Software is eating the world, but AI is going to eat software.

Smart people focus on the right things.

The automation of automation, the automation of intelligence, is such an incredible idea that if we could continue to improve this capability, the applications are really quite boundless.

Virtual reality, all the A.I. work we do, all the robotics work we do - we're as close to realizing science fiction as it gets.


A.I. will make it possible for the Internet to directly engage people in the real world, through robotics and drones and little machines that will do smart things by themselves.

I love that the work that we do is so vital to science. We're in a lot of ways at the scientific front line. The work that we're doing to build up the computational defense system for infectious diseases, whether it's finding the vaccine as fast as possible this time or next time to detect early outbreaks.

Without intellectual honesty, you can't have a culture that's willing to tolerate failure because people cling too much to an idea that likely will be bad or isn't working and they feel like their reputation is tied up in it. They can't admit failure.

One of the things I'm excited about is the observation that gamers are creators and creators are gamers too. We used to think of creators as workstation customers and think of gamers as consumers.

It's very clear that AI is going to impact every industry. I think that every nation needs to make sure that AI is a part of their national strategy. Every country will be impacted.

I think that's what's thrilling about leadership - when you're holding onto literally the worst possible hand on the planet and you know you're still going to win. How are you still going to win? Because that's when the character of the company really comes out.

The single most important thing for any processor is getting adoption by software developers.

People are going to use more and more AI. Acceleration is going to be the path forward for computing. These fundamental trends, I completely believe in them.

The challenge for what everybody's seeing in deep learning - the software richness is really quite high. In training, you have to wait days and weeks before it comes back to tell you whether your model works or not. And in the beginning, they all don't work.

There are very few industries that I know of - I mean, there are companies in fashion, in cosmetics. They're developing AI models and training them in the cloud in the beginning. If they're successful, they build their own datacenters and develop the software in their own datacenter, like Uber does.