NVIDIA (NVDA.US), the world's highest-valued company often dubbed the "most important stock on Earth" and the "AI chip kingpin," released another exceptionally strong quarterly report and future outlook after the U.S. market closed on Wednesday. The latest results clearly underscore that the global frenzy for building AI computing infrastructure is far from over, expanding from AI GPUs and ASICs to data center CPUs, high-performance networking, full-scale server clusters, AI super factories, and enterprise-level large-scale AI cloud computing systems.
On Wall Street, bullish sentiment for NVIDIA, the "global AI leader," is intensifying. The average analyst price target alone suggests a potential market capitalization exceeding $7 trillion. In early trading, NVIDIA's stock price rose to around $227, with its market cap hovering near $5.35 trillion.
For its fiscal first quarter of 2027 (ending April 26, 2026), NVIDIA reported revenue of $81.62 billion, an 85% year-over-year increase, surpassing the average analyst estimate by approximately 3.1%. The core Data Center business generated roughly $75.2 billion, accounting for over 90% of total revenue. Both figures set new quarterly records, demonstrating that AI computing demand remains the absolute growth driver.
The company's revenue guidance for the second fiscal quarter has a midpoint of around $91 billion, which would be another historical high and significantly above average analyst expectations, though below the most optimistic Wall Street forecasts of $96-$98 billion. Notably, NVIDIA management clarified this guidance excludes any data center compute revenue from China, indicating it is currently supported by demand outside China and leaves room for potential future growth.
It is noteworthy that the U.S. Commerce Department has officially approved exports of NVIDIA's H200 AI chips to ten major Chinese companies, including Alibaba, Tencent, and ByteDance, with each buyer allowed to purchase up to 75,000 units.
These results and guidance indicate that demand from North American hyperscale cloud providers, large AI model companies, enterprise AI clouds, and industrial physical AI has not slowed significantly. Concerns about "AI capital expenditure peaking" have not yet materialized in NVIDIA's orders and guidance. More importantly, NVIDIA is presenting Wall Street with an expanded narrative beyond just AI GPU growth, showcasing the explosive expansion of "AI factory economics."
Citigroup noted that NVIDIA's breakdown of data center sales into Hyperscale and ACIE (AI Cloud, Industrial, Enterprise) categories improves visibility. The chip giant also projected its Vera CPU sales to reach $20 billion this year and outlined a $200 billion total addressable market (TAM) for CPUs by 2030. This signals NVIDIA's evolution from an "AI GPU leader" to an "AI infrastructure platform company" encompassing GPUs, CPUs, networking infrastructure, rack-scale systems, and the CUDA developer software ecosystem, even aggressively entering the data center server CPU market long dominated by Intel and AMD.
**AI Wave Unstoppable! NVIDIA Once Again Impresses Wall Street**
For a company of its immense size, NVIDIA's latest performance and guidance are remarkably impressive to Wall Street analysts, a fact retail investors have also noted. Analysts widely praised the tech giant, led by CEO Jensen Huang, for operating at full throttle across its operations.
Wall Street sentiment on NVIDIA remains strongly bullish. MarketBeat data shows a 12-month average price target of $295.10 from 54 analysts, with a high target of $500. Based on the recent trading price of around $223.47, the average target implies approximately 32.1% upside potential, while the high target suggests a staggering 123.7% potential gain. The $295.10 average target corresponds to a market cap of about $7.17 trillion, and the $500 high target implies a market cap of roughly $12.14 trillion.
These figures are extraordinary: the highest target implies NVIDIA could become a company valued over $12 trillion, more than doubling its current market cap. Having surpassed average Wall Street valuation milestones of $1 trillion and $5 trillion, investors now believe $7 trillion could be the next significant threshold.
Citigroup senior analyst Atif Malik wrote in a client note: "NVIDIA is improving visibility into its data center sales by providing two sub-categories: Hyperscale and ACIE (AI Cloud, Industrial & Enterprise), which we view favorably for comparability. CEO Jensen Huang expects Vera CPU sales to reach $20B this year (we believe primarily from Meta and Microsoft) and anticipates a new $200B CPU TAM by 2030. Our prior Data Center sales estimates of over $1 trillion for 2025-2027 did not include Vera CPU and LPX contributions, leading us to raise our FY27/28/29 EPS estimates by 8%/6%/8%, respectively. Furthermore, NVIDIA management seems unified in its view that with superior performance/token metrics, it will gain share at AI model companies like OpenAI and Anthropic, and even SpaceX AI." Malik maintained a "Buy" rating with a $300 price target.
Morgan Stanley senior analyst Joseph Moore likened the earnings report to a top golfer's performance, calling the result "straight down the fairway." Moore wrote: "NVIDIA reported numbers above our estimates and preview, with clean beats and raised guidance across all metrics. The significant ramp of Vera Rubin should validate their claim that NVIDIA's AI super hardware system leads in AI factory economics." Moore raised his price target from $285 to $288. He noted that while the "goalposts are always moving" for this tech behemoth, which once reached a $5.5 trillion market cap, the company continues to prove its superiority over competitors. Moore added: "This doesn't make comparisons meaningless, as $400B run-rate AI capital investment cannot be fully satisfied by Vera Rubin in a few quarters, so comparisons between Blackwell AI GPU clusters and other companies' next-gen products remain a key consideration. But we believe a truth long revealed by NVIDIA holds: until proven otherwise, lower-cost silicon has not been proven to deliver lower cost per token, and having the best-in-class AI infrastructure capability is a significant advantage for customers seeking the longest, most efficient lifespan and most complete ecosystem."
Evercore ISI analyst Mark Lipacis pointed out that the significant increase in capital returns—an $80 billion share buyback and a quarterly dividend hike from $0.01 to $0.25 per share—suggests its earnings multiple may be expanding. Lipacis explained: "We expect NVIDIA's forward 12-month P/E multiple to begin expanding meaningfully as the company increases its capital return program—raising the dividend from $0.01 to $0.25 per share, announcing a new $80B buyback on top of the existing $39B, and planning to return 50% of free cash flow in calendar 2026 (with a higher percentage likely in 2027 and beyond, in our view)." Lipacis raised his price target substantially from $352 to $413. He also noted NVIDIA's CPU business appears ready for takeoff. Lipacis added: "NVIDIA highlighted that it expects to ship $20B of stand-alone Vera CPUs in calendar 2026 (shipments began last week)—notably, this excludes CPUs attached as headnodes within rack-scale system shipments. More broadly, CEO Jensen Huang, often called the 'Godfather of AI,' emphasized a $200B CPU TAM, which aligns with our above-consensus market size estimate of $190B-$275B by 2030. We expect Huang's Computex keynote to be a catalyst, disclosing more details on the company's CPU strategy."
Seeking Alpha analyst Julia Ostian stated that operational metrics "look impressive," and the Blackwell ramp has not hurt profitability as some feared. Ostian commented via email: "NVIDIA remains a premier global tech company, as evidenced by the unprecedented Q2 revenue expectation of $91B (which, if achieved, would imply an 11% sequential beat)."
**The Trillion-Dollar AI Vision Nears Reality! The World's Most Important Stock Still Defining the AI Super Bull Market**
Wall Street's mainstream price targets already price NVIDIA as a $7 trillion company; the most aggressive aggregated targets push it toward the $12 trillion level. The core logic behind these high targets is NVIDIA's upgrade from a GPU leader to a "full-stack AI factory infrastructure platform"—where GPUs, CPUs, networking, rack-scale systems, software ecosystems, and capital returns collectively support valuation expansion. Accelerated computing infrastructure spending is projected to potentially reach $3-$4 trillion by 2030.
As AI agents gain global popularity, the AI computing investment theme is shifting from a "single-point computing race around AI GPUs" to "AI agent-driven full-stack computing systems." The next wave of excess alpha returns will not belong solely to the strongest leaders in AI GPUs/ASICs but will systematically diffuse across the full-stack AI computing infrastructure layer, including data center CPUs, DRAM/NAND/HBM memory, AI PCBs, liquid cooling systems, data center optical interconnects, ABF substrates/glass substrates, and broad foundry services.
On April 30th, cloud computing giants Microsoft, Alphabet (Google), and Amazon all reported strong results on the same night, highlighting the unexpectedly rapid growth of their cloud businesses benefiting from the AI wave, prompting Wall Street to reprice AI's commercial returns.
A recent Morgan Stanley analyst report estimates that the combined capital expenditures of five tech hyperscalers (Amazon, Alphabet, Meta, Microsoft, Oracle) will reach approximately $800 billion in 2026 and are expected to surpass $1.1 trillion in 2027, up from a previous forecast of $950 billion. Morgan Stanley analysts emphasized the core logic behind these massive investments: heavy upfront investment and capacity building, followed by recouping returns through scaled commercial revenue and ROIC based on AI computing resources. The surge in cloud backlogs and AI application tokens is the most direct evidence this model works, and the faster-than-expected growth of these giants' cloud businesses is leading Wall Street to reassess AI's commercial payoff.
Earlier this year, at the GTC conference, CEO Jensen Huang outlined NVIDIA's "unprecedented AI computing revenue super blueprint" in the AI infrastructure space. He informed global investors that driven by strong demand for Blackwell GPU computing and the even more explosive upcoming demand for the Vera Rubin AI computing architecture, the company's future revenue scale in AI chips could reach at least $1 trillion by 2027 (representing total AI infrastructure revenue from 2025-2027), far exceeding the $500 billion AI computing infrastructure blueprint presented at the previous GTC conference for 2026.
NVIDIA is no longer just a semiconductor leader; it has become the price anchor, sentiment anchor, and earnings anchor for the global AI capital expenditure cycle and the trajectory of the global AI bull market. Its performance dictates how the market views AI demand strength. Its supply chain impacts the fundamental growth prospects of upstream semiconductor equipment and foundry players like TSMC, SK Hynix, Micron, and ASML, as well as the entire AI computing infrastructure chain encompassing data center CPUs, HBM/NAND/HDD storage, 2.5D/3D advanced packaging, liquid cooling, optical interconnect supply chains, and data center power. Its stock price movements significantly influence the trajectory of the Nasdaq and S&P 500 bull markets and global stock market risk appetite.
Furthermore, it is worth noting that while NVIDIA's fundamentals continue to exceed expectations, positioning in the stock is extremely crowded. This suggests that NVIDIA's rally may not continue with the same vertical ascent seen in the past, though the long-term uptrend remains intact. Goldman Sachs' trading desk noted NVIDIA's positioning crowdedness scores a 9 out of 10. While short interest stands at approximately $62.5 billion, long positions are very widespread. Simultaneously, institutions holding large amounts of covered call options have put dealers in a long gamma position, suppressing short-term volatility. Therefore, short-term price sluggishness appears more a function of trading structure under conditions of "high expectations, high positioning, high gamma" rather than a sign of weakening AI demand. As long as AI model companies and hyperscalers continue investing in AI factories, Blackwell/Rubin/Vera CPUs ramp up, and the cost-per-token advantage remains defined by NVIDIA's system-level architecture, NVIDIA's earnings ceiling continues to rise.
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