The Nvidia Way: Jensen Huang and the Making of a Tech Giant

by Tae Kim

He told them that he would join them if they could prove that the start-up could eventually generate $50 million in annual sales.

DURING ONE OF NVIDIA’S VERY FIRST board meetings, director Harvey Jones, a former CEO of a leading chip-design-software company called Synopsys, asked Jensen about the NV1: “How would you position this?” At the time, Jensen didn’t realize that Jones was not merely asking about the NV1’s feature set or product specifications. He was asking him to consider how Nvidia would sell the new chip in a highly competitive industry.

For these reasons, and others, it is hard to describe Nvidia’s action as anything other than the construction of a competitive moat. Nvidia made a general-purpose GPU that represented the first major leap forward in computational acceleration since the invention of the CPU. The GPU’s programmable layer, CUDA, was not only easy to use but also opened up a wide range of functions across scientific, technical, and industrial sectors. As more people learned CUDA, the demand for GPUs increased. By the early 2010s, the market for general-purpose GPUs that had once looked moribund appeared to be on the ascent.

NVIDIA BECAME A MATURE COMPANY not as a result of the size of its revenues, the refinement of its internal structure, or the collective brain power of its employees. Rather, it became mature when Jensen learned how to consistently turn the organization away from internal political dysfunction and disorder. Through mechanisms such as direct public feedback, the Top 5 e-mail, and the requirement to present ideas on a whiteboard rather than as a static PowerPoint, Nvidia equips its workforce with powerful weapons in the constant struggle for accuracy and rigor and against groupthink and inertia.

“We can only continue to be relevant if we invest,” Jensen once said. “In my business, if you don’t invest, you’ll be out of business soon.” He believes, in other words, that in the highly technical chip industry, innovative engineering matters far more than financial metrics. That belief is perhaps the single thing that most differentiates Jensen from his peers.

Dally could accelerate Nvidia’s development of GPU technology. For the first fifty years of computing history, the most important chip inside the computer was the central processing unit, or CPU. The CPU is a generalist, capable of performing a wide variety of tasks. It moves from task to task with great speed and can dedicate significant processing power to each operation. Nevertheless, it can handle only a few operations simultaneously because of its limited number of cores, which process only a few computation threads at once. The GPU, in contrast, is optimized for volume over complexity. It contains hundreds or thousands of tiny processing cores, enabling it to break down tasks into numerous simpler operations executed in parallel. While a GPU is less versatile than a CPU, it can vastly outperform a CPU in processing speed for many applications.

For instance, should it be organized around pursuing patents? Molnar thought about it for a while and said, “I don’t see Nvidia as some kind of IP fortress. Our strength is just outrunning the other guy.”

Nvidia has key advantages over other AI chip makers. Similar to Apple’s approach with the iPhone, the company employs a “full-stack” model that optimizes the customer’s experience across hardware, software, and networking. Most of its rivals just make chips. And Nvidia moves faster than its competitors.

The breathtaking speed Nvidia demonstrated was a sign that it will be difficult to dethrone, even though several other large technology companies, including Microsoft, Amazon, Google, Intel, and Advanced Micro Devices, are developing their own AI chips. Nvidia has proved, as it enters its fourth decade, that it can still outrun the competition. Its second, but lesser-known, advantage is pricing power. Nvidia doesn’t believe in building commodity products, because commodities are subject to downward pricing pressure as competition increases. Instead, from the very beginning, its pricing has only gone in the opposite direction: up.

Hiring raw talent is the first essential component of the Nvidia Way. Y Combinator cofounder Paul Graham, who once worked for Yahoo, noticed that once Yahoo started losing the war for the best engineers to Google and Microsoft, the company began slouching toward mediocrity. “Good programmers want to work with other good programmers. So once the quality of programmers at your company starts to drop, you enter a death spiral from which there is no recovery,” he