Jensen Huang

And so inference chips are going to be little tiny chips and they're not like NVIDIA's chips.

Oh, those are going to be complicated and expensive and

And in the future, inference is going to be the biggest market and it's going to be easy and we're going to commoditize and everybody can build their own chips.

And that was always illogical to me because inference is thinking.

And I think thinking is hard.

Thinking is way harder than reading.

You know, pre-training is just memorization and generalization, you know, and looking for patterns and relationships.

You're reading versus thinking, reasoning, solving problems, taking un-

unexplored experiences, new experiences, and breaking it down into decomposing it into solvable pieces that we then go off either through first principle reasoning or through previous examples, prior experiences, or just exploration and search and trying different things.

And

That whole process of test time scaling inference is really about thinking.

And it's about reasoning.

It's about planning.

It's about search.

And so how could that possibly be compute-lite?

And we were absolutely right about that.

So test time scaling is intensely compute-intensive.

Then the question is, okay, now we're at inference and we're at test time scaling.

What's beyond that?

Well, obviously, we have now created one agentic person.