Jensen Huang

And that one agentic person has a large language model that we've now developed.

But during test time, that agentic system goes off and does research and research.

bangs on databases and it goes on and, you know, uses tools.

And one of the most important things it does is spins off and spawns off a whole bunch of sub-agents, which means we're now creating large teams.

It's so much easier to scale NVIDIA by hiring more employees than it is to scale myself.

And so the next scaling law is the agentic scaling law.

It's kind of like multiplying the

AI.

Multiplying AI, we could spin off agents as fast as you want to spin off agents.

And so, you know, you and I have four scaling laws.

And as we use the agentic systems, they're going to create a lot more data.

They're going to create a lot of experiences.

Some of it, we're going to say, wow, this is really good.

We ought to memorize this.

That data set then comes all the way back to pre-training.

We memorize and generalize it.

We then refine it and fine tune it.

back into post-training.

Then we enhance it even more with test time, you know, and the agents and agentic systems, you know, put it out into the industry.

And so this loop, the cycle is going to go on and on and on.