Part IThe Story
The Arms Dealer's Breakfast
In September 2023, Jensen Huang went back to the Denny's on Berryessa Road in San Jose, California, where Nvidia had been conceived three decades earlier. Stephen Witt, who was profiling him for The New Yorker, sat across the booth, and his account of that morning has become one of the defining portraits of the man. Huang ordered a small feast of diner food. Witt had just seen a demonstration of a robot that seemed to recognize its own hands, and it had unsettled him. Huang waved the worry away. "I know how it works, so there's nothing there," he said. "It's no different than how microwaves work."
It was an odd thing to hear from the man supplying the hardware for the A.I. boom. Four months earlier, on May 25, 2023, Nvidia's market value had jumped by roughly two hundred billion dollars in a single session — one of the largest one-day gains in stock-market history — after the company told investors to expect a surge in demand for its data-center chips. "There's a war going on out there in A.I., and Nvidia is the only arms dealer," one Wall Street analyst said. And here was the arms dealer in a diner booth, comparing the technology behind his fortune to a kitchen appliance.
The setting was deliberate. Denny's was presenting Huang with a commemorative plaque, and a TV crew was on hand. Huang had worked at a Denny's in Oregon as a teenager, and he told the waitress about it. "But I worked hard! Like, really hard. So I got to be a busboy." By Witt's account, he left her a thousand-dollar tip.
Between the busboy and the plaque — between the nine-year-old Taiwanese immigrant who cleaned toilets at a Kentucky reform school and the man whose Nvidia stake has made him one of the richest people in the world — lies one of the most improbable arcs in American business. It is the story of a founder who bet his company on parallel computing years before the rest of Silicon Valley saw the point, whose chips became the substrate of the A.I. revolution, and who never stopped running Nvidia as though it were about to die.
By the Numbers
Nvidia's Empire
$5T+Market capitalization (first company to surpass $5 trillion, 2025)
$130.5BRevenue, fiscal year 2025
$72.9BNet income, fiscal year 2025
~70%Gross profit margin on A.I. equipment
80%+Market share in GPUs for training and deploying A.I. models
30+ yearsJensen Huang's tenure as CEO (since 1993)
36,000Employees worldwide
The Swinging Bridge
The story that Jensen Huang tells about himself — and he tells it carefully — begins not in triumph but in displacement. He was born Jen-Hsun Huang on February 17, 1963, in Taiwan — in Taipei, by most accounts, before the family moved to Tainan, the historic capital of the island's south — the second son of Huang Hsing-tai, a chemical engineer, and Lo Tsai-hsiu, a grade-school teacher. When Jensen was five, the family relocated to Thailand. But the Vietnam War was bleeding across borders, and Thailand itself was convulsing — "tanks were rolling down the streets," Huang has recalled, "grenades are going off. It's a full-on battle." His parents, who had formed a positive impression of the United States through his father's participation in a worker-training program at Carrier, the air-conditioning manufacturer, decided their sons' futures lay elsewhere.
In 1973, when Jensen was nine and his brother ten, the boys were sent as unaccompanied minors to the United States. They landed in Tacoma, Washington, to live with an uncle who promptly enrolled them at the Oneida Baptist Institute in rural Oneida, Kentucky — a school the uncle believed to be a prestigious boarding academy. It was, in fact, a religious reform school for troubled youth. Jensen's roommate, a seventeen-year-old, lifted his shirt on their first night together to display the numerous places where he'd been stabbed in fights. "Every student smoked," Huang has said, "and I think I was the only boy at the school without a pocketknife." His roommate was illiterate. The deal they struck — Jensen taught him to read, and the older boy taught Jensen to bench-press — left Huang doing a hundred push-ups every night before bed.
Because he was too young for the institute's classes, Huang went to a nearby public school, where he became friends with Ben Bays, one of six children raised in a house without running water. It was poor tobacco country, and a small Asian boy with long hair and a heavy accent stood out. Huang told Stephen Witt of The New Yorker that he was called a racial slur — "Chinks" — every day, and he said it without apparent emotion.
To get to school, Huang had to cross a rickety rope footbridge strung high over a river, many of its planks missing. Local boys would grab the ropes and shake it while he crossed. "Somehow it never seemed to affect him," Bays later recalled. By the end of the year, the same boys were following Huang into the woods. What Bays remembered most was how carefully Jensen picked his way across. "Actually, it looked like he was having fun."
Huang credits Oneida with building resilience. "Back then, there wasn't a counsellor to talk to," he said. "Back then, you just had to toughen up and move on." In 2019, he donated a building to the school, and spoke fondly of the footbridge — neglecting to mention the bullies who had tried to throw him off it. The omission is characteristic. Huang's relationship with suffering is not one of resentment but of utility. Pain, for him, is raw material. "People with very high expectations have very low resilience," he told Stanford's business school in 2024. "One of my great advantages is that I have very low expectations."
Homework as Courtship
After a couple of years, Huang's parents secured entry to the United States, settling in the suburbs of Portland, Oregon, and the brothers reunited with them. Jensen excelled at Aloha High School. He was a nationally ranked table-tennis player. He joined the math, computer, and science clubs. He skipped two grades and graduated at sixteen. "I did not have a girlfriend," he said.
At Oregon State University, he studied electrical engineering. His lab partner was Lori Mills, one of only a handful of women in a class of roughly two hundred and fifty. Huang, younger than everyone around him, was sure he was outmatched in the contest for her attention. "I was the youngest kid in the class," he has said. "I looked like I was about twelve."
His approach was methodical. Every weekend, he called Mills and asked her to do homework with him. "I tried to impress her — not with my looks, of course, but with my strong capability to complete homework," he said. Six months of homework sessions passed before he worked up the courage to ask her on a date. She accepted. They married after graduation. Lori, herself a microchip designer, initially earned more than Jensen. ("She actually made more than me," he said, with the half-proud, half-sheepish tone of a man who keeps score.) She would eventually leave the workforce to raise their two children — Spencer, who ran a cocktail bar, and Madison, who went into hospitality and luxury marketing. Both eventually joined Nvidia.
The Latin Word for Envy
Following Oregon State, Huang landed at Advanced Micro Devices in Sunnyvale, California, then moved to LSI Logic Corporation, where he designed software tools for chip architects and rose to become director of a company division — all while attending Stanford for a master's in electrical engineering by night. He was, by all accounts, an accomplished product manager with a reputation for speed and technical mastery that exceeded what his titles suggested. He had told his future co-founders, Chris Malachowsky and Curtis Priem, that he aimed to run something by the age of thirty.
Malachowsky and Priem were veteran microchip designers at Sun Microsystems, the fabled Silicon Valley workstation manufacturer. Malachowsky was a hardware architect with deep roots in chip fabrication; Priem, a graphics specialist who dreamed of making competitors "green with envy." When they decided to leave Sun and start a company, they recruited Huang — younger, but already clearly the one who should run things. "He was a fast learner," Malachowsky said, with the understatement of a man who has watched his protégé become one of the wealthiest people alive.
In 1993, the year Huang turned thirty, the three men incorporated the company they had been sketching out in a booth at the Denny's on Berryessa Road. They had $40,000 in capital. They wanted to design graphics chips. They initially called the company NVision, until they discovered the name belonged to a manufacturer of toilet paper. Huang suggested Nvidia, riffing on invidia, the Latin word for "envy." They met at the Denny's because it was quieter than home and had cheap coffee, and the chain suited Huang, who had started there as a dishwasher. "I find that I think best when I'm under adversity," he said. "My heart rate actually goes down. Anyone who's dealt with rush hour in a restaurant knows what I'm talking about."
— Jensen HuangI do everything I can not to go out of business. I do everything I can not to fail.
The founding myth of Nvidia is a Denny's booth and three electrical engineers. But the company nearly died before it could grow. Huang and his co-founders bet on quadrilateral-based graphics primitives rather than the industry-standard triangles. It was a principled technical choice and a near-fatal commercial error: soon after Nvidia shipped its first product, Microsoft announced that its graphics software would support only triangles. Short on money, Huang pivoted to the conventional approach, laid off more than half of the company's hundred employees, and bet Nvidia's remaining funds on a production run of untested microchips he wasn't sure would work. "It was fifty-fifty," he said, "but we were going out of business anyway."
The product, RIVA 128, hit stores in 1997 with Nvidia holding enough cash for one month of payroll. The gamble paid off: a million units sold in four months. But the near-death experience stayed with the company. For years afterward, Huang opened staff presentations with the words "Our company is thirty days from going out of business."
A Fleet of Motorcycles
To understand what Nvidia actually makes — and why it matters — requires a brief excursion into the architecture of computing. A conventional computer relies on a central processing unit, which works through instructions largely in sequence. For decades Intel dominated that model. Nvidia's graphics processor takes the opposite approach: it splits a large mathematical job into thousands of small pieces and computes them at the same time — parallel computing. A C.P.U. is a few fast, versatile workers; a G.P.U. is a vast crew of simpler ones working in unison.
In 1999, the year it went public, Nvidia released the GeForce 256 and marketed it as the world's first "graphics-processing unit." The term had been used before, but Nvidia made it its own. "We invented the category so we could be the leader in it," Dan Vivoli, then head of marketing, has said — a principle Huang would apply repeatedly over three decades: if the market doesn't exist, create it.
GeForce sold on the back of games like Quake, which rewarded exactly the parallel horsepower Nvidia's cards supplied, and gamers upgraded with every generation. Huang suspected the chips could do more. In 2000, Ian Buck, a Stanford graduate student in computer graphics, wired thirty-two GeForce cards together to run Quake across eight projectors. "It was the first gaming rig in 8K resolution, and it took up an entire wall," Buck said. "It was beautiful."
Buck then wondered whether the cards could be put to work on problems that had nothing to do with games. With DARPA funding, he found ways around the cards' limited programming tools to reach the parallel circuitry underneath, turning consumer graphics hardware into a cheap supercomputer. Before long, he was working for Huang.
The Zero-Billion-Dollar Market
Since 2004, under Buck's oversight, Nvidia developed CUDA — Compute Unified Device Architecture — a software platform that would allow any programmer to harness the parallel-computing power of Nvidia's chips for general-purpose tasks. Huang's vision was audacious: put CUDA on every GeForce card, so that anyone with a gaming PC had a supercomputer. "We were democratizing supercomputing," he said.
Wall Street reacted with dismay. Huang was spending billions on a new chip architecture targeting an obscure corner of academic and scientific computing — a market that, at the time, was "certainly less than the billions they were pouring in," as Ben Gilbert of the Acquired podcast noted. Huang argued that the mere existence of CUDA would enlarge the supercomputing sector. It was a classic zero-billion-dollar market play — Huang's phrase for products that have no competitors because they don't yet have obvious customers. "One of the things you can definitely guarantee is where there are no customers, there are also no competitors," he told Caltech graduates in 2024.
The market's skepticism was vindicated, at least initially. By late 2008, Nvidia's stock price had declined by seventy percent. As Stephen Witt reported in The New Yorker, CUDA downloads peaked in 2009 and then declined for three straight years. Board members worried that the depressed stock price would attract corporate raiders. "We did everything we could to protect the company against an activist shareholder who might come in and try to break it up," Jim Gaither, a longtime board member, said. Dawn Hudson, a former N.F.L. marketing executive who joined the board in 2013, described a "distinctly flat, stagnant company."
During this wilderness period, Huang kept faith. He has said that a visit to Ting-Wai Chiu, a physicist at National Taiwan University, helped sustain it. Chiu was simulating the evolution of matter after the Big Bang on a homemade cluster of GeForce cards, cooled by desk fans, in a lab next to his office. "Jensen is a visionary," Chiu said. "He made my life's work possible." He was exactly the customer CUDA had been built for — and there were not many like him.
Nvidia pitched CUDA to traders, oil-and-gas explorers, and biologists; one deal, with General Mills, involved simulating the thermal physics of cooking frozen pizza. Artificial intelligence barely registered. There didn't seem to be much of a market.
Prophets in the Wilderness
At the beginning of the twenty-tens, A.I. was a neglected discipline. Progress in basic tasks — image recognition, speech recognition — had been halting. Within this unpopular field, an even less popular subfield attempted to solve problems using "neural networks," computing structures inspired by the human brain. Many computer scientists considered neural networks discredited. "I was discouraged by my advisers from working on neural nets," Bryan Catanzaro, who would become Nvidia's lead deep-learning researcher, said, "because, at the time, they were considered to be outdated, and they didn't work."
Catanzaro calls the researchers who persisted "prophets in the wilderness." The best known was Geoffrey Hinton of the University of Toronto, who had argued for decades that layered neural networks could learn to represent the world. In 2009, his group used CUDA to train a speech-recognition network, and the results were strong enough that he talked them up to a large audience of machine-learning researchers. He then asked Nvidia for something in return. "I sent an e-mail saying, 'Look, I just told a thousand machine-learning researchers they should go and buy Nvidia cards. Can you send me a free one?'" Hinton told The New Yorker. "They said no."
Hinton kept steering his students toward CUDA anyway. In 2012, two of them, Alex Krizhevsky and Ilya Sutskever, bought a pair of GeForce cards and began training an image-recognition network, with Krizhevsky running the job from his bedroom at his parents' house. "Actually, it was his parents who paid for the quite considerable electricity costs," Hinton said.
The network, later known as AlexNet, was entered in the 2012 ImageNet visual-recognition competition and beat the field by a wide margin. "That was a kind of Big Bang moment," Hinton said. "That was the paradigm shift."
The AlexNet paper has since been cited more than a hundred thousand times, making it one of the most influential papers in modern computer science. Its central lesson was stark: a G.P.U. could train neural networks far faster than a general-purpose C.P.U. "To do machine learning without CUDA would have just been too much trouble," Hinton said. Within two years, every entrant in the ImageNet competition was using a neural network. By the mid-twenty-tens, neural networks trained on G.P.U.s were identifying images with ninety-six-percent accuracy, surpassing humans.
Huang's ten-year crusade to democratize supercomputing — the billions poured into an obscure corner of scientific computing, the frozen-pizza simulations, the seventy-percent stock decline, the board members sweating activist shareholders — had succeeded. Not because frozen pizza needed simulating, but because a graduate student needed two graphics cards and a software platform, and both happened to exist.
— Ilya Sutskever, co-founder of OpenAIG.P.U.s showed up and it felt like a miracle.
The Friday E-mail
What happened next was less a strategic pivot than a detonation. Huang concluded that neural networks would revolutionize society and that he could use CUDA to corner the market on the necessary hardware. He announced — again — that he was betting the company. "He sent out an e-mail on Friday evening saying everything is going to deep learning, and that we were no longer a graphics company," Greg Estes, a vice-president at Nvidia, recalled. "By Monday morning, we were an A.I. company. Literally, it was that fast."
Around the same time, Huang approached Catanzaro with a thought experiment. "He told me to imagine he'd marched all eight thousand of Nvidia's employees into the parking lot," Catanzaro said. "Then he told me I was free to select anyone from the parking lot to join my team." It was the gesture of a CEO willing to subordinate every existing business line — billions of dollars in gaming revenue — to a technology whose commercial applications were, at that moment, almost entirely theoretical. "I didn't want him to fall into the same trap that the A.I. industry has had in the past," Catanzaro said. "But, ten years plus down the road, he was right."
The bet grew. In 2016, Nvidia delivered its first dedicated A.I. supercomputer, the DGX-1, to a small research group at OpenAI. Huang personally carried the computer to OpenAI's offices. Elon Musk, then chairman, opened the package with a box cutter. The following year, researchers at Google introduced the transformer architecture for neural-net training. The year after that, researchers at OpenAI used Google's framework to build the first "generative pre-trained transformer" — G.P.T. — trained on Nvidia supercomputers, absorbing an enormous corpus of text and learning to make humanlike connections. In late 2022, after several versions, ChatGPT was released to the public, and the entire world noticed what Huang had been building for a decade.
Marc Andreessen, of the venture firm Andreessen Horowitz, had seen it coming. "We've been investing in a lot of startups applying deep learning to many areas," he said in 2016, "and every single one effectively comes in building on Nvidia's platform." What Andreessen understood — what Wall Street took another six years to internalize — was that CUDA had done for A.I. what the App Store had done for mobile: created a platform so comprehensive that the ecosystem couldn't leave.
Haiku and Ransom Notes
Nvidia's Santa Clara headquarters is a pair of vast triangular buildings with clipped corners, a motif that repeats throughout the interiors; employees call them "spaceships." Below the main floors are windowless labs where technicians stress-test chips to the point of failure.
Huang runs the company in a way that breaks with most Silicon Valley convention. He has roughly sixty direct reports. He holds no regular one-on-one meetings. There are no fixed divisions or hierarchy. Instead, employees send a weekly list of the five most important things they are working on, and Huang reads them late into the night. His own e-mails are famously terse, often just a few words; Witt reported that employees reached for comparisons ranging from haiku to ransom notes.
Rene Haas — who worked at Nvidia in the early 2010s before becoming CEO of the British chip designer Arm, and who considers Huang both a former boss and personal mentor — identified the logic beneath the apparent chaos. "It's a very unique culture," Haas told the Financial Times. "The benefit of that is transparency and speed. And I think that is one of the things that Nvidia is really, really good at. They move very, very fast, they're very, very purposeful." Huang organizes the company around projects rather than traditional hierarchies, allowing him to reach any layer of management and extract answers directly.
Huang is known for stopping at junior employees' desks and grilling them on their work. The analyst Hans Mosesmann told The New Yorker that bluffing, common elsewhere in the Valley, does not survive contact with Huang: "You can't do that with Jensen. He will kind of lose his temper." Huang himself acknowledges the mismatch between internal and external processing. "It's really about what's going on in my brain versus what's coming out of my mouth," he said. "When the mismatch is great, then it comes out as anger." One employee offered a more visceral comparison: "Interacting with him is kind of like sticking your finger in the electric socket."
Yet Nvidia has remarkably high employee retention. Jeff Fisher, who runs the consumer division and was one of the company's earliest hires, is now extremely wealthy but continues to work. "Many of us are financial volunteers at this point," Fisher said, "but we believe in the mission." Catanzaro left for another company, then came back.
— Bryan Catanzaro, Nvidia VP of Applied Deep Learning ResearchJensen is not an easy person to get along with all of the time. I've been afraid of Jensen sometimes, but I also know that he loves me.
The Architecture of Failure
Perhaps Huang's most radical management belief is that "failure must be shared." In the early two-thousands, Nvidia shipped a faulty graphics card with a loud, overactive fan. Instead of firing the product managers responsible, Huang arranged a meeting in which they presented, to a few hundred colleagues, every decision that had led to the fiasco. Nvidia also distributed to the press a satirical video, starring the product managers, in which the defective card was repurposed as a leaf blower.
Presenting one's failures to an audience has become a beloved ritual at Nvidia — beloved by those who survive it. "You can kind of see right away who is going to last here and who is not," said Dwight Diercks, Nvidia's head of software. "If someone starts getting defensive, I know they're not going to make it." The practice functions as both accountability mechanism and cultural selection pressure. It ensures that institutional knowledge about failure circulates rather than being buried, and it identifies the people capable of the radical intellectual honesty that Huang demands.
This culture of transparency extends to Huang's own reasoning. When he reviews work, he explains how he reached a view rather than simply handing down a verdict. It is a leader's version of showing your work — not dictating conclusions but modeling cognition, so that the organization learns not just what to think but how.
The Cousin Question
Nvidia's fiercest rival is Advanced Micro Devices, and the rivalry carries a familial tang. Since 2014, A.M.D. has been run by Lisa Su — another gifted engineer who immigrated to the United States from Taiwan at a young age, who attended MIT, who rebuilt a struggling chip company into a formidable competitor. In the years since Su became C.E.O., A.M.D.'s stock price has risen thirtyfold, making her arguably second only to Huang among the semiconductor chief executives of this era. Su is also Huang's first cousin once removed.
Huang says he didn't know Su growing up and met her only after she was named C.E.O. "She's terrific," he said. "We're not very competitive." This is the kind of statement that, inside Nvidia, would provoke a knowing silence: employees can recite the relative market share of Nvidia's and A.M.D.'s graphics cards from memory. Their personalities are a study in contrast. Su is reserved, stoic, possessed of what Mosesmann calls "a great poker face." Huang is temperamental and expressive. "Jensen does not," Mosesmann added, "although he'd still find a way to beat you." The gross profit margin on Nvidia's equipment approaches seventy percent, and margins like that invite competition. Google, Tesla, Amazon, and numerous startups including Cerebras, which makes a "mega-chip" the size of a dinner plate, are all developing A.I.-training hardware. "They're just extorting their customers, and nobody will say it out loud," Cerebras's C.E.O., Andrew Feldman, said of Nvidia. Huang's counter was characteristically reframing: "The more you buy, the more you save."
Speaking Universes into Existence
When asked in September 2023 if he was taking any gambles that resembled the ones he'd made twenty years ago, Huang responded immediately with a single word: "Omniverse."
The idea grew out of an experiment in which Huang had Hao Ko, the architect of Nvidia's headquarters, wear a virtual-reality headset driven by a rack of G.P.U.s to preview how light would move through the building. Omniverse extends that into an attempt to simulate the physical world in fine detail, building on the real-time ray tracing — rendering that models how light bounces off surfaces — that Nvidia has put in its graphics cards since 2018. Witt described being shown a photorealistic ramen shop and a lifelike digital avatar whose small imperfections made her convincing; only a faint shimmer in her eyes gave her away.
Huang's vision is to unify Nvidia's computer-graphics research with its generative-A.I. research, creating image-generation systems so sophisticated they can render three-dimensional, inhabitable worlds populated with realistic people — while language-processing A.I.s interpret voice commands instantly. "The programming language of the future will be 'human,'" Huang has said. Users will speak universes into existence. Digital twins of our world will train robots and self-driving cars. Combined with V.R. technology, the Omniverse could allow users to inhabit bespoke realities.
It is a dizzying vision, yet the Nvidia executives Witt interviewed met existential questions with striking calm. Asked whether A.I. might someday kill someone, Catanzaro replied, "Eh, electricity kills people every year." Asked whether it might eliminate art, Diercks said, "It will make art better!" In May 2023, hundreds of industry leaders endorsed a statement putting the risk of extinction from A.I. alongside pandemics and nuclear war. Huang didn't sign it.
"Horses have limited career options," he said, when asked about economists who compared A.I.'s potential displacement of humans to the Industrial Revolution's displacement of horses. "For example, horses can't type."
The Leather Jacket and the Moss Garden
Jensen Huang is compact, polished, and known among colleagues for both a quick temper and a long view. In 2025 TIME named him among the "Architects of A.I." on its Person of the Year cover. Onstage, in the trademark black leather jacket, he projects the optimism of a man who believes he is building the most important technology of his era.
His relationship with public performance is characteristically contradictory. "I hate public speaking," he told a journalist at an event before several hundred architects, then went onstage and performed with relaxed confidence for an hour. "I'm not a great speaker, really, because I'm quite introverted," he told the New Yorker. ("He's a great entertainer," his friend Ben Bays countered.) "I only have one superpower — homework," Huang said. ("He can master any subject over a weekend," Dwight Diercks responded.) "I don't really think I've done anything special here. It's mostly my team." ("He's irreplaceable," Jim Gaither said.) The self-deprecation is sincere, and so is the drive underneath it.
He works seven days a week. He is either working or thinking about work every waking moment, he told Stripe CEO Patrick Collison. His wardrobe — the black leather jacket, black jeans, black shoes — has been featured in the New York Times Style section and is widely imitated by subordinates. (When architect Hao Ko showed up in an identical outfit, Huang spent several minutes roasting his pants for having too many pockets. "Simplify, man!")
He never reads science fiction. He dislikes speculation. He reasons from first principles about what microchips can do today, then gambles with great conviction on what they will do tomorrow. "I'm never satisfied," he said. "No matter what it is, I only see imperfections."
At Caltech's 2024 commencement, Huang closed with a story from Japan. Watching a gardener painstakingly tend to Kyoto's famous moss garden, he realized that when a person is truly dedicated to their craft — when they prioritize their life's work — they always have plenty of time. "Prioritize your life," he told the graduates, "and you will have plenty of time to do the important things."
In 2025, Nvidia became the world's first five-trillion-dollar company after Huang announced plans to build supercomputers for the U.S. government and forecasted an additional five hundred billion dollars in orders for Nvidia's A.I. chips. Memes depicted Nvidia as Atlas, holding the stock market on its shoulders. When asked by TIME if there was anything he was envious of — this man who had named his company for envy — Huang said no. He tallied what he was grateful for: his happy marriage, his adult children, his two dogs, who had both received clean ultrasounds that day.
The deepest revelation about Jensen Huang may be the simplest: he is not, and has never been, motivated by the destination. He is motivated by the work. He told a crowd at TiEcon, when asked what still drives him, that he doesn't have anything else to do besides serve as CEO of Nvidia. He told Caltech's graduates to find their GPU, find their CUDA, find their generative AI — to believe in something unconventional and unexplored and dedicate themselves to making it happen. He has been doing the same thing for more than three decades, from the Denny's booth to the five-trillion-dollar company, and the thing that makes him unusual among the century's great capitalists is not vision or cunning or even the leather jacket. It is endurance.
Part IIThe Playbook
Jensen Huang has led Nvidia for more than three decades — from a Denny's booth to the world's most valuable company. The following principles are distilled from his decisions, his management practices, and the culture he has built. They are not platitudes. They are the operating logic of a man who has bet his company's existence at least three times and won each gamble by margins that looked, in the moment, like recklessness.
The principles
- 1.Build for the zero-billion-dollar market.
- 2.Treat near-death as an operating principle.
- 3.Reason from first principles, then bet with total conviction.
- 4.Invent the category so you can lead it.
- 5.Make failure a public institution.
- 6.Replace hierarchy with information velocity.
- 7.Pursue the speed of light.
- 8.Build the full stack.
- 9.Use suffering as raw material.
- 10.Show your reasoning, not your conclusions.
- 11.Abandon commodity work relentlessly.
- 12.Outlast everyone.
Principle 1
Build for the zero-billion-dollar market.
Huang uses this phrase for products like CUDA — a platform that, when launched in 2006, had no competitors because it had no obvious customers. Wall Street punished Nvidia for the investment; the stock fell seventy percent. But the absence of customers also meant the absence of competitors, which meant that when demand eventually materialized — when AlexNet showed how much faster G.P.U.s could train neural networks — Nvidia owned the entire ecosystem.
This is not the "build it and they will come" naïveté it superficially resembles. Huang's zero-billion-dollar markets are chosen on the basis of deep technical reasoning about where computing architectures are heading. The bet on CUDA was informed by Huang's intuition that Moore's Law was decelerating and that parallel computing would become essential. The bet on A.I. in 2013 was informed by AlexNet's results and Hinton's research. The current bet on Omniverse is informed by the convergence of ray-tracing and generative A.I. Each zero-billion-dollar market is, in Huang's framing, the logical destination of trends already visible to anyone with sufficient technical depth.
At Caltech, he described the choice plainly: "With no more markets to turn to, we decided to build something where we are sure there are no customers."
Tactic: Identify the technology that will be essential in five to ten years but has no commercial application today — then build the platform that makes it accessible, and own the ecosystem before demand arrives.
Principle 2
Treat near-death as an operating principle.
The RIVA 128 episode — betting the company's remaining funds on untested chips when Nvidia had one month of payroll left — could have been a traumatic memory. Instead, Huang institutionalized it. "Our company is thirty days from going out of business" became a refrain, repeated at the start of staff meetings for years.
This is not performative paranoia. It is a deliberately constructed organizational psychology. Nvidia's chip business runs on brutally short product cycles where a company is only as good as its last card. Huang's urgency is calibrated to that reality: in a perpetual upgrade cycle, complacency is indistinguishable from extinction. The near-death framing also inoculates against the bloat that typically accompanies success — the committee-driven decision-making, the political turf wars, the loss of speed that large companies almost inevitably suffer.
Tactic: Embed your company's closest brush with failure into its institutional memory — not as a cautionary tale but as an operating tempo. The urgency of genuine survival is irreplaceable; if you've lost it, reconstruct it deliberately.
Principle 3
Reason from first principles, then bet with total conviction.
Huang starts from what microchips can do today and extrapolates from there. "You can learn how something can be done and then go back to first principles and ask yourself, 'Given the conditions today, given my motivation, given the instruments, the tools, given how things have changed, how would I redo this? How would I reinvent this whole thing?'" he told Stanford.
The critical move is what happens after the reasoning: total commitment. When Huang concluded in 2013 that neural networks would revolutionize society, he sent the Friday e-mail declaring Nvidia an A.I. company. When Microsoft's decision to standardize on triangles made Nvidia's first architecture a dead end in the mid-nineties, he abandoned it, laid off more than half the company, and bet the rest on a production run. The first-principles reasoning provides the warrant; the conviction provides the velocity.
This combination — analytical rigor followed by existential commitment — is rare. Most founders are either careful analysts who hedge their bets or wild gamblers who trust their gut. Huang is both, sequentially.
Tactic: Separate the reasoning phase from the commitment phase. During reasoning, be relentlessly analytical and open to being wrong. Once you've reached a conclusion, commit as if the company's survival depends on it — because in Huang's experience, it usually does.
Principle 4
Invent the category so you can lead it.
When Nvidia introduced the GeForce 256 in 1999, Dan Vivoli didn't just name a product — he claimed a category. The term "graphics-processing unit" had appeared before, but Nvidia's launch marketing made it synonymous with Nvidia. "We invented the category so we could be the leader in it," Vivoli said. The same logic governed CUDA (creating the "general-purpose GPU computing" category) and the DGX line (creating the "A.I. supercomputer" category).
Category creation is Huang's alternative to competing on market share. "We never talk about market share in our company," he has said, "because the concept of market share says that there are a whole bunch of other people who are doing the same thing. And if they are doing the same thing, then why are we doing it? Why am I squandering the lives of these incredibly talented people to go do something that's already been done?"
N
Nvidia's Category-Creation Playbook
Three decades of defining new markets before competing in them.
| Year | Category Created | Conventional Wisdom |
|---|---|---|
| 1999 | GPU (GeForce 256) | Graphics acceleration is a niche feature |
| 2006 | General-purpose GPU computing (CUDA) | Supercomputing belongs to national labs |
| 2016 | A.I. supercomputer (DGX-1) | A.I. is an academic curiosity |
| 2020s | Industrial metaverse (Omniverse) | The metaverse is a gaming concept |
Tactic: Don't fight for market share in existing categories. Define a new category where you are the default leader, then build the ecosystem — developer tools, software stack, community — that makes the category synonymous with your product.
Principle 5
Make failure a public institution.
The defective graphics card with the overactive fan. The satirical video repurposing it as a leaf blower. The product managers presenting, to hundreds of colleagues, every decision that led to the fiasco. This is Huang's most distinctive management innovation: failure as institutional theater.
The practice serves three functions simultaneously. First, it distributes knowledge — the specific decisions that caused the failure become organizational learning rather than private shame. Second, it functions as a selection mechanism: people who get defensive don't survive at Nvidia, and the public-failure ritual identifies them quickly. Third, it normalizes the admission of error at every level, which is essential in a company that makes existential bets and needs rapid course correction when those bets go wrong.
Huang frames this as intellectual honesty. In a company where the CEO routinely bets the entire enterprise on unproven technologies, the ability to say "this didn't work, here's why, here's what we learned" is not a nice-to-have cultural value — it is a survival mechanism.
Tactic: Create a formal ritual for presenting failures publicly — not as punishment but as institutional learning. The people who can do this honestly are the ones you want; the ones who can't will self-select out.
Principle 6
Replace hierarchy with information velocity.
Huang's span of control is enormous, and he skips the one-on-ones that usually go with it. The company is organized around projects rather than fixed divisions, and employees submit a weekly list of five priorities. The result is what Haas described as "transparency and speed" — a flat information architecture that allows Huang to reach any layer of the organization and extract ground truth.
The logic is deliberate. "Prior models of leadership were drawn from the battlefield," Huang has said, "where only the general makes strategic decisions, while the foot soldiers fight on the ground." In Huang's model, information flows at maximum velocity from any point in the organization to any other, with the CEO serving less as a hierarchical apex and more as a pattern-recognition engine operating across the full surface area of the company.
This structure demands an almost inhuman tolerance for cognitive load — Huang reads thousands of employee updates weekly, writes hundreds of e-mails daily, and personally quizzes junior engineers on their work. It also demands a particular kind of employee: one who thrives in radical transparency, can operate without the cover of organizational layers, and doesn't need the psychological comfort of a clearly defined reporting structure.
Tactic: Organize around projects, not divisions. Create mechanisms that allow leadership to access ground-level information without intermediaries. Accept that this requires extraordinary personal bandwidth from leadership — and extraordinary self-direction from employees.
Principle 7
Pursue the speed of light.
When scheduling, Huang asks employees to consider "the speed of light." This is not simply an instruction to move quickly. It means: determine the absolute fastest a task could conceivably be accomplished — the physical limit, the theoretical minimum — then work backward toward an achievable goal.
The V.R.-architecture story illustrates this. When Nvidia was designing its headquarters, architect Hao Ko's V.R. headset originally took five hours to render design changes. At Huang's insistence, the engineering team got the speed down to ten seconds. Ko understood the logic in retrospect: "If the headset took five hours, I'd probably settle on whatever shade of green looked adequate. If it took ten seconds, I'd take the time to pick the best shade of green there was." The speed of light is not about efficiency for its own sake. It is about how velocity changes the quality of decisions by enabling more iterations within the same time frame.
Tactic: For every critical process, identify the theoretical minimum time for completion. Make that the target. The point is not just speed — it's that faster iteration cycles produce fundamentally better outcomes because they enable more experimentation per unit of time.
Principle 8
Build the full stack.
Nvidia designs chips, builds hardware systems, writes the software platform (CUDA), develops the programming tools, maintains the developer ecosystem, and now offers cloud computing services. This vertical integration — from silicon to software to systems — is the structural source of Nvidia's moat.
The full-stack approach means that customers who adopt CUDA become embedded in an ecosystem that is extraordinarily difficult to leave. The millions of developers and thousands of companies building on Nvidia's platform, the nearly two decades of accumulated software libraries — all of this creates switching costs that no individual competitor can replicate by building a better chip. Competing with Nvidia requires not just designing superior silicon but replicating an entire computing paradigm.
Ben Thompson of Stratechery has described this as Nvidia's deepest strategic insight: the company doesn't just sell hardware. It sells a computing platform, and the compound effect of its full-stack approach produced, over the past decade, what Huang calls "a million-x speed-up" in A.I. computing.
Tactic: Don't compete on a single layer of the stack. Build vertically — from infrastructure to tools to ecosystem — so that customers adopt not just a product but a platform, and the switching costs compound over time.
Principle 9
Use suffering as raw material.
The Oneida Baptist Institute. The swinging bridge. The bullies. The reform school. The missing planks. Huang does not narrate these experiences as trauma; he narrates them as manufacturing inputs. "Greatness is not intelligence," he told Stanford students. "Greatness comes from character. And character isn't formed out of smart people, it's formed out of people who suffered."
This is not motivational poster bromide. It is the operating philosophy of a man who was called a racial slur every day as a nine-year-old, who was nearly shaken off a bridge by local boys, and who responded by leading those same boys into the woods by the end of the school year. The experience taught him that resilience is not a personality trait but a practiced skill — one that can be cultivated through exposure to adversity and the deliberate refusal to internalize it.
He applies this framework to his company: the near-death framing, the public failure rituals, the relentless product cycles. Nvidia's culture is designed to produce institutional suffering — not gratuitous pain, but the productive discomfort of operating at the edge of capability under conditions of genuine uncertainty. "If you want to do extraordinary things, it shouldn't be easy," he told 60 Minutes. "It should be like that."
Tactic: Don't insulate your organization from difficulty. Design systems that create productive discomfort — short deadlines, public accountability, existential framing — while providing the psychological safety to learn from failure rather than being destroyed by it.
Principle 10
Show your reasoning, not your conclusions.
Most leaders transmit decisions. Huang transmits cognition. "If you send me something and you want my input on it, and in my review of it, share with you how I reasoned through it, I've made a contribution to you," he told Stanford. "I've made it possible to see how I reason through something. And by reasoning, as you know, how someone reasons through something empowers you."
This is pedagogical leadership. By modeling his thought process rather than issuing directives, Huang builds an organization that can make Huang-quality decisions without Huang being present. It explains how he can manage so many direct reports without one-on-one meetings: the company has internalized his reasoning framework, not just his conclusions.
The approach also explains Nvidia's ability to pivot rapidly. When the Friday e-mail declared the company an A.I. enterprise, the organization could execute the pivot over a weekend because employees already understood how Huang thought about technology transitions. They didn't need step-by-step instructions; they needed to apply the reasoning framework to new conditions.
Tactic: When reviewing work, don't just correct conclusions — narrate your reasoning process aloud. Teach your organization how you think, not what you think. This scales leadership beyond the bottleneck of any individual decision-maker.
Principle 11
Abandon commodity work relentlessly.
Nvidia has walked away from multiple profitable businesses that became commoditized. The logic is brutal but consistent: "Why am I squandering the lives of these incredibly talented people to go do something that's already been done?" Huang asks. He views commodity work as an existential threat — not to margins, but to the talent density that makes Nvidia's next-generation bets possible. The best engineers want to work on problems no one has solved. If the company is competing on price in an existing market, those engineers leave.
This principle is the inverse of the zero-billion-dollar market thesis. You enter markets where there are no customers and no competitors; you exit markets once they become crowded with both. The discipline of abandonment is at least as important as the discipline of investment — and considerably rarer.
Tactic: Regularly audit your product portfolio for commoditization. Walk away from businesses that others can do, even if they're still profitable, to preserve your ability to attract the talent that can build what no one else can.
Principle 12
Outlast everyone.
Jensen Huang has run Nvidia since co-founding it in 1993, making him one of the longest-serving founder-CEOs in the technology industry. The longevity is not incidental to the strategy. The zero-billion-dollar market thesis requires patience measured in decades — CUDA took nearly a decade to find its killer application. The full-stack approach requires compound investment over time horizons that would destroy a CEO who was managing to quarterly earnings. The cultural practices — the failure rituals, the flat hierarchy, the reasoning-first leadership — require decades of reinforcement to become genuinely embedded.
⏱
The Long Bets
Nvidia's major bets and their time-to-payoff.
1993
Nvidia founded, after planning sessions at Denny's, with $40,000 in capital.
1997
RIVA 128 saves the company; 1 million units in four months.
1999
GeForce 256 and the IPO; Nvidia markets the "GPU" as a category.
2006
CUDA launched; stock drops 70% over the next two years.
2012
AlexNet, trained on two GeForce cards, wins ImageNet.
2013
Huang sends the "Friday e-mail" pivoting Nvidia to A.I.
2016
First DGX-1 delivered to OpenAI by Huang personally.
2023
A.I.-driven sales forecast triggers a roughly $200 billion single-day market cap gain.
2025
Nvidia becomes the world's first $5 trillion company.
Huang has never entertained the possibility of stepping down. When asked what still drives him, he said he doesn't have anything else to do. The answer is both self-deprecating and revelatory: Nvidia's strategy is inseparable from Huang's continued presence. No successor could replicate the technical intuition, the institutional memory, the cultural authority, the willingness to bet the company, the hundreds-of-e-mails-a-day cognitive bandwidth. Nvidia's deepest competitive advantage may be that its founder simply refuses to leave.
Tactic: Build for the long term by staying for the long term. The compound returns on culture, institutional knowledge, and ecosystem investment accrue disproportionately to organizations whose leadership endures across multiple technology cycles. Patience is a structural advantage, not a personality trait.
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Part IIIMaxims
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Run as if the runway is short. Urgency is not a response to crisis; it is an operating tempo. The company that behaves as though it has one month of runway will outperform the company that behaves as though it has decades.
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The zero-billion-dollar market is the safest market. Where there are no customers, there are no competitors. Build the platform first; the demand follows the infrastructure.
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Horses can't type. Technological displacement threatens the inflexible, not the human. The correct response to A.I. is not fear but adaptation — and the refusal to be a horse.
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The more you buy, the more you save. When your product creates more value than it costs, price objections are a sign that customers haven't yet understood the economics. Reframe the discussion around total value, not unit cost.
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Failure must be shared. Private failure becomes organizational ignorance. Public failure — presented honestly, without defensiveness — becomes institutional knowledge that compounds over time.
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The speed of light is a planning tool. Determine the theoretical minimum time for a task, then work backward. Faster iteration changes not just efficiency but the quality of decisions, because it enables more experiments per unit of time.
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Don't squander talented lives on commodity work. The best people want to solve problems no one has solved. If the market is crowded, the talent will leave before the margins do.
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Greatness comes from character, and character is formed from suffering. Intelligence is common. The willingness to endure discomfort, absorb failure, and keep building is rare — and it is a far better predictor of extraordinary outcomes.
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Prioritize your life, and you will have plenty of time. Huang's lesson from a Kyoto moss gardener: dedication to the work makes time, rather than consuming it. Urgency and patience are not opposites; they are the same discipline applied at different time scales.
In Their Own Words
There's plenty of time, if you prioritize yourself properly. Make sacrifices.— The Economic Times
Focus on problems that cannot be solved today at all.
I very seldom fire people, I'd rather torture them to greatness.— The Times of India
Software is eating the world, but AI is going to eat…— The Economic Times
When you increase productivity, economies become better - local economies become better, society becomes better.
It's very clear that AI is going to impact every industry. I think that every nation needs to make sure that AI is a part of their national strategy. Every country will be impacted.
Software is the language of automation.
I find that I think best when I'm under adversity. My heart rate actually goes down. Anyone who's dealt with rush hour in a restaurant knows what I'm talking about.— Jensen Huang, at Denny's, September 2023
People with very high expectations have very low resilience — and unfortunately, resilience matters in success. One of my great advantages is that I have very low expectations.— Jensen Huang, Stanford GSB View From The Top, 2024
I hope you believe in something. Something unconventional, something unexplored. But let it be informed, and let it be reasoned, and dedicate yourself to making that happen. You may find your GPU. You may find your CUDA. You may find your generative AI. You may find your NVIDIA.— Jensen Huang, Caltech Commencement, June 14, 2024
Every industry needs it, every company uses it, and every nation needs to build it. This is the single most impactful technology of our time.— Jensen Huang, TIME Person of the Year interview, November 2025
I'm never satisfied. No matter what it is, I only see imperfections.— Jensen Huang, interview with the New Yorker, 2023
