Jensen Huang Navigates China Risks in Ambitious Plan

Jensen Huang, the architect of the current artificial intelligence revolution, has transformed Nvidia into the world’s most valuable company, largely by pioneering the specialized computer chips that power AI. Now, Huang is embarking on a new, ambitious endeavor: to redefine how these critical AI components are financed, aiming to position them as long-term, stable assets akin to commercial real estate or essential infrastructure like toll roads, a move that could unlock hundreds of billions in capital for the burgeoning AI economy.

This strategic pivot is intricately tied to Nvidia’s long-term vision, particularly its competitive stance against China’s rapidly expanding AI capabilities. To achieve this, Huang is attempting a sophisticated financial engineering feat, seeking to convince Wall Street investors that Nvidia’s high-performance graphics processing units (GPUs) represent a secure, appreciating investment rather than rapidly depreciating technology.

This week, Nvidia announced significant partnerships with six of the world’s most influential asset managers: BlackRock, Blackstone, Apollo, KKR, Brookfield, and Goldman Sachs. The collective aim is to establish financing platforms designed to mobilize over $500 billion in third-party capital. This substantial financial pipeline is earmarked for the construction of data centers and GPU clusters, providing crucial funding for companies that may not possess the immediate creditworthiness or capital to acquire millions of dollars worth of cutting-edge silicon.

A cornerstone of Huang’s strategy, unveiled in a recent public appearance alongside the leaders of these financial giants, rests on a fundamental assumption: that Nvidia’s GPUs will retain their value over time. He envisions them behaving more like tangible, enduring assets with established secondary markets, rather than disposable consumer electronics. Huang articulated this vision, stating, “Nvidia’s AI factory platform is really an investable asset, an infrastructure asset. The reason for that is because it’s productive, it’s revenue generating, it is fungible, it’s used by just about every cloud service provider, it runs every AI model.”

Traditionally, asset-backed finance relies on the collateral’s tangible value and longevity. Banks lend against assets like buildings, warehouses, or cargo ships, knowing that if a borrower defaults, these physical assets can be repossessed and sold, often with established secondary markets and lifespans spanning decades. However, the productive and resale lifespan of state-of-the-art GPUs presents a more complex challenge.

While the latest GPUs are indispensable for training cutting-edge AI models, their utility can diminish after a few years, shifting to less demanding inference tasks. This transition directly impacts their resale value and, consequently, their viability as collateral for long-term financing. The inherent risk of rapid technological obsolescence and depreciation is a significant factor that investors must consider.

The potential for faster-than-expected depreciation is a key concern, according to industry observers. Ben Emons, founder of FedWatch Advisors and a veteran in structured finance, highlights this as a critical risk. He noted, “Depreciation is the one key risk here. Nvidia chips could depreciate faster than expected.”

A significant external factor that could exacerbate these depreciation concerns is the competitive landscape, particularly from China. Emons suggests that China’s aggressive ramp-up in domestic compute capacity could lead to a strategic dumping of low-cost silicon into the global market, triggering a price war. Such a scenario could rapidly erode the value of the collateral backing substantial debt investments, leaving investors vulnerable to significant losses.

To mitigate these risks, Emons anticipates that investors will likely demand higher yields, treating GPUs as high-depreciation equipment rather than stable real estate. He estimates that required yields could range from 11% to 17%, depending on the specific position within the capital structure. Furthermore, a Bank of America Securities analysis indicates that the primary borrowers in such financing arrangements are likely to be non-investment grade companies, including nascent AI startups and emerging cloud providers, which typically lack access to traditional debt markets.

If these higher-risk borrowers face financial distress, asset managers could be compelled to repossess and liquidate used GPUs into a potentially declining market, a scenario that could test the efficacy of Nvidia’s innovative financing model. Despite these potential headwinds, the immediate competitive threat from China is somewhat constrained by U.S. export controls. Huawei, a prominent Chinese AI chip provider, has been on the U.S. Commerce Department’s Entity List since 2019. More recently, in May, the U.S. government declared that Huawei’s Ascend AI chips violate U.S. export controls, effectively barring any American company from utilizing them.

In the current U.S. market, Nvidia remains the undisputed leader in AI chip supply, commanding an estimated market share exceeding 75%. The economic dynamics continue to favor Huang’s strategy. Driven by the relentless demand from hyperscalers expanding their AI infrastructure, the rental rates for Nvidia’s H100 GPUs have seen a notable increase. Huang recently pointed out that these rates have climbed from approximately $1.70 per GPU-hour in late 2025 to around $2.35 per GPU-hour this year, reflecting a strong market driven by scarcity.

Crucially, Nvidia’s proprietary CUDA software layer plays a pivotal role in its valuation argument. CUDA enables developers to optimize AI workloads on Nvidia’s GPUs, continuously enhancing hardware performance post-deployment. This software advantage allows older generations of GPUs to maintain productivity and generate revenue for longer periods than might be predicted by conventional accounting models. Ultimately, the success of this ambitious financing initiative, and the substantial investor capital it aims to attract, may hinge on the sustained technological edge and the long-term value proposition of Nvidia’s AI ecosystem.