Nvidia (ASX: NVDA) reported fiscal first-quarter revenue of $81.6 billion, up 85% year over year and ahead of guidance of $78 billion. Nvidia expects April-quarter revenue of $91 billion, up 95% year over year and ahead of the FactSet consensus estimate of $87.2 billion.

Why it matters: There’s no slowdown in demand for Nvidia’s artificial intelligence gear, and the company is doing all it can to expand its supply chain to meet the insatiable demand associated with large language models and agentic AI. Nvidia’s revenue growth is broad-based across customers, and profits remain stellar.

  • Data center, or DC, revenue was $75.2 billion, up 92% year over year. Revenue from hyperscale (mostly large cloud) customers was $37.9 billion, half of the total and up 115% year over year, consistent with the hefty capital expenditure plans of these customers as they race to build out AI.
  • The other half of DC revenue from AI cloud (neocloud), industrial, and enterprise customers was $37.4 billion, up 74% year over year. This customer cohort does not design its own AI accelerators, and we expect Nvidia to dominate this market while still seeing a massive uptick in AI adoption.

The bottom line: We raise our fair value estimate for wide-moat Nvidia to $280 from $260, as near- and medium-term growth continues to modestly outpace our expectations. Shares were basically flattish after hours and appear undervalued to us.

  • In addition to Nvidia’s excellent AI GPU growth, its networking business tripled year over year to nearly $15 billion.

Nvidia remains dominant in a host of AI workloads

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.