Public balance sheets tell one story about the artificial intelligence industry. The footnote disclosures tell another. An investigation by Nikkei Asia found that five US technology giants — Alphabet, Microsoft, Amazon, Meta, and Oracle — carried an estimated $1.65 trillion in debt that does not show up on their headline balance sheets, against only $1.35 trillion they reported officially for the same period. The hidden sum is larger than the reported one, not a rounding difference lost in a footnote.

Futurism, reporting on the Nikkei findings, notes that Meta alone has amassed roughly $420 billion in off-balance-sheet obligations. That single company’s shadow debt exceeds the gross domestic product of entire mid-sized nations, and it sits outside the leverage ratios analysts quote on earnings calls.

This is the central puzzle of the AI economy. The firms building the future are also building a parallel ledger of commitments that their published financials do not fully capture. The question is not whether the debt exists — it does. It is who ultimately pays for it, and when the bill is presented.

How Off-Balance-Sheet Financing Actually Works

The machinery is older than the AI boom. A special-purpose vehicle, or SPV, is a legally distinct entity created to hold an asset or a loan. The parent company can lease capacity from the SPV, promise to buy its output, or guarantee its debt — without consolidating that obligation onto its own books, provided the arrangement meets narrow accounting thresholds for control and risk absorption.

In the data-center era the same trick wears new clothes. Operating leases, sale-leaseback deals, and long-term power purchase agreements let a hyperscaler book a gleaming server farm as “someone else’s problem” for accounting purposes while still depending on it operationally. The asset earns revenue for the parent; the liability lives next door, in a structure the parent does not consolidate.

The result is a balance sheet that looks lighter than the business really is. Equity analysts who screen on debt-to-EBITDA see a healthier company. Creditors who read only the consolidated statements see less risk. The real exposure hides in the commitments section, the variable-interest-entity footnote, and the contractual obligations table that most readers skip past.

There are legitimate reasons for some of these structures: they allocate risk, attract specialist capital, and let operators focus on compute rather than real estate. The analytical problem is not the existence of SPVs. It is the cumulative opacity they create when every major player uses them at the same time.

Why the Labs Reach for Special-Purpose Vehicles

The motivation is not fraud. It is optics and arithmetic. Frontier AI is brutally capital intensive: a single training run can cost $100 million to $1 billion, and the data centers to serve the models cost hundreds of times more. Putting all of that debt on the parent’s balance sheet would pressure credit ratings, trigger covenant limits, and dilute equity.

By routing spending through SPVs and lease structures, a lab can preserve headline leverage ratios that keep its investment-grade rating intact and its borrowing costs low. Rating agencies weight reported net debt heavily; a lower reported number protects the rating, which in turn lowers the interest rate on every subsequent bond. The devices are self-reinforcing.

The firm can also avoid issuing new shares that would dilute existing investors during a valuation peak. Off-balance-sheet financing is, in this framing, a rational response to a genuine funding gap — a way to fund a buildout the equity markets will not fully underwrite at today’s prices.

The danger is that “rational for the individual firm” compounds into “dangerous for the system.” When every major player uses the same devices, the industry’s true leverage becomes invisible precisely when investors most need to see it. Transparency erodes one footnote at a time, and the market prices the clean version of the story.

The Leverage Stacked Into the AI Compute Chain

The leverage is not only on the labs’ books. It is built into the financing of the compute stack itself. A working paper from Columbia Business School estimates that some AI infrastructure vehicles carry a leverage ratio of roughly 90 percent debt — about $27 billion of borrowing against $30 billion of asset value — far above what an investment-grade corporate issuer would tolerate (SSRN).

The private-credit industry has rushed to fill the gap. Blackstone and Apollo are reported to be structuring roughly $36 billion of debt financing for Anthropic’s infrastructure expansion, packaged through special-purpose vehicles, equipment-backed credit, and syndicated private loans. This is not venture capital betting on a model. It is asset finance betting on the picks and shovels.

For the alternative managers, the logic is clean: instead of guessing which lab wins, finance the chips, data centers, and power that every lab must rent. The shift turns AI infrastructure into a new real-asset class — and layers debt onto assets that have no proven long-term cash flow yet, underwritten largely on forecasts of demand that has not materialized.

That transformation matters for systemic risk. When pension funds, insurers, and private-credit funds all hold slices of the same AI infrastructure debt, a slowdown in one lab’s adoption rate can propagate through balance sheets that look, on the surface, completely unrelated.

OpenAI, Stargate, and the Debt-First Buildout

Nowhere is the debt-first model clearer than in the Stargate project. The OpenAI, Oracle, and SoftBank venture launched with an initial $100 billion commitment and a plan to scale to $500 billion by 2029 (Reuters). The capital is not all equity. JPMorgan agreed to lend $2.3 billion for the Abilene, Texas site alone, and OpenAI has said it will pursue “creative financing” — including debt — to lease the chips the data centers require.

Oracle is the most exposed of the major players. CNBC reports the company is carrying more than $100 billion in debt while its free cash flow has turned negative, effectively funding tomorrow’s data centers with tomorrow’s borrowings. When the only major builder leaning on debt this heavily is also the one leasing capacity back to the labs, the circularity is hard to ignore.

The pattern is consistent: equity announces ambition, debt funds the concrete, and operating leases convert the concrete into a recurring obligation that sits, by design, partly off the consolidated statement. Each layer of financing is individually defensible. Stacked together, they form a chain whose weakest link is future demand.

The Sequoia Question: Revenue Versus Capex

All of this spending presumes a revenue future that does not yet exist. Sequoia Capital framed the problem as AI’s $600 Billion Question: the industry must generate roughly $600 billion a year in incremental revenue just to justify the compute buildout already underway. Current realized revenue is a fraction of that figure, and the gap widens with every new data-center groundbreaking.

The gap is not a moral failing. It is a timing mismatch. Capex is spent today, in concrete and silicon. Revenue arrives, if it arrives, over years of enterprise adoption, consumer subscriptions, and new workflows. The bet is that demand compounds faster than the interest bill. History is full of industries that made the opposite bet and discovered the bill arrived first.

The margin math is unforgiving. Inference and API revenue must not only grow but do so at a gross margin high enough to service debt taken on against depreciating hardware. Chips that look cutting-edge at purchase can be commercially obsolete inside three years, while the loan behind them runs for ten. The depreciation clock and the repayment clock rarely align.

For readers tracking the broader market, the same arithmetic sits at the heart of our analysis of what survives when the AI bubble bursts — the survivors will be the ones whose revenue caught up to their capex before the refinancing window closed.

What Happens If Revenue Lags Capex

If revenue lags, the hidden ledger stops being a cosmetic choice and becomes a liability. Three mechanisms matter. First, refinancing risk: SPV debt is often short- to medium-term and must be rolled over. A cooling market raises spreads exactly when the borrower is weakest, turning a manageable coupon into a crushing one.

Second, covenant and rating pressure: if leased capacity cannot cover its own carrying cost, the guarantees parents signed begin to bite, pulling obligations back onto consolidated balance sheets at the worst possible moment. The off-balance-sheet shield was always conditional on the asset performing.

Third, asset fire sales: specialized AI data centers have thin secondary markets, so distressed capacity may sell far below build cost, locking in losses that equity holders absorb. A server farm built for one lab’s workload is not easily repurposed for another’s, which limits who can bid.

None of this requires a dramatic crash. A few quarters of disappointing enterprise uptake, a widening gap between promised and realized margins, and the comfortable off-balance-sheet structure can invert into a visible, rating-agency-defined problem overnight. The speed of the reversal is the part markets consistently underestimate.

Lessons From Enron — and Why This Is Different

The comparison to Enron is tempting and, as Bloomberg Tax reports, already circulating among accountants. Technical accounting consultant Tom Selling warned that while the accounting treatment “is in fashion,” the real risk is “what if one of these companies was a house of cards and was propping itself up with this accounting treatment?”

The distinction matters. Enron used off-balance-sheet vehicles to conceal losses and inflate earnings through outright fraud. Today’s SPVs are generally disclosed in footnotes and are legal under current rules. The labs are not, on the available evidence, falsifying results. They are using permitted structures to present a cleaner picture than the underlying economics warrant.

That is a softer failure, but not a harmless one. Permitted opacity still hides risk from the people who price it. The Enron lesson is not “fraud happened” but “nobody could see the leverage until it was too late.” The current disclosure regime repeats the visibility problem without the criminality, and visibility is the only thing that lets markets price risk correctly.

Reading the Real Balance Sheet

For investors and observers, the published net-debt figure is the starting point, not the answer. The real exposure lives in the 10-K footnotes: variable-interest entities, operating-lease obligations, purchase commitments, and guaranteed residual values on sale-leasebacks. Add those back and effective leverage climbs, sometimes by a factor that changes the investment thesis entirely.

Equally important is concentration. When a handful of labs lean on a handful of private-credit managers and a single dominant leasing partner, a problem at one node propagates across the chain. The AI debt complex is more interconnected than any individual company’s tidy balance sheet suggests, and correlation rises exactly when it is most dangerous.

Disclosure quality also varies by jurisdiction and issuer. A lab that is not yet public may disclose far less than a mature hyperscaler, leaving the fullest picture of industry leverage partly in private credit filings that few retail investors ever see. The most complete ledger is the one least people read.

Regulators have noticed. The same lobbying machinery that shapes AI policy also shapes the accounting rules under which these structures are permitted — a thread we trace in our reporting on record AI lobbying spending in Washington. Disclosure standards are not neutral; they are negotiated, and the negotiators have stakes.

The Bill Always Comes Due

The AI industry has financed a physical capital boom — power plants, chips, and data centers — with a financial architecture that pushes the cost out of sight and into the future. The technology may well deliver enormous value. The financing, however, has borrowed that future against assumptions no one has yet proven.

Off-balance-sheet debt is not free money. It is deferred visibility. When revenue arrives on schedule, the structures look like clever engineering. When it does not, the footnotes become the headline, and the staggering bill the labs papered over returns to the only place it was ever going to land — the consolidated statement, and the investors who trusted the cleaner version of the story.

The only real question is whether the industry’s revenues will compound as fast as its obligations — and if they do not, who is left holding the $1.65 trillion that was never really hidden from everyone, only from the people who needed to see it most?

References

1. Nikkei Asia, “Five US tech giants’ hidden debts soar to $1.65tn on opaque AI funding,” asia.nikkei.com.
2. Futurism, “AI Companies Are Trying to Hide a Staggering Amount of Debt,” futurism.com.
3. Bloomberg Tax, “Big Tech AI Spree Revives Accounting Devices That Toppled Enron,” news.bloombergtax.com.
4. Reuters, “OpenAI widens scope of Stargate, eyes debt finance for chips,” reuters.com.
5. HedgeCo, “Blackstone and Apollo Work on $36 Billion Anthropic Debt Deal,” hedgeco.net.
6. CNBC, “Oracle is building yesterday’s data centers with tomorrow’s debt,” cnbc.com.
7. Sequoia Capital, “AI’s $600 Billion Question,” sequoiacap.com.
8. Columbia Business School, “Financing the AI Buildout” (SSRN working paper on ~90% asset-level leverage), papers.ssrn.com.

The post AI’s Debt Problem: How the Labs Hide a Staggering Bill appeared first on The AI Prism.

The artificial intelligence sector has spent two years training the public to watch one number: how much money the models earn. Quarterly earnings calls lead with cloud growth, and headlines celebrate record funding rounds. Yet a different ledger is growing quietly in the background, and almost no one in the mainstream conversation measures it. The companies building the AI infrastructure are borrowing on a scale that has no precedent in the history of the industry, and they are doing it through channels that don’t show up cleanly in the headlines.

Two recent reports frame the problem from opposite ends. A Fortune investigation places the total of hidden AI-related borrowing at roughly $1.65 trillion, spread across hyperscaler bond issuance and structured financing that sits off the obvious balance-sheet lines [Fortune, 2026]. A separate analysis at wheresyoured.at argues the demand side is itself a bubble, with revenue that depends on the same firms renting capacity back to one another [wheresyoured.at]. Put the two together and a single question sits underneath the entire AI trade: what happens when the borrowing stops being cheap and the demand turns out to be circular?

The $1.65T Number

The headline figure is large enough that it is easy to dismiss as a rounding error of the cloud era. It isn’t. The $1.65 trillion estimate aggregates the debt raised by the largest technology companies and the financial vehicles they use to fund data-center construction, GPU purchases, and power agreements [Fortune, 2026]. Much of it is denominated in investment-grade bonds, which is precisely why it draws little alarm: the issuers are rated well enough that the market treats the borrowing as safe.

What makes the number useful is less its precision than its direction. Capital spending by the hyperscalers has climbed from a meaningful line item to the single largest use of cash on their books. When a company’s capex grows faster than its operating income for several consecutive years, the gap has to be filled somewhere, and equity investors rarely fund that alone.

Debt fills it. The firms have issued bonds at a pace that would have been unthinkable for a software business a decade ago, because the asset they are buying — compute — is treated as a long-lived, defensible moat rather than a depreciating expense [Fortune, 2026]. The bet is that the revenue arrives before the interest comes due.

How the Borrowing Hides

The reason this debt escaped scrutiny for so long is that a meaningful share of it never appears where an ordinary reader looks. Traditional balance sheets capture bonds and bank loans, but the AI buildout leans on structures that sit a step away from the parent company: special-purpose vehicles, sale-leaseback arrangements, and power-purchase agreements that move the obligation to a financing partner [Fortune, 2026]. The capacity still gets used by the same firm, but the liability lives elsewhere.

This is not fraud. These are legal, long-established financing techniques, and they are used across every capital-intensive industry. The distinction matters because the techniques are opaque by design. An analyst who reads only the headline debt figure sees a healthy balance sheet; an analyst who traces the lease obligations and the off-balance-sheet vehicles sees a very different picture of leverage.

The opacity compounds the risk. When the true scale of borrowing is hard to measure, the market cannot price it correctly, and when the market cannot price it, the first sign of strain arrives as a surprise rather than as a gradual repricing. Surprises are what turn a manageable correction into a forced one.

Capex vs Revenue

The cleanest way to see the tension is to put the spending next to the income. The hyperscalers are guiding capital expenditure upward by sums that dwarf the incremental revenue those investments are expected to produce in the near term. The gap between the two is exactly the slice that has to be financed, and financing means borrowing [Fortune, 2026].

Defenders point out that cloud infrastructure has always been built ahead of demand. The difference now is the slope. The spending is not a gentle curve that smooths out as customers arrive; it is a near-vertical line justified by the assumption that AI workloads will absorb every dollar of new capacity. That assumption deserves scrutiny rather than deference.

Revenue, meanwhile, is real but uneven. The firms report strong cloud growth, yet the portion of that growth that traces directly to generative AI remains a smaller fraction than the capex suggests it should be. Until the two lines converge, the financing gap is a standing liability that accrues interest every single day it remains open.

The Demand Question

This is where the second report lands. The wheresyoured.at analysis argues that the demand we celebrate is thinner and more concentrated than the headlines imply [wheresyoured.at]. Inference traffic is growing, but the customers paying for it are themselves a small set of well-funded incumbents, and a large share of the usage is the labs and platforms consuming their own output to train the next model.

A market where the buyers and the sellers are the same handful of companies is not necessarily fake, but it is fragile. Real demand is measured by entities that could walk away; demand that is captive to the firms doing the building cannot be relied on to persist if the financing environment tightens. The question is not whether anyone uses the models, but whether enough independent buyers use them at prices that justify the buildout.

The optimistic case says enterprise adoption is early and will compound. The cautious case says we are watching a self-referential loop where each new data center is justified by the revenue from the previous one. The truth is probably between the two, and the debt does not care which story wins — it comes due either way.

Circular Revenue Worries

The circularity is the part that should make a careful reader pause. A model lab rents GPUs from a cloud provider, builds a product, sells access to developers, and some of that developer activity flows back to the same cloud provider as inference spend. The dollars move in a loop, and at each step a revenue figure is recorded [wheresyoured.at]. Growth that emerges from a closed loop looks identical to growth that emerges from a genuine market until the loop is stressed.

None of this means the products are worthless. Developers are shipping real software on top of these models, and enterprises are finding genuine use cases. The concern is one of proportion: if a meaningful share of the recorded revenue is simply the same money circulating among a small group of giants, then the multiple the market assigns to that revenue is built on a base that is smaller than it appears.

Circular revenue is tolerable when it is a small fraction of the total. It becomes a problem when the entire financing case depends on the total continuing to grow, because the loop has no external force pushing it outward once the participants have saturated their own needs. At that point the growth has to come from someone new, and new buyers are exactly what the cautious reports say are missing.

Who’s Exposed

If the borrowing and the demand are both overstated, the exposure is not limited to the firms doing the building. The lenders who bought the bonds are exposed to the credit risk, and a wave of downgrades would land on insurance companies, pension funds, and asset managers that hold the paper as safe [Fortune, 2026]. The utilities that signed power agreements are exposed to cancellation risk if projects stall.

The AI labs themselves sit in the most precarious spot. They are the smallest balance sheets against the largest ambitions, and many of them depend on the hyperscalers both for compute and for the cloud credits that show up as revenue. A tightening in one relationship propagates quickly through the others, because the ecosystem is more interconnected than its separate branding suggests.

Sovereign wealth funds and other large allocators that poured capital into the theme are exposed as well, though their size gives them patience that a levered startup does not have. The point is that this is not a contained trade. The borrowing was syndicated across the global financial system, which means the bill, if it comes, is shared broadly rather than borne by a single careless actor.

For readers tracking how this could resolve, see The AI Prism’s take on the post-bubble landscape, which maps which parts of the stack are likely to survive a repricing.

Historical Parallels

The pattern has a precedent that is uncomfortable to revisit. In the late 1990s and early 2000s, telecommunications carriers borrowed enormous sums to lay fiber and build network capacity, convinced that internet traffic would grow without bound. The traffic did grow, but not fast enough to service the debt, and the resulting defaults reshaped the industry [Federal Reserve, macro context]. The assets were real; the timing of the payoff was wrong.

AI infrastructure is not telecom, and the firms involved are far more profitable than the carriers ever were. But the structural similarity is the part worth holding onto: when capacity is built on borrowed money against a demand curve that is assumed rather than proven, the discipline is supplied by the credit market, and the credit market is patient only until it isn’t. History suggests the turn is sudden, not gradual.

Another parallel sits closer to the present. The 2021–2022 correction in speculative technology showed how quickly capital that was abundant becomes scarce, and how valuations that looked durable were propped up by a cost of money that changed. The AI debt load is being issued in a different rate environment than the easy-money era, which cuts both ways: the borrowing is more expensive, but the caution is also more warranted.

What a Repricing Would Actually Look Like

It helps to be concrete about the failure mode, because abstraction invites complacency. A repricing does not require a dramatic default. It can begin with a single rating agency placing a cloud provider’s off-balance-sheet vehicle on negative watch, which raises the cost of the next bond, which narrows the spread between borrowing and returns, which quietly slows the next build.

From there the feedback is gentle until it isn’t. The firms most exposed are the ones that borrowed against the most optimistic demand curve; a small downward revision in expected inference growth can turn a comfortable coverage ratio into a strained one. The debt does not need to become unpayable for the financing environment to tighten, and a tighter environment is exactly what stalls the next wave of capacity.

The safeguard is not optimism but optionality. Firms that can slow spending without stranding assets, that have real external demand, and that financed with maturities matched to hardware life will absorb a repricing. The rest will discover that the $1.65 trillion was less a war chest than a timer.

The Bottom Line

The revenue story is real, and no honest account of the AI sector can dismiss the genuine productivity the models have unlocked. The debt story is also real, and it is the one almost nobody is counting. A buildout financed by $1.65 trillion of borrowing, much of it hidden in structures that sit a step away from the balance sheet, only makes sense if the demand underneath it is broader and more independent than the cautious reports allow [Fortune, 2026] [wheresyoured.at]. The two narratives cannot both be comfortably true at the same time, so which one gives first when the credit window narrows?

References

1. Fortune — “AI’s debt binge can’t last, hidden borrowing reaches $1.65T” (2026)
2. wheresyoured.at — “The AI Demand Bubble”
3. The AI Prism — After the AI crash: what survives when the bubble bursts
4. U.S. Securities and Exchange Commission — EDGAR corporate bond and financing filings
5. Federal Reserve — cost of capital and corporate credit conditions

The post The AI Debt Binge: $1.65T of Hidden Borrowing and a Demand Bubble appeared first on The AI Prism.