Jensen Huang compares AI to the industrial revolution because data centers now mass-produce tokens via GPUs, which get transformed into language, images, proteins, and control signals. He calls data centers "AI factories" and points to rapid error-halving trends in models as evidence of coming economic transformation across industries.
Chapters
You've heard me say an industrial revolution. Let me tell you why. We are producing something for the very first time that has never been produced before. And we're producing it in extremely high volume. And the production of this thing requires a new instrument that never existed before. It's a gpu. And the thing that we're producing for the very first time, for the mathematicians and all the computer scientists in the room, for all of you, you know that we're producing tokens. We're producing floating point numbers at high volume for the first time in history. And the floating point numbers have value. The reason why they have value is because it's intelligence, it's artificial intelligence. You can take these floating point numbers, you reformulate it in such a way that it turns into English, French, proteins, chemicals, graphics, images, videos, robotic articulation, steering wheel articulation. We're producing tokens at extraordinary scale. Now we've discovered a way through all of the work that we do with artificial intelligence to produce tokens of almost any kind. So now the world is going to produce an enormous amount of tokens. Now these tokens are going to be produced in new types of data centers, we call them AI factories. Back in the last industrial revolution, water comes into a, a machine. You light the water on fire, turn it into steam, and then it turns into electrons. Atoms come in, electrons go out. In this new industrial revolution, electrons come in and floating point numbers come out. And just like the last industrial revolution, nobody understood why these electricity is so valuable and is now sold, marketed kilowatt kilowatt hours, you know, per dollar. And so now we have, you know, million tokens per dollar. And so that same logic is as incomprehensible to a lot of people as the last industrial revolution, but it's going to be completely normal in the next 10 years. These tokens are going to create new products, new services, enhance productivity on whole slew of industries. $100 trillion worth of industries on top of us. And so this industry is going to be gigantic. Let's start with an example. In 2012, AlexNet was computer vision, ImageNet, image recognition, 82% or something like that, accuracy over the next, almost, not quite 10 years. I think it was like seven years. Every single year, the accuracy error reduced in half, right? Every, every year the error reduced in half, or otherwise known as Moore's Law. Okay, so, so you double the performance, you double the accuracy, and you double its believability every single year over the course of seven years. It's now superhuman. Same thing with speech recognition, same things with, with natural language understanding. We want to believe that the answer that's being predicted to us is accurate. We want to believe that. I have every certainty that we're going to double its accuracy every single year to the point where it is so accurate. And we've, we've largely tested across many of your examples when you interact with it, that you go, you know what? This is really, really good. I believe the answer that it's producing for me, that condition is very important. All of the tests that we currently measure these models with, their error rate is reducing in half every six months. And so there's no reason why we shouldn't expect it all to be superhuman pretty soon.
