No Priors: Artificial Intelligence | Technology | Startups - Building an Autonomous Delivery Experience with DoorDash Co-Founders Andy Fang and Stanley Tang

Episode Date: July 23, 2026

DoorDash is not just a delivery company. From its inception, co-founders Andy Fang and Stanley Tang operated it as a robotics and autonomy company. Andy and Stanley join Sarah Guo to explain how autonomous tech and AI are reshaping consumer habits, commerce, and delivery. Andy and Stanley talk about the rollout of Ask DoorDash, a natural-language interface that’s driving both restaurant discovery and larger grocery orders. They also discuss Dot, their in-house autonomous delivery robot that has operated in Phoenix for over two years, and how it highlights the operational and hardware challenges they have faced and solved in autonomous tech. Andy and Stanley also speak about the “first and last 100 feet problem” in autonomous delivery, why multimodal strategies are the key to success, scaling autonomy and operations, and why they believe that more Dashers, not fewer, are the future of DoorDash.  Sign up for new podcasts every week. Email feedback to [e-mail] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @stanleytang | @andyfang | @DoorDash Chapters: 00:00 – Andy Fang and Stanley Tang Introduction 00:34 – Agentic Commerce and Behavioral Changes 03:52 – Next Steps for Ask DoorDash 06:54 – Investing in Robotics and Autonomy 16:31 – Building Autonomous Tech in the Physical World 21:20 – Dot: DoorDash’s Autonomous Delivery Robot 22:08 – Collecting Realistic Data 25:48 – Why Work at DoorDash 28:04 – Challenges in Scaling Up Autonomy 39:30 – Productivity Benchmarks 44:56 – Future of Agentic Commerce 49:10 – Conclusion

Hi, listeners, welcome back to No Priors. Today I'm here with Andy Fang and Stanley Ting, 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 agenic 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 at you. 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. I 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 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's 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. 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 different? Yeah, so I'd 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. And so that's been big. And then another one is on the grocery side. We're seeing a lot higher basket sizes. 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'll 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 usual.

And like that's a lot easier than tapping through the traditional experience. As wild, I've never thought of DoorDash as difficult to use. But like that suggests there's like actually latent demand that wasn't being served because you. it wasn't easy enough to like 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 like what they're eating, you know.

And so we felt like this experience ended up being a natural way to allow people to express that. Oh, think about the social currency of like my friend Andy found a new, like really good restaurant for me. Andy's awesome. Right. So I feel like that's even a different way people look at door. And, yeah, another thing that was an investment we made was actually, like, incorporating, like, world knowledge into the experience. So 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 into the experience to make people trust it more. How do you think people will buy or think about restaurants differently and, like, five years from now? I don't know about five years from now. I realize this is really hard in the age of AI. Like, next step.

Next step. So for Ask Nordash, 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, 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. 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 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? Like, help me understand, like, how you think about richer context or how to be smarter. 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 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 past care. Yes. Yeah, yeah. And so that was kind of like, I mean, something we'd 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 a less. friction for an agent to kind of like participate in that experience. Okay. Well, while we're here talking about user needs. Yeah. I've got to be like a top percentile DoorDash consumer.

Nice. We're honored. 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. Yeah. Or I can't all the time. And like I do the same thing every time, which is pull everyone, okay, who's coming?

Oh, yeah, yeah. And then these people have these allergies and whatever else. Right. And 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. That is definitely. You just put it on autopilot for me. I show up. I hang out 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.

Like, 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 the first, you know, one-liner for the company was. How long go to 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. And I think that's the nice about being a founder-led company is like we are, we get to think about kind of much more future speculative things. They're on the horizon and 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 door dash that comes along is not going to be someone that builds the exact same version of DoorDash. Maybe with a better UI is going to be. Yeah, that would be dumb. Yeah. going to be like something like, okay, how do we incorporate AI, gentic commerce, how to incorporate

autonomy, robotics, drone deliveries, et cetera. And I think, I mean, fast forward like seven, eight years later, I think you're seeing everything starting to play out in AI and robotics and autonomy. You've seen Waymo's happening. And I think, you know, we're glad that we made that investment early on 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, the timing and sequencing of these very long-term bets and like we just it from a capital allocation perspective like when you can invest in these things yeah i think it's it's probably the same of how we invest in a lot of things at door dashes everything start out as experiments i mean in a way

that's that was a founding story behind door dash door dash was a Stanford college like dorm room experiment it started out as a website called palatal delivery dot com with a pdf menus and a google voice phone number and it was only once we figured out okay there's something here let's turn to in the company. And that's basically we've kind of taken that philosophy throughout the past 13 years and we've kind of applied it to autonomy as well, AI as well. I mean, when first started in 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 skunk works project. It was an experimentation to go, let's go explore, like what's out.

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. 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.

Like, you know, back then we weren't, you know, we didn't know anything about robotics. 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. 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 the space, everyone from the sidewalk robot players

all the way up to the robotaxy 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 was going to happen, not if. And again, fast forward today. You see the way Mo'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. How does autonomy integrate with DoorDash? What deliveries do you take on 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. or even AI just plot the 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 ecosystem. So one of the things that we end 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 economy and robotics and drones are everywhere. What are all the things you have to build? how do you integrate of 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 had 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. Like these things are all built in a vacuum, which is kind of weird because it's like, because in software world, like we went through YC, like we're always taught to, oh,

you ought to start the customer, build something people want. That's 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 robotics, it's people just kind of do the opposite where they try to build a tech first. 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 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 examples that you have these, in Turing World, there's basically two buckets of categories 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. But we quickly realized the speed was like in distance with a huge limitation because the average delivery at DoorDash is about three to five miles.

And the typical delivery times out 15 minutes if you exclude the time takes to make the food. So if you put a two mile per hour sidewalk robot, it's just never going to work. And on the other end of the spectrum, you have kind of the robot taxi players, which really are designed for carrying people around. This is 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. Hey, like, you don't need, if you're only have, if you're only carrying a couple of 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 robotaxies, um, you know, you can walk to a Waymo. I mean, how often have you taken a Waymo where it drops you off, half a block or a block away from what you need to be, which is totally fine because you can, you can walk, but packages can't do that.

Like, how do you, how do you solve that, what I call the first and last hundred 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? You know, like people expect their food to be dropped off, or the vehicle we pulled up straight to the front of the driveway.

or their porch. So, so, so, so, so, so we, we kind of looked around and at an, and asked us, okay, like, if you were to start first principle, then again, this has always been our philosophy at DoorDash, like, like if you were to start from the business, the customer use case, work away 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-taxie. We felt like it was probably something in between. The right metaphor for us, again,

it's like if you're trying to solve that three to five-mile delivery in then suburb, which is where most of the deliveries happen, the right metaphor is probably a autonomous motorcycle or a scooter or a bike profile vehicle. And it doesn't even be 4,000 pounds. It's probably, you know, 300 pounds. But also has to be a lot faster than sideworker. It has to go 20, 25-mile. 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 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 it took many iterations. We started looking at like when testing this with real DoorDash deliveries, looking at our 10 billion delivery 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. 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 always 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 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.

I have a hypothesis. And I'm curious for it resonates with either of you about 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 chibi-t 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 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 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 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 into building the dot form factor.

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 you know, you bring technology into physical world. And the physical world is always a lot mess. year. 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 Doordatch it is. I mean, we do over three billion deliveries a year. There are no two deliveries that look the same. All three 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 downtown San Francisco. 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 to retail and pharmacy and parcels as well. It's like the diversity of deliveries that happen at Dordash is so complex that I think people sometimes don't realize just how nuanced the problem. the problem is. And that's kind of how what we have to solve for at DoorDash. And I think that's part of the been the learning process, especially when it comes to like building autonomy or even AI is how do you manage through all that complexity? And it always comes down to 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 called DoorDash. We have 10 billion deliveries of data to extract from. We have all these consumers, you know, over 40 million consumers during every single month. Like, we understand the complexities of how to handle when things go wrong, how to integrate across all different types of merchants. Like, the way you work with a McDonald's or startup bucks is very different than working with a mom and pop sandwich shop. Like 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. 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, I think about it in terms of the economy thing, but I also think about it in terms of like the human, like how the humanoid robotic space is starting to play out potentially where, I mean, we also launched a product called tasks a couple months ago where we're having people in the Dash Fleet help basically click data points. 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 Chachapit, I think there's a lot of expense needed to invest in just like the V1 of this. I guess Chachybtee 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. 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.

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 Dot. It's an autonomous delivery robot. It's built entirely in-house at DoorDash. It's weighs 300 pounds, traveled up to 20 miles per hour. It's one-tenth of 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 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. If you come up to Phoenix and to Tempe, it really feels like Waymo San Francisco. I'm going to state something and see if this is like a correct. Do 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.

This is a huge problem for everybody where 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. 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,

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 the right to go do this besides you want to do it for the quality of your business. 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 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 started.

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 everything, but instead kind of let's go with a, how do you come up with a multimodal strategy where perhaps you have DoorDash Dot do kind of the three to five mile suburban deliveries from a strip mall? So 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, yet things are still 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 I 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. You can kind of phase in these modalities over time and pick and choose what the right, again, so what the use case, what are the right use cases to solve for one of the right modalities to fit into for each of the use cases? Like, are there certain deliveries can carve out that makes a lot of sense for robotics versus

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. And 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, you know, AI tools and products are going to ship.

And I think, again, it's like, I think that's, that is like, that is like what ultimately like Dordash is building is like, it's really like that ecosystem, uh, for local commerce. And I think that is, and that is like something that is really hard to replicate. 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 jobs, all these different merchants. That's the hard part about the business. Asking 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 like 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, like, do you want to go work on prototypes and demos 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?

And I think that's kind of, you know, 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, we get to ship something where we have real impact. And I think people, at least in autonomy world for the past 10 years, were just fed up, just working on something for 10 years, then, you know, never actually get into a point where they actually saw their products being used in the real world. And, and I think, like, for us, like, it's like, because we, like, we've always been, much more focus on creating, kind of taking this much more pragmatic, practical approach.

Like, we're not here necessarily to do like the, 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 interact with the physical world and start iterating. Because again, like technology, these things aren't built in a vacuum. You have to put something out in the real world, make contact with the real world. and actually learn from that. I think that's, we've, we did that pretty early on for, for Dorosh Data. 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.

I mean, we publicly announced last year or that we've been doing that for over two years. But really, at the beginning, it was just learning. Like, okay, like, again, like, I think you mentioned earlier. It's one thing to just do a fancy demo or have something that works in a one-off environment. It's entirely different to now, okay, how do you turn this into an actual scaled fleet, a scaled service, a scaled business? I mean, the thing I always mentioned to talk about a lot is, you know, building autonomy, business takes more than just autonomy. It's like, how do you actually scale something in the real world, scale fleet, all of a sudden you're running into all these edge cases. You just don't see it.

And we have to do something seven days a week, 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 a dirt covering one of your camera sensors. Okay. Like how robust is your autonomy stack able to handle that? Like there's some leaves on the ground. But it only covers kind of because, again, our dot drives on the road. But it tries to act like a bike.

So it will take the kind of the right side of the road. or the bike plane and if there's kind of leaves located along the kind of where the sideblocks are, maybe half your wheels, the right two wheels are on the leaves. The left two wheels are still on the asphalt. Yeah. Well, all of a sudden, the torque you have to send to the wheels is like very different. And your autonomy stack and your kind of your kind of your middleware and your kind of your low level controls has to, handled that differently. Like, like, that's something I would have never thought of if it was just like driving in a nice demo environment.

it's like things just start breaking. Like how do you handle operations? Like people don't think about actually in order to scale autonomy. There's a lot of non-autonomy or 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 if your battery, like how do you recharge your battery?

Like what if one of your braking system kind of like over, you have to kind of like here here was an issue we ran into. It's like there's certain situations where the, vehicle has to break so hard that it kind of the region braking system overpowers kind of the battery because causes this electric shock, right? Again, like it only happens like extreme edge cases, but, but there's a certain situation where you have to do that because it's something in the real world, like, like this thing has like safety is like something that's super important.

So if it can't handle that, like you got to, you ought to figure that out. Another example we didn't think about as as bloating up the robots. Like when we're doing when this was still a demo project, we like no one thought about, oh, boot up time. All right. So it's literally the original version of the robot boot up was a kind of a simple Jenkins script that one of our engineers hacked together in like in like 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.

And it takes like 30, 45 minutes, but to multiply across 500 robots, all of a sudden, it's like, holy crap. It's like, there's this huge productivity. It becomes this huge productivity issue. And then, and then, of course, it's like, how do you think through like reliability? Now you have to start thinking on manufacturing, supply chain. And of course, the kind of the operational aspect of actually how does this thing integrate with merchants? How do you do the pickup, dropout problem? How do you educate the merchant?

Like, how do you even find the pen, the location of a customer's home? 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. Yeah, absolutely. But if you're 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 can kind of find where the restaurant is. It's the building. It's the front door. You can't do that. her 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's, which front, which storefront is it? Which front door is it? Which gate is it? Now just imagine dot looking around. Exactly. Right. And again, like that's something you have to figure out. But the nice things,

again, the door dash has that data. Like we, yeah, we, we, we can see where people are actually dropping off the package. Yeah. Where did the human dasher drop it off historically? And that is, you know, like, again, it's that first and last hundred feet problem. Like, you don't, that data doesn't exist anywhere else. Doesn't exist in Google Maps. It only exists at DoorDash. Yeah. I think that is a really interesting and genuine advantage. Early on when people are 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. Yes. 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 the database. And it's 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. And I think this is totally like real in, in robotics where I'm an investor in a company called Sunday, right? And one thing that we like deeply believe in this company is 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 I'm like, the cat likes the dishwasher. Yeah. I'm 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 date 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 the setup and the scenario for this robot. Yeah. Like that's clearly not going to be the reality. I just feel like for the next frontier of AI, it's, you know, at least what we're really excited about is like 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, pretend and imagine various demos.

So 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. 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 to figure out how to deploy robots and figure out how to, like, get the fleet operations to work. And I think for a lot of people we talk to, that's very compelling because, like, hey, actually,

there's a, we're not just talking hypothetical here, you know? You're making the deliveries in Phoenix. What are the challenges from here for scale up? I mean, we've been doing delivery in Phoenix for over two years now. I mean, we went fully autonomous L4 last year. I mean, it's like, I think that was a super exciting milestone. And really, it's just a matter like, how do you take this from, again, it's like, originally it was just a couple robots, 10 robots to 100.

Again, it's just like, we ought to make that hill climb. Like, how do you, how do you scale this? And then I think it's really three components is, can we get the autonomy to scale? Five years ago, the question was like, was autonomy even possible? Like, was this just a research project? Is this a science fiction? You've seen kind of now, 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. Our entire autonomy stack is built in-house, but purpose built for delivery, which is, again, it's a little bit different. It's not just copy. I think this is the other thing people miss is you can't just copy and paste what Waymo's done and then plop it into the door dash dot and everything works. Again, the use case is a little bit different. This is a bike claim profile vehicle, but that's constantly navigating between the road and the sidewalks. As far as I know, this is like, there's nothing else like this in the world, besides that even behaves like DoorDashDOT.

But we've kind of built it because we kind of built it uniquely to our use case. So autonomy is definitely one piece, like how to keep scaling across not just Phoenix, but we want to bring to Bay Area, more cities. I'm sure we're going to run into more and more edge cases. 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 than the next two,

which is the second is like operational. Like how do you scale operations? Restaurants behave in Phoenix look different than restaurants in San Francisco versus like, you know, London versus Helsinki. How do you adapt to all these different integrations? So it's the interface layer and then like the fleet management of it. Interface and fleet management.

And then the last piece is hardware. Like how to, 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 hard for it starting to become bought. Like it's like we hand built the first hundred robots ourselves and which is not an issue. But then, okay,

the next thousand or 10,000, well, we're going to have to now starting thinking things like supply chain. Like component reliability. Like it's like, it's like these things has to last for a really long time. It's like how you think about, yeah, it's, it's like it's. And you're not guessing because you can actually tell how long it needs to last and how it's doing the field.

Exactly, right? Like manufacturing, 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 so one of the things we actually did is we actually partnered up with this company called Also, which is this micro mobility company that spun out of Rivian. So RJ is actually the board founder and chairman of the company. So if, you know, why don't we work with someone who knows how to actually scale vehicles? And so that's kind of one of the partnerships we struck up. But it's kind of funny.

It's like 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 Dordash, you know, us to shine with our scale advantage and operation advantage is how do we take this thing from not just zero to one but like 100 one a thousand one to three billion yeah one to three billion and i feel like door dash is just so well positioned to to to take on this like 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 plate plate plate of our strengths so you have these enormous strains 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 agenda commerce and around autonomy. How do you think about just it's a 10,000 plus person company and like a lot of that company is ops. A lot of that company is technology. I'm sure you're thinking deeply about productivity

of that workforce. Like who owns it, what matters today? You're in publishing benchmarks. Like talk about that. I feel like in the past couple of years, what was required to really operate a 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 facing it. I think a lot of startups, I mean, 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 have struggled 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 coding is obviously like the most like obvious place to do transformation. And we've seen a lot of gains there. But there's also work we're doing in terms of how do we do AI enablement, the entire organization. And so I think figuring out how to like benchmark various parts of the company,

I think we announced a benchmark called Dashbench 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. Wow.

Yeah. And so I think it's like, okay, like clearly this has got to get some sort of return. And so, and obviously, like, I think we're seeing a lot of, you know, subjective. Wait, can I ask? You can not answer, but like, since you have inspected this spend, like, has it come down, has it been flat? Has it continued to grow? We're seeing a flat line. Okay.

And I think a lot of it is through some of these intentional efforts. Because 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 R-O. why. 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're just using these close weight models. So I think coding is kind of where we think there's a lot of opportunity, mainly because, 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 in the non-technical organizations because, you know, analysts are finding a lot of value in it, our operators, you know, 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 another thing that is interesting for us is because 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 task? They're like, you know, it works okay. And I think, you know, but then when we do the- And you're like, okay, like $30 million of okay? Yeah, exactly. It's like the cost, but then it's like, okay, 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 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 model. models just don't have in their data distribution or whatever capability set. That is not allowing that step function change enablement in like accounting 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 is 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. Is the long term view like you get rid of all the dashers and it's just dots everywhere What happens? Yeah, well, my take, my prediction actually is in a world where robotics, drones, AI is everywhere. My guess is that in 10 years' time, we're actually going to have more dashers doing deliveries, not less. 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 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 if we want a 5x from here, 10x from here, well, where are the, 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 a case. Like there has to be, we're going to have to find other areas of opportunity to both brand new, modalities as well as improve efficiencies within our business. I think and I think dot, robotics, drones, like Waymo's, like sidewalk with robots. I think we're going to be 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 I think. And I also just think like with introduction, of autonomy and robotics and efficiency gains you're going to see over time. I also think you're just going to see an even stronger surge in demand as autonomy as delivery becomes even more affordable. Probably forward to getting six of these a day.

Amazing. And Annie, when you think about what you've learned with the initial phrase into agentic commerce, like how are people going to buy differently in the future beyond food? Yeah, I mean, I think one of the trends that I've found fascinating is like over the past couple years, Google search query links have gone longer. 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? And, you know, I think we've been testing some of that with the recent DoorDash CLI that we launched last week.

But I just think there's a lot of interesting emerging use cases that can crop up 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 they're 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. 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.

Amazing. I love how ambitious you guys are for both the user experience and the scope and scale DoorDash. Thanks, guys. Yeah, it's a pleasure to be here. Find us on Twitter at No PryorsPod. 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.

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