In AI, the Chinese appear to have innovated their way around U.S. restrictions meant to stifle their progress. What does that mean for the U.S.-Chinese rivalry? From Lorenzo Maria Pacini at strategic-culture.org:
Almost no one expected it, yet it was predictable: China has entered the global AI competition and intends to win.
Chinese companies in the AI field aim to achieve success in the same way that Chinese companies in the electric car, renewable energy and biopharmaceutical sectors are conquering the market: by radically changing the economics of these sectors. In other words, they aim to outcompete their rivals by making AI adoption low-cost and large-scale, thus eliminating rivals that operate with high-cost, high-margin business models.
Since 2018, the US government has sought to hinder the development of Chinese artificial intelligence, imposing restrictions on chip exports and banning access to the most advanced AI models created in the US. The launch of DeepSeek broke this blockade, demonstrating China’s resilience and capacity for innovation.
Subsequently, the US has started to exclude DeepSeek from government agencies, while OpenAI is lobbying for a large-scale ban in the US. It is also possible that the US government will put pressure on its allies to impose restrictions on DeepSeek, as it has already done with Huawei. Other Chinese AI companies may soon face similar restrictions.
The response of Chinese companies is interesting. Since the launch of DeepSeek, there has been a flood of new high-performance AI models from China, such as Qwen from Alibaba, Doubao from ByteDance, Hunyuan from Tencent and Ernie from Baidu. Unlike their US competitors, these models are open source and free: they are available to anyone in the world to download, modify and integrate. But why are Chinese companies adopting this strategy?
Since the launch of ChatGPT in November 2022, major US tech players such as OpenAI, Microsoft, Google and Meta have followed a similar strategy: they have accumulated Nvidia’s most advanced AI chips, invested huge resources in data centers, developed proprietary and closed language models, and applied high subscription or license fees to monetize their products.
