Uncapped #55 | Tony Xu from DoorDash

Tony Xu is the co-founder and CEO of DoorDash, the leading local commerce platform in the United States. Since co-founding DoorDash in 2013, Tony has built the company into a publicly traded business operating across 40 countries, spanning restaurants, grocery, convenience, retail, and advertising. We discussed how Tony thinks about AI spend and what it means to be an "atoms company" in a world racing to consume tokens. Tony shared his framework for directing AI toward customer outcomes rather than pure exploration, and why DoorDash's physical world infrastructure becomes more valuable as AI agents mature. We also got into the 70-year consumer trend behind DoorDash's growth, how the company built the fastest $1B ads business in history while protecting the consumer experience, why every DoorDash employee still does deliveries to this day, and what Tony means when he says DoorDash is still only single digit percentages of its core market. Timestamps (0:00) Intro (0:44) AI spend and token consumption (2:59) Directing AI toward customer outcomes (5:08) How DoorDash uses AI today (8:18) Atoms vs. bits (12:47) What LLMs can do for the DoorDash app (17:09) Was the $35 burrito obvious in 2013? (20:39) The 70-year food consumption trend (22:20) Consumer trends beyond food (26:41) Sequencing: seven years on restaurants before groceries (29:37) Building the ads business (33:13) 1% better every day (34:23) Math and humanity (38:37) Growth levers at scale (41:09) Speed, drones, and autonomous delivery (43:39) Why everyone at DoorDash still does deliveries (44:16) What Tony is most excited about (46:14) Keeping startup intensity at year 13 Links: https://x.com/jaltma https://x.com/t_xu https://uncappedpod.com/ [email protected]

• 0:00Speaker 00

  There are battles for attention, you know, bits and, you know, battles for what's happening in the physical world, you know, for the atoms. And that we largely occupy ourselves in the second category, right? We are in the war for atoms because, you know, one day ultimately, like, what's the point of having a personal assistant? If it can't actually do real things.

• 0:24Speaker 01

  Tony, thanks so much for doing this. One of the things I wanted to start, uh, start with was this concept that if you'll indulge me on it, because it's something I've been thinking about a bunch. Kind of around the idea of like AI spend at companies. And I think the, uh, the trigger sort of anecdote that I saw, um, that I'd like to get your take on was Uber was basically saying, you know, in the first few months of the year, they blew through their whole AI budget. They were just, you know, rampantly consuming tokens. And what did they get out of it? Did they get more rides? Did they get more drivers? Did anything, you know, their margins improve, like what happened? And I think they were kind of saying like, it wasn't obvious. And you're obviously, you know, the leader of a company that's super metrics oriented and has executed super well.

• 1:05Speaker 01

  So I, I assume you must think about this, but I'm just curious kind of at a high level, how you think about like, what, you know, what is a large rising line item? And, you know, as, as it continues to rise, what do you, what do you care about?

• 1:18Speaker 00

  Yeah, I, I, I think every company right now is trying to figure that out where, you know, the ultimate goal of any technology is actually to hopefully solve a problem and actually make things cheaper. I mean, that's the, that's the goal of technology. But as you know, especially when you have constrained resources like compute in the case of these large L L M companies. You know, sometimes you don't get the timing of these things always exactly when you can deliver the outcomes. Or you don't get the cost profiles or the efficiency profiles to serve the technology to match exactly when you can deliver the customer outcomes. And I think that's the world in which we live today. So I think every company is trying to figure this out.

• 2:02Speaker 00

  In terms of how we're thinking about it, um, you know, first and foremost is like, what customer jobs can we actually solve? And so I, I do think like, when you have new technologies, there's always going to be this period of inefficiency. And, you know, almost like discovery where you got this new toy. You don't know exactly what you need it for. You probably kind of know that you don't really need a, you know, frontier model to know what the weather is perhaps. But on the flip side, you don't also know the limits or the ceiling of what the technology can do. And you kind of want to know that. And, and so the way at least I think about how you do this somewhat efficiently, even though by definition, you're going to accept inefficiency

• 2:47Speaker 00

  in the discovery process or in the invention process.

• 2:52Speaker 00

  You want to do it in the, um, most contained set of ways that deliver customer outcomes. So you actually want to put customer outcomes and start there and then actually give teams, you know, as many shots on goal towards those outcomes as possible. Doesn't mean you get there, but at least it's directed.

• 3:10Speaker 01

  Yeah.

• 3:10Speaker 00

  And it's intentional and it's not, you know, entirely just YOLO. There's obviously some of that, uh, but I think if it can be directed towards, you know, in our case, consumers, merchants, dashers. Uh, we've actually found some success.

• 3:24Speaker 01

  It's funny because like as a software company, which is like where so much of, you know, the AI productivity is happening right now.

• 3:31Speaker 01

  It's actually harder in some ways for them to measure the like end outcome of the work that their engineers have done than maybe yours, where you can say, hey, I can measure, did we complete, you know, more deliveries? Did we get more restaurants? Do we have new users or, you know, all the things that you're tracking? You know, I was sort of like, uh, I was reflecting that over the last few years, you've kind of like watched this egg moves through the like snake in the pipeline from, you know, first there's the build out and there's chips and then you get data centers. And then it's like, oh man, are people ever going to use this? And now they are using it and the token spend is ramping. But now it's like, we've got to turn the token spend into like burritos at people's homes. And like, you're in like as good of a position to see that as anybody.

• 4:11Speaker 01

  So like, are you, are you like, you know, we're going to have certain teams try to move the metrics in a market with AI? Or is it kind of just like bottoms up, let the teams explore whatever they want with AI?

• 4:22Speaker 00

  Yeah, I think it's, yeah, it depends on the team. So if you're product teams, you're, you're exactly right. You can direct teams, I think, towards metrics that matter to customers. If you're merchants on DoorDash today, you know, they're onboarding thirty five to fifty percent faster. Because we can actually, you know, have AI produce all of their catalog or their menu in the case of a restaurant. You know, help edit your photos, help set up, you know, what might be the best way to describe yourself. Especially if you're a new retailer, um, coming online and into a city. So there's metrics like that that are very trackable, measurable, have immediate positive, um, benefit to in customers.

• 5:05Speaker 00

  Similarly with dashers, you know, you, um, we, we've had AI help detect, you know, both fraudulent, uh, fraud, uh, fraud as well as safety incidences before they occur. And you can stop those and take preventative action if you knew that was actually happening. So there's things like that where you can, you know, get immediate benefit, you know, so immediate, um, customer benefit.

• 5:29Speaker 00

  But, but you, you said something earlier in your question, you know, well, what about like software teams, for example? And I think one of the things you noticed there is that only certain parts of a software engineer's day is writing code. And it's great that like we have models today to write the code for us. But that helps maybe the whatever twenty five, thirty, fifty percent of our time that's actually, you know, shipping code. But what about everything else? You know, that has dependencies in product reviews, design meetings, you know, alignment with business teams, et cetera, et cetera, et cetera. That also has to change.

• 6:06Speaker 00

  If that doesn't change and come together, uh, um, you're not going to be able to just, um, have perhaps the productivity gain that you hope to have.

• 6:15Speaker 01

  Yeah.

• 6:16Speaker 00

  Um, and I think that's what, you know, companies like ourselves, but I'm sure a bunch of companies, um, across the industry are trying to figure out in getting those workflows right so that you're not just AI native from code development, but you're AI native in how you actually operate.

• 6:29Speaker 01

  Hey, do you do anything to like track different engineering and product teams inside the company comparing like how much more productive they become with AI usage, like ones that are using it more and less heavily and things like that?

• 6:40Speaker 00

  Yeah, I mean, you, you get all these facts and I'm sure, you know, as with all things, there's always a distribution of outcomes. You know, maybe the average is fifty percent more productive. But, you know, you have engineers who might be thirty five times more productive.

• 6:56Speaker 01

  It's crazy.

• 6:56Speaker 00

  Right? And, and it is crazy, but, and, and it's always fun to study that distribution. But, but again, like I, I think that, you know, what's the value of that? The value of that is really knowing what the art of the possible is, especially in understanding your workflows. But what, you know, someone like I have to do is I have to think about, well, how do you create that working environment for everyone? You know, because it's, it's great that we have, you know, engineers who are fifty times more productive. How do you actually get everyone?

• 7:23Speaker 01

  But is that an observation?

• 7:24Speaker 00

  To that new ceiling.

• 7:26Speaker 01

  Exactly. And the question is like, is that an observation and somebody's going to be far out? Or is there a learning there that you can go replicate it?

• 7:31Speaker 00

  Yeah, exactly. And, and, and, and I, I believe it's the latter because with all things, especially if they're newer, you're always going to get a distribution of outcomes. Right? There's always, you know, in sports, in academics, like there's always a distribution of outcomes. But that doesn't mean there aren't classes and teams and, you know, good things that you can teach as a coach or as an instructor so that you can get the rest of the class, you know, to move to the higher plane.

• 7:59Speaker 01

  Yeah. You know, for a lot of companies, when AI came around, it was either like the most like, you know, kind of petrifying new technology ever, or it was like the, oh my God, thank you tailwind. And I feel like for DoorDash, it was probably like neither. And, you know, tell me if you object to this, but like, I would say like, you know, you're mostly operating in the world of atoms, not bits. You know, you've got this two sided, three, you know, three part marketplace with, you know, restaurants and dashers and consumers and all these things. And, you know, you're very, you know, related to cities and all of that stuff. And so I would think it's both insulated from AI in a good way. And then you also maybe didn't, you know, it's not like your business started growing four hundred percent faster because the AI thing showed up and, you know, it wasn't there.

• 8:44Speaker 01

  It's like, how is that been, is that like, is that right? Is that like about what it's been for you? Has it been kind of just insulated and that's both good and bad?

• 8:51Speaker 00

  Yeah, I mean, we're not selling, you know, burritos or Nike shoes or groceries as fast as tokens, you know, at some other companies. But yeah, look, I mean, I, I, I think you're largely correct. I remember, um, I don't know, I think this was twenty twenty one. This is, yeah, this is twenty one before the arrival of chat G P T. We were playing around with some of these models. I mean, I think it was like, it was like G P T two points X, something like that.

• 9:21Speaker 01

  Yeah.

• 9:21Speaker 00

  Right? And just kind of getting a sense of what the world could look like. And obviously, you know, the models back then are very, very different from the models of today. But even then, we kind of had this description of the world where there are, you know, Battles for attention, you know, bits and and and and, you know, battles for what's happening in the physical world. You know, for for the atoms and and and that we largely occupy ourselves in the second category. Right? We are in the war for atoms and moving things around. And if we can do that. And we can build a catalog, for example, for where every item exists.

• 10:01Speaker 00

  Um, inside of a city or every parking spot exists or all of this um, information. We can, as the kind of companies battle for attention, start um, uh, maturing. That we can really partner and work together in very productive ways because, you know, one day ultimately, like, what's the point of having? A personal assistant, uh, if it can't actually do real things for you, right? Exactly, right? And so. Um, and and that's kind of how we thought about it four or five years ago.

• 10:27Speaker 01

  It's actually funny on that point, like, you know, I it it it strikes me that like. Um, one of the it it's very cool, but you know, one of the things that I think there's room for improvement is that so much of tech is just like. Typing on our screens and we're all living in the computer, but at some point to make lives better, you need to. You know, move stuff around. You need education. You need stuff that happens, you know, in a hospital.

• 10:49Speaker 00

  Healthcare, exactly.

• 10:49Speaker 01

  Yeah, you know, and so it's you need, you need, you know, real estate to be developed. It's like all this kind of stuff that's, but it's all physical at the end.

• 10:56Speaker 00

  Yes.

• 10:56Speaker 01

  And so in some ways, it's like all the software ultimately does need to be in service of stuff.

• 11:00Speaker 00

  Yes.

• 11:01Speaker 01

  Yeah.

• 11:01Speaker 00

  Yes, I mean, and and this is and and this is exactly why, you know, what is, I guess it would have been five years ago at this point. Um, the the the kind of the the strategy was very much, no, play the game that we're meant to play. The game we're meant to play is the game of atoms and, you know, be best in class at that. And not only is that good for our business, but I also think it's great for our relationship over time with these companies as they, you know. Finally build out, you know, some of their assistants um, and build out super intelligence and build out. Agents that can actually do things versus, you know, what they currently do. Um, because we naturally will need one another in order to be useful to the communities that we serve.

• 11:43Speaker 01

  Has there been any impact on like recruiting or things like that when everybody's like losing their mind on AI? And you're in this different thing that you're like, I know this is really important and it's going to become even more important eventually, but like, have you had to work through any of that?

• 11:57Speaker 00

  Yeah, I mean, it it it's definitely a battle. We are an applied AI company and we have been, you know, pretty much since day one of the company. Whether you want to call it math or machine learning or now L L Ms or, you know, we can use whatever terminology we want. But as the years have gone by, we are an applied technology company that that also solves real world problems. So, yes, it's it's certainly difficult when you're competing in places, you know, where activity is really hot. You know, in machine learning and um, folks who are looking to do applied research, it's very competitive right now.

• 12:30Speaker 01

  Yep. Are there things like as you think about, you know, like your next frontier and where you want to go? And you look at the AI stuff, it does seem like some of it is pretty direct, like, you know, for example, I could imagine like. Robotics maybe could matter at some point. I could see self-driving matters at some point, like. What are the like next parts of AI outside of like this, like the L L M on the software side that you care about?

• 12:53Speaker 00

  Yeah, well, well, I mean, I I would say first, you know, be before we step away from, you know, some of the L L Ms. We do care about, you know, what L L Ms can do and and I think they have gotten materially smarter. And more powerful, um, not not just, you know, helping with coding, which may be the most prevalent use case today. Uh, and and certainly in the most prevalent um, spend consumption.

• 13:15Speaker 01

  Well, it's definitely a good input to you for building stuff.

• 13:17Speaker 00

  A hundred percent, a hundred percent, but but even, you know, for instance, you know. One of the things that, you know, we always ask ourselves is like, how can a technology actually improve outcomes for customers? Um, so if you take as an example, the DoorDash app, you know, we've grown a tremendous amount over the last. Even five years, forget like, you know, the thirteen years we've been doing this, just in the last five years, kind of the same arc in in which. You know, some of these L L Ms have grown up, you know, DoorDash has moved from one product category, restaurants. One market, uh, US into, I mean, virtually every retail category. The leader now in, you know, deliveries of grocery, convenience items, alcohol items.

• 13:59Speaker 00

  Um, across forty countries, that's, there's lots of positives with that. One of the challenges for a consumer of those. Boy, it's a lot harder to use an app like DoorDash because you now have. Restaurant things in there, you now have grocery items in there, you have retail items in there. You now have um, the ability to make reservations or get deals inside of restaurants. It's getting more complicated to actually use our product. L L Ms, you know, really can help with that and. You know, just as I think there will be, you know, um, personal agents soon. The DoorDash app should be a personal agent. It should be a personal agent to help you do anything inside of your city, right?

• 14:39Speaker 00

  I can't think of any other product and any other utility, frankly, greater than the number of. Connections that you can have between you and the businesses inside of your city. And so. Those are the kinds of things that you'll see us launch just with L L Ms, right?

• 14:55Speaker 01

  Yeah.

• 14:55Speaker 00

  You should be able to have an easier time actually doing research on what items you want to buy. Or what might be great recommendations for you based on all the history that we have across tens of billions of orders on you.

• 15:07Speaker 01

  And you should pretty much be able to get anything to your house and not have to shop for it.

• 15:10Speaker 00

  Yeah, or have the choice of getting it in it later when you're at home or because you may be away or the next day. Because it's better for you. Um, you're exactly right. And so I do think that there's still a lot of excitement just in the L L Ms. But you're right, if you want to go beyond that.

• 15:26Speaker 01

  Yeah.

• 15:27Speaker 00

  Um, there eh eh there there is a a lot of work around um, uh, the physical space. So, for example, we've been working on autonomous vehicles since twenty nineteen. And a lot of that um, started with, yeah, kind of traditional, traditional systems. Kind of like, you know, with machine learning, yet traditional ways of thinking about how to, eh eh, you know, um. Use those techniques to build things like recommendation systems. L L Ms kind of put a complete new spin on it and didn't really require any of those techniques. Something similar is happening in the physical world where. It used to be if you wanted to perhaps, you know, drive autonomously, you know, inside of a market.

• 16:09Speaker 00

  A lot of what you would do is actually build mapping systems and and almost like. Heuristics and rules uh, uh, to create an engine in which you can make good decisions and and kind of weigh them in real time. Or perhaps you can, you know, put all of this into a neural net. And and take similar techniques that some of the L L M companies are using and actually make, you know. Even faster and better decisions, right? And so there there are things like that that are going on. Um, that allow us to uh, do some of the work that we're doing with autonomous delivery, you know, faster as an example.

• 16:43Speaker 01

  Yeah, totally. This is now like going way back in time, but um, are you surprised? How much people are willing to consume on apps? You know, obviously, when you started the company in twenty thirteen, this was the plan. But like, has it gone like a lot further than you expected? You know, like people really will just like. Order, you know, a burrito for, you know. A lot of money, you know, and probably like, and it's worth it, it like, it's turned out that it's worth it to people. But like, was that obvious to you in twenty thirteen that it would go this way?

• 17:13Speaker 00

  No, it was not obvious.

• 17:15Speaker 01

  So you would pay like thirty-five bucks for a burrito?

• 17:16Speaker 00

  For example, when we launched our first um, partnership with. A um, a national brand that was. July of twenty fifteen, I believe, with Taco Bell. I was actually quite skeptical whether or not it would work. You know, on the one hand, you know, you have this legendary brand that's never offered delivery before being offered for the first time. That was the bowl case version. On the flip side, you know, um. You're right, you you're going to pay a premium, you know, to get that order.

• 17:49Speaker 01

  Yeah.

• 17:49Speaker 00

  And and and eh eh.

• 17:51Speaker 01

  Which wouldn't have been intuitive because you said, well, I can get the, you know, I can, I can get this order for four dollars in store.

• 17:56Speaker 00

  Yeah, eh eh, exactly. And and and so that was not obvious to me in twenty fifteen of what would happen. The the bull or the bear, you know, version of that, but clearly people love getting Taco Bell delivered.

• 18:07Speaker 01

  Well, it's also, I mean, it now makes sense to me in a weird way because it's like, you know, you you save a lot of time going back and forth. You can use that time to do all this other stuff. You can use that time to work, like, you know, and I think more people are familiar with those kind of calculations now and all that stuff.

• 18:19Speaker 00

  Yeah, I think it's that. I thi- I I I think there's a lot of things, Jack. I mean, like, um, one of the things that I remember. Um, even in twenty thirteen, looking at eh eh as just this. Marvelous like fact that that that only goes in one direction that there there's a few of them. You know, one of them is that if you looked at um if you looked at just food consumption, you know, in the nineteen fifty's when the US government used to measure this. Um or when they first started measuring this, something like seventy to eighty cents on the dollar was spent on grocery. Okay, this is like in the nineteen fifty's. If you looked at twenty thirteen, when DoorDash was founded, that number was getting closer to fifty five cents towards groceries. You know, forty five cents towards restaurants.

• 19:05Speaker 00

  Today, it's closer to fifty five cents towards restaurants. Forty five cents towards groceries and so over a seventy five seventy six year, you know, arc. Yes, there's ups and downs, but if you look at the trend line, it kind of goes in one direction, which is in the direction of, you know. Uh food prepared by somebody else.

• 19:23Speaker 01

  Do you know by chance if like the relative cost of a steak you made through your own groceries versus a steak prepared by somebody else? If that ratio of cost has changed, like, has one gotten more expensive relative to the other?

• 19:36Speaker 00

  It's a it's a great question, but I think it depends a lot on how you value your time. So this gets me to another fact that I think is pretty interesting, which is if you looked at the percentage of dual income households. Same time period nineteen fifty to twenty twenty, so this, you know, this goes way beyond AI or COVID or any of this sort of stuff. Um you find that the percentage of household has gone from like a quarter, you know, dual income to almost like seventy percent.

• 20:01Speaker 01

  Right.

• 20:02Speaker 00

  You know, um, present day. If you look at the number of restaurants as another example, just just total number of restaurants, total count, okay? In the sixty to seventy years in which this is measured, there's maybe two years in which the total number of restaurants in the current year doesn't exceed the previous year.

• 20:19Speaker 01

  Yeah.

• 20:19Speaker 00

  So even if, you know, new restaurants, you know, which is a very tough business, as you know.

• 20:24Speaker 01

  Yeah.

• 20:24Speaker 00

  Um, I mean, having worked as a dishwasher for my mom's place, I definitely know how hard it is. Um, yes, there there's a risk of going out of business. But there's always more than enough that replenishes the bucket every successive year. I think when you put together some of these, you know, facts over like it seventy plus years or something, they kind of spell out that. People, whether it's through, you know, dollars or time. Are expressing the fact with their activity, not just their words, but their activity, that they much value if somebody else made them the steak.

• 20:55Speaker 01

  It's interesting because like, you know, the the stat about, you know, dual income households. It's like that that's true, but also like the, you know, the cost of a home has gone up crazily. And if you talk to, I think you talk to a lot of like young people today versus young people in the fifty's.

• 21:10Speaker 00

  Sure.

• 21:10Speaker 01

  I think people probably feel like it's harder to get like the home and the car and everything today than they used to and all of that.

• 21:16Speaker 00

  Yes.

• 21:17Speaker 01

  So it's a little counterintuitive that people's willingness to spend on food has done what it's done.

• 21:21Speaker 00

  Well, I think a couple of things, so you know, the first thing I would say is, you know, food happens twenty to twenty five times a week. It it's not like buying a house.

• 21:30Speaker 01

  Buy, yeah, that's right.

• 21:31Speaker 00

  So that's that's the first point I make, so so even if you're the most avid cook, right, that you love making, you know, uh food.

• 21:39Speaker 01

  What's twenty minutes times twenty five?

• 21:41Speaker 00

  It's very, very difficult to do it twenty to twenty five times a week, that's the first point. I think the second point, and I think you make a very good point on the affordability. Um, challenge that we have as a country and probably globally, actually, not just here in the United States. Um, is that um people always want to find a way to feel good.

• 21:58Speaker 01

  Mm hmm.

• 21:59Speaker 00

  Okay, and and and and I think, you know, purchases, especially on food.

• 22:04Speaker 01

  I know.

• 22:04Speaker 00

  You know, you know, they they don't just become like an indulgence anymore, they become like, you know, the ability to actually, you know, feel good.

• 22:11Speaker 01

  It feels great to hit a button and a pizza shows up, it's it's great, yeah.

• 22:14Speaker 00

  Uh uh to to feel good um um more than once and also do something that you know you need to do.

• 22:19Speaker 01

  Yep.

• 22:20Speaker 00

  Food consumption.

• 22:20Speaker 01

  That's right. How much do you think about like consumer trends in general outside of food? Like, do you need to just be myopically focused on food or is it worth your time and headspace to care how people are spending their energy on social media? Or what's going on with Calchi and Polly Market or other consumers, like, does that matter to you? Um, to understand.

• 22:40Speaker 00

  A- as as a consumer business with hundreds of millions of customers now, absolutely, you have to think way beyond food. And as a company that frankly doesn't just do food anymore. And increasingly, our orders are coming outside of food, we have to pay attention.

• 22:54Speaker 01

  So what are like some of the other consumer trends that are like not about food, but that are important and interesting to you?

• 23:01Speaker 00

  Well, I think you named one of them, uh, which is this affordability piece, which isn't just about food. People are looking for affordability across every segment.

• 23:10Speaker 01

  Yes.

• 23:10Speaker 00

  Housing, transportation, eating, groceries, um, healthcare.

• 23:17Speaker 01

  Mm hmm.

• 23:17Speaker 00

  I education, I mean, I we can keep going, uh, uh, banking, uh, uh, every every category, I would say affordability is a huge deal.

• 23:27Speaker 01

  Yep.

• 23:27Speaker 00

  Huge premium and so a lot of um, you know, what we're thinking about is how do you continuously do two things? One, continuously bring down costs and two, how do you bring more value? And and and and I I think those are very hard to do things. Um, we we we again, like mainly try to stay focused in the world of atoms, though. Because one of the things that I think people um on the software side perhaps don't appreciate is, you know, all this information that you can get in software. Um, kind of sometimes gives you this perception that you can structure information pretty easily. And maybe control information and experiences pretty easily end to end.

• 24:07Speaker 00

  That is the complete opposite in the game of atoms where everything is an edge case. Every day, there's this thing called traffic and weather. And every day, by definition, it's not perfectly predictable. And we can argue it's range bounded or not, but look, if there happens to be a traffic jam and, you know, something takes twenty minutes uh uh longer. That's a that's a real problem for that one customer. A lot of what we're doing is actually just staying as expert and proficient as we can in that game.

• 24:38Speaker 01

  What's cool about it is by being so good at logistics and costs and all and coordination and all of that. It's obviously going to apply to stuff outside of food. Um, but even even just within food, it seems like, you know, to to bring more value to the customer. I mean, the the obvious way is you could just keep chipping away at, you know, the cost, which I assume over time. You ought to be able to get to an extremely low place, I would think.

• 25:00Speaker 00

  With food, um, but also with inventory of other types of products, right? For instance, you know, one of the challenges in, you know, grocery or retail is. Um, well, there there is actually separate challenges in the in in the case of grocery, most grocers don't know what items are on shelves. And it's not because of bad technology or outdated systems or a lot of different systems, there are those challenges. But it's honestly also structurally because consumers who go inside the store move things around.

• 25:29Speaker 01

  Right.

• 25:30Speaker 00

  Um, or CPG companies, you know, want, you know, certain items to be promoted or not promoted, and those things change quite often. And as a result of that, you know, that becomes really messy, right? So how do you get great at that? You know why? Because if you don't get great at that and you make mistakes or you have to make a bunch of substitutions, that's extra costs. Backs to your point around affordability, that's that's cost.

• 25:51Speaker 01

  Yeah.

• 25:51Speaker 00

  Even though it's not just about the price of the items, but it's about everything surrounding it to support fulfillment. In the case of retail, if you didn't know that, you know, the um, the pair of shoes that you wanted. Might be a half size off, you know, be because it doesn't fit great, that's extra cost. That's going to get returned somehow or refunded, you you know? Um, and those are all of the challenges that we try to obsess about.

• 26:15Speaker 01

  What are the like tempting adjacent things that kind of makes sense for you to do that you've like said no to in the name of focus? Like, you know, as an example, as I was just listening to that, like, you know, do businesses ever want to use you to like, you know, work with their own suppliers or things like that? And then if you said, you know what, that's just too far afield from what we do. Like, are there are there close by things that you're constantly saying that's a good idea, but it's not a great idea, we're just not going to do that?

• 26:38Speaker 00

  Yeah, it's a good question. I mean, a lot of the separation between good and great internally right now is around sequencing. You know, and and and and so, you know, for instance. I had no idea, back to one of your earlier questions about, well, how big could food be?

• 26:55Speaker 00

  It's very hard as an entrepreneur, you know, when you're working out of your apartment, um, to know.

• 27:00Speaker 01

  And then, yeah.

• 27:01Speaker 00

  That that, you know, what what that size could be one day.

• 27:03Speaker 01

  Turns out a lot of people eat food.

• 27:05Speaker 00

  Turns out a lot of people eat food. Um, it also turns out it's a lot harder than we thought.

• 27:09Speaker 01

  Yeah.

• 27:09Speaker 00

  And we worked on it for seven years before we moved to category number two, which was groceries.

• 27:14Speaker 01

  Funny, it's like I could see getting started, like, oh, this could be a five billion dollar company. And now you're like, this could be a five hundred billion dollar.

• 27:19Speaker 00

  Actually, in our Y Combinator application, there's a question, I don't know if they ask it anymore, which is. How much revenue do you think this company can make one day? And I remember we were just operating in Palo Alto at the time. And so I counted up how many Palo Altos there could be. And how many orders we could do in each one of those types of cities and I estimate something like. A hundred million dollars of revenue or something like that. Thankfully, we're a few orders of magnitude off.

• 27:45Speaker 01

  Funny, yeah.

• 27:45Speaker 00

  But but that's true, that's a but but but to your point about.

• 27:48Speaker 01

  No, but it's surprising.

• 27:49Speaker 00

  It's very surprising and so I think a lot of times it's uh uh you you kind of, as an entrepreneur, have to take the greedy algorithm, right? You have to keep going all the way.

• 27:58Speaker 01

  Right, like if I'm still on a winning swing.

• 28:01Speaker 00

  Yeah.

• 28:01Speaker 01

  Why would you get off?

• 28:01Speaker 00

  And and that's kind of how we think about it when you ask, you know, about like, oh, are there other things we could do? Yeah, the question we ask is.

• 28:07Speaker 01

  Right.

• 28:08Speaker 00

  Yes.

• 28:08Speaker 01

  So then your job is basically just say.

• 28:10Speaker 00

  And what's the opportunity cost?

• 28:12Speaker 01

  Better thing.

• 28:12Speaker 00

  What's what's the opportunity cost relative to the current thing? But, you know, I mean, but but DoorDash is a company that has built a lot of different things now, you know? We have obviously a business in restaurant delivery, we have a business um outside of restaurant delivery, a business outside of the US, a business in advertising, a B two B business. Where we basically take everything we've built for ourselves and we give it to merchants so they can stand up their own first party channels.

• 28:36Speaker 01

  Yeah.

• 28:36Speaker 00

  Right, and so. That came from somewhere, you know, um, uh, but a, a, a lot of it is just constantly weighing, you know, is it the right time now versus is it, you know, a bad idea or a good idea?

• 28:50Speaker 01

  How hard was the advertising business? I feel like those people.

• 28:53Speaker 00

  Hard. Yeah, well, I, I, I, I, I think just building ads, not, not that hard. Building best in class returns for advertisers while you also have best in class returns for the consumer experience?

• 29:05Speaker 01

  Hard.

• 29:06Speaker 00

  Because fundamentally, there is gonna be a conflict. Where if you have an advertisement show up in a product that may not be relevant to a consumer, you have a challenge where you're taking some sort of a trade off.

• 29:19Speaker 01

  Yeah.

• 29:20Speaker 00

  We can say that, oh, it's worth it here and there, but you know, before you know it, you know, after a few years or a decade.

• 29:26Speaker 01

  It's a slight tax.

• 29:27Speaker 00

  It's very, very difficult to kind of unwind all of that. Especially given some of the attractive profile of the economics behind advertising. And so I think, well, one of the things I'm really proud of, you know, I think the team gets a ton of credit for being the fastest company in history to hit a billion dollars in ad revenue, but I'm more proud of how they did it. Which is constantly, constantly, I mean, you know, fighting the restraint effectively or, or, or, or, or living with the constraint that we must achieve both objectives. Best in class returns for advertisers as well as consumers.

• 30:02Speaker 01

  I mean, you know, it's like, um, I think like probably the Facebook ads team, their early ads team must be like one of the greatest. It seems like, um, in, in some ways, it actually seems to me, and I obviously know you're on the board there at, at minute, but I feel like in some ways, it seems like those cultures are, your culture and the meta culture probably have a lot in common. From, at least from the outside in terms of just like, you know, extremely metrics driven, a lot of testing and trying things, like very focused on like, you know, results and stuff like that. Is that, is that, is that like accurate? Is that like what, like was the ads team like an even more distilled version of all of those things?

• 30:35Speaker 00

  I mean, I think you're right in that, in saying that, you know, the ads teams at some of the largest tech companies in the world are some of the, you know, canonically most impressive teams because you, you, you know, it's carried them so far. You know, I think we forget that some of these companies and products that you're, we're talking about here are more than two decades old now at this point. And not, not only do they have the reach of billions of users and things like that. Uh, if you looked at some of the, you know, latest results from these companies, um, it's incredible, the, the, the, um, business growth that they've seen.

• 31:10Speaker 01

  You talk to people from, you know, Google or Meta Ads. I mean, it's brilliant people. Like it's very hard.

• 31:14Speaker 00

  Yeah, it's a, a, a, and, and, and, and the constant working that problem, right? The, the, and it's, and, and, and you're right that this share, you know, i- i- in some ways with the DoorDash side where maybe we work in a different space. It's not fully in our control. It's not all about, you know, digits and, and, and, and attention. It's more about the physical world and we have a lot more constraints where we have to kind of take what the, the, you know, what the, um, defense gives us, so to speak, where you kind of are taking what's happening in the physical world and then you have to react very quickly to it because we don't get to control sources of demand or supply really. Um, but, uh, i- i- it's similar in that you, you have to be very objective,

• 31:56Speaker 00

  um, and unemotional about what is best for customers while living within constraints and then getting one percent better every single day.

• 32:04Speaker 01

  Yeah.

• 32:04Speaker 00

  And not taking for granted that you can't, because I think sometimes it's easy to say, especially intellectually speaking, that like, oh, we've solved the problem. Finished, you know, like delivery is finished.

• 32:14Speaker 01

  Mm-hmm.

• 32:14Speaker 00

  And I think if we ever thought like that, I don't think DoorDash can continue growing. And I think that we've continued to, uh, our teams have continued to, um, just do better by increasing our selection, making fees more affordable, increasing the quality and reliability of our network and delivery, improving our customer support constantly, every single day, one percent better.

• 32:39Speaker 01

  Yeah.

• 32:39Speaker 00

  Um, and that's, that, that, that leads maybe not in one sitting, but over an accumulated period of time, a lot of benefit and surplus.

• 32:47Speaker 01

  Yeah, when, I mean, when you started the company, obviously you were not the only or first person doing this. And there's probably, you know, a, a lot of things that people could say about, you know, what led to all the success. There were good decisions, you know, just starting in the suburbs, I think was like a big one. And probably the way that you, you know, went about acquiring restaurants and all these different things. But in some sense also, I think it's probably like a fair kind of like summary of it all. And what you guys are known for is just like, it's like really great execution. And, you know, this is like the one percent better everyday thing. And this might be hard for you to answer as somebody because it's just the way that you are. Um, but I'm curious if you can sort of speak at all to what it's like to run a company

• 33:28Speaker 01

  with execution as like, you know, a core excellence, you know, like I'm thinking of Amazon, for example, as a company that's had to do this. When the margins are, when the margins are thin, like there's no choice. There are other companies, by the way, where like the sort of, you know, the, the zone of genius has to be something completely different. They don't need to be great at execution, actually. They can just have periodic brilliant insights that are just so unbelievably step changing that, you know, you can actually afford to be sloppy. And the types of people who are gonna have those insights might be types of people who are gonna more likely be, you know, less, you know, tuned to the details anyway, but I'm just curious if you can speak at all to, you know, what your experience is like building a company with sort of these values.

• 34:10Speaker 00

  Yeah, I, I, yeah, that is a hard question. I, I mean, I would say,

• 34:16Speaker 00

  you know, it, it, it starts first and foremost with, um, a, a love and appreciation for how math and humanity come together. Because, and what I mean by that is when I think about the DoorDash business, yes, you, you're right. There are a lot of metrics, there are a lot of constraints, low margins, um, uh, and, and, and therefore you have to be very good at measuring a lot of things. That's the math part. That's the, how do you, you know, take a multivariate problem and make the best set of, you know, trade offs and, and calculus?

• 34:55Speaker 00

  But underneath it though, is the recognition that on every single order we do, we have at least three humans involved. At least, you know, we have, uh, uh, uh, at least a, a dasher, a merchant and a consumer who all participate, you know, to make something productive happen inside that city. And you kind of have to like both. It, it's not good enough to just be very robotic and all we're gonna look at is the numbers and if the numbers are good, we're good. And but, and if it has a negative consequence on somebody, then so be it. That's not good enough in my book. My, my, you know, my book is you have to recognize that if you look at the merchants, right? When I think about my mom who worked inside of a restaurant, this is life.

• 35:40Speaker 00

  This is not a job. This is not a nine to five or, oh, what are you gonna do from this career to the next career? No, this is every single day, my livelihood, every single day. It's my identity. It's certainly my professional income, but it's everything in, in, in the household. You look at couriers, you know, we have tens of millions of couriers who've, um, you know, delivered with us. Um, and they, we are like a stepping stone for most of them. The vast majority of them are, you know, doing only a few hours a week. And that's because they're, they are trying to strive towards, you know, becoming a doctor, a nurse, a realtor, a teacher, et cetera, et cetera, et cetera. And, and, and then obviously we talked about, you know, the benefits to consumers.

• 36:24Speaker 00

  And, and so you have to have that appreciation and love. If you don't have that for either the, the, the, the people or the math, I think it's a very difficult game to sustain because it's just not choosing the right game for you.

• 36:39Speaker 01

  It, it seems like it could be rare to, uh, have both of those in one person. But you've obviously got a company full of, I presume, people who you believe have both of those things. So what do you, how do, how do you figure out if somebody is, uh, not only one or the other, but somehow both of those things where they appreciate the humanity and all these complexities? While also just being, you know, a maniacal sort of stone cold, you know, operator when, when they need to be too.

• 37:07Speaker 00

  Yeah, look, I, I, I think the tests towards, um, you know, someone's.

• 37:13Speaker 00

  Skills or their problem solving or their metric orientation is a lot more straightforward to assess than say someone's values, I would say. Or and and and and and there a lot of it is actually hearing about the things that motivate them as well as the things that demotivate them. And it's okay, by the way, there's no judgment here. It's really around self-selection. Some people, I think, find it awesome, you know, the types of businesses that want to become restaurateurs, retailers, grocers, some find it messy and not for them. That's okay. That's really okay. And and and and so again, a lot of this is about self-selection.

• 37:58Speaker 00

  It's about about people who are gonna do the right thing, even if maybe the numbers belie, you know, that behavior. People who have seen adversity, people who believe in the fact that, you know, if we can be successful, then all these awesome creators and passion projects inside cities will actually continue to be successful and actually, you know, want that to be successful. I think if people can self-select into that, that's really how you can tell. There isn't like this perfect test, but it's really around self-selecting into it.

• 38:34Speaker 01

  It's cool. I'm curious, like, for you, when you think about like growing your business, what are like the biggest levers? Is it is it like city expansion still to some extent? Is it like, is it now about broadening out through more categories? Like, is it M and A? Like, what are the things when you're like, I want my business to grow by X amount next year, here's how I'm gonna ladder my way there? Like, at this stage, obviously a very mature business, what goes into it?

• 39:02Speaker 00

  Well, well, I don't know if we're a very mature business. I mean, I mean, we've, we've, we've certainly surpassed, you know, the the size of my apartment, but but but I would, but I would say that, you know, we're still even our largest, you know, business, our restaurants business in the U.S. is only single-digit percentages of the of the restaurant category. But but to answer your question, I think it's how can we either solve current customer problems better or how do we solve the next problem? And what is that next problem? And, you know, I I think a lot of times, you know, back to the comment you were making earlier about some of the advertising teams at some of these larger technology companies, I I I I think you have to do two things. You have to keep building the core, which, you know, for us has always been food, and

• 39:47Speaker 00

  just constantly work on that problem of improving selection, quality, price, and service. And then you also have to create the new, which is actually a very different set of skills. It's a different management system. It's a different people sometimes, certainly different incentive mechanisms. It's it has a lot more inefficiency before you have efficiency. And and that's where we're searching for new problems to solve. And this is your point around, you know, well, how much do you focus, you know, versus, you know, just doing the core? It is both. I think when you look at, you know, companies that can continue to grow, they tend to do this.

• 40:30Speaker 00

  They tend to keep solving the problems that they've solved for customers continually better, and they also find new problems to solve.

• 40:38Speaker 01

  How much do customers care about speed? Like, if you could deliver everything in five minutes, would that would that dress? Like, can you tell if that would drastically change demand or is that no longer a huge variable?

• 40:49Speaker 00

  No, I think I I think people, I mean, this is like saying, would you like something delivered slower, right? People are always gonna want something delivered faster. It's all in now, is it X, is it X minutes for like, it depends on probably what the product is.

• 41:02Speaker 01

  Yeah.

• 41:03Speaker 00

  But but but but the the short answer I know is definitively is that customers always want something faster.

• 41:08Speaker 01

  Do you think drones will happen for this kind of delivery?

• 41:10Speaker 00

  Of course. I mean, like drones, drones, autonomous vehicles, they will all happen. You know, but but we have to remember something. The delivery part is just one part of the time of a delivery.

• 41:22Speaker 01

  Yeah.

• 41:22Speaker 00

  Right? So while it may be possible to fly in the air and skip a bunch of traffic, it's not possible to skip a busy kitchen, especially if you're understaffed. And so and that's the predominant part, the majority time spent on a delivery is the preparation.

• 41:36Speaker 01

  That's right.

• 41:37Speaker 00

  Whether it's inventory inside of a retail shop or, you know, cooking time inside of a busy kitchen.

• 41:43Speaker 01

  Yeah. And the drone can't like go through the retail store and check.

• 41:45Speaker 00

  Yeah, it, yeah, yeah. So but my perspective, but but but again, I think you're you're calling out a very good point, which is when you think about, you know, products, I think when it comes to digital experiences, a lot of attention is around pixels and every, you know, detail from step to step to step. No different in the physical world, but a lot of the steps are just in the physical world.

• 42:10Speaker 01

  Right.

• 42:10Speaker 00

  And and and it's the coordination and the orchestration of the end-to-end system, of which just the fulfillment is one part, right? Understanding exactly, did you get the right inventory? Understanding the exact prep times, understanding which vehicle to send, you know, whether it's autonomous vehicles or human drivers, understanding what is the right price point, understanding how do you solve, you know, substitutions, understanding how do you do refunds and credits? All of these things have to be orchestrated and, I mean, effectively hidden in terms of the complexity behind the surface to offer a very simple, just get you exactly what you want, you know, to the customer.

• 42:46Speaker 01

  It's funny, like, as I'm thinking about, it's like both, you have both like the, there's like the simplest kind of, you know, objective, like a company of this scale could possibly have, which is like, get that item from that place to that home as quickly and cheaply as possible. Like, that's really simple. But then, like, the complexity from A to B is ridiculous.

• 43:04Speaker 00

  Yeah.

• 43:04Speaker 01

  Yeah.

• 43:05Speaker 00

  That's, I mean, I mean, and that is the DoorDash problem set, right? It it it and for better and for worse. I mean, there are, you know, I remember when we started DoorDash, I remember decomposing that there's almost like twenty mini systems, mini little processes, if you will. If you imagine a checklist of just bring you a burrito, there's twenty little things that you you kind of have to get right because if anything goes wrong, you know, you you would actually need to fix it. And those are, and and and there's no way, by the way, that that that you would know about this unless you actually did the deliveries yourself, right? This is exactly why we still, to this day, I mean, we've done it since day one, but to this day, we still have everyone in the company, you know, dash, which is our way of saying doing deliveries.

• 43:48Speaker 00

  We still all do deliveries because until you actually get into the physical world, until you find yourself stuck in the wrong elevator or the wrong lobby to try to get upstairs to deliver something, until you get to the wrong alleyway for parking, until you get to the wrong place, because you realize actually food gets made sometimes in three different stations at inside of a restaurant, until you actually experience those things, it's very difficult to a priori just intellectualize that and know about it.

• 44:18Speaker 01

  Yeah. What are you most excited about for whatever's coming up with AI or or anything else in the next year or two?

• 44:25Speaker 00

  Well, I think right now it's a period of rapid change for everyone. And so what is very exciting is, I mean, we started this conversation talking about some of the, you know, inefficiencies that perhaps companies are experiencing when it comes to token consumption.

• 44:44Speaker 01

  Token maxing.

• 44:45Speaker 00

  Token maxing and experimentation.

• 44:50Speaker 00

  But there's a lot of fun in that, too.

• 44:52Speaker 01

  Of course.

• 44:53Speaker 00

  You know?

• 44:53Speaker 01

  And there'll be a lot of creative, like, you know, new discoveries that'll come.

• 44:57Speaker 00

  Yeah. And so what I'm excited about is all the things that haven't yet happened, actually. That's what I'm really excited about. Not not just in like products that that that will be great for customer outcomes and and in increasing the surplus, but but actually ways of working. I am really excited because, you know, I I think one of the things you always ask yourself as an entrepreneur is how do you continuously keep up the velocity and pace at a company similar to what you had when you started the company?

• 45:26Speaker 01

  Yeah.

• 45:26Speaker 00

  And it's very hard, as you know, you've done it yourself, and and you see a lot of startups today. And and and so I'm interested in answering both of those questions. It's like, how are we gonna take this period of change to, yes, build better products for customers? But also literally build better products for ourselves so that we can enjoy work to the max?

• 45:46Speaker 01

  Actually, now that you say this, I wanna, my my last question is kind of on the personal because it it was, you know, I didn't do it for as long, but, you know, nine years of it, and, you know, it it's difficult and you're, I guess, thirteen years into this company. Obviously, it's, you know, was and is very intense, but has your own, you know, when you think about how do we keep it as intense as the early days, like, is that kind of the goal in your head? Or do you at some point, you know, you're now, you know, whatever, maybe billion dollar public company, do you at some point say, actually, now I need to find the marathon pace that I can do this for the rest of my life? Or is it just the same intensity as day one for you?

• 46:20Speaker 00

  Well, it's it's less about like the intensity in terms of like, you know, how many hours are you, you know, sprinting or? I I I think it's the feeling of agency and productivity that actually that you always yearn for. It's not, I I don't think any startup founder, at least I personally know, is trying to optimize towards the number of hours worked per week or something like that. No, I think what when when when, you know, the reason why people go towards startups is and why they want to have is because they want to have a lot of impact and and it's the feeling of the agency.

• 46:52Speaker 01

  Yeah.

• 46:53Speaker 00

  And I think, you know, I want to make sure that everybody at the company, you know, continues to feel that agency to hopefully do more and more and more. Because you know what? Because because not only then will they do the best work of their careers at DoorDash, but even if and after, when they, you know, leave to go pursue whatever the next endeavor is in life, personally or professionally, they're gonna have more confidence to do it.

• 47:19Speaker 01

  That's awesome. Well, Tony, this was a pleasure. Thanks so much for sharing it with me.

• 47:22Speaker 00

  Yeah, thanks, Jack.