The Exchanges

Q Yeah. So, what did you learn from Barry Diller?

A Ah, I learned so much, uh, from Barry Diller, but, you know, one of the things that I learned from him is that I'm comfortable going against the grain. You know, Barry was the ultimate counterpuncher. He kind of took on, you know, ABC, NBC, CBS. Uh, you didn't grow up with him, but a lot of people grew up with him, and he, he built Fox, which was a new, uh, a new product. I've always been comfortable going against the grain, and it's one of the things that encouraged me to join Uber. From the outside, things looked really, really difficult, uh, but we got in there, and, uh, at first it was, you know, climbing uphill, uh, but the desire to go against the grain was something that I learned from him.

AI assessment note: “one of the things that I learned from him is that I'm comfortable going”

Q Amazing. So I guess taking a step back, what are biggest misconceptions about tech today?

A So I think the biggest misconception I think comes to AI. I think the public is really worried about what AI means. This, uh, the fears of job displacement. Are real, and the fact is no one knows what those outcomes are going to be, but I think to the average consumer, You know, AI is cool. ChatGPT is awesome. I'm on it constantly. I'm coding with Claude, et cetera, but I'm not the typical consumer. We need to bring out these AI products, uh, and surprise and delight consumers every day so that they see AI working for them, not replacing the work that they do. I think if we do that, we're all going to be okay. And AI is going to be something that's cool for tech companies, but it's going to be even cooler for the general population.

AI assessment note: “So I think the biggest misconception I think comes to AI.”

Q So then how do you set that up? What's the.

A Uh, I asked the team, basically. You know, travel has always been. No, no, we, we did it the right way. But we've always thought about travel. It's, it's a huge use case on, on Uber, and it was natural for us to add hotels. As another product in, in Europe, for example, we already have trains available in the UK and France and Spain. So originally it's like, well, it's wire up everything that moves, but then how do we actually build around the experience of movement and traveling as well? Hotels was a natural for us. The focus for me has been deepening the relationship with Uber one members. You know, we got almost fifty million members. It's a unique offering, which is you get discounts on rides, you get discounts on delivery as well. What else can we offer these members to keep our membership growth really, really high and keep your attention really high as well. And savings on hotels was something that came up. Uh, and so Uber one members, when you book, uh, hotels on Uber, you get a 10% back on all of your bookings, regardless of which hotels you book on. And on 10,000 hotels, kind of a rolling list, you get at least 20% off. So there's an incentive to, uh, book on Uber. And we think it's going to be a delightful experience.

AI assessment note: “The focus for me has been deepening the relationship with Uber one members.”

Q So did you have to rewrite your brain? Like, how did you, how did you come in and do this amazing turnaround? What were the key?

A Well, thank you. I appreciate it. You know, one thing I would tell you is that a lot of people talk about Uber. You know, when I came there, obviously there was a lot of controversy in terms of Uh, the culture, et cetera. But the team had built a good business in terms of demand and the brand and our presence globally. So while things were not easy, I kind of was standing on the shoulders of giants and this was an incredible brand. And for me, I got to bring in some of the new team and then kind of continue some of the old team together and build on the foundations that we already had. The fact is that Uber was incredibly popular. Uh, we really focused on supply. We focused on safety as well, because both our drivers and our riders want to feel safe. Uh, and then once we got to Uber Eats and started building that cross-platform, uh, relationship, you know, we compete with lots of people, but most of our competitors are monolined. They either have rides or they have eats. The fact that we are a complete platform allows us to grow faster than our competitors and be more profitable than our competitors as well.

AI assessment note: “We really focused on supply. We focused on safety as well”

Q And then do you test them internally if it's like actually commercially viable?

A You know, we, so we just test them. We put them out into the market. I mean, one of our philosophies is we just want to build stuff. And if you're not failing, like this is, I think a cool feature is going to be a win, but we want to move faster. We want to take risks. Some of these products are going to work. Most of them we hope, but some of them aren't going to work and that's okay because you know, one of the hallmarks of Uber is We're builders. We want to build fast. Most of our growth is going to come, uh, through building new great products versus going out and buying stuff. Uh, and it's part of the building process that we've got.

AI assessment note: “we just test them. We put them out into the market.”

Q in production. Start building at merge.dev. Founders scale faster on Deal. Set up payroll for any country in minutes, hire anyone anywhere, get visas handled fast, and get back to building. Visit deal.com slash sorcery. That's D-E-E-L dot com slash sorcery. You mentioned how AI is helping inform product decisions. How else is AI impacting the business and operations, whether it is teams, communications, or the next era of Uber?

A So it's like changing how we build in every single way. Um, one of the cool things about Uber is that we've, we've always been comfortable with a probabilistic world. You know, most companies are, are kind of, they want certainty, et cetera, but While you can get certainty in your digital life, you know, with an app, you build the interactions, et cetera. In real life, things can go wrong. You know, you can order that Uber and we told you it's a four minute ETA. The driver cancels. We have to actually get you another driver, et cetera. So there's all kinds of uncertainty that happens in the real world. And we have been built on top of that. So everything that we built It has been algorithmic in nature. Your pricing, your matching, your, uh, your ease feed, et cetera. Now it, it's, it went from very simple algorithms to deep learning algorithms, and now obviously to much larger foundation models. So for persons like culturally, companies are often, uh, uncomfortable with, with, uh, uncertainty. We live in uncertainty because the real world Can punch you in the face sometimes, right? All kinds of things can go wrong. And so approaching with this probabilistic mindset of what outcomes can be is really important in terms of driving our culture. We've had a leg up because we've been, we've been building algorithmically, but our developers are all using Claude and actually Codex is p…

AI assessment note: “our developers are all using Claude and actually Codex is pretty cool as well”