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.

China's push for open-source AI may not be purely strategic but a consequence of US export controls limiting their inference compute. Unable to monetize closed APIs effectively to a skeptical Western market, Chinese labs release models to build influence and attract talent, as few would pay for a sub-frontier, China-hosted service.

Even if Chinese firms use "distillation" to steal capabilities from US models, the process is computationally intensive. Restricting access to advanced chips thus limits direct training *and* makes large-scale IP theft more difficult.

China's push for open-weight models is not just ideological but a strategic necessity. Lacking compute for large-scale inference and facing a tough market for their closed models, open-sourcing is a way to gain traction, talent, and influence where US controls have limited them.

Unable to build frontier models from scratch, some Chinese companies gain a competitive edge by using "scale distillation." This involves training smaller, open models on the outputs of larger, proprietary US models, effectively piggybacking on American R&D to create capable, low-cost alternatives.

Chinese firms are closing the AI capability gap by using "distillation" to replicate the intelligence of leading US models. This creates a strategic vulnerability, as copying software models is easier than replicating China's hardware manufacturing prowess.

Sebastian Malabai argues that U.S. chip export bans are ineffective because China circumvents them by renting GPU capacity in other countries and using "distillation" to reverse-engineer and copycat advanced U.S. models. This suggests a need for a new strategy focused on collaborative safety.

China is creating cheaper, 'good enough' AI models by training them on the outputs of US frontier models. This technique, called distillation, undercuts the revenue of US AI companies, threatening their ability to service the massive debt from their infrastructure buildout.