Accusations that Chinese labs cheat by copying US models are misleading. The practice, known as distillation, is common across the industry (including by Elon Musk's xAI) and academia. Now, with Chinese labs dominating open source, American startups are increasingly building on top of Chinese models.

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 is rapidly closing the AI gap not through pure innovation but through "distillation"—systematically querying Western frontier models via their APIs to harvest their reasoning processes. This allows them to train their own models to a near-frontier level at a fraction of the cost, bypassing years of foundational research.

US officials and AI labs allege Chinese firms are engaged in industrial-scale IP theft. They reportedly use fraudulent accounts to extract capabilities from US models like Claude to train their own, creating a facade of domestic innovation.

Leading Chinese AI models like Kimi appear to be primarily trained on the outputs of US models (a process called distillation) rather than being built from scratch. This suggests China's progress is constrained by its ability to scrape and fine-tune American APIs, indicating the U.S. still holds a significant architectural and innovation advantage in foundational AI.

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.

While foreign AI companies allegedly distill US models to accelerate progress, American counterparts like Meta refrain from the practice. The significant legal and reputational risks in the US create an uneven playing field, effectively handicapping domestic players who cannot leverage this powerful, albeit controversial, technique for model development.

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.

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.