Rene Haas

Mitch, you're experts, 20 billion parameters that can be a mix of inference and training doing reinforcement learning where the chip is now helping learn trained areas.

It's almost like the professor.

teaching a student who can also be a student teacher, who can do a little bit of both.

And then there's inference that over time will be very dedicated, and particularly as you get to endpoints that you can't have a GPU that runs at a kilowatt of power.

It's impossible.

Yeah, physical AI is going to be a gigantic market.

I mean, today, quite candidly, they're using... Bigger than data centers?

Yeah, I think so.

Because I think today they largely use repurposed automotive chips, things that have functional safety, compliance around ADAS, but they're not specific for actuators or specific for smaller parts of the joints.

So physical AI, particularly AI that can learn, is I think going to be a giant market because the robots themselves will have

tens of chips, hundreds of chips.

So yeah, from a unit standpoint, it could be huge.

The numbers are going to be well beyond what we see today.

To some extent, although we don't build anything, our business model is we do the design, someone else has the chip built, mostly at TSMC, some at Samsung, even Intel.

But because we are early in the value chain relative to the software ecosystem, in other words, we probably see what people are doing earlier than anybody else because ultimately we're the link between the hardware and the software.

So on export control, yes, to some extent we have a very big lens into it.

Now today, the China ecosystem actually follows the global ecosystem, which is good from the standpoint that every mobile phone in China

It doesn't run Google Android, but it runs a version of Android.

And it leverages the app ecosystem that comes off of Android.

Same thing with autonomous vehicles.