Economist Writing Every Day

Folks who follow the stock market know that the average company in the S&P 500 has gone essentially nowhere in the last couple of years. What has pulled the averages higher and higher has been the outstanding performance of a handful of big tech stocks. Foremost among these is Nvidia. Its share price has tripled in the past year, after nearly tripling in the previously twelve months. Its market value climbed to $3.3 trillion last week, briefly surpassing tech behemoths Microsoft and Apple as the most valuable company in the world.

What just happened here?

It all began in 1993 when Taiwanese-American electrical engineer Jensen Huang and two other Silicon Valley techies met in a Denny’s in East San Jose and decided to start their own company. Their focus was making graphics acceleration boards for video games. Computing devices such as computers, game stations, and smart phones have at their core a central processing unit, CPU. A strength of CPUs is their versatility. They can do a lot of different tasks, but sequentially and thus at a limited speed.  To oversimplify, a CPU fetches an instruction (command), and then loads maybe two chunks of data, then performs the instructed calculations on those data, and then stores the result somewhere else, and then turns around and fetches the next instruction. With clever programming, some tasks can be broken up into multiple pieces that can be processed in parallel on several CPU cores at once, but that only goes so far.

Processing large amounts of graphics data, such as rendering a high-resolution active video game, requires an enormous amount of computing. However, these calculations are largely all the same type, so a versatile processing chip like a CPU is not required. Graphics processing units (GPUs), originally termed graphics accelerators, are designed to do enormous number of these simple calculations simultaneously. To offload the burden on the CPU, computers and game stations for decades have included on auxiliary GPU (“graphics card”) alongside the CPU.

This was the original target for Nvidia. Video gaming was expanding rapidly, and they saw a niche for innovative graphics processors. Unfortunately, they the processing architecture they choose to work on fell out of favor, and they skated right up to the edge of going bankrupt. In 1993 Nvidia was down to 30 days before closing their doors, but at the last moment they got a $5 million loan to keep them afloat. Nvidia clawed its way back from the brink and managed to make and sell a series of popular graphics processors.

However, management had a vision that the massively parallel processing power of their chips could be applied to more exulted uses than rendering blood spatters in Call of Duty.  The types of matrix calculations done in GPUs can be used in a wide variety of physical simulations such as seismology and molecular dynamics. In 2007, and video released its CUDA platform for using GPUs for accelerated general purpose processing. Since then, Nvidia has promoting the use of its GPUs as general hardware for scientific computing, in addition to the classic graphics applications.

This line of business exploded starting around 2019, with the bitcoin craze. Crypto currencies require enormous amount of computing power, and these types of calculations are amenable to being performed in massively parallel GPUs. Serious bitcoin mining companies set up racks of processors, built on NVIDIA GPUs. GPUs did have serious competition from other types of processors for the crypto mining applications, so they did not have the field to themselves. With people stuck at home in 2020-2021, demand for GPUs rose even further: more folks sitting on couches playing video games, and more cloud computing for remote work.

Nvidia Dominates AI Computing

Now the whole world cannot get enough of machine learning and generative AI. And Nvidia chips totally dominate that market. Nvidia supplies not only the hardware (chips) but also a software platform to allow programmers to make use of the chips. With so many programmers and applications standardized now on the Nvidia platform, its dominance and profitability should persist for many years.

Nearly all their chips are manufactured in Taiwan, so that provides a geopolitical risk, not only for Nvidia but for all enterprises that depend on high end AI processing.