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Jensen Huang

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Jensen Huang in 2023

Jen-Hsun "Jensen" Huang (Chinese:黃仁勳) (born February 17, 1963) is a Taiwanese-born American billionaire businessman, electrical engineer, and the co-founder, president and CEO of Nvidia Corporation.

Quotes

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  • There’s a language to proteins, there’s a language to chemicals, and if we can understand the language and represent it in computer science, imagine the scale at which we can move, we can understand, and we can generate. We can understand proteins and the functions that are associated with them, and we can generate new proteins with new properties and functions. We can do that with generative AI now, now all of a sudden those words make sense and now they’re connecting and fired up and are now applying it to all of their fields of their own companies and see opportunity after opportunity for themselves to apply it.
  • The inference, the scale of inference business has gone through a step function, no doubt, and the type of inference that is being done right now where you know that video will have generative AI added to it to augment the video either to enhance the background, enhance the subject, relight the face, do eye reposing, augment with fun graphics, so on and so forth. All of that generative AI work is done in the cloud and so video has generative AI. We know that there’s imaging and 3D graphics for generative AI, video for generative AI.
  • The computing fabric that compute connects processors needs to be quite high speed. The faster the processors, the greater need for high speed computing fabrics and so it’s a matter of scale and the effectiveness of the scale. For example, if you want to increase to 1000 processors, the linearity of that scale up would be less linear and it would plateau earlier if the interconnects were slower and so that’s basically the trade-off. It’s just a matter of how far can you scale and what is the effectiveness of the scaling, the linearity of the scaling.
  • Ultimately every company needs to have diversity and resilience, that resilience comes from diversity and redundancy and in order to a achieve diversity and redundancy so that every company can have greater resilience implies building fabs in the United States and elsewhere, and those fabs are incrementally more expensive. In the grand scheme of things, those have to be taken into consideration. And so, there’s a price to be paid for diversity and redundancy and we invest ourselves in our company and every large company in order to have resilience. There’s power redundancy, there’s storage redundancy, there’s security redundancy, there’s all kinds of redundancy systems. Even organizations — sales and marketing are dovetailing each other so that they can have some diversity and some redundancy so that you have greater resilience, engineering does the same thing.
  • We build our entire system full stack, and then we build it end-to-end at data center scale but then when we go to market, we disaggregate this entire thing. This is the miracle of what we do, we’re full stack, we’re data center scale, we work in multiple domains, we have quantum computing here, we have computational lithography there, we have computer graphics here and this architecture runs all of these different domains, in artificial intelligence and robotics and such and we operate from the cloud to the edge and we built it in a full system, vertically integrated, but when we go to market, we disaggregate everything and we integrate it into the world’s computing fabric.
  • We already work very deeply with end users and developers who do these things. We do that today and our engagement with the world’s leading important verticals that we focus on, whether it’s healthcare, automotive, of course all the AI startups, we work with some 10,000 AI startups. So industry after industry, if there are industries where we could add a lot of value, the video game industry, we have direct coverage on just about every developer. The automotive industry, we have direct coverage on just about every single car company. The healthcare industry, we’re working with just about every drug discovery company and so we already do that today. It’s just that the fulfillment of the system ultimately comes from somebody else. If you want your stack accelerated, you work with Nvidia.
  • We already work very deeply with end users and developers who do these things. We do that today and our engagement with the world’s leading important verticals that we focus on, whether it’s healthcare, automotive, of course all the AI startups, we work with some 10,000 AI startups. So industry after industry, if there are industries where we could add a lot of value, the video game industry, we have direct coverage on just about every developer. The automotive industry, we have direct coverage on just about every single car company. The healthcare industry, we’re working with just about every drug discovery company and so we already do that today. It’s just that the fulfillment of the system ultimately comes from somebody else. If you want your stack accelerated, you work with Nvidia.
  • If you were building a chip company and you were taping out a chip, the tapeout of a chip is around $100 million, just the tapeout. Not to mention the tools, which are probably another $100 million, and not to mention all the engineers, all the systems you’re bringing up, things like that. In order to build one of our chips, it’s a few billion dollars. And we’re just one chip company. There’s a whole bunch of chip companies. When they tape out a chip it’s no less than $25 million. Writing, developing a large language model–taping out a chip these days, what the software industry is learning is that building these large language models is kind of like taping out a chip.
  • 20 years ago, all of this [artificial intelligence] was science fiction. 10 years ago, it was a dream. Today, we are living it.
    • Jensen Huang
  • Moonshots? How about robots that will design robots that will operate robots that will design new robots.
    • Jensen Huang
  • Never start with a chiplet design; go for the biggest chip you can imagine and build it.
    • Jensen Huang
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