Nvidia
California
United States
Overview
It is sometimes observed that, during a gold rush, the truly profitable ones are those that are selling the shovels. While that grossly undersells the complexity of what chipmaker Nvidia has achieved, it doesn’t ring entirely false, either. For years, Nvidia has been best known as a hardware enabler for the gaming industry due to its development of cutting-edge graphics chips.
But its leadership team, including founder and long-time CEO Jensen Huang, spotted the perfect opportunity for expansion by hitching its graphics processing units (GPUs) wagon to the AI boom. As it turns out, the kind of chips able to handle the complex, simultaneous calculations for computer graphics also turn out to be helpful for the heavy duty math needed in machine learning. This made them a “must have” for data centers around the world, with innovations like Nvidia’s new, powerful (and, at around, $40,000 per unit, expensive) H100 processor perfectly timed to surf the wave of generative AI. Nvidia’s strategic bet has left rivals such as Intel and AMD in the comparative dust. Today, some estimates suggest that Nvidia owns an astronomical 95% of the GPU market for machine learning.
Being in the right place, at the right time, with the right products has helped Nvidia this year soar past the vaunted $1 trillion market cap, putting it in extremely rarefied air among the world’s biggest and most powerful companies. Jensen Huang is now one of the world’s richest individuals thanks to his position in the company. In short, the current AI revolution runs on Nvidia hardware.
It’s not just shovel-selling that makes Nvidia an AI force to be reckoned with, however. It has been cementing its position atop the AI world with no shortage of smart software research as well. Recently Nvidia made a $50 million investment in Recursion Pharma to train AI machines for use in drug discovery.
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But its leadership team, including founder and long-time CEO Jensen Huang, spotted the perfect opportunity for expansion by hitching its graphics processing units (GPUs) wagon to the AI boom. As it turns out, the kind of chips able to handle the complex, simultaneous calculations for computer graphics also turn out to be helpful for the heavy duty math needed in machine learning. This made them a “must have” for data centers around the world, with innovations like Nvidia’s new, powerful (and, at around, $40,000 per unit, expensive) H100 processor perfectly timed to surf the wave of generative AI. Nvidia’s strategic bet has left rivals such as Intel and AMD in the comparative dust. Today, some estimates suggest that Nvidia owns an astronomical 95% of the GPU market for machine learning.
Being in the right place, at the right time, with the right products has helped Nvidia this year soar past the vaunted $1 trillion market cap, putting it in extremely rarefied air among the world’s biggest and most powerful companies. Jensen Huang is now one of the world’s richest individuals thanks to his position in the company. In short, the current AI revolution runs on Nvidia hardware.
It’s not just shovel-selling that makes Nvidia an AI force to be reckoned with, however. It has been cementing its position atop the AI world with no shortage of smart software research as well. Recently Nvidia made a $50 million investment in Recursion Pharma to train AI machines for use in drug discovery.
