Nvidia Scarcity Allegations: Did It Fund Its Own GPU Sales?

Investigations into Nvidia’s business practices allege the chipmaker artificially engineered artificial intelligence hardware scarcity by using circular financing. By aggressively funding startup cloud providers to buy its graphics processing units (GPUs), the firm maintained high demand signals. This strategy has drawn intense scrutiny following the sudden resignation of chief executive Jensen Huang on November 12, 2026.

The Mechanics of “Circular Financing”

Nvidia allegedly established a financial loop by investing billions of dollars into niche “neocloud” providers, which then immediately used those funds to purchase Nvidia graphics processing units. This aggressive vendor financing model sustained artificial demand signals while the chip manufacturer faced massive semiconductor manufacturing commitments.

According to reports, the company was facing an incoming wafer and memory supply pressure from Taiwan Semiconductor Manufacturing Company (TSMC)—Nvidia’s primary foundry partner. According to TIKR financial tracking, Nvidia’s contribution to TSMC’s revenue rose to 19% in 2025, up from 12% in 2024. If hyperscalers began shifting to custom silicon, Nvidia risked being left with massive, unpaid inventory commitments.

To mitigate this, Nvidia allegedly turned to “neocloud” startups. Investigative reports analyzed by The Decoder revealed that Nvidia invested $2 billion each in CoreWeave and Nebius. These investments effectively guaranteed that the capital would flow straight back to Nvidia’s top line as hardware orders.

In August 2026, former CEO Jensen Huang clashed with CFO Colette Kress over these high-risk financial guarantees. Huang reportedly called Kress a “deer in the headlights” during a meeting where she questioned the long-term viability of these commitments. Following Huang’s resignation on November 12, 2026, Kress took over as interim CEO.

The Multi-Billion-Dollar Backstop Portfolio

Interim CEO Colette Kress now inherits a massive, high-risk portfolio containing an estimated $286 billion in total named financial commitments and guarantees, as detailed by TechPowerUp. This exposure includes multi-billion-dollar data center lease backstops, direct startup equity investments, and a major Wall Street infrastructure financing platform.

The scale of this operation is unprecedented. The company’s standard lease guarantees alone had a maximum gross exposure of $3.5 billion as of early fiscal 2027, according to ExplainX. But the larger deals are even more staggering.

For example, Nvidia negotiated a massive $105 billion credit backstop to help OpenAI secure leases for an 8-gigawatt data center in Pike County, Ohio, reported by Unite.ai. This final agreement, signed on August 17, 2026, was actually halved by Kress from an initial proposed guarantee of $250 billion after early leaks caused Nvidia’s stock to tumble.

Nvidia also partnered with major financial institutions like BlackRock and Blackstone to launch the Compute Infrastructure Financing Platform, targeting $500 billion in third-party capital, as reported by Business Standard. Under this arrangement, Nvidia agreed to provide a residual-value support mechanism backstopping up to 25% of the deals, exposing the chipmaker to up to $125 billion in liabilities.

This massive exposure echoes previous major deals, such as Nvidia’s $105B OpenAI Deal, which highlighted the growing systemic risk of vendor-backed infrastructure as documented by Unite.ai. As the dominant player in hardware, Nvidia’s financial health dictates the trajectory of the entire sector.

The India Angle

India’s largest artificial intelligence infrastructure projects remain heavily exposed to Nvidia’s hardware delivery timelines, local pricing markups, and strategic investments. Despite global leadership instability, local cloud providers and enterprise giants continue to deploy thousands of graphics processors to meet sovereign compute targets.

An enterprise Nvidia H100 80GB SXM5 GPU carries a massive landed purchase cost in India, frequently ranging from ₹25,00,000 to ₹34,22,000 (~$30,000 to $41,000 USD) depending on distributor markup, tracked under compute initiatives on the IndiaAI portal. To break down the math, a standard H100 GPU with a US MSRP of approximately $30,000 translates to a base cost of roughly ₹25,20,000 at an exchange rate of ₹84 per USD. Adding India’s Basic Customs Duty (BCD) of 7.5% (approx ₹1,89,000) and the standard 18% Integrated Goods and Services Tax (IGST) (approx ₹4,87,620) brings the total landed cost to approximately ₹31,96,620. This represents a substantial 27.5% brand markup and tax drag over direct US acquisition.

Because of these high capital expenditures, the local cloud rental market is thriving. Developers are turning to domestic providers verified on the IndiaAI compute portal to avoid the 3.5% foreign transaction markups charged by international banks on US dollar credit cards.

For instance, local provider E2E Networks lists on-demand rental rates tracked on the IndiaAI compute portal for an Nvidia H100 80GB SXM5 starting at ₹379.05 per hour, with spot instances dropping as low as ₹70 per hour. This domestic billing bypasses traditional credit card friction entirely. Meanwhile, entry-level Nvidia L4 GPUs (with a landed purchase price of ₹2.3 lakh to ₹4.9 lakh) rent for ₹35 to ₹85 per hour locally.

Nvidia has also begun executing direct equity-for-hardware deals in India. In August 2026, Bengaluru-based Sarvam AI raised a ₹698 crore ($74 million) funding round led by Nvidia, which contributed ₹238 crore ($25 million) for a 1.66% equity stake, as covered by Business Standard. Similarly, in October 2026, Nvidia participated in a $14 million Series B round for Indian AI inference startup Simplismart. This investment directly aligns with local efforts to optimize GPU workloads and reduce compute overhead.

On the infrastructure side, domestic cloud partners like Yotta Data Services are executing a massive $12 billion capital expansion plan to deploy 80,000 Nvidia Vera Rubin and Blackwell GPUs across Greater Noida and Navi Mumbai, coordinating with the IndiaAI compute program. This deployment represents one of the largest single commitments to Nvidia’s next-generation architectures outside the United States.

This massive pipeline is critical for the government’s IndiaAI Mission, which is backed by a ₹10,372 crore (~$1.25 billion) sovereign fund. As outlined on the official IndiaAI compute portal, the initiative aims to democratize access to high-performance computing for domestic researchers and startups. As of late 2026, the IndiaAI Mission faced a 15,000-GPU deficit in its procurement targets. To prevent underutilization of the 30,000 GPUs already contracted, the Ministry of Electronics and Information Technology (MeitY) recently allowed bidders to sell their capacity globally.

What to Watch

Regulatory agencies and financial analysts are closely monitoring whether the US Securities and Exchange Commission will launch a formal probe into Nvidia’s accounting practices. The primary concern is whether these circular transactions inflated revenues during the height of the artificial intelligence infrastructure boom.

If the allegations of artificial demand generation are proven, Nvidia faces severe legal and financial repercussions. Already, the company’s stock has reacted sharply, closing at $166.19 on November 12, 2026, marking a 30.5% drop from its October high of $243.37, as noted by The Decoder.

This market correction has also hit the company’s workforce. For example, Nvidia’s 5,590 employees in India held an estimated 7.05 million unvested RSUs; market swings documented by The Decoder dropped the value of these unvested equity grants from $1.73 billion in early October to $1.11 billion by mid-November 2026.

This leadership transition is occurring at a time when the entire artificial intelligence landscape is recalibrating. As explored in our analysis of how OpenAI Slows Down AI Training, massive model scaling is hitting physical and financial walls. If frontier model developers reduce their hardware acquisition budgets, the “Bank of Nvidia” model could collapse under the weight of its own guarantees.

Furthermore, if neocloud startups fail to attract enough organic enterprise customers, they will default on their multi-billion-dollar data center leases. In that scenario, Nvidia will be forced to honor its massive backstops, turning paper profits into real cash liabilities.