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

Summary

No Priors Podcast – July 23, 2026

Episode: Building an Autonomous Delivery Experience with DoorDash Co-Founders Andy Fang and Stanley Tang
Hosts: Sarah Guo & Elad Gil (Conviction)
Guests: Andy Fang & Stanley Tang – Co-Founders, DoorDash

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Overview: The Future of Agentic Commerce and Autonomous Delivery

In this episode, DoorDash co-founders Andy Fang and Stanley Tang delve into the company's sweeping integration of artificial intelligence, the advent of fully autonomous deliveries, and their vision for commerce where users simply tell DoorDash what they need—no taps required. They share the company’s journey experimenting with conversational interfaces, outlining the technological, operational, and cultural shifts that come with rolling out robotics and AI at DoorDash-scale. The conversation highlights their approach to experimenting, learning from real-world use cases, and what it really takes to deliver 3 billion orders with a network of 9 million Dashers.

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Key Topics and Discussion Highlights

1. Ask DoorDash: From Voice Experiments to Natural Language Commerce

• Initial Focus on Voice:
  DoorDash’s push towards agentic commerce began with an early bet on voice interfaces, which did not take hold as anticipated.

  "It actually, originally we were bullish on voice as the modality, and that ended up not being the thing. But maybe it will in the future." – Andy Fang [01:09]

• Natural Language as 'Ask DoorDash':
  Success arrived with text-based, conversational search—letting people express requests in plain language.

  "People found it easier… to search for things either more nuanced kind of restaurant discovery searches or different tasks on the grocery side." – Andy Fang [01:28]

• Behavior Change & Metrics:

  • 50% of “Ask DoorDash” restaurant orders are from places users haven’t tried before [01:56]
  • Grocery baskets are 40% larger with agentic interface (e.g., meal planning or restocking via photo) [02:14]
  • Lower friction unlocks latent demand:

    "That suggests there's actually latent demand that wasn't being served because it wasn't easy enough to eat at new places." – Sarah Guo [02:47]

• Incorporating World Knowledge:
  Bringing in data from outside the DoorDash ecosystem—like trending restaurants online—to increase trust and novelty.

  "We'll see, hey, what's trending on the Internet?... Stuff that's not in the models, but... people would find because, like, their knowledge cutoff is too early." – Andy Fang [03:32]

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2. The Agent-First Future of Commerce

• Predicting the Next 5 Years:
  Agents will increasingly anticipate and fulfill needs—like office pantry restocking via camera-triggered orders.

  "I can just have one of the cameras on the pantry shelf. It's like, hey, when the shelf starts to get empty, I can fire off... to DoorDash to stock up my shelf." – Andy Fang [05:20]

• From Habit to Discovery:
  The new system encourages both existing habits and exploration ("social currency" for finding new places) [03:15].

• Agent Understanding of User Preferences:
  Agents could manage allergies, dietary restrictions, group ordering (e.g., weekly family dinner) [06:25].

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3. Robotics and Autonomy: Inside DoorDash’s Journey

• Early Robotics Bet (since 2018):
  DoorDash has been investing in autonomy long before it became trendy.

  "We've actually been looking to robotics and autonomy probably much longer than people thought, like since 2018..." – Stanley Tang [07:26]

• Experimentation Philosophy:

  "Everything starts out as experiments." – Stanley Tang [08:53]
  Start small (“skunkworks”), test with real use cases, iterate fast.

• Learning from Partnerships:

  • Early attempts to partner with external robotics and autonomy startups highlighted limits—nobody was focused on DoorDash’s unique “last hundred feet” problem:

    "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." – Stanley Tang [10:20]

• First Principles & In-House Robotics:
  The solution wasn’t sidewalk robots (too slow, too limited distance) nor robotaxis (overkill; built for passengers not food).

  "The right metaphor is probably an autonomous motorcycle or a scooter or bike profile vehicle... 20, 25 miles per hour." – Stanley Tang [12:40]

  Result:

  • DoorDash’s "Dot" – a 300-pound, 20mph, fully autonomous robot for real-world deliveries [21:27]

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4. Real-World Complexity & Data Advantages

• Physical-World Nuance:

  "People don't realize just how complicated DoorDash is... There are no two deliveries that look the same." – Stanley Tang [18:04]

• Operational Complexity:
  Differences in geography, weather, food type, merchant size (e.g., McDonald's vs. mom-and-pop). The advantage lies in DoorDash’s operational data—10 billion deliveries, drop-off/pickup patterns, real-world edge cases (e.g., GPS not exact; robots need to find the right door).

  "That data doesn't exist anywhere else... It only exists at DoorDash." – Stanley Tang [32:29]

• Application to AI/Robotics:
  DoorDash’s real-world operational data is a massive edge over theoretical or demo-world robotics.

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5. Scaling Robotics: From 1 to 1,000, to 3 Billion Deliveries

• Multimodal Delivery Strategy:
  Robots are for some use cases (suburban, 3-5 mile deliveries); drones for others; complex or multi-step orders will remain human; DoorDash orchestrates the mix [23:01].

• Challenges Beyond Autonomy:

  • Operations (fleet management, merchant education, localization)
  • Hardware (manufacturing, reliability, scaling)
  • Real-world failures (e.g., sensor dirt, regen braking, boot-up bottlenecks)

    "Things start breaking... how robust is your autonomy stack?" – Stanley Tang [27:10]

• Partnerships for Scale:
  Collaboration with micro-mobility companies for manufacturing scale [38:15].

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6. Building a Research & Productive Workforce

• Recruiting AI/Robotics Talent:
  DoorDash offers the chance to see deployed, real-world impact (not just research/demos).

  "Do you want to work on prototypes and demos or... ship something in the real world?" – Stanley Tang [26:18]

• Cultural Transformation and Benchmarks:

  • Acquisition of AI-native company Metis to infuse AI thinking across org [40:01]
  • “Dash Bench” for benchmarking AI impact, especially on coding tasks [41:35]
  • AI spend up 20x in six months—with ongoing efforts to optimize model allocation and measure ROI, including for non-engineering tasks [41:58].

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7. The Future of Delivery Work and Human + Machine Collaboration

• Will Robots Replace Dashers? No:

  "My prediction actually is... we're actually going to have more Dashers doing deliveries, not less..." – Stanley Tang [45:01]

  • Explosive demand and multi-modal delivery will drive growth in both autonomous and human delivery.

• Agentic Commerce Beyond Food:

  • Consumer interfaces will seamlessly cross into retail, grocery, and more—reducing friction as users “talk” to agents [47:00].
  • CLI and camera triggers as emerging patterns for recurring or context-aware commerce.

    "The easier we can make it for people to interface with apps or with agents like they would with a person, I think it's going to reduce the friction..." – Andy Fang [47:16]

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Notable Quotes & Memorable Moments

• On agentic commerce revealing hidden demand:
  "That suggests there's actually latent demand that wasn't being served because it wasn't easy enough to eat at new places." – Sarah Guo [02:47]

• On why building for use case matters:
  "If you were to start from the business, the customer use case, work your way backwards... turns out no one’s really building that." – Stanley Tang [12:28]

• On physical world surprises:
  "If we're trying to do the dishes, why is a cat in the dishwasher?... The only way to find out is not by an engineer sitting and being like, let me imagine the setup for this robot." – Sarah Guo [33:35]

• On autonomy, scale, and edge cases:
  "When you have to do something seven days a week or ten hours a day, seven days a week at scale, things start breaking." – Stanley Tang [27:10]

• On long-term human and robot mix:
  "We're going to have this multimodal fleet... going to need our hands on every single modality we can get... you're going to see even more humans as well." – Stanley Tang [45:01]

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Timestamps for Key Segments

• Intro, agentic commerce overview: [00:05–01:51]
• Behavior changes from AI features: [01:51–03:32]
• World knowledge & agent future: [03:32–05:56]
• User needs & family dinner scenario: [05:56–07:00]
• Origin of robotics investment: [07:00–08:36]
• Experimentation & partnerships: [08:36–14:30]
• Building in-house robot ('Dot'): [14:30–21:27]
• DoorDash Dot detailed design/live use: [21:27–23:01]
• Data/operational advantage in real world: [24:55–32:42]
• Recruiting AI/robotics talent: [25:47–26:18]
• Scaling Dot & operational challenges: [35:20–39:29]
• AI-native org transformation: [40:01–41:35]
• Company-wide AI benchmarks/spend: [41:35–43:52]
• Will dashers disappear? [44:54–46:45]
• Agentic commerce beyond food, CLI, new user behaviors: [47:00–48:50]

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Closing Thoughts

DoorDash’s founders provide a candid, detailed look at what it takes to bring cutting-edge AI and robotics to a global, high-volume delivery business. Their iterative, use-case-first approach underpins both their product and organizational change. Instead of chasing moonshots alone, they’re building for real-world deployment and learning, aiming for a future where human and machine delivery networks seamlessly serve ever-expanding, user-driven use cases.

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For more, follow @nopriorspod or visit no-priors.com for transcripts and updates.

Transcript

[A]

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 9 million Dashers and 3 billion deliveries a year. Welcome, Andy. Stanley, thank you so much for being here. Really 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.

[B]

Yeah.

[A]

So what was the backstory here?

[C]

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 that ended

[A]

up not being the thing.

[C]

That ended up not being the thing. But maybe it will in the future. But just that didn't really land. But the thing that was very interesting for us was just this natural conversational experience. And I think 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.

[A]

What are you seeing in terms of behavior change from the user side? Like, do I eat or buy differently?

[C]

Yeah, 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, like, I would say, like 40% larger basket sizes on grocery. And so people are like, you know, they'll take a picture of what's in their fridge and they'll say, me, stock up my fridge. Or they'll do meal planning with maybe they have some dietary constraints, or they're like, hey, I want to cook a pasta dinner this weekend with my family. Or even just like, hey, help me reorder my usuals. And that's a lot easier than tapping through the traditional experience.

[A]

That's wild. I've never thought of Doordash as difficult to use, but that suggests there's actually latent demand that wasn't being served because it wasn't easy enough to eat at new places.

[B]

Correct?

[C]

Yeah. And I think a lot of people on the restaurant side, it's like people build habits, but I think people also want some diversity in terms of what they're eating. And so we felt like this experience ended up being a natural way to allow people to express that.

[A]

Oh, think about the social currency of. My friend Andy found a new really good restaurant for me. Andy's awesome. So I feel like that's even a different way people look at Doordash.

[C]

And yeah, another thing that was an investment we made was actually incorporating, like, world knowledge into the experience.

[A]

So what does that mean here?

[C]

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.

[A]

How do you think people will buy or think about restaurants differently, like, five years from now?

[C]

I don't know, about five years from now.

[A]

I realize it's really hard in the age of AI. Like, next step, next step.

[C]

So for Ask Doordash, I would say to start with maybe that's like the next couple months or so. I think it's making it easier for people to discover the experience and, like, figure out what to do. Because I think it can be intimidating if you just see, like, hey, like, there's like, suggested queries that you can type. But, like, some people don't know what to start with. So figuring out how to experiment and 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 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

[A]

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 there.

[C]

Sure. I mean, one cool example is someone's like, hey, for our office, it's like I can just have one of the cameras on the pantry shelf. It's like, hey, when the shelf starts to get empty, I can fire off like a query to doordash to stock up my shelf.

[A]

Yes. As a human being task here. Yes.

[C]

And so that was kind of like, I mean, something we talk about more later, but kind of our early experimentation with our cli, that's kind of an example of making it less friction for an agent to kind of like, participate in that experience.

[A]

Okay, well, while we're here talking about user needs.

[C]

Yeah.

[A]

I. I've got to be like a top percentile doordash consumer.

[B]

Nice.

[A]

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 not going to cook for all these people every. Every week. Yeah. Or I can't all the time. And, um. And like, I do the same thing every time, which is poll everyone. Okay. Who's coming.

[C]

Oh, yeah.

[A]

And then these people have these allergies and whatever else. Like, does anybody feel like anything special?

[C]

Yeah.

[A]

And then, you know, I order.

[C]

Right.

[A]

Right. And I'm like. I feel like. I feel like that's all within the realm of possibility. That is definitely just put it on autopilot for me. I show up, I hang out with my family. Everything's good.

[C]

That is a use case. That is, I mean, I think us 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.

[A]

So Stanley, you guys are doing a whole bunch of things on the Autonomy and robotics side as well. Like you, 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 one liner for the company was. How long ago did the robotics efforts start?

[B]

Yeah, we've actually been looking to robotics and Autonomy probably much longer than people thought, like since 2018 actually back when it wasn't obvious. Autonomy and 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 thing about being a founder led company is like we get to think about kind of much more future speculative things that are on the horizon and constantly think about how do we make sure we don't get disrupted by the next. I think like, 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 and maybe with a better UI, it's going to be.

[A]

Yeah, that would be dumb.

[B]

Yeah, it's going to be like something like, okay, how do we incorporate AI, agent 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, in robotics and Autonomy, seeing Waymo happening. And I think, you know, we're glad that we, we made that investment early on 2018.

[A]

Deutsche's amazing business in 2018, it was like less amazing than it is today. I feel like that's a fair statement. Right. How do you think about like the timing and sequencing of these very long term bets and like just from a capital allocation perspective, like when you can invest in these things.

[B]

Yeah, I think it's, it's probably the same of how we invest in a lot of things. At DoorDashes, everything start out as experiments. I mean in a way that's the, that was the founding story behind DoorDash. DoorDash was a Stanford College like dorm room experiment. It started out as a website called politodelivery.com with eight PDF menus and a Google voice phone number. And it was only once we figured out, okay, there's something here, let's turn this into company. And 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 we 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 what's out there. We don't even know what autonomy looks like, how robotics is going to impact our space, but let's go explore, 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. Back then, we didn't know anything about robotics. There's all these startups out there that have built robots and autonomy. Why don't we just work with them? We can essentially just be the platform, we'll build the APIs, we'll handle all 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 robo taxi players. I'll say there's three things we learned through that experience, I think. One, is it kind of validated or confirmed our belief that there's something here. Autonomy. It's a question of when was going to happen, not if. And again, fast forward today you see the Waymo's driving, 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 you take on. The operational aspect turned out a lot of things you have to build around autonomy in order to make autonomy possible. It's not just you plop a robot in, or even AI, just plop a LLM 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 ended up building is this thing called the autonomous delivery platform. Essentially, it's like, what are all the products and technology, the APIs, the dispatch you need to build now that in a post autonomy world where autonomy and robotics and drones are everywhere, what are all the things you have to build? How do you integrate with merchants? What does the consumer experience look like? And I think the last thing which I think is probably the most important thing we learned, which eventually led us to realize we 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. These things were all built in a vacuum. Which is kind of weird because in software software world, like when we went through YC like we're always taught to oh, you got to serve 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 the tech first and not really think about the use case they're building towards. And whenever that happens you just end up with something that just wasn't quite the right fit. Like there's. And we went through this process where a lot of these companies out there but always felt like it wasn't exactly what doordash needed. For example, simple example is that you have these in tiny world. There's basically two buckets of companies out there. You have these sidewalk robot companies which are kind of these two 3 mile per hour kind of water cooler on wheels. Super effective, simple technology. But we quickly realized the speed was and distance was a huge limitation because the average delivery at DoorDash is about 3 to 5 miles and the typical delivery times are 15 minutes if you exclude the time it takes to make the food. So if you put a 2 mile per hour sidewalk robot, it's just never going to work. And then on the other end of the spectrum you have kind of the 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. But it turns out the problem around carrying people and carrying goods is actually a little bit different. If you're only carrying a couple burritos around, do you really need a 4,000 pound car with chairs and AC? The pickup drop off problem is also very different. In robo taxis you can walk to a waymo. I mean how often have you Taken a waymo where it drops you off half a block or a block away from where you need to be, which is totally fine because you can walk, but packages can't do that. How do you solve that? What I call the first and last hundred feet problem. How does the food get picked up at the merchant? What does that integration look like? And then on the customer, how do you drop off the food? How do you find the driveway? People expect their food to be dropped off or the vehicle pulled up straight to the front of their driveway or their porch. So when we kind of looked around and, and asked ourselves, okay, like if you were to start first principle, then again, this has always been our philosophy at doordash. If you were to start from the business, the customer use case, work your way backwards, our first principles and you can build exactly what we need to solve our use case. What would that look like? And we looked around, turns out no one's really building that. It's not a sidewalk robot, it's not a robotaxi. 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 3 to 5 mile delivery in dense suburbs, which is where most of the deliveries happen, the right metaphor is probably a autonomous motorcycle or a scooter or bike profile vehicle. And it doesn't need to be £4,000, it's probably £300, but also has to be a lot faster than sidewalker but has to go 20, 25 miles per hour. And when we looked around and saw no one's building that, we decided, well, if no one's going to do that, instead of waiting around and 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 start looking at testing this with real doordash deliveries, looking at our 10 billion deliveries we've done, extracting the insights we have on the operational learnings we have. And that's eventually what led us to launch and ship which is kind of our in house autonomous delivery robot. So it's been quite a journey. But again, this is something we look to bring to every aspect of the business. Whether it's Autonomy Robotics, AI it'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.

[A]

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 if it resonates with either of you about like why a lot of people in this era are building technology first versus customer back. I think people think everything is going to work like ChatGPT. Right. And by the way, there was of course work done on instruction, fine tuning to get it to be shaped in a product that was still a user experience. But I think the mental model that people have, it's a general technology and it's just kind of free to turn into different applications 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.

[C]

Yeah, I agree with you there. I mean, that's basically your methodology into building the dot form factor.

[B]

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're bringing technology into physical world and the physical world is always a lot messier. It's a lot more complicated, a lot more nuanced. I think one of the things I think people don't realize is just how complicated DoorDash is. I mean, we do over 3 billion deliveries a year. There are no two deliveries that look the same. All 3 billion deliveries look different. And they all come in all sorts of shapes and sizes and different geographies. Like a delivery and downtown San Francisco is completely different than a delivery done in Dallas or even in Europe or in Helsinki where it's snowing or you're doing a pizza is very different than ice cream. 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 DoorDash is so complex that I think people sometimes don't realize just how nuanced the problem is. And that's kind of how what we have to solve for at DoorDash, and I think that's part of been the learning process, especially when it comes to building autonomy or even AI, is how do you manage through all that complexity? And again, it always comes down to do you understand the use case? And I think we just have such a huge Advantage over everyone else because we have something that everyone else doesn't have. It's called DoorDash. We have 10 billion deliveries of data to extract from. We have all these consumers, over 40 million consumers ordering every single month. We understand the complexities of how to handle when things go wrong, how to integrate across all different types of merchants. The way you work with McDonald's or Starbucks is very different than working with a mom and pop sandwich shop, like a drive through 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.

[C]

I mean for me, kind of that analogy you brought up, I think about it in terms of the autonomy thing, but I also think about it in terms of how the humanoid robotics space is starting to play up potentially. Where I mean we also launched a product called Tasks a couple months ago where we're having people in the Dasher fleet help basically collect data points to help train some of these world models. And I think we're so early there and I think there's so many different form factors that you can use and there's different opinions on what type of model is going to work versus not. But I think unlike something like ChatGPT, I think there's a lot of expense needed to invest in just like the V1 of this. I guess ChatGPT costs a lot of money too. But I think there's a lot of pressure though to figure out how do I actually provide value. 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 there's real pressure to be better than the alternative from either a quality and, or a cost perspective.

[A]

Yes. Otherwise, what are we doing?

[C]

Yeah, exactly.

[A]

So for those of us who aren't in Phoenix, what is DoorDash dot? And tell us about the design of it.

[B]

Yes, DoorDash dot, it's an autonomous delivery robot. It's built entirely in House at DoorDash. It weighs 300 pounds, travels up to 20 miles per hour. It's 1/10 the size of a car. It's the only delivery robot out there that's designed to travel not just on sidewalks, but also go on bike lanes and on the road as well. It's live in Phoenix. We've been Live doing deliveries for almost two years now we do. It's fully autonomous L4. So if you come up to Phoenix and to Tempe, it really feels like Waymo San Francisco.

[A]

I'm going to state something and see if this is like a correct or you agree. Even beyond understanding the wealth of use cases, like you need to know what the distribution of environments you're going to be playing in is. In robotics this is a huge problem for everybody where like it's not. I think most people familiar with the area understand that it's not that hard to get a cherry pick demo of like one cool success on a task. 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.

[B]

Yeah, no, exactly. And I think that's again, that's also where doordash gets to shine with. Our advantage is we don't necessarily have to solve for 100% of our use cases. I mean that's, that's also part of our again, that was part of the learning with our kind of, the kind of. The first early years when we did the partnerships right, we built our autonomous delivery platform was understanding what kind of deliveries fits into what modality. And I think the vision was always let's not design something to solve for everything, but instead let's go with a. How do you come up with a multimodal strategy where perhaps you have doordash dot do kind of the 3 to 5 miles suburban deliveries from a strip mall. So right now we're live in Phoenix. That's kind of our starting point with dot com. That's kind of the perfect market for dot these dense suburbs. Yet things are still far, far apart enough maybe if it's a rural area where there's poor road infrastructure, maybe you send and it's a lightweight order, maybe you send a drone delivery for that. If it's a complicated multi step grocery order where you have to climb, go up and down stairs and pick and pack orders, you're still going to have a dasher for that. And I think that's the nice thing about doordash is you can kind of. You don't have. It's not an all or nothing approach. You can kind of phase in these modalities over time and pick and choose what the right. Again, it's about the use case. What are the right use cases to solve for? What are the right modalities to fit into for each of the use cases? Like are there certain deliveries you can carve out? That makes a lot of sense. Robotics versus humans.

[A]

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.

[B]

Exactly. And then, and then from the consumer side and the merchant side, it's like the exact same experience. It's still the same app for the customer that you can access everything. And then for the merchant it's just one integration. You already integrated DoorDash, all of a sudden you get not just dashers, but you get drones, you get autonomy, you get access to all the AI tools and products that we want to ship. And I think again, I think

[C]

that

[B]

is what ultimately DoorDash is building. It's really that ecosystem for local commerce. And I think that is something that is really hard to replicate. And I think it's again, trying to do that in the real world across, you know, like 40, 50 plus countries and all these different jobs, all these different merchants. That's the hard part about the business.

[A]

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 the general case. Likely 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?

[B]

Yeah, my pitch is really simple. It's basically do you want to go work on prototypes and demos and be at a PhD lab or do you want to work on something where you can actually ship something in the real world? And I think that's kind of, I think that's kind of really been the culture we kind of set up, you know, both at Doordash Labs and all the AI efforts is like, this is. We're not just here to do pure research. Like at the end of day, like you, we get to ship something where you have real impact. And I think people at least, especially in the autonomy world for the past 10 years were just fed up just working on something for 10 years and you know, never actually getting to a point where they actually saw their products being used in the real world. And I think for us, because we've always been much more focused on creating, kind of taking this much more pragmatic, practical approach, we're not here necessarily to do. It's not about, oh, let's go work on 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 and start iterating. Because again, 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 we did that pretty early on for doordash data. Again, I don't think a lot of people know we've actually been doing autonomous deliveries in Phoenix for over two years now. We publicly announced last year. Or that we've been doing that for over two years. But really at the beginning 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 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 out into all these edge cases. You just don't see it. When you have to do something seven days a week or ten hours a day, seven days a week at scale, things start breaking. It could be something as simple as, I don't know, like a dirt covering one of your camera sensors. Okay, like how does, how robust is your autonomy stock able to, able to handle that? Like there's some, there's some leaves on the ground. But it only covers kind of because 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 bike lane and if there's kind of leaves located along the kind of where the right, where the sidewalks are. Maybe half your wheels, the right side 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, and your kind of, your kind of your middleware and your, and your kind of your kind of low level controls has to handle that Differently like, like that's something I would have never thought of if it was just like driving in a nice little demo environment. It's like things just start breaking. Like how do you handle operations? Like people don't think 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, it's like there's certain situations where the vehicle has to break so hard that it kind of the regen braking system overpowers kind of the battery because it causes this electric shock. Right. Again, it only happens extreme edge cases but there are certain situations where you have to do that because it's something in the real world. Safety is something that's super important. So if it can't handle that, you got to figure that out. Another example we didn't think about is booting up the robots. When this was still a demo project, no one thought about oh, boot up time. So it's literally the original version of the robot boot up was kind of this simple Jenkins script that one of our engineers hacked together in a couple hours, which worked fine, but then now you're doing hundreds of robots a day every morning needs to get booted up and the script crashes. Half the time it takes 30, 45 minutes. But to multiply across 500 robots, all of a sudden it's like holy crap. It's. There's this huge productivity becomes this huge productivity issue. And then of course it's like how do you think through reliability? Now you have to start thinking about manufacturing supply chain and of course the kind of the operational aspect of actually how does this thing integrate with merchants? How do you handle that? How do you do the pickup drop off problem? How do you educate the merchant? How do you even find the pin 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.

[A]

Absolutely.

[B]

But if you're a human, it's like you kind of figure it out. 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, the building, it's at the front door. You can't do that with a robot. The robot's going to show up to a pin and all of a sudden it's like, well, okay, which, which storefront is it, which front door is it? Which gate is it?

[A]

Now I'm just imagining Dot looking around.

[C]

Exactly right.

[B]

And again, like, that's something you have to figure out. But the nice thing is again, DoorDash has that data.

[C]

Like we all the drop offs, we can see where people are actually dropping off the package.

[B]

Yeah. Where did the human dasher drop it off historically? And that is again, it's that first and last hundred feet problem. That data doesn't exist anywhere else. It doesn't exist in Google Maps. It only exists at Doordash.

[A]

Yeah, I think that is a really interesting and genuine advantage. Early on when people were 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 what the incumbent data advantage was because they didn't really think about, well, what are we trying to do? What is the use case? What is the intelligence supposed to accomplish? And so they'd be like, ah, we have the, I don't know, customer records and database. And 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. You can't imagine the distribution.

[C]

Right.

[A]

As soon as you like make contact with the physical world, as you said, or the like the real world, you're like, man, if we're trying to do the dishes, why is a cat in the dishwasher? And like, you know, you're in somebody's real house and like the cat likes the dishwasher. Yeah, like, that's not, you know, that's not something you're gonna go imagine. Just like you're not gonna imagine like, oh, I'm gonna deal with this torque problem where like one wheel is on the leaves and not. And then you like think like, okay, but like, how important is that in the distribution? Then you find another cat in another dishwasher when you have enough data and you're like, like, I don't know how many of these are out there, but like the only Way to find out is not by an engineer sitting and being like, let me imagine the setup and the scenario for this robot. Like, that's clearly not going to be the reality.

[C]

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 demo situations. 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 it's 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.

[A]

You know, you're making the deliveries in Phoenix. What are the challenges from here for scale up?

[B]

I mean, we've been doing deliveries and Phoenix for over two years now. I mean, we went fully autonomous L4 last year. I mean, I think that was a super exciting milestone. And really it's just a matter of 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 got to make that hill climb. How do you scale this? And 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 with, especially with AI like, Waymo's kind of made that breakthrough. I think Tesla's starting to make that breakthrough. We made that breakthrough last year. 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 doordash 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 DoorDash dot. 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 it to Bay Area, more cities. I'm sure we're going to run into more and more edge cases. But the funny thing is the autonomy is probably increasingly becoming less and less of a constraint of a blocker. It's really like now how do you. It's really more. The next two, which is 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? How do you.

[A]

So it's the interface layer and then like the fleet management of it.

[B]

Interface and fleet management. And then the last piece is hardware. Like how. And it's kind of funny. It's like when we first started like five years ago, everyone thought hardware was a commodity. And now it's starting to look like hardware is starting to become a bottle. Like it's like we hand built the first hundred robots ourselves, which is not an issue. But then, okay, the next thousand or ten thousand. Well, we're going to have to now starting figuring out things like supply chain, like component reliability. It's like these things has to last for a really long time. It's like how do you think about. Yeah, it's like it's.

[A]

And you're not guessing because you can actually tell how long it needs to last and how it's doing in the field.

[B]

Exactly right. Like manufacturing, you know, like it's like, it's like 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 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 Doordash gets to shine with our scale advantage and operation advantages. How do we take this thing from not just 0 to 1, but like 101,000? 1 to 3 billion? Yeah, 1 to 3 billion. And I feel like Doordash is just so well positioned to take on this. It's like we have just such a unique advantage here and I think that's where we want to play in terms of our play to our strengths.

[A]

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, 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. And I'm sure you're thinking deeply about productivity of that workforce, like who owns it, what matters today. You're even publishing benchmarks. Talk about that.

[C]

I feel like in the past couple of years what was required to really operate at 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 is 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

[B]

we do our work.

[C]

And I think coding is obviously the most, most 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 across the entire organization. And so I think figuring out how to benchmark various parts of the company, I think we announced a benchmark called 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.

[A]

Wow.

[C]

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,

[A]

wait, can I ask you can. 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?

[C]

We're seeing it flatline. Okay. And I think a lot of it is through some of these intentional efforts. Like, 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 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 gone to a point where it's like, okay, one, there's easy things we can do to make sure that we're not doing wasteful stuff. But two is as it relates to this benchmark that we release, it's like, okay, we actually need to start calculating the ROI if there's a way for us to maximize the intelligence, but maybe delegate to open weight models for some of the cheaper tasks. We can get the fable level of intelligence but actually pay less than if we were just using these closed weight models. So I think coding is kind of where we think there's a lot of opportunity, mainly because the vast majority of that spend is still within engineering related tasks. But we're actually seeing the highest amount of growth in our organization in terms of seats in the non technical organizations because analysts are finding a lot of value in it. Our operators, account managers who are trying to figure out, okay, how do I do my QB are with the strategic merchants, how do we automate a lot of that? And so I think 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, for accounting tasks or analytics tasks, how well do the latest models perform? And I think a challenge that we've run into is we'll ask our teams, hey, how well do the models perform on your task? They're like, it works okay. But then when we do and you're

[A]

like, okay, like $30 million of okay, yeah, exactly.

[C]

It's like the cost. But then it's like, okay, then when we send somebody's data to the labs, we'll have to do the data scrubbing and then we'll have to put in RL environment, whatever. And then, then the models crush it. But then we're like, there's clearly it's kind of like what you're saying with the Sunday robotics example. It's like, okay, if you dumb down the problem, maybe the models do well, but 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 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 in their data distribution or whatever capability set that is not allowing that step function change enablement in accounting, analytics or finance functions. And so I think that's kind of the next step for us beyond the coding stuff, which of course there's a lot of work for us to do, but I think there's a lot of interesting things in terms of how do we really see that step function change

[A]

across the org is the long term view. Like you get rid of all the dashers and it's just dots everywhere what happens.

[B]

Yeah, well 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 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 and we want a 5x from here, 10x from here. Well, where are they? Where's the supply going to come from? Hank, are you going to have half America doing deliveries for us every month? That's probably not going to be the case. We're going to have to find other areas of opportunity to both bring in new modalities as well as improve efficiencies within our business. And I think DOT robotics drones waymos sidewalk robots. I think you're going to see a world where we're going to have this multimodal fleet like we're going to need our hand get our hands on every single modality we can get. So I think you're not only going to see more autonomy and more robotics, but I think you're going to see even more humans as well. And I mean, I mean, and I think, and I also just think, like with the introduction of autonomy and robotics and efficiency gains, you're going to see over time, like, I also think you're just going to see an even stronger surgeon in demand as autonomy as delivery becomes even more affordable.

[A]

I look forward to the next 10 years a day. Amazing. And Andy, when you think about what you've learned with the initial forays into agentic commerce, like, how are people going to buy differently in the future beyond food?

[C]

Yeah, I mean, I think one of the trends that I found fascinating is like, over the past couple years, Google search query links have gone longer. And I think to me, how I've translated that is okay. People feel more comfortable talking to agents or to 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 be reduce the friction in terms of compelling them to place an order, whether that's for food or for 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 what does the agent first experience look like? And 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. One concrete example I can talk about is someone who was really excited to use the DoorDash CLI because they're like, hey, let me 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 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.

[A]

Amazing. I love how ambitious you guys are for both the user experience and the scope and scale. DoorDash thanks guys.

[C]

Yeah, pleasure to be here.

[A]

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