Is Nvidia building an AI bubble itself: what is behind its new deals?
Nvidia is promoting massive deals worth more than $750 billion, including investments, guarantees, and financing for customers purchasing its chips. From the company's perspective, this is a way to accelerate the establishment of AI infrastructure globally. However, critics warn that as more money flows between these companies, the fear grows that a cycle is being created where Nvidia finances the demand for its own products. The question beginning to trouble Wall Street is whether this is a brilliant business model or a risk that might only be revealed if the AI market slows down.
Nvidia is no longer just the most important chip manufacturer in the world. In recent years, it has become a central player in almost every link of the artificial intelligence value chain. The company invests in startups, participates in financing data centers, forms partnerships with cloud providers, holds shares in infrastructure companies, and now even manages negotiations to provide guarantees and financing frameworks for the world's largest customers. In other words, it is no longer satisfied with selling processors, but also helps finance the systems that will purchase them.
The recent deals illustrate how much this strategy has expanded. Nvidia announced a collaboration with the Korean SK Group worth over $500 billion, which includes building new data centers and purchasing chips and memory over many years. Simultaneously, according to reports, the company is discussing providing a guarantee of up to $250 billion that will help OpenAI rent capacity in a massive data center being built by SoftBank in the USA. Additionally, it is in talks about financing OpenAI's chip purchases worth about $350 billion for the same project. If the negotiations come to fruition, this would be one of the largest financing deals ever seen in the technology industry.
Here, the concept of "Circular Financing" arises. The idea is simple: a company helps finance the customers who purchase products from it, and thus part of the demand it presents is created through the capital it provides itself. In Nvidia's case, the money it invests or guarantees may allow customers to build larger data centers, buy more AI processors, and continue to expand operations. As long as real demand continues to grow, this model may accelerate the pace of technology adoption. But if demand turns out to be lower than expectations, those same projects could become a financial burden for both the customers and those who helped finance them.
This is not a phenomenon unique to Nvidia. Google, for example, helped Anthropic obtain financing of tens of billions of dollars through guarantees for data center lease contracts. SoftBank builds a significant part of its strategy around OpenAI and finances huge investments through debt raising. Private funds, banks, and infrastructure companies have also become an integral part of a system whose goal is to continue and inject capital into the AI race. As these companies become dependent on each other, the risk increases that a negative event at one of them will spread quickly to the entire system.
This is exactly the reason why some investors are beginning to compare the AI market to other models of aggressive financing seen in the past. The fear is not that Nvidia is creating demand out of thin air, but that the combination of capital investments, guarantees, loans, and cross-holdings might blur the line between organic demand and demand supported by financing. When all links in the chain depend on investments continuing at the same pace, one slowdown could create a domino effect: fewer data centers, fewer chip purchases, less revenue, and less ability to repay the debts taken to finance the expansion.
On the other hand, Nvidia rejects the claims. The company's CEO, Jensen Huang, has previously argued that the investments it makes constitute only a small part of the total financing that customers are raising anyway, and therefore they should not be seen as a factor creating artificial demand. According to him, the company is simply helping to accelerate projects that would have gone ahead anyway, while also benefiting from a financial return on some of the investments. From his perspective, when a new industry requires investments of hundreds of billions of dollars, it is natural that the central technology supplier will also be a partner on the financial side.
It is also hard to ignore the business logic behind the move. Nvidia understands that the biggest barrier today is not the quality of its chips, but the ability of customers to finance and build data centers quickly. If it can help a customer accelerate a project by two years, the meaning is selling thousands or tens of thousands of processors earlier, strengthening the relationship with the customer, and increasing the chance that they will continue to choose its technology in future generations as well. In this sense, the investment is not just a financial instrument, but also a strategic tool for strengthening Nvidia's position at the center of the AI industry.
The agreement with the SK Group also illustrates this well. Beyond building new data centers in Korea, Nvidia secures long-term access to HBM memory from SK Hynix — one of the main bottlenecks in the industry. Simultaneously, SK commits to purchasing Nvidia's computing systems and expanding its AI infrastructure. Each side strengthens the other, and the success of one depends to a large extent on the success of the other. As more deals of this type are signed, a system is built in which chip suppliers, memory manufacturers, cloud providers, and data center operators are closely linked.
For investors, the important question is not whether these deals are good or bad, but what will happen if the pace of investment in AI slows down. As long as OpenAI, Microsoft, Meta, Amazon, Alphabet, and other companies continue to order more chips and build more data centers, the entire system may continue to grow. But if one of the companies decides to reduce investments, if banks make it difficult to provide credit, or if the economic return on AI projects disappoints, the close financial ties built in recent years could turn from an engine of growth into a source of risk.
Ultimately, Nvidia's new deals symbolize the next stage in the artificial intelligence revolution. The competition is no longer just about who will develop the fastest chip, but also about who will succeed in financing the massive infrastructure on which the entire industry will be built. It is possible that in a decade it will turn out that these investments accelerated AI adoption and created one of the greatest growth periods in the history of technology. But it is also possible that they will be remembered as the moment when the boundary between real demand and demand financed through the same ecosystem began to blur. For Wall Street, this is one of the most important questions of the coming years.
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