The Exchanges

Q People thought the model was gonna take over, right?

A Yeah, look, the model sounds really, um, um, um, reasonable, and I think logical on face value, where if you can, you know, on that spectrum of restaurants where you have, you know, high-end service on one end and hospitality only to perhaps delivery only, or more of a manufacturing concept on the other end, It seems reasonable that you can actually just borrow a small square footage of space, um, not incur a lot of, not just the fixed costs, but also the labor costs of actually running your restaurant, in quotes, and then selling through a delivery, you know, platform, or acquiring your own customers, or, or doing something like both. It just turns out it's extraordinarily difficult, however, unless you're a large brand, or, you know, um, you know, a, a house of brands, someone like DoorDash, To be able to attract enough customers to make that math work.

AI assessment note: “It just turns out it's extraordinarily difficult, however, unless you're a large brand”

Q Do you think you guys were more focused on retention than others?

A I don't know if we're more focused. I mean, I could tell you though, you know, one of the things that was happening, especially when you see a competitive fight, is you see everybody race towards it, right? Everybody is going to try to make offers, you know, to customers, try to give discounts, try to give coupons, you know, free this, free that. One of the things that we had looking backwards is we actually did not have a large budget. In fact, you know, between 2016, 1718, we barely were able to raise a dollar, you know, relative to our peers. As a result of that, Um, that made it a constraint. One of the constraints is, okay, you can grow, um, but you cannot spend in order to do it. So in order to do that, you effectively have to actually come up with ideas in the product to actually stand out and make a difference and have organic growth, um, you know, carry you. And then once we were able to, you know, demonstrate to ourselves first, um, that we had a product with higher retention, you know, than other people and higher frequency, And then we were able to raise capital. Then we actually made the decision to pedal to the metal and actually go and acquire customers because we had an unfair advantage.

AI assessment note: “I don't know if we're more focused... we had a product with higher retention”

Q Hmm. So there's a next best use kind of math. Which restaurants have actually invented something that's hard to copy?

A Well, I actually think any restaurant that's been around for, let's say, two plus decades probably has very interesting IP. And obviously, you know, some of this information is not public. So, you know, um, but, but you can imagine, you know, when you go into At McDonald's around the world. That french fry, you know, perhaps they don't sell the same exact items in every single store in every country, every city, but the french fry almost always tastes the same. That is an extraordinarily difficult feat to accomplish. There's a lot that goes behind the scenes, just like there's a lot that goes behind the scenes at DoorDash in terms of getting you, you know, one order on time to make that sentence true. And The same can be said about a lot of other businesses that have been around for very long periods of time. That's on the, you know, big brand, you know, QSR side where a lot of the innovation, if you will, is in the process innovation, and then also how do you run, um, large groups of people And have very high and consistent standards of service. Extremely difficult, extremely difficult, and that's the IP, I would argue, for a lot of these large brands. Now on the other side, there are small restaurants, you know, some of whom have been around for almost a century, actually. There aren't that many of them, but, but when you look at what makes them special, it tends to be the sa…

AI assessment note: “when you go into At McDonald's around the world. That french fry”

Q So are like Phoenix and Austin, these places doing a good job of permits?

A Yeah, like if you look at, um, you know, um, places in Arizona, like the, the Tri-City area, Phoenix being one of them, um, you know, Scottsdale and the Tempe area there. You look at, um, the Tri-City area in, in, in, um, in Texas, um, near Austin, or you look at, you know, what's happening in Dallas and the, the Dallas Plano area. There's these pockets in, in the, actually, in fact, if you look at the country, You know, a lot of this, um, growth is happening in the south of the country, and that's been true for a couple of decades now, and they tend to be correlated, meaning if it's easy for me to build apartment units and to build just construction in general, it, it, it tends to be a bit easier to also get the licenses to open up a restaurant.

AI assessment note: “Yeah, like if you look at, um, you know, um, places in Arizona”

Q open DoorDash, it's kind of the same categories and things like that. And it's less personalized to me than if I took my DoorDash history and put it into, um, put it into an LLM. Isn't that, like, shouldn't we be somehow using the fact that LLMs are pretty good recommenders? Within products. It's not just DoorDash. It's every product I use. It feels like those recommender capabilities are underutilized.

A No, I think you're, I think you're definitely right that there's an opportunity here where there's almost like the traditional school of thought, which is to use the information that you have, right? And, and you build, um, the best personalized, um, models that you can. And, you know, one of the things that LLMs obviously does is it kind of throws efficiency out of the wall, right? Right. From the wall. And it kind of throws as much compute towards it And interestingly enough, one of the things that spits out, you know, when you put in enough tokens and have big enough context windows is you're right. It has actually better models because it's, I mean, it's just using much larger, you know, data sets.

AI assessment note: “I think you're definitely right that there's an opportunity here”

Q So it's just literally suburbs is like a great place first?

A Yeah, and then I think certain city centers, you know, depending on the actual area though, right? And so, there's, um, there's just a lot of, uh, those are the two types that we're thinking about, you know, distance, And the types of, you know, one of the, um, coolest things that we're actually building in DOT that's actually, you know, not physical is What we're calling our autonomous, you know, development platform, which is basically, um, uh, you know, the algorithm that talks about which orders goes to which type of vehicles, right? So DOT, for example, obviously, um, can also, uh, travel mixed routes with, you know, humans, right? For example, if it's a longer distance delivery, but, um, but there is a part of that journey where Where DOT is more advantage, say parking.

AI assessment note: “Yeah, and then I think certain city centers, you know, depending on the actual”