Cracks in Nvidia's "AI Empire": A Trust Test on Valuation Logic

As the market begins to scrutinize every link in the AI industry chain with a magnifying glass, even giants with a market value exceeding $3 trillion cannot escape the ultimate questioning of capital regarding the quality of growth.

Recently, controversies surrounding Nvidia's financial health have continued to ferment among institutional investors, compounded by a deep correction in the cryptocurrency market, leading to a stress test for global tech assets. The core of this turmoil is not a "Ponzi scheme" conspiracy theory, but rather a deep-seated anxiety in the market regarding the sustainability of AI business models, the reasonableness of valuations, and the risk transmission mechanisms within the industry chain.

1. Questions about the authenticity of financial data

NVIDIA's latest quarterly report has indeed revealed some unconventional signals that are worth a calm analysis:

1. Surge in accounts receivable and cash flow mismatch

As of the third quarter of the fiscal year 2024, NVIDIA's accounts receivable reached $33.4 billion, accounting for 58% of revenue, with the collection cycle extending to 53 days. The abnormality in this data lies not in the absolute value, but in its structural contradiction: the company claims that GPU demand is "snatched up completely," yet the willingness of downstream customers to pay is clearly lagging. More critically, the conversion rate of operating cash flow net ( $14.5 billion) to net profit ( $19.3 billion) is only 75%, far below the healthy level of over 90% in the semiconductor industry. This indicates that about 25% of the reported profits have not yet been converted into real cash, which may be due to lenient payment terms for major customers or the timing of revenue recognition.

2. The Contradictory Narrative of Inventory Backlogs and "Hunger Marketing"

The company's total inventory amounts to $19.8 billion, a month-on-month increase of 32%, contrasting with its claim of "insatiable demand." However, it is necessary to distinguish between strategic stockpiling and unsold inventory. Nvidia has secured TSMC's production capacity in advance to stockpile HBM3e memory for the new B100/B200 products, which is considered normal business practice. The real issue is that if AI infrastructure investments slow down in 2025, this inventory will face impairment risks. More noteworthy is that the capital expenditure growth of its major customers (Microsoft, Meta, Google) has shown a month-on-month decline, indicating that the "virtual fire" on the demand side is fading.

3. The Vulnerability of the Closed Loop in the Industry Chain

OpenAI's annual loss exceeding $5 billion is a public fact, and there are market rumors that part of its procurement funding was completed through financing involving NVIDIA, which constitutes a potential "left hand to right hand" risk. If true, it means that NVIDIA's revenue quality depends on the refinancing ability of its invested companies, rather than the actual commercialization realization of end customers. This model is particularly dangerous during an interest rate hike cycle—when primary market funds are exhausted, the entire chain will quickly contract.

II. The Profit Dilemma of the AI Industry Chain: From NVIDIA to Startups

NVIDIA's problem is a microcosm of the flaws in the entire AI industry's profit model.

The Life and Death of Mining Intermediaries

CoreWeave, representing the computing power rental industry, is burdened with $11.2 billion in high-interest debt, with quarterly interest expenses of $250 million, far exceeding the $19 million operating profit. These companies are essentially using debt leverage to bet on the perpetual growth of AI demand, with a business model of low-interest financing → high-priced card purchases → subleasing to startups. Once a wave of startup bankruptcies hits, the demand for computing power will plummet dramatically, and CoreWeave will be the first to face a disaster, adversely impacting NVIDIA's new orders.

The financing dependence of startups

In the first half of 2024, global AI financing reached $104 billion, but 90% of startups have not generated positive cash flow. These companies are using the funding to purchase NVIDIA chips and complete technological iterations, but ultimately rely on commercialization or the next round of financing to survive. Currently, the pace of VC investment in the U.S. has slowed by 30%, and the IPO window is tightly closed, which means the "passing the parcel" on the demand side is nearing its end. When financing cannot cover computing costs, NVIDIA's "golden customer group" will disappear in large numbers.

III. The Ripple Effect in the Crypto Market: An Overstated Liquidity Crisis?

The original text states "AI startups have raised $26.8 billion in financing backed by Bitcoin", but the data is questionable. However, the logical connection has a certain degree of reasonableness:

Real conduction path

Some overseas AI startups do hold Bitcoin as treasury assets and use institutions like Galaxy Digital and SilverGate Bank for pledged financing for operations. If Nvidia's stock price plummets, triggering a reassessment of AI assets, the equity value of these companies will decline, which may lead to margin call requirements. In extreme cases, forced liquidation of Bitcoin by institutions would increase market selling pressure. However, the $26.8 billion scale lacks authoritative source support, and the actual impact is far lower than this. Bitcoin's decline from $100,000 to $85,000 is mainly due to the Federal Reserve's hawkish expectations and profit-taking by whales, with limited direct correlation to Nvidia.

Key Support Level Analysis

From on-chain data, there is a dense support of chips in the range of $78,000 to $80,000 for Bitcoin, while $50,000 was the peak of the bull market in 2021. A drop below this level would require a significant macroeconomic negative factor (such as the Federal Reserve's unexpected rate hikes or a major exchange crash). Currently, the tug-of-war at the $80,000 level is essentially a market disagreement on the pace of interest rate cuts in 2025, rather than being driven by a single event from Nvidia.

4. Short Selling and Valuation Reassessment: Rationally Understanding the Logic of Short Selling

"Big Short" Michael Burry has indeed increased his position in Nvidia put options, but this is part of his portfolio hedging strategy, not a bet against it. The market's assessment of Nvidia's fair value also shows extreme polarization:

pessimistic scenario

If AI capital expenditures peak and decline in 2025, and NVIDIA's data center business growth rate drops from 200% to 20%, a 25x PE (corresponding to the valuation of mature chip companies) could lead the stock price to adjust to the range of $120-150, representing a 15-30% drop from the current $180.

Neutral scenario

If the AI application layer (autonomous driving, robotics) is successfully commercialized, Nvidia will shift from hardware sales to a "hardware + software + cloud services" platform company, with a valuation that can maintain a PE of 30-35 times, and the stock price will exhibit resilience, fluctuating in the range of $160-200.

Optimistic Scenario

If generative AI has a killer application, global computing power demand will increase tenfold, and Nvidia will enjoy a technological monopoly premium, with a valuation potentially exceeding $250.

V. Investor Response: Seeking a Margin of Safety in Expected Differences

The current market is in a fragile stage of "self-fulfilling expectations", where any slight movement could trigger a programmed trading stampede. For ordinary investors:

1. Do not equate short-term fluctuations with a collapse in fundamentals. Nvidia's CUDA ecosystem, technological barriers, and customer stickiness still serve as a moat; the issue lies in whether the valuation has exhausted the growth potential for the next three years.

2. Pay attention to leading indicators: TSMC CoWoS capacity utilization rate, Microsoft/Google CapEx quarter-on-quarter growth rate, and funding amount for US AI startups, which are more crucial than the stock price itself.

3. Differentiation among A-share industry chain companies: Firms like Industrial Fulian and Inspur Information, which have substantial server orders, show strong performance resilience; those that are purely concept-driven need to be wary of the risk of a corrective decline.

4. Independent assessment of cryptocurrencies: Treat Bitcoin as a macro liquidity indicator, rather than a derivative of Nvidia. Accumulate in batches below 80,000, with 50,000 representing an extremely pessimistic scenario, which has a probability of less than 10%.

The controversy surrounding Nvidia is a necessary pain point for the AI industry as it transitions from "technological fantasy" to "commercial reality." What the market needs is not conspiracy theories, but more transparent financial disclosures, more solid profit models, and a healthier division of labor in the industry. For investors, this is precisely an opportunity to discern the true from the false—real value will become clearer after the bubble bursts. Blind panic and blind optimism are equally dangerous; only data and logic can serve as anchors through the cycles. #逆势上涨币种推荐 #美联储会议纪要将公布 #比特币行情观察