David Senra - Tony Xu, DoorDash

Starting point is 00:00:02 So I want to start with the fact that you said that Paolo Alto Delivery.com, which was DoorDash and FordarDash, was the most minimal version of a minimal viable product. Can you explain how you built it? Well, whenever you can ship something in 43 minutes to test your idea, I think that's pretty good. And certainly this is, you know, 12, 13 years before the rise of LLMs and AI tools to make it so easy to do that. But basically, the four of us wanted to test this idea that if you wanted to offer delivery from places that, that never offered delivery before, what is the fastest way to see whether or not consumers would care? I mean, at the end of the day, delivery is not a new idea. And so we thought actually, one of the reasons why maybe delivery in 2013 hadn't been around yet was just because nobody wanted it. So we shipped Palo Alto Delivery.com that alias was available for $9.

Starting point is 00:00:52 And so that's why we got it. Not a super scalable URL, but we were able to get it. It was a static page where you saw eight PDF menus of restaurants that we frequented in Palo Alto. And the only way in which you can order is you can read through the menus, you can call a Google voice number that would ring the cell phones of the four founders, and one of us would pick up. We would take your order, place the order on your behalf, go and get the order, deliver

Starting point is 00:01:22 it to you. And I used to be an intern at Square, and so I had these card readers, which was one of their earliest products, these white dongles that you could stick into the audio jacks of iPhones, And that's how we would collect payment. Something I didn't remember until, because it feels like DoorDash and Uber Eats and everything else has been around forever. But there wasn't, what was the state of, there was other delivery companies, but you essentially created the market for this. Can you explain? Like, when I was telling people, I'm coming, I'm really excited.

Starting point is 00:01:48 I'm going to go speak Tony from DoorDash. They were like, I can't believe he survived in this, like, competitive market. But they just assumed that all, like, there was other apps out there that were already delivering for restaurants that didn't have a delivery fleet. That didn't exist then. No. No, actually, yeah, I think one of the biggest misconceptions when we were founded was just how wide open the space was, where there were about a million restaurants in the States, and maybe 20 to 25,000 of them offered deliveries. Most of them were pizza shops, places in New York City, some in, you know, Chicago, some in, you know, big city centers. But outside of pizza places, maybe a few Chinese restaurants, nobody offered delivery.

Starting point is 00:02:25 And so the real grand question or experiment of DoorDash, Palo Alto Delivery.com, was, okay, what about everyone else? What if you can enable everyone to actually offer delivery? What would that take? And first of all, would people care? And that's really why we ship something so quickly just to see if people would actually come and place orders. So what were the existing companies doing that? They were mostly, honestly, faxing orders, believe it or not.

Starting point is 00:02:48 So they would be a website that would receive orders if you can believe it. They would fax the orders literally into machines that would sit near the kitchen or the payment systems inside these restaurants. then the restaurants would actually go out and do the deliveries themselves. So there were lead-gen companies at the time. I've heard you talk about developing this last-mile logistics network. Did you think about that back then? Or you're just like, hey, I'm just going to try to expand the market for food delivery?

Starting point is 00:03:16 No, we did. So when we started, I guess to take a step back before we shipped palo Alto Delivery.com or even how we got there, my co-founders and I really got connected because of an interest in small businesses. You know, I think my story I've told publicly, which is really, I mean, I grew up coming to the States as an immigrant from China. And my mom, you know, put food on the table by working three jobs a day for 12 years. One of those jobs happened to be at a Chinese restaurant where she was a waitress. I got to hang out with her, wash a few dishes when she allowed me to. That's kind of how I grew up while my dad was getting his Ph.D. at the University of Illinois.

Starting point is 00:03:54 That was, you know, the first 10 years or so of childhood growing up in the States. And that moment and experience always just gave me a deep appreciation for what small business owners represent. I mean, to them, there's no such thing as work. It's all the same thing. There's no concept of a weekend or a Saturday. It's Saturdays and Tuesdays are exactly the same days. And you just kind of get into this process where that becomes your identity. And it's actually one of the most fascinating things I feel.

Starting point is 00:04:29 find about the great experiment that's America, where, you know, because it becomes this all-consuming thing, one of the nice positive derivatives is actually, they don't just create great experiences, like a restaurant or a bar, or a furniture store, or a T-shirt shop. They actually create the GDP for all the cities that we live in. That GDP is what allows us to have great neighborhoods, schools, all the positive things that happen from a local community. And that was always my fascination with it. We had no idea, though, when we're looking at starting DoorDash about anything related to what these business owners' problems were. And so my co-founders and I, we spoke with 300 maybe businesses up and down the Bay Area from San Jose to San Francisco, restaurants,

Starting point is 00:05:15 retailers, service businesses. And it was actually a baker who showed us a booklet, a three-inch binder of delivery orders she had turned down. She was a one-person shop. She was a one-person shop who had no ability to fulfill or desire, frankly, to fulfill all those orders. And that was just a very strange moment for us where I said, delivery's not a new idea. It's 2013. No one offers delivery. Why? And that's really what prompted us to think about, you know, launching palo Alto Delivery.com

Starting point is 00:05:47 to see if people cared. But to your question on logistics networks, you know, we said, okay, well, if the first place in which we can help local businesses is by building a logistics network. we have to pick a place to start. And this is where, I guess, the math brain comes in for me, where when we studied every category of local retail of where we would start, whether it was deliveries for restaurants, grocery stores, convenience stores, retail shops. Those are all options.

Starting point is 00:06:14 We looked at all of them. And we had this hypothesis that if you wanted a chance of creating a logistics network that could actually be successful, that can be very fast, that can be very flexible, meaning it can deliver in 30 minutes, or it can deliver longer than that. You needed network density. You needed the most number of connections between consumers and stores.

Starting point is 00:06:43 We kind of targeted restaurants because there were a million restaurants. If you compare that to the number of grocery stores, there's maybe a couple hundred thousand grocery stores. And you looked at other categories of retail, restaurants had the highest count of stores. And so very quickly, you know, we made the assumption that if there's any vertical to get started in doing deliveries, it would be restaurants and prepared meals to give us a chance to build the highest density network so that one day we can deliver everything else. I want to tell you about the presenting sponsor of this podcast, Ramp. I've been reading a lot about SpaceX lately.

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Starting point is 00:08:39 Yeah. Well, starting in Palo Alto was, I mean, not a conscious choice. I mean, it was just where we were students at the time. But one of the earliest experiments we ran at DoorDash was doing deliveries in Palo Alto versus doing deliveries in San Francisco. So a city center, if you will, that was close to where we started the company. And one of the fascinating things we found out, and we didn't understand why initially, was we were actually completing deliveries faster inside Palo Alto than we were inside San Francisco. Obviously, San Francisco is a more dense place.

Starting point is 00:09:09 But one of the things we learned early on, though, was that obviously, you know, in Palo Alto, you had much easier parking. You had a lot fewer apartment complexes where you had to go up and down the stairs. and figure out where the lobby was or the right elevator entrance, things like that. Palo Alto had the following, which is if you looked at places like Palo Alto, it's really, you know, represents, I think,

Starting point is 00:09:34 most cities in the U.S. or a lot of the world where you have main streets, and then you kind of have, you know, in the spokes, outside of this main street hub of commerce, you have where the people live. And if you actually thought intelligently about what that really told you, you can actually build a very efficient logistics system if you just understood how to manipulate some of these hubs and spokes.

Starting point is 00:10:00 And so this was one of the earliest hypotheses we had that you can actually make a logistics business as efficient in a place like a Palo Alto versus San Francisco. That was guided by that experiment. But the second thing was actually just in talking to customers. What customers told us was they said, look, in San Francisco, I can just walk down the at the elevator and head out the lobby, and we could probably find a few places to go and eat. And Palo Alto, you'd be walking for miles

Starting point is 00:10:28 before you could achieve something like that. You know, the closest set of restaurants near Stanford University where we started this was two miles away on University Avenue, as an example. And that's true in a lot of places in America. And so if there was any place we thought where there would be the highest interest from consumers and a possibility where you can actually make the math work,

Starting point is 00:10:50 It was places like Palo Alto. And the question to us was just how many of them are there. And the only people doing deliveries at this time are the four founders? Yeah. In the very beginning, it was just the four founders. Okay, so you had a line about this where it said it became obvious that the need was higher outside of the cities. We did not have the data to prove it at the time. We had the conviction that because we were doing the deliveries ourselves that this could be true.

Starting point is 00:11:14 Yeah. I mean, we saw, I mean, one of the benefits when you do the deliveries is, well, one, you see you how hard it is to actually bring you a burrito on time every time correctly. And the second thing is you get to see who the customer is. And you saw the customer actually almost always was a mom, you know, who had young children, who had not a lot of time, who didn't want to cook, you know, every single meal, who wanted just look for any solution to save her time. And so when we did those deliveries, we just saw, wow, well, there are a lot of young families

Starting point is 00:11:48 out there. And let's go find out where they hang out. Let's go find out where they live. And that's why we had that sense that, you know, we can build a business, you know, with this audience to start. Is that another unexpected benefit of starting in these, basically the suburbs or the cities? Think about like the typical city populations, like maybe more single people or maybe like just a couple. Sure. But it's not large families shoved in these buildings. Yeah. I mean, I think that was probably a derivative of the discovery. But no, I think in the beginning, especially when you're looking for product market fit as an entrepreneur, you're looking for someone who actually just wants your product organically.

Starting point is 00:12:23 And we could tell very quickly that someone who has young children who maybe doesn't want to take a stroller, pack it up, pack all the things that come with a stroller, then, you know, put that, you know, stroller and the children into the vehicle, then get it out, and then somehow get into it inside of a crowded parking ladder or restaurant. Well, there are a lot of those people. And if we can solve it for that group, then we believe we could build a business that can easily grow organically. You're right. I mean, there's a second derivative, which is there are more mouths to feed when you have a family than when you have one or two people living inside of a city.

Starting point is 00:12:55 But that wasn't the first thought we had. But even more than a second derivative, because you were just explaining, like, okay, well, if I'm delivering somebody's house, I know where to park? Where to park? As opposed to I'm in a city, you have to navigate. Where's the lobby? How do I get in this building? What floor do I get? How to access the elevator, right?

Starting point is 00:13:08 Yeah, totally. And the presence of single family homes made it a lot easier. for sure. That was one of the benefits of delivering to places like Palo Alto. But again, I think it just came from this very simple experiment, which had an anomalous finding, which is, why is it faster to deliver in Palo Alto than it is in San Francisco? Why is it faster to deliver in a less dense place, in other words? Exactly. This is what is interesting to me. It almost made it sense like your competitors seem to do the most obvious or like the logical thing. It's like, no, I need order density. Where are all the people? Let me just go to the cities.

Starting point is 00:13:42 We chased where the, I think when you're starting out, the number one thing every entrepreneur is looking for is do you have something that someone else wants? And is it real? Meaning like it's not artificially inflated with discounts and marketing dollars and, you know, just other ways to inorganically grow. Will people actually use it? Will they actually tell their friends about it if they actually like the service? And that's what we found early on with places like Palo Alto. Even when you were called Palo Alto Delivery.com. Especially when we were called Palo Alto Delaney, right?

Starting point is 00:14:14 Yeah, we had no money. Exactly. We ran this out of my bank account. And that's why I knew early on, even though, look, we didn't have any models or, you know, unit economic forecast or anything like this. But even though it was running out of my bank account where I also had student debt at the time, my bank account wasn't going down, you know, every single week or every single month. So something was telling me that maybe this is a lot.

Starting point is 00:14:40 a chance of working. What were your costs at the time? Because you have the four founders, essentially labor. You're not paying yourself. We didn't pay yourself anything. You're not paying yourself. You have free labor. Just your time. You built a $9 website. I heard something that was hilarious. We're like, well, we don't have a sophisticated dispatch system. So we just use the find my friends. We just find my friends. We used to find my friends. We used to track the drivers, which just happened to be all of you. Our co-founders. You have a Google voice number. Will you? You're not, there's no marketing and advertising, right? No. No, we had no money to Cricket or it advertised.

Starting point is 00:15:11 So what are the expenses did you have back that? Do you remember? It was all kind of like self-funded. This entire activity was self-funded until we had to start recruiting drivers and actually, you know, testing this out beyond just the four of us. This is when you applied to Y Combinator or no? Yeah. Yeah.

Starting point is 00:15:25 I mean, in that time period. Okay. By the time you apply to Y Combinator, do you have more than drivers than just the founders or no? We may have had one or two. Okay. Yeah, very quickly we realized, well, we're in class. And so, you know, we took turns doing deliveries while we were in class, but at some point it's tough to, you know, be a student and do the deliveries. How many years did you have left of business school?

Starting point is 00:15:47 Like, how many years were you in school and running? We had maybe six months left before graduation. I mean, we were effectively Stanford's delivery service, you know, for the second half of, or for the first half of 2013. We were effectively Stanford's delivery service. Then we get DoorDash, the year. and the company name, and then we launched out of Y Combinator in the summer, June 20. So was it like now, once somebody starts using DoorDash or when I start using DoorDash, right, I'm like, ooh, this is very convenient, I just keep using it over and over again.

Starting point is 00:16:20 Did you see that same behavior pattern back then? Yeah, with a very small group users, because in the beginning, we actually did not have high volume. I mean, it was probably 10 orders a day, something like that. Maybe our high day was like 21 orders a day, something like this. Most of them, however, were done by a small group of users at Stanford. When you see that fact that the same customers are ordering over and again, even though it wasn't growing like wildfire, but our bank account also wasn't getting depleted, it gave us enough conviction to keep going. What were the conversations amongst the founders when you guys are seeing that?

Starting point is 00:16:54 Let's keep going. I think we viewed it as a project more than we viewed it as a company. In fact, we were barely incorporated. We were not incorporated when we were running this at Stanford University, and then we just got incorporated when we actually got into YC. But at the time, it was just like, let's just see what the next phase should be. I think sometimes when you start these projects, you absolutely should have a point of view on maybe where this can go

Starting point is 00:17:23 in terms of going the distance. But the most important thing is to just get started and then to have a sense of what the next two or three steps are. No one is able to, you know, know everything about the future. And for us, the summer was really instructive. I mean, the summer, I think doing the deliveries ourselves for the first six months gave us the clarity that the summer was really about answering three questions. Would consumers want to pay a six bucks, which is what we charged? Are there restaurants who would be willing to partner with us for 15 percent? And, you know, could we afford a wage

Starting point is 00:17:55 that we could pay dashers, the drivers for the service? That was it. That was the entirety of the YC summer. It was not about demo day or raising the most amount of money or becoming the most popular at some event. It was just answering those three questions. And if we had enough conviction

Starting point is 00:18:12 answering those questions, then we'd keep going again. You told this hilarious story where during the summer some of your classmates were like, yeah, I'm going to go ski and stad or something like that. What are you doing, Tony?

Starting point is 00:18:23 Like, I'm delivering hummus in my Honda. Yes, yes. That was, yeah, look, I think we had a lot of classmates at Stanford who looked at us and just thought, boy, like, I thought they were like, you know, smart, but, you know, I guess they want to spend their time doing this. And so, look, in the beginning of a lot of these entrepreneurial ventures, nothing looks that amazing, right?

Starting point is 00:18:47 We were working out of an apartment. We had dashers in that apartment. We had the co-founders live in that apartment. We worked 10 a.m. to 2 a.m. every single day. But it wasn't like this glamorous exercise. But nor did we seek that. You know, we were just trying to answer those three questions that summer. We didn't care that much about what our friends were doing clearly.

Starting point is 00:19:08 We thought that it was interesting enough to keep going that if we can actually answer these questions, I think we're actually on to something. We just had Mark Andreessen on the show, and he's got this great line where he says, I firmly believe that people that do great things are doing them for the first time. Huh. Did anybody have any restaurant or actually not even restaurant experience because you're not even in the restaurant? Any delivery? Any of the founders have anything to do with logistics or deliver anything? No.

Starting point is 00:19:36 No. It's actually why we had to do the deliveries. I mean, the reason why we did so, the reason why we were so helping on doing the deliveries besides the fact that we had no idea whether we had any business recruiting other drivers was how does this work? How should it work? And I think DoorDash early on, even to this day, but early on it was so hard to explain because it was actually even, even, Even to build the MVP, yes, to test it was just this website, you know, paloaljitilmute.com, but we had to build, like, four things. We had to build this website for consumers.

Starting point is 00:20:05 We had to build some app for the restaurants, actually receive the orders. We had to build an app for the drivers, the dashers. And then we had to build a dispatch system, you know, that actually could oversee all of the operations. So even in the very beginning, we realized, wow, this is actually pretty interesting. It's just such a fun problem that you, in order to actually just bring. You have to build these four things. And then to do it really, really well, I mean, that's why we did all the deliveries to figure out how you actually do that.

Starting point is 00:20:33 So you were misunderstood back then. You just said something interesting. You think that's still the case to this day. Absolutely, because I think most people, and I totally get it. I mean, think of DoorDash as a consumer app. You know, most people think of us as lunch and dinner. And I think what they don't see is everything behind the scenes. I think a lot of times, I think you can look at, you know, products like ours, especially

Starting point is 00:20:52 as a consumer, and you say, wow, this looks like any other product. You know, there are so many of them. But then I would ask the question, well, how come one just gets used more often than the next or the others? And it comes down to everything that you can't see. You know, one of the things we say a lot internally at the company at DoorDash is it's always the data that you can't see that kills you. Because if you can see a truck coming at you, you're just going to dodge and get out the way. But if you can't see it, you're dead. And it's no different with our business.

Starting point is 00:21:21 Our business is one where all of the magic or the secret sauce, if you will, or on things that you cannot see. You know, no consumer is sitting there while they're ordering DoorDash thinking about what the Dasher experience should look like or what the operations should be to get the best quality experience at the most affordable price.

Starting point is 00:21:40 Or what are the ways in which you take out every single friction and cost with a restaurant or a retailer and make sure that all the items are actually there even when they're not there? You know, I think all of these things are the things that make DoorDash special and make DoorDash an end.

Starting point is 00:21:56 into an experience that's very difficult to replicate. But yeah, I think early on, we knew that because we did all the deliveries. You know who knows it? You're competitors. So you're not going to like this because you're, in my opinion, really humble, probably too humble for my liking. But people in your industry are afraid of you. And one, I have to tell you a personal story that I don't even think you know.

Starting point is 00:22:18 And I didn't know. I've heard about you before. Obviously, you used DoorDash, but I never thought about it. Exactly. What you just described is exactly my experience. I was just like, I have a magic button that brings me a burrito. Exactly. Okay.

Starting point is 00:22:30 I love that magic button. Don't take that magic button away from me, whatever you do. But I was in Stockholm about a year and a half ago. And Daniel Ack was very kind to host me and a handful of European founders. And one of the European founders that was sitting next to me and Daniel at dinner with somebody I had never met before and is Mickey from Volt. Okay. Right.

Starting point is 00:22:50 Cool. But he told me something interesting because, you know, basically the story was he's just like listen I built the door dash of Europe I guess is how what was described and uh he's like I always thought of myself as an entrepreneur I never thought I would work for anybody and he's just like we were in a head-to-head battle right and he's like I had a term sheet in front of me if I remember the number correctly he was getting like a bill he had the ability to raise another fresh billion dollars of capital yeah and he was looking at the term sheet thinking about signing it and then he said involuntarily something came

Starting point is 00:23:23 out of his mouth. And he says, I can't beat him. He's like, I can't beat him. And he's like, I cannot believe that came out of my mouth. And he's like, and then he looked down, he's like, I can either light this money on fire or I could sell my company for life-changing money and go work for Tony and learn a lot. And I think to the day, he's still directly reports to you, correct? Yeah.

Starting point is 00:23:43 Yeah. He runs all of our European business. And he was trying to explain to me and Daniel about just, you don't see, the magic is very similar. What the stuff that you don't see, how hardy is. is to compete against. You had another interesting quote I want to you to say the way that DoorDash has achieved someone's success is tens of thousands of experience, 95% of which never even make it to the customer before they fail.

Starting point is 00:24:05 The way to get more accurate on a delivery probably requires some level of detail that is lower and deeper than you realize. Can you explain what you meant behind that statement? This, again, starts from actually doing the work ourselves and realizing that if you actually want to get something on time, I think it's very easy to think about when you're just intellectualizing it, you know, on the outside when we're getting started. Oh, maybe there's a traffic issue or maybe, oh, the food is taking longer than it should, whatever the reasons might be. But you actually have no idea, actually, what are all the sources of delay in an order

Starting point is 00:24:46 until you actually go and do the work. Sure, there might be some of the issues. But you actually have that I think you can think about on the outside, but then very, very quickly you realize that there's a lot of seconds of delay in every motion. In fact, there's about 20 steps you can decompose a delivery into, and there's delays at each one of those moments. And that's even more complicated if, you know, the delivery today is they happen outside of restaurants.

Starting point is 00:25:15 They happen inside shopping contacts like groceries or retail items, or if they happen inside malls that are more, multi-story, sometimes below ground, sometimes above ground. And one of the things you start realizing is, wow, actually, there are a lot of causes for delays. And there's no way that you're going to know about all of them until you literally actually encounter it for the first time. A lot of what's difficult about DoorDash is we're trying to build a structured data set in a world that is chaos.

Starting point is 00:25:46 That's the physical world. One of the reasons why there's all these sources for mistakes, for delays. for costs that ultimately yield into costs and good or bad experiences for customers is because there is no data that exists. There is no nice data set that a company like a Google or somebody else has organized for you because it's all physical information and it's also changing all the time. When you go into a grocery store and somebody moves an Apple from aisle 6 to aisle 8, is that always going to get documented?

Starting point is 00:26:18 Of course not. Those are the kinds of things we have to work on every single day. And you wouldn't know that. You know, what if I told you the cause for a delay was because actually somebody was homesick that day? How would you know that actually, you know, until that event actually transpired? And what would you do to respond, you know, to that event if that were to occur, which happens every single day.

Starting point is 00:26:39 You know, when we're doing millions of orders every single day, the one in a million event happens a lot. And the one in a thousand event happens way more than that. And so building a system that can ideally detect and prevent these issues, but then also a very fast-twitched muscle to actually be able to build this, I mean, almost like an emergency response system when something actually goes awry to fix it, that requires doing the work over and again and building the system that can learn over time to get better and better and better.

Starting point is 00:27:13 Most of the time we have no idea. We start with these experiments, and that's why most experiments fail. But when you get enough goodness out of it, if you can get the 5% out of tens of thousands of experiments to work in one year, that has the benefit on all of your audience for the next year. And then you just keep going. And that adds compounding surplus for all of the audiences. Deal will help your business hire, pay, and manage any worker anywhere in the world.

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Starting point is 00:28:44 is trusted by over 40,000 customers and growing fast. Learn how they can help your business by going to deal.com forward slash senra. That is deal.com forward slash senra. How do you do that many experiments though? And on a year, like, is this a yearly basis? This is like over the history of Dordash? Like, you're running thousands of experiments every year? Ideally, yes. I think when we are at our best, that's what's happening. But it starts with actually building a system that actually wants to learn. If you think about, like, why do we have to learn? It's because the physical world, A, is not structured. It's not documented anywhere.

Starting point is 00:29:21 It's, you can't scrape it. It's constantly changing. I mean, there's a winter storm right now, for example, in the Northeast. These are all things that happen differently. It's beautiful here in California. Yeah, I know. We would have no idea here. We're spoiled here in the Bay Area.

Starting point is 00:29:36 But, like, but in general, all these things were happening every single, you know, hour of the day. Okay, there's going to be some missing item today. There's going to be some order that took a lot longer today. There's going to be some incorrect gate we entered at an apartment complex. There's going to be some dasher who's going to get lost coming up the stairs of this office building. There will be, guaranteed. And so the question is like, well, it would be impossible to try to figure out all of that if you can't build a system to learn how to do this.

Starting point is 00:30:09 So the most important thing is actually building systems. and building a system that at DoorDash really starts with testing things in a very operational hacky, do things that don't scale kind of way, into then taking the things that ultimately work, the ideas, and actually building products around them, and then engineering the ones that actually works so that you're actually very efficient with this learning loop. So that you can go from learning to shipping something that actually works

Starting point is 00:30:39 because, you know, it's a resource constraint with how many engineers we have. and how many things that we can actually ship, especially when the stakes are high. And you want to make that loop as tight and as fast as possible. That's how you build a system in which you can learn thousands of things. And you just have to keep doing it over and over. You know, our business is one where we believe we have to earn, you know, the right to serve you the next day.

Starting point is 00:31:03 Even though you ordered with us today, thank you very much for your business. We have to earn it again. You know, the scoreboard goes back down to zero tomorrow, and we have to just do that all over again. Where did you learn the important? of that. Of what?

Starting point is 00:31:14 I'm starting over again every day. I've heard you say that before, and I love that idea. Very early at DoorDash, we learned how hard it is to keep someone's trust and how easy it it is to lose it. And, you know, I think I may have said this before, but there was a Stanford football game in which we lost a lot of trust where we were laid on every single delivery because we didn't have enough drivers on the road. We had no ability to shut down the website, but we had a lot of those kinds of days.

Starting point is 00:31:42 Pause there. So where in DoorDash history is this game? This is the third month of our operation, September of 2013, where it was a Saturday. We had no ability to fulfill the orders that came in. We had no ability to even shut down the website. So we couldn't even stop the floodgates. And usually this- Why were you having floodgates three months in? We had floodgates not three months in.

Starting point is 00:32:05 On that day specifically, for whatever reason, because of when the game ended, people wanted to order DoorDash for dinner in Colorado. And for whatever reason, you know, that volume spiked pretty hard. We had no ability to turn it off and no ability to fulfill. So we were late by at least an hour on every single delivery. I think when you go through experiences like that, but not just once, but we've had a lot of those kinds of experiences at DoorDash. I mean, you know, I still do customer support every day. I see them literally every single day.

Starting point is 00:32:37 When you see that you can lose someone's trust on one order, you realize that you got to earn it again the next day. And there is no such thing as this, you know, just set it and forget it kind of mentality. Yeah, that came a lot from the early days, but I think this daily reminder when I do customer support is also another great reinforcing function. So what happened that night of the game? We were late on every single delivery. And I think it was probably somewhere around 10 p.m. or something where we're tallying up all

Starting point is 00:33:11 the refunds that it would cost us if we wanted to make right and give back everybody their money. Were the customers asking for the refunds? No, no one was asking. No one was asking for anything. The night it was over, we finished our last delivery, and we said, okay, that was a terrible night. What are we going to do about it?

Starting point is 00:33:32 We could complain about the orders or something, but at the end of the day, I think within a very short period of time, 15 seconds. So we decided, okay, we've got to make right by the customer. So we got to refund everybody. Now, the complication is we had no money at the time. I was having a hard time raising. I mean, this is a pattern for me. I've had a hard time raising capital for the company in the earliest years.

Starting point is 00:33:57 And that started right from the beginning. I mean, like, we were maybe two or three weeks of cash out. And this refund would have cost us about 40% of the bank account. So it would have just made the two or three weeks and just shrunk that into even fewer days. But yeah, you're right, nobody asked us for the refunds. I'm sure they were pissed, but nobody asked. We did the refund right away, and then we stayed up that night, actually, baking cookies, and we delivered those cookies at around 5 a.m. before we thought when customers would wake.

Starting point is 00:34:27 And the idea was we'd rather die trying to be excellent, or at least die trying to do the thing that we want to stand for, than to live to be mediocre and not something that we'd be proud of. And that's what we did. That's excellent. So tell me more about building the system, the self-reinforcing learning system. Look, these things kind of happen in steps, right? So it started with the four of us doing the deliveries. And okay, well, we can keep doing the deliveries, but at some point, we're going to start running into scale issues. I mean, four people can only do so many deliveries. So of course, we're going to start recruiting dashers. We're going to start recruiting consumers, selling restaurants. And you start noticing, as you do the deliveries, well, you have to build products to scale yourself.

Starting point is 00:35:19 That's one. Two, you also just start noticing all the problems. And whenever you see a problem recur more than once, you would say to yourself, aha, maybe that's, you know, an example of a problem that we should actually build something for or actually run an experiment to see if we could actually solve. So I think very early on, the bias for action turned into this experimentation. mentality. Now, we didn't have like any organizations at the time or anything like that. It was just like a few of us in my apartments. It wasn't like, okay, there's this like rigorous system that I'm talking about. That's probably the earliest inklings, though, of how we thought about, okay, you can go from doing things that don't scale to identifying hypotheses to test, to then running experiments and then to shipping products. That was probably the earliest like time, the first year of the company. You fast forward maybe a year as we started launching into multiple cities. all of the general managers of different cities. So you could be running Boston, someone else is running Dallas, someone else is running, you know, a different city.

Starting point is 00:36:17 They would be reporting into me. And you start seeing that, oh, okay, well, patterns actually emerge, you know, from city A to city B to city C. But they're still quite local. They're slightly, you know, for example, in Boston, there's not a lot of cars. Car ownership is one of the lowest in Boston, you know, in the United States versus other places. There's some strange setups because of the historic nature of the city in terms of that hub and spoke nature I was describing that that actually violate that setup. So there are like local nuances, and you start realizing, well, okay, well, how do I actually, you know, teach this way of doing things that don't scale all the way to shipping, you know, some feature that we know is going to work to each one of these people so that we could run more experiments at the same time?

Starting point is 00:37:09 And then we would just build more products that would actually, you know, go across all of these different patterns. So that's kind of how this thing, you know, has morphed over the years where you basically start with some basic scientific, you know, process, if you will. You meet some point in which you have to figure out the next iteration in order to scale that process. And then you just keep that going. And you're always testing, you know, against whether or not you're delivering better for customers. That's always going to be the North Star metric of whether or not this process is actually making a difference or not. Is it better for customers if it's faster, cheaper, more efficient?

Starting point is 00:37:45 Like what are the- Yeah, it's all the above. So look, customers, I mean, this business is tough because customers, unfortunately, don't just judge us on one dimension. Some customers, all customers want the widest available selection. They want every item they can get delivered. They want the lowest possible price. They want the fastest possible delivery.

Starting point is 00:38:03 They want obviously no mistakes. They absolutely expected to be on time. And then if something were to go wrong, of course they deserve to be treated correctly. We get judged on all of those things, on every single order. So this is this idea of like you can build a business around things that don't change.

Starting point is 00:38:20 Yes. What are the things that don't change from the customer's perspective for DoorDash then? Customers are always gonna want more and more selection. They're gonna always want more and more affordability. They're gonna want faster deliveries. This is like Amazon, almost the exact mirror what Amazon. Well, I think when you just think about what people want, I actually think it's

Starting point is 00:38:41 pretty easy because we can play that role ourselves. And yeah, I think you just ask the, you can ask very basic questions about what's the direction of travel of certain things. Like, for example, like, do you think people are going to expect more convenience or less convenience? Especially in a world where you think that people are earning more, you know, whether it's today versus the past, tomorrow versus today, what do you think they're going to do with those dollars? Is it going to go more towards consumption? Are they going to expect or demand more convenience or less? I think when you start asking questions just out loud, you get the common sense answers in which you can build a business around.

Starting point is 00:39:20 We were talking about this with the crew of breakfast. It's just like, well, their cornerstone in their businesses, a trait in human nature that's never going to change, which is like we want more convenience. Yeah, always. It's not rocket science, I think. The rocket science is actually, how do you make it happen? Yeah, I love this idea of, like, you're hiding the complexity. I spent several hours at Bezos one-on-one, and I'm obviously a massive fan of his.

Starting point is 00:39:45 I've done like 15 episodes on him, and he listens to my other podcast. And I told him, I was like, dude, you know how crazy it is that they put a guillotine in front of your house in Washington? I go, you made a magic button I can press, that anything I want in the world shows up to my house in two days, and now it's like a few hours. And all I do is press the button

Starting point is 00:40:03 and you handle all the other complexity behind it. I was like, you deserve all the money. I hope you have all the money. He just laughed and laughed and laughed. You said something, you're doing customer support every day. Does customer support emails? What is this? Emails or chats, sometimes phone calls.

Starting point is 00:40:16 Every day. Yeah. Say more about this. Well, why do you do this? You know, I was saying earlier that, for a few reasons. You know, one of the things that we were talking earlier is that so much of the magic or the difficulty of building a company like Dordat,

Starting point is 00:40:31 is in all the things you can't see. And so the first thing you got to do is you got to build observability everywhere. Of course there's observability with dashboards and systems and, you know, increasingly AI tools. But also I can see the inbound of customers who write us, whether it's a consumer, a merchant, a dasher, an advertiser, and I can choose to ignore them. But those are freebies. I mean, like, how lucky am I to actually have a product in which people care enough?

Starting point is 00:41:06 You know, even, I mean, usually they're not very positive emails, but I mean, like, but they care enough to actually let me know. You know, I think the greatest killer of a business is usually silence. And here, they're actually, they care enough to actually let me know something went wrong in their experience. I owe them, you know, certainly not just a response, but actually, I think the, you know, and not the courtesy, but I owe them the responsibility of actually solving that problem, ultimately.

Starting point is 00:41:36 And so first, it's an obligation to the customers. Second, it's actually something that I want the rest of the company to do. You know, I think one of the easiest things as companies get a little bit bigger, perhaps earn a little bit more success, is there are more obstacles between them and the customers or the jobs to be done. For example, when you become a company, you know, all of a sudden there are, the only things that kind of get spotlighted are the financial metrics, your revenue, your profits. None of which are metrics that customers care about. There are no metrics in what we report, you know, as a public company, that customers know about probably or care about, frankly.

Starting point is 00:42:22 And that always is quite bothersome to me because it's because of our ability to serve customers. that can hopefully achieve, you know, strong financial metrics that investors care about. And so a lot of what I'm trying to do is building as many reinforcing and repetitive mechanisms and motions, including things that I do individually, that will allow this company to always recognize that the number one job and the only religion at this company is to solve problems for customers. What do you do when the data and anecdotes conflict? It's a tough one.

Starting point is 00:42:59 I think that usually there's always an element of truth in what customers are saying, and it usually becomes a trade-off discussion for different teams. The reason why it's a tough decision is because it is so easy to always just veer on the side of the data. Because almost always, when a customer notices something that is wrong, or there's an anecdote, That may be a quote-unquote edge case. It's usually at some tail of a distribution, a distribution of the wait times for customer support, a distribution of how friendly we were when we actually took the call, a distribution of how on time we were, or how late we were, or how accurate we were, or what are the number

Starting point is 00:43:48 of items of the types of skews you care about in a particular category of lettuce, just lettuce, not vegetables, but just lettuce. So it's always some tail example. And so the data is probably always going to win when it comes to some sort of a prioritization discussion. But when you actually think about how to make a product better, it's going to almost always always by definition be in improving the edges. And that's why a lot of times what I like to do personally is I love to spend time, you

Starting point is 00:44:19 know, with a lot of our power users, whether it's, you know, the top dashers or the consumers who order the most often or the merchants who we've been doing business with for a very long period of time, and also the new users. They're at the tails of the distribution of almost every outcome. A new user who's never touched DoorDash before for the 13 years that we've been around will absolutely tell us about how easy or difficult it is to place their first order in a way that someone who's been used to all the things that we've been training together with customers on and figured out.

Starting point is 00:44:55 A power user also sees all the issues too because they have the most shots on goal for some chaotic event to happen in the real world that we couldn't capture. And so those edges of the distribution are almost always where the anecdotes are that are the most valuable that you have to pay the most attention to because they almost always will disagree with the data. And they are probably worth the most in terms of improving your product. So let's say you find one of these edge cases as you're doing customer support every day. What's your next step? So the ones I love the most are actually the really long ones, actually. The ones where there's a lot of gold. It's probably like the research you do on founders, which is the longer, almost the better,

Starting point is 00:45:41 because you get to study the distributions. When it's a short email about something you already know about, there's not as much, you know, perhaps interesting material in it. But I love the 2,000 word emails, especially. from dashers who will give many use cases of why the logistics algorithm broke for them. And it becomes almost like a debugging exercise, right, of both physical world things that have occurred, things about our systems that, you know, probably broke, and things in our products that couldn't interface well enough between the physical world and our systems.

Starting point is 00:46:15 And so then I go into our debugging tools, and I actually literally track the order. and every single step I'm watching. You're doing this personally. Yeah. And once I start figuring out potentially where the sources of error are, I'll either generate the hypotheses and call the dasher or email the dasher, depending on the best way to reach them or the consumer, and then actually find out whether or not there's a nugget of insight there of something

Starting point is 00:46:43 we actually could improve. So put a different way, can we put a spotlight on an anecdote that improves the product? That's the opportunity I'm looking for. I've heard you describe this as like this eternal mission, right? Yes. How would you describe what the eternal mission of Doordash is? Yeah, well, the eternal mission of Doordash is to grow and empower local economies. We say this a lot.

Starting point is 00:47:04 And the reason why it's eternal is because I think it's a fight worth fighting for or a cause worth fighting for forever, which is the best way to grow the GDP or the happiness or the safety of a city is by making the small, medium, and large businesses in that city successful. They produce the vast majority of jobs and, you know, consumption dollars for the economy and the monies for the police department, the fire department, the parks, the schools, etc., the hospitals. So the question is like, well, how do you actually make them successful? One of the most positive tailwinds of why this could be a very fruitful eternal mission is because the physical world is always changing, right?

Starting point is 00:47:48 And it's hard to just scrape it. And it's one of the things I love the most about it. It's hard to just scrape all that information, say job is finished, and then put it through some LLM or something. Well, A, that data is always changing. B, it's not organized at all. And C, it's not just an – it's not like some relationship between a text, you know, editor and a person.

Starting point is 00:48:16 I mean, there's a lot of people – There are three people involved on every single order at DoorDash, at least. There is a consumer, there's a dasher, there's a merchant, at least three people. Now, given that we do more complicated things, there's even more sometimes. And, you know, for those people, this could be their identity. Back to, you know, what I was saying about small business owners and how they believe that what they do, it's not a office job or something, you know, that they just use to earn money so that they could spend consumption dollars or something else.

Starting point is 00:48:46 this is like their livelihood. This is like who they are. When I think about those kinds of people, I want those people to win. And so if we have to eternally always look for the edges of the distribution to keep improving the product, of course we will.

Starting point is 00:49:00 And if we can do that and we can make them successful, then they're going to make many things about the cities and the neighborhoods that we live in continue to be sustainable and very, very thriving. And the alternative is terrifying.

Starting point is 00:49:13 You have one or two big players. Yeah, I don't even want to think about the alternative. You're totally right. I mean, the alternative isn't, it's a very robotic world where maybe we buy things in one or two ways or from one or two places. That's not a world in which you're going to grow, you know, the GDP of these cities. And actually, that's a world in which you may take away some of the identity, I would argue, of some of the neighborhoods. I think one of the reasons why people love neighborhoods or that there's certain neighborhoods that they, you know, may be preference, is because there's a personality to it. So much of the personality is

Starting point is 00:49:46 given by who the businesses are, and therefore you and your friends want to go frequent and go hang out in those places in addition to your homes and things like that. And that's what makes it tick. That's what makes the place feel awesome, a city feel awesome. And so I think that's an eternal mission worth fighting for. Yeah, because this is not something that you can accomplish in a year, five years, 10 years. It's constantly changing. What do you do with all this data that you're collecting? Well, I mean, the first is we have to structure it. So, um, You know, one of the things that I think Google, you know, so brilliantly did was they did organize a lot of the information on the Internet, and they made it searchable, you know, to everybody. Right now, the first thing we're doing is we're still collecting lots of information.

Starting point is 00:50:32 And then right now, we're trying to do two things with it. You know, the first thing is we're certainly trying to grow a merchant's business by allowing you to search for their stuff through our app. And, you know, we'll bring them incremental business that way. The other way is we're actually trying to make it useful for them. So we're giving data back to them, telling them when... Data about their own business. Yeah. Like when you're out of stock of certain items or that did you know that you are underpriced

Starting point is 00:50:56 in this particular, you know, menu item versus what, you know, what you could be pricing at? Or that there's an opportunity to bundle certain, or to create certain, you know, skews or new items on your menu or in your catalog. if it, a retailer that we think would grow your actual business. This is like, Bezos said that line about Amazon Prime. He's like, we want to make it so valuable. It's irresponsible if you're not a member. Like, it's just insane. So if you can have data for small businesses, media businesses,

Starting point is 00:51:28 big, even large businesses that they didn't know. Like that pricing thing is interesting to me, where it's like, well, you're charging, you know, $15 for this plate of chicken. Yeah. Where we see all these, I assume you're getting the data from all the other merchants on your platform where it's like you could be, people are willing to pay $25 for that thing. Essentially, it'd be irresponsible not to partner with you

Starting point is 00:51:46 if you have all this insights. We can also take the same approach that we've built for ourselves, the scientific process from doing things that don't scale to shipping things at scale on your behalf. Like, you as a merchant can be running experiments too. Now, maybe you can't because you're a single person. You're literally one person like the baker

Starting point is 00:52:05 that I was telling you about that inspired a lot of our discovery of delivery, who doesn't have all the capabilities to run all these. But why can't we do those things for you? Why can't we, for instance, what do you mean do them for me? We can talk about simple things to more difficult things. The simple things, we can change many prices on your behalf. We can buy different kind of promotions for you based on what return thresholds you want

Starting point is 00:52:25 to achieve. We can talk about more complicated things. For example, there's certain merchants who want to actually grow tremendously. Why not? They want their identity. They want their passion project. project to be exposed to as many people as possible. Some of those businesses, for example, find it very hard, though, to grow from one store

Starting point is 00:52:49 to two stores to then somehow 2,000 stores. But imagine if you baked cookies as an example and you wanted everyone to have your cookies. Why can't we match your products with businesses that don't sell your product and actually create a supply chain in which you can actually, you know, sell those products? products in more places. And you can literally make everyone win. You know, the new business who's selling your product now has a new menu item called a cookie. You get to maximally, you know, increase your exposure. There's a range of things in which we can do with the information and make it productive if we knew what your goals were. And so a lot of what we're doing with a lot of businesses is

Starting point is 00:53:33 at scale, how do we maximally increase, you know, your exposure, your identity? and achieve whatever goal you may have. So that's with restaurants. Tell me some of the- Or retailers. Yeah, like this gets really interesting when you expand out to every physical business. When I think about restaurateurs, retailers,

Starting point is 00:53:51 to me, they are no different from me in the sense that they are entrepreneurs, they want to create something. They want something that they have, an idea they may have, a passion they may have, and they want it to be exposed into the world. That gives them fulfillment of a variety of ways. Okay, so let's say that you want to make T-shirts and sell T-shirts. That's a passion project of yours.

Starting point is 00:54:17 There should be no reason why you can't do that today from, you know, testing that idea with the audiences that we have, with the warehousing and logistics inventory that we have, with the ability very quickly to test in any neighborhood, any city in the tens of thousands of, you know, different neighborhoods that we serve or cities that we serve and operate in and see whether or not you may have something before you actually go out and try to spend a lot of money to open up a store or something like that.

Starting point is 00:54:47 There's no reason why we can't be your business partner for any future creation. Dude, this is blowing my mind because I just think about DoorDash as a way to get food. Yeah. I love the idea behind this. It's all about where you start and how you keep going, right? And by the way, a lot of these ideas came to us from our customers.

Starting point is 00:55:05 You know, back to your question about why do I do customer support? I learn a ton, too. Yeah, of course, I learn about all the edges of the distribution. What are some examples of things that customers have asked? Okay, so one customer in 2014, I'll never forget, was a farmer who runs one of the largest farms in the state of California. And they run hundreds of trucks every day up and down, the state of California, okay, distributing their produce and their meats and other products to a variety of, groceries, restaurants, hotels, etc. And they've been doing this for three generations as a family.

Starting point is 00:55:47 They did not start their farm to drive a bunch of trucks. That is not the business that they aspire to be in or passionate about. And literally, in our second year of operation, they called me, or they wrote in, actually. And then we had a conversation on the phone about what they were interested in. They were curious whether we could solve that problem for them. That's why they even asked you that. And this was the second year of the business. And so, you know, I said not yet at the time.

Starting point is 00:56:14 You know, perhaps I should, you know, I almost feel like I owe him a call. So this conversation is a good reminder. But the, when I think you've earned, you know, our goal over time is to be the first phone call for any business, any business for any issue. Yes, today, the number one calls we get about are about delivery. Totally get it, totally understood. Increasingly, they've been about other things. Can you actually help us build our app? Can you help us acquire customers?

Starting point is 00:56:45 Can you help us analyze customers, retain customers, customer support customers? Can you help us store inventory? So those questions are more and more coming inbound, and that's why we've shipped a lot of the products that we have at DoorDash. But I think if done right, DoorDash can be your first phone call to start any business. I mean, that's really what we want. And we can do it in a way that is very low cost that, you know, doesn't have to scale if you don't want it to.

Starting point is 00:57:12 You know, some people are very happy with one or two locations. Or if you want to become the next McDonald's or you want to become the next Walmart. One of my all-time favorite quotes is from the book zero to one. It says the single most powerful pattern I have noticed is that successful people find value in unexpected places. And they do this by thinking about business from first principles instead of formulas. This is exactly what Apploven has done with their new advertising platform, Axon. Axon is the most powerful advertising platform in a generation. Axon allows you to capture undivided attention.

Starting point is 00:57:45 Axon ads are full-screen videos that are watched for an average of 35 seconds, retention that blows other ad platforms out of the water. And you can launch in minutes. You set the goal and Axon achieves it. No complex setup, no expertise needed. And Axon scales quickly. they can put your ads in front of over a billion potential customers. Other businesses have seen immediate results, scale to hundreds of thousands of dollars of spend

Starting point is 00:58:10 per day, and increase their revenue by millions. And most advertisers aren't even thinking about this channel yet. Less than 1% of advertisers have access to Axon, so you want to get started quickly. And you can do that by going to axon.aI forward slash Senra. That is axon. dot AI forward slash Senra. Yeah. Something that, again, I want to compare your story to Bezos because I just think every time

Starting point is 00:58:34 I hear you speak, I hear a lot of Bezos. He obviously did a ton of customer support at the very beginning of Amazon. He publicized his email and made it public, like, email me all the time. And one, he tells this great story in one of the books that when he realized, you know, they were selling at the time, I think just books, CDs and like VH, maybe DVDs. And somebody's like, he would ask, I think he would send an email to like a thousand customers day or something like that. And he's like, what else would you buy?

Starting point is 00:59:01 And one guy's like, will you sell me windshield wipers? And Basel's like, oh my God, we're going to be able to sell anything. Yeah. Or everything in that case. Yeah. we have to like, educate me now. because I've heard, first of all, you need to do more podcasts. we're trying to obviously deliver everything inside the city.

Starting point is 00:59:32 Okay, in order to, and just to put some, you know, context behind it, there are tens of millions of items inside of a city that you could deliver. DoorDash delivers a fraction of those items to do. What fraction do you deliver today? A very small fraction. Very, very small fraction. There are many times the amount of things to deliver than what we currently offer. But there are challenges in making these deliveries, right?

Starting point is 00:59:58 For instance, you know, how do you actually know what the catalog looks like for each city? How do you know if the catalog is actually accurate? What if the items are not available in store or are available in a warehouse, some are far, far away? There are a lot of these challenges in order to actually, you know, address before you can actually do something like deliver everything inside of a city. So one of the things that we launched... Do you talk about like that internally? We're going to deliver everything in a city? Yeah.

Starting point is 01:00:24 But one of the things that we launched last fall, it was actually in September, we announced Dashmar fulfillment solutions where, you know, for companies like a Kroger or companies like a CBS, we'll actually carry their items. And you can order their items, you know, directly from our site. But sometimes they'll actually come from a warehouse that were operating on their behalf. That is an example of a product of a warehousing in inventory management and a logistics solution in which we are offering, you know, perfect accuracy, fast delivery in a way that retailers don't have access to or the capabilities to do so today.

Starting point is 01:01:03 That's part of how you can deliver, you know, all of a city by actually bring and aggregating and making closer some of the inventory to where someone lives. We're building autonomous vehicles. That's something else that we announced last year, where... Actually, I mean, it was a fascinating journey where, candidly, mostly pain and suffering, but most of the journey was recognizing that you actually have to build a purpose, built or intentional product to do last mile delivery in a way that's very different from, say, robotaxies or delivering humans.

Starting point is 01:01:42 You have to solve problems of getting products, for example, inside and out from the vehicle in a way that passengers naturally can do in a robotaxie that items cannot do on their own. You have to think about what types of vehicles you may need for shorter distance deliveries versus longer distance deliveries, heavier deliveries versus lighter packages. When I kind of think about some of those products, for example, that's all part of this mission of trying to bring you everything inside the city and giving every business a chance to win. And are you making the hardware yourself? Yeah.

Starting point is 01:02:14 So in some of the cases we are. And so we don't have, again, like the only religion we really subscribe to is, making customers win. We don't have a religion about whether or not we have to build the product or someone else has to build the product. Actually, when we started the autonomous project, an autonomous vehicle project, we started with the belief that we did not have to build the vehicles. And in partnering with a lot of different companies, we ultimately realized that nobody actually

Starting point is 01:02:45 wanted to build what we wanted to build. And that's ultimately why we decided to start our own. project in 2019 and, you know, shipped it last year. So that's what, six years, seven years, almost seven years of development. Six years. Six years to get. Why did they not want to build, they just didn't want to build what you wanted? Yeah, well, if you think about it, in the world of autonomous vehicles, you have a lot of

Starting point is 01:03:09 the projects and a lot of the capital and a lot of the attention are going towards ropo taxi. And that's just a very different solution and form factor, in our opinion, than what you need for last mile delivery. You know, when you're, it's very hard, for example, to drive a robot taxi into a crowded hub of merchants, you know, whether it's a mall or, you know, a main street and actually somehow find parking and actually, you know, get access to, you know, the products by itself somehow. You know, I think that's a, that's a difficult endeavor to accomplish.

Starting point is 01:03:42 We built actually door dash dot, which actually will, yes, it will travel on the road, but it also can travel on the sidewalk and in the bike lanes. It's a much smaller form factor. It doesn't go as fast, but it has the ability to actually get to the last 10 feet of actually solving the problem of last mile delivery, which really is the last 10 feet problem. It's just live like right now? It's in Arizona. So it's in the Phoenix Scottsdale area. Is it true?

Starting point is 01:04:09 I heard that Waymo, you guys have partnered with Waymo to close the doors of – is that true? We do partner with Waymo, and we do lots of things together. Is it people just not shutting the door when they get out of Owemo? Is that true? One of the things that I think is fascinating about the problems that a company like A Waymo or a problem like DoorDash has to solve is there's always these funny edge cases in the real world that are very hard to predict. Shutting doors may be one of those examples, right? But you actually wouldn't know about that until literally you read the logs of these customer transcripts of these things.

Starting point is 01:04:48 Look, I think there's going to be lots of things that we could do together over time, but I think it starts with just building the foundation. I think the foundations you need to build one of these companies for the physical world are just very, very different from the digital world. And that's kind of the fun part of the exercise at DoorDash. Okay, so let's talk about the talent needed to do all the things that you're describing. I heard you say that when you were recruiting, you looked for Rhodes Scholars that meet Navy Seals. What does that mean?

Starting point is 01:05:16 Yeah, this was... This was a shorthand, I suppose, early on when we were looking for, I think, the types of people that we thought would do well at DoorDash. And I think it started first from, because we did every job ourselves, whether it was the deliveries, customer support, making menus, selling restaurants. We recognized the personality type, if you will. Yes, you needed to be smart. And you needed to be able to, you know, have high processing power in terms of, you know, analyzing all the information, especially in a world that's very unstructured. But one of the things you really needed was you needed to just do things. So much I think that that's challenging is that's very different about the physical world and, say, building software is you have no control in the physical world.

Starting point is 01:06:15 We don't get to control when you hit that order button. We don't get to control whether or a dash or accepts or a rejects an order. We don't get to control how slow or how fast somebody makes an item or how in stock or out of stock some item is. You have to be able to do things to go figure those things out. So one of the earliest things I did I remember was the interview question, if you made it to the interview with me, your final round interview was most likely a surprise. because, you know, our teams would, you know, ask you to, you know, answer some prompt about fixing some problem in its city or something like that. And you probably would go out and do your analyses and, you know, come ready with a one-pageer of notes or something. And then you would come to me and you thought you might think that the interview is to present that to me.

Starting point is 01:07:05 I would literally ask you, I said, well, this could be a really long or really short interview where I'm going to give you 20 minutes. and you can ask me any question that you want. But after the 20 minutes expires, I'm going to give you $20 that you can use to go and acquire 100 customers for us. And you have eight hours to do so. But here's also a plane ticket. I know you traveled far to come to this interview. In case you want to quit the interview now and just move on and find somewhere else to work. And that was the interview because that's the action part.

Starting point is 01:07:42 Right? So much of what we were trying to test for early on is someone who's going to do something to go and collect information, as opposed to someone who's going to collect data, scrape information from some, you know, internet protocol, and then do some magical analysis on it and then ship code. Okay. I mean, but what if none of that information existed? You have to go and do things in order to actually collect information. That was one big kind of behavior. your bias for action that we were testing for. So that's the Navy seal part. That's the Navy seal part, where you have to be willing to do things and be accountable for things. A lot of that was on the non-engineering front. On the engineering front,

Starting point is 01:08:25 we looked for engineers who certainly were great at coding, but we looked for engineers who would be willing to do deliveries with us. In fact, the interview with me, if you're an engineer, is the final round interview, was we would go and do deliveries together.

Starting point is 01:08:35 So the interview would literally take place in my Honda, and we would be doing deliveries for maybe an hour, or two or something like that. And I'm walking you through the flow of literally the order and asking your opinion of how we could productize this. In Silicon Valley, I think sometimes there's the mythical obsession with the 10X engineer, right? And I totally get it.

Starting point is 01:09:04 And they do absolutely exist. But a lot of times that is about coding prowess. That's great. We have a lot of respect for that. At DoorDash, we also need you to have problem-solving prowess or the coding prowess in quotes at DoorDash is about how do you solve this end-to-end problem. And it takes a certain kind of engineer who's willing to do deliveries and not just think about code all day or what the latest, greatest AI tools are. But is what I'm going to ship actually going to solve a real-world problem? Is there going to be a real customer benefit? Yes or no? That was the type of profile and personality and aptitude and attitude that we're looking for for engineering. Was there a specific source where you were finding people like this? Not really.

Starting point is 01:09:56 In fact, to this day, I don't really look at people's backgrounds that much. I think one of the things I discovered along the way, you know, probably in the 2015 to 2020 era, especially when Dorcas was building out its team, there was more. more attributes that I was listening for than there were things on a resume that I was seeking or looking out. A bias for action. You know, and a lot of the ways I can tell in an interview is actually just what people naturally talk to me about. You know, for example, Christopher Payne, our first chief operating officer, I didn't ask him a single interview question. But after a two-hour discussion about our logistics algorithm, he went home that night.

Starting point is 01:10:36 It was Friday, drove with his son for four hours doing deliveries. I didn't ask him to do that. I also didn't ask him the next morning to write me a 3,000-word email about why our logistics algorithm sucks. But he did it. But he did it. And that told me more than any set of interview questions. You hadn't even hired him yet?

Starting point is 01:10:56 No. Hadn't hired him yet. And then you immediately hired him. And this is certainly beyond the resume, right? Or, you know, we look for the ability to operate at the lowest level of detail. I remember my first, it was actually supposed to be a coffee chat, not a quote, unquote, interview, scheduled for 45 minutes with our, you know, now president, Perbier, then CFO candidate.

Starting point is 01:11:20 And he came to the coffee with his computer and this, like, multi-megabyte file, which was some projection of our financial somehow. I said, like, like, this is supposed to be me getting, we're supposed to get to know each other. And this, but this is how he thinks, right? Like, I don't need to watch the resume or read the resume to disson. to decipher how does this person work. He showed it to him. So he built a model and then he walks you through.

Starting point is 01:11:46 Yeah. How did that take? We, I think, debated it for over four hours. So it was like, but it was literally going line by line to think through that. These kinds of examples, ultimately, and then there's like, you know, three or four other attributes that we look for, those tell me more about, I think, how you operate, what makes you tick, what's the environment in which you'd be most successful, and whether or not I think it matches what's required. So there wasn't, there wasn't like a source.

Starting point is 01:12:12 It wasn't like, oh, yeah, we discovered the secret that it's this company or this school or this background that ultimately. But don't they have to be very different people than would be people satisfied working in like just a completely digital like software company? Like you told a story one time where you were wondering, let me see if I remember correctly, correct me if I'm wrong, but you like you took like a small sample of like 20 dasher drivers and 20 UberX drivers. Yes. And I think the control of this experiment was like, you. you all are getting guaranteed $20 an hour. If I offer you more money, how many of these groups would switch? Yes.

Starting point is 01:12:46 Right? And what is the observation that you discovered from this experiment? Do you remember? Yeah. So yeah, they were making about 20 an hour at the time. I made a guaranteed offer of 25 an hour if you switch jobs. So if the Uber X dash drivers would go to DoorDash,

Starting point is 01:13:00 DoorDash would go to Uber, and one out of two groups of 20. So one out of 40 made the move. And what did you derive? And conclusion you drive? from that. And this was very early. This is weeks within the companies getting started because at the time, one of the, one of the, you know, back to the three questions we were trying to answer, we're trying to figure out whether or not we could acquire enough dashers, enough drivers, was, well, if drivers only cared about money, well, we're ultimately going to lose because obviously it's more valuable to transport David than a burrito or a coffee. And, and so we were testing this, you know, we were almost, you know, trying to confirm or to deny this hypothesis. So I ran, that experiment, what I learned was, well, actually, there are two groups of completely different people. The Dornash drivers, they were younger, or about half of them were female or women, and they had all sorts of vehicles. Some of them drove motorcycles, scooters, bikes, yes, cars, but but but but but not exclusively cars.

Starting point is 01:14:03 The Uber X drivers at the time were usually men in their 40s. I think almost exclusively men. Maybe there were a few women in the group, but almost exclusively men. All of them drove vehicles, cars, sorry, four-wheelers. And they viewed that job almost like a full-time job. You know, in some ways they're moving from taxi 1.0 to taxi 2.0. And because some of them had formerly drove for taxi.

Starting point is 01:14:32 The dashers, on the other hand, came from a variety of places. Schools, hospitals, restaurants. retailers, service businesses, moms. And then if you look at it today, there's almost no overlap, very little overlap between right-sharing drivers and delivery drivers. And the dashers, more than half of them are women today. They come from dozens of industries, literally, I mean, every place.

Starting point is 01:15:02 The average dasher only does three to four hours a week, 90% do drive fewer than 10 hours a week. And so it just became a very different setup. you know, the delivery. They kind of self-selected into what... It's self-selected, and that's why I missed. I wonder if there's that insight that you derive there is like, it's kind of what I'm getting to is like, how do you find the people, like the engineer that is willing to get in your shitty Honda, no offense?

Starting point is 01:15:23 Yeah, no, two, no, two deliveries. Yeah, I feel like that is such a different person. It's a software engineer at Google. It's a super different. It's a different. It's a different. Yeah, it's exactly. No, I think you said it yourself.

Starting point is 01:15:35 I mean, if you think about, you know, the early days, it was, I mean, I remember we would add, and it's a strange memory, but we would take a coding break at 10 p.m. to take out the trash because it was an apartment. So it wasn't like an office building where there was, you know, janitorial services. We were janitorial. And so we would take out the trash. It takes a certain kind of engineer. It takes a certain kind of person to actually want to work in that environment. But I don't think there was like a background.

Starting point is 01:16:04 If anything, it was probably like a personal background as opposed to a professional one in which we were, looking for. But I think all of these people had a bias for action. All of them cared about the details. All of them had the ability to hold opposing ideas in their brains. All of them had strong followership. They tended to move with others as they moved from one. Once they joined a company, a bunch of others followed them. Oh, that's an interesting trade to hire for. Say that. Say that. We'll worry about that again. Yeah, it's strong followership. Okay. They had the, They had this ability where, and I didn't even know the why many times, but when you just look at, you know, company A they worked for or organization A, they started, whatever. And they tend to have these groups that are attracted to them.

Starting point is 01:16:54 And they tend to be quite like-minded. They tend to always want to get better. That was one trait that we discovered. And, you know, and you see this, it's not always professionally. like trying to get better at some skill all the time. Sometimes it was they wanted to be the best burger maker or they wanted to be the best karaoke singer. And they would literally tell you about the process

Starting point is 01:17:19 in which they would on the weekends improve every single week. But that's not that different from, if you think about the scientific process that we were recruiting for or trying to institute in our systems here at DoorDash. It's very similar actually, very, very, very similar. There was an obsession almost. to some activity. And there was a system that they devised for themselves to actually get better.

Starting point is 01:17:44 Those are the traits that we looked for as opposed to what company did you work for, etc. You have a great quote where it says, DoorDash has always been a company where bias for action is the way we solve and settle debates. We don't debate a lot. We tend to ship hundreds of thousands of experiments a week. So you're not saying in office or that conference room behind us mapping things out and like, oh, I have an hypothesis. You're just like, no, we're just going to experiment.

Starting point is 01:18:05 We're going to get to the truth. Is possible? Yeah, I think, well, because so much of the physical, a big part of this is because the physical world, so much of it is, there is no analysis you can run on it sometimes. And, or it can be very counterintuitive. And, you know, for example, had we not done deliveries in both Palo Alto and San Francisco, maybe we never would have landed on the idea that you could deliver maybe faster or more economically inside of a quote-unquote suburb than a city.

Starting point is 01:18:38 It's almost like an earned secret. You read Sam Walton's autobiography? A long time ago. Okay. So he has that line in there because his main competitor, Sears, Kmart. People had the idea before he did. And they're like, they're in the city center. He's like, well, I'm in Bentonville.

Starting point is 01:18:52 I'm in like, he basically said, like, if we didn't, he was resource constrained. And so he's like, I didn't have a lot of money. So I had to start out in these small towns. And if I never did that because I was forced to, because I didn't have the money that Kmart or the other competitors had. I didn't realize how much business there was out in these little towns, and then the earned secret that he had was if he could compete on his organizing mantra was everyday little prices. If I can actually sell this same item to you cheaper, people would drive, in human nature, vast distances to save money.

Starting point is 01:19:25 Yes. And what was interesting is Bernie Marcus, founder of Home Depot, realized that same exact idea, like 30 or 40 years later applied to, a different industry. Well, constraints definitely breed, I mean, creativity. I mean, for us, because we had no money or because I was so unsuccessful, you know, raising money in the earliest years, we have to run these experiments.

Starting point is 01:19:45 If you think about it, if a company has no ability to compete with budget and other companies are outspending them with marketing dollars as an example, you only have one way to compete, which is you have to build a product that has better retention, better engagement. You have to. There is no other way.

Starting point is 01:20:01 So what do you think when you see these giant, like seed rounds that we're seeing now. It's impressive, is what I think. I think it's really, my encouragement to those founders is to actually find a problem worth solving first, but then once you find the problem to actually go and solve the problem. Because at the end of the day, that's going to cover for, you know, whatever financial, you know, metrics that they're going to be solving for. Yeah, one of my favorite, because everybody's like, well, I need more money because if I have more money,

Starting point is 01:20:32 win and one of my favorite historical anecdotes. They ever read The Biotrifice by David McCullough? No. Oh, I know you like to read history in biography. I do. I should probably check that one out. Listen to the audiobook. I know you're busy.

Starting point is 01:20:45 It's great because, you know, human-powered flight was a centuries-old problem. Like, people were trying to figure it out over and over again. At the same exact time, the Wright brothers were trying to do it. They had better funded competitors, better brand names. Oh, sure. More experience, for sure. Samuel Langley was, I think, backed by the Smithsonian. I think he'd raised like $500,000.

Starting point is 01:21:03 This was like a crazy amount of money there. And there's a great line in the book where like essentially the Wright brothers solved the century-year-old problem with the modest profits from their bicycle business. And they tallied up how much it cost them. It was like $1,500. That's incredible. Which I absolutely love. I want to go back.

Starting point is 01:21:19 You make some jokes that I, well, maybe they weren't of jokes, but I laughed when I heard you say this, that you're like, I must be a really bad fundraiser. What is this like thousand days of hell? Can you talk about this for your people? And it's weird because the metrics and the business were all trending in the positive direction. They were. So explain what the hell is going on. Well, so one of the hardest things I think you learn as a founder and certainly as a CEO is you have to learn how to control your own psychology because there's lots of things that are going to be out of your control.

Starting point is 01:21:48 And that won't make sense to you. And this happened very early, you know, with DoorDash. I mentioned earlier that we had a difficult time raising the seed round and then this, you know, difficult, a difficult event. event with Stanford football in which we ran out of even our money faster. But we survived that. We raised the seat round. Our series A, series B were hot rounds. You know, we, I don't know, somehow was able to raise money in less than a week, something

Starting point is 01:22:19 like that in each one of those instances. But in the spring of 2016, you know, a few things happened. And this is probably when I first started learning about the importance of dealing with your own psychology. It was actually the first time I took a vacation. I think it was, so we started the company in June, I guess, January, 2013, January 2013, January of 2016. So about three years or so. So I'm first vacation, five days with my wife.

Starting point is 01:22:44 We didn't go on a honeymoon, so I promised her that we should make up for that. And so we go for five days, I think, to Hawaii. And we were, actually, we had received an inbound term sheet, actually. So things were going pretty well to raise our Series C in the winter of 20. 2015 in the spring of 16. And I asked, I remember specifically asking the investor, why don't we just close this? Like, you know, why don't we just close this now? Like before the year, he's like, no, don't worry about it. You've never taken a vacation. Go take your honeymoon. Everything's going to be fine. You know, we're good for it. And I said, okay, all right, I'll go with you on this one.

Starting point is 01:23:23 You know, my tension and my style is usually to get things done quickly. But I said, I'll go with you on this one. I owe this to my wife. So we go to Hawaii, have a great time, come back in January, and the markets actually tank, the public markets. So that's the first thing that happens. You know, companies at the time, companies I remember, I think it was like LinkedIn when they were still an independent public company or Salesforce. They drop 30, 40%, something like that in value in a matter of like a week or something. All of a sudden, analysts and, you know, Twitter at the time, maybe it wasn't as big as exit as today, but they had all the commentary about how, oh, this is the beginning of the end, right?

Starting point is 01:24:04 Finally, like, the bubble's going to burst. And that very quickly trickles to the private sector, private companies and private financings, where investors start backing out, including from the DoorDash Series C. And so this is the start, I would say, of three years. So, you know, where DoorDash could raise very little money, a fraction of where peers could raise, and where we encounter several bouts of almost running out of cash. But you're right, there was this tension internally because, okay, so here we are. The markets are going down.

Starting point is 01:24:43 All of a sudden, the narrative for DoorDash was this, you know, really hot company, now is a company that can do no right. You can't ever make money. You can't beat all these competitors who were better funded at the time that there was Uber. there was Amazon who were either coming in or who already were in or announcing more expansion. And even if you win, you're going to lose because this is a money-losing business or a forever money-losing business. Those are kind of some of the headlines or the themes behind the headlines. But at the same instance, you look at the metrics on the inside and you actually see everything

Starting point is 01:25:19 going the direction that you would hope as an entrepreneur. You see repeatability from city A to city B to city C. You see unit economics improving. And the reason why the company was improfable is because we were constantly launching new markets. And new markets require investment in the beginning because you're actually paying for drivers to make sure that they can stay on the road even when you have no business. And so that was what was happening internally. That happened for about three years, though, where we were kind of stuck.

Starting point is 01:25:51 in this one of these cycles, macro cycles, macro cycles, investment cycles where the company could do no right, the sector was viewed as toxic. And that was certainly, you know, probably the three years in which I certainly had to learn how to deal with my own psychology. So how were you doing that? There was no one way. I think the first thing is you have to make sure that,

Starting point is 01:26:12 I think a lot of times it's very easy to believe in your own bullshit. And so the first thing, you know, I think a place like DoorDash, which is very intellectually honest is, well, what's actually real versus what maybe people are saying. And so we used to do this because we could fit all in one conference room, the all hands. I would show every metric in the company, including our cash balance, which is obviously going towards the X axis. And people are getting nervous, but people are asking a very good question, which is,

Starting point is 01:26:41 Tony, I don't get it. The cash balance is coming down, but the business is going the opposite direction. It's going up and to the right. And in a very organic way, we weren't spending, we didn't even have a marketing team. let alone in marketing budget, we didn't have money. People were very confused. So job number one to me was actually put the company in the best possible place by focusing on what we could control.

Starting point is 01:27:03 Because otherwise, I'm going to go crazy. I'm going to go crazy, and we're actually now going to put the company in the best chance of success. And so we got a group of, I think was maybe 20, 25 people, the people that kind of ran a lot of different important areas and basically brought them under the 10 and said, look, we have to do the following. We got to keep growing and keep taking share. We've got to get more profitable and we can't run out of cash. And there's no or in any of these statements.

Starting point is 01:27:29 It's an and function across all of these statements. And that was ultimately what I just kind of kept obsessing over. You know, because if I obsessed over anything else, the markets or what people were writing about us or another rejection from an investor, if I just obsessed over what was not in my control, I think I was going to go certainly nuts. That was, you know, certainly part one, focusing on what I can control. Part two, I think is, and this is another lesson I learned during those years, is that I think this can be risky, but I actually think that it's really important and undervalued to have genuine friends at work. And meaning that this can't just be about a financial success or a commercial success or some professional success on the resume,

Starting point is 01:28:19 that there is this adventure, if you will, that we're on, on this worthy eternal mission, and that at least we're going to die trying. Worst case, we're going to die trying, kind of like the Stanford football example day. Yes, of course, we want DoorDash to make it, but what gets you through the next day isn't thinking about DoorDash as much as I just want to make you to be successful, like my teammate to be successful. And so this willingness to think about someone else, in addition to just thinking about your own problems, I think actually made this a bit easier to go through. And then the final thing is, you know, back to trying to find, to build anything out of things that don't change.

Starting point is 01:29:01 One thing that I've been able to keep throughout the DoorDash chapter so far is just my exercise routine. So the routine itself has changed, but, you know, back then I was really into running marathons, things like that. just keeping that up, having something that was a bit of a constant in my life, whereas everything else was out of my control, extremely chaotic, usually extremely negative. And part of the routine was also date nights with my wife. So there was no one thing to answer your question about how to manage my own psychology, and trust me, I didn't, you know, have my thing altogether during every single period. But that was kind of when I look back, what were the things that got me through it?

Starting point is 01:29:40 What were the things that I kept trying to tell myself in my notes? book of what to do every single day, those were the things. Yeah, you control what you control. And I love this idea of having a mission bigger than yourself. Because you have this great line where it's like at some point, willpower is going to give out. Like your own personal willpower is going to give out. Especially because you're doing 100%. This is over 1,000 days.

Starting point is 01:29:58 How many rejections? How many knows you're getting from investors? I stopped counting after 50, but it was over 100. That's incredible. To this day, though, you don't, I heard, so we have a mutual friend of Robbie Gupta. Yeah. And you went on his podcast. And you said, I don't look at the stock price.

Starting point is 01:30:15 You try to get everybody else not to pay it. And the company not to pay that you know. And he goes, you had to remind me of our market cap because I don't know what it is. Yes, yes. That's still the case today. Yeah. I mean, back to the things that I can control. You know, well, I mean, usually our finance team will remind me of the market cap during

Starting point is 01:30:32 earnings calls and things like this. But like, but sincerely speaking, it's not something I get to control. And it's also not what is fulfilling or motivating to me. You know, what am I going to do on a daily basis knowing what the stock prices? Like, am I going to behave any – no, I'm not going to behave any differently. I'm probably going to still stick to my routine, which is I'm going to spend time with, you know, our teams that are trying to make sure that they can hit the year. There's one group and the team that is trying to invent the future, and there's several of those teams.

Starting point is 01:31:03 And then with customers, that's how I spend my time. I want to talk about that. I just be remiss not to mention this because, again, I don't know why. every time I hear you speak and not having this conversation with you personally, it's like there's just so much like Jeff Bezos-esque stuff going on here. I think you already know this, but there was a time in Amazon history where he talks about this. And I think, I don't remember what the exact numbers were, but the stock price went from like 180 down to like six. And his whole point, I think he talks about this in his shareholder.

Starting point is 01:31:28 I think he dropped like 90% or whatever the number was. And he's like, yeah, but I either like, I wasn't focused on the stock price. I was focused on the internal metrics of the business and they were all getting better and better and constantly improving. So he's like, I knew this was just temporary. Like, I will get out of this. I will survive because I'm going in the right direction. What is this idea you had this saying where you're like, as an operator, you need two management systems? I think you just dropped a hint right there in what you said earlier.

Starting point is 01:31:52 Yeah. So if you're so lucky as an entrepreneur to one day find product market fit where you can organically grow and build a business that's self-sustaining that generates cash, in other words, you have the privilege now of making a choice. That choice is, you know, keep doing what I'm doing. in or to keep expanding in service of our mission. And when you look at, I think, and one of the reasons why I think Amazon is inspiring, or a lot of these big tech companies now actually, is they tend to do two things at the same

Starting point is 01:32:29 time. One is they continue to build the core business, the business that kind of got them to their place, both in terms of their place with customers. in terms of what they're known for, as well as their financial place where they can invest from. But they also do new things. And they launch the next thing or the next thing, or they're trying to create the next thing.

Starting point is 01:32:55 And those are two very different systems. One system is about making sure that you can constantly reinvent yourself almost. You're trying to build the next version of the product to disrupt yourself, to build something that is 10 times better than what you have today. while you're also running the machine at the same time. Right?

Starting point is 01:33:13 So it's like you are flying the airplane. It's a big airplane. You're carrying lots of passengers, and you're going to do a mid-air engine transplants, right? That's one type of system that you're constantly trying to build. And then there's new stuff. It's not even an airplane. It's like a paper stick airplane.

Starting point is 01:33:32 It's like a paper airplane. There are no passengers, no nothing. You're in search of product market fit all over again. And they require different ways in which you measure success. They require usually a different talent. They require a different amount of resourcing. They have vastly different timelines in terms of rate of progress. And they tend to have a lot larger air bounds on some of these newer areas.

Starting point is 01:33:58 And it's really hard to do because the more successful your big airplane is, the more probably paper airplanes you're going to have to have. And they may be very expensive some of those paper airplanes that you're going to build. Because you need more shots on gold to keep up, you know, kind of this big business that you're trying to move in service of your mission. So the people scaling the businesses in DoorDash, that are post product market fit, right? And then the inventors. Are they, do you separate these people in the company? Yeah, we try to. Okay.

Starting point is 01:34:32 Yeah, we try to. Like separate buildings? How, how it's changing? No, no, no. I was just saying, like, do you even take a business? at that extreme, like separated, separate him, like physically? Like, how do you do this? Yeah, usually that happens.

Starting point is 01:34:43 But that's, that, that, that I don't know if is as important as you need very different goals, goaling systems and incentive systems. And, you know, that is probably more important than physically necessarily, where they are per se. You know, some, you know, DoorDash today also, you know, operates in more than 40 countries. So it's tough to get every single person in exactly the same location. But, no, it's very important, though, to separate how you actually track, manage, measure, incentivize these projects. And so that's more what I'm referring.

Starting point is 01:35:17 Are you making these decisions about, like, we're going to allocate this amount of resources, this amount of time, this amount of people to these experiments? Like, how do you actually structure this? Yes and no. I mean, if I had to make every single decision, I mean, Zordash would certainly move a lot slower than we would want to move. But certainly I have to set the standards and the pace, if you will. You know, that's kind of what I view a lot of my job. And so usually how it works is, well, first of all, anyone should be able to come up with an idea. It can't be somehow that only the leaders come up with the idea.

Starting point is 01:35:54 Usually it's the people closest to the problems that actually come up with the ideas or have the ideas. And, you know, if they can run an experiment, you know, back to this process, that actually demonstrates some viability of success that customers actually want this product, then it starts, you know, entering the phase where we can evaluate whether or not we should actually pursue it during our planning process. And, you know, through the planning process, then we decide, you know, okay, well, how many chips should we bet in project A versus B versus C? And some projects, look, they're not all starting at the same time.

Starting point is 01:36:26 Some projects are older. or some projects just got born. And so it's almost like an internal venture system, if you will, where it's stagegated. There is no, oh, you get all the money up front. And no, you kind of have to earn your right to the next stage. And that's going to be based on how well you're solving that customer problem. Where did you get that idea from, this internal stagegating,

Starting point is 01:36:52 like essentially trading it as like internal venture capital? Well, I, you know, a lot of it came from DoorDash's own history. where DoorDash kind of worked this way, right? And maybe some of it wasn't in the exact, you know, formulation we wanted. But that's how DoorDash was born. You know, you started with little resources or not a lot. And as we got progressively more successful or discovered more product market fit, we were given more resources.

Starting point is 01:37:21 And to me, when I think about the things that we built that were the most, that most solve customer problems, it tended to be when we were most resource constraint. And it's just, so I do feel like that's important to know whether, because the most important thing, again, when you're starting something, is do you really have something or are you just, you know, believing that you have something? And you don't get to make that call as the inventor. It's the customers that you're inventing for that ultimately are going to tell you

Starting point is 01:37:55 whether or not they're going to buy or not. And so that's the most important thing. We're trying to make sure that we actually can create something that is 10 times better than the status quo. And then if we can do that, yeah, of course, we'll keep scaling. Now, some projects cost more money to start. But that's just the nature of the problem. But we're still relative to its size, giving it a small amount of budget to begin with. Are you also learning from your peers?

Starting point is 01:38:19 Like, the reason I ask is because we just did, I think one of the episodes I'm most proud of so far for this new show is the one we did with Toby Lucke. Okay. And I was really excited to talk to Toby because I'm constantly asking world-class founders. Who are you learning from? Toby is like your favorite founder's favorite founder. And his name kept coming up over and over again. And that was the conversation I had where it's like you ask a question and you cannot predict what's going to come out of his mouth next. Because he has all these like uncorrelated ideas, which makes for a very like fascinating conversation.

Starting point is 01:38:45 So like who are the people that like you've either built relationships with or you've like studied like your peer group that you're also like learning from and like taking ideas from? Yeah, well, I mean, you're right. Toby's absolutely great. And well, first of all, some of the peers I have are just people that I grew up with, right? Like if you think like one of the benefits, which we didn't get into was one of the benefits of, you know, White Combinator besides being a forcing function of whether or not of testing our commitment, you know, to the project was actually the peer group, but we actually never got into that part where, you know, if you think about like the 2010s, right? So the companies that grew out of Wycombinator that we kind of grew alongside with, maybe we were slightly in different batches or, you know, not exactly in the same. But whether it was the Airbnbs, Stripe, Coinbase, we all kind of grew up in the same era, if you will. And so as a result, got to know each other through, you know, different events and venues and things like this. But trading notes, you know, with one another, I think certainly was, and we've all had our shares of. of challenges and triumphs.

Starting point is 01:39:58 And then looking at companies that are ahead of us, right? In my not day job, in my other job, I play a small role in Meta's part where I serve on the board learning from founders like Mark, who certainly have built companies that are a different level of scale versus where DoorDash is at. Let's stay on Mark for a second because we were talking before we recorded. I could spend some time with them. I've had a few conversations with them and came across, like, even more impressed than I thought it would be, given the fact that for his age, he doesn't really have a peer. And I actually told him that. So I was like, I wish you did more podcasts and talked about how you built a company that no one else your age is even remotely close to. But like, what are some things that you're on the board? What are some things like you've learned from observing him? Well, I think the first thing that impresses me a lot about Mark is this willingness to always learn new things. I think one of the traps, if you will, of success, or fighting your own, say, call.

Starting point is 01:40:53 is actually not just the challenging parts about that when things aren't going well, but it's also when after things go well. And maybe you actually have achieved some milestone. And one of the trappings of success is actually wanted to hold onto it. And what you see in someone like Mark and the team, I would argue, at meta, is this willingness to reinvent themselves. betting early, for example, on building a different platform in the case of virtual reality, augments, or reality. Obviously, they're going all in on AI.

Starting point is 01:41:35 And those things take a ton of courage. There's not a lot of data early on in either a platform shift or a new technology as a rival to know whether or not you're on the right track all the time. But you got to place the bets, you know, before you can see the success. And I think that willingness to learn a new domain where you're the rookie, where you're going to stumble, where you're going to get criticized, misunderstood, you don't know the answer, which is the opposite, if you will, of the successes maybe that they came from in terms of the previous businesses they've created, that is really impressive. that willingness to always be the beginner, to always go in the arena and sweat and bleed

Starting point is 01:42:24 and toil and struggle. That's really impressive. You both share a love of jiu-jitsu. Have you found anything from your jiu-jitsu practice that you brought back to your day job? Well, jiu-jitsu is a fascinating activity. I mean, it is, it's like, it's slothed. like some version of physical chess. That's a great way to think about it.

Starting point is 01:42:51 And it's almost like an exercise where there's so many opposites that you have to hold at the same time. The best jujitsu athletes can both be extremely firm and strong, yet at the same time, extremely relaxed. They're very capable of being intentional with their game plan, but then give up and release their agenda within a nanosecond if they see that they're losing their position. I think the willingness of how to be so flexible is certainly something that I think I'm trying to teach both myself and my personal life and also bringing that back to DoorDash.

Starting point is 01:43:32 I think the other thing is just, you know, no different, I think, from frankly, any craft. The willingness to just get 1% better every day in a particular position, in a particular flexibility exercise to actually just improve your balance in order to hold a position. Very small things ultimately compound when you look at the elite athletes, not someone like myself, but the elite jiu-jitsu practitioners who win the world championships or who win medals at events, they all have that. And when you actually talk to them about their craft, it's the tiny details, it's the edges of a move. It's actually not some, you know, silver bullet that they're looking for in a match or

Starting point is 01:44:19 something like, in fact, actually these matches at the most competitive levels are decided by sometimes not even points. They're decided by like what are called advantages. And that is, you know, one thing that I think is just a great reminder that you always have to be trying to master that craft. I have to ask you about how AI is changing the way that you guys running the business. you have this great language said, I think some of the technical advances like AI have given people new ways to run companies. Yes. How are you using it?

Starting point is 01:44:48 What are you doing? How is it affecting your work? Yeah. Well, it changes by the month. So this is a question that if we were to talk in the future, I'm not sure it would be actually the same answer. Well, one of the first things I would say is, you know, I think about some of the systems that we've architected here about how you can learn from doing things that.

Starting point is 01:45:12 that don't scale all the way to shipping, especially with something like coding. Right now, I think where the agents are, they're still good at what I call functional tasks. For example, coding. But outside of coding and looking at cross-functional areas, they're not quite there yet, for a lot of reasons. But within something like coding, the things that you could do today, where, some, anyone, actually, frankly. It doesn't have to be anyone of any function. Anyone can come up with an idea, run the prototype, run the experimentation and the analysis, and then actually

Starting point is 01:45:54 shipped to a small group of people all by themselves. That is very impressive, and that collapses, if you will, the amount of activity required or speeds up the learning loop you can have in any scientific process inside your company, that touches code. That's very cool. Second, LLMs, you know, what are they good at that humans are not good at or less good at? Well, they can have almost infinite memory and infinite contacts and search across any sort of file. Okay, so then the question becomes, how do you actually feed it the right information? and if you can feed it the right information,

Starting point is 01:46:39 it probably can do a lot better than humans can at the same activity. So I think those are two areas in which, whether it's speeding up your learning processes or actually improving the same activities right now that are effectively manually done to be done with higher, not just efficiency, but also effectiveness. Would there be any benefit for you, like, partnering with one of the big model companies

Starting point is 01:47:04 with all the physical data that you guys are collecting, or you would you keep that proprietary? Most of the information to run, you know, Dorash to be a great service, you know, are things that we use for ourselves. And the reason why we use them for ourselves is because it's not just that simple, like, oh, we just give away information and then somehow someone's going to be able to do something positive with it. You also have to take the action that the data kind of suggests.

Starting point is 01:47:30 For example, if the data says something is missing in this order, or the Dasher is at the wrong location and cannot find the customer. Let's say that those are all parts of pieces of information. Some corresponding action has to take place in order to actually solve the end-to-end job. In order to get the item that was missing or in order to actually find the customer

Starting point is 01:47:53 where the Dasher is. And so a lot of Dornash is, sure, we have a lot of information, but we have to do something productive because it's the end-to-end job that ultimately we get judged on. with customers. And so if we can partner with anyone, frankly,

Starting point is 01:48:10 in order to solve the end-to-end problem better, of course we would do that. But I think it's very hard sometimes to just give away something if there is no ability to, you know, correspond that with action that ultimately will solve some customers' problem. Think about what a wild ride you're on.

Starting point is 01:48:26 Like, you start the company and your competitors are literally using fax machines to now we're in, like, the age of AI. It's incredible in 13 years. Yeah, and I love this quote, and we'll end here. But you have this great quote where it's like, there's just no better way to be an expert than just do the work. You might be surprised at how quickly you get to become the expert.

Starting point is 01:48:48 Yeah, that's beautiful. Thank you very much for the time, Tony. I think we just like scratch service. I think there's a lot of things that you said today that haven't heard anywhere else. I'd love if you just come back on every, you know, a few months every year, whenever you want. Sure. That was fun. Thanks for a good time.

Starting point is 01:49:00 Thanks, David. I hope you enjoyed this episode. Please remember to subscribe wherever you're listening and leave a review. And make sure you listen to my other podcast founders. For almost a decade, I've obsessively read over 400 biographies of history's greatest entrepreneurs searching for ideas that you can use in your work. Most of the guests you hear on this show first found me through founders.

David Senra - Tony Xu, DoorDash

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