The Frontier with Brendan Foody
00:22:04 · Andy Fang
[00:22:04] S1: I'll start with some of the stuff we're already doing. So I mean, my co-founder Stanley, he's been leading up our autonomy lab for eight years now, since 2018. Um, and so I think that's like a big investment we've made in exploring like autonomy. That's going to become a bigger part of our future in terms of how the future of our logistics network. And I think we learned a ton about that uh from that in terms of like how the autonomy space is evolving, but also how the technology problem in autonomy is widely solved, but the operational problem is a totally different beast. And like I think this is also applying to robotics as well, which we can probably get into right after this. But um, a demo is super easy, you know, like getting a demo to work, like driving from A to B, that's like easy now. And the hard thing is like, how do you solve for all the edge cases? How do you do all the fleet management? You know, like what happens if the car battery dies, or like what happens if it's raining, or all that stuff. So I think that's something that we've learned a lot about. And we're like an early, we're actually live already in a couple cities with our DoorDash dots. Robotics, like more generally, I think is a very interesting space. That's something where we're trying to figure out like what the future is gonna be. And it's funny because like, I mean, you know this too. It's like there's not even agreed upon paradigm in terms of what the working model is gonna be. And so I think it's like a very messy space right now, but like one we're trying to navigate and see you know what the opportunities there are.
00:23:23 · Brendan Foody
[00:23:23] S0: Yeah, and I find that physical world AI is so challenging because when we talk about this point of like sufficiency thresholds, the room for error is so much lower. As a software engineer, like once we hit 50% on sweet badge, that's like spectacular, right? And like we'll ship it, and if it doesn't pass our tests or like fails in production, we'll just like redo the code versus it's radically different in the fiscal world where making a mistake, you know, even five or 10% of the time can create huge problems. And so I definitely think that will limit adoption uh for for most types of tasks as these things sort of develop. And then I know you guys are starting to work on uh RLHF data in the physical world, or I saw something about that. Yeah. Of course, we don't do much in terms of physical world or the lower skilled work, but I'm curious how you see the vision for that unfolding. Cause I think I certainly agree that most work over time will converge on training the models. And I admire the incredible talent network that DoorDash has built to set you guys up for doing that.
00:24:26 · Andy Fang, Brendan Foody
[00:24:26] S1: I mean, for us, it's like super exploratory right now, to be honest. We launched our DoorDash Tasks app a couple months ago, and you know, that's really like allowing our Dasher uh fleet to basically, you know, pick up jobs on the tasks app and see like, you know, if it's whether it's recording yourself doing something or other sorts of tasks, um, like just seeing like, you know, what traction we can get there. Learning a lot there, but I don't know. It's like so early. Like we're still trying to figure out, to be honest, like what the there there is. [00:24:54] S0: So well, and I also think the labs still have a lot of ambiguity of what exact data shapes that they're trying to figure out. And so it feels like it's more of a research problem of what data shapes actually improve the physical models right now, and less of a problem where you can just pour in human hours in the way that we have in the digital world. [00:25:13] S1: Yeah, exactly. Like I don't think there's like a like, hey, this is I mean, I think even the labs don't even know what they want. Yeah, we're still figuring out if there's a there there here, you know. [00:25:21] S0: Makes sense. What other things do you spend your time on at DoorDash that you're excited about? [00:25:25] S1: I mean, I think there's something you guys probably don't have to deal with as much just because you're smaller in terms of number of people, but just AI enablement in general, I think, is something that is top of mind for us. [00:25:33] S0: Like how to get everyone using Claude and be more productive.
00:25:33 · Brendan Foody, Andy Fang
[00:25:33] S0: Like how to get everyone using Claude and be more productive. [00:25:36] S1: Yeah, exactly. And I think on the engineering side, I think that's been pretty organic. And I think on the non-engineering side, we've seen a lot of adoption and we've rolled out Claude co-work pretty widely within the organization. And I would say based on like surveys and stuff like that, people really like it. But I think it's like, how do we actually get that like step function change has been a little elusive, I'd say. Like, I think there's areas where we've seen like maybe 20, 30% gains, which is you know, we'll take it. But like in terms of like, you know, people are like, hey, I like I was just talking to someone earlier today about this. I think it was like everyone talks about how hey, AI is gonna do my taxes, but I'm like, I don't know a single person who's like literally let AI do their taxes without like touching it, you know. It's like for us, it's like, you know, for various functions, like what are those like step function change type of automations that AI can provide? And we're still in the early innings of discovering what those are. [00:26:27] S0: How much of that is people using the Claude Cowork interface themselves versus a custom harness that you guys have set up that they're able to use so that you can swap between models?
00:26:27 · Brendan Foody, Andy Fang
[00:26:27] S0: How much of that is people using the Claude Cowork interface themselves versus a custom harness that you guys have set up that they're able to use so that you can swap between models? [00:26:37] S1: I would say on the non-edge side, a lot of it right now is through cowork. Like we want to build a paradigm where um everybody can build their own agents. And so we want cowork to kind of be that playground to start with. If we find things that we're really excited about, then that's kind of when we'll do the harness. [00:26:54] S0: That's definitely something I've been interested in how it develops because I think that most large companies are just using cowork directly or ChatGPT directly. Yeah. Whereas a lot of startups are starting to build their own harnesses. Like I know Sierra has posted a lot about this. We have our own harness. We still use Claude a lot of the time and that harness, but it at least gives a little bit of optionality with respect to how we change the model that we're using over time and build more customizations on top of it. So I think it'll be interesting to see how that development unfolds.
00:26:54 · Brendan Foody, Andy Fang
[00:26:54] S0: That's definitely something I've been interested in how it develops because I think that most large companies are just using cowork directly or ChatGPT directly. Yeah. Whereas a lot of startups are starting to build their own harnesses. Like I know Sierra has posted a lot about this. We have our own harness. We still use Claude a lot of the time and that harness, but it at least gives a little bit of optionality with respect to how we change the model that we're using over time and build more customizations on top of it. So I think it'll be interesting to see how that development unfolds. [00:27:26] S1: Yeah. I mean, we have our own harness as well that like only like the power user non-technical people are using right now. But like I think for me, what I'm optimizing for right now is like try to identify the use cases. Yeah. Cause I don't think we've seen enough use cases where we're like, oh my gosh, like like we gotta like kind of like really like supercharge this right now. It's more like we're seeing a lot of like 20, 30% type gains in various areas that people are able to kind of like self-sufficiently do. [00:27:53] S0: And when it's people building their own agents, would that mainly be them building a skills file and setting up like a cloud scheduled action so that they can run that agent on whatever cadence? [00:28:05] S1: Pretty much. And then, but we also have our own like harness where like we can also you can spin up like a agent on a cloud instance and kind of do stuff. But like I think that one requires a little bit more wrangling.