How Nvidia became an AI giant

LOS ANGELES — It all started in 1993 at a Denny’s in San Jose.

Three engineers – Jensen Huang, Chris Malachowsky and Curtis Priem – gathered at the restaurant in what is now the heart of Silicon Valley to talk about building a computer chip that would make graphics for video games faster and more realistic. That conversation, and the ones that followed, led to the creation of Nvidia, the tech company that rose through the ranks of the stock market and this week briefly topped Microsoft as the most valuable company in the S&P 500.

The company is now worth more than $3.2 trillion, with its dominance as a chipmaker cementing Nvidia’s position as the figurehead of the artificial intelligence boom — a moment Nvidia CEO Huang has called “the next industrial revolution.” .

During a conference call with analysts last month, Huang predicted that the companies using Nvidia chips would build a new type of data center called “AI factories.”

Huang added that training AI models will become a faster process as they learn to become “multimodal” — able to understand text, speech, images, video and 3D data — as well as “reason and plan.” .

“People talk about AI as if Jensen just arrived, like in the last 18 months, as if 24 months ago he suddenly realized this,” said Daniel Newman, CEO of The Futurum Group, a technology research firm. “But if you actually go back in time and listen to Jensen talk about accelerated computing, he’s been sharing his vision for more than a decade.”

The Santa Clara, California-based technology company’s invention of the graphics processing unit (GPU) in 1999 helped fuel the growth of the PC gaming market and redefine computer graphics. Now, Nvidia’s specialized chips are key components that help power various forms of artificial intelligence, including the latest generative AI chatbots like ChatGPT and Google’s Gemini.

Nvidia’s GPUs are a key factor in the company’s success in artificial intelligence, Newman added.

“They took an architecture that was used for one thing, to maybe improve gaming, and they figured out how to network these things,” he said. “The GPU became the most compelling architecture for AI, going from gaming, rendering graphics and so on, to actually using it for data. … They ended up creating a market that didn’t exist: GPUs for AI, or GPUs for machine learning.”

AI chips are designed to perform artificial intelligence tasks faster and more efficiently. While general-purpose chips like CPUs can also be used for simpler AI tasks, they are “becoming less and less useful as AI evolves,” according to a 2020 report from Georgetown’s Center for Security and Emerging Technology University.

Tech giants are snapping up Nvidia chips as they dive deeper into AI — a movement that enables cars to drive themselves and generates stories, art and music.

“Jensen has essentially made AI digestible and Apple will then make it consumable,” Newman said.

The company had an early lead in the hardware and software needed to tailor its technology to AI applications, in part because Huang introduced it to a technology that was nascent more than a decade ago.

“Nvidia has been working on different parts of this problem for more than two decades. They have a deep innovation engine that goes all the way back to the early 2000s,” said Chirag Dekate, VP analyst at Gartner, a technology research and consulting firm. “What Nvidia did 20 years ago is they both identified and nurtured a neighboring market, where they discovered that the same processors and the same GPUs they used for graphics could be shaped to solve highly parallel tasks.”

At the time, he said, AI was still in its infancy. But Nvidia’s insight that GPUs would be central to AI development was “the fundamental breakthrough that was needed,” Dekate said.

“Until then, I would say we would have been living in the analytical Dark Ages,” he said. “The analytics were there, but we could never bring these AI elements to life.”

Analysts estimate that Nvidia’s revenue for the fiscal year ending in January 2025 will be $119.9 billion — about double its revenue for fiscal 2024 and more than four times its revenue from the previous year.

“My hypothesis is that the kind of exponential growth we’re seeing at Nvidia today may be a pattern we’ll see repeated more often in the coming decades,” he said. “This is the Golden Age, if you will… this is the best time to be an AI engineer.”