The US and China Are Collaborating More Closely on AI Than You Think | WIREDSkip to main contentMenuSECURITYPOLITICSTHE BIG STORYBUSINESSSCIENCECULTUREREVIEWSMenuAccountAccountNewslettersSecurityPoliticsThe Big StoryBusinessScienceCultureReviewsChevronMoreExpandThe Big InterviewMagazineEventsWIRED InsiderWIRED ConsultingNewslettersPodcastsVideoMerchSearchSearchSign InSign InWill KnightBusinessJan 21, 2026 2:00 PMThe US and China Are Collaborating More Closely on AI Than You ThinkWIRED analyzed more than 5,000 papers from NeurIPS using OpenAI’s Codex to understand the areas where the US and China actually work together on AI research.CommentLoaderSave StorySave this storyCommentLoaderSave StorySave this storyThe US and China are, by many measures, archrivals in the field of artificial intelligence, with companies racing to outdo each other on algorithms, models, and specialized silicon. And yet, the world’s AI superpowers still collaborate to a surprising degree when it comes to cutting-edge research.A WIRED analysis of more than 5,000 AI research papers presented last month at the industry’s premier conference, Neural Information Processing Systems (NeurIPS), reveals a significant amount of collaboration between US and Chinese labs.The analysis found that 141 out of the 5,290 total papers (roughly 3 percent) involve collaboration between authors affiliated with US institutions and those affiliated with Chinese ones. US-China collaboration appears fairly constant, too, with 134 out of 4,497 total papers involving authors from institutions in both countries in 2024.WIRED also looked at how algorithms and models developed in one country are shared and adapted across the Pacific. The transformer architecture, developed by a team of researchers at Google and now widely used across the industry, is featured in 292 papers with authors from Chinese institutions. Meta’s Llama family of models was a key element of the research presented in 106 of these papers. Meanwhile, the increasingly popular large language model Qwen from Chinese tech giant Alibaba appears in 63 papers that include authors from US organizations.Jeffrey Ding, an assistant professor at George Washington University who tracks China’s AI landscape, says he is not surprised to see this level of teamwork. “Whether policymakers on both sides like it or not, the US and Chinese AI ecosystems are inextricably enmeshed—and both benefit from the arrangement,” Ding says.The analysis no doubt simplifies the degree to which the US and China share ideas and talent. Many Chinese-born researchers study in the US, forging bonds with colleagues that last a lifetime.“NeurIPS itself is an example of international collaboration and a testament to its importance in our field,” Katherine Gorman, a spokesperson for NeurIPS, said in a statement. “Collaborations between students and advisors often continue long after the student has left their university. You can see these kinds of signals that indicate cooperation across the field in many places, including professional networks and past collaborators.”The latest issue of WIRED explores the many ways in which China is shaping the current century. But with US politicians and tech executives using fears over China’s rise as a justification for ditching regulations and fueling staggering investments, our analysis is a good reminder that the world’s two AI superpowers still have a lot to gain from working together.A Note on MethodologyI used Codex, OpenAI’s code-writing model, to help analyze NeurIPS papers. After writing a script to download all the papers, I used the model to dip into each one and do some analysis. This involved having Codex write a script to search for US and Chinese institutions in the author field of each paper.The experiment offered a fascinating glimpse into the potential for coding models to automate useful chores. There’s plenty of panic about AI replacing coding jobs, but this is something that I normally wouldn’t have had the time or budget to build. I started out writing scripts and having Codex modify them before just asking Codex to do the analysis itself. This involved the model importing Python libraries, testing different tools, and writing scripts before producing reports for me to vet. The process involved a fair bit of trial and error, and you have to be very careful, because AI models make surprisingly stupid mistakes even when they’re being quite smart. I had to make sure that each report included a way for me to go through the results, and I checked as many as possible manually.This is an edition of Will Knight’s AI Lab newsletter. Read previous newsletters here.CommentsBack to topTriangleYou Might Also LikeIn your inbox: WIRED's most ambitious, future-defining storiesThe ‘super flu’ is spreadingBig Interview: Margaret Atwood wants to keep up with the latest doomThe age of the all-access AI agent Is hereLivestream AMA: Welcome to the Chinese centuryWill Knight is a senior writer for WIRED, covering artificial intelligence. He writes the AI Lab newsletter, a weekly dispatch from beyond the cutting edge of AI—sign up here. He was previously a senior editor at MIT Technology Review, where he wrote about fundamental advances in AI and China’s AI ... 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The US and China are engaging in a surprisingly robust level of collaboration within the rapidly evolving field of artificial intelligence, a dynamic significantly more nuanced than the prevailing image of intense competition between the two nations. According to a recent analysis conducted by WIRED, leveraging OpenAI’s Codex to examine over 5,000 research papers presented at Neural Information Processing Systems (NeurIPS), a notable 3 percent of the papers involved collaborative efforts between US and Chinese institutions. This consistent collaboration, observed across multiple years – specifically 134 out of 4,497 papers in 2024 – highlights a sustained, if somewhat understated, exchange of ideas and expertise.

The analysis focused on the practical sharing of algorithms and models. Notably, Google’s transformer architecture, now a foundational element of the AI industry, appeared in 292 papers authored by Chinese researchers. Meta’s Llama family of models played a crucial role in 106 research papers, and Alibaba’s increasingly influential large language model, Qwen, featured in 63 papers involving US organizations. These instances demonstrate a clear flow of knowledge and adaptation, with Chinese institutions actively leveraging and refining established technologies.

Jeffrey Ding, an assistant professor at George Washington University specializing in China’s AI landscape, offered an insightful perspective, stating that regardless of political tensions, the US and Chinese AI ecosystems are inextricably linked and mutually benefit from this arrangement. This suggests that despite geopolitical complexities, there's a recognized pragmatic value in exchanging research and talent.

The investigation revealed a complex web of interconnectedness beyond mere model sharing. Many Chinese-born researchers pursuing advanced degrees in the United States forged enduring professional relationships, leading to ongoing collaborations. WIRED notes that NeurIPS itself exemplifies this international collaboration, with student-advisor relationships frequently extending beyond the immediate academic setting.

The methodology employed—using OpenAI’s Codex to analyze research papers—offers a unique glimpse into the research landscape. The process, initiated by a script designed to identify US and Chinese institutions, presented unexpected opportunities for automation. This approach, initially demanding significant manual intervention and careful vetting of Codex’s output, ultimately proved invaluable, affording a detailed examination of the data.

The findings serve as a potent reminder that the AI landscape is rarely defined by simple competition. Instead, it's a global ecosystem characterized by complex interactions and shared advancements. While US and Chinese governments are often portrayed as adversaries in the technological arena, the research conducted at NeurIPS suggests a more nuanced reality.

The success of this analysis, reliant on Codex's abilities, offers a valuable lesson in the potential of AI to assist in complex data analysis. While caution is warranted – Codex, like any AI model, can produce mistakes—it democratized the process, enabling a more rapid assessment of global AI trends.

Ultimately, the WIRED investigation underscores the importance of recognizing the interconnectedness within the world's most advanced technological fields. It’s a data-driven argument against simplistic narratives of purely competitive AI rivalry, proposing a more cooperative, and potentially mutually beneficial, approach.