1 00:00:01,600 --> 00:00:04,600 Speaker 1: From Mahart of We're Innovation, Money and Power. 2 00:00:04,480 --> 00:00:06,880 Speaker 2: Collie in Silicon Vallet NBN. 3 00:00:07,200 --> 00:00:10,680 Speaker 1: This is Bloomberg Technology with Caroline Hyde. 4 00:00:10,360 --> 00:00:11,680 Speaker 2: And Ed Ludlow. 5 00:00:25,200 --> 00:00:27,760 Speaker 3: Im Ed Ludlow here in San Francisco. Caroline Hyde is 6 00:00:27,760 --> 00:00:31,000 Speaker 3: off today. This is BlueBag Technology. Let's get after it. 7 00:00:31,000 --> 00:00:34,080 Speaker 3: Coming up, Intel surging as it's longer way to come 8 00:00:34,120 --> 00:00:36,559 Speaker 3: back is underway. We're going to break down the company's 9 00:00:36,560 --> 00:00:40,159 Speaker 3: earnings with the CEO, Pat Gelsinger. Plus we're gonna have 10 00:00:40,159 --> 00:00:42,680 Speaker 3: an exclusive interview with the CEO of by Now Pay 11 00:00:42,760 --> 00:00:47,199 Speaker 3: Later company affirm his outlook on fintech and the payment space. 12 00:00:47,600 --> 00:00:49,360 Speaker 3: And we'll push ahead to what's going to be another 13 00:00:49,400 --> 00:00:52,800 Speaker 3: crazy week in tech earnings with Amazon, Apple, and Qualcom 14 00:00:52,840 --> 00:00:55,880 Speaker 3: all reporting next week. We're going to stick with tech 15 00:00:55,920 --> 00:00:59,320 Speaker 3: out this way out West earnings next week include Apple, Amazon, 16 00:00:59,720 --> 00:01:02,800 Speaker 3: and it's been an interesting dynamic where the market is 17 00:01:02,840 --> 00:01:04,760 Speaker 3: so hyped up about AI, but if you look at 18 00:01:04,760 --> 00:01:07,160 Speaker 3: the trading, I think we're kind of focused on some 19 00:01:07,200 --> 00:01:08,839 Speaker 3: of the core businesses of these names. 20 00:01:08,880 --> 00:01:10,400 Speaker 4: Will that be the story next week? 21 00:01:10,680 --> 00:01:15,040 Speaker 3: Let's ask Jonathan Curtis, director of portfolio management for Franklin Equity, 22 00:01:15,120 --> 00:01:19,240 Speaker 3: overseeing nine point five billion dollars in assets under management. 23 00:01:19,240 --> 00:01:20,400 Speaker 4: What do you make of that, Jonathan? 24 00:01:20,720 --> 00:01:24,399 Speaker 3: Investors love AI, but this week it seems like they 25 00:01:24,440 --> 00:01:27,240 Speaker 3: traded on the fundamentals the core business. 26 00:01:27,800 --> 00:01:30,480 Speaker 5: Yeah, well so, certainly there's a lot of excitement and 27 00:01:30,520 --> 00:01:34,600 Speaker 5: appropriately so around AI, but we are in the stage 28 00:01:34,600 --> 00:01:38,480 Speaker 5: of this opportunity that we like to call the experimentation phase. 29 00:01:38,840 --> 00:01:41,759 Speaker 5: Companies are learning about how to use generative AI. They've 30 00:01:41,760 --> 00:01:48,600 Speaker 5: been reorientary, reorienting their product roadmaps to take advantage of AI. 31 00:01:49,040 --> 00:01:51,400 Speaker 5: They're starting to figure out what their customers are going 32 00:01:51,480 --> 00:01:53,640 Speaker 5: to be willing to pay for some of these capabilities. 33 00:01:53,960 --> 00:01:57,280 Speaker 1: But we're not really in the scale at stage. 34 00:01:57,320 --> 00:01:59,559 Speaker 5: We think that's going to come here in the next 35 00:01:59,600 --> 00:02:02,559 Speaker 5: two day, three, maybe four quarters. And the biggest, most 36 00:02:02,560 --> 00:02:05,600 Speaker 5: important thing to be watching there is what platforms like 37 00:02:05,720 --> 00:02:10,000 Speaker 5: Microsoft or Adobe or other companies that have many hundreds 38 00:02:10,040 --> 00:02:12,800 Speaker 5: of millions of users can do when they start putting 39 00:02:12,840 --> 00:02:16,359 Speaker 5: these AI capabilities in their products where hundreds of millions 40 00:02:16,400 --> 00:02:19,680 Speaker 5: of knowledge workers and media creators operate every single day. 41 00:02:19,840 --> 00:02:21,760 Speaker 1: We're quite excited about what that means. 42 00:02:21,560 --> 00:02:25,160 Speaker 5: And how that will put pressure on the underlying infrastructure 43 00:02:25,200 --> 00:02:28,160 Speaker 5: and semi conductor layer and keep the growth going for 44 00:02:28,240 --> 00:02:29,320 Speaker 5: a lot of these companies. 45 00:02:30,120 --> 00:02:33,520 Speaker 3: So we do this every quarter now, track the number 46 00:02:33,520 --> 00:02:36,680 Speaker 3: of times AI as a phrase, a word is. 47 00:02:36,680 --> 00:02:37,960 Speaker 4: Mentioned on an earnings call. 48 00:02:38,639 --> 00:02:42,160 Speaker 3: Do you go through the transcript and say, okay AI 49 00:02:42,240 --> 00:02:43,560 Speaker 3: number one, AI number two. 50 00:02:43,800 --> 00:02:48,200 Speaker 4: I mean, seriously, how much we did do that? 51 00:02:48,320 --> 00:02:52,160 Speaker 5: Yeah, so this past earning cycle, not this current one, 52 00:02:52,200 --> 00:02:54,639 Speaker 5: because we're not completely through it. We did go through 53 00:02:54,720 --> 00:02:58,359 Speaker 5: and count how many references there were to artificial intelligence, 54 00:02:58,400 --> 00:03:02,320 Speaker 5: and then we compared it to references to things like 55 00:03:02,400 --> 00:03:05,120 Speaker 5: mobile back around the launch of the iPhone, because we 56 00:03:05,160 --> 00:03:08,280 Speaker 5: wanted to get a sense for how quickly this was 57 00:03:08,400 --> 00:03:13,200 Speaker 5: dispersing through the economy relative to other big cycles that 58 00:03:13,240 --> 00:03:14,320 Speaker 5: we've seen in the past. 59 00:03:14,680 --> 00:03:19,680 Speaker 3: And why why, Jonathan, they're just words? What do they 60 00:03:19,720 --> 00:03:20,000 Speaker 3: tell you? 61 00:03:20,760 --> 00:03:21,680 Speaker 1: They're not just words? 62 00:03:21,720 --> 00:03:26,320 Speaker 5: Their intentionality And what's important about that intentionality is it 63 00:03:26,360 --> 00:03:30,640 Speaker 5: tells you how much the other sectors outside of the 64 00:03:31,160 --> 00:03:34,800 Speaker 5: outside of tech are gearing up and getting curious about 65 00:03:34,800 --> 00:03:37,840 Speaker 5: this opportunity and ultimately what the big spending is going 66 00:03:37,920 --> 00:03:40,640 Speaker 5: to come. And what's fascinating about the analysis we did 67 00:03:40,720 --> 00:03:43,600 Speaker 5: ninety days ago was that we saw much more dispersion 68 00:03:44,160 --> 00:03:47,200 Speaker 5: for artificial intelligence than we did for mobile back at 69 00:03:47,200 --> 00:03:50,440 Speaker 5: the start of the iPhone cycle, the iPhone opportunity, so 70 00:03:50,480 --> 00:03:52,960 Speaker 5: that gives us a little bit of confidence that this 71 00:03:53,000 --> 00:03:56,000 Speaker 5: could be even bigger than other big cycles we've seen 72 00:03:56,040 --> 00:03:56,720 Speaker 5: in the past. 73 00:03:56,640 --> 00:03:57,160 Speaker 1: Like mobile. 74 00:03:58,120 --> 00:04:00,320 Speaker 3: Jonathan, I want to talk about the semiconductor name. We're 75 00:04:00,320 --> 00:04:03,240 Speaker 3: going to be speaking to Pat Gelsinger in about five 76 00:04:03,280 --> 00:04:06,480 Speaker 3: minutes time, the CEO of Intel. You do not hold Intel, 77 00:04:06,760 --> 00:04:11,240 Speaker 3: but I believe you do hold Nvidia an AMD. Explain 78 00:04:11,440 --> 00:04:14,120 Speaker 3: that thesis to me and why they are the winners 79 00:04:14,160 --> 00:04:15,160 Speaker 3: in AI. 80 00:04:15,320 --> 00:04:18,440 Speaker 5: Yeah, so, certainly Intel's an amazing company. There are funds 81 00:04:18,440 --> 00:04:22,320 Speaker 5: within Franklin that own Intel. They are in the midst 82 00:04:22,400 --> 00:04:27,400 Speaker 5: of a challenging transformation, and they're a little too PC 83 00:04:27,640 --> 00:04:32,119 Speaker 5: centric for our growth needs, and they're in a heavy 84 00:04:32,160 --> 00:04:35,280 Speaker 5: investment stage right now. So we're watching Intel closely. We 85 00:04:35,400 --> 00:04:38,880 Speaker 5: like Nvidia because they're highly aligned with the AI opportunity 86 00:04:38,880 --> 00:04:42,520 Speaker 5: and an incredible mode there. We like AMD because they 87 00:04:42,560 --> 00:04:45,159 Speaker 5: are a share gainer in the server space at the 88 00:04:45,240 --> 00:04:49,320 Speaker 5: expense of Intel. We also like TSMC, which is one 89 00:04:49,320 --> 00:04:52,880 Speaker 5: of Intel's emerging competitors in the fabrication of chips and 90 00:04:52,960 --> 00:04:58,120 Speaker 5: a MD and TSMC and Nvidia and TSMC together are 91 00:04:58,680 --> 00:05:02,120 Speaker 5: really helping set the next stage of growth in the 92 00:05:02,160 --> 00:05:06,159 Speaker 5: semiconductor space, and Intel is just playing catchup quite frankly and. 93 00:05:06,200 --> 00:05:11,240 Speaker 4: As a little two PC centric for our interests PC centric. 94 00:05:11,279 --> 00:05:13,560 Speaker 3: The other area we want to talk about is cloud, 95 00:05:13,600 --> 00:05:17,120 Speaker 3: and we started this conversation Jonathan by pointing out that 96 00:05:17,240 --> 00:05:18,839 Speaker 3: a lot of the trading this week was done on 97 00:05:18,880 --> 00:05:24,280 Speaker 3: core legacy businesses. Abigail mentioned Microsoft, where investors looked at 98 00:05:24,279 --> 00:05:26,320 Speaker 3: the performance of the cloud business from a top line 99 00:05:26,360 --> 00:05:28,520 Speaker 3: growth perspective and weren't as happy. 100 00:05:29,360 --> 00:05:30,320 Speaker 4: What do you see there? 101 00:05:31,320 --> 00:05:33,880 Speaker 5: Well, encouragingly, we are starting to see some of that 102 00:05:34,000 --> 00:05:37,679 Speaker 5: deceleration that we've been seeing in cloud in this post 103 00:05:37,720 --> 00:05:40,360 Speaker 5: COVID era starting to calm down a bit, and in 104 00:05:40,400 --> 00:05:44,279 Speaker 5: fact the deceleration that Microsoft witnessed and into cloud business 105 00:05:44,640 --> 00:05:47,719 Speaker 5: slowed a bit, and encouragingly, we're starting to see the 106 00:05:47,800 --> 00:05:52,360 Speaker 5: real positive impacts from the AI experimentation phase that we're 107 00:05:52,360 --> 00:05:57,080 Speaker 5: in right now, impacting the cloud operators like Microsoft, like Google. 108 00:05:57,200 --> 00:05:59,680 Speaker 5: So we think that we are getting through the optimization 109 00:06:00,120 --> 00:06:03,920 Speaker 5: is post COVID. We are getting into easier comps there 110 00:06:04,200 --> 00:06:06,839 Speaker 5: and now we're starting to see AI become a material 111 00:06:06,920 --> 00:06:09,760 Speaker 5: contributor to these cloud businesses. So we think in our 112 00:06:09,760 --> 00:06:14,680 Speaker 5: broader digital transformation thesis, AI is still misunderstood by investors. 113 00:06:14,680 --> 00:06:17,719 Speaker 5: They don't understand how profound the impacts of this is 114 00:06:17,760 --> 00:06:20,280 Speaker 5: going to be. And then the cloud opportunity is going 115 00:06:20,279 --> 00:06:23,960 Speaker 5: to be a big participant along with AI. But also 116 00:06:24,040 --> 00:06:28,200 Speaker 5: as digital transformation continues, and we're starting to see that stabilization. 117 00:06:28,279 --> 00:06:30,880 Speaker 5: So we're quite encouraged by both what we're seeing in 118 00:06:30,920 --> 00:06:32,120 Speaker 5: AI and in cloud. 119 00:06:32,960 --> 00:06:37,520 Speaker 3: All right, Jonathan Curtis, Franklin Equity Group Director, Report further Strategy. 120 00:06:37,640 --> 00:06:40,719 Speaker 3: Just such a wide ranging conversation, and as we discuss, 121 00:06:41,080 --> 00:06:43,479 Speaker 3: the next big conversation is going to be about the 122 00:06:43,560 --> 00:06:46,120 Speaker 3: chip sector. Thank you very much. Coming up, stay tuned 123 00:06:46,520 --> 00:06:49,080 Speaker 3: because we will sit down with the CEO of Intel, 124 00:06:49,160 --> 00:06:50,120 Speaker 3: Pat Gelsinger. 125 00:06:50,440 --> 00:06:52,279 Speaker 4: The market really. 126 00:06:52,080 --> 00:06:55,000 Speaker 3: Liked what it had to hear about the quarter just 127 00:06:55,080 --> 00:06:58,600 Speaker 3: gone and the outlook for the current quarter, but there 128 00:06:58,640 --> 00:07:02,479 Speaker 3: are long term questions here about Intel returning to a 129 00:07:02,520 --> 00:07:05,479 Speaker 3: position of leadership when it comes to the cutting edge 130 00:07:05,839 --> 00:07:10,560 Speaker 3: of chip manufacturing, chip making technology, and also what is 131 00:07:10,560 --> 00:07:12,720 Speaker 3: the future of this company in the field of AI. 132 00:07:12,800 --> 00:07:16,160 Speaker 3: Pat Gelsinger, Intel CEO coming up next here here in 133 00:07:16,200 --> 00:07:31,400 Speaker 3: San Francisco. This is Bloomberg Technology. I want to welcome 134 00:07:31,400 --> 00:07:36,600 Speaker 3: our global Bloomberg TV and radio audiences. Intel shares are jumping, 135 00:07:36,720 --> 00:07:40,040 Speaker 3: with investors buying into signs that the chip maker's long 136 00:07:40,040 --> 00:07:44,680 Speaker 3: awaited comeback is underway. Intel's forecasting sales in the current 137 00:07:44,760 --> 00:07:49,400 Speaker 3: quarter of thirteen point nine billion dollars, ahead of expectations. 138 00:07:49,400 --> 00:07:50,840 Speaker 4: The company also notched. 139 00:07:50,760 --> 00:07:53,960 Speaker 3: A surprise profit of thirteen censor sharing in the corner 140 00:07:54,160 --> 00:07:57,840 Speaker 3: just gone as a slump in demand for personal computers 141 00:07:58,280 --> 00:08:01,320 Speaker 3: appears to be come into an it's not all good news. 142 00:08:01,360 --> 00:08:04,840 Speaker 3: Serve demand isn't recovering as quickly. The company is still 143 00:08:05,040 --> 00:08:07,680 Speaker 3: a little bit far from its heyday where margins where 144 00:08:07,680 --> 00:08:11,120 Speaker 3: it's sixty percent sales were nearer to twenty billion dollars. 145 00:08:11,280 --> 00:08:16,679 Speaker 3: Even so, joining us now Intel's CEO Pat Gelsinger, You know, Pat, 146 00:08:16,720 --> 00:08:22,240 Speaker 3: this is the second consecutive quarter where investors have cheered 147 00:08:23,520 --> 00:08:26,440 Speaker 3: the earnings results shares her up and I think before 148 00:08:26,480 --> 00:08:29,520 Speaker 3: twenty twenty three, ten out of the eleven earnings prints 149 00:08:29,520 --> 00:08:32,680 Speaker 3: that you had shares fell. Is this job done for 150 00:08:32,760 --> 00:08:34,320 Speaker 3: you in the turnaround at Intel? 151 00:08:35,840 --> 00:08:39,400 Speaker 6: Well, we have a long way to go yet, but boy, 152 00:08:39,520 --> 00:08:42,920 Speaker 6: having two good beaten Ray quarters in a row, you know, 153 00:08:43,120 --> 00:08:46,040 Speaker 6: was a super positive and really I think in the 154 00:08:46,160 --> 00:08:50,040 Speaker 6: KP of that turning point for the company. 155 00:08:50,320 --> 00:08:52,040 Speaker 1: But we still have a lot of work to do yet. 156 00:08:52,080 --> 00:08:54,319 Speaker 6: You know, as we said, our client business as now 157 00:08:55,000 --> 00:08:57,760 Speaker 6: healthy footing. You know, we've returned markers shared to where 158 00:08:57,760 --> 00:09:01,839 Speaker 6: it traditionally was a strong roadmap. You know, the markets recovering, 159 00:09:01,960 --> 00:09:04,720 Speaker 6: inventory levels are good, you know, Data Center, we still 160 00:09:04,720 --> 00:09:06,800 Speaker 6: have work to do, but boy, you know, two quarters 161 00:09:06,800 --> 00:09:08,440 Speaker 6: in a row where we did a bit better than 162 00:09:08,440 --> 00:09:11,000 Speaker 6: we expected, you know, but we still have challenges and 163 00:09:11,080 --> 00:09:14,280 Speaker 6: AI you know, and many of our really good products 164 00:09:14,400 --> 00:09:15,400 Speaker 6: are only. 165 00:09:15,080 --> 00:09:17,680 Speaker 1: Coming to market over the next year. You know. 166 00:09:17,760 --> 00:09:20,160 Speaker 6: Networking is still a lot of inventory to work through, 167 00:09:20,400 --> 00:09:23,400 Speaker 6: and our foundry business is still just a seedling, just 168 00:09:23,480 --> 00:09:26,199 Speaker 6: starting to show some green shoots. So I'll say, boy, 169 00:09:26,320 --> 00:09:29,000 Speaker 6: far from finish, but it is nice bouncing off the 170 00:09:29,000 --> 00:09:29,720 Speaker 6: bottom of a bit. 171 00:09:29,679 --> 00:09:31,440 Speaker 1: And feeling that momentum in the market. 172 00:09:31,440 --> 00:09:35,200 Speaker 3: Response, Pat, why do you have the confidence to kind 173 00:09:35,200 --> 00:09:39,040 Speaker 3: of cool the end of the PC slump and also 174 00:09:39,080 --> 00:09:41,720 Speaker 3: at the same time state that the server recovery is 175 00:09:41,760 --> 00:09:43,040 Speaker 3: delayed to the end of the year. 176 00:09:44,720 --> 00:09:45,280 Speaker 1: Yeah, and the. 177 00:09:45,240 --> 00:09:49,120 Speaker 6: PC side inventory levels are now healthy, right, you know, 178 00:09:49,280 --> 00:09:52,760 Speaker 6: everything that we've seen and a lot of the issues 179 00:09:52,760 --> 00:09:54,840 Speaker 6: that we worked through Q four, Q one and Q 180 00:09:54,960 --> 00:09:59,000 Speaker 6: two were over inventory levels by the OEMs and the channel, 181 00:09:59,320 --> 00:10:02,800 Speaker 6: and now everything is healthy. Our roadmap is very good. 182 00:10:03,040 --> 00:10:06,280 Speaker 6: We've gained share multiple times in a row. I think 183 00:10:06,320 --> 00:10:08,959 Speaker 6: we're now at five out of six quarters where we've 184 00:10:09,160 --> 00:10:11,240 Speaker 6: gained market share. So I just say in the PC 185 00:10:11,480 --> 00:10:15,840 Speaker 6: business healthy, Our position is good, and we're looking forward 186 00:10:15,920 --> 00:10:18,800 Speaker 6: to the AI PC and with our launch of our 187 00:10:18,800 --> 00:10:22,440 Speaker 6: next generation product, meteor Lake later this year. You know, 188 00:10:22,480 --> 00:10:26,400 Speaker 6: we believe that ushers in the AI PC generation and 189 00:10:26,440 --> 00:10:29,520 Speaker 6: I've compared that to like Centrino and Wi Fi, you 190 00:10:29,559 --> 00:10:32,800 Speaker 6: know two decades ago, a major new use case for 191 00:10:32,920 --> 00:10:35,760 Speaker 6: why the PC is the best platform. So we're quite 192 00:10:35,800 --> 00:10:38,679 Speaker 6: excited about that. On the data center side, you know, 193 00:10:38,720 --> 00:10:41,840 Speaker 6: we still saw that, you know, the inventory levels still persist. 194 00:10:42,240 --> 00:10:45,000 Speaker 6: You know, China was weaker than expected. Their recovery is 195 00:10:45,040 --> 00:10:48,280 Speaker 6: going slower. And you know, cyclically we see the shift 196 00:10:48,320 --> 00:10:52,120 Speaker 6: toward AI you know, these big training machines. Every cloud 197 00:10:52,160 --> 00:10:54,480 Speaker 6: vendor is shifting their dollars to more. 198 00:10:54,320 --> 00:10:55,040 Speaker 1: Focus on that. 199 00:10:55,120 --> 00:10:58,240 Speaker 6: So those three things are leading to a bit longer 200 00:10:58,320 --> 00:10:59,400 Speaker 6: recovery cycle. 201 00:10:59,640 --> 00:11:00,600 Speaker 1: On the data center. 202 00:11:00,679 --> 00:11:02,760 Speaker 6: Well, like I said, we performed a bit better than 203 00:11:02,760 --> 00:11:05,240 Speaker 6: we thought on the data center in Q one AM 204 00:11:05,360 --> 00:11:08,360 Speaker 6: Q two. So we're feeling like our momentum and execution 205 00:11:08,559 --> 00:11:11,200 Speaker 6: is rebuilding despite some of those headwinds that are still 206 00:11:11,240 --> 00:11:12,640 Speaker 6: persist in that area. 207 00:11:13,160 --> 00:11:16,000 Speaker 3: For our global TV and radio audience. Here at Bloomberg, 208 00:11:16,000 --> 00:11:19,960 Speaker 3: we're speaking to Pat Elsing at the Intel CEO. Pat, 209 00:11:20,000 --> 00:11:23,720 Speaker 3: you're forecasting gross margin of forty three percent in the 210 00:11:23,920 --> 00:11:27,520 Speaker 3: current period, but it's a long way from that sixty 211 00:11:27,520 --> 00:11:30,040 Speaker 3: percent gross margin level. You know, Wall Street used to 212 00:11:30,040 --> 00:11:33,480 Speaker 3: look at Intel and say sixty percent. You know, they 213 00:11:33,720 --> 00:11:35,920 Speaker 3: cheer you as a leader in that space. Can you 214 00:11:36,000 --> 00:11:39,720 Speaker 3: just explain to our global audience the timeline and path 215 00:11:39,840 --> 00:11:42,520 Speaker 3: to getting back to profit at that level. 216 00:11:44,080 --> 00:11:46,319 Speaker 6: Yeah, and we're working our way back to margins and 217 00:11:46,360 --> 00:11:49,280 Speaker 6: obviously a nice bet in Q two on margins, and 218 00:11:49,320 --> 00:11:51,840 Speaker 6: we forecast Q three a bit better and Q four 219 00:11:51,920 --> 00:11:54,120 Speaker 6: a bit better, you know. And part of it is 220 00:11:54,320 --> 00:11:59,080 Speaker 6: the cyclicality of the semiconductor industry is brutal on margins. 221 00:11:59,120 --> 00:12:03,720 Speaker 6: And when we had an oversupply situation inventory, you know, 222 00:12:03,760 --> 00:12:07,760 Speaker 6: that just depresses margins because you know, the factories cost 223 00:12:07,840 --> 00:12:10,839 Speaker 6: the same whether they're full or whether they're empty, So 224 00:12:10,920 --> 00:12:14,160 Speaker 6: you end up with these charges that you burden the 225 00:12:14,200 --> 00:12:18,600 Speaker 6: price points and depressed margins. We also realize that our 226 00:12:18,600 --> 00:12:22,760 Speaker 6: own product execution has weakened our product position which doesn't 227 00:12:22,800 --> 00:12:25,679 Speaker 6: have asps as strong as well, so that's another factor. 228 00:12:26,120 --> 00:12:28,640 Speaker 6: And the last factor here is, you know, the plan 229 00:12:28,760 --> 00:12:32,439 Speaker 6: that my CFO Dave and I have laid out is 230 00:12:32,480 --> 00:12:36,560 Speaker 6: an expensive plan. We are making aggressive investments to build 231 00:12:36,640 --> 00:12:40,080 Speaker 6: the capacity to get back to leadership and thus we're 232 00:12:40,160 --> 00:12:43,200 Speaker 6: moving through nodes very rapidly. We sat five nodes in 233 00:12:43,280 --> 00:12:45,640 Speaker 6: four years, so that causes us to have a lot 234 00:12:45,640 --> 00:12:50,559 Speaker 6: of undepreciated capacity that we're working through quite aggressively. Also 235 00:12:50,679 --> 00:12:54,120 Speaker 6: building up a bit more capacity for our foundry initiatives. 236 00:12:54,360 --> 00:12:58,640 Speaker 6: So all of those factors depressed margins to historically low 237 00:12:58,720 --> 00:13:00,720 Speaker 6: levels in the first part of the year and we're 238 00:13:00,720 --> 00:13:04,280 Speaker 6: just seeing ourselves now working to build back to margin levels. 239 00:13:04,400 --> 00:13:07,280 Speaker 6: But we're still very confident that as we build our 240 00:13:07,280 --> 00:13:11,520 Speaker 6: foundry business get back to leadership and process and products. 241 00:13:11,600 --> 00:13:14,000 Speaker 6: You know that those kind of margins, that's exactly what 242 00:13:14,320 --> 00:13:16,600 Speaker 6: Dave and I aspire to to the future, and we 243 00:13:16,679 --> 00:13:19,560 Speaker 6: feel like you two was a good marker. You know that, yes, 244 00:13:19,600 --> 00:13:21,440 Speaker 6: we're building momentum to get back there. 245 00:13:22,400 --> 00:13:25,679 Speaker 3: Thank you for joining us here on Bloomberg Television and 246 00:13:25,800 --> 00:13:31,000 Speaker 3: radio worldwide. We're joined by Pat Gelsinger, Intel CEO. You 247 00:13:31,200 --> 00:13:34,760 Speaker 3: described the foundry business as a seedling, but every time 248 00:13:34,840 --> 00:13:37,960 Speaker 3: you and I have spoken, you've hinted that there is 249 00:13:38,000 --> 00:13:43,640 Speaker 3: a big customer waiting in the wings to give life 250 00:13:43,679 --> 00:13:44,480 Speaker 3: to that business. 251 00:13:44,640 --> 00:13:47,000 Speaker 4: What can you tell us about that, Pat. 252 00:13:47,760 --> 00:13:50,600 Speaker 6: Yeah, and we're having good momentum, and as I said 253 00:13:50,640 --> 00:13:54,240 Speaker 6: on the Ernie's call yesterday, we have two big customers 254 00:13:54,240 --> 00:13:56,600 Speaker 6: in particular that we made very good progress over the 255 00:13:56,640 --> 00:14:00,920 Speaker 6: last quarter for our foundry business. We did have one, 256 00:14:01,000 --> 00:14:04,280 Speaker 6: i'll say confirmatory, not as big a customer, but the 257 00:14:04,400 --> 00:14:07,840 Speaker 6: Ericsson announcement their commitment to eighteen A and our next 258 00:14:07,880 --> 00:14:11,480 Speaker 6: generation work with them that we announced this quarter. So 259 00:14:11,520 --> 00:14:14,640 Speaker 6: I'll say overall, we're seeing good momentum and a really 260 00:14:14,679 --> 00:14:18,079 Speaker 6: strong pipeline of customers, and we hope to make meaningful 261 00:14:18,080 --> 00:14:21,880 Speaker 6: announcements later this year on that. We also pointed out 262 00:14:21,880 --> 00:14:24,440 Speaker 6: on our earnings call that now we're seeing a lot 263 00:14:24,440 --> 00:14:26,640 Speaker 6: of interest in our packaging technologies. 264 00:14:26,680 --> 00:14:28,520 Speaker 1: So it isn't just wayfer. 265 00:14:28,240 --> 00:14:31,400 Speaker 6: Manufacturing, it's also package assembly and tests. 266 00:14:31,400 --> 00:14:33,560 Speaker 1: And Intel has long. 267 00:14:33,320 --> 00:14:37,400 Speaker 6: Term been a leader in packaging technologies, and because of 268 00:14:37,480 --> 00:14:42,040 Speaker 6: key areas like high performance computing and AI, there's tremendous 269 00:14:42,080 --> 00:14:45,640 Speaker 6: interest in these advanced packaging technologies, and we're finding a 270 00:14:45,640 --> 00:14:49,120 Speaker 6: lot of customer interest in that incremental area of the 271 00:14:49,160 --> 00:14:50,400 Speaker 6: foundry business as well. 272 00:14:50,480 --> 00:14:51,600 Speaker 1: So overall, you. 273 00:14:51,520 --> 00:14:53,280 Speaker 6: Know, and the numbers were good for us in Q 274 00:14:53,360 --> 00:14:57,400 Speaker 6: two for Foundry, great pipeline of activities, great progress on 275 00:14:57,480 --> 00:15:00,800 Speaker 6: a couple of these most major opportunities. I'm feeling good 276 00:15:00,840 --> 00:15:03,920 Speaker 6: like we're starting to really see that momentum build in 277 00:15:03,960 --> 00:15:07,600 Speaker 6: this new business area for Intel or Intel Foundery services. 278 00:15:08,520 --> 00:15:12,360 Speaker 3: If we think about what a potential customer might be, 279 00:15:12,480 --> 00:15:15,200 Speaker 3: you know, at the scale of Apple, Google or Video, 280 00:15:15,560 --> 00:15:17,960 Speaker 3: what is it that they want from you? What is 281 00:15:18,000 --> 00:15:19,800 Speaker 3: it do you think that you can provide them? 282 00:15:21,240 --> 00:15:22,880 Speaker 1: Yeah? You know, when I view it, you know, we 283 00:15:22,960 --> 00:15:24,840 Speaker 1: have to go through four stages. You know. 284 00:15:24,920 --> 00:15:28,080 Speaker 6: One is are my transistors good? You know, can they 285 00:15:28,080 --> 00:15:32,440 Speaker 6: build good products using Intel? Second is do I have 286 00:15:32,560 --> 00:15:36,800 Speaker 6: the design tools? You know, the cadences and synopsis, c 287 00:15:36,960 --> 00:15:40,120 Speaker 6: das and the IP libraries. Have we gotten all of 288 00:15:40,160 --> 00:15:42,880 Speaker 6: those basics done so that they can design on us? 289 00:15:43,240 --> 00:15:43,440 Speaker 1: You know? 290 00:15:43,480 --> 00:15:46,320 Speaker 6: Then Third, you know, do we have good terms and conditions? 291 00:15:46,360 --> 00:15:48,840 Speaker 6: Are they better off coming to me versus you know, 292 00:15:49,000 --> 00:15:52,880 Speaker 6: TSMC or Samsung as an alternative, And then finally, are 293 00:15:52,880 --> 00:15:56,760 Speaker 6: we customer oriented? Can they really have the support because 294 00:15:56,800 --> 00:15:59,720 Speaker 6: my factory becomes their factory, you know, So we have 295 00:15:59,760 --> 00:16:02,600 Speaker 6: to work through all four of those stages before they're 296 00:16:02,680 --> 00:16:05,520 Speaker 6: ready to commit major businesses to us. 297 00:16:05,680 --> 00:16:06,760 Speaker 1: And that's why it takes a while. 298 00:16:06,840 --> 00:16:09,800 Speaker 6: You know, they got to do designs and tests and pilots, 299 00:16:09,840 --> 00:16:12,560 Speaker 6: and you know, work through the financials, and you know, 300 00:16:12,600 --> 00:16:15,400 Speaker 6: this isn't a mature business area for us. But I'll 301 00:16:15,440 --> 00:16:18,200 Speaker 6: just say we're making great progress, and in particular, you know, 302 00:16:18,280 --> 00:16:21,160 Speaker 6: the two most significant opportunities. It was a really good 303 00:16:21,240 --> 00:16:24,160 Speaker 6: quarter and I'm feeling very optimistic that yes, we'll bring 304 00:16:24,200 --> 00:16:27,200 Speaker 6: them across the line and start to really accomplish what 305 00:16:27,280 --> 00:16:31,080 Speaker 6: we've laid out, you know, with our reshoring and building 306 00:16:31,120 --> 00:16:32,480 Speaker 6: the Western founder. 307 00:16:32,560 --> 00:16:34,560 Speaker 1: And we also had great success. 308 00:16:34,160 --> 00:16:37,840 Speaker 6: With both the EU and the US chipsack this last quarter, 309 00:16:37,880 --> 00:16:40,960 Speaker 6: which are affirming the strong support you know, of the 310 00:16:41,000 --> 00:16:43,240 Speaker 6: Western governments on this strategy. 311 00:16:43,440 --> 00:16:45,800 Speaker 1: It's the right strategy at the right time. We're making 312 00:16:45,840 --> 00:16:46,520 Speaker 1: good progress. 313 00:16:47,000 --> 00:16:49,080 Speaker 3: A big part of your smart capital approach, we have 314 00:16:49,120 --> 00:16:51,960 Speaker 3: to talk about AI. You see, a world in which 315 00:16:52,040 --> 00:16:55,400 Speaker 3: the PC plays a role for localized running of l 316 00:16:55,520 --> 00:16:58,080 Speaker 3: l ms. But what are the use cases that you 317 00:16:58,160 --> 00:17:04,000 Speaker 3: see PAT, The applications PC with AI specific chips is relevant. 318 00:17:05,119 --> 00:17:08,639 Speaker 6: Yeah, you know, to some degree. They're numerous ed. You know, 319 00:17:08,760 --> 00:17:11,119 Speaker 6: let's just give one example. You know, in the future, 320 00:17:11,160 --> 00:17:13,359 Speaker 6: my word processor, you know, I'm going to hit a 321 00:17:13,359 --> 00:17:15,960 Speaker 6: button and say, give me a legal brief that describes this, 322 00:17:16,240 --> 00:17:18,760 Speaker 6: and it's going to get locally generated. You know, my 323 00:17:19,200 --> 00:17:22,399 Speaker 6: video conferencing, my zine, my teams or zooms. I'm going 324 00:17:22,480 --> 00:17:24,639 Speaker 6: to say, you know, give me, you know, real time 325 00:17:24,800 --> 00:17:29,359 Speaker 6: translation across multiple languages, you know, for this meeting, and 326 00:17:29,440 --> 00:17:31,800 Speaker 6: I'm going to have that in real time on my PC. 327 00:17:32,359 --> 00:17:34,479 Speaker 6: You know, my games, you know, for my you know, 328 00:17:34,520 --> 00:17:37,600 Speaker 6: all of that is going to become synthetically generated worlds 329 00:17:37,720 --> 00:17:41,520 Speaker 6: locally on my PC in real time. So we see 330 00:17:41,520 --> 00:17:45,000 Speaker 6: it across creator, across productivity, you know. And as I've said, 331 00:17:45,040 --> 00:17:47,399 Speaker 6: this is sort of like a Wi Fi moment, you know, 332 00:17:47,520 --> 00:17:50,159 Speaker 6: for the PC of the future, and that begins with 333 00:17:50,200 --> 00:17:53,560 Speaker 6: our meteor like launch in the second half of this year. 334 00:17:54,720 --> 00:17:58,440 Speaker 3: PAT quickly a one billion dollar pipeline for AI products 335 00:17:58,440 --> 00:18:01,320 Speaker 3: through twenty twenty four. Just me a sense of the 336 00:18:01,359 --> 00:18:03,359 Speaker 3: pace at which that pipeline is growing. 337 00:18:03,600 --> 00:18:09,320 Speaker 6: Now, Yeah, we had a super exciting quarter. As I said, 338 00:18:09,320 --> 00:18:12,199 Speaker 6: we six sect that pipeline in Q two, so we 339 00:18:12,240 --> 00:18:14,800 Speaker 6: saw a huge uptick on that, you know, and I've 340 00:18:14,840 --> 00:18:18,879 Speaker 6: deployed a lot more sales resources, software resources, you know, 341 00:18:18,960 --> 00:18:22,399 Speaker 6: to jump on those opportunities worldwide. You know, when we 342 00:18:22,480 --> 00:18:25,640 Speaker 6: have our Gouty two chip that is now in volume. 343 00:18:25,880 --> 00:18:28,119 Speaker 6: You know, we've just seen the first wafers on the 344 00:18:28,160 --> 00:18:30,960 Speaker 6: next generation Gouty three, which will be our twenty twenty 345 00:18:31,000 --> 00:18:33,600 Speaker 6: four product, and then we have our twenty five and 346 00:18:33,640 --> 00:18:35,200 Speaker 6: twenty six products underway. 347 00:18:35,280 --> 00:18:37,600 Speaker 1: So you know, we're seeing a lot of momentum there. 348 00:18:37,800 --> 00:18:40,480 Speaker 6: And the world is looking for a great alternative, an 349 00:18:40,560 --> 00:18:45,080 Speaker 6: open alternative, a more cost effective alternative, and Intel is 350 00:18:45,119 --> 00:18:48,719 Speaker 6: a trusted supplier. We think this is a great area 351 00:18:48,760 --> 00:18:50,679 Speaker 6: for us to put a lot of energy into and 352 00:18:50,680 --> 00:18:52,880 Speaker 6: we're seeing the response from the marketplace now. 353 00:18:54,080 --> 00:18:57,480 Speaker 3: Intel CEO Pat Gelsinger, we appreciate your time here on 354 00:18:57,520 --> 00:18:58,720 Speaker 3: Bloomberg TV and radio. 355 00:18:58,880 --> 00:19:00,360 Speaker 4: Thank you, thank you so much. 356 00:19:00,400 --> 00:19:01,400 Speaker 1: Always a pleasure. 357 00:19:09,480 --> 00:19:10,440 Speaker 4: Time for talking tech. 358 00:19:10,560 --> 00:19:13,800 Speaker 3: First up, KLA reported strong earnings for the current period. 359 00:19:13,800 --> 00:19:17,280 Speaker 3: It signals that the chip industry may be nearing a recovery. 360 00:19:17,359 --> 00:19:20,680 Speaker 3: Shares increase around four point five percent in this session, 361 00:19:20,840 --> 00:19:23,440 Speaker 3: but it's unclear how long this upspring will last. Of course, 362 00:19:23,440 --> 00:19:26,200 Speaker 3: it's a sign that chip makers are ready to spend 363 00:19:26,200 --> 00:19:29,280 Speaker 3: on new equipment. Plus, a burning ship near the Netherlands 364 00:19:29,280 --> 00:19:32,680 Speaker 3: has almost five hundred electric cars on board. The cause 365 00:19:32,720 --> 00:19:34,959 Speaker 3: of the blaze still unknown, but the Coast Guard has 366 00:19:34,960 --> 00:19:37,720 Speaker 3: denied reports that the fire broke out in the section 367 00:19:37,800 --> 00:19:40,240 Speaker 3: of the carrier where the electric cars were stored. And finally, 368 00:19:40,359 --> 00:19:43,560 Speaker 3: China has asked its largest tech companies to provide case 369 00:19:43,560 --> 00:19:47,960 Speaker 3: studies of their most successful startup investments. The askers the 370 00:19:48,040 --> 00:19:51,440 Speaker 3: sign authorities are ready to grant them broaderly, way after 371 00:19:51,480 --> 00:19:54,080 Speaker 3: a crackdown that brought them to a virtual halt two 372 00:19:54,200 --> 00:19:56,439 Speaker 3: years ago. All right, coming up, threads, The Twitter like 373 00:19:56,480 --> 00:19:59,359 Speaker 3: app might help bring the center of Internet culture back 374 00:19:59,400 --> 00:20:00,080 Speaker 3: to meta. 375 00:20:00,200 --> 00:20:02,480 Speaker 4: Look at the new app and its potential impact. 376 00:20:02,720 --> 00:20:17,720 Speaker 3: This is Bloomberg Technology. Welcome back to Bloomberg Technology ed 377 00:20:17,760 --> 00:20:20,840 Speaker 3: lovelow here in San Francisco. This week we mentioned the 378 00:20:20,880 --> 00:20:23,479 Speaker 3: idea of everything apps not only for x the company 379 00:20:23,480 --> 00:20:26,080 Speaker 3: formerly known as Twitter, but that conversation now going to 380 00:20:26,119 --> 00:20:28,960 Speaker 3: TikTok as well. Just twenty four hours ago, we spoke 381 00:20:29,000 --> 00:20:32,600 Speaker 3: to metacfo Susanly about the concept of everything apps and 382 00:20:32,640 --> 00:20:35,800 Speaker 3: if Meta would consider itself heading in that direction. 383 00:20:35,840 --> 00:20:36,760 Speaker 4: Here's what she had to say. 384 00:20:37,440 --> 00:20:40,280 Speaker 7: We're really invested in the opportunities that we have ahead 385 00:20:40,280 --> 00:20:43,760 Speaker 7: of us across our family of apps right now, including Threads, 386 00:20:43,760 --> 00:20:47,000 Speaker 7: which is the newest standalone app in our portfolio. And 387 00:20:47,040 --> 00:20:48,760 Speaker 7: then there's just a lot that we can do to 388 00:20:48,800 --> 00:20:52,080 Speaker 7: make the experiences across our family of apps richer and 389 00:20:52,119 --> 00:20:55,000 Speaker 7: more engaging with the investments that we've made already in 390 00:20:55,080 --> 00:20:59,399 Speaker 7: AI and especially recommending content that you don't already follow, 391 00:20:59,440 --> 00:21:02,879 Speaker 7: and we know that that's brought richer content experiences to people, 392 00:21:03,119 --> 00:21:08,320 Speaker 7: is growing engagement across the apps, and we'll release you know, 393 00:21:08,320 --> 00:21:11,280 Speaker 7: we'll be releasing features over the course of the next years. 394 00:21:11,280 --> 00:21:13,320 Speaker 7: But we're really excited about what we think that this 395 00:21:13,440 --> 00:21:16,000 Speaker 7: is going to bring to bear for the consumer experience 396 00:21:16,080 --> 00:21:19,119 Speaker 7: and of course also eventually for businesses to connect with 397 00:21:19,200 --> 00:21:21,360 Speaker 7: consumers across the family of apps too. 398 00:21:23,000 --> 00:21:24,159 Speaker 4: The Everything app. 399 00:21:24,359 --> 00:21:26,879 Speaker 3: Let's break it down with Bloomberg's Alex Brinka, who covers 400 00:21:26,880 --> 00:21:30,080 Speaker 3: the social platforms for US, and Rachel Tippograph, CEO and 401 00:21:30,080 --> 00:21:33,199 Speaker 3: founder of mick Mac, a global e commerce enablement and 402 00:21:33,280 --> 00:21:36,080 Speaker 3: analytics platform for multi channel brands. 403 00:21:37,040 --> 00:21:39,320 Speaker 4: First of all, Rachel, give me. 404 00:21:39,359 --> 00:21:42,920 Speaker 3: Your definition, your definition of an everything app? 405 00:21:44,760 --> 00:21:46,760 Speaker 8: And everything app is a place where I'm going to 406 00:21:46,800 --> 00:21:49,800 Speaker 8: spend the majority of my time to connect with my 407 00:21:49,840 --> 00:21:54,520 Speaker 8: friends and family, be inspired, potentially, do work, start a business, 408 00:21:54,560 --> 00:21:55,359 Speaker 8: and transact. 409 00:21:57,000 --> 00:22:00,200 Speaker 3: So the newspeg this week is X, the everything app. 410 00:22:00,240 --> 00:22:03,879 Speaker 3: It's officially happened. Twitter logo's gone, the Bluebird's gone, we 411 00:22:03,960 --> 00:22:08,560 Speaker 3: have an X, But TikTok and meta is still part 412 00:22:08,560 --> 00:22:11,840 Speaker 3: of this conversation. Do you realistically see a world in 413 00:22:11,880 --> 00:22:14,399 Speaker 3: which we see all three move towards in everything app? 414 00:22:16,000 --> 00:22:18,560 Speaker 8: I think for everything apps to work, we have to 415 00:22:18,680 --> 00:22:21,560 Speaker 8: let go of the notion of wald gardens because to 416 00:22:21,600 --> 00:22:25,560 Speaker 8: be everything you need to integrate with the infrastructure of society. 417 00:22:26,119 --> 00:22:28,280 Speaker 8: So if you want to drive commerce, you need to 418 00:22:28,280 --> 00:22:32,200 Speaker 8: integrate with the biggest players in commerce, Amazon, Target, Walmart, 419 00:22:32,760 --> 00:22:36,720 Speaker 8: payments like PayPal. If you want to integrate into work, 420 00:22:36,760 --> 00:22:40,000 Speaker 8: you got to integrate into Salesforce and Slack and Oracle 421 00:22:40,080 --> 00:22:40,719 Speaker 8: and SAP. 422 00:22:41,680 --> 00:22:42,920 Speaker 4: The approach that the wald. 423 00:22:42,720 --> 00:22:45,480 Speaker 8: Garden apps have taken is, hey, we want to do 424 00:22:45,640 --> 00:22:47,200 Speaker 8: everything ourselves. 425 00:22:47,960 --> 00:22:49,640 Speaker 4: That typically doesn't work out. 426 00:22:50,240 --> 00:22:52,960 Speaker 8: So for everything apps to come to life, there really 427 00:22:53,000 --> 00:22:55,919 Speaker 8: needs to be a partner ecosystem that allows them to 428 00:22:56,000 --> 00:22:57,560 Speaker 8: flourish in everyday life. 429 00:22:58,760 --> 00:23:00,280 Speaker 3: So I want to go back to the metapart of 430 00:23:00,320 --> 00:23:03,920 Speaker 3: this equation, Alex Brinka, You've written a story published on 431 00:23:03,920 --> 00:23:07,480 Speaker 3: Bloomberg this morning. Meta has a rare opportunity to seize 432 00:23:07,560 --> 00:23:10,280 Speaker 3: momentum with threads, and I know you were watching the 433 00:23:10,280 --> 00:23:14,280 Speaker 3: interview with Susan Lee really closely. They're playing down the 434 00:23:14,400 --> 00:23:18,159 Speaker 3: sort of near term monetization of threads. But is that 435 00:23:18,320 --> 00:23:22,600 Speaker 3: the app that represents an opportunity for Meta to broaden 436 00:23:22,640 --> 00:23:23,440 Speaker 3: itself out a bit? 437 00:23:24,720 --> 00:23:28,560 Speaker 9: And the reason I made that argument is because it's 438 00:23:28,600 --> 00:23:32,359 Speaker 9: not just Twitter or the formerly known as Twitter X. 439 00:23:32,880 --> 00:23:34,800 Speaker 9: It's not just x that is kind of the biggest 440 00:23:34,840 --> 00:23:38,440 Speaker 9: competition here for Meta. Even if, as CFO Susan Lee says, 441 00:23:38,480 --> 00:23:41,480 Speaker 9: the Everything app isn't their kind of current focus, it's 442 00:23:41,560 --> 00:23:45,680 Speaker 9: actually TikTok. Meta used to be kind of the king 443 00:23:45,760 --> 00:23:50,120 Speaker 9: maker of Internet culture. Instagram used to really rule the zeitgeist, 444 00:23:50,320 --> 00:23:53,680 Speaker 9: and things that happened online happen there first. Right now, 445 00:23:53,760 --> 00:23:57,240 Speaker 9: TikTok really has that position and That's the argument I 446 00:23:57,320 --> 00:23:59,440 Speaker 9: kind of laid out it in that piece is Meta 447 00:23:59,440 --> 00:24:02,080 Speaker 9: has a rare community here, not to kill Twitter, but 448 00:24:02,160 --> 00:24:04,520 Speaker 9: to actually bring kind of the center of Internet culture 449 00:24:04,600 --> 00:24:07,920 Speaker 9: back to Meta because TikTok, while that is the place 450 00:24:08,000 --> 00:24:11,040 Speaker 9: right now, TikTok's a video platform. Making videos is a 451 00:24:11,080 --> 00:24:14,639 Speaker 9: lot harder than posting text online, so there could be 452 00:24:14,760 --> 00:24:17,760 Speaker 9: this moment of opportunity and Edwhen you and Caroline chatted 453 00:24:17,800 --> 00:24:20,639 Speaker 9: to Susan Lee yesterday, I was really struck when you 454 00:24:20,680 --> 00:24:23,880 Speaker 9: asked her about kind of how you bring that zeitgeist back. 455 00:24:24,160 --> 00:24:27,600 Speaker 9: She went straight to talking about product updates and changes 456 00:24:27,640 --> 00:24:30,320 Speaker 9: to features, which is a little bit different than I 457 00:24:30,359 --> 00:24:33,320 Speaker 9: know that TikTok thinks of itself. They talk about culture 458 00:24:33,359 --> 00:24:37,480 Speaker 9: and bringing creators and you know, music and fashion, and 459 00:24:37,800 --> 00:24:39,480 Speaker 9: it's a little bit of a different flavor. But I 460 00:24:39,480 --> 00:24:42,560 Speaker 9: think it's an important distinction as they continue to figure 461 00:24:42,560 --> 00:24:46,080 Speaker 9: out what threads fits, how threads fits into the meta universe. 462 00:24:46,640 --> 00:24:49,199 Speaker 3: I like the historic dynamic, which is that you know 463 00:24:49,359 --> 00:24:53,120 Speaker 3: what people say right when something's trending on TikTok, your 464 00:24:53,359 --> 00:24:58,000 Speaker 3: parents might see it on reels or Instagram six weeks later. 465 00:24:58,520 --> 00:25:01,640 Speaker 3: So with that in mind, Rachel, you know Alex tried 466 00:25:01,680 --> 00:25:04,959 Speaker 3: to outline Meta's position, which is that they want to 467 00:25:05,080 --> 00:25:08,040 Speaker 3: seize the cultural moment. Do you see that happening or 468 00:25:08,080 --> 00:25:10,000 Speaker 3: do you think TikTok leads in that respect? 469 00:25:11,560 --> 00:25:15,800 Speaker 8: So first to debunk the myth. There are plenty of 470 00:25:15,880 --> 00:25:18,840 Speaker 8: gen X and boomers on TikTok. Nearly fifty percent of 471 00:25:18,880 --> 00:25:22,040 Speaker 8: their users are not millennials and not Gen Z. So 472 00:25:22,560 --> 00:25:26,240 Speaker 8: culture is happening there, but culture is now multi generational 473 00:25:26,960 --> 00:25:30,680 Speaker 8: in terms of TikTok. Yes, it is taking over search, 474 00:25:30,760 --> 00:25:33,800 Speaker 8: which I think is a key indicator that culture is 475 00:25:33,920 --> 00:25:36,919 Speaker 8: moving there. When it comes to Meta, there is a 476 00:25:37,000 --> 00:25:41,000 Speaker 8: huge difference. Meta is an identity service, and because it's 477 00:25:41,000 --> 00:25:45,080 Speaker 8: an identity service, it had an enormous amount of data 478 00:25:45,160 --> 00:25:48,880 Speaker 8: to build a really really robust advertising business on top 479 00:25:48,920 --> 00:25:53,199 Speaker 8: of and move people down the path to purchase. TikTok 480 00:25:53,600 --> 00:25:56,919 Speaker 8: has stayed more upper funnel has focused on consumer engagement. 481 00:25:56,960 --> 00:26:00,240 Speaker 8: They do a great job of that, and now are 482 00:26:00,320 --> 00:26:03,520 Speaker 8: trying to figure out, hey, can we actually steal market 483 00:26:03,560 --> 00:26:06,639 Speaker 8: share from Meta by monetizing the data that we have 484 00:26:06,680 --> 00:26:09,919 Speaker 8: on the platform. How they both use the data, and 485 00:26:09,960 --> 00:26:13,159 Speaker 8: how they've been building advertising businesses and commerce businesses on 486 00:26:13,240 --> 00:26:16,879 Speaker 8: top of each other have differed because of the nature 487 00:26:16,880 --> 00:26:20,200 Speaker 8: of the apps, but Meta, being a center for culture, 488 00:26:20,320 --> 00:26:22,480 Speaker 8: they have yet to demonstrate that they have the ability 489 00:26:22,520 --> 00:26:23,200 Speaker 8: to do that again. 490 00:26:23,480 --> 00:26:25,719 Speaker 3: All right, thanks to Rachel Tippograph and our own an 491 00:26:25,760 --> 00:26:28,679 Speaker 3: It's Brinker, we probably could do a full hour on 492 00:26:28,800 --> 00:26:30,680 Speaker 3: the media social media landscape right now. 493 00:26:30,680 --> 00:26:42,480 Speaker 4: We will have both of you back. Thank you very much. Affirm. 494 00:26:42,760 --> 00:26:45,639 Speaker 3: The popular buy now pay Lata service has gained traction 495 00:26:45,880 --> 00:26:50,280 Speaker 3: in recent years as inflation repressures way on consumer purchasing power. 496 00:26:50,280 --> 00:26:53,399 Speaker 3: With more than sixteen million active customers and more than 497 00:26:53,400 --> 00:26:56,639 Speaker 3: two hundred and forty five thousand merchants, the company as 498 00:26:56,640 --> 00:27:00,399 Speaker 3: a marker for tracking consumer trends across various set is 499 00:27:00,440 --> 00:27:03,600 Speaker 3: now it's expanding its reach in the travel space by 500 00:27:03,640 --> 00:27:07,480 Speaker 3: partnering with airline Cafe Pacific. Very happy to welcome Max 501 00:27:07,560 --> 00:27:09,920 Speaker 3: Leftchin to the show, CEO of a firm and my 502 00:27:10,040 --> 00:27:12,439 Speaker 3: good friend Blimberg's Shnali Basic. 503 00:27:13,000 --> 00:27:14,840 Speaker 10: Thank you Ed, and thank you Max for joining us. 504 00:27:14,840 --> 00:27:16,439 Speaker 10: When you took a look at this deal that you 505 00:27:16,560 --> 00:27:20,400 Speaker 10: just cut with Cathay Pacific, This idea that more airlines, 506 00:27:20,520 --> 00:27:24,000 Speaker 10: more travel is drawing by now pay later options. You 507 00:27:24,080 --> 00:27:27,480 Speaker 10: know it's very expensive to travel right now. How much 508 00:27:27,840 --> 00:27:31,359 Speaker 10: kind of heat is there in the consumer wallet to 509 00:27:31,520 --> 00:27:35,080 Speaker 10: be traveling with these kinds of prices, given kind of 510 00:27:35,119 --> 00:27:36,560 Speaker 10: strains all their wallet right now. 511 00:27:38,040 --> 00:27:41,439 Speaker 11: Well, I think we're still recovering from COVID and the 512 00:27:41,560 --> 00:27:43,199 Speaker 11: need to get out of the house to see the 513 00:27:43,200 --> 00:27:48,120 Speaker 11: world is intense, and prices are absolutely high. We are 514 00:27:48,160 --> 00:27:52,439 Speaker 11: seeing very expensive travel, and yet we're almost at the 515 00:27:52,520 --> 00:27:53,480 Speaker 11: peak of twenty. 516 00:27:53,400 --> 00:27:55,240 Speaker 2: Nineteen, right before the pandemic. 517 00:27:55,280 --> 00:27:57,880 Speaker 11: And so part of a reason for the reason we're 518 00:27:57,880 --> 00:27:59,560 Speaker 11: seeing so much success, so much pick up in the 519 00:27:59,600 --> 00:28:04,200 Speaker 11: travel industry is the airlines cannot do enough to bring 520 00:28:04,240 --> 00:28:06,960 Speaker 11: all these people chance to travel, but it is expensive. 521 00:28:07,080 --> 00:28:10,760 Speaker 11: So being able to afford it and with affirm without lateps, 522 00:28:10,800 --> 00:28:13,800 Speaker 11: without gimmicks, without tricks, is a really powerful sales pitch. 523 00:28:13,880 --> 00:28:16,360 Speaker 10: Well, here's a course partner I have about the airlines 524 00:28:16,359 --> 00:28:18,640 Speaker 10: as well, because if you think about it, the credit 525 00:28:18,720 --> 00:28:22,440 Speaker 10: card companies just reported earnings, they're extending loans like crazy. 526 00:28:22,800 --> 00:28:25,040 Speaker 10: The consumer looks a little stretched when you look at 527 00:28:25,040 --> 00:28:27,720 Speaker 10: how much debt they've taken out. There's a question about 528 00:28:27,760 --> 00:28:29,920 Speaker 10: whether they'll hit a cliff at the end of this year. 529 00:28:30,280 --> 00:28:31,520 Speaker 4: So how much can. 530 00:28:31,359 --> 00:28:34,720 Speaker 10: They really buy now pay later even more to fund 531 00:28:34,760 --> 00:28:37,160 Speaker 10: things that they want to do, go out and buy 532 00:28:37,280 --> 00:28:40,840 Speaker 10: luxury goods, and to take an airplane to Europe. 533 00:28:42,440 --> 00:28:47,040 Speaker 11: I think fundamentally consumers will borrow to meet their goals, 534 00:28:47,120 --> 00:28:51,320 Speaker 11: and my job is to provide a bible, transparent, and 535 00:28:51,360 --> 00:28:54,760 Speaker 11: fundamentally better alternative credit cards. If you see the growth 536 00:28:54,800 --> 00:28:56,800 Speaker 11: of buy now, pay later, you can see that we 537 00:28:56,920 --> 00:28:59,400 Speaker 11: are making a real dent and taking over more and 538 00:28:59,440 --> 00:29:02,040 Speaker 11: more of than consumer or spend. We are absolutely not 539 00:29:02,440 --> 00:29:05,240 Speaker 11: adding to it in terms of incremental debt. 540 00:29:05,560 --> 00:29:07,400 Speaker 2: That is unsustainable as a firm. 541 00:29:07,520 --> 00:29:10,400 Speaker 11: Other players can do other things, but our fundamental design 542 00:29:10,400 --> 00:29:13,120 Speaker 11: criteria is if we don't believe you can pay us back, 543 00:29:13,520 --> 00:29:16,360 Speaker 11: we will not lend you money. That is what's enshrined 544 00:29:16,440 --> 00:29:19,080 Speaker 11: in our design notion of no light fees, no gimmick, 545 00:29:19,160 --> 00:29:21,560 Speaker 11: no tricks. We will not benefit if you cannot pay 546 00:29:21,600 --> 00:29:24,640 Speaker 11: us back. Therefore will only lend if you can. 547 00:29:27,000 --> 00:29:27,360 Speaker 4: Max. 548 00:29:27,480 --> 00:29:31,160 Speaker 3: I understand the technological and cash flow advantage of buying now, 549 00:29:31,240 --> 00:29:34,680 Speaker 3: pay later. My question is what is the limit in 550 00:29:34,880 --> 00:29:37,320 Speaker 3: use cases? So you know an airline ticket can cost 551 00:29:37,360 --> 00:29:40,240 Speaker 3: you a few hundred dollars or a few thousand dollars. 552 00:29:41,160 --> 00:29:42,960 Speaker 3: I wonder what the point is where a firm says, 553 00:29:42,960 --> 00:29:47,160 Speaker 3: you know what we cannot do this, be it you know, 554 00:29:47,360 --> 00:29:49,960 Speaker 3: another luxury item or another product category. 555 00:29:51,040 --> 00:29:53,400 Speaker 11: So one of the advantage of bin now appilator to 556 00:29:53,440 --> 00:29:56,680 Speaker 11: the consumer is that, in particular with a firm, again, 557 00:29:57,160 --> 00:30:01,440 Speaker 11: we evaluate every transaction separately. So every time when you 558 00:30:01,520 --> 00:30:03,960 Speaker 11: choose to use a firm, there is a chance we 559 00:30:04,000 --> 00:30:06,400 Speaker 11: will tell you we think you are over extending yourself. 560 00:30:06,440 --> 00:30:09,960 Speaker 11: It is not financially healthy for you to make this transaction. 561 00:30:10,080 --> 00:30:13,040 Speaker 11: Please to make a larger down payment, don't transact. Find 562 00:30:13,080 --> 00:30:17,320 Speaker 11: something cheaper, and so you will or someone will hear 563 00:30:17,480 --> 00:30:20,800 Speaker 11: that every minute of the day today, and on and on. 564 00:30:20,960 --> 00:30:24,080 Speaker 11: We underwrite every single transaction. That's why our credit results 565 00:30:24,080 --> 00:30:26,800 Speaker 11: have been as strong as they have been. That is 566 00:30:26,840 --> 00:30:28,880 Speaker 11: part of the design, and that's why buy now apailiator 567 00:30:28,920 --> 00:30:31,320 Speaker 11: and a firm in particular is better than credit cards. 568 00:30:32,880 --> 00:30:33,160 Speaker 4: Max. 569 00:30:33,160 --> 00:30:36,440 Speaker 3: Twenty four hours ago, we had Zach Pere, the Plaid CEO, 570 00:30:36,520 --> 00:30:40,479 Speaker 3: on the show, and he talked about using AI in 571 00:30:40,720 --> 00:30:44,840 Speaker 3: assessing credit worthiness and lending. I wondered if you could 572 00:30:44,840 --> 00:30:47,120 Speaker 3: explain to our audience what a firm is doing with 573 00:30:47,200 --> 00:30:50,880 Speaker 3: AIS in its decisions on buy now, Pay later. 574 00:30:52,200 --> 00:30:54,560 Speaker 11: I'll nerd out for a couple of seconds, because I 575 00:30:54,560 --> 00:30:58,280 Speaker 11: think it's important to AI is now this overused umbrella 576 00:30:58,520 --> 00:31:01,000 Speaker 11: term that means everything to every So there's a couple 577 00:31:01,080 --> 00:31:03,200 Speaker 11: of different flavors of machine intelligence. 578 00:31:03,400 --> 00:31:04,440 Speaker 2: Let's call it something neutral. 579 00:31:04,720 --> 00:31:07,760 Speaker 11: There's machine learning, which has been around for quite some time, 580 00:31:07,920 --> 00:31:11,560 Speaker 11: and it is statistical learning all sorts of different terms, 581 00:31:11,560 --> 00:31:15,000 Speaker 11: but what it means is using statistical analysis and more 582 00:31:15,040 --> 00:31:19,720 Speaker 11: sophisticated mathematical techniques to assess ability to repay by looking 583 00:31:19,760 --> 00:31:20,800 Speaker 11: at past trends and data. 584 00:31:20,800 --> 00:31:22,640 Speaker 2: And we use that and have been using it since inception. 585 00:31:23,080 --> 00:31:26,320 Speaker 11: That's where a lot of our underwriting advantage comes from. 586 00:31:26,880 --> 00:31:31,960 Speaker 11: The other more currently exciting, excitable part of machine intelligence 587 00:31:32,080 --> 00:31:35,560 Speaker 11: is generative AI, things like Dolly and open AI and chatbots. 588 00:31:36,520 --> 00:31:39,440 Speaker 11: We do not use that to assess your credits because 589 00:31:39,520 --> 00:31:41,560 Speaker 11: we don't think the technology is mature enough and frankly, it 590 00:31:41,600 --> 00:31:43,920 Speaker 11: wasn't built for that. We do use it for exciting 591 00:31:43,920 --> 00:31:49,280 Speaker 11: things like developer productivity, making more data available internally faster 592 00:31:49,680 --> 00:31:52,720 Speaker 11: through chat interfaces. So there's lots and lots to gain 593 00:31:53,200 --> 00:31:57,280 Speaker 11: from various flavors of machine intelligence, but each tool is 594 00:31:57,360 --> 00:31:59,400 Speaker 11: for a different job, and we've been using it since 595 00:31:59,560 --> 00:32:00,680 Speaker 11: the day we're start the company. 596 00:32:00,840 --> 00:32:03,280 Speaker 10: You know, Max, You think about technology, You think about 597 00:32:03,640 --> 00:32:06,040 Speaker 10: what the PayPal mafia has done, and when it comes 598 00:32:06,080 --> 00:32:08,880 Speaker 10: to payments online, and you have Elon Musk trying to 599 00:32:08,960 --> 00:32:13,240 Speaker 10: expand on this everything app What role is social media 600 00:32:13,360 --> 00:32:16,080 Speaker 10: going to be playing in payments ultimately and how long 601 00:32:16,120 --> 00:32:17,080 Speaker 10: will it take to get there. 602 00:32:18,640 --> 00:32:20,960 Speaker 11: I think social media has already emerged as kind of 603 00:32:20,960 --> 00:32:24,200 Speaker 11: the next shopping format. We're all sort of swiping up 604 00:32:24,240 --> 00:32:27,400 Speaker 11: on Instagram and TikTok and on and on, and we 605 00:32:27,400 --> 00:32:29,680 Speaker 11: see a beautiful thing warn't by a beautiful person. Sometimes 606 00:32:29,720 --> 00:32:32,640 Speaker 11: we want to buy it. I think again, not to 607 00:32:32,640 --> 00:32:35,000 Speaker 11: serve food my own horm too much. A firm brings 608 00:32:35,160 --> 00:32:39,640 Speaker 11: certainty and sense of control and lack of gimmicks and 609 00:32:39,960 --> 00:32:42,959 Speaker 11: these into a situation that is sped up. When you're 610 00:32:43,000 --> 00:32:45,720 Speaker 11: tapping to buy something on your social media environment, you're 611 00:32:45,720 --> 00:32:47,920 Speaker 11: not thinking very hard about what financial tool you're going 612 00:32:47,960 --> 00:32:49,520 Speaker 11: to use, but you do want that peace of mind, 613 00:32:49,600 --> 00:32:53,120 Speaker 11: so picking a firm will help you, will keep you protected. 614 00:32:53,760 --> 00:32:58,360 Speaker 11: We are seeing a huge percentage of interaction with computers 615 00:32:58,360 --> 00:33:00,760 Speaker 11: and with phones in particular, shift to the bite sized 616 00:33:00,800 --> 00:33:05,920 Speaker 11: social media modalities, and that's just in your normal talk. 617 00:33:05,760 --> 00:33:08,640 Speaker 10: To us about competition, this idea of Apple pay later. 618 00:33:08,800 --> 00:33:12,120 Speaker 10: You are also partnered with large tech companies yourself, like Amazon, 619 00:33:12,400 --> 00:33:14,800 Speaker 10: but Apples move into the buy now, pay later space. 620 00:33:14,920 --> 00:33:16,640 Speaker 10: How much of an impact could that have on you 621 00:33:16,680 --> 00:33:17,200 Speaker 10: in the future. 622 00:33:18,480 --> 00:33:21,160 Speaker 11: You know, for the moment, we're all taking share from 623 00:33:21,240 --> 00:33:25,360 Speaker 11: credit cards and perhaps even more fundamentally cash. The online 624 00:33:25,480 --> 00:33:27,600 Speaker 11: e commerce in the O salone is nearing a trillion 625 00:33:27,680 --> 00:33:32,520 Speaker 11: farmer correctly, offline is four times that size. The penetration 626 00:33:32,680 --> 00:33:36,400 Speaker 11: into that entire thing is sub one percent if you 627 00:33:36,440 --> 00:33:39,000 Speaker 11: take the totality, and sub five percent. 628 00:33:38,760 --> 00:33:40,840 Speaker 2: If you look at just e commerce. 629 00:33:40,880 --> 00:33:43,160 Speaker 11: And so for the moment, most buy now pay latter 630 00:33:43,200 --> 00:33:45,240 Speaker 11: players are not exactly bumping into each other in a 631 00:33:45,280 --> 00:33:47,920 Speaker 11: hallways trying to convince that last consumer. 632 00:33:48,040 --> 00:33:50,480 Speaker 2: There's lots and lots and lots of greenfield before. 633 00:33:50,880 --> 00:33:54,760 Speaker 11: Competition matters more than just converting folks from credit cards 634 00:33:55,200 --> 00:33:58,680 Speaker 11: onto a firm, And we're making pretty good progress. 635 00:33:58,320 --> 00:33:59,960 Speaker 4: There, Max, really quick. 636 00:34:00,240 --> 00:34:02,320 Speaker 3: I do want you to reflect again on your PayPal 637 00:34:02,440 --> 00:34:05,840 Speaker 3: days and the idea of turning X into a place 638 00:34:05,840 --> 00:34:09,120 Speaker 3: of transactional e commerce. Do you see it as a reality? 639 00:34:12,000 --> 00:34:14,279 Speaker 2: You know, don't ever put anything past you on. 640 00:34:14,719 --> 00:34:17,200 Speaker 11: I think I think he has proven time and time 641 00:34:17,239 --> 00:34:19,600 Speaker 11: again that he can achieve crazy things. 642 00:34:19,800 --> 00:34:20,840 Speaker 2: I do think. 643 00:34:20,680 --> 00:34:24,480 Speaker 11: That the Everything app worked really well in a place 644 00:34:24,480 --> 00:34:26,560 Speaker 11: and a time in a different country. I don't think 645 00:34:26,640 --> 00:34:30,200 Speaker 11: us consumer is looking for a version of a ten 646 00:34:30,280 --> 00:34:30,840 Speaker 11: cent product. 647 00:34:31,960 --> 00:34:34,719 Speaker 3: All right, thanks to a firm CEO Max Levchin, alongside, 648 00:34:34,760 --> 00:34:38,439 Speaker 3: of course Bloomberg Snali Bassett, Happy Friday to you both. 649 00:34:39,360 --> 00:34:42,840 Speaker 3: Right back here in SF, California's largest utility, PG and 650 00:34:42,880 --> 00:34:46,280 Speaker 3: E says it's more prepared than ever for the threat 651 00:34:46,400 --> 00:34:50,080 Speaker 3: of wildfires. The company spent five years modernizing its infrastructure 652 00:34:50,200 --> 00:34:53,960 Speaker 3: in America's most popular state. It's now turning to AI 653 00:34:54,280 --> 00:34:57,279 Speaker 3: to predict and control wildfire impact. Take listen to my 654 00:34:57,320 --> 00:35:01,160 Speaker 3: exclusive interview with PG and E CEO Patti Poppy. 655 00:35:02,760 --> 00:35:04,879 Speaker 12: Ed I think you'd be quite impressed if you could 656 00:35:04,880 --> 00:35:09,319 Speaker 12: see the wildfire science that underpins the activation of the technology. 657 00:35:09,360 --> 00:35:11,719 Speaker 12: The hardware is activated by the software we have that 658 00:35:11,840 --> 00:35:15,160 Speaker 12: data system. We've actually divided up our entire service area 659 00:35:15,200 --> 00:35:17,520 Speaker 12: two thirds of the state of California into two kilometer 660 00:35:17,600 --> 00:35:20,799 Speaker 12: polygons we call them, and we have readings because of 661 00:35:20,800 --> 00:35:23,640 Speaker 12: our weather stations, our high definition cameras that are using 662 00:35:23,719 --> 00:35:27,360 Speaker 12: artificial intelligence to know the difference between fog and smoke. 663 00:35:27,719 --> 00:35:31,799 Speaker 12: That are identifying the fuel, moisture levels, the humidity, the 664 00:35:31,840 --> 00:35:36,520 Speaker 12: wind speed, the temperatures, the grass levels, any open maintenance tags, 665 00:35:36,520 --> 00:35:39,439 Speaker 12: and that two kilometer block, any tree that's within strike 666 00:35:39,480 --> 00:35:41,560 Speaker 12: distance of the line and that two kilometer block. And 667 00:35:41,600 --> 00:35:44,360 Speaker 12: we have technology then that we can activate when the 668 00:35:44,400 --> 00:35:47,760 Speaker 12: conditions are such that a catastrophic wildfire is possible. 669 00:35:47,800 --> 00:35:51,280 Speaker 3: So we're talking about spread modeling essentially trying to predict 670 00:35:51,719 --> 00:35:54,360 Speaker 3: the path that a fire would take if it was 671 00:35:54,400 --> 00:35:55,280 Speaker 3: her cust exactly. 672 00:35:55,320 --> 00:35:58,839 Speaker 12: So we use the spread modeling with this artificial intelligence 673 00:35:59,640 --> 00:36:04,040 Speaker 12: engine in to then activate our hardware on any given. 674 00:36:03,840 --> 00:36:04,439 Speaker 4: Day of the year. 675 00:36:04,480 --> 00:36:09,000 Speaker 12: We have an operation center that's monitoring conditions twenty four 676 00:36:09,000 --> 00:36:10,759 Speaker 12: hours a day, seven days a week, three hundred and 677 00:36:10,760 --> 00:36:13,879 Speaker 12: sixty five days a year, and they monitored those real 678 00:36:13,920 --> 00:36:18,040 Speaker 12: time indicators and then use that AI model to predict 679 00:36:18,480 --> 00:36:22,759 Speaker 12: risk and then we activate our hardening system. We've put 680 00:36:22,760 --> 00:36:26,359 Speaker 12: in layers of protection, including our ten thousand mile undergrounding plan, 681 00:36:26,440 --> 00:36:29,400 Speaker 12: which is sort of old school construction, but it works. 682 00:36:29,440 --> 00:36:34,040 Speaker 12: It's very risk mitigating combined with our technology platform to 683 00:36:34,320 --> 00:36:38,799 Speaker 12: be prepared every single day to prevent the next catastrophic wildfire? 684 00:36:39,120 --> 00:36:40,720 Speaker 4: Do you have to make further investment? 685 00:36:40,719 --> 00:36:43,280 Speaker 3: I mean he talked about it being old school putting 686 00:36:43,360 --> 00:36:46,080 Speaker 3: the cabling underground, but that's how it's worked in the past, 687 00:36:46,200 --> 00:36:50,680 Speaker 3: right above ground cables through a number of factors contributing 688 00:36:50,719 --> 00:36:51,480 Speaker 3: to the. 689 00:36:51,320 --> 00:36:52,200 Speaker 4: Cause of a fire. 690 00:36:52,760 --> 00:36:55,319 Speaker 3: Where else do you have to invest and continue to 691 00:36:55,360 --> 00:36:56,720 Speaker 3: grow your tech play? 692 00:36:57,080 --> 00:36:57,719 Speaker 4: Yeah, well, the. 693 00:36:57,760 --> 00:37:00,480 Speaker 12: Tech play in undergrounding is actually quite excited. We use 694 00:37:00,520 --> 00:37:03,640 Speaker 12: those models and all of that AI to determine what's 695 00:37:03,680 --> 00:37:06,040 Speaker 12: the next best mile to bury. We get lots of 696 00:37:06,040 --> 00:37:08,600 Speaker 12: calls people want us to bury the lines. So a 697 00:37:08,640 --> 00:37:10,920 Speaker 12: lot of people would like this to bury their strategic 698 00:37:10,920 --> 00:37:13,160 Speaker 12: about it. Yes, but we have to use ask that 699 00:37:13,280 --> 00:37:15,879 Speaker 12: risk modeling to determine the best miles, and then we're 700 00:37:15,920 --> 00:37:18,839 Speaker 12: deploying all sorts of new technology to make it way 701 00:37:18,920 --> 00:37:23,600 Speaker 12: less old school. We actually demoed at an investor event 702 00:37:23,760 --> 00:37:25,800 Speaker 12: in San Ramon a couple of maybe a month or 703 00:37:25,840 --> 00:37:29,440 Speaker 12: so ago, what we call at grade undergrounding, utilizing a 704 00:37:29,440 --> 00:37:34,480 Speaker 12: new steel form with a polymer insert with conduit where 705 00:37:34,520 --> 00:37:38,399 Speaker 12: we run the conductor and can bury the lines much 706 00:37:38,520 --> 00:37:42,000 Speaker 12: less deeply right at surface, and it takes a diamond 707 00:37:42,000 --> 00:37:46,279 Speaker 12: cutter to cut through this. So there's infrastructure technology that 708 00:37:46,320 --> 00:37:48,719 Speaker 12: we're deploying, and of course we're deploying all sorts of 709 00:37:48,760 --> 00:37:51,399 Speaker 12: tools to make sure that every minute we spend doing work, 710 00:37:51,440 --> 00:37:52,839 Speaker 12: we're doing it in a smarter way. 711 00:37:53,840 --> 00:37:56,719 Speaker 3: That was PG and Eco Patty Poppy the main takeaway. 712 00:37:57,040 --> 00:38:01,520 Speaker 3: Wildfire risks from equipment ninety four sent less likely now 713 00:38:01,800 --> 00:38:04,480 Speaker 3: than in twenty seventeen because of that tech investment. Check 714 00:38:04,520 --> 00:38:23,760 Speaker 3: out the full interview on bloomberg dot Com. 715 00:38:15,160 --> 00:38:16,760 Speaker 4: Time out for What's going viral? 716 00:38:16,880 --> 00:38:19,240 Speaker 3: Do you remember a scare last year when a man 717 00:38:19,360 --> 00:38:23,440 Speaker 3: in possession of ammunition slipped through a clear screening line 718 00:38:23,680 --> 00:38:26,799 Speaker 3: at Reagan National Airport. While that incident was a catalyst 719 00:38:27,120 --> 00:38:31,000 Speaker 3: to a government pro that uncovered flaws in clears practices, 720 00:38:31,040 --> 00:38:35,319 Speaker 3: at times capturing blurry or obscured images of travelers, Clear 721 00:38:35,400 --> 00:38:38,200 Speaker 3: responded to Bloomberg in a statement saying, quote, it is 722 00:38:38,280 --> 00:38:41,319 Speaker 3: deeply disappointed to us that images were shared with the 723 00:38:41,320 --> 00:38:43,160 Speaker 3: federal government as part of its review. 724 00:38:43,640 --> 00:38:45,880 Speaker 4: We share TSA's on waivering. 725 00:38:45,400 --> 00:38:49,520 Speaker 3: Commitment to aviation security and have proven ourselves a capable 726 00:38:49,560 --> 00:38:53,640 Speaker 3: and trusted partner for more than thirteen years. 727 00:38:54,480 --> 00:38:56,919 Speaker 4: One that everyone's talking about on social Media. 728 00:38:57,000 --> 00:38:57,120 Speaker 7: Now. 729 00:38:57,120 --> 00:39:00,840 Speaker 3: That does it for this edition of Bloomberg Technology. Happy Friday, 730 00:39:00,880 --> 00:39:03,400 Speaker 3: have a good weekend, but don't forget. You can recap 731 00:39:03,440 --> 00:39:06,960 Speaker 3: everything for today's episode in the podcast wherever you get 732 00:39:07,000 --> 00:39:10,960 Speaker 3: your podcast, Apple, Spotify, iHeart, and of course, on the 733 00:39:11,000 --> 00:39:13,960 Speaker 3: Bloomberg platforms Big earning season next week. 734 00:39:14,120 --> 00:39:16,120 Speaker 4: This is Bloomberg Technology.