Nvidia (NAS: NVDA) reported fiscal fourth-quarter revenue of $68.1 billion, up 73% year over year, up 20% sequentially, and ahead of guidance. Nvidia expects April-quarter revenue of $78.0 billion, which would be up 77% year over year and ahead of FactSet consensus estimates of $72.9 billion.

Why it matters: We see no signs of slippage at Nvidia, as revenue growth is accelerating from recent quarters, thanks to the massive growth in artificial intelligence capital expenditure announced by large cloud computing companies. An “AI bubble” does not appear imminent.

  • Nvidia expects sequential growth in each of the four quarters of calendar 2026. Supported by a strong fiscal first-quarter outlook, management suggested that its prior guidance of $300 billion of Blackwell and Rubin product revenue in calendar 2026 will be conservative.
  • The forecast does not include any data center computing revenue sold into China, despite prior approvals of H200 sales. Our model does not incorporate a massive uptick in Nvidia’s China revenue in the years ahead.

The bottom line: We maintain our $240 fair value estimate for wide-moat Nvidia. Shares were flattish after hours, and we still view shares as undervalued. We anticipate that leading cloud companies, model builders, and governments will continue to invest in the promise of AI.

  • We maintain our Morningstar Very High Uncertainty Rating as the timing and magnitude of AI adoption are unclear in the decade ahead.
  • One risk for Nvidia is that it hasn’t yet signed its previously announced deal with OpenAI. Nvidia stated that it “believes it is close” on signing the deal, although outside reports suggest it may be a smaller deal in size.

Coming up: In most quarters from calendar 2023 to 2025, Nvidia generally increased its data center revenue by about $4 billion, as new supply came online. We’re impressed that Nvidia’s fiscal first-quarter guidance implies $11 billion of incremental revenue sequentially.

Nvidia remains at the heart of the AI ecosystem

Nvidia has a wide economic moat, thanks to its market leadership in graphics processing units, hardware, software, and networking tools needed to enable the exponentially growing market around artificial intelligence. In the long run, we expect tech titans to strive to find second-sources or in-house solutions to diversify away from Nvidia in AI, but these efforts will, at best, only chip away at Nvidia’s AI dominance.

Nvidia’s GPUs run parallel processing workloads, using many cores to efficiently process data at the same time. In contrast, central processing units, such as Intel’s processors for PCs and servers, or Apple’s processors for its Macs and iPhones, process the data of “0’s and 1’s” in a serial fashion. The wheelhouse of GPUs has been the gaming market, and Nvidia’s GPU graphics cards have long been considered best of breed.

More recently, parallel processing has emerged as a near-requirement to accelerate AI workloads. Nvidia took an early lead in AI GPU hardware, but more importantly, developed a proprietary software platform, Cuda, and these tools allow AI developers to build their models with Nvidia. We believe Nvidia not only has a hardware lead but also benefits from high customer switching costs around Cuda, making it unlikely for another chip designer to emerge as a leader in AI training. Nvidia’s expansion into networking has been impressive, allowing customers to cluster AI GPUs together for AI training.

We think Nvidia’s prospects will be tied to the AI market, for better or worse, for quite some time. We expect leading cloud vendors to continue to invest in in-house, while AMD is also working on GPUs and AI accelerators for the data center. However, we view Nvidia’s GPUs and Cuda as the industry leaders, and the firm’s massive valuation will hinge on the pace of AI buildouts in the years ahead.

Bulls say

  • The AI infrastructure opportunity is massive, and Nvidia foresees $3 trillion-$4 trillion of annual AI infrastructure spending by 2030.
  • Nvidia’s data center GPUs and Cuda software platform have established the company as the dominant vendor for AI model training and inference.
  • Nvidia is expanding nicely within AI, not just supplying industry-leading GPUs but also moving into networking, software, and services to tie these GPUs into even more-powerful clusters.

Bears say

  • Nvidia’s customers are a handful of the largest Tech companies in the world, and they all have an incentive to eventually diversify away from Nvidia to some extent.
  • AI infrastructure spending has been impressive but revenue and use cases are less certain, perhaps providing doubts that there is a good return on investment on AI that might lead to a spending downturn at some point in the future.
  • Geopolitics have entered the AI space, most notably limiting Nvidia’s AI opportunities in China.