Jensen Huang

On a Thursday morning in September 2023, at a Denny's restaurant on Berryessa Road in San Jose, California, Jensen Huang ordered seven items. A Super Bird sandwich. A chicken-fried steak. Pancakes, which he would roll around a sausage with his fingers. The journalist across the booth had just watched a video of a robot staring at its own hands in seeming recognition, then sorting colored blocks — a demonstration that had given him chills, the obsolescence of his species seemingly near. Huang, sixty years old, sarcastic and Teddy-bear-faced with wispy gray hair, dismissed the concern between bites. "I know how it works, so there's nothing there," he said. "It's no different than how microwaves work."

This was a peculiar thing for the most consequential arms dealer of the twenty-first century to say. Four months earlier, on May 25, 2023, when the Nasdaq opened, Nvidia's market capitalization had increased by approximately two hundred billion dollars in a single session — one of the largest single-day gains in stock-market history — after the revelation that OpenAI's ChatGPT had been trained on Nvidia supercomputers. By the close of trading, Nvidia was the sixth most valuable corporation on earth, worth more than Walmart and ExxonMobil combined. "There's a war going on out there in A.I., and Nvidia is the only arms dealer," one Wall Street analyst observed. And the arms dealer was sitting in a Denny's, eating a chicken-fried steak, telling a reporter that the technology undergirding his fortune was about as mysterious as a kitchen appliance.

The Denny's was not incidental. Huang had drafted Nvidia's founding paperwork at this very restaurant thirty years earlier, on his thirtieth birthday, over cheap coffee and Super Bird sandwiches. The CEO of Denny's was now giving him a commemorative plaque; a TV crew hovered nearby. But Huang's return to the booth where he'd bet his career carried none of the sentimental ceremony the occasion might have warranted. He told the waitress he'd once been a dishwasher at a Denny's in Oregon. "But I worked hard! Like, really hard. So I got to be a busboy." Then he tipped her a thousand dollars, stood up, and accepted his award.

Between the dishwashing and the plaque — between the nine-year-old Taiwanese immigrant who cleaned toilets at a Kentucky reform school and the man whose personal stake in Nvidia now exceeds forty billion dollars — lies one of the most improbable arcs in the history of American capitalism. It is the story of a patient monopolist who bet his company's future on artificial intelligence a full decade before the rest of Silicon Valley believed, who built the computing architecture that would make the A.I. revolution possible, and who did it all while opening staff meetings with the words: "Our company is thirty days from going out of business."

The story that Jensen Huang tells about himself — and he is a man who controls his narrative with the precision of a chip architect routing transistors — begins not in triumph but in displacement. He was born Jen-Hsun Huang on February 17, 1963, in Tainan, the historic capital of southern Taiwan, 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 arrangement they struck — Jensen taught him to read; in exchange, the older boy taught Jensen to bench-press — carries the fairy-tale logic of a fable, except that Huang ended up doing a hundred pushups every night before bed, a habit that suggests not enchantment but survival.

Because Huang was too young for the academy's classes, he attended a nearby public school, where he befriended Ben Bays. Bays grew up with five siblings in an old house with no running water — one of Kentucky's tobacco-farming hollers, where most kids were poor and the vocabulary for describing a small Asian immigrant with long hair and heavily accented English had exactly one word. "The way you described Chinese people back then was 'Chinks,'" Huang told the New Yorker's Stephen Witt, with no apparent emotion. "We were called that every day."

To get to school, Huang crossed a swinging footbridge over a river — old planks, most of them missing, suspended by ropes high above the water. Local boys would grab the ropes when Jensen was crossing and try to shake him off. "Somehow it never seemed to affect him," Bays recalled. "He just shook it off." By the end of the school year, the boys who had tried to dislodge him were following him on adventures into the woods. Bays remembered how carefully Jensen stepped around the missing planks. "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, in his cosmology, 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."

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 with a ferocity that reads, in retrospect, as the energy of a boy who'd survived a reform school and was determined to outrun every disadvantage of his biography. 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 majored in electrical engineering. His lab partner in his introductory classes was Lori Mills — earnest, nerdy, with curly brown hair, one of approximately three women among two hundred and fifty electrical-engineering students. Competition among the male undergraduates for Mills's attention was fierce, and Huang felt himself at a disadvantage. "I was the youngest kid in the class," he said. "I looked like I was about twelve."

His strategy was characteristically systematic. Every weekend, he called Mills and pestered 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 became a speakeasy proprietor, and Madison, who went into hospitality, before both, after years of what Huang calls "paternal browbeating," joined Nvidia.

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.

On April 5, 1993 — Huang's thirtieth birthday — the three men sat 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 selected the Denny's because it was quieter than home and had cheap coffee, but Huang had a deeper affinity for the chain. He'd worked at one in Oregon during the nineteen-eighties — first as a dishwasher, then as a busboy. "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."

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 embedded itself in the company's psyche like a constitutional amendment. For years afterward, Huang opened staff presentations with the words "Our company is thirty days from going out of business." The phrase remains the unofficial corporate motto.

To understand what Nvidia actually makes — and why it matters — requires a brief excursion into the architecture of computing. In standard computer architecture, a microchip called a central processing unit does most of the work. Coders write programs; the C.P.U. processes one solution at a time. For decades, the dominant C.P.U. manufacturer was Intel, and Intel's approach — serial processing, one task after another — defined the industry's physics. Nvidia embraced an alternative. Its graphics-processing unit breaks complex mathematical tasks into small calculations and processes them simultaneously, a method known as parallel computing. A C.P.U. functions like a delivery truck, dropping off one package at a time. A G.P.U. is a fleet of motorcycles spreading across a city.

In 1999, shortly after going public, Nvidia introduced a graphics card called GeForce. Dan Vivoli, the company's head of marketing, called it a "graphics-processing unit" — coining the term. "We invented the category so we could be the leader in it," Vivoli said, articulating a principle Huang would deploy repeatedly over three decades: if the market doesn't exist, create it.

GeForce's popularity was driven by the Quake video-game series, whose grenade-launching, monster-rendering gameplay demanded exactly the kind of parallel-computing horsepower that Nvidia's cards provided. PC gamers, perpetually seeking an edge in deathmatch mode, bought new GeForce cards with every upgrade cycle. The business was lucrative, but Huang saw something beyond gaming. In 2000, Ian Buck, a graduate student at Stanford studying computer graphics — intense, balding, radiating intelligence, the kind of computer-science hot-rodder who would spend two decades pushing chips to their limits — chained thirty-two GeForce cards together to play Quake on eight projectors. "It was the first gaming rig in 8K resolution, and it took up an entire wall," Buck said. "It was beautiful."

Then Buck asked the question that would change the trajectory of computing: What if these graphics cards could be used for something other than launching grenades at friends? With a DARPA grant, he hacked the cards' primitive programming tools to access the parallel-computing circuits beneath, effectively repurposing the GeForce into a low-budget supercomputer. Soon, Buck was working for Huang.

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: enable CUDA to work on every GeForce card, effectively democratizing supercomputing. "We were democratizing supercomputing," he said, deploying the verb with the conviction of a man who does not use words loosely.

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 — a term Huang coined to describe 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. Downloads of CUDA peaked in 2009, then fell for three consecutive 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 cited a visit to the office of Ting-Wai Chiu, a professor of physics at National Taiwan University, as sustaining his conviction. Chiu was attempting to simulate the evolution of matter following the Big Bang. He had constructed a homemade supercomputer in a laboratory adjacent to his office — GeForce boxes littering the floor, the machine cooled by oscillating desk fans. "Jensen is a visionary," Chiu said. "He made my life's work possible." Chiu was the model customer. The problem was that there weren't many like him.

Nvidia marketed CUDA to stock traders, oil prospectors, molecular biologists. At one point, the company signed a deal with General Mills to simulate the thermal physics of cooking frozen pizza. One application that Nvidia spent almost no time thinking about was artificial intelligence. There didn't seem to be much of a market.

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 described the researchers who persisted as "prophets in the wilderness." Chief among them was Geoffrey Hinton, a professor at the University of Toronto — a British-born cognitive scientist who had spent decades arguing that layered neural networks could learn to represent the world, largely to polite academic indifference. In 2009, Hinton's research group used Nvidia's CUDA platform to train a neural network to recognize human speech. He was surprised by the results, which he presented at a conference. Then he reached out to Nvidia. "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 said. "They said no."

Despite the snub, Hinton encouraged his students to use CUDA. One of them was Alex Krizhevsky — Ukrainian-born, possibly the finest programmer Hinton had ever encountered. In 2012, Krizhevsky and his research partner, Ilya Sutskever, bought two GeForce cards from Amazon on a tight budget. Krizhevsky began training a visual-recognition neural network on Nvidia's parallel-computing platform, feeding it millions of images in a single week. "He had the two G.P.U. boards whirring in his bedroom," Hinton recalled. "Actually, it was his parents who paid for the quite considerable electricity costs."

What Krizhevsky built can now be mentioned alongside the Wright Flyer and the Edison bulb. AlexNet, the neural network he trained in his parents' house, entered the annual ImageNet visual-recognition contest in 2012. Neural networks were unpopular enough that Krizhevsky was the only contestant to use the technique. AlexNet scored so well that the organizers initially wondered if he had somehow cheated. "That was a kind of Big Bang moment," Hinton said. "That was the paradigm shift."

AlexNet's nine-page architecture paper has been cited more than a hundred thousand times, making it one of the most important documents in the history of computer science. Krizhevsky pioneered several key programming techniques, but the central finding was stark: a specialized G.P.U. could train neural networks up to a hundred times 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. Because a Ukrainian programmer in his parents' bedroom needed two graphics cards and a software platform, and both happened to exist.

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 metastasized. 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.

Nvidia's headquarters in Santa Clara consists of two enormous buildings, each in the shape of a triangle with its corners trimmed — a shape replicated in miniature throughout the interiors, from the couches and carpets to the splash guards in the urinals. Employees call them "spaceships." The buildings are cavernous, filled with light, and largely empty post-Covid. Underneath the north-campus bar, in windowless laboratories, pallid young quality-control technicians wearing earplugs push microchips to the brink of failure amid a constant whine of high-pitched fans trying to cool overheating silicon. It is these chips that have made the A.I. revolution possible.

Huang governs this empire through a management style that defies every Silicon Valley convention. He has approximately sixty direct reports. He holds no regular one-on-one meetings. There are no fixed divisions or hierarchy. Instead, employees submit a weekly list of the five most important things they are working on, and Huang surveys these e-mails late into the night. He communicates by writing hundreds of e-mails per day, often only a few words long. One executive compared the e-mails to haiku. Another compared them 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.

Wandering through Nvidia's campus, Huang often stops at the desks of junior employees and quizzes them on their work. A visit from Huang can transform a cubicle into an interrogation chamber. "Typically, in Silicon Valley, you can get away with fudging it," the industry analyst Hans Mosesmann said. "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 returned. "Jensen is not an easy person to get along with all of the time," he said. "I've been afraid of Jensen sometimes, but I also know that he loves me."

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. At Stanford, he described his philosophy of sharing his thought process: "If you send me something and you want my input on it, and I can be of service to you, and in my review of it, share with you how I reasoned through it, I've made a contribution to you. I've made it possible to see how I reason through something." 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.

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 second only to Huang as the most successful semiconductor chief executive 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 — a ratio that attracts competition the way chum attracts sharks. 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."

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."

Inspired by a V.R.-architecture experiment in which he'd strapped his building's architect, Hao Ko, into a virtual-reality headset connected to a rack of G.P.U.s to simulate the flow of light across Nvidia's headquarters, the Omniverse is Nvidia's attempt to simulate the physical world at extraordinary levels of fine-grained detail. Since 2018, Nvidia's graphics cards have featured "ray-tracing," which replicates the way light bounces off objects to create photorealistic effects. Inside Nvidia's executive meeting center, a product-demo specialist showed a three-dimensional rendering of a gleaming Japanese ramen shop — light reflecting off the metal counter, steam rising from bubbling broth — with nothing to indicate it wasn't real. Then he demonstrated "Diane," a hyper-realistic digital avatar that speaks five languages. The imperfections were the most affecting: blackheads on her nose, trace hairs on her upper lip. The only clue that Diane wasn't human was an uncanny shimmer in the whites of her eyes. "We're working on that," the specialist said.

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 the kind of vision that makes even hardened technologists dizzy. But inside Nvidia's spaceships, the executives building this Manhattan Project of computer science responded to existential questions with the equanimity of appliance salesmen. When a journalist wondered aloud if an A.I. might someday kill someone, Catanzaro said: "Eh, electricity kills people every year." When he wondered if it might eliminate art, Diercks replied: "It will make art better! It will make you much better at your job." In May 2023, hundreds of industry leaders had endorsed a statement equating the risk of runaway A.I. with that of 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."

At sixty-two, Jensen Huang is compact, polished, and known among colleagues for a quick temper and visionary leadership — traits that coexist without apparent tension. He enters a room looking, as TIME described him in 2025 when naming him among the Architects of A.I. for its Person of the Year cover, as though he might erupt or collapse. Then someone puts on music and he dons the trademark black leather jacket and appears to transform — not just the uniform but the body language and optimism of a man who genuinely believes he is building the most impactful technology the world has ever known.

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 contradictions are not affectations. They are the syntax of a man who learned, on a swinging bridge in Kentucky, that survival requires both absolute self-reliance and the performance of nonchalance.

He works seven days a week. He is either working or thinking about work every waking moment, he told Stripe CEO Patrick Collison. He reads the weekly reports of thousands of employees. He writes hundreds of e-mails a day. 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 six 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. President Trump, who had become a regular late-night phone partner, told him: "You're taking over the world, Jensen." 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 thirty-two years, 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.

Back in Kentucky, on the swinging bridge above the river, the planks were rotting and the gaps were wide and the local boys were shaking the ropes, and a nine-year-old immigrant with no pocketknife stepped carefully around the missing boards. Bays, watching from the bank, noticed something he didn't expect. Jensen wasn't just surviving the crossing. He was studying the bridge.