The Neocloud Cartel: How the AI Industry Started Renting Compute From Itself

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TL;DR

In 2026, the AI industry operates as a small cartel where companies rent compute from each other and Nvidia, which controls most supply and financing. This creates a fragile but powerful choke point in AI development.

In 2026, the AI industry has shifted to a model where most companies rent compute hardware from each other, with Nvidia acting as the central gatekeeper, controlling supply and financing. This development highlights a new form of industry power concentrated in a small circle of firms, creating a fragile but dominant choke point.

Nearly all AI companies now lease GPU hardware from a small group of suppliers, including CoreWeave, Meta, OpenAI, and xAI, often paying billions monthly. In May 2026, xAI leased its supercomputer to Anthropic and Google, signaling that even self-described research labs are becoming landlords, decoupling ownership from use.

Financial flows reveal a circular pattern: Nvidia invests heavily in AI firms, financing their hardware purchases, while these firms commit trillions in future compute spending. Nvidia alone captures the majority of AI hardware revenue, controlling GPU allocation and thus industry access.

This creates a tightly interconnected network where compute access is controlled through contracts, financing, and supply chain dependencies, making the entire system susceptible to disruptions if any link weakens.

At a glance
reportWhen: ongoing in 2026
The developmentThe article reports on how the AI industry has formed a compute rental cartel, with companies leasing hardware from each other and Nvidia, centralizing control over AI compute resources.
The Neocloud Cartel — The Control Series, Part 2: Compute
AI Dispatch · The Control Series · Part 2
Chokepoint 02 — Compute

The Neocloud Cartel

Almost no one racing to build AI owns the machine it runs on. They rent — increasingly from each other — and the money loops back to one chip maker that’s also an investor in nearly everyone at the table.

The loop — money, chips & credits circle a dozen firms
invests ~$100B commits ~$1.15T buy GPUs + equity stakes NVIDIA the chokepoint THE LABS OpenAI · Anthropic CLOUDS & CHIPS CoreWeave·Oracle·AMD ↻ each deal lifts the next one’s value
If it seems circular — it is.
Who actually holds the choke
01 · Upstream
Nvidia takes ~$35B of every $50B/GW
Captures most of every buildout dollar, holds equity in the buyers, and controls chip allocation in a shortage.
02 · The landlords
Rent means someone else’s terms
xAI’s lease reportedly lets Musk reclaim compute if Claude “harms humanity.” CoreWeave drew 77% of revenue from 2 customers.
03 · The financing
Suppliers fund their own buyers
Nvidia invests in OpenAI; AMD hands it warrants; Nvidia+MSFT back Anthropic $15B. The money never leaves the circle.
~$3T
datacenter spend ’25–’28 — half on private credit
−$74B
OpenAI projected operating loss, 2028
~3%
of consumers actually pay for AI
−60–75%
H100 rental rates from peak — commoditizing
The take

The cartel isn’t a conspiracy — it’s the endpoint of extreme capital intensity, real scarcity, and one dominant supplier. But the same circularity that makes it powerful makes it a fuse: each cancelled order is someone else’s missing revenue. Don’t be a price-taker at the bottom of a loop you don’t control — own your inference, keep an open-weight fallback, diversify silicon.

Sources: SpaceX filings; TechCrunch; The Register; Bloomberg; CNBC; Reuters; SemiAnalysis; McKinsey; Morgan Stanley; FT (2025–Jun 2026). Figures are reported commitments, often multi-year, not cash on hand.
thorstenmeyerai.com · 02 / 06

Implications of a Centralized Compute Cartel

This development signifies a fundamental shift in AI industry power dynamics, where a small set of firms, led by Nvidia, control critical infrastructure. It raises concerns about market fragility, potential for monopolistic behavior, and the risk of supply disruptions impacting AI progress and innovation.

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Rise of the Neocloud and Industry Consolidation

Over the past three years, the AI hardware market has transitioned from a fragmented industry to a highly concentrated cartel, driven by GPU shortages and the need for rapid scaling. CoreWeave’s backlog exceeds $55 billion, and major firms like Meta and OpenAI have committed tens of billions to this infrastructure, all relying on Nvidia hardware.

The emergence of the ‘neocloud’—a dedicated AI hyperscaler—has further entrenched this structure, with companies leasing compute instead of owning it outright, fundamentally altering traditional cloud and hardware markets.

“A gigawatt of AI data center capacity costs roughly $50 billion, with about $35 billion flowing to Nvidia.”

— Jensen Huang, Nvidia CEO

Amazon

high-performance AI compute servers

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Uncertainties About Market Stability and Risks

It remains unclear how fragile this cartel is in practice, whether alternative suppliers could challenge Nvidia’s dominance, and how regulatory or geopolitical factors might impact this concentrated control. The long-term stability of this system is still uncertain.

Amazon

cloud GPU rental services

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Future Developments and Potential Disruptions

Next steps include monitoring whether new entrants can break Nvidia’s hold, how regulatory scrutiny might increase, and whether the industry will diversify its supply chain or reinforce existing dependencies. The stability of this compute choke point will be tested as industry demands grow.

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Key Questions

How does Nvidia control the AI compute market?

Nvidia controls the market primarily through its dominant share of GPU supply, financial investments in AI firms, and allocation decisions that determine who gets hardware and at what price.

Why are companies leasing compute instead of owning it?

Due to GPU shortages and the high costs of building dedicated infrastructure, leasing provides a flexible, scalable way for AI firms to access necessary hardware without large upfront investments.

Could this compute cartel be broken up or regulated?

It is uncertain whether regulatory authorities could intervene to challenge Nvidia’s dominance or whether new suppliers or technologies might reduce dependency on the current system.

What risks does this concentration pose for AI development?

The primary risks include supply disruptions if Nvidia faces shortages or policy restrictions, potential monopolistic practices, and increased systemic fragility in AI infrastructure.

What happens if Nvidia’s control is challenged?

If Nvidia’s dominance is challenged, it could lead to increased competition, lower hardware prices, or a shift in industry dynamics, but such changes are not yet foreseeable.

Source: ThorstenMeyerAI.com

This content is for general information only and is not financial, tax or legal advice. Consult a qualified professional for decisions about your money.

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