Assess circular-financing risk by tracing both sides of every transaction: who supplies the capital, and what the recipient is required to buy in return. A chip maker or hyperscaler that invests in an AI lab, which then commits to purchasing that investor’s chips or cloud capacity, creates a feedback loop. The loop becomes dangerous when the same counterparties are simultaneously investors, suppliers, customers and guarantors, and when the arrangement depends on continual refinancing or optimistic AI earnings.
The practical test is not whether an AI company has received outside investment. It is whether reported demand would survive without the vendor’s equity, credit support, guarantees, leases or minimum-purchase commitments. Build a complete dependency map, separate independent sales from partner-funded purchases, reconcile all debt-like obligations, compare equipment lives with debt maturities, and stress-test a fall in utilization, prices or refinancing access.
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What circular financing means in AI
Circular financing is a network of reciprocal commitments rather than a single financial product. The prominent pattern identified by the Bank for International Settlements (BIS) is that chip makers and hyperscalers take equity stakes in AI labs or neocloud providers, while those recipients commit to multiyear purchases of chips or computing power. The recipient may also sign a lease, reserve capacity, issue warrants, share revenue or receive a guarantee. A special-purpose vehicle (SPV) can sit between the parties and borrow to fund data-centre equipment.
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| Stage | Transaction | Economic question |
|---|---|---|
| 1. Capital | A chip supplier or cloud provider buys equity, warrants or debt in an AI company. | Would the AI company have obtained comparable funding from an unaffiliated investor? |
| 2. Commitment | The AI company agrees to buy chips, cloud capacity or managed computing for several years. | Is the purchase based on independent customer demand or on the investor’s support? |
| 3. Infrastructure | An SPV, lease or capacity vehicle finances servers and data-centre capacity. | Who bears utilization, residual-value and refinancing risk? |
| 4. Revenue | Payments from the AI company or its customers flow back to the original supplier or connected lender. | Does cash come from end users, or circulate among affiliated parties? |
Ordinary vertical investment can also involve a supplier becoming a customer or investor. Circularity is a risk characteristic when the parties’ solvency depends on the loop continuing. If the investor’s funding enables a purchase that produces the investor’s reported revenue, and that revenue is then used to justify more funding, a small shock can be amplified across the network.
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What circular financing is not
- It is not a published measure of all AI investment, and there is no authoritative sector-wide percentage showing how much AI revenue is circularly financed.
- It is not proof that every reciprocal investment is improper. Strategic partnerships can provide useful capital, technical support and predictable capacity.
- It is not automatically a crisis. The IMF says financial-stability risks remain contained for major hyperscalers for now because they generally have strong balance sheets and free cash flow.
Why the exposure is growing
The amounts involved make the structure important even when individual contracts appear manageable. The International Monetary Fund (IMF) estimates $3.4 trillion in AI-related capital expenditure through 2029. It 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.
| Indicator | Reported amount and period | What it tells an assessor |
|---|---|---|
| AI-related capital expenditure | $3.4 trillion through 2029, IMF estimate (2026) | The potential infrastructure funding base is large enough for correlated losses to reach corporate-credit markets. |
| Hyperscaler bond financing | More than $100 billion since January 2025, IMF (2026) | Part of the build-out is financed through debt rather than only operating cash flow. |
| Hyperscaler gross bond issuance | More than $100 billion in 2025, BIS (2026) | Debt issuance is occurring at a scale that can matter for refinancing conditions and lenders. |
| Implied property, plant and equipment life | About seven years on average for major hyperscalers, IMF (2026) | Book-life assumptions may exceed the useful life of some advanced chips and systems. |
These are scale indicators, not a circularity ratio. They do not show what share of spending is funded by a partner, nor do they establish that a systemic crisis is inevitable.
Start with a complete dependency map
Map the legal and economic links before judging any single investment. Public filings often describe an equity stake but place purchase obligations, guarantees or leases in separate notes. Include private-credit funds, insurers and banks: BIS notes that off-balance-sheet vehicles can connect those providers to hyperscalers.
- List every capital instrument. Record common or preferred equity, convertible debt, warrants, vendor financing, revolving facilities, private-credit loans and any commitment to provide future funding.
- List every purchase obligation. Capture chip orders, cloud minimum-spend provisions, capacity reservations, take-or-pay clauses, data-centre leases, power contracts and revenue-sharing arrangements.
- Record credit support. Identify parent guarantees, letters of credit, completion guarantees, insurance, collateral and triggers that transfer risk if utilization or revenue falls.
- Identify vehicles and owners. For every joint venture or SPV, note the sponsor, ownership percentage, lenders, consolidation treatment and who controls decisions.
- Draw cash-flow direction. Show where funding enters, where payments leave, and whether a payment is made to an investor, a related company or a lender connected to the investor.
- Mark legal versus economic exposure. A minority stake may still carry substantial exposure through a guarantee, lease, capacity reservation or obligation to absorb losses.
A useful map has one line for each obligation and labels whether it is on the buyer’s balance sheet, disclosed as a commitment, or held by another entity. If a filing gives only a broad description, record the missing term rather than treating it as zero.
A small example
Suppose CloudCo buys 15% of LabCo. LabCo then agrees to spend $2 billion on CloudCo’s computing over five years. An SPV leases servers from CloudCo, borrows from a private-credit fund and receives a minimum-capacity guarantee from CloudCo. LabCo’s reported growth may look strong, but the map reveals three dependencies: CloudCo’s equity supports LabCo, LabCo’s purchases support CloudCo’s revenue, and the guarantee can return losses to CloudCo if utilization disappoints. The map also shows that the private-credit fund and any insurer are exposed even though they do not own either company.
Separate independent demand from recycled demand
Revenue is stronger evidence of market demand when it comes from unaffiliated customers paying cash without the seller’s investment or guarantee. Ask management or filings for the share of purchases made by related parties, the number of independent customers, customer concentration and payment terms.
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Questions to ask about each major customer
- Could the customer fund the purchase without equity, a guarantee, discounted compute or a vendor loan from the seller?
- Is the customer paying a commercially comparable price, or is the price offset by credits, rebates, free consulting or a future revenue share?
- Does the customer have external revenue that covers the commitment, or is it relying on another financing round?
- Would the order be cancelled or reduced if the investor withdrew support?
- Are several apparently independent customers funded by the same lender, insurer, power project or AI platform?
Partner-funded purchases are not necessarily false demand. They are weaker evidence of durable demand because the buyer’s ability and incentive to purchase are linked to the vendor’s capital. A high percentage of such purchases warrants a lower confidence in near-term revenue forecasts and a larger liquidity buffer.
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AI infrastructure can shift financing from operating cash flow toward bonds and private credit. BIS identifies that shift as a source of vulnerability. Do not stop at reported borrowings: add lease liabilities, purchase commitments, guarantees, offtake contracts and debt at unconsolidated vehicles.
Build a maturity and coverage schedule
- Collect principal, interest rate, maturity date, amortization and covenant information for bonds, bank loans and private-credit facilities.
- Add the present and maximum potential value of guarantees, letters of credit and take-or-pay commitments.
- Place lease payments, minimum cloud spend and capacity reservations on the same timeline as debt maturities.
- Compare annual obligations with operating cash flow after maintenance capital expenditure, taxes and working-capital needs.
- Identify any year in which several counterparties must refinance simultaneously.
Stress the schedule under higher interest rates, delayed data-centre utilization, lower GPU rental prices and a closed refinancing market. A company that covers interest only under a high-utilization forecast is dependent on both demand and capital-market access. A guarantee can turn a supplier’s apparently fixed investment into a contingent debt obligation when a partner misses payments.
Look for off-balance-sheet “shadow borrowing”
BIS describes joint ventures and SPVs funded by sponsors and private placements, with hyperscaler leases, capacity offtake commitments and guarantees. It calls the result “shadow borrowing”: obligations economically akin to debt but largely outside corporate balance sheets. The structure can replace upfront capital expenditure with multiyear operating expense while leaving the hyperscaler exposed to the vehicle’s performance.
Disclosure points to extract
- The SPV’s sponsor, ownership and consolidation status.
- Lease term, renewal options, residual-value guarantees and who pays for upgrades.
- Capacity offtake, minimum utilization, take-or-pay provisions and termination fees.
- Guarantee triggers, maximum exposure, collateral and recourse to the parent company.
- Private-placement lenders, insurers and any cross-default provisions.
- Whether equipment can be redeployed to another customer if the original AI buyer fails.
Compare the vehicle’s obligations with the parent’s stated capital expenditure. A sharp gap is not proof of concealment, but it is a reason to reconcile commitments, related-party notes and risk-factor language line by line.
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Compare asset life with debt life
Financing is safer when the asset can generate cash for at least as long as the debt remains outstanding. 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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| Test | Lower-risk pattern | Higher-risk pattern |
|---|---|---|
| Useful life | Conservative life supported by replacement history and resale evidence. | Long accounting life despite rapid chip generations and uncertain resale value. |
| Debt maturity | Debt amortizes before or near the end of realistic productive life. | Large bullet maturity after the equipment may require replacement. |
| Utilization | Contracted demand from diverse, unaffiliated customers. | One affiliated buyer or a forecast that assumes near-full utilization. |
| Replacement capex | Cash flow funds upgrades without new borrowing. | New debt is required to replace obsolete accelerators while old debt remains. |
Model an early obsolescence case: prices for older GPUs fall, utilization drops during migration to a new architecture, and replacement capital expenditure arrives before the original loan matures. Include impairment charges and the possibility that lenders demand additional collateral. The relevant question is not the accounting depreciation period alone, but the period during which the equipment can earn a competitive return.
Measure concentration and contagion
Count exposures to the same chip supplier, cloud provider, AI lab, lender, insurer, power project and geographic data-centre cluster. A company may appear diversified by customer name while every customer depends on one platform or one private-credit fund.
The IMF warns that multidirectional links among central AI firms can amplify adverse shocks nonlinearly. For example, a chip-price decline can reduce the value of collateral, weaken a neocloud provider, trigger a guarantee, reduce cloud purchases and then pressure the original chip supplier’s revenue. The same shock can affect lenders that financed several companies in the loop.
Concentration indicators
- Largest customer and largest supplier as percentages of revenue or cost.
- Share of capacity tied to one cloud, chip architecture or data-centre region.
- Common lenders, insurers, private-credit funds and debt maturities.
- Cross-default, cross-guarantee or change-of-control clauses.
- Dependence on one power contract, network provider or scarce engineering workforce.
Run a single-counterparty failure scenario and a correlated scenario. In the first, one buyer defaults but others remain healthy. In the second, several buyers reduce spending after the same model-performance or funding shock. The correlated case is the one that exposes circularity.
Read cloud and partnership contracts for lock-in
The Federal Trade Commission (FTC) has identified risks in large cloud and AI partnerships involving access to compute and talent, higher switching costs, restrictive commitments and sensitive information sharing. Its documentation describes combinations of equity or revenue rights, billions in cloud commitments, discounted compute, consultation or exclusivity rights and information exchange.
Contract terms that change risk
- Minimum spend and take-or-pay: The customer owes money even when demand or utilization falls.
- Exclusivity: The customer cannot move workloads or buy equivalent capacity from another provider.
- Termination fees: Leaving may be more expensive than continuing with uneconomic capacity.
- Migration barriers: Proprietary tooling, data-egress charges, incompatible APIs or scarce staff can delay a switch.
- Price and credit adjustments: Discounts, credits and rebates may make headline revenue hard to compare with cash received.
- Information rights: Shared forecasts, customer data or technical plans can increase dependence and competitive sensitivity.
Assess whether a customer can move workloads to a second provider within the contract’s term and at a known cost. A partnership that improves short-term access to GPUs may still increase long-term dependence and reduce bargaining power.
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Use six comparison axes for any deal
Compare arrangements consistently rather than judging the equity stake in isolation.
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|---|---|---|
| Balance-sheet location | Who records the debt, lease and guarantee? | Material obligations sit in an unconsolidated vehicle with limited disclosure. |
| Demand independence | Who pays, and could the purchase occur without partner support? | Most revenue comes from parties funded or guaranteed by the seller. |
| Type of support | Is support equity, debt, a guarantee, a discount or a minimum purchase? | Equity is paired with binding purchases and contingent liabilities. |
| Asset life versus debt | Can the equipment earn cash through maturity? | Debt outlasts realistic chip usefulness or requires a large bullet repayment. |
| Counterparty diversity | Are customers, lenders and suppliers independent? | Several entities rely on the same platform, lender or insurer. |
| Transparency and lock-in | Can terms, related parties and switching costs be measured? | Complex contracts obscure prices, termination rights or guarantee triggers. |
Stress-test the network, not just the company
A useful stress test changes one assumption at a time and then combines them. Start with a 20% to 30% fall in utilization only if that range is an explicit scenario rather than a claimed forecast; the sources do not establish a standard percentage. The important point is to test a material decline, not to present an invented probability.
- Demand shock: Reduce independent customer orders and test whether minimum-spend commitments remain payable.
- Price shock: Lower GPU rental rates and resale values; recalculate collateral and impairment.
- Technology shock: Shorten the useful life of advanced chips and bring replacement capital expenditure forward.
- Funding shock: Assume refinancing is unavailable at maturity and apply a higher rate to any renewed debt.
- Counterparty shock: Default the largest affiliated buyer and activate guarantees, termination fees and cross-defaults.
- Combined shock: Apply lower utilization, lower prices and closed refinancing simultaneously to reveal nonlinear effects.
For each case, record cash burn, covenant headroom, collateral calls, guarantee payments, impairment, debt due within 12 months and the number of independent customers remaining. A network can pass an isolated demand test but fail when technology obsolescence and refinancing pressure arrive together.
How to inspect filings and financial statements
Income statement and revenue notes
- Separate revenue from related parties and unaffiliated customers.
- Look for large quarter-end purchases, unusually long payment terms, credits, rebates or customer concentration.
- Compare recognized revenue with cash collections and accounts receivable growth.
- Check whether a customer is also an investor, lender, guarantor or joint-venture partner.
Balance sheet and cash-flow statement
- Reconcile bonds, loans, convertible instruments, lease liabilities and restricted cash.
- Compare capital expenditure with depreciation and disclosed equipment additions.
- Examine operating cash flow after capital expenditure, not only EBITDA or adjusted metrics.
- Track inventory, purchase commitments and impairment charges for older accelerators.
Footnotes and risk factors
- Search for “related party,” “commitment,” “guarantee,” “take-or-pay,” “minimum purchase,” “capacity reservation,” “special-purpose entity,” “variable interest entity,” “offtake,” “cross-default” and “material adverse change.”
- Read subsequent-event notes for new financing, guarantees or customer agreements signed after the reporting date.
- Compare management’s description of a partnership with the counterparty’s filing; differences often reveal which party bears the obligation.
When a term is not quantified, treat it as an unresolved exposure. Do not infer that an undisclosed maximum is immaterial merely because the arrangement is described as strategic.
What would make circular financing systemically dangerous?
A broader credit event would require more than large AI investment. The loop would need to connect highly leveraged borrowers, shared lenders and assets whose values fall together. A plausible transmission sequence is: AI earnings disappoint; investors mark down equity and collateral; neocloud or lab customers cut purchases; SPVs miss lease or offtake payments; guarantees and cross-defaults reach hyperscalers; refinancing costs rise; and private-credit funds or banks restrict new lending.
BIS says a sharp repricing could tighten corporate credit broadly and expose private-credit vulnerabilities. The IMF’s assessment is more conditional: current risks for major hyperscalers remain contained, but multidirectional links can amplify shocks, and sustainability depends on AI firms meeting high earnings expectations. Therefore, an analyst should distinguish present balance-sheet resilience from future vulnerability if earnings, utilization or refinancing conditions change.
“The complex multidirectional channels of shock transmission between circle firms created due to the circular structure could result in nonlinear amplification of adverse shocks.” — International Monetary Fund, Global Financial Stability Report, April 2026
“These arrangements amount to ‘shadow borrowing’: obligations that are economically akin to debt but largely reside outside corporate balance sheets.” — Bank for International Settlements, Financing the AI infrastructure boom, 2026
Neither statement establishes that a crisis will occur. They identify mechanisms that become important if earnings, asset values or refinancing access deteriorate.
A practical red-flag checklist
- The investor is also the dominant supplier and the recipient’s largest customer.
- Revenue grows while cash collections lag and related-party sales rise.
- Minimum purchases or capacity reservations exceed independently verified demand.
- An SPV carries substantial debt while the sponsor provides a lease, offtake or guarantee.
- Debt matures after the realistic useful life of the GPUs or networking equipment.
- Several counterparties share one lender, insurer, chip platform or power project.
- Contracts include exclusivity, high termination fees or difficult data migration.
- Forecasts require near-full utilization and repeated access to cheap refinancing.
- Guarantee caps, residual-value obligations or private-credit terms are not quantified.
- Management reports strategic commitments but does not disclose independent-customer percentages.
A decision framework for investors, journalists and auditors
Use a three-level conclusion for each network rather than a binary safe-or-dangerous label.
| Conclusion | Evidence | Action |
|---|---|---|
| Lower concern | Independent customers fund purchases, obligations are consolidated or fully disclosed, maturities fit asset lives, and counterparties are diversified. | Monitor utilization, customer concentration and refinancing dates. |
| Elevated concern | Partner funding supports a material share of purchases, contracts impose minimum spend, or vehicles and guarantees are only partly disclosed. | Model contingent liabilities, demand independence and a closed-refinancing case before relying on growth forecasts. |
| High concern | The same parties provide capital, purchase most output, guarantee vehicles and share lenders, while debt outlasts equipment usefulness. | Assume correlated stress, seek missing contract terms and value the company using cash generation from unaffiliated customers only. |
This framework keeps the analysis focused on economic dependence. A large investment can be healthy when it attracts independent demand and leaves the recipient free to switch suppliers. A smaller investment can be risky when it carries exclusivity, guarantees and a purchase obligation that cannot be funded independently.
What the available evidence establishes—and what it does not
Official sources document reciprocal equity-and-purchase structures, large AI capital requirements, rapid bond issuance, off-balance-sheet vehicles and competition concerns around compute and talent. They do not publish a single authoritative percentage for circularly financed AI revenue or investment. The $3.4 trillion capex estimate and the more-than-$100-billion bond figures therefore describe scale, not the proportion that is circular.
The evidence also does not show that every partnership is improper or that a systemic crisis is inevitable. The relevant conclusion is conditional: circular dependence increases sensitivity to earnings disappointments, technology obsolescence, utilization shortfalls and refinancing closures. The strongest assessment is the one that identifies each obligation, assigns it to the party that bears the economic risk and tests whether independent cash flow can support it.
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