Building an Autonomous Delivery Experience with DoorDash Co-Founders Andy Fang and Stanley Tang

0:05Hi 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 eight 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. Real excited to talk to you about all the crazy stuff DoorDash is doing. I thought we could start with what's going on with agentic commerce at DoorDash.

0:46I feel like you have one of the largest rollouts of actually using AI to change what people consume. Yeah. So what was the backstory here? I mean, it started a couple of 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. And it ended up not being the thing. 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.

1:34Like people just found it easier to search for things, either more nuanced kind of restaurant discovery searches or different tasks on the grocery side. And yeah, we've just seen a lot of interesting traction that's upheld as we've expanded the rollout. What are you seeing in terms of behavior change from the user side? Like, do I eat or buy differently? Yeah. So I would say on the restaurant side, we are seeing people, 50 % of trajectories of people using Ask DoorDash for restaurants. 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 move.

2:15And so that's been big. And then another one is on the grocery side, we're seeing a lot 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, 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 like my, you know, my usuals. And like that's a lot easier than tapping through the traditional experience.

2:46That's wild. I've never thought of DoorDash as difficult to use, but that suggests there's actually latent demand that wasn't being served because it wasn't easy enough to eat at new places. Correct. Yeah. And I think a lot of people on the restaurant side, it's like people build habits, but I think people also want some diversity in terms of what they're eating. And so we felt like this experience ended up being a natural way to allow people to express that. I was thinking about the social currency of like my friend Andy found a new like really good restaurant for me. Andy's awesome. So I feel like that's even a different way people look at DoorDash.

3:25And yeah, another thing that was an investment we made was actually like incorporating like world knowledge into the experience. What does that mean here? 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 people trust it more how do you think uh people will buy or think about restaurants differently like five years from now i don't know about five years from now i realize it's really hard in the age of AI?

4:08Like, next step. 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. Because 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 take care 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.

4:43It'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 think about nowadays, it's 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. And so, you know, I think there's some interesting speculations there, but hard to say. What could my agent know about what I want to eat or what I want to buy from a grocery perspective?

5:16Like, help me understand, like, how you think about richer context or how to be smarter. Sure. I mean, one cool example is someone was like, hey, for our office, it's like, I can just like have the, like one of the cameras on the pantry shelf. 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. Yes. As a human being passed care. Yes. Yeah. And so that was kind of like, I mean, 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 less friction for an agent to kind of like participate in that experience.

5:55Okay. Well, while we're here talking about user needs, I've got to be like a top percentile DoorDash consumer. I don't know. A lot of customers at this point. But I host family dinner for like extended family every Sunday night. And, you know, we eat DoorDash because I'm going to cook for all these people every week or I can't all the time. And like I do the same thing every time, which is poll everyone. Okay. Who's coming? Oh, yeah. And then these people have these allergies and whatever else. I'm like, does anybody feel like anything special? Yeah. And then, you know, I order. Right. Right. And I'm like, I feel like I feel like that's all within the realm of possibility.

6:37That is definitely. You just put it on autopilot for me. I show up. I hang with my family. Everything's good. That is a use case that is, I mean, I think 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. Stanley, you guys are doing a whole bunch of things on the autonomy and robotics side as well.

7:06Like you're 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. How long ago did the robotics efforts start? Yeah, we've actually been looking to robotics autonomy probably much longer than people thought, like since 2018, actually. Back when it wasn't obvious autonomy in robotics was going to be a thing. But we felt like this was going to be a technology that was going to be transformative to our space and potentially disruptive.

7:46And I think that's the nice thing about being a founder-led company is like we get to think about kind of much more future speculative things that are on the horizon and constantly think about like, how do we make sure we don't get disrupted by the next one? I think 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 - Yeah, that would be dumb. Yeah, it's going to be like something like, okay, how do we incorporate AI, agentic commerce? how to incorporate autonomy, robotics, drone deliveries, etc.

8:23And I think, I mean, fast forward seven, eight years later, I think you're seeing everything starting to play out in AI, in robotics and autonomy, seeing Waymo's happening. And I think, you know, we're glad that we made that investment early on in 2018. There's just an amazing business in 2018. It was like less amazing than it is today. I feel like that's a fair statement, right? How do you think about like the timing and sequencing of these very long-term bets? And like just from a capital allocation perspective, like when you can invest in these things? Yeah, I think it's probably the same of how we invest in a lot of things at DoorDash is everything start out as experiments.

8:59I mean, in a way, that's that was a founding story behind DoorDash. DoorDash was a Stanford college dorm room experiment. It started out as a website called politodelivery.com with a PDF menus and a Google voice phone number. And it was only once we figured out, OK, 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 2018 the intention wasn't hey let's go spin up this giant robotics program let's hire roboticists go build hardware it was really we put together it was me and half an engineer's time it was a skunkworks project it was an experimentation to go let's go explore like what's out there we don't even know what autonomy looks like, how robotics is going to impact our space, but let's go explore.

9:50Let's go form partnerships. Let's go learn. Let's go experiment. And I think in the beginning, the intention wasn't to build our own robot. Actually, 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. Back then, we didn't know anything about robotics. There's all these startups out there that have built robots and autonomy. Why don't we just work with them? We can essentially just be the platform. We'll build the APIs. We'll handle the distribution, et cetera. And we did that for about, actually, several years, actually.

10:25We worked with everyone in the space, everyone from the sidewalk robot players all the way up to 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, it's a question of when it was going to happen, not if. And again, fast forward today, you see the Waymo's driving. It's happening. 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.

11:02How does autonomy integrate with DoorDash? What deliveries do you take on? The operational aspect. So it turned 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 or even AI, just plop an LM in and then things just magically happen. There's a lot of things around it and you kind of have to build a platform, an 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 post-autonomy world where autonomy and robotics and drones are everywhere.

11:41What are all the things you have to build? How do you integrate with merchants? What does the consumer experience look like? 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. Yes, there's a lot of autonomy startups out there, but it always felt like these companies weren't really focused on a use case it always felt like they kind of build the technology first and then retroactively try to go find a problem to fit into these things were all built in a vacuum which is kind of weird because in the software world when we went through YC we were always taught to oh you ought to start with a customer build something people want that's kind of drilled into you and then you can iterate But then when it comes to hardware and hard tech and AI and robotics, people just kind of do the opposite where they try to build the tech first and not really think about the use case they're building towards.

12:42And whenever that happens, you just end up with something that just wasn't quite the right fit. And we went through this process where a lot of these companies out there, but I always felt like it wasn't exactly what DoorDash needed. like for example simple example is that you have these in tiny world there's basically two buckets of companies out there you have these sidewalk robot companies which are kind of these 2-3 mile per hour water cooler on wheels super effective simple technology but we quickly realized the speed was like and distance was a huge limitation because the average delivery at DoorDash is about 3-5 miles and the typical delivery times are 15 minutes if you exclude the time it takes to make the food.

13:30So 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. This goes super fast. You're transporting people. But it turns out the problem around carrying people and carrying goods is actually a little bit different. You don't need, if you're only carrying a couple burritos around, do you really need a 4 ,000 pound car with chairs and AC? The pickup drop-off problem is also very different. In robo-taxis, you can walk to a Waymo.

14:06I mean, 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 because you can walk, but packages can't do that. How do you solve that, what I call the first and last 100 feet problem? How does the food 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? People expect their food to be dropped off or the vehicle be pulled up straight to the front of their driveway or their porch. So when we kind of looked around and asked ourselves, okay, like if you were to start first principle, then again, this has always been our philosophy at DoorDash.

14:49Like, 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 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. We felt like it was probably something in between that the right 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, the right metaphor is probably a autonomous motorcycle or a scooter or bike profile vehicle.

15:30And it doesn't even be 4 ,000 pounds. It's probably 300 pounds, but it also has to be a lot faster than sidewalks. 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 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 started looking at like 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.

16:07And that's eventually what led us to launch and ship, which is kind of our in-house autonomous delivery robot. So it's been quite a journey. But again, this is something we look to bring to every aspect of the business, whether it's autonomy, robotics, AI, it always starts out as experiments. It always starts out as what is the customer problem you're solving for? What's the use case you were solving for, work your way backwards and then iterate and validate kind of your hypothesis and slowly build the product over time. 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.

16:49I have a hypothesis and I'm curious if it resonates with either of you about like why a lot of people in this era are building technology first versus customer back. I think people think everything is going to work like chat GPT. Right. I just, and like, by the way, like there was, of course, work done on instruction fine tuning to get it to like be shaped in a product that was still a user experience. But I think the, the mental model that people have, like it's a general technology and it's just kind of like free to turn into different applications is what they're applying to lots of different things now.

17:26And especially in autonomy, my sense is people are like, okay, we'll make the model. And then the other stuff will be, if not easy, at least secondary. This is not my view at all. Yeah, I agree with you there. I mean, that's basically your methodology to building the dot form factor. I think maybe that approach works in software land. But at least for a business like ours, DoorDash is a physical world business. It's like, you know, you're bringing technology into the physical world and the physical world is always a lot messier. It's a lot more complicated, a lot more nuanced. I think one of the things I think people don't realize is just how complicated DoorDash is.

18:07I mean, we do over 3 billion deliveries a year. There are no two deliveries that look the same. All 3 billion deliveries look different. 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 a delivery done in Dallas or even in Europe or in Helsinki where it's snowing or you're doing a pizza is very different than ice cream like your dinner is very different than your grocery order which is very different now that we're expanding into retail and pharmacy and parcels as well. It's like the diversity of deliveries that happen at DoorDash is so complex that I think people sometimes don't realize just how nuanced the problem is.

19:01And that's kind of what we have to solve for at DoorDash. And I think that's part of 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, 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 called DoorDash. We have 10 billion deliveries of data to extract from. We have all these consumers, over 40 million consumers during every single month. We understand the complexities of how to handle when things go wrong, how to integrate across all different types of merchants.

19:41The way you work with a McDonald's or Starbucks is very different than working with a mom and pop sandwich shop. 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 those different use cases, different interaction, different pickup points? I don't know if there's anything you want to add on the AI side. I mean, for me, like the kind of that analogy you brought up, I think about it in terms of the autonomy thing, but I also think about it in terms of like how the humanoid robotics space is starting to play out potentially.

20:17Where, I mean, we also launched a product called Tasks a couple months ago where we're having people in the DASHA fleet help basically collect 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. 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?

20:57Like I have to be better than what people can do today. 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 from either a quality and or a cost perspective. So, yeah. So otherwise, what are we doing? Yeah, exactly. So for those of us who aren't in Phoenix, like what is DoorDash dot and like tell us about the design of it. Yes, DoorDash. It's an autonomous delivery robot. It's built entirely in-house at DoorDash. It weighs 300 pounds, travels up to 20 miles per hour.

21:37It'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 and on the road as well. It's live in Phoenix. We've been live doing deliveries for almost two years now. It's, you know, we do. It's fully autonomous L4. So we come up to Phoenix, to Tempe. It really feels like Waymo San Francisco. I'm going to state something and see if this is like a correct or you agree. 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.

22:23This is a huge problem for everybody where like it's not. I think most people familiar with the area understand that it's not that hard to get a cherry pick demo of like one cool success on a task. Right. The problem is getting it to work on any object or in any environment. And so there's this like, you know, huge question in the industry of like, OK, 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 the right to go do this besides you want to do it for the quality of your business.

23:01Yeah, 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 also part of our, again, that was part of the learning with our kind of the first early years when we did the partnerships route, we built our autonomous delivery platform was understanding what kind of deliveries fits into what modality. And I think the vision was always, let's not design something to solve for everything, but instead, let's go with a, how do you come up with a multimodal strategy where perhaps you have DoorDash Dot do the three to five mile suburban deliveries from a strip mall.

23:48So right now we're live in Phoenix. That's kind of our starting point with Dot. That's kind of the perfect market for Dot. These dense suburbs, yes, things are still far, far apart enough. Maybe if it's a rural area where there's poor road infrastructure, maybe you send it into lightweight order. Maybe you send a drone delivery for that. If it's a complicated multi-step grocery order where you have to climb, go up and down stairs and pick and pack orders, 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, it's not an all or nothing approach.

24:26You can kind of phase in these modalities over time and pick and choose what the right, again, it's about the use case. What are the right use cases to solve for, what are the right modalities to fit into for each of the use cases? Like, are there certain deliveries to carve out that makes a lot of sense for robotics versus humans? Yeah. I also think that's really cool that you have control over the routing and the distribution where you're like, I can accomplish this task. Exactly. 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.

25:03And then for the merchant, it's just one integration. You already integrated DoorDash. All of a sudden, you get not just dashers, but you get drones, you get autonomy. He had access to all the AI tools and products that we're going to ship. And I think, again, that is what ultimately DoorDash is building. It's really that ecosystem for local commerce. And I think that is something that is really hard to replicate. And I think it's trying to do that in the real world across, you know, like 40, 50 plus countries and all these different jobs, all these different merchants. That's the hard part about the business.

25:47Asking for a friend, question of how you got here. There is an insufficient supply of researchers and people who know how to work on robotics or applied AI in the ecosystem for the recognition of all the different cool use cases you go after. And a lot of people gravitate toward the general case, like we can solve it once. I assume you're competing for some of those people. How do you convince people to work at DoorDash on these problems? Yeah, my pitch is really simple. It's basically, do you want to go work on prototypes and demos and be at a PhD lab? Or do you want to work on something where you can actually ship something in the real world?

26:36and I think that's kind of you know 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 like this is we're not just here to do pure research like at the end of the day like we get to ship something where we have real impact and I think people at least in the autonomy world for the past 10 years were just fed up just working on something for 10 years and never actually getting to a point where they actually saw all their products being used in the real world. And I think for us, it's like because we've always been much more focused on creating, kind of taking this much more pragmatic, practical approach.