Casado is arguing that AI will spread like wildfire wherever the solutions look like its training data, as with code and images, and much more slowly where it has to deal with the physical world. His example is self-driving cars, which he remembers being declared around the corner when Sebastian Thrun won the DARPA Grand Challenge while Casado was at Stanford.
…be much harder, take longer. And I think the economic equivalency is going to be much more difficult. Self-driving is a great example. I remember when Sebastian Thrun won the DARPA Grand Challenge when I was at Stanford. Remember that? And we're like, hooray! Self-driving cars is around the corner.
Seriously.
And it took 20 years and $170 billion of investment to get to worse unit economics than Uber, right? So that's, you know, so I think that like solving like solving real problems where you have to deal with the distribution of the real universe is just much, much harder. And so I view as AI as this general. Tool. It's a very effective tool. It's one where, like, you have a distribution of data, and then hopefully you can apply that to like, you know, some problems. And we're going to apply it to a bunch of stuff. And I think economic equilibrium is going to determine what works and what doesn't work because I think it's a very, very general tool. It's like a universal function approximator. And wherever that nets out, I mean, you know,
one of the things that I said earlier was like how AI was training humans to like use it.
Yeah.
And I very often think about like, you know, some of the problems that seem intractable was because the world is kind of that way. And then we're trying to fit AI or the tools that we're building to manage the world as it is. But in some situations, we actually tend to change the world to fit to…
