How Innovative is China in AI?
Hodan Omaar, ITIF
The Smith Richardson Foundation provided support to the Information Technology and Innovation Foundation (ITIF) to assess how innovative Chinese industries are. The report relied on three methods to assess China's innovation performance in AI: an in-depth case study evaluation of two leading AI companies; interviews and held a focus group roundtable with global experts on the Chinese AI industry, allowing participants to speak anonymously unless they asked to be named in the report; and assessing global data on AI innovation, including scientific articles, patents, talent, and access to infrastructure.
The report summarizes the key takeaways as follows:
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China is the global leader in AI research publications and is neck and neck with the United States on generative AI. However, China’s research publications have less impact than U.S. ones, with fewer citations and less private-sector involvement.
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Tsinghua University in Beijing is the breeding ground for China's leading AI start-ups, including four of the country’s “AI tigers”—Zhipu AI, Baichuan AI, Moonshot AI, and MiniMax—which were all founded by faculty and alumni.
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Chinese large language models are closing the performance gap with U.S. models, with some Chinese models outperforming their U.S. counterparts in bilingual benchmarks.
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China has less private AI investment than the United States, but foreign investment in China's generative AI sector is growing, with Saudi Arabia’s Aramco leading the way.
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State-directed capital funds and financial aid are proving effective at supporting high-potential firms in regions of China the private sector typically underinvests in.
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China’s open-source LLM ecosystem is rapidly advancing, with models such as Alibaba’s Qwen 1.5 and Zhipu AI’s ChatGLM3 outperforming some U.S. counterparts and gaining recognition for their impressive capabilities.
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U.S. policymakers should not prioritize containing China, but rather craft and fund a comprehensive national AI strategy that addresses the twin goals of increased AI development and increased AI adoption to stay ahead.
The report outlines the following recommendations for increasing AI development:
1. Stimulate private investment in AI R&D.
2. Revitalize the federal funding process for AI.
3. Avoid policies that undermine U.S. AI leadership and bolster Chinese competitors.
4. Develop a national data strategy to dramatically expand the availability of data for training AI models.
The report outlines the following recommendations for increasing AI adoption:
5. Create a national AI roadmap for adoption.
6. Prioritize the rapid adoption of AI in the federal government.
7. Support digital transformation.
8. Incentivize AI workforce training investment.