US chip export ban is hurting China’s AI startups, not so much the giants yet

Well before Washington banned Nvidia’s exports of high-performance graphic processing units to China, the country’s tech giants had been hoarding them in anticipation of an escalating tech war between the two nations.

Baidu, one of the tech firms building China’s counterparts to OpenAI, has secured enough AI chips to keep training its ChatGPT equivalent Ernie Bot for the “next year or two,” the firm’s CEO Robin Li said on an earnings call this week.

“Also, inference requires less powerful chips, and we believe our chip reserves, as well as other alternatives, will be sufficient to support lots of AI-native apps for the end users,” he said. “And in the long run, having difficulties in acquiring the most advanced chips inevitably impacts the pace of AI development in China. So, we are proactively seeking alternatives.”

Other deep-pocketed Chinese tech companies have also been taking proactive measures in response to U.S. export controls. Baidu, ByteDance, Tencent and Alibaba collectively ordered around 100,000 units of A800 processors Nvidia to be delivered this year, costing them as much as $4 billion, the Financial Times reported in August. They also purchased $1 billion worth of GPUs that are scheduled for delivery in 2024.

Such heavy upfront investments could easily deter many startups from entering the LLM race. Exceptions do exist if the young business manages to secure handsome investments quickly. 01.AI, which was founded in late March by prominent investor Kai-Fu Lee, acquired a substantial number of high-performance inference chips through loans and has already paid off its debt after raising capital that valued it at $1 billion.

With its reserve of GPUs, Baidu recently launched the Ernie Bot 4, which Li claimed is “not inferior in any respect to GPT-4.”

Rating LLMs is tricky thanks to the sheer complexity of these AI models. Many Chinese AI firms have resorted to ranking boosting by diligently fulfilling the criteria of LLM charts, but the effectiveness of these models when applied to real applications real-life is still pending judgment.

Smaller AI players, lacking the cash flow to hoard chips, will have to settle for less powerful processors that aren’t under U.S. export controls. Alternatively, they can await potential acquisition opportunities. Li expects that with a confluence of factors, including the scarcity of advanced chips, high demand for data and AI talent, and huge upfront investments, the industry will soon transition into a “consolidation stage.”