The Nvidia Way — Tae Kim
Read: November 2026Jensen Huang’s story, told through the company he has run for more than thirty years. Tae Kim’s argument is that Nvidia’s edge is not the chips, the market value or luck. It’s an organizational culture of extreme independence, extreme standards and extreme speed, built around a CEO who believes the company’s worst enemy is its own complacency.
The first third felt like time travel. The PCI bus, the first separate graphics cards, Intel’s integrated graphics, the NV1 flop: all the hardware history behind how a GPU came to exist. The book explains the technical parts in plain terms too, so you don’t need a chip background to follow it.
The CUDA playbook. Mark Harris and the early CUDA team were tiny: a handful of engineers on the driver, the math library and the compilers. Getting it adopted took more than good software. Jensen put CUDA on every GeForce card, not just the expensive ones, so it would be everywhere. Nvidia gave away free cards, recruited academics to teach it (David Kirk flew across the country every other week to lecture), recorded the courses and put them online, and ran summits where scientists could ask for a chip feature and get it. Years later, when AI arrived, developers already knew Nvidia and a decade of bug fixes sat in its libraries. cuDNN, built from the first deep learning experiments, turned that into a product.
The mission is the boss. Jensen’s stock phrases keep people pointed at the right thing: “The mission is the boss.” “Speed of Light.” “How hard can it be?” When someone rambles in a meeting he says “LUA”: listen to the question, understand the question, answer the question.
Whiteboards, not slides. It comes up again and again. A slide deck lets you hide an unfinished thought behind nice formatting, and a whiteboard doesn’t. You also have to wipe it clean when you’re done, however good the idea was, and start over. Nvidia’s culture discourages looking back at either errors or successes.
The Top 5 email. Instead of status reports, which get sanitized until they’re useless, everyone sends their top five things in progress and what they’re seeing in the market, each bullet starting with an action word. Jensen read them as a live feed of the company. After a pile of them mentioned machine learning, he decided Nvidia wasn’t moving fast enough on it.
He looks in the mirror and says “you suck.” That’s his self-improvement method: every morning, no pride in the past, focus on the future. He expects the same from everyone at Nvidia, and the book says he’d rather “torture them into greatness” than give up on someone.
Pain and suffering as advice. Whenever anyone asks Jensen how to be successful, his answer is “I wish upon you ample doses of pain and suffering.” It sounds like a joke but the book treats it as his actual philosophy, and it’s the title of chapter 1. His reasoning: “People with very high expectations have very low resilience. Unfortunately, resilience matters in success.” Greatness comes from character, and character only comes from overcoming setbacks. The hours reflect that view (sixty a week is the bare minimum, and the book doesn’t pretend otherwise).
Engineer CEO vs. business CEO. Jensen walks into a product meeting knowing more about a minor Wi-Fi product than the people running it. The book sets this against Intel, which turned down Apple’s iPhone chip in 2006 because the price was below its forecast cost, and sold off its ARM mobile processor unit the same year. Then it missed AI too. Intel’s CEO couldn’t see where the technology was heading, and Jensen could.
Outrunning, not fencing. “I don’t see Nvidia as some kind of IP fortress. Our strength is just outrunning the other guy.” Three teams on staggered schedules (three chips per design cycle) and “the number one feature of any product is the schedule” are that idea in practice.
Bootstrapping the chicken and the egg. Bryan Catanzaro on ray tracing and AI frame generation: you can’t have AI in hundreds of millions of homes without building it first. It took six years to get the frame-generation model accurate enough, and he points out that most academics can’t spend six years on one project because they need to graduate. The book mentions a lot of PhDs, and this is part of why they end up at Nvidia.
Full stack. Like Apple with the iPhone, Nvidia designs the hardware, software and networking together, where most rivals just make chips. The Mellanox acquisition (InfiniBand) is the networking piece, and the same InfiniBand shows up in the hardware chapter of Inference Engineering, so it was a nice connection to see it from both sides.
“Not everything here is transferable. Most companies can’t ask for sixty-hour weeks and get them, but the whiteboard, the Top 5 email and the habit of answering “I don’t know, but I’ll find out” are cheap to copy.”