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The Thinking Machine: Jensen Huang, Nvidia, and the World's Most Coveted Microchip
“Stephen Witt’s deep reporting shines through every page of The Thinking Machine. The result is a page-turning biography of perhaps the most consequential CEO and company in the world.” —David Epstein, New York Times bestselling author of Range
Nvidia is as valuable as Apple and Microsoft. It has shaped the world as we know it. But its story is little known. This is the definitive story of the greatest technology company of our times.
In June of 2024, thirty-one years after its founding in a Denny’s restaurant, Nvidia became the most valuable corporation on Earth. The Thinking Machine is the astonishing story of how a designer of video game equipment conquered the market for AI hardware, and in the process re-invented the computer.
Essential to Nvidia’s meteoric success is its visionary CEO Jensen Huang, who more than a decade ago, on the basis of a few promising scientific results, bet his entire company on AI. Through unprecedented access to Huang, his friends, his investors, and his employees, Witt documents for the first time the company’s epic rise and its single-minded and ferocious leader, now one of Silicon Valley’s most influential figures.
The Thinking Machine is the story of how Nvidia evolved to supplying hundred-million-dollar supercomputers. It is the story of a determined entrepreneur who defied Wall Street to push his radical vision for computing, becoming one of the wealthiest men alive. It is the story of a revolution in computer architecture, and the small group of renegade engineers who made it happen. And it’s the story of our awesome and terrifying AI future, which Huang has billed as the ‘next industrial revolution,’ as a new kind of microchip unlocks hyper-realistic avatars, autonomous robots, self-driving cars, and new movies, art, and books, generated on command.
This is the story of the company that is inventing the future.
Nvidia is as valuable as Apple and Microsoft. It has shaped the world as we know it. But its story is little known. This is the definitive story of the greatest technology company of our times.
In June of 2024, thirty-one years after its founding in a Denny’s restaurant, Nvidia became the most valuable corporation on Earth. The Thinking Machine is the astonishing story of how a designer of video game equipment conquered the market for AI hardware, and in the process re-invented the computer.
Essential to Nvidia’s meteoric success is its visionary CEO Jensen Huang, who more than a decade ago, on the basis of a few promising scientific results, bet his entire company on AI. Through unprecedented access to Huang, his friends, his investors, and his employees, Witt documents for the first time the company’s epic rise and its single-minded and ferocious leader, now one of Silicon Valley’s most influential figures.
The Thinking Machine is the story of how Nvidia evolved to supplying hundred-million-dollar supercomputers. It is the story of a determined entrepreneur who defied Wall Street to push his radical vision for computing, becoming one of the wealthiest men alive. It is the story of a revolution in computer architecture, and the small group of renegade engineers who made it happen. And it’s the story of our awesome and terrifying AI future, which Huang has billed as the ‘next industrial revolution,’ as a new kind of microchip unlocks hyper-realistic avatars, autonomous robots, self-driving cars, and new movies, art, and books, generated on command.
This is the story of the company that is inventing the future.
272 pages, Hardcover
First published April 8, 2025
607 people are currently reading
8740 people want to read
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Displaying 1 - 30 of 184 reviews
June 17, 2025
After reading countless books about both past and present tech leaders, a troubling pattern has emerged: an uncritical glorification of the “visionary CEO.” The Thinking Machine falls into this same trap, fixating on Jensen Huang’s personality and rise rather than interrogating the structural forces behind his ascent. Instead of examining the consolidation of political and economic power Nvidia now represents — or the global implications of its monopoly in AI chips — the book reads more like a tribute than an analysis.
Only a single paragraph is dedicated to the dangers of monopoly power. That omission reflects a deeper problem in tech biographies today: they idolize billionaires rather than question the system that enables such concentrated influence. We need books that pull back the curtain, not polish the throne.
2.5
Only a single paragraph is dedicated to the dangers of monopoly power. That omission reflects a deeper problem in tech biographies today: they idolize billionaires rather than question the system that enables such concentrated influence. We need books that pull back the curtain, not polish the throne.
2.5
May 24, 2025
Frankly, the best business book I have read in awhile. The author, while detailing the story of Jensen Huang and his company Nvidia, manages to explain complex issues relevant to the industry. If you have been confused by artificial intelligence, neural networks, parallel processing or just computer programing in general, this book will give you enough of a grasp to at least frame issues such as whether AI is dangerous or beneficial. Quite a cast of characters and a lot of insight into Huang and how he became one of the richest men in the world while building one of the most successful firms in the industry.
I "read" the audio version which was well done. The book as a whole moves right along.
I "read" the audio version which was well done. The book as a whole moves right along.
August 7, 2025
It is difficult to get a man to understand something, when his salary depends on his not understanding it— Upton Sinclair
Witt is great (he wrote one of the best-ever books about the music industry) and he acquits himself well enough here, but product development stories ("The NV1 launched into a crowded marketplace in the fall of 1995. Customers walking into electronics retailers that Christmas season found
dozens of chipmakers competing for attention. In addition to Nvidia, there were Matrox, S3, 3dfx, Cirrus Logic, and ATI. The confusing situation was not improved by Nvidia’s circuit-board partners, which sold Nvidia chipsets under the trade name “Diamond Edge” while simultaneously selling
competing 3dfx chipsets under the trade name “Diamond Monster.”") are harder to make compelling, even when the products drive the most important technology ever.
He dutifully pushes the unemployment line and the AI takeover line, and as always it's mildly useful to see how weak and unserious Huang and Cantanzaro's responses are:
when I questioned Nvidia executives about the wisdom of unleashing such power, they looked at me like I was questioning the utility of the washing machine... I wondered if someday soon an AI might become self-aware. “In order for you to be a creature, you have to be conscious. You have to have some knowledge of self, right?” Huang said. “So no. I don’t know where that could happen.”
I brought up Geoffrey Hinton’s worries. Huang scoffed: “A lot of researchers don’t understand why he’s saying that. Maybe it’s bringing attention to his own work.”
...He began to lecture me in the voice that one would use with a wayward teenager. He’d placed high expectations in me, he said, and I had disappointed him. I had wasted his time; I had wasted everyone’s time; the whole project of the book was now called into question. The interview was attended by two of Jensen’s PR reps, but neither made any attempt to intervene—they weren’t about to draw fire... His anger seemed uncontained, omnidirectional, and wildly inappropriate. I was not Jensen’s employee, and he had nothing to gain from raging at me. He just seemed tired of being asked about the negative aspects of the tools he was building. He thought the question was stupid, and he had been asked it one too many times.
“This cannot be a ridiculous sci-fi story,” he said. He gestured to his frozen PR reps at the end of the table. “Do you guys understand? I didn’t grow up on a bunch of sci-fi stories, and this is not a sci-fi movie. These are serious people doing serious work!” he said. “This company is not a manifestation of Star Trek! We are not doing those things! We are serious people, doing serious work. And—it’s just a serious company, and I’m a serious person, just doing serious work.”
For the next twenty minutes, in a tone that alternated among accusatory, exasperated, and belittling, Jensen questioned my professionalism, questioned my interview approach, questioned my dedication to the project...
Looking back, it became clear to me that Jensen had wanted to lose his temper; he’d made a conscious decision to thrash me. Once the performance had started, his fury was genuine, but it was all in service of a larger point he wanted to make. It wasn’t just that Jensen didn’t read science fiction—it was that he actually hated science fiction. He was a serious man...
I recalled, too, with sudden clarity, how disinclined those same executives had been to discuss the potential future implications of the technology they were building, a disinclination that I sensed spilled over into discomfort and even fear. Now I saw where the fear was coming from. The executives were more afraid of Jensen yelling at them than they were of wiping out the human race.
Surely, I countered, a superior intelligence could be dangerous. Our own
species, through agriculture, animal husbandry, mineral extraction, and urbanization, had transformed the surface of the planet, decimating or even eliminating all competing species...
“I feel like we get stuck in science fiction perspectives a little bit too often,” Catanzaro said. He leaned back in his chair, and I got a good look at the large owl embroidered on the front of his sweater. “AI isn’t going to be interested in zero-sum games with us because there’s so much more to do in this universe. For example, if an artificial intelligence is trying to build a huge data center—it doesn’t want to put it where the humans live. It wants to put it somewhere else, maybe underground. Do you know how much space there is underground?”
Catanzaro was uncorked now—I sensed that he didn’t often get to share this perspective at his job. “It doesn’t need to inhabit this biosphere. In fact, it doesn’t need to be on the Earth, either, because the thing about artificial intelligence is that it travels at the speed of light. Humans, you know, we actually have to lug bodies around. Artificial intelligence can move along a radio signal as long as there’s an antenna on the other side...
“Humans are naturally confrontational—like, we’re territorial animals, and it’s built into our limbic
system to defend our turf... AI, if it’s truly intelligent, the things that it’s interested in are so much bigger than the little thin crust of Earth that the humans live on. I don’t think that it’s going to be interested in taking that from us. Rather, I feel like AI is going to want to take care of us.”
(If you were trapped in a gravity well with a territorial and violent ape with priors, you wouldn't try to pre-empt it?)
---
Syukuro Manabe, the scientist who won the 2021 Nobel Prize in Physics for showing that trace amounts of carbon dioxide would trap heat in the atmosphere, had arrived at this conclusion in the late 1960s after fashioning a primitive simulation of Earth with an IBM computer that weighed seventy tons and drew as much power as ten city blocks. Using exponentially more powerful computers in the 1980s, NASA scientists had correctly predicted a coming rise in Earth’s average temperature of several degrees Fahrenheit, even though the empirical trend at the time had looked flat. These simulations also predicted that as the planet warmed, the upper atmosphere would cool down, causing atmospheric layers to pancake as heat was trapped near the surface... Almost all of what we understand about climate change is the product of powerful, energy-hungry supercomputers. Scientists had been running climate models on Nvidia hardware since the late 2000s
Last year there were two AI Nobels: one for a technology which will lead to big things but mostly so far hasn't and one for two dead-end precursors to deep learning just because they were physics-inspired. How many Nobels rely on GPUs more generally?
* 2017 Physics (LIGO)
* 2017 Chemistry (cryoelectron microscopy)
* The initial multiscale modelling from the 2013 Chemistry prize was done before CUDAm but all this lineage of work is now on GPU.
These will probably soon be the rule.
---
Misc notes:
* What fraction of all artificial computation is now orchestrated by CUDA? Maybe a tenth?? More interestingly: what's the slope on its trend?
* Witt's lossy compression of technical reality into nontechnical metaphor is mostly fine. I wish he had done a little of this kind of nonsense though.
* Huang takes top billing, but Nickolls (CUDA) and Catanzaro (cuDNN), NVIDIA's first AI researcher in 2011, probably deserve most of the credit for the AI bet. Witt covers them well (and Huang brings them up spontaneously).
* ‘Everybody shut the fuck up—I’ve got Morris Chang on the phone’
*
The building interiors were immaculate; I imagined firing the gun from Portal at the walls. As I later learned, Nvidia tracks employees throughout the building with video cameras and AI. If an employee eats a meal at a conference table, the AI will dispatch a janitor within an hour to clean up after him. A human janitor, for now
*
With a near-monopoly on the hardware, Huang is arguably the most powerful person in AI
The first clause is off: TPUs, Trainium, and Ascend are probably about a sixth of AI compute and increasing. The second clause could still be true.
* The family business:
The genetic connection between Huang and Su was somewhat faint. Huang’s mother had come from a large family and had at least eleven older siblings. One of those siblings was Su’s grandfather; technically speaking, this made the two executives first cousins, once removed. While he was growing up, Huang hadn’t been aware of Su’s existence and learned she was his relative only after she was named AMD’s CEO.
*
In early 2024, an administrator at CalTech’s data center told me that the school’s wait time for delivery on an H100 chip was almost eighteen months. He had encouraged professors at the school to switch to other providers but found few willing to accept. “They’d rather wait for the hardware than switch away from CUDA,” he said. It was all this code that made Nvidia hard to compete against. Upstarts might design a new chip, but that wasn’t enough—Dwight Diercks, Nvidia’s head of software engineering, had ten thousand programmers working for him. “We’re really a software company; that’s the thing people don’t understand,”
*
In 1994 Dahl unveiled Jellyfish, the first neural net ever sold to the public. Jellyfish had trained on many millions of backgammon games, but despite this intensive computational process, the finished product was small enough to fit on a 3.5-inch floppy disk, which Dahl sold via his primitive website. In this way an early distinction was established between the
cumbersome training stage of AI, which was how the computer learned, and the inference stage, which was how the computer deployed its knowledge... He had selected the name “Jellyfish” as an homage to the ancient aquatic cnidarian whose “nerve net” controlled its systems of stimulus and response. His program “had only about a hundred brain cells, which I figured was about on par with the jellyfish".
---
It'd be a better book if it didn't feel the need to hit every product launch, but Witt did his job.
May 8, 2025
"This is the story of how a niche vendor of video game hardware became the most valuable company in the world..."
The Thinking Machine was a well-done look into NVIDIA and its charismatic CEO, Jensen Huang. I wasn't sure what to expect from this one, as these books can often be hit or miss in my experience.
Author Stephen Witt a Los Angeles-based writer, television producer, and investigative journalist.
Stephen Witt :
Witt opens the book with a good intro. He's got a great writing style that I found effective and interesting. The book is very readable.
He drops the quote above near the start of the book, and it continues:
As the book's title implies, the writing here covers the life of Haung, as well as the history of NVIDIA. The narrative proceeds in a chronological fashion. Jensen is a notably mercurial personality, and is well known for giving very public dressing-downs of his employees. Many of these exchanges are also covered here.
There are many interesting tidbits of writing throughout. In this short blurb, the author talks about the difficulty of interviewing Jenson:
NVIDIA went from a small company that made graphics cards for PCs into the largest tech company (by market capitalization) in the world. In recent years, they have made a foray into the emerging field of AI; supplying the world's biggest companies with the hardware needed to crunch large numbers and perform machine learning.
The discussion around AI is a super-interesting one. Leaders in the field have split (roughly) into two opposing camps. One utopian, and the other dystopian. There are interesting arguments on both sides. One of the main themes debated is the "alignment problem." That is - how do you program an AI to make sure that its values are in alignment with human values? Jensen doesn't seem to think this is a problem.
I have to drop just one more quote. It's a funny bit of writing that the author leaves until the end of the book. He describes the response he got when he asked Jensen about the possibility that AI would steal people's jobs. I'll cover it with a spoiler, since it's a bit long:
********************
I enjoyed this one. It was well written, edited, and presented. I would easily recommend it to anyone interested.
5 stars.
The Thinking Machine was a well-done look into NVIDIA and its charismatic CEO, Jensen Huang. I wasn't sure what to expect from this one, as these books can often be hit or miss in my experience.
Author Stephen Witt a Los Angeles-based writer, television producer, and investigative journalist.
Stephen Witt :
Witt opens the book with a good intro. He's got a great writing style that I found effective and interesting. The book is very readable.
He drops the quote above near the start of the book, and it continues:
"...It is the story of a stubborn entrepreneur who pushed his radical vision for computing for thirty years, in the process becoming one of the wealthiest men alive. It is the story of a revolution in silicon and the small group of renegade engineers who defied Wall Street to make it happen. And it is the story of the birth of an awesome and terrifying new category of artificial intelligence, whose long-term implications for the human species cannot be known.
At the center of this story is a propulsive, mercurial, brilliant, and extraordinarily dedicated man. His name is Jensen Huang, and his thirty-two-year tenure is the longest of any technology CEO in the S&P 500.
Huang is a visionary inventor whose familiarity with the inner workings of electronic circuitry approaches a kind of intimacy. He reasons from first principles about what microchips can do today, then gambles with great conviction on what they will do tomorrow. He does not always win, but when he does, he wins big: his early, all-in bet on AI was one of the best investments in Silicon Valley history. Huang’s company, Nvidia, is today worth more than $3 trillion, rivaling both Apple and Microsoft in value."
As the book's title implies, the writing here covers the life of Haung, as well as the history of NVIDIA. The narrative proceeds in a chronological fashion. Jensen is a notably mercurial personality, and is well known for giving very public dressing-downs of his employees. Many of these exchanges are also covered here.
There are many interesting tidbits of writing throughout. In this short blurb, the author talks about the difficulty of interviewing Jenson:
"I found Huang to be an elusive subject, in some ways the most difficult I’ve ever reported on. He hates talking about himself and once responded to one of my questions by physically running away. Before this book was commissioned, I had written a magazine profile of Huang for The New Yorker. Huang told me he hadn’t read it, and had no intention of ever doing so. Informed that I was writing a biography of him, he responded, “I hope I die before it comes out.”
NVIDIA went from a small company that made graphics cards for PCs into the largest tech company (by market capitalization) in the world. In recent years, they have made a foray into the emerging field of AI; supplying the world's biggest companies with the hardware needed to crunch large numbers and perform machine learning.
The discussion around AI is a super-interesting one. Leaders in the field have split (roughly) into two opposing camps. One utopian, and the other dystopian. There are interesting arguments on both sides. One of the main themes debated is the "alignment problem." That is - how do you program an AI to make sure that its values are in alignment with human values? Jensen doesn't seem to think this is a problem.
I have to drop just one more quote. It's a funny bit of writing that the author leaves until the end of the book. He describes the response he got when he asked Jensen about the possibility that AI would steal people's jobs. I'll cover it with a spoiler, since it's a bit long:
********************
I enjoyed this one. It was well written, edited, and presented. I would easily recommend it to anyone interested.
5 stars.
May 20, 2025
The author should have taken Huang’s advice and realize the world is not a science fiction world and that reality defines our existence since at times the author goes beyond the facts.
AI is not artificial intelligence it is a clever set of computer tools that uses parallel processing enabling neuro networks with matrix multiplications and large language models. It is not science-fiction and there is no emergent intelligence coming from it.
There’s a fair amount of hagiography in the telling of this book and that makes this book silly at times.
It’s not luck that made Nvidia successful. They broke Moore’s law and created a need beyond what their customers wanted at the time. They saw a new market and grabbed it.
AI is not artificial intelligence it is a clever set of computer tools that uses parallel processing enabling neuro networks with matrix multiplications and large language models. It is not science-fiction and there is no emergent intelligence coming from it.
There’s a fair amount of hagiography in the telling of this book and that makes this book silly at times.
It’s not luck that made Nvidia successful. They broke Moore’s law and created a need beyond what their customers wanted at the time. They saw a new market and grabbed it.
April 27, 2025
This book offers an insightful account of Nvidia’s journey and how it has evolved to become a key player at the forefront of today’s generative AI and neural network innovations.
It is informative, educational, and truly inspiring to read.
I had previously encountered some of the areas covered in the book — such as computer graphics, machine learning, bitcoin mining, and generative AI — but only in a fragmented way. This book connects these topics through a coherent timeline of events and technological progression.
A bonus is reading it while the book is still fresh and timely. It feels current and at the cutting edge.
It is informative, educational, and truly inspiring to read.
I had previously encountered some of the areas covered in the book — such as computer graphics, machine learning, bitcoin mining, and generative AI — but only in a fragmented way. This book connects these topics through a coherent timeline of events and technological progression.
A bonus is reading it while the book is still fresh and timely. It feels current and at the cutting edge.
July 1, 2025
I really enjoyed learning Nvidia's backstory and the entire semiconductor/chip industry, and Huang's involvement and interactions with the other big tech CEOs. I couldn't help but notice the author's focus on Huang's personality, while a key factor, often feels disproportionate and overshadows other elements of his ascent. This fixation becomes particularly jarring when the author inserts himself into the story, recounting a personal anecdote of being berated by Huang, which feels more like a distracting authorial intrusion than insightful commentary. Ultimately, while I recommend the book for its invaluable content, the writing style unfortunately detracts from the overall experience.
November 17, 2025
A really interesting read. This is a brief history of NVIDIA, which is also the history of AI development to the stage it has reached today.
NVIDIA started as a graphics card company. It made GPUs — which run on parallel processing, when Intel made CPUs— which run on sequential processing. When Moore’s Law would break for CPUs when transistor sizes reached the atomic scale (because at that scale, there would be leakages in electricity), GPUs would still be giving insane outputs because their architecture was fundamentally different.
NVIDIA’s gamble in going whole hog for parallel processing is what in effect led to AI reality. For parallel processing in scientific applications specifically, they developed CUDA, which was the language that made parallel processing possible. From there, curious scientists made their rigs with multiple NVIDIA chips strung together to develop programs that in various iterations led to what today is Chat GPT.
I was curious about the rise of NVIDIA and what it was really. I always thought it to be a graphics card company. It’s far more transformative. In fact, I learnt that it wasn’t even really a hardware company— it’s the software that runs so well with their parallel processing chips that makes NVIDIA stand out. And the specialised suites / tools they make for a variety of scientific uses.
While the first half of the book is a deep dive into NVIDIA’s history, the second half is a good summary of recent AI evolution history. It covers the topics uncritically for most of the narration, but it’s a good starting point for deeper dives on many themes.
Great book to get up to speed with the AI evolution, if you’ve lagged behind following it. And it’s not just ChatGPT— we are going through our most consequential phases of evolution in our human story thus far.
NVIDIA started as a graphics card company. It made GPUs — which run on parallel processing, when Intel made CPUs— which run on sequential processing. When Moore’s Law would break for CPUs when transistor sizes reached the atomic scale (because at that scale, there would be leakages in electricity), GPUs would still be giving insane outputs because their architecture was fundamentally different.
NVIDIA’s gamble in going whole hog for parallel processing is what in effect led to AI reality. For parallel processing in scientific applications specifically, they developed CUDA, which was the language that made parallel processing possible. From there, curious scientists made their rigs with multiple NVIDIA chips strung together to develop programs that in various iterations led to what today is Chat GPT.
I was curious about the rise of NVIDIA and what it was really. I always thought it to be a graphics card company. It’s far more transformative. In fact, I learnt that it wasn’t even really a hardware company— it’s the software that runs so well with their parallel processing chips that makes NVIDIA stand out. And the specialised suites / tools they make for a variety of scientific uses.
While the first half of the book is a deep dive into NVIDIA’s history, the second half is a good summary of recent AI evolution history. It covers the topics uncritically for most of the narration, but it’s a good starting point for deeper dives on many themes.
Great book to get up to speed with the AI evolution, if you’ve lagged behind following it. And it’s not just ChatGPT— we are going through our most consequential phases of evolution in our human story thus far.
June 6, 2025
Super well done long form journalism on the story of Jensen and NVDA, opening the founder character and resulting company culture very well.
And actually, because it is just a side thread, a great compact intro for anyone who wants to understand how the neural networks wave of AI came together over the last decade -- from the individual scientists now behind all the main AI labs buying their first 2 Nvidia gaming GPUs to run in their dorm rooms.
And actually, because it is just a side thread, a great compact intro for anyone who wants to understand how the neural networks wave of AI came together over the last decade -- from the individual scientists now behind all the main AI labs buying their first 2 Nvidia gaming GPUs to run in their dorm rooms.
October 31, 2025
Very good. Combo biography, history, and business management book. Contemporary relevance and tight/interesting narrative.
May 29, 2025
oh my god i flew threw this book. the way i’ve spent my entire life around tech bros and it’s so terrifying but also so entertaining… what even is reality/humanity/conciousness? honestly this book made me feel a lot more positively about the future. whatever happens happens and at the end of the day the only unstoppable force is time. the end of humanity was always going to happen one way or another. i think this book touches on most theories but doesn’t really speak much about how maybe humans could become the technology. would this count as the end of humanity? would this be sad? would this be inevitable? perhaps we need a new word to describe something that’s not quite ~ sad ~ but we have hard feelings about. the feeling that change has to happen but part of us doesn’t want it to happen but then part of us knows that it will.
mono no aware (Japanese) – the awareness of the impermanence of things, and a gentle sadness at their passing. this might come closest in spirit.
to be honest while sometimes i get caught up in the feels i think im just excited to live in this movie, see how the future unfolds, and one day hopefully peacefully die as a human. or maybe i’ll keep honing my selfish side and try and live forever. i think if the people around me did it i would do it as well. hey, im only human with fomo!
also to the point of selfish ai doing better and wiping out the human race. i think as long as humans are more selfish than ai then there should be no risk of ai taking us over…….. perhaps? so maybe ai ethics people should be focused on making sure to only make selfless ai. i wonder if that’s possible!
mono no aware (Japanese) – the awareness of the impermanence of things, and a gentle sadness at their passing. this might come closest in spirit.
to be honest while sometimes i get caught up in the feels i think im just excited to live in this movie, see how the future unfolds, and one day hopefully peacefully die as a human. or maybe i’ll keep honing my selfish side and try and live forever. i think if the people around me did it i would do it as well. hey, im only human with fomo!
also to the point of selfish ai doing better and wiping out the human race. i think as long as humans are more selfish than ai then there should be no risk of ai taking us over…….. perhaps? so maybe ai ethics people should be focused on making sure to only make selfless ai. i wonder if that’s possible!
April 26, 2025
Software is eating the world, but AI is going to eat software.
A good companion book to "The Nvidia Way". The two combined would make for a five-star book. The Nvidia Way, as the name suggests, is about showing the brighter side of Nvidia's story. The Thinking Machine tries to stay balanced, the focus is more on the AI side of the story, and ultimately ends up being worried about the future of humanity.
A good companion book to "The Nvidia Way". The two combined would make for a five-star book. The Nvidia Way, as the name suggests, is about showing the brighter side of Nvidia's story. The Thinking Machine tries to stay balanced, the focus is more on the AI side of the story, and ultimately ends up being worried about the future of humanity.
May 16, 2025
The Thinking Machine: Jensen Huang, Nvidia, and the World’s Most Coveted Microchip
(2025) by Stephen Witt is an excellent book about Nvidia and its founder. Witt is a journalist who has written for the New Yorker and other publications. Witt writes very well.
For anyone interested in Nvidia this book is a great read. For anyone interested in machine learning the book is also very valuable. It’s also worth listening to the multi-part Acquired podcast about Nvidia.
The book starts by profiling Jensen Huang and his remarkable upbringing. Huang was born in Taiwan and moved to Thailand. He then moved to the US into a boarding school which was also a reform school. There he taught tough kids how to read and they taught him to lift weights. He also started on his ability to improve himself.
His parents moved to the US. Then they moved to Oregon. Huang excelled at school there. He went to a state US college to study engineering. There he met his wife and together they moved to Silicon Valley to work on custom chips. Huang excelled at the work. After some time, he joined up with Chris Malachowsky and Curtis Priem. They formed Nvidia to design graphics accelerator chips for computers.
Huang became CEO and worked very hard on improving himself to do so. He read many management books and books on pricing and other things. However, Nvidia would suffer its first near death experience as the first chip they designed didn’t sell well. Priem was the main designer of the chip. Huang’s management style of incredible hard work and also yelling at people for long periods is well described. However Huang is very loyal to the people he hires as long as they work hard and are smart. He avoids dismissing them in contrast to Musk who fires people regularly.
With the company very close to bankruptcy the firm used virtual tooling to build a new chip that did work. It saved the company. Priem was unhappy with what was happening though.
Nvidia went on to build better and better chips and also to build a programmable graphics pipeline. This did in a chip what the experimental Pixelflow computer had done at UNC many years ago. Nvidia called it a GPU and the name stuck. Witt writes about Nvidia’s chips as the first parallel computers.
From there Nvidia would repeatedly iterate on the design and incorporate more pipelines and more function into their chips. They built up a regular schedule of releasing frequently. Their graphics driver software was excellent. However even then Nvidia wasn’t especially profitable and was sometimes in financial trouble.
Nvidia noticed that scientists were using the parallelism available on their chips for scientific work. So they created an API called CUDA to enable this to be better done. For some years CUDA was a very useful scientific library that facilitated Noble Prizes. However it didn’t have much impact on Nvidia’s profits. There is a section here about how Nvidia’s chips work that includes attempted metaphors for parallel computing. These include a DJ, a fleet of motorcycles and then as GPUs as a Wusthof kitchen knife. It doesn’t really work.
The book also writes about how Nvidia were the first company to bring parallel computing mainstream. However Intel’s ‘Core’ series CPUs did this as well. Admittedly this usually allowed more processors to run without inhibiting each other but it’s still significant. The performance increase on the original core chips was dramatic.
This would all change when machine learning (ML) with deep neural nets began to take off. Neural nets, and the crucial backpropagation algorithm had been around since the 1980s. But it wasn’t until the 2010s that ML really took off. Geoff Hinton, Ilya Sutskever and Alex Krizhevsky used CUDA on Nvidia cards to dramatically speed up ML. This has led to the current explosion in ML. First they enabled ML to do image recognition, something that had been a huge computer science problem for decades.
This led to Nvidia cards becoming highly desired items. This drove up Nvidia’s stock price.
At the same time Nvidia cards were also ideal for crypto mining and this further drew up Nvidia’s profits.
The next big step for ML was the creation of the ‘transformer’ architecture. This was described in the paper ‘Attention is all you need’. The paper was written to improve machine translation. It was written by a team of eight authors at Google. This enabled large neural net models to be trained in parallel. Once sufficiently large the models excelled not just at translation but also in response to written prompts.
It was this breakthrough that led to OpenAI creating ChatGPT and the ML explosion that has happened since. At this point Nvidia was the key supplier for many companies and the stock price dramatically rose. This has led to Nvidia become the third most valuable company in the world.
The book then starts to write about where ML may be going. Further advances have enabled ML to create images, videos, music and essays with incredible ability. Witt interviews various figures about their view of what the impact of ML on society will be.
The views vary. Huang and others at Nvidia are pragmatic and not concerned about ML risks. Hinton and other leading ML scientists are. However some, such as Jan LeCun are not. In the meantime ML has helped more people win Nobel Prizes.
Witt does write well about ML and questions where it is likely to end up.
The Thinking Machine is an excellent book. Witt writes really well and has put a great deal of research effort into the book. It’s highly recommended.
(2025) by Stephen Witt is an excellent book about Nvidia and its founder. Witt is a journalist who has written for the New Yorker and other publications. Witt writes very well.
For anyone interested in Nvidia this book is a great read. For anyone interested in machine learning the book is also very valuable. It’s also worth listening to the multi-part Acquired podcast about Nvidia.
The book starts by profiling Jensen Huang and his remarkable upbringing. Huang was born in Taiwan and moved to Thailand. He then moved to the US into a boarding school which was also a reform school. There he taught tough kids how to read and they taught him to lift weights. He also started on his ability to improve himself.
His parents moved to the US. Then they moved to Oregon. Huang excelled at school there. He went to a state US college to study engineering. There he met his wife and together they moved to Silicon Valley to work on custom chips. Huang excelled at the work. After some time, he joined up with Chris Malachowsky and Curtis Priem. They formed Nvidia to design graphics accelerator chips for computers.
Huang became CEO and worked very hard on improving himself to do so. He read many management books and books on pricing and other things. However, Nvidia would suffer its first near death experience as the first chip they designed didn’t sell well. Priem was the main designer of the chip. Huang’s management style of incredible hard work and also yelling at people for long periods is well described. However Huang is very loyal to the people he hires as long as they work hard and are smart. He avoids dismissing them in contrast to Musk who fires people regularly.
With the company very close to bankruptcy the firm used virtual tooling to build a new chip that did work. It saved the company. Priem was unhappy with what was happening though.
Nvidia went on to build better and better chips and also to build a programmable graphics pipeline. This did in a chip what the experimental Pixelflow computer had done at UNC many years ago. Nvidia called it a GPU and the name stuck. Witt writes about Nvidia’s chips as the first parallel computers.
From there Nvidia would repeatedly iterate on the design and incorporate more pipelines and more function into their chips. They built up a regular schedule of releasing frequently. Their graphics driver software was excellent. However even then Nvidia wasn’t especially profitable and was sometimes in financial trouble.
Nvidia noticed that scientists were using the parallelism available on their chips for scientific work. So they created an API called CUDA to enable this to be better done. For some years CUDA was a very useful scientific library that facilitated Noble Prizes. However it didn’t have much impact on Nvidia’s profits. There is a section here about how Nvidia’s chips work that includes attempted metaphors for parallel computing. These include a DJ, a fleet of motorcycles and then as GPUs as a Wusthof kitchen knife. It doesn’t really work.
The book also writes about how Nvidia were the first company to bring parallel computing mainstream. However Intel’s ‘Core’ series CPUs did this as well. Admittedly this usually allowed more processors to run without inhibiting each other but it’s still significant. The performance increase on the original core chips was dramatic.
This would all change when machine learning (ML) with deep neural nets began to take off. Neural nets, and the crucial backpropagation algorithm had been around since the 1980s. But it wasn’t until the 2010s that ML really took off. Geoff Hinton, Ilya Sutskever and Alex Krizhevsky used CUDA on Nvidia cards to dramatically speed up ML. This has led to the current explosion in ML. First they enabled ML to do image recognition, something that had been a huge computer science problem for decades.
This led to Nvidia cards becoming highly desired items. This drove up Nvidia’s stock price.
At the same time Nvidia cards were also ideal for crypto mining and this further drew up Nvidia’s profits.
The next big step for ML was the creation of the ‘transformer’ architecture. This was described in the paper ‘Attention is all you need’. The paper was written to improve machine translation. It was written by a team of eight authors at Google. This enabled large neural net models to be trained in parallel. Once sufficiently large the models excelled not just at translation but also in response to written prompts.
It was this breakthrough that led to OpenAI creating ChatGPT and the ML explosion that has happened since. At this point Nvidia was the key supplier for many companies and the stock price dramatically rose. This has led to Nvidia become the third most valuable company in the world.
The book then starts to write about where ML may be going. Further advances have enabled ML to create images, videos, music and essays with incredible ability. Witt interviews various figures about their view of what the impact of ML on society will be.
The views vary. Huang and others at Nvidia are pragmatic and not concerned about ML risks. Hinton and other leading ML scientists are. However some, such as Jan LeCun are not. In the meantime ML has helped more people win Nobel Prizes.
Witt does write well about ML and questions where it is likely to end up.
The Thinking Machine is an excellent book. Witt writes really well and has put a great deal of research effort into the book. It’s highly recommended.
December 2, 2025
Stephen Witt’s The Thinking Machine is ridiculously readable for a book about chips, neural nets, and the people who built the modern AI wave. I had a great time with it. The history of AlexNet and Nvidia’s early bet on parallel computing is genuinely fascinating, especially when you see how that gamble snowballed into the scale we have today. Some of it feels almost unreal, like watching a tiny spark grow into a planetary power grid.
The portrait of Jensen Huang is sharp. He comes across as icy, relentless, and very far from the free-spirited tech founder stereotype. The book captures that vibe well, although sometimes it leans a bit too lovingly into the myth of Jensen. I found the admiration a little overdone, but it does not take away from how clearly Witt lays out the rise of Nvidia and how its hardware became the backbone of the AI boom.
The future the book hints at is the part that stuck with me. You get this creeping sense that AI is scaling into something that may outgrow us entirely. The idea that a reasoning model could eventually leave us slow, squishy humans behind is unsettling, and the fact that it will likely run on an Nvidia chip is even funnier and scarier at the same time. I just wish Witt had pushed harder into that angle. The implications of the thinking machine feel lightly touched rather than deeply explored.
Still, as an overview of how AI came to be and the lonely CEO who decided to privatise the hardware race and take it somewhere extreme, it works. It is clear, absorbing, and surprisingly fun to read.
The portrait of Jensen Huang is sharp. He comes across as icy, relentless, and very far from the free-spirited tech founder stereotype. The book captures that vibe well, although sometimes it leans a bit too lovingly into the myth of Jensen. I found the admiration a little overdone, but it does not take away from how clearly Witt lays out the rise of Nvidia and how its hardware became the backbone of the AI boom.
The future the book hints at is the part that stuck with me. You get this creeping sense that AI is scaling into something that may outgrow us entirely. The idea that a reasoning model could eventually leave us slow, squishy humans behind is unsettling, and the fact that it will likely run on an Nvidia chip is even funnier and scarier at the same time. I just wish Witt had pushed harder into that angle. The implications of the thinking machine feel lightly touched rather than deeply explored.
Still, as an overview of how AI came to be and the lonely CEO who decided to privatise the hardware race and take it somewhere extreme, it works. It is clear, absorbing, and surprisingly fun to read.
September 29, 2025
One of the best books I picked in 2025, all thanks to Abhimanyu's recommendation. This book translates the emotion of
"outside, looking in"
with such finesse and is filled with so many measured arguments from all sides that it elevates your reading and comprehension to another level.
Finishing this book reminded me of an activity I did with Aslee Vance's book, where I re-read it every 5 years, I'm gonna do the same with this book as well.
Finishing this book reminded me of an activity I did with Aslee Vance's book, where I re-read it every 5 years, I'm gonna do the same with this book as well.
May 1, 2025
Nicely researched biography of the most popular CEO of current times. The deadpan humor mentioned in the book came through very clearly during the recent GTC conference and it was great to see a similar setting towards the end of the book from the prior year event. It was illuminating to see how Jensen thinks and drives his company despite being at it for 30+ years. All the other innovators are also covered very well. A must read for anyone trying to understand the secret of Nvidia's rise.
July 14, 2025
Huang is clearly a genius. I felt it was a good book that managed to explain technical development of semiconductors and the surrounding industries.
A must read for tech and AI enthusiasts. I felt it could have developed the “fear” element further and was interested by Huang’s refusal to see any downside to AI.
A must read for tech and AI enthusiasts. I felt it could have developed the “fear” element further and was interested by Huang’s refusal to see any downside to AI.
May 16, 2025
This book was an interesting read and exactly what I wanted for about the first 75%, but then it really leaned into AI doomerism and became something a little different. That last 25% or so was pretty ominous. I would have rated it lower if I hadn't shared similar opinions to the author.
July 3, 2025
Great AI history and biography , and to understand the timeline of its evolution and Nvidia and Jensens role in it, a book of the times. Highly recommend.
July 24, 2025
Fascinating - a wonderful history of Nvidia, and subtly a meditation on the roles vision, luck, execution, determination, and team-building play in business success.
October 25, 2025
An easy 5/5. Really, really good… exceeded all expectations. Extremely well written. Can’t recommend this enough.
September 6, 2025
enjoyable review of Nvidia, Jensen Huang, and our current trajectory towards AGI
October 17, 2025
Excellent.
So much better than the other book "The Nvidia Way" by Tae Kim
So much better than the other book "The Nvidia Way" by Tae Kim
July 19, 2025
A solid (and understandable) brief on a complex man and the technologies he champions. Fun to compare Jensen (as described) to Elon (as described by WI) - both ascribe to the philosophy that “anything extraordinary should not be easy” (both the problem and the interpersonal interactions to solve them). I found it curious that the author described Jensen as paradoxical. I guess the greats are in that they’re wickedly intelligent and willing to entertain contrarian thoughts far longer / if at all compared to others. How do they get anything done? I am amazed.
Also I now want to visit a data center
Also I now want to visit a data center
August 7, 2025
作為一個普通讀者，這本書涉及到的技術知識剛好夠讀懂。行文比較抓人，但後記裡面的一堆推薦實在太勸退了，感覺像是一本特定行銷給中國讀者的書。
March 16, 2025
sách rất bánh cuốn, hôm qua nhận hôm nay đọc xong.
Nhưng mà nhiều phần viết hơi kém, rất nhiều phàn đi không đủ sâu. 3-4 chap cuối dở vcl.
Thiếu rất nhiều thông tin về các đối thủ mới xuất hiện vào giai đoạn 2023-2024.
Nói chung là 1 số phần ok, nhiều phần hơi dở. Những trang về lúc Jensen quyết định làm CUDA và những ngày đầu lựa chọn cuDNN thật sự mê vd.
Jensen đã đánh cược về 1 tương lai rất cần computation power, và do đó liên tục thực hiện và cải tiến CUDA, tiếp thị tới những nhà khoa học hàng đầu, rồi sau đó những người làm NN đã sử dụng nó. Ngay tiếp đó, Jensen đã push nó mạnh mẽ coi đó là khoảnh khắc cuộc đời, liên tục nhắc lại điều đó với các nhân viên, ngay cả khi toàn bộ các sếp của anh giai làm cuDNN đều không coi đó là dự án tiềm năng và đánh dấu đó là "cần cải thiện".
Chỉ có viễn kiến về tương lai là chưa đủ, chỉ thuần năng lực không cũng vẫn thiếu, bỏ tay vào gây dựng công ty như 1 trong 3 founder cũng vẫn chưa đủ. Thật sự là 1 cuộc chơi khó khăn. Nhớ lại những lời này: dù là 1 trong 3 người sáng lập và có thể coi như là người có nền tảng kỹ thuật cao và sát sao nhất về lĩnh vực card đồ họa, nhưng Curtis Priem cuối cùng vẫn không thể là người lèo lái con thuyền Nvidia ra khơi (thậm chí tý thì lèo xuống hố, btw ổng vẫn rất giàu).
Thật sự là những cuộc chơi khó khăn.
Nhưng mà nhiều phần viết hơi kém, rất nhiều phàn đi không đủ sâu. 3-4 chap cuối dở vcl.
Thiếu rất nhiều thông tin về các đối thủ mới xuất hiện vào giai đoạn 2023-2024.
Nói chung là 1 số phần ok, nhiều phần hơi dở. Những trang về lúc Jensen quyết định làm CUDA và những ngày đầu lựa chọn cuDNN thật sự mê vd.
Jensen đã đánh cược về 1 tương lai rất cần computation power, và do đó liên tục thực hiện và cải tiến CUDA, tiếp thị tới những nhà khoa học hàng đầu, rồi sau đó những người làm NN đã sử dụng nó. Ngay tiếp đó, Jensen đã push nó mạnh mẽ coi đó là khoảnh khắc cuộc đời, liên tục nhắc lại điều đó với các nhân viên, ngay cả khi toàn bộ các sếp của anh giai làm cuDNN đều không coi đó là dự án tiềm năng và đánh dấu đó là "cần cải thiện".
Chỉ có viễn kiến về tương lai là chưa đủ, chỉ thuần năng lực không cũng vẫn thiếu, bỏ tay vào gây dựng công ty như 1 trong 3 founder cũng vẫn chưa đủ. Thật sự là 1 cuộc chơi khó khăn. Nhớ lại những lời này: dù là 1 trong 3 người sáng lập và có thể coi như là người có nền tảng kỹ thuật cao và sát sao nhất về lĩnh vực card đồ họa, nhưng Curtis Priem cuối cùng vẫn không thể là người lèo lái con thuyền Nvidia ra khơi (thậm chí tý thì lèo xuống hố, btw ổng vẫn rất giàu).
Thật sự là những cuộc chơi khó khăn.
June 26, 2025
Книгата е добра история на Nvidia, но още по-добра история за опасността от AI
April 28, 2025
“The Thinking Machine” is truly a life-changing book. It completely transforms your perspective on artificial intelligence, not just as a technology but as a force reshaping humanity’s future. The book offers profound insights into the evolution, possibilities, and ethical dilemmas surrounding AI.
Jensen Huang, around whom much of the inspiration of this book revolves, is portrayed as a true maverick — a relentless, passionate innovator who embodies the spirit of technological revolution. His work ethic is legendary, bordering on obsession, and his vision for the future of AI and computing is awe-inspiring. There is no doubt that he will be remembered as one of history’s great technological pioneers.
If you’re even remotely interested in technology, innovation, or the future of humanity, The Thinking Machine is a must-read. It challenges your assumptions, stirs your imagination, and leaves you with a renewed appreciation for the extraordinary times we live in.
May 18, 2025
Great inside look at the story of Nvidia and Jensen Huang from the early days until now.
I didn’t know that Nvidia began as a video game hardware company. I also didn’t know that Huang has a lot of Steve Jobs-like tendencies, such as publicly ridiculing people when they make mistakes and exploding in anger.
Must be nice to be one of those early Nvidia employees!
I didn’t know that Nvidia began as a video game hardware company. I also didn’t know that Huang has a lot of Steve Jobs-like tendencies, such as publicly ridiculing people when they make mistakes and exploding in anger.
Must be nice to be one of those early Nvidia employees!
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