top of page
Search

AI Economic Internal Circulation and Chip-Collateralized Financing


ree

"Chip-collateralized financing, AI compute economy: The internal circulation among Nvidia, Oracle, and OpenAI." This phrase accurately depicts a tight-knit and symbiotic business model that is currently forming among AI giants. This is not merely a simple supply chain, but a powerful strategic closed loop and ecological alliance.

Let's break down in detail how this "internal circulation" operates, and the role and interests of each company within this cycle.


I. Role Breakdown and Cycle Dynamics

1. Nvidia: The "Arms Dealer" of Compute Power

  • Role:To provide the most fundamental means of production—AI computing chips (such as the H100, H200, B100/Blackwell).

  • Function in the Cycle:

    • For Oracle:Nvidia is the core supplier for Oracle's construction of its AI cloud infrastructure. Oracle needs to spend vast sums to purchase tens or even hundreds of thousands of Nvidia's GPUs to build its compute clusters.

    • For OpenAI:Although OpenAI also procures chips independently, it heavily relies on Nvidia's hardware architecture and software ecosystem (like CUDA) to develop and run its models. The performance of Nvidia's chips directly determines the iteration speed and inference cost of OpenAI's models.

    • The "Chip-Collateralized Financing" Connection:This link is crucial. Cloud vendors like Oracle may require enormous financing when purchasing chips. A possible model is that they can use future cloud service revenue as a guarantee, or even use the purchased chip assets themselves as collateral to obtain loans. This allows them to rapidly expand their compute capacity to meet the demands of clients like OpenAI. While Nvidia does not directly provide loans, the high value and high liquidity of its chips make such financing feasible.


 

2. Oracle: The "Landlord" and "Banker" of Compute Power

  • Role:To provide large-scale, high-performance cloud infrastructure (OCI, Oracle Cloud Infrastructure), especially its AI-focused "Oracle Cloud Infrastructure AI."

  • Function in the Cycle:

    • For Nvidia:Oracle is one of Nvidia's largest customers. It procures Nvidia chips on a massive scale to build powerful AI compute clusters. Oracle has established a deep strategic partnership with Nvidia and is often among the first companies to receive the latest chip supplies.

    • For OpenAI:Oracle is a key compute provider for OpenAI. In 2024, OpenAI reached a significant agreement with Oracle to use its cloud compute power to train its next-generation models (like GPT-5). This provides OpenAI with another major source of compute power besides Microsoft Azure, enhancing its bargaining power and business resilience.

    • The "Chip-Collateralized Financing" Connection:As a capital-rich enterprise, Oracle can leverage its balance sheet to finance the chip purchases from Nvidia. It is essentially playing a "capital game": first investing heavily to build compute capacity, then recouping costs and generating profit by leasing that capacity to companies like OpenAI.


3. OpenAI: The "Consumer" and "Value Creator" of Compute Power

  • Role:To develop the most cutting-edge AI models (like GPT, DALL-E) and offer AI services globally through APIs and products (like ChatGPT).

  • Function in the Cycle:

    • For Oracle:OpenAI is an "anchor customer" and one of the largest sources of traffic for Oracle's cloud services. OpenAI's insatiable demand for compute power brings a stable and massive revenue stream to Oracle's AI cloud business, significantly boosting Oracle's status and brand influence in the AI cloud market.

    • For Nvidia:OpenAI is the "ultimate endorser" of Nvidia's technology. The complexity and scale of OpenAI's models are constantly pushing the boundaries, validating and fueling the demand for Nvidia's most advanced chips. Without companies like OpenAI, Nvidia's AI chip market would not be as explosive as it is today.

    • Creating a Demand Loop:OpenAI collects fees from end-users and enterprises through its services (such as ChatGPT Plus subscriptions and API calls). This revenue is ultimately converted into "compute rent" and "hardware investment" paid to Oracle and Nvidia.


II. The Strengths of This "Internal Circulation"

  1. High Barriers to Entry: This cycle requires top-tier chip design capabilities, massive capital expenditure capacity, and leading-edge AI research and development abilities. It is extremely difficult for new players to break this iron triangle.

  2. Mutual Dependence and Lock-in:

    • Nvidia needs giants like Oracle and OpenAI to consume its exorbitantly priced chips and prove their value.

    • Oracle needs Nvidia's chips to remain competitive and needs OpenAI's business to support its cloud strategy.

    • OpenAI needs Nvidia's hardware and Oracle's compute power to maintain its technological lead.

  3. Dual Drivers of Capital and Technology:This is not just a technological race; it's a capital game. Whoever can raise more capital and deploy more Nvidia chips faster will gain an advantageous position in the AI competition. "Chip-collateralized financing" is a perfect embodiment of this characteristic.

  4. A Counterbalance to Microsoft:This cycle poses a direct challenge to Microsoft. Previously, OpenAI and Microsoft Azure formed a tight alliance. Now, by bringing in Oracle as a second compute provider, OpenAI reduces its dependence on its competitor, Microsoft. Nvidia welcomes this development, as it signifies a larger market for its chips.

This "internal circulation" is a classic strategic ecological closed loop:

  • Nvidia sells the "shovels" (chips).

  • Oracle builds the "mine" and rents out "mining spots" (cloud compute).

  • OpenAI is the biggest "miner" (consuming compute) that digs up "gold" (AI models and services) to sell to the public.

This cycle tightly binds together capital (chip-collateralized financing, etc.), technology (hardware, cloud, models), and market demand (global AI applications) to jointly carve up the enormous profits from the global AI market explosion. The stability of this alliance will, to a large extent, determine the future landscape of the global AI industry.



III. What Are the Risks and Weaknesses?

A. Technology Dependence and Singularity Risk

  1. Nvidia's Hardware Monopoly is the Core Risk

    • CUDA Ecosystem Lock-in: The entire cycle (Oracle's AI services, OpenAI's model training) is deeply tied to Nvidia's CUDA software ecosystem. If there are any issues with Nvidia's hardware supply or software licensing, the entire cycle could grind to a halt instantly. This "all-or-nothing" dependency is the greatest risk.

    • Technological Disruption: If a new, more efficient, and lower-cost AI computing architecture emerges (e.g., breakthroughs in photonic or quantum computing, or another company's ASIC chips surpassing Nvidia in performance and ecosystem), Nvidia's moat will be threatened, shaking the very foundation of this cycle.

  2. Uncertainty in OpenAI's Model Progress

    • Diminishing Marginal Returns from "Training Large Models": If OpenAI encounters insurmountable technical bottlenecks on its path to AGI (Artificial General Intelligence), causing the value added by more powerful models (like GPT-5) to fall short of the soaring compute costs, the business logic of this cycle will be questioned. The return on investment (ROI) could deteriorate.

    • Homogenized Competition: Open-source models and competitors from around the world (like Meta's Llama series) are catching up quickly. If competitors can offer models with similar performance at a lower cost, OpenAI's "technological moat" and bargaining power will be weakened, in turn affecting its ability to purchase compute from Oracle and justify the value of Nvidia's chips.


B. Economic and Business Risks

  1. Extremely High Capital Expenditure and Financial Risk

    • Heavy Debt Burden: "Chip-collateralized financing" implies that Oracle is taking on massive debt to build its compute capacity. This is a high-leverage gamble. If global AI demand growth falls short of expectations, or an economic downturn leads enterprises to cut AI spending, Oracle will face immense financial pressure from idle compute capacity and revenues that cannot cover debt costs.

    • High Fixed Costs: The construction, power, and cooling costs of cloud data centers are fixed. Even without customers, most of these costs must be paid continuously. If the demand side of the cycle (OpenAI) runs into trouble, these fixed costs will rapidly erode profits.

  2. Internal Friction from Mutual Lock-in and Bargaining Power

    • Profit Squeeze: The three parties are currently in a honeymoon phase. However, over time, internal bargaining conflicts will emerge. Nvidia will want to raise chip prices, OpenAI will want to lower compute costs, and Oracle will want to increase its profit margins. The cycle itself does not solve the problem of how to distribute the final profits, and long-term interest-based negotiations could undermine the alliance's stability.

    • The "Lock-in" Effect: Both Oracle and OpenAI have invested enormous sunk costs (capital, time, R&D for adaptation) into Nvidia's hardware, making switching costs extremely high. This leaves them "locked in" to Nvidia to a certain extent, making it difficult to easily switch to other suppliers.


C. External Competition and Market Risks

  1. Competition from Other Giants

    • Cloud Vendor Competition: Amazon AWS, Microsoft Azure, and Google Cloud are also major customers of Nvidia and are developing their own AI chips. They will do whatever it takes to prevent the Oracle-OpenAI alliance from monopolizing the high-end AI compute market. An intense price war and resource battle is inevitable.

    • Threat of Vertical Integration: The biggest customers can also become the biggest competitors. Microsoft is developing its Maia chip, Google has its TPUs, and Amazon has Trainium and Inferentia. Their long-term goal is to reduce their reliance on Nvidia. If OpenAI's models successfully prove the market, these giants will have even more motivation and resources to replicate OpenAI's success, thereby bypassing this cycle.

  2. Uncertainty in Market Demand

    • Lack of a "Killer" AI Application: The current AI investment boom is largely driven by technological possibility rather than clear business returns. If, in the next 1-2 years, no "killer" AI applications emerge—beyond chatbots, code assistants, and image generation—that can significantly boost corporate profits, businesses may reduce their procurement of high-end compute, causing the demand foundation of the entire cycle to collapse.


D. Operational and Geopolitical Risks

  1. Geopolitics and Supply Chain Risk

    • Concentrated Chip Supply: The production of Nvidia's high-end chips is heavily dependent on TSMC. Any political instability in the Taiwan Strait or global supply chain disruptions (like pandemics, earthquakes) would directly lead to a chip shortage, bringing the entire cycle to a standstill.

    • Export Controls: AI chip export control policies among nations (especially between the US and China) can disrupt the smooth operation of this global cycle, increasing compliance costs and operational complexity.

  2. Energy and Environmental Constraints

    • Explosive Growth in Compute Power Consumption: Training and inference for AI models are energy-intensive industries. The operation of this cycle is predicated on the assumption of abundant and cheap energy. If the global energy crisis worsens, or if nations impose carbon taxes on high-energy-consuming data centers, it will directly impact its operational costs and feasibility.



IV. Summary

This "internal circulation" is an extremely powerful yet exceptionally fragile tower built to win the AI arms race.

  • Its strength lies in its ability to efficiently integrate three top-tier resources (hardware, cloud capital, algorithms) in the short term, creating an unparalleled force of impact.

  • Its fragility lies in its lack of elasticity: its over-reliance on a single technological path (Nvidia CUDA), its thirst for continuous capital infusion, and its sensitivity to external market demand and geopolitics.

Whether this cycle can be sustained depends not on its own sturdiness, but on its ability to withstand challenges from external competitors, disruptions from alternative technologies, and the immense tension between its high-cost structure and uncertain market demand. If any single link breaks, it could trigger a chain reaction, leading to a significant reduction in the system's efficiency or even its complete failure.



Comments

bottom of page