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- [00:00]
A
There are battles for attention, you know, bits and, you know, battles for what's happening in the physical world, you know, for the atoms and that we largely occupy ourselves in the second category. Right. We are in the war for atoms. Because, you know, one day, ultimately, like, what's the point of having a personal assistant if it can't actually do real things?
- [00:25]
B
Tony, thanks so much for doing this. One of the things I wanted to start with was this concept that, if you'll indulge me on it, because it's something I've been thinking about a bunch kind of around the idea of AI spend at companies. And I think the trigger sort of anecdote that I saw that I'd like to get your take on was Uber was basically saying in the first few months of the year, they blew through their whole AI budget. They were just rampantly consuming tokens. And what did they get out of it? Did they get more rides? Did they get more drivers? Did anything, you know, their margins improve? Like, what happened? And I think they were kind of saying, like, it wasn't obvious and you're obviously, you know, the leader of a company that's super metrics oriented and has executed super well. So I, I assume you must think about this, but I'm just curious, kind of at a high level how you think about, like what, you know, what is a large rising line item and you know, as, as it continues to rise. What do you, what do you care about?
- [01:19]
A
Yeah, I, I, I, I think every company right now is trying to figure that out. Where the ultimate goal of any technology is actually to hopefully solve a problem and actually make things cheaper. I mean, that's the goal of technology. But as you know, especially when you have constrained resources like compute in the case of these large LLM companies, sometimes you don't get the timing of these things always exactly when you can deliver the outcomes, or you don't get the cost profiles or the efficiency profiles to serve the technology to match exactly when you can deliver the customer outcomes. And I think that's the world in which we live today. So I think every company is trying to figure this out in terms of how we're thinking about it. First and foremost is like, what customer jobs can we actually solve? And so I do think when you have new technologies, there's always going to be this period of inefficiency and almost like discovery where you got this new toy, you don't know exactly what you need it for. You probably kind of know that you don't really need a frontier model. To know what the weather is, perhaps. But on the flip side, you don't also know the limits or the ceiling of what the technology can do, and you kind of want to know that. And so the way at least I think about how you do this somewhat efficiently, even though by definition you're going to accept inefficiency in the discovery process or in the invention process, you want to do it in the most contained set of ways that deliver customer outcomes. So you actually want to put customer outcomes and start there and then actually give teams as many shots on goal towards those outcomes as possible. Doesn't mean you get there, but at least it's directed and it's intentional and it's not entirely just yolo, there's obviously some of that. But I think if it can be directed towards, in our case, consumers, merchants, dashers, we've actually found some success.
- [03:25]
B
Well, it's funny because as a software company, which is where so much of the AI productivity is happening right now, it's actually harder in some ways for them to measure the end outcome of the work that their engineers have done than maybe yours, where you can say, hey, I can measure. Did we complete more deliveries, did we get more restaurants, do we have new users or all the things that you're tracking? I was sort of like, I was reflecting that over the last few years, you've kind of like watched this egg move through the snake in the pipeline. From first there's the build out and there's chips and then you get data centers and then it's like, oh man, are people ever going to use this? And now they are using it and the token spend is ramping. But now it's like we've got to turn the token spend into like burritos at people's homes. And like, you're in like as good of a position to see that as anybody. So like, are you, are you like, you know, we're gonna have certain teams try to move the metrics in a market with AI, or is it kind of just like bottoms up, let the teams explore whatever they want. With AI, yeah, I think it's.
- [04:23]
A
Yeah, it depends on the team. So if you're product teams, you're exactly right. You can direct teams, I think, towards metrics that matter to customers. If you're merchants on doordash today, you know, they're onboarding 35 to 50% faster because we can actually have AI produce all of their catalog or their menu. In the case of a restaurant, help edit your photos, help set up what might be the best way to describe yourself, especially if you're a new retailer coming online into a city, there's metrics like that that are very trackable, measurable, have you immediate positive benefit to end customers. Similarly, with Dashers, we've had AI help detect both fraud as well as safety incidences before they occur. And you can stop those and take preventative action if you knew that was actually happening. So there's things like that where you can get immediate benefit. So immediate customer benefit. But you said something earlier in your question. What about software teams, for example? I think one of the things you notice there is that only certain parts of a software engineer's day is writing code. It's great that we have models today to write the code for us, but that helps maybe the, whatever, 25, 30, 50% of our time that's actually shipping code. But what about everything else, you know, that has dependencies in product reviews, design meetings, you know, alignment with business teams, etc. Etc. Etc. That also has to change. If that doesn't change and come together, you're not going to be able to just have perhaps the productivity gain that you hope to have. Yeah, and I think that's what, you know, companies like ourselves, but I'm sure a bunch of companies across the industry are trying to figure out and getting those workflows right so that you're not just AI native from code development, but you're AI native in how you actually operate.
- [06:30]
B
Have you done anything to like track different engineering and product teams inside the company, comparing, like how much more productive they become with AI usage, like ones that are using it more and less heavily and things like that?
- [06:41]
A
Yeah, I mean, you get all these facts and I'm sure, you know, as with all things, there's always a distribution of outcomes. You know, maybe the average is 50% more productive, but you know, you have engineers who might be 35 times more productive.
- [06:56]
B
It's crazy, right?
- [06:57]
A
And it is crazy, but. And it's always fun to study that distribution. But, but again, like, I think that, you know, what's the value of that? The value of that is really knowing what the art of the possible is, especially in understanding your workflows. But what, you know, someone like I have to do is I have to think about, well, how do you create that working environment for everyone? You know, because it's, it's great that we have, you know, engineers who are 50 times more productive.
- [07:22]
B
Yes.
- [07:22]
A
How do you actually get everyone?
- [07:23]
B
Right. But is that an observation to that new ceiling? Exactly. And the question is, like Is that an observation and somebody's going to be far out or is there a learning there that you can go replicate it?
- [07:32]
A
Exactly, and I believe it's the latter because with all things, especially if they're newer, you're always going to get a distribution of outcomes. Right. There's always, you know, in sports, in academics, like there's always a distribution of outcomes, but that doesn't mean there aren't classes and teams and, you know, good things that you can teach as a coach or as an instructor so that you can get the rest of the class, you know, to move to the higher plane.
- [07:59]
B
Yeah. You know, for a lot of companies when AI came around, it was either like the most like, you know, kind of petrifying new technology ever or it was like the oh my God, thank you tailwind. And I feel like for doordash it was probably like neither. And you know, tell me if you object to this, but like, I would say like, you know, you're mostly operating in the world of atoms, not bits. You know, you've got this two sided, three, you know, three part marketplace with, you know, restaurants and dashers and consumers and all these things. And you know, you're very related to cities and all of that stuff. And so I, I would think it's both insulated from AI in a good way. And then you also maybe didn't, you know, it's not like your business started growing 400% faster because this AI thing showed up and it wasn't there yesterday. So like, how has that been? Is that like, is that right? Is that like about what it's been for you? Has it been kind of just insulated and that's both good and bad?
- [08:51]
A
Yeah, I mean, we're not selling, you know, burritos or Nike shoes or groceries as fast as tokens, you know, at some other companies. But yeah, look, I mean, I think you're largely correct. I remember, I don't know, I think this was 2021. This is, yeah, this is 21. Before the arrival of ChatGPT, we were playing around with some of these models. I mean, I think it was like, it was like GPT 2 point X something like that. Right. And just kind of getting a sense of what the world could look like. And obviously, you know, the models back then are very, very different from the models of today. But even then we kind of had this description of the world where there are, you know, battles for attention, you know, bits and you know, battles for what's happening in the physical world, you know, for the Atoms and that we largely occupy ourselves in the second category. Right. We are in the war for atoms and moving things around. And if we can do that and we can build a catalog, for example, for where every item exists inside of a city or every parking spot exists or all of this information, we can, as the kind of company's battle for attention, start maturing that we can really partner and work together in very productive ways. Because, you know, one day, ultimately, like, what's the point of having a personal assistant if it can't actually do real things?
- [10:23]
B
Totally.
- [10:23]
A
Right, exactly right. And so. And that's kind of how we thought about it four or five years ago.
- [10:28]
B
It's actually funny on that point. Like, you know, it strikes me that, like, one of the. It's very cool, but, you know, one of the things that I think there's room for improvement is that so much of tech is just like typing on our screens and we're all living in the computer. But at some point, to make lives better, you need to, you know, move stuff around. You need education, you need stuff that happens, you know, in a hospital.
- [10:49]
A
Healthcare. Exactly.
- [10:50]
B
Yeah. You know, and so it's. You need. You need, you know, real estate to be developed. It's like all this kind of stuff that's. But it's all physical at the end.
- [10:56]
A
Yes.
- [10:57]
B
And so in some ways, it's like all the software ultimately does need to be in service of stuff.
- [11:01]
A
Yes.
- [11:01]
B
Yeah.
- [11:02]
A
Yes. I mean, and this is. And this is exactly why, you know, I guess it would have been five years ago at this point, kind of the strategy was very much, no, play the game that we're meant to play. The game we're meant to play is the game of atoms. And, you know, be best in class at that. And not only is that good for our business, but I also think it's great for our relationship over time with these companies as they finally build out some of their assistance and build out super intelligence and build out agents that can actually do things versus what they currently do, because we naturally will need one another in order to be useful to the communities that we serve.
- [11:43]
B
Has there been any impact on recruiting or things like that when everybody's, like, losing their mind on AI and you're in this different thing that you're like, I know this is really important and it's going to become even more important eventually, but, like, have you had to work through any of that?
- [11:58]
A
Yeah, I mean, it's definitely a battle. We are an applied AI company and we have been, you know, pretty much since Day one of the company. Whether you want to call it math or machine learning or now LLMs or you know, we can use whatever terminology we want, but as the years have gone by, we are in a platform applied technology company that also solves real world problems. So yes, it's, it's certainly difficult when you're competing in places, you know, where activity is really hot, you know, in machine learning, in folks who are looking to do applied research. It's very competitive right now.
- [12:31]
B
Yeah. Are there things like, as you think about, you know, like your next frontier and where you want to go and you look at the AI stuff, it does seem like some of it is pretty direct. Like you know, for example, I could imagine like robotics maybe could matter at some point. I could see self driving matters at some point. Like what are the like next parts of AI outside of like this, like the LLM on the software side that you care about?
- [12:54]
A
Yeah, well, I mean I would say first, you know, before we step away from, you know, some of the LLMs we do care about, you know, what LLMs can do. And I think they have gotten materially smarter and more powerful, not just, you know, helping with coding, which may be the most prevalent use case today and certainly in the most prevalent spend consumption.
- [13:15]
B
Well, it's definitely a good input to
- [13:17]
A
you for building stuff. 100%. 100%. But even, you know, for instance, you know, one of the things that, you know, we always ask ourselves is like, how can a technology actually improve outcomes for customers? So if you take as an example the Doordash app, you know, we've grown a tremendous amount over the last even five years. Forget like, you know, the 13 years we've been doing this. Just in the last five years, kind of the same arc in which, you know, some of these LLMs have grown up. You know, DoorDash has moved from one product category, restaurants, one market us, into, I mean, virtually every retail category. The leader now in, you know, deliveries of grocery convenience items, alcohol items across 40 countries. There's lots of positives with that. One of the challenges for a consumer though is boy, it's a lot harder to use an app like DoorDash because you now have restaurant things in there, you now have grocery items in there, you have retail items in there, you now have the ability to make reservations or get deals inside of restaurants. It's getting more complicated to actually use our product. LLMs really can help with that. And you know, just as I think there will be, you know, personal agents soon, the DoorDash app should be a personal agent. It should be a personal agent to help you do anything inside of your city. Right. I can't think of any other product and any other utility, frankly, greater than the number of connections that you can have between you and the businesses inside of your city. And so those are the kinds of things that you'll see us launch just with LLMs. Right. You should be able to have an easier time actually doing research on what items you want to buy or what might be great recommendations for you based on all the history that we have across tens of billions of orders on you.
- [15:08]
B
And you should pretty much be able to get anything to your house and shop for it.
- [15:11]
A
Yeah. Or have the choice of getting it later when you're home, or because you may be away or the next day because it's better for you. You're exactly right. And so I do think that there's still a lot of excitement just in the LLMs, but. But you're right. If you want to go beyond that, there is a lot of work around the physical space. So, for example, we've been working on autonomous vehicles since 2019, and a lot of that started with kind of traditional systems, kind of like with machine learning, yet traditional ways of thinking about how to use those techniques to build things like recommendation systems. LLMs kind of put a complete new spin on it and didn't really require any of those techniques. Something similar is happening in the physical world where it used to be. If you wanted to perhaps drive autonomously inside of a market, a lot of what you would do is actually build mapping systems and almost like heuristics and rules to create an engine in which you can make good decisions and kind of weigh them in real time. Or perhaps you can put all of this into a neural net and take similar techniques that some of the LLM companies are using and actually make even faster and better decisions. There are things like that that are going on that allow us to do some of the work that we're doing with autonomous delivery faster as an example.
- [16:44]
B
Yeah, totally. This is now going way back in time, but are you surprised how much people are willing to consume on apps? You know, obviously, when you started the company in 2013, this was the plan, but, like, has it gone, like, a lot further than you expected? You know, like, people really will just, like, order, you know, a burrito for, you know, a lot of money, you know, and probably, like. And it's worth it. It, like, turned out that it's worth it to people. But, like, was that obvious to you in 2013, that it would go this way.
- [17:14]
A
No, it was not obvious.
- [17:15]
B
People pay like 35 bucks for a burrito.
- [17:17]
A
Wait, for example, when we launched our first partnership with a national brand, that was July of 2015, I believe, with Taco Bell, I was actually quite skeptical whether or not it would work. You know, on the one hand, you know, you have this legendary brand that's never offered, delivered before being offered for the first time. That was the bull case version. On the flip side, you know, you're right, you're going to pay a premium, you know, to get that order.
- [17:49]
B
Yeah.
- [17:50]
A
And which wouldn't have been intuitive because
- [17:53]
B
you said, well, I can get the, you know, I can, I can get this order for $4 in store.
- [17:56]
A
Yeah, yeah, exactly. And so that was not obvious to me in 2015 of what would happen, the bull or the bear, you know, version of that. But clearly people love getting Taco Bell delivered, of course.
- [18:07]
B
And, well, it's also, I mean, it now makes sense to me in a weird way because it's like, you know, you save a lot of time going back and forth. You can use that time to do all this other stuff. You can use that time to work, like, you know, and I think more people are familiar with those kind of calculations now and all that stuff.
- [18:20]
A
Yeah, I think it's that. I think, I think there's a lot of things, Jack. I mean, like, one of the things that I remember even in 2013, looking at as just this marvelous, like, fact that only goes in one direction. There's a few of them. You know, one of them is that if you looked at, if you looked at just food consumption, you know, in the 1950s, when the US government used to measure this or when they first started measuring this, something like 70 to 80 cents on the dollar was spent on grocery. Okay. This is like in the 1950s, if you looked at 2013, when DoorDash was founded, that number was getting closer to $0.55 towards groceries, you know, $0.45 towards restaurants. Today, it's closer to $0.55 towards restaurants, $0.45 towards groceries. And so over a 75, 76 year arc, yes, there's ups and downs, but if you look at the trend line, it kind of goes in one direction, which is in the direction of, you know, food prepared by somebody else.
- [19:24]
B
Do you know, by chance if, like the relative cost of a steak you made through your own groceries versus a steak prepared by somebody else, if that ratio of cost has changed, like, has one gotten more expensive relative to the other?
- [19:36]
A
It's a It's a great question, but I think it depends a lot on how you value your time. So this gets me to another fact that I think is pretty interesting, which is if you looked at the percentage of dual income households, same time period, 1950 to 2020, so this, you know, this goes way beyond AI or Covid or any of this sort of stuff. You find that the percentage of household has gone from like a quarter, you know, dual income to almost like 70%.
- [20:02]
B
Right.
- [20:02]
A
You know, present day, if you look at the number of restaurants as another example, just, just total number of restaurants, total count. Okay. In the 60 to 70 years in which this is measured, there's maybe two years in which the total number of restaurants in the current year doesn't exceed the previous year. So even if, you know, new restaurants, you know, which is a very tough business, as you know, I mean, having worked as a dishwasher at my mom's place, I definitely know how hard it is. Yes, there's a risk of going out of business, but there's always more than enough that replenishes the bucket every successive year. I think when you put together some of these facts over like 70 plus years or something, they kind of spell out that people, whether it's through dollars or time, are expressing the fact with their activity, not just their words, but their activity that they must value if somebody else made them the stake.
- [20:56]
B
It's interesting because like, you know, the stat about, you know, dual income households, it's like that, that's true. But also like the, you know, the cost of a home has gone up crazily and you talk to, I think you talk to a lot of like young people today versus young people in the 50s.
- [21:11]
A
Sure.
- [21:11]
B
I think people probably feel like it's harder to get like, yes, the home and the car and everything today than they used to and all of that.
- [21:17]
A
Yes.
- [21:17]
B
So it's a little counterintuitive that people's willingness to spend on food has done when it's done.
- [21:22]
A
Well, I think a couple things. So, you know, the first thing I would say is, you know, food happens 20 to 25 times a week. It's not like buying a house.
- [21:31]
B
Yeah, that's right.
- [21:31]
A
So that's, that's the first point I make. So. So even if you're the most avid cook. Right. That you love making, you know, food.
- [21:40]
B
What's 20 minutes, very difficult.
- [21:42]
A
It's very, very difficult to do it 20 to 25 times.
- [21:44]
B
Of course, that's the first.
- [21:45]
A
But I think the second point, and I think you Make a very good point on the affordability challenge that we have as a country and probably globally, actually, not just here in the United States, is that people always want to find a way to feel good. Okay. And, and, and, and I think, you know, purchases, especially on food, I know, you know, you know, they don't just become like an indulgence anymore. They become like, you know, the ability to actually, you know, feel good.
- [22:11]
B
It feels great to hit a button and a pizza shows up.
- [22:13]
A
It's, it's great to feel good more than once and also do something that, you know, you need to do.
- [22:20]
B
Yep.
- [22:20]
A
Food consumption.
- [22:21]
B
That's right. How much do you think about, like, consumer trends in general outside of food? Like, do you need to just be myopically focused on food or is it worth your time and headspace to care how people are spending their energy on social media or what's going on with Kalshi and polymarket or other consumer like, does that matter to you?
- [22:40]
A
To understand as a consumer business with hundreds of millions of customers now, absolutely. You have to think way beyond food and as a company that frankly doesn't just do food anymore, increasingly our herbs are coming outside of food. We have to pay attention.
- [22:54]
B
So what are like some of the other consumer trends that are like, not about food but that are important and interesting to you?
- [23:01]
A
Well, I think you named one of them, which is this affordability piece, which isn't just about food. People are looking for affiliate affordability across every segment.
- [23:10]
B
Yes.
- [23:11]
A
Housing, transportation, eating, groceries, health care, education. I mean, we can keep going banking every category. I would say affordability is a huge deal, huge premium. And so a lot of, you know, what we're thinking about is how do you continuously do two things. One, continuously bring down costs and two, how do you bring more value? And, and I think those are very hard to do things. We again, mainly try to stay focused in the world of atoms though, because one of the things that I think people on the software side perhaps don't appreciate is all this information that you can get in software kind of sometimes gives you this perception that you can structure information pretty easily and maybe control information and experiences pretty easily end to end. That is the complete opposite in the game of atoms, where everything is an edge case. Every day there's this thing called traffic and weather. And every day, by definition it's not perfectly predictable and we can argue it's range bounded or not. But look, if there happens to be a traffic jam and, you know, something takes 20 minutes longer, that's a real problem for that one customer. A lot of what we're doing is actually just staying as expert and proficient as we can in that game.
- [24:39]
B
What's cool about it is by being so good at logistics and costs and all and coordination and all of that, it's obviously going to apply to stuff outside of food. But even, even just within food, it seems like, you know, to bring more value to the customer. I mean, the obvious way is you could just keep chipping away at the cost, which I assume over time you ought to be able to get to an extreme, extremely low place, I would
- [25:00]
A
think with food, but also with inventory of other types of products. Right. For instance, you know, one of the challenges in, you know, grocery or retail is, well, there's actually separate challenges in the, in the case of grocery, most grocers don't know what items are on shelves. And it's not because of bad technology or outdated systems or a lot of different systems. There are those challenges, but it's honestly also structurally because consumers who go inside the store move things around or CPG companies, you know, want, you know, certain items to be promoted or not promoted. And those things change quite often. And as a result of that, you know, that becomes really messy. Right. So how do you get great at that? You know why? Because if you don't get great at that and you make mistakes or you have to make a bunch of substitutions, that's extra costs. Back to your point around affordability, that's, that's cost, even though it's not just about the price of the items, but it's about everything surrounding it to support fulfillment. In the case of retail, if you didn't know that, you know, the pair of shoes that you wanted might be a half size off, you know, because it doesn't fit great, that's extra cost, that's going to get returned somehow or refunded, you know, and those are all of the challenges that we try to obsess about.
- [26:15]
B
What are the like, tempting adjacent things that kind of make sense for you to do that you've like said no to in the name of focus? Like, you know, as an example, as I was just listening to that, like, you know, do businesses ever want to use you to like, you know, work with their own suppliers or things like that? And then if you said, you know what, that's just too far afield from what we do. Like, are there, are there close by things that you're constantly saying that's a good idea, but it's not a great idea. We're just not Going to do that?
- [26:39]
A
Yeah, it's a good question. I mean, a lot of the separation between good and great internally right now is around sequencing, you know, and so, you know, for instance, I had no idea back to one of your earlier questions about, well, how big could food be? It's very hard as an entrepreneur, you know, when you're working out of your apartment to know, you know, what that size could be one day.
- [27:04]
B
Turns out a lot of people eat food.
- [27:05]
A
Turns out a lot of people eat food. It also turns out it's a lot harder than we thought. And we worked on it for seven years before we moved to category number two, which was groceries.
- [27:14]
B
Funny, it's like I could see getting started like, oh, this could be a $5 billion company. And now you're like, this could be a $500 billion.
- [27:20]
A
Actually, in our Y Combinator application, there's a question, I don't know if they ask it anymore, which is, how much revenue do you think this company can make one day? And I remember we were just operating in Palo Alto at the time, and so I counted up how many Palo Alto's there could be and how many orders we could do in each one of those types of cities. And I estimate something like $100 million of revenue or something like that. Thankfully, we're a few orders of magnitude off. But. But that's true. That's. But to your point.
- [27:48]
B
No, but it's surprising.
- [27:49]
A
It's very surprising. And so I think a lot of times it's you. You kind of, as an entrepreneur have to take the greedy algorithm. Right. You have to keep going all the
- [27:59]
B
way if you want a winning swing.
- [28:01]
A
Yeah.
- [28:01]
B
Why would you get off?
- [28:02]
A
And that's kind of how we think about it. When you ask, you know, about like, oh, are there other things we could do? Yeah. The question we ask is, right, yes.
- [28:09]
B
So then your job is basically just
- [28:10]
A
say, and what's the opportunity cost? What's. What's the opportunity cost relative to the current thing? But, you know, I mean, but. But Doordash is a company that has built a lot of different things. Now, you know, we have obviously a business in restaurant delivery. We have a business outside of restaurant delivery, a business outside of the US a business in advertising, a B2B business where we basically take everything we've built for ourselves and we give it to merchants and so they can stand up their own first party channels. Right. And so that came from somewhere, you know, but a lot of it is just constantly weighing, you know, is it the right time now versus Is it, you know, a bad idea or a good idea?
- [28:51]
B
How hard was the advertising business? I feel like those people.
- [28:53]
A
Hard.
- [28:54]
B
Yeah.
- [28:55]
A
Well, I think just building ads, not that hard. Building best in class returns for advertisers, while you also have best in class returns for the consumer experience. Hard because fundamentally there is going to be a conflict where if you have an advertisement show up in a product that may not be relevant to a consumer, you have a challenge where you're taking some sort of a trade off. Yeah, we can say that, oh, it's worth it here and there. But you know, before you know it, you know, after a few years or a decade, it's a slight tax. It's very, very difficult to kind of unwind all of that, especially given some of the attractive profile of the economics behind advertising. And so I think, well, one of the things I'm really proud of, you know, I think the team gets a ton of credit for being the fastest company in history to hit a billion dollars in ad revenue. But I'm more proud of how they did it. How they did it constantly. Constantly. I mean, you know, fighting the restraint effectively or living with the constraint that we must achieve both objectives. Best in class returns for advertisers as well as consumers.
- [30:03]
B
It's like, I think probably the Facebook ads team, the early ads team, must be one of the greatest. It seems like in some ways it actually seems to me, and obviously I know you're on the board there at Meta, but I feel like in some ways it seems like those cultures are your culture and the meta culture probably have a lot in common from, at least from the outside in terms of just like, you know, extremely metrics driven, a lot of testing and trying, things like very focused on like, you know, results and stuff like that. Is that, is that, is that like accurate? Is that like what, like, was the ads team like an even more distilled version of all of those things?
- [30:36]
A
I mean, I think you're right in that, in saying that, you know, the ads teams at some of the largest tech companies in the world are some of the, you know, candidly most impressive teams because, you know, it's carried them so far. You know, I think we forget that some of these companies and products that you're we're talking about here are more than two decades old now at this point. And not only do they have the reach of billions of users and things like that, if you looked at some of the, you know, latest results from these companies, it's incredible, the business growth.
- [31:09]
B
I mean, also that They've seen you talk to people from, you know, Google or Meta Ads. I mean, it's brilliant. People, like, it's very, very hard.
- [31:14]
A
Yeah. And the constant working that problem. Right. And you're right, that does share, you know, in some ways with the doordash side, where maybe we work in a different space. It's not fully in our control. It's not all about, you know, digits and attention. It's more about the physical world. And we have a lot more constraints where we have to kind of take what the defense gives us, so to speak, where you can kind of are taking what's happening in the physical world and then you have to react very quickly to it because we don't get to control sources of demand or supply, really. But it's similar in that you have to be very objective and unemotional about what is best for customers while living within constraints and then getting 1% better every single day and not taking for granted that you can't. Because I think sometimes it's easy to say, especially intellectually speaking, that like, oh, we've solved the problem. Finished. You know, like, delivery is finished. And I think if we ever thought like that, I don't think doordash can continue growing. And I think that we've continued to. Our teams have continued to just do better by increasing our selection, making fees more affordable, increasing the quality and reliability of our network and delivery, improving our customer support constantly, every single day, 1% better.
- [32:39]
B
Yeah.
- [32:40]
A
And that's that. That, that leads maybe not in one sitting, but over an accumulated period of time. A lot of benefit and surplus.
- [32:48]
B
Yeah. I mean, when you started the company, obviously you were not the only or first person doing this. And there's probably, you know, a lot of things that people could say about, you know, what led to all the success. And there were good decisions, you know, just starting in the suburbs, I think was like a big one. And probably the way that you went about acquiring restaurants and all these different things. But in some sense also, I think it's probably like a fair kind of summary of it all. And what you guys are known for is just like, it's like really great execution and this is like the 1% better everyday thing. And this might be hard for you to answer as somebody because it's just the way that you are, but I'm curious if you can sort of speak at all to what it's like to run a company with execution as like, you know, a core excellence. You know, like, I'm thinking of Amazon, for example, as A company that's had to do this when the margins are, when the margins are thin, like there's no choice. There are other companies, by the way, where like the sort of, you know, the zone of genius has to be something completely different. They don't need to be great at execution actually. They can just have periodic brilliant insights that are just so unbelievably step changing that you can actually afford to be sloppy. And the types of people who are gonna have those insights might be types of people who are gonna more likely be less attuned to the details anyway. But I'm just curious if you can speak at all to what your experience is like building a company with sort of these values.
- [34:11]
A
Yeah, yeah, that is a hard question. I mean, I would say it starts first and foremost with, with a love and appreciation for how math and humanity come together. And what I mean by that is when I think about the doordash business, yes, you're right. There are a lot of metrics, there are a lot of constraints, low margins. And therefore you have to be very good at measuring a lot of things. That's the math part. That's the how do you take a multivariate problem and make the best set of trade offs and calculus? But underneath it though is the recognition that on every single order we do, we have at least three humans involved. least, we have at least a dasher, a merchant and a consumer who all participate to make something big productive happen inside that city. And you kind of have to like both. It's not good enough to just be very robotic. And all we're going to look at is the numbers. And if the numbers are good, we're good. And, but, and if it has a negative consequence on somebody, then so be it. That's not good enough in my book. You know, my book is you have to recognize that if you look at the merchants, right? When I think about my mom who worked inside of a restaurant, this is life. This is not a job. This is not a 9 to 5 or oh, what are you going to do from this career to the next. Career? No, this is every single day, my livelihood, every single day. It's my identity, it's certainly my professional income, but it's everything in the household. You look at couriers, we have tens of millions of couriers who've delivered with us and we are a stepping stone for most of them. The vast majority of them are, you know, doing only a few hours a week. And that's because they're, they are trying to strive towards you know, becoming a doctor, a nurse, a realtor, a teacher, et cetera, et cetera, et cetera. And, and then obviously we talked about, you know, the benefits to consumers. And so you have to have that appreciation and love. If you don't have that for either the people or the math, I think it's a very difficult game to sustain because it's just not choosing the right game for you.
- [36:40]
B
It seems like it could be rare to have both of those in one person, but you've obviously got a company full of, I presume, people who you believe have both of those things. So how do you figure out if somebody is not only one or the other, but somehow both of those things where they appreciate the humanity and all these complexities while also just being a maniacal sort of stone cold operator when they need to be too?
- [37:07]
A
Yeah, look, I think the tests towards someone's skills or their problem solving or their metric orientation is a lot more straightforward to assess than say someone's values, I would say, or. And there a lot of it is actually hearing about the things that motivate them as well as the things that demotivate them. And it's okay, by the way, there's no judgment here. It's really around self selection. Some people, I think find it awesome. The types of businesses that want to become restaurateurs, retailers, grocers, some find it messy and not for them. That's okay. That's really okay. And so again, a lot of this is about self selection. It's about people who are going to do the right thing, even if maybe the numbers belie that behavior. People who have seen adversity, people who believe in the fact that if we can be successful, then all these awesome creators and passion projects inside cities will actually continue to be successful and actually, you know, want that to be successful. I think if people can self select into that, that's really how you can tell. There isn't like this perfect test, but it's really around self selecting into it.
- [38:35]
B
It's cool. I'm curious, like for you, when you think about like growing your business, what are like the biggest levers? Is it, is it like city expansion still to some extent, Is it now about broadening out through more categories? Is it M and A? What are the things when you're like, I want my business to grow by X amount next year, here's how I'm going to ladder my way there at this stage. Obviously a very mature business. What goes into it?
- [39:02]
A
Well, I don't know if we're A very mature business relatively. We've certainly surpassed know the size of my apartment. But I would say that, you know, we're still even our largest, you know, business or restaurants business in the US is only single digit percentages of the restaurant category. But to answer your question, I think it's how can we either solve current customer problems better or how do we solve the next problem? And what is that next problem? And you know, I think a lot of times, you know, back to the comment you were making earlier about some of the advertising teams at some of these larger technology companies, I think you have to do two things. You have to keep building the core, which for us has always been food and just constantly work on that problem of improving selection, quality, price and service. And then you also have to create the new, which is actually a very different set of skills. It's a different management system, I different people sometimes certainly different incentive mechanisms. It has a lot more inefficiency before you have efficiency. And that's where we're searching for new problems to solve. And this is your point around. Well, how much do you focus versus just doing the core? It is both. I think when you look at companies that can continue to grow, they tend to do this, they tend to keep solving the problems that they've solved for customers continually better and they also find new problems to solve.
- [40:38]
B
How much do customers care about speed? Like if you could deliver everything in five minutes, would that, would that drastically, can you tell if that would drastically change demand or is that no longer a huge variable?
- [40:50]
A
No, I think, I think people, I mean this is like saying would you like something delivered slower? Right. People are always going to want something delivered faster, that's all. Now is it, is it X minutes for like. It depends on probably what the product is. Yeah, but, but, but, but, but the short answer I know is definitively is that customers always want something faster.
- [41:09]
B
Do you think drones will happen for this kind of delivery?
- [41:11]
A
Of course. I mean like drones, drones, autonomous vehicles, they will all happen, you know, but, but we have to remember something. The delivery part is just one part of the time of a delivery. Yeah, right. So while it may be possible to fly in the air and skip a bunch of traffic, it's not possible to skip a busy kitchen, especially if you're understaffed. And so, and that's the predominant part. The majority time spent on a delivery is the preparation, whether it's inventory inside of a retail shop or you know, cooking time inside of a busy kitchen.
- [41:44]
B
Yeah, and the drone can't like go through the retail store and check.
- [41:46]
A
Yeah, yeah, yeah. So, but my perspective. But again, I think you're calling out a very good point, which is when you think about products, I think when it comes to digital experiences, a lot of attention is around pixels and every detail from step to step to step. No different in the physical world. But a lot of the steps are just in the physical world. And it's the coordination and the orchestration of the end to end system of which just the fulfillment is one part right. Understanding. Exactly did you get the right inventory? Understanding the exact prep times, Understanding which vehicle to send, you know, whether it's autonomous vehicles or human drivers, understanding what is the right price point, understanding how do you solve, you know, substitutions, understanding how do you do refunds and credits. All of these things have to be orchestrated and I mean effectively hidden in terms of the complexity behind the surface. Surface to offer a very simple. Just get you exactly what you want.
- [42:45]
B
Yeah.
- [42:46]
A
To the customer.
- [42:46]
B
It's funny, like, as I'm thinking about, it's like both. You have both, like the. There's like the simplest kind of, you know, objective, like a company of this scale could possibly have, which is like get that item from that place to that home as quickly and cheaply as possible. Like, that's really simple. But then like the complexity from A to B is ridiculous.
- [43:04]
A
Yeah, yeah, that's. I mean, I mean. And that is the Doordash product problem set. Right. For better and for worse. I mean, there are. You know, I remember when we started Doordash, I remember decomposing that there's almost like 20 mini systems, many little processes, if you will. If you imagine a checklist of just bring you a burrito, there's 20 little things that you kind of have to get right because if anything goes wrong, you know, you would actually need to fix it. And those are. And there's no way, by the way that that, that, that you would know about this unless you actually did the deliveries yourself. Right? This is exactly why we still to this day, I mean, we've done it since day one, but to this day we still have everyone in the company, you know, dash, which is our way of saying doing deliveries. We still all do deliveries because until you actually get into the physical world, until you find yourself stuck in the wrong elevator or the wrong lobby to try to get upstairs to deliver something, until you get to the wrong alleyway for parking, until you get to the wrong place because you realize actually food gets made sometimes in three different stations inside of a restaurant until you actually Experience those things. It's very difficult to a priori just intellectualize that and know about it.
- [44:19]
B
Yeah. What are you most excited about for whatever's coming up with AI or anything else in the next year or two?
- [44:26]
A
Well, I think right now it's a period of rapid change for everyone. And so what is very exciting is we started this conversation talking about some of the inefficiencies that perhaps companies are experiencing when it comes to token consumption, token token maxing and experimentation. But there's a lot of fun in that too, of course. Yeah.
- [44:54]
B
And there'll be a lot of creative new discoveries that'll come.
- [44:57]
A
Yeah. And so what I'm excited about is all the things that haven't yet happened. Actually that's what I'm really excited about. Not just in products that will be great for customer outcomes and increasing the surplus, but actually ways of working. I am really excited because I think one of the things you always ask yourself as an entrepreneur is how do you continuously keep up the velocity and pace at a company similar to what you had when you started the company? And it's very hard as you know, you've done it yourself and you see a lot of startups today. And so I'm interested in answering both of those questions. It's like, how are we going to take this period of change to yes, build better products for customers, but also literally build better products for ourselves so that we can enjoy work to the maximum.
- [45:47]
B
Actually, now that you say this, I want to. My last question is kind of on the personal because it was, you know, I didn't do it for as long but you know, nine years of it and you know, it's difficult and you're I guess 13 years into this company and obviously it's, you know, was and is very intense but has your own, you know, when you think about how do we keep it as intense as the early days, like is that kind of the goal in your head or do you at some point, you know, you're now, you know, whatever many billion dollar public company do you at some point say, I actually now need to find the marathon pace that I can do this for the rest of my life or is it just the same intensity as day one for you?
- [46:21]
A
Well, it's, it's less about like the intensity in terms of like, you know, how many hours are you, you know, sprinting or I think it's the feeling of agency and productivity that actually that you always yearn for. It's not, I don't think any startup founder at least I personally know, is trying to optimize towards the number of hours worked per week or something like that. No, I think the reason why people go towards startups and why they want to have is because they want to have a lot of impact, and it's the feeling of the agency. And I think I want to make sure that everybody at the company continues to feel that agency to hopefully do more and more and more. Because you know what? Because. Because not only then will they do the best work of their careers at Doordash, but even if and when they leave to go pursue whatever the next endeavor is in life, personally or professionally, they're gonna have more confidence to do it.
- [47:20]
B
That's awesome. Well, Tony, this was a pleasure. Thanks a bunch for doing it with me.
- [47:23]
A
Yeah, thanks, Jack.