Nvidia's newly announced initiative to channel more than US$500 billion in third-party financing into artificial intelligence data centers is set to reinforce its global market dominance, potentially undermining the competitiveness of Chinese chips on the world stage. The company revealed on Monday that it had signed memoranda of understanding with six major financial institutions, including BlackRock, Blackstone, and Goldman Sachs, to establish dedicated compute financing platforms. These platforms are designed to help customers purchase Nvidia's chips and build so-called AI factories, effectively turning Nvidia's compute and full-stack AI infrastructure into an investable asset class.
The financing structure offers global capital providers exposure to long-duration, usage-linked revenue tied to Nvidia's chips. However, Chinese commentators have voiced concerns that this move could deal a significant setback to China's AI ambitions. They point to three primary risks: deeper reliance on Nvidia's CUDA software ecosystem, rising prices for imported high-end chips, and fewer opportunities for Chinese AI chips in overseas markets.
Lai Jiaqi, a writer at Guancha.cn, expressed skepticism about the initiative. "Investors worry this is essentially a closed-loop game of moving money from one hand to the other," she said. "Nvidia provides the financing, customers use it to buy Nvidia chips, and that spending feeds back into Nvidia's revenue growth, relying on external leverage to artificially create chip demand rather than reflect real demand in the industry." Lai also warned of potential financial risks, drawing parallels to the 2008 subprime mortgage crisis. "As Wall Street offers customers loans to buy Nvidia's chips, financial risks increase significantly. If returns from AI applications fall short of expectations, it could trigger something like the subprime mortgage crisis of 2008," she added.
An Anhui-based columnist using the pen name "Irresistible Freedom" echoed these concerns, arguing that financial leverage would reinforce the lock-in of the CUDA ecosystem. "Global AI research and model training will become even more dependent on Nvidia hardware," he wrote. "That will narrow the technology paths available and reduce diversity across the global AI industry." He also cautioned that cheap financing could fuel blind expansion of computing capacity, leading to oversupply within two to three years. "GPUs that depreciate 30% to 40% a year are financed with debt that runs five to 10 years, so falling demand or rents could shrink collateral values and trigger a wave of defaults," he noted.
The columnist further suggested that Chinese chipmakers such as Cambricon Technologies, Hygon Information Technology, and Biren Technology would lose their cost advantage overseas, as foreign data centers could borrow cheaply to acquire Nvidia GPUs. This intensifying competition in computing power has already begun to squeeze profit margins at some Chinese tech giants. Tencent Holdings, operator of WeChat, reported an 11% revenue increase to 204.8 billion yuan in the quarter ending June 30, but net profit rose only 0.7% to 56 billion yuan, missing forecasts, as capital expenditure on AI nearly tripled to 52.8 billion yuan.
Hardware curbs widen
Some Chinese commentators see potential opportunities for domestic data center equipment suppliers, as large-scale expansion of overseas AI factories could boost exports of servers, liquid cooling systems, and high-speed connectors. However, such optimism may be premature, given Washington's efforts to bar American data centers from using Chinese-made components. The Federal Communications Commission (FCC) is drafting a rule to ban imports of Chinese-made optical transceivers, which are used in data centers to convert electrical signals into light for high-speed transmission. Sources told Reuters that the agency aims to publish the rule before the end of the year, citing concerns that the components could be used to steal data, install malware, or disrupt operations at critical AI data centers.
Amid rising US-China tensions, many American AI data centers already under construction have avoided Chinese components to preempt any tightening of regulations. Meanwhile, China is developing its own mechanism for data centers to securitize AI computing power and raise fresh capital. In March 2026, Zhang Yunquan, a member of the National Committee of the Chinese People's Political Consultative Conference (CPPCC) and a researcher at the Chinese Academy of Sciences, proposed establishing an AI computing power bourse. "High-end GPU resources are concentrated in large tech companies, and market information is severely asymmetric," Zhang said. "Trading of computing power is still dominated by bilateral deals with no unified price index or risk management tools." He suggested exploring futures and options on computing power and, in the longer term, securitizing computing power assets as tradable products. He predicted China could launch such products within two to three years.
In March 2026, Shanghai began accepting applications for national computing power interconnection nodes, building a unified system of identification, standards, and rules to underpin a future computing power exchange. In June, Shanghai issued guidelines to prepare for computing power futures as part of its push to become a global asset management hub. That same month, 21Vianet Group, a Nasdaq-listed Chinese data center operator, announced plans to explore similar financing mechanisms. These efforts reflect China's determination to build its own AI infrastructure and reduce dependence on foreign technology, even as Nvidia's financing plan threatens to deepen the divide.
The stakes are high for both sides. Nvidia's move could cement its dominance in AI hardware, while China's response—ranging from domestic chip development to financial innovation—will shape the global AI landscape. As the competition intensifies, the financial and technological strategies adopted by both nations will have far-reaching implications for the Indo-Pacific region and beyond.
