A new report from the Center for Security and Emerging Technology (CSET) sheds light on China’s critical stance toward large language models (LLMs) as the dominant approach to achieving general artificial intelligence (GAI). While the United States and Europe have heavily invested in LLMs, such as OpenAI’s GPT models and Google’s Gemini, China is pursuing a diversified AI strategy that incorporates alternative pathways, including brain-inspired AI and embodied intelligence.
The report, titled “Chinese Critiques of Large Language Models: Finding the Path to General Artificial Intelligence,” compiles statements from China’s top AI researchers, government strategies, and academic publications, revealing skepticism over LLMs’ ability to reach human-like reasoning and problem-solving capabilities.
China’s AI development differs from Western approaches by avoiding an overreliance on a single technological pathway. While Chinese companies and institutions have developed LLMs similar to ChatGPT, Chinese scientists have raised concerns about their inherent limitations, including their high computational costs, reliance on vast amounts of training data, and persistent issues such as hallucinations and lack of reasoning abilities.
Chinese researchers argue that GAI requires more than just scaling up LLMs. Instead, they emphasize integrating brain-inspired architectures, real-world sensory experiences, and a stronger emphasis on knowledge graphs and symbolic reasoning. Many top AI institutions in China, including the Beijing Academy of Artificial Intelligence (BAAI) and the Chinese Academy of Sciences, are actively exploring these alternative approaches.
Several leading general AI experts in China have publicly criticized the limitations of LLMs and called for more diversified research efforts. Among them is Tang Jie, a professor at Tsinghua University and a key figure in China’s AI community. He has argued that LLMs, while powerful, do not sufficiently replicate the cognitive mechanisms of the human brain. Instead, he advocates for a deeper exploration of biologically inspired AI.
Similarly, Zhang Yaqin, former president of Baidu and founding dean of Tsinghua’s AI Industry Research Institute, has pointed out LLMs’ inability to interact with the physical world effectively. He suggests that future AI systems must integrate principles from the physical sciences and knowledge graphs to bridge this gap.
Another major critic, Zhu Songchun, director of the Beijing Institute for General Artificial Intelligence, has outright dismissed LLMs as a viable path to GAI. He argues that simply increasing the scale of current models will not achieve true intelligence and that AI development must shift toward new paradigms rooted in cognitive science.
The Chinese government has aligned its AI strategy with these expert critiques, calling for a more comprehensive approach to GAI. In May 2023, Beijing’s municipal government issued a directive promoting research into “new paths” for general AI, including brain-inspired intelligence and embodied learning. The directive emphasized that future AI systems should be capable of autonomous decision-making, interacting with dynamic environments, and operating under a unified theoretical framework.
At the national level, China’s Ministry of Science and Technology has also pushed for a multi-pronged AI development strategy. A speech in March 2024 by Wu Zhaohui, vice president of the Chinese Academy of Sciences, underscored the need for “synergy between large and small models” and called for research into embodied intelligence, group intelligence, and human-machine hybrid intelligence.
China’s emphasis on diverse AI research approaches could give it a strategic advantage in the race to GAI. While Western companies remain heavily invested in LLMs, China’s broader research portfolio ensures that it is not locked into a single AI development path. If LLMs ultimately fail to achieve GAI, China’s investment in alternative models could place it ahead in the long-term AI competition.
Moreover, China’s AI research is increasingly tied to national security and governance priorities. Unlike Western AI development, which is largely driven by private sector initiatives, China’s AI strategy is state-led, ensuring alignment with broader economic and strategic goals. This includes efforts to embed state-approved values into AI models, a concept championed by Zhu Songchun and other Chinese AI leaders.
The CSET report warns that while Western AI development is fixated on scaling up existing LLMs, China is methodically pursuing a broader research agenda. If LLMs prove insufficient for achieving GAI, China’s approach may offer a more sustainable and adaptable roadmap.
The findings of the CSET report highlight a critical divergence in global AI strategy. While Western AI research remains dominated by LLMs, China is positioning itself for long-term success by investing in a wider range of technologies. If China’s diversified approach proves successful, it could shift the balance of AI leadership and redefine the future of artificial intelligence on a global scale.
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