China’s AI strategy is built around efficiency. While the United States pours enormous compute into frontier models, China has focused on making smaller, faster, cheaper versions of those same models through a process called distillation. Distillation takes a large model and compresses its knowledge into a smaller one. The smaller model doesn’t need the same amount of compute, yet it can perform many of the same tasks. This approach lets China stretch its hardware further and get more output per watt, per chip, and per dollar.
This matters because China can train distilled models on top of publicly available Western models. If a U.S. company releases a powerful model, China can scrape the outputs, run them through its own training pipeline, and produce a distilled version that is more efficient. In practice, this means China can use Western compute as a starting point, then optimize it until it runs cheaply on domestic hardware. The result is a long-term advantage in cost and scalability. They don’t need to match U.S. compute if they can reuse U.S. compute indirectly.
Distillation also gives China a way to bypass some of the bottlenecks the U.S. is facing. American companies are approaching the last ten to fifteen percent of available frontier compute. Hardware shortages, energy limits, and rising costs make each new model harder to train. China’s strategy sidesteps this ceiling by focusing on models that require less compute to begin with. If you can get ninety percent of the capability for ten percent of the cost, you don’t need to win the hardware race. You just need to win the efficiency race.
There’s a misconception that China needs to steal massive amounts of raw data to compete. In reality, they mostly need access to model outputs. Distillation works on behavior, not on the underlying training set. If a Western model can answer questions, summarize text, write code, or generate images, China can feed those outputs into its own training pipeline and produce a distilled version. This turns Western AI progress into a resource China can harvest. It’s not espionage in the cinematic sense. It’s more like copying the answers from a very smart calculator and building a cheaper calculator that can do the same thing.
Long term, this strategy could let China use Western compute against the West. Every time a U.S. company spends hundreds of millions of dollars training a new frontier model, China can scrape the outputs, distill them, and deploy a cheaper version at scale. The U.S. pays the full cost of innovation. China pays a fraction of the cost to replicate the capability. If this pattern continues, China’s AI ecosystem could become more sustainable and more widely deployed simply because it is less expensive to operate.
For beginners, the key idea is simple. The U.S. is building giant, expensive models. China is building smaller, efficient models that learn from the giants. Distillation lets China convert Western breakthroughs into compact systems that fit within its hardware limits. This isn’t a dramatic scenario. It’s a practical one. Efficiency wins in the long run, and China has made efficiency its strategy.
