David (2:42)

So what were the existing companies doing then?

Tony Xu (2:44)

They were mostly honestly faxing orders, believe it or not. 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 they were lead gen companies at the time.

David (3:05)

I've heard you talk about developing this like 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?

Tony Xu (3:16)

No, we did. So the, when we started, I guess to take a step back before we shipped palo to delivery.com or even how we got there, you know, 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 PhD at the University of Illinois. 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, work, life. 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 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, retailers, service businesses. And it was actually a big maker who showed us a booklet, a three inch binder of delivery orders she had turned down. 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 is not a new idea. It's 2013. No one offers delivery. Why? And that's really what prompted us to think about launching palo alto delivery.com 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. 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. We kind of targeted restaurants because there were a million restaurants. You know, 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.

Sponsor/Host Voice (7:16)

I want to tell you about the presenting sponsor of this podcast, Ramp. I have been reading a lot about SpaceX lately. SpaceX is one of the most valuable private businesses in the world. And there's an idea from their history that more companies should use. From the very beginning, SpaceX was constantly attacking and questioning their costs. Ramp helps. Many of the most innovative businesses in the world do exactly that. The median company running on Ramp cuts their expenses by 5%. And the important idea that I found by reading about SpaceX is that a religious dedication to controlling costs helps increase revenue because you can pursue opportunities you couldn't otherwise. We see that in the Ramp data, too. The median company running on Ramp also grows their revenue by 16%. So when you're running your business on Ramp and your competitors are not, you have a massive competitive advantage that compounds over time. Ramp is the only platform designed to make your finance team faster and happier. Many of the top founders and CEOs that I know run their business on Ramp. I run my business on Ramp. And you should, too. Go to ramp.com today to learn how they can help your business save time, save money, and grow revenue. That is ramp.com There was other people

David (8:24)

that had maybe a similar idea, but I heard you tell the story one time where you're like, well, they actually went into, like, city centers. And one advantage you. I don't even think this might have been an accidental is that you started in Palo Alto instead of, like, New York City. Can you talk about why that was important?

Tony Xu (8:39)

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. 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. 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. It represents, I think, most cities in the US Or a lot of the world where you have main streets and then you kind of have, 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. 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 elevator and head out the lobby. And we could probably find a few places to go and eat. In Palo Alto, you'd be walking for miles before you could achieve something like that. 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, it was places like Palo Alto. And the question to us was just how. How many of them are there?

David (10:55)

And the only people doing deliveries at this time are the four founders.

Tony Xu (10:59)

Yeah, in the very beginning, it was just the four founders.

David (11:03)

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.

Tony Xu (11:14)

Yeah, I mean, we saw. I mean, one of the benefits when you do the deliveries is, well, one, you see how hard it is to actually, you know, bringing 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 looked 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 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.

David (11:58)

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.

Tony Xu (12:07)

Sure.

David (12:07)

But it's not large families shoved in these buildings.

Tony Xu (12:10)

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. 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 the stroller, then, you know, put that, you know, stroller and the children into the vehicle, then get it out, and then somehow get into inside of a crowded parking lot or a 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, you know, one or two people living inside of a city. But that wasn't the first thought we had.

David (12:57)

But even more than a second derivative, because you were just explaining like, okay, well, if I'm delivering to somebody's house, I know where to park, where to park, as opposed, 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 do I access the elevator?

Tony Xu (13:08)

Right, 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?

David (13:32)

Exactly. This is what is interesting to me. It almost made sense. Like, your competitors seem to do the most obvious or the logical thing. It's like, no, I need order density. Where are all the people? Let me just go to the cities

Tony Xu (13:43)

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.

David (14:10)

Even when you were called Palo Alto

Tony Xu (14:12)

Delivery, Especially when we were called Palo

David (14:14)

Alto, you had no money, right?

Tony Xu (14:14)

Yeah, we had no. Exactly. We were. We were. 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 forecasts 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 chance of working.

David (14:41)

What were your costs at that time? Because you have the four founders, essentially labor you probably are paying yourself.

Tony Xu (14:45)

We didn't pay ourselves.

David (14:46)

You're not paying yourselves free labor.

Tony Xu (14:47)

Yeah.

David (14:48)

Just your time. You built a $9 website.

Tony Xu (14:52)

Yeah.

David (14:52)

I heard something that was hilarious. We were like, well, we don't have a sophisticated dispatch system, so we just used the Find My Friends app.

Tony Xu (14:58)

We used Find My Friends. We used Find My Friends. We used to track the drivers, which

David (15:01)

just happened to be all of you, our co founders. You have a Google voice number.

Tony Xu (15:05)

We do.

David (15:05)

You're not. There's no marketing advertising, right?

Tony Xu (15:07)

No, no. We had no money to market or advertise.

David (15:10)

So what are you. What other expenses did you have back then? Do you remember?

Tony Xu (15:13)

It was all kind of like self funded. This entire, you know, activity was self funded until we had to start recruiting drivers and actually, you know, testing this out beyond just the four of us.

David (15:22)

This is when you applied to Y Combinator or No.

Tony Xu (15:24)

Yeah, yeah. I mean, in that time period.

David (15:26)

Okay. By the time you apply to Y Combinator, do you have more than drivers than just the founders or.

Tony Xu (15:30)

No? We may have had one or two.

David (15:33)

Okay.

Tony Xu (15:34)

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.

David (15:45)

How many years did you have left of business school? Like, how many years were you in school and running?

Tony Xu (15:49)

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. Then we get DoorDash, the URL and the company name, and then we launch out of Y Combinator in the summer, June 20th.

David (16:14)

So was it like now once somebody starts using DoorDash or when I start using DoorDash, right. I'm like, oh, this is very convenient. I just keep using it over and over again. Did you see that same behavior pattern back then?

Tony Xu (16:22)

Yeah. With a very small group of 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.

David (16:50)

What were the conversations amongst the founders when you guys are seeing that?

Tony Xu (16:54)

Let's keep going and let's. 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 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 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. What consumers want to pay us six bucks, which is what we charged. Are there restaurants who would be willing to partner with us for 15%? And, you know, could we afford a wage 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 answering those questions, then we'd keep going.

David (18:14)

Again, you told this hilarious story where during the summer, some of your classmates are like, yeah, I'm gonna go ski in Staad or something like that. What are you doing, Tony? I'm delivering hummus in my Honda.

Tony Xu (18:26)

Yes, yes, that was. Yeah, look, I mean, 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? We were working out of an apartment. We had Dashers in that apartment. We had the co founders live in that apartment. We worked 10am to 2am every single day. But it wasn't like this glamorous exercise. But nor did we seek that. We were just trying to answer those three questions that summer. We didn't care that much about what our friends were doing. Clearly we thought that it was interesting enough to keep going, that if we can actually answer these questions, I think we're actually onto something.

David (19:18)

We just had Marc 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.

Tony Xu (19:25)

Huh.

David (19:26)

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 delivery? Anything?

Tony Xu (19:35)

No, no. It's actually why we had to do the deliveries. I mean, I mean, the reason why we did so, the reason why we were so hell bent on doing the deliveries, besides the fact that we had no idea whether with any business recruiting other drivers, was how does this work? How should it work? And I think Jordache, early on, even to this day, but early on was so hard to explain because it was actually even to build the mvp, yes, to test, it was just this website, you know, paloma.com, but we had to build like four things. We had to build this website for consumers. 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 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 in order to actually just bring you a burrito, 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.

David (20:33)

So you were misunderstood back then. You just said something interesting. You think that's still the case to this day?

Tony Xu (20:38)

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 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. Our business is one where all of the magic, or the secret sauce, if you will, are 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, 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 to end experience that's very difficult to replicate. But yeah, I think early on we knew that because we did all the deliveries.

David (22:02)

You know, who knows it? Your 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. And I didn't know I've heard about you before and didn't really, you know, obviously you use 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.

Tony Xu (22:29)

Exactly.

David (22:29)

Okay, 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 AK was very kind to host me and like a handful of European founders. And one of the European founders that was sitting next to me and Daniel at dinner was somebody I had never met before. And it's Mickey from Voltaire.

Tony Xu (22:50)

Okay, cool.

David (22:51)

But he told me something interesting because, you know, basically the story was he's just like, listen, I built the doordash of Europe, I guess is how it was described. And 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.

Tony Xu (23:16)

Yeah.

David (23:17)

And he was looking at the term sheet, thinking about signing it. And then he said involuntarily, something came 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 could 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 this day he still directly reports to you, correct?

Tony Xu (23:42)

Yeah, yeah. He runs all of our European business.

David (23:45)

And he was trying to explain to me and Daniel about just you don't. It's all the magic is very similar. What the stuff that you don't see how hard he is to. To compete against. You had some. Another interesting quote I wanted you. You say the way that Doordash has achieved so much success is tens of thousands of experience, 95% of which never even make it to the customer before they fail. The way to get a more. 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 statement?

Tony Xu (24:15)

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 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 happen outside of restaurants, they happen inside shopping context like groceries or retail items or if they happen inside malls that are multi storey, 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. 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 six to aisle eight, 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 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 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.

Sponsor/Host Voice (27:33)

Deal will help your business hire, pay and manage any worker anywhere in the world. Deal is the best company in the world at building infrastructure for global hiring. Deal is one platform for payroll, HR, benefits and device management across 150 countries. Deal gets you everything you need to run a high performing global workforce on a single AI native platform. From first offer to final offboarding, Deal handles the complexity so you can stay focused on your business. The best founders and operators in the world have one thing in common. They control as much of their business as possible. And the founders of Deal do exactly this. When you use Deal, you aren't using a third party payroll processor or a messy network of in country providers. Deal built and owns the rails. That means faster speed Better service and total accountability. The founder of eleven Labs, who I use to make transcripts for this podcast, has a great description of the value that deal can give your company. He 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

David (28:42)

corner of the world.

Sponsor/Host Voice (28:44)

Deal is trusted by over 40,000 customers and growing fast. Learn how they can help your business by going to deal.comforward/senra. That is deal.com forward slash senra.

David (28:56)

How do you do that many experiments though? Like, is this a yearly basis? This is like over the history of doordash. Like, you're running thousands of experiments every year.

Tony Xu (29:04)

Ideally, yeah. Yes. I think when we are at our best, that's, that's, that's what's happening. But, but, but it starts with actually building a system that actually wants to learn. If you think about like, like, like, why do we have to learn? It's because the physical world, a is not structured. It's not documented anywhere. It's you, you can't scrape it. It's constantly changing. It's. I mean, there's a winter storm right now, for example, in the northeast. I mean, these are all things that happen differently.

David (29:31)

You know, it's beautiful here in California.

Tony Xu (29:33)

Yeah, I know. We would have no idea here. We're spoiled here in the Bay Area. But like, but in general, all these things are 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 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 and 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. Our business is one where we believe we have to earn 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. The scoreboard goes back down to zero tomorrow, and we have to just do that all over again.

David (31:11)

Where did you learn the importance of that, of starting over again every day? I've heard you say that before, and I love that idea.

Tony Xu (31:17)

Very early at Doordash, we learned how. 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. 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. We had no ability to shut down the website, but we had a lot of.

David (31:42)

Wait, pause there. So where in DoorDash history is this game?

Tony Xu (31:45)

This is the third month of our operations, 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.

David (32:00)

Why were you having floodgates three months in?

Tony Xu (32:03)

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. 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. When you see that you can lose someone's trust on one order, you realize that you gotta earn it again the next day. And there is no such thing as this 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.

David (32:57)

So what happened that night of the game?

Tony Xu (33:00)

We were late on every single delivery, and I think it was probably somewhere around 10pm or something where we're tallying up all the refunds that it would cost us if we wanted to make. Right. And kind of give back everybody their money.

David (33:17)

Were the customers asking for the refunds?

Tony Xu (33:19)

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? 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, we decided, okay, we 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. 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 deliver those cookies at around 5am 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.

David (34:41)

That's excellent. So tell me more about building the system, this self reinforcing learning system.

Tony Xu (34:45)

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. That's 1, 2. 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 if 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 apartment. 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. 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 are still quite local. They're slightly, you know. You know, 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. So 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, and then we would just build more products that would actually go across all of these different patterns. So that's kind of 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, you know, against whether or not you're delivering better for customers. That's always gonna be the North Star metric of whether or not this process is actually making a difference or not.

David (37:41)

Is it better for customers if it's faster, cheaper, more efficient? Like, what are the.

Tony Xu (37:45)

Yeah, 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, you know, delivered. They want the lowest possible price. They want the fastest possible delivery. They want Obviously, no mistakes. They absolutely, you know, 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 in all of those things on every single order.

David (38:17)

So this is this idea of like, you can build a business around things that don't change.

Tony Xu (38:20)

Yes.

David (38:20)

What are the things that don't change from the customer's perspective for doordash, then

Tony Xu (38:24)

customers are always gonna want more and more selection. They're gonna always want more and more affordability. They're gonna want faster deliveries.

David (38:32)

This is like Amazon, almost the exact, like mirror of what Amazon.

Tony Xu (38:36)

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 just asked 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.

David (39:21)

We were talking about this with the crew at breakfast. It's just like, well, they're cornerstone. Their business is a trait in human nature that's never going to change, which is like, we want more convenience.

Tony Xu (39:28)

Yeah, always. It's not rocket science. I think the rocket science is actually how do you make it happen?

David (39:35)

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. 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. This customer support. Emails.

Tony Xu (40:13)

What is this? Emails or chats, sometimes phone calls. Every day yeah.

David (40:18)

Say more about this. Well, why do you do this?

Tony Xu (40:20)

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. You know, of 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, but those are freebies. I mean, how lucky am I to actually have a product in which people care enough even usually they're not very positive emails, but to 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, 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. 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. You know, 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 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.

David (42:55)

What do you do when the data and the anecdotes conflict?

Tony Xu (42:58)

It'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. Just lettuce, not vegetables, but just lettuce. Right. 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 by definition be in improving the edges, you know. And that's why a lot of times what I like to do personally is I love to spend time, you 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 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. 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.

David (45:23)

So let's say you find one of these edge cases as you're doing customer support every day. What's your next step?

Tony Xu (45:28)

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, you know, 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. But I love the 2000 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 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 personally, you're doing this personally? Yeah. And once I start figuring out potentially where the sources of error are, you know, I'll either generate the hypothesis and call the dasher or email the dasher, you know, 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. 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.

David (46:52)

I've heard you describe this as like this eternal mission, right?

Tony Xu (46:55)

Yes.

David (46:56)

How would you describe what the eternal mission of Doordash is?

Tony Xu (46:58)

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, 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 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. Well, A, that data is always changing, B, it's not organized as all 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's. 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 is, 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. 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. 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.

David (49:12)

And the alternative is terrifying. You have one or two big players.

Tony Xu (49:15)

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 may 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 your homes and things like that. And that's what makes it tick. That's what makes a place feel awesome, a city feel awesome. And so I think that's an eternal mission worth fighting for.

David (50:04)

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?

Tony Xu (50:12)

Well, I mean, the first is we have to structure it. So one of the things that I think Google so brilliantly did was they did organize a lot of the information on the Internet and they made it searchable to everybody. Right now the first thing we're doing is we're still collecting lots of information. 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.

David (50:49)

Data about their own business.

Tony Xu (50:50)

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, 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, skus or new items on your. On your menu or in your catalog if you're a retailer that we think would grow your actual business.

David (51:16)

This 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, 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.

Tony Xu (51:36)

Yeah.

David (51:36)

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 be irresponsible not to partner with you.

Tony Xu (51:46)

Yeah.

David (51:47)

You have all this insights.

Tony Xu (51:48)

We can also take the same approach that we built for ourselves. You know, the scientific process from doing things that don't scale, to sh 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 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?

David (52:14)

What do you mean do them for me?

Tony Xu (52:15)

We could talk about simple things to more difficult things. The simple things. We could, you know, change menu prices on your behalf. We can buy different kind of 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, you know, 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. 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 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. 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, you know, your exposure, your identity, and achieve whatever goal you may have?

David (53:42)

So that's with restaurants. Tell me some of the stories.

Tony Xu (53:44)

Or retailers.

David (53:45)

Yeah, like, this gets really interesting when you expand out to every physical business.

Tony Xu (53:49)

When I think about restaurateurs, retailers, to me, they are. They're 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 sort 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, 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. There's no reason why we can't be your business partner for any future creation.

David (54:52)

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.

Tony Xu (54:59)

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.

David (55:13)

What are Some examples of things that

Tony Xu (55:14)

customers have, 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 grocers, restaurants, hotels, etc. And they've been doing this for three generations as a family. They did not start their farm to drive a bunch of trucks. That is not the business that they aspire to be in, 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. That's wild.

David (56:07)

They even asked you that.

Tony Xu (56:09)

And this was the second year of the business. And so I said, not yet. At the time, perhaps I should. I almost feel like I owe him a call. So this conversation is a good reminder. But the, 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? 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 wanted. 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. 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.

Sponsor/Host Voice (57:21)

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 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. 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 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

David (58:29)

dot 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. He obviously did a ton of customer support at the very beginning of Amazon. He publicized his email and, like, made it public. Like, email me all the time. And one, he tells this great story. One of the books that, when he realized, you know, they were selling at the thing at the time, I think just books, CDs, and, like, VH, maybe DVDs. Okay. 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 Bezos was like, oh, my God, we're going to be able to sell anything.

Tony Xu (59:07)

Yeah.

David (59:08)

Or everything in that case.

Tony Xu (59:09)

Yeah.

David (59:10)

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 you need. 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 during the last six months. Tell me about one that you're really excited about.

Tony Xu (59:27)

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. DoorDash delivers a fraction of those items.

David (59:42)

To what fraction do you deliver today?

Tony Xu (59:44)

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. 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. So one of the things that we launched.

David (60:20)

Do you talk about like that internally, we're going to deliver everything in a city?

Tony Xu (60:24)

Yeah. But one of the things that we launched last fall was actually in September we announced Dashboard Fulfillment solutions where for companies like a Kroger or companies like a cbs, we'll actually carry their items and 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, an 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 that's part of how you can deliver all of a city by actually bring and aggregating and making closer some of the inventory to where someone lives. We are 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 robo taxi 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.

David (62:11)

And are you making the hardware yourself?

Tony Xu (62:14)

Yeah, 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. When we don't have a religion about whether or not we have to build, you know, the product or someone else has to build the product. Actually, when we started the autonomous project, 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 start our own project in 2019 and shipped it last year.

David (62:55)

So that's what, six, seven years? Almost seven years of development.

Tony Xu (62:59)

Six years. Six years.

David (63:01)

Why did they not want to build? They just didn't want to build what you wanted.

Tony Xu (63:04)

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. 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 robotaxi 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, that's a difficult endeavor to accomplish. We built actually DoorDash 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 10ft of actually solving the problem of last mile delivery, which really is the last 10ft problem.

David (64:03)

It's just live.

Tony Xu (64:03)

Like right now it's in Arizona, so it's in the Phoenix Scottsdale area.

David (64:09)

Is it true? I heard that Waymo, you guys have partnered with Waymo to close the doors of. Is that true?

Tony Xu (64:16)

We do partner with Waymo and we do lots of things together.

David (64:20)

Is it people just not shutting the door when they get out of a Waymo. Is that true?

Tony Xu (64:23)

One of the things that I think is fascinating about the problems that a company like a Waymo or a problem like Doordash 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. Right, but 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 the fun part of the exercise at DoorDash.

David (65:06)

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?

Tony Xu (65:16)

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 recognize 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. 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. How. 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 ask you to answer some prompt about fixing some problem in a city or something like that. And you probably would go out and do your analyses and, you know, come ready with a one pager of notes or something. And then you 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. And that was the interview, because that's the action part, 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 Internet protocol, and then do some magical analysis on it and then ship code.

David (68:02)

Okay.

Tony Xu (68:03)

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.

David (68:14)

So that's the Navy SEAL part.

Tony Xu (68:15)

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, 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? And 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? 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 engineers.

David (69:51)

Was there a specific source where you were finding people like this?

Tony Xu (69:55)

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, you know, probably in the 2015-2020 era when, especially when Doordash was building out its team, there are 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 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, 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.

David (70:50)

But he did it.

Tony Xu (70:50)

But he did it. And that told me more than any set of interview questions.

David (70:55)

You hadn't even hired him yet.

Tony Xu (70:56)

No, hadn't hired him yet.

David (70:58)

And then you immediately hired him.

Tony Xu (70:59)

And this was certainly, 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 Prabir, then CFO candidate. And he came to the coffee with his computer and this like multi megabyte for file, which was some projection of our financials somehow. I said, what? Like, like this was, this is supposed to be me getting. We're supposed to get to know each other and this. But, 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.

David (71:44)

So he built a model and then he walks you through.

Tony Xu (71:46)

Yeah.

David (71:47)

How long did that take?

Tony Xu (71:48)

We, I think debated it for over four hours. So it was like. And, 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, it wasn't like, oh yeah, we discovered the secret that it's this company or this school or this background that ultimately.

David (72:19)

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 this story one time where you were wondering, let me see if I remember correctly. Correct me if I'm wrong, but you're like you took like a small sample of like 20 Dasher drivers and 20 Uber X drivers. Yes. 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?

Tony Xu (72:46)

Yes, right.

David (72:46)

And what is the observation that you discovered from this experiment? Do you remember?

Tony Xu (72:50)

Yeah, so yeah, they were making about 20 an hour at the time. I made a guaranteed offer of 25 an hour if you, 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.

David (73:08)

And what did you derive and what conclusion derived from that?

Tony Xu (73:11)

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 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 so we were testing this, we were almost 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 doordash drivers, they were younger, 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. The UberX drivers at the time were usually men in their 40s, I think almost exclusively men. Maybe there are 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 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. 90% drive fewer than 10 hours a week. And so it just became a very different setup. You know, the delivery they kind of

David (75:12)

self selected into what?

Tony Xu (75:13)

Self selected. And that's what I wonder if there's

David (75:14)

that insight that you derive there. It's 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 go get in your shitty Honda? No offense.

Tony Xu (75:23)

Yeah, do deliveries.

David (75:25)

Yeah, I feel like that is such a different person than it's a software engineer at Google. It's a super different pheasant or something for launch.

Tony Xu (75:31)

Yeah, it's super different. Yeah, it's exactly. No, I think you said it yourself. I mean, if you think about, you know, the early days, it was, I mean, I remember we would at and it's a strange memory, but we would take a coding break at 10pm 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 a background. If anything it was probably 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.

David (76:30)

Oh, that's an interesting trait to hire for. Say that more about that again.

Tony Xu (76:34)

It's strong followership. Okay. They had this ability where and I didn't even know the why many times. But when you just look at, you know, 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 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 and 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. 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. Those are the traits that we looked for as opposed to what company did you work for, et cetera.

David (77:49)

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, oh, I have a hypothesis. You're just like, no, we're just going to experiment. We're going to get to the truth as fast as possible.

Tony Xu (78:07)

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. And 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.

David (78:38)

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 competitors, 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. 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, these small towns. And if I didn't, 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. His, you know, organizing mantra was everyday low prices. If I can actually sell this same item to you cheaper. People would drive in human nature vast distances to save money.

Tony Xu (79:25)

Yes.

David (79:25)

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, different, different industry trends.

Tony Xu (79:36)

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 had 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, which is you have to build a product that has better retention, better engagement. You have to. There is no other way.

David (80:01)

So what do you think when you see these giant, like, seed rounds that we're seeing now?

Tony Xu (80:06)

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 that I'm to actually go and solve it 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.

David (80:29)

Yeah. One of my favorite, because everybody's like, well, I need more money because if I have more money, I win. And one of my favorite historical anecdotes. You ever read the biography the Wright brothers by David McCullough?

Tony Xu (80:38)

No.

David (80:38)

Oh, I know you like to read history.

Tony Xu (80:40)

I do. I should probably, I should probably check that one out.

David (80:43)

Listen to the audiobook. If I know you're busy, it's great because, you know, human powered fight 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. I think it was Samuel Langley. Samuel Langley was, I think backed by like the Smithsonian. I think he'd raised like $500,000. This is 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.

Tony Xu (81:16)

That's incredible.

David (81:17)

Which I absolutely love. I want to you make some jokes that I. 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? And you talk about this for your trying to. And it's weird because the metrics in the business were all trending in the positive direction.

Tony Xu (81:36)

They were.

David (81:37)

So explain what the hell is going on.

Tony Xu (81:38)

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 career control and that won't make sense to you. And this happened very early, you know, with Door Dash. I mentioned earlier that we had a difficult time raising the seed round. And then this, you know, difficult event at with Stanford football in which we, we ran out of even our money faster. But we survived that. We raised the seed 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 like that in each one of those instances. But in the spring of 2016, 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, 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. We didn't go on a honeymoon, so I promised her that we should. 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 are going pretty well. To raise our Series C in the winter of 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? It's like, no, don't worry about it. You've never taken a vacation. Go, go, take, go take your honeymoon. Everything's going to be fine. You know, we're. We're good for it. And I said, okay, all right, I'll go with you on this one. 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. 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 a week or something. All of a sudden, analysts and Twitter at the time maybe wasn't as big as X it is today, but they had all the commentary about how, oh, this is the beginning of the end, right? Finally 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 what her 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. All of a sudden, the narrative for DoorDash was this 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 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. 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 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 wasn't profitable 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 in this one of these cycles, macro cycles, investment cycles, where the company could do no right. The sector was viewed as toxic. And that was certainly probably the three years in which I certainly had to learn how to deal with my own psychology.

David (86:04)

So how were you doing that?

Tony Xu (86:06)

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. And so the first thing you know, I think a place like Doordash, which is very intellectually honest, is, well, what's, 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, Tony, I don't get it. The, the cash balance is coming down, but the business is going the opposite direction. It's going up and to the right. And the way that in a very organic way, we weren't spending. 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. 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 tent and said, look, we have to do the following. We gotta keep growing and keep taking share. We gotta get more profitable and we can't run out of cash. And 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 kind of kept obsessing over, you know, because if I obsessed over anything else, the markets or what people are 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. 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, 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. And 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 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 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.

David (89:47)

You control what you control. And I love this idea of having a mission bigger than yourself.

Tony Xu (89:51)

Yeah.

David (89:51)

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 this is over a thousand days. How many rejections, how many no's you getting from investors?

Tony Xu (90:01)

I stopped counting after 50, but it was over 100.

David (90:05)

That's incredible. To this day, though, you don't. I heard. So we have a mutual friend of Ravi Gupta.

Tony Xu (90:11)

Yeah.

David (90:11)

And you went on his podcast and you said, I don't look at the stock price. You try to get everybody else not to pay in the company, not to pay attention to it. He goes, you had to remind me of our market cap because I don't know what it is.

Tony Xu (90:22)

Yes, yes.

David (90:22)

That's still the case today.

Tony Xu (90:24)

Yeah. I mean, back to the things that I can control. Well, I mean, usually our finance team will remind me of the market cap during earnings calls and things like this. But 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 different. 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.

David (91:06)

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 they're having this conversation with you personally, it's like, there's just so much 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 6. And his whole point, I think he talks about this in his shareholders. I think it 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, I will survive because I'm going in the right direction. What is this idea you had? This saying where you like as an operator you need two management systems. I think you just dropped a hint right there in what you said earlier.

Tony Xu (91:52)

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 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. 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. 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 ten times better than what you have today while you're also running the machine at the same time. Right. 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 there. 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. And 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.

David (94:23)

So the people scaling the businesses in DoorDash that are post product market fit.

Tony Xu (94:27)

Sure. Right.

David (94:28)

And then the inventors, are they. Are you separate these people? Yeah, we try to. Okay, yeah, we try. Separate buildings. How? I was just saying, like, do you even take it to that extreme? Like separate, Separate them? Yeah, physically. Like how do you do this?

Tony Xu (94:41)

Yeah, usually that happens, but that, that's. That, that, that, that I don't know if is as important as you need very different goals, goaling systems and incentive systems. And that is probably more important than physically necessarily where they are per se. DoorDash today also operates in more than 40 countries, so it's tough to get every single person in exactly the same location. But 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 to.

David (95:17)

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, how do you actually structure this?

Tony Xu (95:24)

Yes and no. I mean, if I had to make every single decision, I mean, Jordache would certainly move a lot slower than, than, than. 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, 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. 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, 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. Some projects are older, 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.

David (96:48)

Where'd you get that idea from this internal stage gating, like essentially treating it as like internal venture capital.

Tony Xu (96:54)

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. 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. And to me, when I think about the things that we built that were the most, that most solved customer problems that 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 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 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 ten 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.

David (98:17)

Are you also learning from your peers? 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. Okay. 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, 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. So like, what are, 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 taking ideas from?

Tony Xu (98:56)

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. 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 at the peer group. 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 Y Combinator 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, you know, challenges and triumphs and then looking at companies that are ahead of us. Right. You know, 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, you know, founders like Mark, who certainly have built companies that are at a different level of scale versus where DoorDash is at.

David (100:18)

Let's stay on Mark for a second because we were talking before we recorded, I got to spend some time with him. I've had a few conversations with him and came across 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 what are some things that you like you're on the board. What are some things you've learned from observing him?

Tony Xu (100:43)

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 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 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, augmented reality, obviously they're going all in on AI, 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 is arrival to know whether or not you're on the right track all the time. But 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. And struggle. That's really impressive.

David (102:29)

You both share a love of jiu jitsu. Have you found anything from your jiu jitsu practice that, like, you brought back to your day job?

Tony Xu (102:39)

Well, Jiu jitsu is a. Is a fascinating activity. I mean, it is. It's like some version of physical chess.

David (102:50)

That's a great way to think about it.

Tony Xu (102:51)

And it's almost like an exercise where there's so many opposites that you have to hold at the same time. The best 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. 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 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 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 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.

David (104:37)

I have to ask you about how AI is changing the way that you guys are running the business. You have this great language. I think some of the technologies, technical advances like AI have given people new ways to run companies.

Tony Xu (104:46)

Yes.

David (104:47)

How are you using it? What are you doing? How's it affecting your work?

Tony Xu (104:50)

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. But 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. You know, 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, 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. Okay, 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.

David (107:01)

Would 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

Tony Xu (107:06)

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 takes. Kind of suggests, for example, if the data says something is missing in this

David (107:33)

order

Tony Xu (107:36)

or the Dasher is at the wrong location and cannot find the customer. Let's say that those are all parts of, you know, 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, you know where the dasher is. And so a lot of doordash 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, 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 correspond that with action that ultimately will solve some customer's problem.

David (108:24)

Think about like what a wild ride you're on. Like you start the company and your competitors are literally using fax machines to now we're in like the age of AI.

Tony Xu (108:34)

You have this incredible in 13 years.

David (108:36)

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.

Tony Xu (108:47)

Yeah, that's beautiful.

David (108:49)

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 haven't heard anywhere else. I'd love if you just come back on every, you know, few months, every year, whenever you want.

Tony Xu (108:58)

Sure. It was fun.

David (108:59)

Thanks for taking the time.

Tony Xu (109:00)

Thanks, David.

Sponsor/Host Voice (109:01)

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.