According to a new financial analysis report from IO Fund, the tight connection between chip giant Nvidia and specialized cloud providers like CoreWeave and Nebius is raising major questions about the transparency of the current GPU market. Analysts are questioning whether the cash flow circulating among these entities is a form of circular financing designed to sustain the AI hardware boom.
Background & Origin
During the boom of generative artificial intelligence, cloud startups like CoreWeave and Nebius emerged as Nvidia's largest customers, purchasing tens of thousands of Hopper and Blackwell GPU chips. However, deeper analysis indicates that Nvidia is not merely a vendor, but also acts as a direct or indirect investor in these very customers. This creates a financial loop: Nvidia injects capital into startups, and the startups use that exact money to order chips from Nvidia, helping the giant's revenue continuously hit record highs.
Technical & Technological Analysis
Regarding technological infrastructure, GPU-specialized cloud providers like CoreWeave build massive data centers optimized specifically for training large language models (LLMs). However, relying entirely on a single hardware architecture from Nvidia exposes this ecosystem to high risks. If algorithmic optimization demands shift toward other specialized chips (such as Google's TPUs or Amazon's custom-designed chips) or when software performance improvements reduce the number of required GPUs, the business model of these highly leveraged clouds risks a rapid collapse.
Expert Opinions & Insights
Financial experts at IO Fund warn that this model shares similarities with past telecom bubbles, where revenues were inflated through cross-transactions of equipment. Although the involved parties assert that investments and purchasing contracts are completely independent and based on real market demand, observers still advise investors to remain cautious when assessing the sustainability of Nvidia's data center revenue growth rate.
Impact & Future
Skepticism over this financial model could lead to serious valuation adjustments for semiconductor and AI stocks in the near future. For the tech community, this serves as an important lesson in evaluating AI infrastructure costs. Instead of over-investing in expensive hardware, enterprises should focus on model optimization and seeking cost-effective alternative solutions.