OpenAI reportedly had zero debt as of March 31, 2026.
At first glance, that sounds like a remarkably clean balance sheet for one of the companies spending more aggressively on artificial intelligence infrastructure than almost anyone else.
But there is another number that deserves just as much attention: roughly $665 billion in purchase commitments.
That does not mean OpenAI has borrowed $665 billion. It means the company has reportedly signed long-term agreements committing it to spend enormous amounts on chips, electricity and data-center capacity.
And that distinction is becoming increasingly important as the AI industry finds new ways to finance the infrastructure needed to keep the boom going.
Zero debt does not mean zero financial commitments
The headline is simple: OpenAI reportedly had no debt, less than $750 million in lease obligations, and about $46 million in buildings and equipment spending during the first quarter of 2026.
But its reported purchase commitments were on an entirely different scale.
Those commitments, estimated at around $665 billion, reportedly cover long-term access to:
- AI chips
- Power
- Data-center capacity
- Infrastructure from companies including Microsoft, Oracle and Amazon
- Projects connected to the broader Stargate initiative
These are not loans sitting on OpenAI’s balance sheet.
That matters.
A purchase commitment is a contractual obligation to spend money in the future. Debt, on the other hand, is borrowed money that has to be repaid.
So saying OpenAI has $665 billion of debt would be inaccurate.
But looking only at the debt figure and ignoring those commitments would also leave out a huge part of the financial picture.
The figures cited here come from reported reviews of OpenAI’s financial statements and have not been independently verified by Augment. They are also private-company figures rather than the kind of detailed public filings investors get from listed companies.
The AI industry is changing who actually carries the debt
This is where the story gets more interesting.
Building AI infrastructure is extraordinarily expensive. The companies operating the models need massive amounts of computing power, but they do not necessarily have to borrow all the money themselves.
Instead, debt can sit with a separate company that owns the data center or the chips.
The operating company then leases the infrastructure or commits to purchasing computing capacity.
That structure can make the operating company’s balance sheet look very different from the economics of the entire project.
Meta offers a useful example
Meta’s Hyperion campus in Louisiana shows how this model can work.
Meta and Blue Owl Capital created a jointly owned company in 2025. Blue Owl-managed funds own 80%, while Meta owns 20%.
Blue Owl contributed roughly $7 billion, while Meta contributed land and assets under construction.
The venture reportedly raised around $27 billion through investment-grade bonds.
Meta operates the campus and leases it from the venture. It also guaranteed the campus’s value for the first 16 years.
So the borrowing belongs to the infrastructure vehicle, while Meta remains the user of the facility and takes on contractual obligations through rent and guarantees.
The details vary from company to company, but the underlying idea is increasingly common: the company using the infrastructure does not necessarily own the debt used to build it.
OpenAI is not the only company using this playbook
Other AI companies are also relying on complicated financing structures to fund enormous infrastructure requirements.
For xAI’s Colossus 2, a separate company reportedly raised approximately $12.5 billion in debt alongside $7.5 billion from its owners, including as much as $2 billion from Nvidia.
That entity owns the chips and leases them to xAI.
CoreWeave has reportedly borrowed $18.8 billion through several separate companies, with GPUs backing the loans.
There is an important lesson here for anyone trying to understand the AI infrastructure boom:
Follow the asset, not just the headline debt number.
Ask:
- Who owns the GPUs?
- Who owns the data center?
- Who borrowed the money?
- Who is paying the lease?
- Who has guaranteed the financing?
- What happens if the equipment loses value?
- Who takes the loss if demand falls?
Those questions can reveal a very different financial picture from simply looking at the operating company’s reported debt.
The biggest risk may be what happens to GPUs later
There is another piece of this story that deserves attention.
The financial world has decades of experience financing assets such as aircraft, buildings and telecom infrastructure.
High-end GPUs are different.
The market for used AI computing equipment is relatively young, and there is far less history showing what these chips will be worth several years from now.
That creates an uncomfortable question for lenders and investors:
What happens if today’s expensive AI hardware becomes tomorrow’s outdated equipment?
Rental prices already show how quickly the economics can move.
Reported H100 rental rates fell from nearly $8 per hour in early 2024 to roughly $1.70 by late 2025, before climbing to around $2.35 by March 2026.
Those figures come from limited third-party observations, so they should not be treated as a complete picture of the market.
Still, the direction is worth watching.
If computing prices fall sharply while companies are locked into long-term infrastructure commitments, the economics of those contracts can become much less attractive.
Nvidia is becoming part of the financing story too
Nvidia’s role in the AI boom is no longer just about selling chips.
The company has reportedly invested $30 billion in OpenAI and has separately been reported to be discussing financing connected to as much as $350 billion of chip sales to OpenAI.
Nvidia has also reportedly supported lease obligations for a partner’s data center and entered an arrangement to lease its GPUs from Lambda.
In July, Nvidia announced a program allowing it to potentially rent unused GPUs from participating cloud providers in exchange for a share of cloud revenue.
That creates an interesting dynamic.
Nvidia understands the performance of its hardware better than almost anyone. It also has a strong view of what customers are willing to pay for computing power.
But when the chipmaker also has financial exposure to the infrastructure ecosystem, investors need to understand where the risk ultimately sits.
If GPUs remain highly valuable and demand stays strong, these structures can work well.
If hardware values fall faster than expected or computing demand disappoints, the question becomes much more important:
Who absorbs the loss?
Private-company investors have a transparency problem
This may be the most important takeaway.
Public companies have to disclose a significant amount of financial information through filings, footnotes, lease schedules and other reporting.
Private companies operate differently.
Some investors may receive extensive financial information under confidentiality agreements. Others may see much less.
That makes private-market valuation considerably harder.
Two companies could appear to have similar valuations while carrying very different financial obligations.
One might own its computing infrastructure outright.
Another might lease it.
A third could have guaranteed financing connected to infrastructure it does not technically own.
The valuation alone does not tell you the difference.
That is why understanding the contracts behind AI infrastructure is becoming just as important as understanding revenue growth or user numbers.
The $46 million versus $665 billion comparison is striking
One of the most eye-catching numbers in the report is the gap between OpenAI’s reported first-quarter capital spending and its long-term commitments.
OpenAI reportedly spent $46 million on buildings and equipment in Q1 2026.
Its reported purchase commitments were approximately $665 billion.
Those numbers measure very different things, so they should not be compared as if they were equivalent forms of spending.
But the contrast highlights the scale of what OpenAI has reportedly agreed to commit to over the long term.
The AI race is no longer simply about who can build the best model.
It is also about who can secure enough chips, power and data-center capacity to operate those models at enormous scale.
The bigger AI investment question
The most important question may not be whether OpenAI has debt today.
It is this:
How much financial risk sits around the company even when that risk does not appear as conventional debt on its balance sheet?
That is a much harder question to answer.
The AI industry is building an extraordinary amount of infrastructure, and companies are experimenting with increasingly creative ways to finance it.
That does not automatically make these structures dangerous.
In many cases, separating asset ownership from operations can be an efficient way to bring long-term capital into infrastructure projects.
But it does mean investors need to look beyond the headline balance sheet.
Debt is only one part of the story.
The real picture includes purchase commitments, leases, guarantees, financing vehicles, asset values and the obligations that companies have promised to take on years into the future.
And with hundreds of billions of dollars tied to the next phase of AI infrastructure, those details are becoming impossible to ignore.
The number to remember
$665 billion.
Not OpenAI’s debt.
Not money it has already spent.
But a reported mountain of long-term commitments that shows just how capital-intensive the AI race has become.
The next phase of the AI boom may be decided not only by who builds the smartest models, but by who can finance the infrastructure behind them, and who ultimately carries the risk when the economics change.