Nvidia AI: Challengers Are Coming for Nvidia’s Crown

It’s hard to overstate Nvidia’s AI dominance. Founded in 1993,
Nvidia first made its mark in the then-new field of graphics processing units (GPUs) for personal computers. But it’s the company’s AI chips, not PC graphics hardware, that vaulted Nvidia into the ranks of the world’s most valuable companies. It turns out that Nvidia’s GPUs are also excellent for AI. As a result, its stock is more than 15 times as valuable as it was at the start of 2020; revenues have ballooned from roughly US $12 billion in its 2019 fiscal year to $60 billion in 2024; and the AI powerhouse’s leading-edge chips are as scarce, and desired, as water in a desert.

Access to
GPUs “has become so much of a worry for AI researchers, that the researchers think about this on a day-to-day basis. Because otherwise they can’t have fun, even if they have the best model,” says Jennifer Prendki, head of AI data at Google DeepMind. Prendki is less reliant on Nvidia than most, as Google has its own homespun AI infrastructure. But other tech giants, like Microsoft and Amazon, are among Nvidia’s biggest customers, and continue to buy its GPUs as quickly as they’re produced. Exactly who gets them and why is the subject of an antitrust investigation by the U.S. Department of Justice, according to press reports.

Nvidia’s AI dominance, like the explosion of machine learning itself, is a recent turn of events. But it’s rooted in the company’s decades-long effort to establish GPUs as general computing hardware that’s useful for many tasks besides rendering graphics. That effort spans not only the company’s GPU architecture, which evolved to include “tensor cores” adept at accelerating AI workloads, but also, critically, its software platform, called
Cuda, to help developers take advantage of the hardware.

“They made sure every computer-science major coming out of university is trained up and knows how to
program CUDA,” says Matt Kimball, principal data-center analyst at Moor Insights & Strategy. “They provide the tooling and the training, and they spend a lot of money on research.”

Released in 2006, CUDA helps developers use an Nvidia GPU’s many cores. That’s proved essential for accelerating highly parallelized compute tasks, including modern generative AI. Nvidia’s success in building the CUDA ecosystem makes its hardware the path of least resistance for AI development. Nvidia chips might be in short supply, but the only thing more difficult to find than AI hardware is experienced AI developers—and many are familiar with CUDA.

That gives Nvidia a deep, broad moat with which to defend its business, but that doesn’t mean it lacks competitors ready to storm the castle, and their tactics vary widely. While decades-old companies like
Advanced Micro Devices (AMD) and Intel are looking to use their own GPUs to rival Nvidia, upstarts like Cerebras and SambaNova have developed radical chip architectures that drastically improve…