Uncapped #55 | Tony Xu from DoorDash

**Tony Xu** (0:00)
There are battles for attention, you know, bits, and you know, battles for what's happening in the physical world, you know, for the atoms. And that we largely occupy ourselves in the second category.
We are in the war for atoms. Because, you know, one day ultimately, like, what's the point of having a personal assistant if it can't actually do real things for you?

**Jack Altman** (0:24)
Tony, thanks so much for doing this. One of the things I wanted to start with was this concept that, if you'll indulge me on it, because it's something I've been thinking about a bunch, kind of around the idea of, like, AI spend at companies. And I think the trigger sort of anecdote that I saw that I'd like to get your take on was Uber was basically saying, you know, in the first few months of the year, they blew through their whole AI budget. They were just, you know, rampantly consuming tokens. And what did they get out of it? Did they get more rides? Did they get more drivers? Did anything, you know, their margins improve? Like, what happened? And I think they were kind of saying, like, it wasn't obvious.
And you're obviously, you know, the leader of a company that's super metrics-oriented and has executed super well. So I assume you must think about this, but I'm just curious kind of at a high level, how you think about, like, what, you know, what is a large rising line item? And, you know, as it continues to rise, what do you care about?

**Tony Xu** (1:18)
Yeah, I think every company right now is trying to figure that out, where, you know, the ultimate goal of any technology is actually to hopefully solve a problem and actually make things cheaper. I mean, that's the that's the goal of technology. But as you know, especially when you have constrained resources like compute, in the case of these large LLM companies, you know, sometimes you don't get the timing of these things always exactly when you can deliver the outcomes, or you don't get the cost profiles or the efficiency profiles to serve the technology to match exactly when you can deliver the customer outcomes. And I think that's the world in which we live today. So I think every company is trying to figure this out. In terms of how we're thinking about it, you know, first and foremost is like what customer jobs can we actually solve? And so I do think like when you have new technologies, there's always going to be this period of inefficiency and, you know, almost like discovery, where you got this new toy. You don't know exactly what you need it for. You probably kind of know that you don't really need a, you know, frontier model to know what the weather is, perhaps. But on the flip side, you don't also know the limits or the ceiling of what the technology can do. And you kind of want to know that.
And so the way, at least I think about how you do this somewhat efficiently, even though by definition, you're going to accept inefficiency in the discovery process or in the invention process, you want to do it in the most contained set of ways that deliver customer outcomes. So you actually want to put customer outcomes and start there, and then actually give teams as many shots on goal towards those outcomes as possible. Doesn't mean you get there, but at least it's directed, and it's intentional, and it's not entirely just YOLO.
There's obviously some of that, but I think if it can be directed towards, in our case, consumers, merchants, dashers, we've actually found some success.

**Jack Altman** (3:24)
It's funny because as a software company, which is where so much of the AI productivity is happening right now, it's actually harder in some ways for them to measure the end outcome of the work that their engineers have done than maybe yours, where you can say, hey, I can measure, did we complete more deliveries? Did we get more restaurants? Do we have new users? Or all the things that you're tracking?
I was reflecting that over the last few years, you've watched this egg move through the snake in the pipeline from, first, there's the build out and there's chips, and then you get data centers, and then it's like, oh man, are people ever going to use this? And now they are using it and the token spend is ramping. But now it's like, we've got to turn the token spend into burritos at people's homes. And you're in as good of a position to see that as anybody. So are you like, we're going to have certain teams try to move the metrics in the market with AI, or is it kind of just bottoms up, let the teams explore whatever they want with AI?