1 00:00:00,800 --> 00:00:04,040 Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney, alongside 2 00:00:04,040 --> 00:00:06,920 Speaker 1: my co host Matt Miller. Every business day, we bring 3 00:00:06,960 --> 00:00:11,520 Speaker 1: you interviews from CEOs, market pros, and Bloomberg experts, along 4 00:00:11,560 --> 00:00:15,600 Speaker 1: with essential market moving news. Find the Bloomberg Markets Podcast 5 00:00:15,600 --> 00:00:18,479 Speaker 1: on Apple Podcasts or wherever you listen to podcasts, and 6 00:00:18,480 --> 00:00:21,960 Speaker 1: at Bloomberg dot com slash podcast. All right, Matt, you'll 7 00:00:21,960 --> 00:00:25,680 Speaker 1: get this. People love dogs, especially the millennial generation. We're 8 00:00:25,680 --> 00:00:28,160 Speaker 1: Owning a pet is more common than having a kid 9 00:00:28,160 --> 00:00:30,440 Speaker 1: in any US cities. In fact, San Francisco is home 10 00:00:30,480 --> 00:00:33,080 Speaker 1: to nearly one hundred and fifty thousand dogs, but just 11 00:00:33,200 --> 00:00:35,880 Speaker 1: one hundred and fifteen thousand children. That's according to the 12 00:00:35,880 --> 00:00:38,879 Speaker 1: American Community Survey. So, uh, you know, I'm not a 13 00:00:38,920 --> 00:00:41,760 Speaker 1: pet person, but I know most people are, and it's 14 00:00:41,760 --> 00:00:45,159 Speaker 1: a big business. Uh So we're happy to welcome Aaron Easterly. 15 00:00:45,200 --> 00:00:48,200 Speaker 1: He's a CEO. But you don't have a dog, never 16 00:00:48,240 --> 00:00:50,519 Speaker 1: had a dog, never had a pet. Your poor children, 17 00:00:50,600 --> 00:00:53,319 Speaker 1: I know, I know that's trust movie and they've never 18 00:00:53,360 --> 00:00:55,720 Speaker 1: been we've never taken him to Disney. So just failure 19 00:00:55,720 --> 00:00:59,560 Speaker 1: on so many fronts, exactly, Aaron, thanks so much for 20 00:00:59,640 --> 00:01:02,320 Speaker 1: joining us forgive me for not being a pet enthusiast, 21 00:01:02,400 --> 00:01:05,640 Speaker 1: but talk to us about Rover, what your company is, 22 00:01:05,680 --> 00:01:08,360 Speaker 1: what you do, and because I know you're going public soon, 23 00:01:08,400 --> 00:01:13,600 Speaker 1: I believe Yeah, that's right. So Rover was founded on 24 00:01:13,640 --> 00:01:17,000 Speaker 1: the premise that all people should deserve the unconditional love 25 00:01:17,040 --> 00:01:19,160 Speaker 1: of a pet in their lives, whether it be dog 26 00:01:19,280 --> 00:01:21,600 Speaker 1: or cat. And we want to make it really easy 27 00:01:21,680 --> 00:01:24,839 Speaker 1: for people to go about their busy, modern hectic lives 28 00:01:24,880 --> 00:01:27,200 Speaker 1: and half pets in their lives. So we started a 29 00:01:27,760 --> 00:01:30,800 Speaker 1: peer to peer marketplace where people can find pet sitters 30 00:01:30,840 --> 00:01:34,039 Speaker 1: and dog walkers, I mean, anything else they're looking for 31 00:01:34,040 --> 00:01:37,920 Speaker 1: for the pet care. So how did how did your 32 00:01:37,959 --> 00:01:45,880 Speaker 1: business get impacted by the pandemic? UM Well tailed two 33 00:01:46,560 --> 00:01:49,400 Speaker 1: parts of the pandemic. The first part was was brutal. 34 00:01:50,160 --> 00:01:53,320 Speaker 1: People stayed indoors, that they didn't go into work, and 35 00:01:53,320 --> 00:01:56,960 Speaker 1: they traveled a lot less. And we're largely a travel 36 00:01:57,000 --> 00:01:59,160 Speaker 1: related business of what to do with your pets when 37 00:01:59,160 --> 00:02:01,280 Speaker 1: you leave town? Sometimes let's to do with your pets 38 00:02:01,280 --> 00:02:03,520 Speaker 1: when you go into work. So our business was hit 39 00:02:03,560 --> 00:02:07,520 Speaker 1: pretty hard. But during that same period of time, um 40 00:02:07,680 --> 00:02:11,720 Speaker 1: the adoption rates of pets of the annual growth rate 41 00:02:11,720 --> 00:02:15,320 Speaker 1: in that roughly quadruple um. So everyone during the pandemic 42 00:02:15,560 --> 00:02:18,280 Speaker 1: went out and got a dog or a cat, and 43 00:02:18,320 --> 00:02:21,480 Speaker 1: so it actually increased our addressable market and we gained 44 00:02:21,520 --> 00:02:24,639 Speaker 1: material share. I think at the beginning of the pandemic 45 00:02:24,720 --> 00:02:26,679 Speaker 1: we were about six and a half times the next 46 00:02:26,680 --> 00:02:28,720 Speaker 1: biggest player if you look at the third party credit 47 00:02:28,720 --> 00:02:32,640 Speaker 1: card data sales data um when we started our go 48 00:02:32,800 --> 00:02:36,280 Speaker 1: public vias back process, or roughly ten times bigger. And 49 00:02:36,320 --> 00:02:38,200 Speaker 1: if you look at the most recent data or something 50 00:02:38,240 --> 00:02:42,160 Speaker 1: about sixteen or seventeen times bigger. UM. So weirdly, the 51 00:02:42,200 --> 00:02:45,280 Speaker 1: pandemic heard us in the short term, but actually expanded 52 00:02:45,280 --> 00:02:49,000 Speaker 1: our addressable market, caused us to gain market share, and 53 00:02:49,080 --> 00:02:51,639 Speaker 1: we've boom coming out of it, saying records the last 54 00:02:51,680 --> 00:02:56,440 Speaker 1: several months. So, uh, most people know my dog Steve 55 00:02:56,680 --> 00:02:59,960 Speaker 1: is not just my pet. He's my very best friend 56 00:03:00,000 --> 00:03:04,359 Speaker 1: and and uh he's you know, been all over with 57 00:03:04,400 --> 00:03:07,880 Speaker 1: me from New York to Bronxville. We live in Berlin now. 58 00:03:07,919 --> 00:03:10,200 Speaker 1: He flew We got him a business class seed. He 59 00:03:10,240 --> 00:03:14,279 Speaker 1: flew with me on the plane, did we really Yeah yeah, Um, 60 00:03:14,360 --> 00:03:17,840 Speaker 1: he had the kosher meal, and uh he didn't drink 61 00:03:17,880 --> 00:03:20,120 Speaker 1: any beers because you know, you had to hold it 62 00:03:20,160 --> 00:03:23,440 Speaker 1: for eight hours. But for for me, the hardest thing 63 00:03:23,800 --> 00:03:27,600 Speaker 1: is getting these services, finding a dog walker when I 64 00:03:27,639 --> 00:03:32,440 Speaker 1: was single, UM or finding someone to watch Steve when 65 00:03:32,440 --> 00:03:37,280 Speaker 1: I was going places that didn't accept pets, and uh, 66 00:03:37,440 --> 00:03:40,520 Speaker 1: I've always had to rely on a mishmash of different 67 00:03:40,680 --> 00:03:45,800 Speaker 1: services UM and different platforms to find this. UM. I 68 00:03:45,840 --> 00:03:49,400 Speaker 1: can understand your popularity, but I wonder if you know 69 00:03:49,440 --> 00:03:52,760 Speaker 1: the popularity of pets at this point is at a 70 00:03:52,880 --> 00:03:55,920 Speaker 1: peak because of the pandemic. Arin do you think that 71 00:03:55,960 --> 00:03:59,280 Speaker 1: we're gonna see, now you know, the need to own 72 00:03:59,280 --> 00:04:03,720 Speaker 1: a dog kind of decline? Well, I think that if 73 00:04:03,760 --> 00:04:06,080 Speaker 1: you look at the pet ownership breaks in the U S, 74 00:04:06,120 --> 00:04:09,240 Speaker 1: they's continued to go up even before the pandemic, and 75 00:04:09,280 --> 00:04:13,280 Speaker 1: that's a multidecade trend, and the spend per pet continues 76 00:04:13,320 --> 00:04:17,760 Speaker 1: to go up. UM. I don't think that coming out 77 00:04:17,760 --> 00:04:19,960 Speaker 1: of the pandemic is going to reverse that. In a 78 00:04:20,080 --> 00:04:22,839 Speaker 1: lot of ways, I think having a dog is similar 79 00:04:22,880 --> 00:04:26,240 Speaker 1: to the emotional relationships sometimes people have with kids, which 80 00:04:26,279 --> 00:04:28,320 Speaker 1: is like, once you're in your life, you can't imagine 81 00:04:28,320 --> 00:04:31,200 Speaker 1: your life without them, And so I expect that will 82 00:04:31,240 --> 00:04:35,280 Speaker 1: be pretty similar that most people once they have their 83 00:04:35,360 --> 00:04:37,800 Speaker 1: furry ball of joy around them will be tough to 84 00:04:37,800 --> 00:04:40,640 Speaker 1: imagine life without them. All right, again, I'm not a 85 00:04:40,680 --> 00:04:43,320 Speaker 1: pet person. I'm an financial analyst. By the way, someone 86 00:04:43,360 --> 00:04:45,840 Speaker 1: writes in and says, I didn't realize Paul hated American 87 00:04:45,920 --> 00:04:51,359 Speaker 1: values dogs in Disney in Disney um, But I have 88 00:04:51,440 --> 00:04:54,400 Speaker 1: a financial analystis stock analy So talk to me about 89 00:04:54,400 --> 00:04:57,160 Speaker 1: your business model. How do you make money in the 90 00:04:57,160 --> 00:05:01,800 Speaker 1: pet business? Sure? So we take a percentage of the 91 00:05:01,920 --> 00:05:07,560 Speaker 1: transactions going through rover Um, typically a small percentage in 92 00:05:07,640 --> 00:05:12,520 Speaker 1: the range of on average um and across all of 93 00:05:12,520 --> 00:05:15,920 Speaker 1: our services is a small percentage. I want to be 94 00:05:16,000 --> 00:05:19,520 Speaker 1: in your business. Can you imagine if your stockbrokers that 95 00:05:19,600 --> 00:05:26,599 Speaker 1: I'll take of every trade? Well, small, I'll say, a 96 00:05:26,640 --> 00:05:29,000 Speaker 1: minority of the dollars going through How about that? All right? 97 00:05:29,040 --> 00:05:30,640 Speaker 1: So give us some some of the metrics. Is that 98 00:05:30,720 --> 00:05:32,560 Speaker 1: a perchase? I mean, well, what are the metrics that 99 00:05:32,600 --> 00:05:36,800 Speaker 1: really drive your business? Yeah? The biggest thing for our 100 00:05:36,839 --> 00:05:40,600 Speaker 1: business is the rate of repeats. The business is typically 101 00:05:40,680 --> 00:05:44,440 Speaker 1: between eight and repeat business in a given month. We 102 00:05:44,480 --> 00:05:46,919 Speaker 1: don't have to do a lot to drive repeat business. 103 00:05:46,960 --> 00:05:51,600 Speaker 1: We have incredible loyalty, so that generates really strong Uh, 104 00:05:51,640 --> 00:05:55,880 Speaker 1: incremental cash flow dynamics, a new customer acquisition and the 105 00:05:56,000 --> 00:05:58,320 Speaker 1: rate of repeat are the big drivers of our business 106 00:05:59,160 --> 00:06:02,279 Speaker 1: UM and then average order value UM. So most people 107 00:06:02,320 --> 00:06:04,360 Speaker 1: when you travel out of town maybe go for three 108 00:06:04,400 --> 00:06:06,720 Speaker 1: or four night time average at the mix of some 109 00:06:06,800 --> 00:06:09,880 Speaker 1: long trips and some small trips UM. And on average 110 00:06:10,480 --> 00:06:14,039 Speaker 1: overnight cares you know, roughly thirty to thirty five dollars 111 00:06:14,480 --> 00:06:18,560 Speaker 1: daytime care like in home daycare and dog walking is 112 00:06:18,600 --> 00:06:22,400 Speaker 1: about twenty Is there any plans, I mean for expansion. 113 00:06:22,440 --> 00:06:26,599 Speaker 1: I could think of some markets that need a platform 114 00:06:26,720 --> 00:06:29,280 Speaker 1: like this. For example, UM, you know, if you want 115 00:06:29,279 --> 00:06:31,359 Speaker 1: to adopt a dog, pet Finder kind of owns that 116 00:06:31,400 --> 00:06:35,280 Speaker 1: market in America. But if you want to um find 117 00:06:35,520 --> 00:06:40,159 Speaker 1: a breeder, it's really difficult to uh sort through all 118 00:06:40,200 --> 00:06:43,280 Speaker 1: the web pages and understand who's reputable and who's not. 119 00:06:43,480 --> 00:06:45,680 Speaker 1: I mean, it seems like there's a lot to be 120 00:06:45,760 --> 00:06:50,760 Speaker 1: done in this space. And obviously pets is a um 121 00:06:51,200 --> 00:06:54,520 Speaker 1: an industry in which people are just willing to dump cash. 122 00:06:54,600 --> 00:07:00,719 Speaker 1: I mean, I will pay whatever it costs for Steve. Yeah. Uh, 123 00:07:00,880 --> 00:07:03,840 Speaker 1: same with mine. That dog London UM lover to death 124 00:07:03,920 --> 00:07:06,560 Speaker 1: and she owns me. Yeah. One of the neat things 125 00:07:06,560 --> 00:07:10,360 Speaker 1: about our business is that there are actually relatively few 126 00:07:10,720 --> 00:07:14,520 Speaker 1: scale tech companies in the pet space. The pet industry 127 00:07:14,560 --> 00:07:17,600 Speaker 1: was associated with dot com access about twenty years ago, 128 00:07:17,720 --> 00:07:21,080 Speaker 1: so suffered from uh, not as much investment as it 129 00:07:21,080 --> 00:07:24,120 Speaker 1: could have had. And so when you think about in 130 00:07:24,160 --> 00:07:29,320 Speaker 1: the US tech companies that have a direct digital relationship 131 00:07:29,360 --> 00:07:33,200 Speaker 1: with pet owners in the seven figure range, there's basically 132 00:07:33,280 --> 00:07:36,320 Speaker 1: two e Amazon and Rover, and we think that puts 133 00:07:36,400 --> 00:07:39,080 Speaker 1: us in a really nice position to continue to expand 134 00:07:39,120 --> 00:07:42,640 Speaker 1: our offerings. When we started, we had just two offerings. 135 00:07:42,680 --> 00:07:45,800 Speaker 1: Both were overnight care boarding, which as you take your 136 00:07:45,800 --> 00:07:48,120 Speaker 1: dog to someone else's home and house sitting someone comes 137 00:07:48,120 --> 00:07:50,760 Speaker 1: to your home. And when we rolled out our daytime 138 00:07:50,800 --> 00:07:57,200 Speaker 1: services dog walking, drop and visit in home daycare. Aaron Easterly, 139 00:07:57,280 --> 00:08:03,520 Speaker 1: CEO of Rover, thanks very much. All right, let's turn 140 00:08:03,640 --> 00:08:07,640 Speaker 1: to cyber security. Let's turn to ransomware. We've seen a 141 00:08:07,640 --> 00:08:10,760 Speaker 1: lot of ransomware stories this year. Seems like they're running, 142 00:08:11,120 --> 00:08:15,000 Speaker 1: you know, much more frequently than we've seen in the past. Yeah, exactly, 143 00:08:15,040 --> 00:08:18,520 Speaker 1: even Microsoft, right, which is like if they can't defend 144 00:08:18,680 --> 00:08:21,440 Speaker 1: against the cyber attack, and who can It's exactly. It's 145 00:08:21,480 --> 00:08:23,800 Speaker 1: one thing for like the Paramount Film Studio to get hacked. 146 00:08:23,800 --> 00:08:28,000 Speaker 1: It's another thing for Microsoft. Patrick, I'm co founder of Ventures, 147 00:08:28,200 --> 00:08:30,840 Speaker 1: former head of trust and security at Dropbox and the 148 00:08:30,960 --> 00:08:34,280 Speaker 1: senior VP, chief Trust Officer of Salesforce joins this Patrick, 149 00:08:34,320 --> 00:08:36,840 Speaker 1: thanks so much for joining us. Is it my imagination 150 00:08:37,080 --> 00:08:41,679 Speaker 1: or are we seeing more ransomware stories on corporate America 151 00:08:41,720 --> 00:08:47,960 Speaker 1: and global uh, you know, corporations. It's not your mantros imagination. 152 00:08:48,040 --> 00:08:50,840 Speaker 1: Were absolutely seeing more on this, and I would say 153 00:08:50,880 --> 00:08:53,800 Speaker 1: a lot of it is tied to just the plicans 154 00:08:53,840 --> 00:08:55,840 Speaker 1: of the business model. It used to be if you 155 00:08:55,880 --> 00:08:58,599 Speaker 1: were cyber criminal in the past and you steal information, 156 00:08:59,200 --> 00:09:00,800 Speaker 1: you have to find them a good place and sell 157 00:09:00,880 --> 00:09:04,440 Speaker 1: instimate money. But nowadays are the ransomware. It's very simple. 158 00:09:04,600 --> 00:09:07,840 Speaker 1: Now you point and click and crypt and you can 159 00:09:07,920 --> 00:09:11,200 Speaker 1: go directly demonetizing by using cryptocurrency. We you know, we 160 00:09:11,280 --> 00:09:16,680 Speaker 1: really didn't have ransomware before cryptocurrency became a thing. Although 161 00:09:17,440 --> 00:09:20,959 Speaker 1: I mean, you can't really hide, um where the money 162 00:09:21,040 --> 00:09:24,480 Speaker 1: is going with cryptocurrency. Everyone can see on the public ledger, 163 00:09:25,000 --> 00:09:28,199 Speaker 1: so it seems pretty dumb in a way too. But 164 00:09:28,240 --> 00:09:30,000 Speaker 1: it was supposed to be you can't trace it, but 165 00:09:30,040 --> 00:09:32,080 Speaker 1: that was never the case. You literally can trace it 166 00:09:32,520 --> 00:09:34,760 Speaker 1: better than anything else in the world. I mean, it's 167 00:09:34,800 --> 00:09:40,599 Speaker 1: like the most traceable there. There is transparency on the transactions, 168 00:09:40,640 --> 00:09:43,040 Speaker 1: and there are companies that look at tracing it, but 169 00:09:43,160 --> 00:09:46,040 Speaker 1: the wallet owners are not known, and there are third 170 00:09:46,080 --> 00:09:49,720 Speaker 1: party services called tumblers that move it through many wallets, 171 00:09:49,800 --> 00:09:53,280 Speaker 1: rapidly splitting transactions to try to anonymize them. So true, 172 00:09:53,320 --> 00:09:55,880 Speaker 1: it's like shredding a paper, but you can just if 173 00:09:55,880 --> 00:09:58,840 Speaker 1: you want to spend the time and assume, assuming the 174 00:09:58,880 --> 00:10:02,240 Speaker 1: CIA and the f I have you know, hired enough 175 00:10:02,280 --> 00:10:05,880 Speaker 1: employees you or or or you if you want to 176 00:10:05,920 --> 00:10:09,120 Speaker 1: write a program, you know, Um, it just takes another 177 00:10:09,160 --> 00:10:11,319 Speaker 1: couple of seconds to do it. It's not I mean, 178 00:10:11,840 --> 00:10:15,439 Speaker 1: it is the most easily traceable thing in the world. 179 00:10:15,520 --> 00:10:22,640 Speaker 1: So clearly, cyber uh, cyber security isn't threatened by crypto, right, 180 00:10:22,800 --> 00:10:25,760 Speaker 1: it's it's that these hackers have figured out a way 181 00:10:25,800 --> 00:10:28,680 Speaker 1: to get in. Why can they even get into Microsoft? 182 00:10:28,720 --> 00:10:32,079 Speaker 1: I mean you were running security at drop Box, You're 183 00:10:32,120 --> 00:10:36,400 Speaker 1: in the in the tech world. Um at Salesforce. Is 184 00:10:36,400 --> 00:10:42,600 Speaker 1: there anyone who's unhappable? Nobody is unhappable. Uh. Yeah, At 185 00:10:42,600 --> 00:10:44,319 Speaker 1: the end of the day, it's a game of statistics. 186 00:10:44,840 --> 00:10:47,040 Speaker 1: I hate to say the larger the company is, the 187 00:10:47,080 --> 00:10:50,080 Speaker 1: more likely it is that you will find that one 188 00:10:50,120 --> 00:10:52,480 Speaker 1: mistake that was made. If all you're looking for is 189 00:10:52,520 --> 00:10:55,199 Speaker 1: one mistake, is the bad guy to find that entry point. 190 00:10:55,480 --> 00:10:58,599 Speaker 1: Once you have an entry point, you start exploring extracting 191 00:10:58,600 --> 00:11:02,920 Speaker 1: information what's called loving laterally, which is broadening your access 192 00:11:02,960 --> 00:11:06,200 Speaker 1: across the network to get into more systems until you 193 00:11:06,280 --> 00:11:08,679 Speaker 1: have enough critical access that you do what you need 194 00:11:08,720 --> 00:11:11,760 Speaker 1: to do. So it's an iterative process. It takes some time, 195 00:11:12,040 --> 00:11:15,360 Speaker 1: but frankly, I think you know, even decades ago, you 196 00:11:15,400 --> 00:11:17,960 Speaker 1: know it's it became common knowledge, at least among the 197 00:11:18,040 --> 00:11:21,280 Speaker 1: cyber compassentity that there is no such thing as being 198 00:11:21,360 --> 00:11:26,000 Speaker 1: perfectly secure. It's all about risk management and prioritization. What 199 00:11:26,640 --> 00:11:30,080 Speaker 1: do you think corporations should do if it's, you know, 200 00:11:30,360 --> 00:11:33,200 Speaker 1: if you really can't prevent a hack? What should be 201 00:11:33,200 --> 00:11:36,880 Speaker 1: the strategy for some of these corporations As I think 202 00:11:36,880 --> 00:11:40,760 Speaker 1: about kind of trying to make more their systems more secure, 203 00:11:42,760 --> 00:11:45,680 Speaker 1: it's I would categorize them into two things. Number one 204 00:11:46,000 --> 00:11:50,080 Speaker 1: is early detection and the ability to do something about it. 205 00:11:50,080 --> 00:11:52,520 Speaker 1: When you see an early indication of a breach coming 206 00:11:52,559 --> 00:11:55,760 Speaker 1: in UM, somebody has gotten into a system, you need 207 00:11:55,800 --> 00:11:58,480 Speaker 1: to have a timely response for that after you detected, 208 00:11:58,520 --> 00:12:01,199 Speaker 1: and detection, by the way, is very difficult at scale. 209 00:12:01,960 --> 00:12:04,559 Speaker 1: My advice is for the vast majority of companies that 210 00:12:04,679 --> 00:12:09,199 Speaker 1: can't build twenty four seven in house monitoring UM systems 211 00:12:09,360 --> 00:12:12,920 Speaker 1: and teams basically outsourced the detection part. You want somebody 212 00:12:12,920 --> 00:12:17,160 Speaker 1: who's watching your network seven who can have early indicators 213 00:12:17,160 --> 00:12:19,880 Speaker 1: that something is going wrong and block it at that 214 00:12:19,920 --> 00:12:22,360 Speaker 1: point in time. The second thing is, you know, let's 215 00:12:22,360 --> 00:12:25,280 Speaker 1: assume that doesn't happen. You need a plan for resilience. So, 216 00:12:25,600 --> 00:12:28,520 Speaker 1: you know, let's assume they've gotten in, they moved laterally, 217 00:12:28,600 --> 00:12:31,679 Speaker 1: they've encrypted a bunch of information or whatnot. You need 218 00:12:31,720 --> 00:12:34,880 Speaker 1: to have the ability to continue your business, and that 219 00:12:34,960 --> 00:12:38,920 Speaker 1: means being able to recover your information UM. A lot 220 00:12:38,960 --> 00:12:41,240 Speaker 1: of the advice I give folks given folks even when 221 00:12:41,280 --> 00:12:43,160 Speaker 1: there was a drop box, is just store things in 222 00:12:43,200 --> 00:12:45,960 Speaker 1: like a cloud based service like a drop box or 223 00:12:46,000 --> 00:12:50,560 Speaker 1: a Google Drive or Microsoft share Point, and you know, 224 00:12:50,600 --> 00:12:53,040 Speaker 1: they can decrypt the or they can encrypt and steal 225 00:12:53,040 --> 00:12:55,160 Speaker 1: the local copy, but they won't be able to get 226 00:12:55,160 --> 00:12:56,880 Speaker 1: to the cloud back at the stair at least not 227 00:12:57,080 --> 00:13:00,400 Speaker 1: very effectively, So that's one element, but of BEE much 228 00:13:00,440 --> 00:13:03,880 Speaker 1: more sophisticated than that. It's really building scenarios and testing 229 00:13:03,920 --> 00:13:06,760 Speaker 1: against them to make sure that you anticipate that something 230 00:13:06,760 --> 00:13:08,800 Speaker 1: like this is going to happen to your business and 231 00:13:08,800 --> 00:13:11,920 Speaker 1: that you can continue operations. What are you investing in? 232 00:13:12,200 --> 00:13:15,000 Speaker 1: You you're starting a v VC fund, What are you 233 00:13:15,000 --> 00:13:20,480 Speaker 1: looking for? We're looking very broadly. You know, my partners 234 00:13:20,520 --> 00:13:23,360 Speaker 1: J and I started send Ventures and March efficially we 235 00:13:23,480 --> 00:13:26,560 Speaker 1: raised the twinter million dollar funds, been actively investing, made 236 00:13:26,559 --> 00:13:31,240 Speaker 1: for investments already. Um, honestly, you know, we we differentiate 237 00:13:31,280 --> 00:13:37,080 Speaker 1: because we're former practitioners. We've been running cybersecurity serve functions 238 00:13:37,120 --> 00:13:40,280 Speaker 1: inside of large enterprises for twenty some one years and 239 00:13:40,400 --> 00:13:42,600 Speaker 1: that gives a slightly different perspective. So you know, what 240 00:13:42,640 --> 00:13:44,600 Speaker 1: we invest in is quite frankly, you know, when we 241 00:13:44,640 --> 00:13:48,640 Speaker 1: look at it from a CAZO security leaders perspective, we're 242 00:13:48,640 --> 00:13:51,080 Speaker 1: looking stuff and say, yeah, this would have made our 243 00:13:51,160 --> 00:13:53,960 Speaker 1: jobs much easier. This is this is a company I 244 00:13:53,960 --> 00:13:56,679 Speaker 1: would have bought as a customer in the past, So 245 00:13:56,840 --> 00:14:02,000 Speaker 1: we're looking through the perspective of buyers ones and obviously 246 00:14:02,160 --> 00:14:05,120 Speaker 1: certain things like ransomware, super interested, but it's a much 247 00:14:05,120 --> 00:14:07,800 Speaker 1: broader landscape that we look at really from the lens 248 00:14:07,800 --> 00:14:10,240 Speaker 1: of people who have been practitioners in space for quite 249 00:14:10,240 --> 00:14:12,520 Speaker 1: a long time. All Right, Patrick, thanks very much for 250 00:14:12,640 --> 00:14:16,520 Speaker 1: joining us. Totally fascinating, obviously totally fascinating topic. Great to 251 00:14:16,520 --> 00:14:19,760 Speaker 1: hear from Patrick him their co founder of sin Ventures. 252 00:14:22,320 --> 00:14:26,680 Speaker 1: Now let's talk about investing in Uh well, I guess 253 00:14:26,800 --> 00:14:32,560 Speaker 1: anti climate change. Investing to stop climate change, I guess 254 00:14:32,600 --> 00:14:35,240 Speaker 1: is the way to put it. Gabriella Herculano, there's a 255 00:14:35,320 --> 00:14:38,600 Speaker 1: CEO of i Clama, and she joins us um with 256 00:14:38,720 --> 00:14:41,120 Speaker 1: her insights on this and got me. The interesting thing 257 00:14:41,200 --> 00:14:44,760 Speaker 1: is it's not so easy to understand how to do 258 00:14:44,800 --> 00:14:47,800 Speaker 1: this correctly, right, because if you want to raise the 259 00:14:47,880 --> 00:14:52,040 Speaker 1: capital costs of those people or companies who emit too 260 00:14:52,160 --> 00:14:55,880 Speaker 1: much carbon, you also end up raising their returns thereby 261 00:14:55,920 --> 00:14:59,280 Speaker 1: giving yourself lower returns. Or I guess if you want 262 00:14:59,320 --> 00:15:01,160 Speaker 1: to say, I want to take a bet that the 263 00:15:01,240 --> 00:15:04,880 Speaker 1: government is going to change regulations so that these you know, 264 00:15:04,960 --> 00:15:08,480 Speaker 1: carbon neutral investments are going to pay off later, that's 265 00:15:08,520 --> 00:15:11,320 Speaker 1: also not really moving the needle yourself. So what do 266 00:15:11,360 --> 00:15:15,240 Speaker 1: you do? Well? Um, Well, first of all, thank you 267 00:15:15,280 --> 00:15:18,080 Speaker 1: so much. Format for having me. What we do is 268 00:15:18,120 --> 00:15:23,200 Speaker 1: we look at the companies that can can really transition us, 269 00:15:23,680 --> 00:15:26,680 Speaker 1: move us away from business as usual based on solutions 270 00:15:26,720 --> 00:15:32,200 Speaker 1: that are um high on carbon footprint. The the motivation 271 00:15:32,240 --> 00:15:34,960 Speaker 1: behind all that we do is the idea that the 272 00:15:35,000 --> 00:15:37,920 Speaker 1: best way to reduce the carbon in the atmosphere is 273 00:15:37,960 --> 00:15:40,480 Speaker 1: by not a meeting in the first place. So what 274 00:15:40,520 --> 00:15:44,520 Speaker 1: are these solutions and how do we provide investors with 275 00:15:44,600 --> 00:15:47,840 Speaker 1: a direct exposure to that. That's pretty much what we've done. 276 00:15:47,880 --> 00:15:52,400 Speaker 1: It took us almost two years to put this product together. Um, 277 00:15:52,760 --> 00:15:57,080 Speaker 1: we think that what we've done is provide exposure to 278 00:15:57,080 --> 00:16:01,200 Speaker 1: a very comprehensive set of relevance visions. And we have 279 00:16:01,280 --> 00:16:05,280 Speaker 1: a tangible metric that allows us to determine and ascertain 280 00:16:05,400 --> 00:16:09,800 Speaker 1: and quantify that relevance, which is potential avoided emissions in 281 00:16:09,920 --> 00:16:14,160 Speaker 1: diggatons of field two equivalent per year. So that's a mouthful, 282 00:16:14,240 --> 00:16:17,480 Speaker 1: but it's that idea that there is a data between 283 00:16:17,920 --> 00:16:20,720 Speaker 1: the emissions that come from you driving from point A 284 00:16:20,880 --> 00:16:24,560 Speaker 1: to point B an internal combustion engine and taking an 285 00:16:24,560 --> 00:16:28,800 Speaker 1: electric vehicle tesla right for for that same need, and 286 00:16:28,880 --> 00:16:31,840 Speaker 1: that delta is the avoidance and that's where the world 287 00:16:31,920 --> 00:16:34,800 Speaker 1: needs to go towards we need to go UM to 288 00:16:34,840 --> 00:16:40,120 Speaker 1: satisfy our needs different UH needs for electricity and needs 289 00:16:40,360 --> 00:16:46,080 Speaker 1: for transportation, for food, UM, all of those relevant ways 290 00:16:46,160 --> 00:16:51,200 Speaker 1: in terms of mitigating climate change. So, Gabby, one of 291 00:16:51,240 --> 00:16:53,960 Speaker 1: the issues that we've heard from E s G investors 292 00:16:54,040 --> 00:16:57,400 Speaker 1: is that you know, the amount available data to make 293 00:16:57,720 --> 00:17:01,240 Speaker 1: informed decisions just really isn't that good. I mean, you know, 294 00:17:01,360 --> 00:17:04,280 Speaker 1: for typical financial analysis, you want the income statement, to 295 00:17:04,320 --> 00:17:06,280 Speaker 1: balance sheet, the cash flow statement, but for E s 296 00:17:06,359 --> 00:17:08,560 Speaker 1: G you need a whole bunch of other metrics and 297 00:17:09,000 --> 00:17:11,720 Speaker 1: they're not really out there or they're not consistent. How 298 00:17:11,760 --> 00:17:15,440 Speaker 1: do you guys deal with that? Well, we we took 299 00:17:15,920 --> 00:17:18,639 Speaker 1: manage in our own hands. We we we gather the 300 00:17:18,680 --> 00:17:22,040 Speaker 1: information ourselves. Where a London day is the ginstant tact 301 00:17:22,480 --> 00:17:26,200 Speaker 1: and in Europe we have what is called EU taxonomy, 302 00:17:26,240 --> 00:17:29,760 Speaker 1: the companies whom have to report what is deemed to 303 00:17:29,800 --> 00:17:34,120 Speaker 1: be products and services that are positive in the impact 304 00:17:34,160 --> 00:17:38,439 Speaker 1: in terms of environmental um changes and benefits. So that 305 00:17:38,720 --> 00:17:42,120 Speaker 1: very long list of what these products and services are 306 00:17:42,520 --> 00:17:46,520 Speaker 1: was a very important UM guideline for us. And what 307 00:17:46,600 --> 00:17:49,720 Speaker 1: we did was we we we have we developed our 308 00:17:49,760 --> 00:17:53,959 Speaker 1: own methodology because there was no, nothing remotely close to 309 00:17:54,000 --> 00:17:56,639 Speaker 1: what we wanted to do in the marketplace. So we 310 00:17:56,800 --> 00:18:00,439 Speaker 1: vertically integrate. We created our own equity benchmarks. So we 311 00:18:00,520 --> 00:18:05,320 Speaker 1: triangulated these these UH finding the data on what is 312 00:18:05,359 --> 00:18:08,760 Speaker 1: green revenue and what is also brown revenue, and surprisingly 313 00:18:08,800 --> 00:18:11,600 Speaker 1: that data is also not there. Investors that want to 314 00:18:11,680 --> 00:18:15,400 Speaker 1: negatively screen struggle to get that information. So we looked 315 00:18:15,400 --> 00:18:17,920 Speaker 1: at the universe that is in line with these products 316 00:18:17,960 --> 00:18:20,639 Speaker 1: and services that move us away from the business as 317 00:18:20,720 --> 00:18:23,879 Speaker 1: usual right like we talked about. So we quantified the 318 00:18:23,920 --> 00:18:26,720 Speaker 1: green revenue and the brown revenue, and then we we 319 00:18:27,040 --> 00:18:32,040 Speaker 1: quantified and estimated the potential avoided emissions, which is another 320 00:18:32,160 --> 00:18:36,520 Speaker 1: very relevant UM influence for us is the framework by 321 00:18:36,600 --> 00:18:41,520 Speaker 1: mission innovation and what constitutes UH that delta, that potential 322 00:18:41,520 --> 00:18:44,359 Speaker 1: avoiding inities. So we quantify. That's why it took us 323 00:18:44,400 --> 00:18:48,440 Speaker 1: two years because we quantified. We went through all the filings, 324 00:18:48,720 --> 00:18:51,560 Speaker 1: all the public information for each of the companies are 325 00:18:51,560 --> 00:18:54,359 Speaker 1: it's about hundred and sixty nins in our universe, and 326 00:18:54,400 --> 00:18:57,240 Speaker 1: we quantify that information for each one of them. What's 327 00:18:57,240 --> 00:18:58,880 Speaker 1: the name of the e t F you're launching later 328 00:18:58,920 --> 00:19:05,359 Speaker 1: this month? UM. We we launched last Wednesday, UM launched too. Yeah, yeah, 329 00:19:05,520 --> 00:19:08,359 Speaker 1: so launching one on New York Stockaching is great. Launching 330 00:19:08,400 --> 00:19:11,439 Speaker 1: too is fantastic. UM it's called i climb a Global 331 00:19:11,520 --> 00:19:17,560 Speaker 1: Decarbonization Transition Leaders and the iclimbate Distributed Renewable Energy Transition Leaders, 332 00:19:17,560 --> 00:19:20,439 Speaker 1: which tells what we think is the most exciting story 333 00:19:20,560 --> 00:19:24,520 Speaker 1: within clean energy space, which is the decentralization of our 334 00:19:24,560 --> 00:19:28,359 Speaker 1: power systems. So we launched both least Wednesday. All right, 335 00:19:28,480 --> 00:19:30,840 Speaker 1: all right, we'll pay attention to those certainly going forward, 336 00:19:30,880 --> 00:19:34,240 Speaker 1: and certainly is G investing is a growing, growing part 337 00:19:34,240 --> 00:19:37,000 Speaker 1: of this marketplace, a lot of investor interest. Gabriella Herculano, 338 00:19:37,119 --> 00:19:40,240 Speaker 1: CEO of I Climate, joining us again talking about E 339 00:19:40,400 --> 00:19:42,840 Speaker 1: s G investing, and as I said, a lot of 340 00:19:42,880 --> 00:19:46,520 Speaker 1: folks are really interested in this UM type of investing, 341 00:19:46,560 --> 00:19:49,640 Speaker 1: and they're really pressing the companies that they own, whether 342 00:19:49,680 --> 00:19:52,320 Speaker 1: it's in their mutual funds and their et s, about 343 00:19:52,560 --> 00:19:56,240 Speaker 1: those companies UH efforts on E s G and again 344 00:19:56,280 --> 00:19:58,840 Speaker 1: there's lots of metrics, lots of grades. Even on the 345 00:19:58,840 --> 00:20:02,080 Speaker 1: Bloomberg terminal under the f A function where you have 346 00:20:02,080 --> 00:20:05,120 Speaker 1: financial analysis, you have income statement balanchee, cash flow statements, 347 00:20:05,160 --> 00:20:07,840 Speaker 1: all that kind of good stuff. Uh and bloombernk also 348 00:20:07,920 --> 00:20:10,800 Speaker 1: in that function has E s G data, so we're 349 00:20:10,840 --> 00:20:17,399 Speaker 1: a big part of that data process right there. You know, 350 00:20:17,440 --> 00:20:21,040 Speaker 1: I'm looking at Tesla t s l a U S 351 00:20:21,080 --> 00:20:23,199 Speaker 1: Equity and then I hit b Q to get that 352 00:20:23,240 --> 00:20:26,119 Speaker 1: Bloomberg quote. It's my favorite quote on the terminal for 353 00:20:26,240 --> 00:20:29,040 Speaker 1: security because it gives you just a snapshot of everything. 354 00:20:29,480 --> 00:20:31,199 Speaker 1: And I'm looking at Tessa right here, and it's up 355 00:20:32,320 --> 00:20:34,760 Speaker 1: on a trailing twelve month basis, but it's down more 356 00:20:34,800 --> 00:20:36,959 Speaker 1: than six percent here on a year to day basis. 357 00:20:36,960 --> 00:20:38,840 Speaker 1: So it feels to me like a stock that's looking 358 00:20:38,880 --> 00:20:42,040 Speaker 1: for a catalyst for that next move. And they we 359 00:20:42,160 --> 00:20:44,200 Speaker 1: have earnings for the company and for the close, and 360 00:20:44,240 --> 00:20:47,080 Speaker 1: so let's get a preview and there's no one better 361 00:20:47,240 --> 00:20:49,720 Speaker 1: than Dan Ives for that preview. Dan Ives as a 362 00:20:49,760 --> 00:20:52,679 Speaker 1: managing director Equity Research at web Bush Securities. He is 363 00:20:52,720 --> 00:20:56,360 Speaker 1: a proud alumnus of the Penn State University. Matt so 364 00:20:56,720 --> 00:20:59,800 Speaker 1: goes goes against your buck eyes every year. Dan, thanks 365 00:20:59,840 --> 00:21:02,520 Speaker 1: for joining us here. Again, I'm looking at the stock, 366 00:21:02,960 --> 00:21:05,240 Speaker 1: you know, kind of not doing much this year. What 367 00:21:05,359 --> 00:21:08,280 Speaker 1: do you think is the next catalyst for this name? 368 00:21:08,280 --> 00:21:12,240 Speaker 1: And will that catalyst come after the close tonight. Yeah, 369 00:21:12,280 --> 00:21:15,000 Speaker 1: I mean it's really been about the China story because 370 00:21:15,080 --> 00:21:18,240 Speaker 1: China is such a windpin to the test of both pieces. 371 00:21:18,280 --> 00:21:22,399 Speaker 1: That's about deliveries going into next year, and China was 372 00:21:22,480 --> 00:21:25,800 Speaker 1: chopping at this quarter in terms of pr issue safety. 373 00:21:25,880 --> 00:21:29,800 Speaker 1: That definitely had, but I'd say a negative impact on demand. 374 00:21:30,320 --> 00:21:33,640 Speaker 1: Tonight's really about Musk hand holding investors through the rest 375 00:21:33,680 --> 00:21:36,720 Speaker 1: of the year in terms of the trajectory, what China 376 00:21:36,840 --> 00:21:40,360 Speaker 1: looks like and alternately, what deliveries could be. I view 377 00:21:40,359 --> 00:21:43,760 Speaker 1: it as a positive catalysts. The stocks really under perform 378 00:21:43,840 --> 00:21:46,120 Speaker 1: this year after what was the story to Yere last year. 379 00:21:46,480 --> 00:21:49,040 Speaker 1: My view to three years out in the green tidalway, 380 00:21:49,160 --> 00:21:50,720 Speaker 1: this continues to being one of the best ways to 381 00:21:50,760 --> 00:21:55,640 Speaker 1: play it. You know, I look through um your recommendations, Dan, 382 00:21:55,880 --> 00:22:00,679 Speaker 1: and you are just crushing it on every other stock 383 00:22:01,040 --> 00:22:03,879 Speaker 1: that that you cover. You have a big universe and 384 00:22:03,920 --> 00:22:09,639 Speaker 1: you're beating your peers almost across the board. The only 385 00:22:10,119 --> 00:22:12,960 Speaker 1: I mean, you're still beating your peers on Tesla, but 386 00:22:13,200 --> 00:22:16,480 Speaker 1: still it's down and you have a thousand dollar price target. 387 00:22:16,520 --> 00:22:18,520 Speaker 1: What do they do wrong or were they not do that? 388 00:22:18,560 --> 00:22:22,680 Speaker 1: You expected? What was the unexpected move from Tesla. Yeah, 389 00:22:22,680 --> 00:22:25,080 Speaker 1: and I think when we make these calls and over 390 00:22:25,119 --> 00:22:28,040 Speaker 1: the last you know called twenty plus years, I I 391 00:22:28,080 --> 00:22:30,320 Speaker 1: don't like to look at stocks over a quarter or two, right, 392 00:22:30,480 --> 00:22:32,879 Speaker 1: it's the longer term thesis. And I think when you 393 00:22:32,920 --> 00:22:35,240 Speaker 1: look at tests for the first part of the thesis 394 00:22:35,240 --> 00:22:39,040 Speaker 1: played out last year, but so far this year, China 395 00:22:39,119 --> 00:22:41,720 Speaker 1: is underperformed. And that's why the stocks on the perform 396 00:22:41,800 --> 00:22:43,960 Speaker 1: as long as you know, when when you have all 397 00:22:44,000 --> 00:22:47,040 Speaker 1: these competitors coming in the e V landscape, from the 398 00:22:47,040 --> 00:22:50,960 Speaker 1: traditional the GM, FORD, b W to the startups, that's 399 00:22:50,960 --> 00:22:54,960 Speaker 1: put a perception almost an overhang issue comes down to China. 400 00:22:55,440 --> 00:22:57,960 Speaker 1: And I think what's been a bit surprising is what 401 00:22:58,040 --> 00:23:02,800 Speaker 1: we saw this quarter. I mean China was disappointing early on. 402 00:23:02,920 --> 00:23:05,240 Speaker 1: I think they started to get their sea legs back, 403 00:23:05,320 --> 00:23:08,160 Speaker 1: have momentum going the second half of the year, and 404 00:23:08,200 --> 00:23:10,680 Speaker 1: this will go up and down. With China. We think up. 405 00:23:10,800 --> 00:23:14,040 Speaker 1: We view it as more of a speed bump rad 406 00:23:14,160 --> 00:23:17,200 Speaker 1: in the start of a more structural negative. That's why 407 00:23:17,200 --> 00:23:20,280 Speaker 1: I think we start to hear tonight, Dan, you mentioned 408 00:23:20,320 --> 00:23:22,600 Speaker 1: some of the new competitors coming into the marketplace, and 409 00:23:22,640 --> 00:23:24,760 Speaker 1: again the big players, the big oms that we've been 410 00:23:24,800 --> 00:23:28,439 Speaker 1: waiting for. How do you think about Tesla going up 411 00:23:28,480 --> 00:23:30,399 Speaker 1: against the g m s and the vws of the 412 00:23:30,400 --> 00:23:32,399 Speaker 1: world is a and the Mercedes even you know what, 413 00:23:32,480 --> 00:23:35,320 Speaker 1: we saw some news recently as they really start to 414 00:23:35,320 --> 00:23:38,000 Speaker 1: put some serious money behind their e M or e 415 00:23:38,119 --> 00:23:41,000 Speaker 1: V efforts. Yea. And why aren't they worth that much? 416 00:23:41,520 --> 00:23:45,160 Speaker 1: I mean, you know, why is GM worth only eight billion? Folkswagen, 417 00:23:45,280 --> 00:23:48,520 Speaker 1: the biggest carmaker in the world, which has such illustrious 418 00:23:48,560 --> 00:23:51,359 Speaker 1: brands as Porsche and Audi, They're only worth a hundred 419 00:23:51,680 --> 00:23:56,280 Speaker 1: four billion dollars and Tesla's worth six fifty. It's a 420 00:23:56,320 --> 00:23:59,080 Speaker 1: great question, and that's why. Look it's our view and 421 00:23:59,320 --> 00:24:02,040 Speaker 1: take a step back. Can you get b W forward 422 00:24:02,080 --> 00:24:05,200 Speaker 1: and especially GM? I believe a lot of these thoughts 423 00:24:05,280 --> 00:24:08,280 Speaker 1: over the coming years get rerated, not just today, their 424 00:24:08,320 --> 00:24:11,280 Speaker 1: auto companies from twather viewed by investors more and more 425 00:24:11,359 --> 00:24:15,320 Speaker 1: start to get disruptive technology multiple because their success and EVS. 426 00:24:15,720 --> 00:24:18,080 Speaker 1: Especially when I look at GM, I think that's a 427 00:24:18,160 --> 00:24:20,720 Speaker 1: name that could be significantly higher as it gets rerded 428 00:24:20,760 --> 00:24:24,000 Speaker 1: on the conversion to e V s and and ultimately 429 00:24:24,040 --> 00:24:28,560 Speaker 1: today automobiles in the US or electric vehicle. So I 430 00:24:28,600 --> 00:24:31,920 Speaker 1: don't view this as necessarily zero sum game. I think 431 00:24:31,960 --> 00:24:36,160 Speaker 1: you're gonna see a lot of beneficiary tests, a disproportional beneficiary, 432 00:24:36,640 --> 00:24:38,919 Speaker 1: but no doubt you are going to start to see 433 00:24:39,040 --> 00:24:42,520 Speaker 1: some share games from the traditional players as part of 434 00:24:42,520 --> 00:24:45,840 Speaker 1: this green tidal wave. Dan, let's focus a little bit 435 00:24:45,920 --> 00:24:48,959 Speaker 1: on the income statement here, some boring old pen l stuff. 436 00:24:49,720 --> 00:24:54,720 Speaker 1: Does Tesla make a profit on an individual unit basis 437 00:24:54,800 --> 00:24:57,800 Speaker 1: just excluding any kind of credits or anything like that, 438 00:24:57,840 --> 00:25:00,480 Speaker 1: And if not, when you expect that to happen, Yeah, 439 00:25:00,560 --> 00:25:03,240 Speaker 1: not yet, and and that and that goes to the 440 00:25:03,280 --> 00:25:07,680 Speaker 1: emotional bold bear thesis. Because of the ev tax credits 441 00:25:07,680 --> 00:25:10,359 Speaker 1: and because of some of those other talents, that's how 442 00:25:10,359 --> 00:25:14,120 Speaker 1: they show profitability. But as we go into the next 443 00:25:14,200 --> 00:25:17,560 Speaker 1: few years, we believe they will be profitable as a 444 00:25:17,560 --> 00:25:20,640 Speaker 1: car company, right, not just from an EVY tax credit perspective. 445 00:25:20,680 --> 00:25:22,879 Speaker 1: And I think it's always been a forest through the 446 00:25:22,960 --> 00:25:26,080 Speaker 1: tree type name where you have to sort of look out, 447 00:25:26,359 --> 00:25:30,119 Speaker 1: especially because of China and where we are, especially on 448 00:25:30,119 --> 00:25:32,359 Speaker 1: the software piece, that you're seeing more and more of 449 00:25:32,400 --> 00:25:35,840 Speaker 1: the software upgrades, which flows to the bottom line. That's 450 00:25:35,840 --> 00:25:39,680 Speaker 1: gonna be a major catalyst for Tesla to really see 451 00:25:39,720 --> 00:25:41,240 Speaker 1: the green and I think a lot of it is 452 00:25:41,280 --> 00:25:44,080 Speaker 1: the red and the rearview mirror, and that's been something 453 00:25:44,119 --> 00:25:46,560 Speaker 1: that we've seen really play out over the last few years. 454 00:25:46,600 --> 00:25:48,320 Speaker 1: If you go back to what's played out in the 455 00:25:48,400 --> 00:25:51,840 Speaker 1: in the Tesla thesis, what's your biggest conviction of all 456 00:25:51,880 --> 00:25:54,880 Speaker 1: the You've got a lot of outperforms here, and granted 457 00:25:54,920 --> 00:25:59,359 Speaker 1: they've all done really well. Um, which one do you 458 00:25:59,400 --> 00:26:03,840 Speaker 1: like the best? It's that company at a Cupertino Apple. 459 00:26:03,920 --> 00:26:07,720 Speaker 1: I mean that's the one where when I just think 460 00:26:07,760 --> 00:26:10,800 Speaker 1: about where we are in the upgrade cycle, what i'll 461 00:26:10,840 --> 00:26:15,280 Speaker 1: call it supercycle thesis going into five G services, that 462 00:26:15,320 --> 00:26:18,640 Speaker 1: continues to get rerated and I think the year from 463 00:26:18,640 --> 00:26:20,679 Speaker 1: now we're going at three trillion dollar mark cap, and 464 00:26:20,720 --> 00:26:24,000 Speaker 1: that continues to be the one along with of course 465 00:26:24,040 --> 00:26:27,639 Speaker 1: Microsoft is a core cloud play. It's how you play 466 00:26:27,760 --> 00:26:30,920 Speaker 1: this tech thesis and really fourth in Dusher Revolution play 467 00:26:31,000 --> 00:26:33,760 Speaker 1: out all right, Well, never reminds me I need to 468 00:26:33,760 --> 00:26:36,320 Speaker 1: go get a new iPhone. Actually, that's hey, Dan can 469 00:26:36,520 --> 00:26:38,760 Speaker 1: He's got these recommendations. He knows how to frame them. 470 00:26:38,880 --> 00:26:40,560 Speaker 1: I love the a on our page for him. It 471 00:26:40,920 --> 00:26:45,800 Speaker 1: looks a plus for Dan Eyes from Wedbush Securities, this 472 00:26:46,760 --> 00:26:50,719 Speaker 1: is Bloomberg. Thanks for listening to the Bloomberg Markets podcast. 473 00:26:51,119 --> 00:26:54,320 Speaker 1: You can subscribe and listen to interviews with Apple Podcasts 474 00:26:54,440 --> 00:26:58,360 Speaker 1: or whatever podcast platform you prefer. I'm Matt Miller. I'm 475 00:26:58,400 --> 00:27:01,880 Speaker 1: on Twitter at Matt Miller nineteen seventy three. And I'm 476 00:27:01,880 --> 00:27:04,960 Speaker 1: Fall Sweeney. I'm on Twitter at pt Sweeney. Before the podcast, 477 00:27:05,000 --> 00:27:07,480 Speaker 1: you can always catch us worldwide at Bloomberg Radio