The Thinking Machine (Stephen Witt) - Amazon USA Store: https://www.amazon.com/dp/B0D47TP8KT?tag=9natree-20 - Amazon Worldwide Store: https://global.buys.trade/The-Thinking-Machine-Stephen-Witt.html - Apple Books: https://books.apple.com/us/audiobook/human-machine-reimagining-work-in-the-age-of-ai-unabridged/id1359264342?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree - eBay: https://www.ebay.com/sch/i.html?_nkw=The+Thinking+Machine+Stephen+Witt+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1 - Read more: https://mybook.top/read/B0D47TP8KT/ #JensenHuang #Nvidia #GPUacceleration #CUDA #TSMC #AIdatacenter #Highbandwidthmemory #Semiconductorgeopolitics #TheThinkingMachine These are takeaways from this book. Firstly, Jensen Huang and the long game of relentless focus, Witt portrays Jensen Huang as a founder who fused technical vision with commercial ruthlessness and personal resilience. The narrative follows his early experiences in chip design, the founding of Nvidia in the nineties, and the many near misses that forged an appetite for existential bets. Central to this arc is an insistence on building for the future rather than optimizing for the next quarter. Huang backed parallel computing when it was unfashionable, nurtured a culture that prized product discipline, and insisted on end to end control from silicon to software. Witt also captures the human side of leadership under pressure, including how story telling, partnerships with researchers, and a willingness to reframe the company mission kept Nvidia aligned through market swings from PC graphics to mobile headwinds to the acceleration era. The result is a portrait of strategic patience where technical compounding and ecosystem building matter more than flashy launches. Secondly, From graphics to general purpose computing and the CUDA moat, The book explains how a chip once optimized for pixels became the engine of neural networks. Witt lays out the basics of GPU parallelism, then shows how Nvidia transformed hardware advantage into a durable moat through CUDA and a layered software stack. Toolchains, libraries like cuDNN, and integration with frameworks made GPU programming productive for scientists and startups, creating powerful lock in. Witt details key breakthroughs such as the deep learning surge following ImageNet, and how Nvidia capitalized by shipping developer kits, DGX systems, and reference designs. Rather than selling raw chips, the company sold time to solution, reducing friction across the stack. This section also explores how CUDA created a two sided network effect. More developers meant more optimized code paths and better performance, which attracted more developers in a flywheel. Competing accelerators struggled not only to match silicon but to replicate a decade of software, documentation, and community. Thirdly, Fabs, packaging, and geopolitics of the supply chain, Witt takes readers into the industrial realities that made the AI boom possible. Nvidia is fabless, but its fate is bound to foundries and memory suppliers. The book explains how TSMC advanced nodes, CoWoS packaging, and high bandwidth memory from SK hynix, Samsung, and Micron became the true bottlenecks behind chip scarcity. Readers learn why advanced packaging is as strategic as transistor scaling, how yield, substrate capacity, and thermal constraints shape product roadmaps, and why allocation rather than list price often decides winners. The story also engages with geopolitics. Export controls to China, the CHIPS Act, and the imperative to diversify manufacturing reshape the calculus for every player in the stack. Witt shows how a single board may embody a web of dependencies that stretch from Arizona to Hsinchu to Seoul. The result is a lucid view of silicon...