China's AI Data Center Boom Turns Bust: Billions in Idle Infrastructure as Market Shifts

China's AI data center boom goes bust: Rush leaves billions of dollars in idle infrastructure

Triggered by the rise of generative AI applications, China rapidly expanded its AI infrastructure in 2023 - 2024 and built hundreds of new data centers using both state and private funding. But this boom has since lost momentum. Facilities that cost billions of dollars now sit underused, returns are falling, and the market for GPU rentals has collapsed. To make the matters worse, many data centers became outdated before they were even fully operational as market conditions have changed, according to MIT Technology Review. The sudden drop in real estate activity following the 2020 COVID-19 pandemic increased pressure to find new economic drivers, and the rise of ChatGPT in late 2022 made AI seem like the next big thing. In 2023 alone, more than 500 data center projects were proposed nationwide, according to KZ Consulting. By late 2024, at least 150 projects were reportedly operational. Local authorities promoted these projects, hoping to boost their regional economies. State-owned companies, government-linked investment funds as well as private companies and investors were eager to back these data centers. But as usually happens with rushed projects, poor planning were their downfall. For example, some facilities were often built without regard for actual demand or technical standards, according to MIT Technology Review's sources among project leads and executives. This is not particularly surprising as engineers with the relevant experience are rare, and many executives depended on middlemen who inflated projections or exploited procurement to get subsidies. As a consequence, many new data centers fell short of expectations as they are expensive to run, difficult to fill, and technically irrelevant for contemporary AI workloads. To make the matters even more complicated, some projects never planned to profit from computation at all. According to multiple reports and industry insiders cited by MIT Technology Review, certain companies used AI data centers to qualify for government-subsidized green energy or land deals. In some cases, electricity earmarked for AI tasks was sold back into the grid for a mark-up. Others secured loans and tax incentives while leaving buildings unused. By late 2024, most people still in the business were aiming to benefit from policy incentives rather than actual AI work, the report claims. When massive AI data centers were built in 2023 - 2024, the envisioned demand for AI training and AI inference performance requirements was different than the actual demand we see today. Nowadays demand is shifting towards inference as this is what makes money for owners of AI models. Inference workloads do not necessarily require massive clusters based on tens of thousands of high-end Nvidia GPUs that are used for training. By contrast, inference workloads can benefit from specialized accelerators with lower cost and power consumption, but faster response times. As a result, monthly rental prices for an H100 server with eight GPUs designed for training have plummeted from ¥180,000 ($24,000) to ¥75,000 ($10,000). Interestingly, despite export restrictions, the H100 continues to flow steadily. As a result, massive rural or inland locations are now far less attractive despite their lower costs. Consequently, some data centers now offer free computing vouchers to local tech firms, but still get underused. By contrast, other data center operators often shut down facilities entirely rather than risk losses from electricity and maintenance that partial rental income cannot cover. One of the biggest shifts came with the rise of DeepSeek, which released a reasoning model called R1 that achieved performance comparable to ChatGPT o1 but at significantly lower cost. This made many AI companies rethink their requirements for hardware and scale. Despite the setbacks, central authorities remain committed to AI development. A government symposium held in early 2025 reaffirmed the need for national self-reliance in this area. Major firms have followed suit: Alibaba announced over $50 billion in planned investments for cloud and AI infrastructure, and ByteDance committed another $20 billion. Insiders believe Chinese officials will not abandon these projects, viewing them as growing pains rather than failures. The government is expected to take over floundering centers and assign them to more capable operators. However, for those operators that cannot rent their capacity to clients that can pay, the bubble has clearly gone bust.

China's AI craze has led to empty data centers and falling GPU rentals

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.

Has the AI Data Center "Bubble" Popped in China? Major AI Clusters Now Sit Idle as China Apparently FOMO'd Into the AI Bandwagon

Well, China was one of the more notable nations to have built up an AI infrastructure rapidly since the technology's inception, but it looks like the "front-running" hasn't worked out for them. There's no doubt that China is currently involved in a technological war with the US, and with AI coming into play, the nation decided not to hold back and instead announced massive investments in the technology to gain superiority. However, this move is now costing the nation a lot, as, according to a report by MIT Technology Review, the data center construction boom in China has now faded, resulting in the industry witnessing a massive slowdown in the economics around AI. Has China's "billions of yuan" in data center investment worked out for them? The report quotes a local data center project manager, who claims that "AI GPU" traders, who once flaunted off when they acquired NVIDIA's cutting-edge accelerators, are now selling off their inventory at "down-to-earth" prices since the demand from domestic markets is far too low. Moreover, data center projects are finding it challenging to secure investment, mainly because the industry doesn't see AI ventures as profitable. Apart from this, it is claimed that more than 80% of China's AI computing resources are idle, and companies based around the data center hype are finding it difficult to "stay afloat." To top it all off, with the release of DeepSeek, the AI economics of the domestic markets shifted rapidly, and a perception has been created that the technology doesn't need high financial resources, which is why investors are now offloading data centers at prices far beyond the market rates, just to stay financially relevant. Now, the reason why China saw a massive AI boom, apart from the global hype, is that the local economic leaders saw AI infrastructure as the next place of "financial stimulus," given that regional real estate and internet industries saw a massive decline in interest. So ultimately, major investors jumped onto the bandwagon, hoping to capitalize on "short-term" profits by boosting stock prices; however, many of these investors found themselves in a mud-pit by apparently FOMO'ing into the hype, which is why the development of AI infrastructure in the nation has seen a slowdown. This case isn't just for China alone, since global tech leaders, notably Microsoft, are also pulling out of data center leases as a "strategic" move to adjust infrastructure. While this certainly doesn't indicate that the AI bubble has popped, it does show that investor sentiment doesn't favor the "long-ball" game with AI.