China’s State Council has twice in four months turned its attention to artificial intelligence, and experts say the two meetings reveal a clear change in emphasis. On June 29, Premier Li Qiang chaired an executive meeting that reviewed overall AI development and stressed seizing the initiative through better supporting policies and governance. On October 16, a second meeting reviewed progress on the “AI Plus” initiative and described AI as a key variable shaping future development, urging that technological innovation be driven by real-world applications.
Experts told 21st Century Business Herald that the first meeting focused on top-level strategy, while the second evaluated how earlier measures were working. Policy attention, they said, has moved from “planning the layout” to “tracking progress and deepening implementation,” with a stronger orientation toward the real economy.
The October meeting set out three priorities: strengthening the foundation through technological breakthroughs, algorithm innovation, coordination between computing power and electricity, and higher-quality data; empowering applications by linking supply and demand, encouraging joint research, and advancing pilot-scale testing; and balancing development with security through tiered, category-based oversight.
Analysts noted that the idea of letting applications lead innovation reverses the old approach of achieving technical breakthroughs first and finding uses later. It plays to China’s complete industrial system, rich application scenarios and strong engineering capacity.
Both meetings stressed AI security and China’s comparative advantages. The differences lay in emphasis: June highlighted basic research, supercomputing clusters and talent and funding, while October added computing-electricity coordination and pilot testing, and shifted from international governance cooperation toward industry-level risk assessment.
Wu Chaoming of Caixin Financial Holdings summed up the change as a move from “capability building” to “value realization.” He said the yardstick for AI policy is shifting from model metrics to productivity metrics, with deployment density replacing parameter scale as the core measure of success.
Source: 21st Century Business Herald, October 17, 2026
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