Where AI value accumulates: margins, cross-financing and competition

By Paul-Antoine TUAL, AI Transformation Leader, Croissance et Transitions, July 2026; revised September 2026.

Following the money in AI requires separating what has already been recognised from what has merely been announced, then examining how margins, capital intensity and bargaining power change at each layer of the chain.

• Published results describe the present: revenue, margins, cash and debt.
• Multi-year agreements describe a future intention or obligation whose execution still depends on industrial schedules and financing.
• Comparisons with 2000 and competitive scenarios are investor frameworks, rather than accomplished facts.
• This article is educational and does not constitute investment advice or a recommendation to buy or sell.

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1. The four floors: who really pockets the cash?

The AI value chain currently distributes revenue, margins and risk very unevenly, from components already sold to business uses whose returns still have to be demonstrated company by company.

• Silicon: reported revenue and margins. For the quarter ended 26 July 2026, Nvidia reported $96.2 billion of revenue, including $89.0 billion from data centres, and a 75.0% GAAP gross margin [1].
• Fabrication and memory: profitable but cyclical bottlenecks. TSMC reported a 67.7% gross margin for the second quarter of 2026 [2], while SK hynix announced a 76% operating margin for the same period [3]; those records reflect strong demand without guaranteeing that it will persist.
• Infrastructure: capital-intensive assets and physical constraints. Data centres require chips, buildings, networks and energy; in its base case, the IEA expects their worldwide electricity consumption to more than double by 2030 [4].
• Models and applications: a more varied economy. Laboratories and software vendors combine subscriptions, APIs, licences and services, but their private revenue, compute costs and purchase commitments are not reported to a common standard.

Directly comparing global capital expenditure with one subsegment’s revenue can produce an arresting number without measuring an investment return, because the periods, accounting boundaries and assets financed do not match.

• Capital expenditure creates capacity used over several years; it should not be compared with one year’s revenue as though it were immediately consumed.
• The same infrastructure supports training, inference, conventional cloud services and sometimes external customers, so assigning all investment to AI overstates its specific cost.
• The gap still matters economically: future revenue must grow substantially, or the unit cost of compute must fall, if installed capacity is to earn an adequate return.
• The useful questions concern utilisation, compute pricing, contract duration and return on capital, rather than a spectacular ratio between incompatible aggregates.

High hardware margins do not overturn a general law of software economics: they chiefly indicate that some scarce components captured economic rent in 2026, while the profitability of models and applications depends more on inference cost, differentiation and customers’ willingness to pay.

• Gross margin is what remains after direct cost of sales; it does not deduct research, selling costs or every investment required for growth.
• Scarcity in accelerators, HBM memory and fabrication capacity supports suppliers further upstream.
• Downstream, competition between models, open source and falling prices may transfer some value to integrators and users.

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2. Cross-financing: read the nature of each flow

The Bank for International Settlements defines circular financing as a reciprocal structure in which a cloud or chip supplier takes an equity stake in an AI laboratory or infrastructure operator that then commits to buying compute or equipment from it [5].

• Capital: the investor acquires a financial asset and provides cash to the funded company.
• Commercial commitment: the recipient promises future purchases, sometimes over several years or subject to deployment conditions.
• Revenue: the supplier should recognise it only when the applicable accounting tests are met, so the face value of an announcement is not cash revenue.
• Risk: if the customer relies on the same funding to meet its purchases, weaker fundraising can affect demand, receivables and investment values at the same time.

Two public agreements show why the label ‘circular’ is insufficient and why the legal status, conditions and timetable of each transaction have to be examined.

• In September 2025, Nvidia and OpenAI announced a letter of intent under which Nvidia said it intended to invest up to $100 billion progressively as at least 10 GW of Nvidia systems were deployed [6].
• In October 2025, AMD and OpenAI announced a definitive agreement covering 6 GW, with the first MI450 deployment expected in the second half of 2026 [7].
• AMD also issued OpenAI with a conditional warrant for up to 160 million shares, vesting in stages against deployment and share-price milestones [7].
• None of those amounts should be presented as revenue already collected: one describes a progressive intention, while the other is a contract and equity incentive whose effects unfold over time.

These relationships can accelerate the construction of a supply chain and allocate risk between partners, but they make final demand harder to read when the same companies act as funders, suppliers and customers.

• In the favourable scenario, the capital seeds capacity that subsequently attracts independent, profitable users.
• In the fragile scenario, purchases remain dependent on new funding rounds, guarantees or supplier commitments.
• Useful indicators include the share of third-party customers, actual utilisation, operating cash flow, receivables, debt and cancellation clauses.
• Circularity is therefore a concentration and transmission risk; it does not prove accounting fraud or a predetermined collapse.

The telecoms precedent illustrates the mechanism without setting a timetable for AI: SEC filings show operators financing purchases from Lucent and Nortel, then struggling to repay when their own revenue failed to keep pace [8][9].

• Vendor credit supported equipment orders and deferred the customer’s cash outflow.
• When funding dried up, the supplier faced both weaker sales and higher credit risk.
• The internet still created lasting economic value; poor capital allocation and excessive valuations did not invalidate the technology.
• Today’s large hyperscalers have diversified businesses and substantial cash flow, limiting a direct analogy with the fragile telecoms operators of that period.

Central banks describe a conditional risk rather than a prediction: in July 2026, the Bank of England said that a reassessment of earnings prospects could be amplified by index concentration, market positioning and leverage [10].

• Market observation: AI-related companies carry a large weight in some global indices.
• Scenario condition: a sharp adjustment requires expectations for adoption, profitability or financing to deteriorate.
• Transmission channel: debt, private credit and reciprocal commitments can spread the shock beyond equities.
• Uncertainty: the same institutions recognise that outcomes could also exceed expectations; their analysis is intended to support financial-system resilience.

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3. Where could competition reduce economic rent?

Competition works differently in frontier-model training, which requires a coherent system at very large scale, and in inference, where cost, latency, energy and integration with a particular workload may carry more weight.

• Training: chips, networking, libraries, compilers and operating tools must work together through long runs.
• Inference: a company can optimise a stable model for specific hardware and make finer trade-offs between performance, cost and availability.
• Migration: CUDA and its ecosystem create a real switching cost, but that cost varies with the models, existing code and production requirements.
• Consequence: a competitor can win targeted workloads without replacing Nvidia across the market.

The alternatives are already industrial, although their announcements still have to turn into deliveries, software availability, utilisation and satisfied customers.

• AMD: the OpenAI agreement called for an initial one-gigawatt MI450 deployment in the second half of 2026, followed by conditional multi-year deployments [7].
• AWS: Amazon said in February 2026 that 1.4 million Trainium2 chips had landed and that its Rainier cluster used more than 500,000 for Anthropic [11].
• Google: TPUs are offered through Google Cloud with capacity commitments, extending their role beyond Google’s own internal workloads [12].
• Specialised chips: their potential advantage depends on a workload being stable and large enough to amortise design, software and supply-chain costs.

The investor thesis is that broader supply could compress accelerator prices and margins, but the outcome depends on demand growth and on competitors’ ability to deliver a complete system.

• If total demand grows faster than competitors gain share, Nvidia can keep increasing revenue despite stronger competition.
• If in-house chips chiefly absorb inference, pressure may first appear in the price per request rather than frontier training.
• TSMC can benefit from several competing chip designers because it fabricates some of their chips; design diversity therefore does not automatically diversify the foundry layer.
• Record margins attract competing investment, but they determine neither its speed nor its success.

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Decisions for business leaders

For the leader of an SME or mid-market company, this framework is mainly a way to buy capability with discipline and preserve bargaining power, without turning an operating decision into a bet on a supplier’s share price.

• Link each expense to a process, a volume, an expected quality level and a responsible business owner.
• Measure the full cost per useful outcome, including integration, human review, security and operations.
• Avoid unnecessary dependency by separating data, business logic and model provider where the economics justify it.
• Revisit price, quality and exit terms regularly because the model and compute markets are changing quickly.

Falling unit compute costs may benefit user companies, but value does not migrate to them automatically: it appears only when a use case sustainably improves revenue, margin, working capital, risk or service quality.

• A lower price without adoption produces no return.
• Poorly integrated automation can shift costs into review, errors and support.
• An industrialised use case can instead convert falling technology costs into an operating advantage.
• The white paper on industrialising AI develops this production discipline, while the Junyr Method™ provides the transformation framework.

Disclaimer. This article is educational and informational; it is neither investment advice nor a recommendation to buy or sell, its data are current to 6 September 2026, and past performance does not predict future performance.

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Sources

Sources consulted on 6 September 2026.

[1] NVIDIA, second-quarter fiscal 2027 results for the quarter ended 26 July 2026.

[2] TSMC, second-quarter 2026 results, 16 July 2026.

[3] SK hynix, second-quarter 2026 results, 29 July 2026.

[4] International Energy Agency, Energy and AI, executive summary.

[5] Bank for International Settlements, Annual Economic Report 2026, ‘Progress and peril’.

[6] NVIDIA and OpenAI, letter of intent announced 22 September 2025.

[7] AMD, Form 8-K and OpenAI agreement dated 6 October 2025.

[8] SEC, Lucent Technologies 2001 annual report.

[9] SEC, an operator’s disclosure of vendor-financing obligations to Nortel and Lucent, 2001.

[10] Bank of England, Financial Policy Committee record, July 2026.

[11] Amazon, fourth-quarter 2025 results published February 2026.

[12] Google Cloud, long-term TPU capacity reservations, updated 26 August 2026.