Nvidia Built a Monopoly on the AI Boom. Now Everyone Is Trying to Break It.
Jensen Huang had a habit, during the most intense period of AI investment frenzy in 2023 and 2024, of showing up to events in his signature black leather jacket and presenting slides that made competitors wince. Quarterly earnings calls that other chip companies approached with careful hedging became, for Nvidia, occasions to raise guidance by margins that analysts struggled to process. Data center revenue that had been measured in billions became tens of billions became approaching a hundred billion annually. The stock, already extraordinary, kept going.
The source of this exceptional position is straightforward to describe and extraordinarily difficult to replicate. Nvidia Graphics Processing Units: GPUs, chips originally designed to render video game graphics: turned out to be almost perfectly suited for the matrix multiplication operations that underlie modern AI training. When deep learning began demonstrating remarkable capabilities in the early 2010s, Nvidia hardware was there. The company invested aggressively in software: particularly its CUDA programming framework, which gave developers tools to write code that ran on Nvidia chips with relative ease. By the time the AI boom arrived in full force, Nvidia had a hardware advantage, a software ecosystem advantage, and a talent pipeline advantage that competitors were years behind on. The result was something close to a monopoly on the hardware powering the most consequential technology of the era.
The numbers are stark. Nvidia held approximately 70 to 80 percent of the AI training chip market through 2024 and into 2025. Its H100 GPU: the chip that became synonymous with AI infrastructure investment: was so constrained in supply during peak demand that companies were paying significant premiums on secondary markets and reporting multi-quarter delivery wait times from Nvidia directly. When OpenAI, Google, Anthropic, and Microsoft talked about compute as the limiting constraint on AI development, what they largely meant was Nvidia H100 availability.
The margins reflect the position. Nvidia data center gross margins have run above 70 percent: extraordinary for a hardware company, more typical of software businesses with near-zero marginal costs. The company is, in effect, extracting software-like economics from a hardware product because its competitive position is strong enough to support pricing that hardware commodity economics would never allow.
The scale of Nvidia margins and market position has attracted every major technology company into the chip business in ways that would have seemed implausible a decade ago.
AMD is the most conventional challenger. Its MI300X GPU is a genuine competitor to Nvidia H100 on raw performance metrics, and AMD has been making targeted progress on the software ecosystem problem: investing heavily in ROCm, its alternative to CUDA, and working with major AI labs to ensure their training frameworks run well on AMD hardware. Several hyperscalers have begun deploying MI300X at meaningful scale, and AMD reported strong data center GPU revenue growth through 2024. It is not close to matching Nvidia market share, but it is establishing a credible alternative.
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