#分享美股交易赢英伟达股票 Nvidia has already reached $5 trillion. Is it still a good time to invest now?

— Nvidia's latest quarterly revenue is $81.6 billion, a year-over-year surge of 85%, with a market value once surpassing $5.7 trillion, topping the global rankings. But the AI chip sector is shifting from "Nvidia's dominance" to "multiple players vying for supremacy," as cloud providers develop in-house solutions, AMD catches up, and the Chinese market is losing ground... The most profitable hardware business on this planet is entering its most complex phase. Is Nvidia worth this valuation now?

Let's start with the conclusion
Where is Nvidia now?
Stock price range: approximately $224 per share (early June 2026), 52-week low $129, 52-week high about $236
Market cap: about $5.4 trillion, ranking second globally (alternating with Apple for first place)
Latest quarterly (Q1 of FY2027) revenue: $81.6 billion, up 85% YoY, well above expectations
Next quarter guidance: $91 billion, still expected to grow over 70% YoY
Three key judgments:
✅ Under what conditions can Nvidia continue to rise?
→ Continuous explosion in demand for AI large model training/inference, cloud providers' capital expenditure remains high
→ Rubin architecture scheduled for mass production in late 2026, performance boosted fivefold to create new demand
→ RTX Spark enters AI PC market, opening a second growth curve, with consumer-side computing power demand following
⚠️ Under what conditions will it face significant pressure?
→ Major clients (Google, Amazon, Microsoft) exceeding expectations in self-developing ASIC chips, diverting orders
→ China market lost + Huawei Ascend accelerates substitution, Asia-Pacific revenue continues to shrink
→ AMD MI350/MI400 series making inroads in inference market, price wars lowering Nvidia’s gross margin
→ Macroeconomic risks: Federal Reserve’s delay in interest rate cuts leading to shrinking valuations of tech stocks
💡 How do ordinary investors view this?
→ Nvidia is not a bubble, but it’s also not a "buy blindly and it will rise" bargain
→ Focus on the erosion rate of CUDA’s moat, which is the most critical indicator
→ Investors who cannot tolerate a 30% pullback should control their positions; historically, Nvidia experiences deep corrections once every 1-2 years

II. What happened this year: From "DeepSeek’s blowout" to "regaining the top spot globally"
Nvidia’s story in 2026 is a classic "desperate turnaround."
End of January 2026: DeepSeek R1 debuts, achieving top inference performance at extremely low training costs, causing market panic—"If AI can be so cost-efficient, does Nvidia still need to exist?"
Nvidia’s stock plummeted nearly 17% in a single day, evaporating about $600 billion in market value, setting a record for the largest single-day loss in U.S. stock history.
February 2026: Nvidia releases full-year FY2026 financial report, with total revenue of $215.9 billion, up 65% YoY, net profit of $120 billion.
Refuting panic: Blackwell chips shipped 6 million units annually, in high demand. The market reinterprets the "DeepSeek effect"—more efficient inference models actually stimulate more applications, and computing demand is structural, not cyclical.
April 2026: H20 export ban suddenly enforced. The Trump administration announced an indefinite ban on Nvidia exporting H20 chips to China, leading to a $5.5 billion impairment loss, nearly cutting off the Chinese market.
May 2026: Are the negatives over? Nvidia releases Q1 FY2027 financials:
Revenue of $81.6 billion, beating expectations by $3 billion
Data center revenue: $75.2 billion, accounting for 92% of total revenue
Net profit: $58.3 billion, up 211% YoY
Q2 guidance: $91 billion, surpassing market expectations again. Meanwhile, Nvidia announced RTX Spark at Computex 2026—a joint effort with MediaTek, using TSMC’s 3nm process, integrating 70 billion transistors, marking its official entry into the AI PC market. OEMs like Dell, Lenovo, Asus will ship in bulk by fall 2026. In the first week of June: Nvidia’s stock hit new highs, closing near $224, with a market cap surpassing $5.4 trillion.

III. Nvidia’s three moats and three tigers
Moats: Why everyone "can't do without" Nvidia
Moat 1: CUDA ecosystem—10 years of accumulated "irreplaceable"
Think of CUDA as "the native language of AI engineers." Millions of AI engineers worldwide, thousands of deep learning frameworks, and vast amounts of production code are built on CUDA. Switching to competing chips is like asking someone who only speaks Mandarin to suddenly work in Cantonese—possible, but costly. It’s estimated Nvidia holds about 70% of the global AI training market, not just because of the chips’ quality (which is also high), but because of CUDA’s migration barrier.
Moat 2: Full-stack layout—selling more than just chips
Nvidia’s core product isn’t just GPUs, but a "full-stack computing solution":
Chips (GPU + Grace CPU)
Interconnects (NVLink, several times faster than PCIe)
Software frameworks (CUDA + cuDNN + TensorRT)
Complete systems (DGX servers, NVL72/NVL144 racks)
Cloud services (DGX Cloud)
This means customers buy an entire solution, not just a chip.
Moat 3: The annual "arms race" leading to dominance
Nvidia announces new architectures every year: Hopper → Blackwell → Rubin (late 2026) → Feynman (2028). Rubin NVL144’s FP4 performance is five times that of Blackwell, ensuring that competitors always lag one generation behind.

Three tigers: invisible risks
Tiger 1: Major clients "both buyers and competitors"—Microsoft, Google, Amazon, Meta are Nvidia’s biggest customers and rivals:
Google’s TPU has internally replaced many Nvidia chips and is starting to sell externally
Amazon’s self-developed ASICs, Trainium and Inferentia, continue to evolve
OpenAI’s joint plan with Broadcom and TSMC to produce chips in 2026
Meta has developed its own AI chip, MTIA—essentially, like a restaurant’s biggest customer starting to learn cooking—short-term still dependent on Nvidia, but long-term gradually taking market share.
Tiger 2: China market is sealed off
China was Nvidia’s second-largest market after the U.S. The indefinite ban on H20 chips means Nvidia almost has no revenue from China, as Huawei Ascend chips accelerate to fill the gap, likely capturing over 50% of China’s AI chip market. This is not just revenue loss (about $17 billion annually), but a strategic loss of an ecosystem development market.
Tiger 3: The inference demand revolution is quietly arriving
Training large models requires high-throughput GPUs like A100/H100/Blackwell, with no substitutes. But inference—getting models to answer questions—is different—AMD, Intel, cloud providers’ self-developed ASICs, and even Nvidia’s own RTX consumer cards can run inference. Inference is the main battlefield for AI deployment at scale. If competitors eat into the inference market, Nvidia’s dominance in training will be limited, and the ceiling will be reached sooner.

IV. AI chip sector panorama: not just Nvidia
By 2026, the global AI chip market is expected to surpass $280 billion, growing over 40% YoY. But the battlefield has shifted from Nvidia’s "monopoly" to a multi-polar landscape.
In short: Nvidia is the most powerful force in this war, but the battlefield is expanding, and opponents are multiplying.

V. Historical top and bottom features: When will Nvidia experience a major drop?
Nvidia’s stock has surged over 30 times in the past five years, but it has also experienced multiple deep corrections of over 30%. Historical top signals include:
Quarterly decline in AI capital expenditure (key signal)
Two consecutive quarters of revenue growth slowdown exceeding 20 percentage points
Major clients publicly announcing reduced GPU orders or shifting to self-developed solutions
Significant macro rate hikes, leading to overall valuation contraction in tech stocks
Competitors gaining over 40% market share in inference (current about 17%)
Major clients’ CEOs tone down capital expenditure guidance during earnings calls
How many of these are currently triggered? Out of the six signals, only about one (China market lost) is clearly triggered; the rest are not yet evident.
Overall, Nvidia is still far from "top of the cycle" signals, but geopolitical risks (tariffs + export bans) can trigger sudden large swings at any time.

Nvidia’s $81.5 billion quarterly revenue proves one thing: the money in AI is initially flowing to the "shovel sellers." But the good times for shovel businesses are never eternal—when gold miners start making their own shovels, or new gold rush sites no longer need shovels, the story enters a new chapter.
Understanding Nvidia isn’t about judging whether it "can keep rising," but about grasping what stage the company is in: from monopoly to multi-stronghold, from data center unicorn to full-stack AI infrastructure provider. Different stages, different logic, and different opportunities and risks.