Jensen Huang

And so we were a pre-recording, human pre-recording, and file retrieving system.

That's what a computer is, largely.

to now AI computers are contextually aware, which means that it has to process and generate tokens in real time.

So we went from a retrieval-based computing system to a generative-based computing system.

We're gonna need a lot more processing in this new world than in the old world.

We need a lot of storage in the old world.

We need a lot of computation in this new world.

And so that's the first part of it.

We fundamentally changed computing and the way how computing is done.

The only thing that would cause it to go back

is if this way of computation, this way of computing, generating information that's contextually relevant, situationally aware, that is grounded on new insight before it generates information, this computation-intensive way of doing computing would only go back if it's not effective.

So for the last 10, 15 years while working on deep learning, if at any single moment

I would have come to the conclusion that, you know what, this is not gonna work out.

I think this is a dead end, or it's not gonna scale, it's not gonna solve this modality, it's not gonna be used in this application.

Then, of course, I would feel very differently about it.

But I think the last five years has given me more confidence than the last 10 years, the previous 10 years.

The second idea is computers, because it was a storage system, it was largely a warehouse.

We're now building factories.

Warehouses don't make much money.

Factories directly correlates with a company's revenues.