How Nvidia Became the World’s Most Valuable Company

On August 27, 2008, in a packed hall at the San Jose Convention Center, Adam Savage and Jamie Hyneman of MythBusters stood beside two machines that looked nothing like computers. The first, nicknamed Leonardo, carried a single paintball gun on a robotic arm and fired one shot at a time, drawing a pixelated face dot by dot on a canvas. The second sat under a tarp. When Savage pulled it off, the audience saw 1,100 paintball barrels lined up in a grid, each one independently addressable. He smiled. “Each of those paintballs will fly across seven feet of space and, in 80 milliseconds, reach its target,” he said, as reported in the MythBusters paintball demonstration at NVISION 08.

A countdown. Three. Two. One. All 80 milliseconds later, a giant pixelated Mona Lisa appeared on the canvas, painted in a single synchronized burst. “Ladies and gentlemen,” Savage declared, “science class is now over.” The keynote crowd clapped. Most read it as spectacle. Nvidia was demonstrating something else: the core idea behind its graphics chip, the one that would, within fifteen years, make the company the most valuable on Earth. A central processing unit does one task at a time. A graphics processing unit does thousands at once.

Three Engineers and a Denny’s Booth

Fifteen years before the paintball keynote, in 1993, three engineers sat in a booth at a Denny’s in East San Jose and sketched out a chip on a paper napkin. Jensen Huang was 30. Chris Malachowsky and Curtis Priem joined him. Personal computers were starting to run 3D games, but the CPU alone could not render the graphics the trio thought were coming. They wanted a dedicated processor for graphics. Huang later recalled the appeal of the venue in an interview with Denny’s CEO Kelli Valade, as covered in Jensen Huang’s plaque unveiling at the original Denny’s: “It had all the coffee you could drink and no one could chase you out.”

Huang had worked at a Denny’s himself at 15, washing dishes, busing tables, waiting on customers. That early restaurant job, he told Valade, taught him humility, hard work, and hospitality. The diner habits stuck. Decades later, he still defaults to “we” when talking about the menu.

The original thesis was narrow. PCs running 3D games needed realistic graphics. The three co-founders believed those graphics deserved their own processor rather than living as a side job for the CPU. That conversation inside the roadside diner became the foundation of Nvidia. The company incorporated that year, with Huang as the surviving president and CEO, a position he still holds.

The GeForce 256 and the Word ‘GPU’

Six years after the Denny’s meeting, Nvidia put a name to the chip it had been building. In October 1999, the company launched the GeForce 256 and marketed it as the world’s first GPU, according to the 25-year retrospective on the GeForce 256. The product moved hardware transform and lighting, two of the heaviest jobs in a 3D scene, off the CPU and onto the graphics card. Quake III Arena, Unreal Tournament, and a generation of PC games ran faster and looked sharper because of it.

What mattered more than the silicon was the category the company named. A CPU and a GPU do different kinds of work. The table below sketches the split:

At the time, “GPU” meant a graphics-only chip. The GeForce 256 did not yet know it would one day train the largest AI models on Earth. It just made games look better, and that was enough to put Nvidia on the map.

CUDA, the Bet Nobody Asked For

In November 2006, Nvidia released a software platform called CUDA, Compute Unified Device Architecture, alongside the GeForce 8800 GTX. CUDA let developers program an Nvidia GPU for tasks that had nothing to do with graphics: simulations, scientific computing, weather modeling, molecular analysis. The hardware was ready. The customers, as far as Nvidia could tell, were not.

“When I launched CUDA, the audience was in complete silence. Nobody wanted it. Nobody asked for it. Nobody understood it,” Nvidia CEO Jensen Huang later recalled in an interview with The Closer.

When I launched CUDA, the audience was in complete silence. Nobody wanted it. Nobody asked for it. Nobody understood it.

Huang, founder and CEO of Nvidia, attributed the quote to his recollection in a conversation with The Closer, as reported by India Today Tech. The market reaction matched the silence. Wall Street treated CUDA as a costly side project. Other chipmakers kept their roadmaps pointed at faster graphics. Nvidia alone kept building.

AlexNet and the Day GPUs Became AI Hardware

Six years after CUDA shipped, the silence broke. In 2012, Alex Krizhevsky, a researcher at the University of Toronto, trained a neural network called AlexNet on Nvidia GPUs and entered it in the ImageNet image recognition competition. The model, trained on a million images, won the contest by a wide margin against handcrafted software written by vision experts. The result is now treated as the founding moment of modern deep learning.

What changed for Nvidia was not just the win. It was the workload. AlexNet and the wave of models that followed did not need a chip that could draw triangles. They needed a chip that could multiply millions of small numbers at once, the exact job a GPU is built for. Researchers at Google, Stanford, and New York University had already been running AI experiments on Nvidia hardware. ImageNet 2012 turned the experiment into a market.

A few numbers put the journey in proportion:

• 1,100 paintball barrels fired simultaneously at NVISION 08.
• $129,000 list price for the DGX-1 in 2016.
• 8 P100 GPUs in each DGX-1.
• 170 teraflops (FP16) per DGX-1.
• $4,714.89 billion market cap as of July 2, 2026.

The Hand-Delivered Supercomputer

In 2016, Nvidia put all eight of its most powerful training GPUs into one machine and called it the DGX-1. It was, in the company’s framing, a supercomputer in a box. The price was $129,000. Each server carried eight Tesla P100 GPUs linked by Nvidia’s NVLink interconnect, yielding 170 teraflops of half-precision performance, according to Top500’s report on the first DGX-1 delivery to OpenAI. The market for a $129,000 AI training machine was, at the time, almost theoretical.

Then Elon Musk called. Musk was helping set up a non-profit AI research lab called OpenAI. He wanted one of the first DGX-1 units. Huang did not arrange a shipment. He loaded the first system into his car and drove it to OpenAI’s office in San Francisco.

Musk posted his thanks on Twitter. “Would like to thank @Nvidia and Jensen (Huang) for donating the first DGX-1 AI supercomputer to @OpenAI in support of democratising AI technology.” Inside that small office, among the early researchers who would receive the machine, was a young Ilya Sutskever, later one of the principal architects of ChatGPT.

The Roads, Not the Cars

A decade later, when ChatGPT launched and the world discovered generative AI, Nvidia was not catching up. It had been building toward that moment for nearly twenty years. The chips, the software, the developer tools, and the networking stack were already in place. Today, every major model family, including OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, and Meta’s Llama, is trained on Nvidia GPUs running CUDA.

The company’s lead sits in the ecosystem more than in any single chip. “During almost two decades, Nvidia has been building a complete AI ecosystem based on its hardware: CUDA, software libraries, developer tools, networking technology and AI framework optimisations,” Devroop Dhar, Co-Founder and India CEO of Primus Partners, told India Today Tech. “This provided an obvious ecosystem advantage as it is easier to build and cheaper to move to Nvidia’s platform compared to anything else.” Atul Arya, Founder and CEO of Blackstraw, made the same point from a different angle. “Nvidia’s real advantage isn’t just the GPU,” he said. “It’s the ecosystem it has built over nearly two decades: the software, the developer tools and the infrastructure that everything else now assumes is there.”

The arc, in seven dates:

Each row is the same company, doing the same thing it has always done, building infrastructure for whoever is computing at the largest scale.

Who Is Building an Alternative, and How Far They Have to Climb

Nvidia’s grip is not unchallenged. The same customers who buy the most Nvidia hardware are also the ones working hardest to design around it. Jalapeño, OpenAI’s first custom-built inference processor, developed with Broadcom, was unveiled on June 24, 2026, according to reporting on OpenAI’s first custom-built inference processor Jalapeño. OpenAI’s earlier collaboration with Broadcom, announced on October 13, 2025, commits the two companies to 10 gigawatts of custom AI accelerators.

The wider customer-side build-out now looks like this:

Atul Arya, the Blackstraw CEO, framed the scale of the replacement job in plain terms. “If Nvidia disappeared tomorrow, innovation would not stop, but it would slow down significantly. Other major players have viable alternatives, but no single player could immediately replace the scale and maturity Nvidia has built.” Two related reads on this build-out sit at how inference economics are reshaping Nvidia’s grip and at Anthropic’s Samsung chip talks and the foundry race. The pattern is consistent. The biggest buyers of Nvidia silicon are also the biggest funders of Nvidia alternatives.

The GPU as the Modern Oil Field

Sachin Dev Duggal, Founder and CEO of SekondBrain, put the stakes in one line. “Nvidia is arguably the most important infrastructure company in AI today,” he told India Today Tech. “Almost every major breakthrough in modern AI has, directly or indirectly, been accelerated by access to Nvidia’s hardware, software stack and developer ecosystem.” On market capitalisation alone, Nvidia is the world’s most valuable company, at $4,714.89 billion as of July 2, 2026, per Macrotrends data.

Duggal pushed the framing further. “In many ways, the GPU has become the modern equivalent of an oil field. AI is no longer just a software discussion, it is increasingly a geopolitical one.” Nvidia set out to build better graphics for video games. Three decades later, the chips it sells for that purpose are the substrate under the largest technology build-out of the decade. The roads are now what matters, and almost everyone is still driving on Nvidia’s.

Frequently Asked Questions

When was Nvidia founded?

Nvidia was founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem. The three co-founders sketched the company’s first business plan at a Denny’s booth in East San Jose, with Huang as the surviving president and CEO from day one.

What was the GeForce 256?

The GeForce 256 was the graphics card Nvidia released in 1999 and marketed as the world’s first GPU. It moved hardware transform and lighting off the CPU and onto the graphics chip, and it gave the category its name.

What is CUDA?

CUDA, Compute Unified Device Architecture, is a software platform Nvidia launched in November 2006 alongside the GeForce 8800 GTX. It lets developers program Nvidia GPUs for tasks beyond graphics, from scientific simulations to deep learning.

Why is Nvidia the world’s most valuable company?

Nvidia’s market cap reached $4,714.89 billion as of July 2, 2026, according to Macrotrends data, making it the largest publicly traded company in the world by that measure. The valuation rests on its position as the dominant supplier of GPUs and the CUDA software platform used to train the major AI models.

Will Nvidia stay on top?

Industry voices quoted in the India Today Tech feature see Nvidia’s lead persisting for the foreseeable future, with OpenAI, Google, Amazon, Microsoft, AMD, and Intel all building alternatives that have not yet matched Nvidia’s scale. Atul Arya of Blackstraw said a sudden Nvidia exit would slow innovation rather than stop it.