Jensen Huang explains why NVIDIA works on things before there are obvious financial returns

Long before I saw the paper, I met some people that needed my help on this thing called deep learning. At a time I didn't even know what deep learning was. They needed us to create a domain specific language so that all of their algorithms could be expressed easily on our processors. And so we created this thing in the middle for them. And the reason why we did it was because even though there were zero, I mean, these researchers had no money. And this is kind of one of the great skills of our company that you're willing to do something even though the financial returns are completely non existent or maybe very, very far out, even if you believed in it. We ask ourselves, is this worthy work to do? Does this advance a field of science? Somewhere that matters? We find inspiration not from the size of a market, but from the importance of the work. Because the importance of the work is the early indicators of a future market. Nobody has to do a business case on it. Nobody has to show me a P and L. Nobody has to show me a financial forecast. The only question is, is this important work? And if we didn't do it, would it happen without us? Now, if we didn't do something and something could happen without us, it gives me tremendous joy, actually. You want the company to be lazy about doing things that other people can do. If somebody else can do it, let them do it. We should go select the things that if we didn't do it, the world would fall apart. You have to convince yourself of that, that if I don't do this, it won't get done. If that work is hard and that work is impactful and important, then it gives you a sense of purpose. Does that make sense? And so our company has been selecting these projects. Deep learning was just one of them.