The Story of Nvidia is the Story of America’s Future
During a recent visit to the Nvidia headquarters in Santa Clara, California, I fondly recalled another revelatory journey I’d taken out West, many years ago. My wife and I had gone on separate trips, her to Mexico and me to San Francisco, and we had decided that before returning home, we would meet for a few days in a place she’d heard was stunningly beautiful. So it was that we experienced the magnificent, soaring red rocks of Sedona, Arizona for the first time. We were so moved by the grandeur of the landscape that we’ve returned regularly ever since.
As I gazed on those exquisitely sculptured rock formations, set against the vastness of the Arizona sky, I thought once again, as so many times before, of what great fortune I’d had as a young immigrant to be welcomed into this country with its remarkable endowments—those being both of such natural splendor and of the unparalleled opportunity to chase one’s dreams.
That first thrilling glimpse of Sedona flashed through my mind when I arrived at the shimmering new Nvidia headquarters. The building is an architectural wonder, designed with a soaring glass-paneled roof to maximize the natural light flooding into workspaces, and is 100 percent powered by renewable energy. The headquarters complex is aptly named Voyager, given that Nvidia has been on a truly astonishing journey since its founding—a journey that, until recently, I knew almost nothing about.
For many of us, the recent skyrocketing market value of Nvidia came out of left field. Since January 2020, the price per share has surged from $8 to over $200 as of this writing, or an increase of about 2,300 percent. While we may know that the surge is due to the sudden advent of generative AI (GenAI), the story of why Nvidia was poised to vastly outperform its competitors, grabbing an estimated 75 to 90 percent of the AI chip market, is one of great importance for America’s future. It’s a story of the combination of a brilliant long-term strategic vision, a culture that inspires and respects reaching for breakthrough achievement, and a deep commitment to research investment rather than short-term pursuit of shareholder value. It is also a story that epitomizes how the foundational American principles of freedom of opportunity and the pursuit of happiness our nation has just celebrated have allowed so many individuals who started with so little to live the American dream, and in doing so, to advance the prospects of the whole country.
I began learning the story when I was invited to Nvidia’s headquarters as part of a group from the Council on Foreign Relations, of which I’m a member. The Council has a keen interest in continued U.S. leadership in the development of AI, which is vital to the country’s security and prosperity, and indeed to global prosperity and security. What we heard about the reasons for Nvidia’s success, and about its current commitments to innovation, was both reassuring about America’s advantages in the AI race and a stark reminder that we must rigorously protect the rights and access to opportunity that made Nvidia possible.
I’ll start with the story of Nvidia co-founder and longtime CEO Jensen Huang. He is a fellow immigrant, and in a recent interview with former Secretary of State Condoleezza Rice, he said that his success “could only have happened in America” and further added, “I am the embodiment of the American dream.”
Huang was born in Taiwan, and when he was 9-years-old, in 1973, he was sent by his parents to live in the U.S., along with his 10-year-old brother. So important was it to his parents that their sons come here that they sent the boys on the journey by themselves, at such young ages, because the parents couldn’t afford to make the trip at that time. Huang’s father, who was a chemical engineer, had become determined to someday move to the U.S. when he’d come in the 1960s to train at the Carrier air conditioning company in New York City. The decision to send the boys was precipitated by unrest in Asia. The family had moved from Taiwan to Thailand, in search of better economic prospects, and when the Vietnam War led to turmoil in Thailand, and then a coup, the parents decided that, wrenching as it was, they had to send the boys on their own for their safety. For his part, Huang has recounted how thrilling arriving in the U.S. was.
The boys initially lived with an uncle in Tacoma, Washington, and Huang recalled to Rice his astonishment upon entering his uncle’s home and walking on a carpeted floor for the first time. It felt like “walking on a bed with your shoes on,” he said. He marveled also at a restaurant that looked like a spaceship and served meals in a box, which was a vintage-era McDonald’s. The uncle sent the boys to a private school in rural Kentucky, population 900, because he’d read the school welcomed foreigners. Despite that, initially, as the only Asian children in town, they were taunted by some fellow students. But before long, they thrived.
Their parents immigrated two years after they’d sent their sons here, arriving with very little money, and carrying all their possessions in their suitcases. Huang’s mother, who had been a schoolteacher, took a job as a maid at a school while his father searched for work, eventually getting a job at an engineering consulting firm and moving the family to a town outside Portland, Oregon. Jensen Huang started working when he was fifteen, at a Denny’s as a dishwasher and busboy, working the late-night shift, which he continued until he graduated from college. He was a brilliant student, skipping two grades and graduating from high school at sixteen. Choosing to attend Oregon State University for his undergraduate degree, in electrical engineering, because of the low cost, he then got a job at a company that would become one of Nvidia’s main competitors, Advanced Micro Devices (AMD). Helped by a program at AMD that paid for graduate education at Stanford for employees who fit the bill, he started night classes there. It took him eight years to get his master’s. The year after he received it, he took the giant leap of teaming up with two engineers, Chris Malachowsky and Curtis Priem, to start Nvidia. The three held all their meetings to devise their plans for the company at a Denny’s because Huang loved the company and felt so loyal to it.
Of his rise to such extraordinary success within one generation, Huang shared with Condi Rice, “My parents had nothing... the jobs I had, the schools I went to, the opportunities I had, that can’t possibly happen anywhere else.” All of us in the U.S. should be enormously grateful that it did, not only because of the contributions Huang and the team at Nvidia have made to the country’s current dominance in AI, but due to how Nvidia’s technology has facilitated the application of AI to a vast range of heretofore intractable problems, from dramatically reducing the time and cost for discovery of new drugs, to vastly improving forecasting of severe weather, to creating new materials for making highly energy-efficient batteries. And Nvidia has great plans for the future.
Our host at the headquarters was Eric Breckenfeld, the company’s Director of Technology Policy. He shared with us the long-term strategy that has catapulted Nvidia and gave us a window into the company’s newest bold initiatives, which include partnership with Mercedes-Benz in development of self-driving vehicle technology and the creation of a quantum computing chip. I followed up with Eric to dig a little deeper into the reasons for Nvidia’s success and get his thoughts about the future for AI and risks we must be addressing.
Eric explained that the dominant story about Nvidia—that the company first set out to become the leader in making chips for computer gaming and then made a hard pivot to creating AI chips—is something of a misunderstanding. The current breakout success is, instead, exactly in keeping with the original vision that propelled Huang and his co-founders to start the company, and Eric emphasized how consistently that vision has driven bold moves the company has made.
Huang and his partners perceived that in order to solve the most difficult problems computing could be used to tackle, a whole new type of chip, which would be much more powerful, would be needed. At the time they started the company, in 1993, the PC era was just getting going. Indicative of just how imaginative, and bold, they were in formulating their aims for the company, at the time they made the commitment, none of them had actually even seen a PC yet. They were all working on mainframe computer technology.
All computers were then running on a single type of computer chip, the Central Processing Unit (CPU), which is only capable of linear problem solving, moving one step at a time. The Nvidia founders foresaw that in order to solve many of the most important problems, the hardest problems, a chip design that would allow for much faster computing would be needed, a type of chip able to do a whole different type of computing: parallel processing. As opposed to the linear processing of the CPU, parallel processing allows for solving on many fronts all at once. The audacity of their commitment to designing such a radically different type of chip is astonishing. In fact, they were so ahead of the curve that there was no market for their first chips.
While they pulled off a stunning engineering feat by creating their first chip in just eighteen months, they found no takers. Undeterred, they quickly designed a second chip that would be easier to work with, but it also flopped. And herein lies a great lesson of their ultimate success. As Huang recalls, in the face of those failures, and with very little funding left, they decided “we have to figure out what is the right path long term and work our way back.” That involved an arduous journey. Co-founder Chris Malachowsky has said, “Everyone wants to know where we came from... this overnight success was thirty years in the making.” As Huang vividly put it, “We suffered our way here.”
The founders had learned that they had to design their chips for an existing market, and that’s what led them to create chips for computer gaming, which was beginning to take off. Due to customer demand, gaming companies desperately needed to create much more impressive graphics. That was a terribly difficult problem to solve, but Nvidia dug into it and succeeded by creating the GPU chip, which stands for graphics processing unit, using parallel processing design. Gaming designers leapt at the offering, and Nvidia chips drove a huge boom in gaming.
The company’s core strategy became, as Eric Breckenfeld explained, “solving computational problems that no one else knows how to solve.” That is the mission that the company’s engineers have been told to pursue, and it’s central to the culture that has continuously propelled Nvidia to new heights. The company has been able to attract—and keep—top talent, despite fierce competition from tech titans that were much more cash-rich, because, as Eric says, the engineers “feel they are doing their life’s work.” He shared that the average senior director or higher-level executive has been with the company for at least fifteen years, highlighting that due to the skyrocketing of Nvidia’s stock, they could retire as multimillionaires. But there is so much stimulating, and important, work still for them to be doing, which includes the aforementioned partnership with Mercedes-Benz to develop self-driving technology and developing a quantum computing chip.
Also crucial to the culture, and to Nvidia’s current AI dominance, is another principle of Nvidia’s leadership I greatly admire: soliciting, and actually respecting, input from those lower in the hierarchy who have insights from the frontlines. In 2004, Jensen Huang hired a Stanford PhD graduate, Ian Buck, who had interned at Nvidia. At Stanford, Buck had co-written a computer language that allowed Nvidia’s GPU chips to be used for broad-ranging computation in addition to their applications to graphics. Huang heard about the work and provided funding to Buck, and then brought him in to help create a pathbreaking computing platform, called CUDA, which allowed developers working in all sorts of fields to use software Nvidia had created for CUDA to specialize Nvidia’s chips for their purposes, whether that was for mathematics research, genomics, or, as fate would have it, AI. The founders’ original vision of creating chips to solve the most difficult problems was beginning to come to fruition.
CUDA launched in 2006, and two years later, Huang was again all ears when an employee, engineer Bryan Catanzaro, came to him with the word that several of the world’s pioneers in AI research had begun tailoring Nvidia’s chips to create a whole new type of AI system. This work was in creating a new type of neural network, which are complex computing algorithms inspired by the neural processing of the human brain. They require chips capable of parallel processing and massive computing power, and they’re the fundamental building blocks of the machine learning that has enabled the large language models of GenAIg. Catanzaro foresaw that these new neural networks would empower a long-awaited but repeatedly thwarted major breakthrough in AI, and the AI business would boom. He asked to speak to Jensen Huang privately and made the case that Nvidia should invest heavily in creating chips, as well as software, specifically for this barely emerging new era of AI. Huang agreed, and Nvidia began developing a range of specialized products to enable the advancement of AI research. That decision in 2008 is what led to the surge in Nvidia’s business in 2020, the year GenAI was released to the public.
The long-term commitment, and massive financial investment, made in initially developing CUDA and then focusing on creating more specialized offerings for AI developers are astonishing. That’s especially true considering that Wall Street pummeled the company’s share price in the course of those years. Nvidia reportedly devoted $1 billion to the creation of CUDA and a total of $12 billion in R&D between 2006 and 2017, during which time the market for CUDA offerings was quite small. The company’s market valuation tanked from $8 billion to $1.5 billion. But the company held firm, because, as Huang said recently, “I deeply believed in the mission.”
Another impressive commitment Nvidia made to long-term investment in R&D is exemplary of the importance of business and government partnering to solve the biggest problems the country, and the world, are facing, which I’ve written about previously. The company worked with the U.S. Department of Energy for ten years on creating the technology to power exascale supercomputers, which are, as the DOE says, “the next milestone in the development of supercomputers.” They’re able to process information much faster, which “will give scientists a new tool for addressing some of the biggest challenges facing our world, from climate change to understanding cancer to designing new kinds of materials.” Eric Breckenfeld shared that Nvidia made little to no profit for this work, but that it was seen as a fantastic investment because “we needed to gain that internal expertise to be able to deploy the now highly profitable AI infrastructure at the scale”—which they’re now doing.
As Jensen Huang stressed in the interview with Condi Rice, Nvidia’s success is not a matter, though, only of vision, culture, and long-term commitments. The founding of the company was only possible, he points out, due to freedoms the U.S. guarantees, and the tailwinds the U.S. provides innovators, as he put it. Founders with breakout ideas can rely on “laws that are understandable and that you can count on,” Huang said, and a “business environment where people are playing by the rules.” The freedoms and the legal protections are great drivers, he emphasized, of the willingness of so many immigrants to risk so much in leaving their home countries to come here to pursue entrepreneurial dreams. “The entrepreneurial spirit and the immigrant spirit are rather similar,” he said, adding, “You want to work hard because you are desperate to succeed. My feelings about Nvidia and my constant desperation to do better are exactly the same feelings my parents had” when deciding to risk all they had to forge a life here.
Huang also beautifully articulated the role of the nation’s founding principles not only in making it the haven it became but in continuing to provide opportunity and fuel innovation in the future. When Rice asked him what he would say to her students at Stanford who are disillusioned with the state of the country and their prospects for the future, he responded, “I would always go back to first principles… America was founded on first principles,” continuing to say, “the American dream has a core foundation…pillars that keep it up.” As he was suggesting, we must protect those pillars if we are to continue to lead the world not only in innovation, but in providing transformative opportunities for both those born here and all those from abroad aspiring to build new lives here.
As I’ll delve into in my next post, about the present and future of AI, we have remarkable opportunities for unprecedented problem solving with the technology, but we must assure that we apply our founding principles to affording those opportunities to all Americans, including new arrivals.
For now, I’ll leave you with a message of hope. The story of Nvidia has profound lessons for the future of America. Yes, for the past couple of years, we have witnessed a deviation from our path in pursuit of fulfilling the foundational principles and nurturing the national culture that has produced Nvidia, and so many other great companies, and a country of people hungry for success that goes hand in hand with purpose, meaning, and justice. But this country that opened its arms to Jensen Huang and his family has a deep commitment to its first principles, and in the long-term quest to live up to them, I am confident we will build a future on the same principled foundation. We will prevail.
Notes
https://www.wsj.com/tech/ai/nvidia-nvda-1st-quarter-earnings-report-2026-stock-c2bb9c1c
https://www.nytimes.com/2025/10/29/technology/nvidia-value-market-ai.html;
and
https://www.marketsandmarkets.com/blog/SE/nvidia-dominance-in-the-ai-chip-market
https://www.hoover.org/research/jensen-huang-vision-risk-and-gpu
https://sequoiacap.com/podcast/crucible-moments-nvidia/
https://en.wikipedia.org/wiki/CUDA
https://aakashgupta.medium.com/what-really-is-nvidias-moat-8f99f8932072
https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
https://www.energy.gov/science/doe-explainsexascale-computing
Wonderful! Thank you Peter.