One of the most surprising stories in the technology world over the last few months has been how the AI infrastructure ecosystem is not only powered by demand but also by a complicated matrix of money, contracts, and cross-shareholdings. Bloomberg recently described it as a "circular" economy of deals and cautioned that the AI boom is now sustained by a network of transactions that, if combined, could go beyond $1 trillion.

This trillion-dollar amount does not refer to a single deal but to the total of the overlapping and interdependent binding agreements between the big players—OpenAI, Nvidia, AMD, Oracle, and others. The transactions mix the buying of hardware, the taking of equity stakes, the signing of capacity guarantees, and the building of a network of the most entangled kinds of systems in which money often returns.

We can untangle this web by figuring out who the players are, what the structures look like, what risks there are, and what outcomes might be probable.

Key Players & Their Roles

OpenAI is the main player in this network. As the top generative AI lab (responsible for ChatGPT and similar models), it has been going all out to obtain the computational resources necessary to both train and later deploy its new models. As a result, it has been making large-scale and long-term deals with both chipmakers and cloud infrastructure providers.

Nvidia is in the system as a vendor and investor, on the other hand. One of the most talked-about deals among the announcements is the planned investment of up to $100 billion by Nvidia in OpenAI, which is dependent on OpenAI using millions of Nvidia chips in its data centers.

AMD also has a partnership with OpenAI: the latter agreed to buy the next-generation GPUs from AMD with a total value of tens of billions of dollars, and OpenAI may become one of AMD's largest shareholders.

Anatomy of the Web: Circular Deals & Interdependence

The reason these relationships are called "circular" is that the same resources and obligations are going through several layers and are often coming back to the place from where they started but in a different form. As an illustration:

Nvidia uses its funds or makes an investment in a company that later becomes a customer of Nvidia by buying hardware.

While OpenAI is purchasing from AMD, it is also becoming an AMD stockholder.

Oracle is placing big bets on OpenAI by hosting its infrastructure, and in return, OpenAI is buying hardware from Nvidia or AMD.

The Scale: How It Reaches $1 Trillion

The total exposure has been estimated differently in various places. However, the $1 trillion value is quite often referred to as the total sum of the overlapping commitment OpenAI has with the core partners.

Some of the breakdowns that have been reported:

Nvidia commitments (in hardware, investment, and related contracts) are speculated to be valued at around $500 billion.

As per Business Insider, AMD deals could be the reason for an exposure of $270 billion.

Oracle’s hosting and infrastructure contracts with OpenAI are valued at approximately $300 billion.

CoreWeave and a few more are adding a couple more incremental billions.

What is more, lots of these are long-term agreements, bonds to buy capacity or keep the minimum usage level—though the actual consumption may be behind.

Why Are They Doing This?

The reasons for people to play such a confusing game vary greatly:

Lock in supply and capacity

Semiconductor supply, most notably for powerful AI chips, is a limited one. Through large-scale, long-term contracts only, AI labs and chipmakers are able to ensure access to capacity and, at the same time, to lessen the risk of supply shortages.

Align incentives and revenue certainty

Committed buyers are an incentive for hardware makers to have steady revenues and thus predictability of their business. On the other hand, equity stakes or shared profits give the purchasers a feeling of partnership and engagement.

The Risks & Fragilities

Although the scale and ambitions are impressive, to say the least, the web also brings along large systemic risks of a serious nature:

Overcapacity & Overhyped Demand

If the use of AI in reality is not as extensive as the infrastructures that have been built for it, then most of the capacity will just be lying there unused. This mismatch would put a lot of pressure on the financers, because in many contracts it is stipulated that payment should be made regardless of whether the service is used or not.

Hidden Financial Exposure

In a situation where a vendor is simultaneously a customer and a cross-owner of a company, the revenue may be inflated while at the same time the cash flow is weak. These internal transfers can shelter the true level of demand that exists.

Take-or-Pay & Backstop Clauses

There are many contracts that require minimum payments or penalties even if the infrastructure is not utilized to full capacity. Such financial rigidity can overburden the entrepreneur's capital when demand is falling.

Conclusion

The AI-related deals that span across the globe and are worth trillions of dollars is one thing to be admired as an outstanding feat of engineering and another thing to be worried about as a financial tightrope. On one hand, it exhibits fairly easy coordination among chipmakers, AI labs, and cloud providers, which has never been seen before. However, on the other hand, it is susceptible to breakdowns due to dependencies that go in circles and limited transparency.

The interlocking agreements may be worth the investment if AI keeps going into the core of businesses and becoming integral to consumer life. However, if demand drops and distortions arise, some parts of the network will detach, and overcommitment and inflated valuations will be revealed.

Anyway, this complex network can be considered the most daring infrastructural project of recent tech history, which will determine not only the technical side of AI but also the business and financial side of it for the next years.