1 00:00:02,400 --> 00:00:07,520 Speaker 1: Bloomberg Audio Studios, podcasts, radio news LIFT. 2 00:00:07,600 --> 00:00:09,720 Speaker 2: It's been holding its first over investor day this week, 3 00:00:09,760 --> 00:00:12,959 Speaker 2: with the company projecting growth bookings to grow about fifteen 4 00:00:13,000 --> 00:00:15,800 Speaker 2: percent and a compound annual rate over the next three years. 5 00:00:15,960 --> 00:00:18,360 Speaker 2: Investors liking that clarity a stick in a little bit 6 00:00:18,400 --> 00:00:20,119 Speaker 2: more in the LIFT CEO David Rusher, who's here in 7 00:00:20,160 --> 00:00:22,160 Speaker 2: New York City. It was a big event. It was 8 00:00:22,600 --> 00:00:25,639 Speaker 2: lots of musical performances, and then the serious matter of 9 00:00:25,800 --> 00:00:30,680 Speaker 2: numbers of margin. What gets you that fifteen percent compound increase? 10 00:00:30,720 --> 00:00:33,559 Speaker 2: What gets you an improvement in margin and profitability too. 11 00:00:33,840 --> 00:00:35,479 Speaker 1: So I don't think you're going to be surprised about this, 12 00:00:35,520 --> 00:00:38,040 Speaker 1: but the answer is customer obsession is what gets us there, right. 13 00:00:38,080 --> 00:00:40,080 Speaker 1: I mean, we're a scale business. We're a scale business. 14 00:00:40,080 --> 00:00:42,080 Speaker 1: We do two million rides a day to day. We're 15 00:00:42,120 --> 00:00:44,000 Speaker 1: going to get to twenty five billion dollars in bookings 16 00:00:44,040 --> 00:00:45,920 Speaker 1: over the next couple of years. That just means doing 17 00:00:45,920 --> 00:00:48,840 Speaker 1: more of customers, like picking them up faster, pricing better, 18 00:00:49,040 --> 00:00:51,199 Speaker 1: and then innovating like with women plus connect and some 19 00:00:51,200 --> 00:00:53,120 Speaker 1: of the other things we've talked about before. It's really 20 00:00:53,120 --> 00:00:55,000 Speaker 1: the basics, but you do them really well. Over and 21 00:00:55,040 --> 00:00:57,040 Speaker 1: over again, and you can grow an incredibly big and 22 00:00:57,040 --> 00:00:57,880 Speaker 1: profitable business. 23 00:00:58,040 --> 00:01:00,400 Speaker 2: I can see that helping bookings. How does it do 24 00:01:00,400 --> 00:01:03,240 Speaker 2: the profitability? Do you have to start to charge people 25 00:01:03,520 --> 00:01:05,880 Speaker 2: a little bit more to be able to get that margin? 26 00:01:06,080 --> 00:01:06,360 Speaker 3: No? 27 00:01:06,360 --> 00:01:07,800 Speaker 1: No, in fact, that's not a lot on the plan, 28 00:01:07,880 --> 00:01:10,360 Speaker 1: and standward, it's in the plan are things like lift media. 29 00:01:10,560 --> 00:01:12,959 Speaker 1: So this is such an interesting thing. Think about all 30 00:01:12,959 --> 00:01:15,119 Speaker 1: the different ways brands want to talk to you today. 31 00:01:15,360 --> 00:01:17,679 Speaker 1: A lot of them are online, right, They're through Google, 32 00:01:17,840 --> 00:01:20,320 Speaker 1: maybe Meta whatever it is. Well, we actually have people 33 00:01:20,360 --> 00:01:23,480 Speaker 1: in cars going to grocery stores. And is that interesting 34 00:01:23,480 --> 00:01:26,000 Speaker 1: that maybe you know Cres versus Collgate. Maybe you don't 35 00:01:26,000 --> 00:01:27,960 Speaker 1: care too much, but maybe there's an advertiser who'd like 36 00:01:28,040 --> 00:01:29,960 Speaker 1: to talk to you. Is you're about to walk into Kroger, 37 00:01:30,040 --> 00:01:31,920 Speaker 1: for example. So that's part of the way that we 38 00:01:31,920 --> 00:01:33,840 Speaker 1: can kind of work with margins. We can also work 39 00:01:33,880 --> 00:01:35,960 Speaker 1: with modes and some other things. A lot of good ideas. 40 00:01:35,720 --> 00:01:39,760 Speaker 3: There, David, good morning, is ed. I'd like to look 41 00:01:39,800 --> 00:01:42,080 Speaker 3: past the numbers a little bit. I understand the seven 42 00:01:42,160 --> 00:01:44,720 Speaker 3: hundred and nine million rides you did in twenty twenty three, 43 00:01:44,920 --> 00:01:48,760 Speaker 3: and you make this argument that your target market or 44 00:01:48,800 --> 00:01:52,840 Speaker 3: total addressable market is based on the total miles driven 45 00:01:52,920 --> 00:01:56,240 Speaker 3: in personal cars. But what I don't see is how 46 00:01:56,240 --> 00:01:59,560 Speaker 3: you're going to go after that addressable market convincing people 47 00:01:59,760 --> 00:02:03,240 Speaker 3: not to use their own vehicles but take you instead. 48 00:02:03,840 --> 00:02:05,800 Speaker 1: Right, Well, you know I don't have to do a 49 00:02:05,840 --> 00:02:07,360 Speaker 1: lot of that. To be honest, it's one hundred and 50 00:02:07,400 --> 00:02:10,800 Speaker 1: sixty billion trips that people take a year. Let's just 51 00:02:10,840 --> 00:02:14,720 Speaker 1: look at the daily commute. Every single year, people commute 52 00:02:14,720 --> 00:02:19,160 Speaker 1: to and from works work fifty five billion times, fifty 53 00:02:19,200 --> 00:02:22,720 Speaker 1: five soul crushing billion times. So you don't have to 54 00:02:22,760 --> 00:02:24,960 Speaker 1: take much of that to make a real difference in 55 00:02:25,000 --> 00:02:27,560 Speaker 1: our seven hundred to eight hundred million rides. We've got 56 00:02:27,560 --> 00:02:29,840 Speaker 1: a new product that we're just in the process of developing, 57 00:02:29,880 --> 00:02:32,280 Speaker 1: for example, that will allow you to lock in your price, 58 00:02:32,600 --> 00:02:35,320 Speaker 1: so every single day, instead of a pobace that bounces around, 59 00:02:35,440 --> 00:02:37,600 Speaker 1: you get a consistent price. And that's something we know 60 00:02:37,680 --> 00:02:40,160 Speaker 1: people stress about. Literally, people wake up early in the 61 00:02:40,200 --> 00:02:42,359 Speaker 1: morning just to check a couple of different apps to 62 00:02:42,360 --> 00:02:44,120 Speaker 1: figure out what the least expensive way to get to 63 00:02:44,120 --> 00:02:46,079 Speaker 1: work is. So if we can take that off the table, 64 00:02:46,280 --> 00:02:48,359 Speaker 1: that might increase us by call it a ride a week. 65 00:02:48,400 --> 00:02:50,400 Speaker 1: But we ride a week, times fifty two weeks a year, 66 00:02:50,560 --> 00:02:53,640 Speaker 1: times millions of people. It adds up pretty fast. 67 00:02:54,120 --> 00:02:55,880 Speaker 3: David. When I said you were coming on the show, 68 00:02:56,320 --> 00:02:59,640 Speaker 3: the majority of questions from the audience were about robotaxi. 69 00:03:00,280 --> 00:03:02,280 Speaker 3: You and I've discussed this in the past, but Lift 70 00:03:02,320 --> 00:03:05,799 Speaker 3: has a complicated history with robotaxi. I think at the 71 00:03:05,800 --> 00:03:10,360 Speaker 3: core of their question is will you survive in a 72 00:03:10,400 --> 00:03:13,360 Speaker 3: world where you know, Zeukes has a really big runway, 73 00:03:13,440 --> 00:03:15,520 Speaker 3: Waimo appears to have a really big runway. 74 00:03:15,639 --> 00:03:18,400 Speaker 1: Yeah, yeah, so we will survive. Let's just take that 75 00:03:18,440 --> 00:03:20,440 Speaker 1: off the table right now, because I think the way 76 00:03:20,480 --> 00:03:22,799 Speaker 1: to think about it is this. You know, it's one 77 00:03:22,840 --> 00:03:25,359 Speaker 1: thing to create a robotaxi, and both of those companies 78 00:03:25,440 --> 00:03:28,160 Speaker 1: are doing really incredible work. I'm super super impressed with 79 00:03:28,160 --> 00:03:30,519 Speaker 1: the technology and how it's coming together. But it's a 80 00:03:30,639 --> 00:03:34,280 Speaker 1: very different thing to two million times a day, pick 81 00:03:34,320 --> 00:03:37,160 Speaker 1: people up, drop them off, do it in weird weather, 82 00:03:37,360 --> 00:03:39,160 Speaker 1: do it at airports, and so on and so on, 83 00:03:39,360 --> 00:03:41,560 Speaker 1: and so I think it's a much more likely scenario 84 00:03:41,640 --> 00:03:45,120 Speaker 1: that one displaces the other, that actually we work together, right, 85 00:03:45,160 --> 00:03:47,400 Speaker 1: that we end up welcoming them to our network. Because 86 00:03:47,400 --> 00:03:50,040 Speaker 1: if you're a ROBOTAXI company, and I know you've taken 87 00:03:50,080 --> 00:03:52,680 Speaker 1: for example, the Zoukes ROBOTAXI. Gosh, you need a lot 88 00:03:52,680 --> 00:03:55,880 Speaker 1: of volume to really drive the numbers. Otherwise, you know, 89 00:03:55,880 --> 00:03:58,520 Speaker 1: I mean you make ten ten cars, you know, ten 90 00:03:58,560 --> 00:04:00,120 Speaker 1: million dollars a car, it doesn't make any sense. So 91 00:04:00,320 --> 00:04:02,320 Speaker 1: you've got to have someone like us as a partner 92 00:04:02,400 --> 00:04:05,040 Speaker 1: who can really incorporate them into our network. And I 93 00:04:05,080 --> 00:04:07,120 Speaker 1: think that's the most likely scenario that you know, a 94 00:04:07,120 --> 00:04:08,920 Speaker 1: bunch of different companies. I think they're going to work on. 95 00:04:09,160 --> 00:04:11,320 Speaker 2: No soul crushing commute for Ed when he's in that 96 00:04:11,400 --> 00:04:15,360 Speaker 2: zoos about heading around that, David, I'm interested in you 97 00:04:15,400 --> 00:04:17,839 Speaker 2: can work together with robotaxis, but you're not going to 98 00:04:17,839 --> 00:04:20,640 Speaker 2: work together with Uber And I'm interested is to Ultimately 99 00:04:20,720 --> 00:04:24,120 Speaker 2: they're really focusing on this advertising focus too. Is there 100 00:04:24,200 --> 00:04:25,800 Speaker 2: room enough of both of you? How are you going 101 00:04:25,800 --> 00:04:26,719 Speaker 2: to compete for that? 102 00:04:27,160 --> 00:04:29,160 Speaker 1: I don't think we have to compete for them because 103 00:04:29,200 --> 00:04:30,400 Speaker 1: I think this. You know, one of the things I 104 00:04:30,480 --> 00:04:33,640 Speaker 1: learned from Jeff Bezos is work on things that are durable. 105 00:04:33,720 --> 00:04:35,320 Speaker 1: If you're going to build a big business, work on 106 00:04:35,320 --> 00:04:38,400 Speaker 1: something that's durable. Okay, I can predict in five years 107 00:04:38,440 --> 00:04:40,320 Speaker 1: ten years, one hundred years, there are going to be 108 00:04:40,320 --> 00:04:44,120 Speaker 1: companies like the ones we work with today, Chase, You, NBC, Universal, 109 00:04:44,680 --> 00:04:48,719 Speaker 1: Delta with their skymiles program, Kroger, Ihop. These are five 110 00:04:48,760 --> 00:04:51,120 Speaker 1: companies that we're working with today. Every single one of 111 00:04:51,120 --> 00:04:54,360 Speaker 1: those companies or their siblings or their brethren are going 112 00:04:54,400 --> 00:04:56,200 Speaker 1: to want to talk to customers in new ways. And 113 00:04:56,279 --> 00:04:58,840 Speaker 1: we've got two million rides every single day. So no, 114 00:04:58,960 --> 00:05:00,400 Speaker 1: I don't think that this is one of the zero 115 00:05:00,480 --> 00:05:02,320 Speaker 1: some things. I think actually this is a whole new 116 00:05:02,360 --> 00:05:05,320 Speaker 1: category of mobile advertising. I think we're going to get 117 00:05:05,320 --> 00:05:06,560 Speaker 1: their first. I think we're going to do it better. 118 00:05:06,600 --> 00:05:08,280 Speaker 1: But if other people want to follow us, and more 119 00:05:08,279 --> 00:05:08,880 Speaker 1: power to them. 120 00:05:09,160 --> 00:05:11,200 Speaker 2: Of course works Famaz and you work for Microsoft. Do 121 00:05:11,200 --> 00:05:12,960 Speaker 2: you learn a lot from those businesses? And both of 122 00:05:12,960 --> 00:05:16,040 Speaker 2: those are plowing forward and artificial intelligence, I'm sure you 123 00:05:16,080 --> 00:05:19,360 Speaker 2: are too. How much can you use my data as 124 00:05:19,400 --> 00:05:22,000 Speaker 2: a lift user to dictate what ads I'm seeing? Make 125 00:05:22,040 --> 00:05:24,400 Speaker 2: it more specific for me without wading me out? 126 00:05:24,720 --> 00:05:26,440 Speaker 1: So great question. I mean I would actually turn it 127 00:05:26,480 --> 00:05:28,600 Speaker 1: around a little bit and say, of course, artificial intelligence 128 00:05:28,680 --> 00:05:31,760 Speaker 1: is going to revolutionize a lot of different businesses. I 129 00:05:31,760 --> 00:05:34,000 Speaker 1: would start actually with our drivers. Look at all the 130 00:05:34,040 --> 00:05:36,320 Speaker 1: different ways drivers interact with on our platform. Right now, 131 00:05:36,400 --> 00:05:38,279 Speaker 1: by the way, forty one percent of our drivers don't 132 00:05:38,279 --> 00:05:40,840 Speaker 1: speak English as their first language at home. So imaginal 133 00:05:40,839 --> 00:05:44,440 Speaker 1: world where artificial intelligence can help with simultings translation. Every 134 00:05:44,440 --> 00:05:46,960 Speaker 1: single one of our drivers wants to earn more our platform. 135 00:05:47,120 --> 00:05:50,320 Speaker 1: Imaginar world where drivers can use an intelligent assistant to 136 00:05:50,360 --> 00:05:52,159 Speaker 1: help them figure out how to spend their time so 137 00:05:52,200 --> 00:05:54,080 Speaker 1: they can earn even more on the platform. And then 138 00:05:54,120 --> 00:05:56,120 Speaker 1: you get to the targeting side, and our goal is 139 00:05:56,120 --> 00:05:58,480 Speaker 1: going to be to make the targeting so seamless. You 140 00:05:58,520 --> 00:06:00,159 Speaker 1: think of it as a benefit, like, oh wow, this 141 00:06:00,240 --> 00:06:02,760 Speaker 1: is fantastic. I'm literally getting a coupon off of my 142 00:06:02,760 --> 00:06:05,159 Speaker 1: fe what's your favorite toothpiste? Out of curiosity. 143 00:06:05,400 --> 00:06:07,520 Speaker 2: I'm using some weird one made in India that doesn't 144 00:06:07,520 --> 00:06:09,160 Speaker 2: have fluor ride in it. I'm like going into that 145 00:06:09,279 --> 00:06:10,080 Speaker 2: big rabbit hole on. 146 00:06:10,120 --> 00:06:12,160 Speaker 1: You're one of those people, Okay, fair enough, Well you 147 00:06:12,200 --> 00:06:14,400 Speaker 1: know what, I use a product called David Toothpaste. I 148 00:06:14,400 --> 00:06:16,160 Speaker 1: don't know if they're on the line now, Sorry, I've 149 00:06:16,160 --> 00:06:19,359 Speaker 1: got a personalized it's not personalized, it's just pure coincidence. 150 00:06:19,360 --> 00:06:22,159 Speaker 1: But there also I think they have a different formulation 151 00:06:22,200 --> 00:06:23,680 Speaker 1: than Florid. I don't know. I'm not so focused on that, 152 00:06:23,760 --> 00:06:25,240 Speaker 1: but it's a very nice tube I can tell you that. 153 00:06:25,279 --> 00:06:28,200 Speaker 1: So imagine I'm going to CVS and I get five 154 00:06:28,240 --> 00:06:31,320 Speaker 1: dollars off my David's Tuesdays instead of the big, long, 155 00:06:31,400 --> 00:06:33,160 Speaker 1: ridiculous thing that you get after you check out there. 156 00:06:33,200 --> 00:06:34,279 Speaker 1: I think it's a better experience. 157 00:06:35,160 --> 00:06:37,279 Speaker 3: David on the AI note, I got a very specific 158 00:06:37,320 --> 00:06:40,200 Speaker 3: question from an audience member on X how is lift 159 00:06:40,279 --> 00:06:43,440 Speaker 3: leveraging AI? Is it truly a buzzword for them or 160 00:06:43,440 --> 00:06:46,599 Speaker 3: are they really making use of it in a meaningful way? 161 00:06:46,600 --> 00:06:49,160 Speaker 3: And you gave the example or a couple of examples 162 00:06:49,200 --> 00:06:52,680 Speaker 3: there from the driver's perspective, what about the rider's perspective? 163 00:06:52,680 --> 00:06:54,080 Speaker 3: And what about your internal use? 164 00:06:54,279 --> 00:06:57,080 Speaker 1: Sure? So all three? Right? So internal is really about 165 00:06:57,200 --> 00:07:00,800 Speaker 1: productivity and quality? Right? We use AI to check our code. 166 00:07:00,839 --> 00:07:03,200 Speaker 1: We use AI to come up with different ideas, sometimes 167 00:07:03,200 --> 00:07:05,440 Speaker 1: as kind of idea starters. Anyway, a lot of interesting 168 00:07:05,440 --> 00:07:07,920 Speaker 1: things there. For drivers. I just mentioned a great way 169 00:07:07,960 --> 00:07:10,200 Speaker 1: I think to sort of maximize earnings as an example, 170 00:07:10,480 --> 00:07:12,560 Speaker 1: and I think for riders, imagine a world. Here's a 171 00:07:12,600 --> 00:07:14,559 Speaker 1: scenario that I bet has never happened to anyone listening. 172 00:07:14,680 --> 00:07:18,360 Speaker 1: You ever leave your iPhone in the car, and how 173 00:07:18,640 --> 00:07:20,520 Speaker 1: how fun is that experience is? You know two minutes 174 00:07:20,560 --> 00:07:22,360 Speaker 1: later when you realize you don't have it, well, you know, 175 00:07:22,440 --> 00:07:25,200 Speaker 1: AI is pretty good at detecting patterns and understanding the 176 00:07:25,200 --> 00:07:27,320 Speaker 1: gosh the car is moving one way. You know, you've 177 00:07:27,320 --> 00:07:29,240 Speaker 1: just gotten out of the car. What's going on here? 178 00:07:29,280 --> 00:07:31,520 Speaker 1: Maybe you can reroot the driver back to drop the 179 00:07:31,520 --> 00:07:33,520 Speaker 1: phone off for you. So I think, look, you can 180 00:07:33,600 --> 00:07:35,560 Speaker 1: overhype AI. You know, maybe it's not going to cure 181 00:07:35,600 --> 00:07:38,000 Speaker 1: cancer tomorrow. Probably isn't, to be honest, but I do 182 00:07:38,040 --> 00:07:40,720 Speaker 1: think that every single day. If we're customer obsessed and 183 00:07:40,720 --> 00:07:43,360 Speaker 1: that's going to be our focus, customer obsessed AI, we 184 00:07:43,400 --> 00:07:45,119 Speaker 1: can use it in really innovative ways. 185 00:07:45,840 --> 00:07:47,760 Speaker 3: Let's see it. David Richard, thank you so much. We 186 00:07:47,800 --> 00:07:49,560 Speaker 3: appreciate you coming into a well head quolt. 187 00:07:49,560 --> 00:07:50,360 Speaker 1: It is always a pleasure. 188 00:07:50,520 --> 00:07:51,240 Speaker 3: You'll good to see