Under these circumstances, Chinese enterprises have little choice but to look for alternative routes for AI development, such as industrial applications and deployment in specific sectors, for instance, personal computers, the Internet of Things, robotics and driverless vehicles.
And although it’s a choice driven by necessity, many industry players seem confident the strategy shows promise. That’s because it entails lower costs and higher cost-performance ratios and plays to China’s competitive advantages: a rich and diversified set of use cases, huge market demand and great industrial capacity. That in turn is built on China’s traditional advantages in manufacturing, such as trained workers with lower labour costs, the scale of its economy, supportive government policies and so on.
All these factors point to this direction for AI development in China: applications in specific industries. These require fewer but specific parameters, which means they don’t need as many advanced AI chips to provide high computer power to train models.
China’s AI Plus initiative, released in its 2024 government work report, strongly reinforces this thrust, aiming to further integrate AI and various industries in the real economy. And industry is following Beijing’s lead. Tech giants in China such as Baidu, Alibaba, Tencent and Huawei have focused on developing highly specific AI applications in areas such as autonomous driving, biology, weather forecasting, education, mining and the like.
However, great concerns remain among the Chinese high-tech industrial community as to whether China’s alternative approach will succeed.
Indeed, a widening gap with the United States in fundamental model development could derail the strategy. Continuously improving fundamental AI models such as GPT-4, or multimodal tools such as Sora, may soon be capable of managing all key functions in specific sectors.
Simply put, improved fundamental AI models could kill most of the start-ups that have developed industry-specific AI tools, based on the large, general models. As OpenAI’s CEO Sam Altman has said, continuous improvement of AI models could eliminate the need for additional features, as the large models become more versatile.
Looking ahead, the United States is likely to retain its lead in this sphere. Given restricted availability of AI chips, quality data and algorithms, the best scenarios for China’s big companies such as Baidu, Alibaba, Tencent, Huawei and ByteDance would be to continue to work to gain ground in fundamental model development, while focusing on specific applications.
Chinese companies have a history of innovation built on US technological breakthroughs, as in the era of mobile internet in the early 2010s. In that instance, China caught up with and even surpassed the United States in applications such as e-commerce, mobile payments, and early voice and facial recognition.
This time, again, instead of seeking technological breakthroughs and leading revolutionary innovation, Chinese companies are focusing on use cases and applications that help consumers or businesses to make quick money. The approach underlies the ongoing chaotic phenomenon in China’s fundamental model market, in which more than 100 LLMs emerged in a single year following ChatGPT’s release.
Most of these models are basically fine-tuned versions of LLMs that duplicate open-source models such as Llama or are patchworks based on different models that are seeking to quickly profit in China’s fledging LLM market.
There are only a few developed by leading companies, such as Baidu’s Ernie 3.0 (Wenxin Yiyan) and Alibaba’s Tongyi Qianwen, and start-ups such as Baichuan, Moonshot AI, Zhipu AI and MiniMax are trying hard to get advanced AI chips and billions or even trillions of parameters with which to train their models.
There is a possibility, as some Chinese observers have claimed, that the widespread industrial applications of AI in the huge number of use cases could help China repeat its history of innovation built on US technological breakthroughs and create more high-quality business-specific data, This, in turn, would provide more application scenarios and thus create a real industrial revolutionary moment.
For example, AI’s widespread use in disease diagnosis and treatment in medical scenarios in China could create even more quality data to help the development of the AI solutions market and bring sweeping changes in future health care. Industry verticals in a variety of use cases, once integrated with fundamental models, could provide a well-established base for China to catch up or even lead in AI-driven industrial transformation in the future.
The debate continues. Will the constant improvement of fundamental models by big companies in the United States replace most other vertical models in specific industries? If the answer is yes, China’s alternative route for AI development could be a dead end. But if the answer is no, there may be an opportunity for the country to reap the harvest of AI development. The final answer, as yet unknown, may fall between the two.
