Circular financing, artificial margins, and financially engineered demand may be distorting NVIDIA’s true growth — and exposing investors to a brutal re-rating risk.

NVIDIA: When the AI Boom Starts to Look Like a Financial Illusion

For the market, NVIDIA represents the purest expression of the artificial intelligence boom. Explosive revenue growth, near-monopolistic positioning, and margins that would be extraordinary even for a pure software company have turned NVDA into the poster child of the AI super-cycle.

But beneath the surface of this apparent dominance, a growing number of structural distortions are emerging. Distortions that suggest part of NVIDIA’s demand may be financially engineered, its margins structurally unsustainable, and its valuation dangerously exposed to even modest changes in industry dynamics.

What today looks like an unstoppable compounding machine may, in reality, be far more fragile than investors are willing to admit.

1. Circular Financing: When the Vendor Becomes the Bank

One of the least discussed — and most dangerous — aspects of NVIDIA’s growth model is the increasing use of what can effectively be described as circular financing.

NVIDIA has become an active investor across the AI ecosystem, taking equity stakes and providing capital to startups, partners, and customers that, in turn, become buyers of NVIDIA hardware. While this can be framed as “ecosystem building,” the financial reality is more uncomfortable: NVIDIA is, directly or indirectly, helping to finance the demand for its own products.

This blurs the line between organic market demand and financially stimulated demand.

In practical terms, NVIDIA is pulling forward future sales by injecting capital into entities that then use that capital to purchase NVIDIA GPUs. The short-term effect is higher reported revenue. The long-term risk is that NVIDIA is accumulating hidden credit and equity risk on its balance sheet.

If even a portion of this ecosystem runs into financial trouble, NVIDIA faces a double hit:

• Write-downs on equity investments
• Potential bad debts on receivables and contractual commitments

What looks like revenue today may become losses tomorrow.

Case Study: The CoreWeave Deal — Circular Financing in Plain Sight

This dynamic has now become explicit.

NVIDIA has just announced a $2 billion investment in CoreWeave Class A common stock, further deepening its financial and operational ties with one of the most aggressive buyers of NVIDIA GPUs.

CoreWeave is not a traditional hyperscaler with diversified cash flows. It is a highly leveraged, capital-intensive AI cloud provider whose business model is structurally dependent on access to NVIDIA hardware and on continued external financing.

By injecting $2 billion of equity into CoreWeave, NVIDIA is not simply making a passive financial investment. It is directly strengthening the balance sheet of a customer whose primary use of capital is to purchase and deploy NVIDIA GPUs.

In substance, this is vendor financing under another name.

The implications are significant:

• NVIDIA is helping to fund a major source of demand for its own products
• Reported GPU sales increasingly reflect capital that originated, directly or indirectly, from NVIDIA itself
• NVIDIA is accumulating concentrated exposure to a highly leveraged, cyclical infrastructure operator

This creates a self-reinforcing loop:

NVIDIA provides capital → customer buys NVIDIA GPUs → NVIDIA reports revenue → NVIDIA takes equity risk on the same customer.

In a benign environment, this inflates growth and reinforces the narrative of unstoppable demand.

In a stressed environment, it becomes a textbook example of circular risk:

• Equity write-downs
• Potential credit stress
• And simultaneous weakening of end-demand

What is being presented as strategic ecosystem investment increasingly resembles financial engineering designed to sustain near-term revenue momentum.

For investors, this is not a sign of strength. It is a sign that organic, fully self-funded demand may not be as robust as headline numbers suggest.

2. Software Lock-In and Artificial Margins

NVIDIA’s gross margins — approaching levels more typical of monopoly software platforms than hardware manufacturers — are historically abnormal.

These margins are not the result of manufacturing efficiency. They are the result of control over the CUDA software ecosystem, which has created powerful switching costs and developer lock-in.

But this moat is no longer uncontested.

Hyperscalers and competitors are investing aggressively to reduce their dependence on CUDA:

• Google with TPU and its proprietary software stack
• AMD with ROCm and growing software maturity
• Open-source frameworks optimized for heterogeneous compute
• Internal software layers designed to abstract away NVIDIA-specific dependencies

CUDA does not need to collapse for NVIDIA’s economics to change dramatically. It only needs to weaken.

Even a partial erosion of software lock-in would transform NVIDIA’s pricing power — and once pricing power fades, margins in hardware businesses do not compress gently. They collapse.

The market is valuing NVIDIA as if its monopoly economics are permanent. History suggests they never are.

3. Financial Engineering in Hyperscaler Capex

The AI infrastructure build-out is being financed in increasingly creative ways.

Hyperscalers are shifting capital expenditures, using leasing structures, special-purpose vehicles, and long-term contractual commitments to support massive GPU deployments. The now infamous OpenAI–Oracle agreement — reportedly involving up to $300 billion in infrastructure commitments — is a striking example.

OpenAI is a company that burns cash at an extraordinary rate. Its business model remains deeply uncertain. Yet it is committing to infrastructure spending on a scale that would normally require the balance sheet of a mega-cap tech company.

This is not normal demand. This is demand sustained by financial structures.

When demand depends on financing rather than cash flow, it becomes inherently fragile. The moment capital becomes more expensive, or investor risk appetite changes, the entire structure becomes vulnerable.

4. The Accounting Fiction of GPU Depreciation

Another underappreciated distortion lies in depreciation schedules.

Many companies are depreciating high-end AI GPUs over five to six years. Economically, this borders on fantasy.

The real useful life of cutting-edge AI accelerators is likely closer to two to three years before they become functionally obsolete for state-of-the-art workloads.

This means:

• Profits today are artificially inflated
• Return on invested capital is overstated
• Future write-downs are almost inevitable

Across the ecosystem, accounting is lagging technological reality. The result is a profit picture that looks far healthier than the underlying economics justify.

5. The Build-Out Phase Is Not Permanent

The current phase of AI infrastructure spending is a classic front-loaded build-out.

Companies may be spending $100 billion per year to build capacity. But once the infrastructure is in place, maintaining and upgrading that capacity may require only $20 billion per year.

That is an 80% reduction in incremental demand.

NVIDIA is priced as if the build-out phase is a permanent state of the world. It is not.

Infrastructure markets are cyclical by nature. They overshoot. They overbuild. Then they correct.

The market is treating NVIDIA as structurally immune to this cycle. Semiconductor history suggests the opposite.

6. Customer Concentration and the Rise of Custom Silicon

A large portion of NVIDIA’s revenue is concentrated among a small number of hyperscalers.

These customers have both the incentive and the financial capability to reduce dependence on NVIDIA over time:

• Google with TPUs
• Amazon with Trainium and Inferentia
• Microsoft developing internal AI silicon
• Meta investing in custom accelerators

Custom silicon is not a marginal threat. It is a structural one.

Hyperscalers do not want to be permanently taxed by NVIDIA’s margins. Over time, they will internalize more of the value chain. This puts long-term structural pressure on both NVIDIA’s growth and its margins.

7. What Happens to the Valuation When Reality Intrudes?

NVIDIA is priced for perfection:

• Sustained hyper-growth
• Monopoly-level margins
• Permanent pricing power
• No meaningful cyclicality

The problem is that even a modest deviation from this narrative can be devastating for the stock.

If:

• Margins compress by 10–20 percentage points
• Revenue growth slows or turns slightly negative
• Large customers reduce orders or shift to internal solutions

The multiple does not gently adjust. It collapses.

High-multiple stocks with monopoly narratives tend to re-rate violently when the story changes.

8. The Short Signal: When Even the Bulls Start Hedging

It is telling that investors like Michael Burry have reportedly taken bearish positions or hedges related to NVIDIA.

This is not proof of anything. But it is a signal.

When some of the most historically contrarian investors begin positioning for downside, it suggests that the perceived asymmetry may be shifting — from upside optionality to downside fragility.

Conclusion: The AI Boom Is Starting to Look Like a Leverage Cycle

NVIDIA may continue to report strong numbers in the coming quarters. In late-cycle environments, financial structures often allow growth narratives to persist longer than fundamentals would justify.

But structurally, the foundations of this growth appear increasingly fragile.

When demand is partially funded by the vendor itself, when customer capex is supported by aggressive financial engineering, when accounting assumptions extend far beyond economic reality, and when monopoly-like margins depend on software lock-in that is actively being dismantled, the risk profile changes materially.

This is no longer just a story of technology leadership.

It is becoming a story of leverage, financial circularity, and front-loaded infrastructure spending.

In such environments, turning points are rarely gradual.

They tend to arrive when one or two stress points appear:

• A major customer slows orders
• A leveraged AI infrastructure provider struggles to refinance
• A wave of GPU write-downs hits reported profitability
• Or margins compress faster than consensus models assume

When that happens, the narrative does not slowly adjust.

It breaks.

And when high-multiple, narrative-driven stocks reprice, they do not correct by 20–30%. They reset.

Investors currently treat NVIDIA as if it were a permanent AI toll booth.

But toll booths only work as long as traffic grows and alternatives remain uneconomic.

If traffic slows, if alternative routes emerge, and if the cost structure is revealed to be more fragile than advertised, the toll booth quickly turns into a stranded asset.

The risk for NVIDIA investors is not that growth merely slows.

The risk is that a business priced for perpetual hyper-growth and monopoly margins is suddenly reclassified by the market as what it economically is becoming:

A highly cyclical, capital-intensive infrastructure supplier at the late stage of an unprecedented build-out.

History is unkind to stocks at that point in the cycle.

Not because the company disappears.

But because valuations built on perfection do not survive contact with reality.