OpenAI and Anthropic are signing an artificial intelligence infrastructure agreement worth tens of billions of dollars, although neither company is required to pay the full amount in one lump sum at the time of the contract announcement.

The reason lies in the way long-term cloud computing contracts operate. AI Developers do not directly purchase all GPU and data centers; instead, they agree to purchase computing services over multiple years. This allows infrastructure providers to spread the construction costs and payment arrangements over a longer period of time.

But these substantial commitments also present an important question: what if in the future, AI's income is not sufficient to cover the bills?

How do contracts worth hundreds of billions of dollars AI actually function?

In April 2026, Anthropic announced a commitment to spend over $100 billion with Amazon Web Services (Amazon Web Services) within 10 years in order to obtain up to 5 gigawatts of computing power capacity.

Microsoft separately disclosed that OpenAI has signed an additional contract to purchase $250 billion worth of Azure services. Neither of these statements implies that the total amounts mentioned in the headlines will be paid in cash immediately.

Such agreements typically set forth future expenditure obligations, capacity reservations, and commercial terms. Payments may occur as infrastructure is put into use, services are delivered, or contract milestones are met.

For example, in a hypothetical contract worth $10 billion that spans 10 years, if the expenditures are evenly distributed, it would amount to about $1 billion per year. The actual payment schedule can vary significantly, and some contracts may also require a deposit, advance payments, or minimum purchase commitments.

Who will foot the bill for the construction of the AI data center?

Cloud service providers or infrastructure operators often finance the procurement and construction of equipment before receiving payments from most of their customers.

Companies like CoreWeave will utilize debt, equity, and financing secured by GPU to expand their production capacity. Long-term customer agreements can strengthen the rationale for lending, as they provide evidence of future demand.

However, signing a contract does not eliminate financial risks. CoreWeave There are approximately $104 billion in backlogged orders, which correspond to quarterly net interest expenses of $640 million. This indicates that even with strong demand and signed contracts, financing costs can still be quite high.

The broader issue of data center ownership is also important. The company that builds the facility, the institution that provides loans secured by that facility, and the AI developers who purchase its computing power could be three different enterprises.

What will happen if AI is unable to make the payment?

The consequences depend on the terms of the contract. Some agreements include minimum expenditure requirements or "pay-as-you-go" clauses, which means that payments may still be required even if the actual computing power used by the customer is lower than expected.

Other protocols include termination rights, performance conditions, or negotiated remedial mechanisms. It is not possible to determine from the contract title and amount alone how much of the expenditures are unconditional or legally enforceable.

If AI customers fail to make payments, infrastructure operators may face revenue gaps, while still having to fulfill their payment obligations to lenders and equipment suppliers. At that time, the assets used as collateral in the form of GPU may become a focal point in restructuring negotiations.

For investors, this distinction is of paramount importance: the contract amount is not equal to income, income is not equal to cash flow, and future commitments are not equal to guaranteed profits.

Ultimately, the reason why trillion-dollar-level AI transactions are possible is that the payment obligations can be extended over multiple years. Whether they will ultimately generate profits depends on how much computing power the customers actually use, when the payments are made, and whether AI companies can generate sufficient revenue to fulfill these commitments.