The Exchanges

Q Well, talking about the differentiator that being like being on the ground, being in person, when you launch a new city, can you just talk me through that city playbook rollout? Do you just pick random people and send them? How many do you pick? What's the organization? Can you just walk me through that?

A Yeah. So at Uber, we saw almost every city as its own startup, and that was an incredibly freeing thing. For people that were young in their twenties and thirties to kind of go into a market and create it from scratch. And the playbook was probably a 180 steps. We should probably open source it at some point. It's on an Asana checklist somewhere. But how we thought about it was we wanted the right team. And, you know, for us, the right team meant we were hiring for three roles. One was we'd send in a launcher, um, that would kind of pop around from city to city. Started out in L.A., then did Philadelphia, then did Atlanta, and sort of the profile of the launcher was, ah, MBA type, private equity, banking, and, you know, the launcher would be responsible for hiring a team. So in a general market, we would want a general manager who acted as like the CEO of the city, overseeing both driver and rider, and that person was, ah, very similar background to the launcher, banking, private equity, consulting, MBA. Stanford class of 20 12 was very good to us. And then The second role that we hired was an operations manager, and that was more like banker consultant types like me, um, that would oversee sort of the driver, uh, area of the operation and be responsible for growing that. And then we'd also work on, uh, getting a marketing manager that would oversee the rider side, DD, uh, part…

AI assessment note: “how we thought about it was we wanted the right team”

Q I think, I think for me, it's like input and output metrics. The amount of times it's like, hey, revenues are metric. And I'm like, no, number of rides per week is our metric. I don't care about the revenue. That is the output metric. Final one. What's the best growth strategy that you've seen in the last 12 months? And why have you been so impressed by that one?

A I'm a huge perplexity user. I think it's an amazing product. Um, huge fanboy to use your language. And, uh, you know, I think one of the things they've done recently that's really interesting is they're offering a free pro membership, um, to anybody with a LinkedIn premium account or an Uber one membership. So instead of spending 20 dollars a month or 250 dollars a year, you plug in your info and they'll give you the free usage. So they basically kind of locked in people for a year and, you know, if you believe that perplexity could be like the next Google search, you know, you're happy to pay that CAC or sort of that free membership and sort of The GPUs over the year to acquire those users and generate stickiness when a lot of tools are facing high term.

AI assessment note: “they're offering a free pro membership, um, to anybody with a LinkedIn premium account”

Q How did you retain the drivers when it was one out of 10, two out of 10? Because the hard thing is like the symmetry of timing, making sure it's aligned. How did you keep them when there was nothing coming?

A What I guess people call the chicken and egg problem or the cold start problem in marketplaces by doing a couple things. In the early days, we wanted to make sure that drivers, when they were sitting around, were kind of like paid for that time. So in the early days, we paid a driver 20 or 30 dollars an hour to sit there, right? And you know, this lasted probably 60 or 90 days into a market launch, and then after, you know, it became clear that the driver was making 20, more than 20 or 30 dollars an hour, We removed that guarantee and sort of let the marketplace float naturally. We also did things like we put drivers near places where we knew it would have high demand in cities. Um, and then we made it really easy on the demand side to refer your friends. So if you were riding in a car with somebody and they hadn't used Uber yet, by the time you got out of the car, you would have referred them and you each would have gotten like 10 dollars off your next ride.

AI assessment note: “we paid a driver 20 or 30 dollars an hour to sit there”

Q Now, I would love to start just with a little bit of context before we dive into the incredible stories that are coming. Uh, tell me, how did you make your way into the world of growth, join Uber, I believe as employee number 99?

A Harry, it's a crazy story. So, I finished up school in 2011, and when I finished up school, there were really only two things that people did. They either went to investment banking, or they went to consulting. I chose consulting, and very early on, I realized that it wasn't for me, um, probably on, like, week three. But the silver lining of the experience was, on the weekends, I could go out and fly to San Francisco as long as I was back, as long as I was back at the client site on Monday morning. So, this was 2011, 2012, and, you know, I was going out and meeting companies like Airbnb, Square, fairly early, along with, like, 50 other companies. But nothing really clicked, um, until I was in Chicago one night. I was trying to get to a work dinner, and I was trying to find a taxi. It was raining, and I had heard about this app where if you press the button, you could get a ride. So I downloaded the app, and two minutes later, my first Uber showed up. It was an Escalade, and I was immediately in love. An absolutely magical experience. So that night, uh, was working on a deck pretty late at night, probably wrapped around midnight or one a.m., And had this Jerry Maguire moment where I was like, huh, I need to be a part of this Uber thing. What's the most, like, simple thing to do? So I decided to email Travis at Uber.com, not thinking that I would hear anything. And I got an email…

AI assessment note: “I decided to email Travis at Uber.com, not thinking that I would hear anything.”

Q Which do you think is the best there? That's an interesting one, because, like, I would argue that rider spend is not a true reflection. I could take one long trip, and it doesn't mean that I really love Uber, but if I do velocity of trips, 10 trips, it means that I do. What, which one did you really focus on?

A I would say rides per month is probably the most important in understanding rides per week, and sort of how it maps to people's routine. Because some days, like you said, you might have like a long airport trip, and that might distort the cohort numbers over time. And then on the driver side for, uh, retention, what we were looking at is like, hey, how far are they retaining it? Like, 28 days, you know, 56 days, 96 days, and just understanding like, hey, like, if a driver stays with us for three months, they're likely to stay with us longer. And sort of looking at the underlying trip metrics, how many trips they're doing per week, how many, like, what their ratings are. And then also, like, You know, how many hours they're putting in, right? Some people, this is a full-time thing, and for like, 90% of drivers, it's very part-time, right? Less than like, 10 hours a week.

AI assessment note: “I would say rides per month is probably the most important”

Q How did you retain the drivers when it was one out of 10, two out of 10? Because the hard thing is like the symmetry of timing, making sure it's aligned. How did you keep them when there was nothing coming?

A What I guess people call the chicken and egg problem or the cold start problem in marketplaces by doing a couple things. In the early days, we wanted to make sure that drivers, when they were sitting around, were kind of like paid for that time. So in the early days, we paid a driver 20 or 30 dollars an hour to sit there, right? And you know, this lasted probably 60 or 90 days into a market launch, and then after, you know, it became clear that the driver was making 20, more than 20 or 30 dollars an hour, We removed that guarantee and sort of let the marketplace float naturally. We also did things like we put drivers near places where we knew it would have high demand in cities. Um, and then we made it really easy on the demand side to refer your friends. So if you were riding in a car with somebody and they hadn't used Uber yet, by the time you got out of the car, you would have referred them and you each would have gotten like 10 dollars off your next ride.

AI assessment note: “we paid a driver 20 or 30 dollars an hour to sit there”

Q What were the biggest things that worked in cities that you brought everywhere? So renting a hotel room, giving it to them for downtime, and then pitching. What else?

A I think, honestly, people underestimate It's an incredible mechanism to get people through the door. And then the other thing, Harry, that worked super well, um, is referrals, right? So when we started doing referrals, um, of drivers, we would, you know, pay like a 25 or 50 dollar bonus for a driver to bring their friend, and then when they completed, like, their first 10 trips, we'd pay out the bonus on both sides. And, you know, obviously, as the business scaled up, the referral bounties got quite a bit bigger, but we found that when we tapped into a specific driver community, They all kind of knew each other and were very happy to refer each other because Uber allowed them to buy more cars and expand their business. And it really just helped them grow, um, as sort of the business, uh, at large.

AI assessment note: “the other thing, Harry, that worked super well, um, is referrals”

Q With the increase in competition, and increasing cash to competition, did you actively see it become harder to acquire drivers?

A Yeah. Um, you know, I think there was a point in San Francisco, and don't quote me on this number, Is over time, right? Like we were paying 25 or 50 dollars for referrals in Atlanta. And then we started paying like 250 dollars to each side, the right, the driver that referred and the driver that signed up. We, and then we escalated to 505 hundred, and I think there was a point in time where in San Francisco and a couple of other very competitive cities, we were paying a thousand dollars to each side. So 2000 dollars for driver acquisition. And, you know, we would put some sort of thresholds around that, right? The new driver would have to do X amount of trips and maintain this quality rating and do this sort of acceptance rate. But there was a period of time, probably like, 2014, 20 15, 20 16, where things got very gnarly.

AI assessment note: “we were paying a thousand dollars to each side. So 2000 dollars for driver acquisition.”

Q So I didn't know the context there when I asked that question. I did not know that it was like a direct cold email to Travis. That is, that is awesome. Um, that's also a lot of responsibility on the shoulders of a twenty-three-year-old to open a city. How did you build in Atlanta then? You go back Take me to that.

A Yeah, so it was an incredible experience. Uh, I was living in Atlanta at the same time, so I didn't really have to move. Um, but when I got back to Atlanta to launch Uber, it was really about building the operation from scratch. Um, when you're starting a marketplace, you really need to do two things. You need to get supply and you need to get demand. So for Uber, the hardest thing was the driver side, and we need to get a lot of drivers very quickly. So When we went into the initial market, we got a Yelp list of all the drivers, um, in the city and sort of like called through and trying to get them to sign up with Uber and, you know, got our initial list of drivers that way and launched a few weeks later, um, with something we call a rider zero.

AI assessment note: “we got a Yelp list of all the drivers... and sort of like called through”

Q What were the biggest things that worked in cities that you brought everywhere? So renting a hotel room, giving it to them for downtime, and then pitching. What else?

A I think, honestly, people underestimate It's an incredible mechanism to get people through the door. And then the other thing, Harry, that worked super well, um, is referrals, right? So when we started doing referrals, um, of drivers, we would, you know, pay like a 25 or 50 dollar bonus for a driver to bring their friend, and then when they completed, like, their first 10 trips, we'd pay out the bonus on both sides. And, you know, obviously, as the business scaled up, the referral bounties got quite a bit bigger, but we found that when we tapped into a specific driver community, They all kind of knew each other and were very happy to refer each other because Uber allowed them to buy more cars and expand their business. And it really just helped them grow, um, as sort of the business, uh, at large.

AI assessment note: “the other thing, Harry, that worked super well, um, is referrals”

Q Which do you think is the best there? That's an interesting one, because, like, I would argue that rider spend is not a true reflection. I could take one long trip, and it doesn't mean that I really love Uber, but if I do velocity of trips, 10 trips, it means that I do. What, which one did you really focus on?

A I would say rides per month is probably the most important in understanding rides per week, and sort of how it maps to people's routine. Because some days, like you said, you might have like a long airport trip, and that might distort the cohort numbers over time. And then on the driver side for, uh, retention, what we were looking at is like, hey, how far are they retaining it? Like, 28 days, you know, 56 days, 96 days, and just understanding like, hey, like, if a driver stays with us for three months, they're likely to stay with us longer. And sort of looking at the underlying trip metrics, how many trips they're doing per week, how many, like, what their ratings are. And then also, like, You know, how many hours they're putting in, right? Some people, this is a full-time thing, and for like, 90% of drivers, it's very part-time, right? Less than like, 10 hours a week.

AI assessment note: “I would say rides per month is probably the most important”

Q When you get a regulatory warning, when you get regulatory controls put on you, how does that feel? What does that look like? Can you take me to one?

A South by Southwest, Austin, um, the mecca for all things tech. And in Austin, we were not allowed to operate because there were two rules in Austin that prevented us from doing so. If you ordered a black car in Austin, The minimum fare, whether you're going a block or a mile, was 55 dollars, and if you ordered a car and it showed up in one minute, you had to legally stare at it for another 29 minutes before being allowed to get in. So, it made absolutely no sense, Harry. So, what we did in twenty-fourteen was we decided that we were going to do South by Southwest if we'd done the previous years, And this year we decided to do black cars, and we actually did the 55 dollar minimum fare, um, which was a bonanza. A lot of drivers did very well, um, and we brought in drivers from Austin, from Dallas, from Houston, from San Antonio, and it was incredible. Um, the authorities weren't super concerned about, like, the pre-reservation, like, get a car, and then it shows up 29 minutes later, et cetera. But what we did on UberX really inflamed them. So, what we decided to do Was we decided to do free UberX for all of South by Southwest. But what ended up happening was that, you know, we paid the drivers a set dollar amount per hour. So the drivers were paid. And then we basically said, okay, cool. We're going to let the riders ride for free. Right? So what ended up happening was the regula…

AI assessment note: “South by Southwest, Austin... we were not allowed to operate because there were two rules”

Q What did you say to them when you called them up? What did they say? What percent said yes? Just take me to that.

A Yeah. So I'd be like, Hey, this is Scott. Um, I work at Uber in Atlanta. We're starting a new ride sharing service where, you know, you can pick Pick up, uh, riders between your trips to the airport. Um, and, you know, we help you fill your downtime. Um, is this something you might be interested in? And, you know, while most drivers hadn't heard of Uber and were a little bit skeptical, they were willing to give us a shot because it didn't cost them anything to join, right? They would come to our office and, you know, we'd give them an iPhone, which is a story at some point. When we gave them the iPhone, they'd be able to pick up a rider 20 minutes later, right? So we'd say, hey, if you don't like this, bring it back and no skin off our back.

AI assessment note: “Hey, this is Scott. Um, I work at Uber in Atlanta.”

Q What did you do that didn't scale? I'm just intrigued.

A Yeah, so, For probably the first million drivers that we onboarded, a lot of our processes were manual, right? So we weren't doing a lot of like paid spend on Facebook. We were doing a little bit on Craigslist. We weren't doing a ton on Google, but it was really the operational teams going out and finding drivers. It was that cold calling, right? It was getting drivers to show up to the office and batching onboardings first with like one driver at a time, then five, then 10. Then you would basically get, you know, a conference room at a hotel to onboard 25 drivers at a At the time or a hundred drivers at a time. And it just kind of like scaled up other things that we did that I think were like super effective were we go where the drivers weren't, right? Like we knew that we had a captive audience on Monday mornings and Thursday afternoons when drivers picked up and dropped off people from the airport. And we knew that if we went there with snacks or coffee or rented sort of conference room where they could chill out between rides, we can pitch them on driving Uber. And, you know, we saw it as a very incremental to what they were building. You know, every city at Uber was kind of like running a playbook, but if we found something that worked in one city, we'd bring it everywhere else too.

AI assessment note: “a lot of our processes were manual, right? So we weren't doing a lot”

Q Can I ask, in terms of those kind of margin improvements, how did you see margins and economics change as the company progressed in new cities? Like, did the maturation rate become much quicker? What were some lessons from that? I'm just intrigued.

A Yeah, so I would say the biggest difference on the unit economics over time was We had to play the game on the field, right? Um, there were a lot of competitors in different markets, and at the same time as we were raising money, um, SoftBank was pouring money into all the different competitors, right? So even though we were in 2014 and the business was five years along, like, the unit economics as we launched Hooper X and had more competitors actually got worse over time because you started spending so much money to acquire drivers, to acquire riders, and until the competitive market like rationalized, right, and SoftBank sort of like, Pulled back a little bit, or decided, or Uber decided exactly, like, where we wanted to play. It was very challenging to basically be like, hey, Harry, we're gonna cut all driver incentives tomorrow. We're gonna cut all rider incentives, because we would have seen that market share reflected, and that would have stopped our growth.

AI assessment note: “the unit economics as we launched Hooper X and had more competitors actually got worse”

Q With the increase in competition, and increasing cash to competition, did you actively see it become harder to acquire drivers?

A Yeah. Um, you know, I think there was a point in San Francisco, and don't quote me on this number, Is over time, right? Like we were paying 25 or 50 dollars for referrals in Atlanta. And then we started paying like 250 dollars to each side, the right, the driver that referred and the driver that signed up. We, and then we escalated to 505 hundred, and I think there was a point in time where in San Francisco and a couple of other very competitive cities, we were paying a thousand dollars to each side. So 2000 dollars for driver acquisition. And, you know, we would put some sort of thresholds around that, right? The new driver would have to do X amount of trips and maintain this quality rating and do this sort of acceptance rate. But there was a period of time, probably like, 2014, 20 15, 20 16, where things got very gnarly.

AI assessment note: “we were paying a thousand dollars to each side. So 2000 dollars for driver acquisition.”

Q Would you say now, with the benefit of hindsight, Free UberX Week was incredibly, incredibly instrumental to the success of UberX?

A I think it would have worked no matter what. Um, I think, like, we had strong product market fit, but I think, like, what was particularly interesting about Free UberX Week, looking back on it 10 years later, uh, in hindsight, was the competitive dynamics, right? So, in a lot of ways, when we launched UberX, the main competitor was Lyft, and Lyft's flagship product was UberX. And what a lot of people don't know about Lyft is when we started launching UberX, we let Lyft go first, and we had a policy that said, hey, if Lyft, uh, launches in a city, we are going to wait 30 days to see if the law enforcement, uh, comes after them, and then we're going to launch after that. So, as you probably know from Uber, that was a very abrupt, uh, sort of, It was a very different tack than we had taken on other launches and it didn't last very long, right? We probably did that for like three or four or five cities, and then we just started launching at the same time. So when we launched UberX, it was about market share and it was about getting as many drivers as possible as quickly as possible so that we could win that market and carve out a position, uh, that we felt really confident in.

AI assessment note: “I think it would have worked no matter what.”

Q Well, talking about the differentiator that being like being on the ground, being in person, when you launch a new city, can you just talk me through that city playbook rollout? Do you just pick random people and send them? How many do you pick? What's the organization? Can you just walk me through that?

A Yeah. So at Uber, we saw almost every city as its own startup, and that was an incredibly freeing thing. For people that were young in their twenties and thirties to kind of go into a market and create it from scratch. And the playbook was probably a 180 steps. We should probably open source it at some point. It's on an Asana checklist somewhere. But how we thought about it was we wanted the right team. And, you know, for us, the right team meant we were hiring for three roles. One was we'd send in a launcher, um, that would kind of pop around from city to city. Started out in L.A., then did Philadelphia, then did Atlanta, and sort of the profile of the launcher was, ah, MBA type, private equity, banking, and, you know, the launcher would be responsible for hiring a team. So in a general market, we would want a general manager who acted as like the CEO of the city, overseeing both driver and rider, and that person was, ah, very similar background to the launcher, banking, private equity, consulting, MBA. Stanford class of 20 12 was very good to us. And then The second role that we hired was an operations manager, and that was more like banker consultant types like me, um, that would oversee sort of the driver, uh, area of the operation and be responsible for growing that. And then we'd also work on, uh, getting a marketing manager that would oversee the rider side, DD, uh, part…

AI assessment note: “we were hiring for three roles. One was we'd send in a launcher”

Q What did you do that didn't scale? I'm just intrigued.

A Yeah, so, For probably the first million drivers that we onboarded, a lot of our processes were manual, right? So we weren't doing a lot of like paid spend on Facebook. We were doing a little bit on Craigslist. We weren't doing a ton on Google, but it was really the operational teams going out and finding drivers. It was that cold calling, right? It was getting drivers to show up to the office and batching onboardings first with like one driver at a time, then five, then 10. Then you would basically get, you know, a conference room at a hotel to onboard 25 drivers at a At the time or a hundred drivers at a time. And it just kind of like scaled up other things that we did that I think were like super effective were we go where the drivers weren't, right? Like we knew that we had a captive audience on Monday mornings and Thursday afternoons when drivers picked up and dropped off people from the airport. And we knew that if we went there with snacks or coffee or rented sort of conference room where they could chill out between rides, we can pitch them on driving Uber. And, you know, we saw it as a very incremental to what they were building. You know, every city at Uber was kind of like running a playbook, but if we found something that worked in one city, we'd bring it everywhere else too.

AI assessment note: “a lot of our processes were manual”

Q What's your favorite lesson from working with Travis? You saw him across different stages, across different expansion segments. When you look back, what was your biggest takeaway or lesson from seeing him operate?

A I think what Travis is really good at is speed, right? He has a quote that's, fear is the disease. Hustle is the antidote. And I think what he did better than anybody else and does better than anybody else is he sees something and he moves quickly, right? It's, hey, like, let's try this out. If it works, amazing. We're going to pour more gasoline on it. And if it doesn't, we're going to keep trying until we figure it out. And I think what's really unique about Travis is as a CEO, Is he, he's just level of understanding across like product and operations is very unmatched. Like he could dive into the weeds with engineering, but then he could nerd out with you on sort of like positioning cars in different cities and like talking to drivers, uh, like 30 seconds later. The context switching and just the, the ability to kind of create things at speed and empower his teams to think that they can run through walls was unmatched.

AI assessment note: “I think what Travis is really good at is speed”

Q So I didn't know the context there when I asked that question. I did not know that it was like a direct cold email to Travis. That is, that is awesome. Um, that's also a lot of responsibility on the shoulders of a twenty-three-year-old to open a city. How did you build in Atlanta then? You go back Take me to that.

A Yeah, so it was an incredible experience. Uh, I was living in Atlanta at the same time, so I didn't really have to move. Um, but when I got back to Atlanta to launch Uber, it was really about building the operation from scratch. Um, when you're starting a marketplace, you really need to do two things. You need to get supply and you need to get demand. So for Uber, the hardest thing was the driver side, and we need to get a lot of drivers very quickly. So When we went into the initial market, we got a Yelp list of all the drivers, um, in the city and sort of like called through and trying to get them to sign up with Uber and, you know, got our initial list of drivers that way and launched a few weeks later, um, with something we call a rider zero.

AI assessment note: “we got a Yelp list of all the drivers, um, in the city”

Q Can I ask what didn't work? What was some early mistakes in driver acquisition specifically actually that were like, oh, that, that was a bad one.

A Yeah. So we made a lot of mistakes, right? So I would say like the number one mistake we made in a lot of cities is We probably got kicked out of every office, um, that we joined early because, you know, we were renting like space and like coworking spaces. And a lot of these coworking spaces weren't too happy with drivers coming by like all hours of the day and sort of disrupting the flow. So I, I think that was sort of a major error that we made early on. Um, I think other things that we did that were kind of a little bit challenging, um, in markets is I think that. You know, where, where we messed up was, it was a very, like, 2407, three 65 operation. There were people in cars every single hour of the day, and we probably understaffed a little in the early days, right? So, you know, for the first year in Atlanta, I was the only person handling the driver side. We had Keith, uh, overseeing the city as a general manager, and he was incredible, and we had a marketing manager. But, you know, I probably had, like, a thousand or 1500 drivers that were just me, and, like, You know, we didn't have any of the AI tools that we did today. It was just like on a Google voice and sort of back and forth textings and desk. We, we let a lot of things slip through the cracks, but the business on the foundation was working.

AI assessment note: “the number one mistake we made in a lot of cities is We probably got kicked out”

Q Can I ask, in terms of those kind of margin improvements, how did you see margins and economics change as the company progressed in new cities? Like, did the maturation rate become much quicker? What were some lessons from that? I'm just intrigued.

A Yeah, so I would say the biggest difference on the unit economics over time was We had to play the game on the field, right? Um, there were a lot of competitors in different markets, and at the same time as we were raising money, um, SoftBank was pouring money into all the different competitors, right? So even though we were in 2014 and the business was five years along, like, the unit economics as we launched Hooper X and had more competitors actually got worse over time because you started spending so much money to acquire drivers, to acquire riders, and until the competitive market like rationalized, right, and SoftBank sort of like, Pulled back a little bit, or decided, or Uber decided exactly, like, where we wanted to play. It was very challenging to basically be like, hey, Harry, we're gonna cut all driver incentives tomorrow. We're gonna cut all rider incentives, because we would have seen that market share reflected, and that would have stopped our growth.

AI assessment note: “unit economics as we launched Hooper X and had more competitors actually got worse over time”

Q Well, did it explode from pretty much day one? Like, was there immediate product market fit on UberX?

A On UberX, yeah. Um, there was immediate product market fit on UberX. And what was really interesting about UberX is when we launched UberX, and this was an operational nightmare, in a lot of cities, we actually did free UberX week. So for an entire week, we would get free UberX. It was a marketing expense, right? And we were basically running at probably 8090, a hundred percent utilization all the time. And You know, we did press around it. We knew that when we did it, um, that it would be hard to kind of fill all that demand, but people were really excited about UberX and like a better, faster, cheaper option to use Uber. And, uh, you know, over time we were able to build more supply.

AI assessment note: “On UberX, yeah. Um, there was immediate product market fit on UberX.”

Q Okay, so they're sitting in an office day to day, and they have complete, complete free reign over the city. How does that control and decision making look like from their perspective?

A Yeah, so everything was pretty autonomous, right? In the early days, uh, we all, I think, reported in, all the cities reported to Ryan Graves, uh, who was the first CEO before Travis. Incredible guy. And, uh, basically what happened every city had like the freedom to experiment and try things. And I think we had a pretty flat work structure in the early days. Everybody reported into gray. And later became the COO. Um, and he's an incredible guy. Um, love grades. And what's really interesting sort of about that is we, we all kept each other accountable, right? We were all doing really hard things. And every week we would get on like a city call, um, where an all hands call and every city would go down the list and say how much gross bookings you did it last week, how many trips you completed, how many drivers you onboarded and sort of general highlights from the city. And we had everything in a dashboard. Everybody at the company could see exactly what city was doing what, who was growing fastest, and it became like a really competitive dynamic that all just kind of, like, pushed each other forward, um, which was a really interesting approach.

AI assessment note: “every city had like the freedom to experiment and try things”

Q What did Uber get most wrong, do you think? When you look back now, what's the, that was the mistake?

A I think our aggressive stance with writers and politicians clearly worked because we were able to create a lot of fans of Uber over the years. But if I could go back or we could go back and do it all over again, I think we would have found a way to present our image in a way that was less combative, right? Especially with media. I wish that, you know, we could have built like a, a better story. Um, because I think like when you come out and, you know, I think we had a lot of bravado, we had a lot of swagger. I think when you come out like that, everybody wants to build you up on the way up and then they want to tear you down on the way down. You know, I probably would have been combative in the areas that we were with, uh, with like writers and And politicians, but a little bit more humble and gentle with sort of media.

AI assessment note: “we would have found a way to present our image in a way that was less combative”

Q more broad startup advice where we do a quick fire. He inspired this like followership. I think the best founders inspire followership. You see it with the Collisons, Toby Lutker at Shopify, Travis. Where I know many early Uber employees, and they just fucking love him, and they love each other, and it's like this intense familial sentiment, which is really special. What did Travis do to generate this followership?

A The best founders build cults, and they're cult leaders, and I think Travis, what he was really good at was inspiring the team. We would look left, we would look right, we would all have like a lot of like work on our plate, but we knew that If we put our heads down and did it, there would be an incredible journey on sort of the other side. And Travis was really good at explaining the what and the why behind everything, you know, during Zurb, a lot of founder CEOs got a little soft. Um, and I think like Travis was always willing to roll up the sleeves, dive in and figure out the answers to the messiest problems. And I think like the best idea always won with Travis and As a, as a team, we had a lot of respect for each other, and we were, we had strong opinions, but they were loosely held, and we were all kind of, like, going to war together every day, ah, on this, like, mission together, and that felt really good, and that's what built the Lifelong Bonds.

AI assessment note: “Travis was really good at explaining the what and the why behind everything”

Q more broad startup advice where we do a quick fire. He inspired this like followership. I think the best founders inspire followership. You see it with the Collisons, Toby Lutker at Shopify, Travis. Where I know many early Uber employees, and they just fucking love him, and they love each other, and it's like this intense familial sentiment, which is really special. What did Travis do to generate this followership?

A The best founders build cults, and they're cult leaders, and I think Travis, what he was really good at was inspiring the team. We would look left, we would look right, we would all have like a lot of like work on our plate, but we knew that If we put our heads down and did it, there would be an incredible journey on sort of the other side. And Travis was really good at explaining the what and the why behind everything, you know, during Zurb, a lot of founder CEOs got a little soft. Um, and I think like Travis was always willing to roll up the sleeves, dive in and figure out the answers to the messiest problems. And I think like the best idea always won with Travis and As a, as a team, we had a lot of respect for each other, and we were, we had strong opinions, but they were loosely held, and we were all kind of, like, going to war together every day, ah, on this, like, mission together, and that felt really good, and that's what built the Lifelong Bonds.

AI assessment note: “Travis was really good at explaining the what and the why behind everything”

Q Okay. Listen, like not hugely dissimilar to consumer subscription to be honest. How did the product offering look when you were doing Atlanta and the subsequent city rollout in terms of the different tiers of Ubers in terms of luxury? What did that look like?

A Yeah. So when I started at Uber, we were only black cars. Um, so when we were launching cities, it was really only black cars. So for the first year in Atlanta, we were only black car. Um, and What was challenging about that is there are only a certain number of black cars in a given city, right? Like in Atlanta, there might've been a thousand black cars. And while you can create more and get more on board, you're never going to build like the massive TAM business unless you open it up for UberX. So we started launching UberX more probably in late, early, early, early, and then sort of, as we started rolling out UberX in more markets, um, that was probably in That's when the business really started to explode.

AI assessment note: “when I started at Uber, we were only black cars.”

Q Were there cities that were clearly behind and struggling, and what were the reasons for those struggling cities?

A I think there were definitely cities that fell behind, um, but in a lot of cases, like, the teams were just incredible, um, It was more a function of the regs. So a lot of times, you know, if a city was struggling, it would be due to the regulations and the laws. So, you know, they would make it really difficult to operate. They would take your drivers. They would try to shut us down. And over time that regulatory situation cleared up where, you know, we were able to operate more freely or, you know, we ended up moving the teams to other cities or sort of working on other projects. But I would say like, it wasn't, it wasn't like certain markets were underperforming because of, you know, the, Sort of like underperformance. It was more like external factors that people couldn't control.

AI assessment note: “there were definitely cities that fell behind... It was more a function of the regs”