A trip to Shanghai’s AI mega-conference showed me that China’s developers are still playing catch-up to Silicon Valley

A trip to last week's World Artificial Intelligence Conference revealed that China's AI companies have yet to catch up to Silicon Valley.

Last week, Shanghai hosted China’s largest AI event: The World Artificial Intelligence Conference (WAIC), with 500 exhibitors, 1,500 exhibits, over 300,000 attendees, and even an appearance from Chinese premier Li Qiang.

But despite its impressive scale, the conference left me disenchanted. I’d hoped to witness the sector’s technological advancements. Instead, WAIC confirmed my suspicions: There’s a gap between what China’s AI can do and the cutting-edge innovations emerging from Silicon Valley.

WAIC exhibitors focused on robotics and large language models (LLMs), with only a few generative AI companies in the mix. Over half the companies at WAIC, including big tech companies and even some state-owned telecommunications companies, were showcasing their new models.

In Shanghai, Baidu founder Robin Li encouraged attendees to start developing practical AI applications rather than continue to refine their LLMs. He stressed that a powerful and widely-used AI application will benefit society more than another model that can process vast amounts of data yet had no practical use.

The generative AI applications on display in Shanghai were mostly ChatGPT-like chatbots, except for Kuaishou’s text-to-visual application Kling, a Sora-like product that I found genuinely impressive.

As I wandered the showroom, I noticed that most chatbots required prompts in English, instead of Chinese. That leads me to suspect that many of China’s AI programs are, in fact, running on models developed outside of China.

It’s obvious that the models still need some fine-tuning. One consumer prompted a text-to-visual app from Moore Threads with “a cute baby boy with brown hair, sitting in the garden.” The result was a baby with bright fuchsia skin, eyes that didn’t align on the face, and a disproportionately small body.

I left the conference agreeing with Alibaba chairman Joe Tsai’s candid admission earlier this year that China’s generative AI development is at least two years behind the U.S. That means U.S. and Chinese companies aren’t really playing in the same leagues, and so it’s difficult to directly compare them.

The critical problem is that China’s LLMs are limited to using data within the Great Firewall. As investment bank Goldman Sachs noted late last year, “LLM performance improves with scale—more parameters, more and better training data, more training runs and more computation.” There is simply less information in the isolated Chinese-language internet compared to an open internet with sources in many different languages.

AI companies outside of China just have far more data they can use for training. An AI developer in China will struggle to keep pace.

The constraints caused by limited access to advanced GPUs are also glaringly apparent. U.S. policies that curtail access to cutting-edge chips and chipmaking technology will mean that Chinese companies are lagging behind their non-Chinese peers.

Yet despite these limitations, China’s AI developers are searching for opportunities to innovate.

A lot of strong talent from the country’s mature consumer tech ecosystem is pivoting to AI. Most of the founding members of the hyped “four tigers”—Baichuan, Zhipu AI, Moonshot AI and MiniMax—had a stint at a big tech company. Their strong intuitions regarding consumers and products are why they’re now leading China’s AI application space. From a consumer’s perspective, their products are on par with many of the leading U.S. applications.

There’s progress on the hardware front too. Huawei’s Ascend AI processors, in particular, seem to be miles ahead of their competitors. The Chinese tech giant, now using SMIC’s manufactured chips, claims its Ascend 910B AI chip can outperform Nvidia’s A100 chip in some tests, especially in the use of large AI model training.

Chinese AI developers face some fundamental hurdles, such as a challenging environment, a lack of advanced chips, geopolitical isolation, and national security concerns that limit talent and capital mobility.

Together these constraints will create two parallel AI ecosystems: one inside of China, and one outside of it. The U.S. is going to maintain its lead in developing this transformative technology.

But just because the U.S. has the technological edge doesn’t mean that China’s AI developers will be left behind. Chinese companies have always started off a step behind their non-Chinese peers, yet fierce competition and a willingness to experiment helped them catch up to—and in the case of consumer internet companies, even outcompete—the rest of the world.

In the world of AI, the U.S. and China are both frenemies and competitors. We should hope that the geopolitical competition between them doesn’t get in the way of innovation and collaboration.

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