AI Circular Financing: Who Is Paying for the Growth?

Circular financing matters when the money funding a customer’s purchases comes from businesses that also benefit from those purchases. It creates a question about the independence and durability of demand, not automatic proof of fraud or fictitious revenue.

The AI debate often jumps straight from a diagram of connected companies to a verdict about the whole industry. A useful financial analysis takes the opposite route: identify the contracts, trace the cash and ask what happens if the next funding round does not arrive.

Sources reviewed: 26 October 2026. The announced transaction below is historical context. The worked example is wholly invented and does not represent any named company, Altys dataset or estimate of the AI industry.

A real announcement, with its original status intact

On 22 September 2025, OpenAI and NVIDIA announced a letter of intent for deployment of at least 10 gigawatts of NVIDIA systems. The announcement described NVIDIA’s intention to invest up to $100 billion progressively as deployment occurred.

Those words matter. An intended investment is not evidence that the full amount was paid. A deployment ambition is not completed capacity, recognised revenue or independent end-customer spending. This article does not certify the transaction’s subsequent execution status as of October 2026.

The announcement illustrates a structural question: when an infrastructure supplier is also a potential investor in an infrastructure customer, commercial demand and funding can become connected. That connection deserves analysis without implying that the arrangement is improper.

Three transactions, not three sources of final demand

Consider three fictional businesses: an equipment supplier, a cloud operator and an AI application company. Assume all transactions occur in one period, there are no opening balances and there are no taxes, fees, loans or other cash movements.

The supplier invests ₹100 crore of equity in the application company. The application company pays ₹70 crore to the cloud operator for a service fully delivered during the period. The operator buys and receives ₹50 crore of equipment from the supplier. Separately, unrelated customers pay the application company ₹20 crore for services delivered in the period.

Cash movement	Amount	What it represents
Supplier to application company	₹100 crore	Equity funding, not the recipient’s revenue
Application company to cloud operator	₹70 crore	Payment for the assumed delivered service
Cloud operator to supplier	₹50 crore	Purchase of the assumed delivered equipment
Unrelated customers to application company	₹20 crore	External customer receipts in this example

The application company ends with ₹50 crore: ₹100 crore funding + ₹20 crore receipts − ₹70 crore cloud payment. The operator ends with ₹20 crore, while the supplier has a net ₹50 crore outflow. The three cash changes total ₹20 crore, matching the outside customer inflow under these deliberately narrow assumptions.

The ₹70 crore and ₹50 crore payments are real commercial cash flows in the example. But adding them to ₹20 crore and calling ₹140 crore “independent end-user demand” would count successive stages of the same chain. Adding the equity funding as sales would be a different error.

This is not a complete profit or cash-flow statement. The operator’s equipment purchase may be capital expenditure rather than an immediate expense; the supplier has production costs that the example excludes. Consolidation, revenue recognition and cash-flow classification depend on actual arrangements and applicable standards. The lesson is about tracing funding, not prescribing journal entries.

The question is what sustains the next period

There are several legitimate possibilities. Independent customer receipts might grow enough to fund ongoing service purchases. Investors might deliberately fund an early loss-making expansion that later becomes viable. Or the business might continue to require fresh funding for longer than its providers expected.

The first funding loop cannot tell us which outcome follows. Nor does the existence of external customers settle the issue. Their spending needs to be material, repeatable and commercially viable relative to the obligations being created.

Separate paid customers from trials and credits. Separate contracted amounts from usage-based spending. Record which obligations can be reduced if demand is weaker and which remain payable. If a minimum-purchase commitment is disclosed, its timing may matter as much as its headline size.

Our results, presentation and concall reconciliation guide explains how to retain differences in definitions rather than force them into an artificial match.

Why lenders and investors look beyond the first counterparty

The Bank of England’s September 2026 FPC record, published on 30 September, identifies leverage, opacity and circular arrangements as factors that can complicate risk assessment and amplify losses if expectations disappoint. This is an attributed risk assessment, not a finding that every connected AI transaction is unsound.

Altys interpretation: a counterparty can look diversified on a customer list while still depending on a narrow funding ecosystem. Several customers may share the same backers or depend on the same spending assumption. That is an exposure to investigate, not a number that can be inferred from a network drawing alone.

Avoid the opposite overstatement too. A supplier’s investment does not mean every rupee of its customer’s spending comes from that investment. Funds are fungible, businesses have other revenues and financing sources, and public disclosure rarely provides a perfect money trail. Show what can be evidenced and leave the rest unknown.

What Indian research teams can take from this

The question extends beyond US chip and cloud companies. An Indian supplier working on an overseas infrastructure project may need to understand who funds its customer, whether milestones are financed and how payment obligations are secured.

Do not treat every Indian business with an AI association as part of a financing loop. Establish the actual relationship first. A disclosed contract can support a commercial connection; it does not establish the customer’s entire funding position.

Likewise, do not infer that an investor is a related party solely because it owns a stake. Related-party status and disclosure obligations depend on the applicable rules and facts. Commercial interdependence is the broader analytical question here, not a legal classification.

Our AI capex and India report covers orders, delivery and collection. The AI bubble analysis addresses the separate distinction between useful technology and sustainable investment economics.

Follow the evidence, not the accusation

For each material relationship, retain the source date, committed versus paid amount, delivery condition, funding source and evidence of independent customer receipts. Revisit the record when financial statements or contract disclosures change. Do not fill a missing figure with zero or present an undisclosed funding split as an estimate.

Altys’s source-linked company research and monitoring support this evidence-led approach. Available Excel exports let teams review inputs and calculations alongside their own research. Request access to explore that workflow.

The most useful question is not whether money travels in a circle. It is whether the businesses can ultimately support their commitments with demand and cash that persist outside that circle.

Educational business and accounting analysis only. No allegation of wrongdoing, securities recommendation, price target, expected return or portfolio allocation. Altys Labs is not a SEBI-registered Research Analyst or Investment Adviser.