The Exchanges

Q Was there ever any thought in your mind that maybe we're too early? We, we might be the ones who actually make this revolutionary technology, but we might not be the ones to bring it to the promised land.

A Yeah, all the time, but that's, you know, then there was 900 other smart strategies and, you know, the list of new ideas that we came up with to keep the company alive and, you know, successful for just another few more days, and It was countless. And so you're solving the problem both in making sure that you stay alive long enough to proliferate this technology everywhere, looking for every possible way. You're making it easier and easier for people to use this technology. You're teaching people to do it. You're talking to software developers, and you're saying, hey, you know that imaging software that you had? Maybe Photoshop, for example, or some video imaging system for broadcast. Can we modify that so that it runs on CUDA?

AI assessment note: “Yeah, all the time, but that's, you know, then there was 900 other smart strategies”

Q kids gonna do in, in five years? What are the jobs gonna be? I mean, if law firms are using AI for discovery, if consulting firms are using AI for, you know, what, what an entry level job would be if finance firms, et cetera, et cetera. Would you argue that that's a lack of imagination, that we actually are reaching those conclusions because we can't think beyond that scenario?

A Partly. I believe the opportunities, the potential for a new college grad in computer science or computer engineering or software engineering or chip design today is far, far greater than the opportunities and potential when I came out of school. The tools they have to work with is a billion times more capable. It's super highly automated already today, and yet they're busier than ever, and the reason for that is because we have ideas of the things that we want to build that we didn't conceive of at the time without the tools that we have. AI is going to help this next generation of new college grads achieve greater things, build greater things, not take their jobs. For us to scare them into not even want to go to college, Ok, not even wanted to be a computer scientist is a disservice to society. You're not saving anybody. You're talking a whole bunch of people that out of professions that we need in the future.

AI assessment note: “Partly. I believe the opportunities, the potential for a new college grad”

Q you're a publicly traded company, your stock price was terrible, you probably had a lot of pressure from investors. What made you and the people that you worked with say, we're going to keep our heads down and keep going? Because it was a long time. We're talking Six, seven, eight years on this thing that nobody understood and was, and had zero, not zero, but very little commercial success.

A Well, that's, that's when CEOs had to be CEOs. We believed on first principles. This should be quite useful. And I had to believe that it's quite useful. Now the question is, what's the strategy for creating this new architecture for computing that everybody would be able to enjoy? And the problem with computer architectures is this chicken or the egg problem. Let's say you created a brand new architecture. It's incredible. It's the most amazing thing in the world. But computers are built to run software, and if your install base is not large enough, it doesn't attract software developers, because developers want to program on large install-based computers like iPhone and PC, and, and so the problem is, even though we believed that this architecture was going to be incredible, that CUDA was going to be everywhere, Or could be everywhere. How do you get it everywhere? And if you don't get it everywhere, how do you attract the developer? And if you had no developers, who would write the killer app? And if there's no killer app, then why would people buy it? And so the answer was very simple. It was literally sitting in front of us. And it just required enormous sacrifice. The answer was, let's use GeForce, which is the GPU that is now everywhere in the world used for playing video games.

AI assessment note: “We believed on first principles. This should be quite useful.”

Q All right, so I want to jump ahead a little, because in the late nineties, NVIDIA developed a new technology called parallel computing, and this basically gave your chips the ability to perform multiple calculations at the same time, multiple tasks, right? But this was another gamble, because I think a lot of other companies had tried and failed to produce parallel computing chips, right?

A Um, uh, during that time, there were all kinds of different processors being created, and, um, people were trying to come up with new ways of doing computation, and then we realized that computer graphics, if it was just beautiful, but the world was static, it was hard to create beautiful and immersive worlds, and so you really need to find a way to bring physics into that virtual world so that, you know, waters would flow and Yeah. You know, leaves would blow in the wind, and explosions would look like explosions, and, and so we would, we would try to use the processor, which was incredibly parallel, to express, you know, the types of algorithms that represent real-time physics today, and so that was really the beginning of our journey down that world of general purpose programmability. Meanwhile, uh, scientists around the world noticed That NVIDIA's processors were super powerful.

AI assessment note: “we would try to use the processor, which was incredibly parallel, to express”