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Circular financing becomes risky when the same companies are investors, suppliers, customers, lenders or guarantors in one chain. Start with a transaction map and ask: Who is financing the buyer, and what is the buyer committed to purchasing from that financier or its affiliates? A large investment is not automatically circular, and reciprocal deals are not automatically improper. The danger is dependence: revenue, asset utilization and debt service may all rely on counterparties whose own cash flow depends on the same deal continuing.
This guide explains how to trace those links, separate independent AI demand from partner-funded purchases, uncover debt-like obligations outside the balance sheet, and test whether a financing network could transmit losses through cloud providers, chip companies, AI labs, private credit and banks.
What circular financing means in AI
In the most prominent pattern identified by the Bank for International Settlements (BIS), chip makers or hyperscalers take equity stakes in AI labs or neocloud providers. Those recipients then commit to buying chips, cloud capacity or computing power from the same companies or connected entities. The arrangement can also include leases, warrants, guarantees, revenue-sharing agreements and special-purpose vehicles (SPVs).
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- A chip supplier or cloud provider invests cash, extends credit or provides a guarantee to an AI company.
- The AI company uses that support to reserve capacity, purchase chips or sign a multiyear cloud contract.
- The supplier records sales, backlog or utilization, while the AI company reports access to capital and infrastructure.
- Both parties become more dependent on the other remaining solvent and on future financing being available.
The key distinction is between independent demand and recycled demand. Cash sales to unaffiliated customers demonstrate demand more strongly than purchases that exist only because a vendor supplied the buyer with equity, a guarantee, discounted compute or another form of support.
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Why the structure can look healthy at first
Each individual contract may be legal and disclosed. An investment can help a young lab buy scarce GPUs; a cloud commitment can give a provider predictable capacity planning; and a lease can spread infrastructure payments over time. The risk appears when several contracts are treated as independent even though the same counterparties support every leg of the transaction.
For example, an AI lab may owe minimum payments to a cloud provider, while that provider owns warrants in the lab and receives a revenue share. If the lab cannot attract unaffiliated customers, the cloud provider may face both a falling investment value and lower utilization. If the provider financed the data center with debt or an SPV guarantee, the loss can travel to lenders and investors.
Why scale matters in 2026
The amounts involved make small weaknesses potentially material. The International Monetary Fund (IMF) estimates $3.4 trillion in AI-related capital expenditure through 2029. The IMF also reports that hyperscalers raised more than $100 billion in bond financing since January 2025. Separately, BIS reports that hyperscaler gross bond issuance topped $100 billion in 2025. These figures measure related but different populations and should not be added together or interpreted as a circular-financing ratio.
They do show that AI infrastructure is moving beyond a purely operating-cash-funded expansion. BIS says financing is shifting toward debt and private credit. As commitments grow, the relevant question is not simply whether AI investment is large, but whether the underlying cash flows can support the obligations when utilization, prices or technology change.
Build a complete dependency map before judging a deal
Begin with every legal and economic connection, not just the headline equity investment. Record the party, instrument, amount, term, trigger and beneficiary for each item.
- Equity stakes, preferred shares, warrants and options.
- Loans, convertible notes, private-credit facilities and bond obligations.
- Cloud minimum-spend commitments, capacity reservations and chip purchase agreements.
- Leases for data centers, servers, networking equipment and power capacity.
- Guarantees, letters of credit, indemnities and residual-value support.
- Revenue shares, rebates, discounts and termination payments.
- Joint ventures and SPVs, including the sponsor, outside investors and ownership percentages.
- Exclusivity, right-of-first-refusal and information-sharing provisions.
- Exposure held by banks, insurers, pension funds and private-credit vehicles.
Draw two versions of the map. The first shows legal ownership. The second shows economic exposure: who bears utilization risk, who absorbs a lower chip resale value, who must fund a cash shortfall and who is entitled to the first dollars of revenue. A minority stake can still create substantial exposure if it comes with guarantees, offtake rights or a requirement to provide additional funding.
Seven tests for circularity and credit risk
1. Separate partner-funded purchases from independent demand
For each major customer or commitment, identify whether the buyer is unaffiliated, whether it pays in cash, and whether it could make the purchase without the vendor’s investment or guarantee. Ask management for revenue and committed-capacity amounts attributable to affiliated or economically connected counterparties.
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Do not treat a signed reservation as equivalent to realized demand. Compare deposits, invoices and collections with reported bookings. Examine whether the customer has enough outside revenue, cash and financing to meet minimum payments if the supplier stops investing.
2. Reconcile all leverage and refinancing dates
Combine funded debt with obligations that may behave like debt: lease liabilities, guaranteed SPV borrowings, minimum cloud payments, purchase commitments and termination fees. Build a maturity schedule showing when each obligation must be paid or refinanced.
Stress the schedule under higher interest rates, delayed data-center utilization, lower GPU prices and a closed refinancing market. A structure that works only if every new bond or private-credit facility is available is more fragile than one covered by recurring operating cash flow.
3. Look through off-balance-sheet vehicles
BIS describes joint ventures and SPVs funded by sponsors and private placements, with hyperscaler leases, capacity offtake commitments and guarantees. These arrangements can replace an upfront capital expenditure with multiyear operating expense while keeping much of the debt outside the hyperscaler’s balance sheet.
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For every vehicle, identify:
- The sponsor and each minority investor.
- Debt raised by the vehicle and the lenders or private-credit funds.
- The lease, offtake or capacity contract with the hyperscaler.
- Guarantee triggers, maintenance covenants and step-in rights.
- Who bears residual-value, utilization and technology-obsolescence risk.
- What happens if the AI customer defaults or the vehicle cannot refinance.
BIS calls obligations of this kind “shadow borrowing”: economically debt-like commitments that largely reside outside corporate balance sheets. A company can therefore appear lightly levered while being committed to fixed payments through a related vehicle.
4. Measure concentration and possible contagion
List exposure to the same chip supplier, cloud provider, AI lab, lender, insurer, power project and geographic data-center cluster. Concentration should be measured by both revenue and obligations. A provider may have many customers but still depend on one chip architecture, one cloud region or one financing channel.
The IMF warns that multidirectional links among central AI firms can amplify adverse shocks nonlinearly. A single event—such as a delayed model launch, chip-price decline or financing shutdown—can reduce demand for computing, weaken an AI lab’s equity value, impair a lender’s collateral and force a cloud provider to write down capacity at the same time.
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5. Compare asset life with debt life
Match the maturity of each loan, lease or offtake obligation with the useful life of the equipment supporting it. The IMF reports an average implied property, plant and equipment life of about seven years for major hyperscalers, while noting that GPUs and advanced chips may become obsolete sooner.
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Model a replacement cycle that arrives before debt maturity. Include lower resale prices, impairment charges, redeployment costs and the capital required to buy the next generation of accelerators. Long debt against short-lived equipment can leave a company paying for capacity that no longer earns competitive prices.
6. Examine contractual lock-in and competition effects
Read cloud and partnership agreements for minimum spend, exclusivity, termination fees, migration restrictions, data-portability terms and access to scarce GPUs. The U.S. Federal Trade Commission (FTC) has identified potential effects on access to compute and talent, higher switching costs, restrictive commitments and sensitive information sharing.
A customer may be profitable only because it cannot move workloads elsewhere, while the supplier may depend on the same customer to fill a dedicated cluster. Record the practical cost and time to migrate data, models, networking configurations and engineers—not just the legal termination fee.
7. Identify who absorbs a downside
For every stress scenario, name the first-loss holder and the parties exposed after that protection is exhausted. Trace losses through equity, warrants, guarantees, leases, lenders, insurers and any public or private funds that own the debt. A transaction is less transparent when the party bearing the economic risk is different from the party reporting the asset or revenue.
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Use the same questions for a direct investment, a capacity contract, a lease-backed SPV and a strategic partnership. The following axes reveal whether growth depends on correlated counterparties, refinancing or accounting opacity.
| Comparison axis | Lower-risk characteristics | Higher-risk characteristics |
|---|---|---|
| Balance-sheet location | Debt and payment obligations are fully consolidated and clearly disclosed. | Leases, guarantees or SPV commitments leave substantial debt-like exposure outside the main balance sheet. |
| Demand source | Unaffiliated customers pay cash from diversified end-market revenue. | Purchases are funded by the supplier’s equity, guarantee, discount or credit support. |
| Type of support | Limited equity investment with no required purchase or guarantee. | Equity is combined with minimum purchases, offtake, warrants, revenue rights or guarantees. |
| Asset and liability timing | Debt maturity allows repayment before equipment becomes uneconomic. | Debt or leases extend beyond the expected competitive life of GPUs and networking equipment. |
| Counterparty mix | Many independent customers, lenders and infrastructure sites. | One chip supplier, cloud provider, lender, insurer or power cluster dominates exposure. |
| Contract flexibility | Clear termination rights, portable data and practical multi-cloud options. | Exclusivity, high termination fees, minimum spend and technical migration barriers. |
| Transparency | Terms, related parties, guarantees and vehicle debt are easy to reconcile. | Material obligations depend on estimates, side agreements or opaque special-purpose structures. |
What to inspect in filings and contracts
Financial statements and notes
- Related-party transactions and investments in customers, suppliers or joint ventures.
- Contractual purchase commitments and minimum cloud or capacity payments.
- Lease liabilities, guarantees, indemnities and contingent obligations.
- Debt maturities, covenant tests, collateral and refinancing assumptions.
- Consolidation judgments for SPVs and variable-interest entities.
- Impairment policies and useful-life assumptions for GPUs, servers and data centers.
- Revenue concentration, deferred revenue, customer deposits and receivables aging.
Equity and partnership documents
- Whether the investor receives warrants, preferred returns or revenue-sharing rights.
- Whether the recipient must buy chips, cloud capacity or services from the investor or an affiliate.
- Exclusivity, volume floors, take-or-pay clauses and renewal conditions.
- Guarantee triggers if utilization, valuation or cash balances fall.
- Rights to customer data, model information, engineering talent or future financing.
- Termination fees, migration assistance and data-export obligations.
Reconcile the contract language with reported revenue. A vendor that books a large commitment should explain how much has been delivered, billed and collected, and how much remains a conditional reservation.
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Stress scenarios that expose weak circular structures
Utilization falls
Assume a data center or GPU cluster operates below its planned level for several quarters. Recalculate customer-level minimum payments, operating cash flow and the ability to cover lease or debt service. Then test whether the sponsor must inject additional capital.
Compute prices decline
Lower GPU rental rates reduce revenue even if physical utilization is unchanged. Test collateral values, impairment risk, lease economics and the ability to refinance equipment at the original valuation.
Technology is replaced early
Assume advanced chips become uncompetitive before the modeled useful life. Add replacement capex while the old equipment still carries debt or contractual payments. Check whether customers can migrate and whether the provider can sell or redeploy the equipment.
Refinancing closes
Remove access to new bonds and private credit for a defined period. Identify which obligations mature first, which guarantees are called and whether a liquidity reserve is sufficient. This scenario distinguishes a cash-generating business from one that must continually roll obligations.
A major counterparty defaults
Apply a default to the largest connected AI lab, cloud provider or chip supplier. Trace second-order effects: lost offtake, lower utilization, impairment, covenant breaches, collateral calls and insurer or bank losses. IMF describes current financial-stability risks as contained for major hyperscalers because of strong balance sheets and free cash flow, but that resilience does not eliminate a future transmission channel.
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Watch for several signals appearing together rather than treating any single item as proof of a bubble:
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- More financing supplied by the same firms that receive the resulting sales.
- Debt, leases and guarantees growing faster than recurring operating cash flow.
- SPVs or joint ventures taking on infrastructure debt with sponsor guarantees.
- Long contracts financing equipment likely to age faster than the liability.
- Customer or supplier concentration hidden by a large number of legal entities.
- Exclusivity and minimum-spend clauses that make switching uneconomic.
- Repeated equity raises, private-credit refinancings or asset sales needed to meet ordinary payments.
These signs indicate correlated exposure, not necessarily misconduct. The important follow-up is to quantify who loses money under each downside case and whether that loss can be absorbed without emergency refinancing.
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Could circular AI financing trigger a wider credit crisis?
It could, but the available evidence does not establish that a systemic crisis is inevitable. A wider event would require several conditions: weak independent demand, large interconnected obligations, losses on assets or guarantees, and limited ability to refinance or absorb impairments.
The IMF’s description is conditional: “the complex multidirectional channels of shock transmission between circle firms created due to the circular structure could result in nonlinear amplification of adverse shocks.” BIS likewise links sustainability to AI companies meeting high earnings expectations. If earnings disappoint while debt and private-credit exposure are high, a repricing could tighten corporate credit more broadly and expose vulnerabilities in private lending.
Conversely, major hyperscalers currently have strong balance sheets and free cash flow, which the IMF says keeps financial-stability risks contained for now. That is a statement about present resilience, not a guarantee that every lab, neocloud provider or financing vehicle is safe.
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There is no single authoritative percentage for the share of AI revenue or investment financed circularly. The IMF and BIS figures are scale indicators, not a sector-wide circularity ratio. Nor does a reciprocal investment prove that revenue is artificial, that a contract violates competition law or that a crisis will follow.
The defensible conclusion is narrower: reciprocal equity, purchase commitments, leases, guarantees and SPVs can create dependence that ordinary revenue and debt metrics miss. An assessment is strongest when it identifies every connected obligation, measures independent cash demand, matches asset life to liability maturity and shows who bears losses under stressed conditions.
A practical review checklist
- Draw legal-ownership and economic-exposure maps.
- List every equity, warrant, loan, lease, guarantee, offtake and minimum-spend term.
- Separate unaffiliated cash sales from purchases supported by a connected counterparty.
- Reconcile on-balance-sheet debt with SPV, lease and guarantee obligations.
- Build a maturity schedule and test higher rates and unavailable refinancing.
- Model lower utilization, lower compute prices and earlier chip obsolescence.
- Measure exposure to common suppliers, customers, lenders, insurers and regions.
- Read exclusivity, termination, portability and information-sharing clauses.
- Identify the first-loss holder and every party exposed after guarantees are exhausted.
- State which conclusions are observed contractual facts and which are scenario assumptions.
This process answers the central question without relying on a headline investment figure: is the AI business generating independent cash demand, or is a network of connected firms financing purchases that support one another’s reported growth?
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