Investment Tycoon Nobul Warns: Cycle Financing Plants Seeds for Collapse, Nvidia (NVDA.US) May Repeat Cisco's Mistake

According to Zhitong Finance APP, veteran investor George Noble issued a warning on Thursday, stating that Nvidia (NVDA.US) is heading towards a crisis similar to the reckoning during the dot-com bubble era, as the company is providing financial support to customers who are purchasing its artificial intelligence (AI) hardware.

Noble, who previously worked as an analyst under Peter Lynch, emphasized in a social media post that Nvidia currently accounts for about 8% of the S&P 500 index, double the weighting Cisco (CSCO.US) held at the peak of the dot-com bubble in 2000. He believes the semiconductor giant is falling into the “exact same” trap that led to the crash of Cisco’s stock over 20 years ago.

Noble directly compared the current situation to Cisco’s reliance on vendor financing models at the turn of the millennium, when the network equipment supplier offered loans to telecom startups unable to pay upfront for equipment. Noble pointed out that when these telecom companies successively went bankrupt, Cisco’s stock plummeted by 89% and dragged down the S&P 500 by half overall.

Noble noted that today Nvidia is adopting a modern version of this approach: guaranteeing customers' leasing agreements and taking equity stakes in startups that purchase its chips. He warned that this circular financing structure exposes Nvidia to considerable default risk, stating that the more hardware Nvidia sells, the higher the risk once customers are unable to continue paying their bills. He wrote, “This is the largest capital misallocation in history,” warning that such a strategy creates dangerous systemic vulnerabilities for both Nvidia and the broader market.

Beyond fundamental warnings, Noble also cited technical analysis for support. He shared a chart provided by macro investor Sam Kovac. The chart shows that Nvidia’s years-long daily uptrend is narrowing, forming a rising wedge pattern that spans several years; a close-up view on the chart shows the stock beginning to break below the lower, upward-slanting support line.

Technical analysts closely monitor rising wedge patterns, as these are typically viewed as bearish reversal formations, indicating prices may reverse from rising to falling. As the market increases scrutiny of Nvidia’s balance sheet, the momentum behind the stock may finally begin to crack, especially as the price is now lagging below the 200-day moving average.

Nvidia’s Deep Involvement in AI Infrastructure Financing Draws Attention to Scale of Guarantees

In fact, in recent years Nvidia has moved beyond being just an AI chip supplier, gradually extending into data center infrastructure construction, customer financing support, and investments in the AI ecosystem. The company aims to help clients overcome funding and infrastructure bottlenecks, further expand the deployment of its GPU products, and solidify its dominance in the AI computing market.

According to Nvidia’s filings for the second quarter of 2026, as of July 26, 2026, the company’s equity investments amounted to $99 billion, with an additional $25 billion in equity investment commitments. At the same time, Nvidia also disclosed that the total cloud service commitments signed with some AI cloud service providers reached $36 billion, generally over a period of six years. Under these arrangements, AI cloud service providers purchase Nvidia’s data center infrastructure products, while Nvidia commits to buying their cloud service capacity; if these providers fail to sell enough of this capacity to third-party customers, Nvidia may be obliged to purchase the unsold capacity.

More notably, Nvidia has started providing credit support for data center leases of some AI cloud service providers. The company disclosed that as of July 26, 2026, its maximum exposure to guarantees for these data center land, power, and building shell leases for certain AI cloud partners was $3.5 billion.

Additionally, in August 2026, Nvidia signed a guarantee arrangement with SB Energy to provide up to $10.5 billion in credit support for the PORTS Technology Campus project in Ohio, USA. This project will provide OpenAI with about 4.25 GW of IT load capacity, including nine data center construction phases and 20-year leasing arrangements. According to the agreement, the guarantee obligations will take effect gradually as each data center phase is completed and put into use, and will also decrease as OpenAI fulfills its lease payment obligations.

Nvidia emphasized that these guarantees only cover specific portions of rent and power payments, not the entire project cost or all customer obligations; guarantees may also terminate if OpenAI achieves a satisfactory credit rating and other specified conditions. Based on Nvidia’s disclosures, the combined maximum exposure of the $10.5 billion and the $3.5 billion guarantees brings the company’s total related guarantee risk to $10.85 billion.

These arrangements indicate that Nvidia is helping upstream and downstream in the AI industry obtain the funding and infrastructure needed to build data centers through guarantees, investments, and long-term procurement commitments. However, if those customers fail to secure sufficient external financing, experience project construction delays, or if AI computing demand falls short of expectations, Nvidia could bear additional costs and financial risks as a result.

$500 Billion Financing Plan Faces Wall Street Scrutiny, Value of GPU Collateral in Spotlight

In August this year, Nvidia also announced cooperation with financial institutions including Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR, planning to establish an independent AI computing infrastructure financing platform. The goal is to mobilize over $500 billion in third-party capital to support AI infrastructure construction in the future.

The core idea of the plan is to turn Nvidia GPUs and related AI infrastructure into financeable assets, using methods such as chip-backed loans to provide funding for AI developers and infrastructure operators. Nvidia CEO Jensen Huang believes that AI computing resources are productive, durable, and transferable, capable of generating revenue over an extended period, and therefore suitable as long-term financing assets.

However, this financing model has not completely dispelled Wall Street’s concerns. According to a Reuters report on October 1, some banks and credit investors are cautious about the value of GPUs as long-term collateral, believing that Nvidia’s projections for chip lifespan and future residual value may be overly optimistic. Jensen Huang has said that Nvidia's high-end GPUs can have a lifespan of up to ten years, but some banks typically value GPU assets using a depreciation period of three to four years.

This means that if the iteration speed of AI chips accelerates, or market demand for previous-generation GPUs quickly declines, the actual value of the collateral may fall short of the valuation at the time of financing. If the borrower’s business worsens, and the chip residual value cannot cover outstanding debts, both lenders and guarantor companies could face losses.

Reuters also pointed out that certain completed chip-collateralized financing deals received high credit ratings mainly because of stable third-party customer payment commitments. For example, CoreWeave, a company in which Nvidia holds equity, previously obtained $8.5 billion in GPU-collateralized loans, with lenders relying primarily on Meta’s contract payments to guarantee debt repayment. By contrast, if a financing project lacks investment-grade customers providing long-term revenue guarantees, lenders may require higher guarantee ratios, more substantial risk buffers, and stricter repayment assurances.

Additionally, Nvidia’s financing support is not limited to disclosed projects. On October 29, Reuters referenced The Information, reporting that Nvidia is in talks with AI cloud service provider Nscale to provide billions of dollars in credit guarantees to support debt financing for building data centers for Anthropic. However, as of the report's release, the arrangement was still under discussion, and specific guarantee amounts and financing terms had not yet been disclosed.

Whether AI Demand Can Be Converted Into Real Cash Flow Will Determine the Sustainability of the Financing Model

From Nvidia’s perspective, offering financing to support clients' AI infrastructure construction can ease capital constraints in the industry chain, drive more GPUs into deployment, and create ongoing hardware sales and ecosystem software revenue for the company. Particularly as AI cloud service providers and model developers rapidly expand but lack the balance sheet and long-term credit strength to support large-scale infrastructure investment, Nvidia’s intervention in financing has its commercial logic.

However, whether the financing arrangement can be sustainable ultimately still depends on whether AI computing demand can be converted into sufficient commercial revenue and cash flow. If AI model developers cannot achieve profitability, if enterprise clients’ willingness to pay for AI services is below expectations, or if data center utilization falls short, the corresponding operators may struggle to cover GPU procurement, financing interest, power expenditures, and long-term leasing costs.

In such scenarios, Nvidia may not only face declining chip demand but could also have to bear multiple layers of pressure from guarantee obligations, procurement commitments, and equity investments. Particularly if AI infrastructure construction becomes highly reliant on debt financing, a tightening financing environment or a drop in collateral valuation could spread risks across the interconnected networks of chip suppliers, AI cloud providers, model developers, and financial institutions.

Of course, today’s AI industry is not entirely comparable to the telecom sector of 2000. Now, cloud computing giants and large technology companies have more mature business models, stronger balance sheets, and genuine needs for AI infrastructure. Nvidia also emphasizes that its financing platform will engage independent financial institutions, with partners independently assessing and underwriting specific projects. Therefore, one cannot simply equate the current financing model to the vendor financing of the dot-com bubble era.

However, as Nvidia transitions from a chip supplier to an investor, guarantor, and long-term procurement party in the AI infrastructure ecosystem, the market’s focus on Nvidia’s risk exposure continues to rise. The critical question for investors is no longer just how many GPUs Nvidia can sell, but whether those GPUs can generate sufficient ongoing cash flow, and whether customers can maintain operations without further financial support from Nvidia.