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Jensen Huang on why the AI revolution will be similar to the industrial revolution

November 06, 2025

“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 is tokens. We’re producing floating point numbers at high volume for the first time in history. And the reason these floating point numbers have value is because it’s intelligence.”

Jensen continues:

“You can take these floating point numbers and reformulate it in such a way that it turns into English, French, proteins, chemicals, graphics, images, videos, robotic articulation, steering wheel articulation… So now, the world is going to produce an enormous amount of tokens. And these tokens are going to be produced in new types of data centers - we call them AI factories.”

He compares this to the industrial revolution:

“Back in the last industrial revolution, water comes into a machine, you light the water on fire, it turns it into steam, and then turns it into electrons. Atoms come in, electrons go out. In this new industrial revolution, electrons come in and floating point numbers come out. And in the last industrial revolution, nobody understood why electricity was so valuable but it’s now sold and marketed at kilowatt hours per dollar. Now we have 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, enhanced productivity in a whole slew of industries, and a hundred trillion dollars worth of industries on top of us.”

The major problem holding AI back from a lot of these use cases right now is accuracy, but Jensen thinks this will soon be solved. The way to think about these models, Jensen argues, s that the error rate will be cut in half every six months to a year. Which means the accuracy and believability will double at a rate faster than Moore’s Law.

“All the tests that we currently measure these models with, their error rate is reducing in half every six months. And there’s no reason why we shouldn’t expect it all to be superhuman pretty soon.”