Prologue: a bedroom in Toronto
A few days ago, I read a post from a well-known community manager. Once again: Nvidia, $6 trillion. Once again, the big number, the VC jargon, the hype. And not a word on how the company actually got there.
So I went looking for the story behind the number. What I found is the opposite of the hype. The largest AI company in the world, the largest market cap on the planet, was built on human instinct, human innovation and human intent.
It starts in a bedroom.
Toronto, 2012. A family house like any other. Upstairs, in the bedroom where he grew up, a graduate student named Alex Krizhevsky leaves a computer running day and night.
Inside the case, two graphics cards. GeForce GTX 580, the kind teenagers buy to play games, about $500 each. For five or six days, they grind through more than a million images, again and again.
On September 30, the results of ImageNet come out. It is the world’s reference contest for image recognition, and the big labs compete in it. Krizhevsky’s model, built with his fellow student Ilya Sutskever and their professor Geoffrey Hinton, wins by a wide margin. The paper will be cited more than 172,000 times.
That autumn, modern AI was born in a bedroom. On a machine built for video games.
Ask how that was possible, and you pull a thread. It runs back through a Stanford lab, a letter mailed to Taiwan, a humbling flight to Japan and a booth in a San Jose diner. It runs forward to a company worth close to $6 trillion.
This is the story of that thread, told the way a seller reads a company. The bets, the offer, the buyers, the money and the timing.
Chapter 1: six months to live
The thread starts in 1993, at a Denny’s on the corner of Capitol and Berryessa in East San Jose. Jensen Huang knows the place. He washed dishes at Denny’s, bused tables, waited on customers. He likes the bottomless coffee.
In a booth, with two engineers from Sun Microsystems, Chris Malachowsky and Curtis Priem, he decides to start a company. The idea: chips that bring 3D graphics to ordinary PCs. The market for it? Sequoia’s Mark Stevens, who would join the board, says any research firm would have sized it at zero. They start with $40,000 in the bank, then raise $20 million from Sequoia, Sutter Hill and others.
Their first chip, the NV1, ships in 1995. It fails. Nvidia had chosen its own way of drawing 3D shapes, and then Microsoft set the standard for PC games and chose another. Brilliant engineering, wrong side of history. Huang would later call the chip technically poor.
Worse, Sega had hired Nvidia to build the next chip for its console, on the same flawed design. Nvidia had cut half its staff. The cash was running out.
So in 1996, Huang gets on a plane to Japan. Picture the scene. A young CEO walks into the office of his most important client. He tells them the product is wrong. He tells them to cancel the contract. And he tells them that Nvidia still needs the last $5 million payment, or it will not survive.
Every sales instinct screams not to do this. You protect the deal. You manage the message. You buy time.
Huang told the truth. And Sega’s American CEO, Shoichiro Irimajiri, convinced his company to pay and to invest. Huang later said that generosity gave Nvidia six months to live.
Six months. Nvidia put everything into one chip, the RIVA 128, built in record time and launched in 1997. A million units in four months. Twenty percent of the PC graphics market. The first quarterly profit. Sega would later sell its Nvidia shares for $15 million. Three times its money, for the price of believing a man who admitted he was wrong.
The first lesson of this story is the oldest one in sales. When you are truthful and direct, even with bad news, you give the client a reason to bet on you.
Chapter 2: a letter to Hsinchu
To understand the second bet, you need to know what a chip is.
A chip is a slab of silicon carrying billions of microscopic switches, the transistors. Each one is either on or off, 1 or 0. Think of a city with billions of traffic lights. Designing a chip means drawing the city: where the roads go, how the lights are timed, so data flows to the right place, fast. Manufacturing it means actually building that city, with roads thousands of times thinner than a hair. Only a handful of companies on Earth can do the second part.
Nvidia knew how to draw cities. It had no way to build them. A chip factory costs billions. Nvidia had about 50 to 60 employees.
The answer was in Taiwan. TSMC, founded by Morris Chang, did one thing only: build chips designed by others. Nvidia contacted TSMC’s office in San Jose. Nobody took the small startup seriously.
So in 1997, Huang did what founders do when the front door is closed. He wrote to the top. A letter, by regular mail, to Morris Chang himself. It reached Chang in Hsinchu, Taiwan. A small company, it said, with some very promising chips. Would he please call.
The letter itself has never been published. What we have is Chang’s memory of it, in his memoir and in a rare interview. He remembers being curious, and a little irritated that his own team had ignored a customer. On a trip to California, he picked up the phone and dialed the number printed on the letterhead.
Huang answered himself. Behind him, a noisy room, a heated discussion. Chang introduced himself. And Huang shouted to his team: “Quiet, Morris Chang is calling me”.
At their first meeting, Huang made his pitch. Nvidia is in financial trouble. But this chip will save the company, and make us one of your biggest customers. Chang found it a bold statement. Within two to three years, Nvidia was one of TSMC’s top five clients. In 1998, when Nvidia was short of hands, TSMC sent two of its production planners to live inside Nvidia for a month.
That letter solved the hardest equation in the business: how to build the most complex objects on Earth without owning a single factory. It set Nvidia’s model for thirty years. Nvidia draws. TSMC builds. Every dollar Nvidia raised could go into design, software and people.
Sixteen years later, in 2013, Chang tried to hire Huang as TSMC’s CEO. Huang said no within ten minutes. He already had a job.
Chapter 3: developers, developers, developers
Around 2000, Steve Ballmer jumped across a Microsoft stage chanting one word, over and over: developers. People laughed. I spent years selling to developers, at Borland and at Microsoft, and I never laughed. Ballmer was right. Whoever wins the developers wins the platform.
That same year, at Stanford, a PhD student named Ian Buck was playing games. Seriously. He wired 32 GeForce cards together to run Quake in 8K. Then a question took hold of him. If these chips can do that much math for a game, why not for anything else?
Here is why the question made sense. Nvidia’s chips were built very differently from the processor in your laptop, the CPU. A CPU is a few brilliant generalists. Think of four star chefs who can cook any recipe, four plates at a time. A graphics chip, the GPU, is thousands of simple workers doing the same small task at once. A thousand line cooks who can only chop onions. Useless for a soufflé. Unbeatable for a mountain of onions.
A video game is a mountain of onions. A million pixels, redrawn sixty times a second, each needing the same small calculation. Underneath, that calculation is just math: multiplying large grids of numbers. Buck saw that science was full of the same math.
The problem was language. A chip only understands its own machine language, simple orders in 1s and 0s. Developers write in human-readable languages, and a translator, the compiler, converts their code for the chip. For graphics chips, the only languages available spoke “graphics.” Almost no scientist did.
So Buck built Brook, a way to write ordinary code for a graphics card. It became his thesis. Nvidia hired him in 2004 and paired him with its architect John Nickolls. Together they turned Brook into CUDA, released in early 2007. CUDA let any engineer who knew C, the most common language in engineering, put thousands of line cooks to work. Buck’s lesson from the field was simple: nobody wants to learn a new language.
Then came the go-to-market decision that changed everything. CUDA was free. And Nvidia put it on its chips across the line, gaming cards included. Every teenager’s gaming PC became a small supercomputer, and every student a future customer. From day one, Buck also ran a team building ready-made libraries, so a researcher never started from a blank page. His priority order: “Scientists first, programmers a very distant second”.
For years, the paying market was a small community of scientists. Who paid for the bet? Gamers. Huang called gaming the flywheel that funded huge R&D. Training followed, and universities started teaching CUDA. Nvidia’s Deep Learning Institute alone has trained hundreds of thousands of developers. Today, estimates of the CUDA community run from about 4 million to 6 million developers.
Ballmer chanted it. Nvidia built it, quietly, one scientist at a time.
Five years later, the thread pulls tight, in a bedroom in Toronto.
Before AlexNet, Krizhevsky had already written his own CUDA code to train neural networks on Nvidia cards. He was one of those students. Free software, a gaming card he could afford, libraries already there. Ian Buck’s community, five years after its birth, produced the moment that would change everything.
Training an AI model, it turns out, is the biggest mountain of onions ever seen. The same multiplication of large grids of numbers, repeated billions of times. Graphics was always math. AI is the same math, at a scale nobody had imagined.
Was it luck? Partly, and Huang says so. Had the team in Toronto been using AMD cards, he admits, the story might have gone differently.
But luck needs somewhere to land. In 2012, only one company had spent five years making its chips easy for scientists to use, and putting them in every student’s hands. In my reading, CUDA is the real reason the world was ready for AI. The bedroom was the spark. The community was the fuel.
In 2012, Nvidia was worth about $7 billion. Still, on paper, a gaming company.
Chapter 4: never stop at the current model
Most companies, handed a winning product, protect it. Nvidia kept climbing. Four stages, three leaps, each one selling a bigger problem solved, to a more senior buyer, at a higher price.
From chip to box. Huang dates the shift to deep learning to 2012. In 2016 it became a product: the DGX-1, a complete AI computer at about $300,000, pitched as the power of 250 servers in one box. A chip is a part an engineer chooses. A box is a solution an executive signs for.
In August 2016, Huang carried the first unit himself to a small nonprofit lab in San Francisco called OpenAI. He signed it in marker for the team. A small honesty note, in the spirit of this series: Huang now tells it as a launch nobody ordered. An April 2016 email to Elon Musk, filed in court, shows him writing at the time that demand was coming from everywhere. Founders polish their legends. Documents keep the dates.
From box to network. AI doesn’t run on one box. It runs on thousands of them talking to each other, and the connections between them become the bottleneck. In March 2019, Nvidia bought Mellanox, an Israeli networking company, for $6.9 billion in cash, outbidding Intel. It was almost half of Nvidia’s annual revenue and nearly all its cash. Huang said the plan out loud: from a chip company, to a server company, to a data-center-scale company. Mellanox made about $1 billion that year. In fiscal 2026, Nvidia’s networking business made more than $31 billion.
From network to AI factory. Then, in November 2022, ChatGPT. The world suddenly wanted what Nvidia had spent a decade assembling. Since then, its data center business has grown nearly 13 times. Nvidia now sells “AI factories”: entire racks of chips, networking and software that turn electricity into answers. Its newest platform, Vera Rubin, is in full production at CoreWeave, Google Cloud, Microsoft Azure, Oracle Cloud and Nebius.
The gaming company is a memory. Data center is now 92.7% of sales. Revenue went from $60.9 billion in fiscal 2024 to $216 billion in fiscal 2026.
Chapter 5: many rivers to the sea
Success brought a danger every seller recognizes. A few very large accounts.
The big cloud providers, the Microsofts, Googles, Amazons and Metas of the world, represent about half of Nvidia’s revenue. In fiscal 2026, one direct customer alone accounted for 22% of revenue, another for 14%. And those same giants are designing their own AI chips. Your biggest clients slowly becoming your competitors: I have seen that movie in software, and it rarely ends well for the vendor who waits.
Nvidia didn’t wait. It opened new rivers, each with its own kind of buyer:
AI clouds. The “neoclouds” like CoreWeave and Nebius, which rent Nvidia computing by the hour to anyone who needs it. AI cloud revenue alone tripled year over year in early fiscal 2027.
Frontier AI partnerships. The labs building the most advanced models, OpenAI, Anthropic, xAI and others. Nvidia sells to them, works with them on future chips, and often invests in them.
Governments. Nvidia calls it sovereign AI: countries building their own national AI infrastructure. More than $30 billion in fiscal 2026, triple the year before, led by Canada, France, the Netherlands, Singapore and the UK. A whole new buyer persona: a nation.
Enterprise and industry. The long, patient business every platform eventually needs. Nvidia groups AI clouds, industry and enterprise together, and that group made $37 billion in a single quarter in early fiscal 2027.
Four rivers, four sales motions, four kinds of conversation. A cloud buyer counts cost per hour. A frontier lab wants the next chip before anyone else. A minister talks about national sovereignty. An enterprise wants proof it works for its business. Same company, very different pitches.
Chapter 6: lending your client the money to buy
Follow the money through this story and it turns completely around.
In 1993, Nvidia lived on $40,000 and $20 million of venture capital. In 1996, it lived on $5 million from a client who believed it. For the next two decades, gamers paid for its future.
Today Nvidia is the one writing the checks. In fiscal 2026, it invested $17.5 billion in private companies and infrastructure funds, including AI model makers that buy its products directly or through the clouds. In the first months of 2026 alone it committed more than $40 billion, including $30 billion in OpenAI and another $2 billion in CoreWeave. At the same time, it returned $41.1 billion to its own shareholders.
In plain sales terms: I help my clients afford my product, so they can grow, and buy more of my product.
Critics call it circular financing. Nvidia funds the buyer, the buyer buys Nvidia, and the revenue looks bigger than the real demand. Huang answers that these investments are a small share of what those companies must raise elsewhere.
I have built partner programs most of my career. Funding the ecosystem to speed up adoption is one of the oldest plays in the book. What is new is the scale, and the flexibility: equity in one place, help securing land and power for a cloud partner in another. At $40 billion, it is a growth engine. It also ties Nvidia’s health to the health of the clients it finances. Both are true.
Epilogue: a story of bets
Pull the whole thread and you find people. Human instinct: a CEO who chose the truth over the deal. Human innovation: a gamer at Stanford who wanted Quake in 8K, and a company that gave his idea to every student on Earth. Human intent: years of betting on developers before anyone paid for it.
What I keep from it:
The community was the real bet. Ian Buck’s CUDA, given away to every gamer and student, is what made a bedroom in Toronto possible. AI found Nvidia ready because Nvidia had spent years winning developers.
Solve the hardest equation with a partner. One letter to Hsinchu turned the impossible economics of chip factories into a thirty-year partnership.
Truth is a survival skill. A CEO flew to Japan to tell his biggest client the product was wrong. The client paid, invested, and tripled its money.
Never stop at the current model. Chip, box, network, AI factory. Each leap sold a bigger answer to a bigger buyer.
Open new rivers before the old one dries. AI clouds, frontier labs, governments, enterprises: four buyers, four pitches, one platform.
Be flexible with money. Help your clients afford you, so they can grow and buy more. Know the risk that comes with it.
Why I tell these stories
Markets tell this story as a stock chart. I read it as a story of human intent, human purpose and human innovation. That is what my work is about.
That is how I look at every company I work with, through three lenses. Commercial intelligence: what are you really selling, to whom, through which channels, and where is the money? A sense of human innovation: who are the people and ideas inside the company that can carry the next offer, the way Ian Buck carried CUDA? A sense of intent: what are you deciding to do, and when, while the market is still moving?
Most companies are not Nvidia. They face the same questions at their own scale, every quarter. That is the work I do, and this series is where I will keep pulling the threads behind the numbers.
If you want to pull the threads behind your own numbers, let’s talk. Book a conversation with me here: [YOUR CALENDLY LINK]