1 00:00:02,720 --> 00:00:09,040 Speaker 1: Bloomberg Audio Studios, podcasts, radio news, and Android auto with 2 00:00:09,119 --> 00:00:12,240 Speaker 1: the Bloomberg Business App. Listen on demand wherever you get 3 00:00:12,240 --> 00:00:15,040 Speaker 1: your podcasts, or watch us live on YouTube. 4 00:00:16,040 --> 00:00:20,200 Speaker 2: A trade team heading over to Switzerland today for trade 5 00:00:20,200 --> 00:00:23,919 Speaker 2: talks with China, and President Trump out today with a 6 00:00:23,960 --> 00:00:29,040 Speaker 2: tweet saying eighty percent China tariff. Quote seems right ahead 7 00:00:29,040 --> 00:00:30,880 Speaker 2: of the talks, so we'll see how that plays out. 8 00:00:30,920 --> 00:00:33,400 Speaker 2: Seawan Donna joins us. He is senior economics writer for 9 00:00:33,440 --> 00:00:37,280 Speaker 2: Bloomberg News. He's based down in Washington, DC. Seems right. 10 00:00:37,760 --> 00:00:38,080 Speaker 3: I don't know. 11 00:00:38,080 --> 00:00:40,320 Speaker 2: I'm not sure where that number came from here, Sean, 12 00:00:40,360 --> 00:00:43,920 Speaker 2: what are expectations going into some of these trade discussions 13 00:00:44,400 --> 00:00:46,360 Speaker 2: with the Chinese in Switzerland. 14 00:00:47,000 --> 00:00:50,919 Speaker 4: Look, the administration from President Trump on down has made 15 00:00:51,000 --> 00:00:53,280 Speaker 4: very clear they want to do some de escalation this 16 00:00:53,360 --> 00:00:57,040 Speaker 4: weekend and that they want to bring parish down from 17 00:00:57,200 --> 00:01:00,959 Speaker 4: the one hundred and forty five percent plus level that 18 00:01:00,960 --> 00:01:05,120 Speaker 4: they're at. We had heard in recent days below sixty 19 00:01:05,160 --> 00:01:07,560 Speaker 4: percent was something that they're targeting. The President's out with 20 00:01:08,040 --> 00:01:10,320 Speaker 4: eighty percent. But look, the thing to keep in mind 21 00:01:10,360 --> 00:01:12,800 Speaker 4: here is even if we get what would be a 22 00:01:12,880 --> 00:01:17,119 Speaker 4: dramatic the escalation dramatic reduction from one hundred and forty 23 00:01:17,120 --> 00:01:21,039 Speaker 4: five percent to eighty percent or sixty percent. Those levels 24 00:01:21,080 --> 00:01:23,679 Speaker 4: are still incredibly high, and they're still going to be 25 00:01:23,680 --> 00:01:26,120 Speaker 4: prohibitive for a lot of trade. And you know, the 26 00:01:26,160 --> 00:01:30,119 Speaker 4: folks a Bloomberg Economics have been running the numbers and 27 00:01:30,200 --> 00:01:33,080 Speaker 4: you still get plenty of damage to the US economy 28 00:01:33,520 --> 00:01:38,880 Speaker 4: to trade to the Chinese economy. Those are not good numbers. 29 00:01:40,240 --> 00:01:42,400 Speaker 4: I mean this it speaks to how far we've come 30 00:01:42,400 --> 00:01:43,959 Speaker 4: in the last month. You know, it was literally a 31 00:01:43,959 --> 00:01:48,920 Speaker 4: month ago today that these reciprocal tariffs went into place 32 00:01:49,160 --> 00:01:51,240 Speaker 4: from President Trump and then he had a bit of 33 00:01:51,280 --> 00:01:53,800 Speaker 4: a U turn put on a ninety day puz. But 34 00:01:54,800 --> 00:01:58,600 Speaker 4: the window on what is being discussed is just has 35 00:01:58,640 --> 00:02:00,240 Speaker 4: shifted remarkably over that time. 36 00:02:00,760 --> 00:02:02,520 Speaker 5: And that's what we forget kind of yesterday too, like 37 00:02:02,560 --> 00:02:04,000 Speaker 5: even though there was a deal with the UK, like 38 00:02:04,040 --> 00:02:06,960 Speaker 5: those ten percent tariffs are still in effect, They're still there. 39 00:02:08,160 --> 00:02:10,560 Speaker 5: What was so interesting though, is that he also said 40 00:02:10,560 --> 00:02:14,280 Speaker 5: that it's up to Besson't why, like, what do we 41 00:02:14,320 --> 00:02:15,840 Speaker 5: make of that? And is that a good or a 42 00:02:15,880 --> 00:02:17,680 Speaker 5: bad thing as far as markets are concerned. 43 00:02:18,600 --> 00:02:20,760 Speaker 4: Well, look, I mean, Besson's the guy for now. Right, 44 00:02:20,840 --> 00:02:24,720 Speaker 4: I mean, he's leading the delegation there, and President Trumps 45 00:02:24,919 --> 00:02:29,280 Speaker 4: has moved around. I mean a few weeks ago, it 46 00:02:29,360 --> 00:02:31,840 Speaker 4: was Howard Latnik who was the guy right and on 47 00:02:32,240 --> 00:02:36,080 Speaker 4: China and other things. It looks like the Secretary of 48 00:02:36,080 --> 00:02:40,080 Speaker 4: Commerce is now dealing with other countries and there's a 49 00:02:40,080 --> 00:02:44,880 Speaker 4: lot of negotiations going on there. Scott Besson is the 50 00:02:44,919 --> 00:02:47,520 Speaker 4: man of the moment this weekend. He's being joined by 51 00:02:47,600 --> 00:02:52,320 Speaker 4: Jamison Greer, who really is the trade wonk in the mission. 52 00:02:52,400 --> 00:02:57,240 Speaker 4: He's the US trade representative of course. So look, you know, 53 00:02:57,320 --> 00:02:59,320 Speaker 4: one of the things we learned in the first Trump 54 00:02:59,360 --> 00:03:03,799 Speaker 4: administration is President Trump has lots of emissaries, and he 55 00:03:03,880 --> 00:03:08,400 Speaker 4: often shifts emissaries over time. So let's see what Scott 56 00:03:08,400 --> 00:03:11,720 Speaker 4: Bessen comes back with, Let's see what President Trump makes 57 00:03:11,720 --> 00:03:14,560 Speaker 4: of it, and let's see where the talks go from here. 58 00:03:14,840 --> 00:03:18,280 Speaker 2: What's the current thinking Sean about when will consumers feel this? 59 00:03:18,400 --> 00:03:20,480 Speaker 2: Gene Soroka, who runs a port of la was in 60 00:03:20,520 --> 00:03:23,040 Speaker 2: our studio last week and he said bookings for this week, 61 00:03:23,200 --> 00:03:26,640 Speaker 2: sailings from China into his port are down thirty to 62 00:03:26,680 --> 00:03:28,120 Speaker 2: thirty five percent for this week. 63 00:03:28,720 --> 00:03:30,080 Speaker 3: When's that going to reach consumers? 64 00:03:31,520 --> 00:03:35,600 Speaker 4: Look it's it's going to reach consumers and soon. I mean, 65 00:03:35,880 --> 00:03:38,120 Speaker 4: I think you know, we had new trade numbers out 66 00:03:38,160 --> 00:03:44,080 Speaker 4: of China today and exports from China to the US 67 00:03:44,080 --> 00:03:47,320 Speaker 4: are down by twenty one percent, and we're down by 68 00:03:47,320 --> 00:03:49,040 Speaker 4: twenty one percent in April, and they're only going to 69 00:03:49,040 --> 00:03:52,360 Speaker 4: get that. You know, they're only going to continue downward 70 00:03:52,480 --> 00:03:54,640 Speaker 4: from from here, even if we get some kind of 71 00:03:54,640 --> 00:03:58,560 Speaker 4: de escalation at the weekend. So the one thing that 72 00:03:58,760 --> 00:04:02,000 Speaker 4: is is kind of pushing back against the impact for 73 00:04:02,040 --> 00:04:04,920 Speaker 4: consumers is that companies have built up a lot of inventories. 74 00:04:05,720 --> 00:04:07,440 Speaker 4: So you talk to companies and they'll say, we've got 75 00:04:07,440 --> 00:04:10,600 Speaker 4: three maybe six months worth of inventory there, and that 76 00:04:10,720 --> 00:04:13,440 Speaker 4: might get us through. So we're going to see empty shelves, 77 00:04:13,480 --> 00:04:15,720 Speaker 4: but not all the shelves are likely to be empty 78 00:04:15,760 --> 00:04:16,200 Speaker 4: this summer. 79 00:04:16,400 --> 00:04:17,960 Speaker 5: And then it becomes a question of when do we 80 00:04:18,000 --> 00:04:20,520 Speaker 5: see those jobs be cut? So if you don't have 81 00:04:20,640 --> 00:04:25,080 Speaker 5: the volume coming in, do those dock workers get scrapped? 82 00:04:25,120 --> 00:04:27,680 Speaker 5: And that was a question posed to President Trump yesterday 83 00:04:27,720 --> 00:04:29,560 Speaker 5: and he's like, well, good, because a long term it's 84 00:04:29,600 --> 00:04:31,479 Speaker 5: going to be good for America. But what's that trickle 85 00:04:31,560 --> 00:04:33,479 Speaker 5: down looking like? 86 00:04:34,160 --> 00:04:37,000 Speaker 4: Yeah, and we're already seeing job cuts, and you know, 87 00:04:37,160 --> 00:04:40,720 Speaker 4: we're also seeing hiring slow down, and I think that's 88 00:04:40,800 --> 00:04:42,760 Speaker 4: That's the other thing to remember is a lot of 89 00:04:42,800 --> 00:04:46,960 Speaker 4: the uncertainty around trade isn't necessarily about cutting jobs or 90 00:04:48,480 --> 00:04:52,920 Speaker 4: closing factories in the short term. It's about not expanding factories. 91 00:04:53,160 --> 00:04:56,440 Speaker 4: It's about not growing, it's about not hiring more people, 92 00:04:56,440 --> 00:04:58,760 Speaker 4: and that, of course is what's really toxic for the expoment. 93 00:04:59,000 --> 00:05:00,599 Speaker 5: All right, Sean, we gotta leave it there. Thanks lash 94 00:05:00,680 --> 00:05:03,440 Speaker 5: onon Don and joining us. He heads up all trade 95 00:05:03,520 --> 00:05:05,560 Speaker 5: and economic coverage for Bloomberg. 96 00:05:07,160 --> 00:05:10,840 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 97 00:05:10,920 --> 00:05:14,320 Speaker 1: weekdays at ten am Eastern on Applecarclay and Android Auto 98 00:05:14,440 --> 00:05:17,479 Speaker 1: with the Bloomberg Business app. Listen on demand wherever you 99 00:05:17,520 --> 00:05:20,520 Speaker 1: get your podcasts, or watch us live on YouTube. 100 00:05:21,279 --> 00:05:21,760 Speaker 3: AI. 101 00:05:21,839 --> 00:05:23,800 Speaker 2: Well, you remember when the time was we talked about 102 00:05:23,839 --> 00:05:27,960 Speaker 2: AI NonStop, and then we switched the narrative to terrors. 103 00:05:28,040 --> 00:05:29,320 Speaker 3: I kind of want to go back to AI. That 104 00:05:29,360 --> 00:05:30,760 Speaker 3: seems like a lot more fun. 105 00:05:31,839 --> 00:05:34,120 Speaker 2: But it's affecting all the industries we know that, including 106 00:05:34,120 --> 00:05:37,040 Speaker 2: financial services industry. One of our next guests is really 107 00:05:37,040 --> 00:05:40,400 Speaker 2: involved in that. Josh Panthony, CEO CO founder of boosted 108 00:05:40,560 --> 00:05:42,760 Speaker 2: dot AI Joints, is here in our Bloomberg and Arrective 109 00:05:42,760 --> 00:05:46,600 Speaker 2: Brokers studio based in Chicago, so I mean in Toronto 110 00:05:46,680 --> 00:05:51,320 Speaker 2: for our Canadian friends. Welcome, Josh, appreciate it. 111 00:05:51,360 --> 00:05:53,120 Speaker 3: Talk to us about boost to AI. What do you 112 00:05:53,160 --> 00:05:53,560 Speaker 3: guys do? 113 00:05:54,000 --> 00:05:57,320 Speaker 6: So we make it easy for professional investment managers to 114 00:05:57,440 --> 00:05:59,240 Speaker 6: build these little sort of AI workers the kind of 115 00:05:59,240 --> 00:06:01,720 Speaker 6: automate different parts of the process. So you might give 116 00:06:01,720 --> 00:06:04,960 Speaker 6: it some task related to maybe doing a competitive analysis 117 00:06:05,080 --> 00:06:07,960 Speaker 6: or tracking what's happening with tariffs, and then have the 118 00:06:08,000 --> 00:06:10,920 Speaker 6: worker kind of automatically scan through the world and inform 119 00:06:10,920 --> 00:06:12,080 Speaker 6: you about really important. 120 00:06:11,800 --> 00:06:13,880 Speaker 7: Information that could have impacts on your process. 121 00:06:14,240 --> 00:06:16,919 Speaker 5: So what's the ROI for something like that? Like, if 122 00:06:16,960 --> 00:06:19,200 Speaker 5: I'm a portfolio manager, what does it look like? What 123 00:06:19,240 --> 00:06:20,760 Speaker 5: has my job get better or worse? 124 00:06:21,400 --> 00:06:26,039 Speaker 6: Yeah, well we mix your job much better. Point we 125 00:06:26,120 --> 00:06:27,680 Speaker 6: kind of think of it in three different ways. We 126 00:06:27,720 --> 00:06:29,800 Speaker 6: can help automate drudgery. So if you're someone has to 127 00:06:29,800 --> 00:06:32,840 Speaker 6: do something like write a deep due diligence document, or 128 00:06:32,839 --> 00:06:35,320 Speaker 6: if you do a report or a swat analysis, or 129 00:06:35,360 --> 00:06:38,280 Speaker 6: you know, write like a ninety page investment thesis. These 130 00:06:38,320 --> 00:06:40,159 Speaker 6: are all examples of task that system could help you 131 00:06:40,160 --> 00:06:42,480 Speaker 6: do just a lot faster. The second way we think 132 00:06:42,520 --> 00:06:44,599 Speaker 6: about it sort of monitoring the world. If you have 133 00:06:44,680 --> 00:06:47,040 Speaker 6: a portfolio and you wanted to sort of scan through 134 00:06:47,480 --> 00:06:50,080 Speaker 6: everything happening in particular sector, maybe look forever you like 135 00:06:50,120 --> 00:06:52,800 Speaker 6: eight K, ten K scan to corporate events. This doesn't 136 00:06:52,800 --> 00:06:55,040 Speaker 6: can form you real time. And then the last category 137 00:06:55,040 --> 00:06:57,480 Speaker 6: I think of is sort of like superpowers. A good 138 00:06:57,520 --> 00:07:00,719 Speaker 6: example of that is when the video head there Blackwell 139 00:07:00,760 --> 00:07:03,560 Speaker 6: chipped away. He historically analyze that by looking at sell 140 00:07:03,600 --> 00:07:06,120 Speaker 6: side research talking to our groups. You'd still do that, 141 00:07:06,600 --> 00:07:08,400 Speaker 6: but you could use our system to look through like 142 00:07:08,600 --> 00:07:11,520 Speaker 6: three thousand companies, learnings calls, summer as across all of them, 143 00:07:12,040 --> 00:07:14,000 Speaker 6: what are the major macro effects of this, and how 144 00:07:14,040 --> 00:07:16,000 Speaker 6: many are still bullish and working with the video versus 145 00:07:16,040 --> 00:07:19,160 Speaker 6: other providers, And that kind of analysis just wasn't really 146 00:07:19,160 --> 00:07:21,559 Speaker 6: possible at scale until our technology came around. 147 00:07:22,200 --> 00:07:25,360 Speaker 2: As a former cell side research channels, would my job 148 00:07:25,400 --> 00:07:27,840 Speaker 2: be at risk here? People still need me? 149 00:07:32,200 --> 00:07:34,000 Speaker 7: Yeah? I think the jobs just going to change. 150 00:07:34,160 --> 00:07:34,440 Speaker 3: Okay. 151 00:07:34,520 --> 00:07:36,760 Speaker 7: I think there's kind of two elements of that work, right. 152 00:07:36,800 --> 00:07:41,800 Speaker 6: There's the element of producing text, analyzing text, producing numerical analysis. 153 00:07:42,200 --> 00:07:44,000 Speaker 6: We think AI is going to more or less automate 154 00:07:44,040 --> 00:07:46,280 Speaker 6: ninety to ninety five percent of that. But there's another 155 00:07:46,360 --> 00:07:48,120 Speaker 6: deep part of your job where you were going out 156 00:07:48,160 --> 00:07:50,440 Speaker 6: and actually talking to humans. You were running conferences, you 157 00:07:50,480 --> 00:07:53,840 Speaker 6: were talking to management, you were talking to experts. Humans 158 00:07:53,840 --> 00:07:56,200 Speaker 6: are always going to have a massive competitive advantage, and 159 00:07:56,320 --> 00:07:57,960 Speaker 6: we think that the job almost gets more humanized. You 160 00:07:58,000 --> 00:08:00,600 Speaker 6: spend more of your life talking to other people, get information, 161 00:08:00,880 --> 00:08:01,760 Speaker 6: learning what matters, and. 162 00:08:01,760 --> 00:08:03,680 Speaker 7: Taking to the polls. Well, the machine keeps track of 163 00:08:03,680 --> 00:08:04,720 Speaker 7: all the information in the world. 164 00:08:05,040 --> 00:08:07,120 Speaker 5: So one of the criticism is that you know, by 165 00:08:07,160 --> 00:08:09,720 Speaker 5: going through all those earnings reports or whatever salesye reports, 166 00:08:09,800 --> 00:08:13,320 Speaker 5: you learn stuff, and that if you have AI do it, 167 00:08:13,560 --> 00:08:15,280 Speaker 5: you take away that learning tool. 168 00:08:15,400 --> 00:08:16,360 Speaker 7: How do you combat that? 169 00:08:16,920 --> 00:08:19,320 Speaker 6: Yeah, absolutely, there's still going to be some earnings calls 170 00:08:19,360 --> 00:08:22,720 Speaker 6: you're going to cover, but there's probably hundreds of earnings 171 00:08:22,720 --> 00:08:25,120 Speaker 6: calls maybe up the supply chain, down the supply chain, 172 00:08:25,160 --> 00:08:27,880 Speaker 6: relatedt competitors, related names that you don't cover, that have 173 00:08:28,040 --> 00:08:30,640 Speaker 6: really big impacts on your stocks you're probably not looking 174 00:08:30,680 --> 00:08:33,120 Speaker 6: at today. By adding in something like this, the sort 175 00:08:33,120 --> 00:08:34,760 Speaker 6: of set of information you can use as part of 176 00:08:34,760 --> 00:08:36,800 Speaker 6: your analysis just increases a lot more. 177 00:08:37,440 --> 00:08:40,280 Speaker 2: What did you make of the news that's maybe came 178 00:08:40,320 --> 00:08:41,920 Speaker 2: to a little bit of a head in this past 179 00:08:41,920 --> 00:08:46,000 Speaker 2: week about AI as a threat to Google Search? 180 00:08:46,360 --> 00:08:47,480 Speaker 3: Yeah, what did you make of that? 181 00:08:47,960 --> 00:08:48,080 Speaker 4: So? 182 00:08:48,600 --> 00:08:51,120 Speaker 6: Number one, it absolutely is, But I think at the 183 00:08:51,160 --> 00:08:53,720 Speaker 6: high level there's kind of two interesting things with AI. 184 00:08:54,040 --> 00:08:56,640 Speaker 6: Number One, we think we're still very early innings. There's 185 00:08:56,640 --> 00:08:58,080 Speaker 6: a lot of room to expand we don't think the 186 00:08:58,080 --> 00:09:01,200 Speaker 6: markets priced that in. But number two, there is huge 187 00:09:01,240 --> 00:09:02,400 Speaker 6: disruption that's coming. 188 00:09:03,320 --> 00:09:03,520 Speaker 7: You know. 189 00:09:03,559 --> 00:09:06,719 Speaker 6: In particular, we think the cost of producing software is 190 00:09:06,800 --> 00:09:10,000 Speaker 6: going to reduce like ninety nine percent of the next 191 00:09:10,080 --> 00:09:12,240 Speaker 6: year or two. And so there's a lot of traditional 192 00:09:12,280 --> 00:09:15,400 Speaker 6: companies that had very strong tech motes where those motes 193 00:09:15,440 --> 00:09:17,800 Speaker 6: are going to basically evaporate overnight, and we don't think 194 00:09:17,800 --> 00:09:18,800 Speaker 6: that's been priced in yet. 195 00:09:20,000 --> 00:09:21,840 Speaker 5: How are you looking to expand your business? How do 196 00:09:21,880 --> 00:09:23,959 Speaker 5: you keep growing? I'm sure there's a lot of competition. 197 00:09:24,880 --> 00:09:28,880 Speaker 6: Yeah, you know, long story short, we're just under a 198 00:09:28,920 --> 00:09:32,200 Speaker 6: thousand users right now. There's a ton more out there, 199 00:09:32,280 --> 00:09:34,920 Speaker 6: you know, So we're growing really heavily quarter over quarter, 200 00:09:35,480 --> 00:09:36,280 Speaker 6: but it's still. 201 00:09:36,080 --> 00:09:37,640 Speaker 7: Maybe one percent of the market. 202 00:09:37,760 --> 00:09:40,040 Speaker 6: And really my mission right now is to capture as 203 00:09:40,080 --> 00:09:41,680 Speaker 6: much as a can over the next year or two. 204 00:09:42,040 --> 00:09:45,760 Speaker 2: So what's the feedback from you go into you know, 205 00:09:45,880 --> 00:09:46,960 Speaker 2: a hedge fund or something. 206 00:09:47,679 --> 00:09:48,920 Speaker 3: Are they receptive to it? 207 00:09:48,960 --> 00:09:52,000 Speaker 2: Do they feel like the quality may not be there, 208 00:09:52,040 --> 00:09:52,839 Speaker 2: they're unsure about it? 209 00:09:52,920 --> 00:09:53,600 Speaker 3: What's the reception? 210 00:09:54,360 --> 00:09:54,640 Speaker 7: Yeah? 211 00:09:55,120 --> 00:09:57,800 Speaker 6: Oftentimes when we engage, we like to start with sort 212 00:09:57,840 --> 00:10:01,600 Speaker 6: of asking, what's the task you lease like doing. Tell me, 213 00:10:02,000 --> 00:10:04,400 Speaker 6: you know you work eighty hours a week, ninety hours 214 00:10:04,400 --> 00:10:05,880 Speaker 6: a week, what are the ten hours that you just 215 00:10:06,000 --> 00:10:08,600 Speaker 6: absolutely do not want to do? And really a lot 216 00:10:08,640 --> 00:10:11,120 Speaker 6: of that journey is walking them through how to automate 217 00:10:11,360 --> 00:10:14,240 Speaker 6: that ten hours, that twenty hours, giving them that time back, 218 00:10:14,240 --> 00:10:15,640 Speaker 6: and then we kind of build on top of that. 219 00:10:17,160 --> 00:10:19,520 Speaker 5: How hard is it to find a team, like I'm 220 00:10:19,559 --> 00:10:23,839 Speaker 5: assuming that competition is huge right now, what's that like, like. 221 00:10:23,800 --> 00:10:25,439 Speaker 7: The technology team to actually build it? 222 00:10:25,480 --> 00:10:25,640 Speaker 3: Yeah? 223 00:10:25,720 --> 00:10:27,160 Speaker 7: Yeah, yeah, probably very hard. 224 00:10:28,000 --> 00:10:29,960 Speaker 6: I'm really lucky. I've been in the space since about 225 00:10:29,960 --> 00:10:33,800 Speaker 6: two thousand and nine. Built my first company at first 226 00:10:33,800 --> 00:10:36,200 Speaker 6: AI company back in twenty ten that eventually got sold 227 00:10:36,240 --> 00:10:37,440 Speaker 6: to Microsoft. 228 00:10:37,679 --> 00:10:39,599 Speaker 3: Working before like any of us knew what a I was. 229 00:10:39,760 --> 00:10:41,080 Speaker 7: Yeah, no, back then it was a bad word. 230 00:10:41,280 --> 00:10:43,200 Speaker 6: I had trouble raising money in two thousand and nine 231 00:10:43,240 --> 00:10:44,760 Speaker 6: because no one thought AI was going to be a 232 00:10:44,760 --> 00:10:46,920 Speaker 6: real thing. 233 00:10:47,240 --> 00:10:48,200 Speaker 7: Just crazy to think about. 234 00:10:48,280 --> 00:10:52,280 Speaker 3: Uh huh, what's like raising money today? A little bit easier? 235 00:10:53,679 --> 00:10:56,079 Speaker 6: No, I mean this company, I've raised about sixty five 236 00:10:56,080 --> 00:10:58,960 Speaker 6: million dollars in that range. You know, jump back in 237 00:10:59,000 --> 00:11:03,920 Speaker 6: time Maluba raised maybe thirteen million, So very different fundraising environment. 238 00:11:05,120 --> 00:11:06,280 Speaker 3: Do you go to different tools? 239 00:11:06,360 --> 00:11:10,160 Speaker 5: So I how do you keep refining your tools to 240 00:11:10,280 --> 00:11:12,800 Speaker 5: broaden and make it even more useful? And also the 241 00:11:12,840 --> 00:11:13,839 Speaker 5: accuracy level? 242 00:11:13,880 --> 00:11:14,839 Speaker 3: How do you track that? 243 00:11:15,760 --> 00:11:18,840 Speaker 6: Yeah, so kind of two different questions there. I'll start 244 00:11:18,840 --> 00:11:22,240 Speaker 6: with maybe taking track of technology, So we start by 245 00:11:22,240 --> 00:11:24,520 Speaker 6: having a very deep empathy of understanding what are the 246 00:11:24,600 --> 00:11:27,280 Speaker 6: kinds of workflows our users want us to automate, what 247 00:11:27,280 --> 00:11:29,280 Speaker 6: are the kinds of things that actually matter, and then 248 00:11:29,640 --> 00:11:32,040 Speaker 6: constantly tracking what a success look like for those kind 249 00:11:32,080 --> 00:11:36,360 Speaker 6: of workflows and where we have success with that. 250 00:11:36,360 --> 00:11:38,040 Speaker 7: And I'm sorry was reminding the second question. 251 00:11:38,520 --> 00:11:44,280 Speaker 5: Uh, the second question was it was really brilliant changing 252 00:11:44,280 --> 00:11:45,359 Speaker 5: the tool accuracy? 253 00:11:45,640 --> 00:11:46,400 Speaker 3: Oh an accuracy? 254 00:11:46,520 --> 00:11:50,200 Speaker 6: Yes, this is actually really really vital in the inside 255 00:11:50,200 --> 00:11:52,679 Speaker 6: of finance. So the biggest problem is, like, you're not 256 00:11:52,720 --> 00:11:54,160 Speaker 6: going to make one hundred million dollars trade if you 257 00:11:54,200 --> 00:11:56,600 Speaker 6: don't absolutely trust the information that's coming out. 258 00:11:56,440 --> 00:11:57,000 Speaker 7: Of the system. 259 00:11:57,320 --> 00:11:59,520 Speaker 6: So we really think about that in two ways. Number One, 260 00:11:59,520 --> 00:12:01,400 Speaker 6: we have to have the most accurate system in the space. 261 00:12:01,559 --> 00:12:03,520 Speaker 6: Number two, we have to have the most auditible system 262 00:12:03,559 --> 00:12:05,719 Speaker 6: in the space. When the system actually makes a recommendation 263 00:12:05,840 --> 00:12:07,959 Speaker 6: to it's not enough to just give. 264 00:12:07,760 --> 00:12:08,400 Speaker 7: You an answer. 265 00:12:08,679 --> 00:12:10,560 Speaker 6: It has to give you a very detail explanation of 266 00:12:10,559 --> 00:12:12,400 Speaker 6: where it came to, that conclusion from where it came from. 267 00:12:12,480 --> 00:12:13,520 Speaker 7: And that's something we excel at. 268 00:12:14,280 --> 00:12:17,000 Speaker 3: Josh, thanks so much, coming in the studio. Josh Pentony. 269 00:12:17,040 --> 00:12:20,800 Speaker 2: He's a CEO and co founder of boosted AI company 270 00:12:20,800 --> 00:12:24,040 Speaker 2: based in Toronto. Have an operation here in New York City, 271 00:12:24,280 --> 00:12:27,280 Speaker 2: showing once again how we're very friendly US and Canada. 272 00:12:27,360 --> 00:12:28,920 Speaker 3: We like, we love these guys. 273 00:12:28,960 --> 00:12:30,760 Speaker 5: I mean, yeah, I know, so I don't know if 274 00:12:30,760 --> 00:12:32,880 Speaker 5: that feeling is going to stay, but sure, no, it's 275 00:12:33,000 --> 00:12:33,400 Speaker 5: all good. 276 00:12:33,440 --> 00:12:34,480 Speaker 3: It's all good. We're all friends. 277 00:12:36,160 --> 00:12:39,840 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 278 00:12:39,920 --> 00:12:43,040 Speaker 1: weekdays at ten am Eastern on Apple, Coarcklay, and Android 279 00:12:43,040 --> 00:12:46,360 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 280 00:12:46,400 --> 00:12:50,080 Speaker 1: you get your podcasts, or watch us live on YouTube. 281 00:12:50,400 --> 00:12:52,880 Speaker 3: All right, let's go down to Washington, DC. You got 282 00:12:52,880 --> 00:12:53,600 Speaker 3: a lot going on there. 283 00:12:53,640 --> 00:12:56,920 Speaker 2: Henrietta trez joined your season managing partner, director of Economic 284 00:12:57,000 --> 00:12:58,200 Speaker 2: Policy at Vada Partners. 285 00:12:58,600 --> 00:13:01,679 Speaker 3: She's actually lives in Louisiana. How cool is that? I 286 00:13:01,720 --> 00:13:02,360 Speaker 3: think New Orleans. 287 00:13:02,400 --> 00:13:07,200 Speaker 2: That's awesome, Hey, Henriette, I mean from your perspective and 288 00:13:07,280 --> 00:13:10,000 Speaker 2: you talk to your sources in Washington. Should we expect 289 00:13:10,040 --> 00:13:12,679 Speaker 2: anything coming out of Switzerland this weekend as it relates 290 00:13:12,679 --> 00:13:14,640 Speaker 2: to US China and trade. 291 00:13:15,520 --> 00:13:16,480 Speaker 8: Yeah, it's a good question. 292 00:13:16,520 --> 00:13:18,920 Speaker 9: Obviously, the President out kicked his coverage here with the 293 00:13:18,920 --> 00:13:21,520 Speaker 9: one hundred and forty five percent tariffs on China. The 294 00:13:21,600 --> 00:13:24,240 Speaker 9: problem is eighty percent, which he's talking about now on 295 00:13:24,280 --> 00:13:27,199 Speaker 9: truth social is still prohibitively high and doesn't really move 296 00:13:27,200 --> 00:13:30,520 Speaker 9: the needle for small businesses or anybody importing from China 297 00:13:30,640 --> 00:13:34,320 Speaker 9: or overseas right now. So the expectation I had going 298 00:13:34,320 --> 00:13:36,439 Speaker 9: into this before the President started tweeting this morning was 299 00:13:36,480 --> 00:13:40,920 Speaker 9: effectively that Secretary Bessett is trying to establish himself with 300 00:13:41,040 --> 00:13:44,160 Speaker 9: the Vice Premier of China at these meetings in Switzerland 301 00:13:44,240 --> 00:13:47,880 Speaker 9: this weekend. Historically, what we know from China and what 302 00:13:47,920 --> 00:13:50,720 Speaker 9: we've gotten from MOFCOM in the last couple of days 303 00:13:50,800 --> 00:13:53,920 Speaker 9: is that they are extraordinarily cautious and not at all 304 00:13:53,960 --> 00:13:57,800 Speaker 9: optimistic about whether Secretary Bessett is able to speak for 305 00:13:57,840 --> 00:14:01,360 Speaker 9: the president, whether the United States is serious about actually 306 00:14:01,440 --> 00:14:03,080 Speaker 9: reducing terrifs. 307 00:14:03,240 --> 00:14:07,600 Speaker 8: And so the President is trying to give the Secretary. 308 00:14:07,160 --> 00:14:10,679 Speaker 9: Vesset effectively the permission structure that China needs to enter 309 00:14:10,720 --> 00:14:12,680 Speaker 9: into the conversation any kind of meaningful way. 310 00:14:12,920 --> 00:14:15,040 Speaker 8: So my sorry, Yeah, So did he. 311 00:14:15,120 --> 00:14:17,160 Speaker 5: Do it by saying eighty percent looks good and it's 312 00:14:17,160 --> 00:14:17,800 Speaker 5: all up to Scott? 313 00:14:17,840 --> 00:14:19,040 Speaker 3: And was he able to accomplish that? 314 00:14:20,160 --> 00:14:24,120 Speaker 9: I think that from a directional standpoint, it's pretty clear 315 00:14:24,200 --> 00:14:27,160 Speaker 9: now that Secretary Besson does have this authority to go 316 00:14:27,200 --> 00:14:29,680 Speaker 9: to eighty percent. Whether he has the ability to go 317 00:14:29,760 --> 00:14:32,360 Speaker 9: further than that, I think is not something that China 318 00:14:32,360 --> 00:14:35,720 Speaker 9: can necessarily accept because obviously the President, through this tweet, 319 00:14:35,920 --> 00:14:37,880 Speaker 9: is the one who actually set the terms of what's 320 00:14:37,920 --> 00:14:39,560 Speaker 9: going to happen, not Secretary Beston. 321 00:14:39,680 --> 00:14:42,000 Speaker 8: So it does undermine the Secretary a little bit. 322 00:14:42,960 --> 00:14:45,920 Speaker 9: And I think what we should hope at best case 323 00:14:45,960 --> 00:14:49,320 Speaker 9: scenario is some sort of joint statement, which we don't 324 00:14:49,360 --> 00:14:52,080 Speaker 9: often get. Usually, the US will put out a statement 325 00:14:52,320 --> 00:14:55,040 Speaker 9: and China will put out another statement, sometimes hours or even 326 00:14:55,080 --> 00:14:58,320 Speaker 9: days afterwards, and they're very short, they're very curt and they. 327 00:14:58,200 --> 00:14:59,480 Speaker 8: Effectively say we met. 328 00:15:00,040 --> 00:15:01,880 Speaker 9: And that's sort of the best that we can hope 329 00:15:01,920 --> 00:15:04,600 Speaker 9: for going into this before the President tweeted about it. 330 00:15:05,040 --> 00:15:07,040 Speaker 9: I think that what we'll see now is the business 331 00:15:07,040 --> 00:15:08,960 Speaker 9: community to come out and say, hey, eighty percent is 332 00:15:09,000 --> 00:15:11,600 Speaker 9: still too high. We need this to come much much lower, 333 00:15:11,640 --> 00:15:13,600 Speaker 9: if not all the way off, which the President does 334 00:15:13,640 --> 00:15:14,520 Speaker 9: not seem inclined to do. 335 00:15:15,520 --> 00:15:19,880 Speaker 2: This trade policy the President Trump is deploying, How is 336 00:15:19,880 --> 00:15:20,720 Speaker 2: that good politics? 337 00:15:22,840 --> 00:15:23,360 Speaker 8: I think the. 338 00:15:24,840 --> 00:15:27,960 Speaker 9: Supporters of President Trump really like the tariffs. They like 339 00:15:28,040 --> 00:15:30,640 Speaker 9: this sort of sticking it to the man mentality. And 340 00:15:31,120 --> 00:15:34,760 Speaker 9: so even though you're seeing the polling drop pretty materially, 341 00:15:34,840 --> 00:15:37,960 Speaker 9: even with Republican voters, his core base. I think the 342 00:15:38,000 --> 00:15:39,920 Speaker 9: last time I saw numbers was like forty three forty 343 00:15:39,960 --> 00:15:43,200 Speaker 9: seven percent of the Republican conference is very supportive of 344 00:15:43,200 --> 00:15:46,040 Speaker 9: the President's tariffs and his strategy, and they're willing to 345 00:15:46,040 --> 00:15:47,760 Speaker 9: give him this opportunity to wait and see. 346 00:15:48,120 --> 00:15:50,840 Speaker 8: But on Capitol Hill you have absolute panic. 347 00:15:50,880 --> 00:15:52,640 Speaker 9: It's sort of like ducks sitting on top of the 348 00:15:52,640 --> 00:15:56,200 Speaker 9: water and then furiously paddling underneath the Republican Party trying 349 00:15:56,240 --> 00:15:58,480 Speaker 9: to write this tax bill as running into the reality 350 00:15:58,480 --> 00:16:01,600 Speaker 9: that tariffs do not raise two trillion dollars if they do. 351 00:16:01,480 --> 00:16:02,600 Speaker 8: Not bring in revenue. 352 00:16:02,760 --> 00:16:05,120 Speaker 9: And of course, if you're stopped imports, you're not bringing 353 00:16:05,120 --> 00:16:05,960 Speaker 9: in tariff revenue. 354 00:16:06,000 --> 00:16:07,800 Speaker 8: So there's a lot of panic on Capitol Hill. 355 00:16:08,040 --> 00:16:12,240 Speaker 5: So clearly though, the narrative is trying to be shifted 356 00:16:12,680 --> 00:16:15,200 Speaker 5: to lower taxes. I guess as long as you're making 357 00:16:15,320 --> 00:16:19,320 Speaker 5: under five million dollars as a couple, and also deregulation 358 00:16:19,520 --> 00:16:21,560 Speaker 5: like that desperately seems to be the narrative that that 359 00:16:21,640 --> 00:16:24,120 Speaker 5: needs to be shifted for the administration. Is it happening, 360 00:16:24,200 --> 00:16:25,040 Speaker 5: like is that legit? 361 00:16:26,080 --> 00:16:27,160 Speaker 8: Well, that's interesting. 362 00:16:27,280 --> 00:16:28,840 Speaker 9: The one thing I touch on with this two and 363 00:16:28,880 --> 00:16:32,240 Speaker 9: a half million for individuals and five million for couples 364 00:16:32,240 --> 00:16:35,160 Speaker 9: filing jointly. This new tax policy for the president is 365 00:16:35,160 --> 00:16:37,200 Speaker 9: that you know, he's the ultimate salesman, right, and he 366 00:16:37,280 --> 00:16:40,760 Speaker 9: recognizes that this tax bill has a serious image problem. 367 00:16:41,000 --> 00:16:43,320 Speaker 9: And the image is one that the American public is 368 00:16:43,360 --> 00:16:46,640 Speaker 9: really primed to notice, which is cuts to medicaid. You know, 369 00:16:46,680 --> 00:16:50,080 Speaker 9: we have been talking about medicaid cuts for twenty years now, 370 00:16:50,280 --> 00:16:52,680 Speaker 9: since Obamacare. It's something we talk about on our web 371 00:16:52,920 --> 00:16:56,400 Speaker 9: Wednesday webcasts. I wish we could move on for it, 372 00:16:56,400 --> 00:16:59,480 Speaker 9: but the American public is so attuned to any cuts 373 00:16:59,480 --> 00:17:03,240 Speaker 9: to Medicaid that they sort of viscerally react negatively to 374 00:17:03,320 --> 00:17:06,560 Speaker 9: any tax bill that would include those data sets. So 375 00:17:06,760 --> 00:17:10,720 Speaker 9: in this circumstance, the President has realized that supporting taxes 376 00:17:10,720 --> 00:17:13,480 Speaker 9: on the wealthy is actually very popular, and this is 377 00:17:13,520 --> 00:17:17,240 Speaker 9: a great way to sort of reimagine the tax bill 378 00:17:17,280 --> 00:17:20,919 Speaker 9: and potentially get more support for the package from the 379 00:17:20,920 --> 00:17:24,080 Speaker 9: American public. But obviously the Republicans on Capitol Hill do 380 00:17:24,119 --> 00:17:24,920 Speaker 9: not like that idea. 381 00:17:25,359 --> 00:17:27,240 Speaker 2: All right, Henrietta, thank you so much for joining us. 382 00:17:27,320 --> 00:17:30,760 Speaker 2: Appreciate your time always. Henrietta Trez, Managing partner and director 383 00:17:30,760 --> 00:17:32,600 Speaker 2: of Economic Policy at Veda Partners. 384 00:17:33,000 --> 00:17:36,360 Speaker 3: Coming to us via New Orleans via that zoom thing. 385 00:17:36,440 --> 00:17:41,879 Speaker 1: Here, you're listening to the Bloomberg Intelligence Podcast. Catch us 386 00:17:41,960 --> 00:17:45,000 Speaker 1: live weekdays at ten am Eastern on Apple, Cocklay, and 387 00:17:45,000 --> 00:17:48,280 Speaker 1: Android Auto with the Bloomberg Business App. Listen on demand 388 00:17:48,320 --> 00:17:51,880 Speaker 1: wherever you get your podcasts, or watch us live on YouTube. 389 00:17:52,600 --> 00:17:55,000 Speaker 5: Let's get to some of the earnings that came out 390 00:17:55,040 --> 00:17:58,000 Speaker 5: over the last twelve hours in terms of technology, and 391 00:17:58,040 --> 00:17:59,800 Speaker 5: one of them is a care round strike. 392 00:18:01,000 --> 00:18:03,200 Speaker 8: So the US is now probe being a role. 393 00:18:03,000 --> 00:18:06,800 Speaker 5: Of CrowdStrike fosses in a Karasoft deal. I don't really 394 00:18:06,840 --> 00:18:07,880 Speaker 5: know what that means. 395 00:18:07,680 --> 00:18:08,639 Speaker 3: So let's get to that. 396 00:18:08,760 --> 00:18:11,359 Speaker 5: But it wasn't good for crowdsource. A man deep Seeingloomberg 397 00:18:11,400 --> 00:18:13,719 Speaker 5: intelligence in your tech industry analyst joins us. Now, so 398 00:18:13,800 --> 00:18:14,840 Speaker 5: what's the latest with this thing? 399 00:18:15,720 --> 00:18:19,640 Speaker 10: Well, so I think this goes back a couple of months. 400 00:18:20,320 --> 00:18:25,960 Speaker 10: In fact, Karasoft is a reseller of software, so they 401 00:18:26,400 --> 00:18:30,159 Speaker 10: sell all kinds of software to government agencies and in 402 00:18:30,160 --> 00:18:35,040 Speaker 10: this case, the contract is like thirty two million dollars 403 00:18:35,160 --> 00:18:40,480 Speaker 10: that was the end customer's IRS. And it seems the 404 00:18:40,640 --> 00:18:44,280 Speaker 10: product was never deployed, so even though it was accounted 405 00:18:44,400 --> 00:18:46,840 Speaker 10: for in CrowdStrike. 406 00:18:46,160 --> 00:18:47,240 Speaker 7: Early, I think I remember this. 407 00:18:47,400 --> 00:18:51,800 Speaker 10: Yes, so it's getting probe. But to my mind, you know, 408 00:18:52,720 --> 00:18:56,280 Speaker 10: for a company like CrowdStrike with a four billion dollar 409 00:18:56,400 --> 00:19:01,439 Speaker 10: revenue run, right, you know they have exposure, but it's 410 00:19:01,480 --> 00:19:04,280 Speaker 10: like maybe mid to high single digits, so not a 411 00:19:04,320 --> 00:19:09,119 Speaker 10: whole lot of government exposure, and it is more of 412 00:19:09,200 --> 00:19:12,120 Speaker 10: a one off. I can't imagine, you know, that being 413 00:19:12,119 --> 00:19:15,119 Speaker 10: a problem with how they do their accounting. It is 414 00:19:15,119 --> 00:19:20,000 Speaker 10: more about how they source the contract through this reseller caarasoft, 415 00:19:20,600 --> 00:19:23,920 Speaker 10: and when did it show up in the income statement 416 00:19:24,040 --> 00:19:28,640 Speaker 10: as revenue. So nothing, to my best of my knowledge 417 00:19:28,680 --> 00:19:31,840 Speaker 10: is accounting related. It's more about the sourcing. And we've 418 00:19:31,920 --> 00:19:35,680 Speaker 10: seen that with other companies like Service Now in the past, 419 00:19:36,240 --> 00:19:41,520 Speaker 10: where you know, you have government agencies that award contracts 420 00:19:41,920 --> 00:19:45,240 Speaker 10: and these are lumpy like think of them as twelve 421 00:19:45,320 --> 00:19:47,920 Speaker 10: months in the making and then suddenly. 422 00:19:47,680 --> 00:19:49,320 Speaker 3: You get awarded a big contract. 423 00:19:49,840 --> 00:19:55,800 Speaker 10: But nothing really that affects overall companies' sort of sourcing 424 00:19:55,840 --> 00:19:56,560 Speaker 10: of contracts. 425 00:19:56,640 --> 00:20:00,000 Speaker 2: I think that's a competition in the car hailing business. 426 00:20:00,160 --> 00:20:03,800 Speaker 2: Look at the Lift of twenty one percent crowds. 427 00:20:04,560 --> 00:20:06,520 Speaker 3: I mean again, Lift, I pitch you. 428 00:20:06,520 --> 00:20:09,640 Speaker 2: Over the last ten rides I've done, I've probably taken 429 00:20:09,720 --> 00:20:12,879 Speaker 2: Lift seven times because they just had the better pricing 430 00:20:12,960 --> 00:20:16,520 Speaker 2: and materially better pricing. Yeah, so somebody's algorithms not working, 431 00:20:16,560 --> 00:20:17,600 Speaker 2: either ubers or lifts. 432 00:20:17,600 --> 00:20:20,360 Speaker 3: But I'm taking advantage. I'm arbitrage in that bad Boy. 433 00:20:20,640 --> 00:20:21,840 Speaker 3: What'd you learn from Lyft? 434 00:20:22,160 --> 00:20:26,080 Speaker 10: Well that pricing is these things to focus on. If 435 00:20:26,119 --> 00:20:30,360 Speaker 10: you have lower prices, that helps you get repeat customers. 436 00:20:30,400 --> 00:20:34,200 Speaker 10: And I think that you what you just said absolutely 437 00:20:34,240 --> 00:20:36,800 Speaker 10: make sense. And look when you compare a Lyft to 438 00:20:37,280 --> 00:20:40,360 Speaker 10: let's say door Dash or Uber. Lyft has got about 439 00:20:40,400 --> 00:20:44,800 Speaker 10: twenty four million monthly active users, door Dash has forty 440 00:20:44,840 --> 00:20:47,640 Speaker 10: two million, and ubers is much higher because they have 441 00:20:47,920 --> 00:20:54,560 Speaker 10: geographic diversification. But when I compare the overall rides, Lift 442 00:20:54,640 --> 00:20:58,240 Speaker 10: does less than a billion rides in a year. DoorDash 443 00:20:58,320 --> 00:21:02,120 Speaker 10: does three billion orders for with forty two million users, 444 00:21:02,160 --> 00:21:03,960 Speaker 10: So that just goes to show there is a lot 445 00:21:04,000 --> 00:21:08,240 Speaker 10: of room for Lift to increase frequency. And people are 446 00:21:08,280 --> 00:21:10,120 Speaker 10: price sensitive, so that's where. 447 00:21:10,160 --> 00:21:12,359 Speaker 3: The grouping at people that you can do different? Am 448 00:21:12,400 --> 00:21:15,640 Speaker 3: I like the person? Again? Me, it's just person because 449 00:21:15,680 --> 00:21:18,520 Speaker 3: I know it is in most cases the same car, 450 00:21:18,760 --> 00:21:19,520 Speaker 3: the same carver. 451 00:21:20,160 --> 00:21:23,280 Speaker 10: Yeah, and they have tiered you know the customers, so 452 00:21:23,320 --> 00:21:25,399 Speaker 10: if you want to go for a high end service, 453 00:21:25,480 --> 00:21:28,760 Speaker 10: they have vehicles available. So from that perspective, there is 454 00:21:28,800 --> 00:21:31,800 Speaker 10: not much differentiation between an Uber and a Lyft. 455 00:21:32,119 --> 00:21:34,600 Speaker 3: And the game in the system here very totally. And 456 00:21:34,880 --> 00:21:37,880 Speaker 3: you told everyone now prices for everything. 457 00:21:38,600 --> 00:21:41,159 Speaker 5: But what did Lift do to manage their couse to 458 00:21:41,200 --> 00:21:43,760 Speaker 5: help their margins is considering that their market share they 459 00:21:43,800 --> 00:21:44,280 Speaker 5: didn't grow. 460 00:21:45,000 --> 00:21:48,119 Speaker 10: I mean, look, in all fairness, the valuation was less 461 00:21:48,160 --> 00:21:51,480 Speaker 10: than one time sales, so you compare it to other marketplaces. 462 00:21:51,800 --> 00:21:54,760 Speaker 10: Broad Ash traids at five times ev DO sales. Suber 463 00:21:54,880 --> 00:21:58,639 Speaker 10: is more four x ev DO sales. So lift A 464 00:21:58,680 --> 00:22:00,800 Speaker 10: lot of the bad news was price in that this 465 00:22:00,880 --> 00:22:04,119 Speaker 10: company is losing share, they would never really be profitable, 466 00:22:04,400 --> 00:22:09,000 Speaker 10: and they just managed to execute much better operationally. You know, 467 00:22:09,040 --> 00:22:11,920 Speaker 10: they've been cutting costs on the operation side, sales and 468 00:22:12,080 --> 00:22:16,120 Speaker 10: marketing side, and they have limited their ambitions to those 469 00:22:16,160 --> 00:22:19,520 Speaker 10: twenty five million monthly active users. They're not going after 470 00:22:19,560 --> 00:22:22,800 Speaker 10: one hundred million what Uber has. They're saying, if I 471 00:22:22,960 --> 00:22:26,280 Speaker 10: keep getting repeat business from this twenty five million, that 472 00:22:26,320 --> 00:22:29,720 Speaker 10: will help me sustain mid to iteen's growth. I don't 473 00:22:29,760 --> 00:22:33,320 Speaker 10: have to go for user acquisition right now. And look, 474 00:22:33,359 --> 00:22:36,720 Speaker 10: they are making an acquisition in Europe now free now, 475 00:22:36,800 --> 00:22:40,639 Speaker 10: so they are looking to expand geographically. But it's really 476 00:22:40,760 --> 00:22:44,119 Speaker 10: finding your sweet spot and niches where Uber is not 477 00:22:44,240 --> 00:22:46,840 Speaker 10: that big, and that strategy is good enough because at 478 00:22:46,880 --> 00:22:48,480 Speaker 10: the end of the day, it's still a duopoly in 479 00:22:48,600 --> 00:22:50,280 Speaker 10: right sharing platinize. 480 00:22:50,320 --> 00:22:51,840 Speaker 2: When I was down at Duke a few weeks ago, 481 00:22:51,920 --> 00:22:53,080 Speaker 2: when I was a student. 482 00:22:52,800 --> 00:22:56,320 Speaker 3: There, parking was impossible, impossible. 483 00:22:56,800 --> 00:22:59,200 Speaker 2: Now it's not a problem because not as many kids 484 00:22:59,200 --> 00:23:01,280 Speaker 2: bring to cars because they because they just rely on Uber. 485 00:23:01,680 --> 00:23:04,080 Speaker 3: Yeah, so it's like you don't have to worry about Yeah. 486 00:23:04,200 --> 00:23:06,359 Speaker 3: So it's it's they were telling me it's not an 487 00:23:06,400 --> 00:23:08,040 Speaker 3: issue anymore. So how about that? 488 00:23:08,119 --> 00:23:08,399 Speaker 7: All right? 489 00:23:08,400 --> 00:23:10,320 Speaker 2: Man Deep Sing, Thank you so much. Appreciate that. Senior 490 00:23:10,359 --> 00:23:12,600 Speaker 2: tech analyst for Bloomberg Intelligence. 491 00:23:13,240 --> 00:23:17,959 Speaker 1: This is the Bloomberg Intelligence podcast, available on Apple, Spotify, 492 00:23:18,160 --> 00:23:22,120 Speaker 1: and anywhere else you get your podcasts. Listen live each weekday, 493 00:23:22,320 --> 00:23:25,600 Speaker 1: ten am to noon Eastern on Bloomberg dot com, the 494 00:23:25,680 --> 00:23:29,560 Speaker 1: iHeartRadio app, tune In, and the Bloomberg Business app. You 495 00:23:29,600 --> 00:23:32,879 Speaker 1: can also watch us live every weekday on YouTube and 496 00:23:33,080 --> 00:23:35,040 Speaker 1: always on the Bloomberg terminal