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Transcript / Stripe Cheeky Pint

Tony Xu 談 DoorDash、餐飲經濟與配送機器人

這頁整理的是 YouTube 影片的英文逐字稿,保留時間戳,方便搜尋、定位與回到原影片播放。訪談主題包括 DoorDash 早期勝出原因、中國外送市場、餐飲業成本結構、詐欺與實體世界資料、Dot 自動配送車,以及 stablecoin / AI commerce。

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0:00

Oh, wow. We got Dot coming.

0:02

It's like a TARDIS.

0:05

Tony Xu is the co-founder and CEO of on-demand delivery giant, DoorDash.

0:11

He's grown the business from a Stanford side project into America's dominant food

0:14

delivery platform, and is now pushing into everything

0:16

from grocery to retail to autonomous vehicles and financial

0:19

products for gig workers.

0:20

Great. Cheers.

0:20

Cheers. Good to see you.

0:23

Good to see you too, John.

0:25

As I think back to that era where you guys got started, what year

0:28

was DoorDash founded? 2013.

0:30

As I think back to 2013 and those early days,

0:34

the iPhone and the apps that were made possible by the iPhone was

0:40

the defining tech trend of the era.

0:43

Uber and Lyft in ride-sharing, Instacart, you guys, all of the magic wand apps

0:48

that made your iPhone useful for bringing things to you and manipulating

0:51

the wider world.

0:53

Loads of people were going after food delivery,

0:58

and way more than are around now.

1:00

As you look back on that journey from, again, it was really kicked off

1:05

by the iPhone, in my opinion.

1:07

Not that there wasn't food delivery before the iPhone, but that really massively

1:10

increased the market size.

1:12

As you think about from then to today, why did you guys win?

1:19

The short answer is we got more customers than other people.

1:22

Sure, but let's have the five whys here.

1:25

I think these are very tough things to study because many

1:29

things are happening in the same moment, but I'll just talk about it

1:33

from the perspective of things that I think we got right.

1:35

When you think about something like restaurant delivery,

1:41

you actually get judged on multiple dimensions as a service.

1:45

We get judged on what restaurants we bring you, certainly,

1:48

whether it showed up on time and the quality and condition you expect,

1:51

how much did it cost, if we screwed up, what did we do about it?

1:56

It's not one thing that you have to be good at, actually.

1:59

It's all of the above.

2:00

Unfortunately or fortunately, this is literally the game that we're

2:04

playing where customers are judging us in all of these dimensions.

2:07

I think getting that right better than anyone else as measured by whether or not

2:12

people are coming back to the app and using us, using us even when we had no

2:16

money to market to them or discount offers to them, things like that,

2:20

I think was the tell.

2:22

Are you saying it was a very complex, multivariate challenge even from early on,

2:28

and you guys just embraced that complexity and said, "Okay, this is going to be super

2:32

complex to get right," whereas maybe others took a bit more simplicity?

2:35

I think if I were to become overly reductionist and purely looked at it—

2:39

That's what I like to do.

2:42

From the product perspective, yes, because at the end of the day,

2:46

any consumer product is judged very simply by its retention and its usage.

2:52

That's how you know whether you have a differentiated product.

2:54

I think it's very easy to have differences in opinion about

2:57

whose app do you like more or whether or not certain apps look

3:01

similar or different.

3:02

At the end of the day, though, if our app is performing at a much higher

3:07

retention and frequency of use than others, that's how we know whether or not

3:11

the things that we say actually are making a difference to customers.

3:15

Getting all of that right very, very early and then building the systems

3:22

to actually instrument that as well as to repeat that over and again,

3:27

I think was very, very important in the development of the company.

3:30

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

3:34

I don't know if we were more focused.

3:35

I could tell you, though, one of the things that was happening,

3:38

especially when you see a competitive fight, is you see

3:41

everybody race towards it.

3:43

Everybody is going to try to make offers to customers, try to give discounts,

3:47

try to give coupons, free this, free that.

3:51

One of the things that we had looking backwards is we actually

3:54

did not have a large budget.

3:55

In fact, between 2016, '17, '18, we barely were able to raise

3:59

a dollar relative to our peers.

4:01

As a result of that, that made it a constraint.

4:05

One of the constraints is, okay, you can grow,

4:08

but you cannot spend in order to do it.

4:12

In order to do that, you effectively have to actually come up

4:15

with ideas in the product to actually stand out and make a difference

4:18

and have organic growth carry you.

4:21

Then once we were able to demonstrate to ourselves first that we had a product

4:26

with higher retention than other people and higher frequency,

4:30

and then we were able to raise capital, then we actually made the decision

4:34

to pedal to the metal and actually go and acquire customers because

4:37

we had an unfair advantage.

4:39

Were you more customer-obsessed than others?

4:41

I think it's very difficult to measure us against other people because

4:46

I didn't really work at those companies.

4:48

But again, I really just look at the early DoorDash crew and what were the things

4:54

that we did that were very useful.

4:56

We all did deliveries. We all did customer support.

4:59

We all made menus.

5:00

We all sold restaurants.

5:02

We all customer serviced restaurants.

5:04

We all worked inside restaurants.

5:06

I think at the end of the day, I think the term customer obsession can

5:12

be fairly generic and vague and applied.

5:15

It really just goes to show what actions actually demonstrate that?

5:20

For us, some of the ones that came to mind, and frankly,

5:23

the story that actually inspired the value in the first place was

5:26

when our backs were against the wall, and we had, I think,

5:29

less than two weeks of cash runway, and we had a terrible night where every

5:35

single order was late—this was a Stanford football game in 2013,

5:38

and I had trouble raising the seed round—we made the decision

5:43

to refund everyone.

5:45

That cost us over 40% of the bank account.

5:47

When you have 2 weeks of runway, 40% of the bank—

5:49

You issued a refund that was 40% of your funds' runway?

5:51

Yeah, maybe a little over that.

5:53

We baked everyone cookies and delivered them at 05:00 AM before everybody woke up.

5:59

Look, this was maybe 100-ish customers or something like this.

6:02

But I think it demonstrates that this was a real value.

6:07

It's actually my test, really, which is, what are the actions

6:10

that are naturally occurring?

6:11

What are the behaviors that are actually occurring inside of an organization?

6:17

Then how do you actually articulate it versus the inverse,

6:20

which is you start with maybe some articulation, and maybe some of the times

6:24

it matches and other times it doesn't.

6:27

I've always believed that these cultural norms or behaviors are really

6:31

80% of what you've done.

6:32

I think that's the best way to actually find what they are for your company.

6:36

We're the only platform today to take care of Dashers when there is these increasing

6:42

gas prices and making sure that we have their back and that we're actually trying

6:46

to help them save $1.40 to $1.90 per gallon for Dashers.

6:51

Or in COVID, we were the only company to cut commissions by 50% when we

6:55

were not yet profitable as a company.

6:57

That was an expense over $100 million.

7:00

We then, on top of that, spend millions of dollars buying national

7:04

TV campaigns to tell people to order, whether it's on us or our competitors,

7:08

because restaurants have, on average, seventeen days of cash on hand.

7:13

You need to order.

7:14

This is do or die for them.

7:18

I can give you many of those examples where I think at the end of the day,

7:22

whether the actions first speak, and then you can articulate the

7:27

actual words and norms and cultural behaviors that the company

7:31

is trying to speak.

7:32

That's ultimately, I think, how I think about customer obsession.

7:35

If I try to draw what these values are, it seems to be

7:42

a very clear-eyed focus on the best product experience

7:46

and taking care of Dashers.

7:48

I didn't realize you guys had a time when you had only two

7:51

weeks of cash in the bank account.

7:53

We had several moments where we ran out of money.

7:57

We had that moment, obviously, in the fall of 2013.

8:03

We had three very difficult years, 2016, '17, '18,

8:07

where we had two rounds of financing that were incredibly difficult for us,

8:10

the Series C and Series D.

8:12

COVID was interesting because COVID started actually…

8:17

Again, I think it's easy to forget these things because I think humans,

8:20

we adjust very quickly.

8:21

COVID, the first week, actually, the business tanked 80%.

8:26

It tanked massively because everything was shut down.

8:31

Then the next week, all of the dining rooms were closed,

8:35

but the kitchens were open.

8:37

Boom, we see this massive doubling of the business in a week.

8:41

There are a lot of these moments.

8:44

You've seen this at Stripe, obviously, a lot, where you have these moments as

8:51

an entrepreneur where you have very, very low lows.

8:55

The highs don't feel as high as maybe they get, but that's what you get.

9:01

As I think about the funding rounds you mentioned being hard,

9:06

one of the investors I know who has consistently

9:10

had the most steadfast belief about DoorDash is Michael Abramson,

9:14

who was at Sequoia back in the day.

9:17

Why was he so convicted? What was it?

9:19

I think you're going to have to have him on the show to ask him.

9:21

Okay, yeah, we will. That'd be good.

9:22

But I think Michael always had a fairly

9:29

simplistic view of the company, where if

9:32

three things worked, the business works.

9:36

When I met Michael, it actually reminded me a lot of

9:41

our journey in Y Combinator, because I think in Y Combinator,

9:45

I think we were just happy to be there, but a lot of people were really racing

9:49

towards demo day where the objective function was to raise as much money

9:52

at the highest possible valuation.

9:54

For us, actually, the objective function was answering three questions.

9:58

It was, would consumers pay for this product, pay a premium for delivery?

10:01

Would merchants actually work with us and pay us commission?

10:04

And would Dashers partner with us for a wage that we could afford?

10:08

I think Michael had a very similar set of very simple questions about the company.

10:14

I think so long as those dimensions looked right to him, he kept on investing.

10:20

I think he said, "You have to do the math and work out that the economics of this

10:24

business do work," and they do, therefore, a more compelling product

10:27

experience will win.

10:29

Yeah, that he believed that if the unit economics were there,

10:32

that we could secure enough Dashers.

10:36

I think his third comment was really that we can continue executing

10:42

into the market opportunity that things will work out.

10:46

He turned out to be right.

10:48

Thinking more about this ecosystem, food delivery is very

10:51

popular in the United States.

10:52

It is even more popular in China.

10:55

I feel like the experience of just being in a major Chinese city

11:00

is one of seeing food delivery…

11:01

You go to a restaurant and there's mopeds, you're on the roads and there's just wild

11:06

flocks of mopeds everywhere delivering food.

11:10

The apartment buildings and the office buildings have dedicated

11:14

zones to streamline it.

11:16

Why is food delivery so much bigger in China than it is in the US?

11:20

Yeah, there are a few things.

11:22

This is one where I'm always, on the one hand, so impressed

11:27

by how far ahead sometimes behavior is in markets like China.

11:32

Then on the other hand, I have to remind myself that there are

11:35

differences and why you can't just clone some of these markets.

11:39

One of the first big differences is the eating out culture in China is very,

11:46

very high and very, very affordable.

11:48

Eating out in China is about as affordable as cooking at home.

11:53

As a result, nobody cooks.

11:54

As a result, nobody cooks, exactly.

11:56

There are a lot of reasons for this, and we can go super deep on retail

12:02

history and grocery history in China.

12:05

But anyways, that's a huge phenomenon.

12:07

I think second, because of labor market dynamics, both the availability as well as

12:13

the cost of labor in the Chinese market, that's allowed a lot of these businesses

12:19

to make this activity almost as affordable as if you were to pick

12:24

up the orders yourself.

12:26

I think third is that, especially it's not every city in China,

12:31

but more and more, the Chinese…

12:35

It started really with the Eastern Seaboard cities,

12:38

where the density just allowed a very easy delivery setup, almost like every

12:44

city was like New York City.

12:45

I think in America, we think New York City is very special.

12:48

There's more mega cities in China. It has millions of people.

12:50

In China, there are many dozens of cities, over a million in population,

12:55

and the population density is even perhaps higher than that of Manhattan.

12:59

Now, that's actually morphed to the Western cities, too.

13:03

It's not just the Eastern Seaboard cities.

13:05

If you actually study this over a time series, whether it's the '80s to the '90s

13:10

and certainly to today, because of how fast China is able

13:16

to manufacture things, city plan things, deploy things.

13:20

I mean, literally, they can build a train station in an entire day.

13:24

Because they can do things like this, the market potential has really expanded,

13:28

even as people moved now from Eastern Seaboard cities to other cities.

13:33

You're saying a big part of it is lower labor costs mean

13:37

food delivery is quite inexpensive.

13:39

Does that affect then—

13:40

The cost of the food, I would start with that, is a lot lower.

13:44

Then you have lower labor costs, and you also have mass availability of labor.

13:49

Then now you also have rising incomes

13:55

because of how fast cities are getting developed.

13:58

It used to be that most of the income for consumer discretionary spend,

14:02

if you studied as a percent of China's GDP,

14:04

was along the Eastern Seaboard, say take circa the early 2000s

14:09

or even up to the 2010s.

14:11

But if you looked over the last 15, 20 years, that has now become a lot more

14:15

uniformly distributed, kind of like the US, actually,

14:19

whereas this is not true in other parts of the world.

14:23

Where I was going with this is, does that then predict the behavior

14:25

of other markets where is there also a lot of food delivery in other markets

14:30

where food is cheap and labor is cheap.

14:31

And whether or not there are a lot of restaurants.

14:36

People who can afford delivery, sure, but also the production of food.

14:43

I think one of the things that I've always thought about is

14:47

restaurants have two very special properties about them.

14:51

I do think that more and more, they are almost going towards

14:54

the ends of these two spectrums.

14:56

Same thing with retail, and we can talk about that, too.

14:58

But restaurants, on one hand I think are centers of hospitality.

15:03

It's incredible, the level of attention to service they have.

15:07

I think that will never get replaced by AI, that human-to-human connection.

15:12

On the other hand, where technology and autonomous technology

15:17

can make a difference is actually restaurants are almost like

15:19

manufacturing production sites.

15:21

They're a third space and a factory all in one.

15:24

It's no different in retail.

15:25

You have showrooms and warehouses.

15:28

I think that In the case of, back to your point around China,

15:33

China has a lot of restaurants.

15:36

Over seven million restaurants versus what we have here in the US at about a million

15:41

restaurants in comparison, for example.

15:43

In China, you also have a very deeply

15:49

capitalistic society in the sense that wherever there is opportunity,

15:54

you're going to see lots of people go and fill that.

15:57

It doesn't have to be a restaurant.

15:58

It could be a kitchen.

16:00

It could be coming up with different merchandising.

16:03

If you're a restaurant, you can sell not just food.

16:05

You can sell T-shirts, and you can sell hats, and you can sell cookbooks.

16:10

I think there's a lot of that commercialism also in that industry

16:16

in a way that is much more advanced than other countries.

16:20

Restaurants are very hard businesses, and they're one of the most

16:26

common businesses that people start, and yet they're incredibly sophisticated,

16:29

even on a small scale.

16:31

Yes, and they are also incredibly sought after.

16:33

In fact, if you looked at the census data in America now, and this is also true

16:38

in China, but if you looked in America, in the last 60 years,

16:42

I think there's only a couple of years where the number of restaurants

16:45

in the current year didn't exceed that in the previous year.

16:49

Even though they are such difficult businesses because of our love for food,

16:54

for socializing, being with each other and what a meal represents and how it can

16:58

connect different people, it continues to grow.

17:02

In fact, it's the number one type of establishment that malls and a lot

17:07

of shopping centers want the most of.

17:10

Talk to me about the trends in the restaurant industry these days.

17:13

If I think about what people talk about, there's dark kitchens.

17:17

That was more a few years ago, people were talking about dark kitchens.

17:20

There's the fact that high-end restaurants can't make any money these

17:23

days because no one drinks.

17:24

There's maybe the emergence of new fast casual trends, but also some criticism

17:29

of the slop bowls and everything.

17:31

But that's just what people are… You're in it.

17:33

Tell me what's actually going on in the restaurant industry.

17:35

One of the most difficult things is how do you actually staff your restaurant?

17:39

This is the number one challenge.

17:41

This has always been the number one challenge.

17:43

There's no easy ways around this, really.

17:48

I think because the cost of labor only goes in one direction and only goes up,

17:53

restaurants are increasingly making this choice of on the continuum

17:57

of service to manufacturing.

18:00

Where do I want to sit on that spectrum?

18:03

I think that is one very big trend that more and more restaurants feel

18:08

like they have to go towards the ends of the spectrum, whereas before—

18:11

You have a local restaurant that people were sitting and eating their dinner and

18:15

also is fulfilling a few takeout orders.

18:17

You're saying that's becoming less common?

18:19

Yes, because there's a lot more pressure in order to make sure that everyone

18:23

deservedly gets paid what is required to actually work there, especially

18:28

inside some of these city centers.

18:30

I think that's a perennial trend.

18:32

I think a second perennial trend, and this is why I think people love

18:35

restaurants, is the innovation in restaurants is a little

18:39

bit different than the innovation in, say, software, where innovation in a restaurant

18:43

is just different types of cuisines and food.

18:46

You're always going to see the next type of restaurant.

18:49

I think this is best shown in what I was saying earlier around

18:53

the number of restaurants.

18:54

The number of restaurants always grows.

18:56

That's a perennial trend.

18:58

That's something that is not changing, even though the types of maybe food is

19:01

changing or what's hot, what's not, that's changing.

19:04

What's not changing is the consumer's appetite for actually these restaurants.

19:10

I would say number three is restaurants are thinking about

19:16

increasingly how to scale, and this takes on different forms.

19:21

In the case of some of these large brands,

19:26

the big QSRs that you've heard of,

19:28

which represents almost 50 cents on the dollar of restaurant spend

19:33

in a place like the US, they're thinking about, how do I take

19:38

my economies of scale and just keep going?

19:41

Because I have that as a big advantage.

19:48

You also see smaller restaurants who are trying to figure out,

19:52

"How do I open up location number two?" It's really difficult.

19:55

It's actually really, really difficult in a lot of cities,

19:58

even for the most popular restaurants, the hottest chefs in town

20:04

who you'd be shocked at how hard it is for them to raise the capital for location

20:09

number two or even get the permits to start construction on location number two.

20:14

I think that's another perennial trend that you see happening

20:19

in the restaurant ecosystem.

20:21

Those are the ones that I think a lot about, which are what are the things

20:25

that are not changing that are incredibly difficult

20:29

that if you can solve these big rocks, you can unlock even more value.

20:35

I presented a lot of obstacles, but if you actually look

20:39

at the totality of the data, you would see that the amount spent

20:44

on restaurants or eating out has increased over the last 75 years, every single year.

20:50

It used to be, these are rough numbers, but in the 1950s, when the government was

20:55

measuring this, we would spend in this country,

20:58

in America, maybe 75–80 cents on the dollar on groceries versus restaurants.

21:04

In present day, those numbers are almost reversed, where it's

21:09

almost 60 cents on the dollar on takeout and 40 cents on grocery.

21:12

That number has only gone in these proportions or this direction.

21:16

Even though it is so difficult to make it as a single restaurateur,

21:22

especially when I think about the days of washing dishes in my mom's restaurant,

21:26

the totality of the industry continues to remain very strong,

21:30

and they're moving in one direction.

21:34

When you talk about permits, famously, a lot of cities

21:41

make it harsh to open a restaurant.

21:42

San Francisco people have a lot of complaints.

21:45

One of the innovations that you see these days in restaurant culture is the food

21:49

truck gardens and food truck collections.

21:51

I always find those bittersweet, where often you have really cool new

21:54

concepts and stuff, but we have societally lost the ability to allow people to open

21:59

restaurants and therefore, we're reduced to a collection of food

22:03

trucks in a parking lot somewhere.

22:05

Are permits getting better or worse?

22:06

I think in general, similar to building things,

22:10

they've gotten largely worse on average.

22:13

That doesn't mean there aren't cities who aren't green-lighting

22:16

and making it a lot easier.

22:17

In fact, if you look at some of the fastest-growing cities

22:20

in the country, they tend to also be the fastest-growing

22:24

cities for restaurants.

22:26

It's probably not San Francisco.

22:28

Phoenix and Austin, these places are doing a good job of permits.

22:31

If you look at places in Arizona, like the tri-city area,

22:35

Phoenix being one of them, Scottsdale and the Tempe area there.

22:39

You look at the tri-city area in

22:43

Texas, near Austin, or you look at what's

22:46

happening in Dallas and the Dallas-Plano area, there's these pockets.

22:52

In fact, if you look at the country, a lot of this growth is happening

22:56

in the south of the country.

22:58

That's been true for a couple of decades now.

23:01

They tend to be correlated, meaning if it's easy for me to build

23:04

apartment units and to build just construction in general, it

23:09

tends to be a bit easier to also get the licenses to open up a restaurant.

23:13

That there's some bright spots amidst a nationwide, maybe negative

23:18

trend when it comes to permitting.

23:19

I think when you're in any business, certainly the restaurant industry or even

23:23

our line of work, I would argue, you have to be an optimist.

23:26

You have to see the silver lining on the way out of how to get

23:32

there, but it's a slog.

23:34

I do think that one of the responsibilities we now have as

23:39

a company, as we're a little bit bigger now, is to help on behalf,

23:42

particularly of these small businesses, to represent them almost as a class

23:47

and actually try to even the playing field a bit and make it a bit easier

23:52

to actually be a small business.

23:54

What do you argue for on behalf of small businesses?

23:56

When you talk to someone in Washington, what are you saying?

24:01

One of the first ones is actually making it easier to actually open

24:04

up location number two.

24:06

Permitting reform.

24:08

Permitting reform is one of those things that we certainly stand for.

24:13

Making sure that labor laws are

24:20

changed or when they are changed, that

24:23

they take into account the restaurants.

24:26

We're talking about one of the largest sectors of job creation here that is

24:31

always growing, by the way, every single year, which is a rarity,

24:34

but a truism in every economy, to take into account

24:40

what the restaurateurs have to say.

24:41

Those are some of the big topics.

24:43

Again, I look at the things that aren't changing in terms of the difficult

24:47

challenges of being a restaurateur, and we try to fight on their behalf.

24:52

Where has technology meaningfully changed how restaurants work?

24:58

A very visible example to people might be in fast-casual restaurants,

25:03

they have managed to change the labor equation, it seems.

25:06

You might know the actual number is because it's self-serve ordering and pick

25:10

up at the counter and stuff like that.

25:12

That's much more common than it was, and so you need fewer people for the same

25:16

volume of orders, or you can run much more scale with the same number of people.

25:20

I'm guessing there's supply chain stuff, but it's less visible to us as people

25:23

who visit restaurants, but you'd know.

25:25

As you think about where tech has made the biggest impact

25:29

versus 10 or 20 years ago with restaurants,

25:31

what would be your top three list?

25:35

Not to speak our book, but one of the first things that comes to

25:37

mind is actually something like delivery.

25:39

When you look at the economics for a restaurant, rough math...

25:43

For every dollar of food that if you and I bought inside of a restaurant,

25:48

from the cost structure, you're going to have 30%

25:51

of that in the food and the packaging, you're going to have 30% of it

25:55

in the rent, you're going to have 30% of it in the labor, something like that.

26:00

On that dollar, a restaurant can net 10 cents.

26:02

On a takeaway order, you're still paying for the food

26:06

and the packaging, but you're, largely speaking, using the same

26:09

labor and you're paying the same rent.

26:12

There are some exceptions where some restaurants have become delivery

26:15

only and things like that.

26:16

But by and large, on average, you're going to make 3-5x the incremental margin.

26:24

I think, number two, I think restaurants recognize that

26:31

they need to build relationships with customers that are not just over

26:36

the telephone or over in-person visits where they're relying on their memory.

26:44

That area is still, I would say, somewhat incomplete,

26:50

where there are products where you can manage your social media pages and maybe

26:54

have a database of relationships that you recall.

26:57

The challenge, however, though, is you don't get to see everything.

27:00

You don't get to see the full visibility of your online orders,

27:03

your in-store orders.

27:04

Another challenge is your staff turns over.

27:06

The average staff may turn over every other day.

27:10

Not every other week, every other day.

27:12

I do think that there's still a long road ahead there in both making that product

27:18

a lot more robust for restaurateurs to be able to have those relationships.

27:23

Back to one of the things we talked about in this conversation about the importance

27:27

of retention in a consumer business, it is so important for a restaurant

27:30

to build the concept of a regular, which is a retained customer.

27:35

You and I, I know we travel a lot for work and so we wouldn't always be

27:39

great customers for a restaurant.

27:41

What's more important, actually, is if they knew that there was a regular

27:46

that is actually coming that should be prioritized.

27:51

Those are the kinds of things that I think, hopefully, we, DoorDash,

27:56

can help and actually solve.

27:59

Don't most restaurants do a bad job of recognizing regulars?

28:04

You think about the airlines as an industry.

28:06

They've made it where it's highly irrational as a frequent traveler

28:11

to travel not your preferred airline.

28:13

They've really created a lot of lock-in there.

28:15

Whereas as I think about restaurants, there is the super-mass market punch card,

28:21

10th coffee free stuff.

28:23

Then there's the really old-school restaurant where the owner might greet you

28:27

and might comp your dessert or something, but it's very sporadic and very dependent

28:33

on the owner being there because as you say, when the new waiter or waitress

28:37

is there, they don't know you.

28:38

But there's nothing in between systematic, and it feels like some way

28:42

of rewarding the regulars is missing.

28:44

It's a hard problem.

28:45

Let me ask you, what is your favorite food to eat?

28:50

At the weekend, we went and we got good Taiwanese food, and that was fabulous.

28:54

All right. How often do you eat that?

28:56

Once a month, once every two months.

28:58

Once a month, once every two months. That's the challenge.

29:00

Even your favorite food is probably not something you're going

29:03

to do every single day.

29:04

When you're talking about an activity like eating, which is 20–25 times a week—

29:08

It's not like coffee. Yeah, it's not like coffee.

29:11

It's even different from travel where the product may be the same every single time.

29:16

I think it is tough to create what you're describing, which is this program

29:21

in which you'd be known as regular.

29:23

But that, I think, is the opportunity that at least we're

29:26

thinking about at DoorDash, because if you think about it, our product

29:30

with now over 100 million-plus annual customers,

29:34

tens of millions of monthly customers, they shop at various frequencies

29:39

and they're building relationships with all of these businesses,

29:43

sometimes many times a day, sometimes once every other month, sometimes once a year.

29:49

I think because there's 20–25 moments per week, I think that there is an opportunity

29:56

in which we can create that.

29:58

Now, we recently acquired, last year, a company called SevenRooms that manages

30:02

a lot of the in-store bookings of restaurants, particularly

30:05

of higher-end restaurants.

30:07

If we can democratize that and give that to everybody,

30:10

and then also layer on the DoorDash data set and actually

30:15

tell you that John is a regular, and he tends to like the following things,

30:19

and here's other things that he tends to do, perhaps we can

30:24

crack open that question.

30:25

You want to help restaurants take care of regulars?

30:27

Yeah, because that is how you build a great consumer business.

30:30

You need the retention.

30:32

That is true for every restaurant, every consumer business.

30:36

That's interesting.

30:41

A core challenge for marketplaces like DoorDash is streamlining

30:44

courier operations.

30:45

Like letting their network of couriers and shoppers make specific

30:47

purchases for customers.

30:49

Think about it, you can't use a traditional corporate card here.

30:52

You don't have the right permissions.

30:53

You don't actually get to interface programmatically with

30:55

the card in real time.

30:58

Stripe Issuing is our answer to this problem.

31:01

With Stripe Issuing, you can use Stripe to create virtual or physical credit cards.

31:05

They can be single use, single vendor, programmatically controlled,

31:08

whatever your use case needs.

31:10

People are even these days giving their OpenClaw a credit card so it

31:13

can spend money on the internet.

31:15

If you need to create credit cards to manage spending in the real world, see

31:19

what you can build with Stripe Issuing.

31:24

We were talking a lot about ghost kitchens, I don't know,

31:26

five or seven years ago.

31:28

You don't really hear about them that much.

31:30

How big have ghost kitchens become?

31:33

They're relatively small.

31:34

People thought the model was going to take over, right?

31:38

Yeah, the model sounds really reasonable,

31:41

and I think logical on face value where if

31:45

you can, on that spectrum of restaurants where you have

31:48

high-end service on one end and hospitality only to perhaps delivery

31:53

only or more of a manufacturing concept on the other end,

31:57

it seems reasonable that you can actually just borrow a small square footage

32:02

of space, not incur a lot of not just the fixed

32:05

cost, but also the labor cost of actually running your "restaurant,"

32:09

and then selling through a delivery platform or acquiring your own

32:13

customers or doing something like both.

32:15

It just turns out it's extraordinarily difficult, however, unless you're a large

32:20

brand or a house of brands, someone like DoorDash,

32:25

to be able to attract enough customers to make that math work.

32:31

Is another way of putting it that it's hard for a ghost kitchen to be as

32:36

efficient as a restaurant that is also churning out orders across both

32:42

in-restaurant and delivery?

32:46

It's just hard for them to compete on the economics?

32:49

Yeah, it's slightly different.

32:50

Perhaps we can look at two types of examples.

32:54

You can have one type of example, which is perhaps my mom's Chinese restaurant.

32:58

It's an SMB where the name value outside of literally

33:04

its locale isn't that well known.

33:07

It's very difficult for that restaurant to acquire enough customers to make

33:12

that math work because they still have to…

33:14

Remember, I mentioned that staffing was the number one challenge

33:16

that restaurants face.

33:19

They still have to staff these kitchens.

33:21

That's one of the challenges.

33:23

You go all the way to the other side.

33:25

Pretend you are a large QSR with a big brand presence.

33:29

You certainly can attract lots of customers, but you're going to have

33:32

to think for yourself, "What's my opportunity cost?" My

33:35

opportunity cost might be to open up another restaurant.

33:37

Why not do that and take my economies of scale and actually apply it to more

33:41

orders, in-store orders as well as to-go orders?

33:44

Because I can do that with a restaurant.

33:46

Hard to do that with a delivery-only kitchen.

33:48

I think because of that, it's a tricky model to scale

33:55

to every type of restaurant brand.

33:57

I think people maybe thought that big chains were going to do it more,

34:03

where you're right, the billboard effect for an actual retail

34:07

presence is hard to compete with, where you get this very cheap customer

34:11

acquisition from the fact that people know your name from the high street or maybe

34:14

have been to you, and as a ghost kitchen, you need to make up

34:17

for that awareness gap.

34:18

But if you're Chipotle, maybe people thought that the orders get

34:24

fulfilled out of a dedicated Chipotle central factory, and then

34:29

the retail space are separate.

34:30

But also that hasn't really happened that much.

34:32

That happens occasionally.

34:37

I think these are not decisions where we're accounting for all of the variables.

34:42

One variable we're not accounting for is "What else could I do with the space?" You

34:47

have to remember for businesses like Chipotle who tend to identify real estate

34:53

in fairly expensive areas, there's high opportunity cost

34:59

of what you do with that space.

35:00

Yes, you're right.

35:01

One choice is to turn it into a kitchen or a delivery-only kitchen,

35:05

and sometimes that happens, but there's also a massive

35:09

opportunity to recoup the expense.

35:11

If you start introducing, for example, franchises.

35:15

Chipotle is not really franchised, but if you look at other types of brands

35:18

where it is franchised, I think the economics become even trickier.

35:23

There's a next best use math.

35:25

Which restaurants have actually invented something that's hard to copy?

35:29

I actually think any restaurant that's been around for, let's say, two-plus

35:34

decades, probably has very interesting IP.

35:38

Obviously, some of this information is not public.

35:42

You can imagine when you go into a McDonald's around the world,

35:47

that french fry, perhaps they don't sell the same exact items in every single store

35:52

in every country, every city, but the french fry almost

35:56

always tastes the same.

35:57

That is an extraordinarily difficult feat to accomplish.

36:00

There's a lot that goes behind the scenes, just like there's a lot that goes behind

36:04

the scenes at DoorDash in terms of getting you one order on time

36:08

to make that sentence true.

36:10

The same can be said about a lot of other businesses that have been around

36:15

for very long periods of time.

36:17

That's on the big brand QSR side where a lot of the innovation, if you will,

36:23

is in the process innovation.

36:25

Also, how do you run large groups of people and have very

36:32

high and consistent standards of service?

36:35

Extremely difficult.

36:36

That's the IP, I would argue, for a lot of these large brands.

36:41

On the other side, there are small restaurants,

36:43

some of whom have been around for almost a century, actually.

36:47

There aren't that many of them.

36:49

But when you look at what makes them special, it tends to be the same things

36:53

that actually make startups special.

36:55

It tends to be a small group of people that really believe in a certain idea,

37:00

some commitment towards some standard of excellence.

37:02

It could be excellent towards the food, the service, or takeout.

37:06

There are some takeout-only businesses that have been around for many decades.

37:09

As a result, they have high retention,

37:14

high usage, and they're very efficient.

37:17

Can you speak about any specific examples of the impressive IP that these

37:22

chains have conducted over the years?

37:24

It could be the McDonald's french fry, how they actually make it taste

37:27

consistent, I don't know the answer to that, or any of your favorite restaurants?

37:32

One of the things that I'll never forget

37:35

was I went through a hospitality

37:39

training at Chick-fil-A.

37:42

I want to say it was 2017, maybe it was 2018.

37:47

I don't remember everything

37:50

on the checklist, but one of them that stands out

37:53

is that the floors must be so clean that babies would lick them or could lick them.

38:00

The babies will lick them regardless of how clean they are.

38:03

Is Chick-fil-A still the highest grossing fast-casual restaurant per square foot?

38:08

I don't know, actually, but it's definitely up there.

38:10

People talk about it. It's up there.

38:12

It's definitely up there. Why is it so successful?

38:15

It actually goes back to, I think, the standard ingredients

38:18

of a great consumer business.

38:20

It has a highly retentive following, which it spends very little money

38:24

acquiring, and they come back often.

38:30

They're extremely efficient with all of the scale economies behind the scenes

38:35

around equipment, which is quite custom to that company, to the service training,

38:42

to how they treat their staff.

38:45

One of the most impressive facts about that business is that many people actually

38:51

that I met with have spent over two, three

38:54

decades of their careers at Chick-fil-A.

38:58

We find that to be a novelty actually here in Silicon Valley or in technology.

39:02

And in the restaurant industry.

39:04

In the restaurant industry, one where we described all of the churn

39:08

and the difficulty of staffing, Chick-fil-A has extremely

39:11

high retention of staff.

39:15

It has so many repeatable stories of where

39:20

it gives somebody who comes from not a lot

39:24

of means, frankly, to become the general manager of one of these stores

39:28

and earns a really healthy living, pays their entire family

39:33

through college and beyond.

39:37

In many ways, a lot of these restaurant stories, Chick-fil-A being one of them,

39:42

have a lot of the essence still of the American dream.

39:45

We're talking a lot about QSR chains here, and people often like to ask the question

39:50

of, why is there not a Chipotle of my preferred cuisine,

39:56

where it's slightly elevated versus regular fast food, very good,

40:01

consistent quality, good execution?

40:03

There is no chain that serves you really good Neapolitan thin-crust pizza

40:07

in whatever city you're in or Indian food, or again, pick your favorite cuisine.

40:11

Why don't we have more chains delivering just a really good

40:18

standard version of a cuisine?

40:21

Have you ever cooked for a large group of people?

40:24

Or do you cook often?

40:26

Yeah, we cook, but not for huge groups.

40:28

One of the things I found is that as the number of people you have to cook

40:32

for grows, the difficulty to keep the standard of service,

40:36

whether it's the taste, the temperature, the speed in which you receive

40:41

the food, goes up exponentially.

40:44

That is what's so hard about exactly what you just said, around keeping

40:48

that quality control really high.

40:50

It's maybe easy to build you the Chipotle for one type of cuisine

40:55

for one restaurant.

40:58

That's just on the skill perspective.

41:01

Although I do have some ideas on how perhaps you can do that.

41:04

But the second reason also is think about who you're competing against.

41:12

Let's say that you want to make a Neapolitan pizza.

41:15

You mentioned that. Or any kind of pizza.

41:17

Great. I can't wait to taste it, John.

41:21

Did you know that there are over 1,000 different pizza choices

41:25

in the Bay Area on DoorDash? Just on DoorDash.

41:30

We don't have all of the restaurants.

41:33

That is just one of the many things that you're going

41:37

to have to do to be able to create this concept and actually scale it.

41:42

Then don't forget all the other challenges we talked about: the staffing,

41:48

What determines whether chains succeed versus whether they don't?

41:53

Because there's a lot of pizza restaurants in the Bay Area, but at the same time,

41:56

there's a lot of Mexican restaurants in LA that are really good, and yet Chipotle

42:01

still has plenty of locations there.

42:03

It doesn't just seem to be, do there exist local options?

42:07

Sometimes a chain can break through.

42:09

I also don't think it's a skill issue because it's very processable

42:13

to make a good Neapolitan pizza.

42:15

It's about the ingredients and the temperature and humidity control

42:18

in proofing the dough and then cooking it at very high temperature,

42:23

but it's not rocket science.

42:25

You could make a process around this. Potentially.

42:28

I think the fact that there only exists so many of these QS...

42:33

Accountable number of these brands who have actually scaled to the size

42:38

that we're talking about, where they're ubiquitous, I think

42:43

should be some evidence—

42:45

It's harder than you think.

42:47

That it's a lot harder than you think.

42:49

Because of what I just said, there's a lot of process skills that you

42:53

have to be extraordinarily good at.

42:54

In fact, I remember we partnered with one brand where we launched this brand to

43:01

great fanfare and sold a lot of burgers actually on a weekend,

43:09

testing this idea, can you make a healthy-tasting burger

43:14

if you put a brand name around it?

43:16

The answer is yes, and the answer is it's really hard to retain

43:19

any of those customers.

43:20

Because it's too healthy?

43:22

No, because half of the reviews said, "Your burgers were great," and the other

43:26

half said that they were inconsistent in quality.

43:30

It's because it's extremely difficult.

43:32

That exponential challenge as you try to grow scale, and keep up the quality

43:38

is very, very, very difficult.

43:40

You have to get right the service, you have to get right the production,

43:45

you have to get right the pricing, the packaging.

43:48

There's lots of things you have to get right.

43:50

Do you think reliability is the thing that people underestimate

43:53

about the restaurant industry?

43:55

Yes, or about a service like DoorDash.

43:57

I think when you have high volumes of activity,

44:00

I think keeping the reliability as reliable as the electricity we have or

44:05

the water inside of our buildings, that is extraordinarily difficult.

44:08

Can we talk about the future of delivery and how you see it playing

44:12

out in 10 years time?

44:14

There's a few different modalities being discussed.

44:16

There's drone delivery, there's sidewalk robot delivery, and you guys have Dot.

44:21

I'd love you to talk about Dot.

44:22

I don't know how much you're doing in drones.

44:25

Maybe you can talk about that, too.

44:26

Maybe there's autonomous vehicles, like autonomous cars.

44:29

People don't talk about that that much, but maybe in the suburbs,

44:31

it would kind of work.

44:33

This is what we have at the moment.

44:34

Just like, what's the mix in 10 years time?

44:38

Even before we go there, I think the first question might be what might be delivered?

44:43

Because I think that's actually still not that well understood.

44:46

Today, yes, DoorDash is probably most known

44:50

for food, potentially now increasingly groceries and household items.

44:55

But that is a fraction of the tens of millions of items

44:59

inside every single city.

45:00

One of the biggest things that we're going to have to do before we can just fulfill

45:05

the items, which is what we'll get to, is where are the items,

45:10

and what are the items?

45:12

There's tens of millions of items literally inside these cities,

45:16

whether it's in the US or different countries within Europe, other parts

45:19

of the world, they're not cataloged.

45:22

There's no structured data that you can scrape in order to figure that out.

45:27

I think that's going to be a long journey.

45:29

Sometimes the inventory is not even close enough, and so you may have to house

45:32

the inventory so that you can actually bring those goods and even have the right…

45:37

A lot of what I believe makes for great logistics is you need a great setup.

45:42

Because if you're always trying to figure it out on the fly, and you're pulling some

45:45

magic trick out of thin air, and you have to be the hero, that is not a system.

45:50

That is an exception.

45:52

I think great logistics or great reliability,

45:56

they require building systems.

46:00

You have the catalog, you got maybe the inventory close enough

46:03

to where people live, now we can talk about the fulfillment.

46:07

The entirety of that is what you have to build before we can get

46:11

into the great technologies.

46:13

The staging of what it is that you're delivering.

46:14

Because if you don't know where to get the items or if you can't get the right

46:20

item, what difference does it make if you have the right vehicle?

46:25

You're talking about the fact that you don't actually know what's

46:28

on a supermarket's shelves where the fact that you have to actually be able

46:33

to predict when the restaurant will have the stuff ready, all that

46:36

knowing what is ready to be delivered. Yeah.

46:39

Or take when the Cheeky Pint becomes an established bar, and maybe DoorDash can

46:44

deliver from one day, what's inside the Cheeky Pint?

46:48

What's on the actual menu?

46:50

What's off menu and actually, you can order?

46:52

These are complicated issues before we even get to the fulfillment

46:56

of the actual items.

46:59

Then you can get to fulfillment.

47:01

I think that's one where we're still exploring.

47:05

To me, the most important question we always ask on any, frankly, technology,

47:12

but certainly a technology like this, where there's long cycles of development

47:15

is, what problem does it actually solve?

47:18

Obviously, in the case of drones, you mentioned it, it can cover

47:21

a lot of ground very quickly.

47:23

Longer distance orders, that seems to be a great job for it to solve.

47:27

In a busy high-rise area where you got short, dense orders, which is the majority

47:33

of these orders, maybe not the best solution for such product.

47:38

This is actually what led us to the creation of Dot,

47:41

which was when we started the Autonomy Project in 2018, 2019,

47:47

we actually did not set out thinking that we needed to build

47:50

anything, that we would…

47:51

Surely there would be someone who would—

47:53

We'll just pick the best one, and I'm sure we can buy some tech off the shelves.

47:57

Yes, we were out there raising our hand, asking for a partner to the dance,

48:01

so to speak, and someone who could specialize in that,

48:04

and we could specialize in the operations and things like this.

48:07

It turned out that no one was that interested.

48:10

A lot of people were interested in building Robotaxis, and obviously,

48:14

we've seen that come to fruition.

48:16

A lot of people did build, to your point, sidewalk robots,

48:20

but we found those to be too slow.

48:23

Back to what problems are you solving?

48:24

Because you got to remember, back to opportunity costs, humans are very

48:30

good at delivery, actually, it turns out.

48:32

We're also very good at driving, too.

48:33

The bar is actually a fairly high bar.

48:36

It's not as simple as just saying, "We can make the technology, let it rip."

48:40

Then we said, "It has to be fast enough.

48:45

It probably doesn't need to be the size of a car because cars aren't very good

48:50

at parking inside these busy locations where a lot of these restaurants are."

48:54

We had to think about all these things.

48:56

We had to think about how to load the vehicle.

48:59

We had to think about unloading the vehicle because, unlike Robotaxi,

49:02

the passenger solves all of those problems, but in our case,

49:05

we have to solve that. That's how we ended up on this.

49:08

Where are you in rolling out Dot?

49:10

We're primarily testing right now.

49:13

We really want to get it right in one market.

49:16

We're doing a lot of this in Arizona because it's not just the technology.

49:21

One of the things I've learned and come to appreciate about building

49:26

something like Dot is that these autonomous vehicle companies are almost

49:30

many companies inside one company, or certainly many different types

49:33

of skills you have to be good at.

49:35

Yes, I think everyone obsesses over the autonomy,

49:39

but there's also the hardware, there's also the manufacturing,

49:43

there's also the operations.

49:44

There's hardware reliability.

49:45

There's also the maintenance.

49:47

There's also the regulatory.

49:49

There are a lot of different components that you actually have to be good at.

49:53

Then we can talk about the stack required to actually build the inputs

49:56

to some of these things.

49:57

For example, there's also understanding how to procure supply when you have

50:03

sometimes geopolitical tensions or when you have supply chain disruptions.

50:09

Where do you do assembly versus where do you do construction?

50:14

Not super obvious answers to some of these questions

50:18

at times, especially when you have to make these decisions sometimes years out.

50:22

I think that's the stage that we're in right now, which is,

50:27

"We got to figure this out correctly before we can actually scale it."

50:30

Where do drones fit in, if at all? They definitely do.

50:32

As I mentioned, back to jobs to be done, I think drones obviously can do a lot

50:38

of these longer-distance orders.

50:40

We've been doing drone deliveries actually for a couple of years now, mostly outside

50:43

of the United States, outside of the US. Whereabouts?

50:45

Places like Australia.

50:48

We're going to bring them to Europe, bring them to the United States as well.

50:52

Again, you have all of those problems you have to solve.

50:56

The autonomy is a little bit easier.

50:58

You still have a routing problem.

51:00

You have hardware problems.

51:02

Obviously, you still have permits and regulation, set up, loading inside

51:07

different stores, things like that.

51:10

Very interesting.

51:11

We're talking a lot about some of these scale tech problems,

51:14

and one of the ones that people might not think about is fraud, but I feel like

51:21

Stripe and DoorDash obviously work together in many ways, but one of the

51:24

things we work together on is fraud.

51:26

I feel like the number of potential fraud vectors is much more

51:31

complex than people think.

51:32

Maybe you can just talk about trust and safety at DoorDash,

51:35

all the different ways that people try…

51:37

You're running this complex system that people try to game

51:39

and what it takes to run that.

51:42

Many times I think about my freshman-year

51:48

electrical engineering course on

51:51

state machines, where DoorDash in some ways,

51:55

is like a large state machine, where you have many systems in place,

52:02

and they're observing what's happening.

52:05

There are things that must happen when things are green to keep them green.

52:09

But as we start degrading gracefully, from green to yellow to orange to red,

52:15

something like fraud, as you mentioned, other systems have to kick in and do

52:19

things like exception handling.

52:21

Obviously, you're spending most of your time building prevention systems,

52:24

but of course, it's tricky.

52:27

You also have to have the 911 response system that literally totally responds

52:30

within minutes, because sometimes we're talking about physical safety.

52:34

We're not just talking about fraud.

52:35

Given the volumes that DoorDash carries, billions of orders per year,

52:39

the one-in-a-million incident, unfortunately, does occur.

52:43

That's DoorDash as a system.

52:47

Now, in fraud, you're right.

52:49

We partner, for example, a lot on online fraud.

52:52

You have the largest database of all of these transactions, and therefore,

52:57

you can give us lots of information that we ourselves couldn't do on our own.

53:01

On the flip side, there's also offline fraud, which is very tricky to catch.

53:06

What do you mean by offline fraud?

53:07

For example, let's say that a consumer suggested that an order

53:12

was never dropped off.

53:14

Let's say that a Dasher said that an order at a grocery store was already shopped

53:20

and picked, but we're not sure.

53:22

Or let's say that a Dasher said an item is missing inside of a store, any store,

53:27

maybe perhaps an apparel store.

53:30

All of those, I would argue, have offline components that are difficult to verify.

53:35

Building the eval, if you will, for this set of conditions and use cases

53:41

is actually extraordinarily difficult.

53:43

A lot of what we're trying to do is we have to build those signals ourselves.

53:47

There is no perfect science here.

53:49

I wish there were, but in many ways, we have to invent this.

53:53

This is actually why…

53:54

I'll give you a few examples.

53:56

One of the things that we shipped, this is actually a couple of years ago,

53:59

was called SafeChat, where we noticed that prior to any

54:03

physical altercation that may, unfortunately, happen between audiences,

54:07

90-something percent of the time, it's always preceded by a verbal altercation.

54:12

Knowing that alone allows us to prevent a lot of tough situations.

54:18

Or building alert systems.

54:20

For example, sadly, there are shootings, particularly in the United States.

54:27

There was one shooting in Manhattan, where

54:32

several people died in the lobby

54:34

of a large office building.

54:38

Within minutes of a Dasher seeing the shooter walk in to the building,

54:45

we have an alert system that doesn't just alert all Dashers and consumers

54:49

and merchants within that vicinity, but also all local law enforcement.

54:53

Actually, we were the first to report that to the NYPD who try their

55:00

best to respond in time.

55:04

There are a lot of those events.

55:07

It's not always about fraud is what I'm trying to say, and that you have to build

55:13

extremely responsive systems, and you also have to build as many

55:19

signal-detecting systems, too.

55:21

You're running a fairly large slice of the economy, and you're trying to have

55:25

everything run smoothly, and there's quite a lot of failure modes

55:27

that people might not think about that are possible when you're just seeing so

55:31

many economic interactions all day. Exactly.

55:33

We're trying to be the digital representation of these cities,

55:38

whether it's a city, a suburb, a neighborhood, a rural area.

55:42

Sometimes it's not always of what's transacting, but it's just

55:46

what activities are occurring.

55:48

One of the hardest

55:50

kinds of frauds to prevent is the classic,

55:56

say, credit card fraud, the one that people think about,

55:58

is your card got stolen, and it's actually someone overseas buying

56:02

stuff for themselves rather than you buying it for yourself.

56:05

That we've actually now got very good at detecting.

56:08

It's quite hard to do.

56:09

There's just so much data we can bring to bear on that problem.

56:12

A much harder one that we see that businesses face is the kind you're saying…

56:17

The industry term of ours, which is funny, is friendly fraud,

56:20

which is a bit of a euphemism.

56:23

The customer buys something and just says it never arrived when it actually did,

56:28

and they're just straight up lying.

56:30

You don't really want to call them on it.

56:31

You also don't have proof that they're lying, but it definitely

56:34

does happen to some degree.

56:36

How do you deal with…

56:39

Maybe there's a photo of the Dasher having delivered it, but I'm curious how you deal

56:42

with people just defecting from the system.

56:45

There's no one answer, as you know, with fraud, because as soon as you figure

56:49

out how to measure the risk and contain the risk, obviously,

56:55

the incentive for the fraudster goes towards the next axis of breaking in.

56:59

And so, I don't think there's a perfect solution to the problems that you're

57:04

describing, but you're right.

57:05

A lot of this is about...

57:06

A lot of how you build systems, where obviously the metric is around

57:12

reliability and consistency and great customer outcomes.

57:15

The inputs are a lot of measuring things before things happen.

57:18

I talked about, I think one of the most important things about

57:21

logistics is the setup.

57:22

That happens even before a delivery, if somebody hits place order.

57:25

The setup is extraordinarily important.

57:28

Did you get the r ight catalog?

57:30

label correctly the catalog? Blah, blah, blah, blah, blah.

57:33

A lot of that sort of stuff.

57:36

In the case that you're describing, we can track several things.

57:41

We obviously can track what is happening with the Dasher.

57:44

You mentioned some of these ideas, taking photos of receipts

57:47

or orders being dropped off.

57:49

We have our own mapping system, for example, that we built.

57:52

Why did we build our own?

57:54

It's because we care much more than any third-party mapping system of exactly

57:59

where the last two feet, forget, 20 or 200 feet of some apartment

58:04

unit door is, for example.

58:06

We know if we reliably deliver to that door and that we saw the

58:11

pin actually hit exactly where, we have a little bit more fidelity

58:14

on whether something was dropped off.

58:16

We build profiles of customers, I'm sure you do, too, of their

58:19

behavior and what they tend to say.

58:21

There's lots of things that we're trying to do behind the scenes to build these

58:25

signals to figure out what is the likelihood here of fraud.

58:31

Makes a lot of sense.

58:32

Tell me about DoorDash Tasks.

58:35

DoorDash Tasks is something that we shipped recently where…

58:39

Actually it started with a project where we were trying to solve our own problem.

58:43

It started with trying to figure out where is every item inside the city.

58:48

We have millions of Dashers,

58:53

and they're usually frequenting

58:56

different types of stores.

58:58

We know that consumers move around items a lot, which makes it extraordinarily

59:01

difficult for any store to track their own inventory.

59:05

Dashers sometimes are collecting this information.

59:08

That was one of the problems that we were solving.

59:12

Interestingly, while we were doing this, we started getting calls

59:16

from people you probably would expect—retailers and CPG companies who

59:22

might be interested in this information.

59:25

Then especially as companies…

59:30

I think it's well known at this point that a lot of the large LLM companies have

59:35

been building lots of repositories of online information.

59:40

What about the offline information to bolster some of those models,

59:46

or just to help some of these companies building robotics and things of the sort?

59:51

We said, "Interesting.

59:52

We're building a catalog for ourselves, but we ought to be able

59:56

to help other people, too.

59:57

If it's an opportunity to help Dashers earn more give them more

1:00:00

choices to earn more, great.

1:00:02

Why not build that solution?"

1:00:04

That's really the story of DoorDash Tasks, which started organically probably

1:00:08

a couple of years ago as we were solving our own problem.

1:00:11

We started realizing that there may be other people who have similar tasks.

1:00:15

It's a platform for having fairly small tasks done?

1:00:19

You can give the task to DoorDash and you guys could complete it?

1:00:23

Do you get excited about an AR angle for DoorDash where…

1:00:26

Are you in the best use cases of the Meta glasses, where now you can get directions

1:00:31

within the store and everything?

1:00:32

You'd actually speed things up quite a bit, rather than wandering around lost.

1:00:37

I think we definitely could be.

1:00:38

Again, back to the question of asking, "What problem does this solve?" The human

1:00:43

is pretty good with their eyes at some of this stuff.

1:00:47

The bar is pretty high in order to do some of the things you're talking about.

1:00:53

As I think through AI applications for DoorDash, one thing I'm struck by is

1:00:57

that the LLMs are pretty good recommenders,

1:01:01

just by tossing things into context.

1:01:03

You don't need to train a custom model.

1:01:05

I don't know if you've ever tried for book recommendations or TV recommendations.

1:01:09

You just tell it a bunch of the stuff you like already.

1:01:11

Restaurant recommendations, it's like, "Here are 15 restaurants we like to go to.

1:01:15

Give us other recommendations," and it'll do a really nice job.

1:01:18

Yet within products, if I open DoorDash, it's the same categories,

1:01:22

and it's less personalized to me than if I took my DoorDash history

1:01:30

Shouldn't we be somehow using the fact that LLMs are pretty good

1:01:35

recommenders within products?

1:01:37

This is not just DoorDash, it's every product I use, it feels like.

1:01:41

Those recommender capabilities are underutilized.

1:01:43

No, I think you're definitely right that there's an opportunity here where…

1:01:46

There's almost like the traditional school of thought,

1:01:49

which is to use the information that you have, and you build the best

1:01:53

personalized models that you can.

1:01:55

One of the things that LLMs obviously does is they throw efficiency out of the wall,

1:02:00

and they throw as much compute towards it.

1:02:05

Interestingly enough, one of the things that spits out when you

1:02:08

put in enough tokens and have big enough context windows is you're right.

1:02:12

It has actually better models because it's just using much larger data sets.

1:02:22

I do think there's an opportunity in what you're saying, which is that products like

1:02:28

DoorDash, like consumer products, will definitely have their own ordering agents.

1:02:34

I think we're all trying to figure out what is that right modality, because

1:02:40

it's not obvious to me that everything's going to be done through text.

1:02:43

We don't buy things through text.

1:02:47

We don't always get inspired through text either.

1:02:51

I think it's something that we're all figuring out.

1:02:55

What else are you excited about these days from a product point of view?

1:02:58

What's the new part of the product that is close

1:03:02

to your heart, or where do you want to take things?

1:03:04

We have a few missions that we're on, and we talked about several of them,

1:03:07

I think, in this conversation.

1:03:08

One of them is we got to bring you everything inside the city, and we're

1:03:10

a long, long, long ways from that.

1:03:12

We can go super deep on restaurants and food, but as you start going

1:03:18

into category n+1, we are just a smaller and smaller and smaller drop in the ocean.

1:03:23

We got a lot of work to do.

1:03:25

A second mission is, obviously, we also want all of the businesses

1:03:29

that to have their own software.

1:03:32

DoorDash's goal is not just to grow your business by sending you customers.

1:03:37

We also want you to learn how to do it on your own—that's why we have products

1:03:41

like DoorDash Drive or Storefront or SevenRooms, which are B2B products,

1:03:46

which is probably why consumers don't think of us for them—and help you

1:03:50

build your own omni-channel business.

1:03:52

We have a mission in which we want to actually bring you inside stores.

1:03:58

That's actually something that we're trying to do.

1:04:01

We started that about a year ago.

1:04:03

I mentioned earlier that one of the perennial challenges or trends

1:04:07

for restaurants is, how do you grow your own brand?

1:04:10

If we can also help you with that, both by helping build

1:04:15

for you a CRM of a 360-view of guests where we can also send you customers

1:04:21

inside stores, we'd love to do that.

1:04:23

We're starting with restaurants with two products.

1:04:25

One is called Going Out, the other is Reservations.

1:04:28

That's a big mission that we're on.

1:04:31

We have several of these missions that I'm pretty excited about.

1:04:37

You mentioned reservations.

1:04:39

There are a number of quite established companies in that space,

1:04:42

and restaurants are famously not exactly eager to wake up and switch

1:04:47

all their systems one morning.

1:04:48

How do you plan to unseat the existing guys in reservations?

1:04:52

I think it starts with the idea that I believe that no restaurant should own

1:04:58

their own reservations, ideally, technology.

1:05:02

A reservations book, we won't get into super deep here.

1:05:04

It's actually quite complicated to run a book, actually.

1:05:08

I mentioned earlier this idea that restaurants' North Star really should be

1:05:13

to build regulars, but that's not how every reservation system is designed.

1:05:20

I think the most important thing is that restaurants can have

1:05:23

reservations on any platform.

1:05:28

That's one of the main premises behind SevenRooms, too.

1:05:34

It's that it's agnostic to who generates the demand for you.

1:05:38

What it's trying to do is to make sure it achieves your objective function.

1:05:42

It may be to grow profits one year, it may be to grow locations

1:05:47

the next year, or maybe to get a certain type of customer the next year,

1:05:52

I think it starts by building what's best for the merchants.

1:05:56

Hopefully DoorDash can make a difference, too, where we just have more customers

1:06:02

than people that play with restaurants, and also more information

1:06:07

that we can supply to these restaurants on, how do you make more money

1:06:12

to achieve whatever outcome you want?

1:06:13

If we can help achieve a restaurant's objective function

1:06:18

better than anyone else, then hopefully we have a seat at the table.

1:06:22

Tock in reservations carved out a successful niche in high-end reservations.

1:06:28

I think about one of the beliefs that informed their products most strongly

1:06:32

was that no-shows are terrible for restaurants,

1:06:35

and a restaurant reservation should be like a flight reservation,

1:06:38

where you don't just get to decide you're not going to go to the restaurant.

1:06:42

You put a little deposit. Exactly, you put a little deposit down.

1:06:44

Do you subscribe to that philosophy?

1:06:48

Do you think it's maybe less relevant, they were really dealing

1:06:50

with very high-end restaurants?

1:06:52

Do you think it's less relevant for regular restaurants?

1:06:54

I'm just curious what you think of that viewpoint that we're doing

1:06:57

reservations all wrong, which was, I think, one of the founding

1:07:00

insights of Tock. I think it makes a ton of sense.

1:07:02

The implementation of the idea is what's difficult.

1:07:05

Restaurants that have very limited seating and very long seatings,

1:07:11

the opportunity cost for a cancellation is very high, versus a restaurant that may

1:07:18

have infinite production capacity, if you will.

1:07:21

So I think the implementation of the idea is what matters, but I think

1:07:27

the idea itself makes a ton of sense.

1:07:28

How do you, with reservations, plan to ensure that people

1:07:31

aren't no-showing to restaurants?

1:07:35

We're working on that problem right now, but I don't think there is one thing.

1:07:40

I think the first thing is we have to help you find reservations

1:07:45

I think there's a lot of room left in the space to innovate there.

1:07:48

I don't think there's been that much innovation in that area.

1:07:52

So discovery is almost a bigger problem?

1:07:54

I think discovery is very difficult.

1:07:56

How do you find out about restaurants today?

1:08:00

My wife finds them on Instagram.

1:08:04

I think that's right, because there are so many different sources now.

1:08:08

I'm sure other social channels are another.

1:08:10

LLMs, you mentioned earlier about throwing things in there and getting something out.

1:08:17

I think it's really difficult still.

1:08:22

No matter the sources of input, most restaurants still struggle

1:08:27

generating their own demand.

1:08:30

I think so long as that's true, we have a shot at making a difference.

1:08:36

You talked about your European expansion.

1:08:39

You just acquired Deliveroo.

1:08:41

In what ways are the Deliveroo and DoorDash businesses different?

1:08:46

Fundamentally, they're more the same.

1:08:47

I would start with that. Hence, the acquisition.

1:08:54

We talked a lot about systems and logistics.

1:08:58

I'm sure you've probably been there many times.

1:09:00

You probably know it better than I do.

1:09:02

It is an interesting city in that it doesn't actually have a lot

1:09:06

of the grid-like hub-and-spoke properties of a lot of cities.

1:09:11

There are a lot of reasons for that.

1:09:13

We could go into the history.

1:09:17

No, there are other cities that are actually designed differently

1:09:20

in other parts of Europe.

1:09:21

We can get into other countries and cities, but age is one of them.

1:09:24

Obviously, it's not meant to be a city where you drive a lot of vehicles.

1:09:30

I think it's older than even a lot of other European cities.

1:09:33

Even as European cities go, London is really old.

1:09:36

As a result, that alone makes the logistics

1:09:39

problem very, very different.

1:09:40

The logistics, not just the algorithm,

1:09:43

which I think is just one part of the system, but the signals that you'd

1:09:46

want to collect are very, very different.

1:09:49

Vehicles are very different.

1:09:51

We predominantly are non-autos in the City of London, which is very different

1:09:56

in the States, as an example.

1:10:01

The regulatory setup is different.

1:10:05

It has parts that rhyme with the US, but as you well know, each country

1:10:12

Even though, I guess, the UK is not under the EU,

1:10:15

but even within the EU, each country has their own local version of regulation.

1:10:19

That's very, very different.

1:10:21

You know this probably better than anyone in the world.

1:10:24

Payment processing is very different.

1:10:25

There are many more card types in parts of Europe than, say, in the US.

1:10:32

Industry dynamics on retail are very different.

1:10:37

In the US, we have a fragmented retail industry that has a lot

1:10:43

That's more concentrated in the UK?

1:10:44

Yeah, in the UK, in Germany, in France, in a lot of European countries,

1:10:49

this is more the norm than the exception.

1:10:52

Whereas in the US, you have maybe hundreds of different big brands that are

1:10:57

strong in certain regions.

1:10:58

But in Europe, that's less true.

1:11:02

Those are some of the immediate differences that come to mind.

1:11:05

A mystery to me is why the pharmacies are so much better in the UK versus the US.

1:11:11

You go into a Boots versus you go into a CVS or Walgreens,

1:11:15

and it's like night and day.

1:11:18

One of the things I would say, just as a standard matter,

1:11:20

is because of the concentration.

1:11:25

It's really concentrated by category.

1:11:27

Pharmacies, it could be a category.

1:11:29

Apparel can be another category, et cetera.

1:11:31

This is true in Australia, and this is true in Germany.

1:11:36

The standards are higher, and they're more

1:11:40

consistent of that service.

1:11:41

In the US when you have tens of thousands of supermarkets, hundreds of big brands,

1:11:49

If you look even more upper-funnel, say CPG, there are a lot more CPGs

1:11:55

in the US market that are competing than in some of these other countries,

1:11:59

which make things like inventory management, the SKU counts,

1:12:03

the size of the stores, all of that, therefore is different.

1:12:08

You referenced some of the stuff we're doing together.

1:12:10

I think of Stripe and DoorDash as almost having grown up together,

1:12:14

founded very similar times, and then you guys were early users of all

1:12:18

manner of products, be it Stripe Connect or Radar or Issuing for cards or things.

1:12:22

What would you like to see Stripe do that it doesn't today,

1:12:27

either because you guys would like to use it or just because you think it's

1:12:30

something we should be doing?

1:12:31

I've been giving you all my silly product suggestions.

1:12:34

This is where you get to turn the tables and give me yours.

1:12:37

I would always start with what our teams would say.

1:12:40

Probably the first piece of advice always is, "What problems are you solving

1:12:44

for us?" I think especially as companies grow in size, success,

1:12:52

variety of products, there's a natural temptation to start

1:12:56

imposing what the company wants to do or

1:13:01

wants to solve or wants to sell versus,

1:13:03

say, "What problem does it solve

1:13:06

for the customer?" I am sure one of the things that could be true is

1:13:12

making sure that you're solving our top problems, versus just

1:13:16

introducing the range of products.

1:13:19

That's probably one thing that we probably both share as advice for one another.

1:13:34

It's kind of like a TARDIS.

1:13:37

It's very cute, but I can't believe that we actually have Dot with us today.

1:13:43

It's traveling backwards, but here you go.

1:13:48

The LED screen is for the delivery?

1:13:50

Yeah, the LED—Is for person?

1:13:52

Exactly, where we can send different messages.

1:13:54

We can even give messages through the eyes.

1:13:57

We thought a lot about the eye contact between Dot and a human.

1:14:02

You see part of the sensing stack there in terms of the cameras,

1:14:07

the lighter up there. This is a lot bigger than I expected.

1:14:09

It's like a small car, not a big robot.

1:14:12

Yes, although it's still— It's like a Smart car.

1:14:15

Yeah, it's like one-tenths the size of a car, but it

1:14:18

can travel up to 20 miles an hour.

1:14:20

When you think about how that compares to what a car travels in traffic,

1:14:27

Wow! This is how we load it up.

1:14:30

We got you some non-alcoholic Guinness. Have you had Taytos?

1:14:33

I actually have not had Taytos. Ireland's preferred.

1:14:35

Here, have some of those. These are the chips?

1:14:37

These are the Tayto chips. Very good.

1:14:38

That's cool. That's awesome.

1:14:42

The compartment is totally custom and modular, which means that depending

1:14:47

on your needs or what we want to fit, whether it's heating units, cooling units,

1:14:52

all of that can be configurable as well as built modularly.

1:15:00

Where will this first be most useful?

1:15:03

There are a lot of deliveries.

1:15:06

You can think of almost suburban-like

1:15:11

environments, where the deliveries are

1:15:13

probably under 5 miles, and there's some challenge for a Dasher,

1:15:18

for example, parking. We talked a lot about earlier.

1:15:21

So it's just literally suburbs is a great place for it?

1:15:24

Yeah, then I think certain city centers, depending on the actual area, though.

1:15:32

Those are the two types that we're thinking about, distance and the types of…

1:15:37

One of the coolest things that we're actually building in Dot,

1:15:41

that's actually not physical, is what we're calling our autonomous

1:15:46

development platform, which is basically

1:15:51

the algorithm that talks about which

1:15:53

orders goes to which type of vehicles.

1:15:55

Dot, for example, obviously can also travel mixed routes with humans.

1:16:02

For example, if it's a longer distance delivery, but there is a part

1:16:08

of that journey where Dot is more advantaged, say, parking,

1:16:13

it can do the first leg of the order.

1:16:16

First leg and then hand it off to a Dasher?

1:16:18

Yeah. Thinking carefully about—

1:16:19

Can it go on the roads? Yes.

1:16:23

The sidewalk and the bike lane.

1:16:24

It's the only vehicle in the world that does that.

1:16:28

Thinking carefully about the handoffs is very,

1:16:32

very important to actually making autonomy work because this is very different

1:16:35

from Robotaxis, where obviously a passenger can solve

1:16:39

a lot of these "edge cases", but we actually have to think about all of it.

1:16:45

I actually had no idea. This is cool to see Dot here.

1:16:47

How far can it go on a single charge?

1:16:51

Or how many deliveries really is it?

1:16:52

It could do hundreds. Really?

1:16:58

We probably wouldn't test it to failure, so to speak.

1:17:01

A lot of this is making sure how do you actually build…

1:17:06

We talked about one of the skills you have to do in building autonomous vehicles is

1:17:10

you have to build it ready for mass manufacturing.

1:17:13

You also have to build it for easy operations.

1:17:16

The swapping in and out of the right vehicle parts is very important.

1:17:21

Where is the battery? All the base is batteries?

1:17:24

There's some compute in there, there are some batteries in there,

1:17:29

and there are some more sensors.

1:17:31

The autonomy is all local?

1:17:36

There are a lot of other things that we're going to have to keep doing to

1:17:41

test Dot in different types of weather.

1:17:44

This is much more sophisticated sensor stack than I expected.

1:17:48

If you're traveling the road, and you're going up to 20 miles an hour,

1:17:51

there are some physical parameters that you're solving for,

1:17:57

which is what guides the development of the actual stack.

1:18:00

You're right in saying that—

1:18:02

Just when you see it, it's surprising.

1:18:06

It's not a baby sensor stack. It's not a baby sensor stack.

1:18:08

I guess we're not trying to build any particular technology.

1:18:14

We're just trying to make it work. Is the fan the fridge or the comput?

1:18:18

There are a lot of different versions that we're

1:18:23

working right now, both in terms of what the temperature settings could be

1:18:27

internally, what the heating systems are,

1:18:33

and making sure that we don't overheat.

1:18:36

There's some interesting braking systems that we're building as well, too, in Dot.

1:18:42

Why is the braking system interesting?

1:18:46

A lot of the question is, what type of brake are you going to make

1:18:53

that makes sure that at different speeds or in different types of weather,

1:18:58

that you can actually make sure that you stop on a dime, especially if

1:19:00

you're not going just on the road?

1:19:02

For example, if you're going on the sidewalk or when you're making a turn.

1:19:06

You need to be able to stop much more suddenly?

1:19:07

Yes, because if you're going to move from, say, the bike lane into the sidewalk

1:19:13

or any quick turn like that, those are the types of things that are more sensitive.

1:19:25

See you, Dot. Good robot.

1:19:30

That's so cool. These are really good, by the way.

1:19:32

They're good. I've never had Tayto.

1:19:35

Wait, you're saying this is like… This is Ireland's…

1:19:38

This is like the— This is Ireland's potato chips.

1:19:42

Yeah, it's the Lay's chips of Ireland.

1:19:48

I'm introducing you to all my culture. We're talking about…

1:19:51

What are we talking about?

1:19:52

One of the things I'll never forget talking to you and Patrick about is we

1:19:55

grew up together in the years of building our companies.

1:19:59

You've always had the idea that Stripe is a technology company

1:20:06

that happens to be in payments.

1:20:08

It's not a payments company.

1:20:11

You've always challenged, I remember, your customers to think about,

1:20:17

"Don't think about just accepting payments.

1:20:18

There's more that you can do." I'm curious, what more can we do?

1:20:23

Should we be doing with things like crypto or stablecoins?"

1:20:26

Two topics that are on the minds of all our customers today

1:20:30

are, yes, stablecoins and then AI, specifically with respect

1:20:35

to changing buying, agentic commerce.

1:20:38

On stablecoins, I think maybe DoorDash is not where I would start.

1:20:43

Two places that we're seeing tons of interesting stuff is, one,

1:20:47

any cross-border use case where if you want to send money to 100 countries,

1:20:53

it's already the case that stablecoin is a much better way to do

1:20:57

That's where we're seeing a ton of adoption where just that long-tail

1:21:00

coverage, and they really work for that.

1:21:02

The second, and this is where Tempo,

1:21:06

our new initiative that you guys were nice enough to work

1:21:09

with us on, is coming in, is very scalable crypto payments.

1:21:17

Obviously, agents is what we have in mind as we're developing Tempo,

1:21:23

where if you want to pay for your API consumption or if your agent wants to be

1:21:28

able to pay for things around the internet, you actually need a really

1:21:30

good scalable blockchain for that.

1:21:31

Okay, so that's on the crypto side of things.

1:21:34

The other thing that we're thinking about a lot is just how does commerce change?

1:21:40

Now that people's expectations are increasingly, they ask their AI for stuff.

1:21:45

It feels like you should be able to ask your AI, whether that's ChatGPT or Gemini

1:21:51

or whether that's Siri or whatever your interface is to AI, it's like,

1:21:57

"Let's just order again what we ordered on Sunday." You should be able to make

1:22:00

that natural language query and have it take care of interfacing with DoorDash

1:22:04

and payment and everything like that.

1:22:06

I'm curious how you guys think about the interface changing.

1:22:09

I mean, you just had a lot.

1:22:10

Do you want to enable people to order within the AI apps?

1:22:14

Do you want to have just more of a natural language input to DoorDash?

1:22:17

How is that going to happen given that that is presumably what people want

1:22:21

in terms of the low friction AI experience?

1:22:24

Well, I think what people want is definitely low friction,

1:22:26

but I think people also want the products to show up on time and think they want—

1:22:31

You only get a right to do this cool UI stuff if the logistics is right.

1:22:35

The challenge with the product like a DoorDash is you have to do the whole

1:22:40

thing end-to-end, or you have to have great coordination

1:22:44

or communication mechanisms.

1:22:46

We definitely agree with you that I think there's definitely going to be a place

1:22:51

where in the future, as these AIs develop their—whatever we

1:22:56

want to call them—app ecosystems, relationships, I think,

1:22:59

with companies like ourselves that can, exactly what you just said, do the jobs

1:23:03

to be done in their regular life.

1:23:05

I think that will be a source of top-of-funnel for us.

1:23:08

I think the hard part is making sure that we can actually deliver on the promise.

1:23:15

Sometimes, a cautionary tale of the past, I remember services,

1:23:22

Google food ordering launched in, I forget, 2015 or '16,

1:23:26

and they allowed you to order delivery, for example, from various Google surfaces:

1:23:32

Google Maps, Google Search, et cetera.

1:23:34

While on the one hand, it was low friction,

1:23:37

and they could send lots of traffic, they couldn't get the retention.

1:23:40

This is where we're talking about consumer businesses and retention

1:23:43

being the name of the game.

1:23:45

The reason was because they didn't know how to contact the driver,

1:23:49

they didn't know if the driver couldn't find you, if something was taking a little

1:23:52

bit longer, if something was out of stock.

1:23:55

There's a lot of things that happen after the checkout button.

1:23:58

I think this is one thing, back to what we were talking about

1:24:01

earlier about what is the correct UI.

1:24:04

It's not obvious to me that it's just a chat interface,

1:24:07

that there are all of these different components that have to be solved.

1:24:12

The way I think about it is, okay, regardless of whatever we ship,

1:24:16

whether it's on our own surface, other people's surfaces,

1:24:21

are we solving the end-to-end job?

1:24:23

If we are not solving the end-to-end job, it's not going to matter.

1:24:26

What is food that you can get in other places when you visit London,

1:24:32

when you visit some other city that you wish you could get in the Bay Area,

1:24:37

Actually, I really like tea sandwiches.

1:24:39

I was literally in London—

1:24:42

Yeah, little cucumber sandwiches.

1:24:46

I'm always looking for a healthy snack in the afternoon.

1:24:49

Today, I had an awesome, thanks to you— Healthy snack?

1:24:53

Have you done the proper afternoon tea with the three-level platters

1:24:57

and the little cakes and everything? I did it once.

1:25:00

I took my kids a couple of summers ago.

1:25:04

That was an experience, I think, in part because it was new to me

1:25:07

and my kids, in part because I had kids with me playing with all the different

1:25:12

things that they gave us.

1:25:16

One of the things that's really interesting about some of the grocery

1:25:20

stores, for example, in European markets is prepared meals

1:25:25

is a massive focus, massive.

1:25:27

We have some of that here in the US, to be fair.

1:25:31

But in London, I just found myself finding that in every store, as I went

1:25:35

to shop, you would see so many.

1:25:37

There's a few of these things that I find that are so much better back

1:25:41

home in Ireland, UK, Europe.

1:25:44

Prepared food is one of them.

1:25:45

A sandwich you get in Heathrow will probably be pretty good.

1:25:48

A sandwich you get in JFK, don't go there.

1:25:56

Some of the stores we have, the cookies, obviously, much better back home.

1:26:00

There's some delicious cookies.

1:26:02

The teas, there's a lot more choices of teas inside restaurants, I found.

1:26:08

I think one of the coolest part and greatest privileges, frankly,

1:26:12

that I have at DoorDash is because we get to interface with all the businesses

1:26:18

of all these different wonderful cities and really get to meet the people

1:26:23

who are building the GDP of those cities.

1:26:26

It's not just the cool economic impact that they're having, but you get to find

1:26:30

out about all their passion projects.

1:26:32

Everyone is passionate about this stuff.

1:26:34

Everyone's passionate about something.

1:26:35

That's one of the coolest things about my job where I don't just…

1:26:39

You asked me a bunch of questions about different IP and process

1:26:44

I promise you there is that for every single business inside every city.

1:26:49

It's a cool job where you get to work with people who are incredibly passionate

1:26:54

about what they do all day long. Yes.

1:26:56

Well, Tony, thank you. Thanks so much, John.