The Circular Financing Trap in Nvidia's Ecosystem: The Capital Undercurrents Behind the GPU Boom

Introduction: A Self-Reinforcing Capital Game

As the AI boom sweeps across the globe, Nvidia has undoubtedly emerged as the biggest winner. Yet behind this wave of GPU prosperity lies a capital operation model worth watching closely—Circular Financing. Discussions on Hacker News about the three-way relationship between Nvidia, CoreWeave, and Nebius have peeled back a corner of this finely tuned mechanism.

Circular financing, put simply, refers to capital flowing repeatedly among a few key players before ultimately returning to its origin, forming a closed loop that appears prosperous but is in fact self-reinforcing. Nvidia injects capital into cloud computing companies, which then use those funds to purchase Nvidia GPUs, and Nvidia's revenue and valuation rise accordingly—a perfect positive feedback loop.

It's worth noting that circular financing is not an invention of the AI era. In essence, it is a distorted form of Supply Chain Finance, with traces found across multiple historical bubble cycles. Supply chain finance is originally a financial tool designed to improve overall efficiency by optimizing the cash flows of accounts receivable, inventory, and accounts payable, and it is widely used in manufacturing, retail, and other sectors. However, when a supplier directly provides financing to a buyer to purchase its own products, this arrangement veers into its distorted form. The most classic case occurred during the telecom boom of the late 1990s: equipment giants like Nortel and Lucent provided massive loans to cash-strapped carriers through so-called "Vendor Financing," on the condition that the money be used to buy back their own equipment. This arrangement produced impressive revenue figures on paper and drove up the stock prices of both parties, yet buried the real credit risk deep within their balance sheets. When the bubble burst in 2001, the market value of these companies evaporated by more than 90%; Nortel eventually went bankrupt in 2009, while Lucent was acquired by Alcatel at a steep discount. The echoes of history make today's AI capital structure especially worthy of scrutiny.

Anatomy of the Three-Way Relationship

Nvidia: Both Supplier and Investor

Nvidia plays a dual role in this game. As the absolute dominant player in global AI chips, it controls the lifeline of GPU supply; at the same time, through strategic investments, it injects capital into emerging GPU cloud service providers, helping them scale up their computing capacity.

The ingenuity of this arrangement lies in the fact that a large portion of the capital Nvidia invests flows back in the form of purchase orders. This both locks in downstream demand and, to a certain extent, polishes Nvidia's own revenue growth curve.

Nvidia's investment footprint goes far beyond CoreWeave and Nebius. Its strategic investment arm, NVentures, established in 2022, focuses on early- and growth-stage investments in the AI ecosystem and has taken stakes in dozens of AI startups spanning large models, robotics, autonomous driving, and other domains. This approach closely mirrors Intel Capital's strategy during the PC era—locking in upstream chip demand by investing in downstream ecosystems. Back then, Intel built the demand-side support for the "Wintel" ecosystem by investing in operating systems, application software, and peripheral makers, essentially using capital to lock in chip demand. The difference is that the PC ecosystem had multiple influential players such as Microsoft and AMD, whereas in the current AI training market Nvidia GPUs hold more than 80% market share. This near-monopoly position makes the demand-locking effect of its investments far more profound than Intel's was, and the self-reinforcing effect of the loop far more pronounced.

To understand the technical foundation of this monopoly, one must look at the CUDA ecosystem. CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model launched by Nvidia in 2006, allowing developers to use GPUs for general-purpose parallel computing. After nearly two decades of ecosystem accumulation, CUDA is deeply embedded in the underlying implementations of mainstream deep learning frameworks such as PyTorch and TensorFlow, with thousands of research papers, open-source models, and production-grade systems all written on top of CUDA. This deep integration creates extremely high migration costs: even if competitors (such as AMD's ROCm platform) gradually close the hardware performance gap, developers still face real-world obstacles such as code rewrites, extended debugging cycles, and missing ecosystem tools. A vast amount of research code and production-grade models are deeply bound to CUDA, making the substitution cost for AMD, Intel, or even domestic chips extremely high. This "software moat" means that even if competitors gradually catch up in hardware performance, migration costs will generate strong path dependency among users, thereby maintaining Nvidia's unshakable core position within the circular financing structure.

CoreWeave: The Newcomer in GPU Cloud Computing

CoreWeave is a representative company that rose to prominence in this wave of AI infrastructure. Its background is rather dramatic—the company originally started as an Ethereum mining operation. When the cryptocurrency market cooled in 2019, its founders astutely repurposed their accumulated GPU computing resources into cloud services, successfully completing a stunning business model pivot.

CoreWeave represents a new breed of "Hyperscaler Specialist." Unlike comprehensive cloud platforms such as AWS, Azure, and GCP, it focuses on the single track of GPU compute leasing. The core logic of this model is: obtain hardware discounts and priority supply through large-scale procurement, then lease out compute to AI companies at prices above its own cost of capital, earning the spread. Its business model is built on purchasing GPUs at scale and renting out compute to AI companies—essentially a capital-intensive, high-leverage operating model: procuring GPUs through large-scale debt financing, leasing out compute via long-term contracts, and anchoring on tech giants such as Microsoft and Meta as clients. However, the capital-intensive model requires sustained large capital expenditures (CapEx). GPU depreciation cycles are typically 3–5 years, yet the pace of AI technology iteration may cause hardware to depreciate even faster, creating a significant maturity mismatch risk on the financial side. Reports indicate that its debt once exceeded $7 billion, giving it extremely high financial leverage. CoreWeave completed its IPO in 2024, but its prospectus explicitly disclosed its heavy reliance on Nvidia hardware as one of its core risk factors—this transparency itself reflects the structural depth of that dependency.

Nvidia's support—whether through hardware priority supply or capital-level entanglement—forms an important source of CoreWeave's competitiveness, and also creates a deep dependency on Nvidia's ecosystem.

Nebius: Another Node in the Closed Loop

Nebius's background is equally noteworthy. It emerged from the Russian internet giant Yandex, and its very birth is a textbook case of geopolitics reshaping the commercial landscape. After the outbreak of the Russia-Ukraine conflict in 2022, Nasdaq-listed Yandex faced serious sanctions risks and investor pressure, forcing it to launch a complex business spin-off and restructuring: its international cloud business, autonomous driving, and other non-Russian assets were carved out and bundled, re-registered in the Netherlands under the name Nebius, and independently listed on Nasdaq in 2024, primarily providing GPU cloud services to European and North American markets. Nvidia's stake at this juncture both secured it a positioning node in the European GPU cloud market and provided Nebius with the credit endorsement it urgently needed to build market trust. Nvidia's equity stake both offered capital backing and ensured priority in the hardware supply chain. This structure of a "chip supplier directly taking a stake in a compute-leasing provider" has also sparked discussions at the regulatory level about vertical integration and market competition.

When multiple cloud service providers become bound to chip suppliers in similar ways, the flow of capital across the entire industry takes on a high degree of interdependence, further reinforcing the circular characteristics.

The Hidden Dangers of Circular Financing

Questionable Revenue Quality

The most fundamental problem with circular financing is that it can distort judgments about true market demand. When part of a supplier's revenue comes from the downstream companies it invests in, it becomes difficult to disentangle exactly how much of those figures reflect genuine end-user demand.

During the telecom bubble of the late 20th century, equipment suppliers provided financing to carriers to purchase their own equipment, ultimately triggering a chain reaction when demand collapsed. This history warns us: although circular financing can inflate the paper figures of all parties in the short term, it may quietly accumulate systemic risk.

An Amplifier of Valuation Bubbles

Within this closed loop, the high valuations of all parties mutually endorse one another: Nvidia's stock price supports its capacity to invest in cloud providers, and the expansion of cloud providers in turn validates Nvidia's growth story. Once a crack appears at any link, this mutually reinforcing valuation logic could trigger a domino-like chain collapse.

From a financial perspective, the network built by circular financing exhibits the classic characteristics of "Common Risk Exposure." This concept comes from financial network theory and refers to multiple institutions forming high correlation on the same risk factor because they hold similar assets or cross-hold each other's equity. When multiple institutions each hold each other's equity and serve as each other's customers, stress at a single node is transmitted synchronously through multiple paths, amplifying overall volatility. The 2008 financial crisis profoundly revealed the destructive power of this network effect: AIG formed implicit connections with hundreds of financial institutions through credit default swaps (CDS), and the liquidity crisis at this single institution directly threatened the stability of the entire financial system, ultimately forcing the U.S. government to deploy over $180 billion in emergency bailouts. Although the AI infrastructure space has not yet formed financial derivative leverage of the same magnitude, as more institutions participate, the fragility arising from the network effect of cross-held equity among multiple parties still warrants continued attention.

Concentration Risk

The entire AI infrastructure ecosystem's heavy reliance on Nvidia is itself a form of structural fragility. When supply, capital, and demand all converge on the same central node, any shock directed at that node—technological substitution, regulatory intervention, or shifting demand—will be amplified and transmitted throughout the entire network.

The high migration cost of the CUDA ecosystem consolidates this concentration in the short term, but it also means that once a substitute technology breaks through a critical threshold, the pace of industry restructuring could exceed expectations. Notably, Groq's LPU (Language Processing Unit), Cerebras's wafer-scale chips, and the AI accelerators developed in-house by major cloud providers (Google TPU, Amazon Trainium) are forming a diversified competitive landscape. Once any substitute path gains recognition from the developer ecosystem, the entire circular financing structure built on the CUDA monopoly will face the risk of its foundations being shaken.

Viewing the GPU Boom Rationally

It must be acknowledged that the growth in AI compute demand is real. Training and inference of large models do consume massive GPU resources—this is the fundamental support underpinning the current boom. Circular financing itself is not entirely negative either—at certain stages it can accelerate infrastructure construction and meet rapidly swelling compute demand.

However, investors and industry observers need to stay clear-headed: when capital circulates within a closed loop, prosperity on paper does not necessarily equate to sustainable value creation. Distinguishing between genuine end demand and the false demand manufactured by internal circulation is the key to judging whether this AI infrastructure investment is healthy. An effective analytical framework is to trace where the "compute consumption" ultimately points: do the trained models generate real commercial returns? Do the inference services reach paying end users? Or does a large amount of compute consumption still remain at the internal experiment and proof-of-concept stage?

Although the Hacker News community's discussion of this topic is not large in scale, it reflects the rising vigilance within the tech circle about the capital structure of AI infrastructure. This bottom-up scrutiny is often the first line of defense in identifying potential risks.

Conclusion

The circular financing among Nvidia, CoreWeave, and Nebius offers an important lens for understanding the current AI boom. It reveals both the immense power of capital in driving a technological revolution and the structural hidden dangers beneath the surface of prosperity. The technological monopoly built by the CUDA moat, the high-leverage operations and maturity-mismatch risks of capital-intensive cloud providers, and the common risk exposure formed by cross-held equity among multiple parties together weave a delicate yet fragile web of capital.

For every participant caught up in this transformation, seeing the true path of capital flows clearly is more important than chasing short-term paper growth. Whether the GPU boom can translate into sustainable industry development ultimately depends on whether genuine end-user demand can be realized, rather than on the illusion manufactured by capital circulation. History has repeatedly proven that when an industry's prosperity relies heavily on the self-circulation of internal capital rather than the sustained influx of external demand, the boundary between rationality and bubble is often far more fragile than people realize.