Behind China’s AI gift, a constraint
At the ninth World AI Conference in Shanghai this month, Chinese President Xi Jinping addressed the gathering for the first time in nine years. It
At the ninth World AI Conference in Shanghai this month, Chinese President Xi Jinping addressed the gathering for the first time in nine years. It is indicative of the priority that AI occupies for the leadership. At the conference, Mr. Xi pitched Chinese AI models as a global public good. The contrast was clear as day. America is selling closed-source, proprietary models while China is offering open-weight models to the world. This distinction is not merely technical, but equally political. Unlike Anthropic, OpenAI, and Google, which host their frontier models on their respective servers and offer services via an application programming interface (API), Chinese labs such as DeepSeek, Qwen, and Moonshot also release their weights for anyone to download. Unlike American models, these can be customised, modified, and even further trained. There is no subscription to cancel or terms of service. The maker cannot revoke or restrict access. So long as one can manage the requisite compute to host the model, even Papua New Guinea or Senegal can run it locally on their servers. And most importantly, the data never leaves the border. AI diplomacy However, one may wonder what explains China’s benevolence. Well, to begin with, the objective is to project China as a responsible AI player in the comity of nations. Unlike the Americans, who he alleged to have overstretched the concept of national security to AI, and thereby adopted an exclusionary approach, China is committed to sharing the benefits and fruits of AI.
Mr. Xi’s framing was indicative in this regard. He described AI as “humanity’s collective wisdom,” a “historic opportunity” to “bridge the AI and digital divides,” and something that “should not be a solo performance by a single country but a symphony of international cooperation.” The underlying message, aimed at Washington, projected Beijing as the open and cooperative alternative to the U.S.’s AI models. But there is a limiting factor at play too that explains Beijing’s benevolence — its compute constraint, both for training and inference. Since 2022, the U.S. administration, in concert with its allies, has imposed a series of export restrictions on China, limiting its ability to both mass-import and mass-manufacture high-end chips. The compute constraint To understand this constraint, one must separate two very different tasks — training a model and serving it. Training is a one-time cost. A lab assembles a cluster of chips, runs it for a few months and comes up with a finished model. The fleet required is numerically small. For instance, GPT-4 was trained on an estimated 25,000 Nvidia chips, and Meta trained its Llama 3.1 on ~16,400 chips. DeepSeek, on the other hand, trained its acclaimed V3 model on ~2,000 export-compliant H800s. A frontier model, in other words, needs chips in the thousands, and it needs them only once. Serving that model to the public, however, is an altogether different proposition. Inference scales with the user base and requires continuous expansion as it must answer hundreds of millions of user queries.
The BriefWire