The Nvidia Way: Jensen Huang and the Making of a Tech Giant

One should not stop at learning technical processes but always be open to how technical change impacts society. “The NVIDIA Way” (Norton, 2025) by Tae Kim explores NVIDIA and how they turned technological success into societal change. Jensen Huang, NVIDIA’s CEO, and the longest active serving technical CEO, plays a key part in that success. The book divides into a chronological approach: before 1993, 1993-2003, 2002-2013, and from 2013 to the present. This should be essential reading for anyone managing a startup, working on product issues, and desiring a better look at the marketplace.The chronological approach is unusual as it focuses on Jensen first and then moves to the technological change. The early NVIDIA processes had setbacks and potential fails, but it is the company's structure and culture moving them forward. I do not know if all the NVIDIA successes are paths for me, but it highlights multiple paths to success. One could skip directly to NVIDIA current successes but would lose how those decisions became the organic culture.Employees often describe NVIDIA as not a 24x7 approach but a 25x8 approach, never ceasing, never stopping, and eliminating opponents by sheer hard work. There is a quote from Jensen, “Don’t worry about the score, it’s how you play the game” that resonates strongly with me. As a martial arts coach, I always tell students to seek technical perfection, and the wins will arrive in time. In boarding school, ping pong, and his undergraduate studies, Jensen’s early years show this as he excels not only at the task but earns the money to support those achievements with multiple janitorial jobs Think about character strength and resilience needed to work as a janitor in the same location one attends school. This constant resilience enabled success as NVIDA launched in later years.The company's first success was the founders departing LSI Logic to focus on graphics cards for PCs and the gaming industry. When NVIDIA first launched, the goal was for the first card NV1, to do everything from graphics to audio to processing. The computer game DOOM proved this was impossible as even excelling at graphics, it fell behind audio standards. Succeeding from the early failures required purchasing testing equipment and speeding up software development so drivers were ready at the same time as the chip. This was drastically different than other chip manufacturing companies and more in tune with a modern DevOps approach. The end result was the RIVA 128 chip.Essential NVIDIA growth depended on the “ship the whole cow” concept. This meant that chips failing high-level testing were sold at a reduced price.. Many vendors gradually reduced selling prices, but NVIDIA always believed in their value, keeping prices high. Despite selling lower quality parts at lower cost, the parts were still market leaders. This applies years later in selling advanced GPUs when linking cheaper graphics cards could perform similar functions. An example of this appears in a meeting with Steve Jobs about laptop NVIDIA chips. Steve believed the chips were overpowered for the laptops but NVIDIA engineers explained simply lowering the clock speed made the chips perform, leading to their inclusion in all systems.NVIDIA’s market growth is best tied to the GPU market. This was when research scientists realized the same technical specifications creating success for graphics could be engineered to address extremely large matrix math, with up to millions of parameters. Matrix math is a key to current machine learning and AI algorithms, even if those were not the immediate answers at the time. The first major use was life sciences, measuring protein folding and biological interaction. One key disagreement with NVIDIA strategy happened whan a scientist, Ross Walker, first using the proprietary CUDA code, wanted to purchase only commercial GEFORCE graphics cards instead of the higher-end GPUS. Despite NVIDIA introducing technical controls, Ross continued his approach even with a later career at GlaxoSmithKline. NVIDIA continued to advocate always purchasing the highest quality and, consequently, expensive models.The success continued into the modern era as NVIDIA supplies top-end solutions, emphasizing engineering first over profit-taking incentives. Jensen emphasized several approaches to focus on technical success. Even during expansion, he introduced a flat organization with all employees, sending a top five concerns to the CEO weekly. One might see this from recent news about DOGE, requesting government employees to take a similar approach. Another emphasis was all employees had to respond to any email within two hours, again hitting the 25x8 structure. A third approach was all employees were accountable for all ideas with Jensen frequently calling out individuals to defend ideas in public meetings. I do not necessarily agree with public shaming, but Jensen followed it up with the concept that no one should fail alone, encouraging others to ask for help frequently.One area where I found fault with Kim’s writing was the book becomes very disjointed in later sections. Maintaining a chronological approach creates challenges as the company expands and more areas grow rapidly. The later section might have benefited more from a functional approach from 2018 to current, highlighting marketing, product, CUDA, GPUs, and other developments separately. The chronological approach fails as these items are difficult to follow in later chapters.Overall, “The NVIDIA Way” should be a must-read for tech professionals. The essential learning points are demonstrating resilience, leading innovation, and maintaining a consistent strategic vision. Along the way, the book demonstrates how companies like Google, Silicon Graphics, and many graphics companies failed by concentrating on profit rather than technology. Jensen shows up as a unique individual, and a key behind NVIDIA’s success. I don’t think the model can be duplicated but there are several good industry lessons. The book reads quickly and I recommend it for CPOs, CTOs, and CEOs looking to grow their knowledge.

I've gifted this several times since I read because it's such a unique look at Jensen's 30y+ tenure and how many times NVDA has faced existential threats and changed the business. Author got some incredible inside baseball from founders (ex. Jensen wouldn't join until he had high conviction to pathway to $50m+ of sales, meetings are open and meant to help people understand decision making even if tense, Jensen can give anyone RSU grant on spot). Even the history of TSMC which isn't the focal point of the book provides unique looks at the semi industry. I loved this book and unlike a lot of other non-fiction, it felt like a chronological story with real character development that wasn't weighed down by overly dense explanations. Tae Kim has a talent for story-telling and simple explanations of complex ideas.

This is a good summary of rise of Nvidia. Although a bit technical, it is a good book to know this successful company.

I have been reading a lot of books about the recent history of AI and this has been the most mind-blowing.It is a book about Nvidia and its CEO Jensen Huang. That aspect is very well covered, but the author goes a lot further.Most books on the subject cover the evolution of the GPT model, but in this book Mr. Witt provides much greater detail about the critical-path advances and the serendipity involved.This is the part of the history that surprised me:In 2002, Ian Buck, then a Stanford PhD student, was working on BrookGPU, an academic project to make GPUs programmable for scientific tasks. Buck’s work caught the attention of Nvidia co-founder Chris Malachowsky and CTO David Kirk. They quietly invited him to join Nvidia to see if GPUs could be programmed with a C-like language.This effort evolved into the powerhouse parallel computing platform named CUDA, released in 2006.This was a risky bet. Parallel computing had proved a dead end for decades. No one had made it work, and it was considered career self-immolation.The project was a money pit for years, and Nvidia’s board was not happy, but Huang persisted – he knew this was important. He went all in and bet the farm.Meanwhile, in 2010, Geoffrey Hinton’s lab at the University of Toronto had been experimenting with deep convolutional neural networks for image recognition – another subject area that had seen decades of failure and was a major career-ender.Hinton’s team – Alex Krizhevsky and Ilya Sutskever – noticed that the parallel nature of CUDA was ideal for training neural networks. They reached out to Nvidia but – crickets.Research grants for neural networks were nonexistent, so Krizhevsky and Sutskever emptied their own pockets to purchase two Nvidia cards and set them up in Krizhevsky’s basement apartment – at his parents’ house. The electric bills from the 24/7 training runs were enormous, and his parents were kind enough to cover those costs. Thus, the first modern convolutional neural network, AlexNet, was born.That is how it all started – two groups of people persisting in pursuing what were considered technological dead ends.There are other major milestones and pivots in the story, but that one I found the most inspiring.Back to the book review: a great tech and business book, probably the best I have read this year.