Tony Xu, DoorDash

23:54You had another interesting quote I want to read to you. You 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. 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 on the outside when we're getting started.

24:33Oh, 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 until you actually go and do the work. Sure, there might be some of the issues 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 the delivery today, they happen outside of restaurants.

25:15They happen inside shopping contexts like groceries or retail items or if they happen inside malls that are 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. 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.

25:59There 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? Of course not. Those are the kinds of things we have to work on every single day. And you wouldn't know that. What if I told you the cause for a delay was because actually somebody was homesick that day? How would you know that actually until that event actually transpired? And what would you do to respond to that event if that were to occur, which happens every single day.

26:38You 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 twitch 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. Most of the time, we have no idea. We start with these experiments.

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28:29He said, we built 11 labs to break down language and communication barriers. With Deal enabling us to hire and support exceptional talent anywhere, we can accelerate our innovation and bring more voices, stories, and ideas to every corner of the world. Deal 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 like, is this a yearly basis? Is this like over the history of DoorDash? Like you're running thousands of experiments every year?

29:04Ideally, yeah. 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. 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. But in general, all these things are happening every single hour of the day.

29:42OK, 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. 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.

30:23And to then taking the things that ultimately work, the ideas, and actually building products around them, and then engineering the ones that actually work so that you're actually very efficient with this learning loop. So that you can go from learning to shipping something that actually works 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.

30:58You know, our business is one where we believe we have to earn, you know, the right to serve you the next day. 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 importance of that? Of what? Of 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 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 late on every single delivery because we didn't have enough drivers on the road.

31:39We had no ability to shut down the website. But we had a lot of those kinds of days. Pause there. So where in DoorDash's 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, like, stop the floodgates. Why were you having floodgates three months in? We had floodgates not three months in. On that day specifically, for whatever reason, because of when the game ended, people wanted to order DoorDash for dinner in Palo Alto.

32:13And for whatever reason, 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 still do customer support every day. I see them literally every single day. 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.

32:51But 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 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?

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

34:11But 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. 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?

34:50So 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. Of course, we're going to start recruiting dashers, we're going to start recruiting consumers, selling restaurants. You start noticing as you do the deliveries, well, you have to build products to scale yourself. That's one. Two, you also just start noticing all the problems. Whenever you see a problem recur more than once, you would say to yourself, aha, maybe that's an example of a problem that we should actually build something for or actually run an experiment to see if we could actually solve.

35:34So I think very early on, the bias for action turned into this experimentation mentality. Now, we didn't have any organizations at the time or anything like that. It was just a few of us in my apartment. It wasn't like, OK, there's this rigorous system that I'm talking about. That's probably the earliest inklings, though, of how we thought about, OK, 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 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.

36:13You could be running Boston, someone else is running Dallas, someone else is running a different city. They would be reporting into me. You start seeing that, oh, okay, well, patterns actually emerge from City A to City B to City C, but they are still quite Local, there's slightly, for example, in Boston, there's not a lot of cars. Car ownership is one of the lowest in Boston 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 actually violate that setup. There are local nuances and you start realizing, well, okay, well, how do I actually teach this way of doing things that don't scale all the way to shipping 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.

37:09Then we would just build more products that would actually go across all of these different patterns. So that's how this thing has morphed over the years where you basically start with some basic scientific 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 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?

37:45Yeah, it's all of 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. They want, obviously, no mistakes. They absolutely expect it 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.

38:20Yes. What are the things that don't change from the customer's perspective for DoorDash then? Customers are always going to want more and more selection. They're going to always want more and more affordability. They're going to want faster deliveries. This is like Amazon, almost the exact mirror of what Amazon does. Well, I think when you just think about what people want, I actually think it's pretty easy because we can play that role ourselves. And yeah, I think you can ask very basic questions about what's the direction of travel of certain things. Like, for example, do you think people are going to expect more convenience or less convenience?

38:57Especially in a world where you think that people are earning more, 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. We were talking about this with the crew at Breakfast. It's just like, well, their cornerstone, their business is a trait in human nature that's never going to change, which is like we want more convenience.

39:28Yeah, 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. 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 and you handle all the other complexity behind it.

40:05I 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. Is this customer support emails? What is this? Emails or chats, sometimes phone calls. 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 about earlier is that so much of the magic or the difficulty of building a company like DoorDash 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.

40:38Of course, there's observability with dashboards and systems and 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. Those are freebies. How lucky am I to actually have a product in which people care enough? 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 care enough to actually let me know something went wrong in their experience.

41:24I owe them, you know, certainly not just a response, but actually, I think, and not the courtesy, but I owe them the responsibility of actually solving that problem ultimately. 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. 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, all of a sudden, the only things that get spotlighted are the financial metrics, your revenue, your profits, none of which are metrics that customers care about.

42:12There are no metrics in what we report as a public company that customers know about, probably, or care about, frankly. And that always is quite bothersome to me because it's because of our ability to serve customers that can hopefully achieve 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 the anecdotes conflict?

42:57It's a tough one. 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 of items of the types of SKUs you care about in a particular category of lettuce.

43:53Just lettuce, not vegetables, but just lettuce. It's always some tail example. The data is probably always going to win when it comes to some sort of a prioritization discussion. When you actually think about how to make a product better, it's going to almost always by definition, be in improving the edges. That's why a lot of times what I like to do personally is I love to spend time with a lot of our power users, whether it's 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.

44:36A 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 have figured out. 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.

45:20And 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 because you get to study the distributions. When it's a short email about something you already know about, there's not as much perhaps interesting material in it. I love the 2 ,000-word emails, especially from dashers who will give many use cases of why the logistics algorithm broke for them.

45:59And it becomes almost like a debugging exercise, right, of both physical world things that have occurred, things about our systems that probably broke, and things in our products that couldn't interface well enough between the physical world and our systems. And so then I go into our debugging tools, and I actually literally track the order. And every single step, I'm watching. And you're doing this personally? Yeah. And once I start figuring out potentially where the sources of error are, I'll either generate the hypothesis 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 we actually could improve.

46:45So, 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. 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.

47:25They produce the vast majority of jobs and consumption dollars for the economy and the monies for the police department, the fire department, the parks, the schools, et cetera, 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? 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, you know, scrape all that information, say job is finished, and then put it through some LLM or something.

48:01Well, 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 editor and a person. 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 for those people, this could be their identity. Back to what I was saying about small business owners and how they believe that what they do. It's not an office job or something that they just use to earn money so that they could spend consumption dollars or something else.

48:46This is like their livelihood. This is like who they are. When I think about those kinds of people, I want those people to win. If we have to eternally always look for the edges of the distribution to keep improving the product, of course, we will. 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. ED HARRISON And the alternative is terrifying. You have one or two big players. RONALD BOOTH Yeah, I don't even want to think about the alternative. You're totally right.

49:18I mean, the alternative, 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 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 are certain neighborhoods that they, you know, maybe preference is because there's a personality to it. So much of the personality is 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, you know, your homes and things like that.

49:54And 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, ten 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, 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.

50:32And 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 know, you are underpriced in this particular menu item versus what you could be pricing at. Or that there's an opportunity to bundle certain or to create certain SKUs or new items on your menu or in your catalog of a retailer that we think would grow your actual business.

51:16This is like, Bezos has 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, medium businesses, 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 where we see all these other, 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 if you have all those insights.

51:48We can also take the same approach that we've built for ourselves, you know, the scientific process from doing things that don't scale to shipping things at scale on your behalf. 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 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. 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.

52:17The simple things, we can change menu prices on your behalf. We can buy different promotions for you based on what return thresholds you want to achieve. We can talk about more complicated things. For example, there are certain merchants who want to actually grow tremendously. Why not? They want their identity, their passion 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 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.

52:59Why 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 sell those products in more places? And you can literally make everyone win. The new business who's selling your product now has a new menu item called a cookie. You get to maximally 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 at scale, how do we maximally increase your exposure, your identity, and achieve whatever goal you may have?

53:41So that's with restaurants. Tell me some of the stuff. Or retailers. Yeah. This gets really interesting when you expand out to every physical business. When I think about restaurateurs, retailers, to me, they are no different from me in the sense that they're 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. There should be no reason why you can't do that today from 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 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.

54:47there'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. 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? So one customer in 2014, I'll never forget, was a farmer who runs one of the largest farms in the state of California.

55:24And 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 grocers, restaurants, hotels, etc. And they've been doing this for three generations. as a family. And they did not start their farm to drive a bunch of trucks. That is not the business that they aspire to be in or are 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.

56:07That's wild. They even asked you that. And this was the second year of the business. And so, I said, not yet at the time. 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?

56:45Can 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 wanted. And we can do it in a way that is very low cost that, you know, It doesn't have to scale if you don't want it to. 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.

57:21One 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 AppLovin 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. 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.

57:55You 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 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.ai forward slash Senra. Something that, again, I want to compare your story to Bezos, because I just think every time I hear you speak, I hear a lot of Bezos.

58:35He 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 he tells this great story in one of the books that when he realized they were selling at the time, I think, just books, CDs, and maybe DVDs. And somebody's like, he would ask, I think he would send an email to like a thousand customers a day or something like that. And he's like, what else would you buy? 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.

59:10Yeah. So, yeah, I love that idea. What are these other products that you're building? Okay. We have to, like, educate me now because I've heard, first of all, you need to do more podcasts because I've listened to all of them. I didn't know some of the stuff you're telling me right now. But I know you've launched a bunch of different products in the last, like, 12 months. Tell me about one that you're really excited about. Well, one of the things that we're trying to do is we're trying to obviously deliver everything inside the city. Okay. In order to, and just to put some context behind it, there are tens of millions of items inside of a city that you could deliver.

59:40DoorDash delivers a fraction of those items. RAOUL PAL, What fraction do you deliver today? MICHAEL GREENSTONE, 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. For instance, 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 but are available in a warehouse somewhere far, far away? There are a lot of these challenges in order to actually address before you can actually do something like deliver everything inside of a city.

1:00:18So one of the things that we launched - Do you talk about like that internally? We're going to deliver everything in a city? Yeah. Yeah. But one of the things that we launched last fall, it was actually in September, we announced Dashbar fulfillment solutions where for companies like a Kroger or companies like a CVS, we'll actually carry their items. You can order their items directly from our site, but sometimes they'll actually come from a warehouse that we're operating on their behalf. That is an example of a product of a warehousing and inventory management and a logistics solution in which we are offering perfect accuracy, fast delivery in a way that retailers don't have access to or the capabilities to do so today.

1:01:03That's part of how you can deliver all of a city by actually bringing 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, robo-taxis or delivering humans. 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 robotaxi that items cannot do on their own.

1:01:51You 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 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. ED HARRISON And are you making the hardware yourself? MICHAEL GREENSTONE Yeah. In some of the cases, we are. We don't have, again, 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.

1:02:28Actually, when we started the 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 wanted to build what we wanted to build. And that's ultimately why we decided to, you know, 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. 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 the projects and a lot of the capital and a lot of the attention are going towards robotaxi.

1:03:14And that's just a very different solution and form factor, in our opinion, than what you need for last mile delivery. It's very hard, for example, to drive a robotaxi into a crowded hub of merchants, whether it's a mall or a main streets and actually somehow find parking and actually get access to the products by itself somehow. I think that's a difficult endeavor to accomplish. We built actually DoorDash. 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 a last 10 feet problem.

1:04:03It's just live, like right now? It's in Arizona. So it's in the Phoenix, Scottsdale area. Is it true? 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 the Waymo? Is that true? ED HARRISON One of the things that I think is fascinating about the problems that a company like Awaymo or a company 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.

1:04:41But you actually wouldn't know about that until literally you read the logs of these customer transcripts of these things. 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.

1:05:14What does that mean? Yeah, 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 have high processing power in terms of 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.

1:06:03So 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. We don't get to control when you hit that order button. We don't get to control whether or not a Dasher accepts or 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. 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 our teams would ask you to answer some prompt about fixing some problem in a city or something like that.

1:06:52And you probably would go out and do your analyses and come ready with a one-pager 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. 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.

1:07:38And that was the interview because that's the action part. 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 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, bias for action that we were testing for. So it'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.

1:08:21A lot of that was on the non-engineering front. On the engineering front, 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. 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?

1:09:02And I totally get it. 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? 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 engineering.

1:09:51Was there a specific source where you were finding people like this? Not really. There wasn't. 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, probably in the 2015 to 2020 era, especially when DoorDash was building out its team, there were 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. And a lot of the ways I can tell in an interview is actually just what people naturally talk to me about. For example, Christopher Payne, our first chief operating officer, I didn't ask him a single interview question.

1:10:31But after a two-hour discussion about our logistics algorithm, he went home that night. 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? 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.

1:11:07I remember my first, it was actually supposed to be a coffee chat, not a quote-unquote interview. He was scheduled for 45 minutes with our now president, Probeer, then CFO candidate. And he came to the coffee with his computer and this like multi-megabyte file, which was some projection of our financials somehow. I said, what? This is supposed to be me getting, we're supposed to get to know each other. But this is how he thinks, right? Like, I don't need to watch the resume or read the resume to decipher how does this person work. He showed it to me. So he built a model, and then he walks you through.

1:11:46Yeah. How long 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 like a source. It wasn't like, oh, yeah, we discovered the secret that it's this company or this school or this background that ultimately wins.

1:12:19But 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 took like a small sample of like 20 Dasher drivers and 20 UberX drivers. And I think the control of this experiment was like you all are getting guaranteed$20 an hour. If I offer you more money, how many of these groups would switch? Yes. Right? And what is the observation that you discovered from this experiment? Do you remember?

1:12:50Yeah. 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 UberX drivers would go to DoorDash, 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 from that? And this was very early. This is weeks within the companies getting started because at the time, one of the, you know, back to the three questions we're 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.

1:13:33And so, we were testing this. We're almost trying to confirm or to deny this hypothesis. So I ran that experiment. What I learned was, well, actually, they're two groups of completely different people. The DoorDash 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 not exclusively cars. The UberX 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.

1:14:20And they viewed that job almost like a full-time job. In some ways, they're moving from taxi 1.0 to taxi 2.0 because some of them had formerly drove for taxi. 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 ride-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. The average dasher only does three to four hours a week.

1:15:0590 % 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 - They self-selected and that's what I missed. I wonder if there's that insight that you derived 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, and do deliveries. Yeah, I feel like that is such a different person than the software engineer at Google. It's a super different... It's eating pheasant or something for lunch. Yeah, it's super different.

1:15:33Yeah, it's exactly. No, I think you said it yourself. I mean, if you think about 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 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. If anything, it was probably like a personal background as opposed to a professional one in which we were looking for.

1:16:11But 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 trait to hire for. Say more about that again. Yeah, they had strong followership. Okay, they had this ability where, and I didn't even know the why many times, But when you just look at company A they worked for or organization A they started, whatever, and they tend to have these groups that are attracted to them.

1:16:55And they tend to be quite like-minded. They tend to always want to get better. That was one trait that we discovered. 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 like tell you about the process 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're recruiting for or trying to institute in our systems here at DoorDash.

1:17:31It'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. Those were the traits that we looked for as opposed to what company did you work for, et cetera. 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 sitting in an office or that conference room behind us mapping things out and like, like, oh, I have a hypothesis.

1:18:04You're just like, no, we're just going to experiment. We're going to get to the truth as fast as 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. Or it can be very counterintuitive. 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. It's almost like an earned secret. You read Sam Walton's autobiography a long time ago.

1:18:43Okay, so he has that line in there because his main competitors, Sears, Kmart, there was 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. I'm in like, he's 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 iron secret that he had was if he could compete on his organizing mantra was everyday low prices.

1:19:16If I can actually sell this same item to you cheaper, people would drive, in human nature, vast distances to save money. 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. 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.

1:19:53which is you have to build a product that has better retention, better engagement. You have to. There is no other way. 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 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, I win.

1:20:33And one of my favorite historical anecdotes, have you ever read the biography of the Wright Brothers by David McCullough? No. Oh, I know you like to read history and biography. I do. I should probably check that one out. Listen to the audio book. I know you're busy. 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. I think it was Samuel Langley. More experience, for sure. Samuel Langley was, I think, backed by like the Smithsonian.

1:21:01I think he'd raised like$500 ,000. 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 centuries-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. You make some jokes that I – well, maybe they weren't 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? You're trying to – and it's weird because the metrics in the business were all trending in the positive direction, right?

1:21:36They were, yeah. 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, and that won't make sense to you. This happened very early with DoorDash. I mentioned earlier that we had a difficult time raising the seed round and then this difficult event with Stanford football in which we ran out of even our money faster. We survived that. We raised the seed round. Our Series A, Series B were hot rounds.

1:22:12We, I don't know, somehow was able to raise money in less than a week, something like that in each one of those instances. But in the spring of 2016, a few things happened. 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, I guess, January 2013, January of 2016, so about three years or so. So, first vacation, five days with my wife. 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.

1:22:53And we were actually, we had received an inbound term sheet, actually. So, things are going pretty well to raise our Series C in the winter of 2015, in the spring of 2016. 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, OK, all right, I'll go with you on this one. You know, my attention, my style is usually to get things done quickly.

1:23:27But 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. 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 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?

1:24:04Finally, the bubble is going to burst. That very quickly trickles to the private sector, private companies and private financings, where investors start backing out, including from the DoorDash, Series C. This is the start, I would say, of three years, where DoorDash could raise very little money, a fraction of what our 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, OK, so here we are, the markets are going down. All of a sudden, the narrative for DoorDash was this really hot company now is a company that can do no right.

1:24:49You can't ever make money. You can't beat all these competitors who are better funded. at the time, there was Uber, there was Amazon, who were either coming in or who already were in or announcing more expansion. Even if you win, you're going to lose because this is a money losing business or a forever money losing business. Those are some of the headlines or the themes behind the headlines. At the same instance, you look at the metrics on the inside, and you actually see everything going the direction that you would hope as an entrepreneur. You see repeatability from city A to city B to city C.

1:25:25You see unit economics improving. The reason why the company wasn't profitable is because we were constantly launching new markets. 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. That was what was happening internally. That happened for about three years, though, where we were stuck in this one of these cycles, macro cycles, investment cycles, where the company could do no right, the sector was viewed as toxic. That was certainly probably the three years in which I certainly had to learn how to deal with my own psychology.

1:26:04ED HARRISON So, how were you doing that? RONALD BOOTH There was no one way. I think the first thing is you have to make sure that I think a lot of times, it's very easy to believe in your own bullshit. It's a good market. The first thing, I think a place like DoorDash, which is very intellectually honest is, well, what's actually real versus what maybe people are saying? 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. People are getting nervous, but people are asking a very good question, which is, Tony, I don't get it.

1:26:42the 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 didn't even have a marketing team, let alone a 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. 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. We got a group of, I think it was maybe 20, 25 people, the people that ran a lot of different important areas and basically brought them under the tent and said, look, we have to do the following.

1:27:22We got to keep growing and keep taking share. We got to get more profitable and we can't run out of cash. There's no or in any of these statements. It's an and function across all of these statements. And that was ultimately what I just kept obsessing over. 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 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.

1:28:10And meaning that this can't just be about a financial success or a commercial success or some professional success on the resume. 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, right? 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.

1:28:54And then the final thing is, you know, back to trying to find, to build anything out of things that don't change. 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 then 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.

1:29:30And trust me, I didn't have my thing all together during every single period. But that was kind of when I look back, what were the things that got me through it? what were the things that I kept trying to tell myself, you know, in my notebook 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. Yeah. 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 a thousand days.

1:29:58How many rejections, how many no's you're getting from investors? I stopped counting after 50, but it It was over 100. That's incredible. To this day, though, you don't—I heard—so we have a mutual friend, Robbie Gupta. Yeah. And you went on his podcast. And you said, don't look at the stock price. You try to get everybody else not to pay—and the company not to pay that. 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 earnings calls and things like this.

1:30:33But sincerely speaking, it's not something I get to control. And it's also not what is fulfilling or motivating to me. What am I going to do on a daily basis knowing what the stock price is? Am I going to behave? 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 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. and then with customers. That's how I spend my time. I want to talk about that. 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.

1:31:17I think you already know this, but there was a time in Amazon history where he talks about this. And I think, I don't know 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 shareholders. I think he dropped like 90 % or whatever the number was. And he's like, yeah, but 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 better and constantly improving. So he's like, I knew this was temporary. Like, I will get out of this.

1:31:42I 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. Yeah. 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 keep doing what I'm doing 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 time.

1:32:30One is they continue to build the core business, the business that 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. 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.

1:33:13Right? 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 transplant. 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. 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 different talent. They require a different amount of resourcing. They have vastly different timelines in terms of rate of progress.

1:33:53And they tend to have a lot larger air bounds on some of these newer areas. 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 goal to keep up 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, do you separate these people in the company? Yeah, we try to.

1:34:32Okay. Yeah, we try to. Like separate buildings? No, no, no. I was just saying, do you even take it to that extreme, like separating them physically? Like, how do you do this? Yeah, usually that happens. But that, I don't know, 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, DoorDash today also operates in more than 40 countries. So it's tough to get every single person in exactly the same location. But, you know, it's very important, though, to separate how you actually track, manage, measure, incentivize, you know, these projects.

1:35:15And so that's more what I'm referring to. 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.

1:35:51It can't be somehow that only the leaders come up with the idea. Usually, it's the people closest to the problems that actually come up with the ideas or have the ideas. And if they can run an experiment back to this process that actually demonstrates some viability of success that customers actually want this product, then it starts entering the phase where we can evaluate whether or not we should actually pursue it during our planning process. And through the planning process, then we decide, OK, 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.

1:36:26Some 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 stage-gated. 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 stage-gating, like essentially trading it as like internal venture capital? Well, 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 formulation we wanted, but that's how DoorDash was born.

1:37:09You 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. 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 constrained. 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.

1:37:52It's the customers that you're inventing for that ultimately are going to tell you 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? 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 Luque.

1:38:25Okay. And I was really excited to talk to Toby because I'm constantly asking world-class founders, who are you learning from? Toby's 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 it a very, like, fascinating conversation. 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?

1:38:56Yeah. Well, I mean, you're right. Toby is 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 Y Combinator, besides being a forcing function of whether or not of testing our commitment to the project was actually the peer group, but we actually never got into that part where if you think about like the 2010s, the companies that grew out of Y Combinator that we grew alongside with, maybe we were slightly in different batches or not exactly in the same, But whether it was the Airbnbs, Stripe, Coinbase, we all grew up in the same era, if you will.

1:39:42And so, as a result, got to know each other through different events and venues and things like this. But trading notes with one another, I think, certainly was, and we've all had our shares of challenges and triumphs. And then looking at companies that are ahead of us. Right. You know, in my not day job and my other job, I play a small role in Meta's part where I serve on the board. I'm learning from, you know, 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.

1:40:22I could spend some time with him. I've had a few conversations with him and came across like even more impressed than I thought I 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 like you're on the board? What are some things that 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.

1:40:48I think one of the traps, if you will, of success or fighting your own psychology 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 wanting to hold on to 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 your reality. Obviously, they're going all in on AI.

1:41:36Those things take a ton of courage. There's not a lot of data early on in either a platform shift or a new technology's arrival to know whether or not you're on the right track all the time. You got to place the bets 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 and toil and struggle.

1:42:28That'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's like some version of physical chess. That's a great way to think about it. And it's almost like an exercise where there's so many opposites that you have to hold at the same time. The best jiu-jitsu 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.

1:43:18I 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 bring that back to DoorDash. I think the other thing is just 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 jujitsu practitioners who win the world championships or who win medals at events, they all have that.

1:44:08And 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 silver bullet that they're looking for in a match or something like that. 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 are running the business. You have this great line where you said, I think some of the technical advances like AI have given people new ways to run companies.

1:44:47Yes. How are you using it? What are you doing? How's 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'd be actually the same answer. Well, one of the first things I would say is, I think about some of the systems that we've architected here, about how you can learn from doing things 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.

1:45:34But within something like coding, the things that you could do today where 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 ship 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.

1:46:20Second, LLMs, what are they good at that humans are not good at or less good at? Well, they can have almost infinite memory and infinite context and search across any sort of file. OK, so then the question becomes, how do you actually feed it the right information? And if you can feed it the right information, 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.

1:47:01Would there be any benefit for you like partnering with one of the big model companies with all the physical data that you guys are collecting or you would keep that proprietary? Most of the information to run DoorDash to be a great service 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. 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.

1:47:45Some 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 where the dasher is. and so a lot of DoorNash 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, you know, we get judged on with customers. And so if we can partner with anyone, frankly, 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.

1:48:24Think about what a wild ride you're on. You start the company and your competitors are literally using fax machines to now we're in 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 to just do the work. You might be surprised at how quickly you get to become the expert. Yeah. That's beautiful. Thank you very much for the time, Tony. I think we just like scratched service. I think there's a lot of things that you said today that I haven't heard anywhere else.

1:48:55I'd love if you just come back on every, you know, a few months, every year, whenever you want. Sure, it was fun. Thanks for having your time. Thanks, David. Bye. 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. You