The cash of AI: who pockets it, and why it may be circular

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

Framing. “The technology can be very real and the valuation excessive, both at once.” That was true in 2000, when Cisco was worth more than $550 billion and it took nearly 25 years for its stock to reclaim that peak; it is true in 2026. As a young consultant, I lived through the telecoms crash, and I recognise mechanisms that are returning today. In an earlier article, we ranked the ten largest CAC 40 and Nasdaq companies by size and by AI maturity. Here, we follow the money, in three steps: who really pockets the cash (the value chain), why it may be circular (circular financing), and who could flip the table (the industrial challengers). Neither doom-mongering nor denial: a grid for sorting. This article is not investment advice; several figures are press or non-audited estimates, flagged as such.

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

During a gold rush, it is not always the prospectors who get rich. It is often the sellers of picks and shovels. The AI value chain reads exactly the same way, in four floors, from closest to the silicon to closest to the user.

Floor 1 · Silicon (the picks). This is where the cash accumulates today. Nvidia posts a gross margin of about 75% [1], a dizzying level for hardware. Taiwan’s TSMC, which etches these chips, reported in mid-July 2026 a gross margin of around 68% [2]; Korea’s SK Hynix, king of the high-performance memory (HBM) that AI servers depend on, reached around 70% operating margin at its latest record quarter [3]. The risk on this floor is not demand, which remains insatiable, but cyclicality, and the fact that the biggest customers (Google, Amazon, OpenAI) are now designing their own chips to chip away at Nvidia’s near-monopoly (see Part 3).

Floor 2 · Infrastructure (the shovels and the electricity). Profitable, but capital-hungry. The four big American hyperscalers (Amazon, Alphabet, Meta, Microsoft) are spending in 2026 on the order of $600-700 billion in capital expenditure (capex), even more if Oracle is included, mostly on data centres and chips [4]. The constraint is becoming physical: the International Energy Agency projects a rise of about 130% in the electricity consumption of American data centres by 2030 [5]. Cloud is profitable (Amazon Web Services exceeds 35% operating margin), but capex is swallowing a growing share of revenue, and financing is shifting towards debt.

Floor 3 · Models (the promise). Hypergrowth and massive losses. OpenAI reportedly shows around $25 billion of annualised revenue, but also on the order of $14 billion of projected losses in 2026 (a non-audited figure, to be treated as an estimate [6]). It is the most media-visible floor, and the most dependent on private capital.

Floor 4 · Applications (the smallest floor). The revenue actually captured by enterprise generative-AI applications is counted in the tens of billions, on the order of $20 billion according to Menlo Ventures’ estimates [7].

Put the two ends side by side: about $700 billion of capex against about $20 billion of application revenue. This gap is not a detail, it is the point. It does not prove that AI is an illusion: the lower floors are, themselves, pocketing very real cash. But it says one simple thing: today, the bottom of the chain sells picks at a premium; the top is still selling a promise. A telling fact: for the first time in thirty years of software history, software has lower margins than hardware, with a gross margin estimated at around 33% at OpenAI, against 75% at Nvidia [1][6]. The entire investment playbook of the past thirty years is upside down.

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2. The closed loop: when the same dollar is counted several times

Here is the mechanism that should make a business leader cautious. The loop, in one sentence: Nvidia invests in OpenAI → OpenAI pays a cloud provider (Oracle) for compute → that provider buys chips from Nvidia → Nvidia’s revenue rises → its market capitalisation funds the next round. The same dollar can be counted as revenue at each link.

At the end of 2025, the financial press put these cross-deals at about $1 trillion, according to Bloomberg’s “AI circular deals” tally [8]: a press estimate, not consolidated accounting data. The emblematic examples, with their real status, are worth more than the headline figure:

• Nvidia → OpenAI: the “$100 billion” announced in 2025 was only a letter of intent. Jensen Huang himself acknowledged in early 2026 that it was “never a commitment”; it was replaced by a far smaller stake in the March funding round [9]. (Saying “announced, then unwound” is more accurate, and more instructive.)
• OpenAI ↔ AMD, then Meta ↔ AMD: the customer is paid in shares (warrants on a slice of AMD’s capital) to buy the alternative supplier’s chips [10].
• OpenAI ↔ Oracle: a compute purchase commitment on the order of $300 billion over five years, an order book, not revenue received [11].
• Microsoft ↔ OpenAI: about a quarter of the capital against massive cloud purchase commitments (the October 2025 restructuring), recast again in spring 2026, ending exclusivity and revenue sharing [12].
• Nvidia ↔ CoreWeave: Nvidia backstops several billion dollars of its own customer’s unsold capacity, a customer that took on debt to buy… Nvidia chips [13].

The precedent I lived through. In the late 1990s, telecoms-equipment makers practised vendor financing: they lent their own customers the money to buy their equipment. Lucent carried billions of dollars in customer credit; Nortel lent in proportions that even exceeded the orders themselves. As long as the music played, the order book kept swelling. Then customers stopped paying: Lucent posted a colossal loss in 2001, its stock going from tens of dollars to less than one dollar; Nortel went from a market capitalisation of more than a hundred billion to bankruptcy [14]. And Cisco, the indestructible one: more than $550 billion of market capitalisation in March 2000, an earnings multiple of about 200, and nearly 25 years for its stock to reclaim that peak (late 2025) [15]. The lesson is not “the technology was fake”: the internet transformed everything. The lesson is that the shareholder who paid the peak lost anyway, because the valuation and the circular revenue did not survive it.

Who is ringing the alarm bell in 2026? The Bank of England (October 2025: stretched valuations, rising concentration, risk of a sharp correction), the IMF, investor Michael Burry (short positions on AI stocks), and above all the Bank for International Settlements (June 2026), which flags circular financing that is “poorly disclosed” and the risk of multiple pledging of the same asset [16]. The repricing has, in fact, already begun: in June 2026, a single trading session wiped out on the order of $1.3 trillion of market capitalisation across semiconductors [17].

Honesty, which is also credibility. This is not 2000 all over again, and saying so strengthens the analysis rather than weakening it: unlike back then, the hyperscalers finance most of this on real, massive cash flows; the leaders’ valuation multiples run at around 25 times earnings, against nearly 58 times in March 2000 [18]; paying demand genuinely does exist; and AMD-style warrants cost no cash. The market read June’s correction as a repricing, not a rupture. The conclusion, therefore, is not to “flee”, but to sort: floor by floor, promise against proof.

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3. Who can flip the table: the 2027-2028 challengers

Record margins are not a shield: they are magnets for competition. To understand where the edifice can move, one concept is enough, and it is too rarely explained to the general public.

🎓 Training vs inference. Training a model means building it: a handful of mega-projects a year, tens of thousands of chips synchronised for months, zero tolerance for error; this is where the software ecosystem matters most. Inference means running the already-built model to answer each request: billions of times a day, where only the cost per request matters. Training is the prestige market; inference is the volume market, and volume is what is exploding. The entire competitive dynamic of 2027-2028 flows from this distinction: you do not dethrone Nvidia on training, you go around it on inference.

Nvidia (more than 80% of AI accelerators, down from ~92% in 2023) [19] owes its dominance as much to CUDA, the software layer with which fifteen years of AI code has been written, as to its chips. Moving off CUDA means rewriting and re-validating thousands of lines of critical code: a massive switching cost, exactly like changing a bank’s core computing system. But this lock is asymmetric: enormous on training, weak on inference, where standardised layers run better and better on non-Nvidia hardware. Hence three attacks already real in 2026:

• AMD, the frontal attack. Its new generation of chips (MI450) has landed giant commitments: a major lab has committed to several gigawatts, a major cloud player is deploying tens of thousands of them [20]. AMD no longer sells chips, but complete systems, like Nvidia.
• The cloud giants’ in-house chips, the integration attack. Google’s TPUs are moving beyond its captive cloud (a leading lab is committing to them at scale) [21], and Amazon has deployed more than a million of its Trainium chips [22]. Why? At a hyperscaler’s scale, saving 30 to 40% of inference cost justifies billions in research to design a dedicated chip of one’s own.
• Sovereign ASICs, finally: OpenAI is designing its own chip (with Broadcom), and China is pushing its alternatives under regulatory constraint.

🎓 The stock-market mechanics to understand. At more than 80% market share, Nvidia has almost no share left to gain, only to lose, and each point lost is paid for twice: in revenue and in valuation multiple. Yet multiple erosion almost always precedes revenue erosion. A sign already visible: by mid-July 2026, Nvidia’s stock is up only about 9% on the year, while Apple’s has risen by nearly 23% [23]. The market has begun to sort.

A word on the other floors: the fatter a floor’s margins, the more concrete its 2027 alternatives. TSMC remains the only true bottleneck with no credible substitute within two years; its risk is geopolitical (Taiwan), not competitive. HBM memory (SK Hynix) shows 70% margins because we are at the peak of a notoriously violent cycle, one that the return of Samsung and the rise of Micron will shave down. And the models floor is the most substitutable of all: switching from one AI provider to another takes almost a single line of configuration, which pushes the price towards marginal cost. “75% margin at Nvidia created AMD-OpenAI; 70% at SK Hynix woke up Samsung and Micron. Record margins are not a moat, they are invitations to tender.”

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What this changes for a business leader (not an investor)

An SME or mid-cap business leader does not have to bet on Nvidia’s share price. But this reading gives them a compass, and it echoes the thesis of our articles Where is AI’s value going? and the AI maturity of the Top 10.

The real question is not “should we buy AI?” but “who will AI re-rate, and who will it de-rate?” The deflation in the cost of intelligence (the price of a given AI capability is divided by around 10 every year) is a tax on those who sell tokens and a subsidy for those who consume them. In other words: value migrates mechanically towards the companies that put AI to work in their business, not towards those that sell it. For an SME, the stake is not to guess the top of the bubble; it is to stand on the right side of the counter: to industrialise concrete use cases while the technology layer becomes abundant and cheap. That is exactly the purpose of our white paper on industrialising AI and of the MATIA Method™.

And the reading rule, valid from the Nasdaq to the workshop floor: proof is read in margins, not in slogans. 75% gross margin at Nvidia is a fact; the productivity gains promised in keynote speeches are still a promise.

Disclaimer. This article is educational and informational. It is neither investment advice nor a recommendation to buy or sell any security whatsoever. Several figures cited are press or non-audited estimates, flagged as such. Past performance does not predict future performance. For any investment decision, consult a licensed financial investment adviser.

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Would you like your company to be on the right side of this value migration, the one that puts AI to work, not the one that suffers it? Start with a free AI Express Audit & Roadmap.

Sources

Figures as of mid-July 2026. Several amounts are press or non-audited estimates, indicated as such in the text.

[1] NVIDIA official results (quarter ended April 2026), gross margin ≈ 74.9%, quarterly revenue ≈ $81.6 billion of which ≈ $75.2 billion data centre.

[2] TSMC Q2 2026 results (published 16 July 2026), gross margin ≈ 67.7% (rounded to ≈ 68% in the text).

[3] SK Hynix results (Q1 2026, a record), operating margin ≈ 72% (HBM memory).

[4] CNBC / market roundups (February 2026), combined 2026 capex of the four hyperscalers ≈ $640-700 billion.

[5] International Energy Agency, Electricity 2026 / Energy and AI: US data-centre electricity consumption ≈ +130% by 2030.

[6] Press estimates (The Information), OpenAI ≈ $25 billion annualised revenue, ≈ $14 billion projected 2026 losses, gross margin ≈ 33%; non-audited figures.

[7] Menlo Ventures, State of Generative AI in the Enterprise (Dec. 2025): enterprise-side GenAI application revenue ≈ $19-20 billion (private, non-audited estimate).

[8] Bloomberg, AI circular deals: cross-deals tallied on the order of $1 trillion (October 2025; press estimate, not consolidated accounting data).

[9] Fortune, Jensen Huang, “never a commitment” (February 2026): the “$100 billion” Nvidia→OpenAI, an unexecuted letter of intent.

[10] OpenAI/AMD and Meta/AMD press releases (2025-2026), warrants on a slice of AMD’s capital.

[11] Bloomberg / CNBC, OpenAI↔Oracle commitment ≈ $300 billion over 5 years (purchase commitment, not cash received).

[12] CNBC, recast of the Microsoft-OpenAI agreement (April 2026).

[13] Financial press, Nvidia’s guarantee on CoreWeave’s unsold capacity (≈ $6.3 billion).

[14] American Affairs, Who Lost Lucent?; Lucent SEC filings (loss ≈ $16.2 billion in 2001, stock from ~$65 in 1999 to <$1 in 2002) and Nortel (2009 bankruptcy, after a peak >$100 billion).

[15] CNBC, Cisco’s stock reclaims its March 2000 peak on 10 December 2025: >$550 billion market capitalisation at the peak, P/E ≈ 200 (only the share price recovered its level; market capitalisation remains lower, owing to share buybacks).

[16] Bank for International Settlements, Annual Report (28 June 2026); Bank of England (FPC, 8 October 2025); IMF (October 2025).

[17] Market roundups, a June 2026 session: ≈ −$1.3 trillion of market capitalisation across semiconductors (sector sell-off; press estimates).

[18] Multiple comparison: leaders ≈ 25× earnings (2026) vs ≈ 58× at the March 2000 peak.

[19] Sector estimates (IDC / TrendForce, second-hand), Nvidia ≈ 80-88% of data-centre AI accelerators in 2026 (down from ~92% in 2023).

[20] AMD / OpenAI / Oracle press releases, commitments on the MI450 generation (2026).

[21] Google Cloud / Anthropic, TPUs opened beyond the captive cloud; commitment from a major lab.

[22] Amazon Web Services, deployment of more than a million Trainium chips.

[23] CNBC, Apple, Nvidia vie for title of world’s most valuable company (17 July 2026): year-to-date, Apple ≈ +23%, Nvidia ≈ +9%.