No PriorsBuilding an Autonomous Delivery Experience with DoorDash Co-Founders Andy Fang and Stanley Tang
EVERY SPOKEN WORD
50 min read · 10,186 words- 0:00 – 0:34
Andy Fang and Stanley Tang Introduction
- SGSarah Guo
[upbeat music] 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 eight years, the data advantages of their network, and what all this means for nine million Dashers and three billion deliveries a year. Welcome.
- 0:34 – 3:52
Agentic Commerce and Behavioral Changes
- SGSarah Guo
Andy, Stanley, thank you so much for being here. Real excited to talk to you about, um, all the crazy stuff DoorDash is doing. 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.
- AFAndy Fang
Yeah.
- SGSarah Guo
Um, so w- what was the backstory here?
- AFAndy Fang
[sighs] 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-
- SGSarah Guo
And it ended up not being the thing.
- AFAndy Fang
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 or 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 h- uh, upheld as we've expanded the rollout.
- SGSarah Guo
What are you seeing in terms of behavior change from the user side? Like, do I eat or buy differently?
- AFAndy Fang
Yeah. So I would say on the restaurant side, we are seeing people, fifty percent of trajectories of people using Ask DoorDash for restaurants, uh, they're or- fifty percent 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, forty percent 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, "H-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 wanna, 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, the traditional experience.
- SGSarah Guo
That's wild. I've never thought of DoorDash as difficult to use-
- AFAndy Fang
[laughs]
- SGSarah Guo
... but, like, that suggests there's, like, actually latent demand that wasn't-
- AFAndy Fang
Yeah
- SGSarah Guo
... being served because you, you, it wasn't easy enough to, like, eat at new places.
- AFAndy Fang
Correct. Yeah, and I think a lot of people on the restaurant side, it's like people build habits.
- SGSarah Guo
Mm-hmm.
- AFAndy Fang
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.
- SGSarah Guo
Oh, think about the social currency of, like, my friend Andy found a new, like, really good restaurant for me.
- AFAndy Fang
Right. Yeah.
- SGSarah Guo
Andy's awesome, right?
- AFAndy Fang
[laughs]
- SGSarah Guo
So I feel like that's even a different way people look at DoorDash.
- AFAndy Fang
And yeah, another thing that was an investment we made was actually, like, incorporating, like, world knowledge into the experience. So it's like-
- SGSarah Guo
What does that mean here?
- AFAndy Fang
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
- 3:52 – 6:54
Next Steps for Ask DoorDash
- AFAndy Fang
what's cool. And so, like, that was something we tried to incorporate, uh [chuckles] , into the experience to make, uh, people trust it more.
- SGSarah Guo
How do you think, uh, people will buy or think about restaurants differently in, like, five years from now?
- AFAndy Fang
I don't know about five years from now.
- SGSarah Guo
I realize it's, like, really hard-
- AFAndy Fang
Just to start-
- SGSarah Guo
... in the age of AI.
- AFAndy Fang
[laughs] I would say-
- SGSarah Guo
Like, next step, next step.
- AFAndy Fang
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.
- SGSarah Guo
Mm-hmm.
- AFAndy Fang
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.
- SGSarah Guo
What could my agent know about what I wanna eat or what I wanna, um, buy from a grocery perspective? Like, help me understand, like, how you think about richer context or how to be smarter there.
- AFAndy Fang
Sure. I mean, one cool example is someone's like, 'Hey, [chuckles] for our office," it's like I can just, like, have the, like, one of the cameras on the, uh, pantry shelf.
- SGSarah Guo
Mm-hmm.
- AFAndy Fang
It's like, hey, when the shelf starts to get empty, like-
- SGSarah Guo
Oh
- AFAndy Fang
... I can fire off, like, a query to DoorDash to, like, stock up my shelf.
- SGSarah Guo
Yes, as a human being past care. Yes.
- AFAndy Fang
Yeah, yeah.
- SGSarah Guo
Yeah.
- AFAndy Fang
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-
- SGSarah Guo
Mm-hmm
- AFAndy Fang
... that's kind of an example of, like, making it less friction for an agent to kind of, like, participate in that experience.
- SGSarah Guo
Okay. Well, while we're here talking about user needs-
- AFAndy Fang
Yeah
- SGSarah Guo
... I'm-- I've got to be, like, a top percentile DoorDash consumer.
- AFAndy Fang
[laughs] Nice.
- SGSarah Guo
I don't know. I'm, you know-
- AFAndy Fang
We're honored
- SGSarah Guo
... a lot of, lot of customers at this point.
- 6:54 – 16:31
Investing in Robotics and Autonomy
- STStanley Tang
lunch at this time, otherwise it's not gonna show up." And it's like, again, everyone has their own, like, allergies or dietary preferences and stuff, so.
- SGSarah Guo
Stanley, you guys are doing, uh, a whole bunch of things on the autonomy and robotics side as well. Like, y- 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 what- whatever the first, you know, one-liner for the company was. Um, how long ago did the robotics efforts start?
- STStanley Tang
Yeah. We've actually been looking into robotics and autonomy probably much longer than people thought, like since 2018 actually, uh, back when it wasn't obvious autonomy and robotics was gonna be a thing. Uh, but we felt like this was gonna be a technology that was gonna be transform- formative to our space and potentially disruptive. And I think, I think that's the nice thing about being a f- 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-
- SGSarah Guo
Hmm
- STStanley Tang
... by the next one. I think like, like Andy said, like the next DoorDash if it, that comes along is not gonna be someone that builds the exact same version of DoorDash but maybe with a better UI. It's gonna be-
- SGSarah Guo
Yeah, that would be dumb.
- STStanley Tang
Yeah.
- SGSarah Guo
Yeah. [laughs]
- STStanley Tang
It's gonna be like something like, okay, how do we incorporate AI, agentic commerce? How do we 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 and autonomy. You've seen Waymo tapping in. I think, you know, we're, we're glad that we, we made that investment early on 2018.
- SGSarah Guo
DoorDash is an amazing business. In 2018, it was, like, less amazing than it is today.
- STStanley Tang
Yeah.
- SGSarah Guo
I feel like that's a fair-
- STStanley Tang
Mm-hmm
- SGSarah Guo
... 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?
- STStanley Tang
Yeah. I think it's, it's probably the same of how we invest in a lot of things at, at DoorDash, is y- everything start 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 paollodeliver.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 a 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 a roboticist, 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. Like, we don't even know what autonomy looks like, how robotics is gonna impact our space, but let's go explore. Let's go form partnerships. Let's go learn. Let's go experiment. Um, and, and in the, 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 ourse- 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 all the distribution, et cetera. And we did that for about actually s- 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 robotaxi 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, it's a question of when it was gonna happen, not if. And again, fast-forward today, you're seeing, you see the Waymo's driving-
- SGSarah Guo
It's happening
- STStanley Tang
... right, it's happening.
- SGSarah Guo
Yeah, yeah.
- STStanley Tang
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 l- it turned out that 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 y- like, or even AI, just plop a LLM in and then things just magically happen. There's-
- SGSarah Guo
Hmm
- STStanley Tang
... a lot of things around it and, and, and, and you kinda 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 i- in a, in a post-autonomy world where autonomy and robotics and drones are, are every... What, what are all the things you have to build? How do you integrate with 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 had to build this technology ourselves, is really this idea of building towards a use case.
- SGSarah Guo
Mm-hmm.
- STStanley Tang
Yes, there's a lot of autonomy startups out there, um, but we, it always felt like these, uh, these companies weren't really focused on a use case. It, it always felt like they kind of build the technology first-
- SGSarah Guo
Mm-hmm
- STStanley Tang
... 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 kinda weird 'cause, 'cause it's, it's like, 'cause in, in, in, in, in software world, like when we went through YC, like we're always taught to, oh, you gotta serve the customer, build something people want. That was kinda 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 s- people just kinda 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. And, and whenever that happens, you just end up with something that just wasn't quite the right fit. Like, like there's... Like we could... 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 tiny world, there's basically two buckets or category of, of companies out there. You have these sidewalk robot companies, which are kinda 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 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 gonna work. And then on the, on the end of the spectrum, you have kinda the robotaxi players, which really are designed for carrying people around. It's a 4,000-pound vehicle. This goes super fast. You're transporting people. And, and but turns out the problem around carrying people and carrying goods is actually a little bit different. Uh, 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's also very different in robotaxis. 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. [laughs] Like, how do you, how do you s- solve that, what I call the first and last 100 feet problem? How does it, 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 and, or, or, or the, the vehicle to be pulled up straight to the front of their driveway or their, or their porch. Um, so, so, so we, when we kinda looked around and a- and asked ourselves, okay, like if you were to start first principle, and again this has always been our philosophy at DoorDash, like s- like if you were to start from the bus- the customer use case, work your way backwards, start 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, it's not a sidewalk robot. It's not a robotaxi. Um, we felt like the, it, 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 deli- deliveries happen, the right metaphor is probably a autonomous motorcycle or scooter or bike profile vehicle. And, uh, you know, it doesn't need to be 4,000 pound. It's probably, you know, 300 pounds. Uh, but it also has to be a lot faster than a sidewalk robot. 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, if no one's gonna do that, instead of waiting around, and let's, [laughs] you know, and wait, and wait for this to happen, we're gonna control our own destiny here. Let's invest in this and see what we can build. And, and it took many iterations. It, you know, like we start look- looking at like, went testing this with real DoorDash deliveries, looking at our 10 billion deliveries we've done, extracting the insights we have, um, 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 always starts out, start out as experiments. S- it always starts out as what is the customer problem you're solving
- 16:31 – 21:20
Building Autonomous Tech in the Physical World
- STStanley Tang
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.
- SGSarah Guo
That sounds extremely rational. I have a hypothesis, and it's very cool. I wanna ask you where we are in the life cycle of-
- STStanley Tang
Mm-hmm
- SGSarah Guo
... everybody getting these automated deliveries. Um, I have a hypothesis, and I'm curious if it resonates with either of you, about like w- why, uh, um, a lot of people in this area are, are building technology first versus customer back. I think people think everything is gonna work like ChatGPT.
- STStanley Tang
Mm-hmm.
- AFAndy Fang
Yeah.
- SGSarah Guo
Right? I just, uh, 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 kinda 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.
- AFAndy Fang
I, yeah, I agree with you there. I mean, that's basically your methodology into building the Dot Form Factor.
- STStanley Tang
Yeah. 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. [laughs] It's like we're, we're, 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. Uh, I, I think one of the things I think people don't realize is just how complicated DoorDash is. I mean, we do what, over 3 billion deliveries a year. There are no two deliveries that look the same. [laughs] 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 in-- or, or even in Europe or in Helsinki where it's snowing or you're doing a, uh, pizza is very different than ice 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 pa-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 then that's kind of how, what we have to solve for at, at, at DoorDash, and, and I think that's part of the been, 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 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.
- AFAndy Fang
[laughs]
- STStanley Tang
We have ten billion deliveries of data to extract from. We have, uh, all these consumers, like, you know, over forty 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-through restaurant is, again, is very different than a restaurant at a strip mall or a 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.
- AFAndy Fang
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 hum-- like how the humanoid robotic 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, uh, 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 are so many different form factors that you can use, and there's like different opinions on like what type of model is gonna 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. Eh, 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.
- SGSarah Guo
Yes. Otherwise, what are we doing?
- AFAndy Fang
Yeah, exactly.
- 21:20 – 22:08
Dot: DoorDash’s Autonomous Delivery Robot
- SGSarah Guo
[laughs] 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.
- STStanley Tang
Yeah. So DoorDash Dot, it's a autonomous delivery robot. It's built entirely in-house, uh, at DoorDash. It's, uh, weighs three hundred pounds, travels up to twenty 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, 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, uh, to Tempe, it really feels like, uh, Waymo San Francisco.
- 22:08 – 25:48
Collecting Realistic Data
- SGSarah Guo
I'm gonna state something and see if this is like 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 gonna be playing in is in robotics.
- STStanley Tang
Yeah. Yes.
- SGSarah Guo
This is a huge problem for everybody where like it's not-
- STStanley Tang
Right
- SGSarah Guo
... 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.
- STStanley Tang
Right.
- SGSarah Guo
The problem is getting it to work on any object or-
- STStanley Tang
Uh-huh
- SGSarah Guo
... in any environment.
- STStanley Tang
Yeah.
- SGSarah Guo
Um, and so there's this like, you know, huge question in the industry of like, okay, how are we gonna go get data that feels like realistic data? And like the best realistic data is the real-world data actually.
- STStanley Tang
Mm-hmm. Yes.
- SGSarah Guo
And so I, I think that's like a really interesting premise of like why you might have the right to go do this-
- STStanley Tang
Mm-hmm
- SGSarah Guo
... besides you want to do it for the quality of your business.
- STStanley Tang
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 one hundred percent of our use cases. I mean, that's, that's also part of our, again, that was part, part of the learning with our kind of the, kind of the first early years when, when, when we did the partnerships for how we built our autonomous delivery platform, was understanding what kind of deliveries fits into what modality.
- SGSarah Guo
Mm-hmm.
- STStanley Tang
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 mile suburban deliveries from a strip mall. Um, so, so right 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 d-- 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 s-stairs and pick and pack orders, like you're still gonna have a, 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 the right... Again, so what the use case, what are the right use cases, uh, to, to solve for? What are the right 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, robotics versus, versus humans?
- SGSarah Guo
Yeah. Um, I also think that's really cool that you have control over the routing and the distribution-
- STStanley Tang
Mm-hmm
- SGSarah Guo
... where you're like, "I can-"
- STStanley Tang
Yeah
- SGSarah Guo
"... I can accomplish this task."
- STStanley Tang
Exactly.
- SGSarah Guo
Yeah. [laughs]
- STStanley Tang
And then, and then from the consumer side-
- SGSarah Guo
Yeah
- STStanley Tang
... and the merchant side, it's like the exact same experience.
- SGSarah Guo
Yeah.
- STStanley Tang
It's still, it's the same app for the customer, uh, that you can access everything, and then for the merchant, it's just one integration. Uh, you already integrated with 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 to- 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, yeah, that is like something that is really hard to replicate [laughs] 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 jar- all these different merchants. That's, that's the hard part about, about the business.
- 25:48 – 28:04
Why Work at DoorDash
- SGSarah Guo
for a friend, question of how you got here.
- STStanley Tang
Mm-hmm.
- SGSarah Guo
Um, there is, uh, 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. Um, and a lot of people gravitate toward, like, the general case.
- STStanley Tang
Mm-hmm.
- SGSarah Guo
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?
- STStanley Tang
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 we have real impact, and I think people, at least especially in 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 be- like, for us, like, it's like, because we k- 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 and then start iterating because ag- 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've, we did that pretty early on for, for DoorDash Dot. I actually, I, I, again, I don't, I don't think a lot of people know, we've actually been doing autonomous deliveries in Phoenix for over two years now, and we, we publicly announced last year 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, you, you mentioned earlier,
- 28:04 – 39:30
Challenges in Scaling Up Autonomy
- STStanley Tang
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 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 b- uh, business takes more than just autonomy. It's like how do you actually scale something in the real, real world, scale fleets? Yeah. All of a sudden, you're running out into all these edge cases, [laughs] right? Like, you just don't see it, and when, when, 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, right? [laughs] 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 the right side of the road or the, the bike lane.
- SGSarah Guo
Mm-hmm.
- STStanley Tang
And if there's kind of leaves located along the kind of where the s- it's right at where the sidewalks are-
- SGSarah Guo
Mm-hmm
- STStanley Tang
... maybe half your wheels, the right two wheels are on the leaves, the left two wheels are still on the asphalt.
- SGSarah Guo
Yeah.
- STStanley Tang
Well, all of a sudden, the, the torque you have to send to the wheels is, like, [laughs] 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, 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-
- SGSarah Guo
Demo
- STStanley Tang
... demo env- environment.
- SGSarah Guo
[laughs]
- STStanley Tang
Uh, it's like, 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 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 if 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, you ha- you have to kind of... Like, here, here is an issue we ran into. It's like, it's like there are 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, s- extreme edge cases, but But there are certain situations where you have to do that because it's, again, it's something in the real world, like, like safe- like this thing ha- like safety is like some- like is something that's super important. So if it can't handle that, like you gotta, you gotta, you gotta figure that out. The, another example we, we didn't think about is, is, is booting up the robots. Like [laughs] , like when we're doing... When, when this was still a demo project, we, like no one thought about, oh, boot up time, right? Like, so, so it's literally the, the, the original version of, of, of, of the robot boot up was a s- kind of this 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 there's this huge productivity, it's becomes this, 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, m- 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 the, how do you do the pickup, dropoff 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 an apartment complex, it's never kind of... I mean, I, I mean, it's, it's not like always the exact same spot.
- SGSarah Guo
Yeah, absolutely.
- STStanley Tang
But if you're-
- SGSarah Guo
Yeah
- STStanley Tang
... 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.
- SGSarah Guo
Yeah, it's this building, yeah.
- STStanley Tang
It's the building.
- SGSarah Guo
Yeah.
- STStanley Tang
It's at the front door. You can't do that with a robot.
- SGSarah Guo
[laughs]
- STStanley Tang
The robot's gonna show up to a pin, and all of a sudden it's like, well, okay, which, wh- which, where, where, where is, which, which front, which storefront is it? Which front door is it? Which gate is it? Um-
- SGSarah Guo
Now I'm just imagining Dot looking around.
- STStanley Tang
Exactly, right?
- SGSarah Guo
[laughs]
- STStanley Tang
And again, like that's something you have to figure out. But the nice thing is, again, DoorDash has that data. Like we-
- SGSarah Guo
Yeah
- STStanley Tang
... we-
- SGSarah Guo
All the dropoffs-
- STStanley Tang
Yeah, we-
- SGSarah Guo
... like we, we can see where people are actually dropping off the package.
- 39:30 – 44:56
Productivity Benchmarks
- SGSarah Guo
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, uh, I'm sure you're thinking deeply about productivity of that workforce.
- STStanley Tang
Mm.
- SGSarah Guo
Like who owns it, what matters today. You're even publishing benchmarks. Like talk about that.
- STStanley Tang
I feel like in the past couple years, what was required to really operate at a high level in the tech knowledge 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 s- they have struggled to s- 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-
- SGSarah Guo
Wow.
- STStanley Tang
Yeah. And so I think it's like, okay, like clearly this has gotta get some sort of return. And so, um, and obviously, like I think we're seeing a lot of, um, you know, subjective-
- SGSarah Guo
Wait, can I ask you, you can, you can, uh-
- STStanley Tang
Sure
- SGSarah Guo
... not answer, but like since you have inspected this spend, like has it come down? Has it been flat? Has it continued to grow?
- STStanley Tang
Um, we're seeing a flat line.
- SGSarah Guo
Okay.
- STStanley Tang
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 was 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 gotten 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 released, 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, closed 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-
- AFAndy Fang
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 task?" They're like, "You know, it works okay." And I think, you know, but then when we do the-
- SGSarah Guo
And you're like, okay, like thirty million dollars of okay. [laughs]
- AFAndy Fang
Yeah, exactly.
- SGSarah Guo
Yeah. [laughs]
- AFAndy Fang
It's like the, the cost, but then it's like, okay, when we, then when we send some of this 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 kinda 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.
- SGSarah Guo
Mm-hmm.
- AFAndy Fang
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, 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?
- SGSarah Guo
Is the long-term
- 44:56 – 49:10
Future of Agentic Commerce
- SGSarah Guo
view, like you get rid of all the Dashers and it's just Dots everywhere? What happens?
- STStanley Tang
Yeah. Well, my take, my prediction actually is in a world where robotics, drones, AI is, is everywhere, uh, my guess is that in ten years' time, we're actually gonna have more Dashers doing, doing deliveries, not less. Uh, simply just because, again, I, I think it's just a... 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 nine million Dashers doing deliveries and the business is growing twenty-five percent year over year. Like fast forward ten years' time, like, like, and, and we want to five X from here, ten X from here. Well, where are the-- where's the supply gonna come from? Like, are you gonna have half America doing, doing deliveries for us every month? Like that's probably not gonna be the case. Like, like there has to be-- we're gonna 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 Waymos, like sidewalk robots, I think we're gonna-- you're gonna see a world where we're gonna have this multimodal fleet. Like we're gonna need our hand-- get our hands on every single modality we can get. So I think you're not only gonna see more autonomy and more robotics, but I think you're gonna 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 gonna see over time, like I also think you're just gonna see a even stronger surge in, in demand as autonomy be-- as delivery becomes, um, even more affordable, uh, in, in the next ten years.
- SGSarah Guo
I look forward to getting six of these a day.
- STStanley Tang
Yeah. [laughs]
- SGSarah Guo
[laughs]
- STStanley Tang
Mm-hmm.
- SGSarah Guo
Uh, amazing. And um, Andy, when you think about what you've learned with the initial forays into agentic commerce, like how are people gonna buy differently in the future beyond food?
- AFAndy Fang
Yeah, I mean, I think one of the trends that I've found fascinating is like over the past couple years, Google Search query lengths have gotten 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 gonna 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 gonna be true is I think we're all gonna 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 gonna 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 gonna become a lot more interesting.
- SGSarah Guo
Amazing. I love how, uh, ambitious you guys are for both the user experience and the, uh, scope and scale of DoorDash. Thanks, guys.
- AFAndy Fang
Yeah.
- STStanley Tang
Thank you.
- AFAndy Fang
It's a pleasure to be here. [upbeat music]
- SGSarah Guo
Find us on Twitter at No Priors Pod. Subscribe to our YouTube channel if you wanna see our faces. Follow the show on Apple Podcasts, Spotify,
- 49:10 – 49:18
Conclusion
- SGSarah Guo
or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.
Episode duration: 49:19
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Transcript of episode vNpcg_Ma-FA
