In a rare moment of public candor, leading figures from China's artificial intelligence sector have delivered a sobering assessment: the country is not poised to overtake the United States in the global AI race in the near future. The warnings came from top executives at tech giants Alibaba and Tencent, as well as from the founder of prominent AI firm Zhipu.
A Candid Reality Check at Beijing Summit
The stark evaluation was shared during a panel at the AGI-Next summit in Beijing's Zhongguancun technology hub, often called China's Silicon Valley, on Saturday. The event was co-organized by Zhipu AI and Tsinghua University.
Justin Lin, who leads the development of Alibaba's Qwen series of open-source AI models, put the chances of any Chinese company achieving fundamental breakthroughs to leapfrog leaders like OpenAI and Anthropic in the next three to five years at less than 20%.
He pointed to a critical disparity in resources. "A massive amount of OpenAI's compute is dedicated to next-generation research, whereas we are stretched thin — just meeting delivery demands consumes most of our resources," Lin stated. He framed it as an age-old dilemma: "Does innovation happen in the hands of the rich, or the poor?"
His caution was echoed by Tang Jie, founder and chief AI scientist at Zhipu AI, and Yao Shunyu, a recent OpenAI hire now leading the AI push at Tencent, China's most valuable company.
Why the Gap May Be Widening
Despite recent market enthusiasm and progress, the leaders identified several key constraints holding China back.
Tang Jie directly addressed the perception that recent open-source model releases signal China catching up. "We just released some open-source models, and some might feel excited, thinking Chinese models have surpassed the US," he said. "But the real answer is that the gap may actually be widening."
The speakers collectively cited two major hurdles:
- Limited Computational Resources: Unlike US counterparts who can allocate vast computing power to pure research, Chinese teams are often consumed by product delivery demands.
- US Export Controls: Restrictions on advanced chips and lithography equipment continue to pose a significant long-term challenge for China's AI ambitions.
Yao Shunyu urged the industry to focus on core bottlenecks for next-generation models, such as improving long-term memory and self-learning capabilities.
Market Success vs. Technical Reality
The candid discussion followed a week of notable financial successes for Chinese AI firms. Zhipu AI and Shanghai-based MiniMax Group collectively raised more than $1 billion in market debuts. MiniMax shares more than doubled on their Friday debut, while Zhipu climbed 36% since its listing a day earlier.
This market optimism was partly fueled by the breakout success of DeepSeek's R1 model in early 2025, which spurred a wave of Chinese companies—from Alibaba to startups—to open-source their latest AI models. These open-source efforts have rapidly narrowed the gap with US proprietary offerings.
However, the summit made clear that financial market performance does not equate to fundamental technological leadership.
Future Bets and a Call for Unity
The tech leaders also outlined their strategic priorities for the coming year. Yao Shunyu is focusing on helping Tencent leverage AI to generate greater value for its massive user base, for instance by linking its Yuanbao AI assistant with WeChat chat history.
Justin Lin highlighted Alibaba's investments in multimodality (AI that processes text, images, and sound) and real-world AI agents. Both Tang Jie and Yang Zhilin, founder of Moonshot AI, touted upcoming releases of their flagship foundation models.
Tang concluded with a call for cohesive national effort over internal rivalry. "Meaningless internal competition serves no purpose," he asserted. "We should represent China to push AGI (Artificial General Intelligence) further for the world."
The summit's overarching message was one of ambitious long-term goals tempered by a clear-eyed recognition of present-day challenges. While China's AI industry is advancing rapidly and attracting significant capital, its top minds believe surpassing US innovation in core AI research remains a distant prospect, hindered by resource allocation and geopolitical constraints.
