The Dispatch


The Great AI Race

AI is reigniting the US-China rivalry in higher education to shape talent pipelines and drive innovation, as the top two economies compete for global influence.

By Gauri Kohli

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“China has prioritised AI education at all levels of its education system, aligning curricula with national goals from an early stage."
“One of the most immediate consequences of AI’s growing prominence in higher education is the intensifying battle for talent."

The United States and China are locked in an increasingly intense competition over artificial intelligence (AI) - and universities are at the heart of it. As the world’s top two economies invest in AI research, talent development and education reforms, higher education is becoming a key front in the global race for technological leadership. Universities are pivotal in shaping national AI strategies, spearheading innovation and exerting global influence.

China’s Centralised Ambition

China’s approach to AI in higher education is guided by a top-down, state-backed model that aligns university research and development with broader national goals. This model is characterised by centralised planning, generous government funding and a highly coordinated research agenda. According to Ruibin Bai, professor and Head of the Artificial Intelligence and Optimisation Research Group at the University of Nottingham Ningbo China, Chinese national key research grants are distributed through central agencies like the National Natural Science Foundation of China (NSFC) and the Ministry of Science and Technology (MOST).

The process unfolds in three stages: first, corporations and universities submit research topic suggestions; next, a panel of experts from academia and industry develops funding guidelines based on these inputs; finally, institutions compete for grants through an open bidding process aligned with the outlined priorities. “The advantage is clear—topics are more aligned with government priorities,” Professor Bai explains.

These research grants carry the highest prestige and benefit China’s top-tier institutions. Professor Bai notes that this model ensures that research efforts are not only well-funded but also aligned with long-term national objectives, from economic modernisation to technological sovereignty. At the same time, some sophisticated oversight mechanisms are put in place to enhance transparency and fairness.

Beyond research, Chinese universities are aggressively scaling their AI capabilities across academic disciplines. Students are encouraged to pursue AI-related fields, and successful graduates can command salaries up to ten times higher than those in traditional manufacturing sectors. This wage premium reflects the national push to retain and attract AI talent domestically, as well as the increasing importance of AI across industrial sectors. “China has prioritised AI education at all levels of its education system, aligning curricula with national goals from an early stage,” says Professor Bai.

The country is also advancing efforts to transform its education system through a comprehensive guideline issued by the Ministry of Education alongside eight other departments. The directive by the ministry in April 2025 focuses on building an AI-driven educational framework that incorporates smart technologies into teaching, learning, assessment and academic research.

The plan includes fast-tracking the development of large-scale AI models to deepen their integration within education. It also calls for updates to academic curricula in both higher education and vocational training to align with the evolving needs of advanced manufacturing and modern service industries.

China aims to continue developing educational AI models, expand pilot programs and establish model classrooms where AI tools are embedded in teaching and evaluation processes. Educators will receive training to leverage AI in creating more engaging and innovative learning environments.

As part of this initiative, national AI learning platforms have been rolled out across all levels of education. Specialised models have been introduced for 13 disciplines, including computer science, chemistry and materials science. For example, Wuhan University of Technology has implemented AI Assistant 2.0 and developed tools like the AI Study Assistant and AI HR Assistant to support the development of a smart campus.

China’s AI surge is also mirrored in its research output. According to the 2025 Stanford AI Index Report, although the US remains ahead in terms of output, China has made significant strides in quality - narrowing the performance gap on key benchmarks like Massive Multitask Language Understanding (MMLU), (used to evaluate the language understanding abilities of large language models), and HumanEval (a tool for assessing the capabilities of AI models in understanding and generating code) to near equality.

At the same time, China continues to dominate in AI research publications and patent filings. Notably, AI model development is becoming more globally distributed, with emerging contributions from regions including the Middle East, Latin America and Southeast Asia.

US: Innovation Through Decentralisation

In contrast, the US higher education system is grounded in decentralisation, academic freedom and a competitive funding landscape. While this model has long fuelled innovation and cross-disciplinary breakthroughs, it also presents unique challenges, especially in the context of AI.

C. Raymond Perrault, a distinguished computer scientist at SRI International and Co-Director of the 2025 Stanford AI Index Report, points out that US universities are highly reliant on international students, particularly in computer science and engineering disciplines. “About 80 percent of US computer science students are foreign,” Perrault observes, highlighting recent moves by the Trump administration that could change this given the policy instability in DC.

The AI Index Report offers a mixed picture of US leadership. In 2024, US-based institutions produced 40 notable AI models, significantly outpacing China’s 15 and Europe’s three.

Despite these advantages, US universities are struggling to meet the rise in student demand for AI education. Hodan Omaar, Senior Policy Manager at the Information Technology and Innovation Foundation’s Center for Data Innovation (CDI), warns that under-resourced departments are turning away qualified students.

“Departments cap enrolment, ration classes or raise tuition for computer science majors—pricing out lower-income students and narrowing the pathway into AI. Without stronger state support and internal funding realignment, these limitations could stifle the country’s long-term AI ambitions,” she says.

US universities boast strong AI programs, drawing students from around the world. Moreover, interest in studying AI and AI-related courses at US universities is growing as the market for these skills soars, says the US AI Policy Report Card authored by Omaar for the CDI in 2022.

Many federal agencies are also prioritising investment in AI higher education. For example, the 2021 National Defense Authorization Act directs the National Science Foundation to fund AI initiatives for higher education (e.g., fellowships for faculty recruitment in AI) as well as AI curricula, certifications and other adult learning and retraining programs.

Competing AI Values

While Western narratives often frame China’s AI strategy as focused on control and surveillance, Professor Bai offers a different view. He argues that in China, in addition to fairness and transparency, the government assumes primary oversight responsibility for ensuring AI safety and minimising societal risks. “The so-called ‘control’ is mainly focused on safety and social and societal impacts, instead of controlling the business operations of each AI enterprise,” he notes.

In the United States, there is growing emphasis on Responsible AI (RAI), particularly within academia and civil society. Perrault confirms that ethical deployment, transparency and fairness are increasingly prominent themes in US and European AI research. The Stanford AI Index Report tracks a rising number of RAI-focused publications, reflecting this ethical shift. Since 2019, the overall geographic distribution of RAI publications has remained relatively consistent, with the US accounting for the most (3,158), followed by China (1,100).

Experts reflect the broader reality that ethical AI remains a fragmented and evolving domain, where education will play a crucial role in shaping future norms.

Talent Pipelines Under Strain

One of the most immediate consequences of AI’s growing prominence in higher education is the intensifying battle for talent. A large number of students from China study at Western universities, particularly in the US. However, shifting visa policies, national security concerns and geopolitical tensions are reshaping these flows.

Perrault points out that it remains unclear whether Chinese graduate students trained in the US will stay or return home. Recent US policy decisions—including tech export restrictions and increased visa scrutiny—have prompted Chinese students to consider alternative destinations such as the UK, Singapore and Australia. Professor Bai confirms this trend, suggesting that domestic options in China are also becoming more attractive as the country bolsters its AI ecosystem.

As both countries deepen their AI commitments, universities are becoming central to national narratives and global influence strategies. Educational partnerships, exchange programs, and joint research initiatives serve as conduits for soft power. But the landscape is becoming more fragmented.

Perrault is more cautious, noting the unpredictability of US policy and the growing potential for a reversal depending on future administrations.

Toward a divided AI world?

While AI continues to drive innovation and reshape economies, its integration into higher education is also redrawing the geopolitical map. If the US and China continue down divergent paths, the world may face a bifurcated AI ecosystem—not just in terms of technology, but in values, talent flows, and research norms, say experts.

Yet a collaborative reset is not entirely off the table. International AI conferences, academic exchanges and joint research papers still offer points of engagement. As Professor Bai notes, “At the individual level, I still see a lot of exchanges and communications in international AI conferences.”

To preserve these channels and ensure that AI development serves global, rather than purely national interests, universities will have to play a delicate balancing act—championing both innovation and inclusion, autonomy and alignment.