At first glance, OpenAI’s financial picture looks almost unusually clean.
The company reportedly had no debt as of March 31, 2026, along with less than $750 million in lease obligations and around $46 million in spending on buildings and equipment during the first quarter.
But there is another number that changes the conversation completely:
OpenAI reportedly has around $665 billion in purchase commitments.
That does not mean OpenAI borrowed $665 billion. It means the company has reportedly signed long-term agreements tied to access to chips, electricity, data centers and computing capacity.
And that distinction matters.
$0 debt does not mean $0 financial obligations
Debt and purchase commitments are not the same thing.
Debt is money a company has borrowed and is obligated to repay. Purchase commitments are agreements to spend money in the future, often under long-term contracts.
For OpenAI, the reported commitments are tied to infrastructure needed to run and expand its AI operations.
The reported figure of roughly $665 billion includes long-term commitments involving companies and projects connected to:
- Microsoft
- Oracle
- Amazon
- Stargate
- Chips and computing infrastructure
- Power and data-center capacity
So while OpenAI may not have traditional debt on its books, it has reportedly committed to spending enormous amounts over time.
That creates a very different picture from simply looking at the debt line.
The $665 billion number needs an asterisk
There is an important caveat here.
The figures come from third-party reporting based on a review of OpenAI’s financial statements, rather than a public filing from OpenAI.
The reported numbers are also as of March 31, 2026, and are described as unaudited based on the reporting available.
That means the $665 billion figure should not be treated as a precise measure of OpenAI’s current liabilities or as money that the company owes today.
It is better understood as the reported value of its long-term purchase commitments.
That distinction is especially important when looking at a private company where investors do not have the same level of public financial disclosure available for a listed company.
AI infrastructure is becoming a financing game
OpenAI is not the only company facing this challenge.
AI companies need massive amounts of computing power, and building that infrastructure requires huge amounts of capital.
But the company using the GPUs does not always have to be the company borrowing the money.
That is where things get interesting.
A data-center company or separate financing vehicle can raise debt, buy the chips or build the infrastructure, and then lease that equipment or facility to the AI company.
The AI company gets access to the infrastructure without necessarily carrying all of the borrowing directly on its own balance sheet.
The debt exists. It just may sit somewhere else.
Meta offers a useful example
Meta’s Hyperion campus in Louisiana shows how this type of structure can work.
Meta and Blue Owl Capital created a jointly owned company for the project in 2025.
Reportedly:
- Blue Owl-managed funds own 80%
- Meta owns 20%
- Blue Owl contributed roughly $7 billion
- 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.
Meta also reportedly guaranteed the campus’s value for the first 16 years of operations.
The important point is that the financing sits with the separate venture, while Meta remains the operator and tenant.
That structure can allow companies to build enormous AI infrastructure without putting every dollar of borrowing directly onto the operating company’s balance sheet.
OpenAI isn’t alone in using this model
Similar structures have reportedly appeared across the AI infrastructure market.
For xAI’s Colossus 2, a separate company was reportedly funded with around:
- $12.5 billion in borrowed money
- $7.5 billion from owners
- As much as $2 billion from Nvidia
The separate entity reportedly owns the chips and leases them to xAI.
CoreWeave has also reportedly borrowed $18.8 billion through several separate companies, with GPUs backing the loans.
The structures are not identical, but the basic idea is similar.
The company using the hardware may not be the same company that borrowed the money to buy it.
That makes it harder to understand the true financial exposure simply by looking at the operating company’s debt.
Why this matters for private-market investors
This becomes especially important when people are looking at private companies and their valuations.
With a public company, investors can typically dig through:
- Financial statements
- Debt schedules
- Lease obligations
- Footnotes
- Credit reports
- Financing arrangements
Private companies are different.
Investors may receive detailed information under confidentiality agreements, but others may have access to much less.
A headline valuation therefore does not tell the whole story.
Two companies could have similar valuations but very different financial structures.
One could own its computing equipment outright.
Another could lease almost everything.
A third could have guarantees attached to its infrastructure financing.
Same valuation. Very different risk.
The biggest question may be what those GPUs are worth later
There is another risk that is easy to overlook.
High-end AI chips are expensive, but they are also evolving extremely quickly.
The financing model assumes that the hardware will retain enough value to support the debt or lease arrangements attached to it.
That is not necessarily easy to predict.
The market for used GPUs does not have the decades of historical data available for other types of assets.
Aircraft, for example, have established resale markets, maintenance records and decades of data that lenders can use to estimate future values.
High-end AI GPUs do not have that same history.
And the economics are moving quickly.
Reported H100 rental rates fell from nearly $8 per hour in early 2024 to around $1.70 by late 2025, before rising to roughly $2.35 by March 2026.
Those numbers come from limited third-party observations and may not represent every type of contract or GPU deployment.
Still, the broader point is important:
The value and earning power of AI hardware can change quickly.
Who takes the risk if the economics change?
This is where the financing structure becomes particularly important.
Imagine a company finances billions of dollars worth of GPUs based on an assumption about how much those GPUs will earn over several years.
Then newer chips arrive.
Rental prices fall.
Demand slows.
Or the hardware becomes less valuable than expected.
Someone has to absorb that difference.
That could be:
- The company that owns the GPUs
- The lender
- The AI company leasing them
- A guarantor
- Or, in some arrangements, a chip supplier
The key question is not simply “How much debt does OpenAI have?”
It is:
“Who ultimately carries the risk if the economics of this infrastructure change?”
Nvidia is becoming part of the financing ecosystem
Nvidia is not simply selling chips into this market.
The company has also reportedly become involved in financing and supporting some of the infrastructure surrounding AI computing.
Nvidia has reportedly invested $30 billion in OpenAI and has separately been reported to be discussing financing for as much as $350 billion of chip sales to the company.
It has also reportedly:
- Supported $860 million of lease obligations for a data-center partner
- Entered a $1.5 billion arrangement to lease its GPUs from Lambda
- Launched a program that could allow it to rent unused GPUs from participating cloud providers
Nvidia described that program as a revenue-sharing and credit-support model.
That creates an interesting dynamic.
Nvidia knows its hardware better than almost anyone.
It also has a strong understanding of demand for AI computing.
But if Nvidia is simultaneously selling chips, investing in AI companies, supporting financing and potentially taking exposure to GPU utilization, its own financial interests can become connected to the success of the infrastructure ecosystem.
That does not automatically make these arrangements problematic.
It simply makes the terms worth watching.
The real story behind OpenAI’s $46 million
One of the most striking numbers in the report is actually much smaller.
OpenAI reportedly spent around $46 million on buildings and equipment in Q1 2026.
On its own, that number might suggest OpenAI is not spending heavily on physical infrastructure.
But that would miss the larger picture.
OpenAI’s reported purchase commitments were around $665 billion.
The difference highlights how much of the company’s infrastructure strategy can involve long-term agreements and external financing structures rather than simply buying everything directly.
The company can commit to enormous amounts of future computing capacity without recording the entire commitment as debt today.
So is OpenAI really debt-free?
Technically, based on the reported figures, OpenAI reportedly had no debt as of March 31, 2026.
But “debt-free” can sound more reassuring than it should.
It does not mean OpenAI has no major financial commitments.
The company reportedly has hundreds of billions of dollars in long-term purchase commitments connected to the infrastructure required to operate its AI business.
That is why looking at only one number can be misleading.
$0 debt tells you one thing.
$665 billion in commitments tells you something else.
Neither number, on its own, explains the full financial picture.
What investors should really be watching
For anyone evaluating OpenAI or other private AI companies, the important questions go beyond debt.
Look at:
- Who owns the GPUs?
- Who borrowed the money to buy them?
- Who is responsible for lease payments?
- Are there guarantees attached to the financing?
- What happens if GPU values fall?
- Who absorbs losses if demand for computing capacity drops?
- How long are the purchase commitments?
- What happens if the company does not need all of the capacity it contracted for?
- Are the obligations held directly by the AI company or by separate entities?
Those details can completely change the risk profile.
The bigger takeaway
The AI infrastructure race is no longer just about who can build the most powerful models.
It is also becoming a story about capital, financing, leases, guarantees and who ultimately carries the risk.
OpenAI’s reported financial position is a good example.
The company reportedly had $0 in debt at the end of March.
At the same time, it reportedly had around $665 billion in purchase commitments.
That does not mean OpenAI owes $665 billion today.
But it does show the scale of the infrastructure commitments being made to support the AI boom.
And as more companies finance GPUs, data centers and power capacity through separate entities, leases and long-term contracts, investors may need to look beyond the headline debt number.
In the AI infrastructure race, the question isn’t only who is borrowing the money.
It’s who is ultimately on the hook when the bill comes due.
Data point of the day
$46 million
OpenAI’s reported spending on buildings and equipment during Q1 2026.
Compared with that:
~$665 billion in reported purchase commitments.
That gap is what makes OpenAI’s financial structure worth watching closely.
The figures discussed here are based on reported private-company financial information and secondary reporting. They may be unaudited, incomplete or non-standard and should not be treated as fair value, executable pricing or investment advice.