Dara Khosrowshahi on replacing Uber drivers — and himself — with AI

Today, I’m talking with Uber CEO Dara Khosrowshahi. It’s become something of an annual tradition to have Dara join us in the studio when he comes to New York for Uber’s big GO-GET event every year, and it’s always a lot of fun.

The big news this year is that Dara is really starting to think about Uber as a much larger platform for travel — starting with the ability to book hotels in the Uber app, thanks to a partnership with Expedia. There’s also new services, like being able to have coffee and snacks in your Uber when it arrives, and even personal shopping. Uber is going so far as to call this an everything app, so I wanted to see how far Dara thinks everything actually goes — and whether he’s feeling pressure to own more of the user experience in a world where AI companies keep promising that their chatbots will book all the cars for you.

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I also wanted to know if those chatbots have created any opportunities for Uber. Last year Dara told me he was wide open to partnerships just to see if they were meaningful, but all the AI Uber integrations I’ve seen so far have been pretty clunky, and far slower than just using the app myself. So we dug into what Dara is seeing there — and if there’s any potential in the future. 

I’ve also been dying to talk to software CEOs about what AI is doing inside their companies, as AI coding tools and agentic systems upend software development. Just a couple of weeks ago, Uber’s CTO said the company had already burned through its entire token budget for the year by the start of April, and Dara told me he was rethinking how fast the company would hire people as it spent more money on tokens. That’s a big bet, and I wanted to know if Dara was rethinking how his software teams were structured as AI starts to muddle the relationship between product managers, designers, and engineers.

Then we talked about Uber’s increasingly large investments in autonomous cars — especially its big investment in Rivian, what kinds of milestones Dara is looking for as the technology evolves, and what happens to all of its drivers in a future where robots do all the work.

And lastly, I asked Dara when he thinks AI will be ready to replace the CEO — it turns out there’s already a rogue AI Dara inside Uber. 

There’s a lot in this one — Dara was as clear and candid as ever, and I think you’ll like it.

Okay: Uber CEO Dara Khosrowshahi. Here we go.

This interview has been lightly edited for length and clarity.

Dara Khosrowshahi, you are the CEO of Uber. Welcome back to Decoder. 

Thank you very much. It’s good to be back.

I’m happy to have you; it’s like a yearly tradition. You guys do your GO-GET event, you have a bunch of news, and then you come down to where we are.

Chock full of news for you. Yeah. Chock full of news.

And we hang out together in person, which is my very favorite thing. So thank you for doing it. I want to talk about the news that you can now book hotels and other experiences in the Uber app, which is a big deal. But I always ask everybody the same two Decoder questions about how companies are structured in decision making, and I just want to do them as a little lightning round at the top.

So, last year on Decoder, I said, “How do you make decisions?” And you gave me the Amazon answer. You said, “One-way doors and two-way doors.”

Mmm-hmm.

A lot of pressure on decision making. Lately, you’re making big decisions. Even expanding the app is a big decision. Has your fundamental framework changed?

The fundamental framework has not changed. Now, I will tell you that I am pushing the company in something that we talked about, taking smart risks. The pattern that I keep seeing is that as companies get larger, they become more hesitant in terms of risk taking. It’s more about playing safe. You’re a public company, you have to hit your quarterly numbers, et cetera. And to some extent, as companies get larger, they get more resilient. They can actually make bigger mistakes. For us, we’ve got almost $10 billion in cash flow. When I first joined, if we made a billion dollar mistake, it would be a disaster. It would put the company on its knees. And I’m not saying that I want to make a billion dollar mistake, but the risks that we have to take in order to get the right return, in order to keep innovating in the world – for example, autonomous vehicles (AV), which I’m sure we’ll talk about – are getting bigger.

We have to be willing to take those risks. And the patterning that I’ve seen with a lot of companies is that as they get bigger, they get more conservative, and the way they operate gets more set in stone. You have more management layers, et cetera. We very much want to avoid that. And it’s taking me really pushing that “one-way door, two-way doors” as one framework of looking at decisions, but then smart risk taking as well. We’ve got to keep taking smart risks as a company. It means once in a while taking risks that in hindsight look dumb. But we’ve gotta push the envelope, especially during this time when there’s so much innovation going on.

Risks, everyone wants to talk about it, but taking the blame for when things fail is like the other part of risk.

Yeah.

It’s the other side of the coin. Also empowering people to take the risk without that fear of failure is really important. How do you think about the stakes? How big of a risk is an individual software engineer at Uber allowed to take?

[laugh] I think if you can’t identify the downside, don’t take the risk. But if you can identify the downside, whether it’s time that you’re spending on a feature, compute that you are dedicating to a feature, or you’ve gotta invest a certain amount of capital in building something, or going after expanding a new line of business in a country – we’re launching Uber Eats in seven countries in Europe as well – then you can make the right calculus in terms of whether you should take the risk or not. 

We want to learn from our mistakes. Some people talk about celebrating mistakes. I’m not going to celebrate a mistake. But I do want to be able to make sure that I learn from a mistake so that the next decision I make can be incrementally better. That’s usually the construct that we use. I think sometimes we overexamine our mistakes. We have meetings, we talk about it. We document the issues, what we did wrong, what could have gone better. I’m honestly not a big fan of that. It’s a big engineering thing, et cetera: “Understand why you made a mistake, what you could have done better, and then move on with life. Let’s go build the next thing.”

Put this into practice for me. What’s a risk that came outside of your sphere of management control that worked out, and what’s one that didn’t?

One that absolutely worked out, that I was involved with – but it was the team that really pushed forward – was women riders and drivers preferred. There was some question as to the liquidity in a marketplace. One of the big things about Uber is that you push a button, you get a car in four to five minutes. There was a question as to whether or not we would have enough women drivers to introduce this feature for women riders. Because if you introduce a feature, it’s not like “women riders, women drivers preferred, and maybe you’ll get one if you’re lucky.” That’s not a good feature. So there was a real question as to the reliability of the marketplace to the extent that the vast majority of our drivers are men, in the US for example.

But because of our size and scale, we have been able to build liquidity in terms of women drivers. And now that women drivers can request women riders, we’re looking to increase the number of women drivers as well. So you get this great flywheel. That’s a risk that worked.

We have built a taxi product twice. We tried it early on and we tried to build taxis the same way that we built peer-to-peer rideshare, which is kind of a one-on-one hail. And it failed, didn’t work. Taxis didn’t trust us. They didn’t sign up. About six years later, Sachin Kansal, who’s now our CPO – he used to build a taxi app – said, “Let’s try this again.” And so, while it failed the first time, this time, we approached it differently.

For example, with taxis, because we don’t have the data inside of the taxi as to whether or not they have a rider in the car, what we did was a little bit different. We introduced blast dispatch. When you ask for an Uber and we want to hail a taxi, we will dispatch to 10 different taxis. And whoever says yes first accepts that ride. So we’re able to get higher reliability and adjust the way that we’ve built the product for taxis. Taxis are now one of our fastest growing products. That’s also an example of making a mistake once, but then actually sometimes you have to try things again, even though it didn’t work for the first time. A different flavor, a different approach. I’m really glad that we took that shot on taxis.

We’re going to come back to risk, because you have a bunch of new products that seem risky.

I want to ask you the other Decoder question about structure. Last time you were here, I felt like I could have talked to you about the structure of Uber for the entire conversation. You had a wild answer that was very lengthy. I encourage people to go back to listen to that part of the conversation. But the short version is, you said, “We have a combination matrix and line of business structure.” You have global leads for mobility and delivery and everything else is matrixed. And importantly, the thing that you had changed was you had made product a central function. You didn’t have separate product teams for each.

And the ride business, obviously – I’m guessing something has changed here because you have many new lines of business. You have an autonomy division. Quickly describe how Uber’s structure has changed.

The only change in structure – because I do value stability – is that I now have a president and COO, Andrew Macdonald. Andrew ran our mobility global business. What we observed is that the platform that is mobility and delivery is coming together, and particularly users who use both mobility and delivery have been growing much, much faster than the individual use cases of mobility and delivery. And it was always my hypothesis – one of the visions that I had, coming to Uber, was that once we had the delivery business post-COVID grow so quickly and show that it has the potential of being just as big as a mobility business, we compete against mobility players and we compete against delivery pure play players. You could have a hypothesis, which is actually being a pure play could be an advantage.

It’s all Lyft. The only thing Lyft cares about, at least historically, was US rideshare, they’re starting to expand internationally as well. Good for them. About time, you could argue. And the only thing DoorDash cares about, let’s say, is food delivery. We’re trying to do both. And it’s hard as a company to do multiple things at once, to have skill sets in multiple business lines. To make up for that, we had a mobility team, a delivery team, and a bunch of common structures and services platforms. Where it came together was the technology platform. We started really pushing this idea of consumer-side platform, driver-side platform. To the extent we could get consumers to use both Rides and Eats, we had a hypothesis that we would retain them for longer. It turns out not only is the retention better, but they spend much more.

Multi-platform consumers spent three times as much as single line consumers as well. We launched the Uber One membership, now almost to 50 million members, growing really, really quickly. They spend three times more, and they tend to be multi-platform versus single platform as well. And that we thought could be our secret sauce, which could differentiate us from the model line players and allow us to acquire more customers, bring them into the platform, get them to use more stuff, have better retention, et cetera. That sounded great, but the P&L often got in the way. It’s every pixel – it sounds easy, let’s use our mobility, let’s cross-promote delivery as well, sounds easy, but that delivery pixel on the mobility app could be taking away from your mobility experience as well. And also could be costing mobility. It’s P&L, I’m sending a customer over to do something else. So, sometimes a P&L got in the way.

I do a lot of stuff. I was pushing platform on the side here, in addition to everything else I do. I really wanted one member of our management team – and Andrew Macdonald’s been here, he’s one of the longest tenured employees, and one of the most capable team members that we have. I said, “Andrew, it’s time for you to move from running global mobility to actually becoming president and COO of the company, and thinking about the platform as a whole.” It’s been a big success, and it frees me up to work more directly with the product and tech teams. So, it’s kind of a double benefit for me. But the platform is really starting to sing. The number of consumers using both Rides and Eats has increased six times in the past five years, and it’s growing 50% faster than our general audience. It’s definitely, definitely working, and I want to lean into it.

It strikes me just as you’re talking here that you’re describing everything in terms of trade-offs. Even risk, you’re describing in terms of trade-off.

Everything’s a trade-off of life. Yeah.

“We might use this compute instead of doing this other thing and putting a pixel on this screen might take a customer away from this line of business.” So you’ve installed the COO just to manage that trade-off more holistically?

Yeah. He negotiates the trade-offs on the ground, he’s ultimately responsible for one number, if you want to call that, whether it’s a customer happiness or that it’s a P&L, and obviously, often you have to manage for all of the above.

My joke on this show constantly is, “If you told me your org chart, I can tell you 80% of your problems.” All companies are kind of the same and I can get to about 80% of the tension if you just tell me where all the executives are lined up and who controls what budget.

Kevin Scott at Microsoft as the CTO once was the person in charge of distributing the GPUs. And I was like, “That’s all I need to know.” I know almost everything about Microsoft at this moment in time. Now, it seems much more complicated for a variety of reasons, but at that moment I could just tell. It sounds like – and obviously the secret is in the last 20 percent – but it sounds like you’ve installed an executive just to oversee the 20% of the prioritization and the trade-offs here.

It’s the 20% of the prioritization of the trade-offs, but you could argue it’s our most important 20%. It’s a 20% that no one else has. And in one year, the 20% doesn’t really matter, but when you compound it over five years, over 10 years, you get the results that we’ve got, which is generally that we’ve grown faster than our competitors, and we’re able to be more profitable than our competitors. That’s the power of the platform, and I really wanted to lean in. At some point it was getting up to a scale where it wasn’t a part-time job, I needed someone really focused on the whole thing.

So, the news here in that context feels like, “We’re going to bet on the platform more.”

We have bet on platform for the past five years. It’s a vision that we’ve always had, it’s working, and when something works, you want to double down.

I’m going to be very reductive here though. The last time you were here, I described Uber as a magic button that made a Toyota Highlander appear in my life. Wherever I’m in the world, almost statistically, a Toyota Highlander is going to arrive. That’s great, and then it’s going to move me around. And the jump from there to “the Toyota Highlander has food in it” is reasonably small. “We’re moving things around, we’re a logistics business.” The news here is you’re also doing hotel booking in partnership with Expedia, you’ve got shopping assistance, and now cars might have coffee in them.

We got a lot going on.

This is far beyond logistics for a platform that was pretty much organized around logistics. Tell me about that in the context of risk and trade-offs and platform bet.

First I would say, these are different kinds of bets that we’re making. Not all of them are going to succeed, and if they do, we’re being too conservative. I expect some of this stuff not to work, but hopefully most of it will. One that I’m quite confident that’s going to work is actually travel and hotel bookings. In that Uber already is very highly used by the global traveler. We operate in more than 70 countries. Often what’s the first thing that you do when you arrive in an airport in a city other than your home city? You open the Uber app. Part of what we announced is usually that the Uber app is the same app regardless of the context that you have. And if you think about it, when you open Uber at home, and we know you’re in your home city, that should be a different experience than if you’ve just landed in Paris and you open Uber.

So, for example, we have what’s called travel mode. You open up the app, and we first give you step-by-step instructions as to how to get to an Uber, and how long is the walk going to take? How long is the pickup? What are typical rides? We make it context aware, so to speak. And we give you highlights on what’s going on in Paris. Lots of good stuff. Now, the sheer numbers that we’ve got, which is that we have over 100 million riders taking rides to and from airports every single year. 100 million, that’s a huge audience. We do 1.5 billion trips a year outside of your home city. We have the perfect audience and Uber’s built for travel in terms of our being present all over the place. It’s a perfect audience to start to build out the travel offerings.

We started experimenting, actually, with trains in the UK, and it’s worked out really well, it drives frequency, which is pretty cool. And now we announced a deal with Expedia, where now we offer hotel bookings through Uber. It’s smooth, we have all your information, we’ve got your context. And what’s really cool is for Uber One members, they get 10% off every single Uber, every single hotel booking you get credits back to use, and then you get 20% off a rolling list of 10,000 hotels. We’re making it really worth your while to book hotels on Uber.

Tell me about the insight that led to that risk. Because I think about Uber and I’m either, “I just need to get somewhere so I’m going to open the app,” and the time sense of Uber is right now, I need something right now, or “I’m going to the airport tomorrow and I live in a reasonably remote area and I need to make sure the car’s going to arrive.”

Tomorrow is about as far out as I go. I never land at an airport and think, “I need a hotel.” Something bad has happened if that is the occurrence. The time horizon of needing hotels feels much longer than anything Uber has previously offered, at least in my experience.

So, that’s a bet. You have to get people to think about Uber months or weeks before they need it.

What’s the insight that said, “We can get people to do that”?

It’s a bet, and you just described an adjustment to your behavior, which is that Uber has always been about on-demand. One of the questions that we had is, “Can we move from on-demand transportation to transportation by appointment, for example?” The first step that we took was actually Uber Reserve probably three, four years ago. And if you remember, we used to have an old Reserve product, where you would reserve an Uber, but we would be hacking it in the backend. You wouldn’t actually reserve an Uber. We would then call the Uber on-demand when we thought that it could get to you by that reservation time. It was okay, but it didn’t get you the reliability that you needed, it wasn’t a guaranteed reservation, so to speak.

We took the signal, which is that some people were trying the product, but it wasn’t that good, to be honest. We said, “Listen, what if we really up the reliability game?” And we sent the dispatch to drivers in advance, we did some research. Drivers are like, “I like knowing what my next day is going to be like.” So, it was good for drivers. We were able to charge a premium, give it to the driver, essentially to up reliability, and we started building the habit of this as an on-demand service to “Actually, this is more than an on-demand service, and I’m going to think about scheduling things in my life often having to do with travel.” Now, what we’re finding is actually some people are hacking Reserve, if you want to call it that, for reliability.

If you’re in Westchester County, in Armonk, and the liquidity for Uber is lower, you may not want to use on-demand for your commute, but you can use Reserve for your commute as well. What started as, “Let’s try this for travel,” is now being used to hack reliability to some extent. That insight of Reserve building – and we’ve been at it for four to five years, reliability is not perfect perfect, but it’s 99% now, and we’re always working that trade-off between reliability and price, because we want the price premium to be as low as possible, but you can’t lose too much reliability. That insight led us to believe that you actually can move from on-demand to scheduled, and the offerings, like the Uber One discounts, we think will hopefully, over a period of time, change behavior.

So you’ll actually come to Uber to reserve your booking in advance. We don’t think this is going to be a last-minute thing. If you get to a city and you don’t have a hotel, there is something wrong, maybe it’ll be there on a cancellation basis, but we are trying to drive reservation behavior and we’ve demonstrated previously then we can.

Yeah. I feel like hardcore travelers who know to reserve an Uber, who are some of your best customers, they like price shopping hotels, and there’s a lot of credit card points.

Yes, totally.

My sister’s a credit card points person. It’s frankly a little terrifying, but she’s really good at it. How are you going to compete with that? Because that’s the customer. In my mind, the customer who knows how to book a hotel and Uber is also the person with five different credit cards trying to get the best deal, and they know that this portal is where they need to go at this time. How do you compete with them?

So, I actually had an earlier interview with The Points Guy, and I asked them, “What’s the best credit card for travel?” Because I was curious. Turns out Amex Platinum, according to The Points Guy, is the best credit card for travel.

I don’t believe you, because this worked out too well.

I’m just saying. It was amazing. We have a great relationship with Amex, where you get benefits and free bookings on Ubers as well. There’s actually a lot of layering that we’re doing. If you’ve got Delta Sky Miles, you can get Delta Sky Miles for booking on Uber. We have a relationship with Marriott Bonvoy. We’ve got Travelers using Uber all the time. We’ve got the Amex Platinum card, the best card for Travelers as well. We have the right elements coming together to get some percentage of our Uber One members to try the booking experience, and then we’ll go from there. I do think that this would be a failure if it ends with hotel booking. One of the pieces of magic that Uber brings is it’s actually the backend experience.

One of my learnings when I was at Expedia was basically that after the booking, there weren’t that many services that Expedia offered other than if something went wrong. You do everything you can to help the customer, but actually what we can do is connect all these logistical elements of your travel. So, obviously, your Uber to the airport, if you did your hotel booking, we already know where your Uber is, maybe we’ll give you a discount to the hotel. I’m hoping that as we build out travel, we can actually improve the in-market experience. I don’t know about you, but why do I need to check into a hotel?

What’s the deal with that? I’ve got my phone, and if you have a hotel booking, maybe you can walk into the hotel and we can give you all the information and you can just go up to your room, and maybe your app can act as a key, et cetera. There’s a lot more that we want to do in terms of the in-market experience, and it’s something that Uber is uniquely positioned to do because we’re already in-market in almost every city that you’re going to want to travel to.

There are competitors in these markets. Expedia is an interesting partner because you used to be the CEO of Expedia. I assume you just made a phone call and said, “Hey, what’s up? It’s me.”

I actually had to recuse myself from the process entirely. The idea, the strategy, “Let’s get deeper into travel,” obviously I was involved with all that, but because of the conflict – I’m still on the Expedia board – I had to recuse myself from the process. The team ran it, and I’m like, “Guys, what’s going on?” They’re like, “We can’t talk to you.” Expedia won because of the great job that that team did, they got no help from me. I’m sorry, Expedia.

The CEO of Expedia wasn’t like, “I’ve got a board member breathing down my neck.”

I had to recuse myself in those discussions. It was a little awkward, but it all worked out well.

So, obviously Expedia would be a competitor, but they’re your partner. There are other competitors: there are the hotel loyalty programs, Booking.com exists. They say the same sorts of things that you say. They’ve been on the show saying literally the same sorts of things that you say.

Connected trip I think they talk about, right?

All the time. Yeah. Why do you need a hotel? I think a lot of people like checking in the hotel, the free water especially is very useful when you arrive in a new hotel. That piece of the puzzle, where you’re going to connect everybody’s backend systems together and build one unified experience where the Uber app is the primary interface, I could abstract that away and say, “Well, that’s everything, that’s what OpenAI would like to do. That’s what Google would like to do.”

Why is Uber going to win that fight?

It’s a different question or service offering in terms of offering the availability of the service, but to the extent that you can actually deliver it in-market, it is truly different. OpenAI is an incredible company, they build a lot of cool things, but they don’t live in the probabilistic real world that we live in. There’s a Mike Tyson saying, I think: “Everything is theory until you get punched in the face.”

“Everyone has a plan until you get punched in the face.”

Yeah. “Everyone has a plan.” And we get punched in the face daily, which is drivers are canceling, riders are having issues, deliveries are late, et cetera. We already deal with this probabilistic world on the back end where things go wrong all the time, and it’s one thing to try to chain all of these events together, and get the logistics right, but to adjust to real world traffic conditions, cancellations, road closures, all of that stuff, we do daily. We’re much better equipped to actually fulfill this seamless, delightful end-to-end experience from planning to booking, making it incredibly easy, and then to delivery, the actual experience on the ground.

Your partnership with Marriott, for example, Marriott wants those to be their customers. If you’re the app that everyone’s doing everything in, that relationship gets intermediated, is that a tension?

It’s a tension. At the same time, it’s a tension that everyone deals with. Marriott competes with Expedia, to some extent you could argue that they compete with us, although we’re a much smaller player today in travel, maybe we’ll get bigger. We work with Starbucks at Uber Eats, and of course they’d rather have people come directly to their app, but the fact is that Uber Eats brings them a lot of incremental demand as well. So, this “coopetition” theme is something that many, many players have been comfortable with for many, many years.

Comfortable with for many, many years is in one context, right? Everybody has an app, and it doesn’t really matter, you’re all going to open the apps and maybe we can get you to open our app with a discount or a point system or something. Now you’re in a world where you’re going to open an app and maybe an agent’s going to go off and do something for you. The idea of being the everything app in that context – Uber is describing this as a step to being in everything. It’s in the press materials.

Brian Chesky was on the show. Airbnb is going to do concierge services for travel, and they’re going to get way out of their lane, and maybe that’s working, maybe it’s not. I haven’t talked to Brian in a minute about it. OpenAI wants to be in everything. X famously is already “the Everything App,” as you know. We’re all using X all day long for everything. Do you think the pressure on needing to be that interface is going up because of AI?

I think the pressure is going up to some extent, but I think AI is making it possible in a way that it wasn’t possible previously. One is these models can adjust to real world conditions in a way that deterministic code can’t. That’s really cool. Whereas you had to build UI interfaces that were tight and relatively limited, AI is allowing for an interface that is unlimited, essentially. You can just tell the app what you want, and you can have agents then take that and break up that request and try to deliver it as best you can. AI is making it possible now. You can just build much faster. To go to smart risk, the cost of taking risks is going down. All of that is coming together in an opportunity set that I think a lot of companies recognize, including us, including Airbnb and the other companies, and it’s going to be a race to many of these new markets, and we’re confident.

We’ve raced before, we love competition. But this is another trillion dollar plus opportunity, and we’ve done well with mobility, we’ve done well with delivery. All of these businesses have been built organically. There’s a builder mindset at Uber, and we’re going to give it a shot, and so far the signal’s pretty damn good.

Last time you were on the show, we talked a lot about agents and accessing Uber as a service inside of an agentic workflow. I will tell you, I asked a lot of CEOs at that time this question, and everybody who had a physical product was like, “We’ll be fine.” And then it was Amazon, who has an interface to a bunch of dropshippers, that is filing the lawsuits.

They have a virtual product. Everybody who was in the world of atoms was like, “Go ahead and try. Try to make another Uber, you just give it a shot, we’ll be here when you’re waiting.” That was very much your attitude. What you said to me was, “The price of calling an Uber on ChatGPT should be zero until they prove it’s valuable and then I’ll figure out what the rate should be.” It’s been a year. Have you seen any meaningful uptake of calling Ubers from ChatGPT?

No. And it doesn’t seem to be at this point a priority for a lot of the foundation model companies, whether it’s ChatGPT or Gemini. I think they’re experimenting with it, but I think the enterprise market is growing much faster than anyone thought that it was going to. There’s been a pivot towards enterprise. Rightly so, based on the growth rates that we see, based on our internal usage of these foundation models. At this point, that part of the market hasn’t developed, and the cool thing is, we’re building some really cool products. You can scribble a shopping list, you can take a picture of food that looks really tasty, and we’ll put together a shopping list for you. If you tell us what merchant you want to go shopping at, we’ll put together the list for you and we’ll get it delivered automatically.

A lot of these experiences that I think people thought you’d find on OpenAI, et cetera, you’re actually going to find first on an Uber. I wouldn’t be surprised if it’s built over a period of time, but right now enterprise is coming first and you could argue rightly so.

Uber is a favorite of agentic demos. You pop up all the time. I’m just going to go down the list.

Is that right?

Yeah.

Well, it’s an everyday use case. It’s great.

Google and Samsung announced Gemini task integration on the newest Samsung phones, where the model will literally open the Uber app in the background in a virtual container, and click around it to get you a car. Have you seen any meaningful rides from that integration?

Not yet. Not yet, but we’d be delighted to see it. We want to bring more experimentation, more opportunity for our drivers, it’s just really small now. It doesn’t mean it’s not going to be big 10 years from now.

We had a whole year of these things. Has Alexa sent you any meaningful rides?

No. Small. Very, very small.

Okay. And I can keep going, but it seems like the answer –

Have you used any of these products?

I have to, I’m required.

And how is it?

I think they all have the same problem. Down the line, they’re slower than me just doing it myself. Also, I’m only ever calling a car from work to home or home to work or to the airport. The app is one tap away for all of those experiences.

Exactly. And it’s pretty easy to use. One area that, for example, we are looking at is while the front end, the initial demand may come from any agent, I am going to want our pixels in front of you. For example, I’m perfectly fine with OpenAI calling Uber, but then I want in that web interface and within the ChatGPT app, the Uber pixels and the Uber brand so that you know who is fulfilling that ride for you. We’ll see how things turn out. If you’re an Uber One member, you’re going to want to use our product, especially for travel.

Again, this is the fight that I’ve seen coming, where getting people out of your app and just using Uber as a backend service, as a commodity against every other service, pure play or not, nobody’s going to want this. But it seems like they’ve all pivoted to enterprise so fast that that fight is delayed or maybe never coming.

I think it’s delayed. It’s going to happen because I think the size of the prize is too big. If you talk about history not repeating itself but rhyming, there’s some of what I went through in my former job at Expedia. If you remember during those times, there was a big debate about metasearch. There were these metasearch players – Kayak, TripAdvisor, Trivago – that were amalgamating a bunch of travel content, and there was a point at which metasearch was quite powerful in terms of customer acquisition, et cetera. But as supply consolidated, really the value started accruing to the suppliers much more than the meta players and the travel business consolidated to Expedia, Booking.com, and Airbnb. There’s more, but those are the three very, very big players. On the supply side, when you look at mobility, when you look at delivery, there’s usually two or three players in every market.

Even if you get that front end being particularly big, in a consolidated, let’s say, supply marketplace and with our size and scale, multi-platform, all the countries that we operate in, I think we’re going to be more than okay, in terms of the leverage and the negotiations that happen. I always try to push the negotiations to the backend – build a great experience, figure out the economic balance later – but sometimes you’ve got to figure that stuff out upfront.

This is a slight difference from the last time you were here, and I just noted the companies are all different – not Uber, but the AI companies – they’re all in a slightly different posture than they were a year ago.

Yeah, totally.

They’re racing towards IPO, they are constantly calling code reds. Every week it’s a code red at OpenAI.

It’s a cool thing to do.

Yeah. We’ve had CEOs come on the show and say they’ve called a code red, and I’m like, “Did you actually do it? And they’re like, “No.” They just wanted to say.

We definitely had our share of code reds. And there’s a danger of code red fatigue in companies too because then it becomes meaningless. It’s a real issue.

OpenAI was a partner of yours, you’ve obviously launched things with them, you’ve used the products. As you broadly think about, “We’re going to build AI services, we need a model provider,” do they feel like a stable partner?

Yes, their products are excellent. For example, we’ve used, I think, ChatGPT 5.5 for some of the cool stuff that we demoed today in terms of the shopping list or taking pictures, et cetera. Codex is something that a lot of our devs use. OpenAI has been a strong partner. Whatever drama that you see in the markets isn’t showing up in terms of the quality of their product. They continue to be first-rate.

The drama in the market is all encompassing. As you and I sit here today, Sam Altman and Elon Musk are in a courtroom arguing with each other.

Listen, it used to be Uber. When I was looking at joining the company, it reminds me of that, and we got through it. We got through it, and it’s a great company now, and I think that it’s an adjustment that every company has to go through. So many people are interested in how OpenAI does things because it’s an important company in the world. They’ll get through this.

Do the model companies feel interchangeable in a way that has always seemed like a small danger here?

I think interchangeable is a little bit too strong a word. I do think that what Anthropic is building, Claude, it’s spectacular. Our developers are using it all the time. Codex is definitely picking up use by our developers. What we do do is we use some of the frontier models and some of the more advanced models to pilot, build demos if you want to build something quickly. And then what we do look to do is – it’s much more than an API layer – we’ve got a platform, Michelangelo, that has all the data feeds, and then essentially you’re able to switch models. And early on when we’re trying to explore something, we will use some of the more advanced models, but then once you get up to larger volumes, we will try to switch out either cheaper models or open source models to control the costs and the token costs on the backend.

Interchangeable is too strong a word, but we definitely experiment with various ones, and at this point, nothing is hard coded into our systems. And frankly, we’re going to make sure that none of them are hard coded into our systems.

That seems like a hedge against the companies and their needs and also cost, right? The cost of tokens is still quite high.

Yeah. You never want to be overly dependent on one technology unless you’re highly confident or it is very, very, very proprietary. Part of it is that all this stuff is so new. You and I were talking about Cursor last year. And Claude wasn’t a thing, at least internally. Now Claude is really, really increasing at incredibly surprising rates internally. So, early on, as this market is developing, we want lots of experimentation, and we want to give our devs the freedom to try a bunch of stuff. I don’t want this to be top down, “thou shalt here or there.” Of course, there’s going to be optimization, but right now there’s a lot of experimentation going on internally.

Let me ask you about running a software company in 2026. This is the thing I was most excited to talk to you about. It is true, the last time you were here, we were talking broadly about AI, and had all these questions about agents and the big labs coming for you with their consumer chatbots. Maybe that’s not happening yet. The thing that we did end up talking about just as you were walking out was, “We had GitHub Copilot, but all the engineers want to use Cursor.” And now you’re saying, “And Cursor’s around, but they’re all using Claude Code. Or maybe they’re using Codex.”

The increase in Claude Code usage and sometimes the replacement of Cursor usage is fairly remarkable. We use both, they’re both terrific products. And then there’s a group that’s using Codex. And they’re all really good. And I’d say the big change is with Cursor. It was coding and coding assist, so to speak, complete, but now these agents and agentic coding is something that is just blowing people away. It’s very, very cool.

And when you say “blowing people away,” I would say many of your peers have gone crazy. They have seen agentic coding, it’s looked them in the eye, and they’ve responded by losing their minds and saying that the entire structure of a company should change around this. I’ll give you some examples. Meta is reportedly going to have teams where 50 people report to one manager; Jack Dorsey can’t lay off enough people fast enough, and his goal, he said this out loud, he wants all 6,000 people agentically assisted to report to him at Block. I don’t even know how you would do that. It’s a show about org charts, and I read that, and I thought, well, our show’s going to keep going for another decade. We’re on the cusp of the weirdest org charts in history. Are you there? Are you saying, “Agentic coding is going to fundamentally change how you construct a software company”?

We have not gone and examined the fundamental org chart of the company yet. I’m not saying it won’t happen. We are pushing the company hard, and I’ve got to push the company harder to go to first principles in terms of how you work, period. Our culture is like bottom-up, let people do a bunch of stuff, and listen. The engineers are using it, the debugging, all the cool stuff is happening, as it should. But what we saw is in sales. Salespeople now use agents to summarize information on a client that they’re going to call to build out a really cool presentation. We’re using agents and AI, I would describe, around the edges of how we work. That’s one. And we’re not thinking about, “Let’s think about the sales function from the bottom up,” I’d want to push a company to do that.

Customer service is another example where we’ve got agents who generally follow policies. There’s a policy, if you’re an Uber One member and your order is delayed by 20 minutes, we’re going to give you $15 back because you’re a loyal customer, et cetera. That’s a policy that’s in place, and there are agents that are following those policies, et cetera.

Human agents you mean. Your current agents.

Human agents. And we then said, “Let’s build virtual agents to follow those policies,” and it turns out that actually our policies on a global basis, the documentation is complete crap, to use a technical term. What happens is, an agent, a human agent, I’ll be sitting next to you and be like, “What does this policy mean? It’s kind of unclear.” And you coach me and then I figure it out. Humans are quite flexible. When we had AI agents go through these policies, they just went nuts. One approach was, “Let’s rebuild all the documentation and policies the right way, and then let’s have the agents work based on these policies.”

But why do we put those policies together in the first place? It was to get to goals and outcomes based on standardized ways. I don’t want to go bankrupt, but I want to keep you, the Uber One member, happy. And so we made a policy to approximate the optimal outcome for the population. But now I can just tell the agent what that outcome is. I want actually to be fair to a person, I want Uber One members to be happy, I don’t want to go bankrupt, et cetera. The approach that we’re taking now within customer service is to throw away the policies, describe to the agent what you’re trying to accomplish, and then let the agents go and obviously train them on good interactions, bad interactions, and give them feedback, et cetera.

Wait, just a foundational philosophical question.

Yeah.

Why trust computers to make those determinations and not people?

Because the model can learn based on the population of everything that is happening, versus an individual human just learning based on the experience that he or she is having that day, and models are easier to track and tune than humans are to train.

Okay. So, this is a scale answer. It can see all the data.

Yeah.

So, you can just describe a generalized outcome.

You can retrain based on that data and you have perfect visibility into the actions, reactions. The retraining output you don’t have perfect visibility into, but you can iterate around that. It does demand a different approach and it’s a little bit back to what you and I were talking about, which is a smart risk. It’s a riskier approach. We got to throw stuff out and just completely rebuild in a different way. And I’m really glad. In this case, it wasn’t me who pushed the customer ops team to throw everything out. They were frustrated with the results that they were seeing early on, like, “We have to be able to do better, we’re going to try this out.” The signal looks really promising, but I can’t tell you if it’s actually going to work in the end.

That kind of dynamic customer response – in terms of pricing, people are making it illegal in this country to do dynamic pricing in that way because it feels unfair.

Yeah. That is actually an issue. What we don’t want to do is have different outcomes based on targeting you versus another person versus another person. But you can have different outcomes because there were circumstances that were different. So, for you, if your food was 15 minutes late, another person’s food was 45 minutes late, and you’re both Uber One members, you could actually have different outcomes because the circumstances are different. It’s not based on targeting or optimizing based on targeting, it’s optimizing based on context.

That’s really interesting, and it strikes me that we could probably do another whole hour on, “We wrote a bunch of rules for humans, and now we have to write a system prompt that isn’t the rules.”

It’s actually the outcomes that you’re trying to get at. Yeah.

Yeah. That’s another ad.

We’ll see if it works. 

You’re going to come back next year, I’m going to ask you if it worked.

But let me ask you just more at the base level. When I think about software companies generally, the creative tension of any software group is you have a PM, you have a designer, you’ve got some engineers. They all want to be in charge. They all think they are going to do it right. And they all need to work together. If you can get that right, it’s magic. It feels like with the power of vibe coding, everyone is going to try to do everyone else’s job, and no one’s going to be good at it, and it’s all a mess. I can see it happening all over the place already.

Totally. 

Are you rethinking that basic triad inside of Uber?

It depends on the kind of project that you’re working on. There are some larger projects where you need design, you need proper planning, et cetera. But we are having some product team members, whereas previously, if there were some simple bugs in the code or very, very simple features, they would have to then prioritize it with their engineers, et cetera. Now, they’re just going in and they are vibe coding, and an engineer is going to review the code, but essentially the product person is going directly into the code base, so to speak, or going directly with an agent into the code base. I do think for simpler problems, smaller problems, the dynamics are going to change. We’re going to try it out, we’re going to see what happens.

When you look at a company like Meta, which seems to just be in the midst of endless rolling layoffs, they’re saying it’s because AI is making everybody more productive, it might be because they’re just freeing up capital expenditures (CapEx) to go spend on whatever they’re spending CapEx on, to whatever end that Meta is going to do AI. Super intelligence, I’m told. Are you in that same spot where you’re like, “We’re getting more productive, I need fewer people”?

No. My view is if an engineer is going to be 50% or 200% more productive, I want more engineers. The list of ideas in terms of what we want to build so outscales our throughput at this point, that generally we are looking to add more engineers to our employee base. Now, there is a trade-off, and we are dealing with the trade-off right now as we speak. I don’t know if you saw it, but our CTO was talking to a reporter, and made a comment, which is true, that we have blown through our AI token and infrastructure budget for the whole year in about three to four months. And it was a big thing when that happened, but it happened. And the trade-off is going to be headcount. We are budgeting differently. Previously, you would have a headcount budget or plan, doesn’t mean it would actually happen, but as a plan going in, you would have an infra budget.

Now there’s an active trade-off going on between the two, and to the extent that we have overages in terms of token spend or infra spend – theoretically those overages are products that are being built and are productivity that’s being added to our engineers – we’re going to hire less aggressively, so to speak. That is a live trade-off. How far it’s going to go, I don’t know at this point.

Are you all the way at, “I’m spending so much on tokens that it’s costing me more than hiring one junior engineer”?

We are spending a lot on tokens. I haven’t done the math yet, but it’s significant. But the throughput is really accelerating. At this point, it’s something that needs to be managed, and I do think it’s just taking different muscles. The way that we’re managing budgets, especially on tech, is fundamentally different from how we did three, four years ago.

All right. One last AI question, then I want to talk to you about autonomy. Which is also AI, but in a very different way.

Physical world AI. Yeah.

You were on Diary of a CEO, and you said the employees at Uber have created an AI version of Dara to practice presenting and pitching to. Is that real and how close are we to AI replacing the CEO?

It is real, I have not witnessed the Dara AI, but it is real. People have done it. Honestly, I don’t know how good it is. It’s clearly not as good as the real thing. Come on, how is that possible?

Decoder listeners, every time we do an AI episode, they say the AI should replace the CEO. It is a reflexive comment we get.

I’m not there yet. I think that the AI powered CEO is going to be better than the AI CEO. I think there’s a magic in terms of teaming up humans with AI and with agents, and based on what I see, that is a superior product than pure play AI or pure play human.

You should recuse yourself from this. You have a deep conflict of interest here.

Of course I do. I’m hoping the board sees it that way as well. Maybe the board is planning this and I had no idea.

That would be, in keeping with the Uber story, that would be there.

Exactly. How is AI changing our board processes? I’ve got to think about that one.

Oh, trust, I get those pitches. You don’t want anything to do with those. Let’s talk about robots, actual robots, actual AI in the world.

Yes.

Uber has made a bunch of big investments in robotaxis. I want to start with Rivian. It’s over a billion dollars, I think it’s $1.2 billion in total commitment to Rivian over some number of years. That’s a partnership announced in March, that you’re going to buy up to 50,000 fully autonomous R2 robotaxis by 2031, but it’s also called an investment. And I’m just doing the math – at the price of the R2 platform, you’re just buying a bunch of cars. Is buying a bunch of cars an investment or are you actually getting equity in Rivian?

We actually invested in Rivian equity, and we’ve invested in a number of our partners. Usually we will invest in our partners like Lucid, in WeRide, in Avride, for example. It is an investment and it’s a vehicle commitment as well. It’s both. It’s based on deliverables, obviously. They’ve got to deliver, and based on everything that we’ve seen from RJ and team, putting together a first-class AI team, we’re confident that they can deliver on those R2s.

Yeah. The deliverables are very vague, I’m just going to read you the press release. “Uber will invest up to $1.25 billion in Rivian through 2031 subject to,” and I quote, “the achievement of certain autonomous milestones by specific dates.”

Well, they are very specific contractually, and they’re fairly fuzzy as far as what you know.

I put this into five different AI systems and no one can tell me what they are. What are the autonomous milestones?

I could tell you, but then I’d have to kill you.

The reason I’m asking is that I desperately want to know the specifics.

I’m looking at this industry in total, and I will tell you that we’ve thrown out whatever autonomous milestones we used to have, the level system that everyone used to talk about. That’s all gone. No one cares about this anymore. No one’s like, “We shouldn’t do level four.” We’re doing it. And I can’t quite tell you what milestone an autonomy platform has to hit before I can say “This is a robotaxi.”

I’ll give you examples of milestones, not specific to Rivian. Usually there’s a milestone, for example, if you release in-market with a vehicle operator. Usually one other milestone may be if you take the vehicle operator out, you can only take the vehicle operator out to the extent that you complete a safety case that we put together along with an autonomy provider, then another deliverable might be delivering a certain number of cars that are NVO-capable, that have a redundancy at a certain bill of materials as well, at a certain cost. Those are examples of deliverables that have to do with either capability or economics, because ultimately this is about going to market with a product that’s proving to be a very, very popular product.

Your big partner in the past was Waymo.

Yes.

Waymo has gotten there in many cases to some of the kinds of milestones you’re describing.

Yeah, sure.

You’re obviously diversifying away from Waymo, you’ve got the Rivian deal, you mentioned Lucid, you’re going to buy at least 35,000 Lucid vehicles designed exclusively for use as part of Uber’s robotaxis.

Yeah, and a partnership with Nuro.

And a partnership with Nuro, which is the platform there.

Yes.

Overall, you’re going to commit some $10 billion to autonomous efforts. You launched Uber Autonomous Solutions, which feels like a bet on this is happening, but we don’t know who’s going to win. You’re diversifying.

It’s a little bit different from that in that we believe that it is going to happen, and we believe that just like there isn’t going to be one foundation model to rule them all, there isn’t going to be one physical world foundation model to rule them all. And all the evidence that we see is, yes, Waymo has passed the finish line, they’re the leader, they are in many ways inspiration for many, many companies in this industry. They’re a great partner of ours in Atlanta and Austin. There are many other companies that are getting to the finish line. WeRide, for example, or Pony.ai or Baidu – these are Chinese companies – are already at the finish line, and we are in-market, for example, with WeRide in the Middle East. There are players like Nuro, Waabi, Avride or Wayve, all of whom are accelerating to the finish line.

And if anything, the speed of getting to the finish line is accelerating. One, model capabilities are much, much better now. It used to be deterministic code that you had to slog through, now obviously it’s learning AI models. SIM capability is much better so that data will go much further in terms of model training. And what we’re trying to do with AV Solutions is build out the whole necessary ecosystem around these companies so that they can focus on what they do best, which is training these models to get them to superhuman safety. We can help them get there, for example, with data collection, and we can both then get to market as quickly as possible.

It’s not, I would say, a diversification bet. It’s a bet that there are going to be many players. And as a platform, we’ve always been supply-led. The way to grow our platform is to build out supply, whether that’s more drivers or more restaurants or more hotels. As we build out liquidity of supply, demand shows up, and just like we want every safe human driver on the platform, we want every safe robot driver on the platform, whether that’s a Waymo driver or a Nuro driver or an Avride or a WeRide. It’s a bet that we’re making, which is that there won’t be one physical AI model to rule them all.

There’s some real confidence in this bet. I’ve talked to a lot of rideshare CEOs over the years, a lot of autonomy CEOs over the years, and it’s always been 10 years away.

Yeah, yeah.

The confidence I’m hearing from you is, “This is happening. We’re spending a lot of money to get there faster.”

All the evidence we see is that it’s happening. Waymo has shown the way. A lot of Waymo engineers now are working in other companies. For example, the Chinese players have shown the way, and you’ve seen it, the speed of foundation model development, whether it’s digital foundation models or physical foundation models. Nvidia is betting on this as well. These are big bets made by capable companies and we think we’re on the right track here.

In the context of our conversation, I’m going to bring up the trade-off.

Sure.

By saying it’s going to be more real, you no longer get to kick the can on, “We’re not going to have drivers in the cars,” which famously got Travis Kalanick in a lot of trouble by saying, “I want to get the driver out of the car,” long, long ago.

Yeah.

Because autonomy was so far away, we just didn’t have to solve this problem. You have been on podcasts recently saying, “This problem is here. I don’t know what’s going to happen to 9.5 million Uber drivers when autonomy comes.” You literally said, “I don’t know,” to Steven Bartlett.

Well, if you don’t know, you should say it. Now, here’s what I know. 10 years from now, I am 90% certain that we’re going to have more drivers on our overall platform than we do today. I don’t know if that’s going to be true in San Francisco, but with the way that the business is growing, and the capability of building these cars at the right bill of materials in all the markets that we operated, not just the high cost markets, we’re going to have plenty of drivers, and we also are actively looking to build out more use cases for drivers that are more complex. One of the announcements that we made was about a personal shopper. It was Courier, people started hacking Courier, asking Uber Couriers to go shop for them, so we decided to productize that as well. That’s a very, very complicated interaction.

It’s a random store, take a picture of the goods, “This is what I want.” We’re building out much more complex use cases for humans to migrate onto as more of the work is being automated. 20 years from now, I don’t know what that’s going to look like, because then you really start increasing capabilities. I think these are big societal questions. It’s going to be true of white-collar workers, and it’s going to be true of certain kinds of blue-collar work as well. CEOs should talk about this, not in a way to scare people, but we should also be honest about it. I’ve never seen a wave of technology that has such a direct impact on how companies work and how people have worked with the accelerated pace that I’m seeing today. Doesn’t mean that society can’t adjust, but the pace of change here, it’s pretty remarkable.

One of my theories about the extremely negative polling on AI is that it’s fundamentally an enterprise technology. You’ve described this even in this conversation, the frontier models, those companies are moving to enterprise use cases, you at Uber are using them in enterprise context, and there are not great consumer products in front of people.

Not yet. Yeah.

I haven’t seen them. Maybe they’re coming.

We’re trying to do that and it’s these moments of surprise and delight where you can talk to your Uber to get an Uber, lots of complex situations, you can transcript a shopping list, take a picture –

Sure. But I don’t think that stuff is going to change the overall polling on, “This is a threat that will take my job away.”

Yeah. Listen, if it’s your job, I think you’re right. Yeah.

This dynamic of everybody showing up saying the jobs are going away, and mostly because it’s so good at writing code, this is a weird disconnected dynamic for regular people. Uber needs customers, you need people with money to want to ride around. How do you see that economy playing around?

Right now talk is louder than what we see in the market. The economy remains robust, the consumer remains robust, we don’t see white collar people out of work at this point. I just don’t see it in-market. Now, the fear that you see might be a leading indicator of what’s to come, but at this point, I see no signal in our actual business that it’s having an impact on consumers at large.

What do you ascribe the extremely negative polling around AI to?

I do think that it’s some fearmongering from the press. They love the drama. Are you part of the press or no? A little bit, okay.

Yeah. Can I have this level of influence?

Can I point at you here?

You can point at me all you want.

But listen, it’s a conversation that people are constantly having, it’s a dramatic conversation. And I do think machines replacing humans has been a theme for eons. What you do see in manufacturing, for example, with automation is that machines complement humans, and then there are other capabilities that humans always adjust to. It’s just things are moving so fast now that I think the fear is out there. I’ve got 14-year-old twin boys and two other older kids. My 14-year-old kid is like, “Dad, why should I study? I’m not going to be able to have a job.” And I was just blown away. My 14-year old is asking me. Now maybe he doesn’t want to study. 

This does feel like the main thing 14-year-olds say.

Yeah, exactly. So, it’s in the ether, you see signals, there are some companies, like you mentioned, who are acting on it. We’ll see what happens in the next two years. But I don’t see how it’s going to reverse. Once we get more data, maybe the reality will be less dramatic than someone makes it out to be, and then we’ll see. We’ll do our best.

Yeah. I would love for it to be real that it’s the press. The media history is not at a moment of intense strength right now. It is contracting.

Yeah, but there have been some. I do think that the media is incentivized sometimes to overdramatize these things. Could be real, maybe it’s not. I do think that there is a reality in it. The question is, how quickly is change going to happen, and will society be able to adjust fast enough?

Look, I get all my news from X the Everything App, which assures me on the daily that AGI is just around the corner.

I want to ask you the question I ask every time I talk to you. I always take an Uber to come see you, it’s just my little tradition, and the drivers always have the same question. So, I have the same question every year.

Sure, sure.

And then this time I actually got a very detailed follow-up question to ask you.

Oh, cool. All right.

The drivers all want to know: How are they going to get paid more?

They are going to get paid more by some of the newer jobs that we’re giving them. Shopping, for example, on a per-hour basis can pay more. But I do think that driver pay is based on what market rate pay is, essentially. The local pay goes up and down based on the spot cost of labor in a particular market. The way that drivers are going to get paid more is the cost of labor generally goes up, or it goes down. Right now, the cost of labor is fairly steady, and driver pay has been fairly steady. Nationwide, it’s probably $32, $33 per utilized hour. Here in New York City, it’s over $50 per utilized hour. Drivers are making decent money. Of course, they’re going to want to make more money.

They all want to make more money.

Of course.

Do you think autonomy changes that rate?

I don’t think it will significantly. I think that drivers are probably going to take longer trips. When we see autonomous inventory coming into a market, we slow down driver recruitment because we want the drivers who are in-market making as much. At this point, in markets like Atlanta, like Austin, where we have a significant autonomy presence, because we’ve reduced recruiting, driver pay is actually up. And I’m hoping that we can continue those trends for a long time.

I’m glad you brought up utilized hours because this is my very detailed follow-up. It’s actually good because you brought up all the keywords of this question.

Sure.

So, you mentioned Westchester, I live in Westchester. The drivers in Westchester are allowed to drive into New York City, but they’re not allowed to pick up in New York City and drive back to Westchester. So they literally lose one utilized hour. I’ve been directly requested that you go and lobby the city and state so that they can go home with a utilized hour instead of an empty ride.

We have already been lobbying. Some of these regulations have unintended consequences. New York is unfortunately one of the most highly regulated markets out there. A significant amount of your fare goes to the city, et cetera. I think Ubers are too expensive here, and I think regulation sometimes goes over the top. It’s something that I will absolutely take to the powers that be.

The powers that be in this city is Zohran Mamdani. Have you met with Zohran Mamdani?

I have seen him speak, I have not met him one-on-one yet, but I look forward to that dialogue.

Well, here’s my tips. One, say you love New York City, he loves it when you say you love New York City.

Cool, cool. I do.

Two, tell him the drivers want the return trips from both the airports and the city.

I will absolutely relay that to him. Maybe he listens to your podcast, you never know.

We know some people. The same thing, I can’t tell you. I can’t tell you what the milestones are.

Dara, this is always a pleasure, thank you so much for coming.

Thank you. I really appreciate it.