The Chinese AI industry is undergoing a fundamental shift in 2026, moving from conversational models to task-oriented agentic AI, according to multiple authoritative reports.
At the "AGI-Next" summit hosted by Tsinghua University this month, industry experts reached a consensus: the "Chat" paradigm centered on dialogue is over, and AI competition has pivoted to an era of intelligent agents that can "get things done."
This shift is reflected in the numbers. China's AI core industry revenue is expected to exceed 1.2 trillion yuan in 2026, up nearly 30% year-on-year, with over 6,000 AI enterprises and domestic open-source large models downloaded globally more than 10 billion times cumulatively.
**Technical Paradigm: From Chat to Action**
In January, DeepSeek published two papers co-authored by Liang Wenfeng, addressing memory bottlenecks and stability issues in training large models. Industry observers say this clarifies the shape of next-generation models.
"DeepSeek marks a differentiated breakthrough in China's AI technical route," said Zhang Yaqin, founding dean of Tsinghua's Institute for AI Industry Research. "China is turning to embrace lighter models, smarter architectures, higher efficiency, and lower prices."
**The Density Law**
A paper titled "The Density Law of Large Models" by ModelBest (面壁智能) and Tsinghua University argues that AI evolution will proceed along both capability and cost dimensions, with efficiency as a central theme. The industry is moving from "scale competition" to "density competition," focusing on refined mechanisms, algorithm architecture, and training methods rather than sheer parameter size.
Wei Liang, vice president of the China Academy of Information and Communications Technology (CAICT), said the industry no longer relies solely on increasing parameter scale for performance breakthroughs.
**Agentic AI: The Next Frontier**
Zhang Yaqin believes AI is accelerating toward agentic AI, which can set tasks, plan paths, learn from trial and error, and possess autonomy, generalization, and long-term memory. "If chatbots are 'talking dictionaries,' agentic AI is a 'butler that can work independently,'" said Huang Jin, a researcher at the Chinese Academy of Sciences.
However, Wei Kai, director of CAICT's AI Research Institute, cautioned that agents still need improvements in reliability, context memory, and long-horizon tasks before large-scale deployment.
**Application Race**
Major tech firms are racing to deploy AI in real-world scenarios. Tencent has integrated its self-developed large model into over 900 internal scenarios. Baidu has established new departments for basic model R&D and application model R&D. Founder Robin Li predicted that only a few basic models will survive, but many application-layer players will succeed.
The "Hundred Models War" is winding down, replaced by a marathon focused on real-world penetration, ecosystem building, and value mining.
**Looking Ahead**
Experts see AI expanding beyond the digital realm into physical and biological intelligence. In January, a Chinese embodied intelligence model ranked first in a global benchmark, signaling progress in understanding and executing tasks in the physical world.
As Zhang Yaqin noted, "The scaling law hasn't failed—we still need compute and data—but the marginal returns of brute-force compute are diminishing." The next breakthroughs will likely come from algorithmic innovation and efficient architectures.
Why it matters
- Signals a fundamental shift in China's AI strategy from model scale to real-world utility and efficiency.
- Implies that global AI competition is moving toward agentic systems and application-layer innovation.
- Indicates that Chinese AI firms are prioritizing cost-effective, deployable solutions over massive parameter counts.
What to watch next
- How quickly agentic AI applications mature and achieve reliability for enterprise use.
- Whether the 'density law' becomes a dominant paradigm in global AI research.
- Which Chinese companies successfully translate AI into revenue from real-world deployments.
Sources
- 新华深读｜2026年中国AI发展趋势前瞻 (Xinhua)
- 2026年中国AI发展趋势前瞻 (Tsinghua University)
Confidence: 90%