1 00:00:01,800 --> 00:00:07,080 Speaker 1: From Marhard where Innovation, money and power Collie in Silicon Valley, NBN. 2 00:00:07,400 --> 00:00:20,880 Speaker 1: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 3 00:00:25,320 --> 00:00:28,320 Speaker 2: I'm Caroline Hyder a Bloomberg's World headquarters in New York, and. 4 00:00:28,280 --> 00:00:32,360 Speaker 3: I'm Ed Ludlow in San Francisco. This is Bloomberg Technology. 5 00:00:31,880 --> 00:00:35,639 Speaker 2: Coming up ed full earnings coverage ahead was rake down 6 00:00:35,680 --> 00:00:38,280 Speaker 2: the results from Qualcom. From Etsy, we push her head 7 00:00:38,280 --> 00:00:40,000 Speaker 2: to the all important Apple out after the bell. 8 00:00:40,800 --> 00:00:43,760 Speaker 3: Plus we dive deep into the world of artificial intelligence 9 00:00:43,760 --> 00:00:47,159 Speaker 3: and talk AI safety, as well as investing with Soundbenture's 10 00:00:47,159 --> 00:00:49,120 Speaker 3: general partner Ashton Kutcher. 11 00:00:49,520 --> 00:00:53,200 Speaker 2: Plus Ali Baba weighing a USIPO for its online commerce business. 12 00:00:53,520 --> 00:00:56,040 Speaker 2: Could that be valued at thirty nine billion dollars? Will 13 00:00:56,040 --> 00:00:58,400 Speaker 2: bring you the latest details this hour, but of course 14 00:00:58,440 --> 00:01:00,600 Speaker 2: we've got so much to discuss, particularly with the world 15 00:01:00,920 --> 00:01:04,240 Speaker 2: of executives coming up right now. And we welcome bron 16 00:01:04,319 --> 00:01:07,080 Speaker 2: Chesky of Airbnb and I'm very peace to say Blue Megs, 17 00:01:07,120 --> 00:01:09,560 Speaker 2: Emily Chang. Emily do take away the conversation. 18 00:01:09,880 --> 00:01:12,319 Speaker 4: Hey there, Brian, thank you so much for joining us. 19 00:01:12,360 --> 00:01:15,680 Speaker 4: Obviously you've got your huge summer release out now, lots 20 00:01:15,680 --> 00:01:18,160 Speaker 4: of different new features and categories. The one I want 21 00:01:18,200 --> 00:01:21,640 Speaker 4: to focus on is rooms, which, as I know, the 22 00:01:21,680 --> 00:01:25,679 Speaker 4: headline here feels a little like Groundhog Day because rooms 23 00:01:25,680 --> 00:01:26,800 Speaker 4: were where you started. 24 00:01:27,240 --> 00:01:27,880 Speaker 5: What do you want? 25 00:01:28,120 --> 00:01:30,200 Speaker 4: What's the real pain point you're trying to solve with 26 00:01:30,280 --> 00:01:31,160 Speaker 4: these new features. 27 00:01:31,360 --> 00:01:35,680 Speaker 6: Well, the primary pain point is that people, once again, 28 00:01:36,360 --> 00:01:39,400 Speaker 6: just like when we started Airbnb in two thousand and eight, 29 00:01:40,319 --> 00:01:43,080 Speaker 6: want an affordable way to travel, and the most one 30 00:01:43,080 --> 00:01:45,000 Speaker 6: of the most affordable ways to travel is to stay 31 00:01:45,000 --> 00:01:47,960 Speaker 6: in a room in someone's house. That's how airbanbe started. 32 00:01:48,160 --> 00:01:51,240 Speaker 6: The average room on Airbnb is only sixty seven dollars 33 00:01:51,240 --> 00:01:53,279 Speaker 6: a night, but we talked to a lot of people 34 00:01:53,520 --> 00:01:55,800 Speaker 6: and a lot of people said they weren't comfortable staying 35 00:01:55,800 --> 00:01:57,520 Speaker 6: in a room in someone's house. Who's the other person 36 00:01:57,600 --> 00:01:59,520 Speaker 6: I'm going to be staying with. So that's why we 37 00:01:59,560 --> 00:02:02,720 Speaker 6: created this new thing called the host Passport. The host 38 00:02:02,800 --> 00:02:06,560 Speaker 6: Passport is basically this really robust profile We verify the 39 00:02:06,600 --> 00:02:09,040 Speaker 6: identity of every host and get you can get to 40 00:02:09,040 --> 00:02:11,280 Speaker 6: know the host before you book. We also have really 41 00:02:11,280 --> 00:02:13,840 Speaker 6: cool privacy features, like we show if the bathroom is 42 00:02:13,880 --> 00:02:15,840 Speaker 6: share a private, and if there's going to be a 43 00:02:15,840 --> 00:02:17,919 Speaker 6: lock in the bedroom door, So this is I think 44 00:02:17,919 --> 00:02:20,160 Speaker 6: what we're trying to do. Additionally, we think this is 45 00:02:20,200 --> 00:02:23,600 Speaker 6: a really good way to experience a local culture. If 46 00:02:23,639 --> 00:02:25,040 Speaker 6: you want to go to a new city for the 47 00:02:25,080 --> 00:02:27,160 Speaker 6: first time, this is a great way because you get 48 00:02:27,160 --> 00:02:28,919 Speaker 6: to step in the shoes as someone who lives there. 49 00:02:30,200 --> 00:02:33,120 Speaker 4: You of course invented Airbnb in the middle of the 50 00:02:33,200 --> 00:02:35,320 Speaker 4: last recession, and here we are on the verge of 51 00:02:35,360 --> 00:02:38,320 Speaker 4: another one, which you know, obviously not good for consumers, 52 00:02:38,320 --> 00:02:41,079 Speaker 4: potentially not good for travel. How much is the economic 53 00:02:41,200 --> 00:02:43,240 Speaker 4: climate impacting. 54 00:02:42,800 --> 00:02:43,600 Speaker 2: This new release. 55 00:02:44,480 --> 00:02:48,520 Speaker 6: It's absolutely impacting what people care about. A year ago, 56 00:02:48,600 --> 00:02:51,720 Speaker 6: what people cared about primarily was flexibility, and so we 57 00:02:51,800 --> 00:02:54,720 Speaker 6: launch air and B categories which allow you to inspire 58 00:02:54,800 --> 00:02:57,800 Speaker 6: you to be able to travel beyond any place you 59 00:02:57,800 --> 00:03:01,640 Speaker 6: can think. Now, I think the equations now people really 60 00:03:01,680 --> 00:03:04,600 Speaker 6: really are focused on affordability more than the even we're 61 00:03:04,720 --> 00:03:05,320 Speaker 6: a year ago. 62 00:03:06,240 --> 00:03:07,080 Speaker 5: That's our roots. 63 00:03:07,160 --> 00:03:10,200 Speaker 6: I mean, our roots are started in affordable travel, being 64 00:03:10,200 --> 00:03:12,440 Speaker 6: on the share space, So this is what we're really 65 00:03:12,440 --> 00:03:14,960 Speaker 6: focused on. We're also just really trying to focus on 66 00:03:15,000 --> 00:03:18,680 Speaker 6: providing a great service. So we've been really obsessed over 67 00:03:19,080 --> 00:03:21,760 Speaker 6: the feedback we're getting from customers. We've been pouring over 68 00:03:21,880 --> 00:03:24,880 Speaker 6: millions of pieces of feedback from customer service. We've been 69 00:03:24,919 --> 00:03:27,359 Speaker 6: talking to guests and hosts, and based on that, we've 70 00:03:27,360 --> 00:03:30,800 Speaker 6: made over fifty core improvements to the service based on 71 00:03:30,880 --> 00:03:33,800 Speaker 6: feedback directly from our community. And so hopefully if people 72 00:03:33,880 --> 00:03:36,800 Speaker 6: use earing the summer, they'll find it's more affordable and 73 00:03:36,960 --> 00:03:38,160 Speaker 6: even better level service. 74 00:03:38,920 --> 00:03:42,240 Speaker 4: You're seeing that with simpler, more affordable monthly stays, you've 75 00:03:42,240 --> 00:03:46,440 Speaker 4: got priority customer service, you've got more transparent and upfront pricing. 76 00:03:46,480 --> 00:03:48,960 Speaker 4: I know you're using AI and machine learning to make 77 00:03:49,000 --> 00:03:51,120 Speaker 4: a lot of this work on the back end. How 78 00:03:51,160 --> 00:03:53,600 Speaker 4: are you thinking about the AI hype cycle and is 79 00:03:53,600 --> 00:03:55,280 Speaker 4: it going to be as big as everyone says it's 80 00:03:55,280 --> 00:03:57,600 Speaker 4: going to be, and what that means for Airbnb. 81 00:03:58,240 --> 00:04:00,000 Speaker 6: It's probably going to be a lot bigger than everyone 82 00:04:00,000 --> 00:04:01,960 Speaker 6: and says it's going to be. In every few years, 83 00:04:01,960 --> 00:04:05,480 Speaker 6: there's new AI hypes, there's new technology hype cycle. But 84 00:04:05,520 --> 00:04:08,280 Speaker 6: the reason why is because there have been technologies that 85 00:04:08,360 --> 00:04:10,520 Speaker 6: have changed the world and change how we all live, 86 00:04:10,960 --> 00:04:15,120 Speaker 6: the Internet, the personal computer revolution, Mobile AI will certainly 87 00:04:15,120 --> 00:04:17,800 Speaker 6: be as big as all of them, maybe bigger than 88 00:04:17,839 --> 00:04:20,520 Speaker 6: all of them combined. And I think this is just 89 00:04:20,520 --> 00:04:23,640 Speaker 6: the very beginning. It's going to play out over a 90 00:04:23,800 --> 00:04:26,719 Speaker 6: long period of time. It's happening quickly. It's going to 91 00:04:26,800 --> 00:04:28,839 Speaker 6: change I think how people travel, it's going to change 92 00:04:28,839 --> 00:04:30,479 Speaker 6: how we learn, It's going to change how we get 93 00:04:30,480 --> 00:04:32,680 Speaker 6: care for. It's going to change a lot of things 94 00:04:32,760 --> 00:04:35,240 Speaker 6: in society. What we're trying to do right now is 95 00:04:35,320 --> 00:04:37,800 Speaker 6: number One, we want to use the tools to make 96 00:04:37,880 --> 00:04:40,000 Speaker 6: everyone to air and be more productive. I don't think 97 00:04:40,040 --> 00:04:42,560 Speaker 6: this will replace people initially as much as will augment 98 00:04:42,640 --> 00:04:45,320 Speaker 6: people and make them more productive. Two, I think it 99 00:04:45,360 --> 00:04:48,320 Speaker 6: can help customer service. We have a very complated customer 100 00:04:48,360 --> 00:04:50,720 Speaker 6: service problem. We have millions of people living together every 101 00:04:50,800 --> 00:04:53,640 Speaker 6: night from different countries. This will help augment our agents 102 00:04:53,680 --> 00:04:56,800 Speaker 6: provide better service faster. And finally, AI can be like 103 00:04:56,839 --> 00:05:00,160 Speaker 6: the ultimate matchmaker. Imagine if your app being almost like 104 00:05:00,160 --> 00:05:02,160 Speaker 6: the ultimate concierge and they can match you whatever you're 105 00:05:02,160 --> 00:05:05,160 Speaker 6: looking for. These are the near term things. Beyond that, 106 00:05:05,279 --> 00:05:07,760 Speaker 6: it's really up to everyone's imagination. What you could do, 107 00:05:08,000 --> 00:05:09,680 Speaker 6: kind of like the computer forty years ago. 108 00:05:10,080 --> 00:05:13,200 Speaker 2: Call just about matching your talent for this new AI 109 00:05:13,279 --> 00:05:14,719 Speaker 2: door and you say you want to be at the vanguard. 110 00:05:14,760 --> 00:05:16,320 Speaker 2: Have you got the right people within the business to 111 00:05:16,320 --> 00:05:17,200 Speaker 2: be building it for you. 112 00:05:17,600 --> 00:05:20,120 Speaker 6: I absolutely think so. I mean, of course we're still hiring. 113 00:05:20,360 --> 00:05:22,760 Speaker 6: If people are really great at this area, we certainly 114 00:05:22,760 --> 00:05:24,440 Speaker 6: want to talk to them, But we have one of 115 00:05:24,480 --> 00:05:26,400 Speaker 6: the we have a great team. I mean, we have 116 00:05:26,440 --> 00:05:28,280 Speaker 6: people that have been working in this area for quite 117 00:05:28,279 --> 00:05:30,080 Speaker 6: a long period of time. And the other thing I 118 00:05:30,120 --> 00:05:32,560 Speaker 6: just want to say is there's many different layers the 119 00:05:32,560 --> 00:05:35,159 Speaker 6: stack of AI. You have Open Ai or Google that 120 00:05:35,160 --> 00:05:37,160 Speaker 6: are working on the base models. And we're not going 121 00:05:37,240 --> 00:05:39,479 Speaker 6: to be a research lab building base models. That's like 122 00:05:39,480 --> 00:05:42,480 Speaker 6: building bridges infrastructure. We'll build the cars on the bridge, 123 00:05:42,520 --> 00:05:44,800 Speaker 6: so to speak, the applications. So what we're going to 124 00:05:44,839 --> 00:05:47,880 Speaker 6: be really good at, I hope is building personalized layers 125 00:05:47,920 --> 00:05:50,080 Speaker 6: on top of AI. We can tune the models and 126 00:05:50,200 --> 00:05:52,240 Speaker 6: ultimately I want airbe to feel like the kind of 127 00:05:52,279 --> 00:05:55,400 Speaker 6: company that it knows you, it understands you, it learns 128 00:05:55,440 --> 00:05:57,720 Speaker 6: about your preferences, and I think that's what we can 129 00:05:57,800 --> 00:05:58,440 Speaker 6: use AI for. 130 00:05:58,760 --> 00:06:01,080 Speaker 2: What's interesting, We've got one of early back is coming on. 131 00:06:01,120 --> 00:06:03,440 Speaker 2: Who's all about AI today? Ashton Kutch Show. 132 00:06:03,440 --> 00:06:03,800 Speaker 6: Oh yeah. 133 00:06:04,839 --> 00:06:07,880 Speaker 2: Also, we're thinking about how it can lead to more productivity, 134 00:06:07,920 --> 00:06:11,040 Speaker 2: more profitability. Go back to your announcements that you're making 135 00:06:11,120 --> 00:06:14,760 Speaker 2: in this economic environment, we are thinking about how you 136 00:06:14,760 --> 00:06:16,960 Speaker 2: can be more profitable. You're saying how you can be 137 00:06:17,040 --> 00:06:20,920 Speaker 2: more affordable, reducing fees. Does that ultimately impact your bottom line? 138 00:06:20,920 --> 00:06:23,200 Speaker 6: In the longer term, I think the AI is going 139 00:06:23,240 --> 00:06:26,800 Speaker 6: to affect everything. AI is going to make people significantly 140 00:06:26,800 --> 00:06:29,320 Speaker 6: more productive. AI is going to be able to also 141 00:06:29,520 --> 00:06:31,719 Speaker 6: allow things to happen that you could never have done 142 00:06:31,720 --> 00:06:34,479 Speaker 6: without that technology. Again, it's a little hard to know 143 00:06:34,839 --> 00:06:36,560 Speaker 6: exactly what it's going to affect. I think it's going 144 00:06:36,560 --> 00:06:38,720 Speaker 6: to reduce the cost of our service. I think it's 145 00:06:38,720 --> 00:06:41,039 Speaker 6: going to make the product significantly more efficient. It's going 146 00:06:41,120 --> 00:06:44,480 Speaker 6: to allow engineers to be at least thirty percent more productive, 147 00:06:44,480 --> 00:06:47,440 Speaker 6: eventually twice as productive. So that is the equivalent of 148 00:06:47,480 --> 00:06:50,160 Speaker 6: reduction at fists costs or a lot more throughput. So 149 00:06:50,200 --> 00:06:53,000 Speaker 6: those are the near term things we'll have. But beyond that, 150 00:06:53,120 --> 00:06:54,920 Speaker 6: I think we're just getting started. I think we're at 151 00:06:54,960 --> 00:06:57,479 Speaker 6: the beginning of something that we're probably many years from now, 152 00:06:57,760 --> 00:06:59,440 Speaker 6: going to look back in this period of time and 153 00:06:59,480 --> 00:07:02,080 Speaker 6: remember alive at this period and being in a seminal 154 00:07:02,120 --> 00:07:04,159 Speaker 6: moment in history. That's what I think we're going to 155 00:07:04,160 --> 00:07:05,239 Speaker 6: think about this period in Ai. 156 00:07:06,600 --> 00:07:08,640 Speaker 4: Brian, You've been hosting guests in your own home for 157 00:07:08,680 --> 00:07:11,080 Speaker 4: the first time and act, yeah, so excited. I got 158 00:07:11,080 --> 00:07:12,440 Speaker 4: to take a peek. You're going to see that on 159 00:07:12,480 --> 00:07:14,240 Speaker 4: my new show. I don't want to give too much away, 160 00:07:14,280 --> 00:07:17,040 Speaker 4: but there were some Cheskey's chips involved. What are you 161 00:07:17,160 --> 00:07:20,360 Speaker 4: learning from, you know, your own experience hosting, and how 162 00:07:20,400 --> 00:07:23,600 Speaker 4: you want to integrate that back into Airbnb. You know, 163 00:07:23,640 --> 00:07:26,040 Speaker 4: given you know what you've been hearing from hosts and guests, 164 00:07:26,080 --> 00:07:30,840 Speaker 4: whether it comes to fees or affordability or privacy. 165 00:07:31,400 --> 00:07:35,200 Speaker 6: I mean, you know, I always feel like companies that 166 00:07:35,280 --> 00:07:39,400 Speaker 6: create great products create them for themselves because then you 167 00:07:39,440 --> 00:07:42,400 Speaker 6: have total empathy. You understand what the data means. And 168 00:07:42,480 --> 00:07:44,080 Speaker 6: I wanted to be a host again now. I was 169 00:07:44,120 --> 00:07:46,920 Speaker 6: one of the first hosts of my roommate fifteen years 170 00:07:46,920 --> 00:07:50,640 Speaker 6: ago in Airbnb, and as I started hosting again, I 171 00:07:50,680 --> 00:07:52,680 Speaker 6: have people my house with me. They stay in the 172 00:07:52,680 --> 00:07:56,320 Speaker 6: guest room, I'm there with them, and it really helped 173 00:07:56,320 --> 00:07:59,720 Speaker 6: me understand Number one, trust and safety is paramount. You know, 174 00:08:00,080 --> 00:08:03,120 Speaker 6: now verify the identity of every guest booking around the 175 00:08:03,160 --> 00:08:06,280 Speaker 6: world on Airbnb. But it also helped me understand that 176 00:08:06,520 --> 00:08:09,040 Speaker 6: we need to make hosting easy, and as we make 177 00:08:09,080 --> 00:08:11,720 Speaker 6: the product easier for hosts, it will become a better 178 00:08:11,760 --> 00:08:14,080 Speaker 6: experience for guests. I'll give you one example. A lot 179 00:08:14,080 --> 00:08:17,400 Speaker 6: of people have complained about fees on Airbnb and host 180 00:08:17,480 --> 00:08:18,400 Speaker 6: charging cleaning fees. 181 00:08:18,680 --> 00:08:20,480 Speaker 5: Well, as I started hosting. 182 00:08:20,520 --> 00:08:22,920 Speaker 6: I noticed that it was kind of complicated to figure 183 00:08:22,920 --> 00:08:25,120 Speaker 6: out how much to charge, and we weren't doing a 184 00:08:25,120 --> 00:08:27,920 Speaker 6: great job coaching hosts about how to provide a great 185 00:08:28,040 --> 00:08:30,000 Speaker 6: value to guest. And as you use the product, you 186 00:08:30,040 --> 00:08:34,040 Speaker 6: start to see these things and posting this actually helped 187 00:08:34,120 --> 00:08:36,600 Speaker 6: lead some of the innovations that we announced yesterday. 188 00:08:37,080 --> 00:08:40,040 Speaker 2: Brad Chesky, we thank you of Airbnb and of course 189 00:08:40,400 --> 00:08:41,520 Speaker 2: our own Emily Chang. 190 00:08:42,880 --> 00:08:45,599 Speaker 3: Let's go to AI. Earlier this week at Milky and 191 00:08:45,720 --> 00:08:49,559 Speaker 3: actor and entrepreneur Ashton Kutcher said that companies not investing 192 00:08:49,880 --> 00:08:52,560 Speaker 3: in AI will go out of business. But does that 193 00:08:52,640 --> 00:08:56,600 Speaker 3: mean that all Way I startups are equally good investments. 194 00:08:56,640 --> 00:08:59,440 Speaker 3: Here's what Cosla Ventures found of in our Cosla told 195 00:08:59,480 --> 00:09:00,120 Speaker 3: us yesterday. 196 00:09:00,200 --> 00:09:00,720 Speaker 5: Have a listen. 197 00:09:01,440 --> 00:09:06,120 Speaker 7: I would say there's many more bad AI startups than 198 00:09:06,240 --> 00:09:10,240 Speaker 7: good startups, and it's very hard to differentiate if you're 199 00:09:10,280 --> 00:09:13,800 Speaker 7: not experienced with AI, so I do think lots of 200 00:09:13,840 --> 00:09:17,960 Speaker 7: bad investments will be made, but overall more money will 201 00:09:18,000 --> 00:09:20,760 Speaker 7: be made than lost, even if ninety percent of the 202 00:09:20,760 --> 00:09:22,160 Speaker 7: startups fail, which they. 203 00:09:22,000 --> 00:09:26,200 Speaker 3: Will sound ventures Ashton Kutscher joins us now for more 204 00:09:26,679 --> 00:09:28,920 Speaker 3: and on his firm recently closing a day I fund 205 00:09:28,960 --> 00:09:32,360 Speaker 3: oversubscribed about two hundred and forty million dollars. It's interesting 206 00:09:32,360 --> 00:09:35,720 Speaker 3: action because you're really focusing on foundational models. That's what 207 00:09:35,800 --> 00:09:38,600 Speaker 3: jumped out at me from the announcement. That's where a 208 00:09:38,600 --> 00:09:42,400 Speaker 3: lot of people are putting their activity. Why so focused there? 209 00:09:43,120 --> 00:09:45,240 Speaker 5: So we have multiple vehicles. 210 00:09:45,360 --> 00:09:49,280 Speaker 8: Our core vehicle will be focusing on the application layer, 211 00:09:50,679 --> 00:09:53,760 Speaker 8: and that's where there's going to be an absolute boom 212 00:09:53,920 --> 00:09:58,480 Speaker 8: of innovation from business models that are being unleashed that 213 00:09:58,559 --> 00:10:00,160 Speaker 8: were never possible before. 214 00:10:01,040 --> 00:10:02,679 Speaker 5: But our core. 215 00:10:03,200 --> 00:10:07,559 Speaker 8: AI fund that we basically we saw in November when 216 00:10:07,640 --> 00:10:10,839 Speaker 8: GPT was launched. We've been investing in AI for the 217 00:10:10,920 --> 00:10:15,160 Speaker 8: last seven years, several companies that are narrow AI companies 218 00:10:15,200 --> 00:10:19,960 Speaker 8: that are application based. But when we saw GBT be launched, 219 00:10:20,320 --> 00:10:24,640 Speaker 8: we realized that this was an absolute breakthrough and these 220 00:10:24,679 --> 00:10:29,160 Speaker 8: foundational models are going to be the underpinning of the 221 00:10:29,200 --> 00:10:34,040 Speaker 8: next absolute transformation for technology. When we first started investing 222 00:10:34,200 --> 00:10:36,600 Speaker 8: in two thousand and eight two thousand and nine, it 223 00:10:36,679 --> 00:10:40,559 Speaker 8: was right around the mobile revolution, and companies that didn't 224 00:10:41,000 --> 00:10:47,040 Speaker 8: didn't embrace that in a forward basis really struggled. But 225 00:10:47,200 --> 00:10:50,600 Speaker 8: it also unleashed extraordinary new business models that never could 226 00:10:50,640 --> 00:10:53,439 Speaker 8: have existed before. When you had a GPS in your pocket, 227 00:10:53,480 --> 00:10:55,360 Speaker 8: when you had a camera in your pocket, when you 228 00:10:55,400 --> 00:10:57,880 Speaker 8: had a radio in your pocket, when you had all 229 00:10:57,920 --> 00:11:01,760 Speaker 8: of that capability in your pocket, unleashed these business models 230 00:11:02,080 --> 00:11:05,240 Speaker 8: that were never possible before. And what's about to happen 231 00:11:05,720 --> 00:11:09,280 Speaker 8: because of these large transform of models that have so 232 00:11:09,760 --> 00:11:13,920 Speaker 8: such extraordinary output, We're going to see innovation of business 233 00:11:13,920 --> 00:11:17,040 Speaker 8: models that were never capable before that are that are 234 00:11:17,040 --> 00:11:21,520 Speaker 8: about to be launched. Yes, and and and and that's 235 00:11:21,800 --> 00:11:24,680 Speaker 8: that that is just going to be an absolute And 236 00:11:24,800 --> 00:11:27,440 Speaker 8: what I said at Milken was that if you were 237 00:11:27,440 --> 00:11:30,600 Speaker 8: a commerce company ten years ago and you didn't embrace 238 00:11:30,640 --> 00:11:33,680 Speaker 8: e commerce, you're probably not in business anymore. 239 00:11:34,200 --> 00:11:35,400 Speaker 5: And I firmly. 240 00:11:35,080 --> 00:11:37,599 Speaker 8: Believe that if you're a company today and you're not 241 00:11:37,880 --> 00:11:41,520 Speaker 8: embracing the changes that are taking place with AI, you're 242 00:11:41,559 --> 00:11:43,520 Speaker 8: going to be behind and you're going to have a 243 00:11:43,559 --> 00:11:45,520 Speaker 8: hard time catching up Ashton. 244 00:11:45,520 --> 00:11:47,520 Speaker 3: I'm really interested in how quickly you moved on this 245 00:11:47,559 --> 00:11:50,839 Speaker 3: thematic fund as well. Who are the LPs and how 246 00:11:50,880 --> 00:11:52,080 Speaker 3: quickly did it come together? 247 00:11:53,720 --> 00:11:53,840 Speaker 2: Uh? 248 00:11:54,200 --> 00:11:57,120 Speaker 8: We we pulled the fund together in about five weeks. 249 00:11:58,080 --> 00:11:58,360 Speaker 5: Wow. 250 00:11:59,120 --> 00:12:02,560 Speaker 8: And we have an extraordinary We have a base of 251 00:12:02,679 --> 00:12:05,679 Speaker 8: LPs that have been with us for years on end. 252 00:12:06,080 --> 00:12:08,080 Speaker 5: So we have about a billion dollars. 253 00:12:07,800 --> 00:12:11,000 Speaker 8: Under management at Sound Ventures and have been working with 254 00:12:11,120 --> 00:12:13,880 Speaker 8: some really extraordinary LPs over the years, and so we 255 00:12:13,920 --> 00:12:16,720 Speaker 8: reached out to them and said, hey, this is happening now. 256 00:12:17,640 --> 00:12:19,600 Speaker 8: These large transformer models a lot of them. You look 257 00:12:19,640 --> 00:12:22,760 Speaker 8: at open Ai, you look at Anthropic, you look at Stability. 258 00:12:23,040 --> 00:12:25,520 Speaker 8: They didn't they didn't start yesterday. They've been around for 259 00:12:25,520 --> 00:12:27,160 Speaker 8: a while. They've been building this. You look at what 260 00:12:27,240 --> 00:12:30,360 Speaker 8: Google has been doing with deep Mind and Barred and 261 00:12:30,880 --> 00:12:33,520 Speaker 8: those those companies have been in development for a while. 262 00:12:34,000 --> 00:12:36,760 Speaker 8: What we've just seen is an extraordinary breakthrough where you 263 00:12:36,800 --> 00:12:39,920 Speaker 8: can take a massive corpus of data and be able 264 00:12:39,920 --> 00:12:44,280 Speaker 8: to index through it and again create statistical, highly likely 265 00:12:44,320 --> 00:12:49,240 Speaker 8: outputs that are beneficial to humans. And these utilities are 266 00:12:49,320 --> 00:12:54,560 Speaker 8: really really valuable. It's it is going to exponentially change. 267 00:12:54,600 --> 00:12:58,720 Speaker 8: I agree with everything that Brian saying before I came on. 268 00:12:59,240 --> 00:13:02,480 Speaker 8: This is going to change business forever, and we need 269 00:13:02,520 --> 00:13:04,720 Speaker 8: to embrace it because right now this AI can be 270 00:13:04,880 --> 00:13:07,440 Speaker 8: used to improve humanity. 271 00:13:08,880 --> 00:13:09,679 Speaker 5: I actually look at. 272 00:13:09,640 --> 00:13:12,520 Speaker 8: It as an equity and an inclusion play, where you know, 273 00:13:13,120 --> 00:13:18,080 Speaker 8: as we commoditize things like legal advice, in medical advice 274 00:13:18,160 --> 00:13:22,880 Speaker 8: and education and personalize those things down to the individual consumer, 275 00:13:24,120 --> 00:13:26,320 Speaker 8: all of a sudden, people that can't get a doctor 276 00:13:26,360 --> 00:13:28,040 Speaker 8: on the phone, or can't get a lawyer on the phone, 277 00:13:28,080 --> 00:13:30,679 Speaker 8: or can't get a pediatrician on the phone, they're going 278 00:13:30,720 --> 00:13:34,120 Speaker 8: to have access to these services and access to these 279 00:13:34,120 --> 00:13:37,199 Speaker 8: services at an affordable price. Yeah, and I think that's 280 00:13:37,240 --> 00:13:40,400 Speaker 8: extraordinary for humanity and extraordinary for all of us. 281 00:13:40,520 --> 00:13:43,880 Speaker 2: It augments you mentioned education there, and I think about 282 00:13:43,960 --> 00:13:48,679 Speaker 2: your exited portfolio company, cheg, which lost about half of 283 00:13:48,720 --> 00:13:51,040 Speaker 2: its market value over the course of the week because 284 00:13:51,320 --> 00:13:54,280 Speaker 2: already chat GPT is upending its own business model. What 285 00:13:54,320 --> 00:13:57,240 Speaker 2: are you doing with your current portfolio, Ashton, to see 286 00:13:57,280 --> 00:14:00,000 Speaker 2: around this corner to ensure that they are augmented rather 287 00:14:00,120 --> 00:14:01,559 Speaker 2: then completely disrupted. 288 00:14:02,360 --> 00:14:04,360 Speaker 8: So that was the very first thing that we did 289 00:14:04,400 --> 00:14:07,480 Speaker 8: before we even thought about putting a vehicle together to 290 00:14:07,559 --> 00:14:10,079 Speaker 8: invest in these things. Is we reached out to every 291 00:14:10,120 --> 00:14:12,240 Speaker 8: one of our portfolio companies and said, how are you 292 00:14:12,280 --> 00:14:13,520 Speaker 8: embracing this technology? 293 00:14:13,800 --> 00:14:15,760 Speaker 5: How are you implementing this technology? 294 00:14:16,120 --> 00:14:20,680 Speaker 8: And I think, I think maybe from my perspective, the 295 00:14:21,760 --> 00:14:24,800 Speaker 8: what happened with CHEG the other day is somewhat short sighted. 296 00:14:25,440 --> 00:14:28,960 Speaker 8: CHEG is implementing GPT four into their product they announced 297 00:14:29,040 --> 00:14:31,920 Speaker 8: check Mate, and and I think a lot of the 298 00:14:32,040 --> 00:14:34,640 Speaker 8: value is going to fall to the incumbents. So if 299 00:14:34,640 --> 00:14:37,840 Speaker 8: you have an extraordinary product, you have consumers and you're 300 00:14:37,880 --> 00:14:40,880 Speaker 8: implementing this, I think there, I think value is going 301 00:14:40,920 --> 00:14:45,560 Speaker 8: to fall to some of those incumbents, and as it should. 302 00:14:46,200 --> 00:14:49,160 Speaker 8: And don't dismiss the fact that a lot of these 303 00:14:49,160 --> 00:14:53,200 Speaker 8: companies have extraordinary data sets that are their own proprietary 304 00:14:53,280 --> 00:14:56,840 Speaker 8: data sets, and they'll tune their own models on top 305 00:14:56,960 --> 00:15:01,280 Speaker 8: of these large transformer models, which will will create unique 306 00:15:01,800 --> 00:15:03,400 Speaker 8: value propositions for consumers. 307 00:15:03,880 --> 00:15:06,280 Speaker 2: Actually, the word that keeps coming up Timing time again 308 00:15:06,640 --> 00:15:09,920 Speaker 2: is hype cycle and being able to see wood for 309 00:15:09,960 --> 00:15:12,720 Speaker 2: the trees, but also being able to understand that this 310 00:15:12,760 --> 00:15:16,440 Speaker 2: isn't kind of like crypto, where yes, there's still ultimate value. 311 00:15:16,440 --> 00:15:18,080 Speaker 2: We see it with the OG's at Bitcoin but we 312 00:15:18,120 --> 00:15:20,760 Speaker 2: have seen in up ending in valuations of NFTs an 313 00:15:20,800 --> 00:15:23,120 Speaker 2: area that you were playing heavily in. How do you 314 00:15:23,240 --> 00:15:25,640 Speaker 2: worry about some of the similarities there and how do 315 00:15:25,680 --> 00:15:28,440 Speaker 2: you ensure that the same mistakes aren't remade. 316 00:15:29,800 --> 00:15:32,040 Speaker 8: Well, I think the biggest mistake that was made in 317 00:15:32,160 --> 00:15:36,520 Speaker 8: crypto was is and still is, just the absolute lack 318 00:15:37,200 --> 00:15:41,080 Speaker 8: of clear cut regulations so people know what the rules 319 00:15:41,080 --> 00:15:43,560 Speaker 8: are that they should be playing by. I think that 320 00:15:43,640 --> 00:15:47,560 Speaker 8: there were a lot of companies that were assuming that 321 00:15:47,640 --> 00:15:50,960 Speaker 8: blockchain technology could be used for a lot of different 322 00:15:51,000 --> 00:15:53,600 Speaker 8: things that it really shouldn't have been used for. Look, 323 00:15:53,800 --> 00:15:58,320 Speaker 8: it's a public ledger database, so it's really that blockchain 324 00:15:58,400 --> 00:16:01,840 Speaker 8: is super valuable for any of transaction between two parties 325 00:16:01,840 --> 00:16:04,360 Speaker 8: that don't trust one another. So if two parties don't 326 00:16:04,360 --> 00:16:06,320 Speaker 8: trust one another, they want to transact, they want to 327 00:16:06,360 --> 00:16:09,560 Speaker 8: transact out in the open, so everybody could see what 328 00:16:09,600 --> 00:16:13,360 Speaker 8: that transaction is and have a consistent, constant, historic ledger 329 00:16:13,440 --> 00:16:17,400 Speaker 8: of those transactions. It's really valuable for that. I actually 330 00:16:17,440 --> 00:16:21,080 Speaker 8: think for artists that want to have enduring art out 331 00:16:21,120 --> 00:16:24,400 Speaker 8: in the market and be able to timestamp this new 332 00:16:24,480 --> 00:16:27,080 Speaker 8: piece of art as hey, I made this at this 333 00:16:27,160 --> 00:16:30,080 Speaker 8: date and everything else that comes after that is derivative 334 00:16:30,120 --> 00:16:34,600 Speaker 8: of that's it's extraordinarily valuable to have a public ledger 335 00:16:34,640 --> 00:16:38,440 Speaker 8: for that. I think some of the tokenization that was 336 00:16:38,440 --> 00:16:43,600 Speaker 8: taking place was really short sighted and pretty manipulative. But 337 00:16:43,680 --> 00:16:46,720 Speaker 8: the biggest problem was there was there's been no clear 338 00:16:46,760 --> 00:16:47,720 Speaker 8: regulation and there's. 339 00:16:47,560 --> 00:16:50,080 Speaker 2: No clear regulation for AI either. Is that needed? 340 00:16:51,160 --> 00:16:54,840 Speaker 8: Yes, it is needed, And I think that the most 341 00:16:54,840 --> 00:16:57,600 Speaker 8: promising thing because by the way, it's not just needed, 342 00:16:57,640 --> 00:16:59,880 Speaker 8: it's needed badly. And I think that we've seen some 343 00:17:00,400 --> 00:17:03,800 Speaker 8: extraordinarily intelligent people that have come out and it said 344 00:17:03,920 --> 00:17:06,800 Speaker 8: it's needed and we need to really focus on this. 345 00:17:07,200 --> 00:17:10,719 Speaker 8: I think the companies that are building this recognize that 346 00:17:11,040 --> 00:17:12,159 Speaker 8: regulation is needed. 347 00:17:12,520 --> 00:17:14,119 Speaker 5: And I think, and what. 348 00:17:14,000 --> 00:17:17,400 Speaker 8: I'm seeing is really promising is the founders of these companies, 349 00:17:17,440 --> 00:17:20,359 Speaker 8: the founders of Stability, the founders of Entropic, the founders 350 00:17:20,400 --> 00:17:25,040 Speaker 8: of open Ai, Google and otherwise are really forward thinking 351 00:17:25,200 --> 00:17:29,000 Speaker 8: about what regulation should be because I don't think anybody 352 00:17:29,000 --> 00:17:32,120 Speaker 8: wants this to be an unfettered market. And I think 353 00:17:32,119 --> 00:17:35,359 Speaker 8: there are data privacy implications. I think there are data 354 00:17:35,480 --> 00:17:39,120 Speaker 8: there's what is the value of the data? I think 355 00:17:39,160 --> 00:17:43,880 Speaker 8: there's misinformation issues. I work really heavily in eliminating child 356 00:17:43,920 --> 00:17:47,600 Speaker 8: sexual abuse material from the Internet. I think that's an 357 00:17:47,640 --> 00:17:51,360 Speaker 8: issue that needs to be considered. I think biases need 358 00:17:51,400 --> 00:17:54,960 Speaker 8: to be considered. I think that the traceability of this 359 00:17:55,080 --> 00:17:57,040 Speaker 8: data needs to be considered. All of these things need 360 00:17:57,080 --> 00:17:59,480 Speaker 8: to be considered. The good news is is that the 361 00:17:59,520 --> 00:18:03,159 Speaker 8: founders these companies and the people that are building this technology, 362 00:18:03,160 --> 00:18:07,199 Speaker 8: they're cognizant of it, they're proactive about it. They're considering 363 00:18:07,280 --> 00:18:10,160 Speaker 8: what needs to be done and what good regulation would 364 00:18:10,200 --> 00:18:14,040 Speaker 8: look like, because it's really hard if you don't understand 365 00:18:14,119 --> 00:18:17,359 Speaker 8: how this technology works to understand how to regulate it. 366 00:18:17,440 --> 00:18:18,879 Speaker 5: And the truth, there's a lot. 367 00:18:18,720 --> 00:18:23,439 Speaker 8: Of people that are fear mongering on general artificial intelligence, 368 00:18:23,480 --> 00:18:27,000 Speaker 8: and we're not there. We do not have general artificial 369 00:18:27,000 --> 00:18:33,639 Speaker 8: intelligence yet. It's closer, and we need to be really 370 00:18:33,680 --> 00:18:37,200 Speaker 8: considering what the implications of that are. But the benefits 371 00:18:37,240 --> 00:18:41,199 Speaker 8: to society for the utilization of this technology are going 372 00:18:41,240 --> 00:18:43,520 Speaker 8: to be extraordinary. And what we don't want to do 373 00:18:44,000 --> 00:18:45,720 Speaker 8: is have it regulated in a way that puts it 374 00:18:45,720 --> 00:18:49,040 Speaker 8: inside of a box in box and doesn't allow us 375 00:18:49,080 --> 00:18:53,720 Speaker 8: to continue to innovate, because this isn't just happening domestically. 376 00:18:54,560 --> 00:18:58,919 Speaker 8: This is happening all around the world, and the push 377 00:18:59,080 --> 00:19:03,600 Speaker 8: towards general order official intelligence is going to happen. If 378 00:19:03,640 --> 00:19:05,520 Speaker 8: not here, it's going to happen in China, it's going 379 00:19:05,520 --> 00:19:07,920 Speaker 8: to happen all over the world, and the implications of 380 00:19:07,960 --> 00:19:08,399 Speaker 8: it are. 381 00:19:08,240 --> 00:19:09,200 Speaker 5: Going to be massive. 382 00:19:09,760 --> 00:19:12,800 Speaker 8: So we don't amstring ourselves in a way where we 383 00:19:12,880 --> 00:19:17,160 Speaker 8: are now behind and we're underneath the rock of somebody 384 00:19:17,160 --> 00:19:20,880 Speaker 8: else's general AI. And so it needs to be done 385 00:19:20,920 --> 00:19:25,359 Speaker 8: in an intelligent, thoughtful, proactive way. And the good news 386 00:19:25,480 --> 00:19:28,240 Speaker 8: is that the founders of these companies recognize that, realize that, 387 00:19:28,359 --> 00:19:29,640 Speaker 8: and are being proactive about it. 388 00:19:29,720 --> 00:19:33,240 Speaker 2: Ashton Koscher, thanks for being proactive on the subject with us, 389 00:19:33,280 --> 00:19:36,359 Speaker 2: Sam Venture's co founder general partner. And we're going to 390 00:19:36,400 --> 00:19:38,919 Speaker 2: stick so much more with what's happening in the private markets, 391 00:19:38,920 --> 00:19:41,320 Speaker 2: also what's happening in the public market. It's Etsy earnings 392 00:19:41,359 --> 00:19:44,320 Speaker 2: out last night, first calling on revenue beating analyst estimates. 393 00:19:44,400 --> 00:19:47,959 Speaker 2: Joining us now ever Core II analyst Shwedder Kajuria she 394 00:19:48,000 --> 00:19:50,520 Speaker 2: has an outperform rating on the Etsy stop with one 395 00:19:50,560 --> 00:19:53,840 Speaker 2: hundred and twenty dollars price target. But ultimately we're just 396 00:19:53,840 --> 00:19:56,640 Speaker 2: talking about how AI is going to OpenD everything, and 397 00:19:56,720 --> 00:19:58,840 Speaker 2: if you weren't an e commerce player back in the 398 00:19:58,920 --> 00:20:01,359 Speaker 2: day that managed to pivot. How are you seeing Etsy 399 00:20:01,480 --> 00:20:04,040 Speaker 2: navigate these current macro conditions? 400 00:20:04,440 --> 00:20:07,399 Speaker 9: A Hi, thanks for having me well in terms of 401 00:20:07,440 --> 00:20:10,920 Speaker 9: the In terms of Etsy platform, I first of all, 402 00:20:11,160 --> 00:20:14,040 Speaker 9: just lay the land. We do have an outperform rating, 403 00:20:14,080 --> 00:20:16,840 Speaker 9: but we also had a tactical underperform rating into the 404 00:20:16,880 --> 00:20:19,840 Speaker 9: print into the year, which we don't have right now. 405 00:20:19,720 --> 00:20:22,480 Speaker 10: Because we remain near from cautious on et Sea. 406 00:20:22,800 --> 00:20:25,520 Speaker 9: And the biggest reason really is that the macro environment 407 00:20:25,760 --> 00:20:28,639 Speaker 9: is very unclear. There is a lot of volatility and 408 00:20:28,760 --> 00:20:33,680 Speaker 9: there is a lot of choppy waters that creates cloudiness 409 00:20:33,680 --> 00:20:35,320 Speaker 9: in terms of lack of visibility of. 410 00:20:35,280 --> 00:20:37,040 Speaker 10: What's to come and so ETS. 411 00:20:37,240 --> 00:20:41,600 Speaker 9: Actually the management team did comment on discretionary spend being pressured. 412 00:20:42,080 --> 00:20:45,560 Speaker 9: There's a mix shift of spend away from goods into services, 413 00:20:45,840 --> 00:20:50,240 Speaker 9: and the lower household income folks are actually watching their 414 00:20:50,240 --> 00:20:53,120 Speaker 9: spend even more closely and so they're feeling the pressure. 415 00:20:53,200 --> 00:20:55,960 Speaker 9: And some two of their biggest categories are moment living 416 00:20:56,000 --> 00:20:59,760 Speaker 9: in crafts and those are seeing pricing pressures as people 417 00:20:59,760 --> 00:21:03,080 Speaker 9: are shifting away from higher priced items to low price items. 418 00:21:03,200 --> 00:21:04,640 Speaker 10: All this put together and you. 419 00:21:04,640 --> 00:21:07,240 Speaker 9: See pressure on ETS stop line growth and we did 420 00:21:07,280 --> 00:21:09,680 Speaker 9: see that. So in the first quarter they did beat 421 00:21:09,720 --> 00:21:13,520 Speaker 9: all the estimates. But the guidance for the second quarter suggests. 422 00:21:13,280 --> 00:21:14,520 Speaker 10: Fairly muted trends. 423 00:21:14,600 --> 00:21:17,400 Speaker 9: And most importantly, it is unclear what the back half 424 00:21:17,440 --> 00:21:19,719 Speaker 9: of this year will bring, especially if we head into 425 00:21:19,840 --> 00:21:22,560 Speaker 9: a deeper recession. So near term I remain cautious, but 426 00:21:22,720 --> 00:21:24,359 Speaker 9: long term I'm bullish. 427 00:21:24,400 --> 00:21:27,199 Speaker 3: On the platform, Sweer, good morning to you. I mean, 428 00:21:27,240 --> 00:21:31,280 Speaker 3: a lot of your cell side colleagues complemented ETS on 429 00:21:31,359 --> 00:21:34,960 Speaker 3: its execution in that tough macro environment. They complemented ETS 430 00:21:35,359 --> 00:21:39,120 Speaker 3: on how they communicated guidance. But the stocks down significantly. 431 00:21:39,160 --> 00:21:40,679 Speaker 3: Why are they not being rewarded for that? 432 00:21:41,200 --> 00:21:44,520 Speaker 10: Well, because the execution has been good. This is a 433 00:21:44,560 --> 00:21:45,760 Speaker 10: stellar management team. 434 00:21:45,800 --> 00:21:49,000 Speaker 9: They've done a fantastic job in terms of turning around 435 00:21:49,000 --> 00:21:50,800 Speaker 9: at Sea over the past several years. 436 00:21:51,200 --> 00:21:51,560 Speaker 4: Now. 437 00:21:51,720 --> 00:21:55,560 Speaker 9: The challenge is that there's not a ton of clarity 438 00:21:55,640 --> 00:21:56,360 Speaker 9: on the back half. 439 00:21:56,359 --> 00:21:58,960 Speaker 10: If we just take what the guidance calls for in 440 00:21:58,960 --> 00:22:01,399 Speaker 10: the second quarter, it is calling for a. 441 00:22:01,440 --> 00:22:06,000 Speaker 9: Europear decline, meaningful decline at the low end of their guide, 442 00:22:06,040 --> 00:22:08,480 Speaker 9: and then some some growth at the high end of 443 00:22:08,520 --> 00:22:09,000 Speaker 9: their guide. 444 00:22:09,040 --> 00:22:11,280 Speaker 10: And so what that tells us is that the back 445 00:22:11,400 --> 00:22:14,440 Speaker 10: half we could potentially see low single digit growth rate 446 00:22:14,520 --> 00:22:16,840 Speaker 10: in GMS grow and then what does that tell us 447 00:22:16,840 --> 00:22:19,920 Speaker 10: about next year? And I think that's that's what's creating 448 00:22:20,560 --> 00:22:22,280 Speaker 10: uncertainty among among investors. 449 00:22:22,320 --> 00:22:25,600 Speaker 9: And then just one more point, there are definitely great 450 00:22:25,640 --> 00:22:27,760 Speaker 9: things to call out, and that's what probably some of 451 00:22:27,800 --> 00:22:30,000 Speaker 9: the other other folks were talking about. His new buyer 452 00:22:30,040 --> 00:22:33,760 Speaker 9: growth came in strong, they reactivated a lot of new 453 00:22:34,359 --> 00:22:35,280 Speaker 9: other buyers as well. 454 00:22:35,400 --> 00:22:37,800 Speaker 10: Those were those were pretty strong metrics. 455 00:22:37,920 --> 00:22:40,320 Speaker 9: But at the same time, habitual buyers, which down for 456 00:22:40,400 --> 00:22:43,040 Speaker 9: about forty three percent of their GMS, came in lighter 457 00:22:43,040 --> 00:22:45,320 Speaker 9: than we thought. And that's that's something not to miss 458 00:22:45,440 --> 00:22:45,800 Speaker 9: as well. 459 00:22:45,960 --> 00:22:50,280 Speaker 3: Schwideck, cojerior of Evical Things, Etsy, thank you so much. Now, 460 00:22:50,359 --> 00:22:53,280 Speaker 3: coming out the Biden administration meeting with CEOs from the 461 00:22:53,320 --> 00:22:57,280 Speaker 3: top AI names pressuring them to implement safeguards for the 462 00:22:57,280 --> 00:22:59,320 Speaker 3: emerging technology, We're going to talk to Adam wensch Or, 463 00:22:59,400 --> 00:23:03,399 Speaker 3: co founder CEO of Arthur about the tools necessary to 464 00:23:03,520 --> 00:23:07,440 Speaker 3: navigate the risks in artificial intelligence. From San Francisco and 465 00:23:07,520 --> 00:23:12,520 Speaker 3: from New York. This is Bloomberg. 466 00:23:19,400 --> 00:23:21,840 Speaker 2: Welcome back to Bloomberg Technology. I'm Caroline hired in New. 467 00:23:21,760 --> 00:23:24,960 Speaker 3: York and I'm ed Ludlow in San Francisco. Let's turn 468 00:23:25,000 --> 00:23:28,160 Speaker 3: to Qualcom learnings, where the largest maker of smartphone process 469 00:23:28,280 --> 00:23:32,000 Speaker 3: is offered a disappointing outlook on demand for mobile phones. 470 00:23:32,080 --> 00:23:35,840 Speaker 3: Joining us here in San Francisco, Cunjan Sabani of Bloomberg Intelligence. 471 00:23:35,880 --> 00:23:38,119 Speaker 3: I look at the forecast eight point one billion dollars 472 00:23:38,440 --> 00:23:41,000 Speaker 3: to eight point nine billion dollars for the fiscal third 473 00:23:41,520 --> 00:23:44,520 Speaker 3: What does that tell us about the recovery for qua 474 00:23:44,600 --> 00:23:45,560 Speaker 3: COM's end markets? 475 00:23:45,840 --> 00:23:47,600 Speaker 11: And my mentor al is used to tell me, the 476 00:23:47,600 --> 00:23:49,919 Speaker 11: longer the party, the bigger the hangover. And that's what 477 00:23:50,080 --> 00:23:53,240 Speaker 11: exactly is happening here. Smartphone makers are not drawing down 478 00:23:53,280 --> 00:23:56,280 Speaker 11: in inventry as fast as the company expected, and it's 479 00:23:56,359 --> 00:23:59,200 Speaker 11: driven by lack of visible signs of demand coming back. 480 00:23:59,359 --> 00:24:01,840 Speaker 11: So you have this combination of high inventories and low 481 00:24:01,920 --> 00:24:03,680 Speaker 11: demand that's causing really the pain. 482 00:24:04,440 --> 00:24:07,720 Speaker 5: Longer the party, bigger the hangover. Remember that one. 483 00:24:08,000 --> 00:24:10,320 Speaker 3: I guess that our expectations at the beginning of this 484 00:24:10,400 --> 00:24:14,040 Speaker 3: year were the inventories would clear up in the second quarter. 485 00:24:14,760 --> 00:24:16,439 Speaker 3: What do we now think is going to happen in 486 00:24:16,480 --> 00:24:19,040 Speaker 3: the rest of the year for the smartphone market, but 487 00:24:19,119 --> 00:24:21,239 Speaker 3: also Quowcomm in particular. 488 00:24:21,640 --> 00:24:24,679 Speaker 11: So we expected the first half to be the bottom, 489 00:24:24,720 --> 00:24:27,240 Speaker 11: which now looks to be kind of pushed out, and 490 00:24:27,720 --> 00:24:30,919 Speaker 11: we don't think the recovery will come until the fourth quarter. 491 00:24:31,400 --> 00:24:34,160 Speaker 11: There is no current trigger for the demand. The next 492 00:24:34,160 --> 00:24:37,320 Speaker 11: trigger will be in September when Apple launches his flagship 493 00:24:37,359 --> 00:24:40,040 Speaker 11: device and then going into December in the holiday season 494 00:24:40,200 --> 00:24:42,560 Speaker 11: that generally spurs up demand. So until then, there is 495 00:24:42,600 --> 00:24:44,400 Speaker 11: no clear trigger for demand to come back. 496 00:24:44,600 --> 00:24:47,760 Speaker 3: The CFO mentioned their modem only customer, which is code 497 00:24:47,800 --> 00:24:49,879 Speaker 3: for like Apple wink wink, and I think the idea 498 00:24:49,920 --> 00:24:52,320 Speaker 3: is they've done a big order earlier in the year. 499 00:24:52,480 --> 00:24:56,239 Speaker 3: Bloomberg Intelligence, you also spotted something about China. What are 500 00:24:56,240 --> 00:24:57,800 Speaker 3: we learning about quowcom in China? 501 00:24:58,359 --> 00:25:00,760 Speaker 11: So China is a big market for them. It's over 502 00:25:00,840 --> 00:25:04,520 Speaker 11: fifty percent exposure for them. So like China had was 503 00:25:04,560 --> 00:25:06,919 Speaker 11: the last to go in lockdowns, and that's where the 504 00:25:06,960 --> 00:25:09,920 Speaker 11: demand has not come back yet. So when we look 505 00:25:09,920 --> 00:25:13,919 Speaker 11: at China data, we were expecting that coming second quarter calendar, 506 00:25:13,960 --> 00:25:16,240 Speaker 11: second quarter demand will start picking up, but we're not 507 00:25:16,280 --> 00:25:18,560 Speaker 11: seeing the signals for it to come back. And again 508 00:25:18,600 --> 00:25:21,720 Speaker 11: it's that we are no longer in the supply constraint environment, 509 00:25:21,800 --> 00:25:26,280 Speaker 11: so smartphone manufacturers don't have the incentive to overorder ahead 510 00:25:26,320 --> 00:25:27,919 Speaker 11: in anticipating demand ahead. 511 00:25:28,000 --> 00:25:31,640 Speaker 3: Let's go back to Apple really quick that that commentary 512 00:25:31,640 --> 00:25:35,000 Speaker 3: around modemon only ordering earlier. How did that impact the 513 00:25:35,040 --> 00:25:36,280 Speaker 3: guide going forward? 514 00:25:37,200 --> 00:25:40,359 Speaker 11: So typically they would start ordering up for the September launch, 515 00:25:40,640 --> 00:25:43,840 Speaker 11: but because they had ordered over ordered ahead, they have 516 00:25:43,960 --> 00:25:47,119 Speaker 11: enough inventory and they're trying to order in time looking 517 00:25:47,160 --> 00:25:49,240 Speaker 11: at near term demand instead of overordering. 518 00:25:49,760 --> 00:25:54,040 Speaker 3: Conjen Sabani new to the team here at Bloomberg Intelligence, Caroline. 519 00:25:53,880 --> 00:25:57,600 Speaker 2: Great conversation, and let's well go back to artificial intelligence again, 520 00:25:57,640 --> 00:26:00,399 Speaker 2: shall we, because look, it's important on Capital Health President 521 00:26:00,440 --> 00:26:03,280 Speaker 2: Kamala Harris of course, well meet with leaders of major 522 00:26:03,320 --> 00:26:06,280 Speaker 2: AI companies including Google, Microsoft, Open Ai to address the 523 00:26:06,400 --> 00:26:10,440 Speaker 2: risks that can mitigate from their systems. QAI company Arthur 524 00:26:10,680 --> 00:26:13,640 Speaker 2: launching its new service called Arthur Shield Look. It acts 525 00:26:13,680 --> 00:26:16,760 Speaker 2: as a firewall to protect firms against the risks posed 526 00:26:16,760 --> 00:26:21,280 Speaker 2: from large language models like chatchipt afur CEO Anam Winschel 527 00:26:21,440 --> 00:26:24,119 Speaker 2: joins us now to discuss in the news day in, 528 00:26:24,280 --> 00:26:28,520 Speaker 2: day out, is the opportunity around artificial intelligence, but the 529 00:26:28,600 --> 00:26:31,400 Speaker 2: nerves around it too is Kamala Harris is currently discussing 530 00:26:32,119 --> 00:26:34,720 Speaker 2: what worries are you seeing with your clients. You serve 531 00:26:34,760 --> 00:26:36,320 Speaker 2: some of the biggest banks, you serve some of the 532 00:26:36,359 --> 00:26:39,480 Speaker 2: key companies department defense. How is chat ChiPT becoming a 533 00:26:39,560 --> 00:26:40,120 Speaker 2: risk for them? 534 00:26:40,520 --> 00:26:41,119 Speaker 5: Absolutely? 535 00:26:41,200 --> 00:26:42,600 Speaker 12: So, I think you know, the first thing is they 536 00:26:42,640 --> 00:26:45,040 Speaker 12: see the opportunity. Right there's you know, studies that have 537 00:26:45,080 --> 00:26:47,159 Speaker 12: shown that in certain job families they can get up 538 00:26:47,200 --> 00:26:49,560 Speaker 12: to a fifty percent increase in productivity, which is just 539 00:26:49,800 --> 00:26:51,960 Speaker 12: a game changer. But in the rush to kind of 540 00:26:51,960 --> 00:26:55,320 Speaker 12: adopt it and take advantage of those those benefits, they 541 00:26:55,400 --> 00:26:57,639 Speaker 12: run into a lot of walls. And those things include 542 00:26:57,720 --> 00:26:59,400 Speaker 12: the fact that it makes things up with some people 543 00:26:59,520 --> 00:27:01,640 Speaker 12: term hallution and nations, the fact that it can leak 544 00:27:01,720 --> 00:27:04,320 Speaker 12: back sensitive data that it was trained on or that 545 00:27:04,359 --> 00:27:06,919 Speaker 12: it was asked about. The fact that sometimes it can 546 00:27:07,000 --> 00:27:11,840 Speaker 12: return toxic or responses that aren't value aligned with your organization. 547 00:27:11,920 --> 00:27:14,560 Speaker 12: And so, you know, we're helping our customers solve these 548 00:27:14,560 --> 00:27:16,360 Speaker 12: problems so that they can take advantage of this game 549 00:27:16,440 --> 00:27:17,400 Speaker 12: changing technology. 550 00:27:17,640 --> 00:27:21,359 Speaker 2: Describe how that happens, because there are rivals to open Aiyes, 551 00:27:21,400 --> 00:27:23,480 Speaker 2: chat GPT looking at at least showing you where the 552 00:27:23,480 --> 00:27:26,359 Speaker 2: information has come from. How do you ensure that when 553 00:27:26,760 --> 00:27:30,639 Speaker 2: we get incorrect information or indeed we're worried about data leaks, 554 00:27:30,680 --> 00:27:31,320 Speaker 2: we're flagged. 555 00:27:31,800 --> 00:27:34,440 Speaker 12: Yeah, absolutely so, I mean these models fundamentally, they don't 556 00:27:34,440 --> 00:27:36,800 Speaker 12: contain all the data they were trained on. They contain 557 00:27:36,880 --> 00:27:40,040 Speaker 12: sort of some like really efficient summaries of it, right, 558 00:27:40,119 --> 00:27:42,240 Speaker 12: and so they don't really know what's true and what's 559 00:27:42,280 --> 00:27:42,720 Speaker 12: not true. 560 00:27:42,960 --> 00:27:44,120 Speaker 5: What they know is what is. 561 00:27:44,119 --> 00:27:47,480 Speaker 12: Probably could be likely to be true. And as they're 562 00:27:47,520 --> 00:27:50,800 Speaker 12: making these determinations, there are certain flags and signals which 563 00:27:50,840 --> 00:27:53,720 Speaker 12: you can key off of to Jenet to decide like 564 00:27:53,760 --> 00:27:56,040 Speaker 12: what are the how confident are we that this model 565 00:27:56,200 --> 00:27:59,399 Speaker 12: knows what it's talking about. And that's what our research 566 00:27:59,440 --> 00:28:02,040 Speaker 12: team has developed is the way to to really kind 567 00:28:02,080 --> 00:28:03,879 Speaker 12: of set threshold and block things that you know you 568 00:28:03,920 --> 00:28:06,840 Speaker 12: really shouldn't be confident are correct. Because these are getting 569 00:28:06,840 --> 00:28:10,640 Speaker 12: deployed into legal and medical contexts and you know, business 570 00:28:10,640 --> 00:28:13,040 Speaker 12: people and uh, you know when you're when you're deploying 571 00:28:13,080 --> 00:28:14,480 Speaker 12: them in this way and people are actioning off them, 572 00:28:14,480 --> 00:28:17,240 Speaker 12: there's real consequences to having misinformation out there. 573 00:28:17,359 --> 00:28:19,440 Speaker 2: How many of your clients just want to now add 574 00:28:19,440 --> 00:28:23,440 Speaker 2: out bad of open ais chat GIPT within the four 575 00:28:23,480 --> 00:28:26,040 Speaker 2: walls of their businesses or virtual four walls, because we 576 00:28:26,080 --> 00:28:27,320 Speaker 2: just saw it was Samsung for example. 577 00:28:27,359 --> 00:28:29,800 Speaker 12: Yeah, the majority of them absolutely there. You can't use 578 00:28:29,800 --> 00:28:32,720 Speaker 12: the public open AI. They're starting to be able to 579 00:28:32,720 --> 00:28:35,600 Speaker 12: stand it up with inside of Microsoft Azure Security Zone. 580 00:28:35,600 --> 00:28:37,600 Speaker 12: But even that, there's there's a number of problems that 581 00:28:37,640 --> 00:28:39,800 Speaker 12: come with that because ultimately, what these firms want to 582 00:28:39,840 --> 00:28:42,080 Speaker 12: do is be able to take something like a chat GPT, 583 00:28:42,240 --> 00:28:44,680 Speaker 12: but then train it on augment it with training it 584 00:28:44,720 --> 00:28:46,720 Speaker 12: on their own data so that they can ask it 585 00:28:46,840 --> 00:28:49,520 Speaker 12: questions that are you know, more informed by their their 586 00:28:49,560 --> 00:28:52,800 Speaker 12: business knowledge, and that's one of the areas where you know, 587 00:28:52,880 --> 00:28:54,920 Speaker 12: leaking that data back in the wrong context can be 588 00:28:54,960 --> 00:28:56,360 Speaker 12: really harmful and really damaging. 589 00:28:57,200 --> 00:29:00,239 Speaker 2: What ED seems to be in every part of our 590 00:29:00,280 --> 00:29:02,880 Speaker 2: conversation is the pros and the cons here we just 591 00:29:02,880 --> 00:29:05,200 Speaker 2: heard it from Ashton Kutsher. We're now talking about really 592 00:29:05,280 --> 00:29:07,880 Speaker 2: cyber can be helped and hindered by. 593 00:29:07,760 --> 00:29:10,640 Speaker 3: This, right, Yeah, Adam, I'm interesting for your take on this. 594 00:29:10,680 --> 00:29:13,440 Speaker 3: So as at RSA conference last week and everyone's talking 595 00:29:13,440 --> 00:29:17,320 Speaker 3: about generative AI balance between how useful it's going to 596 00:29:17,320 --> 00:29:22,000 Speaker 3: be in making data secure and tracking attacks versus how 597 00:29:22,160 --> 00:29:25,400 Speaker 3: dangerous it is in the hands of bad actors, author 598 00:29:25,440 --> 00:29:26,800 Speaker 3: sits at the kind of the intersection of that. 599 00:29:26,960 --> 00:29:30,360 Speaker 12: What's your view, Yeah, absolutely, I think Look, the risks 600 00:29:30,360 --> 00:29:32,680 Speaker 12: around it are just beginning to be understood as it's 601 00:29:32,760 --> 00:29:35,240 Speaker 12: rolled out in some of these large applications, and so 602 00:29:35,280 --> 00:29:37,480 Speaker 12: I think we're sort of in the early days of 603 00:29:37,920 --> 00:29:41,000 Speaker 12: really figuring out how to effectively operationalize lms and the 604 00:29:41,040 --> 00:29:44,040 Speaker 12: benefits they bring. But even in the early days, we've 605 00:29:44,040 --> 00:29:47,239 Speaker 12: seen lots of examples of all sorts of problems that, 606 00:29:47,480 --> 00:29:49,600 Speaker 12: you know, just just make it prevent people from putting 607 00:29:49,600 --> 00:29:51,760 Speaker 12: it into production without the appropriate safeguards. 608 00:29:53,200 --> 00:29:57,040 Speaker 3: So Vice President Harris is welcoming all these great names 609 00:29:57,040 --> 00:30:01,760 Speaker 3: across Open AI Microsoft today to have a conversation about 610 00:30:01,960 --> 00:30:06,520 Speaker 3: artificial intelligence. When industry participants like you see something like that, 611 00:30:06,680 --> 00:30:08,600 Speaker 3: what do you actually expect to come out of it? 612 00:30:09,440 --> 00:30:12,080 Speaker 12: Yeah, Look, I think it's a it's a great first step, 613 00:30:12,080 --> 00:30:14,800 Speaker 12: and there's a lot of value in convening that and 614 00:30:14,800 --> 00:30:17,000 Speaker 12: certainly there's a really important role for the government to 615 00:30:17,040 --> 00:30:19,320 Speaker 12: play in this, and so I'm glad the conversation is 616 00:30:19,360 --> 00:30:21,920 Speaker 12: getting started. My hope is that they also, you know, 617 00:30:21,960 --> 00:30:25,320 Speaker 12: solicit the opinions of not just the people making the technology, 618 00:30:25,320 --> 00:30:26,080 Speaker 12: but those who. 619 00:30:25,920 --> 00:30:27,560 Speaker 5: Are part of the process. 620 00:30:27,240 --> 00:30:29,360 Speaker 12: Of actually deploying it in the world and making sure 621 00:30:29,360 --> 00:30:31,920 Speaker 12: that it works properly and that it and that it 622 00:30:32,000 --> 00:30:36,040 Speaker 12: you know, serves its purpose in a credible and unharmful way. 623 00:30:37,280 --> 00:30:39,800 Speaker 3: All right, thanks to Adam Wench or Arthur co founder, 624 00:30:40,160 --> 00:30:52,160 Speaker 3: and see. 625 00:30:48,560 --> 00:30:48,600 Speaker 9: That. 626 00:30:48,720 --> 00:30:50,800 Speaker 2: We keep talking about the macro, We keep talking about 627 00:30:50,840 --> 00:30:53,560 Speaker 2: the banking fallout as it continues to be a crisis, 628 00:30:53,600 --> 00:30:57,280 Speaker 2: how that's impacting startups general to AI apparently seems to 629 00:30:57,280 --> 00:30:59,440 Speaker 2: be some sort of green shoot for the venture capitalists 630 00:30:59,480 --> 00:31:01,520 Speaker 2: out there and in terms of the industry, but there's 631 00:31:01,560 --> 00:31:05,680 Speaker 2: perhaps a broader area higher growth potential the female economy, 632 00:31:05,800 --> 00:31:07,920 Speaker 2: from women's health, tech to e commerce and much more. 633 00:31:07,960 --> 00:31:11,520 Speaker 2: That's the topic of Monique Woodard's white paper out today 634 00:31:11,560 --> 00:31:14,440 Speaker 2: and it's called Finding Alpha the Trillion Dollar Female Economy. 635 00:31:14,640 --> 00:31:17,720 Speaker 2: Kate Lynch's founding partner and managing director Money wood joins 636 00:31:17,800 --> 00:31:20,400 Speaker 2: us right now and great to welcome you back. Many 637 00:31:20,560 --> 00:31:23,280 Speaker 2: talk to us about sort of the focus you have, 638 00:31:23,400 --> 00:31:26,080 Speaker 2: What problems do you want to solve for women that 639 00:31:26,160 --> 00:31:30,040 Speaker 2: you think is so highly advantageous for investment too. 640 00:31:32,120 --> 00:31:36,640 Speaker 13: Absolutely so, investors are always looking for these growth markets, 641 00:31:36,720 --> 00:31:39,880 Speaker 13: and I think that women are an emerging consumer that's 642 00:31:39,960 --> 00:31:44,120 Speaker 13: emerged for quite some time. Women are now now control 643 00:31:44,200 --> 00:31:49,440 Speaker 13: eighty five percent of consumer spending, they control a significant 644 00:31:49,440 --> 00:31:51,920 Speaker 13: amount of assets, and that number is expected to triple 645 00:31:51,960 --> 00:31:55,160 Speaker 13: over the next decade. And that points to a few 646 00:31:55,160 --> 00:31:59,480 Speaker 13: different things. It points to opportunities in consumer retail and 647 00:31:59,520 --> 00:32:02,200 Speaker 13: e commerce, of course, but also things like women help, 648 00:32:02,240 --> 00:32:06,800 Speaker 13: women's health, semtech, and the care economy. We have to 649 00:32:06,800 --> 00:32:10,440 Speaker 13: figure out how both care and work get done as 650 00:32:10,520 --> 00:32:14,000 Speaker 13: more women enter the workforce and start to move up 651 00:32:14,000 --> 00:32:14,480 Speaker 13: the ladder. 652 00:32:15,000 --> 00:32:17,840 Speaker 2: You name check some companies Maven Health of course, which 653 00:32:17,880 --> 00:32:21,800 Speaker 2: has been proven, and Unicorn and Resilient in this headwind 654 00:32:22,120 --> 00:32:24,680 Speaker 2: macro headwind kind of environment. You mentioned him in hers 655 00:32:24,760 --> 00:32:29,280 Speaker 2: carot fertility. I'm interested, though, like why write this white paper? 656 00:32:29,880 --> 00:32:32,800 Speaker 2: We talk a lot about well, the need to invest 657 00:32:32,840 --> 00:32:35,040 Speaker 2: in women, the need for female investors, people who look 658 00:32:35,040 --> 00:32:38,120 Speaker 2: like you managing the money, and yet ultimately things don't 659 00:32:38,120 --> 00:32:40,400 Speaker 2: really change. What did you need to spell out here 660 00:32:40,400 --> 00:32:41,080 Speaker 2: that was different? 661 00:32:42,520 --> 00:32:44,920 Speaker 13: So the problem is twofold. You do have the problem 662 00:32:44,960 --> 00:32:48,840 Speaker 13: of not enough investors investing in companies led by women 663 00:32:48,960 --> 00:32:52,320 Speaker 13: founders and CEOs. But I also think there is an 664 00:32:52,400 --> 00:32:55,560 Speaker 13: opportunity to show people that, look, there is a massive 665 00:32:55,600 --> 00:33:00,280 Speaker 13: consumer market here that is being underinvested in, and we 666 00:33:00,320 --> 00:33:04,480 Speaker 13: are under investing in companies and products that solve the 667 00:33:04,520 --> 00:33:07,560 Speaker 13: problems and meet the needs of female consumers. And I 668 00:33:07,560 --> 00:33:10,400 Speaker 13: think that is a really interesting opportunity to try to solve. 669 00:33:11,200 --> 00:33:16,840 Speaker 13: People don't, especially investors, don't change their behavior based on 670 00:33:17,320 --> 00:33:20,360 Speaker 13: things that are nice to do. They change their behavior 671 00:33:20,440 --> 00:33:23,800 Speaker 13: based on the movement of capital markets and discovery of 672 00:33:23,840 --> 00:33:25,000 Speaker 13: new growth markets. 673 00:33:26,560 --> 00:33:29,360 Speaker 3: On that note, Manie, how actively have you discussed your 674 00:33:29,400 --> 00:33:35,040 Speaker 3: thesis with your LPs, with other vench capital piers to 675 00:33:35,160 --> 00:33:39,280 Speaker 3: try and make this into something more substantive in terms 676 00:33:39,280 --> 00:33:42,680 Speaker 3: of deploying capital extremely active. 677 00:33:42,720 --> 00:33:45,400 Speaker 13: It's the second layer of the cake ventures thesis, So 678 00:33:46,360 --> 00:33:50,080 Speaker 13: cake ventures investing companies that touch areas of demographic change 679 00:33:50,080 --> 00:33:52,920 Speaker 13: that are changing technology. The second layer of the cake 680 00:33:53,040 --> 00:33:57,600 Speaker 13: is the increased spending power of women, and I have 681 00:33:57,760 --> 00:34:01,240 Speaker 13: been very open about that with both my pis the 682 00:34:01,240 --> 00:34:03,520 Speaker 13: companies that I invest in, and I think that's why 683 00:34:03,600 --> 00:34:06,000 Speaker 13: founders want to work with me. They know that I 684 00:34:06,120 --> 00:34:09,080 Speaker 13: understand that this is not just a niche. It is 685 00:34:09,840 --> 00:34:13,280 Speaker 13: moving women are moving into you know, a majority position 686 00:34:13,360 --> 00:34:15,080 Speaker 13: in a lot of in a lot of ways, and 687 00:34:15,080 --> 00:34:19,480 Speaker 13: that presents a really compelling opportunity for both my investors 688 00:34:19,480 --> 00:34:22,280 Speaker 13: in the fund and founders who are building businesses. 689 00:34:23,320 --> 00:34:29,080 Speaker 3: Monique, how is the ongoing regional banks crisis impacting you 690 00:34:29,080 --> 00:34:31,200 Speaker 3: your firm but also your portfolio companies? 691 00:34:33,440 --> 00:34:38,200 Speaker 13: Yeah, it's it's you know, candidly, First Republic is my bank. 692 00:34:38,640 --> 00:34:43,200 Speaker 13: I also have portfolio companies who banked with Silicon Valley Bank. 693 00:34:44,040 --> 00:34:46,799 Speaker 13: But looking broadly at the challenges that we've seen with 694 00:34:46,920 --> 00:34:50,600 Speaker 13: Silicon Valley Bank, First Republic and now potentially path West, 695 00:34:51,760 --> 00:34:54,759 Speaker 13: you know, I think the public has has started to 696 00:34:54,760 --> 00:34:58,359 Speaker 13: see these as very high dollar venture capital problems. When 697 00:34:58,880 --> 00:35:02,000 Speaker 13: regional banks are are, you know, providers to small and 698 00:35:02,080 --> 00:35:08,120 Speaker 13: medium sized businesses all over America and these are their customers. 699 00:35:09,560 --> 00:35:13,239 Speaker 13: I think it's become a challenging position that startups and 700 00:35:13,320 --> 00:35:17,000 Speaker 13: small businesses and venture capitalists are all in and I 701 00:35:17,040 --> 00:35:18,760 Speaker 13: think that we need regional banks. 702 00:35:18,800 --> 00:35:19,560 Speaker 10: We need We. 703 00:35:19,520 --> 00:35:22,640 Speaker 13: Don't need more consolidation. But I am very excited that 704 00:35:22,760 --> 00:35:26,880 Speaker 13: JP Morgan is now taking over the First Republic. But 705 00:35:26,920 --> 00:35:30,319 Speaker 13: we do need those regional banks to exist. You also 706 00:35:30,560 --> 00:35:34,680 Speaker 13: worry about carry on, Sorry, I also worry about you know, 707 00:35:34,680 --> 00:35:38,799 Speaker 13: how it will affect underrepresented founders, women founders, women who 708 00:35:39,000 --> 00:35:40,960 Speaker 13: lead venture capital funds and the like. 709 00:35:41,600 --> 00:35:46,080 Speaker 2: Yeah, access equality basically, and the worry that we revert 710 00:35:46,520 --> 00:35:50,439 Speaker 2: to pass behavior. I'm interested in how you're finding those 711 00:35:50,440 --> 00:35:54,920 Speaker 2: portfolio companies that you have back that are led by minorities. 712 00:35:55,239 --> 00:35:57,920 Speaker 2: Are they getting follow on checks or indeed are they 713 00:35:57,960 --> 00:36:00,800 Speaker 2: having to trim more than most? Are they well savvy 714 00:36:00,840 --> 00:36:03,160 Speaker 2: with cash in this current environment to be able to 715 00:36:03,239 --> 00:36:04,799 Speaker 2: weather this macro headwindstorm. 716 00:36:07,080 --> 00:36:12,000 Speaker 13: I find women led companies and companies led by underrepresented 717 00:36:12,000 --> 00:36:15,400 Speaker 13: founders to be really resilient in times of crisis, and 718 00:36:15,480 --> 00:36:17,600 Speaker 13: I think that we are in a time of crisis, 719 00:36:17,600 --> 00:36:20,520 Speaker 13: and I think those companies are proving themselves to be 720 00:36:20,560 --> 00:36:25,560 Speaker 13: incredibly resilient. Unfortunately, many of them never had the kind 721 00:36:25,560 --> 00:36:28,200 Speaker 13: of access to capital that their peers did and so 722 00:36:28,800 --> 00:36:30,960 Speaker 13: learn to do more with left and so I think 723 00:36:31,000 --> 00:36:35,440 Speaker 13: that is created extremely strong companies that will weather the storm. 724 00:36:35,960 --> 00:36:39,840 Speaker 13: But you know, they are still firmed out here like 725 00:36:39,880 --> 00:36:43,359 Speaker 13: myself and like Cake Ventures, that are actively investing in 726 00:36:43,440 --> 00:36:47,000 Speaker 13: women and founders entrepreneurs of all types. 727 00:36:48,080 --> 00:36:51,040 Speaker 2: Mynie, what I could always talk longer for you. Thank you. 728 00:36:51,120 --> 00:36:54,160 Speaker 2: Kate bench, A's founding partner and managing director, such a 729 00:36:54,160 --> 00:36:56,759 Speaker 2: great conversation. Mean while coming up ed so much more 730 00:36:56,840 --> 00:36:59,160 Speaker 2: to do with you from Weimo says it's ready to 731 00:36:59,200 --> 00:37:03,000 Speaker 2: expand right giving riders in San Francisco and Phoenix area 732 00:37:03,120 --> 00:37:05,680 Speaker 2: what options to get around town. We're going to talk 733 00:37:05,680 --> 00:37:08,279 Speaker 2: a bit about dating too with a Waymochief Product. 734 00:37:08,000 --> 00:37:12,480 Speaker 3: Officer ed, Yeah, that's saswap panigrah. We're going to talk 735 00:37:12,480 --> 00:37:14,719 Speaker 3: about that. Keeping our eyes though right now in Ali 736 00:37:14,800 --> 00:37:18,359 Speaker 3: barber Is, its international online shopping unit, is looking at 737 00:37:18,360 --> 00:37:21,680 Speaker 3: a US initial public offering is it weighs its options 738 00:37:21,719 --> 00:37:24,680 Speaker 3: to spur growth for that business, and that includes major 739 00:37:24,680 --> 00:37:27,320 Speaker 3: e commerce brands LAZARDA and Ali Express, the firms in 740 00:37:27,360 --> 00:37:31,520 Speaker 3: the early stages of consideration around the IPO size, which 741 00:37:31,600 --> 00:37:33,239 Speaker 3: is yet to be determined. 742 00:37:34,080 --> 00:37:47,239 Speaker 2: This is Bloomberg, the biggest US IPO since twenty twenty one, 743 00:37:47,680 --> 00:37:50,680 Speaker 2: and shares are popping as they start trading. Health company 744 00:37:50,760 --> 00:37:54,920 Speaker 2: ken View has begun its trading as a independent company 745 00:37:54,960 --> 00:37:57,400 Speaker 2: from J and Jay and shares run the Rise tailan 746 00:37:57,480 --> 00:38:00,520 Speaker 2: old band aid all the sexy stuff ed current up 747 00:38:00,520 --> 00:38:02,720 Speaker 2: for grabs and it looks as though there's some interest 748 00:38:02,719 --> 00:38:05,839 Speaker 2: in this particular stock after what has been kind of 749 00:38:05,880 --> 00:38:08,840 Speaker 2: a drought in the overall world of initial public offerings. 750 00:38:08,880 --> 00:38:09,560 Speaker 2: It was upsized. 751 00:38:10,600 --> 00:38:10,799 Speaker 5: Yeah. 752 00:38:10,840 --> 00:38:13,160 Speaker 3: Look, the shares opened at twenty five dollars fifty three 753 00:38:13,239 --> 00:38:16,440 Speaker 3: cents to share. They're kind of floating around that level. 754 00:38:16,560 --> 00:38:19,360 Speaker 3: Three point eight billion dollars raised from the IPO, biggest 755 00:38:19,400 --> 00:38:21,719 Speaker 3: since twenty twenty one. As you say, care, remember this 756 00:38:21,800 --> 00:38:25,360 Speaker 3: was Johnson and Johnson's consumer health business. They're essentially spinning 757 00:38:25,400 --> 00:38:27,440 Speaker 3: it off. But this is one that we've been waiting 758 00:38:27,480 --> 00:38:31,120 Speaker 3: for a little bit of activity in the IPO market. 759 00:38:31,239 --> 00:38:33,520 Speaker 2: Yeah, gold Mas, Sachs, JP, Morgan Bank of America are 760 00:38:33,600 --> 00:38:36,280 Speaker 2: leading this particular charge on the IPO front. I remember 761 00:38:36,280 --> 00:38:39,160 Speaker 2: this is a company that still perhaps has some overhanging concerns, 762 00:38:39,280 --> 00:38:42,080 Speaker 2: legal concerns. Ken You already has been sued over TALC 763 00:38:42,160 --> 00:38:44,440 Speaker 2: injury claims. In caution, it might be subject to claims 764 00:38:44,480 --> 00:38:47,040 Speaker 2: arising outside the US and Canada. But I got to 765 00:38:47,040 --> 00:38:49,560 Speaker 2: say it, I unfortunately was dosing up my kid with 766 00:38:49,600 --> 00:38:52,480 Speaker 2: taalanol last night. I was usering listery in this morning. 767 00:38:52,480 --> 00:38:56,200 Speaker 2: These things are pretty pretty resilient in the face of 768 00:38:56,239 --> 00:38:57,080 Speaker 2: economic headwinds. 769 00:38:57,120 --> 00:38:58,239 Speaker 5: Yeah. 770 00:38:58,400 --> 00:39:00,920 Speaker 3: Well, it will continue to monitor Chem shares throughout the 771 00:39:00,960 --> 00:39:04,719 Speaker 3: hour as they begin trading here on Bloomberg Television now 772 00:39:04,760 --> 00:39:08,040 Speaker 3: Alphabet's waymos as it plans to expand its self driving 773 00:39:08,080 --> 00:39:11,040 Speaker 3: taxi service in its two main markets. Customers in the 774 00:39:11,080 --> 00:39:14,480 Speaker 3: Greater Phoenix area of Scottsdale and other neighboring cities will 775 00:39:14,480 --> 00:39:17,240 Speaker 3: be able to hail Rise from waymost driverless cars. Service 776 00:39:17,280 --> 00:39:21,280 Speaker 3: will also offer trips in new neighborhoods here in San Francisco. 777 00:39:21,400 --> 00:39:24,720 Speaker 3: Joining us now with the details is Saswat Panagrahi, Weaymo's 778 00:39:24,760 --> 00:39:29,760 Speaker 3: chief product officer. Welcome to the program, Saswat. That's the expansion. Okay, 779 00:39:30,280 --> 00:39:33,200 Speaker 3: so how do we now start measuring progress for WEIMO? 780 00:39:34,320 --> 00:39:36,000 Speaker 5: Yeah, hey, Ed, great to be with you. 781 00:39:36,600 --> 00:39:37,799 Speaker 14: Yeah, it's an exciting time. 782 00:39:38,600 --> 00:39:38,839 Speaker 3: You know. 783 00:39:38,920 --> 00:39:41,800 Speaker 14: We have been the first company to open up in 784 00:39:41,920 --> 00:39:44,920 Speaker 14: the first right healing service to the public, now expanding 785 00:39:44,920 --> 00:39:47,800 Speaker 14: it to the largest contiguous service area on the planet 786 00:39:47,920 --> 00:39:51,080 Speaker 14: with av access, the first to serve airports and we're 787 00:39:51,080 --> 00:39:54,120 Speaker 14: expanding that and in San Francisco, as you did, we're 788 00:39:54,120 --> 00:39:56,080 Speaker 14: expanding that as well. So what you can expect from 789 00:39:56,120 --> 00:39:59,960 Speaker 14: us is we cross ten thousand trips are fully outumum 790 00:40:00,080 --> 00:40:02,440 Speaker 14: strips to riders and we were going to tenext that 791 00:40:02,600 --> 00:40:05,319 Speaker 14: over the course of the next year, roughly by about 792 00:40:05,400 --> 00:40:10,120 Speaker 14: next summer, so more cities, more occasions, more riders the 793 00:40:10,160 --> 00:40:11,040 Speaker 14: same way MO Rider. 794 00:40:12,120 --> 00:40:15,080 Speaker 3: Have you set a date or a target for full 795 00:40:15,120 --> 00:40:17,720 Speaker 3: commercial deployment in any of these markets? 796 00:40:19,239 --> 00:40:20,880 Speaker 14: Ed, I would say in Phoenix we are in a 797 00:40:20,880 --> 00:40:23,640 Speaker 14: full commercial deployment. You just download the app and ride. 798 00:40:23,719 --> 00:40:27,680 Speaker 14: There's no wait lists, there's no NDA, there's no approvals, 799 00:40:27,719 --> 00:40:29,640 Speaker 14: you just download the app and ride. So to give 800 00:40:29,680 --> 00:40:31,759 Speaker 14: you an idea, during Super Bowl, we work with the 801 00:40:31,760 --> 00:40:35,720 Speaker 14: host committee to deal with all the riders and visitors 802 00:40:35,719 --> 00:40:38,560 Speaker 14: to Super Bowl. So that's pretty massive scale already. And 803 00:40:38,600 --> 00:40:40,719 Speaker 14: now that if you look at it, we're covering most 804 00:40:40,760 --> 00:40:43,520 Speaker 14: of major metro Phoenix and anybody can download an app 805 00:40:43,520 --> 00:40:46,600 Speaker 14: and rider, and we're seeing pretty healthy ridership as well 806 00:40:46,640 --> 00:40:48,880 Speaker 14: as engagement as well as retention. So I would say 807 00:40:48,880 --> 00:40:52,880 Speaker 14: it's pretty commercial in Phoenix already full commercial deployment. 808 00:40:55,520 --> 00:40:56,960 Speaker 5: No sorry, SATISFI continue. 809 00:40:57,520 --> 00:40:57,719 Speaker 8: Yeah. 810 00:40:57,719 --> 00:41:00,680 Speaker 14: And in San Francisco we have done of thousands of 811 00:41:00,760 --> 00:41:03,240 Speaker 14: riders in our waitlist. We're waiting for a paid permit 812 00:41:03,280 --> 00:41:05,640 Speaker 14: to be able to begin charging them, but we're still 813 00:41:05,680 --> 00:41:08,480 Speaker 14: offering almost the entirety of the city and today have 814 00:41:08,600 --> 00:41:12,040 Speaker 14: opened up North Beach as well as fishermen's work some 815 00:41:12,080 --> 00:41:15,480 Speaker 14: of the busiest locations of San Francisco to trust testers 816 00:41:15,520 --> 00:41:15,879 Speaker 14: as well. 817 00:41:17,160 --> 00:41:19,200 Speaker 3: There's a lot of discussion at the moment around ours 818 00:41:19,239 --> 00:41:22,359 Speaker 3: fisheral intelligence. My first exposure to that field was talking 819 00:41:22,360 --> 00:41:26,600 Speaker 3: about machine learning in the context of training the perception 820 00:41:26,880 --> 00:41:30,040 Speaker 3: side and the compute side of self driving. So what 821 00:41:30,080 --> 00:41:31,960 Speaker 3: are you doing in the field of AI. How are 822 00:41:32,000 --> 00:41:35,960 Speaker 3: you ramping up? Are you using lllms to improve your 823 00:41:36,000 --> 00:41:37,040 Speaker 3: existing technology? 824 00:41:38,280 --> 00:41:41,400 Speaker 14: Yeah, Ed, I mean we have been focusing on machine 825 00:41:41,480 --> 00:41:44,040 Speaker 14: learning for a very very long time and it's in 826 00:41:44,600 --> 00:41:47,080 Speaker 14: every part of our stack. Everything from perception, like you 827 00:41:47,120 --> 00:41:49,920 Speaker 14: mentioned on how we perceive the world, how we predict 828 00:41:49,920 --> 00:41:53,040 Speaker 14: other people's behavior, being able to distinguish when a pedestrians 829 00:41:53,080 --> 00:41:56,200 Speaker 14: is standing by a curb but not intending to cross 830 00:41:56,360 --> 00:41:59,680 Speaker 14: versus crossing. Also in how we plan how we drive 831 00:41:59,760 --> 00:42:02,520 Speaker 14: through in every part of it that are deep learned 832 00:42:02,560 --> 00:42:05,560 Speaker 14: models all across our stack. We're also in simulation as 833 00:42:05,560 --> 00:42:09,160 Speaker 14: well as in validation, for example simulating rain, fog and 834 00:42:09,200 --> 00:42:11,920 Speaker 14: those things. There's a tremendous amount of machine learning and 835 00:42:11,960 --> 00:42:13,960 Speaker 14: we're staying at the cutting edge of it with our 836 00:42:14,000 --> 00:42:15,960 Speaker 14: research and have published some of it as well. 837 00:42:16,920 --> 00:42:19,960 Speaker 3: All right, thank you, disessat panagra he Weimo's chief product office. 838 00:42:20,080 --> 00:42:22,120 Speaker 3: We'll get both of us in a Weymo car very 839 00:42:22,160 --> 00:42:23,400 Speaker 3: seeing Caroline. 840 00:42:23,080 --> 00:42:25,760 Speaker 2: Excited for it. Meanwhile, while we always talk about cars 841 00:42:25,760 --> 00:42:27,360 Speaker 2: with Apple, but then it seems to be quite on 842 00:42:27,360 --> 00:42:29,160 Speaker 2: the horizon, let's talk about the here and the now. 843 00:42:29,200 --> 00:42:31,400 Speaker 2: The earnings are out after the ball for a preview, 844 00:42:31,680 --> 00:42:33,719 Speaker 2: let's go to mark Gum and then we're likely to 845 00:42:33,719 --> 00:42:34,680 Speaker 2: see sales drop again. 846 00:42:35,680 --> 00:42:38,680 Speaker 15: Yeah, there will be no Apple car announced today during 847 00:42:38,719 --> 00:42:43,040 Speaker 15: earnings Like you said, in terms of Apple earnings today, yeah, 848 00:42:43,080 --> 00:42:47,040 Speaker 15: we're likely looking at another five percent sales decline. Don't 849 00:42:47,080 --> 00:42:49,640 Speaker 15: take my word for it. That's what Apple said would 850 00:42:49,680 --> 00:42:53,680 Speaker 15: be the case when it provided its color on the 851 00:42:53,719 --> 00:42:55,680 Speaker 15: current quarter during its last earnings call. 852 00:42:55,760 --> 00:42:55,960 Speaker 9: Right. 853 00:42:56,160 --> 00:42:58,319 Speaker 15: Traditionally they like to go a little bit under to 854 00:42:58,360 --> 00:43:01,319 Speaker 15: show more of a beat. But it does seem likely 855 00:43:01,360 --> 00:43:04,120 Speaker 15: based on everything that Wall Street is saying, combined with 856 00:43:04,120 --> 00:43:06,239 Speaker 15: what Apple is saying, that we are likely in for 857 00:43:06,320 --> 00:43:09,800 Speaker 15: revenue around ninety two billion, which would be a decline 858 00:43:09,800 --> 00:43:12,719 Speaker 15: from around ninety seven point three billion reported in the 859 00:43:12,760 --> 00:43:15,759 Speaker 15: year ago quarter. This will be the second quarter in 860 00:43:15,760 --> 00:43:18,279 Speaker 15: a rower Apple's going to show an annual decline. So 861 00:43:18,400 --> 00:43:22,560 Speaker 15: that's something that's obviously concerning. Last quarter around they had 862 00:43:22,719 --> 00:43:25,840 Speaker 15: a bit of an explanation, right, the iPhone fourteen pro 863 00:43:26,200 --> 00:43:29,440 Speaker 15: had major supply chain constraints. They were just not able 864 00:43:29,480 --> 00:43:32,560 Speaker 15: to produce produce them because of the COVID zero policies 865 00:43:32,600 --> 00:43:35,160 Speaker 15: at the time in China. Now I'm curious to see 866 00:43:35,160 --> 00:43:37,040 Speaker 15: what they're going to say the reason for this decline 867 00:43:37,080 --> 00:43:40,520 Speaker 15: is whether that's the economy. Maybe they're supply chain issues 868 00:43:40,560 --> 00:43:42,759 Speaker 15: we don't know about. So we'll be interested to see 869 00:43:42,800 --> 00:43:43,840 Speaker 15: how that goes later today. 870 00:43:44,040 --> 00:43:45,880 Speaker 2: I'll wait to see you on the blog, Mark German, 871 00:43:46,000 --> 00:43:48,480 Speaker 2: We thank you. Meanwhile, well we've got some action in 872 00:43:48,520 --> 00:43:51,160 Speaker 2: the IPO market, don't we add finally the popping of Kenvy. 873 00:43:52,080 --> 00:43:52,239 Speaker 1: Yeah. 874 00:43:52,320 --> 00:43:54,840 Speaker 3: Look, we're still trading around that twenty five dollars fifty 875 00:43:54,880 --> 00:43:58,920 Speaker 3: cents mark, but eagerly anticipates we finally got a big ipo, 876 00:43:59,000 --> 00:44:01,520 Speaker 3: the biggest since twenty twenty one. 877 00:44:01,560 --> 00:44:03,759 Speaker 2: That does it for this edition of Bloomberg Technology Ed. 878 00:44:04,760 --> 00:44:08,080 Speaker 3: Yep, don't forget recap the podcast. Wherever you get yours. 879 00:44:08,320 --> 00:44:09,200 Speaker 3: This is Bloomberg