NVIDIA

Nvidia is the dominant supplier of the specialized chips that power modern artificial intelligence, and its rise from a gaming-graphics company to the infrastructure backbone of the AI boom is one of the fastest wealth-creation stories in corporate history. Founded in 1993 and headquartered in Santa Clara, California, Nvidia's GPUs (graphics processing units), originally built to render video game graphics, turned out to be exceptionally well suited to the parallel math that trains and runs large AI models — a fit that has made Nvidia the single most important company in the AI supply chain.

The scale of that shift shows up directly in Nvidia's financials. Fiscal 2026 revenue reached a record $215.9 billion, up 65% year over year, and the most recent quarter (ended April 2026) posted $81.6 billion in revenue, up 85% year over year. Data center revenue — the segment that includes AI training and inference chips — brought in over $130 billion for the fiscal year, up from just $15 billion three years earlier, and hit a record $75.2 billion in the most recent quarter alone, up 92% year over year, driven by the ramp of Nvidia's Blackwell 300-generation products alongside networking technologies like InfiniBand and Spectrum-X Ethernet. Roughly half of that data center revenue now comes from large hyperscale cloud providers, with the rest increasingly diversified across AI-specialized clouds, enterprises, industrial customers, and government ("sovereign AI") buyers — a sign that AI infrastructure demand has broadened well beyond the handful of technology giants that drove Nvidia's earliest growth.

Nvidia's competitive moat is not just its chips but the software layer built around them, principally CUDA, the programming platform that has become the de facto standard for AI development over more than a decade. That software lock-in is a large part of why Nvidia holds an estimated 80%-plus share of the AI training chip market despite AMD, Intel, and the hyperscalers themselves (Google's TPUs, Amazon's Trainium, Microsoft's Maia) all building competing or complementary silicon. Nvidia has pushed to extend that moat further into networking (with its Mellanox-derived InfiniBand and Ethernet products) and into full data-center-scale systems, effectively selling entire AI factories rather than individual chips.

The company's core risk is one of concentration and cyclicality dressed up as a growth story: an enormous share of Nvidia's revenue now depends on a relatively small number of hyperscale customers continuing to spend tens of billions of dollars a year building AI data centers, a spending pattern that has never been tested through a full economic downturn. Export restrictions on advanced chips to China have also repeatedly reshaped Nvidia's addressable market and forced the company to design China-specific, compliance-constrained chip variants, adding a persistent geopolitical variable to what is otherwise a demand story most companies would envy. Nvidia's roughly $3 trillion-plus market capitalization already prices in years of continued hypergrowth, which means any meaningful slowdown in AI infrastructure spending — whether from a hyperscaler pullback, a competitive breakthrough, or a broader pause in AI investment — would likely hit Nvidia's stock disproportionately hard relative to peers with more diversified revenue.