Unlike the Western discourse, which is often framed as a race to achieve AGI by a certain date, the Chinese AI community has significantly less discussion of specific AGI timelines or a clear "finish line." The focus is on technological self-sufficiency, practical applications, and commercial success.
While U.S. advocates for AI cooperation with China often feel they are in a marginalized minority fighting a hawkish narrative, their counterparts in China feel their position is mainstream. Chinese academia, industry, and think tanks broadly view international governance collaboration as a priority, not just an acceptable option.
In China, mayors and governors are promoted based on their ability to meet national priorities. As AI safety becomes a central government goal, these local leaders are now incentivized to create experimental zones and novel regulatory approaches, driving bottom-up policy innovation that can later be adopted nationally.
Top Chinese officials use the metaphor "if the braking system isn't under control, you can't really step on the accelerator with confidence." This reflects a core belief that robust safety measures enable, rather than hinder, the aggressive development and deployment of powerful AI systems, viewing the two as synergistic.
Contrary to common Western assumptions, China's official AI blueprint focuses on practical applications like scientific discovery and industrial transformation, with no mention of AGI or superintelligence. This suggests a more grounded, cautious approach aimed at boosting the real economy rather than winning a speculative tech race.
In China, academics have significant influence on policymaking, partly due to a cultural tradition that highly values scholars. Experts deeply concerned about existential AI risks have briefed the highest levels of government, suggesting that policy may be less susceptible to capture by commercial tech interests compared to the West.
Unlike the US's voluntary approach, Chinese AI developers must register their models with the government before public release. This involved process requires safety testing against a national standard of 31 risks and giving regulators pre-deployment access for approval, creating a de facto licensing regime for consumer AI.
China can compensate for less energy-efficient domestic AI chips by utilizing its vast and rapidly expanding power grid. Since the primary trade-off for lower-end chips is energy efficiency, China's ability to absorb higher energy costs allows it to scale large model training despite semiconductor limitations.
China's binding regulations mean companies focus safety efforts on the 31 specific risks defined by the government. This compliance-driven approach can leave them less prepared for emergent risks like CBRN or loss of control, as resources are directed toward meeting existing legal requirements rather than proactive, voluntary measures.
China remains committed to open-weight models, seeing them as beneficial for innovation. Its primary safety strategy is to remove hazardous knowledge (e.g., bioweapons information) from the training data itself. This makes the public model inherently safer, rather than relying solely on post-training refusal mechanisms that can be circumvented.
China's refusal to buy NVIDIA's export-compliant H20 chips is a strategic decision, not just a reaction to lower quality. It stems from concerns about embedded backdoors (like remote shutdown) and growing confidence in domestic options like Huawei's Ascend chips, signaling a decisive push for a self-reliant tech stack.