Chinese AI models appear close to the frontier primarily because they are trained on the outputs of leading U.S. models. This creates a dependency loop: they can only catch up by using the latest from the West, ensuring they remain followers rather than innovators who can achieve a true breakthrough.

Despite impressive models from companies like DeepSeek, China's AI ecosystem is heavily reliant on "distilling"—essentially copying and refining—open-source models from the US. This dependency on an external innovation engine is a major weakness in their national strategy to achieve genuine AI leadership and self-sufficiency.

The perception of China's AI industry as a "fast follower" is outdated. Models like ByteDance's SeedDance 2.0 are not just catching up on quality but introducing technical breakthroughs—like simultaneous sound generation—that haven't yet appeared in Western models, signaling a shift to true innovation.

China is gaining an efficiency edge in AI by using "distillation"—training smaller, cheaper models from larger ones. This "train the trainer" approach is much faster and challenges the capital-intensive US strategy, highlighting how inefficient and "bloated" current Western foundational models are.

A critical, under-discussed constraint on Chinese AI progress is the compute bottleneck caused by inference. Their massive user base consumes available GPU capacity serving requests, leaving little compute for the R&D and training needed to innovate and improve their models.

The performance gap between Chinese and American frontier AI models is not due to a lack of talent or different training techniques. Instead, it is primarily constrained by access to massive-scale compute and the capital required to procure it.

An Alibaba tech lead claims the US compute advantage allows for wasteful but effective "rich people innovation" (running many experiments). In contrast, Chinese firms are forced into "poor people innovation," bogged down by operational needs and unable to risk compute on next-gen research.

Chinese labs use 'smart distillation,' a sophisticated technique where a frontier model acts as a 'teacher' to guide a smaller model's judgment and data labeling. This is viewed as a legitimate and efficient catch-up method, distinct from simply copy-pasting answers.

While Chinese AI labs are brilliant at efficiency and quickly replicating existing breakthroughs, they have not demonstrated the distinct skillset required for true frontier innovation. Their ecosystem is built around a different type of talent. Even with a sudden influx of compute, they would face a significant cultural and technical learning curve to lead the race.