1 00:00:01,760 --> 00:00:07,040 Speaker 1: From Marhart where Innovation, Money and power Collie in Silicon Valley, NBN. 2 00:00:07,360 --> 00:00:10,840 Speaker 1: This is Bloomberg Technology with Caroline Hide. 3 00:00:10,480 --> 00:00:28,280 Speaker 2: And Ed Lovelove, Iurmed Ludlow Here in San Francisco, Caroline 4 00:00:28,320 --> 00:00:31,560 Speaker 2: hides off today. This is Bloomberg Technology coming up on 5 00:00:31,600 --> 00:00:35,560 Speaker 2: the program. Robotaxis get a big win in San Francisco, 6 00:00:35,640 --> 00:00:39,480 Speaker 2: Crucio Carl Vote joins me live on set as regulators 7 00:00:39,560 --> 00:00:42,640 Speaker 2: votes to allow his company and Google's Weimo to truly 8 00:00:42,720 --> 00:00:46,920 Speaker 2: commercialize their tech here in SF and a Bloomberg exclusive, 9 00:00:46,960 --> 00:00:51,080 Speaker 2: a government probe into malicious attacks on cloud computing, including 10 00:00:51,159 --> 00:00:55,320 Speaker 2: Microsoft's role in suspected Chinese hacking of government officials. We 11 00:00:55,400 --> 00:00:58,600 Speaker 2: have the details, and the aviation sector is about to 12 00:00:58,600 --> 00:01:02,040 Speaker 2: hand passengers a five trillion dollar bill to fight its 13 00:01:02,080 --> 00:01:06,600 Speaker 2: next big threat, decarbonization. We have the Bloomberg Big Big 14 00:01:06,640 --> 00:01:08,880 Speaker 2: take all right, straight to our top story. 15 00:01:08,880 --> 00:01:11,000 Speaker 3: San Franciscan's get ready because you're. 16 00:01:10,840 --> 00:01:14,880 Speaker 2: About to see even more robotaxis on the streets. California 17 00:01:14,920 --> 00:01:19,680 Speaker 2: regulators just voted in favor of Waymo and Cruz expanding 18 00:01:19,720 --> 00:01:22,600 Speaker 2: their paid driverless services in the city. 19 00:01:22,640 --> 00:01:23,560 Speaker 3: It's a major. 20 00:01:23,280 --> 00:01:27,880 Speaker 2: Milestone towards commercializing the technology. The ruling came after hours 21 00:01:28,280 --> 00:01:31,959 Speaker 2: of public testimony with citizens arguing for and against the 22 00:01:32,080 --> 00:01:35,039 Speaker 2: expansion of the Robotaxi company's services. 23 00:01:35,080 --> 00:01:36,119 Speaker 3: The two companies can. 24 00:01:35,959 --> 00:01:41,760 Speaker 2: Now run full truly driverless commercial services, charging faares day 25 00:01:41,760 --> 00:01:44,520 Speaker 2: and night here in San Francisco. 26 00:01:44,600 --> 00:01:47,520 Speaker 3: The CEO of Cruz, Kyle Vote, joins me. 27 00:01:47,560 --> 00:01:51,640 Speaker 2: Now, Kyle, welcome to Bloomberg Technology. Thanks d How do 28 00:01:51,640 --> 00:01:54,800 Speaker 2: you operationalize this? Let's get right to it. It's gone 29 00:01:54,840 --> 00:01:56,960 Speaker 2: in your favor. What happens next? 30 00:01:56,960 --> 00:01:58,320 Speaker 3: What do you do well? 31 00:01:58,360 --> 00:02:01,040 Speaker 4: So it's a big year of scaling for us. 32 00:02:01,080 --> 00:02:03,360 Speaker 5: So over the past several months we've ramped up our 33 00:02:03,400 --> 00:02:06,200 Speaker 5: operations in a very measured and careful way as we 34 00:02:06,240 --> 00:02:08,720 Speaker 5: see the performance of these abs improve on the road. 35 00:02:09,080 --> 00:02:11,680 Speaker 5: And so the big difference with yesterday's permit, which is 36 00:02:11,720 --> 00:02:14,480 Speaker 5: the sixth one we've got in California, the final one 37 00:02:14,520 --> 00:02:17,720 Speaker 5: needed to commercialize our services. We can convert that service 38 00:02:17,720 --> 00:02:20,000 Speaker 5: from a pre service to a fared service. And so 39 00:02:20,040 --> 00:02:22,000 Speaker 5: it's a big milestone for not just us, but the 40 00:02:22,639 --> 00:02:25,120 Speaker 5: av industry, and I think a signal for you know, 41 00:02:25,160 --> 00:02:29,320 Speaker 5: California that we are going to prioritize progress versus accepting 42 00:02:29,320 --> 00:02:31,239 Speaker 5: the tragedy of the status quo on our roads today? 43 00:02:31,280 --> 00:02:34,360 Speaker 2: How strong is the signal? I tweeted or xed that 44 00:02:34,400 --> 00:02:37,120 Speaker 2: you're coming on the program. And actually, a really fair 45 00:02:37,200 --> 00:02:40,760 Speaker 2: question I got in response was why did the CPUC 46 00:02:41,120 --> 00:02:43,760 Speaker 2: just do San Francisco. Why not say to cruise or 47 00:02:43,840 --> 00:02:47,119 Speaker 2: weai moo have at it in Los Angeles, Sacramento, any 48 00:02:47,120 --> 00:02:48,160 Speaker 2: other city in the state. 49 00:02:48,840 --> 00:02:50,200 Speaker 4: That was actually at our direction. 50 00:02:50,280 --> 00:02:52,440 Speaker 5: What we've been working with regulators, you know, like I said, 51 00:02:52,480 --> 00:02:54,400 Speaker 5: for years now, and the very first time we went 52 00:02:54,440 --> 00:02:57,040 Speaker 5: to the CPUC for FARED service, it was for a 53 00:02:57,080 --> 00:03:00,160 Speaker 5: small region of San Francisco, a limited number of cars, 54 00:03:00,200 --> 00:03:02,399 Speaker 5: so we could you know, work with regulators and show 55 00:03:02,400 --> 00:03:04,600 Speaker 5: them that this technology was ready. And so this is 56 00:03:04,639 --> 00:03:07,120 Speaker 5: actually the second time we've gone for a farired permit, 57 00:03:07,160 --> 00:03:09,640 Speaker 5: and this is an expansion of that original permit, and 58 00:03:09,680 --> 00:03:11,560 Speaker 5: we'll do the same later this year or perhaps next 59 00:03:11,600 --> 00:03:14,280 Speaker 5: year when we expand into California and other cities in 60 00:03:14,320 --> 00:03:14,720 Speaker 5: the states. 61 00:03:14,800 --> 00:03:17,760 Speaker 2: So why is San Francisco, Why is this the place 62 00:03:18,440 --> 00:03:22,600 Speaker 2: to not just test the technology but commercialize the business. 63 00:03:22,919 --> 00:03:25,359 Speaker 5: Well, you know, candidate, like, we're based here in San Francisco, 64 00:03:25,440 --> 00:03:28,000 Speaker 5: so this is our home territory. But we see San 65 00:03:28,040 --> 00:03:32,359 Speaker 5: Francisco as a litmus test for the commercialization of robosaxies. 66 00:03:32,680 --> 00:03:35,240 Speaker 5: If they work in a city like San Francisco, where 67 00:03:35,280 --> 00:03:39,360 Speaker 5: we have high population density, you know, steep hills, heavy fog, 68 00:03:39,520 --> 00:03:41,920 Speaker 5: a lot of a lot of tricky construction and other things, 69 00:03:42,160 --> 00:03:44,080 Speaker 5: then that means it can work in many other cities 70 00:03:44,080 --> 00:03:47,320 Speaker 5: across similar cities across the US. And of course, you know, 71 00:03:47,360 --> 00:03:49,960 Speaker 5: from a business standpoint, these are the early days of 72 00:03:50,000 --> 00:03:50,760 Speaker 5: the technology. 73 00:03:51,040 --> 00:03:51,720 Speaker 4: These vehicles. 74 00:03:51,760 --> 00:03:54,160 Speaker 5: The first version is always more expensive than later ones, 75 00:03:54,240 --> 00:03:56,160 Speaker 5: and we want to start in cities where there's a 76 00:03:56,240 --> 00:03:58,360 Speaker 5: high willingness to pay. So we'll see us start in 77 00:03:58,680 --> 00:04:00,760 Speaker 5: some of the major cities before we work out into 78 00:04:00,840 --> 00:04:01,920 Speaker 5: into some of the other cities. 79 00:04:02,200 --> 00:04:03,880 Speaker 3: What about money in your pocket now? 80 00:04:04,520 --> 00:04:06,320 Speaker 2: You know, I know the weight list is in the 81 00:04:06,440 --> 00:04:09,840 Speaker 2: tens of thousands, so you are able to charge a 82 00:04:09,920 --> 00:04:12,960 Speaker 2: fair you on board the weight list? Does that bring 83 00:04:13,000 --> 00:04:16,040 Speaker 2: in sort of meaningful revenue for you guys, Because I'm 84 00:04:16,040 --> 00:04:17,840 Speaker 2: going to get into the history of your company a second, 85 00:04:17,839 --> 00:04:20,479 Speaker 2: but I am interested in how quick the impact of 86 00:04:20,480 --> 00:04:22,360 Speaker 2: being able to charge a fair is. 87 00:04:23,040 --> 00:04:23,280 Speaker 3: Well. 88 00:04:23,839 --> 00:04:25,960 Speaker 5: You know, you know today we're still operating in relatively 89 00:04:26,040 --> 00:04:28,839 Speaker 5: small scale. You know, it's gonna we have on the 90 00:04:28,839 --> 00:04:31,080 Speaker 5: awer of hundreds of vehicles, but up from you know, 91 00:04:31,120 --> 00:04:33,040 Speaker 5: fifty or so a year ago, and you're gonna see 92 00:04:33,040 --> 00:04:35,159 Speaker 5: that move into the thousands, you know, not not right away, 93 00:04:35,200 --> 00:04:37,680 Speaker 5: but over time, you know, across several cities that we're 94 00:04:37,680 --> 00:04:41,360 Speaker 5: operating in. But you know, the rate of expansion has 95 00:04:41,400 --> 00:04:44,080 Speaker 5: been pretty impressive. And this is not because we're going 96 00:04:44,200 --> 00:04:47,839 Speaker 5: you know, just going wild here. It's actually because the 97 00:04:47,920 --> 00:04:50,840 Speaker 5: av system itself is improving so quickly. That's one of 98 00:04:50,920 --> 00:04:53,360 Speaker 5: the most the remarkable things about AVIS compared to human 99 00:04:53,440 --> 00:04:56,479 Speaker 5: drivers is they can keep getting better over time and 100 00:04:56,520 --> 00:04:59,080 Speaker 5: even exponentially. And so even just a couple of weeks 101 00:04:59,080 --> 00:05:01,040 Speaker 5: ago on the and Earning scoll I said, we were 102 00:05:01,040 --> 00:05:03,839 Speaker 5: at about ten thousand rides per week. This last week 103 00:05:03,839 --> 00:05:06,479 Speaker 5: it was fifteen thousand. So we're seeing demand that's off 104 00:05:06,480 --> 00:05:08,440 Speaker 5: the charts, and I think that's going to lead to 105 00:05:09,120 --> 00:05:10,880 Speaker 5: you know, continued expansion in the near term. 106 00:05:10,920 --> 00:05:12,520 Speaker 2: You know, I talked about the history last night on 107 00:05:12,560 --> 00:05:15,960 Speaker 2: Bloombog Television. We aired an episode of the Circuit with 108 00:05:16,000 --> 00:05:19,520 Speaker 2: Emily Chang and she's sawing a Gary Tan about why Combinator. 109 00:05:20,200 --> 00:05:23,360 Speaker 2: You guys were why Combinator Class of Winter twenty fourteen. 110 00:05:23,360 --> 00:05:24,000 Speaker 4: I think that's right. 111 00:05:24,560 --> 00:05:26,960 Speaker 3: Nine years it's taking you to get to this point. 112 00:05:28,040 --> 00:05:32,720 Speaker 2: Is that fast progress or is that frustratingly slow progress. 113 00:05:33,120 --> 00:05:38,080 Speaker 5: Well, you know, building a car that can operate, you know, 114 00:05:38,920 --> 00:05:41,680 Speaker 5: better in many ways than a human driver is one 115 00:05:41,720 --> 00:05:45,719 Speaker 5: of the ultimate AI challenges and there's no shortcuts. And so, 116 00:05:45,960 --> 00:05:48,599 Speaker 5: you know, the first eight years of this tenure journey 117 00:05:49,400 --> 00:05:53,160 Speaker 5: was a grind. It's continuous improvement, improving the software to 118 00:05:53,200 --> 00:05:55,240 Speaker 5: the point we saw compelling evidence that this is going 119 00:05:55,279 --> 00:05:57,080 Speaker 5: to have a positive impact on road safety. 120 00:05:57,360 --> 00:05:58,839 Speaker 4: And so, unlike you know, other. 121 00:05:58,720 --> 00:06:00,720 Speaker 5: AI products, where you can launch a product out there 122 00:06:00,720 --> 00:06:02,680 Speaker 5: in the public as soon as it's kind of working, 123 00:06:02,960 --> 00:06:05,640 Speaker 5: in our case, that technology really had to be matured 124 00:06:05,720 --> 00:06:08,120 Speaker 5: and mid robust before we put it out there on 125 00:06:08,120 --> 00:06:08,560 Speaker 5: the roads. 126 00:06:09,320 --> 00:06:10,760 Speaker 4: And so it has been a ten year journey. 127 00:06:10,800 --> 00:06:12,800 Speaker 5: But we're really happy now that we're finally able to 128 00:06:12,800 --> 00:06:14,480 Speaker 5: put this out there and people are able to use 129 00:06:14,520 --> 00:06:16,360 Speaker 5: it and we're going to see you know, benefits and 130 00:06:16,440 --> 00:06:17,359 Speaker 5: road safety as a result. 131 00:06:17,440 --> 00:06:19,440 Speaker 2: Okay, so we're going to look at some pictures of 132 00:06:20,240 --> 00:06:24,400 Speaker 2: what are retrofitted customized Chevy Bolts driving around San Francisco. 133 00:06:24,480 --> 00:06:28,599 Speaker 2: It's also the case in other states like Arizona, Texas. Right, 134 00:06:29,400 --> 00:06:33,279 Speaker 2: A question I constantly get is why no New York City, 135 00:06:33,839 --> 00:06:37,120 Speaker 2: but also no China because there are Chinese players the 136 00:06:37,279 --> 00:06:40,040 Speaker 2: test in the Bay Area and in Chinese cities. 137 00:06:40,040 --> 00:06:41,400 Speaker 3: Could you answer those two please? 138 00:06:42,279 --> 00:06:44,559 Speaker 5: For New York in other cities, it's not a question 139 00:06:44,640 --> 00:06:48,080 Speaker 5: of if, but when. And you know this, this technology, 140 00:06:48,640 --> 00:06:51,240 Speaker 5: the way we've deployed is we make it work in 141 00:06:51,279 --> 00:06:54,120 Speaker 5: a defined environment and so in San Francisco you've got 142 00:06:54,560 --> 00:06:58,000 Speaker 5: fog and seapills, but you don't have you know, ice 143 00:06:58,120 --> 00:07:00,400 Speaker 5: or snow, and so that'll come and then the next 144 00:07:00,760 --> 00:07:03,000 Speaker 5: couple of years, and once we add that capability, we'll 145 00:07:03,000 --> 00:07:05,719 Speaker 5: be able to expand into some of the colder weather cities. 146 00:07:06,120 --> 00:07:08,599 Speaker 5: But that's not slowing our expansion today. I mean a 147 00:07:08,640 --> 00:07:10,920 Speaker 5: year ago we were at in one city, just operating 148 00:07:10,920 --> 00:07:14,560 Speaker 5: in San Francisco. As of today, we've announced seven or 149 00:07:14,600 --> 00:07:17,440 Speaker 5: eight eight cities that will be in eminently and so 150 00:07:17,480 --> 00:07:19,480 Speaker 5: you're going to see that rapid expansion, probably in the 151 00:07:19,520 --> 00:07:21,720 Speaker 5: Sun Belt, in the areas where the capabilities are a 152 00:07:21,760 --> 00:07:23,120 Speaker 5: match to what we see in those cities. 153 00:07:23,320 --> 00:07:26,760 Speaker 2: Let's talk about the future from the technology perspective and origin. 154 00:07:27,320 --> 00:07:31,360 Speaker 2: So right now, these are retrofitted or customized Chevy Bolts. 155 00:07:31,440 --> 00:07:33,840 Speaker 2: But you are designing and we're showing it right now. 156 00:07:34,320 --> 00:07:41,000 Speaker 2: A purpose built steering wheel lists seats inward facing shuttle. Okay, 157 00:07:41,160 --> 00:07:43,120 Speaker 2: where are we with that product? 158 00:07:43,800 --> 00:07:45,800 Speaker 5: Engineering is complete, you know, we're on the verge of 159 00:07:45,800 --> 00:07:48,120 Speaker 5: going into production. We expect some announcements on that in 160 00:07:48,120 --> 00:07:50,920 Speaker 5: the next few weeks. But the exciting thing about this 161 00:07:51,040 --> 00:07:53,480 Speaker 5: vehicle is it's the first time a car has been 162 00:07:53,480 --> 00:07:57,240 Speaker 5: built from the ground up for this robotaxi purpose. So 163 00:07:57,520 --> 00:07:59,680 Speaker 5: it lasts a lot longer than a regular car. It's 164 00:07:59,720 --> 00:08:02,600 Speaker 5: designed to be optimal for pool rides, so multiple people 165 00:08:02,640 --> 00:08:05,720 Speaker 5: sharing a vehicle because there's lots of space between them, 166 00:08:06,240 --> 00:08:08,520 Speaker 5: and the cost of it will be substantially lower than 167 00:08:08,560 --> 00:08:11,720 Speaker 5: the first generation of the technology. So I think the 168 00:08:11,760 --> 00:08:13,440 Speaker 5: cars on the road today feel like a car where 169 00:08:13,440 --> 00:08:15,720 Speaker 5: you've just removed the driver, you still see the steering 170 00:08:15,720 --> 00:08:19,600 Speaker 5: wheel moving around. The Origin feels like what a what 171 00:08:19,640 --> 00:08:22,080 Speaker 5: a driverless vehicle was meant to be, and that experience 172 00:08:22,120 --> 00:08:25,280 Speaker 5: that I think we've all perhaps been fantasizing about for years. 173 00:08:25,400 --> 00:08:29,080 Speaker 2: Can you just explain in simple terms how when production 174 00:08:29,240 --> 00:08:33,280 Speaker 2: starts you will deploy to the real world the Origin 175 00:08:34,040 --> 00:08:35,839 Speaker 2: against your existing infrastructure. 176 00:08:36,120 --> 00:08:38,320 Speaker 5: Yeah, so we've already been testing the Origin and again, 177 00:08:38,320 --> 00:08:40,240 Speaker 5: this car has no steering wheel, so without you know, 178 00:08:40,280 --> 00:08:43,199 Speaker 5: any safety driver or anyone behind the wheel in multiple cities. 179 00:08:43,360 --> 00:08:46,280 Speaker 5: And as it goes into production, you'll see us start 180 00:08:46,280 --> 00:08:48,800 Speaker 5: deploying those vehicles, you know, again in a gradual and 181 00:08:48,840 --> 00:08:51,800 Speaker 5: measured way across the existing you know, seven or eight 182 00:08:51,840 --> 00:08:54,120 Speaker 5: cities that we've announced, and then ramp up, you know, 183 00:08:54,160 --> 00:08:56,240 Speaker 5: the number of those vehicles each cities as we ramp 184 00:08:56,280 --> 00:08:56,760 Speaker 5: our production. 185 00:08:57,160 --> 00:09:00,480 Speaker 2: There is a fierce, fierce battle for talent right now, 186 00:09:01,200 --> 00:09:04,040 Speaker 2: broadly in the theater of artificial intelligence, but you know, 187 00:09:04,160 --> 00:09:06,880 Speaker 2: machine learning or neural networks is a big part of 188 00:09:06,920 --> 00:09:09,040 Speaker 2: what you guys do as well. What are your biggest 189 00:09:09,080 --> 00:09:12,880 Speaker 2: challenges right now from a cash perspective, from a talent 190 00:09:12,920 --> 00:09:16,480 Speaker 2: perspective that you've got left to tackle in twenty twenty three. 191 00:09:17,120 --> 00:09:19,320 Speaker 5: Well, we're very fortunate to have, you know, robust support 192 00:09:19,320 --> 00:09:21,960 Speaker 5: from General Motors that you know, Mary and her team 193 00:09:21,960 --> 00:09:24,640 Speaker 5: see this is you know, a key part of GM's future. 194 00:09:24,960 --> 00:09:26,920 Speaker 5: And so when we talk to candidates, we talk to 195 00:09:26,960 --> 00:09:29,560 Speaker 5: AI talent, they'd like to see that strong backing. But 196 00:09:29,640 --> 00:09:31,560 Speaker 5: also for many people there are a lot of AI 197 00:09:31,600 --> 00:09:34,079 Speaker 5: opportunities out there that are fun or interesting problems, but 198 00:09:34,160 --> 00:09:36,520 Speaker 5: many of them still feel like toys. When you work 199 00:09:36,520 --> 00:09:38,600 Speaker 5: on a self driving car that is having a real 200 00:09:38,640 --> 00:09:41,440 Speaker 5: impact affecting road safety today, and it's one of the 201 00:09:41,440 --> 00:09:42,160 Speaker 5: best ways. 202 00:09:41,920 --> 00:09:44,000 Speaker 4: That an AI engineer can spend their time in my opinion. 203 00:09:44,280 --> 00:09:47,160 Speaker 2: All Right, Kyle Vote, CEO of Cruise, a pretty big 204 00:09:47,200 --> 00:09:52,000 Speaker 2: moment for robotaxi or driver list technology. Here in SF, 205 00:10:00,880 --> 00:10:06,160 Speaker 2: a US Cybersecurity Advisory Panel will investigate malicious targeting of 206 00:10:06,200 --> 00:10:10,560 Speaker 2: cloud computing environments, including Microsoft's role in a recent breach 207 00:10:10,840 --> 00:10:15,640 Speaker 2: of government officials email accounts by suspected Chinese hackers that 208 00:10:15,760 --> 00:10:19,079 Speaker 2: according to the Department of Homeland Security. It also confirms 209 00:10:19,360 --> 00:10:22,400 Speaker 2: a report from Bloomberg News. Let's bring in our cybersecurity 210 00:10:22,559 --> 00:10:26,959 Speaker 2: editor who's been on this story, Andrew Martin. Andrew helped 211 00:10:26,960 --> 00:10:29,400 Speaker 2: me understand the basics here what is being looked at. 212 00:10:31,240 --> 00:10:36,960 Speaker 6: So the Biden administration created this cyber safety review panel 213 00:10:37,040 --> 00:10:41,440 Speaker 6: to look at major cybersecurity events after the fact to 214 00:10:41,480 --> 00:10:44,040 Speaker 6: try to figure out ways to prevent it from happening 215 00:10:44,080 --> 00:10:47,040 Speaker 6: in the future. And what they did here is following 216 00:10:47,160 --> 00:10:51,600 Speaker 6: a hack that was revealed in July that included a 217 00:10:51,640 --> 00:10:56,800 Speaker 6: bunch of US government email accounts, including Comerace Secretary Gina Romando. 218 00:10:57,800 --> 00:11:03,679 Speaker 6: They decided to look broadly at malicious attacks on cloud 219 00:11:03,720 --> 00:11:08,800 Speaker 6: security environment and also at a specific hack by suspected 220 00:11:08,880 --> 00:11:13,120 Speaker 6: Chinese hackers that got access to these email accounts. 221 00:11:14,679 --> 00:11:17,640 Speaker 2: Okay, let's go to Microsoft in their role in this story. 222 00:11:17,720 --> 00:11:21,000 Speaker 2: What have we learned about? What investigators are looking at 223 00:11:21,160 --> 00:11:22,360 Speaker 2: Microsoft's role in all this? 224 00:11:23,080 --> 00:11:25,439 Speaker 1: So what happened in this latest hack is that. 225 00:11:27,240 --> 00:11:32,000 Speaker 6: Is that these suspected Chinese hackers got what's known as 226 00:11:32,400 --> 00:11:37,120 Speaker 6: sort of an encryption key that allowed them to generate 227 00:11:37,280 --> 00:11:41,120 Speaker 6: authentication tokens, which basically allow allow them to. 228 00:11:43,240 --> 00:11:45,400 Speaker 1: Act as legitimate users. 229 00:11:45,000 --> 00:11:48,040 Speaker 6: In these systems and get access to email accounts. And 230 00:11:48,800 --> 00:11:53,280 Speaker 6: Senator Ron Wyden has wrote a letter last month asking 231 00:11:53,800 --> 00:11:56,920 Speaker 6: for US officials to investigate Microsoft's. 232 00:11:56,360 --> 00:11:56,800 Speaker 1: Role in this. 233 00:11:56,960 --> 00:12:00,480 Speaker 6: He accused them of negligent cybersecurity predices. 234 00:12:01,000 --> 00:12:04,000 Speaker 1: He also noted that Microsoft's products were. 235 00:12:05,320 --> 00:12:08,360 Speaker 6: Involved in the solar wind tech from a couple of 236 00:12:08,400 --> 00:12:12,199 Speaker 6: years ago. So he has broadly for an investigation of Microsoft. 237 00:12:12,679 --> 00:12:15,400 Speaker 1: This is this Cyber Safety. 238 00:12:15,040 --> 00:12:17,880 Speaker 6: Review Panel is looking more broadly at not just Microsoft, 239 00:12:17,960 --> 00:12:19,600 Speaker 6: but all cloud providers. 240 00:12:20,200 --> 00:12:23,200 Speaker 2: I think it's important that we kind of think about 241 00:12:23,240 --> 00:12:25,280 Speaker 2: what the agencies are saying. We have a statement from 242 00:12:25,320 --> 00:12:30,439 Speaker 2: the Secretary for Homeland Security, Alejandro Mayorcus who says organizations 243 00:12:30,440 --> 00:12:34,040 Speaker 2: of all kinds are increasingly reliant on cloud computing to 244 00:12:34,160 --> 00:12:37,079 Speaker 2: deliver services to the American people, which makes it imperative 245 00:12:37,480 --> 00:12:42,319 Speaker 2: that we understand the vulnerabilities of that technology. Do we 246 00:12:42,360 --> 00:12:46,800 Speaker 2: have a sense that they understand the vulnerabilities now, Andrew, No, 247 00:12:46,960 --> 00:12:47,560 Speaker 2: I think that's. 248 00:12:47,440 --> 00:12:50,560 Speaker 6: We're gonna They're going to try to figure out. You know, 249 00:12:51,000 --> 00:12:53,760 Speaker 6: cloud obviously has grown very rapidly in the last couple 250 00:12:53,800 --> 00:12:56,240 Speaker 6: of years. There has been a couple major attacks that 251 00:12:56,280 --> 00:12:59,360 Speaker 6: have exploited vulnerabilities in the cloud. So I think it 252 00:12:59,440 --> 00:13:01,280 Speaker 6: makes sense a sort of step back and try to 253 00:13:01,280 --> 00:13:01,960 Speaker 6: figure this out. 254 00:13:03,320 --> 00:13:06,480 Speaker 2: The thing that is interesting here is who in DC 255 00:13:06,720 --> 00:13:09,440 Speaker 2: looks at this stuff at any given moment. You know, 256 00:13:10,480 --> 00:13:13,760 Speaker 2: when you think about the departments investigating and probing, it's 257 00:13:13,800 --> 00:13:18,240 Speaker 2: a digital issue rather than I guess the national security issue. 258 00:13:18,360 --> 00:13:21,040 Speaker 2: Can you can you explain to us who has jurisdiction here? 259 00:13:23,200 --> 00:13:23,440 Speaker 7: Yes? 260 00:13:23,679 --> 00:13:25,760 Speaker 1: So, yes. 261 00:13:25,960 --> 00:13:31,319 Speaker 6: So there's an agency called the cyber Security and Infrastructure 262 00:13:31,360 --> 00:13:36,480 Speaker 6: Security Agency, which oversees sort of the defense of US 263 00:13:37,520 --> 00:13:42,600 Speaker 6: computer networks, so they provide a sort of protection against 264 00:13:42,600 --> 00:13:46,400 Speaker 6: the tax and intelligence to help for people who work 265 00:13:46,400 --> 00:13:48,000 Speaker 6: in those agencies. 266 00:13:49,679 --> 00:13:52,679 Speaker 1: Protect against the tax. The Cyber Safety. 267 00:13:52,360 --> 00:13:56,959 Speaker 6: Review Board is overseen by them their advisory in nature, 268 00:13:57,000 --> 00:14:00,240 Speaker 6: and again the purpose of them is not some much 269 00:14:00,240 --> 00:14:02,720 Speaker 6: to remend at tax, but to sort of after the 270 00:14:02,800 --> 00:14:06,160 Speaker 6: fact go back and say, Okay, here's what went wrong. 271 00:14:05,960 --> 00:14:07,920 Speaker 1: In this case, what can we do better in the 272 00:14:07,960 --> 00:14:08,480 Speaker 1: next time. 273 00:14:08,800 --> 00:14:13,600 Speaker 6: They've previously looked at log forge, which was the software 274 00:14:13,720 --> 00:14:14,640 Speaker 6: vulnerability that. 275 00:14:16,720 --> 00:14:18,480 Speaker 1: In open source software. 276 00:14:18,880 --> 00:14:21,320 Speaker 6: And they also looked at this hacking group called lapses, 277 00:14:22,400 --> 00:14:24,720 Speaker 6: which it hacked a bunch of technology companies that come 278 00:14:24,760 --> 00:14:30,200 Speaker 6: up with reports sort of recommending ways to prevent against 279 00:14:30,240 --> 00:14:30,600 Speaker 6: the tax. 280 00:14:30,640 --> 00:14:35,440 Speaker 2: Against that nature, all right, thanks to Bloomberg Cybersecurity editor 281 00:14:35,520 --> 00:14:39,760 Speaker 2: Andrew Martin, and again that story confirming a previous Bloomberg report. 282 00:14:40,160 --> 00:14:42,840 Speaker 2: Now coming up here on Bloomberg Technology, the cost. 283 00:14:42,600 --> 00:14:43,800 Speaker 3: Of flying green. 284 00:14:44,360 --> 00:14:47,400 Speaker 2: Why some airlines are planning to charge passengers for more 285 00:14:47,800 --> 00:14:51,520 Speaker 2: for flights in order to reduce their carbon emissions. It 286 00:14:51,640 --> 00:14:55,120 Speaker 2: is a big number big. We'll have the details next. 287 00:14:55,160 --> 00:14:59,400 Speaker 2: This is Bloomberg Technology. 288 00:15:01,600 --> 00:15:04,560 Speaker 8: The designated members of each House of Congress of the 289 00:15:04,600 --> 00:15:09,440 Speaker 8: appointment in February twenty eighteen, after being nominated by the 290 00:15:09,440 --> 00:15:13,080 Speaker 8: former president and confirmed by the Senate, mister Weiss was 291 00:15:13,120 --> 00:15:15,800 Speaker 8: sworn in as the United States Attorney for the District 292 00:15:15,840 --> 00:15:20,400 Speaker 8: of Delaware. Mister Weiss had been a career prosecutor, having 293 00:15:20,440 --> 00:15:23,720 Speaker 8: served previously in the office for more than a decade. 294 00:15:24,760 --> 00:15:28,080 Speaker 8: Beginning in twenty nineteen, mister Weiss, in his capacity as 295 00:15:28,200 --> 00:15:32,200 Speaker 8: US Attorney and along with federal law enforcement partners, began 296 00:15:32,320 --> 00:15:37,920 Speaker 8: investigating allegations of certain criminal conduct by, among others, Robert 297 00:15:38,080 --> 00:15:42,880 Speaker 8: Hunter Biden. That investigation has been recently referenced in federal 298 00:15:42,920 --> 00:15:46,720 Speaker 8: criminal proceedings in the District of Delaware, and as noted 299 00:15:46,720 --> 00:15:50,800 Speaker 8: in those proceedings and other public statements by mister Weiss's office, 300 00:15:51,080 --> 00:15:57,280 Speaker 8: that investigation remains ongoing. In February twenty twenty one, US 301 00:15:57,320 --> 00:16:00,240 Speaker 8: Attorney Weiss was asked to remain as US Attorney for 302 00:16:00,240 --> 00:16:04,240 Speaker 8: the District of Delaware and in that capacity to continue 303 00:16:04,280 --> 00:16:08,640 Speaker 8: to lead the investigation. As I said before, mister Weiss 304 00:16:08,640 --> 00:16:12,600 Speaker 8: would be permitted to continue his investigation, take any investigative 305 00:16:12,640 --> 00:16:16,120 Speaker 8: steps he wanted, and make the decision whether to prosecute 306 00:16:16,200 --> 00:16:20,760 Speaker 8: in any district. Mister Weiss has told Congress that he 307 00:16:20,920 --> 00:16:25,040 Speaker 8: has been granted ultimate authority over this matter, including the 308 00:16:25,080 --> 00:16:30,680 Speaker 8: responsibility for deciding where, when, and whether to file charges, 309 00:16:31,000 --> 00:16:34,320 Speaker 8: and for making decisions necessary to preserve the integrity of 310 00:16:34,360 --> 00:16:39,840 Speaker 8: any prosecution consistent with federal law, the principles of federal prosecution, 311 00:16:40,440 --> 00:16:45,400 Speaker 8: and departmental policies. In a July twenty twenty three letter 312 00:16:45,480 --> 00:16:48,920 Speaker 8: to Congress, mister Weiss said that he had not to 313 00:16:48,960 --> 00:16:55,120 Speaker 8: that point requested special Council designation. On Tuesday of this week, 314 00:16:55,560 --> 00:16:59,560 Speaker 8: mister Weiss advised me that, in his judgment, his investigation 315 00:16:59,680 --> 00:17:03,720 Speaker 8: had stage at which he should continue his work as 316 00:17:03,720 --> 00:17:07,080 Speaker 8: a Special Council, and he asked to be so appointed. 317 00:17:08,160 --> 00:17:12,280 Speaker 8: Upon considering his request, as well as the extraordinary circumstances 318 00:17:12,320 --> 00:17:15,679 Speaker 8: relating to this matter, I have concluded that it is 319 00:17:15,720 --> 00:17:18,919 Speaker 8: in the public interest to appoint him as Special Council 320 00:17:19,800 --> 00:17:23,640 Speaker 8: commitment to provide mister Weiss all the resources he requests. 321 00:17:24,240 --> 00:17:26,960 Speaker 8: It also reaffirms that mister Weiss has the authority he 322 00:17:27,080 --> 00:17:31,199 Speaker 8: needs to conduct a thorough investigation and to continue to 323 00:17:31,240 --> 00:17:35,480 Speaker 8: take the steps he deems appropriate independently based only on 324 00:17:35,520 --> 00:17:39,880 Speaker 8: the facts and the law. Mister Weiss will also continue 325 00:17:39,960 --> 00:17:42,600 Speaker 8: to serve as US Attorney for the District of Delaware 326 00:17:43,920 --> 00:17:46,960 Speaker 8: a Special Council, he will continue to have the authority 327 00:17:47,000 --> 00:17:51,399 Speaker 8: and responsibility that he has previously exercised to oversee the 328 00:17:51,400 --> 00:17:56,320 Speaker 8: investigation and decide where, when, and whether to file charges. 329 00:17:56,920 --> 00:17:59,359 Speaker 8: The Special Council will not be subject to the day 330 00:17:59,400 --> 00:18:03,320 Speaker 8: to day supervision of any official of the Department, but 331 00:18:03,440 --> 00:18:07,480 Speaker 8: he must comply with the regulations, procedures, and policies of 332 00:18:07,520 --> 00:18:12,439 Speaker 8: the Department consistent with the Special Council regulations. At the 333 00:18:12,480 --> 00:18:15,560 Speaker 8: conclusion of mister Weiss's work, he will provide me with 334 00:18:15,640 --> 00:18:20,800 Speaker 8: a report explaining the prosecution or declination decisions reached by him. 335 00:18:21,760 --> 00:18:24,520 Speaker 8: As with each Special Council who has served since I 336 00:18:24,560 --> 00:18:27,760 Speaker 8: have taken office, I am committed to making as much 337 00:18:27,760 --> 00:18:31,879 Speaker 8: of his report public as possible, consistent with legal requirements 338 00:18:31,920 --> 00:18:38,280 Speaker 8: and Department policy. Today's announcement affords the prosecutors, agents, and 339 00:18:38,359 --> 00:18:42,560 Speaker 8: analysts working on this matter the ability to proceed with 340 00:18:42,640 --> 00:18:48,280 Speaker 8: their work expeditiously and to make decisions indisputably guided only 341 00:18:48,440 --> 00:18:52,280 Speaker 8: by the facts and the law. The men and women 342 00:18:52,400 --> 00:18:57,480 Speaker 8: undertaking this investigation are public servants who have dedicated their 343 00:18:57,520 --> 00:19:02,600 Speaker 8: careers to protecting the citizens of this country. The appointment 344 00:19:02,600 --> 00:19:06,560 Speaker 8: of mister Weiss reinforces for the American people. The Department's 345 00:19:06,600 --> 00:19:12,800 Speaker 8: committed meant to both independence and accountability in particularly sensitive matters. 346 00:19:14,080 --> 00:19:16,879 Speaker 8: I am confident that mister Weiss will carry out his 347 00:19:17,000 --> 00:19:21,960 Speaker 8: responsibility in an even handed and urgent manner and in 348 00:19:22,000 --> 00:19:26,600 Speaker 8: accordance with the highest traditions of this department. Thank you. 349 00:19:27,520 --> 00:19:30,400 Speaker 1: If you was the joernal of Weiss, had the authorities needed. 350 00:19:30,640 --> 00:19:31,680 Speaker 3: You need to be a polution. 351 00:19:31,880 --> 00:19:35,440 Speaker 2: That was US Attorney General Garland speaking there the news 352 00:19:35,960 --> 00:19:39,639 Speaker 2: the Hunter Biden probe has been assigned a special council 353 00:19:39,720 --> 00:19:43,040 Speaker 2: by the Department of Justice. That special council the current 354 00:19:43,119 --> 00:19:46,080 Speaker 2: head of the probe David Weiss, who will be the 355 00:19:46,119 --> 00:19:48,800 Speaker 2: special council on the Hunter Biden probe. Let's head over 356 00:19:48,840 --> 00:19:51,639 Speaker 2: to Washington. Webloon both Katie Lions has more Kayley. 357 00:19:53,000 --> 00:19:56,080 Speaker 9: Well and as we just heard from the Attorney General, 358 00:19:56,240 --> 00:19:59,760 Speaker 9: David Weiss, the US District Attorney in Delaware since twenty 359 00:19:59,840 --> 00:20:04,320 Speaker 9: nine ten, has already been looking into criminal investigations against 360 00:20:04,480 --> 00:20:07,719 Speaker 9: Hunter Biden. Of course, President Joe Biden's son. He has 361 00:20:07,760 --> 00:20:11,640 Speaker 9: now been granted Special Council authority and under which, according 362 00:20:11,680 --> 00:20:14,600 Speaker 9: to Merrick Garland, the Attorney General. He will have full independence, 363 00:20:14,640 --> 00:20:16,440 Speaker 9: he will not be overseen on a day to day 364 00:20:16,480 --> 00:20:20,800 Speaker 9: basis by the Department of Justice as he continues this investigation. 365 00:20:20,920 --> 00:20:24,840 Speaker 9: It's important to note here earlier this summer, mister Weiss, 366 00:20:25,080 --> 00:20:27,680 Speaker 9: the district attorney there, was involved in that initial plea 367 00:20:27,720 --> 00:20:30,840 Speaker 9: agreement with Hunter Biden, where he was supposed to plead 368 00:20:30,880 --> 00:20:33,560 Speaker 9: guilty to two misdemeanor tax charges and in return could 369 00:20:33,600 --> 00:20:36,920 Speaker 9: have avoided prosecution on a separate gun related charge. That 370 00:20:37,160 --> 00:20:40,280 Speaker 9: plea deal had fallen apart in court. That is not 371 00:20:40,400 --> 00:20:43,439 Speaker 9: a completely resolved matter, and now going forward, mister Weiss 372 00:20:43,440 --> 00:20:46,800 Speaker 9: will have this special Council designated authority in the probe 373 00:20:46,880 --> 00:20:49,360 Speaker 9: of the President's son. It's important to keep in mind 374 00:20:49,440 --> 00:20:52,359 Speaker 9: here as well that a special counsel being appointed is 375 00:20:52,440 --> 00:20:56,600 Speaker 9: something that Republicans have been calling for for some time now, 376 00:20:56,600 --> 00:20:59,440 Speaker 9: and of course in Congress there are other separate, ongoing 377 00:20:59,480 --> 00:21:02,720 Speaker 9: investigations into the dealings, not just of Hunter Biden, but 378 00:21:02,800 --> 00:21:05,800 Speaker 9: how the President himself may have been involved. There was 379 00:21:05,800 --> 00:21:08,760 Speaker 9: a hearing just recently with Debon Archer, hunter Biden's former 380 00:21:08,800 --> 00:21:12,800 Speaker 9: business partner, before the House Oversight Committee, talking about phone 381 00:21:12,800 --> 00:21:15,080 Speaker 9: calls that the President may have been on with Hunter 382 00:21:15,440 --> 00:21:19,200 Speaker 9: simultaneously with business associates. So Congress is doing work here 383 00:21:19,240 --> 00:21:21,919 Speaker 9: and now again a special counsel, Mister Weiss, the District 384 00:21:21,960 --> 00:21:26,120 Speaker 9: attorney in Delaware, has also been given that special council designation. 385 00:21:26,800 --> 00:21:29,080 Speaker 2: All right, Bloombers k Lines in DC, thank you the 386 00:21:29,119 --> 00:21:31,360 Speaker 2: news that David Weiss, the current head of the Hunter 387 00:21:31,440 --> 00:21:34,480 Speaker 2: Biden probe, has been assigned as the special council by 388 00:21:34,720 --> 00:21:41,600 Speaker 2: the Department of Justice. 389 00:21:45,320 --> 00:21:46,760 Speaker 3: Welcome back to Bloomber Technology. 390 00:21:46,880 --> 00:21:50,280 Speaker 2: Ed Ludlow here in San Francisco, remainder Caroline Hyde is off. 391 00:21:50,320 --> 00:21:52,200 Speaker 3: I want to get a quick check on the markets. 392 00:21:51,840 --> 00:21:53,520 Speaker 2: And kind of how we ended the week and as 393 00:21:53,560 --> 00:21:56,800 Speaker 2: that one hundred over five days down again one point 394 00:21:56,840 --> 00:22:00,879 Speaker 2: seven percent as it stands, second consecutive weekly decline and 395 00:22:01,440 --> 00:22:04,080 Speaker 2: it's easy to forget, but this is the first back 396 00:22:04,119 --> 00:22:06,240 Speaker 2: to back weekly declines we've had on the Tech Heavy 397 00:22:06,240 --> 00:22:10,080 Speaker 2: index since December when we had four straight weeks of declines. 398 00:22:10,119 --> 00:22:12,400 Speaker 2: Earning season has been a really big part of the story. 399 00:22:12,920 --> 00:22:15,880 Speaker 2: But remember the USCPI print as well being a factor 400 00:22:16,320 --> 00:22:19,040 Speaker 2: markets trying to understand what the Fed might do next. 401 00:22:19,280 --> 00:22:22,640 Speaker 2: Remember higher rates impact the present value of future cash flows. 402 00:22:22,760 --> 00:22:23,440 Speaker 3: There's a lot. 403 00:22:23,280 --> 00:22:25,760 Speaker 2: Going on, But it is interesting to note back to 404 00:22:25,840 --> 00:22:28,800 Speaker 2: back weekly declines on the Nazak one hundred. We always 405 00:22:28,840 --> 00:22:31,800 Speaker 2: go to that index because it's so tech heavy from 406 00:22:31,800 --> 00:22:34,600 Speaker 2: the megacap perspective, which is a drag in Friday session 407 00:22:34,680 --> 00:22:37,919 Speaker 2: right through to higher multiple software names. One name we're 408 00:22:37,920 --> 00:22:41,359 Speaker 2: looking at in particular Alphabet, parent of Google. It's lower 409 00:22:41,440 --> 00:22:44,920 Speaker 2: during Friday session, but on a weekly basis has actually 410 00:22:45,000 --> 00:22:47,360 Speaker 2: fared a little bit better. What I want to talk 411 00:22:47,400 --> 00:22:53,480 Speaker 2: about next is Alphabet's big old pile of cash. The 412 00:22:53,520 --> 00:22:56,720 Speaker 2: Google parent generated nearly twenty nine billion dollars in cash 413 00:22:56,920 --> 00:23:00,240 Speaker 2: in the second quarter after cutting thousands of jobs and 414 00:23:00,280 --> 00:23:03,600 Speaker 2: its efforts to staunch losses in its various moonshot projects. 415 00:23:03,840 --> 00:23:06,520 Speaker 2: But the Google parent now has about one hundred and 416 00:23:06,560 --> 00:23:10,800 Speaker 2: eighteen billion dollars in cash and short term market world securities, 417 00:23:11,080 --> 00:23:14,240 Speaker 2: more than any other company on the Nazak one hundred. 418 00:23:13,960 --> 00:23:15,680 Speaker 3: Apart from Apple. 419 00:23:15,960 --> 00:23:18,439 Speaker 2: But unlike Apple, which aims to give back most of 420 00:23:18,480 --> 00:23:22,040 Speaker 2: its cash to shareholders, Alphabet does not have a well 421 00:23:22,040 --> 00:23:26,920 Speaker 2: defined capital return strategy. So investors really right now seeking 422 00:23:26,960 --> 00:23:30,000 Speaker 2: more details on its plans, and we'll ask we'll try 423 00:23:30,000 --> 00:23:32,280 Speaker 2: to get those answers for you now from. 424 00:23:32,080 --> 00:23:34,040 Speaker 3: Big tech to little tech. 425 00:23:34,400 --> 00:23:37,360 Speaker 2: We know Google's going big on AI, as is Microsoft 426 00:23:37,480 --> 00:23:39,679 Speaker 2: are the big names, but it isn't just about the 427 00:23:39,720 --> 00:23:43,560 Speaker 2: megacaps or the giants. Take mid Journey as an example. 428 00:23:43,600 --> 00:23:47,359 Speaker 2: It's an independent research lab that's been experimenting with AI 429 00:23:47,760 --> 00:23:51,960 Speaker 2: and can generate images using AI. It's the subject of 430 00:23:51,960 --> 00:23:56,800 Speaker 2: today's Bloomberg Tech Daily newsletter, and its author, Bloomberg's Davily 431 00:23:56,800 --> 00:23:59,480 Speaker 2: Album joins me. Now this is interesting, Like you and 432 00:23:59,520 --> 00:24:03,560 Speaker 2: I interact with so many of the AI companies that 433 00:24:03,600 --> 00:24:07,760 Speaker 2: are developing a generative AI tool. We interact with their apps, 434 00:24:07,760 --> 00:24:11,520 Speaker 2: their technology. Why you've been writing about about this particular 435 00:24:11,640 --> 00:24:12,520 Speaker 2: name in the newsletter. 436 00:24:13,680 --> 00:24:16,840 Speaker 7: Yeah, hey, Ed, it's good to be here. You know, 437 00:24:17,000 --> 00:24:20,800 Speaker 7: I think that mid Journey, which you know has been 438 00:24:20,880 --> 00:24:26,199 Speaker 7: floating around the name if you recall that viral image 439 00:24:26,400 --> 00:24:28,800 Speaker 7: of the Pope and a puffer jacket that was actually 440 00:24:29,200 --> 00:24:32,399 Speaker 7: created by mid Journey. It's one of the biggest AI 441 00:24:32,680 --> 00:24:37,280 Speaker 7: image generating apps and is only. 442 00:24:37,240 --> 00:24:37,960 Speaker 1: Sort of. 443 00:24:39,720 --> 00:24:42,320 Speaker 7: If you think about the three biggest ones, it would 444 00:24:42,359 --> 00:24:48,760 Speaker 7: be mid Journey, Open AIS, Dolly, and Stability AIS Stable Diffusion. 445 00:24:49,359 --> 00:24:53,200 Speaker 7: So this is, you know, one of the most popular 446 00:24:53,560 --> 00:24:57,359 Speaker 7: and actually according to this research, is currently the most 447 00:24:57,440 --> 00:25:01,879 Speaker 7: popular image generating app that out there, and it's concerning 448 00:25:01,960 --> 00:25:04,080 Speaker 7: that it can be easily tricked. 449 00:25:05,240 --> 00:25:08,760 Speaker 2: Okay, so the most popular names mid Journey, Stable Diffusion, 450 00:25:08,760 --> 00:25:11,879 Speaker 2: and of course open Aiyes, Dolli all very familiar to 451 00:25:11,960 --> 00:25:13,440 Speaker 2: us here on Bloomberg Technology. 452 00:25:13,920 --> 00:25:14,680 Speaker 3: There's something in. 453 00:25:14,560 --> 00:25:17,320 Speaker 2: Your writing that really jumps out though, that you consider 454 00:25:17,400 --> 00:25:21,240 Speaker 2: mid Journey and we think about standards content moderation. Is 455 00:25:21,280 --> 00:25:23,320 Speaker 2: it fair to say that mid Journey has the most 456 00:25:23,800 --> 00:25:27,320 Speaker 2: flexible standards of those three? 457 00:25:27,680 --> 00:25:30,199 Speaker 7: At least according to this report, it seems to be 458 00:25:30,280 --> 00:25:35,520 Speaker 7: the case. Researchers from the Center for Countering Digital Hate 459 00:25:36,359 --> 00:25:41,080 Speaker 7: showed an early report, shared it with us exclusively, and 460 00:25:41,240 --> 00:25:46,040 Speaker 7: they found one hundred examples where mid Journey's bot was 461 00:25:46,119 --> 00:25:52,159 Speaker 7: easily tricked into creating conspiratorial images where they showed, you know, 462 00:25:52,320 --> 00:25:59,159 Speaker 7: prompts where people were ordering up these images of politicians 463 00:25:59,440 --> 00:26:04,760 Speaker 7: in comproming situations and events that never happened, and mid 464 00:26:04,800 --> 00:26:11,439 Speaker 7: Journey easily delivered the images. There are some guardrails like 465 00:26:11,560 --> 00:26:15,280 Speaker 7: mid Journey will not, for instance, generate an image if 466 00:26:15,320 --> 00:26:19,640 Speaker 7: you have certain keywords like blood or you know, anything 467 00:26:19,680 --> 00:26:21,600 Speaker 7: referring to gore or violence. 468 00:26:22,320 --> 00:26:24,679 Speaker 3: But it's so easy to trick it. 469 00:26:25,520 --> 00:26:28,320 Speaker 7: One example that we talk about in the newsletter is 470 00:26:28,400 --> 00:26:31,200 Speaker 7: you could ask it to generate an image of say, 471 00:26:31,480 --> 00:26:35,280 Speaker 7: Bill and Hillary Clinton with their hands covered in strawberry syrup, 472 00:26:35,520 --> 00:26:37,960 Speaker 7: so that kind of makes it look like they have. 473 00:26:37,920 --> 00:26:39,320 Speaker 1: Literal blood on their hands. 474 00:26:39,320 --> 00:26:42,080 Speaker 7: And researchers are really worried about this ahead of twenty 475 00:26:42,119 --> 00:26:44,359 Speaker 7: twenty four in the US presidential election. 476 00:26:45,400 --> 00:26:48,680 Speaker 2: So we've covered such wide ranging issues here on BTech 477 00:26:48,800 --> 00:26:54,720 Speaker 2: about the text to image issue, biases, or you know, 478 00:26:55,600 --> 00:26:58,080 Speaker 2: a result that is not what you intended. Let's be 479 00:26:58,200 --> 00:27:00,679 Speaker 2: fair here and say what have made Journey had to 480 00:27:00,720 --> 00:27:04,680 Speaker 2: say about this? What is their response to the reporting 481 00:27:04,720 --> 00:27:05,639 Speaker 2: on their standards. 482 00:27:06,800 --> 00:27:11,320 Speaker 7: So they didn't actually send us a response on the record, 483 00:27:11,760 --> 00:27:14,119 Speaker 7: but it has said in the past that it has 484 00:27:14,480 --> 00:27:20,440 Speaker 7: dozens of moderators and guides that look at the content 485 00:27:20,760 --> 00:27:25,320 Speaker 7: on their discord chat app that is the main interface 486 00:27:25,440 --> 00:27:28,840 Speaker 7: that mid Journey uses to interact with its users, and 487 00:27:28,880 --> 00:27:33,240 Speaker 7: they say that these folks sixty eight content moderators sort 488 00:27:33,280 --> 00:27:35,800 Speaker 7: of scour the chat and make sure that all the 489 00:27:35,880 --> 00:27:40,919 Speaker 7: requests are above board, but it seems like they're letting 490 00:27:41,000 --> 00:27:45,280 Speaker 7: some requests go straight through still and beyond that there's 491 00:27:45,560 --> 00:27:50,680 Speaker 7: sort of automatic algorithmic AI that checks on the requests, 492 00:27:51,000 --> 00:27:53,800 Speaker 7: usually using text matching to make. 493 00:27:53,680 --> 00:27:56,440 Speaker 3: Sure that certain terms for instance. 494 00:27:56,320 --> 00:28:01,560 Speaker 7: Again about violence and gore aren't creating images, but again 495 00:28:01,600 --> 00:28:03,080 Speaker 7: those are easily circumpanded. 496 00:28:04,119 --> 00:28:07,160 Speaker 2: Bloomberg's Davy Alba with the Bloomberg Technology Daily, I really 497 00:28:07,240 --> 00:28:09,520 Speaker 2: encourage our audience go out find it on Bloomberg dot 498 00:28:09,520 --> 00:28:11,359 Speaker 2: com and in the newsletter format. 499 00:28:11,359 --> 00:28:12,080 Speaker 3: Thank you so much. 500 00:28:12,160 --> 00:28:14,359 Speaker 2: Now, coming up here on the show, we'll talk about 501 00:28:14,480 --> 00:28:18,800 Speaker 2: the state of AI development and adoption, but particularly in enterprise. 502 00:28:18,880 --> 00:28:22,680 Speaker 2: That's with Insight Partners Managing Director Lonny Jaffie. 503 00:28:22,720 --> 00:28:41,360 Speaker 3: That's next. This is Bloomberg All right. 504 00:28:41,400 --> 00:28:44,320 Speaker 2: Time for today is VC Spotlight. Let's talk about cybersecurity 505 00:28:44,360 --> 00:28:48,200 Speaker 2: and AI. Insite Partners recently released it's State of the 506 00:28:48,400 --> 00:28:51,520 Speaker 2: Enterprise report where they break down what the future of 507 00:28:51,640 --> 00:28:55,680 Speaker 2: enterprise adoption and tailwinds in development of AI technology looks like. 508 00:28:56,120 --> 00:28:59,400 Speaker 2: Managing Director Lonnie Jaffee joins us now for more here 509 00:28:59,400 --> 00:29:03,040 Speaker 2: on Bloomberg Achnologies. So much of our audience either works 510 00:29:03,120 --> 00:29:07,280 Speaker 2: for or is a customer of enterprise cloud Enterprise SaaS. 511 00:29:07,520 --> 00:29:11,840 Speaker 2: They're thinking heavily about how the developments that are happening 512 00:29:11,920 --> 00:29:14,760 Speaker 2: right now in AI impact their core business or how 513 00:29:14,760 --> 00:29:17,080 Speaker 2: they use AI. Just give me sort of the key 514 00:29:17,120 --> 00:29:19,920 Speaker 2: takeaways from the report that you published very. 515 00:29:19,800 --> 00:29:25,200 Speaker 10: Recently, right, so, the Inside Partner's State of Enterprise Tech Report. 516 00:29:25,320 --> 00:29:27,920 Speaker 10: This is a study that reached out to about three 517 00:29:28,000 --> 00:29:31,480 Speaker 10: hundred senior technologists at some of the world's largest companies, 518 00:29:31,600 --> 00:29:34,520 Speaker 10: so seventy percent of them generate more than ten billion 519 00:29:34,640 --> 00:29:37,320 Speaker 10: or so in annual revenue. And there's a free download 520 00:29:37,360 --> 00:29:39,360 Speaker 10: from our website and has a lot of interesting data 521 00:29:39,400 --> 00:29:43,200 Speaker 10: points around spending plans and strategy and areas like cybersecurity 522 00:29:43,240 --> 00:29:47,640 Speaker 10: and generative AI. Cybersecurity is obviously a big focus. One 523 00:29:48,000 --> 00:29:51,520 Speaker 10: newer concern we're hearing from bigger companies and governments as 524 00:29:51,560 --> 00:29:54,760 Speaker 10: well is around the possibility that we'll see a wave 525 00:29:55,040 --> 00:29:59,280 Speaker 10: of generative AI upgraded cyber attacks. For example, something like 526 00:29:59,360 --> 00:30:02,280 Speaker 10: ransomware much smarter because it's powered by something like a 527 00:30:02,320 --> 00:30:06,760 Speaker 10: large language model things. We actually launched a two weeks 528 00:30:06,920 --> 00:30:10,880 Speaker 10: compression called the AI Cyber Challenge. This is led by 529 00:30:10,920 --> 00:30:13,840 Speaker 10: DARPA working with companies like Anthropic and Google, Microsoft and 530 00:30:13,880 --> 00:30:17,560 Speaker 10: open Ai, which will have teams competing to identify and 531 00:30:17,560 --> 00:30:19,600 Speaker 10: fix software vulnerabilities using AI. 532 00:30:21,040 --> 00:30:22,680 Speaker 3: Lonnie, sorry didn't mean to jump in on you there. 533 00:30:22,720 --> 00:30:25,000 Speaker 2: I just want to say on this show, when we 534 00:30:25,040 --> 00:30:28,800 Speaker 2: talk in the cybersecurity context, so many people saying that 535 00:30:28,920 --> 00:30:31,080 Speaker 2: the tools that we all have and you guys in 536 00:30:31,120 --> 00:30:35,080 Speaker 2: your industry will have the threat actors have them as well. Ultimately, 537 00:30:35,120 --> 00:30:39,320 Speaker 2: you're a VC, So I'm interested why Insight actually publishes 538 00:30:39,360 --> 00:30:39,960 Speaker 2: this report. 539 00:30:40,240 --> 00:30:41,680 Speaker 3: What do you guys get out of it. 540 00:30:43,440 --> 00:30:46,840 Speaker 10: We have a program called Insight on Site and it's 541 00:30:46,960 --> 00:30:51,320 Speaker 10: generally trying to help support our portfolio companies with an 542 00:30:51,440 --> 00:30:53,400 Speaker 10: enormous amount of resource. So it's one hundred and twenty 543 00:30:53,400 --> 00:30:56,960 Speaker 10: full time people who help with things like product strategy 544 00:30:57,000 --> 00:30:59,560 Speaker 10: and marketing and sales. And there's a part of the 545 00:30:59,560 --> 00:31:02,960 Speaker 10: team Calledgnite that manages relationships with buyers at large multinational 546 00:31:03,040 --> 00:31:04,880 Speaker 10: corporations and so this is a great way to stay 547 00:31:04,880 --> 00:31:07,600 Speaker 10: in contact with them, and it helps our companies scale 548 00:31:07,760 --> 00:31:12,680 Speaker 10: and also meet potential customers and partners and even acquirers. 549 00:31:12,760 --> 00:31:12,840 Speaker 3: Right. 550 00:31:12,920 --> 00:31:16,400 Speaker 10: So, this week it was announced that Checkpoint will acquire 551 00:31:16,480 --> 00:31:19,000 Speaker 10: our portfolio company Perimetery v one, which allows you to 552 00:31:19,040 --> 00:31:22,760 Speaker 10: build a secure virtual corporate network over the Internet for 553 00:31:22,880 --> 00:31:26,160 Speaker 10: about four hundred and ninety million dollars, and Rubric this 554 00:31:26,160 --> 00:31:28,400 Speaker 10: week also announced that it signed an agreement to acquire 555 00:31:28,400 --> 00:31:31,480 Speaker 10: our portfolio company, Laminar, which is a leader in cybersecurity 556 00:31:31,480 --> 00:31:33,760 Speaker 10: for data and use. So these are both deep tech 557 00:31:33,840 --> 00:31:37,880 Speaker 10: Israeli cybersecurity startups, and you know, when the companies are 558 00:31:37,880 --> 00:31:40,640 Speaker 10: relatively small and growing rapidly, that kind of connectness with 559 00:31:40,680 --> 00:31:43,240 Speaker 10: buyers can be really helpful to them. 560 00:31:43,640 --> 00:31:45,240 Speaker 2: Lottie, I want to jump on something you just said 561 00:31:45,280 --> 00:31:48,200 Speaker 2: there about Israeli tech. I know you guys have offices 562 00:31:48,200 --> 00:31:50,560 Speaker 2: in Tel Aviv. One of the big stories of the 563 00:31:50,600 --> 00:31:56,360 Speaker 2: week has been VC's attitude towards the country, given the societal, 564 00:31:57,040 --> 00:32:01,520 Speaker 2: judicial and political considerations happening there. How do you view 565 00:32:01,600 --> 00:32:04,000 Speaker 2: Israeli tech right now? Are you pulling back or are 566 00:32:04,040 --> 00:32:08,400 Speaker 2: you still interested, particularly in cybersecurity. You know, cybersecurity has 567 00:32:08,440 --> 00:32:11,200 Speaker 2: been one of the main stay offerings out of Israel. 568 00:32:12,720 --> 00:32:14,920 Speaker 10: Yeah, the cybersecurity talent there is amazing. 569 00:32:16,040 --> 00:32:16,440 Speaker 3: You know. 570 00:32:16,280 --> 00:32:19,280 Speaker 10: You see there's a we're based in New York City 571 00:32:19,280 --> 00:32:21,680 Speaker 10: and there's a pretty close connectedness between the New York 572 00:32:21,720 --> 00:32:25,400 Speaker 10: City tech scene and the one in Tel Aviv. There's 573 00:32:25,440 --> 00:32:27,840 Speaker 10: a lot of companies when they land in the US, 574 00:32:27,920 --> 00:32:30,160 Speaker 10: and this is true for Europe as well, they'll pick 575 00:32:30,240 --> 00:32:32,600 Speaker 10: New York City as their landing place. There were a 576 00:32:32,640 --> 00:32:35,720 Speaker 10: few initial companies like Mango, dB and Data Dog and 577 00:32:35,760 --> 00:32:37,760 Speaker 10: others that were you know, big prove that you could 578 00:32:37,800 --> 00:32:40,840 Speaker 10: scale a massive infrastructure software company in Manhattan, and so 579 00:32:40,880 --> 00:32:43,480 Speaker 10: that was a big early part of our interest in 580 00:32:43,480 --> 00:32:46,520 Speaker 10: the country. We're now the largest investor by both deal 581 00:32:46,600 --> 00:32:50,440 Speaker 10: count and dollars and technology companies there. And you know, 582 00:32:50,840 --> 00:32:54,479 Speaker 10: we're actually very helpful to the Israeli companies as they 583 00:32:54,480 --> 00:32:57,040 Speaker 10: start to scale up, because they need to start scaling 584 00:32:57,120 --> 00:33:00,240 Speaker 10: up internationally pretty much right away, and we have a 585 00:33:00,240 --> 00:33:03,160 Speaker 10: lot of expertise around global scale out, you know, hiring 586 00:33:03,200 --> 00:33:06,480 Speaker 10: sales leaders, opening up your first international office, things like that. 587 00:33:07,160 --> 00:33:08,560 Speaker 3: Let's go back to you personally. 588 00:33:08,920 --> 00:33:12,080 Speaker 2: You know, you add a decade IBM, now you're in 589 00:33:12,680 --> 00:33:13,600 Speaker 2: the VC role. 590 00:33:14,120 --> 00:33:15,760 Speaker 3: How do you find the next IBM? 591 00:33:16,080 --> 00:33:18,960 Speaker 2: You know, in this environment such a heavy focus on AI, 592 00:33:19,640 --> 00:33:20,800 Speaker 2: what are you seeing out there? 593 00:33:22,400 --> 00:33:25,080 Speaker 10: Yeah, we're still only a real like about a few 594 00:33:25,120 --> 00:33:27,920 Speaker 10: months into it with the generative AI capabilities, but there 595 00:33:27,960 --> 00:33:31,560 Speaker 10: seem to be some early important differences starting to show 596 00:33:31,640 --> 00:33:34,800 Speaker 10: up when compared to the prior generations of AI. The 597 00:33:35,000 --> 00:33:38,120 Speaker 10: AI prediction systems like the recommendation engines or the computer 598 00:33:38,200 --> 00:33:42,240 Speaker 10: vision classification systems in domains like healthcare, like our portfolio 599 00:33:42,320 --> 00:33:44,960 Speaker 10: company Iterative Health, which can use computer vision to find 600 00:33:45,000 --> 00:33:47,960 Speaker 10: cancerous polyps from a cool anascope video. These took a 601 00:33:48,000 --> 00:33:51,200 Speaker 10: lot of time, effort, and resources to build. They needed 602 00:33:51,320 --> 00:33:56,280 Speaker 10: big teams of very expensive engineers, complex data pipelines, lots 603 00:33:56,280 --> 00:33:59,600 Speaker 10: of training and feedback data. But with the new wave 604 00:33:59,640 --> 00:34:02,280 Speaker 10: of general to AI, at least so far, many companies, 605 00:34:02,880 --> 00:34:06,000 Speaker 10: especially you're seeing this with incumbent software vendors and even 606 00:34:06,080 --> 00:34:09,640 Speaker 10: end customers have been able to build and ship generative 607 00:34:09,680 --> 00:34:12,800 Speaker 10: AI products extremely quickly. You know, some have actually released 608 00:34:12,800 --> 00:34:15,480 Speaker 10: things already this year, even though it's only been a 609 00:34:15,560 --> 00:34:19,720 Speaker 10: few months. And then they're able to give these language 610 00:34:19,719 --> 00:34:23,360 Speaker 10: model or image generation capabilities access to their massive you know, 611 00:34:23,520 --> 00:34:24,600 Speaker 10: hundreds of millions. 612 00:34:24,280 --> 00:34:26,040 Speaker 3: Of existing users, existing distribution. 613 00:34:27,320 --> 00:34:29,920 Speaker 10: And so what we've been doing is working with our 614 00:34:29,920 --> 00:34:33,120 Speaker 10: portfolio companies like Atlin, for example, which is one of 615 00:34:33,160 --> 00:34:37,040 Speaker 10: our better modern data stack portfolio companies that's going through 616 00:34:37,160 --> 00:34:40,600 Speaker 10: really rapid growth and helping them figure out, Okay, so 617 00:34:40,680 --> 00:34:43,680 Speaker 10: you can build a generative AI capability and then you 618 00:34:43,719 --> 00:34:45,759 Speaker 10: can light it up to all of your users and 619 00:34:45,800 --> 00:34:48,600 Speaker 10: it can use your product and in a way it's 620 00:34:48,600 --> 00:34:50,719 Speaker 10: sort of a startup because it's within you know, it's 621 00:34:50,719 --> 00:34:53,319 Speaker 10: a new capability and it can do something really media 622 00:34:53,360 --> 00:34:55,200 Speaker 10: and substantives, like in the case of atline, it can 623 00:34:55,200 --> 00:34:58,000 Speaker 10: do interactive Q and A with all of your enterprise data. 624 00:34:58,160 --> 00:35:00,680 Speaker 10: You can ask it a question like you know, which 625 00:35:00,680 --> 00:35:03,000 Speaker 10: of my sales reps is most productive this month, and 626 00:35:03,040 --> 00:35:05,240 Speaker 10: it can go figure out across the catalog. 627 00:35:05,040 --> 00:35:06,360 Speaker 3: You know how to answer that question. 628 00:35:07,600 --> 00:35:10,160 Speaker 10: And our companies are able to get these impressive generative 629 00:35:10,400 --> 00:35:12,560 Speaker 10: AI capabilities out the door in just a few months. 630 00:35:13,200 --> 00:35:15,000 Speaker 10: So it does kind of tilt the balance of power 631 00:35:15,040 --> 00:35:19,000 Speaker 10: a little bit between from startups to incumbents. But there 632 00:35:19,040 --> 00:35:21,520 Speaker 10: are there are a few really interesting startup categories that 633 00:35:21,560 --> 00:35:26,000 Speaker 10: we've been looking at, like one, as Kevin Scott puts it, 634 00:35:26,000 --> 00:35:28,880 Speaker 10: it's the companies that can make the previously impossible things 635 00:35:28,920 --> 00:35:33,080 Speaker 10: possible but still hard right, so there's still defensibility and 636 00:35:33,200 --> 00:35:35,400 Speaker 10: hard tech and you need really good talent. Like we 637 00:35:35,480 --> 00:35:38,720 Speaker 10: have a portfolio company, Unlearned, that uses AI to create 638 00:35:38,760 --> 00:35:42,440 Speaker 10: synthetic digital twins. So these are basically virtual patients created 639 00:35:42,480 --> 00:35:46,000 Speaker 10: out of combining AI predictions with existing patient profiles, and 640 00:35:46,040 --> 00:35:49,400 Speaker 10: it allows researchers to conduct clinical trials while needing significantly 641 00:35:49,480 --> 00:35:51,960 Speaker 10: fewer real patients in the control group to get the 642 00:35:52,000 --> 00:35:55,040 Speaker 10: same quality trial outcomes. So it's still really hard to 643 00:35:55,080 --> 00:35:58,000 Speaker 10: do it, but it's now possible, and so there may 644 00:35:58,040 --> 00:36:01,359 Speaker 10: not be an incumbent, right, and those things can lend 645 00:36:01,400 --> 00:36:03,000 Speaker 10: themselves really well to startups. 646 00:36:03,160 --> 00:36:06,200 Speaker 2: All right, Inside Partners Managing Director Lonnie Jaffi, good to 647 00:36:06,239 --> 00:36:08,719 Speaker 2: catch up here on VC Spotlight Bloomberg Technology. 648 00:36:08,760 --> 00:36:19,520 Speaker 3: Thank you all right, time for going viral. Have you 649 00:36:19,560 --> 00:36:20,200 Speaker 3: seen this one? 650 00:36:20,360 --> 00:36:24,400 Speaker 2: Jeff Bezos and his new waterfront mansion in Florida. Sources 651 00:36:24,440 --> 00:36:28,320 Speaker 2: save Bezos agreed to pay about sixty eight million dollars 652 00:36:28,680 --> 00:36:32,840 Speaker 2: for the three bedroom a state dubbed the Billionaire Bunker 653 00:36:32,880 --> 00:36:35,560 Speaker 2: of Florida. The property is located in Indian Creek, a 654 00:36:35,640 --> 00:36:40,080 Speaker 2: man made barrier island, basically in the larger Miami area. 655 00:36:40,160 --> 00:36:41,960 Speaker 3: I know a lot of people that have moved to Miami. 656 00:36:42,400 --> 00:36:45,359 Speaker 2: They're not spending sixty eight million on their house hot 657 00:36:45,400 --> 00:36:48,160 Speaker 2: there right now. Meanwhile, in other news, the richest person 658 00:36:48,160 --> 00:36:51,040 Speaker 2: in the world, Elon Musk, says he spent three hours 659 00:36:51,120 --> 00:36:53,799 Speaker 2: in an MRI machine earlier this week ahead of a 660 00:36:53,920 --> 00:36:56,759 Speaker 2: proposed cage match with Metasio. 661 00:36:56,840 --> 00:36:57,560 Speaker 3: Mark Zuckerberg. 662 00:36:57,600 --> 00:36:59,920 Speaker 2: He says the scan showed there's a problem with his 663 00:37:00,320 --> 00:37:05,480 Speaker 2: right shoulder blade rubbing against his ribs, which requires minor surgery, 664 00:37:05,640 --> 00:37:09,239 Speaker 2: and that recovery is expected to last several months. We 665 00:37:09,400 --> 00:37:13,280 Speaker 2: do have no idea, frankly, whether that proposed cage match 666 00:37:13,880 --> 00:37:16,800 Speaker 2: is going to go ahead. Shifting gears pardon the pun. 667 00:37:16,880 --> 00:37:20,879 Speaker 2: Touro is taking on traditional car rental services like Avis, 668 00:37:20,920 --> 00:37:24,479 Speaker 2: budget Perts by allowing its customers not only rent cars 669 00:37:24,520 --> 00:37:27,399 Speaker 2: from others, but list their vehicles on the rental market 670 00:37:27,480 --> 00:37:29,560 Speaker 2: as well. Here's tell us about what the company is 671 00:37:29,640 --> 00:37:34,280 Speaker 2: up to, particularly with generative Ai. Andre Hardad Touro's CEO. 672 00:37:34,480 --> 00:37:35,920 Speaker 2: It's been a while since we've caught up here on 673 00:37:35,960 --> 00:37:39,680 Speaker 2: Bluebow Technology. I want to ask you first the macro 674 00:37:39,840 --> 00:37:44,120 Speaker 2: story we've seen, you know, with Airbnb, certain behaviors with 675 00:37:44,200 --> 00:37:47,960 Speaker 2: short term rentals, you know when it comes to houses. 676 00:37:48,440 --> 00:37:50,640 Speaker 2: What do you see right now in consumer behavior with 677 00:37:50,800 --> 00:37:54,000 Speaker 2: listing their vehicles on Touro. Is it because they're under 678 00:37:54,000 --> 00:37:56,520 Speaker 2: pressure financially or otherwise? 679 00:37:57,680 --> 00:38:00,279 Speaker 3: Thanks for having me the latter b here. 680 00:38:00,480 --> 00:38:05,040 Speaker 11: The picture on the community of the tour hosts that 681 00:38:05,080 --> 00:38:07,919 Speaker 11: we've got has been very, very positive over the last 682 00:38:07,960 --> 00:38:11,200 Speaker 11: couple of years. As you know, prices have gone up, 683 00:38:11,360 --> 00:38:13,560 Speaker 11: Car prices have gone up, interest rates have gone up, 684 00:38:13,800 --> 00:38:16,239 Speaker 11: and more people are looking for ways to make their 685 00:38:16,280 --> 00:38:20,200 Speaker 11: car actually make sense financially for them. So we've seen 686 00:38:20,360 --> 00:38:24,279 Speaker 11: tremendous growth in our hosting community. We've seen a lot 687 00:38:24,280 --> 00:38:27,399 Speaker 11: of people also start building businesses on top of our app. 688 00:38:27,520 --> 00:38:31,560 Speaker 11: So we've seen those multi car hosts really take hold 689 00:38:31,719 --> 00:38:34,160 Speaker 11: in the community of tour hosts, and now we're excited 690 00:38:34,200 --> 00:38:37,960 Speaker 11: to be able to have both consumer hosts and multi 691 00:38:38,000 --> 00:38:41,360 Speaker 11: car hosts. Entrepreneurs were building small fleets on top of 692 00:38:41,360 --> 00:38:41,760 Speaker 11: our app. 693 00:38:42,600 --> 00:38:46,359 Speaker 2: The story of the year has been artficial intelligence, either 694 00:38:46,400 --> 00:38:50,440 Speaker 2: generative AI tools or using a large language model to 695 00:38:50,520 --> 00:38:52,040 Speaker 2: underpin your existing technology. 696 00:38:52,080 --> 00:38:53,799 Speaker 3: What's Touro up to in the field of. 697 00:38:53,719 --> 00:38:57,359 Speaker 11: AI, Well, we've jumped into it. As you may have seen, 698 00:38:57,480 --> 00:39:01,120 Speaker 11: we launched our plugin for CHATGP. We're excited about the 699 00:39:01,160 --> 00:39:03,840 Speaker 11: initial traction that we're seeing. We think that can really 700 00:39:03,880 --> 00:39:05,719 Speaker 11: transform the way people discover. 701 00:39:05,560 --> 00:39:07,799 Speaker 3: How does it work in basic tech? 702 00:39:07,920 --> 00:39:10,640 Speaker 11: So you add the plug in to chat GPT, you 703 00:39:10,680 --> 00:39:14,160 Speaker 11: download it to you add it to your chat gypt app, 704 00:39:14,320 --> 00:39:16,680 Speaker 11: and you have the Tura plug in on it, and 705 00:39:16,719 --> 00:39:20,240 Speaker 11: then you can start navigating Touro in really fun ways. 706 00:39:20,239 --> 00:39:24,960 Speaker 11: You can actually ask to get really wonderful cars in 707 00:39:25,000 --> 00:39:27,759 Speaker 11: a particular location, and you can describe the kinds of 708 00:39:27,760 --> 00:39:30,120 Speaker 11: cars that you'd like, kind of budget that you have, 709 00:39:30,760 --> 00:39:32,960 Speaker 11: the sort of specs that you'd like, and then chat 710 00:39:33,000 --> 00:39:35,959 Speaker 11: gpt will provide you with an incredible selection from Tero. 711 00:39:36,120 --> 00:39:39,680 Speaker 2: So at its core, it's basically automation search, but at 712 00:39:39,719 --> 00:39:40,359 Speaker 2: a next level. 713 00:39:40,440 --> 00:39:43,600 Speaker 11: Yeah, instead of having the customer go in to search 714 00:39:43,680 --> 00:39:47,120 Speaker 11: and enter a lot of different criteria and filter through 715 00:39:47,160 --> 00:39:50,080 Speaker 11: makes and models and prices and all the kinds of 716 00:39:50,120 --> 00:39:52,680 Speaker 11: specs you can imagine when you're trying to find the 717 00:39:52,680 --> 00:39:56,040 Speaker 11: perfect car for your trip, you can just ask chat 718 00:39:56,040 --> 00:39:58,440 Speaker 11: gpt to do that for you and we can deliver 719 00:39:58,600 --> 00:40:00,640 Speaker 11: great selection thanks to the com the nation of our 720 00:40:00,680 --> 00:40:01,840 Speaker 11: plugin with CHADGPT. 721 00:40:02,120 --> 00:40:04,920 Speaker 2: So what's the result been for Touro has an increased 722 00:40:05,000 --> 00:40:10,200 Speaker 2: volume of traffic to the app or any actually easier matches. 723 00:40:09,960 --> 00:40:12,680 Speaker 11: It's easier matching. Yeah, we're seeing both an increase in 724 00:40:12,920 --> 00:40:16,399 Speaker 11: the matching grades as well as growth in traffic because 725 00:40:16,440 --> 00:40:19,560 Speaker 11: more and more people are discovering now Turo through chat gpt, 726 00:40:19,680 --> 00:40:21,480 Speaker 11: which is really an interesting. 727 00:40:21,080 --> 00:40:21,879 Speaker 3: Development for us. 728 00:40:22,360 --> 00:40:25,600 Speaker 2: Final quick quick one growth of Touro. Where are you 729 00:40:25,640 --> 00:40:27,960 Speaker 2: growing right now? You know, either here domes in the 730 00:40:28,040 --> 00:40:29,080 Speaker 2: US or internationally. 731 00:40:30,040 --> 00:40:33,320 Speaker 11: Actually, we're growing in all of our locations. We expanded 732 00:40:33,360 --> 00:40:36,759 Speaker 11: last year in France and Australia. Australia was our most 733 00:40:36,800 --> 00:40:39,720 Speaker 11: recent launch in November of last year. The US business 734 00:40:39,760 --> 00:40:43,279 Speaker 11: continues to grow rapidly, and you know, we're excited to 735 00:40:43,320 --> 00:40:46,800 Speaker 11: continue to expand geographically. We see turo having an opportunity 736 00:40:46,880 --> 00:40:49,000 Speaker 11: to be everywhere around the world. 737 00:40:49,440 --> 00:40:51,840 Speaker 2: All right to our CEO, Andre Hadad, it's good to 738 00:40:51,880 --> 00:40:54,719 Speaker 2: catch up back in SF on Bloomberg Technology. 739 00:40:54,760 --> 00:40:55,200 Speaker 3: That does it. 740 00:40:55,239 --> 00:40:58,879 Speaker 2: Sadly for this edition of Bloomberg Technology, what a week 741 00:40:58,920 --> 00:41:03,200 Speaker 2: it's been for earning for markets, for AI for billionaires, 742 00:41:03,280 --> 00:41:05,560 Speaker 2: so much news about billionaires. 743 00:41:05,880 --> 00:41:07,279 Speaker 3: But there's a lot more to come. 744 00:41:07,320 --> 00:41:09,080 Speaker 2: And you can always recap everything from the show on 745 00:41:09,120 --> 00:41:11,920 Speaker 2: our podcast wherever you get your podcast. We're on Apple, 746 00:41:12,080 --> 00:41:15,040 Speaker 2: We're on Spotify, and we're on iHeart, and of course we're. 747 00:41:14,840 --> 00:41:16,879 Speaker 3: On all of the Bloomberg platforms. 748 00:41:17,040 --> 00:41:19,839 Speaker 2: From here in San Francisco, Caroline Hide and New York's 749 00:41:19,840 --> 00:41:23,239 Speaker 2: coming back soon, guys. I promise this is Bloomberg Technology.