Author: 137Labs

On February 25, 2026, global AI chip leader Nvidia (NVDA) released its financial report for the fourth quarter of fiscal year 2026 (ending January 25, 2026) and for the entire year: revenue, profit, and data center income largely exceeded expectations, while the guidance for the next quarter was also revised upward. According to the traditional logic of "performance drives stock price," such a financial report often implies certainty of an increase.

However, the market responded with the opposite answer. The day after the earnings report was released, NVDA's stock price fell by about 5.46%, and there were widespread statistics indicating a "single-day evaporation of about $260 billion in market value." The sharp divergence between strong fundamentals and weak stock prices is not due to "the truth of the performance," but rather that the pricing weight in the capital market is shifting from "quarterly profits" to "growth duration, capital expenditure slope, and structural risks."

1. First, let's nail down the financial report: How strong is it?

According to Nvidia's official disclosure, the core data for Q4 and the full year of fiscal year 2026 is as follows:

·Q4 revenue: $68.127 billion, up 73% year-on-year, up 20% quarter-on-quarter

·Q4 data center revenue: $62.3 billion, up 75% year-on-year, up 22% quarter-on-quarter, continuing to set records

·Q4 GAAP net profit: $42.96 billion; non-GAAP net profit: $39.552 billion

·Full-year revenue: $215.938 billion, up 65% year-on-year

·Full-year GAAP net profit: $120.067 billion

·Guidance for the next quarter (Q1 of fiscal year 2027): revenue around $78 billion (±2%)

This set of data indicates two things: first, the demand for AI infrastructure is still in a strong expansion phase; second, Nvidia's revenue structure is further concentrating on the "data center single engine."

2. Strength is turning into single point risk: Excessive data center proportion

The brilliance of the financial report is precisely the most sensitive point for the market: Q4 data center revenue of $62.3 billion / total revenue of $68.1 billion, accounting for about 91.5%. This means Nvidia is almost betting all its growth on the "AI capital expenditure cycle"—the more aggressive the cloud vendors, sovereign countries, and large enterprises are in capital investment, the more Nvidia resembles a high-speed growth machine; once capital expenditure shifts from expansion to contraction, volatility will also be amplified.

Meanwhile, even if non-data center businesses grow, they are unlikely to create effective hedges. The automotive, gaming, and professional visualization businesses are not on the same scale as the data center. For example, automotive revenue in a single quarter is about $604 million, which is far from enough to counter the cyclical fluctuations of the data center. This structure is viewed as "highly focused efficiency" during bull markets, but can quickly be transformed into a discount for "single-engine dependence" at sentiment turning points.

3. Increasing customer concentration: The accelerator is in the hands of a few

The market often summarizes Nvidia's customer structure with "the five major cloud vendors contribute more than half of the revenue." Nvidia's sales concentration has increased in fiscal year 2026, noting that two customers accounted for 36% of total sales. The conclusion is straightforward—Nvidia's super growth is deeply tied to a few super large customers.

This binding creates a double-edged sword effect:

·Upturn: The faster top customers expand, the more Nvidia can "collect taxes";

·Downturn: Once top customers slow down capital expenditures, Nvidia's orders and valuations will be under pressure simultaneously;

·A more subtle risk lies in the change in bargaining power: when customers begin to systematically support second suppliers or self-develop alternatives, Nvidia's "monopoly premium" will be compressed to "leading premium."

The decline in response to the financial report is largely a preemptive discounting of the combined risks of "concentration of growth + migration of bargaining power."

4. Why did "beyond expectations" become a negative? Pricing logic shifts from the current quarter to duration

Nvidia has exceeded expectations for several consecutive quarters, thus "beyond expectations" has gradually lost its marginal surprise. Funds fully priced in the "strong earnings" before the report through positions and derivatives, resulting in a typical trading outcome: no matter how strong the earnings, as long as there is a lack of "new increments that exceed existing narratives," it becomes easy to trigger profit-taking.

This type of move often manifests as "good news being realized." When the market expects the growth path to extend into 2027 and beyond, the financial report needs to address not "can it continue to exceed in the current quarter," but rather "how long can growth be sustained, maintaining what structure, and in what competitive environment." A lack of longer-term certainty can lead to the abnormal combination of "strong fundamentals, weak stock prices."

5. Is the AI bubble a false proposition? More like a reassessment of capital expenditure and credit

The "AI bubble" is often misinterpreted as "AI has no value." A closer reflection of the true divergence is: the value of AI is beyond doubt, but the mismatch of investment and returns over time is being priced in seriously.

The scale of AI capital expenditure by cloud vendors continues to climb, with substantial investments being made, while commercial returns are still in the early stages of growth. Against a backdrop of high interest rates or pressure on profits, the market will naturally question: with such a massive investment in computing power, when will it be transformed into sustainable profits? If it continues to present "only investments without earnings" in the short term, once the slope of capital expenditures slows down, the valuation center of upstream computing power suppliers will be reassessed.

This is not unfamiliar territory to the cycle of the cryptocurrency industry: infrastructure expansion often precedes the realization of applications. When "supply expansion" runs ahead of "demand realization," prices and valuations become extremely sensitive to changes in sentiment. AI is at a similar stage, but this time the "accounting book" is not on the blockchain but in the financial reports of cloud vendors and semiconductor leaders.

6. The real threat of competition: It’s not "someone can make GPUs," but "customers don't want to buy exclusively from one supplier"

For a long time, Nvidia has relied on its GPU leadership, CUDA ecosystem, and system solutions to form a protective moat. However, the key change in the competitive landscape is not a single breakthrough by a company, but rather a structural shift on the customer side—introducing second suppliers + self-developed chips + replacing single card procurement with system procurement.

1) AMD × Meta: The second supplier strategy is becoming institutionalized

A high-level, high-volume long-term cooperation between Meta and AMD is not just aimed at immediately changing market share, but more importantly, it releases a signal: super large customers are using certain orders to support alternative solutions and reduce reliance on a single supplier. The direct consequence of this strategy is that Nvidia's "bargaining power marginal decline" in future negotiations will compress its valuation premium.

2) The arrival of the inference era: The competition for computing power shifts from "training" to "cost and latency"

The focus of the AI industry is shifting from cost-unconcerned training to cost-sensitive inference. The inference side pays attention to throughput, latency, energy consumption, and unit cost, opening the door for more niche new architecture players. Nvidia is addressing its shortcomings by introducing inference-related technologies and teams (such as technical licensing with inference chip company Groq and personnel integration) to demonstrate that the competition of the inference era has expanded from "chip performance" to "full-stack system efficiency" combat.

7. Nvidia is pursuing a second curve: from cloud computing power to an operating system for the physical world

Understanding Nvidia solely as a "GPU-selling company" underestimates its strategic depth. During the earnings cycle, Nvidia has continuously promoted its platform layout in "physical AI" directions such as autonomous driving, robotics, and industrial simulation, and launched open-source capabilities for autonomous driving inference and safety validation (e.g., Alpamayo). This line contributes limited short-term value but represents a direction: upgrading Nvidia from "selling shovels" to "providing an operating system-level foundation," locking customers in from "buying hardware" to "buying platforms and ecosystems."

Once this platformization succeeds, Nvidia's growth duration will no longer be entirely determined by the capital expenditure of cloud vendors, but will derive more from industrial digitization, industrial robotics, and autonomous driving, which have longer cycle demands. However, before the second curve truly scales, the market will continue to prioritize pricing within the framework of "data center single engine + capex cycle assets."

8. The key variables for 2026: What determines stock price are three curves, not a profit statement

The core determining factor for Nvidia's valuation center in 2026 is not "can it continue to grow?" but rather "how long can growth be sustained, in what structure?" The market will focus on three verifiable curves:

1) Cloud vendors' capital expenditure slope: continuing to accelerate, or slowing down marginally?

2) Inference revenue structure and systematic penetration: can the transition from "selling GPUs" to "selling complete system solutions (network interconnect, software stack, platform tools)" continue to enhance stickiness and customer value?

3) Speed of penetration by second suppliers and self-developed solutions: the faster alternative solutions move from pilot to scaled procurement, the more easily Nvidia's premium space will be compressed.

Conclusion: The earnings report proves that the myth of computing power continues, but pricing is entering "duration judgment"

This financial report proves that the AI infrastructure boom is still ongoing, and Nvidia remains the strongest cash flow machine for computing power. However, the drop in stock price reminds the market that when "exceeding expectations" becomes the norm, pricing logic has shifted from growth rates to sustainability, from profits to growth duration, and from monopoly premium to competitive landscape.

The adjustments following the financial report do not necessarily imply a reversal in fundamentals, but rather resemble a shift in valuation center. Nvidia remains strong, but the real test lies in—how long can growth be maintained, and can the structure become more stable?

This answer will determine the valuation boundaries for Nvidia in 2026 and will also influence the risk appetite direction for AI assets.

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。