1 00:00:02,440 --> 00:00:10,280 Speaker 1: Bloomberg Audio Studios, podcasts, radio news from Marhart where Innovation, 2 00:00:10,560 --> 00:00:14,920 Speaker 1: money and power Collie in Silicon Valley, NBN. This is 3 00:00:14,960 --> 00:00:18,400 Speaker 1: Bloomberg Technology with Caroline Hyde and Ed Ludlow. 4 00:00:31,360 --> 00:00:33,800 Speaker 2: I'm Caroline Heinde and Bloomberg's world headquarters in New York, 5 00:00:34,200 --> 00:00:35,920 Speaker 2: and I met Ludlow in San Francisco. 6 00:00:36,080 --> 00:00:38,000 Speaker 3: This is Bloomberg Technology coming up. 7 00:00:38,200 --> 00:00:42,720 Speaker 2: Amazon Web Services CEO Adam Slipski is stepping down from 8 00:00:42,760 --> 00:00:44,320 Speaker 2: the job. Details to come. 9 00:00:44,880 --> 00:00:48,479 Speaker 4: Plus Open Ai unveils its updated AI models as Google 10 00:00:48,600 --> 00:00:52,120 Speaker 4: kicks off It's AI event today for coverage ahead, and we. 11 00:00:52,080 --> 00:00:53,800 Speaker 2: Sit down with the CEO of the buy now, pay 12 00:00:53,920 --> 00:00:57,520 Speaker 2: later firm Klana as a company eyes that long awaited IPO, 13 00:00:57,640 --> 00:00:59,600 Speaker 2: But first set's check in on what's already training on 14 00:00:59,600 --> 00:01:02,200 Speaker 2: these public markets, and maybe a little bit of a 15 00:01:02,600 --> 00:01:04,920 Speaker 2: reprieve in some animal spirits even though we see that 16 00:01:04,959 --> 00:01:08,440 Speaker 2: PPI number that produce a price index coming in hotter 17 00:01:08,480 --> 00:01:11,120 Speaker 2: than anticipated, but you get more granular and maybe there's 18 00:01:11,160 --> 00:01:13,520 Speaker 2: sign of cooling in some of the key areas that 19 00:01:13,520 --> 00:01:16,240 Speaker 2: the FED will be focusing on from inflatory pressures here 20 00:01:16,240 --> 00:01:17,800 Speaker 2: in the US and managing to pull up some four 21 00:01:17,840 --> 00:01:20,480 Speaker 2: tens percent on the Nasdaq, and notably, even though we 22 00:01:20,520 --> 00:01:22,720 Speaker 2: saw an initial knee jerk sell off, we're back to 23 00:01:22,800 --> 00:01:25,000 Speaker 2: rallying on the bond market, particularly at the two year yield. 24 00:01:25,240 --> 00:01:27,400 Speaker 2: I'm also shining light on what's happening on those highly 25 00:01:27,440 --> 00:01:30,440 Speaker 2: shortened names. Basically, what's happening with the likes in gamestoc 26 00:01:30,480 --> 00:01:34,120 Speaker 2: AMC Another extraordinary rally, no fundamental reason, but we go 27 00:01:34,200 --> 00:01:37,360 Speaker 2: a long they particularly high short interest stocks, those that 28 00:01:37,400 --> 00:01:39,640 Speaker 2: are widely beed against by the broader market. We're up 29 00:01:39,640 --> 00:01:41,840 Speaker 2: seven percent on that particular index. Move on, have a 30 00:01:41,880 --> 00:01:44,520 Speaker 2: look at what's happening on the world a Bitcoin. Actually 31 00:01:44,640 --> 00:01:48,280 Speaker 2: no move across or read across from the meme frenzy 32 00:01:48,520 --> 00:01:50,840 Speaker 2: to the crypto frenzy. Today Bitcoin just selling off a 33 00:01:50,880 --> 00:01:51,240 Speaker 2: little bit. 34 00:01:51,240 --> 00:01:52,200 Speaker 3: In fact, we've. 35 00:01:52,000 --> 00:01:55,560 Speaker 2: Heard from some key players within the cryptosphere and thinking 36 00:01:55,560 --> 00:01:57,200 Speaker 2: of Might Novogratz saying, actually, look, we're going to be 37 00:01:57,280 --> 00:01:59,480 Speaker 2: ranged brown from fifty five to seventy five thousand. We're 38 00:01:59,480 --> 00:02:01,400 Speaker 2: currently at six two thousand. Let's call it today, ed, 39 00:02:01,440 --> 00:02:02,600 Speaker 2: what are you watching on the micro. 40 00:02:03,360 --> 00:02:06,200 Speaker 4: There is a lot of technology to fit in today's show. 41 00:02:06,240 --> 00:02:08,760 Speaker 4: Let's go to China and the US listed shares of 42 00:02:08,800 --> 00:02:11,560 Speaker 4: Ali Barbera and Tencent Ali Barbera. The story is low 43 00:02:11,639 --> 00:02:15,480 Speaker 4: single digit growth in e commerce and cloud. There's fighting 44 00:02:15,560 --> 00:02:18,960 Speaker 4: talk about AI and fighting talk about the spending power 45 00:02:18,960 --> 00:02:21,520 Speaker 4: of the Chinese consumer, but those US listed shares are 46 00:02:21,520 --> 00:02:24,680 Speaker 4: lower compare contrast with Tencent. They're seeing a lot of 47 00:02:24,760 --> 00:02:28,440 Speaker 4: ad growth, particularly in video, and we Chat is seeing 48 00:02:28,480 --> 00:02:31,560 Speaker 4: a really big surge in usage. Hour later in the program, 49 00:02:31,639 --> 00:02:34,560 Speaker 4: we'll go to team coverage on those two key China names, 50 00:02:34,560 --> 00:02:38,720 Speaker 4: and you mentioned it Google Io. Later today Alphabet will 51 00:02:38,720 --> 00:02:41,440 Speaker 4: show its hand, we think on its next offerings in 52 00:02:41,480 --> 00:02:42,480 Speaker 4: our official intelligence. 53 00:02:42,560 --> 00:02:44,200 Speaker 3: Let's get to our top story. 54 00:02:44,160 --> 00:02:48,200 Speaker 4: And that is Amazon and Amazon Web Services or AWS, 55 00:02:48,639 --> 00:02:52,600 Speaker 4: the cloud unit of Amazon. The company announcing that Adams 56 00:02:52,680 --> 00:02:57,200 Speaker 4: Lipski is stepping down as CEO of AWS effective June third. 57 00:02:57,320 --> 00:03:00,200 Speaker 4: Matt Garman, who started as an intern and am in 58 00:03:00,400 --> 00:03:02,880 Speaker 4: two thousand and five, was one of the early product 59 00:03:03,000 --> 00:03:07,000 Speaker 4: leaders in AWS, will take over as the CEO. The 60 00:03:07,040 --> 00:03:09,919 Speaker 4: shares are off session lows, but still down around eight 61 00:03:09,919 --> 00:03:12,640 Speaker 4: tens of a percent in this Tuesday session, and the 62 00:03:12,680 --> 00:03:16,119 Speaker 4: market asking questions, well, is this bad for Amazon's cash cow? 63 00:03:16,360 --> 00:03:18,480 Speaker 4: And who on earth is Matt Garman. Let's get out 64 00:03:18,520 --> 00:03:21,680 Speaker 4: to Seattle. Bloomberg's Matt Day joins us, and let's start 65 00:03:21,680 --> 00:03:25,240 Speaker 4: with the news at hand. Why is Adam Zelyipsky stepping 66 00:03:25,280 --> 00:03:29,240 Speaker 4: down as CEO? And should we be worried? I suppose 67 00:03:29,560 --> 00:03:31,160 Speaker 4: about the health of AWS. 68 00:03:31,960 --> 00:03:34,880 Speaker 5: I should listened to Amazon CEO Andy Jase tell it, No, 69 00:03:35,000 --> 00:03:38,360 Speaker 5: we shouldn't be worried, he said. When he recruited Adam Selipski, 70 00:03:38,400 --> 00:03:41,000 Speaker 5: who was his right hand in building the WS business, 71 00:03:41,080 --> 00:03:44,120 Speaker 5: back to Amazon in twenty twenty one, they'd had a conversation, said, listen, 72 00:03:44,160 --> 00:03:46,320 Speaker 5: give me a few years and get the next generation 73 00:03:46,360 --> 00:03:49,320 Speaker 5: of leaders in shape, and then we'll talk later. Andy 74 00:03:49,400 --> 00:03:52,480 Speaker 5: Jesse said, Adam Selipsky is considering other challenges. We don't 75 00:03:52,480 --> 00:03:54,680 Speaker 5: know what those might be. Nobody detailed that in their 76 00:03:54,680 --> 00:03:57,160 Speaker 5: emails to staff today. We just know that he is 77 00:03:57,160 --> 00:03:58,480 Speaker 5: out as of next month. 78 00:03:59,080 --> 00:04:02,160 Speaker 2: Some well and rest with family seems to insinuate before 79 00:04:02,200 --> 00:04:04,800 Speaker 2: he launches onto his next challenge. Memory was CEO of 80 00:04:04,800 --> 00:04:08,160 Speaker 2: Tableau for an interim basis in his long career with Amazon. 81 00:04:08,200 --> 00:04:11,480 Speaker 2: I'm interested, therefore, on who Mac Garmon is as we know, 82 00:04:11,680 --> 00:04:15,000 Speaker 2: like what an MBA intern turns now one of the 83 00:04:15,120 --> 00:04:15,960 Speaker 2: key executives. 84 00:04:16,760 --> 00:04:19,520 Speaker 5: That's right, so Matt Garmon, he's a lifer at Amazon 85 00:04:19,640 --> 00:04:21,680 Speaker 5: joined and two thousand and five, is an intern two 86 00:04:21,680 --> 00:04:23,839 Speaker 5: thousand and six as a full timer and led some 87 00:04:23,880 --> 00:04:27,080 Speaker 5: of their initial services, helped set pricing the first names 88 00:04:27,120 --> 00:04:29,599 Speaker 5: for the products AWS put on the market, and since 89 00:04:29,640 --> 00:04:32,159 Speaker 5: then he's spent most of his time as an engineering leader. 90 00:04:32,400 --> 00:04:35,520 Speaker 5: He was leading one of thems on's most important divisions 91 00:04:35,520 --> 00:04:37,960 Speaker 5: of the sort of computing power for rent group, and 92 00:04:38,000 --> 00:04:40,839 Speaker 5: then back in twenty twenty he found himself running sales 93 00:04:40,839 --> 00:04:43,039 Speaker 5: and marketing, and to a lot of AWS insiders that 94 00:04:43,080 --> 00:04:44,880 Speaker 5: really looked like he was getting set up to succeed 95 00:04:44,960 --> 00:04:48,680 Speaker 5: Andy Jasse, the division's founding CEO. Turns out Andy brought 96 00:04:48,720 --> 00:04:51,599 Speaker 5: back Adam Sleipski instead. But this isn't a surprise anybody 97 00:04:51,640 --> 00:04:54,600 Speaker 5: who's been following either Matt Garman's trajectory or or AWSS. 98 00:04:56,600 --> 00:04:57,800 Speaker 3: The market was a bit worried. 99 00:04:57,960 --> 00:05:00,720 Speaker 4: There was a knee jerk reaction if you ca percentage 100 00:05:00,760 --> 00:05:03,479 Speaker 4: point decline and knee jet reaction. Matt and I guess 101 00:05:03,480 --> 00:05:07,400 Speaker 4: the idea is what happens next for AWS? Because Adams 102 00:05:07,440 --> 00:05:11,440 Speaker 4: Lipski left that still accounting for the majority of operating 103 00:05:11,480 --> 00:05:15,159 Speaker 4: income for the entirety of Amazon, and the new figure 104 00:05:15,160 --> 00:05:18,359 Speaker 4: they're giving us is this run rate of one hundred 105 00:05:18,400 --> 00:05:23,080 Speaker 4: billion dollars of revenue sum up where AWS sits today. 106 00:05:24,080 --> 00:05:26,720 Speaker 3: So ABS is the biggest cloud computing company. 107 00:05:27,040 --> 00:05:28,960 Speaker 5: But that said, they've had a bunch of pressure put 108 00:05:29,000 --> 00:05:31,000 Speaker 5: on them in the last year coming out of the pandemic. 109 00:05:31,000 --> 00:05:32,960 Speaker 5: A lot of their biggest corporate customers were looking to 110 00:05:33,000 --> 00:05:36,400 Speaker 5: cut costs. That dropped ABS's growth down to record lows. 111 00:05:36,640 --> 00:05:40,160 Speaker 5: They've bumped up beyond that, which the market seems to 112 00:05:40,200 --> 00:05:42,760 Speaker 5: like well enough, market likes even more though that they 113 00:05:42,760 --> 00:05:44,760 Speaker 5: have said that artificial intelligence. 114 00:05:44,279 --> 00:05:45,880 Speaker 3: Revenue is starting to materialize. 115 00:05:46,120 --> 00:05:48,640 Speaker 5: Last month they said AWS is on pace for it's 116 00:05:48,640 --> 00:05:51,279 Speaker 5: a couple billion dollar business over the course of a year, 117 00:05:51,640 --> 00:05:54,360 Speaker 5: which's not a particularly detailed number, but for a really, 118 00:05:54,400 --> 00:05:56,920 Speaker 5: really hot corner of technology. It's a sign that Amazon 119 00:05:57,000 --> 00:05:59,200 Speaker 5: is getting momentum after really getting kind of beaten out 120 00:05:59,200 --> 00:06:01,600 Speaker 5: of the gate in terms of generated AAI services in 121 00:06:01,640 --> 00:06:03,960 Speaker 5: the last couple of years. So, you know, AWS still 122 00:06:04,040 --> 00:06:06,599 Speaker 5: Amazon's cash cow, still accounts for, you know, the vast 123 00:06:06,600 --> 00:06:09,200 Speaker 5: majority of their operating profit quarter to quarter, you know, 124 00:06:09,200 --> 00:06:11,000 Speaker 5: but that set it's a business. It's been under pressure 125 00:06:11,000 --> 00:06:12,840 Speaker 5: for for the last couple of years, certainly. 126 00:06:12,600 --> 00:06:15,720 Speaker 2: And MAP is it fair to characterize is it being 127 00:06:15,880 --> 00:06:17,960 Speaker 2: beaten out the gate or is it that it just 128 00:06:18,000 --> 00:06:21,200 Speaker 2: didn't use generative AI and it's lexicon quickly enough from 129 00:06:21,200 --> 00:06:24,120 Speaker 2: your perspective when we're anticipating what Google's about to unveil 130 00:06:24,400 --> 00:06:26,080 Speaker 2: for today, I. 131 00:06:26,080 --> 00:06:27,320 Speaker 3: Think it is certainly a little bit of both. 132 00:06:27,320 --> 00:06:29,160 Speaker 5: I mean, the Amazon's been working on machine learning and 133 00:06:29,200 --> 00:06:31,200 Speaker 5: AI services for years and years, as they will tell 134 00:06:31,240 --> 00:06:33,039 Speaker 5: you. You know, if you go back a couple of years 135 00:06:33,520 --> 00:06:36,000 Speaker 5: to win Open AI releases, you know that the chat 136 00:06:36,000 --> 00:06:38,520 Speaker 5: GPT that shocks the world. You know, Amazon wasn't really 137 00:06:38,520 --> 00:06:40,760 Speaker 5: even talking in the same lexicon at that point. Jenerative 138 00:06:40,760 --> 00:06:42,919 Speaker 5: A I wasn't in the conversation. And then when it 139 00:06:42,920 --> 00:06:46,240 Speaker 5: came to embedding, you know, generative AI products into into 140 00:06:46,240 --> 00:06:49,120 Speaker 5: services already on the market. Microsoft got there real quick, 141 00:06:49,160 --> 00:06:51,520 Speaker 5: Google got there real quick. Amazon, you know, caught up 142 00:06:52,080 --> 00:06:53,839 Speaker 5: and is working to catch up further. I guess, but 143 00:06:53,880 --> 00:06:55,440 Speaker 5: you know, I think it's pretty safe to say that 144 00:06:55,800 --> 00:06:58,120 Speaker 5: at least at the outset, there were a couple of steps. 145 00:06:57,839 --> 00:07:00,280 Speaker 3: Behind that day things. 146 00:07:00,320 --> 00:07:03,680 Speaker 2: Matt Garmon on All Things AWS, we thank you so much. 147 00:07:11,560 --> 00:07:13,440 Speaker 2: We've got to talk about Klana, the buy now, pay 148 00:07:13,560 --> 00:07:15,960 Speaker 2: later firm that well, as we all know, is eyeing 149 00:07:16,040 --> 00:07:19,880 Speaker 2: an IPO and we're wondering at what particular point they're 150 00:07:19,880 --> 00:07:21,160 Speaker 2: going to do. That is at the first quarter of 151 00:07:21,160 --> 00:07:23,480 Speaker 2: next year. That's according to reports right now, this comes 152 00:07:23,480 --> 00:07:26,160 Speaker 2: on the hills. What's been a busy year for IPO 153 00:07:26,240 --> 00:07:28,640 Speaker 2: offerings and also a busy year for integrating General to AI, 154 00:07:28,680 --> 00:07:31,360 Speaker 2: which this company is doing. I please say that Sebastian 155 00:07:31,360 --> 00:07:33,360 Speaker 2: see Murkowski is with us, is a clan a CEO, 156 00:07:33,480 --> 00:07:36,600 Speaker 2: and I want to start on what all investors are 157 00:07:36,600 --> 00:07:38,480 Speaker 2: so interested in is the fact that you are eyeing 158 00:07:38,480 --> 00:07:39,360 Speaker 2: the public markets. 159 00:07:39,520 --> 00:07:40,240 Speaker 6: We're hearing as. 160 00:07:40,120 --> 00:07:44,240 Speaker 2: Soon as Q one how much more Tea's crossing. I's 161 00:07:44,280 --> 00:07:45,760 Speaker 2: donning you having to be doing at the moment. 162 00:07:46,520 --> 00:07:50,240 Speaker 7: I've heard that rumor as well, said, well, you know, 163 00:07:50,320 --> 00:07:53,480 Speaker 7: it's an interesting rumor. I but I think for US 164 00:07:53,560 --> 00:07:56,320 Speaker 7: it's always been like really critical. We wanted to establish 165 00:07:56,360 --> 00:07:58,680 Speaker 7: ourselves as a global business. That meant success in the 166 00:07:58,760 --> 00:08:02,440 Speaker 7: US meant both in awareness and in profitability. And now 167 00:08:02,480 --> 00:08:04,960 Speaker 7: with almost forty million users in the US and it 168 00:08:05,040 --> 00:08:07,880 Speaker 7: being a profitable and actually our largest market by revenue, 169 00:08:08,240 --> 00:08:10,760 Speaker 7: those kind of criteria has been met, so we're definitely ready. 170 00:08:10,920 --> 00:08:13,560 Speaker 2: You'd been doing interesting partnerships with eber, You've been launching 171 00:08:13,560 --> 00:08:15,080 Speaker 2: a wait list for a credit card. We'll get into 172 00:08:15,120 --> 00:08:16,960 Speaker 2: those products in a moment. But when you say it's 173 00:08:16,960 --> 00:08:19,600 Speaker 2: an interesting rumor, could you go earlier than Q. 174 00:08:19,640 --> 00:08:23,920 Speaker 7: One, Everything's possible, okay, ed. 175 00:08:25,960 --> 00:08:26,000 Speaker 8: It? 176 00:08:26,040 --> 00:08:27,120 Speaker 3: So, actually, good morning to you. 177 00:08:28,600 --> 00:08:30,680 Speaker 4: Let's think about them where you might list in the 178 00:08:30,680 --> 00:08:34,880 Speaker 4: context of your business. You've grown thirty two percent in 179 00:08:34,920 --> 00:08:36,600 Speaker 4: this country over the last year. 180 00:08:38,040 --> 00:08:40,600 Speaker 3: Why is that? Why are you seeing strength in the 181 00:08:40,720 --> 00:08:41,520 Speaker 3: US market? 182 00:08:42,000 --> 00:08:44,200 Speaker 7: Well, I think that what's interesting Actually it comes back 183 00:08:44,200 --> 00:08:47,360 Speaker 7: to an interesting McKinsey study from fifteen and it showed 184 00:08:47,400 --> 00:08:50,600 Speaker 7: that in this market there is a growing number of 185 00:08:50,640 --> 00:08:53,160 Speaker 7: consumers that are very tired of the credit cards and 186 00:08:53,200 --> 00:08:56,120 Speaker 7: how credit cards work. There's a great Netflix documentary call 187 00:08:56,160 --> 00:08:58,200 Speaker 7: Credit Cards Explain. You will see all the bad practices 188 00:08:58,240 --> 00:09:00,520 Speaker 7: that banks have accumulated over the years to push people 189 00:09:00,600 --> 00:09:03,400 Speaker 7: into debt, to revolve to build up as big balances 190 00:09:03,440 --> 00:09:07,520 Speaker 7: as possible. And these group is self aware. A voiders 191 00:09:07,559 --> 00:09:10,640 Speaker 7: people that are looking for simple credit products, zero interest, 192 00:09:10,920 --> 00:09:14,560 Speaker 7: fixed installment. They're about twenty percent of the US population 193 00:09:15,200 --> 00:09:18,040 Speaker 7: and buy now, pay later speaks directly to this audience, 194 00:09:18,080 --> 00:09:21,160 Speaker 7: and I think that is the most important explanation for 195 00:09:21,200 --> 00:09:23,640 Speaker 7: the success in the US Sebastia. 196 00:09:23,720 --> 00:09:25,400 Speaker 4: Later in the program, we're going to talk a lot 197 00:09:25,400 --> 00:09:28,520 Speaker 4: about Ali Barber's earnings, and I know that that China 198 00:09:28,559 --> 00:09:30,199 Speaker 4: is not something that's supper mined for you. 199 00:09:30,200 --> 00:09:31,040 Speaker 3: But bear with me. 200 00:09:31,720 --> 00:09:34,320 Speaker 4: They talked about the consumer in China having a lot 201 00:09:34,360 --> 00:09:39,360 Speaker 4: of cash, a lot of savings, but sentimentally they're not 202 00:09:39,480 --> 00:09:43,600 Speaker 4: prepared to spend. It's a really psychological market. Now, compare 203 00:09:43,600 --> 00:09:45,800 Speaker 4: and contrast that with the United States. A lot of 204 00:09:45,800 --> 00:09:48,960 Speaker 4: people I hear from are talking about credit card use 205 00:09:49,320 --> 00:09:53,560 Speaker 4: and credit card levels, but psychologically the US consumer seems 206 00:09:53,760 --> 00:09:56,280 Speaker 4: prepared to spend. What do you make of all that? 207 00:09:56,400 --> 00:09:58,640 Speaker 4: What do you see through your platform? 208 00:09:59,000 --> 00:10:00,000 Speaker 3: Exactly that right. 209 00:10:00,200 --> 00:10:01,600 Speaker 1: I was actually a little bit worried. 210 00:10:01,360 --> 00:10:03,440 Speaker 7: Around Christmas when I saw the Christmas cells. I felt 211 00:10:03,440 --> 00:10:06,280 Speaker 7: that Christmas Cells this year was a little bit discount driven, 212 00:10:06,280 --> 00:10:08,640 Speaker 7: and I wasn't clear to how much was merchants kind 213 00:10:08,640 --> 00:10:12,360 Speaker 7: of trying to offload stock do through discount and how 214 00:10:12,440 --> 00:10:14,880 Speaker 7: much was consumer strength, so to speak. But I think 215 00:10:14,920 --> 00:10:17,840 Speaker 7: now with a few moments in the bag, you see 216 00:10:17,840 --> 00:10:19,880 Speaker 7: that like consumers spending in the US is holding up 217 00:10:20,000 --> 00:10:22,520 Speaker 7: quite well, and there is there is that demand. So 218 00:10:22,520 --> 00:10:24,840 Speaker 7: we're seeing that across all categories that we cover, and 219 00:10:24,880 --> 00:10:27,600 Speaker 7: as much as we're associated with kind of fashions so forth, 220 00:10:27,720 --> 00:10:29,840 Speaker 7: like with being live with Uber, with Airbnb, with a 221 00:10:29,840 --> 00:10:32,079 Speaker 7: lot of other big American brands, we can see that. 222 00:10:32,280 --> 00:10:32,960 Speaker 1: Across the board. 223 00:10:33,480 --> 00:10:35,960 Speaker 2: What's so interesting is you have been building up a 224 00:10:36,000 --> 00:10:37,840 Speaker 2: focus here in the US and you have got this 225 00:10:37,880 --> 00:10:39,600 Speaker 2: weight list for a credit card, but you meant to 226 00:10:39,600 --> 00:10:42,480 Speaker 2: be the anti credit card company. I thought this whole 227 00:10:42,520 --> 00:10:45,640 Speaker 2: idea that the spiraling debt that mounts there, how are 228 00:10:45,640 --> 00:10:46,400 Speaker 2: you doing it differently? 229 00:10:46,480 --> 00:10:47,480 Speaker 3: Why do that differently? 230 00:10:48,040 --> 00:10:50,120 Speaker 7: Well, I think that, Like the interesting thing is that 231 00:10:50,160 --> 00:10:52,360 Speaker 7: to me, the card is just a delivery mechanism. It 232 00:10:52,360 --> 00:10:54,760 Speaker 7: allows the consumers to use Klana everywhere, as opposed to 233 00:10:54,840 --> 00:10:57,160 Speaker 7: only the places where merchants have integrated US. And even 234 00:10:57,200 --> 00:10:59,000 Speaker 7: if we cover forty of the top one hundred US 235 00:10:59,000 --> 00:11:00,880 Speaker 7: retailers online today, we still want to. 236 00:11:00,840 --> 00:11:01,920 Speaker 1: Make it available everywhere. 237 00:11:01,960 --> 00:11:04,560 Speaker 7: Right, But I think the key difference versus the credit 238 00:11:04,559 --> 00:11:06,520 Speaker 7: card is how you use it now. When I used 239 00:11:06,520 --> 00:11:08,920 Speaker 7: to work at Burkeying back in the days when you 240 00:11:08,920 --> 00:11:11,000 Speaker 7: would swipe a card you would press one for debit 241 00:11:11,040 --> 00:11:13,080 Speaker 7: and two for credit, because it made a lot of 242 00:11:13,120 --> 00:11:16,000 Speaker 7: sense that you didn't use credit for every purchase, but 243 00:11:16,280 --> 00:11:18,720 Speaker 7: the banks abandoned that because they wanted you to build 244 00:11:18,800 --> 00:11:21,440 Speaker 7: up your monthly statement to be as big as possible, 245 00:11:21,480 --> 00:11:23,480 Speaker 7: to put everything on it. So it increases the likely 246 00:11:23,520 --> 00:11:27,079 Speaker 7: to view lending and borrowing money and kind of increasing 247 00:11:27,120 --> 00:11:28,880 Speaker 7: the revolver and right like, you have to remember, a 248 00:11:28,880 --> 00:11:32,600 Speaker 7: credit card balance is usually five five hundred dollars on average, 249 00:11:32,720 --> 00:11:35,959 Speaker 7: with KLON it's one hundred and fifty dollars. So to us, 250 00:11:36,200 --> 00:11:39,160 Speaker 7: the ability to always have the option between debiting credit 251 00:11:39,240 --> 00:11:41,720 Speaker 7: is fundamental. That's one of the key things that will 252 00:11:41,760 --> 00:11:45,800 Speaker 7: provide a healthier, responsible usage of credit. And in addition 253 00:11:45,840 --> 00:11:48,600 Speaker 7: to that having zero interest and fixed installments. So those 254 00:11:48,640 --> 00:11:50,719 Speaker 7: are the primary concepts, and we can bring them through 255 00:11:50,760 --> 00:11:53,640 Speaker 7: a card, we can bring them through offering them directly 256 00:11:53,679 --> 00:11:55,360 Speaker 7: on the merchant to website or in the. 257 00:11:55,320 --> 00:11:58,360 Speaker 2: Sort and it's heart you're doing things differently by being 258 00:11:58,840 --> 00:12:01,720 Speaker 2: tech driven. A lot of credit card companies are now 259 00:12:01,760 --> 00:12:04,280 Speaker 2: pretty tech driven as well. I want to focus in 260 00:12:04,320 --> 00:12:07,200 Speaker 2: on the genera of AI side, because you caught a 261 00:12:07,240 --> 00:12:09,960 Speaker 2: lot of headlines by the fact that you said, look, now, 262 00:12:10,200 --> 00:12:12,760 Speaker 2: general to AI within our customer services is doing the 263 00:12:12,840 --> 00:12:15,959 Speaker 2: job of seven hundred people. Is it still seven hundred? 264 00:12:16,080 --> 00:12:16,760 Speaker 3: Is it more than that? 265 00:12:17,120 --> 00:12:20,000 Speaker 7: It's probably slightly more by now. I think the reason 266 00:12:20,000 --> 00:12:22,040 Speaker 7: we share we knew that number would kind of catch 267 00:12:22,080 --> 00:12:25,080 Speaker 7: the attention. But it's partially because I feel there's a 268 00:12:25,080 --> 00:12:27,280 Speaker 7: lot of buzz around AI and there is, you know, 269 00:12:27,320 --> 00:12:29,200 Speaker 7: a lot of things demos being announced, and you were 270 00:12:29,200 --> 00:12:31,880 Speaker 7: talking about you know, Google today announcing something, but there 271 00:12:31,880 --> 00:12:34,559 Speaker 7: are few things that are practically there, and a lot 272 00:12:34,600 --> 00:12:36,840 Speaker 7: of business leaders are asking themselves like, where is the 273 00:12:36,880 --> 00:12:39,680 Speaker 7: actual implications on my business? And this was the first 274 00:12:39,679 --> 00:12:41,679 Speaker 7: time we at least felt like, look, here's a practical 275 00:12:41,720 --> 00:12:45,080 Speaker 7: application that isn't like just as small. It's real life 276 00:12:45,240 --> 00:12:48,600 Speaker 7: consumers prefer it because it's actually higher customer satisfaction than 277 00:12:48,600 --> 00:12:52,040 Speaker 7: the human agent, it's faster to use, its higher quality, 278 00:12:52,400 --> 00:12:55,400 Speaker 7: and it's having profound impact. But we also believe that 279 00:12:55,520 --> 00:12:58,320 Speaker 7: society and politicians needs to recognize that this is not 280 00:12:58,440 --> 00:13:03,160 Speaker 7: something that's going toge in ten years. The implications for 281 00:13:03,320 --> 00:13:05,240 Speaker 7: society are going to come in the coming years, and 282 00:13:05,280 --> 00:13:07,480 Speaker 7: it's time to think a little bit proactively about what 283 00:13:07,559 --> 00:13:09,360 Speaker 7: measures could be taken as a consequence. 284 00:13:10,280 --> 00:13:10,839 Speaker 3: Sebastian. 285 00:13:10,880 --> 00:13:14,079 Speaker 4: Exactly one month ago, we had the European Commissioner for 286 00:13:14,200 --> 00:13:18,160 Speaker 4: Financial Services McGuinness visit US here in San Francisco, and 287 00:13:18,200 --> 00:13:25,199 Speaker 4: we talked about fintech regulation. Essentially, you operate in both markets, 288 00:13:26,320 --> 00:13:29,120 Speaker 4: which do you find to be more friendly of the 289 00:13:29,160 --> 00:13:32,360 Speaker 4: regulators between the United States and Europe. 290 00:13:32,440 --> 00:13:36,199 Speaker 7: Well, I think amazingly, the EU introduced some legislation back 291 00:13:36,240 --> 00:13:38,360 Speaker 7: in four or five that meant that we, who are 292 00:13:38,400 --> 00:13:41,800 Speaker 7: a fully regulated bank in Sweden, can password that license 293 00:13:41,800 --> 00:13:45,959 Speaker 7: across all European states, and that is a tremendous advantage, 294 00:13:46,080 --> 00:13:50,200 Speaker 7: and to some degree it's actually easier even to operate 295 00:13:50,200 --> 00:13:52,800 Speaker 7: than across all of the US states, where there's bigger 296 00:13:52,800 --> 00:13:56,400 Speaker 7: differences on consumer credit lending in different US states than 297 00:13:56,400 --> 00:13:59,200 Speaker 7: there is among the European nations to some degree. So 298 00:13:59,240 --> 00:14:01,760 Speaker 7: it's actually, i would say fairly even a little bit 299 00:14:01,800 --> 00:14:07,079 Speaker 7: sometimes at the intanges to be in Europe. But we 300 00:14:07,200 --> 00:14:11,160 Speaker 7: definitely think that there is more competition, openness, and willingness 301 00:14:11,240 --> 00:14:14,480 Speaker 7: to promote competition in the American society than in the 302 00:14:14,480 --> 00:14:16,920 Speaker 7: European Union, and so I think that's very favorable in 303 00:14:17,720 --> 00:14:19,880 Speaker 7: American context. 304 00:14:20,040 --> 00:14:23,960 Speaker 4: Especially when you do go public, investors will be very 305 00:14:23,960 --> 00:14:26,880 Speaker 4: focused on profit. Could you speak a little bit about 306 00:14:26,880 --> 00:14:27,400 Speaker 4: your profit? 307 00:14:28,600 --> 00:14:32,440 Speaker 7: Of course now, I think look from my perspective, I 308 00:14:32,480 --> 00:14:34,840 Speaker 7: hope we will be able to deliver some very interesting 309 00:14:36,280 --> 00:14:39,160 Speaker 7: perspectives when we are ready for that, because we are 310 00:14:39,160 --> 00:14:43,400 Speaker 7: seeing a very strong growth currently within Klana. You know, 311 00:14:43,440 --> 00:14:47,120 Speaker 7: we are definitely about two billion dollars of revenue. We're 312 00:14:47,960 --> 00:14:51,400 Speaker 7: seeing very high volume growth at the same point of time. 313 00:14:51,600 --> 00:14:56,440 Speaker 7: Due to the implications of AI. Since September October, we 314 00:14:56,520 --> 00:15:00,280 Speaker 7: have stopped recruitment and in our case, with normal trition 315 00:15:00,400 --> 00:15:02,480 Speaker 7: rates that most tech companies have where people stay about 316 00:15:02,480 --> 00:15:07,040 Speaker 7: five years, this means that we are actually shrinking in 317 00:15:07,160 --> 00:15:09,560 Speaker 7: number of employees by about twenty percent per year. 318 00:15:10,560 --> 00:15:12,080 Speaker 3: So we hope that by the time. 319 00:15:11,960 --> 00:15:13,440 Speaker 7: We kind of get through that perspective, we're going to 320 00:15:13,440 --> 00:15:16,000 Speaker 7: be able to present something that looks like revenue growth 321 00:15:16,400 --> 00:15:19,120 Speaker 7: wild costs actually diminishing at the same point of time. 322 00:15:20,720 --> 00:15:22,960 Speaker 2: Sebastian, it's been great having some time with you. Thank 323 00:15:23,000 --> 00:15:25,960 Speaker 2: you for coming on and like how you've been navigating 324 00:15:26,000 --> 00:15:28,520 Speaker 2: some of the timing questions with us. Sebastian Si mccowski. 325 00:15:28,560 --> 00:15:30,760 Speaker 2: He's a Klana CEO and what for got coming up 326 00:15:30,760 --> 00:15:31,200 Speaker 2: more in AI. 327 00:15:31,240 --> 00:15:33,640 Speaker 3: I think, yeah, more on AI. I really enjoyed that. 328 00:15:33,680 --> 00:15:37,080 Speaker 4: By the way, open ai unveils its updated chat model 329 00:15:37,360 --> 00:15:40,240 Speaker 4: as Google kicks off it's AI event today. We've got 330 00:15:40,240 --> 00:15:42,800 Speaker 4: really important discussions coming out next stick with us. This 331 00:15:42,880 --> 00:15:53,040 Speaker 4: is Bloomberg Technology. 332 00:15:58,680 --> 00:16:02,440 Speaker 2: Open Ai sing faster, cheaper versions of the AI model 333 00:16:02,480 --> 00:16:05,520 Speaker 2: that underpins its chatbot chat GPT, and it says the 334 00:16:05,560 --> 00:16:08,120 Speaker 2: startup works to hold on to its seeming lead and 335 00:16:08,120 --> 00:16:11,200 Speaker 2: then an increasingly crowded market joining us. Now on the 336 00:16:11,200 --> 00:16:14,400 Speaker 2: announcements made yesterday, Blue MoG's Rachel Mets and we heard 337 00:16:14,440 --> 00:16:17,680 Speaker 2: in particular from the key executives from Marati as well, 338 00:16:17,720 --> 00:16:22,760 Speaker 2: really outlining well, me, as an unpaying user, it is 339 00:16:22,800 --> 00:16:24,800 Speaker 2: going to get a whole load more for free. 340 00:16:26,240 --> 00:16:28,280 Speaker 9: Yeah, I mean that's a big part of what they 341 00:16:28,320 --> 00:16:31,880 Speaker 9: were announcing is that they are giving people a lot 342 00:16:31,880 --> 00:16:35,320 Speaker 9: of capabilities that until now had been reserved just for 343 00:16:35,400 --> 00:16:39,160 Speaker 9: paying users of GPT for but also overall they're saying 344 00:16:39,200 --> 00:16:42,680 Speaker 9: the model is faster and more efficient at this point. 345 00:16:42,760 --> 00:16:45,800 Speaker 9: And there are also some additional interesting things that they 346 00:16:45,960 --> 00:16:50,600 Speaker 9: rolled out as well, such as some audio capabilities that 347 00:16:50,640 --> 00:16:54,320 Speaker 9: are much faster than what we've seen before. They already 348 00:16:54,360 --> 00:16:58,360 Speaker 9: had the ability to talk to chat GPT via the app. 349 00:16:58,640 --> 00:17:01,000 Speaker 9: I believe that was just for paid users in the past, 350 00:17:02,160 --> 00:17:06,159 Speaker 9: but now you can use that in a much more 351 00:17:06,200 --> 00:17:09,000 Speaker 9: real time way. And it also it's much more capable 352 00:17:09,040 --> 00:17:10,480 Speaker 9: than it appeared to be in the past. That'll be 353 00:17:10,640 --> 00:17:12,359 Speaker 9: rolling out the next in the coming weeks. 354 00:17:12,359 --> 00:17:16,040 Speaker 3: I believe Rachel la zero in on the underlying model. 355 00:17:16,200 --> 00:17:20,000 Speaker 4: GPT for oh is that right, GBT for oh, I 356 00:17:20,040 --> 00:17:20,600 Speaker 4: got it right? 357 00:17:20,680 --> 00:17:21,479 Speaker 3: Look at this chart. 358 00:17:22,280 --> 00:17:25,919 Speaker 4: Yeah, so this this is the its performance against benchmarks, right, 359 00:17:25,920 --> 00:17:28,359 Speaker 4: which everyone does when they release a new model. But 360 00:17:28,400 --> 00:17:29,960 Speaker 4: I guess the main thing that open ai I was 361 00:17:30,000 --> 00:17:34,479 Speaker 4: talking about is that this is dramatically reduced latency in text, 362 00:17:34,640 --> 00:17:38,240 Speaker 4: image and video use cases get nitty gritty and nerdy 363 00:17:38,280 --> 00:17:39,000 Speaker 4: for us. 364 00:17:40,280 --> 00:17:43,280 Speaker 9: So I mean, really, this should just make all kinds 365 00:17:43,280 --> 00:17:45,720 Speaker 9: of things faster that people want to do with the model. 366 00:17:45,760 --> 00:17:48,560 Speaker 9: Make it faster to have it back and forth with it. 367 00:17:48,560 --> 00:17:51,760 Speaker 9: It could make it. It could make it feel more 368 00:17:51,760 --> 00:17:54,520 Speaker 9: life like if you're talking to it and it's talking 369 00:17:54,560 --> 00:17:57,320 Speaker 9: back to you. Analyzing pictures. I mean, what they were 370 00:17:57,320 --> 00:17:59,280 Speaker 9: showing this demo that you've got on the screen is 371 00:17:59,320 --> 00:18:02,359 Speaker 9: showing an open camera view rather than right now, if 372 00:18:02,400 --> 00:18:05,119 Speaker 9: you're using the chat GBT app, you have to like 373 00:18:05,200 --> 00:18:08,479 Speaker 9: take a picture. And in the future they said they 374 00:18:08,480 --> 00:18:12,040 Speaker 9: will be adding the ability to analyze videos as well, 375 00:18:12,119 --> 00:18:15,080 Speaker 9: not just still images. So they're trying to bigger, better, 376 00:18:15,119 --> 00:18:17,680 Speaker 9: faster more with this model and some of it. I 377 00:18:17,680 --> 00:18:19,400 Speaker 9: guess we're just gonna have to wait and see how 378 00:18:19,400 --> 00:18:20,920 Speaker 9: well it works. 379 00:18:21,200 --> 00:18:24,639 Speaker 3: Bloomberg's racial mets terrific reporting. Great to have you on 380 00:18:24,640 --> 00:18:25,040 Speaker 3: the program. 381 00:18:25,040 --> 00:18:27,520 Speaker 4: There is one thing that I open Aye did not 382 00:18:27,680 --> 00:18:31,800 Speaker 4: do or reveal, which was search. And the Google Io 383 00:18:31,920 --> 00:18:35,920 Speaker 4: conference is kicking off in Mountain View. Let's hear about 384 00:18:35,920 --> 00:18:39,359 Speaker 4: how Alphabet ceo is to the pitch, I characterized his 385 00:18:39,520 --> 00:18:42,120 Speaker 4: competition with the CEO of Microsoft. 386 00:18:42,240 --> 00:18:46,240 Speaker 3: Just listen to this, I think you'd one of the 387 00:18:46,280 --> 00:18:47,120 Speaker 3: wa is you can. 388 00:18:48,880 --> 00:18:52,200 Speaker 10: Do the wrong thing is by listening to noiself talent 389 00:18:52,240 --> 00:18:53,879 Speaker 10: playing to someone else's dance music. 390 00:18:54,119 --> 00:18:56,639 Speaker 3: I've always been very clear. I think we have a 391 00:18:56,680 --> 00:18:58,240 Speaker 3: clear sense of what we need to do. 392 00:19:00,080 --> 00:19:02,159 Speaker 4: That was part of the conversation that he had on 393 00:19:02,200 --> 00:19:03,360 Speaker 4: the circuit with Emily Chang. 394 00:19:03,400 --> 00:19:04,639 Speaker 3: He was talking about Microsoft. 395 00:19:04,920 --> 00:19:07,720 Speaker 4: But open Ay has been pretty noisy as well, didn't 396 00:19:07,880 --> 00:19:11,000 Speaker 4: reveal its own search. Let's bringing Bloombergs Jackie Deablos zero 397 00:19:11,160 --> 00:19:13,920 Speaker 4: in on Google Io and Jackie, what are you looking 398 00:19:13,960 --> 00:19:14,239 Speaker 4: out for? 399 00:19:14,720 --> 00:19:17,280 Speaker 8: Well, there's a few things, and as center Bridge I 400 00:19:17,320 --> 00:19:19,959 Speaker 8: mentioned the dancing using it's pretty hard to ignore, especially 401 00:19:19,960 --> 00:19:22,560 Speaker 8: with an announcement like that coming out of Opening Eye yesterday. 402 00:19:22,600 --> 00:19:25,000 Speaker 8: It's really raising the bar for Google. One of the 403 00:19:25,040 --> 00:19:28,080 Speaker 8: things that will be keenly focused on is, of course, 404 00:19:28,200 --> 00:19:32,840 Speaker 8: it's updates to its current flagship Gemini model. We're expecting 405 00:19:33,240 --> 00:19:36,680 Speaker 8: a few more details on just how that model will 406 00:19:36,720 --> 00:19:40,240 Speaker 8: be interspersed throat to threat its most ubiquitous products, think 407 00:19:40,440 --> 00:19:45,000 Speaker 8: Gmail Maps and especially like you mentioned, ed search. It's 408 00:19:45,040 --> 00:19:48,440 Speaker 8: been experimenting with some features, but hasn't rolled anything out 409 00:19:48,480 --> 00:19:51,359 Speaker 8: really broadly yet. We'll get some more color there. And 410 00:19:51,400 --> 00:19:53,520 Speaker 8: of course, you know this isn't just about software. We're 411 00:19:53,560 --> 00:19:57,440 Speaker 8: expecting some updates on it's hardware line, it's tablets, it's smartphones, 412 00:19:57,640 --> 00:20:02,600 Speaker 8: and of course any updates to it Android fifteen operating system. 413 00:20:03,040 --> 00:20:06,199 Speaker 2: I mean, the context with all of this is that 414 00:20:07,080 --> 00:20:09,919 Speaker 2: they seem to be behind the curve, even though for 415 00:20:10,040 --> 00:20:12,879 Speaker 2: many generative AI was only made possible because of the 416 00:20:12,920 --> 00:20:15,720 Speaker 2: research them by Google, but then they managed to rush 417 00:20:15,720 --> 00:20:18,760 Speaker 2: out send products there was a little of concern around 418 00:20:18,840 --> 00:20:21,600 Speaker 2: the image generation in particular with Gemini. How are we 419 00:20:21,640 --> 00:20:23,800 Speaker 2: seeing them try to write size the perception? 420 00:20:25,720 --> 00:20:29,640 Speaker 8: Well, if you said it perfectly, Caroline, they were early pioneers. 421 00:20:29,720 --> 00:20:32,159 Speaker 8: They have some of the best and brightest talent in 422 00:20:32,280 --> 00:20:36,040 Speaker 8: artificial intelligence. But where they really fell behind is really 423 00:20:36,119 --> 00:20:41,280 Speaker 8: kind of creating this bigger, generative AI ecosystem that is 424 00:20:41,359 --> 00:20:44,480 Speaker 8: embedded across its products. It has an advantage in search, 425 00:20:44,640 --> 00:20:47,800 Speaker 8: as we all know, but we've seen Microsoft and Open 426 00:20:47,840 --> 00:20:51,560 Speaker 8: Eye really kind of product make it make its way 427 00:20:51,560 --> 00:20:55,639 Speaker 8: into products much quicker and really commercialize that technology in 428 00:20:55,680 --> 00:20:59,040 Speaker 8: a much more broader way. And so today we're really 429 00:20:59,080 --> 00:21:02,119 Speaker 8: going to be looking to learn how exactly is this 430 00:21:02,200 --> 00:21:05,040 Speaker 8: flowing through across the business, not just kind of one 431 00:21:05,080 --> 00:21:08,359 Speaker 8: off features here and there. It's also unveiling a new 432 00:21:08,960 --> 00:21:12,000 Speaker 8: open source it's a smaller model called Gemma, so it's 433 00:21:12,040 --> 00:21:14,840 Speaker 8: looking to really kind of catch up and offer a 434 00:21:14,840 --> 00:21:19,320 Speaker 8: more kind of broad array of models, just as we've 435 00:21:19,320 --> 00:21:20,359 Speaker 8: seen Open Eye. 436 00:21:20,119 --> 00:21:20,600 Speaker 11: Do as well. 437 00:21:21,000 --> 00:21:22,480 Speaker 4: Jackie, really quick, I don't want to put you on 438 00:21:22,480 --> 00:21:24,920 Speaker 4: the spot, but of all the announcements, what's the one 439 00:21:24,960 --> 00:21:27,680 Speaker 4: piece of AI technology you're actually using right now? 440 00:21:28,680 --> 00:21:29,240 Speaker 3: Personally? 441 00:21:29,400 --> 00:21:33,679 Speaker 8: I love how it you know, in Google Docs, it 442 00:21:33,720 --> 00:21:36,240 Speaker 8: makes my needing templates. I don't really have to worry 443 00:21:36,240 --> 00:21:39,720 Speaker 8: about kind of doing all the drudgery there. I don't 444 00:21:39,800 --> 00:21:42,720 Speaker 8: use it quite yet for work, but it's kind of 445 00:21:42,760 --> 00:21:44,480 Speaker 8: the you know, if I need to write up a 446 00:21:44,560 --> 00:21:48,119 Speaker 8: quick note, half the template is already there. It's predictive, 447 00:21:48,480 --> 00:21:50,399 Speaker 8: so it kind of knows what I'm looking to write 448 00:21:51,040 --> 00:21:53,639 Speaker 8: and it takes some of that boring aspect out of 449 00:21:53,640 --> 00:21:54,200 Speaker 8: my work there. 450 00:21:54,640 --> 00:21:58,080 Speaker 2: I love that Jackie Devlo's ps on agnostic don't use 451 00:21:58,160 --> 00:21:58,480 Speaker 2: all of. 452 00:21:58,400 --> 00:22:00,560 Speaker 3: The above and many but others available. 453 00:22:00,720 --> 00:22:02,960 Speaker 2: Yeah, absolutely love how you're giving us the nuance of 454 00:22:02,960 --> 00:22:04,440 Speaker 2: how are you using it in real day life? Thank 455 00:22:04,480 --> 00:22:14,520 Speaker 2: you very much. Indeed, Jackie Davlosk welcome Actively made Technology. 456 00:22:14,520 --> 00:22:15,840 Speaker 2: And Caroline Hyde in New York. 457 00:22:16,160 --> 00:22:18,560 Speaker 4: Elam Ed Ludlow in San Francisco. Let's get a quick 458 00:22:18,600 --> 00:22:19,960 Speaker 4: check on the market. So this is kind of where 459 00:22:19,960 --> 00:22:22,880 Speaker 4: we stand in the moment. We are basically treading water. 460 00:22:23,160 --> 00:22:25,840 Speaker 4: When it comes to the nads that one hundred, there 461 00:22:25,880 --> 00:22:28,640 Speaker 4: are as equal number of names in the green as 462 00:22:28,640 --> 00:22:31,960 Speaker 4: there are in the red. And then we look at 463 00:22:32,080 --> 00:22:35,119 Speaker 4: the Golden Dragon China Index. This is basically the US 464 00:22:35,160 --> 00:22:39,200 Speaker 4: listed shares of China's biggest technology companies. 465 00:22:39,440 --> 00:22:41,920 Speaker 3: You have the earning story, which we're about to touch. 466 00:22:41,680 --> 00:22:44,800 Speaker 4: On, and then you have the impact of Biden administration 467 00:22:45,400 --> 00:22:50,480 Speaker 4: tariffs on the automotive sector in China. Those are some 468 00:22:50,600 --> 00:22:53,760 Speaker 4: usisted names that also are impacted in terms of the specifics. 469 00:22:53,960 --> 00:22:56,040 Speaker 4: Spend a lot of the morning looking at Ali Barba 470 00:22:56,359 --> 00:22:59,639 Speaker 4: and Tencent. The stories are really clear. Ali Barbara pretty 471 00:22:59,640 --> 00:23:02,800 Speaker 4: slug single digit growth on its core e commerce business 472 00:23:02,800 --> 00:23:05,720 Speaker 4: and its cloud unit. Cloud adoption slow in China, but 473 00:23:05,840 --> 00:23:08,560 Speaker 4: Ai is there. And then ten Cent great strength in 474 00:23:08,680 --> 00:23:12,040 Speaker 4: video advertising, great strength in the we chat user base 475 00:23:12,080 --> 00:23:14,200 Speaker 4: in terms of the number of hours that are going on. 476 00:23:14,560 --> 00:23:16,600 Speaker 4: So one stop going lower in the US and another 477 00:23:16,640 --> 00:23:19,240 Speaker 4: stop going higher. Let's wrap it all up and bring 478 00:23:19,240 --> 00:23:21,760 Speaker 4: in Bloomberg's Henry Wren and Isabelle. 479 00:23:21,400 --> 00:23:22,840 Speaker 3: Lee and isabel stop with you. 480 00:23:22,880 --> 00:23:25,639 Speaker 4: I think that's the summary right when it comes to 481 00:23:26,160 --> 00:23:29,360 Speaker 4: this earnings in what I outlined, but start with Ali 482 00:23:29,400 --> 00:23:30,399 Speaker 4: Barbera and what they said. 483 00:23:30,880 --> 00:23:32,560 Speaker 11: So this is really the tale of two cities. We 484 00:23:32,600 --> 00:23:35,080 Speaker 11: have ten Cent reporting a better than projected sixty two 485 00:23:35,119 --> 00:23:38,600 Speaker 11: percent surgeon earnings, but Ali Baba's profit plunge and the 486 00:23:38,640 --> 00:23:42,159 Speaker 11: fact that the two biggest internet bihimots and China reported 487 00:23:42,240 --> 00:23:44,240 Speaker 11: on the same day is a rarity and the fact 488 00:23:44,240 --> 00:23:46,800 Speaker 11: that they did just really highlighted the tale of two 489 00:23:46,800 --> 00:23:49,920 Speaker 11: fortunes for both. And yes, both reported better than expected 490 00:23:50,040 --> 00:23:53,359 Speaker 11: single digit revenue growth, but it's the profit performance where 491 00:23:53,359 --> 00:23:55,560 Speaker 11: they diverge, and investors are really closely looking at the 492 00:23:55,560 --> 00:23:57,960 Speaker 11: bottom line of these two companies because they're both really 493 00:23:58,000 --> 00:24:01,440 Speaker 11: seen as a barometer of Chinese health, especially China recovers 494 00:24:01,480 --> 00:24:04,240 Speaker 11: from this rocky COVID recovery, and of course we have 495 00:24:04,359 --> 00:24:07,200 Speaker 11: also that year's long big tech crackdown and get this, 496 00:24:07,600 --> 00:24:11,280 Speaker 11: neither company offered investors strong reassurances of the plight of 497 00:24:11,320 --> 00:24:14,440 Speaker 11: Chinese economy moving forward, So that's something investors are hanging on. 498 00:24:14,640 --> 00:24:15,480 Speaker 3: We love how you give. 499 00:24:15,440 --> 00:24:18,320 Speaker 2: Us the macro context. It's about, Henry, let's go micro 500 00:24:18,480 --> 00:24:21,879 Speaker 2: with you and let's go optimistic first. Why was Tencent 501 00:24:22,080 --> 00:24:24,720 Speaker 2: really managing to pull it out for the investor base today. 502 00:24:25,760 --> 00:24:30,920 Speaker 10: Yeah, Tensen's profitability really shines out today's in today's results. 503 00:24:30,920 --> 00:24:35,720 Speaker 10: So basically the video streaming is huge positive, but also 504 00:24:35,720 --> 00:24:39,000 Speaker 10: in the meantime, the company is cutting hugely on costs. 505 00:24:39,400 --> 00:24:42,200 Speaker 10: Remember the company is booking on revenue growth of six 506 00:24:42,240 --> 00:24:44,800 Speaker 10: percent in the quarter, but its cost of revenue is 507 00:24:44,840 --> 00:24:47,440 Speaker 10: actually down eight percent from a year ago, and the 508 00:24:47,520 --> 00:24:50,360 Speaker 10: key to that is lower countent costs as far as 509 00:24:50,480 --> 00:24:55,320 Speaker 10: lower costs associated with crowd projects deployment, but also in 510 00:24:55,320 --> 00:24:58,040 Speaker 10: the meantime the company is cutting down other costs, for 511 00:24:58,080 --> 00:25:02,680 Speaker 10: example employee benefits based compensation, and that really stands out. 512 00:25:03,040 --> 00:25:06,120 Speaker 10: A key worry for Tencent in recent quarters has been 513 00:25:06,640 --> 00:25:10,159 Speaker 10: it's domestic gaming business, but it's also showing some signs 514 00:25:10,160 --> 00:25:13,080 Speaker 10: of green shoots as well. The companies say two of 515 00:25:13,119 --> 00:25:19,040 Speaker 10: its flagship games actually received positive gross received growth in March, 516 00:25:19,119 --> 00:25:22,280 Speaker 10: so that's a passive signal taken away from investors too. 517 00:25:22,320 --> 00:25:24,440 Speaker 3: No I was interested now on the gaming sites. 518 00:25:24,440 --> 00:25:26,240 Speaker 4: They were talking about, how like rather than have some 519 00:25:26,280 --> 00:25:29,600 Speaker 4: big standalone organic title, they would have the kind of 520 00:25:29,640 --> 00:25:33,640 Speaker 4: games coming where you can update them regularly. Both companies 521 00:25:33,680 --> 00:25:36,840 Speaker 4: or executives on both sides. Henry also had some basically 522 00:25:36,920 --> 00:25:40,159 Speaker 4: fighting talk saying that they were each the best at AI. 523 00:25:40,760 --> 00:25:43,680 Speaker 4: Did we learn anything new about either company's AI strategy? 524 00:25:44,960 --> 00:25:45,240 Speaker 3: Yes. 525 00:25:45,280 --> 00:25:49,080 Speaker 10: Indeed, for Ali Baba, for example, it's pretty confident that 526 00:25:49,440 --> 00:25:52,720 Speaker 10: AI world drive growth in this cloud business. The company said, 527 00:25:54,440 --> 00:25:57,920 Speaker 10: with its AIS models gaining steam in China, it's confident 528 00:25:57,960 --> 00:26:02,360 Speaker 10: that it will turbo charge cloud units growth. Remember it's 529 00:26:02,400 --> 00:26:05,639 Speaker 10: still pretty tepic growth in this quarter percent of revenue growth, 530 00:26:05,760 --> 00:26:07,920 Speaker 10: but it says it's confident that in the second half 531 00:26:08,200 --> 00:26:11,639 Speaker 10: of fiscal twenty five it can actually return back to 532 00:26:11,800 --> 00:26:14,320 Speaker 10: double digit growth for its cloud unit, which should be 533 00:26:14,400 --> 00:26:16,880 Speaker 10: a good news because cloud unit has always been seen 534 00:26:17,200 --> 00:26:21,439 Speaker 10: as a growth driver for Ali Baba and the for 535 00:26:21,520 --> 00:26:24,440 Speaker 10: the other side of Tensents. It's actually already bearing fruit. 536 00:26:24,800 --> 00:26:29,359 Speaker 10: Remember that we mentioned about the strong advertising momentum for 537 00:26:29,480 --> 00:26:36,760 Speaker 10: Tensen's video streaming business. Remember that the company is actually 538 00:26:36,880 --> 00:26:41,600 Speaker 10: already deploying many of its AI organism in recommending videos 539 00:26:41,640 --> 00:26:44,439 Speaker 10: for its users, so it has already been seeing some 540 00:26:44,480 --> 00:26:46,119 Speaker 10: of the early signs of benefits. 541 00:26:46,560 --> 00:26:48,800 Speaker 2: It's a well, push us forward a little bit here, 542 00:26:48,840 --> 00:26:51,600 Speaker 2: because you're giving us the sort of context of the 543 00:26:51,640 --> 00:26:55,399 Speaker 2: consumer as well. We've got JD dot Com coming later. 544 00:26:55,160 --> 00:26:55,640 Speaker 3: In the week. 545 00:26:56,000 --> 00:26:58,359 Speaker 2: I mean, they aren't the only company by do as well. 546 00:26:58,840 --> 00:27:00,880 Speaker 2: Are we going to get any kind on just how 547 00:27:01,000 --> 00:27:03,320 Speaker 2: resilient a Chinese consumer is right now? 548 00:27:03,560 --> 00:27:06,640 Speaker 11: I think we will, because Chinese consumers still remain really cautious. 549 00:27:06,680 --> 00:27:08,800 Speaker 11: I mean they're dealing with a property meltdown, they're dealing 550 00:27:08,880 --> 00:27:11,520 Speaker 11: with persistent use employment, and the Chinese economy is still 551 00:27:11,520 --> 00:27:14,320 Speaker 11: slipping deeper and deeper into deflation. But to your point, 552 00:27:14,480 --> 00:27:16,639 Speaker 11: it is a big month for Chinese company. So we 553 00:27:16,680 --> 00:27:19,800 Speaker 11: have Baidujd, dot com CLM, net Ease, and Chinese Tech 554 00:27:19,840 --> 00:27:22,800 Speaker 11: Company is actually surprised to be upside for the last 555 00:27:22,840 --> 00:27:25,000 Speaker 11: eight quarters. So this is kind of good news. It 556 00:27:25,080 --> 00:27:27,639 Speaker 11: means that you know, recovery is there, slowly but surely. 557 00:27:27,800 --> 00:27:29,600 Speaker 11: But the money managers have been talking to look at 558 00:27:29,680 --> 00:27:32,199 Speaker 11: China two ways. It's either too cheap that's why they 559 00:27:32,240 --> 00:27:35,200 Speaker 11: want to go in, or it's either it's too cheap 560 00:27:35,320 --> 00:27:38,119 Speaker 11: for a reason. So there are really like diverging views 561 00:27:38,280 --> 00:27:40,560 Speaker 11: among them. But I think this is really going to 562 00:27:40,600 --> 00:27:44,920 Speaker 11: be a big tell moving forward this month as well. 563 00:27:45,040 --> 00:27:47,160 Speaker 2: Me we brace ourselves, so the feather un needs to come. 564 00:27:47,200 --> 00:27:49,439 Speaker 2: Henry Wren, great wrap up on the micro of all 565 00:27:49,560 --> 00:27:52,359 Speaker 2: that we've just digested. Meanwhile, let's turn our attention to 566 00:27:52,600 --> 00:27:56,360 Speaker 2: Washington and US China relationships from a geopolitical perspective. Now, 567 00:27:56,520 --> 00:27:59,280 Speaker 2: President Biden hiking those tariffs as we expected on a 568 00:27:59,320 --> 00:28:03,880 Speaker 2: wide range Chinese impults, including semiconductors, batteries, sona cells, critical 569 00:28:03,920 --> 00:28:07,240 Speaker 2: minerals from all Let's bring in news Kaye lines in Washington, 570 00:28:07,400 --> 00:28:11,400 Speaker 2: a trend started by Trump continued by the current administration. 571 00:28:11,640 --> 00:28:14,040 Speaker 2: But there is nuance to how they're doing this, Kailey. 572 00:28:15,240 --> 00:28:18,399 Speaker 12: There is, Caroline. Largely the Trump era tariffs have remained 573 00:28:18,440 --> 00:28:21,520 Speaker 12: intact under this administration, and in fact, no tariffs are 574 00:28:21,560 --> 00:28:23,880 Speaker 12: actually being reduced today. There are just some new ones 575 00:28:23,880 --> 00:28:26,240 Speaker 12: being implemented in others being raised. All said, this will 576 00:28:26,240 --> 00:28:30,080 Speaker 12: effect about eighteen billion dollars of Chinese imports into the US. 577 00:28:30,119 --> 00:28:32,159 Speaker 12: All of this will take effect between this year and 578 00:28:32,240 --> 00:28:34,240 Speaker 12: twenty twenty five. As you say, a number of key 579 00:28:34,240 --> 00:28:37,399 Speaker 12: industries are in focus here. Semiconductors will see the tariff 580 00:28:37,440 --> 00:28:40,280 Speaker 12: rate go from twenty five percent to fifty percent by 581 00:28:40,360 --> 00:28:44,280 Speaker 12: twenty twenty five. That's aimed specifically, it's so called legacy chips. 582 00:28:44,320 --> 00:28:45,120 Speaker 3: You'll also see. 583 00:28:44,920 --> 00:28:48,280 Speaker 12: Critical mineral tariffs a new tariff of twenty five percent 584 00:28:48,360 --> 00:28:50,160 Speaker 12: this year. That is one area in which this could 585 00:28:50,160 --> 00:28:54,160 Speaker 12: actually be more disruptive than just symbolic, considering the US 586 00:28:54,200 --> 00:28:57,720 Speaker 12: is still largely dependent on China for things like critical 587 00:28:57,760 --> 00:29:01,160 Speaker 12: minerals than others we saw forecasts and voting solar cells 588 00:29:01,200 --> 00:29:04,840 Speaker 12: and batteries, specifically lithium ion batteries. We'll see that tariff 589 00:29:04,920 --> 00:29:06,920 Speaker 12: rate go higher. It's going to jump to about twenty 590 00:29:06,960 --> 00:29:10,440 Speaker 12: five percent. Cell tariffs will jump from twenty five percent 591 00:29:10,480 --> 00:29:13,160 Speaker 12: to fifty percent. And then the big quadrupling is in 592 00:29:13,280 --> 00:29:15,440 Speaker 12: Chinese made electric vehicles that goes all the way up 593 00:29:15,480 --> 00:29:17,760 Speaker 12: to one hundred and two and a half percent. But 594 00:29:17,800 --> 00:29:20,560 Speaker 12: that is one area in which largely it is symbolic 595 00:29:20,600 --> 00:29:23,280 Speaker 12: as because of existing tariffs on Chinese evs already, they 596 00:29:23,280 --> 00:29:27,160 Speaker 12: weren't really looking at the US consumer market to get 597 00:29:27,200 --> 00:29:29,600 Speaker 12: that demand. What this really is about, though, is making 598 00:29:29,680 --> 00:29:33,600 Speaker 12: sure that no further supply necessarily could come dumping into 599 00:29:33,640 --> 00:29:36,760 Speaker 12: the US. Given the concerns the administration has about Chinese 600 00:29:36,800 --> 00:29:39,200 Speaker 12: over capacity, They really with these tariffs are trying to 601 00:29:39,200 --> 00:29:43,920 Speaker 12: aim at shoring up US production in these critical areas. 602 00:29:43,960 --> 00:29:47,240 Speaker 12: And we'll see what effect this may have in return 603 00:29:47,320 --> 00:29:49,840 Speaker 12: on the US if there's any retaliatory moves that could 604 00:29:49,880 --> 00:29:53,120 Speaker 12: potentially come to China, which said today it's resolutely opposed 605 00:29:53,240 --> 00:29:54,160 Speaker 12: to these new measures. 606 00:29:55,120 --> 00:29:57,440 Speaker 4: We discussed on the show yesterday and I checked there 607 00:29:57,440 --> 00:30:00,680 Speaker 4: are just full China made EV you can buy in 608 00:30:00,720 --> 00:30:02,280 Speaker 4: this country. So, as you point out, it's like more 609 00:30:02,320 --> 00:30:05,280 Speaker 4: of a deterrent for more models to come. There's a 610 00:30:05,320 --> 00:30:08,200 Speaker 4: balancing act here so that everyone doesn't get hurt, you know. 611 00:30:08,280 --> 00:30:13,080 Speaker 4: Amory Horden Bloomberg's Amory spoke to the Treasury Secretary, and 612 00:30:13,160 --> 00:30:15,400 Speaker 4: I think we're getting a sense here that there is 613 00:30:15,400 --> 00:30:18,600 Speaker 4: a clear message from the administration, but they're basically saying, 614 00:30:18,640 --> 00:30:20,640 Speaker 4: we're just being fair with what China's doing. 615 00:30:21,000 --> 00:30:23,200 Speaker 12: Yeah, Essentially, they want to make sure that the US 616 00:30:23,320 --> 00:30:27,560 Speaker 12: is competitive, noting that China has some uncompetitive or unfair practices, 617 00:30:27,560 --> 00:30:29,880 Speaker 12: including with things like subsidies. This is something that we 618 00:30:29,960 --> 00:30:33,240 Speaker 12: heard the Treasury Secretary talking about. However, when Amory asked 619 00:30:33,240 --> 00:30:35,520 Speaker 12: heree yesterday if the US is looking for another outright 620 00:30:35,560 --> 00:30:38,400 Speaker 12: trade war with China, the Treasury Secretary didn't seem to 621 00:30:38,480 --> 00:30:40,480 Speaker 12: want to confirm that, because they are trying to walk 622 00:30:40,520 --> 00:30:42,560 Speaker 12: this very delicate line here where they don't want to 623 00:30:42,560 --> 00:30:45,840 Speaker 12: have blowback on US consumers. That is perhaps why, as well, 624 00:30:45,840 --> 00:30:49,120 Speaker 12: you are seeing this more calibrated targeting of certain areas 625 00:30:49,320 --> 00:30:51,640 Speaker 12: coming from the Biden administration in this section three oh 626 00:30:51,720 --> 00:30:54,920 Speaker 12: one review, rather than something like the blanket sixty percent. 627 00:30:54,960 --> 00:30:56,760 Speaker 12: HERI IFFs that Donald Trump says he would look to 628 00:30:56,800 --> 00:31:00,320 Speaker 12: do on all Chinese imports in a second administration should 629 00:31:00,320 --> 00:31:02,880 Speaker 12: he win the White House in November, because there is 630 00:31:02,920 --> 00:31:05,360 Speaker 12: a concern as well about the inflationary effect that this 631 00:31:05,440 --> 00:31:08,160 Speaker 12: could have here at home. Obviously, while you would like 632 00:31:08,200 --> 00:31:10,560 Speaker 12: to say that this hurts China, its costs that are 633 00:31:10,560 --> 00:31:12,800 Speaker 12: paid by importers that could then get passed on to 634 00:31:12,920 --> 00:31:14,920 Speaker 12: the end consumer could result in higher costs for the 635 00:31:14,960 --> 00:31:17,520 Speaker 12: American people at a time when this administration is not 636 00:31:17,520 --> 00:31:21,400 Speaker 12: only struggling geopolitically to navigate its relationship with China and 637 00:31:21,440 --> 00:31:24,360 Speaker 12: make sure it's competitive but still cooperative, but also here 638 00:31:24,400 --> 00:31:27,479 Speaker 12: at home as they're seeking reelection, really struggling with an 639 00:31:27,480 --> 00:31:30,600 Speaker 12: economy and an inflation outlook that many Americans are unhappy with. 640 00:31:30,960 --> 00:31:33,719 Speaker 3: Investigated lines out of Washington, d C. Thank you very much. 641 00:31:33,720 --> 00:31:36,040 Speaker 4: All right, Coming up on Bloomberg Technology, we're going to 642 00:31:36,080 --> 00:31:39,240 Speaker 4: be joined by Craft co founder and partner David. 643 00:31:39,040 --> 00:31:41,640 Speaker 3: Sachs on a brand new AI. 644 00:31:41,440 --> 00:31:44,880 Speaker 4: Communication tool and company that he's leading. That conversation coming 645 00:31:44,880 --> 00:31:46,560 Speaker 4: out and stay with us because we'll be right back. 646 00:31:46,840 --> 00:32:06,040 Speaker 4: This is Bloomberg Technology, a work chat for the AI era. 647 00:32:06,120 --> 00:32:08,920 Speaker 4: It's called Glue, and it's a new AI native communication 648 00:32:09,040 --> 00:32:12,400 Speaker 4: platform going public, coming out of Stealth today after six 649 00:32:12,440 --> 00:32:14,040 Speaker 4: months of private beta. 650 00:32:14,120 --> 00:32:15,440 Speaker 3: The founders say. 651 00:32:15,360 --> 00:32:17,680 Speaker 4: Glue takes the best of both worlds from Chat, GPT 652 00:32:17,800 --> 00:32:20,800 Speaker 4: and Slack, and it was built for the way people 653 00:32:20,840 --> 00:32:24,520 Speaker 4: now collaborate, both with colleagues and with their AI assistance. 654 00:32:24,600 --> 00:32:26,240 Speaker 3: Let's bring in Craft partner. 655 00:32:26,120 --> 00:32:28,040 Speaker 4: And Glue co founder and chairman de like to say 656 00:32:28,040 --> 00:32:31,240 Speaker 4: that David Sachs is whe us here on Bloomberg Technology. David, 657 00:32:31,280 --> 00:32:32,800 Speaker 4: if it's OK, I actually just want to start with 658 00:32:33,000 --> 00:32:37,280 Speaker 4: the technology itself. You know, Glue's built to support both 659 00:32:38,040 --> 00:32:40,640 Speaker 4: GBT four and Claude three, and I just wondered if 660 00:32:40,640 --> 00:32:43,480 Speaker 4: you talk us through the approach you took in building 661 00:32:43,520 --> 00:32:44,160 Speaker 4: it right. 662 00:32:44,200 --> 00:32:46,880 Speaker 1: Well, we want to be agnostic. 663 00:32:46,960 --> 00:32:49,840 Speaker 13: We want to work with really all the major models, 664 00:32:49,880 --> 00:32:52,280 Speaker 13: and we're starting with Chat GBT and we're starting with Claude. 665 00:32:52,400 --> 00:32:54,720 Speaker 13: And by the way, we're using the latest version of 666 00:32:54,800 --> 00:32:58,760 Speaker 13: Chat GPT, Chat GBT four Army that they just launched yesterday, 667 00:32:58,760 --> 00:33:03,360 Speaker 13: and it is pretty awesome. The performances terrific. But stepping 668 00:33:03,360 --> 00:33:05,440 Speaker 13: back a second, what we're trying to do with Glue 669 00:33:05,480 --> 00:33:09,320 Speaker 13: AI is create the first AI native chat app, and 670 00:33:09,400 --> 00:33:12,200 Speaker 13: what it does is it creates one place where you 671 00:33:12,240 --> 00:33:14,800 Speaker 13: can have your chats with AI and your chats with 672 00:33:15,000 --> 00:33:18,400 Speaker 13: humans with your team, because we think people, we think 673 00:33:18,480 --> 00:33:20,680 Speaker 13: employees want to do that in one place instead of going. 674 00:33:20,600 --> 00:33:22,120 Speaker 1: To two separate apps. 675 00:33:22,480 --> 00:33:25,040 Speaker 13: The problem with chat GPT, as awesome as it is, 676 00:33:25,040 --> 00:33:27,880 Speaker 13: is that it's not a multiplayer experience. There's no way 677 00:33:27,880 --> 00:33:32,200 Speaker 13: to bring your coworkers into the chat with you. And 678 00:33:32,240 --> 00:33:35,080 Speaker 13: the problem with Slack is really that the channels get 679 00:33:35,080 --> 00:33:38,320 Speaker 13: in the way, and we hear from lots of teams 680 00:33:38,320 --> 00:33:41,680 Speaker 13: that they have channel fatigue and really want this new 681 00:33:42,240 --> 00:33:45,480 Speaker 13: threaded conversation model without all the channels. 682 00:33:45,600 --> 00:33:46,800 Speaker 1: So that's what we've done with Glue. 683 00:33:46,920 --> 00:33:49,360 Speaker 4: David, we know you as a bench capus right, a 684 00:33:49,360 --> 00:33:53,400 Speaker 4: long time investor in technology companies. What was the need 685 00:33:53,640 --> 00:33:56,640 Speaker 4: that you identified or the gap in the marketplace that 686 00:33:57,200 --> 00:34:00,640 Speaker 4: you wanted to start and build a company yourself and 687 00:34:00,720 --> 00:34:03,680 Speaker 4: the tool rather than them back, I guess, or invest 688 00:34:03,920 --> 00:34:04,720 Speaker 4: in another player. 689 00:34:05,080 --> 00:34:09,160 Speaker 13: Well, you know, before becoming an investor and before becoming 690 00:34:09,200 --> 00:34:12,000 Speaker 13: a podcaster, I actually was an Internet product eye. I 691 00:34:12,040 --> 00:34:13,799 Speaker 13: was a head of product at PayPal, and then I 692 00:34:13,800 --> 00:34:16,279 Speaker 13: created a company called yamor back in two thousand and eight, 693 00:34:16,280 --> 00:34:18,800 Speaker 13: which was the first or one of the first enterprise 694 00:34:18,840 --> 00:34:22,319 Speaker 13: messaging platforms, and back then it was based on the 695 00:34:22,360 --> 00:34:25,600 Speaker 13: idea of feeds, which thanks to social networking, were kind 696 00:34:25,600 --> 00:34:29,320 Speaker 13: of the conversation paradigm. Then you know, the next decade 697 00:34:29,360 --> 00:34:31,720 Speaker 13: we had Slack, which is based on IOC and channels, 698 00:34:31,760 --> 00:34:34,560 Speaker 13: and now we have a new conversation paradigm that's based 699 00:34:34,600 --> 00:34:37,760 Speaker 13: on AI. It's based on chat GPT. I think every 700 00:34:37,840 --> 00:34:41,000 Speaker 13: decade you get a new paradigm like this and you 701 00:34:41,080 --> 00:34:43,359 Speaker 13: need a new communication tool, and that's what we've built 702 00:34:43,360 --> 00:34:45,960 Speaker 13: with Glue. I just didn't see that tool on the 703 00:34:46,000 --> 00:34:49,600 Speaker 13: marketplace that was reacting fast enough to this disruption, and 704 00:34:49,680 --> 00:34:52,720 Speaker 13: so I partnered with Evan Owen, who's my co founder 705 00:34:52,760 --> 00:34:56,400 Speaker 13: and CEO, in order to create in order to create Clue. 706 00:34:56,680 --> 00:34:58,759 Speaker 2: Now, David, I know that you like to start your 707 00:34:58,760 --> 00:35:01,279 Speaker 2: pictures with a demo, So let's demo in a little bit. 708 00:35:01,280 --> 00:35:02,880 Speaker 2: I want to look at how it looks, it feels, 709 00:35:02,920 --> 00:35:05,880 Speaker 2: and how it's going to interact. And as the founder, 710 00:35:06,360 --> 00:35:08,799 Speaker 2: as well as putting the money into this, are you 711 00:35:08,840 --> 00:35:11,080 Speaker 2: not worried about, well, the competition that's out there because 712 00:35:11,120 --> 00:35:13,400 Speaker 2: everyone's getting into enterprise generator AI. 713 00:35:15,400 --> 00:35:15,799 Speaker 1: For sure. 714 00:35:15,840 --> 00:35:20,000 Speaker 13: I mean right now every enterprise SaaS app is trying 715 00:35:20,040 --> 00:35:23,040 Speaker 13: to figure out how to incorporate AI into their product roadmap. 716 00:35:23,040 --> 00:35:24,000 Speaker 1: There's no question about that. 717 00:35:24,040 --> 00:35:26,080 Speaker 13: But I think there's still a pretty big debate about 718 00:35:26,120 --> 00:35:28,960 Speaker 13: where AI is going to live in the enterprise. Where 719 00:35:28,960 --> 00:35:31,560 Speaker 13: are you going to go to ask those sort of 720 00:35:31,600 --> 00:35:35,480 Speaker 13: broad based questions about, for example, who in your enterprise 721 00:35:35,560 --> 00:35:38,680 Speaker 13: has the right expertise, who should be talking to Are 722 00:35:38,680 --> 00:35:42,399 Speaker 13: those sort of core enterprise collaboration use cases. And our 723 00:35:42,480 --> 00:35:45,239 Speaker 13: view on it is that AI should live in the 724 00:35:45,360 --> 00:35:47,960 Speaker 13: chat because again, you don't want to have one application 725 00:35:48,040 --> 00:35:50,680 Speaker 13: for your AI chats and one application for human chats. 726 00:35:50,840 --> 00:35:53,240 Speaker 1: It all belongs together and the AI. 727 00:35:53,520 --> 00:35:55,759 Speaker 13: What we found is that when you do incorporate the 728 00:35:55,840 --> 00:35:59,080 Speaker 13: AI as a full fledged participant into the chat, it's 729 00:35:59,160 --> 00:36:02,640 Speaker 13: able to use all of the live chat history as context, 730 00:36:03,080 --> 00:36:07,440 Speaker 13: and so your work chat app is able to give 731 00:36:07,480 --> 00:36:11,480 Speaker 13: you answers that regular chat GPT could not. We've had 732 00:36:11,680 --> 00:36:15,360 Speaker 13: really staggering results at craft Ventures using this product. Internally, 733 00:36:15,760 --> 00:36:18,160 Speaker 13: I've asked the AI to write Investment my most for 734 00:36:18,239 --> 00:36:20,840 Speaker 13: me summarize the arguments for and against investing in a company. 735 00:36:20,920 --> 00:36:23,960 Speaker 13: It's done an unbelievable a job just assembling those arguments 736 00:36:24,160 --> 00:36:27,000 Speaker 13: based on the chat history. I've asked it who is 737 00:36:27,040 --> 00:36:29,319 Speaker 13: contributing the most to deal flow at craft, and the 738 00:36:29,360 --> 00:36:31,880 Speaker 13: AI is able to tell me. It's really amazing the 739 00:36:31,920 --> 00:36:35,040 Speaker 13: answers you can get when you give AI access to 740 00:36:35,040 --> 00:36:35,800 Speaker 13: your chat history. 741 00:36:36,800 --> 00:36:40,400 Speaker 2: Amazing answers built by in many ways, amazing large language 742 00:36:40,400 --> 00:36:44,920 Speaker 2: models that underpin it. I'm interested in you having sold 743 00:36:45,120 --> 00:36:48,880 Speaker 2: Yamma to Microsoft previously, having understood the way the M 744 00:36:48,880 --> 00:36:50,800 Speaker 2: and A works, and the company that looks for exits 745 00:36:51,040 --> 00:36:53,440 Speaker 2: is now seeing all these interesting partnerships, shall we call 746 00:36:53,520 --> 00:36:56,160 Speaker 2: them being done, whether it's Aquahy's, whether it is opening 747 00:36:56,680 --> 00:36:59,520 Speaker 2: Timmy when Microsoft and throp Aic of course going in 748 00:36:59,520 --> 00:37:01,480 Speaker 2: with Amazon and I'm Google at the same time. How 749 00:37:01,520 --> 00:37:02,800 Speaker 2: do you judge that as a VC. 750 00:37:03,680 --> 00:37:06,760 Speaker 13: Well, there's innovation happening at every level of the stack. 751 00:37:06,880 --> 00:37:11,160 Speaker 13: I mean you're seeing tremendous innovation at the foundation model layer. 752 00:37:11,239 --> 00:37:14,040 Speaker 13: You've got open AI, You've got Anthropic like you've mentioned, 753 00:37:14,080 --> 00:37:18,480 Speaker 13: you've got x Ai now launching that we just invested in. 754 00:37:18,960 --> 00:37:22,200 Speaker 13: Then you've got innovation happening at the application layer of 755 00:37:22,320 --> 00:37:25,400 Speaker 13: the stack, where applications are able to take advantage of 756 00:37:25,440 --> 00:37:29,200 Speaker 13: all the new capabilities created by these lllms, And if 757 00:37:29,200 --> 00:37:32,359 Speaker 13: you're positioned correctly in the application layer, you don't want 758 00:37:32,400 --> 00:37:33,680 Speaker 13: to compete against the models. 759 00:37:33,760 --> 00:37:34,880 Speaker 1: You want to harness them. 760 00:37:35,040 --> 00:37:37,719 Speaker 13: So as the models get better and better, your application 761 00:37:37,760 --> 00:37:39,920 Speaker 13: gets better and better. And that's what we've seen with 762 00:37:40,080 --> 00:37:43,240 Speaker 13: Glue is that with each new release of Chat, GPT 763 00:37:43,400 --> 00:37:46,759 Speaker 13: or these other models that we've incorporated, like Flaud, the 764 00:37:46,760 --> 00:37:48,600 Speaker 13: Glue Chat tool just gets better and better. 765 00:37:48,640 --> 00:37:50,000 Speaker 1: So we're going to ride that wave. 766 00:37:50,360 --> 00:37:53,520 Speaker 13: As the underlying model innovation continues and the models get 767 00:37:53,520 --> 00:37:56,080 Speaker 13: better and better, the features of our application will get 768 00:37:56,080 --> 00:37:56,760 Speaker 13: better and better. 769 00:37:57,239 --> 00:37:58,800 Speaker 1: And that's how you want to position yourself. 770 00:38:00,000 --> 00:38:02,440 Speaker 4: Tru David, you had Sam Outman on the All Limpod 771 00:38:02,560 --> 00:38:05,000 Speaker 4: last week, and I think on social media there was 772 00:38:05,040 --> 00:38:08,120 Speaker 4: some debate which you participated in, about whether he'd said 773 00:38:08,120 --> 00:38:12,560 Speaker 4: anything new. But go to yesterday's presentation from open AI. 774 00:38:12,960 --> 00:38:15,080 Speaker 4: How much of it then surprised you based on the 775 00:38:15,080 --> 00:38:16,480 Speaker 4: conversation you'd had last week. 776 00:38:17,640 --> 00:38:20,040 Speaker 13: Well, in retrospect, he gave us a bunch of hints 777 00:38:20,200 --> 00:38:22,960 Speaker 13: last week, so I was I shouldn't have I was 778 00:38:23,000 --> 00:38:25,560 Speaker 13: being a little bit uncharitable, but really I wasn't criticizing Sam, 779 00:38:25,560 --> 00:38:29,840 Speaker 13: I was just trying to ratio Jason, But yeah, no, 780 00:38:30,320 --> 00:38:32,520 Speaker 13: Sam gave us a bunch of hints last week that 781 00:38:32,600 --> 00:38:34,719 Speaker 13: now make a lot more sense in the light of 782 00:38:35,080 --> 00:38:39,560 Speaker 13: chat gpto yesterday, which really was an impressive demo, and 783 00:38:39,600 --> 00:38:42,279 Speaker 13: like I said, we've already incorporated it into Glue, and 784 00:38:42,640 --> 00:38:44,880 Speaker 13: I can tell you the performance of it is way 785 00:38:44,920 --> 00:38:48,120 Speaker 13: better than GPT four Turbo. So kudos to the open 786 00:38:48,160 --> 00:38:49,840 Speaker 13: Ai team. They've really done a great job with that. 787 00:38:50,760 --> 00:38:54,640 Speaker 4: David, quick question, just thinking about Craft's portfolio. Did you 788 00:38:54,719 --> 00:38:56,680 Speaker 4: try to get into the Xai round. 789 00:38:57,120 --> 00:38:59,080 Speaker 1: Yeah, we did, We've participated in that. 790 00:38:59,320 --> 00:39:02,880 Speaker 2: And from the follow will you integrate Xai into Glue 791 00:39:02,960 --> 00:39:05,279 Speaker 2: and how are you thinking about whether winner takes all 792 00:39:05,400 --> 00:39:07,839 Speaker 2: or whether there will be, of course, well a commoditized 793 00:39:07,920 --> 00:39:09,359 Speaker 2: version of large language models here. 794 00:39:10,480 --> 00:39:12,680 Speaker 13: Yeah, I mean I expect that we'll integrate with all 795 00:39:12,719 --> 00:39:17,719 Speaker 13: the major llms. Our view on it is that we're agnostic. 796 00:39:17,840 --> 00:39:21,200 Speaker 13: We want to give our users the choice of which 797 00:39:21,360 --> 00:39:23,759 Speaker 13: LM to use, and in fact, we're going to plug 798 00:39:23,760 --> 00:39:25,960 Speaker 13: into all the major lms and then we can actually 799 00:39:26,000 --> 00:39:28,680 Speaker 13: make the choice for them in terms of which model 800 00:39:28,680 --> 00:39:32,240 Speaker 13: to use based on the query that they're trying to ask, 801 00:39:32,680 --> 00:39:35,080 Speaker 13: So we actually think that the more models the better. 802 00:39:35,760 --> 00:39:37,880 Speaker 2: Do you think Xai could have got a large evaluation? 803 00:39:40,080 --> 00:39:42,800 Speaker 13: You know, I'm going to let them announce their own round. 804 00:39:43,040 --> 00:39:44,920 Speaker 13: I don't want to announce their round for them. But 805 00:39:46,120 --> 00:39:48,160 Speaker 13: if the question is whether we participated. 806 00:39:47,640 --> 00:39:47,920 Speaker 1: We did. 807 00:39:49,080 --> 00:39:49,360 Speaker 3: David. 808 00:39:49,360 --> 00:39:52,560 Speaker 4: Look, it's an election year, presidential election year. You talks 809 00:39:52,560 --> 00:39:55,680 Speaker 4: about your background in technology and being a tech investor, 810 00:39:55,960 --> 00:39:58,440 Speaker 4: and you've been vocal on a political front. Will you 811 00:39:58,480 --> 00:40:01,280 Speaker 4: be more active this year in this election cycle? 812 00:40:03,360 --> 00:40:05,640 Speaker 13: Well, you know, we talk about politics. Is one of 813 00:40:05,400 --> 00:40:09,040 Speaker 13: the topics on the All In pod. We discussed current events, 814 00:40:09,120 --> 00:40:12,520 Speaker 13: business markets, politics, foreign policy, events all over the world, 815 00:40:12,600 --> 00:40:15,719 Speaker 13: and so obviously the election is unavoidable, and it's no 816 00:40:15,800 --> 00:40:19,560 Speaker 13: secret that I've been a critic of Biden. So I 817 00:40:19,600 --> 00:40:24,680 Speaker 13: may get more involved in the sense of maybe hosting events, 818 00:40:24,680 --> 00:40:29,400 Speaker 13: maybe contributing, but it's really it's not It's certainly not 819 00:40:29,440 --> 00:40:31,319 Speaker 13: the main thing I do. Let's just put it that way. 820 00:40:31,360 --> 00:40:33,760 Speaker 13: It's the way that you get involved in the political 821 00:40:33,800 --> 00:40:37,359 Speaker 13: process is that you typically host events and you get 822 00:40:37,360 --> 00:40:40,200 Speaker 13: to maybe meet the candidates that way, and that's something 823 00:40:40,239 --> 00:40:43,160 Speaker 13: we've tried to do, me and my co host and 824 00:40:43,200 --> 00:40:46,799 Speaker 13: the all in pod. We've done that now for Robert F. 825 00:40:46,840 --> 00:40:49,600 Speaker 13: Kennedy Junior. We did that for a Vake Gramaswami. We 826 00:40:49,600 --> 00:40:52,000 Speaker 13: had them on the pod and that we did events 827 00:40:52,000 --> 00:40:56,439 Speaker 13: for them. And we've extended an offer to the other 828 00:40:56,520 --> 00:40:58,880 Speaker 13: major candidates and hope that they accept. 829 00:40:59,640 --> 00:41:01,960 Speaker 2: Well, it's not the only thing you do. It's on 830 00:41:02,000 --> 00:41:03,480 Speaker 2: top of the pod, it's on top of the investing 831 00:41:03,560 --> 00:41:07,040 Speaker 2: and on top of founding new businesses as well. Come back, 832 00:41:07,120 --> 00:41:09,120 Speaker 2: tell us how it's going. David Sackscraft partner AT and 833 00:41:09,160 --> 00:41:18,880 Speaker 2: Glue chairman. Thank you. Let's get to the return and 834 00:41:18,880 --> 00:41:23,200 Speaker 2: the mean stocks. GameStop AMC surging that retail frenzy or 835 00:41:23,280 --> 00:41:26,319 Speaker 2: is it a retail frenzy that's actually driving this whatever 836 00:41:26,360 --> 00:41:28,640 Speaker 2: it's happened, it was Roaring Kitty that seems to revive 837 00:41:28,680 --> 00:41:32,000 Speaker 2: the animal spirits. Whoomberg's belly Lipschaltz joins us for more 838 00:41:32,239 --> 00:41:34,279 Speaker 2: a nice sixty six percent higher on the day. I'm 839 00:41:34,280 --> 00:41:36,200 Speaker 2: not gonna ask you about fundamentals of the business or 840 00:41:36,239 --> 00:41:38,359 Speaker 2: the business model of selling real games and heart and 841 00:41:38,640 --> 00:41:42,760 Speaker 2: retail stores, but who is buying? Who is driving this higher? 842 00:41:43,280 --> 00:41:44,239 Speaker 3: It seems like. 843 00:41:44,200 --> 00:41:46,400 Speaker 6: This is fingerprints of Wall Street when We're looking at 844 00:41:46,440 --> 00:41:49,160 Speaker 6: some of the flow data across Fidelity, Interactive brokers, and 845 00:41:49,200 --> 00:41:51,040 Speaker 6: some of the other ways that we track what retail 846 00:41:51,160 --> 00:41:53,839 Speaker 6: is buying in as real time as possible. It doesn't 847 00:41:53,880 --> 00:41:55,880 Speaker 6: seem like they're actually buying GameStop. It seems like on 848 00:41:55,920 --> 00:41:59,960 Speaker 6: the whole net, sales across Fidelity's platform for GameStop outpacing 849 00:42:00,160 --> 00:42:02,600 Speaker 6: those to buy in GameStop, though they do seem to 850 00:42:02,600 --> 00:42:05,800 Speaker 6: be buying a bit more AMC relative to those cell orders. 851 00:42:05,920 --> 00:42:07,920 Speaker 6: But there's a lot to try to make of some 852 00:42:08,000 --> 00:42:10,680 Speaker 6: of the trends that we've been seeing. Again, GameStop has 853 00:42:10,719 --> 00:42:12,600 Speaker 6: a shot at getting back to those all time highs. 854 00:42:12,680 --> 00:42:15,720 Speaker 6: AMC is still down like ninety seven percent from those records. 855 00:42:16,800 --> 00:42:19,440 Speaker 4: Bailey, do we have any more clarity on that social 856 00:42:19,480 --> 00:42:21,320 Speaker 4: media post post by Roaring Kitty? 857 00:42:21,520 --> 00:42:24,399 Speaker 6: Yes or no? Now he's added more to it. He's 858 00:42:24,440 --> 00:42:27,000 Speaker 6: posted again today. I have no idea and what the 859 00:42:27,080 --> 00:42:30,160 Speaker 6: logic is. I've DMed him, Ryan Kitty, if you're watching this, 860 00:42:30,239 --> 00:42:33,239 Speaker 6: please DM me back, but no one knows anything, and 861 00:42:33,320 --> 00:42:36,279 Speaker 6: it's there's so much uncertainty around what he's doing and 862 00:42:36,320 --> 00:42:36,960 Speaker 6: what it could mean. 863 00:42:37,160 --> 00:42:39,440 Speaker 2: I mean, he's going on because it was initially lifting 864 00:42:39,520 --> 00:42:42,640 Speaker 2: one of gamestop's own posts back from back in February. 865 00:42:42,680 --> 00:42:45,120 Speaker 2: But now it's just video after video. Go check it 866 00:42:45,120 --> 00:42:47,920 Speaker 2: out Bailly Lipschultz, He's across all of it. Meanwhile, that 867 00:42:47,960 --> 00:42:49,719 Speaker 2: does it for this edition of Blue Big Technology ED. 868 00:42:49,760 --> 00:42:50,560 Speaker 2: It was a busy one. 869 00:42:51,280 --> 00:42:53,600 Speaker 3: Check out our podcast our pod. 870 00:42:53,680 --> 00:42:56,520 Speaker 4: You can find the pod on Apple, Spotify, iHeart, and 871 00:42:56,520 --> 00:42:57,600 Speaker 4: the Bluebot platforms. 872 00:42:58,000 --> 00:43:00,440 Speaker 3: We love the pod from San Francisco and New York. 873 00:43:00,440 --> 00:43:02,920 Speaker 3: This is Bloombay Technology