John Collison

So then, technically, what I'm most excited about is all of the

rapid progress in AI and the world models, the foundational model work.

And it is just such a massive boost to how much we can simplify the system, how much we can bring down the cost and how we can scale globally.

And there's some magic that happens that I don't think I would have anticipated, you know, a few years ago.

So that I find from the technical perspective, just

I think it's seeing the capability and the scaling laws from this approach of starting, you know, with that cornerstone of the foundational model and then specializing to T-shirts and then, you know, distilling.

It just, you get such big wins in performance across the board.

I understand.

You, you,

invest something into the architecture or get better at data or training recipes, and then you invest in that early stage, and then it just has massive amplification and ripple effects.

So that in some ways is magical.

Then you see it on the car, and I've had some moments where a car does something, and you look at a log,

And I've been surprised.

Like, it does things that I didn't think it was capable of doing.

So it's that... When you see emergent behavior, that's kind of a proud moment.

One example, yeah.

When you build a system and then you think you understand...

you know, how it works and you understand fully, you know, the limits of its capability and performance, and then it does something, you know, kind of almost magical, it's exhilarating.

So one example I can give you, I think I've shared some videos of that publicly in some talks, was this example where the situation that happened in San Francisco, you know, fairly

benign situation we're at an intersection our light is red there's new cross traffic a bus goes by and you know it stops partially blocking our light light turns green so we start to go we're nudging around the bus and then you see a pedestrian being detected on the other side of the bus and