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

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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. 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 waiting in your Uber when it arrives, and even personal shopping. Uber is going so far is to call itself in everything at now. 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 the world where AI companies keep promising that their chatbots will book all the cars and hotels for you. I also wanted to know if these chatbots have created any opportunities for Uber. Last year Dara told me he was wide open to partnerships with AI companies 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 he sees 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 up and software development. Just a couple 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 starting to rethink 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 also rethinking how his software team's restructured as AI starts to muddle the relationship between product managers, designers, and engineers. Lastly, we talked about Uber's increasingly large investments in autonomous cars, especially its big investment in RIVIAN, and what kinds of milestones Dara is looking for is the technology of alls. I also wanted to know what happens to all of Uber's drivers in the future, or robots are doing all of that work. Of course, that means I also asked Dara when he thinks AI will be ready to replace him as CEO. It turns out there's already a rogue AI Dara operating inside of Uber. There's a lot going on in this one. Dara was as clear and candid as ever, and I think you're going to like it. Okay, Uber's CEO, Dara, Custro, Shawi. Here we go. Dara Custro, Shawi, and CEO of Uber, welcome back to Dakota. Thank you very much. Good to be back. I'm having 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. It's a talk full of news for you. Yeah. And we hang out together in person, which is my very favorite thing. So thank you for doing it. There's a lot of news to talk about. As I was telling you just before we started, I'm very curious what it means to run a software company. Yes. That's 2026 in that age of AI coding agents. And I'm very curious if you're going to have 6,000 people report directly to you, as Jack Dorsea said. So I want to ask about all that. I want to talk about the news, which 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 Dakota questions about how companies are structured and decision-making. And I just want to do them as a little lightning round at the top. Sure. So last year on Dakota, I said, how do you make decisions and you get, you get the Amazon answer? You said one way doors and two way doors. A lot of pressure on decision-making. Totally. Totally. Lately, making big decisions, even expanding the app is a big decision, is your fundamental framework changed? Fundamental framework has not changed. Now, I will tell you that I am pushing the company in something that we talk about taking smart risks. The pattern that I keep saying is that as companies get larger, they become more hesitant in terms of risk-taking. You know, it's more about playing a safe. It's your 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. And you know, for us, we've got almost $10 billion in cash flow. And you know, when I first joined, if we made a billion dollar mistake, it would be a disaster, right? 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, you know, for example, with AV, which I'm sure we'll talk about, are getting bigger. And 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, the way they operate gets more sense-stone, you have more management layers, et cetera. And we very much want to avoid that. And it's taking me really pushing kind of one way door to a door as one framework of looking at decisions, but then smart risk taking as well. We've got to keep taking smart risk as a company. It means once in a while, taking risk that in hindsight, look dumb, but we've got to 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. It's the other side of the coin. So getting, empowering people to take the risk without that fear of failure is really important. How do you think about the stakes? Like how big of a risk is an individual software engineer due to a lot of risk? So I think as long as you can identify the downside of risk, if you can 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, whether it's compute that you're dedicated to a feature or you've got to invest the certain amount of capital in building something or going after expanding a new line of business in a country where launching Uber Eats in seven countries in Europe as well. As long as you can identify the downside, then you can make the right calculus in terms of whether you should take the risk or not. We absolutely, you know, we want to learn from our mistakes. Like there's just some people talking about celebrating mistakes. Like I'm not going to celebrate a mistake, right? 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 over examine our mistakes and, you know, 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, is, hey, understand why you made a mistake, what you could have done better and then move on with life. Like let's go build the next thing. But this is in a practice for me. What's a risk that came outside of your sphere in management control that worked out and what's one that didn't? So one that absolutely worked out, for example, I was involved with, but it was the team that really pushed forward was women, riders and drivers preferred. There was some question asked to the liquidity in a marketplace. Anytime, you know, one of the big things about Uber is, you know, 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, right? So there was a real question as to the reliability of the marketplace to the extent that you, 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 kind of this great flywheel. So that's a risk that worked. You know, we have built a taxi product twice. We tried it actually early on, and we tried to build taxi the same way that we built Peter Peeer ride chair, which is kind of a one-on-one hail to you. And it failed, didn't work, taxis didn't trust us, they didn't sign up. About six years later, Sachin Consul, who's now our CPO, he used to build kind of a taxi app, he said, let's try this again. And so while it failed the first time, this time we approach it differently. And 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 or not. What we did was a little bit different, which is we introduced Blast Dispatch. So when you ask for an Uber, and we want a hail taxi, we will dispatch to 10 different taxis. And whoever says, yes, first, accepts that ride. So we're able to get the higher reliability and kind of adjust the way that we've built the product for taxis. Taxis is now one of our fastest-growing products. So that's an example also of like, you make a mistake once. But then actually sometimes you have to try things again, even though it didn't work for the first time with a different flavor, with a different approach. And I'm really glad that we took that shot on taxi. - We're going back to risk, because you have a bunch of new products that seem risky. - Yeah. - 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, it was very lengthy, I encourage people to go back to listen to that part of the conversation. But the short version is you said, quote, we have a combination matrix in 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 ease. - Yes. - And the ride business. Obviously, I'm guessing something has changed here because you have many new lines of business. You have an autonomy to vision. Quickly describe how Uber's structure has changed. - The only change in structure, 'cause I do value stability is that, I now have a president, COO, Andrew McDonald. And that was about, I, Andrew ran our mobility global business. What we observed is that the platform, that is mobility and delivery coming together and particularly users who use both mobility and delivery has 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 have the delivery business post-COVID, grow so quickly and show that it has potential of being just as big as the mobility business. At a hypothesis, which is, you know, 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, right? It's all lift, the only thing lift cares about, at least historically it was, U.S. ride, share, they're starting to expand internationally as well, good for them, about time, you could argue. And the only thing door-dash cares about, let's say, is food delivery, we're trying to do both, right? And it's hard as a company to do multiple things that wants to have skill sets and multiple business lines. And so to make up for that, we had mobility team, delivery team, we had a bunch of common structures and services platform. So the work 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 eeps, we had a hypothesis that we would retain them for longer. Turns out not only is it retention better, but they spend much more, multi-platform consumers spend three times, 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. That 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 gone the way, right? It's every pixel that sounds easy. Well, 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 the P&L gone the way. And I do a lot of stuff. And I was pushing platform kind of on the side here in addition to everything else I do. I really wanted one member of our management team, and Andrew McDonald's been here. He's one of the longest 10-year employees and most capable team members that we have. I said, "Angelo, it's time for you to move from running global mobility to actually become president, CEO of the company." And think 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. We've the number of consumers using both rides and eats has six ecks in the past five years. And it's growing 50% faster than our general audience. So it's definitely, definitely working. I don't want to lean into it. - Yeah. It strikes me as 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-offs. - Everything's a trade-off. - 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. And so you've installed a 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 above. - My joke on this show constantly is if you told me your org chart, I can tell you 80% of your problems. You know, all the companies are kind of the same. And I can get to about 80% of the attention if you just tell me where all the executives are lined up and who controls what budget. Like 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. Like I know almost everything about Microsoft at this moment. 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%. It sounds like you've installed an executive just to oversee the 20% of the prioritization in 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 compounded over five years, over 10 years, you get the results that we've gone, which is generally 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 want to lead 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, oh, we're gonna 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 worse, you want to double down. - Okay, I'm gonna be very reductive here. The last time you were here, I described Uber as a magic button that made a Toyota Highlander appear in my life. Wherever I am in the world, almost statistically like the-- - Something like a Toyota Highlander is job. - Well, Toyota Highlander is gonna arrive. That's great. And then it's gonna move me around. And the jump from there to the Toyota Highlander has food in it is reasonably small to its a career service is reasonable. We're moving things around. We're a logistics business. The news here is you're doing hotel booking in partnership with Expedia. You've got shopping assistants. Now that 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 in platform bet. - Yeah, absolutely. So first I would say, and these are different kinds of bets that we're making. And by the way, 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. Hopefully most of it will. One that I'm quite confident that's gonna work is actually travel on hotel bookings. In that Uber is 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 a home city, you open the Uber app. And part of what we announced is usually that Uber app is kind of 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 if you've just landed in Paris and you open Uber, right? It's like, that's a different context. 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 it walking a 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 we have over 100 million of them. of our riders now are 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. So we have the perfect audience and Uber's built for travel in terms of our being present all over the place. So it's a perfect audience to start to build out the travel offerings. We started experimenting actually with train in the UK and it's worked out really well. It drives frequency, which is pretty cool. And now we announced the 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 hotel booking. You get credits back to use. And then you get 20% off a rolling list of 10,000 hotels. So we're making it really worth your while too, but hotels on Uber. - Tell me about the insight that led to that risk. 'Cause I think about Uber and I'm either, I just need to get somewhere or something, open the app. And the time sense of Uber is like right now. I need something right. - Totally. - 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 gonna arrive. So I'm gonna pick tomorrow. It's about as far out as I go. I never land an airport and think, I need a hotel. Like that's a, - Yes. - Something that has happened. - Yeah. - If that is the occurrence. A hotel feels like the time horizon of needing a hotel is much longer than anything Uber has previously offered. At least in my experience. - Yeah. - Yeah. - So that's a bet. You gotta 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? - So it's a bet. And you just described actually an adjustment to your behavior, which is Uber has always been about on demand, right? And one of the questions that we had is, can we move from on demand transportation to transportation by appointment, for example? So 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 back end. 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. So we took the signal, which is 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 like, hey, 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 upper reliability. And we started building the habit of, this is 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. So if you're in Westchester County, in Armank, 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. So 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 believing that you actually can move from on-demand to scheduled. And the offerings that Uber won kind of discounts, we think will, hopefully, over a period of time, change behavior. So you actually come to Uber to reserve your booking 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 that we can. Yeah. I feel hardcore travelers who know to reserve in Uber, who are some of your best customers, they like price-shopping hotels. Yes. And there's a lot of credit card points. And my sister's a credit card points person. Yes. It's frankly a little terrifying, which he's really good at. How are you going to compete with that? Because that's the customer. In my mind, the customer who knows to book a hotel in 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. So how do you compete with that? So I actually-- I had an earlier interview with the point sky, and I asked them, what's the best credit car for travel? Because I was curious. Turns out, Amix Platinum, according to the point sky, is the best credit card for travel. And by the way-- I don't believe you because this worked out too well. I just wanted to say it was amazing. And we have a great relationship with Amix, where you get benefits and free bookings on Uber's as well. So it's actually-- there's 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 Mary on Bonvoy. We've got travelers using Uber all the time. We've got the Amix Platinum card. The best card for travelers as well. So I think we have kind of the right elements coming together to get some percentage of our Uber one of members to try the booking experience. And then we'll go from there. And 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 backhand experience. One of my learnings, when I was at Expedia, it was basically after the booking, there weren't that many services that Expedia offered other than if something went wrong. And 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, that you're a hotel booking, where you already know where your Uber is, maybe we'll give you a discount to the hotel. And 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 you 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. So actually, I had to recuse myself from the process entirely. The idea of the strategy, let's get deeper into travel. Obviously, I was involved with, 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? We can't talk to you. So they got to Expedia one because of the great job that that team did. They got no help from me. I'm sorry, I'm sorry. The CEO of Expedia wasn't like, I got a board member branding on my neck. I had to like 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 hotel loyalty programs. Booking.com exists. They say the same sorts of things that you say. Of course. They've been on the show saying literally the same sorts of things. Connected trip, I think they talk about, right? All the time. 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, the puzzle, where you're going to connect everybody's back end systems together and build one unified experience where 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? Well, I think 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. 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. It's a Mike Tyson saying is like everything is there until you get punched in the face. Everyone has a plan. Everyone has a plan, I think. And we get punched in the face daily, which is drivers are canceling. Riders are having issues, et cetera. Deliveries are late. And so 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, but and get the logistics right, but to adjust to real world traffic conditions, cancellations, road closures, all of that stuff, we do daily. So I just think 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. - You know, your partnership with Marriott, for example. Marriott wants those to be their customers. You're the app that everyone's doing everything in that relationship that gets intermediate. Is that attention? - I mean, it's attention at the same time. It's attention that everyone deals with, right? Marriott competes with Expedia. To some extent, you could argue that they compete with us, although we're 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 direct to their app, but the fact is that Uber Eats brings them a lot of incremental demand as well. So this co-opitation theme is something that many, many players have been comfortable with for many, many years. - Comfort with for many, many years is in one context, right? Everybody has an app, and it doesn't really matter. You're all gonna open the apps, maybe we can get you to open our app. Now you're in a world where you're gonna open an app, and maybe an agent's gonna go off and do something for you. And the idea of being the everything app in that context, Uber is describing this as a step to being in everything. - Totally. - It's in the press materials. Brian Chesky was on the show. Airbnb is gonna do concierge services for travel. And they're gonna get way out of their lane. And maybe that's where I get my answer. I haven't talked to Brian in a minute about it. Open AI wants to be in everything. X, famously, is already the everything app. As you know, we're all using X all the long for everything. Is 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 determinants the code can't, right? That's really cool. Whereas you have 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 as best you can. So AI is making possible now. And by the way, you can just build much faster. So to going to Smart Risk, the cost of taking risks is going down. So I think 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 gonna be a race to many of these new markets and we're confident. We've race 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. So I think there's kind of a builder mindset at Uber and we're gonna give it a shot and so far the signal's pretty damn good. (upbeat music) We have to pause here for a quick break. We'll be right back. (upbeat music) Support for the show comes from Upwork. Think of the fastest growing businesses you know. You might think they've gotten where they're at just by doing more, but that's not always the case. Chances are, they're just delegating smarter. Upwork helps you bring in expert freelance help fast so you can delegate and keep moving. Upwork is a one stop platform to find, hire and pay expert freelancers. Find specialized talent across web and software development, data and analytics, marketing, business operations, and more. Upwork also has business plus, which gives you access to the top 1% of talent on their platform. With AI powered short listing, you get matched to the right freelancer in under six hours. No endless searching required. 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I'm talking with Uber CEO, Darakasha Shawi, about how everyone seems to want their agent AI to call you an Uber. Why that's maybe not such a great idea. 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, everybody who had a physical product was like, well, we'll be fine. And then it was Amazon who has an interface to a bunch of dropshippers that is filing the lawsuits. Right? They have a virtual product. Everybody who is 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 and Chatchy-B-T to 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 Uber's from Chatchy-B-T? No. And it doesn't seem to be at this point a priority for a lot of the foundation model companies, whether it's Chatchy-B-T 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. So I think there's been a pivot towards enterprise. And by the way, rightly so based on the growth rates that we see based on our internal usage of these foundation models. So 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 squirreble shopping list. You can take a picture of food that either 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 a list for you and we'll get it delivered automatically. So a lot of these experiences that I think people thought you'd find on an open AI, et cetera, you're actually going to find first on an Uber. I wouldn't be surprised if it's built over a per-totime. 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 line. Is that right? Yes. It's kind of like 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. If you see any meaningful rides from that integration. Not yet. Not yet, but we'd be delighted to see it. I mean, 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. I'm just-- We had a whole year of these times. Totally, totally. Alexa has Alexa sent you any meaningful rides. It's all very, very small. OK, when I can keep going. But it seems like the answer-- Have you used any of these? I have. I'm required. And now is it. I think they all have the problem. They're slower than me just doing it myself. Kind of down the line. 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 exercises. Exactly. And it's pretty easy to use. Now, I do think that one area that, first example we are looking at is, while the front and the initial demand may come from any agent, I am going to want our pixels in front of you. So for example, I'm perfectly fine with OpenAI calling Uber. But then I want in that web interface. And within that chat GBT app, kind of the Uber pixels and the Uber brand so that you know who is fulfilling that ride for you. So 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. I mean, again, this is a 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 player, not-- nobody's going to want this. But it seems like they've all pivoted enterprise so fast that fight is delayed or maybe never coming. - I think it's delayed. It's going to happen 'cause I think the size of the prize is too big. Now, if you talk about kind of history rhyming not repeating itself, 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 meta search, right? There were these meta search players, kayak, TripAdvisor, Travago, that were amalgamating a bunch of travel content and there was a point at which meta search was quite powerful in terms of customer acquisition, et cetera. But as a supply consolidated, really the value started accruing to the suppliers much more than the meta players and the travel business consolidates Expedia, booking.com, Airbnb, there's more, but three very, very big players. So I do think also on the supply side, when you look at mobility, when you look at delivery, there's usually two or three players in every market. So 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 gonna be more than okay. In terms of kind of the leverage and the negotiations that happen. I always try to push the negotiations to the back end, build a great experience, figure out kind of the balance, the economic balance later, but sometimes you gotta figure that stuff out upfront. >> This is a slight difference in the last time you were here. I'm just note that 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. >> Right, they're racing towards IPO, they are constantly calling code reds, like every week it's a code red open. >> It's a cool thing to do. >> Yeah, I mean, we've had CEOs come on the show and say they've called the code red, I'm like, did you actually do it like no? We just wanted to set our share of code reds. And there's a danger of code red fatigue in companies too, 'cause then it becomes meaningless. So it's a real issue. >> Open AI was a partner of yours, you've obviously launched things with them. You've used the products. As you broadly think about, okay, we're gonna build AI services, we're a need a model provider. Do they feel like a stable partner? >> Yes, their products are excellent. For example, we've used, I think Chatchy BT55 for some of the cool stuff that we demoed today in terms of the shopping lists or taking pictures, et cetera. Codex is something that a lot of our devs use. Open AI has been a strong partner in whatever drama that you see in the markets isn't showing up in terms of the quality of the product. They continue to be first right. >> 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 you. >> Listen, it used to be Uber when I was looking to join 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 Open AI does 'cause it's an important company in the world. So 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 word. I mean, I do think that what Anthropica's building, Clawed is it's spectacular. Our developers are using it all the time. Codex is definitely picking up use of our developers. Now, 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 we have, it's much more than an API layer, but 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 kind of 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 inter-systems. And frankly, we're gonna make sure that none of them are hard-coded inter-systems. - Right, that seems like a hedge against the companies and their needs and also cost, right? The cost of tokens is still very. - Yeah, I mean, you never want to be overly dependent on one technology unless you're highly confident or it is very, very, very proprietary. And part of it is that all this stuff is so new. I mean, you and I were talking about cursor last year, right? And cloud wasn't a thing at least internally. Now, cloud is really, really increasing at incredibly surprising rates internally. So early on as this market is developing, we want lots of experimentation and we wanna give our devs the freedom to try a bunch of stuff. I don't want this to be top-down, vowshout here or there. Of course, there's gonna be optimization, but right now there's a lot of experimentation going on internally. - We have to take on a short break. We'll be back in just a minute. (upbeat music) - Support for the show comes from hosting her. Every business has its impact. And with AI changing the landscape, the barrier to entry has never been lower. Whether you're starting a side hustle or building the next big thing, hosting her lets you go live in minutes, not weeks. Hosting her is an all-in-one platform that brings everything into one place. You can create websites, online stores, and even custom apps without coding or design skills. Then use AI agents to automate tedious tasks and help grow your business. Turn your one day into day one. Go to hostingher.com/decoder to bring your idea online for under $3 a month. Plus get an extra 20% off with promo code decoder. That's less than the price of a cup of coffee per month. That's hostingher.com/decoder. promo code decoder for an extra 20% off. Do you wanna live forever? Influential journalist, Cara Swisher, is taking a hard look at the longevity industry to separate the influencer hype from evidence-backed science. In her new CNN original series, Cara's talking to Silicon Valley power players and trying out the latest in anti-aging technology to see what works and what's a waste. Cara Swisher wants to live forever. Go to CNN.com/subscribe to get started and save 40% for a limited time, terms apply. Support for the show comes from AWS. How much of your workday is actually work and how much is just hunting for information? The answer you need is buried in a slack thread. The data's in Salesforce or an email from two weeks ago. By the time you've pulled it all together, half your morning is gone. That's the problem Amazon Quick was built to solve. Quick is an intelligent workplace assistant that connects to all of your systems, your documents, your dashboards, Salesforce, Jura, Slack, email, and gives you complete answers in seconds. Not links to dig through, actual answers with full context. And here's where it gets interesting. Quick doesn't just find answers, it turns them into action, create a deck, update a ticket, send a message right there in the conversation without switching tools. It's AI that actually works the way you do. Learn more at aws.com/quick. Welcome back. I'm talking with Darakas Shabby, CEO of Uber, about just how weird the experience of running a software company is getting right now. Let me ask you about running a software company in 2020. - Yeah, yes. 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 we did end up talking about just as you were walking. As you said, we had GitHub co-pilot, but all the engineers want to use cursor and now you're saying, and cursor's around, but they're all using Cloud Code. Or maybe they're using codecs, right? - The increase in Cloud 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 codecs 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 agentec coding is something that is it's just blowing people away. It's very, very cool. - And when you say blow people away, I would say many of your peers have gone crazy. Like they have seen agentec coding, it's looked them in the eye and they have 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 or 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. It's a show about orgfarts and I read that and I thought, well, our show is going to keep going for another decade. I know what kind of. we're on the cusp of the weirdest org charts in history. Yeah, yeah. Are you there? Are you saying, okay, 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. By the way, I've got to push a company harder to go to first principles in terms of how you work period. What we found is. and again, our culture is like bottoms up, let people do a bunch of stuff. And listen, the engineers are using it, the debugging, like all the cool stuff is happening as it should. But what we saw is like in sales, right? Most people 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, though I would describe around the edges of how we work. So that's one. And we're not kind of thinking about. well, I was thinking about the sales function from the bottom up. Our 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 bucks 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. Human agents. Human agents. And we then said, well, let's build, first, we'll 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. And what happens is, you know, an agent, a human agent, I'll be sitting next to you and be like, hey, what is 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. And so 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 to get those outcomes. I don't want to go bankrupt, but I want to keep you the Uber one customer member happy. And so we made a policy to approximate the optimal outcome for the population, right? 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, et cetera. I don't want to go bankrupt. So the approach that we're taking now with customer services, 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, I can give them feedback, et cetera. >> Just a foundational philosophical question. 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 happening that day. And models are easier to track and tune than humans are to train. >> This is scale answer. It can see all the data. >> Yeah. >> So you can just describe a generalized outcome and we'll just allow some-- >> And you can retrain based on that data and you have perfect visibility into the actions, reactions, and the retraining output, you don't have perfect visibility into it, but you can kind of iterate around that. So 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. Like we got to throw stuff out and just completely rebuild in a different way. And I'm really glad. Like it wasn't in this case. It wasn't me who pushed the Kostop's team to throw everything out. They were frustrated with the results of it. That they were seeing early on that 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 it's actually going to work in the end. >> That kind of dynamic customer response. >> Yeah. In terms of pricing, people are making illegal in this country. It's your dynamic pricing in that way. >> Well, because it feels unfair. >> Yeah. We're not going to. That is actually an issue, which is 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, you're both Uber one members, another person's food was 45 minutes late. You could actually have different outcomes because actually circumstances are different. So it's not based on targeting or optimizing based on targeting. It's optimizing based on context. >> That's really interesting. 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. >> That's not about that. >> You'll see if it works. >> You're going to come back next year. I'm going to ask you if it works. >> Yeah, exactly. >> 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 right now. And they all need to work together. And 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 that is, it's all a mess. I can see it happening over my sorry. Are you rethinking that basic triad inside of everyone? >> So it depends on the kind of project that you're working on. There are some larger projects that you need, design you need, proper planning, etc. 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, etc. Now they're just going in and they are vibe coding. And an engineer is going to review it, the code, but essentially the product person is going direct into the code base, so to speak, or going direct with an agent into the code base. So 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 has been making everybody more productive at my business because they're just freeing up cat-backs to go spend on whatever they're spending cash on to whatever end that Meta is going to do AI. Superintelligence, I'm told. Are you in that same spot where you're like, we're getting more productive, I need those people? >> No. We are, my view is if an engineer is going to be 50% or 200% more productive, I want more engineers. There are 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 a trade-off right now as we speak, which is, I don't know if you saw it, but our CTO was talking to a reporter and Meta comment, which is true, 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 happens, but it happened. And the trade-off is going to be headcount. So the, the, we are budgeting differently. Previously you would have a headcount budget or plan, you know, doesn't mean it would actually happen, but it's a plan going into you. You know, to input a 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 infrastructure spend, which 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, 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, it's significant. But the throughput is really accelerating. So at this point, it's, 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, it's just, especially on tech, it's fundamentally different than how we did three, four years ago. Well, let's say a question that I want to talk to you about autonomy. Sure. Which is also AI, but in a very different way. Physical world. Yeah. And you said the employees at Uber have created an AI version of Dart practice presenting resentment. Is that real and how close are we to AI replacing the CEO? So 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 a real thing. I mean, come on, how's that possible? - Dakota 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 just, I think that the AI-powered CEO is gonna 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. - I mean, that would be, in keeping with the Uber story, that would be there. - Yeah, exactly. - How is AI changing a board process? I've got to think about that one. - Oh, to try to, I get those pictures. They're very, you don't want anything to do with those. Let's talk about robots, actual robots, actually AI in the world. Uber has made a bunch of big investments in robotaxies. I wanna start with Rivian. It's over a billion dollars thing. It's $1.2 billion in total commitment to Rivian over some number of years. I just have a really basic question. You're in that's partnership in March. You're gonna buy up to 50,000 fully autonomous R2 robotaxies by 2031. But it's also called an investment. And I'm just doing the math. Like that's the price of the R2 platform. You're just buying a bunch of cars. Is buying a bunch of cars and investments are actually getting equity in Rivian? - So we actually invested in Rivian equity. And we've invested a number of our partners. Usually we will invest in our partners in a lucid, in a we ride, in an av ride, for example. So it is an investment and it's a vehicle commitment as well. It's both. And it's based on deliverables, obviously. They've got a deliver and based on everything that we've seen from RJ and team, putting together a first first class AIT, and we're confident that they can deliver on those R2s. - Yeah, the deliverables are very vague. I'm just gonna 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 contracts. - I put this into five different AI systems and we know it's a control. - Plus E is what you know. What are the autonomous milestones? - I could tell you, but then I'd have to kill you. - The reason I'm asking is not, I mean, I desperately wanna know those 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 when a car, what milestone an autonomy platform has to hit before I can say this is a real attack. - So I mean, usually, 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, maybe 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. So those are examples of deliverables that have to do with either capability or economics, because ultimately this is why I'm going to market with a product that's proven 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 striving. You're obviously diversiting 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 robotaxi. - Yeah, an apprenticeship with Nuro. - An apprenticeship with Nuro, which is the platform there. And overall, you're going to commit some $10 billion to autonomous efforts. You launched Uber, autonomous solutions. That feels like a bet on this is happening, but we don't know who's going to win. You're diversified. - So it's a little bit different from that. And 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 a finish line. They are the leader. They are in many ways of inspiration for many, many companies in this industry. They're great partner of ours in Atlanta and Austin. There are many other companies that are getting to the finish line. We ride, for example, or a pony.ai or a bido. These are Chinese companies are already at the finish line. And we are in market, for example, with we ride in the Middle East. And there are players like a Neuro or a Wabi or an Avride or a Wave, 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. Used to be kind of deterministic, kind of code that you have 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 we're trying to 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 be superhuman safety. We can help them get there, for example, with data collect. And we can both kind of then get to market as quickly as possible. So 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, which is the way to grow our platform is to build out supply, whether that's more drivers or whether that's more restaurant or more hotels. Then we're able to, 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 Neuro driver or an av ride or a We Ride. It's a bet that we're making, which is 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 ride share CEOs over the years, a lot of autonomy CEOs over the years. And it's always been 10 years away. Confidence I'm hearing from you is, oh, this is happening. We're spending a lot of money to get there faster. All the evidence we see is that it's happening. And you can, 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 the-- you've seen it. The speed of foundation model development, whether it's digital foundation models or physical foundation models in video, is betting on this as well. These are big bets made by capable companies. And we think one of the right track here. The context of our conversation I'm going to bring on 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 Kalnik in a lot of trouble by saying, I want to get the driver on the car long, long ago. Because autonomy was so far away, we just didn't have to solve this problem. You have been on podcasts recently saying, oh, this problem is here. I don't know what's going to happen to 9.5 million Uber drivers when autonomy comes. So you literally said, I don't know to Steven Barley. 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. Now, 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 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. So 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. And 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. And I think CEOs should talk about this, not in a way to scare people. But we should also be honest about it, which is I've never seen a wave of technology that has 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 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. - Not yet. - You've got people. - Not yet, yeah. - I haven't seen them, maybe they're coming. - I mean, listen, 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 gonna change the overall polling on this. There's a threat that will take my job. - Listen, if it's your job, I think you're right. - And so this dynamic of everybody is showing up saying the jobs are going away, and mostly, because it's so good at writing code, right? This is a weird kind of disconnected dynamic for regular people. Uber needs customers, you need people with money to want to write around. How do you see that economy playing around? - So I think that right now, the talk is louder than what we see in the market, right? The economy remains robust, the consumer remains robust. We don't see why color people out of work at this point. So 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 going around AI2? - I do think that it's some fear mongering from the press. They love the drama. Are you part of the press or no? - Yeah, I'm a little bit. Can I have this? Can I have this? - Can I have this a little bit of influence? 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 like machines replacing humans has been a theme for eons, right? And 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 it's out there. My, 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 gonna be able to have a job." We had an house just blown away. My 14 year old is asking me now, maybe he don't want to study. - Does that feel like a main thing 14 year old? - Yeah, exactly. So it's in the ether. You see signal, there are some companies like you mentioned who are acting on it. We'll see when then what happens in the next two years. But I don't see how it's gonna reverse. Once we get more data, maybe societal will, maybe the reality will be less dramatic than some would make it out to be. And then we'll see, we'll do our best. - I mean, I would love for the, to be real that it's the press. I just, the media history is not in a moment of intense strength right now, right? - Yeah, it is. - Contracting and image. - But you know, there has been some, some, I do think that the media is incentivized sometimes to over dramatize these things. Could be real, maybe it's not. I do think that that there is a reality in it. The question is, how quickly is it gonna change gonna happen? And will we be able to, will society be able to adjust fast enough? - Look, I get all my news from X, the everything app, which ensures me on the daily that AGI is just around the corner. I wanna 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 I always ask the driver. - Thank you. - The driver's always have the same question. So the same question over here. - Sure. - And then at this time, I actually got a very detailed follow-up question. - That sounds cool. - All right. - The drivers, I wanna know, how are they gonna get paid more? - Well, they are going to get paid more by some of the newer jobs that we're giving them. You know, shopping, for example, on a per hour basis can pay more. But I do think that driver pay isn't, is based on kind of what market rate pay is, essentially, right? The local pay goes up and down, based on the cost, kind of the spot cost of labor in a particular market. So I think the way that drivers are gonna get paid more is the cost of labor generally goes up or goes down. Right now the cost of labor is fairly steady and driver pay has been fairly steady. Nationwide is probably $32, $33 per utilize hour. Here near city is over $50 per utilize hour. So drivers are making decent money. Of course they're gonna wanna make more money. - They'll wanna make more money. - Of course. - You think autonomy changes that right? (sighs) - I don't think significantly. I think that drivers are going to probably take longer trips. When we see autonomous, autonomous inventory coming into a market, we slow down driver recruitment 'cause we want the drivers who are in market making as much. So 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 utilize hours because this is the very detailed file. - Yeah. - It's actually good 'cause you brought up all the keywords of discussion. - Sure. - See, you mentioned Westchester, I love the Westchester. The drivers in Westchester are allowed to drive into New York City. They're not allowed to pick up a New York City and drive back to Westchester. So they lose, it's an hour. They literally lose one utilized hour. So I have 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 run. - 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, significant amount of your fare goes to the city, et cetera. I think Uber's are too expensive here. And I think regulations sometimes goes over the top. It's something that I will absolutely take to the powers of B. - The power that the B in this city is, is there on Monday? Have you met with there on Monday? - 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. Who loves it when you say you love New York City. - Cool, cool, I do. - To tell them 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. - All right, cool. - Dara, this is always a pleasure. Thank you so much. - Thank you. Really appreciate it. - I'd like to thank Dara for taking the time and joining me on Decoder and thank you for listening. I hope you enjoyed it. To look at the snow, what you thought about this episode or really anything else at all, drop us a line. You can email us at decoderatthroed.com. We really do read all the emails. Or you can hit me up directly on Threads or Blue Sky. We're also on YouTube. You can check out full episodes at DecoderPod. It's the same handle on TikTok and Instagram. If you'd like to code her, please share it with your friends and subscribe wherever you're podcast. Decoder is a production of the Virgin, part of the Boxing Media Podcast Network. Show us produced by Kate Cox and Nick Statt. This episode was edited by Zander Adams. Our editorial director is Kevin McShane. The Decoder music is by great master cylinder. We'll see you next time. - Support for this show comes from Odo. So why make it harder? With a dozen different apps that don't talk to each other. Introducing Odo. In the best part, Odo replaces multiple expensive platforms for a fraction of the cost. So why not you? That's odo.com.