The "Silicon Curtain" drawn between Washington and Beijing may not be as impenetrable as it seems. Even as geopolitical tensions lead to export restrictions and regulations on advanced technologies, the massive engine of Artificial Intelligence (AI) research continues to be fueled by cross-border cooperation. Recent data analyzing over 5,000 papers submitted to NeurIPS, the world's premier AI academic conference, reveals that technical ties between the two nations are deeper and more specific than our intuition might suggest.

A "Strange Symbiosis" Proven by Data

Despite the intensifying competition for technological hegemony, US and Chinese researchers continue to maintain high-density cooperation in specific domains. A quantitative analysis of over 5,000 NeurIPS papers using OpenAI's Codex model confirmed that 141 papers, approximately 3% of the total, were co-authored by researchers from the US and China. While this figure may seem small numerically, it is significant that these efforts are concentrated in Large Language Models (LLM) and Natural Language Processing (NLP).

According to the analysis, US-China cooperation goes beyond merely filling out headcount. While private sector laboratories such as Google and Meta lead the collaboration in the United States, China's state-led academic ecosystems, including Tsinghua University and the Chinese Academy of Sciences (CAS), step forward as partners. This suggests the formation of a unique technological bloc that combines American capital and infrastructure with Chinese academic human resources.

However, signs of cracks are appearing in this strange symbiotic relationship. Compared to the past, the growth rate of collaborative papers between the two countries has slowed significantly. Government regulations have induced a psychological chilling effect among researchers, leading to a reorganization of research networks. Specifically, points where different entities—private corporations and universities—previously met are now increasingly bifurcating according to each nation's strategic priorities.

A Precarious Tightrope Between Barriers and Cooperation

The implications of this research data are clear. Even if politics attempts to confine technology, completely blocking the flow of knowledge is practically impossible. Advanced fields like AI, particularly LLMs, cannot maintain their pace of development through the closed ecosystem of a single nation. This is because open-source communities and academic exchanges are the core drivers of AI advancement.

From a critical perspective, however, concerns persist that the continuation of such cooperation could pose a risk to national security. The controversy surrounding "technology leakage"—where the research achievements of US companies nourish China's state-led projects, or conversely, where Chinese data and algorithms are integrated into US private services—remains an unavoidable challenge. Indeed, regulatory authorities in both countries are wary of private-led joint research escaping government control, which is likely to impact future research funding and visa issuance policies.

Ultimately, the current AI landscape is shifting toward "strategic selectivity" rather than "complete decoupling." Researchers are seeking partners who can deliver the most efficient research results while navigating regulatory hurdles, and the intersection of these efforts is reflected in the 141 papers identified at NeurIPS.

Survival Strategies in the Field: Responses Seen Through Data

Researchers and technology leaders in the field must now operate with political risk as a constant variable. The fact that cooperation in LLM and NLP remains active, as shown in the analysis, serves as evidence that the technical complexity of these fields is so high that breakthroughs are difficult to achieve without global collaboration.

Practical scenarios that developers and companies should consider are as follows: First, the ownership of Intellectual Property (IP) for joint research outputs must be clearly defined. Since the current cooperation structure often involves asymmetrical relationships between US corporations and Chinese universities, there is significant potential for technology ownership disputes if regulations change in the future. Second, diversification of research networks is necessary. Organizations must move away from existing models that rely solely on the US and China and distribute risk by securing third-party research hubs in regions such as Europe or Southeast Asia.

FAQ

Q: What was the role of the Codex model used to analyze the 5,000+ papers?
A: It was used to quantitatively extract correlations between authors' affiliated institutions, countries, and research fields from vast amounts of academic data. It served as a key tool for analyzing large-scale text data that would be difficult for humans to survey manually, identifying the specific scale (141 papers) and focus areas of US-China cooperation.

Q: Why is cooperation in the LLM field still active despite tightening regulations?
A: LLM is a field that simultaneously requires massive computing resources and high-level algorithm optimization. The analysis suggests that the synergy generated when US corporate infrastructure and Chinese academic human resources combine is perceived to be greater than the costs imposed by regulation. However, the growth rate has slowed compared to the past.

Q: Why do the research entities in the US and China differ?
A: The momentum for AI research in the US stems from Big Tech companies with vast capital, such as Google and Meta, whereas China focuses on nurturing key educational and research institutions like Tsinghua University and CAS as part of its national strategy. These structural differences are directly reflected in the joint research networks.

The Future of Invisible Links

US-China AI research cooperation is like an undercurrent that does not stop even amidst massive waves. While the 3% figure may seem small, the technical value contained within it touches upon the core models that form the foundation of modern AI. Those in the field already know that technological isolation ultimately leads to weakened competitiveness.

The point we must watch moving forward is how long this "strategic cooperation" will remain viable. As national regulations begin to penetrate the finer details of research, the number of collaborative papers will inevitably decrease further. However, knowledge tends to flow, and as barriers grow higher, researchers will find more sophisticated pathways. The true outcome of the AI hegemony war will depend not on who builds the highest wall, but on who connects most efficiently with the most capable partners.

참고 자료

• 🛡️ AI Index Report 2023 - Stanford HAI
• 🏛️ China and the U.S. produce more impactful AI research when collaborating together