1 00:00:01,639 --> 00:00:06,920 Speaker 1: From Marhart where Innovation, Money and power Collie in Silicon Valley, NBN. 2 00:00:07,280 --> 00:00:11,320 Speaker 1: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 3 00:00:25,160 --> 00:00:27,639 Speaker 2: I'm Caroline Hyde at Bloomberg's World headquarters in New York 4 00:00:27,920 --> 00:00:29,440 Speaker 2: and I'm Ed Lodlow in San Francisco. 5 00:00:29,560 --> 00:00:32,040 Speaker 3: Big Friday. This is Bloomberg Technology. 6 00:00:32,320 --> 00:00:35,200 Speaker 2: It is big coming up full coverage of all those 7 00:00:35,280 --> 00:00:37,800 Speaker 2: tech earnings from Snapchat to Amazon to Pinterest. 8 00:00:37,840 --> 00:00:40,839 Speaker 3: We have got you covered and sticking with earnings, I'm 9 00:00:40,840 --> 00:00:43,840 Speaker 3: going to bring you my interview with Intel CEO Pat Gelsinger, 10 00:00:44,000 --> 00:00:47,400 Speaker 3: the company projecting a return to free cash flow, plus. 11 00:00:47,040 --> 00:00:49,479 Speaker 2: How AI is going to impact hiring and managing in 12 00:00:49,520 --> 00:00:53,800 Speaker 2: the workplace. We'll discuss the CEO of Lettice, Jack Altman. 13 00:00:54,280 --> 00:00:57,160 Speaker 2: But first from those privately VC back companies to the 14 00:00:57,160 --> 00:01:00,480 Speaker 2: public markets, where we're a little bit more cautious. Some 15 00:01:00,520 --> 00:01:03,040 Speaker 2: of the big eco data that we got, the drops 16 00:01:03,080 --> 00:01:05,160 Speaker 2: on the inflation reads ahead of the FED next week, 17 00:01:05,440 --> 00:01:07,920 Speaker 2: and look, inflation is still brisk here in the United States. 18 00:01:07,959 --> 00:01:10,479 Speaker 2: It's still brisk over in Europe, but maybe trading down 19 00:01:10,480 --> 00:01:12,160 Speaker 2: a little bit. We've seen the NASLAG still managing to 20 00:01:12,160 --> 00:01:14,200 Speaker 2: punch up three tens percent even though Amazon is that 21 00:01:14,280 --> 00:01:17,200 Speaker 2: drag two year yield though we're seeing two basis points lower. 22 00:01:17,200 --> 00:01:19,960 Speaker 2: In fact, we're seeing a buying into US bonds day 23 00:01:20,120 --> 00:01:22,399 Speaker 2: and mid risker version. Perhaps that's around the banking sector. 24 00:01:22,440 --> 00:01:24,640 Speaker 2: All eyes on First Republic. I'm looking at MSCI or 25 00:01:24,680 --> 00:01:27,440 Speaker 2: Country World innext up about half a percentage point. Europe 26 00:01:27,440 --> 00:01:29,960 Speaker 2: managed to just push on through towards the end of trading. 27 00:01:30,120 --> 00:01:31,520 Speaker 2: Let's slick it on and look at what's happening in 28 00:01:31,520 --> 00:01:33,479 Speaker 2: the great big world of crypto, because we are seeing 29 00:01:33,600 --> 00:01:36,520 Speaker 2: don ogcoin having a good set of five days. We're 30 00:01:36,600 --> 00:01:38,840 Speaker 2: up six percent, We're back at near that thirty thousand level. 31 00:01:38,840 --> 00:01:41,160 Speaker 2: There's been a lot of volatility along the way for bitcoin, 32 00:01:41,400 --> 00:01:43,560 Speaker 2: but edit does feel as though that's still some sort 33 00:01:43,600 --> 00:01:44,680 Speaker 2: of safe harbor on the day. 34 00:01:45,440 --> 00:01:47,440 Speaker 3: Yeah, I'm still really focused on earning season. Take a 35 00:01:47,440 --> 00:01:49,120 Speaker 3: look at Intel. We kind of paired some of our 36 00:01:49,160 --> 00:01:51,800 Speaker 3: earlier games. A tough first quarter, to be honest to 37 00:01:51,840 --> 00:01:54,080 Speaker 3: trough on the top and bottom line, but the outlook 38 00:01:54,120 --> 00:01:55,840 Speaker 3: for the second half of this year a bit more 39 00:01:55,920 --> 00:01:58,280 Speaker 3: rosy and analyst really buying into that. We will hear 40 00:01:58,280 --> 00:02:01,240 Speaker 3: from the CEO Pat Gelsinger later in the show about 41 00:02:01,240 --> 00:02:03,680 Speaker 3: what the green shoots are that are giving the market 42 00:02:03,720 --> 00:02:06,360 Speaker 3: confidence elsewhere. Social media a really big theme of this 43 00:02:06,400 --> 00:02:09,480 Speaker 3: earning season. Snap usually reports earlier. It's a little bit 44 00:02:09,560 --> 00:02:13,000 Speaker 3: later this quarter, but basically it's first ever revenue decline, 45 00:02:13,040 --> 00:02:14,840 Speaker 3: and you can see how that's weighing on the shares. 46 00:02:14,960 --> 00:02:18,079 Speaker 3: Biggest dropping around six months. Pinterest another one where it's 47 00:02:18,200 --> 00:02:21,080 Speaker 3: kind of failing to gain traction, particularly in the US 48 00:02:21,120 --> 00:02:23,840 Speaker 3: on the user growth perspective, and in this world of AI, 49 00:02:24,000 --> 00:02:27,119 Speaker 3: we're worried about visibility. How is this relevant anymore? We'll 50 00:02:27,120 --> 00:02:29,240 Speaker 3: dig into that. And then Amazon down four percent. This 51 00:02:29,320 --> 00:02:32,359 Speaker 3: is all about the cloud. Sequentially into April from the 52 00:02:32,400 --> 00:02:35,720 Speaker 3: first quarter, AWS top line growth slowing by around five 53 00:02:35,800 --> 00:02:38,679 Speaker 3: hundred basis points. This has been the cash cow for Amazon, 54 00:02:38,919 --> 00:02:41,399 Speaker 3: but it hasn't come to the rescue this quarter actually 55 00:02:41,600 --> 00:02:43,959 Speaker 3: cost discipline on the top and bottom line beating the 56 00:02:44,000 --> 00:02:46,240 Speaker 3: course of gone. The strength of the e commerce business 57 00:02:46,440 --> 00:02:49,160 Speaker 3: definitely there that the cloud a little more worrying, Caroline. 58 00:02:49,360 --> 00:02:50,920 Speaker 2: Yeah, and as you say, that has been such an 59 00:02:50,960 --> 00:02:53,880 Speaker 2: important growth driver. Let's talk about it even more. Now 60 00:02:53,919 --> 00:02:56,400 Speaker 2: we've got pack to you, president and co founder and 61 00:02:56,400 --> 00:02:58,440 Speaker 2: of course someone who used to work over at Amazon. 62 00:02:58,480 --> 00:03:01,799 Speaker 2: Melissa Verdick. You were previously of Cool, spending a decade 63 00:03:01,800 --> 00:03:04,000 Speaker 2: at the company. You helped launch the CpG, the health 64 00:03:04,000 --> 00:03:07,200 Speaker 2: beauty retail business. You understand the ad product, but focus 65 00:03:07,240 --> 00:03:09,840 Speaker 2: on the cloud for a minute, in particular, how warring 66 00:03:09,919 --> 00:03:12,440 Speaker 2: is this cash cow is thoughting to slow out in 67 00:03:12,480 --> 00:03:13,120 Speaker 2: terms of growth. 68 00:03:13,919 --> 00:03:15,840 Speaker 4: Yeah, there's well, there's a couple of things for sall 69 00:03:15,919 --> 00:03:19,040 Speaker 4: things for having me glad to be back, but you know, 70 00:03:19,120 --> 00:03:24,280 Speaker 4: sixteen percent growth in AWS still not anything to sneeze about. 71 00:03:24,360 --> 00:03:27,160 Speaker 4: But there's a couple of worrying things. I think One 72 00:03:27,200 --> 00:03:30,120 Speaker 4: is AWS has been the leader. They had forty percent 73 00:03:30,160 --> 00:03:31,000 Speaker 4: growth a year ago. 74 00:03:31,480 --> 00:03:32,240 Speaker 5: Now it's slowing. 75 00:03:32,280 --> 00:03:35,480 Speaker 4: Their competitors are catching up. So Azure and Google Cloud 76 00:03:35,880 --> 00:03:39,000 Speaker 4: both grew faster. Another thing that I think there are 77 00:03:39,000 --> 00:03:41,040 Speaker 4: two other things that are a little bit concerning. One 78 00:03:41,080 --> 00:03:44,440 Speaker 4: is it is impacted by macroeconomics. Amazon talked about how 79 00:03:44,680 --> 00:03:47,360 Speaker 4: customers of all sizes are looking to optimize their cost 80 00:03:47,360 --> 00:03:50,720 Speaker 4: savings and that is where we're seeing some slowing growth. 81 00:03:50,760 --> 00:03:53,640 Speaker 4: And then lastly is on the AI front. So one 82 00:03:53,680 --> 00:03:57,440 Speaker 4: of the things you know, Chat GBT OpenAI launch, they're 83 00:03:57,480 --> 00:04:01,840 Speaker 4: the market leader in Microsoft Azure has a direct integration 84 00:04:01,960 --> 00:04:05,120 Speaker 4: with them. Yes, so that's going to be one challenge 85 00:04:05,120 --> 00:04:09,080 Speaker 4: for them. Amazon did announce Bedrock and their solution for 86 00:04:10,120 --> 00:04:13,200 Speaker 4: Microsoft's Copilot called co Whisper. So I think the question 87 00:04:13,280 --> 00:04:15,320 Speaker 4: is going to be how much adoption can they get 88 00:04:15,360 --> 00:04:18,680 Speaker 4: of their integrations with ADBs given the fact that Chatter 89 00:04:19,160 --> 00:04:20,040 Speaker 4: is leader. 90 00:04:20,960 --> 00:04:23,719 Speaker 3: You know, they're offering something broad right, foundational models for 91 00:04:23,760 --> 00:04:26,680 Speaker 3: those that want to use third party or Titan within Bedrock. 92 00:04:27,000 --> 00:04:29,080 Speaker 3: One thing that really caught my eye the impact of 93 00:04:29,200 --> 00:04:31,680 Speaker 3: machine learning on advertising. To take a look at this 94 00:04:31,760 --> 00:04:34,679 Speaker 3: chart that shows ad growth across the advertising based companies, 95 00:04:34,720 --> 00:04:37,560 Speaker 3: and actually Amazon comes top of the pile. Milist a 96 00:04:37,560 --> 00:04:40,000 Speaker 3: real bright spot. What's your read into that? 97 00:04:40,960 --> 00:04:41,760 Speaker 5: Yeah, definitely. 98 00:04:41,760 --> 00:04:44,479 Speaker 4: I mean my company pack You sits on top of 99 00:04:44,480 --> 00:04:47,200 Speaker 4: the Amazon's APIs for ads, and it's been a great 100 00:04:47,240 --> 00:04:51,440 Speaker 4: business for us. Advertising is highly profitable. Actually, this week 101 00:04:51,839 --> 00:04:55,560 Speaker 4: we looked at earnings across Meta, Google, Snap and Amazon 102 00:04:56,320 --> 00:04:59,880 Speaker 4: was way better than twenty three percent growth, whereas you 103 00:05:00,320 --> 00:05:03,000 Speaker 4: did not do as well. There's an interesting new partnership 104 00:05:03,040 --> 00:05:06,920 Speaker 4: that Amazon announced Whiches with Pinterest. Pinterest has been struggling 105 00:05:06,960 --> 00:05:09,680 Speaker 4: a little bit. They want to monetize their pins, and 106 00:05:09,760 --> 00:05:12,160 Speaker 4: so this is kind of a win for Amazon, this 107 00:05:12,200 --> 00:05:15,040 Speaker 4: new partnership that will drive traffic to them. And then 108 00:05:15,080 --> 00:05:17,000 Speaker 4: one of the other bright spots with the Amazon is 109 00:05:17,000 --> 00:05:19,919 Speaker 4: that they have lots of injury. You know, they continue 110 00:05:19,920 --> 00:05:22,799 Speaker 4: to add more placements. They're pushing more towards upper funnel 111 00:05:22,880 --> 00:05:26,040 Speaker 4: and streaming TV. There's a big opportunity around non indemic 112 00:05:26,200 --> 00:05:30,240 Speaker 4: So even you know, cars that don't sell products on Amazon, 113 00:05:30,320 --> 00:05:34,120 Speaker 4: you can leverage Amazon's audience. So there's just there's a 114 00:05:34,160 --> 00:05:36,760 Speaker 4: lot of opportunity. Amazon has great space to grow in 115 00:05:36,760 --> 00:05:37,839 Speaker 4: the advertising business. 116 00:05:38,560 --> 00:05:40,280 Speaker 3: All right, Melissa Bird, I could pack for you, Thank 117 00:05:40,360 --> 00:05:42,080 Speaker 3: you so much. The other name we're tracking them, course, 118 00:05:42,080 --> 00:05:44,680 Speaker 3: Snap shares set for the worst day in six months 119 00:05:44,720 --> 00:05:48,040 Speaker 3: after posting its first ever revenue decline. Joining us now 120 00:05:48,240 --> 00:05:51,680 Speaker 3: is Jasmine Mburg Insider Intelligence Principle analysts. There was a 121 00:05:51,720 --> 00:05:56,320 Speaker 3: phrase that jumped out, continued disruption in demand in advertising. 122 00:05:56,839 --> 00:05:57,440 Speaker 2: What is that? 123 00:05:58,839 --> 00:06:01,960 Speaker 6: Well, I think over all, looking at Snap's revenue decline, 124 00:06:02,000 --> 00:06:05,760 Speaker 6: what we see is that it has really deep challenges within. 125 00:06:05,520 --> 00:06:06,359 Speaker 5: Its ad business. 126 00:06:06,640 --> 00:06:08,960 Speaker 6: So yes, there was a disruption and demand in the 127 00:06:09,000 --> 00:06:12,920 Speaker 6: sense that it is revamping its ad platform. It is 128 00:06:13,120 --> 00:06:16,440 Speaker 6: unifying its ad formats to make them easier for users, 129 00:06:16,480 --> 00:06:19,640 Speaker 6: it's investing in machine learning, and all of this, of 130 00:06:19,680 --> 00:06:23,880 Speaker 6: course is to provide more ROI for advertisers, and Snap 131 00:06:23,960 --> 00:06:26,520 Speaker 6: is right, you know, these things take time to ramp 132 00:06:26,600 --> 00:06:30,159 Speaker 6: up and really you know, impact its bottom line. But 133 00:06:30,279 --> 00:06:33,040 Speaker 6: for me, you know, this issue really runs deeper, and 134 00:06:33,080 --> 00:06:35,800 Speaker 6: that's that Snap is a much smaller player than say 135 00:06:35,839 --> 00:06:38,919 Speaker 6: Facebook or Instagram. And we saw Meta come in, you know, 136 00:06:39,000 --> 00:06:41,880 Speaker 6: with a three percent revenue growth in Q one, and 137 00:06:42,160 --> 00:06:45,760 Speaker 6: you know, Snapchat also isn't a textbook ad supported platform 138 00:06:45,800 --> 00:06:47,440 Speaker 6: the way that those two players are. 139 00:06:47,680 --> 00:06:49,560 Speaker 7: It's been very clear about this itself. 140 00:06:49,600 --> 00:06:52,880 Speaker 6: It is a messaging app and its users primarily use 141 00:06:52,920 --> 00:06:56,719 Speaker 6: it for visual messaging, and messaging apps are notoriously difficult 142 00:06:56,760 --> 00:06:57,719 Speaker 6: to monetize. 143 00:06:58,080 --> 00:07:01,080 Speaker 2: Three hundred and eighty three million users daily is nothing 144 00:07:01,080 --> 00:07:04,200 Speaker 2: to be sniffed out, though overalled, How do they end 145 00:07:04,279 --> 00:07:08,240 Speaker 2: up trying to set themselves apart from not being well 146 00:07:08,279 --> 00:07:11,960 Speaker 2: and also RAN, but a different RAN, offering something different, 147 00:07:12,000 --> 00:07:13,800 Speaker 2: particularly when it comes to VRAR. 148 00:07:14,960 --> 00:07:16,920 Speaker 6: Yeah, and that's something that Snap has been trying to 149 00:07:16,960 --> 00:07:20,080 Speaker 6: do from the very beginning. You know, they position themselves 150 00:07:19,840 --> 00:07:24,120 Speaker 6: as the as as a gen Z platform, calling it 151 00:07:24,160 --> 00:07:25,280 Speaker 6: the Snapchat generation. 152 00:07:25,360 --> 00:07:25,960 Speaker 5: And it's true. 153 00:07:25,960 --> 00:07:28,800 Speaker 6: It really does have a stronghold on gen Z. Where 154 00:07:28,800 --> 00:07:32,480 Speaker 6: it has struggled is being able to attract audiences outside 155 00:07:32,520 --> 00:07:35,920 Speaker 6: of gen Z, older audiences in particular, and a lot 156 00:07:35,920 --> 00:07:39,239 Speaker 6: of the things that they're doing, particularly around AR, really 157 00:07:39,280 --> 00:07:43,080 Speaker 6: do appeal to its audience. I would say that, you know, 158 00:07:43,680 --> 00:07:48,840 Speaker 6: convincing advertisers that AR in advertising is valuable, especially in 159 00:07:49,080 --> 00:07:52,600 Speaker 6: this economic environment, has been difficult. But it's also working 160 00:07:52,640 --> 00:07:56,679 Speaker 6: towards bringing AR off platform. It has a new RS 161 00:07:57,000 --> 00:08:01,320 Speaker 6: which is its Augmented Reality Enterprise Service division, where it's 162 00:08:01,360 --> 00:08:07,360 Speaker 6: incorporating it's AR technology into brand apps and websites, as 163 00:08:07,400 --> 00:08:10,560 Speaker 6: well as bringing some of that technology eventually in store. 164 00:08:10,920 --> 00:08:12,480 Speaker 6: So this of course is you know, part of it 165 00:08:12,560 --> 00:08:17,200 Speaker 6: diversifying its revenue streams and being able to monetize AR 166 00:08:17,320 --> 00:08:19,400 Speaker 6: outside of its core Snapchat platform. 167 00:08:19,480 --> 00:08:22,120 Speaker 2: All about the monetization Jasmine Great hasent time with the 168 00:08:22,240 --> 00:08:25,520 Speaker 2: jasmin Enberg of insider intelligence from Snap to Pinterest. Let's 169 00:08:25,520 --> 00:08:27,760 Speaker 2: have a look at how those shares perform, because we 170 00:08:27,800 --> 00:08:29,560 Speaker 2: know that it's under pressure. We know that it's more 171 00:08:29,600 --> 00:08:32,120 Speaker 2: perhaps about the lack of cost cutting going on to 172 00:08:32,200 --> 00:08:35,640 Speaker 2: that particular business right now. Tom Forte, DA Davidson's senior 173 00:08:35,679 --> 00:08:37,440 Speaker 2: research analyst who has a neutral rating on the stock. 174 00:08:37,480 --> 00:08:40,240 Speaker 2: I understand, and you're sticking with a twenty two dollar 175 00:08:40,280 --> 00:08:42,280 Speaker 2: price target even though we're still seeing sort of the 176 00:08:42,840 --> 00:08:44,880 Speaker 2: pain trade that is the restructuring going on. 177 00:08:45,920 --> 00:08:48,160 Speaker 7: Sure, so I think I lowered my price target by 178 00:08:48,160 --> 00:08:48,960 Speaker 7: a dollar. 179 00:08:48,760 --> 00:08:52,400 Speaker 8: To twenty two, and I think the good news is 180 00:08:52,440 --> 00:08:56,480 Speaker 8: that pinterest has made strides and engagement. They found an 181 00:08:56,600 --> 00:09:02,640 Speaker 8: interesting way to solve their monetization in e commerce by 182 00:09:02,640 --> 00:09:05,679 Speaker 8: partnering with Amazon. The bad news, and what I think 183 00:09:05,760 --> 00:09:07,880 Speaker 8: is weighing on the shares in addition to year of 184 00:09:07,920 --> 00:09:12,840 Speaker 8: comments on cost pressure, is that their sales expectations for 185 00:09:12,920 --> 00:09:15,520 Speaker 8: the June quarter suggests. 186 00:09:15,080 --> 00:09:16,320 Speaker 7: They were lower than expected. 187 00:09:16,679 --> 00:09:19,000 Speaker 8: So the digital ad market, when you look at Snapchat, 188 00:09:19,280 --> 00:09:21,600 Speaker 8: when you look at pinterest, it's still challenging out there. 189 00:09:22,400 --> 00:09:22,800 Speaker 5: Hey Tom. 190 00:09:22,800 --> 00:09:25,319 Speaker 3: When we look at pinterest, when you think video and 191 00:09:25,400 --> 00:09:28,560 Speaker 3: the emphasis from its pays, then you think about artificial intelligence. 192 00:09:28,559 --> 00:09:30,920 Speaker 3: There seems to be a narrative in the market that 193 00:09:31,000 --> 00:09:33,839 Speaker 3: pinterest is at risk of being left behind. Do you 194 00:09:33,880 --> 00:09:34,720 Speaker 3: share that concern? 195 00:09:36,280 --> 00:09:38,560 Speaker 8: I do not to the extent that if you say 196 00:09:38,600 --> 00:09:44,040 Speaker 8: that Pinterest's functionality is as a visual social network an 197 00:09:44,040 --> 00:09:46,960 Speaker 8: excellent place to view for example, kitchens if you're thinking 198 00:09:46,960 --> 00:09:50,000 Speaker 8: about doing a kitchen remodel, or a parel if you're 199 00:09:50,040 --> 00:09:54,440 Speaker 8: thinking about buying new clothes. I do think that they 200 00:09:54,679 --> 00:09:58,880 Speaker 8: leverage artificial intelligence to the extent that they use artificial 201 00:09:58,880 --> 00:10:01,559 Speaker 8: intelligence to find more things that you might be interested 202 00:10:01,559 --> 00:10:02,679 Speaker 8: in based. 203 00:10:02,400 --> 00:10:04,720 Speaker 7: On what you've looked at so far. So I'm not 204 00:10:04,760 --> 00:10:06,559 Speaker 7: overly concerned about it being left behind. 205 00:10:06,679 --> 00:10:11,559 Speaker 2: No what therefore can Pinterest run home to the investor 206 00:10:11,559 --> 00:10:14,800 Speaker 2: base right now? About how close it is to actual 207 00:10:14,880 --> 00:10:19,160 Speaker 2: people making investment purchase decisions. How can they continue to 208 00:10:19,880 --> 00:10:22,280 Speaker 2: ultimately be a need to have for many of these 209 00:10:22,280 --> 00:10:24,200 Speaker 2: companies looking to advertise a little bit closer. 210 00:10:25,240 --> 00:10:29,400 Speaker 8: So here's where Amazon comes in from an advertising standpoint. 211 00:10:29,600 --> 00:10:31,439 Speaker 7: So to the extent that they're going to work. 212 00:10:31,240 --> 00:10:35,400 Speaker 8: With Amazon to improve their advertising and e commerce make 213 00:10:35,440 --> 00:10:39,000 Speaker 8: it easier for consumers to find things on Pinterest, to 214 00:10:39,120 --> 00:10:42,480 Speaker 8: find ads on Pinterest, to click on those ads and 215 00:10:42,520 --> 00:10:45,760 Speaker 8: buy them with Amazon. I think that's going to help 216 00:10:45,840 --> 00:10:49,080 Speaker 8: Pinterest over the next twelve to eighteen months. And the 217 00:10:49,200 --> 00:10:54,079 Speaker 8: challenge for everyone not named meta platforms is monetization. Pinterest 218 00:10:54,160 --> 00:10:56,400 Speaker 8: has a large global user based for under ninety three 219 00:10:56,400 --> 00:10:59,520 Speaker 8: million monthly active users, but they need to improve their 220 00:10:59,559 --> 00:11:02,839 Speaker 8: MATIZI and I think over time that ad advertising effort 221 00:11:02,880 --> 00:11:04,400 Speaker 8: with Amazon can help. 222 00:11:04,559 --> 00:11:16,160 Speaker 3: It's on forte DA Davidson, thank you so much. Intel 223 00:11:16,200 --> 00:11:19,360 Speaker 3: shares pushing higher after chipmaker projected a return to free 224 00:11:19,400 --> 00:11:21,280 Speaker 3: cash flow in the second half of the year, with 225 00:11:21,320 --> 00:11:24,040 Speaker 3: analysts saying the worst now may be over for the company. 226 00:11:24,040 --> 00:11:27,040 Speaker 3: Moments ago, I sat down with Intel CEO Patal Singer. 227 00:11:27,080 --> 00:11:27,520 Speaker 5: Have listen. 228 00:11:28,120 --> 00:11:30,120 Speaker 9: You know, the second half in our industry is typically 229 00:11:30,160 --> 00:11:32,400 Speaker 9: stronger than the first half, and we expect that to 230 00:11:32,400 --> 00:11:34,880 Speaker 9: be the case your second You know, a lot of 231 00:11:35,160 --> 00:11:38,320 Speaker 9: inventory adjustments are occurring in the first half of the year, 232 00:11:38,640 --> 00:11:43,000 Speaker 9: and particularly we see those finished in the client the 233 00:11:43,080 --> 00:11:46,839 Speaker 9: PC industry, but we're also seeing progress through our networking 234 00:11:46,920 --> 00:11:49,920 Speaker 9: and data center and cloud business as well, so improving 235 00:11:50,040 --> 00:11:53,800 Speaker 9: inventory position, and you know that's very typical of the industry. 236 00:11:53,840 --> 00:11:56,360 Speaker 9: You go through a sharp decline, you have inventory, a 237 00:11:56,400 --> 00:11:59,240 Speaker 9: lot of fixed costs, and then you gradually work through that. 238 00:11:59,360 --> 00:12:01,640 Speaker 9: So we see that as the second factor. But the 239 00:12:01,679 --> 00:12:05,199 Speaker 9: third one is an improving product position that improves our 240 00:12:05,200 --> 00:12:08,400 Speaker 9: market share overall, and that I think in particular you're 241 00:12:08,400 --> 00:12:11,559 Speaker 9: our data center business in particular, we're seeing Q one 242 00:12:11,600 --> 00:12:14,960 Speaker 9: as a turning point where a stronger product line the 243 00:12:15,040 --> 00:12:17,160 Speaker 9: market is looking at you know what we're saying for 244 00:12:17,240 --> 00:12:20,240 Speaker 9: our process and product and saying, boy, that's a little 245 00:12:20,280 --> 00:12:22,600 Speaker 9: bit better than we were expecting. So seeing some of 246 00:12:22,640 --> 00:12:25,640 Speaker 9: those positive green shoots, and maybe finally, you know, at 247 00:12:25,720 --> 00:12:28,720 Speaker 9: least parts of the world, China clearly is showing some 248 00:12:28,840 --> 00:12:32,840 Speaker 9: economic strength as well, so hopefully some economic green shoots 249 00:12:32,880 --> 00:12:34,160 Speaker 9: in the second half as well. 250 00:12:34,600 --> 00:12:36,800 Speaker 3: The turning point idea is really interesting. There's a number 251 00:12:36,800 --> 00:12:40,720 Speaker 3: of analysts that say Intel has hit its troth essentially 252 00:12:40,720 --> 00:12:44,120 Speaker 3: when they look at margins and top line growth. Is 253 00:12:44,200 --> 00:12:46,520 Speaker 3: Intel in its trough coming out of its trough? 254 00:12:46,559 --> 00:12:49,560 Speaker 9: To your mind, well, you know, we guided Q two 255 00:12:49,960 --> 00:12:52,240 Speaker 9: better than Q one, and we expect second half to 256 00:12:52,240 --> 00:12:54,920 Speaker 9: be stronger than that. So clearly from a top line 257 00:12:55,120 --> 00:12:58,440 Speaker 9: growth perspective, but also from a margin perspective, you know, 258 00:12:58,520 --> 00:13:01,680 Speaker 9: we expect that we're so margins in Q two, but 259 00:13:01,800 --> 00:13:04,400 Speaker 9: improving as we go through the years. We get out 260 00:13:04,440 --> 00:13:07,440 Speaker 9: of the thirties and back into comfortably into the forties 261 00:13:07,440 --> 00:13:10,240 Speaker 9: as we go through the year. So an improving position 262 00:13:10,320 --> 00:13:13,040 Speaker 9: on margins and an improving position then on the bottom 263 00:13:13,080 --> 00:13:15,640 Speaker 9: line as we go through the year. So clearly, you know, 264 00:13:15,679 --> 00:13:18,200 Speaker 9: we're seeing that momentum, but I'd also say there's a 265 00:13:18,200 --> 00:13:22,760 Speaker 9: lot of turbulence macroeconomic uncertainty, so it also requires a 266 00:13:22,800 --> 00:13:26,440 Speaker 9: lot of execution and financial discipline, which we demonstrated in 267 00:13:26,520 --> 00:13:29,240 Speaker 9: Q one. So overall, I think the company is doing 268 00:13:29,280 --> 00:13:31,800 Speaker 9: what I've asked them to do, to be very thoughtful 269 00:13:31,800 --> 00:13:36,559 Speaker 9: with how we spend, but execute, execute, execute, product process, 270 00:13:36,679 --> 00:13:39,800 Speaker 9: new areas like foundry, all of those gaining momentum as 271 00:13:39,800 --> 00:13:40,560 Speaker 9: we go through the year. 272 00:13:42,480 --> 00:13:45,320 Speaker 3: Intel CEO Pat Gelsinger there, Caroline. 273 00:13:44,960 --> 00:13:48,360 Speaker 2: Really fascinating conversation overall, and the focus on chips, which 274 00:13:48,400 --> 00:13:50,200 Speaker 2: of course we know have been a bit of a laggot. 275 00:13:50,240 --> 00:13:52,160 Speaker 2: Over the course of the week, we've got to dig 276 00:13:52,200 --> 00:13:54,440 Speaker 2: in a little bit more about Intel's earnings and the 277 00:13:54,480 --> 00:13:58,080 Speaker 2: chip sector more broadly, bringing Bloomberg Intelligences, man needs sing 278 00:13:58,760 --> 00:14:01,240 Speaker 2: what do you make of some studying? Is the worst 279 00:14:01,240 --> 00:14:04,480 Speaker 2: behind us? To that extent, I think it. 280 00:14:04,440 --> 00:14:07,000 Speaker 10: Was interesting that they said that the PC market is 281 00:14:07,040 --> 00:14:09,840 Speaker 10: stabilizing and they call for two hundred and seventy million 282 00:14:10,000 --> 00:14:11,040 Speaker 10: units for the year. 283 00:14:11,559 --> 00:14:14,880 Speaker 7: My sense is, you know, the inventory. 284 00:14:14,440 --> 00:14:18,080 Speaker 10: Situation is still not clear in terms of you know, 285 00:14:18,200 --> 00:14:22,080 Speaker 10: the channel inventory clearing they sounded a lot more optimistic, 286 00:14:22,240 --> 00:14:24,560 Speaker 10: So you have to take the word because Intel is 287 00:14:24,560 --> 00:14:27,640 Speaker 10: the largest player. And look, on the client side, they 288 00:14:27,640 --> 00:14:31,080 Speaker 10: have less of competitive issues than on the data center side, 289 00:14:31,120 --> 00:14:34,640 Speaker 10: where we know the demand is high for high performance 290 00:14:34,680 --> 00:14:39,360 Speaker 10: computing chips. We've seen expectations for Nvidia. So clearly Intel 291 00:14:39,480 --> 00:14:42,800 Speaker 10: has more secular headbinds to deal with on the data 292 00:14:42,800 --> 00:14:45,760 Speaker 10: center side. The client side will be cyclical, whether it 293 00:14:45,840 --> 00:14:48,440 Speaker 10: comes back in the second half or it takes two 294 00:14:48,560 --> 00:14:49,480 Speaker 10: or three more quarters. 295 00:14:49,800 --> 00:14:50,680 Speaker 1: I think we. 296 00:14:50,840 --> 00:14:52,280 Speaker 5: Still have to figure that part out. 297 00:14:53,160 --> 00:14:55,680 Speaker 3: Yeah, I think he said he was confident the inventory 298 00:14:56,240 --> 00:14:58,720 Speaker 3: would be corrected, it would be completed, though he didn't 299 00:14:58,760 --> 00:15:01,840 Speaker 3: say when you know, relatively. Pat's trying to sell this 300 00:15:01,920 --> 00:15:04,920 Speaker 3: story right, mandate that Intel can return to being a 301 00:15:04,960 --> 00:15:08,280 Speaker 3: technological leader. Do you see evidence that he is managing 302 00:15:08,280 --> 00:15:11,280 Speaker 3: that process well while also sort of catching up on 303 00:15:11,320 --> 00:15:12,960 Speaker 3: the day to day of running a chip company. 304 00:15:13,480 --> 00:15:16,600 Speaker 10: I mean, look, turn around stories in tech are tough, 305 00:15:16,800 --> 00:15:20,400 Speaker 10: and look, he has a very tough hand in this quorder. 306 00:15:20,480 --> 00:15:25,240 Speaker 10: Intel actually had negative nine billion dollars in free cash flow, 307 00:15:25,320 --> 00:15:27,200 Speaker 10: so that just goes to show what he is dealing 308 00:15:27,280 --> 00:15:31,640 Speaker 10: with in terms of, you know, the fab aspect of 309 00:15:31,680 --> 00:15:35,960 Speaker 10: it catching up to TSMC and really kind of dealing 310 00:15:35,960 --> 00:15:38,800 Speaker 10: with the data center headminds that I alluded to before. 311 00:15:39,280 --> 00:15:42,920 Speaker 10: A lot I think depends on the government and the 312 00:15:43,080 --> 00:15:47,120 Speaker 10: Chips Act, because that's where things can be supported, you know, 313 00:15:47,200 --> 00:15:50,560 Speaker 10: in terms of driving more onshore manufacturing of chips, and 314 00:15:51,000 --> 00:15:53,920 Speaker 10: that's where the free cash flow headminds can be eased, 315 00:15:53,960 --> 00:15:56,840 Speaker 10: and you know, it can be more kind of dealt 316 00:15:56,960 --> 00:15:59,600 Speaker 10: with down the line as opposed to, you know, convincing 317 00:15:59,640 --> 00:16:02,720 Speaker 10: investor that Intel won't keep losing money for the next 318 00:16:02,720 --> 00:16:03,520 Speaker 10: two three years. 319 00:16:03,680 --> 00:16:07,000 Speaker 3: Caroline, as you can imagine, I couldn't resist the temptation, 320 00:16:07,200 --> 00:16:10,840 Speaker 3: and I asked, we asked a question about artificial intelligence. 321 00:16:11,400 --> 00:16:14,360 Speaker 2: Yeah, and it seems as though that's got to be 322 00:16:14,400 --> 00:16:17,040 Speaker 2: an area they focus on. I mean, Amandie your point 323 00:16:17,240 --> 00:16:21,000 Speaker 2: that we're looking at Amazon developing chips to help with 324 00:16:21,040 --> 00:16:24,600 Speaker 2: that sort of capacity that computer needed with generative AI. 325 00:16:24,840 --> 00:16:26,920 Speaker 2: Is this something that Intel just has to be a 326 00:16:26,960 --> 00:16:29,000 Speaker 2: part of and are they a significant part of it? 327 00:16:30,120 --> 00:16:33,960 Speaker 10: And that's the challenge, right, So the hyperscalers all want 328 00:16:34,000 --> 00:16:36,320 Speaker 10: to do their own chips. I mean, Google is doing 329 00:16:36,360 --> 00:16:40,560 Speaker 10: their own TPUs, Amazon the Graviton, and Microsoft has I 330 00:16:40,560 --> 00:16:42,720 Speaker 10: think There'll been rumors that they're doing their own chips. 331 00:16:42,720 --> 00:16:45,720 Speaker 10: So that's the challenge for somebody like Intel, which is 332 00:16:45,760 --> 00:16:48,720 Speaker 10: the leading market share leader when it comes to you know, 333 00:16:48,800 --> 00:16:52,360 Speaker 10: the data center chips, and now they are partnering with ARM. 334 00:16:52,440 --> 00:16:55,320 Speaker 10: I mean, we know Intel is eighty six, and so 335 00:16:55,520 --> 00:16:58,560 Speaker 10: it's not very clear whether they have that you know 336 00:16:58,680 --> 00:17:03,560 Speaker 10: technology parac with PSMC now to really get more enterprises 337 00:17:03,680 --> 00:17:07,879 Speaker 10: at least in terms of joining you know, the AI 338 00:17:08,040 --> 00:17:10,640 Speaker 10: side of things. And in Vidia at the same time 339 00:17:10,800 --> 00:17:12,240 Speaker 10: is running away with that market. 340 00:17:12,320 --> 00:17:13,240 Speaker 7: AMD is doing well. 341 00:17:13,320 --> 00:17:16,719 Speaker 10: So clearly a lot of competitive pressure and there aren't 342 00:17:16,880 --> 00:17:20,600 Speaker 10: enough foll points in terms of you know, uh manufacturing 343 00:17:20,640 --> 00:17:24,320 Speaker 10: coming back. That's where the government supports is key and yeah, 344 00:17:24,400 --> 00:17:26,800 Speaker 10: they have to start showing that in bad manufacturing. 345 00:17:27,160 --> 00:17:30,439 Speaker 2: So take us there the bait you laid in. What 346 00:17:30,520 --> 00:17:31,800 Speaker 2: did he say in terms of AI? 347 00:17:32,760 --> 00:17:35,080 Speaker 3: So Mandy's flat was really interesting because he was pretty 348 00:17:35,119 --> 00:17:39,240 Speaker 3: committed to competing with Nvidia those high of higher endgpus 349 00:17:39,359 --> 00:17:42,280 Speaker 3: for the deep learning part. But really where Intel's looking 350 00:17:42,320 --> 00:17:45,000 Speaker 3: man Deepa's inference right, they have a range of CPU 351 00:17:45,040 --> 00:17:47,439 Speaker 3: and GPU products that they think can compete there. Just 352 00:17:47,480 --> 00:17:50,000 Speaker 3: so what you said about the chips ACT. Pat seems 353 00:17:50,040 --> 00:17:53,400 Speaker 3: confident that those dollars flow through this year. How crucial 354 00:17:53,440 --> 00:17:55,359 Speaker 3: is it to then to get that public sector money 355 00:17:55,440 --> 00:17:57,560 Speaker 3: to drive that vision that Pat has. 356 00:17:57,920 --> 00:18:01,280 Speaker 10: Yeah, look, I think that is key because the more 357 00:18:01,359 --> 00:18:04,840 Speaker 10: these tabs are underutilized, it's going to. 358 00:18:04,359 --> 00:18:05,800 Speaker 1: Be a drag on the gross margin. 359 00:18:05,840 --> 00:18:09,080 Speaker 10: And we've seen that fifteen percentage points in gross margin 360 00:18:09,119 --> 00:18:11,840 Speaker 10: compression in the last two three quarters. Guess what. The 361 00:18:11,880 --> 00:18:15,600 Speaker 10: more lower utilization for these fabs, it's going to continue 362 00:18:15,600 --> 00:18:18,879 Speaker 10: to pressure THATAB. So if he gets that funding and 363 00:18:19,080 --> 00:18:21,840 Speaker 10: if he gets something tangible from the Chips ACT, I 364 00:18:21,840 --> 00:18:23,720 Speaker 10: think that will certainly is that pressure. 365 00:18:24,040 --> 00:18:26,000 Speaker 1: And also the inference market. 366 00:18:25,840 --> 00:18:28,760 Speaker 10: Is sort of running away from them because Apple does 367 00:18:28,800 --> 00:18:32,120 Speaker 10: its own chips for its PCs and max. That's where 368 00:18:32,160 --> 00:18:35,400 Speaker 10: a lot of infrints will also happen. So Intel really 369 00:18:35,520 --> 00:18:37,640 Speaker 10: wants to make sure that you know the x eighty 370 00:18:37,680 --> 00:18:40,879 Speaker 10: six ecosystem, the Windows ecosystem stays with them on the 371 00:18:40,960 --> 00:18:41,800 Speaker 10: client side. 372 00:18:42,400 --> 00:18:45,359 Speaker 2: Always great to have some time with you of Freebag Intelligence. 373 00:18:45,560 --> 00:18:47,240 Speaker 2: The one and only man leaps saying, one of the 374 00:18:47,240 --> 00:18:50,600 Speaker 2: busiest people in this building. Mean while coming up, we've 375 00:18:50,600 --> 00:18:53,000 Speaker 2: got to talk about that bitcoin scam, a Moonland, a 376 00:18:53,080 --> 00:18:55,199 Speaker 2: craft so much more, but it all coming up and 377 00:18:55,240 --> 00:18:57,600 Speaker 2: talking tech that's next, and this is Blomberg. 378 00:19:15,560 --> 00:19:18,200 Speaker 3: Time for talking tech. A US jarge ordered a South 379 00:19:18,200 --> 00:19:21,520 Speaker 3: African executive to pay more than three point four billion 380 00:19:21,560 --> 00:19:25,760 Speaker 3: dollars in restitution and fines for a fraud scheme involving bitcoin, 381 00:19:25,800 --> 00:19:29,080 Speaker 3: which impacted more than twenty three thousand people in the US. 382 00:19:29,160 --> 00:19:32,560 Speaker 3: This is the highest ever civil monetary penalty in any 383 00:19:32,640 --> 00:19:36,680 Speaker 3: CFTC case. Ice Faces Moonlander crash is now wiped out 384 00:19:36,720 --> 00:19:39,720 Speaker 3: about six hundred million dollars, almost half of the Tokyo 385 00:19:39,760 --> 00:19:43,960 Speaker 3: based startups value of shares slumping for a third straight session, 386 00:19:44,280 --> 00:19:47,600 Speaker 3: and Sony casting doubt on its PlayStation momentum. The company 387 00:19:47,840 --> 00:19:50,840 Speaker 3: offered a pretty conservative profit outlook after warning about the 388 00:19:50,880 --> 00:19:54,720 Speaker 3: impact of the global consumer spending slump on its electronics 389 00:19:54,840 --> 00:19:58,399 Speaker 3: and entertainment business. And finally, Baine Capital has built a 390 00:19:58,440 --> 00:20:02,040 Speaker 3: stake in Software Ag, raising the prospect of a takeover 391 00:20:02,119 --> 00:20:05,159 Speaker 3: battle for the German company that, according to sources, the 392 00:20:05,240 --> 00:20:08,120 Speaker 3: firms areying a potential merger with its own portfolio firm, 393 00:20:08,560 --> 00:20:09,720 Speaker 3: Rocket Software. 394 00:20:17,640 --> 00:20:20,600 Speaker 2: Welcome back to Bloomerg Technology. I'm Paralyine Hyde in New York. 395 00:20:20,920 --> 00:20:23,680 Speaker 3: And Amed Lovelow in San Francisco. It's the final trading day, 396 00:20:23,720 --> 00:20:26,399 Speaker 3: Caroline of April, so I thought I get some scores 397 00:20:26,400 --> 00:20:28,200 Speaker 3: on the doors in terms of where we're at. Now's 398 00:20:28,200 --> 00:20:30,040 Speaker 3: that one hundred out performance, right, We're up four and 399 00:20:30,080 --> 00:20:33,159 Speaker 3: a half percent relative to sort of other specific areas 400 00:20:33,160 --> 00:20:36,879 Speaker 3: of the technology space, the Philadelphia Semiconductor Index being one 401 00:20:36,960 --> 00:20:39,159 Speaker 3: laggage you talked about earlier in the show. It's been 402 00:20:39,200 --> 00:20:42,080 Speaker 3: a really interesting month because of the banking concerns that 403 00:20:42,119 --> 00:20:45,440 Speaker 3: we've had, but then a really condensed period of earnings 404 00:20:45,480 --> 00:20:48,040 Speaker 3: in the final sort of ten days of April when 405 00:20:48,040 --> 00:20:49,959 Speaker 3: it comes to the technology sector, and honestly, it's been 406 00:20:49,960 --> 00:20:51,719 Speaker 3: a real mixed bag. I think you and I can 407 00:20:51,720 --> 00:20:54,800 Speaker 3: talk about that on our Twitter spaces later. On Megacap's 408 00:20:54,880 --> 00:20:57,520 Speaker 3: NYC Fangpus index, you also have some of the US 409 00:20:57,520 --> 00:21:01,000 Speaker 3: listed shares of Chinese tech companies sprinkled in there. Speaking 410 00:21:01,000 --> 00:21:02,879 Speaker 3: of which, then there's that Gorden Dragon index is a 411 00:21:03,000 --> 00:21:05,000 Speaker 3: clear laggard in the month of April, and you and 412 00:21:05,080 --> 00:21:08,119 Speaker 3: I have been talking about how geopolitical concerns have ramped 413 00:21:08,200 --> 00:21:10,320 Speaker 3: up in that period between the US and China. In 414 00:21:10,359 --> 00:21:13,400 Speaker 3: the moment during Friday session, Cloud is a big concern. 415 00:21:13,440 --> 00:21:15,879 Speaker 3: We've been talking about how Amazon is down because of 416 00:21:16,000 --> 00:21:19,400 Speaker 3: their commentary around the sequential decline in growth at AWS, 417 00:21:19,440 --> 00:21:21,800 Speaker 3: but take a look at cloud Flare. That is a big, 418 00:21:21,840 --> 00:21:25,000 Speaker 3: big drop for that name all cloud related. There are 419 00:21:25,040 --> 00:21:29,000 Speaker 3: concerns about growth, concerns about structural issues one name that 420 00:21:29,000 --> 00:21:31,160 Speaker 3: we don't often cover, but that is a pretty steep drop. 421 00:21:31,600 --> 00:21:33,840 Speaker 2: Talking of steep drops, let's get straight to it. When 422 00:21:33,880 --> 00:21:36,439 Speaker 2: we're talking about the banking sector. And because US officials 423 00:21:36,480 --> 00:21:39,480 Speaker 2: are reportedly in talks with government agencies to throw First 424 00:21:39,520 --> 00:21:42,320 Speaker 2: republic banker lifeline, now, the San Francisco based lender has 425 00:21:42,359 --> 00:21:45,280 Speaker 2: really struggled to navigate through the aftermath of failed Silicon 426 00:21:45,320 --> 00:21:48,040 Speaker 2: Valley Bank and to other regional lenders. In March, for 427 00:21:48,080 --> 00:21:50,359 Speaker 2: more on this, we turned to Lumnus Wall Street reporter 428 00:21:50,440 --> 00:21:53,800 Speaker 2: Schnarali Bassak, who has been I'm sure of all the 429 00:21:53,880 --> 00:21:57,280 Speaker 2: volatility in the name, the pausing that we're seeing, what's the. 430 00:21:57,320 --> 00:22:00,359 Speaker 11: Latest beyond that in the center of a lurry of 431 00:22:00,359 --> 00:22:03,000 Speaker 11: telephone calls as well. Remember is we describe the situation, 432 00:22:03,119 --> 00:22:06,639 Speaker 11: it is a live situation and can best be described 433 00:22:06,720 --> 00:22:10,199 Speaker 11: as a frenzied last minute effort to save the bank. 434 00:22:10,400 --> 00:22:13,560 Speaker 11: Now whether it falls into FDIC receivership. Now there's a 435 00:22:13,560 --> 00:22:15,359 Speaker 11: sense for the last couple of days that this could 436 00:22:15,359 --> 00:22:18,760 Speaker 11: continue to limp along as a bank with a clear 437 00:22:18,760 --> 00:22:22,200 Speaker 11: hole in its balance sheet. However, at this point, remember 438 00:22:22,240 --> 00:22:24,520 Speaker 11: we have reported a few times now that some of 439 00:22:24,520 --> 00:22:27,080 Speaker 11: the banks that were involved in the rescue actually even 440 00:22:27,200 --> 00:22:31,680 Speaker 11: prefer an FDIC receivership in some manners. Here now, remember 441 00:22:31,680 --> 00:22:34,560 Speaker 11: there's another thing about the FDIC receivership. The FDIC, the 442 00:22:34,600 --> 00:22:37,200 Speaker 11: deposit fund has already taken a massive hit of more 443 00:22:37,200 --> 00:22:39,560 Speaker 11: than twenty two billion dollars here are tied to the 444 00:22:39,600 --> 00:22:42,080 Speaker 11: rescue of two prior banks to fail banks that is 445 00:22:42,080 --> 00:22:44,880 Speaker 11: Silicon Valley Bank as well as Signature Bank. So this 446 00:22:45,000 --> 00:22:48,280 Speaker 11: kind of a hole is a thirty billion dollar hole 447 00:22:48,520 --> 00:22:50,720 Speaker 11: or and then some. I mean, the exact amount is 448 00:22:50,760 --> 00:22:53,359 Speaker 11: not quite understood, but it is a large one. So 449 00:22:53,440 --> 00:22:55,159 Speaker 11: no matter what happens to this bank, it is a 450 00:22:55,200 --> 00:22:58,959 Speaker 11: costly situation for the banks that are in and around 451 00:22:58,960 --> 00:23:02,800 Speaker 11: the situation already, the government itself, and of course for 452 00:23:03,000 --> 00:23:06,160 Speaker 11: the shareholders that are remaining for FRC. 453 00:23:07,840 --> 00:23:11,040 Speaker 3: I think the idea SNALI is that investors are struggling 454 00:23:11,119 --> 00:23:15,480 Speaker 3: to understand what of the many possible scenarios is most 455 00:23:15,520 --> 00:23:18,120 Speaker 3: likely to happen. This talk in the market that now 456 00:23:18,160 --> 00:23:20,560 Speaker 3: a US takeover is the most likely outcome. 457 00:23:21,240 --> 00:23:23,080 Speaker 11: Listen, and that's what I was trying to convey here. 458 00:23:23,560 --> 00:23:27,440 Speaker 11: This is very much a search for things to come 459 00:23:27,440 --> 00:23:32,240 Speaker 11: together here. Now, getting a consortium of people together is 460 00:23:32,280 --> 00:23:35,560 Speaker 11: a difficult thing to do. And remember we've reported multiple 461 00:23:35,560 --> 00:23:37,520 Speaker 11: times here at as well that the government would need 462 00:23:37,560 --> 00:23:40,639 Speaker 11: to be involved in such a manner and pony up 463 00:23:40,680 --> 00:23:44,879 Speaker 11: a little bit here under the FDS receivership plan. The 464 00:23:44,960 --> 00:23:49,159 Speaker 11: issue here is which is a more costly solution. The FDIC, 465 00:23:49,320 --> 00:23:53,480 Speaker 11: as we've been saying, would take another massive hit here IFFRC, 466 00:23:53,600 --> 00:23:56,680 Speaker 11: we're supposed to fall under receivership. So yes, the market 467 00:23:56,760 --> 00:23:58,280 Speaker 11: is trying to set us out to your point, the 468 00:23:58,280 --> 00:24:02,320 Speaker 11: most likely outcomes here, Again, this is not you cannot 469 00:24:02,359 --> 00:24:05,159 Speaker 11: say that this bank will necessarily fail. It's not to 470 00:24:05,200 --> 00:24:08,240 Speaker 11: that point yet, but it is possible that it does 471 00:24:08,280 --> 00:24:09,960 Speaker 11: get to that point in some scenario if they don't 472 00:24:09,960 --> 00:24:10,680 Speaker 11: find a solution. 473 00:24:11,720 --> 00:24:14,920 Speaker 3: All right, Bloombergshnili Bassi, you've earned your weekend alongside Man 474 00:24:14,920 --> 00:24:17,080 Speaker 3: Deep singer bi Off. You go go and have some fun. 475 00:24:18,000 --> 00:24:21,399 Speaker 3: Let's pivot a little bit Can AI replace our jobs? 476 00:24:21,440 --> 00:24:23,440 Speaker 3: That is a question Caroline and I have been asking 477 00:24:23,680 --> 00:24:26,119 Speaker 3: quite a lot recently the rise of generature of AI. 478 00:24:26,800 --> 00:24:29,320 Speaker 3: It may not expect to impact all jobs in the 479 00:24:29,400 --> 00:24:32,040 Speaker 3: same way uniformly, but there's going to be an impact 480 00:24:32,080 --> 00:24:35,400 Speaker 3: on hiring and managing in the workplace at large. That's 481 00:24:35,400 --> 00:24:39,080 Speaker 3: according to the CEO of People Management Software Lattice. Jack 482 00:24:39,080 --> 00:24:42,600 Speaker 3: Oltman is here with me. Now, how many times a 483 00:24:42,680 --> 00:24:46,760 Speaker 3: day internally do you say the words artificial intelligence? Or 484 00:24:46,800 --> 00:24:49,200 Speaker 3: are you already sick of it? Because actually you've been 485 00:24:49,200 --> 00:24:50,400 Speaker 3: doing it for a while. 486 00:24:51,800 --> 00:24:51,919 Speaker 12: Now. 487 00:24:52,280 --> 00:24:54,200 Speaker 5: First of all, thanks for having me. I don't get 488 00:24:54,240 --> 00:24:54,639 Speaker 5: sick of it. 489 00:24:54,680 --> 00:24:56,919 Speaker 13: I think it is like the most important topic in 490 00:24:57,119 --> 00:25:01,240 Speaker 13: tech right now. I think they're is still a lot 491 00:25:01,280 --> 00:25:03,760 Speaker 13: to be understood, but I think we're already seeing a 492 00:25:03,760 --> 00:25:07,160 Speaker 13: lot of ways in which AI is starting to impact companies, 493 00:25:07,240 --> 00:25:11,960 Speaker 13: individuals productivity. So I think, finally, after many many years 494 00:25:11,960 --> 00:25:15,000 Speaker 13: of people anticipating this with beta breath, we're we're here 495 00:25:15,040 --> 00:25:16,560 Speaker 13: and we're seeing a lot of the impacts, and so 496 00:25:16,880 --> 00:25:17,639 Speaker 13: it's super exciting. 497 00:25:17,680 --> 00:25:18,280 Speaker 1: Not sick of it. 498 00:25:19,400 --> 00:25:21,360 Speaker 3: LA's this is a really interesting example. 499 00:25:21,480 --> 00:25:21,760 Speaker 1: Jack. 500 00:25:21,840 --> 00:25:25,160 Speaker 3: You know you had a billion dollar valuation twenty twenty 501 00:25:25,160 --> 00:25:27,560 Speaker 3: one raise new funds the beginning of the year three 502 00:25:27,600 --> 00:25:30,720 Speaker 3: billion dollar valuation. But you're kind of a key example 503 00:25:30,800 --> 00:25:34,560 Speaker 3: of where AI tools can be applied in enterprise and SaaS. 504 00:25:35,119 --> 00:25:36,880 Speaker 3: Why is that such a good marriage? 505 00:25:38,840 --> 00:25:41,800 Speaker 13: So the way I've been thinking about large language models, 506 00:25:41,840 --> 00:25:44,040 Speaker 13: which is sort of the thrust of what's been happening 507 00:25:44,080 --> 00:25:47,719 Speaker 13: in the last couple quarters in AI, is kind of 508 00:25:47,760 --> 00:25:50,879 Speaker 13: like what calculators did for Math. I believe large language 509 00:25:50,920 --> 00:25:53,399 Speaker 13: models are going to do in many ways for things 510 00:25:53,480 --> 00:25:56,320 Speaker 13: like reading and writing. So they're going to make people 511 00:25:56,440 --> 00:26:01,200 Speaker 13: much more readily able to digest and comprehend and text 512 00:26:01,240 --> 00:26:04,000 Speaker 13: and information. We're going to be able to help people 513 00:26:04,600 --> 00:26:07,840 Speaker 13: draft concepts, so like, for example, in a Lattice world 514 00:26:07,840 --> 00:26:10,959 Speaker 13: where people are doing lots of writing around performance reviews 515 00:26:11,080 --> 00:26:13,800 Speaker 13: or goals or feedback. There's a lot of ways in 516 00:26:13,840 --> 00:26:16,080 Speaker 13: which AI can help people get to good answers more 517 00:26:16,160 --> 00:26:18,800 Speaker 13: quickly and get to good content more quickly. But I 518 00:26:18,960 --> 00:26:21,800 Speaker 13: see this as boosters for people, and the way that 519 00:26:21,880 --> 00:26:24,359 Speaker 13: calculators did for Math, I see that happening for reading 520 00:26:24,359 --> 00:26:26,960 Speaker 13: and writing, and so for companies like Lattis, but for 521 00:26:27,000 --> 00:26:29,080 Speaker 13: tons of enterprise software, I think this is going to 522 00:26:29,080 --> 00:26:30,960 Speaker 13: be one of the ways in which we see AI 523 00:26:31,080 --> 00:26:31,800 Speaker 13: really help people. 524 00:26:32,080 --> 00:26:34,960 Speaker 2: Jack. Of course, your entire business is HR tools is 525 00:26:34,960 --> 00:26:37,680 Speaker 2: efficiency as culture in many ways, and I know you've 526 00:26:37,680 --> 00:26:40,000 Speaker 2: personally had to navigate that in januarly having to let 527 00:26:40,080 --> 00:26:43,520 Speaker 2: go of people within your team and trying to think 528 00:26:43,600 --> 00:26:47,480 Speaker 2: about how you can support them. Is Ultimately this general 529 00:26:47,560 --> 00:26:49,840 Speaker 2: of AI and AI going to mean more people get 530 00:26:49,920 --> 00:26:51,840 Speaker 2: let go because they can be replaced in some. 531 00:26:51,800 --> 00:26:55,480 Speaker 13: Way, sort of like you mentioned at the beginning, We're 532 00:26:55,480 --> 00:26:59,680 Speaker 13: definitely going to see different industries and roles be impacted 533 00:26:59,680 --> 00:27:02,800 Speaker 13: in different it is. But in my view, it's most 534 00:27:02,960 --> 00:27:05,479 Speaker 13: likely that AI is not going to reduce the demand 535 00:27:05,520 --> 00:27:07,879 Speaker 13: for labor. I think it's going to increase the demand 536 00:27:07,880 --> 00:27:11,320 Speaker 13: for labor. Generally, what we see when any asset in 537 00:27:11,359 --> 00:27:15,240 Speaker 13: the economy becomes more productive is there's more demand for it. 538 00:27:15,640 --> 00:27:19,520 Speaker 13: So if a software engineer becomes three times more productive 539 00:27:19,560 --> 00:27:22,560 Speaker 13: as a result all of AI supporting them, more companies 540 00:27:22,560 --> 00:27:24,480 Speaker 13: are going to want to hire that person because they're 541 00:27:24,520 --> 00:27:26,640 Speaker 13: going to be able to produce so much more. So, 542 00:27:27,119 --> 00:27:29,959 Speaker 13: in my view, the lens should be that we're going 543 00:27:30,000 --> 00:27:32,199 Speaker 13: to become so much more productive and therefore there's going 544 00:27:32,240 --> 00:27:36,320 Speaker 13: to be much more demand for labor from companies thinking. 545 00:27:36,080 --> 00:27:39,040 Speaker 2: About lenses and AI is something that you're doing a 546 00:27:39,040 --> 00:27:41,280 Speaker 2: lot of your brother's doing a lot of of course, 547 00:27:41,320 --> 00:27:43,240 Speaker 2: in any way, some altman who I know you've built 548 00:27:43,280 --> 00:27:47,639 Speaker 2: businesses with, you live with previously, he's at the cutting 549 00:27:47,760 --> 00:27:50,959 Speaker 2: edge of where we think ethically general to AI can 550 00:27:51,040 --> 00:27:54,080 Speaker 2: be built. Do you are you on the optimistic side? 551 00:27:54,480 --> 00:27:56,760 Speaker 2: Are you thinking in general that we can harness the 552 00:27:56,760 --> 00:27:58,800 Speaker 2: productivity and we're not going to have some of the 553 00:27:59,000 --> 00:28:01,960 Speaker 2: worries about regulation this, or that it's developing just too 554 00:28:01,960 --> 00:28:03,000 Speaker 2: swiftly for humans. 555 00:28:05,160 --> 00:28:07,879 Speaker 13: I am optimistic about it. I do tend to believe 556 00:28:07,960 --> 00:28:11,640 Speaker 13: that it is going to be wildly exciting and positive 557 00:28:11,680 --> 00:28:15,520 Speaker 13: for humanity. I think it is something that is extremely potent, 558 00:28:15,600 --> 00:28:17,359 Speaker 13: and so I think it is really smart to be 559 00:28:17,400 --> 00:28:19,359 Speaker 13: paying a lot of attention. I do think it's something 560 00:28:19,440 --> 00:28:23,760 Speaker 13: that requires quite a bit of care and attention. But 561 00:28:24,000 --> 00:28:27,040 Speaker 13: that's why I think having companies with thoughtful leaders who 562 00:28:27,119 --> 00:28:30,240 Speaker 13: want to really invest into AI safety and making sure 563 00:28:30,280 --> 00:28:32,920 Speaker 13: that we're testing things before we release them to the world, 564 00:28:32,920 --> 00:28:34,960 Speaker 13: and that we're working hard on alignment. I think those 565 00:28:35,000 --> 00:28:38,120 Speaker 13: things are critical. But the way I see it, this 566 00:28:38,200 --> 00:28:40,120 Speaker 13: has got the potential to be one of the most 567 00:28:40,120 --> 00:28:43,960 Speaker 13: amazing things for humanity, and so I think investing in 568 00:28:44,000 --> 00:28:49,440 Speaker 13: it hopefully optimistically, with a lot of conviction and desire 569 00:28:49,480 --> 00:28:53,040 Speaker 13: to build that kind of world, it is the right mindset. 570 00:28:53,080 --> 00:28:55,120 Speaker 13: But of course, you know, being careful because this is 571 00:28:55,160 --> 00:28:58,200 Speaker 13: such a potent technology. I think that really matters. 572 00:28:59,360 --> 00:29:04,440 Speaker 3: Jack. Founders all across the country are considering how they 573 00:29:05,000 --> 00:29:09,160 Speaker 3: can become relevant or not miss the opportunity that AI presents. 574 00:29:09,160 --> 00:29:11,800 Speaker 3: And that's just one fact. I know you've been tweeting 575 00:29:11,880 --> 00:29:16,280 Speaker 3: about things that founders have been getting wrong. How hard 576 00:29:16,360 --> 00:29:18,520 Speaker 3: is it out there right now? Is really the question? 577 00:29:18,680 --> 00:29:21,200 Speaker 3: You know, you raised more money at the start of 578 00:29:21,200 --> 00:29:24,440 Speaker 3: the year at a significantly high evaluation at a time 579 00:29:24,480 --> 00:29:26,440 Speaker 3: where others were doing the opposite down rounds. 580 00:29:28,400 --> 00:29:31,320 Speaker 13: Yeah, I think it is a It is a challenging 581 00:29:31,360 --> 00:29:34,080 Speaker 13: time for founders in some sense relative to the last 582 00:29:34,120 --> 00:29:37,959 Speaker 13: couple of years, you know, separate from this whole AI acceleration, 583 00:29:38,080 --> 00:29:42,720 Speaker 13: which is incredibly exciting. On a completely unrelated path, we've 584 00:29:42,760 --> 00:29:47,320 Speaker 13: had this real economically challenging time for tech and specific 585 00:29:47,360 --> 00:29:51,080 Speaker 13: we've seen this happen. As you know, you've covered extremely 586 00:29:51,120 --> 00:29:54,280 Speaker 13: well tech stocks in the public markets have really suffered 587 00:29:54,320 --> 00:29:56,960 Speaker 13: for a lot of reasons, and that's led to hard 588 00:29:56,960 --> 00:29:59,080 Speaker 13: times for startups. And so you have two things going 589 00:29:59,120 --> 00:30:02,680 Speaker 13: on here se, which is that the world of investing 590 00:30:02,720 --> 00:30:05,520 Speaker 13: and raising capital for startups has really changed. But we 591 00:30:05,600 --> 00:30:10,440 Speaker 13: have this really exciting platform shift in a new technology 592 00:30:10,440 --> 00:30:12,800 Speaker 13: being developed around AI, and we're seeing more and more 593 00:30:12,840 --> 00:30:15,960 Speaker 13: startups be built with these concepts in mind. I don't think, 594 00:30:16,000 --> 00:30:19,480 Speaker 13: you know, most startups should say AI is an entire 595 00:30:19,640 --> 00:30:22,840 Speaker 13: concept for building a new company. But any idea that 596 00:30:22,920 --> 00:30:25,280 Speaker 13: you had two years ago, I think can now you 597 00:30:25,360 --> 00:30:28,800 Speaker 13: can re ask the question, in what ways does AI 598 00:30:28,920 --> 00:30:32,800 Speaker 13: help us solve this problem for customers? You know, it's 599 00:30:32,840 --> 00:30:36,120 Speaker 13: sort of a misunderstanding of the moment to say I'm 600 00:30:36,160 --> 00:30:37,840 Speaker 13: going to build AI and we'll see what happens. I 601 00:30:37,840 --> 00:30:40,240 Speaker 13: think you still need to build companies rooted in solving 602 00:30:40,280 --> 00:30:43,280 Speaker 13: real problems to real people. But AI is this technology 603 00:30:43,280 --> 00:30:45,240 Speaker 13: that can really help in a lot of in a 604 00:30:45,280 --> 00:30:46,800 Speaker 13: surprisingly large number of cases. 605 00:30:46,840 --> 00:30:49,200 Speaker 2: And let's talk about the problems that Lattice is solving, 606 00:30:49,240 --> 00:30:52,000 Speaker 2: and it's trying to make people more efficient, particularly in 607 00:30:52,080 --> 00:30:57,040 Speaker 2: working smoother, being more productive, ensuring that your workflow becomes 608 00:30:57,160 --> 00:31:00,720 Speaker 2: one that you get feedback more efficiently. About are people 609 00:31:00,760 --> 00:31:02,719 Speaker 2: wanting that at the moment you had five thousand customers, 610 00:31:02,800 --> 00:31:04,240 Speaker 2: is that still building in this environment? 611 00:31:05,800 --> 00:31:07,760 Speaker 1: Yeah? I mean I think what people want. 612 00:31:08,400 --> 00:31:11,080 Speaker 13: There are things that individuals want and that companies want, 613 00:31:11,120 --> 00:31:13,400 Speaker 13: and I think there is a deep alignment when management 614 00:31:13,440 --> 00:31:16,480 Speaker 13: has done really well. And so I think what individuals 615 00:31:16,520 --> 00:31:19,760 Speaker 13: want is they want a sense of impact. They want 616 00:31:19,800 --> 00:31:22,680 Speaker 13: to understand that their company and their manager cares about them. 617 00:31:22,920 --> 00:31:24,920 Speaker 13: They want to be part of a team doing something important, 618 00:31:24,960 --> 00:31:26,080 Speaker 13: and they want to know how they're going to be 619 00:31:26,080 --> 00:31:28,640 Speaker 13: able to grow. What companies want is to build strong 620 00:31:28,680 --> 00:31:30,920 Speaker 13: cultures of people who are going to be well equipped 621 00:31:31,240 --> 00:31:34,800 Speaker 13: to do great things and drive the business forward. And 622 00:31:34,880 --> 00:31:36,800 Speaker 13: so I think what Lattice strives to be is this 623 00:31:36,880 --> 00:31:40,520 Speaker 13: intersection of those where you find that individual and company 624 00:31:40,560 --> 00:31:44,200 Speaker 13: alignment to such a degree where those things are incredibly 625 00:31:44,200 --> 00:31:47,240 Speaker 13: in harmony. And so I think whether we are in 626 00:31:47,600 --> 00:31:50,680 Speaker 13: the sort of boon times of twenty twenty one or 627 00:31:51,000 --> 00:31:54,480 Speaker 13: the new sort of paradigm of twenty twenty three, I 628 00:31:54,480 --> 00:31:56,680 Speaker 13: think those principles hold no matter what. And so I think, 629 00:31:56,720 --> 00:31:59,640 Speaker 13: you know, for Lattice, just finding the ways to do 630 00:31:59,680 --> 00:32:01,960 Speaker 13: that for customers is still the north star. 631 00:32:02,680 --> 00:32:05,240 Speaker 2: See you have of Lattice. Thank you, Jack Altman, I 632 00:32:05,280 --> 00:32:08,360 Speaker 2: appreciate the time. Meanwhile, coming up, well, we've got to 633 00:32:08,400 --> 00:32:10,640 Speaker 2: talk more about AI in all sorts of manners. Wing 634 00:32:10,720 --> 00:32:13,280 Speaker 2: Venture Capital has actually just been backing a new AI startup, 635 00:32:13,320 --> 00:32:15,480 Speaker 2: Pine Code. It's actually New York based, valued at seven 636 00:32:15,520 --> 00:32:18,000 Speaker 2: hundred and fifty million dollars. Now we're on that next. 637 00:32:18,160 --> 00:32:18,920 Speaker 2: This is a Bloomberg. 638 00:32:34,840 --> 00:32:37,560 Speaker 3: We were just talking about generative AI and its impact 639 00:32:37,640 --> 00:32:40,720 Speaker 3: on the workplace. We've been talking about generative AI and 640 00:32:40,760 --> 00:32:43,200 Speaker 3: the arms race going on in tech all week because 641 00:32:43,520 --> 00:32:47,240 Speaker 3: at the core of earnings was artificial intelligence. Speaking of 642 00:32:47,280 --> 00:32:49,200 Speaker 3: some of the big players that reported numbers, we asked 643 00:32:49,240 --> 00:32:51,320 Speaker 3: our audience, Caro, who's. 644 00:32:51,040 --> 00:32:51,720 Speaker 5: Won for you? 645 00:32:51,840 --> 00:32:54,240 Speaker 3: Who, to your mind, has made the most convincing case 646 00:32:54,480 --> 00:32:57,240 Speaker 3: that they're leading in AI. Those answers speak for themselves. 647 00:32:57,320 --> 00:33:00,920 Speaker 2: They do. Clearly Microsoft, with their tie up being integrating 648 00:33:00,920 --> 00:33:03,600 Speaker 2: open AI's chatchipt for the fact that they're managing to 649 00:33:03,640 --> 00:33:08,520 Speaker 2: brag ed about a quadrupling in app downloads that they've had. 650 00:33:08,920 --> 00:33:11,600 Speaker 2: Clearly sat In Adela has stall in the march on 651 00:33:11,640 --> 00:33:14,120 Speaker 2: this one. I mean, what was it? Overall? Though more 652 00:33:14,200 --> 00:33:17,840 Speaker 2: than two hundred times AI was mentioned in these earnings 653 00:33:17,880 --> 00:33:20,560 Speaker 2: calls this week alone, so they're all trying to really 654 00:33:20,640 --> 00:33:22,120 Speaker 2: still a little bit of the line. Like Meta, I 655 00:33:22,120 --> 00:33:23,240 Speaker 2: think did a pretty good job on it. 656 00:33:23,280 --> 00:33:26,240 Speaker 3: Two, Yeah, I did, And I did account on the 657 00:33:26,240 --> 00:33:28,960 Speaker 3: blogs of how many times AI was mentioned in each 658 00:33:28,960 --> 00:33:31,400 Speaker 3: earning's press release, because that's the kind of guy I am. 659 00:33:31,440 --> 00:33:33,600 Speaker 3: Amazon's going to feel agreed by those results, though we'll 660 00:33:33,600 --> 00:33:36,280 Speaker 3: come back to that. So, speaking of AI, pine Cone, 661 00:33:36,280 --> 00:33:39,840 Speaker 3: the startup whose platform supports AI software, just raised one 662 00:33:39,880 --> 00:33:42,160 Speaker 3: hundred million dollars in a funding round that values the 663 00:33:42,200 --> 00:33:45,400 Speaker 3: company at around seven hundred and fifty million dollars. The 664 00:33:45,480 --> 00:33:49,400 Speaker 3: round led by Andres and Horowitz, with participation from existing 665 00:33:49,600 --> 00:33:54,240 Speaker 3: investors Menlo Ventures and Wing Venture Capital. So it's bringing 666 00:33:54,320 --> 00:33:59,520 Speaker 3: Wing Venture partner Jake Flumenberg for more the round, the timing, 667 00:33:59,840 --> 00:34:04,120 Speaker 3: the valuation, explain you'll rationale behind getting involved. 668 00:34:05,120 --> 00:34:07,320 Speaker 5: Sure, well, thank you for having me on the show. 669 00:34:07,680 --> 00:34:10,840 Speaker 14: We've been involved with this company for a long time, 670 00:34:10,960 --> 00:34:15,200 Speaker 14: certainly before the rapid adoption that we've seen as of late, 671 00:34:15,680 --> 00:34:18,080 Speaker 14: and it fundamentally has to do with the types of 672 00:34:18,120 --> 00:34:23,080 Speaker 14: things that you need for machine learning. Most relational databases, 673 00:34:23,080 --> 00:34:26,600 Speaker 14: most data warehouses, they need just data that most people 674 00:34:26,680 --> 00:34:28,759 Speaker 14: understand numbers, texts, things like this. 675 00:34:29,320 --> 00:34:32,200 Speaker 5: Machine learning algorithms operate on vectors, embeddings. 676 00:34:32,239 --> 00:34:34,360 Speaker 14: This is a different type of data that needs a 677 00:34:34,400 --> 00:34:37,640 Speaker 14: different type of database. And so if you're building a 678 00:34:37,719 --> 00:34:41,319 Speaker 14: generative AI startup or a generative AI application and you 679 00:34:41,360 --> 00:34:44,120 Speaker 14: want to marry that with your own company's data, or 680 00:34:44,160 --> 00:34:48,080 Speaker 14: if you want to remember things from one one question 681 00:34:48,200 --> 00:34:51,080 Speaker 14: to the next, you need a vector database like incomb 682 00:34:51,440 --> 00:34:54,840 Speaker 14: And so with the rise of all these generative AI applications, 683 00:34:54,880 --> 00:34:58,040 Speaker 14: we're seeing more and more people adopt Mincombe. Pine Cone 684 00:34:58,080 --> 00:35:01,120 Speaker 14: is still a really early company. Oh, public market metric 685 00:35:01,239 --> 00:35:03,560 Speaker 14: is going to say, you know, justify this valuation. 686 00:35:03,680 --> 00:35:06,080 Speaker 5: This is a valuation that's based off of the potential. 687 00:35:06,120 --> 00:35:09,480 Speaker 14: If this is the database for AI, that's a tremendous 688 00:35:09,520 --> 00:35:10,600 Speaker 14: opportunity going for. 689 00:35:11,120 --> 00:35:14,040 Speaker 2: New York based company. Jake. What's really interesting about your 690 00:35:14,040 --> 00:35:15,839 Speaker 2: background and the fact that you've been investing these sorts 691 00:35:15,840 --> 00:35:18,440 Speaker 2: of companies for a while now, including pine Cone, is 692 00:35:18,960 --> 00:35:22,160 Speaker 2: you're trying to have an expertise about how you operationalize AI. 693 00:35:22,239 --> 00:35:24,200 Speaker 2: And this is kind of at the core of all 694 00:35:24,239 --> 00:35:27,279 Speaker 2: of our conversations. Everyone's trying to work out where and 695 00:35:27,320 --> 00:35:29,520 Speaker 2: sort of the stack is the most value to be had. 696 00:35:29,760 --> 00:35:32,920 Speaker 2: You about infrastructure, Are you about big companies applying AI? 697 00:35:33,600 --> 00:35:35,719 Speaker 2: Where do you lie in terms of where the most 698 00:35:35,719 --> 00:35:36,480 Speaker 2: disruption will be? 699 00:35:37,080 --> 00:35:39,440 Speaker 14: Yeah, I mean, well, we invest in early stage companies 700 00:35:39,480 --> 00:35:41,799 Speaker 14: and so we're certainly believers that they're going to have 701 00:35:42,520 --> 00:35:44,800 Speaker 14: an outsized portion of the returns. 702 00:35:44,960 --> 00:35:46,479 Speaker 5: But we invest up and down the stack. 703 00:35:46,560 --> 00:35:49,800 Speaker 14: We've made investments in semiconductor companies, and certainly the first 704 00:35:49,800 --> 00:35:52,080 Speaker 14: group of companies are the core infrastructure. 705 00:35:52,280 --> 00:35:53,879 Speaker 5: Then you see the cloud vendors. 706 00:35:53,600 --> 00:35:57,080 Speaker 14: That are making money hosting some of these foundation in 707 00:35:57,160 --> 00:36:00,440 Speaker 14: large language models and in the limit. A lot of 708 00:36:00,480 --> 00:36:02,920 Speaker 14: these things are going to be new pieces of infrastructure, 709 00:36:02,920 --> 00:36:04,800 Speaker 14: and just the way we turn to the cloud vendors 710 00:36:04,840 --> 00:36:07,680 Speaker 14: you mentioned, we're going to turn to these AI foundation 711 00:36:07,800 --> 00:36:10,799 Speaker 14: model vendors for some of that technology, and some of 712 00:36:10,800 --> 00:36:13,560 Speaker 14: them are partnering up. But we're most excited about the 713 00:36:13,600 --> 00:36:16,279 Speaker 14: applications on top because there'll be a few winners in 714 00:36:16,320 --> 00:36:18,719 Speaker 14: each of those lower levels, but there'll be hundreds, if 715 00:36:18,760 --> 00:36:23,640 Speaker 14: not thousands of opportunities to build interesting applications powered by AI. 716 00:36:25,000 --> 00:36:28,240 Speaker 3: Jake, I'm hearing a lot of companies as similar size 717 00:36:28,239 --> 00:36:30,960 Speaker 3: to pine Cone are trying to get rounds done. You know, 718 00:36:31,080 --> 00:36:36,239 Speaker 3: early through growth stage. How wary are you all of 719 00:36:36,320 --> 00:36:40,600 Speaker 3: open ai because open ai also has a small fund 720 00:36:40,680 --> 00:36:44,680 Speaker 3: that it's investing across startups in AI and those that's 721 00:36:44,719 --> 00:36:47,160 Speaker 3: deploying its own technology. 722 00:36:47,239 --> 00:36:50,239 Speaker 14: Yeah, I would say we're not worried at all about 723 00:36:50,320 --> 00:36:54,320 Speaker 14: open ai as a source of venture capital. There's lots 724 00:36:54,320 --> 00:36:57,840 Speaker 14: and lots of dollars and certainly open ai has a 725 00:36:57,840 --> 00:36:59,759 Speaker 14: bunch of things that can bring to bear. But we 726 00:36:59,800 --> 00:37:02,400 Speaker 14: were with companies at the really early stages on that 727 00:37:02,480 --> 00:37:05,319 Speaker 14: journey from zero to partner market fit, which is a 728 00:37:05,480 --> 00:37:08,680 Speaker 14: really hands on endeavor that takes a lot of time 729 00:37:08,719 --> 00:37:11,160 Speaker 14: and energy, and I think there's a role for people 730 00:37:11,239 --> 00:37:11,520 Speaker 14: that have. 731 00:37:11,480 --> 00:37:13,600 Speaker 5: Been doing that for a long time. 732 00:37:13,840 --> 00:37:17,600 Speaker 14: In terms of open Ai as a competitor, absolutely, you know, 733 00:37:17,680 --> 00:37:20,319 Speaker 14: as a competitor it's something to watch. But it's just 734 00:37:20,360 --> 00:37:22,440 Speaker 14: like the cloud vendor landscape. We don't think it's going 735 00:37:22,520 --> 00:37:24,279 Speaker 14: to be a winner take all. We do think it's 736 00:37:24,280 --> 00:37:27,880 Speaker 14: going to settle into a small number of larger foundation 737 00:37:28,040 --> 00:37:30,960 Speaker 14: model players and we're going to use them as infrastructure. 738 00:37:31,120 --> 00:37:33,000 Speaker 5: So they're a building block, they're an enabler. 739 00:37:33,680 --> 00:37:36,719 Speaker 2: Well, you're someone who's had leadership roles that it's cloud era, 740 00:37:36,840 --> 00:37:39,239 Speaker 2: so got a lot of experience from software to the 741 00:37:39,239 --> 00:37:41,440 Speaker 2: AI journey. We thank you so much. We venture capital 742 00:37:41,440 --> 00:37:53,080 Speaker 2: partner Jake Flumenberg, great to have you. From Cochella live 743 00:37:53,080 --> 00:37:55,560 Speaker 2: stream to having influences belled out the hottest viral song 744 00:37:55,600 --> 00:37:58,399 Speaker 2: on a short YouTube. It seems to always be looking 745 00:37:58,440 --> 00:38:01,000 Speaker 2: for new innovative ways to bring music to the masses, 746 00:38:01,000 --> 00:38:04,479 Speaker 2: despite some pretty tough competition in the digital space. Neil 747 00:38:04,600 --> 00:38:07,239 Speaker 2: Cohen YouTube Global had of Music speak to us Ana. 748 00:38:08,239 --> 00:38:13,840 Speaker 12: We're a global platform and we're focused on Our mission 749 00:38:14,760 --> 00:38:17,640 Speaker 12: is give everybody a voice and show them the world. 750 00:38:18,160 --> 00:38:22,440 Speaker 1: And boy of we've done that. We have gone from. 751 00:38:22,360 --> 00:38:26,080 Speaker 12: Three stages where we live stream to six stages. 752 00:38:26,880 --> 00:38:29,000 Speaker 1: We've introduced shorts. 753 00:38:28,600 --> 00:38:33,479 Speaker 12: As a really fun way for the kids to suggest 754 00:38:34,520 --> 00:38:39,960 Speaker 12: their favorite artists to do some of their songs that 755 00:38:40,000 --> 00:38:45,160 Speaker 12: they want, and so they're influencing the sets they are 756 00:38:45,600 --> 00:38:52,600 Speaker 12: able to be and create lightweight short form video to 757 00:38:52,680 --> 00:38:56,279 Speaker 12: explain to the rest of the world that they were there, 758 00:38:56,440 --> 00:38:59,680 Speaker 12: they were enjoyed the experience, whether they were at Coachella 759 00:38:59,800 --> 00:39:02,600 Speaker 12: or experiencing from their living room. 760 00:39:03,360 --> 00:39:06,399 Speaker 1: We introduced for this. 761 00:39:06,480 --> 00:39:09,319 Speaker 12: It's actually the second year where shopping has been an 762 00:39:09,400 --> 00:39:13,000 Speaker 12: integral part of the experience where kids are able to 763 00:39:13,040 --> 00:39:18,640 Speaker 12: buy merchandise and you know, rock their t shirts that 764 00:39:18,719 --> 00:39:23,000 Speaker 12: they were there, that they experienced it. And of course 765 00:39:23,040 --> 00:39:27,040 Speaker 12: the production has gotten so much better, and we introduced 766 00:39:27,120 --> 00:39:31,320 Speaker 12: live chats and and so there's a lot of work 767 00:39:31,360 --> 00:39:38,239 Speaker 12: that goes into it, but it's been incredibly fun and enjoyable. 768 00:39:39,160 --> 00:39:42,240 Speaker 2: Tell us about the international focus, but this was interesting 769 00:39:42,239 --> 00:39:44,480 Speaker 2: about the lineup of Coachella this year was, Yes, there 770 00:39:44,480 --> 00:39:48,120 Speaker 2: were the big American artists, but I'm also thinking Black Pink, 771 00:39:48,160 --> 00:39:51,480 Speaker 2: I'm thinking Bad Bunny, these truly international stars. So how 772 00:39:51,520 --> 00:39:53,520 Speaker 2: much was the viewership coming internationally? 773 00:39:54,400 --> 00:39:59,160 Speaker 12: Well over sixty five percent of viewership came from outside 774 00:39:59,160 --> 00:40:04,480 Speaker 12: of America. And when the fans are able to experience 775 00:40:05,400 --> 00:40:11,640 Speaker 12: Nigerian Burner Boy, Korean Black Pink, and I don't know 776 00:40:11,680 --> 00:40:15,200 Speaker 12: if you saw, but Rosalia shut the place down. She's 777 00:40:15,239 --> 00:40:19,600 Speaker 12: from Spain and was one of the most moving performances. 778 00:40:20,200 --> 00:40:25,719 Speaker 12: And we had the first Punjabi artist perform and they're 779 00:40:26,000 --> 00:40:30,080 Speaker 12: celebrating in India the fact that they have an Indian 780 00:40:30,200 --> 00:40:37,000 Speaker 12: Pujabi artist on Coachella, and it's just fabulous to see. 781 00:40:35,960 --> 00:40:38,640 Speaker 2: Leo Cohen there, global head of Music at YouTube now 782 00:40:38,800 --> 00:40:41,480 Speaker 2: ed but He's sort of like the man almost that 783 00:40:41,560 --> 00:40:43,759 Speaker 2: helped drive the birth of hip hop. He was there 784 00:40:44,080 --> 00:40:47,880 Speaker 2: and unsigned Rumdiancy. He can see around corners. What's interesting 785 00:40:47,960 --> 00:40:50,360 Speaker 2: is pretty optimist about AI's impact on music. 786 00:40:50,840 --> 00:40:53,359 Speaker 3: Yeah, you and I grew up in the MTV era, right, 787 00:40:53,520 --> 00:40:55,920 Speaker 3: video and music? How do you push it forward? It 788 00:40:55,960 --> 00:40:56,840 Speaker 3: is so fascinating. 789 00:40:57,000 --> 00:40:59,360 Speaker 2: Yeah, his artists don't seem to be worried about official 790 00:40:59,400 --> 00:41:01,880 Speaker 2: intelligency in their lunch for now. That does it for 791 00:41:01,920 --> 00:41:03,200 Speaker 2: this edition of Bloomberg Technology. 792 00:41:03,239 --> 00:41:05,960 Speaker 3: Yet, such a huge week, so much recap, So don't 793 00:41:05,960 --> 00:41:09,640 Speaker 3: forget the podcast, Apple, Spotify, iHeart Bloomberg. Join us on 794 00:41:09,680 --> 00:41:13,560 Speaker 3: Twitter spaces right now on Twitter, This is Bloomberg