If there is a single technology America needs to bring about the “thrilling new era of national success” that President Donald Trump promised in his inauguration speech, it is generative artificial intelligence. At the very least, AI will add to the next decade’s productivity gains, fuelling economic growth. At the most, it will power humanity through a transformation comparable to the Industrial Revolution.
Mr Trump’s hosting the next day of the launch of “the largest AI infrastructure project in history” shows that he grasps the potential. But so does the rest of the world—and most of all, China. Even as Mr Trump was giving his inaugural oration, a Chinese firm released the latest impressive large language model (LLM). Suddenly, America’s lead over China in AI looks smaller than it has at any time since ChatGPT became famous.
China’s catch-up is startling because it had been so far behind—and because America had set out to slow it down. Joe Biden’s administration feared that advanced AI could secure the Chinese Communist Party (CCP) military supremacy. So, America has curtailed exports to China of the best chips for training AI and cut off China’s access to many of the machines needed to make substitutes. Behind its protective wall, Silicon Valley has swaggered. Chinese researchers devour American papers on AI; Americans have rarely returned the compliment.
Yet China’s most recent progress is upending the industry and embarrassing American policymakers. The success of the Chinese models, combined with industry-wide changes, could turn the economics of AI on its head. America must prepare for a world in which Chinese AI is breathing down its neck.
China’s LLMs are not the very best. But they are far cheaper to make. QwQ, owned by Alibaba, an e-commerce giant, was launched in November and is less than three months behind America’s top models. DeepSeek, whose creator was spun out of an investment firm, ranks seventh by one benchmark. It was apparently trained using 2,000 second-rate chips—versus 16,000 first-class chips for Meta’s model, which DeepSeek beats on some rankings. The cost of training an American LLM is tens of millions of dollars and rising. DeepSeek’s owner says it spent under $6mn.
American firms can copy DeepSeek’s techniques if they want to, because its model is open-source. But cheap training will change the industry at the same time as model design is evolving. China’s inauguration-day release was DeepSeek’s “reasoning” model, designed to compete with a state-of-the-art offering by OpenAI. These models talk to themselves before answering a query. This “thinking” produces a better answer, but it also uses more electricity. As the quality of output goes up, the costs mount.
The result is that, just as China has brought down the fixed cost of building models, so the marginal cost of querying them is going up. If those two trends continue, the economics of the tech industry would invert. In web search and social networking, replicating a giant incumbent like Google involved enormous fixed costs of investment and the capacity to bear huge losses. But the cost per search was infinitesimal. This—and the network effects inherent to many web technologies—made such markets winner-takes-all.
If good enough AI models can be trained relatively cheaply, then models will proliferate, especially as many countries are desperate to have their own. And a high cost-per-query may likewise encourage more built-for-purpose models that yield efficient, specialised answers with minimal querying.
