NVIDIA Earnings Quick Take: AI has been surging for so long—is computing demand still delivering?

Google and NVIDIA, the application and underlying infrastructure giants of the AI industry, have both delivered their reports this week.

If Google I/O showcased the imagination of AI applications, then NVIDIA's earnings report verifies whether the computing power demand behind those visions has materialized.

After the market closed on May 20th Eastern Time, NVIDIA reported its FY2027 first-quarter fiscal results, with revenue reaching $81.615 billion, an 85% year-over-year increase and a 20% sequential increase; Data Center revenue hit $75.2 billion, up 92% year-over-year and 21% sequentially; NVIDIA also announced an additional $80 billion share repurchase authorization and raised its quarterly cash dividend from $0.01 per share to $0.25 per share.

These figures are robust in themselves, but what the market truly cares about is not "is NVIDIA still growing," but given the already high market expectations, can it continue to prove that the AI narrative remains strong, computing power demand hasn't peaked, and NVIDIA's pricing power is still solid?

1. A Glance at Revenue, Guidance, and Gross Margin: Is the AI Engine Still Accelerating?

First, it's crucial to clarify that NVIDIA's core business is no longer "graphics cards" in the traditional sense, but rather the Data Center, the computing power infrastructure behind AI factories.

In this quarter, NVIDIA's Data Center revenue reached $75.2 billion, accounting for over 92% of total revenue. Breaking it down, under the old business segment classification, Data Center compute revenue was $60.4 billion, up 77% year-over-year; Data Center networking revenue reached $14.8 billion, up 199% year-over-year, also hitting an all-time high.

This highlights a key issue: AI demand is not just limited to individual GPUs but is expanding towards the entire AI infrastructure stack—where GPUs handle computation, networking connects the computing power, and systems like full-rack cabinets, NVLink, InfiniBand, Ethernet, optical communications, power, and cooling all become part of the AI factory.

Therefore, the significance of this Data Center revenue isn't just "NVIDIA sold a lot," but it shows that global cloud providers, AI model companies, enterprise clients, and sovereign AI entities have not significantly cooled their investment in computing power. From this perspective, if Data Center revenue continues to exceed expectations in the future, risk appetite in the AI hardware chain is likely to broaden. Conversely, if this indicator starts to fall short, the market will truly begin to worry about a peak in AI capital expenditure.

Of course, besides revenue, for a high-expectation stock like NVIDIA, the stock price reaction post-earnings often depends not just on the current quarter's numbers, but more on the analysis of the next quarter's guidance.

NVIDIA provided Q2 FY2027 revenue guidance of $91 billion (plus or minus 2%), significantly exceeding the pre-earnings market consensus expectation of around $86-87 billion. The company explicitly stated that this guidance does not assume any Data Center compute revenue from China. This is crucial: if guidance reaches $91 billion even without China's Data Center compute revenue, it indicates that demand from overseas cloud providers, AI factories, enterprise AI, and other regions is sufficient to sustain high growth.

In other words, the market's original worry was that NVIDIA's growth has been too fast, making it difficult to exceed expectations going forward. However, this guidance signals that, at least for the next quarter, demand for AI computing power hasn't significantly slowed.

However, it's also important to note that as market expectations rise, NVIDIA needs to deliver not just "good earnings," but "clearly better-than-expected earnings." So, whether the stock price surges in the short term still depends on whether investors believe this guidance is sufficient to justify its high valuation.

At the same time, NVIDIA's high valuation stems not only from its high revenue growth but also from its exceptionally strong profitability.

This quarter, NVIDIA's GAAP gross margin was 74.9%, and Non-GAAP gross margin was 75.0%. The company's guidance for next quarter's gross margin is similarly 74.9% (GAAP) and 75.0% (Non-GAAP), with a 50-basis-point leeway.

This shows that despite higher costs associated with the Blackwell system, HBM, advanced packaging, and full-rack cabinet solutions, NVIDIA can still maintain a gross margin around 75%. For the market, this undoubtedly represents two things:

• NVIDIA still possesses strong pricing power. Customers aren't just buying a chip; they are buying a complete platform capability.
• While competition in AI chips is intensifying, it hasn't significantly compressed NVIDIA's profit margins yet. Google TPU, Amazon Trainium, AMD GPUs, and ASIC custom chips will all bring competition, but based on this report, NVIDIA's profitability hasn't been notably shaken.

Of course, if gross margins significantly fall below 74% in the future, the market will start worrying about product transition costs, customer bargaining power, and pressure from alternatives. This requires long-term monitoring.

2. Is NVIDIA Beginning to Transition into an "AI Cash Flow Platform"?

A noteworthy change in this report is the shareholder returns.

In Q1, NVIDIA returned approximately $20 billion to shareholders, including share buybacks and cash dividends. By the end of the first quarter, the company had $38.5 billion remaining under its existing repurchase authorization. Subsequently, the Board of Directors approved an additional $80 billion share repurchase authorization and raised the quarterly dividend from $0.01 per share to $0.25 per share.

The implication here extends beyond just having ample cash on hand. It signals to the market that the benefits of the AI boom are not only flowing to ecosystem partners, AI startups, and the supply chain but are also starting to return to shareholders.

After all, the market previously worried that NVIDIA's significant investments in AI ecosystem partners like OpenAI and Anthropic might be a form of "circular financing." However, by simultaneously increasing buybacks and dividends, the company can partially alleviate long-term investors' concerns about capital allocation efficiency.

This is also shifting NVIDIA's profile from a pure high-growth AI stock to increasingly possessing characteristics of an "AI cash flow platform."

3. Beyond Blackwell, What is the Market Looking At?

Another key aspect for NVIDIA is whether its product cycle can continue.

This quarter, NVIDIA highlighted the Vera Rubin platform, including products like the Vera CPU and BlueField-4 STX, and mentioned collaborations with Google Cloud, including A5X instances powered by Vera Rubin and a preview of Google's Gemini model on NVIDIA Blackwell and Blackwell Ultra GPUs.

This indicates that NVIDIA isn't ending its narrative with Blackwell but is proactively laying the groundwork for the next platform generation.

This is important for investors. If Blackwell were just a single strong cycle, the market would fear a growth slowdown after the peak. But if Vera Rubin can smoothly take over, NVIDIA possesses not just one-off product success but sustained platform iteration capability.

Regarding whether Google TPU and CPUs will threaten NVIDIA, I believe this should be viewed on two levels.

In the short term, TPUs, ASICs, and CPUs will indeed take on more tasks in specific scenarios, especially for large companies' in-house models and inference workloads. However, in the medium term, this is more about multiple routes coexisting due to the sheer magnitude of AI demand, rather than NVIDIA being immediately replaced.

NVIDIA's true advantage isn't just the GPU itself, but the "platform capability" combining GPU, CPU, networking, software, full-rack cabinets, and ecosystem partners. As long as customers need to rapidly deploy large-scale AI factories, NVIDIA remains at the core of the industry chain.

Final Thoughts

This earnings report proves at least one thing: the AI narrative remains intact.

Data Center revenue continues to set records, next quarter's guidance beats expectations, gross margins hold around 75%, buybacks and dividends have significantly increased, and the product cycle has extended from Blackwell to Vera Rubin. All of this shows that NVIDIA is still at the center of the AI infrastructure expansion.

However, for the stock price, the question isn't "were the earnings good," but "were they good enough to exceed the market's already high expectations." If the market sees this report as merely validating expectations, there might be short-term volatility. If investors further revise up their projections for AI capital expenditure and NVIDIA's long-term revenue potential, the AI chain could continue to broaden.

Furthermore, from an industry chain perspective, a strong NVIDIA report doesn't just affect NVDA.M. It also prompts the market to re-evaluate the entire AI infrastructure chain:

• ASIC / Manufacturing / HBM: AVGO.M, TSM.M, MU.M
• Networking & Interconnect: ANET.M, MRVL.M, CRDO.M
• Optical Communications: COHR.M, LITE.M, AAOI.M
• Power & Cooling: VRT.M, ETN.M, MPWR.M

Of course, working backwards from the applications, since Google I/O this week demonstrated that AI applications are still proliferating, it's easy to understand why the computing power demand in NVIDIA's report is still being realized—as long as the application side continues to generate demand, the AI infrastructure chain is far from over.