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The Thinking Machine summary

Name: The Thinking Machine
Author: Stephen Witt

Stephen Witt

Jensen Huang, Nvidia, and the World's Most Coveted Microchip

22 mins

Brief summary

The Thinking Machine delves into the complex network of global piracy and the digital music revolution. It uncovers the individuals and technological shifts driving the transformation of how we access and consume music.

Topics

Career Progress Strategy Success Innovation Big Tech Entrepreneurship

Table of Contents

The Thinking Machine
Summary of 8 key ideas

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Key idea 1 of 8

What’s in it for me? Find out how a graphics chip held the secret to today’s entire technological landscape.

If you were deep into playing Half-Life 2 or Counter-Strike in the early 2000s, you probably knew all about Nvidia. Back then, Nvidia’s graphic processor chips, known as GPUs, were all the rage and obsessive gamers loved to show them off in custom see-through rigs with glowing cases.

But few people knew that the advancements Nvidia had made in its gaming chips had actually unlocked the processing power needed for things like machine learning, which would in turn make innovations like Chat-GPT possible. This happened in large part because of Jensen Huang, the head of Nvidia, who kept pushing his company into the field of academic research even when it was far from profitable.

In this Blink, we’ll look at the remarkable life of Jensen Huang and the story of how Nvidia went from niche products to powering most of today’s technology.

A new Kentucky home

The year was 1973, and Jensen Huang was far from home. Born in Taiwan, and raised in Thailand, Jensen was ten years old and waking up in a drafty dorm room in rural Kentucky. His parents had sent him to America to escape political unrest and, through a twist of fate, he ended up at a small, rough-around-the-edges boarding school called the Oneida Baptist Institute. Chances are, his father thought it was an elite private school, when in reality it was more like a reform school.

Being the only Asian kid in a sea of white classmates, Jensen stuck out. To make matters worse, while his English wasn’t great, he immediately proved himself to be one of the smartest kids in school. This made him an easy target for bullies.

But Huang wasn’t a pushover. He pretended to be a martial arts expert and if someone forced him into a fight, he’d be the last one to back down or give up. His classmates quickly learned that Huang had grit. So, being stuck in the middle of rural Kentucky didn’t bother him. He saw it as a challenge – one that only helped to nurture his preternatural self-confidence and determination.

In 1976, when it came time for high school, his father had finally found work in the US and the family was reunited in Oregon. This is when, in the suburbs of Portland, Huang found his people – fellow math and science nerds, and the misfits of the local computer club. He dove headfirst into the new Apple II computer, fell in love with coding, and found a surprising second passion in table tennis. Like everything else, he gave ping-pong everything he had, and within a year, he was nationally ranked.

Around this time is when Huang picked up a job as a busboy at a local Denny’s. Meanwhile, he aced his classes and decided to ignore the lure of the Ivy Leagues and enroll at Oregon State University instead. It was kismet, as during the very first week of classes he met his future wife, Lori Mills. They were assigned as lab partners and the two bonded over homework. Huang considered doing homework his “superpower,” and Lori was apparently impressed.

When he graduated with honors, microchips were starting to take over the world. His timing couldn’t have been better. Skilled circuit designers were suddenly the rock stars of the new tech era, and Huang was ready to join them in Silicon Valley.

The trio’s first chip

By the time Huang proposed to Mills, he was already making a name for himself in Silicon Valley. It was the mid-80s, and 20-year-old Huang was working for Advanced Micro Devices, better known as AMD, one of the Valley’s most influential chipmakers. He still remembers his starting salary – $28,700 – not just as a number, but as a sign of how far he would come.

Huang’s early days at AMD were a hands-on crash course in chip design, but he soon moved on to LSI Logic, where his intensity and work ethic quickly set him apart. This is where Huang came to know two important figures in his life: Curtis Priem and Chris Malachowsky. They were engineers who worked at Sun Microsystems, a demanding client of LSI Logic.

The NV1 goes bust

Part of the business plan made perfect sense. In the early ’90s, there was a growing consumer video game market that the big players like Sun Microsystems and Silicon Graphics were choosing to ignore. But it was also true that a whole stampede of startups were rushing to fill in this gap – to turn high-end graphics tech into something PC gamers could use. The key invention was the “graphics accelerator” – a plug-in circuit board with a graphics chip at its heart.

For Huang, this was an attractive challenge, and after a few meetings with Priem and Malachowsky in the back of the local Denny’s, tinkering with projections, he talked himself into the risk. There was, however, a personal benchmark: if they couldn’t clear $50 million in yearly revenue, it wasn’t going to be worth his time.

The risk pays off

The mood at Nvidia in 1996? “Funereal” would be a nice way of putting it. When Huang was done cleaning house, all that was left was a skeleton crew of 35 engineers. Their survival was based on coming up with a last-ditch graphics card – and fast.

The NV1 disaster was the first time people got a taste of Huang’s leadership style. In a word, it’s explosive. And public. Huang doesn’t scream at you behind closed doors, he does it at meetings and in the open so that everyone gets the message. Even Priem was not safe from Huang’s wrath.

Parallel successes

In the late ’90s, all Nvidia was trying to do was make the best graphics cards on the market. And by committing itself to coming out with updates every six months, it was, to put it bluntly, kicking ass. But in cracking the parallel computing hurdle, it unwittingly created a product for a whole new demographic: scientists.

Since the 1970s, researchers had been trying to create a neural net – a version of artificial intelligence inspired by the structure of the human brain. Many in the field thought neural nets were a waste of time, but in 1986, the tide turned. David Rumelhart, Geoffrey Hinton, and Ronald Williams unveiled the backpropagation algorithm – a way for neural nets to learn from their successes and mistakes and make adjustments, just like a real brain.

Huang’s investment pays off

While most of the world shrugged at what CUDA was capable of, Huang was hiring academic legends like Bill Dally to turn Nvidia Research into a world-class R&D engine.

But Geoffrey Hinton’s AI group in Toronto was aware of CUDA’s power. Among his group was a quiet genius named Alex Krizhevsky who used CUDA and some Nvidia GPUs to build a neural network that could recognize images faster and better than anything that came before. The model was called AlexNet. The machine learning revolution had begun and Huang’s decade of persistence was about to start paying off.

Good news and bad news

The real judgment day arrived on February 21, 2024, the day of Nvidia’s earnings call. Over the past few years, Nvidia had expanded with the $7 billion purchase of the Israeli data center and networking company Mellanox, and it had signed a $100 million deal with OpenAI to train GPT-4. It now controlled 90 percent of the AI chip market, and its chips were seeing gross profit margins of over 90 percent. No one was harnessing raw computational power like Nvidia. But the earnings call would tell the true story.

Beginning just after the stock market closed, it was one of the most listened-to earnings reports in Wall Street history. Colette Kress, Nvidia’s CFO, opened with the news: annual revenues had more than doubled to $60 billion, and the company had an impressive 70 percent gross margin – beating even Apple. The net income was almost $30 billion, more than Nvidia had earned in the past 30 years combined. To put it another way: every employee at Nvidia was now generating a million bucks in profit.

Future threats or endless hope?

The last thing to consider is the future of AI itself. Is the current progress really sustainable?

Putting aside questions of whether AI could become self-aware and threaten humanity, there are already some very real threats to Earth to consider. Nvidia’s rise sparked a huge increase in global electricity demand. Companies like Google and Microsoft are seeing emissions skyrocket due to their ever-expanding data centers. Nvidia’s chips, powering most of them, are massive consumers of electricity. Just a few years ago, their DGX boxes used about the same power as a clothes dryer. Today’s systems are sucking up enough electricity to power entire homes.

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What is The Thinking Machine about?

The Thinking Machine (2025) pulls back the curtain on the company that is powering the current revolution in AI, cloud computing, cryptocurrency and more. It’s the unlikely story of a company that made its name in PC gaming chips, only to turn itself into the most valuable business in the world.

Who should read The Thinking Machine?

Tech enthusiasts
People interested in ethics and philosophy
Business leaders and entrepreneurs

About the Author

Stephen Witt is an author and journalist whose work often delves into the impact of innovation on industries, with a particular focus on the tech world and its social consequences. His book How Music Got Free explores the intersection of technology, music piracy, and the digital revolution. He’s also contributed to publications like the New Yorker, the New York Times, and Wired.

Categories with The Thinking Machine

Technology & the Future Career & Success Entrepreneurship

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