Nvidia is at the center of the global artificial intelligence boom, supplying the GPUs and computing infrastructure that power some of the world’s most ambitious AI projects. But the company’s role in the industry is becoming more complicated than simply selling chips.
Nvidia has committed billions of dollars to AI companies that are also among the biggest potential buyers of its hardware. The company has invested almost $50 billion in AI labs and related businesses, while partnerships and financing platforms could eventually help unlock more than $500 billion in outside capital for AI infrastructure.
That has raised an important question on Wall Street: Is Nvidia financing the customers that ultimately generate revenue for Nvidia itself?
The arrangement has increasingly been described as circular financing, a term Nvidia itself acknowledged during its latest earnings discussion.
Nvidia Says AI Labs Could Drive a Quarter of Its Business
Nvidia CFO Colette Kress told analysts on August 26 that demand from AI laboratories and companies supported by Nvidia’s balance sheet could account for approximately one-quarter of the company’s business next year.
The statement highlights just how closely Nvidia’s financial interests are becoming linked to the companies building the next generation of AI systems.
The basic model is relatively straightforward.
Nvidia invests in an AI company or helps that company gain access to financing. The AI company then uses the capital to construct or expand data centers. Those facilities require massive amounts of computing equipment, including Nvidia GPUs and networking systems.
The AI company purchases Nvidia hardware, generating revenue for Nvidia. That revenue strengthens Nvidia’s financial position, potentially allowing the company to make additional investments in the AI ecosystem.
This creates a cycle involving investment, infrastructure construction, Nvidia hardware purchases and further investment.
What Nvidia Has Committed So Far
Kress outlined several major financing relationships during the earnings call.
Nvidia has partnerships with six major investment firms:
- Apollo Global Management
- BlackRock
- Blackstone
- Brookfield Asset Management
- Goldman Sachs
- KKR
According to Kress, these relationships are intended to establish financing platforms capable of raising more than $500 billion in outside capital for AI infrastructure.
However, there is an important distinction between an announced intention and money that is already available.
Nvidia’s earnings materials indicate that several of these arrangements remain subject to definitive agreements. That means the entire $500 billion should not be interpreted as capital that has already been raised or committed to specific projects.
Instead, it represents the potential financing capacity associated with these partnerships.
Nvidia and OpenAI’s Massive Computing Plans
One of the most significant examples involves OpenAI.
Nvidia said it has secured land, power and construction capacity through SB Energy for facilities designed to use Nvidia equipment. The first phase represents approximately 4.25 gigawatts of capacity and is expected to support OpenAI.
Kress also indicated that OpenAI’s existing and planned commitments could involve roughly 12 gigawatts of Nvidia computing capacity through 2030.
The scale is enormous because modern AI systems require data centers containing thousands or even hundreds of thousands of advanced processors.
Nvidia also disclosed credit support for another AI laboratory, although the company did not publicly identify the lab. The support covers almost two gigawatts of capacity.
The identity of that company remains an important unanswered question.
Why Nvidia Does Not Consider It Traditional Circular Financing
Nvidia understands why investors and analysts use the phrase “circular financing.” Kress acknowledged the terminology during the earnings call but argued that Nvidia’s arrangements are different from a simple closed financial loop.
Her argument rests on several factors.
First, Nvidia says external financial institutions still evaluate individual projects based on their own economics. Nvidia is not simply providing all the financing itself.
Second, Nvidia says its hardware customers are generally investment-grade companies or businesses supported by financially strong counterparties.
Third, Nvidia believes its exposure is protected by the underlying value of its equipment.
If an AI company were to experience financial problems, Nvidia argues that its GPUs and other infrastructure could potentially be transferred or sold to another customer.
That final assumption is particularly important.
The protection works best if demand for AI computing remains extremely strong. If demand were to weaken substantially, the resale value and utilization of specialized infrastructure could also come under pressure.
Why AI Labs Need Nvidia’s Financial Support
The reason for these arrangements is connected to the unusual economics of today’s AI industry.
AI companies can have enormous demand for computing resources without having the balance sheets needed to finance the infrastructure themselves.
Many AI startups are relatively young businesses. Traditional lenders often prefer borrowers with long operating histories, predictable cash flows, strong credit ratings and long-term contractual revenue.
AI laboratories can have strong technology and substantial customer demand but still lack those traditional characteristics.
As Kress explained, access to computing can become a bigger constraint than access to customers or technology.
That creates an opportunity for Nvidia.
By helping customers obtain financing or providing credit support that makes lenders more comfortable, Nvidia can potentially accelerate the construction of new data centers—and consequently increase demand for Nvidia’s GPUs.
Nvidia’s Cloud Operator Strategy
The company is also using a similar model with smaller cloud operators.
According to Kress, Nvidia can agree to rent a portion of a cloud provider’s capacity. That commitment effectively provides the operator with a guaranteed revenue floor.
The operator can then use that expected income to secure financing from lenders.
Nvidia may also receive a portion of revenue generated above the guaranteed level.
This creates another potential source of income for Nvidia.
In Kress’s description, Nvidia can benefit from the arrangement in two ways: first through the sale of computing equipment and later through rental-related income.
That model could become increasingly important as specialized AI infrastructure becomes a major asset class.
Nvidia’s Bigger Bet: The Rise of AI Agents
Behind these investments is an even larger assumption about how quickly AI computing demand will grow.
Kress told Morgan Stanley analyst Joseph Moore that AI agents could require 15 to 100 times more computing power than a person interacting with the same AI system.
Nvidia CEO Jensen Huang has also argued that the industry has recently entered a period where AI is becoming predominantly agentic.
AI agents are designed to perform multi-step tasks rather than simply respond to individual prompts. Instead of answering one question and stopping, an agent can plan, use tools, analyze information and execute several actions.
If businesses adopt these systems at scale, the amount of computing required could rise dramatically.
That expectation forms a major part of Nvidia’s investment thesis.
Nvidia Expects Strong Revenue Growth, but Supply Remains a Constraint
Nvidia’s financial outlook reflects its confidence in continued AI infrastructure demand.
The company guided toward approximately $108 billion in revenue for the quarter and indicated that it preliminarily expects around 70% growth in revenue in the year to January 2028.
Kress suggested that the limitation is increasingly supply rather than demand.
In other words, Nvidia believes customers want more AI computing capacity than the industry can currently produce.
That imbalance has been one of the central forces behind the company’s extraordinary growth.
Rising Memory Costs Could Pressure Nvidia’s Margins
There is, however, another challenge emerging from the AI infrastructure boom: memory prices.
Kress warned that memory costs are rising faster than Nvidia had previously anticipated.
The company expects gross margins to decline to approximately 74% in the current quarter, with margins potentially reaching a low of around 71% to 72% in the fourth quarter.
The AI boom itself is partly responsible for the pressure because advanced AI systems require enormous quantities of high-performance memory alongside GPUs.
As demand for AI accelerators increases, suppliers across the semiconductor and memory ecosystem face growing pressure to expand production.
What Could Go Wrong With Nvidia’s Financing Model?
The strategy offers Nvidia significant upside, but it also introduces risks.
If AI companies continue to experience explosive demand, Nvidia can benefit from both its investments and the resulting hardware purchases.
The bigger concern is what happens if the economics of AI infrastructure deteriorate.
If an AI laboratory cannot meet its financial obligations, Nvidia could potentially face losses on both sides of the relationship—its investment and the associated hardware demand.
Nvidia’s response is that the equipment can be redeployed to another customer.
That argument depends heavily on continued demand for AI computing.
If demand remains above supply, GPUs and data-center capacity should retain strong economic value. But if the industry eventually experiences overcapacity, the assumption becomes less certain.
Nvidia’s AI Financing Strategy Is Becoming a Major Part of the Story
Nvidia is no longer simply a semiconductor company benefiting from the AI boom. It is increasingly becoming an investor, infrastructure partner, financing facilitator and computing provider within the AI ecosystem.
Its relationships with AI labs and major financial institutions could unlock hundreds of billions of dollars for data-center construction.
At the same time, those investments may help generate additional demand for Nvidia’s own products.
That is why the company’s financing strategy deserves close attention.
The model could accelerate AI infrastructure spending and strengthen Nvidia’s position if demand continues to grow rapidly. But it also means investors will need to watch the quality of the underlying AI businesses, the economics of data centers and the sustainability of AI computing demand.
Nvidia is scheduled to report its next earnings results on November 17, when investors will get another opportunity to assess how quickly AI infrastructure spending is developing and how much of that growth is connected to companies Nvidia is helping finance.
In short, Nvidia’s circular-financing debate is really a debate about the future economics of AI. If AI demand keeps expanding faster than computing supply, Nvidia’s strategy could prove extremely powerful. If the cycle eventually slows, however, the financial links between Nvidia and its customers could become a much greater source of risk.
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