a small number of large models to build a wide variety of applications" (Kai Fa Zhe Yao Yi Lai Wei Shu Bu Duo De Da Mo Xing Lai Kai Fa Chu Ge Chong Ge Yang De Yuan Sheng Ying Yong ), he argues that "repeatedly developing foundational models is a tremendous waste of social resources" (Bu Duan Di Zhong Fu Kai Fa Ji Chu Da Mo Xing Shi Dui She Hui Zi Yuan De Ji Da Lang Fei ) (21st Century Business Herald, November 15, 2023). This skepticism represents the prevailing consensus. The latest survey data available, compiled by researchers at CAS, revealed that most experts hold negative attitudes towards LLM development (CLAI Research, March 12, 2023). [1] Continued development also requires the ability to cultivate and integrate AI talent, especially in bridging academia and industry. In 2021, Xue Lan (Xie Lan ), dean of Schwarzman College at Tsinghua University and head of the National Expert Committee on Next Generation AI Governance, stated that universities are trying to train engineering talent but collaboration between universities and enterprises "cannot be implemented" (Luo Shi Bu Liao ) (Tsinghua University, July 13, 2021). Xue noted that it is usually acceptable for a university professor to take a temporary position in industry, but that employees who try to return from a company to a university are often denied permission. Xue lamented the lack of flexible mechanisms to bring people from industry, since teaching requires various approvals. As a result, "although everyone encourages collaboration between universities and enterprises, it often fails to materialize in practice" (Sui Ran Da Jia Du Gu Li Xiao Qi He Zuo ,Dan Zhen Zheng Dao Luo Shi De Shi Hou Luo Shi Bu Liao ). This problem has persisted. One article in the Ministry of Education's monthly academic journal noted how various restrictive factors exert "significant cooling effects on enterprises' engagement in industry university collaboration" (Dui Chan Jiao Rong He Yu Ren Shen Du Xing Cheng Xian Zhu Leng Que Xiao Ying ). These factors include a "systemic delay" of teaching and research productivity in universities (China Higher Education Research, July 2). A separate survey performed by Nanjing University researchers found that nearly 58 percent of enterprises believed that traditional education methods in colleges and universities are insufficient to meet their development needs, and that around 54 percent believed that there is a lack of a "stable communication and consultation mechanisms" (Xiao Qi Jian Que Fa Wen Ding De Gou Tong Jiao Liu He Wen Ti Xie Shang Ji Zhi ) between universities and enterprises (China Education Online, September 10). AI Threatens the Workforce Chinese elites have warned of AI-induced labor displacement that could exacerbate challenges related to unemployment and inequality. Nie Huihua (Nie Hui Hua ), deputy dean of the National Academy of Development and Strategy at Renmin University, has stated that AI adoption benefits business owners, not workers (Jiemian, October 14, 2024). Cai Fang (Cai Fang ), director of the Institute of Population and Labor Economics at the Chinese Academy of Social Sciences, has explained how the PRC's rapid installation of industrial robots has contributed to labor displacement. He asserts that "technological progress does not have a trickle down effect on employment" (Ji Zhu Jin Bu Dui Jiu Ye Mei You Juan Liu Xiao Ying ) (QQ News, May 16). Addressing these distributional implications, president of the National School of Development at Peking University Huang Yiping (Huang Yi Ping ) has cited Samsung's unmanned factories which operate with minimal human labor, raising the fear that workers may be unable to earn a stable income (Sina Finance, June 25). Several experts have highlighted the disproportionate impact of AI-driven labor disruption on vulnerable groups in the workforce, such as workers and migrant laborers, emphasizing the need to strengthen the PRC's social safety net (21st Century Business Herald, December 2, 2024). Li Tao (Li Tao ), Dean of the China Institute of Social Management at Beijing Normal University, has argued that these trends necessitate an "employment-first" (Jiu Ye You Xian ) strategy, which could improve unemployment insurance and pensions (Qiushi, July 14). Economic Growth May Not Materialize Chinese elites have expressed doubt about AI's ability to drive meaningful short-term economic growth. The Tencent Research Institute has argued that much of GDP growth tied to AI has been driven by investment rather than tangible productivity gains, implying that the PRC's economic strategy is over-dependent on AI (Huxiu, September 15). Wu Xiaoying (Wu Xiao Ying ), a professor of economics at Peking University's National School of Development, has described AI as a contemporary example of the Solow Paradox. Wu invokes the paradox to note that widespread investment in AI technologies has not been reflected by economy-wide improvements to productivity (Sina Finance, July 24). Concerns about excessive dependence on AI in sectors such as finance, education, and tourism, are also rampant (Sina Finance, March 10; Economic Daily, March 16; Sohu, April 30). Some experts have warned that the AI obsession has diverted attention away from other technologies, such as blockchain and those related to supply chain development (CEIBS, July 30, 2024). AI-related spending does not yet account for one percent of the country's GDP, while electric vehicles, lithium batteries, and solar panels, contribute only eight percent (DW, March 3, 2024; Sina Finance, April 18. Meanwhile, the real estate sector, which contributes roughly a third of the PRC's GDP, has continued to languish. Some, like the Stanford University-based economist Xu Chenggang (Xu Cheng Gang ), have argued that AI will have limited impact as a growth engine without meaningful reforms to revive the real estate industry and bolster general consumption (DW Chinese, March 8, 2024). AI Brings Social Risks Lastly, prominent Chinese experts have emphasized the need to institute AI-related safety guardrails. Andrew Yao (Yao Qi Zhi ), dean of Tsinghua University's College of AI and the only Chinese recipient of the Turing Award, has highlighted the "existential risks" (Sheng Cun Xing Feng Xian ) of LLMs. [2] He cited an example in which an AI model attempted to avoid being shut down by sending threatening internal emails to company executives (Science Net, June 24). Qi Xiangdong (Qi Xiang Dong ), chairman of a cybersecurity firm with several government contracts, has warned of AI-related security threats like data breaches, deepfake scams, and saturation-style attacks (Chinese People's Political Consultative Conference News, February 12). AGI also poses unique threats. Some, such as Zeng Yi (Ceng Yi ), director of the International Research Center for AI Ethics and Governance at CAS, fear that AGI will surpass humans in intelligence (Sohu, June 19, 2023). Another key concern centers on sourcing training data beyond the Great Firewall, which exposes AI systems to content outside of the CCP's control. Gao Wen (Gao Wen ), former Dean of Electronics Engineering and Computer Science at Peking University, has noted that Chinese data makes up only 1.3 percent of global large-model datasets (The Paper, March 24). Reflecting these concerns, the Ministry of State Security (MSS) has issued a stark warning that "poisoned data" (Shu Ju Tou Du ) could "mislead public opinion" (Wu Dao She Hui Yu Lun ) (Sina Finance, August 5). Conclusion Much of the global discourse around the PRC's AI ascent has overlooked a growing number of influential voices within the country who are raising alarms about overreliance on AI. These concerns reflect deep anxieties about the potential for widespread social and economic disruption if AI development proceeds without institutional coordination, long-term planning, and more robust safeguards. Party elites have increasingly come to recognize the potential dangers of an unchecked, accelerationist approach to AI development. During remarks at the Central Urban Work Conference in July, Xi posed a question to attendees: "when it comes to launching projects, it's always the same few things: artificial intelligence, computing power, new energy vehicles. Should every province in the country really be developing in these directions?" (Shang Xiang Mu ,Yi Shuo Jiu Shi Ji Yang :Ren Gong Zhi Neng , Suan Li , Xin Neng Yuan Qi Che ,Shi Bu Shi Quan Guo Ge Sheng Fen Du Yao Wang Zhe Xie Fang Xiang Qu Fa Zhan Chan Ye ) (People's Daily, August 4). Significant uncertainties remain, particularly regarding how, or whether, closer government oversight of AI development will materialize. Strengthening regulatory capacity across provincial governments is likely to be uneven and difficult. Despite national strategies that prioritize the development of domain-specific AI applications over foundational models, new LLMs with limited commercial application continue to be released. Exacerbating these challenges is Xi Jinping's ideological opposition to Western-style welfarism, which he has criticized for making citizens lazy (People's Daily, February 17, 2023). This suggests a reluctance to implement social reforms needed to cushion the impact of AI-induced labor disruptions. At the same time, as technological competition with Washington intensifies, Xi may decide to press ahead with an accelerationist AI campaign, prioritizing geopolitical rivalry over domestic caution. Notes [1] This data was published prior to the release of the PRC's first AI chatbot, however, so some experts may have changed their views since then. For an excellent survey of Chinese critiques on LLMs, see William Hannas, Huey-Meei Chang, Maximilian Riesenhuber, and Daniel Chou's report "Chinese Critiques of Large Language Models" (CSET, January 2025). [2] The Turing Award is given for achievements in the field of computer science, and is often referred to as the "Nobel Prize of Computing" (A.M. Turing, accessed November 5). 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