Competition or ‘co-opetition’: how is convergence shaping Sino-US AI race?

There was a surprise guest speaker at Nvidia’s widely watched GPU Technology Conference (GTC) in San Jose, California, last month: Yang Zhilin, the founder of Beijing-based Moonshot AI, the developer behind the Kimi family of foundational artificial intelligence models.

Amid heated rhetoric about US-China AI competition, which some have likened to an “arms race”, the participation of a Chinese AI start-up’s CEO at the flagship event of American chipmaking giant Nvidia might have struck some as odd.

Yang, a 30-something doctoral graduate of Pittsburgh’s Carnegie Mellon University from Shantou, Guangdong province, delivered an almost identical presentation less than two weeks later at China’s state-backed Zhongguancun Forum in Beijing, where attendees included Beijing Mayor Yin Yong and Ding Xuexiang, a member of the Politburo Standing Committee – the Communist Party’s highest decision-making body.

Kyle Chan, a fellow at the Washington-based Brookings Institution think tank, said it was the latest example of new forms of convergence emerging between the AI ecosystems in the United States and China, even as geopolitical tensions tried to pull them apart.

“There’s many different cross-cutting factors and interests that are pulling in either direction,” he said. “Even among the US companies themselves, they have very different perspectives and views on Chinese AI and competition.”

Nvidia was a key driver of convergence as it stood to be its biggest beneficiary, Chan said. The company, the world’s leading designer of advanced semiconductor chips – the core hardware powering the global AI industry – announced at GTC a revenue outlook of at least US$1 trillion through to 2027, driven by exploding demand for its most advanced Blackwell and Rubin chips.