The author is well-respected for his ability to dig deeply, and spent two years shadowing Musk in order to write this book. Whether you like or dislike Musk, he is an intriguing figure, but for me, I wanted to better understand this man and his motives. I read this book before he took a role in Trump’s administration.Musk is on the autism spectrum—which often explains his rants of frustration, his moments of brilliance, and his many times of defeat. When I read this book, he was running six businesses—he often spent time flying from business to business. His drive was predicated on two beliefs: we needed to eventually be able to move to Mars as our planet collapses, and he felt humans were not repopulating enough to sustain human life.This is what created his space business. Once he was successful in sending flights to the space station to resupply the astronauts there, he developed a lucrative contract with the U.S. to be the primary resupplier. He also designed his own factory for designing and building space rockets. He tested many types of materials, and found that stainless steel seemed to handle the variances of temperatures. In any of his factories, he put the designers desks on the same floor as product being produced—-his thinking was that if a problem arose with manufacturing, the designers could experience the problem first hand, and make the needed adjustments.Musk often worked well late into the night and early mornings—when he was tired, he slept on the floor under his desk.His belief in repopulation meant that he aligned himself with many willing women, and at my last count, had 14 children.He is an intriguing, very complex person whose beliefs drive his actions. I keep wondering why, with all his resources, he does not assist with keeping this planet, Earth, in better health, and advocate for it instead of putting so much effort in trying to colonize Mars.If you want to better understand this man, I found the author did a wonderful job in researching Musk, shadowing him, and spending time with other people who play important roles in his life. Assimilating all this data had to be challenging for this writer, but I think he painted a picture for the reader that helps unravel some of Musk’s complexity.

As interesting a biography as I’ve ever read and by an author of so many great ones. Einstein, Jobs, Kissiniger, et al. But this one is about a man of our times — changing the world via leading-edge technology that will separate America from the rest of the world and fuel our economic and military superiority.Musk’s genius is unquestioned. His ability to set and achieve near imposible goals for himself, his companies and his team is mind-boggling. His personal life is constantly in shambles: an abusive father in South Africa; multiple marriages; children from several ladies; ever-increasing personal responsibilities while driving more and more corporations; to become today’s globe trotting ‘world’s richest man’ which opens every door with a simple phone call.He thrives on chaos. His surges and problem solving ideas are legendary. He doesn’t accept no — even if it turns out not to be right. Break it and build it back up (Kipling’s ‘IF’). Take 2500 engineers at Twitter and reduce that number to 150 in sixty days. Take a $1500 NASA spacecraft valve and replace it with one that cost $30. Eliminate ‘cost-plus’ sourcing by NASA and upend its entire cost structure. It’s focus on steroids, and when he is infrequently silent with a far-off stare that can sometimes last minutes — shut up and listen, because a decision is about to be communicated and then, (most importantly) executed. Risk is not an enemy, but a motivator to succeed or fail quickly.I cannot recommend this book too much. For although Steve Jobs (whom I knew) displayed similar product genius while sharing Elon’s few interpersonal skills, Steve had no idea how to actually produce the end result. Elon not only creates unbelievable ideas, he sleeps on the shop floors alongside his team as he makes it happen. Musk nightly roams the shop — workstation to workstation examining procedures and making changes on the fly to maximize efficiency and reduce costs. There’s little wonder that Trump selected Musk to initiate draining the swamp in Washington as he cannot tolerate waste and inefficiency. And little wonder why he was happy to escape that feckless group of status quo politicians and do-nothing bureaucrats after launching D.O.G.E. Stay tuned — I doubt it will be his last assignment.From rockets (SpaceX) to Satellites to Starlink, to Starship, to EVs, to Neualink, to Full Self-Driving (FSD), Elon Musk is the perfect technology genius for today’s America as we transition to a new world order. His imperfections as a human (Asperger’s, Bi-polar, mood swings, even destructive behaviors/tweets), along with his abrasive, crisis-driven managment style will clearly not appeal to everyone. Many could not work for the man. But those who can have already accomplished much for America and humankind. All Americans should thank him for his bold vision and ability to bring those dreams to reality. It’s an extremely rare talent. I wish him many more years of exceptional accomplishments.Now…On to Mars!

Everyone who wants to talk about so-called “AI” needs to read this book.If someone is going to advocate for so-called “AI” they need to read this book first and then answer one question:Why do you trust Sam Altman about anything?

Informative about OpenAI. It gives details beyond what the media has reported, re: about Altman’s firing/rehiring and other behind-the-scenes details. Fits well into the genre of Silicon Valley business books.However, the author is biased and clearly hates Sam Altman. She couches everything Altman did as sinister and dishonest. If the author’s reporting is accurate, then clearly Altman has done dishonest things. Altman shares this dishonesty with Musk and all of the other OpenAI founders (e.g. Brockman) who, disingenuously, started OpenAI as a non-profit. Hard to believe that Musk and the head of Y Combinator could not foresee OpenAI needing cash to produce AGI and would eventually thus recast OpenAI as a for-profit company. (of course they knew) But, after all that, Sam Altman comes across as similar to the other Silicon Valley founders (Uber, Amazon, Facebook, plus We Work, Theranos, etc.) who bent the rules to achieve their vision — it’s not good, but Sam Altman does not appear to be especially bad in that company. Moreover, Altman, probably to the author’s chagrin, has led OpenAI to produce one of the best tech products of the 21st Century.Minor note, but it is funny/strange that every time the author mentions the Polish OpenAI employees Pachocki and Zaremba, she makes sure to write that they are from Poland. They are mentioned about a dozen times, and almost each time, they are described as “Polish researchers.” Hao doesn’t do this for any other employee, no matter their nationality. Why? Who knows. The book needed editing.Worse and not funny is Hao’s gossip about the allegations made by Altman’s sister. The book goes into unsubstantiated (he-said, she-said) detail, and is clearly part of Hao’s hatred of Altman. The gossip says nothing about Altman, especially since their entire family acted the same way toward her, and it was Altman who actually gave his sister some financial support. That whole section was tabloid-gossipy and took away from the seriousness of the book.The “empire” theme is forced an unconvincing. The author is trying to make a bigger, longer splash by creating an “empire” narrative. It is enough to say that AI production incorporates low-paid labor in the Global South, along with the use of precious land, water, and other resources at a huge scale. But Meta and Google had been doing this well before AI, and Hao doesn’t cast it all as empire. This is all horrible, but describing AI as a new “empire” is wannbe-academic claptrap.So, if you want to know about OpenAI’s founding and the ups and downs of its thus-far short tenure, this is a decent book to read. If you want a broader perspective on the deleterious human and environmental toll of Silicon Valley companies, this provides it. If you hate Sam Altman with a furious passion, this is definitely your book.

Great book with great content

An incredibly well written and thoroughly well researched book by Hao. The density of facts, references, interviews and quotes to create a riveting narrative is remarkable. I was able to finish the +400 pages in 5 sittings, while taking notes. It was a page turner, but one I intend to reference going forward. And the notes section is a great guide for further reading.Some will say the book is Doomer’ism. Sure. If you say so.I guess telling people not to smoke, to eat healthy and treat each other with kindness could be a form of Doomer’ism too. Yet, we know smoking, processed foods and being mean isn’t good for you and others. But, if you’re hooked on the nicotine rush of smoking, the cravings of processed foods, or the dopamine rush of dunking on someone on Twitter… well. When people tell you those things are bad, you may just say, “Stop being such a Doomer! Live a little!”Likewise, when someone points out that AI has a lot of bad outcomes, some of which are not in plain sight or immediately felt, that may not sit well with folks who are addicted, get a rush from it or will benefit from it. More, importantly, these companies are driven by VC firms whose goal is power, profit and consolidating control through every more obfuscation at the expense of the consumer.Is the book good? YES. Should you buy it, read it? YES.I have worked in the field directly since 2018, indirectly for several years prior. I have done enterprise business directly with OpenAI, Anthropic and other GenAI vendors, as well as the consultancies pushing said solutions. I deeply familiar with their solutions and businesses. Hao’s book does a spectacular job of uncovering the behind the scenes details of these companies, from how the models are trained, where the data comes from, the utter lack of transparency; to the questionable ethos and actions of Altman, Dario and others.More, Hao’s conclusions and recommendations about how to responsibly us AI are spot on. She is NOT saying there is no use for AI. Rather, there is a better way. And the better way is not what is being done by OpenAI, Anthropic, Meta, Google, Microsoft or any of the other major tech vendors.I highly recommend the book.

The book basically tells us the origin story of OpenAI and how it has became an empire.Key insights- Interesting stories about Sam Altman and Elon Musk.- Altman behavior to control power and influence with people.- How large language model was invented- Why data centers consume so much power and waterCons- book has lot filler and I felt everything was not worth reading- The word and narrative felt complex, it could have been written in a simple language so that normal people could understand- I felt the book adopted a non linear narrative like in the movies but as an average reader who reads occasionally you feel lost. Finally after reading the book you can’t remember most of the details because of this.

The first half is more destined to those who have yet to use generative AI on a regular basis – in all probability a shrinking crowd. Anyone who interacted with ChatGPT 3.5 and then 4 will have similar anecdotes. And while certainly useful, I found Ethan’s four principles to mix rules of thumb with prompting techniques and more general observations.Part two I personally found of much greater interest, as Ethan delves into the implications for workers, demonstrating how they can learn to use AI to great effect, e.g. in creative work. Ethan dives deep into how gen-AI can produce efficiencies and superior results, but also lead to disaster if users get too comfy and “fall asleep at the wheel”. While several studies and research papers exist on these topics, it’s one of the first books that explains these concepts to laymen. It’s also enjoying to see him, rather than trying to dissect office jobs he may not be familiar with, instead humbly take himself as a guinea pig to explain in great minutiae how he wields this revolutionary tool in his work, at times using the very paragraphs we read as examples.I found in several occasions echoes of my own book, e.g. when Ethan insists on how technology cannot be treated in a vat but goes hand in hand with frameworks and trends, thus how the way corporations have work organized is likely to change in serious ways, as it did in previous industrial revolutions. He too does not fall for the fallacy that because certain tasks in a job cannot be automated, the job is immune to disruption. He too notes the risks associated with a greater deployment of metrics and the diktat of data, what others have called “Digital Taylorism”, and alludes to what I termed being “pushed off of the sumo ring of cognition” by an AI that calls the shots.He also takes a lucid and pragmatic approach to how such disruptive technology will be deployed in the workforce, detailing how employees use it covertly out of fear that their managers find out, though less convincing are his recommendations to leaders on how to instill a pro Gen-AI culture in the company – a pity given how this is fast becoming a concern for managers. A few other considerations also went neglected, like how those companies that fail to adapt will quickly fall behind those that do. But I feel this is also because he is primarily addressing employees, and that is nice to see in contrast to all the books guiding managers.My favorite is his insistence that AI is proving most beneficial to juniors, who can boost their performance level to the vicinity of veterans; rather than concluding in the lines that “See, this is a great enhancement tool, not something that will eliminate jobs – so don’t be afraid!”, or “it’s not AI that will displace workers, but the workers who master AI that will displace those who don’t” as the majority of so-called experts yell and parrot from every hilltop, he questions what implications this might have for seasoned workers, for those whose expertise becomes somewhat eroded and may no longer justify their wages. Following what I called the commoditization effect, AI could become a great equalizer, but (Duh!!) overall this will drag wages down. He notes how now some companies hire fresh graduates for jobs there where they used to hire people with at least five years of experience, because they can do practically just as well now with the help of AI.Alas he also rightly note that expertise will still play a role, with solid arguments. But he dares not venture say how much of this expertise will still be required and how this will reshape companies – indeed, only time will tell. We could end up with a split of say 80% of juniors – or rather people paid with junior salaries – and only 20% of experts there where we had a more balanced split before, and such junior talent could be outsourced instead of employed, etc.I pass on the final chapters on education and the future of AI, not for lack of interest but to avoid too long a commentary. Also great observations, for instance on how a future AI-mentor would be superior both for providing more constant feedback but also in its ability to take on several different roles (as opposed to the subjectivity of a single human coach, teacher or mentor). And here again there is the risk of overdependence, for instance (with his example of architect) consulting the AI on every single stroke.All in all, a very good read that remains concise and echoes some of my concerns which I believe will only balloon with time as organizations reshape work.

Here’s a refined and insightful book review based on your thoughts:—Ethan Mollick’s book serves as an excellent primer for those new to artificial intelligence, breaking down complex concepts into accessible insights. As someone who does not consider themselves an expert in AI, I found this book particularly helpful in demystifying key ideas and making them more understandable.One of the highlights of the book is Mollick’s “Four Rules for Co-Intelligence,” which offer a structured and thoughtful approach to engaging with AI. Rather than viewing AI as a replacement for human thinking, Mollick emphasizes its role as a tool that enhances our ability to create, analyze, and innovate. He also delves into the mechanisms behind AI—how it processes information, the sources it draws from, and the inherent biases that can shape its outputs. This discussion is crucial, as it encourages readers to approach AI with both curiosity and critical thinking.What I found particularly compelling was Mollick’s perspective on AI in education. By allowing his students to use AI, he pushes them to think more deeply rather than simply outsourcing their work. This approach aligns with the broader theme of the book: AI is not just a technology to be feared or blindly accepted, but one to be understood and harnessed effectively.For those unfamiliar with terms like GPT and large language models (LLMs), this book provides a solid foundation. It equips readers with the knowledge needed to make informed decisions about the benefits and risks of AI. Given the rapid advancement of this technology, Mollick’s insights are both timely and practical. Personally, reading this book has enhanced my own use of AI, giving me a clearer perspective on how to integrate it thoughtfully into my daily work.Overall, this is a must-read for anyone looking to gain a foundational understanding of AI without getting lost in technical jargon. Whether you’re a student, educator, or professional, Mollick’s balanced and insightful approach makes AI feel less intimidating and more like an opportunity to be explored.

This book offers a refreshingly balanced and thoughtful perspective on how AI will reshape our personal and professional lives without falling into either dystopian fear-mongering or blind techno-optimism. The author does an excellent job explaining complex AI concepts in accessible language while providing practical frameworks for adapting to an AI-integrated world. What I appreciated most was the focus on collaboration rather than replacement – showing how humans and AI can work together effectively rather than viewing it as a zero-sum competition. The real-world examples and case studies make the concepts tangible and immediately applicable to current workplace challenges. While some sections can feel a bit dense with information, overall it’s an essential read for anyone looking to understand and thrive in our rapidly evolving technological landscape.

This book explains AI – from how it started to where it is going – in a way that is understandable by non-techie people. It gives detailed instructions for how to start to use AI for oneself. I found it to be very helpful and I recommend it.

The Coming Wave: Technology, Power, and the Twenty-first Century’s Greatest Dilemma by Mustafa Suleyman“Mustafa Suleyman” examines the future of Artificial Intelligence (AI): the good, the bad and the scary in realistic terms. Co-founder and CEO of Inflection AI and cofounder of DeepMind, Mustafa Suleyman takes the readers on a ride to the future of AI and its implications. This thought-provoking 500-page book includes fourteen chapters broken out by the following four parts: I. Homo Technologicus, II. The Next Wave, III. States of Failure, and IV. Through the Wave.Positives:1. Well-researched an insightful book. Suleyman has the expertise and experience to write such a book.2. Artificial Intelligence (AI) is a fascinating topic, specifically whether containment is possible.3. The book flows very well and it’s accessible. The reader is not expected to have expertise in the area to understand it.4. Clearly defines the key topic of the book, the wave. “So, what is a wave? Put simply, a wave is a set of technologies coming together around the same time, powered by one or several new general-purpose technologies with profound societal implications.”5. Goes over the history of technological waves. “One major study pegged the number of general-purpose technologies that have emerged over the entire span of human history at just twenty-four, naming inventions ranging from farming, the factory system, the development of materials like iron and bronze, through to printing presses, electricity, and of course the internet. There aren’t many of them, but they matter; it’s why in the popular imagination we still use terms like the Bronze Age and the Age of Sail.”6. Defines the most important topic in the book, containment. “Containment is the overarching ability to control, limit, and, if need be, close down technologies at any stage of their development or deployment.” “Technical containment refers to what happens in a lab or an R&D facility. In AI, for example, it means air gaps, sandboxes, simulations, off switches, hard built-in safety and security measures—protocols for verifying the safety or integrity or uncompromised nature of a system and taking it offline if needed.”7. The two technologies threatening to surpass our own intelligence. “The coming wave of technology is built primarily on two general-purpose technologies capable of operating at the grandest and most granular levels alike: artificial intelligence and synthetic biology.”8. Describes breakthroughs in AI. “The breakthrough moment took nearly half a century, finally arriving in 2012 in the form of a system called AlexNet. AlexNet was powered by the resurgence of an old technique that has now become fundamental to AI, one that has supercharged the field and was integral to us at DeepMind: deep learning.”9. Interesting observations and predictions. “In the words of an eminent computer scientist, “It seems totally obvious to me that of course all programs in the future will ultimately be written by AIs, with humans relegated to, at best, a supervisory role.”10. The often-used term Singularity defined. “Over the last decade, intellectual and political elites in tech circles became absorbed by the idea that a recursively self-improving AI would lead to an “intelligence explosion” known as the Singularity.”11. Examines synthetic biology. “Companies such as DNA Script are commercializing DNA printers that train and adapt enzymes to build de novo, or completely new, molecules. This capability has given rise to the new field of synthetic biology—the ability to read, edit, and now write the code of life.”12. Looks at other transformative technologies that are part of the wider wave. “Amazon’s “first fully autonomous mobile robot,” called Proteus, can buzz around warehouses in great fleets, picking up parcels. Equipped with “advanced safety, perception, and navigation technology,” it can do this comfortably alongside humans. Amazon’s Sparrow is the first that can “detect, select, and handle individual products in [its] inventory.””13. Describes the quest for quantum supremacy. “In 2019, Google announced that it had reached “quantum supremacy.” Researchers had built a quantum computer, one using the peculiar properties of the subatomic world.”14. Describes the four features that define the coming wave. “The coming wave is, however, characterized by a set of four intrinsic features compounding the problem of containment. First among them is the primary lesson of this section: hugely asymmetric impact. You don’t need to hit like with like, mass with mass; instead, new technologies create previously unthinkable vulnerabilities and pressure points against seemingly dominant powers.”15. Explores containment issues. “However, any discussion of containment has to acknowledge that if or when AGI-like technologies do emerge, they will present containment problems beyond anything else we’ve ever encountered. Humans dominate our environment because of our intelligence. A more intelligent entity could, it follows, dominate us.”16. The race for AI supremacy between China and the U.S. “China is already ahead of the United States in green energy, 5G, and AI and is on a trajectory to overtake it in quantum and biotech in the next few years. The Pentagon’s first chief software officer resigned in protest in 2021 because he was so dismayed by the situation. “We have no competing fighting chance against China in 15 to 20 years. Right now, it’s already a done deal; it is already over in my opinion,” he told the Financial Times.”17. Statements that resonate. “Science has to be converted into useful and desirable products for it to truly spread far and wide. Put simply: most technology is made to earn money.”18. Examines the implications of this coming wave and democracy. “A meta-analysis published in the journal Nature reviewed the results of nearly five hundred studies, concluding there is a clear correlation between growing use of digital media and rising distrust in politics, populist movements, hate, and polarization.”19. Cyber threats examined. “Today’s cyberattacks are not the real threat; they are the canary in the coal mine of a new age of vulnerability and instability, degrading the nation-state’s role as the sole arbiter of security.”20. The dangers of highest of high tech in the hands of a few. “This raises the prospect of totalitarianism to a new plane. It won’t happen everywhere, and not all at once. But if AI, biotech, quantum, robotics, and the rest of it are centralized in the hands of a repressive state, the resulting entity would be palpably different from any yet seen.”21. Describes varieties of catastrophe. “AI is both valuable and dangerous precisely because it’s an extension of our best and worst selves.”22. Describes keys to containment. “Deft regulation, balancing the need to make progress alongside sensible safety constraints, on national and supranational levels, spanning everything from tech giants and militaries to small university research groups and start-ups, tied up in a comprehensive, enforceable framework.”23. Defines the key problem of the twenty-first century. “The central problem for humanity in the twenty-first century is how we can nurture sufficient legitimate political power and wisdom, adequate technical mastery, and robust norms to constrain technologies to ensure they continue to do far more good than harm.”24. Lists the ten steps to containment. “There’s a clear must-do here: encourage, incentivize, and directly fund much more work in this area. It’s time for an Apollo program on AI safety and biosafety.”25. Notes and a link to the bibliography provided.Negatives:1. If you have watched Suleyman’s YouTube Videos there is very little new here.2. Emphasis of speculation over technical details. I wanted more details; if the purpose is to keep the book accessible such details can be included in an appendix.3. Redundancy.4. Lack visual supplementary material: charts, diagrams, and sketches to complement the narrative.5. Lacks a formal bibliography.In summary, this is an important topic a plea of sorts to global leadership and subject matter experts to watch this issue carefully and to take the proper precautions to contain the worst of AI’s potential. Suleyman makes clear that AI has potential for much good but also to catastrophe if humanity loses containment. The Coming Wave of AI and synthetic biology creates immense challenges for humanity, are we ready for it? An excellent, provocative book. I highly recommend it!Further suggestions: “Superintelligence” by Nick Bostrom, “The Age of AI” by Matt Ridley, “AGI: Age of Superintelligence” by Richard A. Mann,“The Singularity Is Near” and “How to Create a Mind” by Ray Kurzweil, “Our Final Invention” by James Barrat, “Surviving AI” by Calum, “ Chace, “When Computers Can Think” by Anthony Berglas, “What to Think About Machines That Think” edited by John Brockman, and “Rise of the Robots” by Martin Ford.

I was apprehensive about the rapid pace of AI development and its potential societal impacts. While some concern remains, the book “The Coming Wave” by Mustafa Suleyman, co-founder of two major contemporary AI companies Google DeepMind and Inflection AI, offers a glimmer of hope.Suleyman predicts that “AI has been climbing the ladder of cognitive abilities for decades, and it now looks set to reach human-level performance across a very wide range of tasks within the next three years” (9). Published in 2023, this prognosis gives me pause as a college student. Will emerging technologies automate the white-collar jobs that leverage human cognitive skills?Suleyman’s unease extends beyond career disruption. He worries that swift improvements in technologies like robotics, quantum computing, nuclear fusion, and especially AI and synthetic biology will profoundly transform society.Historically, the proliferation of transformative innovations has become self-perpetuating. As Suleyman notes, “As demand soared, costs plummeted. One analysis estimates that the introduction of the printing press in the fifteenth century caused a 340-fold decrease in the price of a book, further driving adoption and yet more demand” (30).He observes this pattern recurrently, most recently with smartphones saturating the global consumer market in just a few years: “Now the number of computers, smartphones, and connected devices is estimated at 14 billion. It took smartphones a few years to go from niche product to utterly essential item for two-thirds of the planet” (33).The problem with new technology is that often the sweeping impacts of new technologies are unintended even by their creators. As Suleyman writes, “Gutenberg just wanted to make money printing Bibles. Yet his press catalyzed the Scientific Revolution and the Reformation, and so became the greatest threat to the Catholic Church since its establishment” (35).He continues: “What’s different about the coming wave is how quickly it is being embedded, how globally it spreads, how easily it can be componentized into swappable parts, and just how powerful and able all broad its applications could be” (112). While innovations like AI can be leveraged for social good, vulnerabilities remain for misuse and unintended consequences.What drives leading technologists and scientists to advance capabilities so rapidly, even when risks are apparent? Beyond financial incentives, Suleyman points to status-seeking and ego: “Scientists and technologists are all too human. They crave status, success, and a legacy. … AI scientists and engineers are among the best-paid people in the world, and yet what really gets them out of bed is the prospect of being first to a breakthrough or seeing their name on a landmark paper” (140).Reading this gave me pause for self-reflection. I’ve envisioned starting my own company primarily for financial success. However, if I’m honest with myself, ego also motivates my ambition – the desire for recognition, influence, and fame.This was a surprising personal insight. I had seen myself as independent and indifferent to others’ opinions. Yet I likely harbor an underlying craving for status, rooted in a childhood lacking leadership opportunities. I perhaps even envied the popularity of others then in unconscious ways.From an evolutionary view, it’s natural to seek elevated standing. Higher status can confer greater access to resources, furthering the core purpose of survival and reproduction. My own social programming probably tapped into these innate drives without conscious awareness. Suleyman’s book helped illuminate these hidden motivations.Back to the book, statistics on pandemic misinformation were striking: “Early in the COVID-19 pandemic a blizzard of disinformation had deadly consequences. A Carnegie Mellon study analyzed more than 200 million tweets discussing COVID-19 at the height of the first lockdown. Eighty-two percent of influential users advocating for “reopening America” were bots” (172).As a regular X (formerly Twitter) user, the notion that viral spread could be manufactured horrifies me. I could grasp Suleyman’s apprehension – deception scaled globally, programmed automatically by AI.So how can we prepare for The Coming Wave?Suleyman focuses more on societal precautions than personal guidance, at least that’s how I felt. Investing in leading tech firms seems prudent: “NVIDIA’s share price rose 1,000 percent in the five years after AlexNet” (130). “Those huge centralized coming-wave firms (Google, Apple, Amazon, etc) I just mentioned? They likely end up bigger, richer, and more entrenched than businesses in the past” (190).However, even experts make poor predictions sometimes: “Early in the decade IBM’s president, Thomas J. Watson, had allegedly (and notoriously) said, “I think there is a world market for about five computers” (32). “Only a few isolated researchers like Geoffrey Hinton and Yann LeCun kept them going through a period when the word “neural” was so controversial that researchers would deliberately remove it from their papers. It seemed impossible in the 1990s, but neural networks came to dominate AI. And yet it was also LeCun who said AlphaGo was impossible just days before it made its first big breakthrough. That’s no discredit to him; it just shows that no one can ever be sure of anything at the research frontier” (130).So Suleyman’s outlook may also prove inaccurate. Still, his book left me pensive yet grounded about the future.

A huge doom and gloom senario. The human race has been through far worse than AI will ever cause. It’s hard to say who really wants to control who. It’s a great idealistic dream, but control of AI will never happen for the controllers that want to regulate it. It will never be controlled. It seems that this book is trying to establish that AI control is possible…Lol…Human behavior will never allow control. Way to narrow in scope in regard to people behavior and day to day human existence. A lot of personal and group lofty human self importance to push voodoo and magic and fear…… The old doom and gloom fear game is a old as the first humans in existence. Another nuclear winter threat. Society will find that AI will find it’s place like every other new idea. It is not that important just another thing for what group of humans are going to gain more power and control over another group of humans. Is it necessary ? Of course, who will control the world ?Yes there is no doubt it will destroy lives. I recommend take it easy kick back and enjoy the ride. It’s going to be fun to watch.

I’ve read lots of books on evolutionary, socio-, and other kinds of biology; as well as many related science books — they tend to be informative, well-researched, and turgid. What really distinguishes this book is that the author actually makes his subject matter fun. Easy to read. Amusing. The concept of telling this kind of history through the invention of the people behind the history is innovative and makes the book feel more like you’re somehow reading a biography (albeit a made up one). All in all, one of the best books of its kind around. Highly recommended.

Interesting and a fun read. Short chapters make it easy to read at your leisure.

I have had a growing set of books to use in the lavatory and to get in that library there has to be short and interesting items that I can use to keep busy. This one is quite wonderful as well as his How to Survive History. Funny and smart. Hard to pull off but voila!

I love this book and have bought many copies of it for gifts.Cody Cassidy writes about the discoveries made by archeologists with a sense of humor and awe. There are 17 chapters. I loved them all but my favorite was Who Invested the Wheel? Historians feel it had to at first been a toy because trying to invent an axel on a large cart would have been really tough. Also, the invention of the axel had to have happened when people were involved with metals, because nothing other than metal would have been tough enough to engage with the wheel. In any case, a toy horse on wheels was discovered in the grave of a child 5400 years ago. Historians feel the mother must have made it for her child. But a father could have, too.

Cody Cassidy chose interesting points in human development and made compelling arguments for their inclusion as pivotal to our societal development (such as who drank the first beer or who ate the first oyster). I love that he names each character and explains his choices (a woman probably ate the first oyster and drank the first beer, but a man probably performed the first surgery).The book reads as a series of fun short essays and combine anthropology, sociology, primatology and story telling to make a compelling and quick read.

This is a GREAT book that gives a view of history WAY before history was documented in any way. These people changed our lives, and changed the world, and nobody will ever know their names (well, maybe one – the person who ‘invented writing’. Short, easy to read chapters about each discoverer. Can’t wait to share it.

My husband, who isn’t usually a reader, devoured this book, and he highly recommends it as a source of unique, fascinating information.

This is just delightful – full of fun stuff. A good read.

Stephen Witt’s “The Thinking Machine” is an exploration of the captivating and often enigmatic world of artificial intelligence. Witt’s ability to weave intricate narratives with meticulous research makes this book not only a fascinating read but also an invaluable resource for anyone intrigued by the advancements and implications of AI.Despite the complexity of the subject matter, Witt’s use of accessible language makes “The Thinking Machine” a suitable read for a wide audience. He avoids jargon and explains technical terms in a straightforward manner, allowing readers from various backgrounds to grasp the concepts with ease. I wrote an AI overview for a broad audience and understand its difficulty from experience.A crucial aspect that Witt addresses in “The Thinking Machine” is the environmental impact of AI, particularly the power consumption of data centers that support AI technologies. Witt sheds light on how the growing demand for AI applications contributes to increased energy usage, raising important questions about sustainability. The intricate nature of AI systems, characterized by continuous operation and complex computational demands, necessitates a strategic approach to energy efficiency. Data centers must prioritize optimization of resource usage, reduction of energy consumption, and facilitation of renewable energy transitions.The recently published book “Making IT Sustainable: Techniques and Applications” by Academic Press provides further insights into these critical developments, offering a comprehensive exploration of sustainable IT strategies and challenges of AI development.

The Thinking Machine is the story of Nvidia and mainly its founder Jensen Huang. It is both engaging and illuminating and takes the reader through the company’s whole history and dramas. The visionary leadership of Jensen is a common thread throughout and the author discusses how AI is affecting our outlook and Jensen’s disregard of any negativity on the potential misuse of his hardware. Given the importance of Nvidia this is a really great book to get to know the company’s history and journey to where it is today, which is somewhat miraculous.The Thinking Machine really takes the tour of Nvidia as a byproduct of the biography of Jensen Huang, one of the three founders of the company. The author starts by telling where Jensen came from, his early childhood and subsequent college experience, how he met his wife and how he navigated his career in Silicon Valley. The author then brings the reader through the founding of Nvidia, the Denny’s Jensen worked at but still goes to and the early days of computer graphics which was quite the mess. The author then takes the reader on the journey of Nvidia and how it got its first early adopters, how they consolidated an industry through relentless competition and how there were many instances of near failure. The path through the internet bubble is discussed and then the slow recovery riddled with bumps.The author spends a lot of time on the growth of parallel computing, the role of GPUs and the development of CUDA. CUDA takes up a lot of the book as its development was not appreciated by the market for which activists saw it as an unnecessary cash-burn. The story discusses how parallel computation in scientific research was a core customer for NVidia’s program but was not the killer app needed to sustain the business. The evolution of neural networks and their enablement from hacked GPUs takes up several chapters and one learns about Hinton, Sutskever and the early pronouncements of Elon Musk, This is where the story really picks up as the growth of use cases in academia for neural networks was immense but image classification was not chat GPT, so all of this laid the groundwork for the eventual growth in large language models. The author discusses Google’s role in being the R&D center responsible for the transformer architecture but also how the team disbanded given the overall lack of interest in developing neural network architectures but the story goes back to the further developement of all of this as well as how Nvidia was at the center because of Cuda and its difficulty in replacement. The author briefly discusses AMD and Jensen’s blood relation to Lisa Su, but the author then moves into the existential threat of AI as believed by Hinton and felt by the author himself.The book ends with a discussion of how Jensen felt and responded to these criticisms of the potential endpoint of AI and his complete dismissal of such concerns. There are personal stories which give some perspective into the mind of the man and gives a perspective that would have been hard to grasp in the absence of this book. Overall there is a history here woven into an entertaining story. The reader will gain a picture of the technology, the history, the roadmap and the future with this technology more and more embedded in it. Definitely worth the read for its entertainment value.

The Thinking Machine:Jensen Huang, Nvidia, and the World’s Most Coveted MicrochipBy Stephen WittThe Thinking Machine: Jensen Huang, Nvidia, and the World’s Most Coveted Microchip by Stephen Witt describes the evolution of Nvidia (pronounced IN-vidia) under the leadership of founder and CEO Jensen Huang (“Jensen”) as well as parallel developments in computer technology and Artificial Intelligence in the 21st century.After graduation from Oregon State, Jensen worked at LSI Logic where despite his youth, he quickly rose to a leadership position. In the early 1990s together with two colleagues from Sun Microsystems, Nvidia was formed to provide chips for PC video games. With the support of Will Corrigan, founder and CEO of LSI Logic, Nvidia secured an initial round of venture funding. Like many entrepreneurial startups, Nvidia had its ups and downs. In 1997 with funding about exhausted its Riva 128 (Real-Time Interactive Video and Animation accelerator) took off selling over 1 million cards in four months.Gamers and game designers constantly want more features: pixel shaders, multiplayer combat, increased screens per second, these demands were straining traditional chip capabilities. At that time most chips processed one instruction at a time sequentially. Jensen began to explore a chip that would process multiple instructions at once, referred to as “parallel computing.” This technology was at the heart of Nvidia’s GeForce, referred to as a Graphics Processing Unit (“GPU”) that ushered in a golden age of gaming in the early 2000’s.Jensen believed that parallel processing could also be more effective with certain scientific applications: weather simulation, Black Hole modeling or breast imaging. Nvidia invested heavily in Computer Unified Domain Architecture (“CUDA”), but the demand for this technology was limited. CUDA launched in 2006 was a drag on earnings and by 2013, with Nvidia stock stagnant, investors were losing patience.The human brain (and other animals) recognize objects through a neural network. At the lowest level neurons in the brain recognize points of light, at the next level, neurons combine these points to make a line; then a shape; ultimately a cat that the brain recognizes. Professor Geoffrey Hinton at the University of Toronto believed that a neural network could be created with transistors on silicon serving as neurons. With error correction technology, back propagation, a computer neural network could self-correct, learn to identify objects. In 2012 he tasked Alex Krizhevsky and Ilya Sutskever, two students with recent PhDs in computer science to create a neural network with CUDA. The two graduates pooled their savings, $1,000, to purchase 2 GeForce GPU’s from Nvidia and in Krizhevsky’s bedroom tried to create a neural computer network about equivalent to the brain of a honeybee. At first the results were awful, but then the network began to learn: correct identification “moving to 1 percent, then 10 percent, then 40 percent, then 60 percent ….” (page 124). They entered their network in a 2012 Artificial Intelligence contest recording a stunning 80% correct identification, 10 points above the nearest competitor. Shortly thereafter, Krizhevsky presented these results at an Artificial Intelligence conference in Florence and, overnight, a discipline was transformed.Let me pause to reflect, in an age with one-hundred million dollar + research budgets and huge science teams it is heartwarming that two recent graduates with a $1,000 capital budget working in a bedroom develop a Nobel Prize winning technology that transforms a discipline.Google, Microsoft, Facebook and numerous other companies and research institutions immediately recognized the potential of this technology. Nvidia has the chip design and has worked closely with Taiwan Semiconductor who manufactures these very complex devices; Nvidia has the software support. The billions of dollars invested to develop Artificial Intelligence has led to dramatic revenues and earnings gains for Nvidia as well as staggering investment returns (this is not an investment recommendation).For many years parallel computing and neural networks were backwater technologies. Jensen with imagination, hard work and persistence, as well as the assembly of a very capable team, and Hinton, Krizhevsky and Sutskeyer with the development of neural networks on silicon drove the revolution in artificial intelligence. This book is the history of that evolution.Julian Schroeder

I don’t normally have the time for reviews anymore but I had to do one for this book. This book is excellent. The level of detail and range of topics was just right. With some books I’ve had to force myself to finish. This one kept me interested throughout the entire book and provided everything clearly. I’m more interested in usage of foundation models (LLM’s, RAG, etc.) but the chapters on model pre-training/training/evaluation provided great detail. I’m looking forward to more works from Chip.

It’s always daunting to pick up a technical book that’s over 500 pages long or 21 hours long. However, this book did not disappoint. Not every section, of course, addressed my particular needs. However, the entire treatise was clearly communicated with a broader technical audience in mind. That should be no surprise because Chip Huyen, besides being an AI expert, taught graduate school classes in AI at Stanford and writes science fiction as a side hobby. This book is simply the best technical introduction I’ve encountered to date.The book starts with high-level concepts about AI, which would be accessible to all sorts of scientific folks. Then it focuses on technical topics that are of most interest to engineers. It does an excellent job of centering around concepts first and not being wedded to particular technologies which will soon change. I valued the insights so much that, after listening to the audiobook, I even bought a paper copy to have for a reference.I plan to continue to read about AI engineering, but given that I haven’t taken formal coursework in the topic, this book served as an equivalent to a graduate school class to give me confidence to dive deeper. Although some math were presented, the audiobook was incredibly accessible, unlike with some technical books. For those who spend time commuting in cars, I recommend listening to the text if you don’t have time to flip through a paper book.Overall, this book raised my game significantly about AI. Where other books obscure with technical jargon, this book enlightens with clear concepts. I still need to brush up on a few focused topics to ready myself for a project, but I’m much more fluent about the ideas than before. I highly recommend this in-depth introduction, at least for the next few years until the field outpaces our knowledge once again.

Fantastic book, very useful skills to apply to my job as an MLE immediately! Each chapter goes super in-depth and covers different approaches, weighing the pros and cons of each. Chapters discuss theory, production, and business, with a ton of citations and visuals throughout.

Chip Huyen has done it again—delivering a smart, thorough guide that takes readers step by step through complex material with remarkable clarity. Through simple, accessible examples, she empowers readers to achieve their goals. The modular structure allows experienced readers to navigate at their own pace, while her unmatched coverage of practical applications sets this work apart.Her approachable tone builds reader confidence, ensuring full comprehension of the material. Well-documented and diverse sources provide a robust foundation, while her presentation style—concise, clear, and thoughtfully structured with short, easy to digest paragraphs—creates an ideal learning experience. Important points and deeper insights are segregated and clearly marked for easy reference.This resource will undoubtedly become a valued reference, likely to evolve alongside the field itself. Thank you, Chip! A worthy successor to your first volume — and we eagerly await your next contribution to the field. ~ Denise Shekerjian, author Uncommon Genius (Viking, Penguin)

A must have for ANYONE willing to understand the models, applications, and how everything works inside with a view on how it can be applied outside.Really great job, Chip Huyen!

Great comprehensive book on AI engineering. This book simplifies the concepts and techniques of advanced AI development with practical applications across Generative AI

In academia, there is the concept of a “review article” — it summarizes and organizes the major research findings into a framework that makes it easy to come up to speed on a topic. Frequently, the review articles themselves end up defining the area, and this is what Chip Huyen manages to achieve in this comprehensive book. The quality of the writing and diagams are uniformly high — Chip uses simple language to great effect.I think of myself as being somewhat up to date, but I have learned something new every chapter and not just minor details. For example, I had missed the Deep Mind paper pointing to “self-delusion” as the reason for hallucinations. Chip provides a clear explanation and shows an example. This fundamentally affects my intuitive understanding of model errors.Of course, there’s a danger with writing a review of a fast moving field. Just today, DeepSeek published an article showing that they can avoid SFT altogether and do just train a model on preferences, alphago-style. If this takes off, Chapter 7 will need a second edition.Strongly recommend this book. It’s invaluable for anyone building applications using GenAI models.

I enjoyed the full book. It is detailed enough to understand the fundamentals but recommend you other materials that go even deeply.The narrative is excellent as well.I really like the contrast with real applications, so it’s not only theoretical but also practical.

One of the most thought provoking books I’ve read. Karp does a great job articulating the malaise the US currently finds itself in and asks pointed questions that we all need to ask and answer if the United States is going to remain true to the ideas and culture on which it was founded. Through numerous citations, Karp takes on the journey of how the US has lost its way and become, in many important respects, a shell of its once great self. How did we loose our National identity? Why is it so important that the technology sector re-engages with public/private partnerships to create the most dominant versions of Artificial Intelligence in the world? Why have we waisted the talents of bright engineers on making the next ‘App’ instead of building something truly revolutionary? The iPhone is a great example of this. Updated but hardly ‘innovated’ since its beginning. Apple Corp. has gone from making the sides angular to rounded, to angular, to rounded over and over. The side buttons have gone from round to elongated. So? Where’s the functionality besides being a ‘library, mailbox, and home shopping device’? There are hundreds of Apps to choose from to take your hard earned dollar every month, but use Peter Thiel’s comment about current technology as a summary example; “We wanted flying cars, instead we got 140 characters”.Karp recommends The United States needs to find a collective purpose. A purpose that President Kennedy described in his famous Moon Speech in 1962 “…(a) goal that will serve to organize and measure the best of our energies and skills.” Karp advocates for this. He doesn’t provided a checklist on how to accomplish it, but in todays age where the thought police are seemingly everywhere, just writing this book is daring enough and should provide the necessary shove we need to get a discussion started.If you’re frustrated about the current state of the US and want something better, then I don’t think you can find a more articulate book to describe the situation we find ourselves in and explore ideas of what we can do to get ourselves on a better path forward.

This book addresses the myopic vision of many in silicon valley. And points out that the USA will fall from graces if we stay on our present course of copying the EU or world global elites that avoid war and pay off adversaries. I was hoping to gain more insight into( PLTR) Palantir company. Instead it is an essay format on the USA fall from a high position of being technologically advanced.

This is a cultivated and persuasive book about putting our best private tech minds and government funding to work on defending the West in general and the US in particular. It’s also a pitching exercise for Palantir as the company which does this the best. Protection would be both external by protecting troops and winning wars, and internally reducing crime and making rough neighborhoods safe. This, for example by using AI to analyze and preempt enemy/gangs movements or use intelligence software to coordinate drones amidst other tools. The book is dense in quotations and examples of past achievements of the West and its figures to argue that we are privileged to live in this area and time and should honor its past to protect its future. It preempts also tech AI community objections to work on defense as saying its about time bright minds from silicon valley stop designing better apps for shopping or socialize and engage in something with higher a purpose. Its very persuasive for both external and internal purposes, saying those who shy away from using high tech in dangerous community show moral disregard for all the victims of crime who live in those communities. An interesting quote regarding this is that “a tolerance for everything to devolve in support of nothing”. It is very pragmatic with several on target criticisms of those who just say pompous things because they sound good but leave serious problems unresolved. Overall is an ambitious complex book which hammers the message above in multiple aways without going to deep in the science of how this would be accomplished but it does provides an overview of how Palantir’s AI intelligence software would mimic decentralized coordination of bees and starlings, adapting to different circumstances.

I am a little disappointed in Dr. Karp. There were many good insights, which I wish the book explored further. But this book lacked refinements in its analysis and opinions.Fundamental questions about the West, and what exactly the authors are advocating for on a systemic level (not technological), were not answered. Its call to action was more limited and narrow than I hoped.The historical examples put a superficial gloss on figures like Oppenheimer. The book seemed tailored to advocate to a certain kind of person, but lost something along the way.Finally, we are indeed a lawyerly society. It’s true we are not China. Red tape and litigation slows things down. But if the USA’s identity hinges on anything, it is the law – our Constitution. So what are they advocating for? We must protect the West (to me, law and science) by putting aside those values and systems for faster engineering and tech? I wasn’t sure.That said, I agree we are a great country and the West has uplifted many people. We would benefit from a renewed sense of shared purpose. Social media and consumer product software is shallow and has hollowed out our country’s internal moral fiber. Duty, mission, responsible government, and generational-thinking are virtues.But making killer drones and Palantir being the next big defense company is not nearly ambitious enough. Climate change and pollution are destroying our planet. I’d like to see AI and big tech focus on solving generational problems that impact us all. Freshwater resource depletion, food security, microplastics, nuclear waste and energy, environmental remediation, and man-made climate change and adaptation. These are what must be the Manhattan Projects of the 21st century.

A very well written and timely book in one of the most dynamic fields of our times. This volume transcends foundational LLM utility, diving into the strategic implementation of Agentic AI systems. The authors, grounded in Intelligent Automation, deliver a structured framework encompassing maturity models and phased roadmaps. It rigorously defines the shift toward autonomous execution engines that utilize LLMs for complex, goal-driven orchestration across enterprise architecture. The text clarifies the critical gap between content generation and actionable intelligence, asserting that agents build compounding competitive advantage through persistent learning loops. For CTOs and engineering leadership, this is a mandatory, timely primer on architecting the next era of intelligent systems.

“Agentic Artificial Intelligence” is a must-read for leaders and innovators who want more than AI buzzwords. It clearly explains how agentic systems, intelligent AI agents that act and collaborate and also how they are reshaping business and daily life. The authors combine real-world transparency with deep insight, showing both how to harness these tools and the mindset needed to lead in this new era. The case studies and frameworks are realistic and very actionable. Great book!This book doesn’t just predict the future—it shows how to build it responsibly. A timely, empowering guide for anyone serious about leveraging AI for meaningful impact.

Agentic Artificial Intelligence is serious reading. The book resembles a novel at about 530 pages. That said, it is 100% approachable by non-technical and technical readers alike. Examples of prompts and use cases abound, however, most code is placed in the appendix to the book.A broad array of expertise is found in the assembly of co-authors to this book. Like any responsible AI book, there is a discussion of the development of AI in the BC era (before ChatGPT) which lends needed context and understanding. Broken into parts which encompass two to several chapters each, the book shines its light on “what can AI do?”, “how does it do it?”, “how can one monetize AI and develop entrepreneurial offerings, many of which are just beginning to be imagined?”, and “what does the future of AI and work look like?”.Most significantly, the co-authors are practitioners in the field. AI will fail, drain resources, and weaken organizations unless the organization’s culture and team members are part of a positive change management process. Tales of failures and skinned knees are frequently encountered in this book as well.What I appreciate about this book is the ability to hear the authors genuine voices and appreciation for each other. They frequently “hand off” the story to another author who has specific expertise in a related area. While not a play or a script, the tone of the authors together is cordial, uplifting, and encouraging.I recommend Agentic Artificial Intelligence to any executive and manager with a need to successfully lead their business into the next age of human-artificial intelligence collaboration. This guide will be helpful to AI entrepreneurs and software developers as well.

I am listening to this on audio book and realized there was just too much I would highlight if I had the book so I went ahead and scooped it up for a pretty reasonable price compared to a lot of new books on this subject matter.The cons:1. There is a ton of historical content and examples which may be great for some, but I personally like to focus on the actionable stuff more. I’d say at least 1/5 of this book could have been cut.2. The authors DEFINITELY used AI to help write this – it shows in a few too many ways. Particularly amusing in the audio format. Not a deal killer at all but it does lack that human feel most of the time.The pros:1. Mostly agnostic to specific technologies so this could be useful to you regardless if you are going all code or nearly like CrewAI or using low-code/nocode tools for creating agents.2. Structured well and easy to understand3. Not overly opinionated but highly optimistic of what is currently happening and what is to come in AI4. Large pool of authors and expertise with excellent relevant experience makes this feel current and reliable.

Pascal and team demystify a lot of the concepts about Agentic AI, and address the topic head-on with not only a comprehensive technology assessment and roadmap for success, but also with the human elements related to the future impacts and opportunities that this technology presents. Wonderful read and I would put it at the top of the list for anyone interested in AI Agents and their potential. I would also highly-recommend the book Intelligent Automation, also be Pascal and others. It will enlighten you as much as this book will.

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 24×7 approach but a 25×8 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 25×8 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.