# Building an Autonomous Delivery Experience with DoorDash Co-Founders Andy Fang and Stanley Tang No Priors: AI, Machine Learning, Tech, & Startups · 2026-07-23 DoorDash co-founders Andy Fang and Stanley Tang explain how they are building an autonomous delivery ecosystem with their in-house robot Dot, which has operated at L4 autonomy in Phoenix for over two years, weighing 300 pounds and traveling 20 mph. They discuss how Ask DoorDash, a natural-language interface, drives 50% of restaurant trajectories to new places and 40% larger grocery basket sizes. The founders argue that a multimodal strategy—using Dot for 3-5 mile suburban deliveries, drones for rural areas, and Dashers for complex orders—is key, and that despite autonomy, DoorDash will have more Dashers in 10 years due to 25% annual growth and 9 million active Dashers. They emphasize that their advantage comes from combining world-class operations with real-world data from 10 billion deliveries, solving the first and last 100 feet problem that general-purpose autonomy companies overlook. ## Questions this episode answers ### How has Ask DoorDash changed user ordering behavior on the platform? Andy Fang reports that 50% of Ask DoorDash restaurant searches lead to orders from places users haven't tried before, breaking habits that previously limited discovery. Grocery basket sizes increase by 40% because natural language lets people describe needs like restocking a fridge from a photo or meal planning with dietary constraints, reducing the friction of manual menu navigation. [0:55](https://nopriors.podhood.com/70dbf31d-3c96-49c8-a61d-f31b59c3f25c?t=55000) ### Why did DoorDash decide to build its own delivery robot, Dot, and how does it differ from other autonomous vehicles? Stanley Tang explains that after partnering with sidewalk robots (too slow at 2-3 mph for 3-5 mile deliveries) and robo-taxis (overbuilt and unable to solve the 'first and last 100 feet' pickup/drop-off problem), DoorDash built Dot in-house. Dot is a 300-pound robot traveling 20 mph, designed for bike lanes and roads, and has been making fully autonomous L4 deliveries in Phoenix for over two years. [9:55](https://nopriors.podhood.com/70dbf31d-3c96-49c8-a61d-f31b59c3f25c?t=595000) ### What operational challenges did DoorDash face when scaling its autonomous delivery fleet? Stanley Tang recalls that scaling beyond a few robots revealed many edge cases: a boot-up script that crashed half the time and took 30-45 minutes became a huge productivity issue; dirt on sensors, leaves causing torque differentials, and regen braking overloading the battery all surfaced. The 'first and last 100 feet' problem required using historical Dasher drop-off data to pinpoint exact delivery locations for apartments and storefronts. [28:44](https://nopriors.podhood.com/70dbf31d-3c96-49c8-a61d-f31b59c3f25c?t=1724000) ## Key moments - **[0:00] Intro** - **[0:41] Ask DoorDash** - [2:00] Ask DoorDash drives 50% of users to order from new restaurants, says Andy Fang. - [4:26] Next-generation DoorDash would be agentic-first, Andy Fang predicts. - **[7:02] Autonomy Roots** - [7:23] DoorDash has been exploring robotics and autonomy since 2018, Stanley Tang reveals. - [9:26] DoorDash's robotics efforts began as a skunkworks project with half an engineer, Stanley Tang recounts. - [11:59] Many autonomy startups build technology first then retroactively find a use case, Stanley Tang observes. - [14:16] "The first and last 100 feet problem," Stanley Tang describes autonomous delivery. - [18:01] "No two deliveries look the same" out of 3 billion yearly DoorDash orders, says Stanley Tang. - [20:05] DoorDash launched Tasks to collect data for training world models for robotics, says Andy Fang. - **[21:21] Dot** - **[22:08] Data Edge** - [23:02] DoorDash's multimodal strategy uses different modalities (robot, drone, Dasher) for different delivery types, Stanley Tang explains. - **[25:48] Talent** - [26:19] "Do you want to work on prototypes, or do you want to ship something in the real world?" asks Stanley Tang. - **[28:04] Scaling Up** - [28:44] Real-world edge cases like leaves causing torque imbalance challenged DoorDash Dot's autonomy, Stanley Tang recounts. - [30:47] A boot-up script that took 45 minutes per robot became a huge productivity bottleneck at scale, Stanley Tang recalls. - [33:36] Real-world robotics: "Why is a cat in the dishwasher?" asks Sarah Guo, highlighting unexpected edge cases. - [35:45] Stanley Tang outlines three scale-up challenges for Dot: autonomy, operations, and hardware manufacturing. - [38:36] DoorDash partnered with Rivian spin-out Also to scale manufacturing of Dot robots. - **[39:30] Productivity** - [41:36] DoorDash's AI spend increased 20X from January to June then flatlined through benchmarking, says Andy Fang. - **[44:56] Agentic Future** - [45:02] Stanley Tang predicts more Dashers in 10 years despite robotics, due to demand growth and new modalities. - [47:20] "People feel more comfortable talking to agents like they would a normal human being," says Andy Fang. - **[49:10] Outro** ## Speakers - **Sarah Guo** (host) - **Andy Fang** (guest) - **Stanley Tang** (guest) ## Topics Autonomous Systems ## Mentioned Also (company), DoorDash (company), Metis (company), Tesla (company), Waymo (company), Ask DoorDash (product), ChatGPT (product), Dash Bench (product), DoorDash CLI (product), Dot (product), Tasks (product) ## Transcript ### Intro **Sarah Guo** [0:06] Hi listeners, welcome back to "No Priors." Today I'm here with Andy Fang and Stanley Tang, co-founders at DoorDash. We talk about how you can ask DoorDash in natural language for food and groceries, what that means for the future of Agentic Commerce, their delivery robot Dot, how DoorDash has been a robotics company for the last 8 years, the data advantages of their network, and what all this means for 9 million Dashers and 3 billion deliveries a year. Welcome. Andy, Stanley, thank you so much for being here. **Andy Fang** [0:37] Real excited to talk to you about, um, all the crazy stuff DoorDash is doing. **Sarah Guo** [0:41] I thought we could start with what's going on with, uh, Agentic Commerce at DoorDash. I feel like you have one of the largest rollouts of actually using AI to change what people consume. ### Ask DoorDash **Andy Fang** [0:52] Yeah. **Sarah Guo** [0:52] Um, so what was the backstory here? **Andy Fang** [0:55] Uh, I mean, it started a couple years ago, honestly, in terms of like our attempts to try to make a play here. It actually, originally we were bullish on voice as the modality. Um, that. **Sarah Guo** [1:09] And that ended up not being the thing. **Andy Fang** [1:10] That ended up not being the thing, but maybe it will in the future, but just that didn't really land. But the thing that was very interesting for us was just this natural conversational experience. And I think, you know, what we've seen is just like people being able to like just like naturally just translate what's in their head into this interface versus trying to like do some research online and then try to do some like keyword optimization stuff. Like people just found it easier to search for things, either more nuanced kind of restaurant discovery searches or different tasks on the grocery side. Um, and yeah, we've just seen a lot of interesting traction that's, uh, upheld as we've expanded the rollout. **Sarah Guo** [1:52] What are you seeing in terms of behavior change from the user side? Like do I eat or buy differently? **Andy Fang** [1:57] Yeah. So I would say on the restaurant side, we are seeing people, 50% of trajectories of people using Ask DoorDash for restaurants. Uh, they're 50% of those trajectories are people ordering from places they've never ordered from before, which is huge because that's one of the hardest metrics historically for DoorDash for us to, uh, move. And so that's been big. And then another one is on the grocery side, we're seeing a lot, uh, higher basket sizes. Like I would say like 40% larger basket sizes on grocery. And so people are like, you know, they'll take a picture of what's in their fridge and they'll say, "Hey, help me stock up my fridge." Or they'll do meal planning with like maybe they have some dietary constraints or they're like, "Hey, I want to like cook a pasta dinner this weekend with my family." Or even just like, "Hey, help me reorder, uh, like my, you know, my usuals." And like that's a lot easier than tapping through the traditional experience. **Sarah Guo** [2:47] That's wild. I've never thought of DoorDash as difficult to use, but like that suggests there's like actually late in demand that wasn't being served because you, it wasn't easy enough to like eat at new places. **Andy Fang** [2:59] Correct. Yeah. And I think a lot of people on the restaurant side, it's like people build habits. **Sarah Guo** [3:04] Mm-hmm. **Andy Fang** [3:05] But I think people also want some diversity in terms of like what they're eating, you know? Um, and so we felt like this experience ended up being a natural way to allow people to express that. **Sarah Guo** [3:16] Well, think about the social currency of like my friend Andy found a new, like really good restaurant for me. **Andy Fang** [3:22] Right. Yeah. **Sarah Guo** [3:22] And he's awesome,right? So I feel like that's even a different way people look at DoorDash. **Andy Fang** [3:26] And yeah, another thing that was an investment we made was actually like incorporating like world knowledge into the experience. So it's like. **Sarah Guo** [3:32] What does that mean here? **Andy Fang** [3:33] Things that are going on with restaurants outside of DoorDash. So like, you know, we'll see, hey, what's trending on the internet or what's stuff that's not in the models, but stuff that people would find because like their knowledge cutoff is too early, but maybe it's like, hey, what's trending online or what are people talking about in various forums or whatever. And kind of goes to your point of like, hey, like kind of want to eat what's cool. And so like that was something we tried to incorporate, uh, into the experience to make, uh, people trust it more. **Sarah Guo** [3:59] How do you think, uh, people will buy or think about restaurants differently in like five years from now? **Andy Fang** [4:05] I don't know about five years from now. **Sarah Guo** [4:07] I realize this is really hard in the age of AI. Like next step, next step. **Andy Fang** [4:11] So for Ask DoorDash, I would say to start with, maybe that's like the next couple months or so. I think it's making it easier for people to discover the experience and like figure out what to do. 'Cause I think it can be intimidating if you just see like, hey, like there's like suggested queries that you can type, but like some people don't know what to start with. So figuring out how to experiment and tinker with the user experience to kind of get people or encourage people to find use cases for it. I think if I think further out, then it's a little more speculative, but you know, Stanley and I talk about this all the time. It's like if someone were to create DoorDash today, like I don't know, like college kids in a garage trying to start DoorDash, I think it would look very different. Probably more agentic first. You know, one stat that I always like to, uh, think about nowadays is just like there's more agent traffic on the web than human traffic, you know? And so it's like how do we have a DoorDash type experience that plays into that trend? Um, and so, you know, I think there's some interesting speculations there, but hard to say. **Sarah Guo** [5:09] What could my agent know about what I want to eat or what I want to, um, buy from a grocery perspective? Like help me understand like how you think about richer context or how to be smarter there. **Andy Fang** [5:21] Sure. I mean, one cool example is someone's like, "Hey, for our office, it's like I can just like have the, like one of the cameras on the, uh, pantry shelf." **Sarah Guo** [5:31] Mm-hmm. **Andy Fang** [5:31] It's like, "Hey, when the shelf starts to get empty, like I can fire off like a query to DoorDash to like stock up my shelf." **Sarah Guo** [5:38] Yes. As a human being, task care. Yes. **Andy Fang** [5:40] Yeah. Yeah. And so that was kind of like, I mean, it's something we talk about more later, but like kind of our like early experimentation with our CLI is like that's kind of an example of like making it less friction for an agent to kind of like participate in that experience. **Sarah Guo** [5:56] Okay. Well, while we're here talking about user needs. **Andy Fang** [5:59] Yeah. **Sarah Guo** [6:00] I'm, I've got to be like a top percentile DoorDash consumer. **Andy Fang** [6:03] Nice. **Sarah Guo** [6:04] I don't know. I'm, you know, a lot of, a lot of customers at this point, but, uh, I host family dinner for like extended family every Sunday night. And, you know, we eat DoorDash because I'm not going to cook for all these people every, every week. **Andy Fang** [6:16] Yeah. **Sarah Guo** [6:17] Or I can't all the time. Um, and, uh, like I do the same thing every time, which is pull everyone. Okay. Who's coming? **Andy Fang** [6:26] Oh, yeah. **Sarah Guo** [6:26] You know, and then these people have these allergies and whatever else. **Andy Fang** [6:29] Right. **Sarah Guo** [6:29] And like, does anybody feel like anything special? **Andy Fang** [6:31] Yeah. **Sarah Guo** [6:32] And then, you know, I order. **Andy Fang** [6:33] Right. **Sarah Guo** [6:34] Right. And I'm like, I feel like, I feel like that's all within the realm of possibility. **Andy Fang** [6:37] That is definitely what's in. **Sarah Guo** [6:38] You just put it on autopilot for me. I show up, I hang out with my family, everything's good. **Andy Fang** [6:41] That is a use case that is, I mean, I think, uh, not exactly the same, but like a similar use case is like the office lunch ordering kind of thing. It's like if you're the office manager, it's like, I don't want to like, and then you got to like, hey, make sure you ordered lunch at this time, otherwise it's not going to show up. And it's like, again, everyone has their own like allergies or dietary preferences and stuff. So. **Sarah Guo** [7:02] Stanley, you guys are doing, uh, a whole bunch of things on the autonomy and robotics side as well. Like you, your clearly your view of DoorDash as founders is broader and more ambitious than, I don't know, maybe just like the surface level view of it's a food delivery network or whatever, whatever the first, you know, one-liner for the company was. ### Autonomy Roots **Sarah Guo** [7:23] Um, how long ago did the robotics efforts start? **Andy Fang** [7:26] Yeah, we've actually been looking to robotics autonomy probably much longer than people thought, like since 2018, actually. Uh, back when it wasn't obvious autonomy and robotics was going to be a thing. Uh, but we felt like this was going to be a technology that was going to be transformative to our space and potentially disruptive. And I think, I think that's nice about being a founder-led company is like we are, we get to think about kind of much more future speculative things that are on the horizon and, and, and constantly think about like how do we make sure we don't get disrupted by the next one. I think like, like Andy said, like the next DoorDash that comes along is not going to be someone that builds the exact same version of DoorDash, but maybe with a better UI is going to be. **Sarah Guo** [8:14] Yeah, that would be dumb. **Andy Fang** [8:15] Yeah. It's going to be like something like, okay, how do we incorporate AI, agentic commerce, how to incorporate autonomy, robotics, drone deliveries, uh, et cetera. And, and I think, I mean, fast forward like seven, eight years later, I think you're seeing everything starting to play out in AI, in robotics, in autonomy. You're seeing way more that's happening. And I think, you know, we're, we're glad that we, we made that investment early on 2018. **Sarah Guo** [8:37] DoorDash is an amazing business. In 2018, it was like less amazing than it is today. **Andy Fang** [8:42] Yeah. **Sarah Guo** [8:42] I feel like that's a fair statement,right? Um, how do you think about like the timing and sequencing of these very long-term bets and like just it from a capital allocation perspective, like when you can invest in these things? **Andy Fang** [8:53] Yeah. I think it's, it's probably the same with how we invest in a lot of things at DoorDash is everything started out as experiments. I mean, in a way, that's a, that was a founding story behind DoorDash. DoorDash was a Stanford college like dorm room experiment. It started out as a website called pilotodelivery.com with eight PDF menus and a Google Voice phone number. And it was only once we figured out, okay, there's something here, let's turn this into company. And, and, and that's basically we've kind of taken that philosophy throughout the past 13 years and, and we've kind of applied it to autonomy as well, AI as well. I mean, when we first started in 2018, the intention wasn't, hey, let's go spin up this giant robotics program, let's hire robot assists, go build hardware. It was really, we put together, it was me and half an engineer's time. It was a skunk works project. It was an experimentation to go, let's go explore like what's out there. Like we don't even know what autonomy looks like, how robotics is going to impact our space, but let's go explore, let's go form partnerships, let's go learn, let's go experiment. Um, and, and, and, and, and I think in the beginning, the intention wasn't to build our own robot. Actually, we, we didn't think we needed to build any of this technology ourselves. We thought, okay, we can just partner up with a bunch of folks. Like, you know, back then we weren't, you know, we didn't know anything about robotics. Uh, there's all these startups out there that have built robots and autonomy. Like why don't we just work with them? We can essentially just be the platform. Uh, we'll build the APIs, we'll handle the distribution, et cetera. And we did that for about actually several years, actually. We worked with everyone in, in, in the space, everyone from the sidewalk robot players all the way up to the, the robo-taxi players. I'll say there's three things we learned through that experience. I think one is it kind of validated or confirmed our belief that there's something here. Autonomy is a question of when it was going to happen, not if. And again, fast forward today, you're seeing, you see the Waymos driving. **Sarah Guo** [10:49] It's happening. **Andy Fang** [10:49] Right. It's happening. So we should keep investing. The second is I think it allowed us to learn what it takes to actually enable autonomy because it turns out there's a lot of things you have to build around autonomy, the infrastructure, the ecosystem. How does autonomy integrate with DoorDash? What deliveries do you take on? Like, um, the operational aspect, it turns out a lot of things you have to build around autonomy in order to make autonomy possible. It's not just you plop a robot in, uh, or like, or even AI just plop an LM in and then things just magically happen. There's a lot of things around it. And, and, and, and you kind of have to build a platform ecosystem. So one of the things that we ended up building is this thing called the autonomous delivery platform. Essentially, it's like what are all the products and technology, the APIs, the dispatch you need to build now that in a, in a post-autonomy world where autonomy and robotics and drones are everywhere, what are all the things you have to build? How do you integrate merchants? What does the consumer experience look like? Uh, and I think the last thing, which I think is probably the most important thing we learned, which eventually led us to realize we have to build this technology ourselves, is really this idea of building towards a use case. **Sarah Guo** [11:58] Mm-hmm. **Andy Fang** [11:59] Yes, there's a lot of autonomy startups out there. Um, but we, it always felt like these, these companies weren't really focused on a use case. It always felt like they kind of build the technology first. **Sarah Guo** [12:11] Mm-hmm. **Andy Fang** [12:12] And then retroactively try to go find a problem to fit into,right? Like these, these things were all built in a vacuum, which is kind of weird 'cause, 'cause it's, it's like, 'cause in, in, in, in, in software world, like when we went through YC, like we were always taught to, oh, you got to serve the customer, build something people want. That was kind of like drilled into you and then you can iterate. But then when it comes to like hardware and hard tech and AI and, and robotics, it's, it's people just kind of do the opposite where they try to build the tech first and, and, and not really think about the use case they're building towards it. And whenever that happens, you just end up with something that just wasn't quite theright fit. Like, like there's, and, and we went through this, this, this process where a lot of these companies out there, but it always felt like it wasn't exactly what DoorDash needed. Um, like, like for example, you, you, a simple example is that you have these in, in, in autonomy world, there's basically two buckets of category of companies out there. You have these sidewalk robot companies, which are kind of these two, three mile per hour, kind of water cooler on wheels, super effective, simple technology. Uh, but we quickly realized the speed was like and distance was a huge limitation 'cause if you, 'cause the average delivery at DoorDash is about three to five miles. Uh, and, and, and the typical delivery time's about 15 minutes if you exclude the time it takes to make the food. So if you put a two mile per hour sidewalk robot, it's just never going to work. And then on the other end of the spectrum, you have kind of the robo-taxi players, which really are designed for carrying people around. It's a 4,000 pound vehicle. It's goes super fast. You're transporting people. And, but it turns out the problem around carrying people and carrying goods is actually a little bit different. Like you don't need, if you only have, if you're only carrying a couple burritos around, do you really need a 4,000, 4,000 pound car with chairs and AC? Uh, the pickup drop-off problem is also very different in robo-taxis. Um, you know, you can walk to a Waymo. I mean, how, how often have you taken a Waymo where it drops you off half a block or a block away from where you need to be, which is totally fine 'cause you, you can, you can walk, but packages can't do that. Like, like, how do you, how do you solve that? What I call the first and last 100 feet problem. How does the food, um, get picked up at the merchant? What does that integration look like? And then on the customer, like how do you drop off the food? How do you find the driveway? You know, like people expect their food to be dropped off or, or, or the, the vehicles we pull up straight to the front of their driveway or their, or their porch. Um, so, so, so when we kind of looked around and, and asked ourselves, okay, like if you were to start first principle, and again, this has always been our philosophy at DoorDash, like, like if you were to start from the business, the customer use case, work your way backwards, our first principles, and you can build exactly, um, what we need to solve our use case, what would that look like? And we looked around, turns out no one's really building that. It's not a sidewalk robot. It's not a robo-taxi. Um, we felt like the, it was probably something in between, theright metaphor for us. Again, it's like if you're trying to solve that three to five mile delivery in dense suburbs, which is where most of the deliveries happen, theright metaphor is probably an autonomous motorcycle or a scooter or bike profile vehicle. And, and, you know, it doesn't even be 4,000 pound. It's probably, you know, 300 pounds. Uh, but it also has to be a lot faster than sidewalk robots. It has to go 20, 25 miles per hour. And when we looked around and saw no one's building that, we decided, well, if no one's going to do that, instead of waiting around and let's, you know, and wait for this to happen, we're going to control our own destiny here. Let's invest in this and see what we can build. And, and it took many iterations, you know, like we start looking at, like we went testing this with real DoorDash deliveries, looking at our 10 billion deliveries we've done, extracting the insights we have on the operational learnings we have. And that's eventually what led us to launch and ship, which is kind of our in-house autonomous delivery robot. Um, so it's been a, it's been quite a journey. And, but again, this is something we look to bring to every aspect of the business, whether it's autonomy, robotics, AI, like it's, it's always starts out, start out as experiments. It always starts out as what is the customer problem you're solving for? What's the use case you're solving for? Work your way backwards and then iterate and, and validate kind of your hypothesis and slowly, um, build the product over time. **Sarah Guo** [16:40] That sounds extremely rational. I have a hypothesis and it's very cool. I want to ask you where we are in the life cycle of everybody getting these automated deliveries. Um, I have a hypothesis and I'm curious if it resonates with either of you about like why, uh, a lot of people in this area are, are building technology first versus customer back. I think people think everything is going to work like ChatGPT. **Andy Fang** [17:04] Mm-hmm. **Sarah Guo** [17:05] Right. I just, and like, by the way, like there was of course work done on, uh, instruction fine-tuning to get it to like be shaped in a product that was still a user experience. But I, I think the, the mental model that people have of like it's a general technology and it's just kind of like free to turn into different applications, uh, is what they're applying to lots of different things now. And especially in autonomy, my sense is people are like, okay, we'll make the model and then like the other stuff will be, uh, if not easy, at least secondary. This is not my view at all. **Andy Fang** [17:40] I, yeah, I agree with you there. I mean, that's basically your methodology into building the Dot form factor. **Stanley Tang** [17:47] I think maybe that approach works in like software land, but like for, at least for a business like ours, like DoorDash is a physical world business. It's like we're, you know, you bring technology into physical world and the physical world is always a lot messier. It's a lot more complicated, a lot more nuanced. Uh, I, I think one of the things I think people don't realize is just how complicated DoorDash is. I mean, we do over 3 billion deliveries a year. There are no two deliveries that look the same. All 3 billion deliveries look, look different. Uh, and they all come in all sorts of shapes and sizes and different geographies. Like a delivery in downtown San Francisco is completely different than, uh, a delivery done in Dallas or, or, or even in Europe or in Helsinki where it's snowing. Or if you're doing a, uh, pizza is very different thanice cream. Like your, your dinner is very different than your grocery order, which is very different. Now that we're expanding to retail and, and, and, and pharmacy and parcels as well. It's like the diversity of deliveries that happen at DoorDash is so complex that I, I think people sometimes don't realize just how nuanced the problem, the problem, the problem is. And, and that's kind of how, what we have to solve for at, at, at DoorDash. And, and I think that's part of the, the, the learning process, especially when it comes to like building autonomy or even AI is how do you manage through all that complexity? And again, it always comes down to like, like, like do you understand the use case? And I think we just have such a huge advantage over everyone else because we have something that everyone else doesn't have. It's, it's called DoorDash. We have 10 billion deliveries of data to extract from. We have, uh, all these consumers, like, you know, over 40 million consumers ordering every single month. Like we understand the complexities of how to handle when things go wrong, how to integrate all across all different types of merchants. Like, like the way you work with a McDonald's or a Starbucks is very different than working with a mom-and-pop sandwich shop. Like a, a drive-thru restaurant is, again, it's very different than a restaurant at a strip mall or downtown main street. And how do you handle kind of those different use cases,right? Different interaction, different pickup points. Um, I don't know if there's anything you want to add on the AI side. **Andy Fang** [20:05] I mean, for me, like the kind of that analogy you brought up, I think, I think about it in terms of the autonomy thing, but I also think about it in terms of like the human, like how the humanoid robotics space is starting to play out potentially where, I mean, we, we also launched a product called Tasks a couple months ago where we're, we're having people in the Dash fleet help basically collect, uh, data points to help train some of these world models. And I think we're so early there and I think there's so many different form factors that you can use and there's like different opinions on like what type of model is going to work versus not. Um, but I think unlike something like ChatGPT, I think there's a lot of expense needed to invest in just like the V1 of this. I guess ChatGPT costs a lot of money too, but I think there's a lot of pressure though to figure out how do I actually provide value? Like I have to be better than what people can do today. Um, and you know, whether it's Dot and like delivering something end to end or I mean, you probably invest in like a bunch of different players in this space, but like there's real pressure to like be better than the alternative, uh, from either a quality and/or a cost perspective. So yeah. **Sarah Guo** [21:18] Yeah. So otherwise what are we doing? **Andy Fang** [21:20] Yeah, exactly. **Sarah Guo** [21:21] Um, so for those of us who aren't in Phoenix, like what is DoorDash Dot and like tell us about the design of it. ### Dot **Stanley Tang** [21:27] Yeah. So DoorDash Dot, it's an autonomous delivery robot. It's built entirely in-house, uh, at DoorDash. It's, uh, weighs 300 pounds, travels up to 20 miles per hour. It's one-tenth the size of a car. It's the only delivery robot out there that's designed to travel not just on sidewalks, but also go on bike lanes, on, on, and, and on the road as well. It's, it's live in Phoenix. We've been live doing deliveries for, uh, almost two years now. Uh, it's, you know, we, we do, it's fully autonomous L4. So if you come up to Phoenix and to Tempe, it really feels like, uh, Waymo San Francisco. ### Data Edge **Sarah Guo** [22:08] I'm going to state something and see if this is like a correct or you agree. Uh, even beyond understanding the wealth of use cases, like you need to know what the distribution of environments you're going to be playing in is in robotics. This is a huge problem for everybody where like it's not, I think most people, uh, familiar with the area understand that it's not that hard to get a cherry-picked demo of like one cool success on a task. **Andy Fang** [22:36] Right. **Sarah Guo** [22:36] The problem is getting it to work on any object or in any environment. **Andy Fang** [22:41] Yeah. **Sarah Guo** [22:41] Um, and so there's this like, you know, huge question in the industry of like, okay, how are we going to go get data that feels like realistic data? And like the best realistic data is the real world data actually. And so I think that's like a really interesting premise of like why you might have theright to go do this besides you want to do it for the quality of your business. **Stanley Tang** [23:02] Yeah. No, exactly. And I think that's, again, that's also where DoorDash gets to shine with our advantage is we don't necessarily have to solve for 100% of our use cases. I mean, that's, that's also part of our, again, that was part of the learning with our kind of the kind of the first early years when, when we did the partnerships route, we built our autonomous delivery platform was understanding what kind of deliveries fits into what modality. **Sarah Guo** [23:28] Mm-hmm. **Stanley Tang** [23:29] And, and I think the vision was always, was always, let's not design something to solve for everything, but instead kind of let's, let's go with a, with a, how do you come up with a multimodal strategy where perhaps, you know, you have DoorDash Dot do kind of the, the three to five miles suburban deliveries from a strip mall. Um, so, soright now we're live in, in Phoenix. That's kind of our starting point with Dot. Uh, that's kind of the perfect market for Dot,right? These dense suburbs, yet things are still far, far apart enough. Maybe if it's, if it's a, um, rural area where there's poor road infrastructure, maybe you send a, and it's a lightweight order, maybe you send a drone delivery for that. Uh, if it's a complicated multi-step grocery order where you have to climb, go up and down stairs and pick and pack orders, like you're still going to have a Dasher for that. And I think that's the nice thing about DoorDash is you can kind of, you don't have to, it's not an all or nothing approach. You can kind of phase in these modalities over time and pick and choose what theright, again, it's about the use case. What are theright use cases, uh, to, to solve for what are theright modalities to, to, to fit into for, for each of the, each of the use cases? Like are there certain deliveries you can carve out that makes a lot of sense for robotics versus, versus humans? **Sarah Guo** [24:48] Yeah. Um, I also think that's really cool that you have control over the routing and the distribution where you're like, I can, I can accomplish this task. **Stanley Tang** [24:56] Exactly. And then, and then from the consumer side and the merchant side, it's like the exact same experience. It's still the same app for the customer that you can access everything. And then for the merchant, it's just one integration. Uh, you already integrated DoorDash. All of a sudden you, you get not just Dashers, but you get drones, you get autonomy, you know, you get access to all the, you know, AI tool, uh, tools and products that we're going to ship. And I think, again, it's, it's like, I think that's, that is like, that is like what ultimately like DoorDash is, is building is like, it's, it's really like that ecosystem, uh, for local commerce. And I think that is, again, that is like something that is really hard to replicate. And I think, and I think it's, again, it's trying to, trying to do that in the real world across, you know, you know, like 40, 50 plus countries and all these different jars, all these different merchants. That's, that's the hard part about, about the business. **Sarah Guo** [25:48] Asking for a friend question of how you got here. Um, there is a, uh, an insufficient supply of researchers and people who, you know, know how to work on robotics or applied AI, uh, in the ecosystem for the recognition of all the different cool use cases you go after. ### Talent **Sarah Guo** [26:07] Um, and a lot of people gravitate toward like the general case. **Andy Fang** [26:10] Mm-hmm. **Sarah Guo** [26:10] Like we can solve it once. Um, uh, I assume you're competing for some of those people. How do you convince people to work at DoorDash on these problems? **Stanley Tang** [26:19] Yeah, my pitch is really simple. It's, it's, it's basically like, do you want to go work on prototypes and demos and, and do, and be at a PhD lab, or do you want to work on something where you can actually ship something in the real world? Uh, and I think that's kind of, you know, I, I think, I think, I think that's kind of really been the culture we kind of set up, you know, both at DoorDash Labs and all the AI efforts is, is like, this is, we're not just here to do pure research. Like at the end of the day, like you, we get to ship something where you have real impact. And I think people, at least especially in the autonomy world for the past 10 years, were just fed up just working on something for 10 years and, you know, never actually getting to a point where they actually saw their products being used in the real world. And, and I think like, like for us, like it's like because we, like we've always been much more focused on creating, kind of taking this much more pragmatic, practical approach. Like we're not here necessarily to do like the, it's, it's not about, oh, let's go work on like a crazy moonshot idea. It's like, let's get something out that can be shipped in the real world and actually start learning how these technologies, um, interact with the physical world and start iterating because again, like technology, these, these things aren't built in, in, in a vacuum. You have to put something out in the real world, make contact with the real world, um, and, and actually learn, learn from that. And I think that's, we've, we, we did that pretty early on for, for DoorDash Dot. I actually, again, I don't think a lot of people know we've actually been doing autonomous deliveries in Phoenix for over two years now. Like we, we publicly announced last year or that we've been doing it for over two years, but really at the beginning it was just learning like, okay, like again, like I think you, you, you mentioned earlier, it's one thing to just do a fancy demo or have something that works in a one-off environment. ### Scaling Up **Stanley Tang** [28:10] It's entirely different to now, okay, how do you turn this into a, an actual scaled fleet, a scaled service, a scaled business? I mean, the thing I always mention, talk about a lot is, you know, building autonomy, uh, business takes more than just autonomy. It's like, how do you actually scale something in the real, real world, scale fleets, all of a sudden you're running into all these edge cases like you just don't see it. And when you have to do something seven, seven days a week, uh, or 10 hours a day, seven days a week at scale, things start breaking. Like it could be something as simple as, I don't know, like, like, uh, like a dirt covering one of your camera sensors. Okay. Like how does, how robust is your autonomy stack able to, able to handle that? Like there's some, there's some leaves on the ground, uh, but it only covers kind of, 'cause again, our Dot drives on the road, but it would, it tries to act like a bike. So it'll take the kind of theright side of the road or the, the bike lane. And if there's kind of leaves located along the kind of where the,right where the sidewalks are, maybe half your wheels, theright two wheels are on the leaves, the left two wheels are still on the asphalt. **Sarah Guo** [29:20] Yeah. **Stanley Tang** [29:20] Well, all of a sudden the, the torque you have to send to the wheels is like very different and your autonomy stack and your, and your kind of your, um, kind of your middleware and your, and your kind of your, your kind of low level controls has to handle that differently. Like, like that's something I would have never thought of if it was just like driving in a nice little demo environment. Uh, it's like, like things just start breaking. Like how do you handle operations? Like people don't think about actually, you know, when you scale autonomy, there's a lot of non-autonomy work, like operations. Like you have to set up depots. Again, it's a physical world business. You have to set up depots, maintenance, like what of your battery? Like how do you recharge your battery? Like what if one of your braking system kind of, uh, like over, uh, you, you, you have, you have to kind of like, like here, here was an issue we ran into. It's like, it's like there's certain situations where the, the vehicle has to brake so hard that it kind of, the regen braking system overpowers kind of the, the battery. 'Cause it causes this electric shock,right? Again, like it only happens like extreme edge cases, but, but there's certain situations where you have to do that because it's something in the real world, like safe, like this thing has like safety is like something, like it's something that's super important. So if it can't handle that, like you gotta, you gotta, you gotta figure that out. Another example we, we didn't think about is, is, is booting up the robots. Like, like when we're doing, when, when this was still a demo project, we, like no one thought about, oh, boot up time,right? So, so it's literally the, the, the original version of, of, of, of the robot boot up was a kind of a simple Jenkins script that one of our engineers hacked together in like, in like, in like a couple hours. And then, which worked, which worked fine, but then now you're doing like hundreds of robots a day every morning needs to get booted up and the script, you know, like crashes half the time. It takes like 30, 45 minutes, but to multiply across 500 robots, all of a sudden it's like, holy crap, it's like, it's like, this is huge productivity. It becomes this huge productivity issue. Um, and, and then, and then of course it's like, how do you think through like reliability? Uh, you know, now you have to start thinking about manufacturing supply chain. Uh, and of course the kind of the operational aspect of, of actually how does, how does this thing integrate with merchants? Uh, how do you handle that? How do you do the pickup drop-off problem? How do you educate the merchant? Uh, like how do you even find the pin, the, the location of a customer's home? Uh, which again, sounds kind of silly, but when you punch in someone's address on Google Maps, like the GPS pin, it's like, especially if you're going to apartment complex, it's never kind of, I mean, I mean, it's not like always the exact same spot. **Sarah Guo** [31:59] Yeah, absolutely. **Stanley Tang** [31:59] But if you're a human, it's like you kind of figure it out,right? Like you kind of don't think about it. It's like, oh yeah, a human Dasher shows up. They, they can kind of find where the restaurant is. **Sarah Guo** [32:06] Yeah, it's the building. **Stanley Tang** [32:07] It's the building. It's at the front door. You can't do that with a robot. The robot's going to show up to a pin and all of a sudden it's like, well, okay, which, which, where, where, where is which, which front, which storefront is it? Which front door is it? Which gate is it? **Sarah Guo** [32:18] Not just imagine Dot looking around. **Stanley Tang** [32:20] Exactly. Right. And again, like that's something you have to figure out. But the nice thing is, again, DoorDash has that data. Like we. **Sarah Guo** [32:25] Yeah. All the drop-offs. Like we, we can see where people are actually dropping off the package. **Stanley Tang** [32:30] Yeah. Where did the human Dasher drop it off historically? And that is, you know, like again, it's that first and last 100 feet problem. Like you don't, that, that data doesn't exist anywhere else. It doesn't exist in Google Maps. **Sarah Guo** [32:40] Yeah. **Stanley Tang** [32:40] It only exists at, on, at DoorDash. **Sarah Guo** [32:43] Yeah. I think that is a, uh, a really interesting and genuine advantage. Um, early on when people were like talking about what's going to happen with AI and incumbents and startups, there were a lot of people I think had a very surface level view of like what the incumbent data advantage was. **Andy Fang** [33:02] Yes. **Sarah Guo** [33:03] Um, because they didn't like really think about like, well, what are we trying to do,right? What is the use case? What is the intelligence supposed to accomplish? And so they'd be like, ah, like we have the, I don't know, customer records and database. And I was like, that actually has like very little to do with the thing we're trying to, we could try to accomplish with an agent,right? **Andy Fang** [33:19] Yes. **Sarah Guo** [33:19] And I think this is totally like real in, um, in robotics where, um, I'm an investor in a company called Sunday,right? And, um, one thing that we like deeply believe in this company is you, you can't imagine the distribution,right? As soon as you like make contact with the physical world, as you said, or the, like the real world, you're like, man, if we're trying to do the dishes, why is a cat in the dishwasher? And like, you know, you're in somebody's real house and like the cat likes the dishwasher. **Andy Fang** [33:50] Yeah. **Sarah Guo** [33:50] Like that's not, you know, that's not something you're going to go imagine. Just like you're not going to imagine like, oh, I'm going to deal with this torque problem where like one wheel is on the leaves and not, and then you like think like, okay, but like how important is that in the distribution? Then you find another cat in another dishwasher when you have enough data and you're like, like, I don't know how many of these are out there, but like the only way to find out is not by an engineer sitting and being like, let me imagine this, the, the setup and the scenario for this robot. **Andy Fang** [34:18] Yeah. **Sarah Guo** [34:18] Like that's clearly not going to be the reality. **Andy Fang** [34:20] I just feel like for the next frontier of AI, it's, you know, at least what we're really excited about is like what, how it's going to affect the physical world, you know? And I think to your point, it's like you can only simulate so much. You can only like, you know, uh, you know, pretend and imagine various demo situations. So, um, I think one thing that we're very, I think another thing that makes us very confident is like pairing that world-class operational expertise that we have with world-class technology. **Sarah Guo** [34:50] Mm-hmm. **Andy Fang** [34:50] And I think, you know, a lot of AI researchers are very hesitant to do a lot of the operational stuff, or they think it's like easy to handle. But I think one thing that's really powerful about what we have here at DoorDash is we have a world-class operations team that you can partner with, whether it's to collect or annotate data, whether it's to figure out how to deploy robots and figure out how to like get the fleet operations to work. Um, and I think for a lot of people we talk to, that's very compelling because it's like, hey, actually there's a, we're not just talking hypothetical here, you know? **Sarah Guo** [35:22] You're making the deliveries in Phoenix. What are the challenges from here for scale up? **Stanley Tang** [35:26] I mean, we've been doing deliveries in Phoenix for over two years now. Uh, I mean, we went fully autonomous L4 last year. Uh, I mean, it's, it's like, I think that was a super exciting milestone and, and really it's just a matter of like how do you take this from, again, it's like originally it was just a couple robots, 10 robots to a hundred. Again, it's just like, we got to make that hill climb of like how do you, how do you scale this? Whether, and then I think it's really just three, three components is can we get the autonomy to scale? Five years ago, the question was like, was autonomy even possible? Like was this, was this just a research project? Is this a science fiction? Um, you've seen kind of now with, especially with AI, like Waymo's kind of made that breakthrough. I think Tesla's starting to make that breakthrough. We made that breakthrough last year. Um, like our entire autonomy stack is built in-house, but purpose built for, for delivery, which is again, it's, it's a little bit different. You can't, it's not just copy. I think this is the other thing people miss is you don't, you can't just copy and paste what Waymo's done and then plop it into the DoorDash Dot and everything works. It's, it's, again, it's the use case is a little bit different. This is a bike lane profile vehicle, but it's, that's constantly navigating between the road and the sidewalks. Uh, as far as I know, this is like, there's nothing else like this in the world, uh, besides that even behaves like DoorDash Dot. Uh, but we kind of built it because we kind of built it uniquely to our use case. So autonomy is definitely one piece, like how do you keep scaling, um, across not just Phoenix, but we want to breed to Bay Area, more cities, you know, I'm sure we're going to run into more, more, more edge cases. Um, but the funny thing is like autonomy is probably increasingly becoming less and less of a constraint of a blocker. It's really like now how do you, it's really more the next two, which is, um, the second is like operational. Like how do you scale operations? Restaurants behave in, in Phoenix look different than restaurants in, in San Francisco versus like, you know, London versus Helsinki. How do you adapt to all these different integrations? How do you. **Sarah Guo** [37:31] So it's the interface layer and then like the fleet management of it. **Stanley Tang** [37:35] Interface and fleet management. And then the last piece is, is, is hardware. Like how do you, and it's kind of funny. It's like when we first started like five years ago, like everyone thought hardware was a commodity and now it's starting to look like hardware is starting to become a bottleneck. It's like we hand built the first hundred robots ourselves and which is not an issue, but then okay, then the next thousand or 10,000, well, we're going to have to now starting to think of, think about things like supply chain and like, like component reliability. Like it's, it's like, it's like, it's like these things have to last for a really long time. Uh, it's like how do you think about, um, yeah, it's, it's, it's like, it's, it's, it's. **Sarah Guo** [38:11] And you're not guessing because you can actually tell how long it needs to last and how it's doing in the field. **Stanley Tang** [38:16] Exactly. Right. Like manufacturing. Uh, you know, like, it's like, it's like learning, learning all that. And that turns out to be a pretty hard problem at scale. And, and so, so one of the things we, we actually did was, is, is, um, uh, we actually, uh, partnered up with this company called Also, which is this, um, micro mobility company that spun out of Rivian. So, um, RJ is actually the board of founder and chairman of the company. So if, you know, like, why don't we work with someone who's, who knows how to actually scale vehicles? And, and so, so that's kind of one of the, uh, partnerships we, we, we struck up, but it's kind of funny. It's like the, the problem five years ago was autonomy. Now it's increasingly becoming more about operations, commercialization, hardware, manufacturing. And again, it's like this is, I feel like this is where DoorDash gets to shine with our scale advantage and operation advantage is how do we take this thing from not just zero to one, but like one to a hundred, one to a thousand. **Sarah Guo** [39:14] One to three billion. **Stanley Tang** [39:15] Yeah. One to three billion. Right. And I feel like DoorDash is just so well positioned to, to, to take on this. Like it's, it's like we have, it's just such a unique advantage here. And I think that's, that's what, that's where we want to play in terms of our play to our strengths. ### Productivity **Sarah Guo** [39:30] So you have these enormous strengths. You've got the network and the existing great business and these like two, you know, amongst others, I'm sure, like two really big plays around agentic commerce and around autonomy. How do you think about just, it's, it's a 10,000 plus person company and like a lot of that company is ops, a lot of that company is technology. Um, and I'm sure you're thinking deeply about productivity of that workforce. Like who owns it? What matters today? You're even publishing benchmarks. Like talk about that. **Andy Fang** [40:01] I feel like in the past couple of years, what was required to really operate at high level in the technology industry has changed a lot. And I think one of the reasons why we were so excited to acquire a company called Metis last year was really to just infuse some of that AI native thinking into the company. And I think for a company of our size, it's been really, and I think every company is, every large company at least is facing it. I think a lot of startups, I mean, you, you see this better than anyone else probably is like the way they operate is so different. And I think a lot of people at our company, they, they have struggled to, to see what's possible because they're so used to how things have worked historically. And so I think really figuring out how do we bring in people who actually have seen what is possible on the frontier and incorporating that into how we do our work. And I think, you know, coding is obviously like the most like obvious place to do transformation and we've seen a lot of gains there. Um, but there's also work we're doing in terms of how do we do AI enablement across the entire organization. And so I think, you know, figuring out how to like benchmark various parts of the company. I think we, we announced a benchmark called, uh, Dash Bench a couple weeks ago now that was mainly focused on our ability to figure out how well various models and harness performed on coding tasks. And so that was a really good initial exercise for us to figure out how do we calculate the ROI on all this money we're spending? I mean, I think I was looking at it a week ago. I think our spend in June went up like 20X versus what the spend was in January. Uh. **Sarah Guo** [41:36] Wow. **Andy Fang** [41:37] Yeah. And so I think it's like, okay, like clearly this has got to get some sort of return. And so, um, and obviously like I think we're seeing a lot of, um, you know, subjective. **Sarah Guo** [41:48] Wait, can I ask? You can, you can, uh, not answer, but like since you have inspected this spend, like has it come down? Has it been flat? Has it continued to grow? **Andy Fang** [41:59] Um, we're seeing it flatline. **Sarah Guo** [42:00] Okay. **Andy Fang** [42:01] Um, and I think a lot of it is through, through some of these intentional efforts, like, 'cause I think, you know, when people were experimenting with, especially at the beginning of the year or like maybe like December last year, it's like, I think there's just like a step function change in terms of what was possible. And so I think a lot of it was just experimenting and letting people run with it, but it's gone to a point where it's like, okay, one, there's like easy things we can do to like make sure that like we're not doing wasteful stuff. But two is like, you know, as it relates to this benchmark that we release, it's like, okay, we actually need to start calculating the ROI. Like, you know, if there's a way for us to get maximize the intelligence, but maybe like delegate to open weight models for some of the cheaper tasks, we can actually do, we can get the fable level of intelligence, but actually pay less than if we were just using these, uh, close weight models. So I think coding is kind of where we think there's a lot of opportunity, mainly 'cause I mean, the vast majority of that spend is still within like engineering related tasks, but we're actually seeing the highest amount of growth in our organization in terms of like seats, uh, in the non-technical organizations because, you know, analysts are finding a lot of value in it. Our operators, you know, um, account managers who are trying to figure out, okay, how do I do my QBR with the strategic merchants? How do we like automate a lot of that? And so I think, you know, there's work we're doing there to figure out, okay, how do we benchmark some of the work we're doing in some of these other areas? And I think the, another thing that is interesting for us is 'cause we work with some of these frontier labs on like, okay, like for like accounting tasks or analytics tasks, like how well do the latest models perform? And I think a challenge that we've run into is like, we'll ask our teams like, hey, how well do the models perform on your tasks? They're like, you know, it works okay. And I think, you know, but then when we do. **Sarah Guo** [43:50] And you're like, okay, like 30 million dollars of okay. **Andy Fang** [43:52] Yeah, exactly. It's like the, the cost, but then it's like, okay, when we, then when we send some of these data to the labs, we'll have to do like the data scrubbing and then we'll have to like, you know, you know, put in like RL environment, whatever. And then, you know, then the models crush it, but then we're like, there's clearly, it's kind of like what you're saying with like the, the Sunday robotics example. It's like, okay, if you like dumb down the problem, maybe the models do well, but like for some reason, and when we actually have it with the enterprise data and all the real stuff, it's not performing as well. And so I think for us, it's a question of like, hey, is it because like there's just things that we need to do with the harness to get the model to perform better, or are there inherently things that the models just don't have, uh, in their data distribution or whatever capability set that is not allowing that step function change enablement in like accounting and analytics or, you know, finance functions? And so I think that's like kind of like the next step for us beyond the coding stuff, which of course there's a lot of work for us to do, but I think there's a lot of interesting things in terms of like how do we really see that step function change across the work. **Sarah Guo** [44:56] Is the long-term view like you get rid of all the Dashers and it's just dots everywhere? What happens? ### Agentic Future **Stanley Tang** [45:02] Yeah. Well, my take, my prediction actually is in a world where robotics, drones, AI is, is everywhere. Uh, my guess is that in 10 years' time, we're actually going to have more Dashers doing, doing deliveries, not less. Uh, simply just because again, I think it's just the, well, one, I think the pace at which DoorDash is growing is just, I mean, and the scale at which we're operating is, is pretty insane. I, I don't know if people know, but like we have over 9 million Dashers doing deliveries and the business growing 25% year over year. Like fast forward 10 years' time, like, like, and, and we want a five X from here, a 10 X from here. Well, where are they, where's the supply going to come from? Like, are you going to have half America doing, doing deliveries for us every month? Like that's probably not going to be the case. Like, like there has to be, we're going to have to find other areas of opportunity to both bring new modalities as well as improve efficiencies within our business. And I think, and I think Dot, robotics, drones, like Waymo's, like sidewalk robots, I think we're going to, you're going to see a world where we're going to have this multimodal fleet. Like we're going to need our hand, get our hands on every single modality we can get. So I think you're not only going to see more autonomy and more robotics, but I think you're going to see even more humans as well. And I mean, I mean, and, and, and I think, and I also just think like with the introduction of autonomy and robotics, like, and, and efficiency gains, you're going to see over time. Like I also think you're just going to see an even stronger surge in demand as autonomy, as delivery becomes, um, even more affordable. **Sarah Guo** [46:46] Probably forward to getting 600 of these a day. **Stanley Tang** [46:48] Yeah. **Sarah Guo** [46:50] Uh, amazing. And, um, Andy, when you think about what you've learned with the initial forays into agentic commerce, like how are people going to buy differently in the future beyond food? **Andy Fang** [47:00] Yeah. I mean, I think one of the trends that I found fascinating is like over the past couple years, Google search query links have gone longer. Um, and I think to me, how I've translated that is like, okay, people feel more comfortable like talking to like agents or to like apps, like they would a normal human being. And so I think if we fast forward and look ahead to the future, I think the easier we can make it for people to kind of interface with apps or with agents like they would with a person, I think it's going to reduce the friction in terms of they're compelling them to place an order, whether that's for food or for like their groceries or for retail, what have you. And I think another thing that I think is going to be true is I think we're all going to need to think about like what does the agent first experience look like? Um, and you know, I think we've been testing some of that with the recent DoorDash CLI that we launched last week. Um, but I just think there's a lot of interesting emerging use cases that can crop up, um, once, once you start thinking about this. Like one concrete example I can talk about is like someone who was really excited to use the DoorDash CLI because like, hey, let me like basically streamline my office manager use case for my startup. And when they found out that DoorDash did more than just lunch, they're like, oh, actually, wait, DoorDash can order me like convenience and groceries. So then they just pointed a camera at their pantry shelf and whenever the shelf was getting empty, like they would fire off, uh, the agent to basically restock the shelf. So I think those types of use cases that you wouldn't really think of, but I think it's going to unlock some interesting use cases that I think would not really be as feasible or possible like in today's world, but as we make things more naturally agent first, I think some of these use cases are going to become a lot more interesting. **Sarah Guo** [48:51] Amazing. I love how, uh, ambitious you guys are for both the user experience and the, uh, scope and scale of DoorDash. Thanks, guys. **Andy Fang** [48:59] Yeah, it's a pleasure to be here. **Sarah Guo** [49:02] Find us on Twitter at No Priors Pod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. ### Outro **Sarah Guo** [49:14] And sign up for emails or find transcripts for every episode at no-priors.com. --- This library is powered by PodHood (https://podhood.com), the podcast website platform.