sortiwa
TL;DR: In the wake of ChatGPT’s explosive debut in late 2022, China’s AI industry experienced a surge of excitement and investment. However, this initial fervor has given way to a sobering reality as the country grapples with an oversupply of underutilized data centers and shifting market dynamics.
Xiao Li, a former real estate contractor who pivoted to AI infrastructure in 2023, has witnessed this transformation firsthand through the fluctuating demand for Nvidia GPUs. A year ago, traders in his network boasted about acquiring high-performance Nvidia GPUs despite U.S. export restrictions. Many of these chips were illegally funneled into Shenzhen through international channels. At the market’s peak, an Nvidia H100 – crucial for training AI models – could fetch as much as 200,000 yuan ($28,000) on the black market.
Today, Li noticed that traders have become more discreet and GPU prices have stabilized. Additionally, two data center projects he is acquainted with are struggling to attract further investment as backers anticipate weak returns. This financial strain has forced project leaders to offload excess GPUs. “Everyone seems to be selling, but there aren’t many buyers,” he told MIT Technology Review.
In short, leasing GPUs to businesses for AI model training – a core strategy for the latest generation of data centers – was once considered a guaranteed success. However, the emergence of DeepSeek and shifting economic factors in the AI sector have put the country’s data center industry on unstable ground.
The rapid construction of data centers across China, from Inner Mongolia to Guangdong, was fueled by a combination of government directives and private investment. Over 500 new projects were announced in 2023 and 2024, with at least 150 completed by the end of 2024. However, this building boom has led to a paradoxical situation: an abundance of computational power, particularly in central and western China, coupled with a shortage of chips that meet the current needs for inference and regulatory realities.
The rise of DeepSeek, a company that developed an open-source reasoning model matching the performance of ChatGPT but at a fraction of the cost, has further disrupted the market. Hancheng Cao, an assistant professor at Emory University, noted that this breakthrough has shifted the focus from model development to practical applications. “The burning question shifted from ‘Who can make the best large language model?’ to ‘Who can use them better?'”
This shift has exposed the limitations of many hastily constructed data centers. Many facilities optimized for large-scale AI training are ill-suited for the low-latency requirements of inference tasks needed for real-time reasoning models. As a result, data centers in remote areas with cheaper electricity and land are losing their appeal to AI companies.
The oversupply of computational power has led to a dramatic drop in GPU rental prices. An Nvidia H100 server with eight GPUs now rents for 75,000 yuan per month (around $10,345), down from previous highs of around 180,000 yuan ($25,141). Some data center operators chose to leave their facilities idle rather than operate at a loss.
Jimmy Goodrich, senior technology advisor to the RAND Corporation, attributes this predicament to inexperienced players jumping on the AI bandwagon. “The growing pain China’s AI industry is going through is largely a result of inexperienced players – corporations and local governments – jumping on the hype train, building facilities that aren’t optimal for today’s needs,” he explains.
China’s political system, with its emphasis on short-term economic projects for career advancement, has played a significant role in the data center boom. Local officials, seeking to boost their political careers and stimulate the economy in the face of a post-pandemic downturn, turned to AI infrastructure as a new growth driver.
This top-down approach often disregarded actual demand or technical feasibility. Many projects were led by executives and investors with limited expertise in AI infrastructure, resulting in hastily constructed facilities that fell short of industry standards.
The rise of reasoning models like DeepSeek’s R1 and OpenAI’s ChatGPT has shifted computing needs from large-scale training to real-time inference. This change requires hardware with low latency, often located near major tech hubs, to minimize transmission delays and ensure access to skilled staff.
As a result, many data centers built in central, western, and rural China are struggling to attract clients. Some, like a newly built facility in Zhengzhou, even distribute free computing vouchers to local tech firms but still struggle to find users.
Despite the challenges, China’s central government prioritizes AI infrastructure development. In early 2025, it convened an AI industry symposium emphasizing the importance of self-reliance in this technology.
Major tech companies like Alibaba and ByteDance have announced significant investments in cloud computing and AI hardware infrastructure.
Goodrich suggests that the Chinese government views the current situation as a necessary growing pain. “The Chinese central government will likely see [underused data centers] as a necessary evil to develop an important capability… They see the end, not the means,” he says.
As the industry evolves, demand remains strong for Nvidia chips, particularly the H20 model designed for the Chinese market. However, for many in the field, like data center project manager Fang Cunbao, the current state of the market has prompted a reevaluation.
At the beginning of the year, Fang left the data center industry entirely. “The market is too chaotic. The early adopters profited, but now it’s just people chasing policy loopholes,” he explains. He’s now shifting his focus to AI education.
Source link