1 00:00:01,520 --> 00:00:02,240 Speaker 1: From Marhart. 2 00:00:02,400 --> 00:00:06,800 Speaker 2: We're Innovation, Money and Power Collie in Silicon Valley, NBN. 3 00:00:07,160 --> 00:00:22,480 Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 4 00:00:25,160 --> 00:00:27,160 Speaker 3: I'm Caroline Heidet Bloomberg's Weld head quarters in. 5 00:00:27,080 --> 00:00:30,160 Speaker 4: New York, and I'm Ed Ludlow in San Francisco. This 6 00:00:30,200 --> 00:00:31,400 Speaker 4: is Bloomberg Technology. 7 00:00:31,600 --> 00:00:32,040 Speaker 5: Coming up. 8 00:00:32,120 --> 00:00:34,520 Speaker 3: We'll get the read on the state of the e 9 00:00:34,560 --> 00:00:37,040 Speaker 3: commerce consumer as we break down the early from Walmart 10 00:00:37,280 --> 00:00:38,360 Speaker 3: for strong online. 11 00:00:38,080 --> 00:00:42,560 Speaker 4: Growth plus the largest maker of computer networking equipment making 12 00:00:42,600 --> 00:00:45,160 Speaker 4: headway in AI and security technology. 13 00:00:45,159 --> 00:00:46,879 Speaker 6: We break down Cisco's results. 14 00:00:47,159 --> 00:00:51,040 Speaker 3: Meanwhile, Adien eraising twenty billion dollars of market value in 15 00:00:51,040 --> 00:00:53,720 Speaker 3: one day alone after posting the slowest revenue growth since 16 00:00:53,760 --> 00:00:55,840 Speaker 3: that European payments giants IPO. 17 00:00:56,320 --> 00:00:58,520 Speaker 4: Here in the US, two names that we're watching in 18 00:00:58,560 --> 00:01:02,680 Speaker 4: the yearnings context. The first the AI story Cisco benefiting 19 00:01:02,720 --> 00:01:06,000 Speaker 4: booking five hundred million dollars of sales already on its 20 00:01:06,000 --> 00:01:10,200 Speaker 4: AI products. Growth slowing, but the street seems to light 21 00:01:10,240 --> 00:01:12,520 Speaker 4: the forecast. We're going to get details from our editors 22 00:01:12,800 --> 00:01:15,760 Speaker 4: in the next block. Walmart now down one point six 23 00:01:15,800 --> 00:01:19,560 Speaker 4: percent second consecutive quarter where the outlook's been raised. But 24 00:01:19,600 --> 00:01:22,080 Speaker 4: here's the one for me. E Commerce sales in the 25 00:01:22,160 --> 00:01:25,080 Speaker 4: quarter jumped twenty four percent. This is an e commerce 26 00:01:25,080 --> 00:01:27,760 Speaker 4: story of Walmart. So let's get more details. Bringing Bloomberg's 27 00:01:27,760 --> 00:01:30,360 Speaker 4: Brendan Case, who covers Walmart for US out of Dallas. 28 00:01:30,360 --> 00:01:32,959 Speaker 4: Brendan just run us through the top line of these earnings. 29 00:01:33,720 --> 00:01:36,520 Speaker 7: Yeah, so the big picture for Walmart is that they 30 00:01:37,080 --> 00:01:41,639 Speaker 7: easily surpassed Wall Streets estimates for the second quarter. Little 31 00:01:41,640 --> 00:01:44,600 Speaker 7: bit light on the third quarter forecast, so I think 32 00:01:44,600 --> 00:01:47,039 Speaker 7: that's probably why shares are down. Another reason is that 33 00:01:47,080 --> 00:01:50,040 Speaker 7: there's just been so much enthusiasm about Walmart this year 34 00:01:50,120 --> 00:01:52,640 Speaker 7: that anything less than perfect is going to hurt their share. 35 00:01:53,960 --> 00:01:57,320 Speaker 7: Dropping down to e commerce though, that was a real 36 00:01:57,600 --> 00:02:01,680 Speaker 7: bright spot, up twenty four percent, and that's the second 37 00:02:01,760 --> 00:02:04,120 Speaker 7: grade quarter of really strong. 38 00:02:03,800 --> 00:02:05,480 Speaker 6: Growth in that regard. 39 00:02:05,800 --> 00:02:09,080 Speaker 7: So definitely a big, a big point of pride for 40 00:02:09,120 --> 00:02:09,720 Speaker 7: Walmart there. 41 00:02:09,880 --> 00:02:12,720 Speaker 3: Yeah, Brandon just talked to us about what they're doing 42 00:02:12,800 --> 00:02:15,480 Speaker 3: right here. Is it that they're gaining market share, Is 43 00:02:15,480 --> 00:02:18,560 Speaker 3: it they're able to claw more e commerce box of 44 00:02:18,680 --> 00:02:21,840 Speaker 3: their own customer already, or they're peeling it away from Amazon. 45 00:02:22,840 --> 00:02:24,959 Speaker 7: So there's a couple a couple of different things to 46 00:02:25,080 --> 00:02:28,280 Speaker 7: sort of unpack there, and one of them is something 47 00:02:28,280 --> 00:02:31,440 Speaker 7: that doesn't have anything to do with with placing orders 48 00:02:31,440 --> 00:02:34,640 Speaker 7: for groceries. It's advertising, which is included in their e 49 00:02:34,680 --> 00:02:39,200 Speaker 7: commerce business. Walmart has a big, multi billion dollar advertising 50 00:02:39,240 --> 00:02:43,640 Speaker 7: business that in all respects except for one, is you know, 51 00:02:43,960 --> 00:02:46,840 Speaker 7: big and successful. The one way in which it isn't 52 00:02:46,919 --> 00:02:50,600 Speaker 7: is that it still dramatically trails Amazon. So, you know, 53 00:02:50,720 --> 00:02:54,560 Speaker 7: boosting that business is a big priority for Walmart. They're 54 00:02:54,560 --> 00:02:56,600 Speaker 7: doing it, but they still have a long way to go. 55 00:02:57,360 --> 00:02:59,720 Speaker 7: And then if you sort of twitch to the shopper side, 56 00:03:00,440 --> 00:03:02,960 Speaker 7: what you're seeing a couple interesting trends there. You're seeing 57 00:03:03,360 --> 00:03:05,880 Speaker 7: strong growth in drive up, you know, people going to 58 00:03:05,919 --> 00:03:08,959 Speaker 7: the store to pick up online orders. But you're also 59 00:03:09,080 --> 00:03:14,440 Speaker 7: seeing growth which actually surpassed driver up this quarter, in delivery, 60 00:03:14,480 --> 00:03:17,360 Speaker 7: So you're getting more and more customers who are tapping 61 00:03:17,360 --> 00:03:20,280 Speaker 7: into the delivery options that Walmart offers. 62 00:03:21,040 --> 00:03:23,280 Speaker 4: Brendan, you and I wrote a story almost a year 63 00:03:23,320 --> 00:03:27,000 Speaker 4: ago now together about how Walmart does have nearly as 64 00:03:27,000 --> 00:03:30,520 Speaker 4: many shelf pickers for online orders as it does you 65 00:03:30,560 --> 00:03:33,799 Speaker 4: know people working in physical stores. Do we get any 66 00:03:33,800 --> 00:03:37,480 Speaker 4: sense from the call about structurally how Walmart's adjusting its 67 00:03:37,520 --> 00:03:40,320 Speaker 4: business for this kind of surge and e commerce demand 68 00:03:40,400 --> 00:03:42,520 Speaker 4: that you outline twenty four percent jump in the quarter. 69 00:03:43,720 --> 00:03:46,600 Speaker 7: Yeah, they give a bit of an update on their 70 00:03:46,640 --> 00:03:48,560 Speaker 7: supply chain efforts, and so what they're trying to do 71 00:03:48,680 --> 00:03:50,840 Speaker 7: is they've got a lot more automation going to their 72 00:03:50,880 --> 00:03:53,520 Speaker 7: big distribution centers. The other thing they're doing is they're 73 00:03:53,560 --> 00:03:57,080 Speaker 7: starting to build out what they call fulfillment centers, which 74 00:03:57,120 --> 00:04:01,040 Speaker 7: are inside stores but not visible to customers. And what 75 00:04:01,080 --> 00:04:02,880 Speaker 7: they're designed to do is get a lot of the 76 00:04:03,880 --> 00:04:07,160 Speaker 7: product pickers out of the aisles and have a dedicated 77 00:04:07,160 --> 00:04:11,120 Speaker 7: part of the store that is designed to send stuff 78 00:04:11,120 --> 00:04:13,640 Speaker 7: out to customers. What you're going to see in the 79 00:04:13,680 --> 00:04:17,440 Speaker 7: coming years as that ramps up is just a bigger 80 00:04:17,480 --> 00:04:19,320 Speaker 7: and bigger capability. 81 00:04:18,720 --> 00:04:20,160 Speaker 1: To do that delivery. 82 00:04:20,960 --> 00:04:23,360 Speaker 7: It looks based on the second quarter results like there 83 00:04:23,400 --> 00:04:25,240 Speaker 7: is a lot of demand for that, but time will tell. 84 00:04:26,360 --> 00:04:29,440 Speaker 3: Brendan great analysis. We thank you so much, Brendan, case 85 00:04:29,440 --> 00:04:31,320 Speaker 3: of course of Bloomberg. Meanwhile, we want to dig into 86 00:04:31,320 --> 00:04:34,560 Speaker 3: some of the advertising flare that Brendan was just outlining 87 00:04:34,600 --> 00:04:37,440 Speaker 3: there and ultimately how Walmart is digging up its e 88 00:04:37,480 --> 00:04:41,200 Speaker 3: commerce proess. Alistair Macleanforman's with a CEO of Tigametrics is 89 00:04:41,240 --> 00:04:45,080 Speaker 3: the leading optimization platform for marketplace brands ten million dollars 90 00:04:45,080 --> 00:04:47,880 Speaker 3: plus in total annual ad sales optimized. And I know, 91 00:04:48,480 --> 00:04:53,040 Speaker 3: you know Walmart intimately Alistair what in the playbook. 92 00:04:52,560 --> 00:04:53,760 Speaker 5: Is really working for them? 93 00:04:53,800 --> 00:04:56,839 Speaker 3: How are they managing to lure over new brands for example. 94 00:04:58,440 --> 00:04:59,679 Speaker 8: Well, they're brilliant results. 95 00:05:00,080 --> 00:05:03,320 Speaker 9: Walmart's certainly gaining share and taken metrics, we're using AI 96 00:05:03,400 --> 00:05:06,560 Speaker 9: to optimize for the largest share of brands selling on. 97 00:05:06,520 --> 00:05:07,640 Speaker 8: Walmart dot com. 98 00:05:07,839 --> 00:05:09,680 Speaker 9: We've got billions of dollars of data, so we knew 99 00:05:09,720 --> 00:05:13,800 Speaker 9: these results would be good. The marketplace seller volume is accelerating. 100 00:05:13,880 --> 00:05:17,840 Speaker 9: They're building a flywheel of selection of thousands of brands, 101 00:05:18,279 --> 00:05:20,960 Speaker 9: and they're turning into a technology powerhouse. They're investing in 102 00:05:21,000 --> 00:05:24,080 Speaker 9: the APIs building a lot of tech to enable AI 103 00:05:24,200 --> 00:05:26,919 Speaker 9: companies just like us to go even faster. And I 104 00:05:26,920 --> 00:05:29,839 Speaker 9: think that's a big part of it, the technology. 105 00:05:30,640 --> 00:05:34,880 Speaker 4: You know, we kick off the show, right Carrie Bloomberg Technology, 106 00:05:34,920 --> 00:05:36,640 Speaker 4: and we start talking about Walmart and some of the 107 00:05:36,680 --> 00:05:39,320 Speaker 4: audience out there going Walmart, what are you talking about? 108 00:05:39,480 --> 00:05:44,360 Speaker 4: But e commerce advertising? And Alistairs just brought in AI. Alistair, 109 00:05:45,120 --> 00:05:48,479 Speaker 4: what's your read on how competent Walmart is in those 110 00:05:48,520 --> 00:05:51,839 Speaker 4: fields relative to Amazon, because in the context of ADS 111 00:05:51,880 --> 00:05:55,400 Speaker 4: or AI, we've been talking about Amazon for a while. Walmart, 112 00:05:55,400 --> 00:05:56,480 Speaker 4: it's a newer conversation. 113 00:05:59,000 --> 00:06:02,440 Speaker 8: Well, are doing very very well. They're investing a ton. 114 00:06:03,080 --> 00:06:06,080 Speaker 9: Seth de Laire is the cro over at Walmart and 115 00:06:06,120 --> 00:06:10,400 Speaker 9: the Walmart Connects segment, that's the Walmart ADS business is 116 00:06:10,880 --> 00:06:14,000 Speaker 9: really growing leaps and bounds in terms of the technology 117 00:06:14,400 --> 00:06:17,880 Speaker 9: and the investment, and you know, they're really moving very quickly, 118 00:06:18,200 --> 00:06:18,960 Speaker 9: very very quickly. 119 00:06:20,680 --> 00:06:24,360 Speaker 4: It's interesting as well, Carrie, consumers in this equation, why 120 00:06:24,520 --> 00:06:26,920 Speaker 4: e commerce jump in the corrider twenty four percent is 121 00:06:26,960 --> 00:06:27,520 Speaker 4: a big move? 122 00:06:27,920 --> 00:06:30,280 Speaker 3: Yeah, and I mean tell us the read on data 123 00:06:30,320 --> 00:06:33,560 Speaker 3: there alistair as to whether, well some of your underlying 124 00:06:33,600 --> 00:06:36,040 Speaker 3: thoughts on whether the consumer is resilient at this moment, 125 00:06:36,040 --> 00:06:38,159 Speaker 3: whether it's just Walmart has got all the right price 126 00:06:38,200 --> 00:06:40,960 Speaker 3: points for any type of consumer in this moment, because 127 00:06:41,120 --> 00:06:43,240 Speaker 3: all of us are trying to read these tea leaves. Yes, 128 00:06:43,279 --> 00:06:46,720 Speaker 3: the numbers look great, but both Target and Walmart sounded 129 00:06:46,800 --> 00:06:50,400 Speaker 3: pretty conservative and indeed a little cautious about the consumer 130 00:06:50,480 --> 00:06:50,880 Speaker 3: right now. 131 00:06:51,960 --> 00:06:52,200 Speaker 2: Yeah. 132 00:06:52,240 --> 00:06:54,159 Speaker 9: I think one of the most interesting things of the 133 00:06:54,240 --> 00:06:56,919 Speaker 9: data we see is the change in the Walmart consumer. 134 00:06:57,000 --> 00:07:01,520 Speaker 9: I think there's this stigma that maybe Walmart is all 135 00:07:01,560 --> 00:07:06,680 Speaker 9: about older shoppers or traditional shoppers, but the growth in 136 00:07:06,720 --> 00:07:11,120 Speaker 9: the modern, younger consumer that's opening up their wallets. They've 137 00:07:11,160 --> 00:07:13,960 Speaker 9: got one of the fastest growing apps on the mobile 138 00:07:14,000 --> 00:07:17,239 Speaker 9: side of things, and there's a really really big play 139 00:07:17,440 --> 00:07:21,680 Speaker 9: around omni channel. The fact that Walmart dot Com can 140 00:07:21,960 --> 00:07:26,280 Speaker 9: really do things that other marketplaces cannot is really really powerful, 141 00:07:26,320 --> 00:07:28,080 Speaker 9: and they can connect the dots between. 142 00:07:27,920 --> 00:07:30,800 Speaker 8: Online and offline, and that's what every brand wants. That 143 00:07:30,920 --> 00:07:31,960 Speaker 8: omni channel player. 144 00:07:32,920 --> 00:07:36,440 Speaker 3: Just remind us how international allmarts as well, because for me, 145 00:07:36,480 --> 00:07:39,760 Speaker 3: it's a purely American name, but no, it's in India, 146 00:07:39,960 --> 00:07:42,920 Speaker 3: it's in Mexico. Were seeing strength. I mean, we will 147 00:07:43,280 --> 00:07:45,320 Speaker 3: as UK three voices here. We all knew it for 148 00:07:45,360 --> 00:07:46,600 Speaker 3: having had Asda at one point. 149 00:07:48,000 --> 00:07:48,880 Speaker 8: Yes, absolutely. 150 00:07:49,440 --> 00:07:49,640 Speaker 1: You know. 151 00:07:49,720 --> 00:07:52,840 Speaker 9: There's of course the flip cart asset they have in India. 152 00:07:53,080 --> 00:07:55,720 Speaker 8: I think what is very very interesting from an international 153 00:07:55,760 --> 00:07:57,720 Speaker 8: perspective is their focus. 154 00:07:57,400 --> 00:08:02,440 Speaker 10: On recruiting and acquiring those marketplace sellers from overseas to 155 00:08:02,600 --> 00:08:05,720 Speaker 10: sell into Warmlot dot com. That was one of the 156 00:08:05,720 --> 00:08:10,680 Speaker 10: biggest drive of Amazon's success in its marketplace the flywheel 157 00:08:10,720 --> 00:08:13,760 Speaker 10: of sellers. So we're seeing Walmart doing a lot globally 158 00:08:14,240 --> 00:08:18,360 Speaker 10: attracting brands from overseas, very very similar to the Amazon playbook, 159 00:08:18,400 --> 00:08:19,760 Speaker 10: and it is suddenly working. 160 00:08:21,600 --> 00:08:25,920 Speaker 4: Alista, if you're an advertiser, why is Walmart an attractive 161 00:08:25,960 --> 00:08:28,640 Speaker 4: option relative to any other e commerce name? 162 00:08:29,960 --> 00:08:31,160 Speaker 8: Well, I can tell you. 163 00:08:31,360 --> 00:08:33,440 Speaker 9: The data that we have is the return on ad 164 00:08:33,480 --> 00:08:36,599 Speaker 9: spend or row ASS, which is the primary metric that 165 00:08:36,800 --> 00:08:41,040 Speaker 9: advertisers use to determine their ROI. Those results on Walmart 166 00:08:41,080 --> 00:08:44,760 Speaker 9: and the Walmart Connect platform are fantastic. It is very 167 00:08:44,840 --> 00:08:48,760 Speaker 9: very profitable to sell and advertise on Walmart dot com 168 00:08:49,080 --> 00:08:51,959 Speaker 9: and really that's what's attracting. 169 00:08:51,520 --> 00:08:53,000 Speaker 8: More and more of sellers coming over. 170 00:08:53,480 --> 00:08:57,360 Speaker 9: It's less competitive, it's more of a greenfield. And as 171 00:08:57,400 --> 00:09:00,760 Speaker 9: I said, the ROI, the row as on ads fund 172 00:09:00,960 --> 00:09:04,520 Speaker 9: is really fasting class and we expect that to continue. 173 00:09:05,880 --> 00:09:08,559 Speaker 4: All right, Alistair MacLean Foreman have taken metrics new name 174 00:09:08,600 --> 00:09:10,600 Speaker 4: to the show. Thank you for joining us here on 175 00:09:10,679 --> 00:09:14,439 Speaker 4: Bloomberg Technology and our Speaking of Walmart, China's Internet regulator 176 00:09:14,760 --> 00:09:17,880 Speaker 4: are reaching out to foreign firms, including the retail giant. 177 00:09:18,000 --> 00:09:21,440 Speaker 4: They plan to discuss ways to navigate Beijing's new data 178 00:09:21,520 --> 00:09:25,680 Speaker 4: security rules in an effort to reassure multinationals worried about 179 00:09:25,720 --> 00:09:36,360 Speaker 4: their ability to operate in China under these latest regulations. 180 00:09:38,000 --> 00:09:41,200 Speaker 3: Earning still thinking fast setstick into Cisco now shares moving 181 00:09:41,240 --> 00:09:44,520 Speaker 3: higher after delivering results that actually pointed to some resiliency 182 00:09:44,559 --> 00:09:47,280 Speaker 3: there and demand. CEO Chuck Robins really focusing on the 183 00:09:47,280 --> 00:09:50,760 Speaker 3: company's AI potential. Who'd have thought it, saying, quote, this 184 00:09:50,800 --> 00:09:53,480 Speaker 3: is a huge opportunity for Cisco. We are laser focused 185 00:09:53,480 --> 00:09:55,640 Speaker 3: on leading and winning in this space. 186 00:09:56,080 --> 00:09:58,400 Speaker 5: Let's get more on the results. Nick Turner's with us, 187 00:09:58,440 --> 00:09:59,000 Speaker 5: and Nick, I. 188 00:09:59,000 --> 00:10:01,720 Speaker 3: Mean, how do they profit, how do they capitalize on 189 00:10:01,720 --> 00:10:02,480 Speaker 3: this AI moment? 190 00:10:03,840 --> 00:10:05,160 Speaker 1: Well, it's still pretty early days. 191 00:10:05,200 --> 00:10:07,240 Speaker 11: I mean they did point to five hundred million dollars 192 00:10:07,240 --> 00:10:10,120 Speaker 11: in sales from AI products. I mean we're talking about 193 00:10:10,120 --> 00:10:12,800 Speaker 11: stuff that sort of helps things speed through the process 194 00:10:12,840 --> 00:10:16,720 Speaker 11: of training large language models and general of AI. So 195 00:10:17,080 --> 00:10:18,760 Speaker 11: just we've been focused a lot on the kind of 196 00:10:18,800 --> 00:10:21,640 Speaker 11: chips that people need to run that from Nvidia and others. 197 00:10:21,960 --> 00:10:23,319 Speaker 1: But obviously network's part of that. 198 00:10:23,559 --> 00:10:26,480 Speaker 11: Networking is part of that equation as well, and they 199 00:10:26,520 --> 00:10:28,400 Speaker 11: expect to sort of be a big player in the space. 200 00:10:29,280 --> 00:10:31,480 Speaker 3: Let's geek out for a moment, because Ed, you're a 201 00:10:31,520 --> 00:10:33,640 Speaker 3: manner gets excited about networking gear, aren't you. 202 00:10:33,920 --> 00:10:36,280 Speaker 4: Yeah, Like you know, Cisco's like the old school name 203 00:10:36,320 --> 00:10:38,240 Speaker 4: is so they can Valley write down in San Jose. 204 00:10:38,440 --> 00:10:41,680 Speaker 4: But the logic's really clear, as all of the hyperscalers 205 00:10:42,160 --> 00:10:45,280 Speaker 4: cloud providers want to offer more compute, they need more 206 00:10:45,320 --> 00:10:48,160 Speaker 4: networking gear for the data centers, and so Cisco like 207 00:10:48,240 --> 00:10:50,720 Speaker 4: sweeps in, like we got it, guys, We're the biggest 208 00:10:50,760 --> 00:10:51,679 Speaker 4: networking gear player. 209 00:10:51,720 --> 00:10:52,400 Speaker 6: No worries. 210 00:10:52,800 --> 00:10:54,839 Speaker 4: And what was interesting though, Nick, is that you look 211 00:10:54,880 --> 00:10:56,840 Speaker 4: at the forecast for the rest of the year, it 212 00:10:56,880 --> 00:10:58,880 Speaker 4: didn't really have much to do with AI. They're kind 213 00:10:58,880 --> 00:11:01,240 Speaker 4: of coming out of a period they were playing catch 214 00:11:01,320 --> 00:11:02,679 Speaker 4: up anyway from the pandemic. 215 00:11:04,120 --> 00:11:06,640 Speaker 11: No, yeah, I mean if you look at their sales 216 00:11:06,840 --> 00:11:09,720 Speaker 11: forecast itself, it looks pretty bad. It's a really terrible 217 00:11:09,720 --> 00:11:12,400 Speaker 11: comparison with the last year of the year they just 218 00:11:12,480 --> 00:11:15,480 Speaker 11: ended because they had this huge backlog that built up 219 00:11:15,480 --> 00:11:19,360 Speaker 11: when there was no supplies available, so sales surged eleven 220 00:11:19,400 --> 00:11:21,640 Speaker 11: percent and now this year they're only going to grow 221 00:11:21,679 --> 00:11:24,520 Speaker 11: at two percent or so. So it's obviously a big comdown. 222 00:11:24,600 --> 00:11:27,559 Speaker 11: But the executives have been like, look, we knew kind 223 00:11:27,600 --> 00:11:30,040 Speaker 11: of this was going to happen, and if you look 224 00:11:30,080 --> 00:11:33,280 Speaker 11: at sort of growth in a more longer term perspective, 225 00:11:33,640 --> 00:11:34,760 Speaker 11: it's not a huge issue. 226 00:11:35,600 --> 00:11:38,480 Speaker 4: We're just entering the first quarter of FIS school twenty 227 00:11:38,520 --> 00:11:41,200 Speaker 4: twenty four, so we just finished FIST school twenty twenty three. 228 00:11:41,240 --> 00:11:42,640 Speaker 6: To a point, Chuck. 229 00:11:42,440 --> 00:11:46,240 Speaker 4: Robbins talks a lot about recurring revenue. Just explain of 230 00:11:46,280 --> 00:11:49,280 Speaker 4: recurring revenue and what Cisco's doing to make money elsewhere. 231 00:11:50,720 --> 00:11:53,880 Speaker 11: Well, so, traditionally, as you said, this was the massive 232 00:11:53,920 --> 00:11:56,360 Speaker 11: networking gear company that could kind of do no wrong 233 00:11:56,400 --> 00:11:58,200 Speaker 11: in the old days of Silicon value, and they would 234 00:11:58,200 --> 00:12:01,319 Speaker 11: sell these systems everybody needed them, you know, big hardware, 235 00:12:01,360 --> 00:12:04,360 Speaker 11: expensive hardware, and maybe they would sell you some software 236 00:12:04,400 --> 00:12:06,920 Speaker 11: with that. The problem is is that it does tend 237 00:12:06,920 --> 00:12:09,959 Speaker 11: to be lumpy, as they say, in terms of the sales, 238 00:12:10,000 --> 00:12:11,360 Speaker 11: like you'll have a lot of sales one quarter and 239 00:12:11,360 --> 00:12:13,480 Speaker 11: then less the next. They're trying to get people more 240 00:12:13,480 --> 00:12:16,920 Speaker 11: on a subscription model, so you're paying a certain amount 241 00:12:16,960 --> 00:12:19,160 Speaker 11: each quarter, and it's just it's more reliable. 242 00:12:20,840 --> 00:12:24,200 Speaker 3: Where are analysts on the view of Cisco. Where is 243 00:12:25,280 --> 00:12:28,240 Speaker 3: a sentiment more broadly on this particular name. 244 00:12:28,240 --> 00:12:29,200 Speaker 5: What's the run up been like. 245 00:12:30,400 --> 00:12:32,920 Speaker 11: Well, I think people have been pretty excited about sort 246 00:12:32,920 --> 00:12:35,760 Speaker 11: of the last year or so and just you know, 247 00:12:35,840 --> 00:12:38,679 Speaker 11: optimistic that this is a you know, if you look 248 00:12:38,720 --> 00:12:41,000 Speaker 11: at a year or a year or you know, the 249 00:12:41,000 --> 00:12:43,080 Speaker 11: past five years or so, it's been a little bit 250 00:12:43,160 --> 00:12:46,240 Speaker 11: inconsistent in general in terms of growth. I think they 251 00:12:46,320 --> 00:12:48,640 Speaker 11: obviously Cisco had this moment in the last year where 252 00:12:48,640 --> 00:12:51,680 Speaker 11: they really were able to you know, turn the engines 253 00:12:51,720 --> 00:12:55,479 Speaker 11: on full throttle, and I think people are more optimistic 254 00:12:55,520 --> 00:12:58,160 Speaker 11: than might be expected about the coming year, even though 255 00:12:58,160 --> 00:12:59,439 Speaker 11: sales are decelerating. 256 00:13:00,520 --> 00:13:04,280 Speaker 4: The final component we wrote about was security as a 257 00:13:04,559 --> 00:13:06,800 Speaker 4: sort of standalone mark of Cisco. Really quick, Nick, what 258 00:13:06,800 --> 00:13:08,040 Speaker 4: did they say about security? 259 00:13:09,960 --> 00:13:11,800 Speaker 11: It was one of those you know, there's three things 260 00:13:11,800 --> 00:13:16,040 Speaker 11: they sort of called out AI, data center security, and 261 00:13:16,120 --> 00:13:19,400 Speaker 11: obviously those are all three huge buzzy things that people 262 00:13:19,400 --> 00:13:22,640 Speaker 11: want to see you growing in, and they said they've 263 00:13:22,720 --> 00:13:24,960 Speaker 11: kind of made progress in all of them. I you know, 264 00:13:25,080 --> 00:13:26,760 Speaker 11: I mean, I don't know if they were quite as 265 00:13:26,760 --> 00:13:29,400 Speaker 11: specific on security in terms of how much progress lately, 266 00:13:30,440 --> 00:13:33,120 Speaker 11: but it's definitely going to be a key thing for them. 267 00:13:33,400 --> 00:13:34,880 Speaker 5: Nick. Great to catch up with you. 268 00:13:34,920 --> 00:13:37,760 Speaker 3: Thank you one of their shunning lights today in terms 269 00:13:37,760 --> 00:13:40,240 Speaker 3: of earnings, all things Cisco mean while coming. 270 00:13:40,160 --> 00:13:41,880 Speaker 5: Up look good all things lobal. 271 00:13:41,600 --> 00:13:45,400 Speaker 3: Smartphone market there is heading for the worst year in 272 00:13:45,480 --> 00:13:46,240 Speaker 3: a decade. 273 00:13:46,559 --> 00:13:49,160 Speaker 5: We'll discuss why. Next to speaking of smartphones. 274 00:13:48,679 --> 00:13:51,880 Speaker 3: Look Apple a big tug on the broader NASDAC and 275 00:13:51,920 --> 00:13:54,280 Speaker 3: as that one hundred today, it's really pulling. 276 00:13:54,040 --> 00:13:56,959 Speaker 5: Down from a points perspective. Disappointing earnings report. 277 00:13:56,760 --> 00:13:58,960 Speaker 3: Goes back a week or so, but really the pressure 278 00:13:59,000 --> 00:14:00,920 Speaker 3: is off this company now to liver it's latest version 279 00:14:00,960 --> 00:14:03,400 Speaker 3: of the iPhone. There's plenty of juicy content on the 280 00:14:03,440 --> 00:14:06,760 Speaker 3: Bloomberg about this about how well in about six weeks time, 281 00:14:06,840 --> 00:14:08,680 Speaker 3: we've got to be eyeing up what the tech gine 282 00:14:08,720 --> 00:14:10,880 Speaker 3: is going to be doing, having reported its third straightcord 283 00:14:11,000 --> 00:14:14,480 Speaker 3: declining sales. We've got some analyst expectations out there, shares 284 00:14:14,720 --> 00:14:16,599 Speaker 3: expected to rise ahead of the event. 285 00:14:16,440 --> 00:14:17,640 Speaker 5: Citing historical data. 286 00:14:18,200 --> 00:14:20,880 Speaker 3: But look tell you what's also not rising ed check 287 00:14:20,880 --> 00:14:22,240 Speaker 3: out what's happening in the world of crypto. I just 288 00:14:22,240 --> 00:14:23,920 Speaker 3: want to shine a light and that really just before 289 00:14:23,960 --> 00:14:26,240 Speaker 3: the show, we took another leg lower on the world 290 00:14:26,280 --> 00:14:29,600 Speaker 3: of bitcoin. And this is more about market sentiment. This 291 00:14:29,720 --> 00:14:32,880 Speaker 3: is more about bomb markets, selling off yields, driving higher 292 00:14:32,920 --> 00:14:34,240 Speaker 3: the dollar in focus. 293 00:14:36,960 --> 00:14:39,520 Speaker 6: Yeah, rate concerns the top story. This is Bloomberg. 294 00:14:50,160 --> 00:14:52,640 Speaker 4: Let's get your daily dose of talking tech. First up, 295 00:14:52,640 --> 00:14:56,320 Speaker 4: Global smartphone shipments ahead for their worst year in over 296 00:14:56,360 --> 00:14:59,720 Speaker 4: a decade. Shipments expected to drop six percent year over year, 297 00:15:00,000 --> 00:15:03,200 Speaker 4: according to the latest Counterpoint Research estimate. That's due to 298 00:15:03,200 --> 00:15:06,880 Speaker 4: a disappointing demand, particularly in the US, but a deteriorating 299 00:15:07,200 --> 00:15:10,560 Speaker 4: Chinese economy plus a smartphone sales dwindle. The top three 300 00:15:10,720 --> 00:15:14,360 Speaker 4: US wireless carrier carriers have lost billions in revenue. AT 301 00:15:14,440 --> 00:15:17,800 Speaker 4: and T, T Mobile, and Verizon have collectively lost nearly 302 00:15:17,880 --> 00:15:21,000 Speaker 4: five billion dollars in equipment sales over the past twelve 303 00:15:21,000 --> 00:15:23,000 Speaker 4: months compared to the previous year. 304 00:15:23,040 --> 00:15:24,440 Speaker 6: And it's not just smartphones. 305 00:15:24,680 --> 00:15:28,720 Speaker 4: The world's biggest PC maker, Lenovo, missed profit estimates for 306 00:15:28,760 --> 00:15:32,520 Speaker 4: the second quarter, underscoring the depth of the global electronics 307 00:15:32,560 --> 00:15:33,440 Speaker 4: market downturn. 308 00:15:33,560 --> 00:15:36,280 Speaker 3: Caroline, I mean, let's just talk about missing expectations for 309 00:15:36,320 --> 00:15:38,440 Speaker 3: a moment. Ed We're going to pivot away to Europe 310 00:15:38,600 --> 00:15:42,240 Speaker 3: because Adian wiping out more than thirteen billion, in fact, 311 00:15:42,280 --> 00:15:44,760 Speaker 3: I think it's twenty in the end market value falling 312 00:15:44,800 --> 00:15:47,600 Speaker 3: over as you'll see thirty almost forty percent in a 313 00:15:47,640 --> 00:15:50,560 Speaker 3: training day after missing first half revenue estimates. For more 314 00:15:50,640 --> 00:15:53,600 Speaker 3: on the Dutch payment processing company and on the competition 315 00:15:53,640 --> 00:15:56,080 Speaker 3: that's probably rife over here in the US, bloombergs Henry 316 00:15:56,120 --> 00:15:58,040 Speaker 3: Wren is joining us as well as Sarah Jacob is 317 00:15:58,120 --> 00:16:00,720 Speaker 3: brilliant to have you both with us, And I mean, 318 00:16:00,960 --> 00:16:04,080 Speaker 3: first and foremost, Sarah, you cover the company from a 319 00:16:04,120 --> 00:16:06,240 Speaker 3: real focal point over there in Amsterdam. 320 00:16:06,440 --> 00:16:08,200 Speaker 5: What did you make of these numbers? 321 00:16:08,240 --> 00:16:10,840 Speaker 3: Why was it such a shock this sort of focus 322 00:16:10,880 --> 00:16:13,920 Speaker 3: for growth instead of profitability and obvious with everyone else. 323 00:16:15,080 --> 00:16:16,120 Speaker 12: Yes, that's right. 324 00:16:16,240 --> 00:16:19,360 Speaker 13: So Adune reported earnings for the first half that bes 325 00:16:19,360 --> 00:16:24,640 Speaker 13: testaments on both revenue and margins. It boasted its slowest 326 00:16:24,680 --> 00:16:27,840 Speaker 13: net revenue growth since it was listed, and the company 327 00:16:27,880 --> 00:16:32,440 Speaker 13: attributed this to a weaker economic climate with higher interest rates, inflation, 328 00:16:32,560 --> 00:16:37,040 Speaker 13: and particularly increased price competition in North America. The other 329 00:16:37,080 --> 00:16:39,440 Speaker 13: thing with Adian is that they continue to hire, and 330 00:16:39,480 --> 00:16:43,040 Speaker 13: they had about five hundred and fifty employees or so 331 00:16:43,200 --> 00:16:45,320 Speaker 13: in the first half, and that too has weighed on 332 00:16:45,440 --> 00:16:46,560 Speaker 13: margins the first half. 333 00:16:47,680 --> 00:16:50,400 Speaker 4: Henry, let's bring the shares up and talk about this 334 00:16:50,480 --> 00:16:53,600 Speaker 4: big drop. What's the Street saying because forty percent drop 335 00:16:53,720 --> 00:16:55,160 Speaker 4: essentially biggest drop on record. 336 00:16:56,280 --> 00:16:57,040 Speaker 1: Yeah, definitely. 337 00:16:57,120 --> 00:17:00,320 Speaker 14: So when you think about a growth stock like and 338 00:17:00,360 --> 00:17:02,840 Speaker 14: the scariest moment that you can have is when the 339 00:17:02,960 --> 00:17:05,320 Speaker 14: three things that it's no longer a growth stock. So 340 00:17:05,720 --> 00:17:08,080 Speaker 14: we saw these kind of things happening with Mata before 341 00:17:08,119 --> 00:17:12,600 Speaker 14: the Facebook parent when it's flagship at Apps hit the bump, 342 00:17:13,080 --> 00:17:15,000 Speaker 14: and we see this kind of thing is happening with 343 00:17:15,080 --> 00:17:19,280 Speaker 14: Alien for sure as well, because we saw North American 344 00:17:19,320 --> 00:17:24,080 Speaker 14: revenue the growth actually halved during the first half. Now, 345 00:17:24,320 --> 00:17:28,680 Speaker 14: so the issue is because we have seen several quotas 346 00:17:28,840 --> 00:17:32,119 Speaker 14: so adding and missed earnings sessments, but it's the first 347 00:17:32,119 --> 00:17:35,040 Speaker 14: time that we've seen seen its IPO that it missed 348 00:17:35,640 --> 00:17:39,960 Speaker 14: processing revenue estimates, processing volume estimate as well. And it's 349 00:17:40,000 --> 00:17:42,480 Speaker 14: not just a myss, it's a miss of eight percent. 350 00:17:42,920 --> 00:17:45,960 Speaker 14: So the question is whether the company can revive its growth. 351 00:17:45,960 --> 00:17:49,160 Speaker 14: We know the company has reiterated its medium term guidance, 352 00:17:49,440 --> 00:17:52,280 Speaker 14: but there is now some concerns on whether the company 353 00:17:52,280 --> 00:17:56,719 Speaker 14: can achieve it's growth trajectory anymore given the competition, especially 354 00:17:56,760 --> 00:17:57,640 Speaker 14: in the US. 355 00:17:57,480 --> 00:18:00,960 Speaker 3: And Sarah dwell on that for a moment, they have 356 00:18:01,160 --> 00:18:04,919 Speaker 3: spoken out about this being this hiring, this focus on 357 00:18:05,040 --> 00:18:07,320 Speaker 3: growth still, which is at honest with basically everyone else 358 00:18:07,359 --> 00:18:10,119 Speaker 3: in the market. It's the longer term potential here. But 359 00:18:11,119 --> 00:18:13,360 Speaker 3: is this a cultural focus that they've got at the moment, 360 00:18:13,400 --> 00:18:15,280 Speaker 3: the fact they want to be hiring in this market. 361 00:18:15,440 --> 00:18:17,960 Speaker 3: How do you think they managed to convince investors? 362 00:18:18,840 --> 00:18:22,080 Speaker 13: Well, Adian has been very vocal about the fact that 363 00:18:22,119 --> 00:18:25,480 Speaker 13: they are in an investment mode. They're preparing the company 364 00:18:25,520 --> 00:18:28,800 Speaker 13: for the next growth phase. I mean they are. They 365 00:18:28,920 --> 00:18:33,359 Speaker 13: listed only a few years back. So they hired about 366 00:18:33,920 --> 00:18:36,480 Speaker 13: two hundred employees last year and they expect to hire 367 00:18:36,520 --> 00:18:39,200 Speaker 13: a similar amount this year. So as I said, about 368 00:18:39,240 --> 00:18:41,960 Speaker 13: five hundred and fifty fifty one employees in the first 369 00:18:41,960 --> 00:18:46,080 Speaker 13: half itself. So the company has has said that this 370 00:18:46,119 --> 00:18:49,000 Speaker 13: is their focus, although they did mention that they will 371 00:18:49,040 --> 00:18:50,840 Speaker 13: slow down hiring from next year. 372 00:18:51,200 --> 00:18:52,320 Speaker 14: So yes, it is. 373 00:18:52,359 --> 00:18:55,240 Speaker 13: It is in contrast with a lot of their peers 374 00:18:55,280 --> 00:18:58,919 Speaker 13: who have announced job cuts in the past year or so. 375 00:19:00,000 --> 00:19:02,960 Speaker 4: All right, Bloomberg's Henry Rahn and Sarah Jacobut in Amsterdam, 376 00:19:03,040 --> 00:19:05,920 Speaker 4: thank you so much, carry team coverage. But the reason 377 00:19:05,920 --> 00:19:08,400 Speaker 4: I love this story is we're talking about a European 378 00:19:08,440 --> 00:19:09,680 Speaker 4: tech name that's quite big. 379 00:19:10,040 --> 00:19:11,520 Speaker 6: You read Henry's street rap. 380 00:19:11,560 --> 00:19:14,399 Speaker 4: What they're worried about is that it's not competing against 381 00:19:14,440 --> 00:19:17,359 Speaker 4: the big US names for merchants attention. 382 00:19:17,680 --> 00:19:20,200 Speaker 3: And whether or not they're just not willing to fight 383 00:19:20,280 --> 00:19:22,439 Speaker 3: on a price perspective at the moment, whether or not 384 00:19:22,480 --> 00:19:25,439 Speaker 3: it's there, are they actually losing customers, customers that just 385 00:19:25,480 --> 00:19:27,720 Speaker 3: are using them a little bit less, they're not, of course, 386 00:19:27,760 --> 00:19:29,720 Speaker 3: in this economic environment, as willing to be. 387 00:19:29,680 --> 00:19:31,440 Speaker 5: Paying up for their higher priced products. 388 00:19:31,560 --> 00:19:34,680 Speaker 3: It's really interesting as to also how the competition coalesces, 389 00:19:34,760 --> 00:19:37,080 Speaker 3: of course, all eyes and what is the still private 390 00:19:37,160 --> 00:19:40,359 Speaker 3: juggernaut that is Stripe and how much they managed to 391 00:19:40,359 --> 00:19:41,560 Speaker 3: take in terms of market share. 392 00:19:42,160 --> 00:19:44,600 Speaker 4: Yeah, but a commitment to hiring while the US peers 393 00:19:44,600 --> 00:19:56,520 Speaker 4: are trimming jobs was quite interesting. Welcome back to Bloomberg Technology, 394 00:19:56,600 --> 00:19:58,560 Speaker 4: Ed love loow here in San Francisco. 395 00:19:58,080 --> 00:19:59,239 Speaker 5: And I'm Karenine hiding New York. 396 00:19:59,280 --> 00:20:01,239 Speaker 3: Let'stig into the mark it halfway through this trading day 397 00:20:01,280 --> 00:20:03,879 Speaker 3: and actually, well then as that one hundred, the bigger 398 00:20:03,880 --> 00:20:07,080 Speaker 3: benchmark of the mighty powers of technology, actually you sort 399 00:20:07,080 --> 00:20:09,960 Speaker 3: of trading flat. We've seen the Nasdaq more broadly, the 400 00:20:09,960 --> 00:20:13,600 Speaker 3: benchmark under pressure today. Sentiment has been changing. We're worrying about, well, 401 00:20:13,640 --> 00:20:16,120 Speaker 3: the backup in yeals, the tenure yield is up six 402 00:20:16,680 --> 00:20:19,040 Speaker 3: call it five to six basis points at the moment 403 00:20:19,080 --> 00:20:21,359 Speaker 3: on the ten uere we really have seen this sudden 404 00:20:21,359 --> 00:20:23,320 Speaker 3: shift higher in terms of longer term rates. 405 00:20:23,359 --> 00:20:25,800 Speaker 5: How far they're going much higher? And indeed, well. 406 00:20:25,640 --> 00:20:28,600 Speaker 3: That signals about the Federal Reserve because well, this resilient 407 00:20:28,720 --> 00:20:31,000 Speaker 3: US economy means perhaps they have to keep those rates 408 00:20:31,040 --> 00:20:31,680 Speaker 3: higher for longer. 409 00:20:31,680 --> 00:20:32,879 Speaker 5: What does that mean for risk assets? 410 00:20:32,880 --> 00:20:35,120 Speaker 3: Well, what it means so this risk asset, which is bitcoin, 411 00:20:35,520 --> 00:20:37,560 Speaker 3: is down for the day. In fact, we're breaking through 412 00:20:37,600 --> 00:20:40,680 Speaker 3: that level that we've sustained for the last couple of weeks. 413 00:20:40,720 --> 00:20:43,160 Speaker 3: The fact that we're now sub twenty eight thousand, we've 414 00:20:43,200 --> 00:20:45,359 Speaker 3: really been in this trading range and now we break 415 00:20:45,600 --> 00:20:48,400 Speaker 3: to the lowest in two months for bitcoin overall. Moving 416 00:20:48,480 --> 00:20:50,520 Speaker 3: on to some individual names, because the reason the NASA 417 00:20:50,560 --> 00:20:52,879 Speaker 3: one hundred is perhaps just managing to poke into the 418 00:20:52,920 --> 00:20:55,760 Speaker 3: green is one individual mover that I'll finish on. But first, 419 00:20:55,960 --> 00:20:58,919 Speaker 3: Apple a tug lower in terms of points on some 420 00:20:58,960 --> 00:21:01,200 Speaker 3: of the main benchmarks by a percentage point. 421 00:21:01,200 --> 00:21:01,320 Speaker 6: Ok. 422 00:21:01,640 --> 00:21:04,439 Speaker 3: We're all worrying about really how they can stimulate and 423 00:21:04,520 --> 00:21:07,280 Speaker 3: drive forward growth, particularly of iPhone sales after their recent 424 00:21:07,320 --> 00:21:10,800 Speaker 3: earnings disappointed. New Bank China light on well this particular player, 425 00:21:10,840 --> 00:21:14,720 Speaker 3: of course, over in Latin America a key fintech focus 426 00:21:14,800 --> 00:21:17,400 Speaker 3: on new banking, of course, were by five percent. Why 427 00:21:17,440 --> 00:21:19,399 Speaker 3: will the founder, the co founder and the CEO is 428 00:21:19,400 --> 00:21:21,480 Speaker 3: actually selling out some of his steak about three percent, 429 00:21:21,720 --> 00:21:24,400 Speaker 3: So we dive lower on this particular company. Cisco up 430 00:21:24,520 --> 00:21:27,240 Speaker 3: more than four percent. Ed this we outlined already the 431 00:21:27,359 --> 00:21:30,280 Speaker 3: numbers looking good. AI already half a billion dollars worth 432 00:21:30,280 --> 00:21:30,879 Speaker 3: of sales ed. 433 00:21:31,800 --> 00:21:35,200 Speaker 4: Yeah, Cisco really a story about the corporate world down 434 00:21:35,240 --> 00:21:36,480 Speaker 4: the chain investing in AI. 435 00:21:36,560 --> 00:21:38,280 Speaker 6: But what about the labor market? Let me bring you 436 00:21:38,280 --> 00:21:38,880 Speaker 6: this one. 437 00:21:39,040 --> 00:21:43,240 Speaker 4: A new survey finds that many Americans say automation could 438 00:21:43,320 --> 00:21:47,560 Speaker 4: easily replace their jobs. Younger workers, particularly black people and 439 00:21:47,680 --> 00:21:52,000 Speaker 4: Hispanic Americans, feel most threatened by AI in the workplace 440 00:21:52,280 --> 00:21:56,200 Speaker 4: compared to white counterparts. Some three quarters of those polled 441 00:21:56,680 --> 00:22:00,080 Speaker 4: expect increased use of automation and AI to lead to 442 00:22:00,160 --> 00:22:03,600 Speaker 4: more unemployment, with women more inclined to see that than men. 443 00:22:03,720 --> 00:22:06,520 Speaker 4: That said, the pole also found that most Americans believe 444 00:22:06,760 --> 00:22:09,800 Speaker 4: the increased use of the technology will generally be a 445 00:22:09,840 --> 00:22:12,359 Speaker 4: good thing for workers. So some slight contradiction in that 446 00:22:12,480 --> 00:22:13,000 Speaker 4: data set. 447 00:22:13,160 --> 00:22:15,679 Speaker 3: Yeah, wow, I mean it just outlines though, doesn't it 448 00:22:15,760 --> 00:22:18,360 Speaker 3: end that there are anxieties deep but there are also 449 00:22:18,359 --> 00:22:19,760 Speaker 3: so many opportunities. 450 00:22:19,240 --> 00:22:20,800 Speaker 5: When it comes to artificial intelligence. 451 00:22:20,800 --> 00:22:22,800 Speaker 3: And in fact, we've got the perfect guest to talk 452 00:22:22,840 --> 00:22:24,360 Speaker 3: around that anun mentionals with us. 453 00:22:24,440 --> 00:22:25,480 Speaker 5: The CEO of Arthur. 454 00:22:26,080 --> 00:22:28,359 Speaker 3: It's a company that focuses on ensuring that these AI 455 00:22:28,440 --> 00:22:31,840 Speaker 3: systems that many people are worrying about are actually well managed, 456 00:22:31,880 --> 00:22:35,120 Speaker 3: that they're deployed responsibly. And you've got this new tool 457 00:22:35,160 --> 00:22:38,240 Speaker 3: right that's helped and compare the plethora or large language 458 00:22:38,280 --> 00:22:40,360 Speaker 3: models that are out there and see whether they fit 459 00:22:40,480 --> 00:22:43,760 Speaker 3: your business case in particular, and just talk us through well, 460 00:22:43,840 --> 00:22:47,359 Speaker 3: ultimately this anxiety that lays deep that we're just talking about, 461 00:22:47,440 --> 00:22:52,120 Speaker 3: whether you think it's well vindicated, Well, I. 462 00:22:52,040 --> 00:22:54,520 Speaker 15: Think you know, it's very understandable, the anxiety, and when 463 00:22:54,520 --> 00:22:57,119 Speaker 15: you interact with chat, GPT or some of these other systems, 464 00:22:57,320 --> 00:22:59,960 Speaker 15: you really kind of it's breathtaking, right, how human like 465 00:23:00,080 --> 00:23:01,000 Speaker 15: the responses are. 466 00:23:01,440 --> 00:23:03,120 Speaker 1: But fortunately, look, AI has. 467 00:23:03,040 --> 00:23:06,159 Speaker 15: Been being deployed and you know, just a lot in 468 00:23:06,200 --> 00:23:10,920 Speaker 15: the last five years and accelerating every year and unemployments 469 00:23:10,920 --> 00:23:14,960 Speaker 15: still historically low, and there's like, there's very very little 470 00:23:15,000 --> 00:23:17,760 Speaker 15: evidence that AI is taking jobs. It is evolving jobs, 471 00:23:17,800 --> 00:23:21,400 Speaker 15: it's evolving jobs quickly, but there's not mass job loss 472 00:23:21,400 --> 00:23:24,240 Speaker 15: coming from it that's been observed anywhere. 473 00:23:25,280 --> 00:23:26,960 Speaker 6: So we go back to the Cisco story. 474 00:23:27,000 --> 00:23:30,760 Speaker 4: What we know is that companies are investing either way, 475 00:23:30,840 --> 00:23:34,080 Speaker 4: they're thinking about the use of large language models. The 476 00:23:34,080 --> 00:23:36,639 Speaker 4: way that I see Arthur bench your product is like 477 00:23:36,680 --> 00:23:39,280 Speaker 4: when I'm buying a new laptop, right, I go into 478 00:23:39,359 --> 00:23:42,840 Speaker 4: a grid side by side, I compare processing power, I 479 00:23:42,920 --> 00:23:47,960 Speaker 4: compare the memory, I compare operating system. You're basically offering 480 00:23:48,000 --> 00:23:52,520 Speaker 4: the equivalent for companies to choose the best LM for them. 481 00:23:52,640 --> 00:23:53,280 Speaker 1: Yeah. Absolutely. 482 00:23:53,320 --> 00:23:55,480 Speaker 15: I think the important thing is that you're allowed you 483 00:23:55,480 --> 00:23:57,440 Speaker 15: can you can test it on exactly what you want 484 00:23:57,440 --> 00:23:59,040 Speaker 15: to do with it, right, And so the way you 485 00:23:59,119 --> 00:24:01,879 Speaker 15: use your laptop is different from maybe the way you know, 486 00:24:01,920 --> 00:24:04,480 Speaker 15: the way my high schooler does or the way my 487 00:24:04,600 --> 00:24:06,480 Speaker 15: father does, and so you want to know how this 488 00:24:06,560 --> 00:24:07,560 Speaker 15: is going to perform for me. 489 00:24:07,880 --> 00:24:11,000 Speaker 1: And so I have like my own kind of proprietary data. 490 00:24:10,680 --> 00:24:12,439 Speaker 15: That I want to feed into the LM, and I 491 00:24:12,480 --> 00:24:14,800 Speaker 15: want to ask it certain types of questions, put certain 492 00:24:14,840 --> 00:24:16,720 Speaker 15: types of prompts in that, and there's a lot of 493 00:24:16,720 --> 00:24:19,280 Speaker 15: really nuanced differences that can't be reduced down to just 494 00:24:19,359 --> 00:24:21,000 Speaker 15: a single number on a leaderboard. 495 00:24:21,080 --> 00:24:23,240 Speaker 1: And so that's where Arthur Bench comes in. Allows people 496 00:24:23,240 --> 00:24:24,560 Speaker 1: to really test. 497 00:24:24,359 --> 00:24:26,640 Speaker 15: Like the kinds of prompts that their users are providing 498 00:24:26,760 --> 00:24:29,120 Speaker 15: with their data and see how it performs. 499 00:24:30,280 --> 00:24:34,119 Speaker 4: No l l M Caroline Large language model, it's just 500 00:24:34,119 --> 00:24:37,560 Speaker 4: become a blanket term. But there's such variation between so 501 00:24:37,680 --> 00:24:40,919 Speaker 4: that the multiple billions of parameters large language model and 502 00:24:41,000 --> 00:24:42,160 Speaker 4: something for everyone out there. 503 00:24:42,280 --> 00:24:44,199 Speaker 3: Yeah, and I mean, I'll go back to when you 504 00:24:44,200 --> 00:24:46,840 Speaker 3: and I first started doing the show together as November 505 00:24:46,880 --> 00:24:49,480 Speaker 3: and what came out the gate but chat GPT that 506 00:24:49,600 --> 00:24:52,399 Speaker 3: really seemed to set the world alight. And and to 507 00:24:52,440 --> 00:24:54,800 Speaker 3: that point, you know, when you're looking at the nuances 508 00:24:54,800 --> 00:24:57,400 Speaker 3: within the large language models, how are we seeing them 509 00:24:57,400 --> 00:25:00,119 Speaker 3: differentiated in particular? For example, it feels like anthrop make 510 00:25:00,200 --> 00:25:02,040 Speaker 3: us on a bit of a role recently with Claude two. 511 00:25:03,119 --> 00:25:05,639 Speaker 1: Yeah, Cloud two has been been great in our testing. 512 00:25:05,720 --> 00:25:08,320 Speaker 15: It's doing a good job of not you know, not 513 00:25:08,760 --> 00:25:11,000 Speaker 15: answering questions when it should be answering questions and not 514 00:25:11,040 --> 00:25:14,680 Speaker 15: answering when it doesn't know the answer, and so there's 515 00:25:14,800 --> 00:25:16,360 Speaker 15: you know, that can be very important. 516 00:25:16,800 --> 00:25:18,840 Speaker 1: Yeah. Cloud two definitely performs suppressively. 517 00:25:19,200 --> 00:25:21,240 Speaker 15: You know, there's also we see big differences in terms 518 00:25:21,320 --> 00:25:23,959 Speaker 15: of the way they hedge, for instance, and so some 519 00:25:24,000 --> 00:25:26,360 Speaker 15: of the models are much more likely to not want 520 00:25:26,359 --> 00:25:29,159 Speaker 15: to answer a question and to to say, hey, I'm. 521 00:25:29,000 --> 00:25:31,080 Speaker 1: An ll M, I shouldn't be answering this, or it's 522 00:25:31,119 --> 00:25:31,720 Speaker 1: not my place. 523 00:25:31,760 --> 00:25:34,200 Speaker 15: And so depending on the application, that may be appropriate 524 00:25:34,320 --> 00:25:36,359 Speaker 15: or it may be a big drawback. And so that's 525 00:25:36,440 --> 00:25:38,679 Speaker 15: that's why those are some of the differences that we 526 00:25:39,000 --> 00:25:41,680 Speaker 15: that are testing our tool can tease out for people. 527 00:25:42,960 --> 00:25:48,600 Speaker 4: Adam, how do we democratize AI technology access to it 528 00:25:48,680 --> 00:25:50,960 Speaker 4: so that we look like lower the threshold right where 529 00:25:50,960 --> 00:25:53,840 Speaker 4: you don't need to have control of all the Nvidio 530 00:25:54,000 --> 00:25:57,200 Speaker 4: H one, hundreds or billions of dollars for the training. 531 00:25:57,240 --> 00:25:58,159 Speaker 6: How does that happen? 532 00:25:59,680 --> 00:26:02,080 Speaker 15: Yeah, absolutely, it's a big concern right now access to 533 00:26:02,480 --> 00:26:05,520 Speaker 15: that hardware, both the expense and just the availability of it. 534 00:26:05,720 --> 00:26:09,080 Speaker 15: I think fortunately these systems will become more efficient over time, 535 00:26:09,200 --> 00:26:12,200 Speaker 15: and you know there are other vendors who are starting 536 00:26:12,200 --> 00:26:14,879 Speaker 15: to make hardware more capacity coming online it's going to 537 00:26:14,920 --> 00:26:18,160 Speaker 15: take a little while until that happens. But the nice 538 00:26:18,160 --> 00:26:20,520 Speaker 15: thing with open source models is you can get a 539 00:26:20,560 --> 00:26:23,560 Speaker 15: pre trained model that you can either just use or modify. 540 00:26:23,920 --> 00:26:26,240 Speaker 15: And so you know what Facebook's done with Lama V 541 00:26:26,320 --> 00:26:29,119 Speaker 15: two and a number of other people have done with 542 00:26:29,560 --> 00:26:32,679 Speaker 15: the open release of these models, that's really put them 543 00:26:32,680 --> 00:26:34,120 Speaker 15: in the hands of a lot of people who might 544 00:26:34,160 --> 00:26:35,520 Speaker 15: not have had access to them before. 545 00:26:36,640 --> 00:26:40,840 Speaker 4: Can often make money with this business model, particularly if 546 00:26:40,840 --> 00:26:43,200 Speaker 4: we move towards an increasingly open sourced world. 547 00:26:44,480 --> 00:26:44,680 Speaker 1: Yeah. 548 00:26:44,680 --> 00:26:47,920 Speaker 15: Absolutely, Look, there are things that we do that are proprietary, 549 00:26:48,040 --> 00:26:51,760 Speaker 15: like our ability to detect and block hallucinations, and so 550 00:26:52,160 --> 00:26:54,160 Speaker 15: not everything we do is open source, but in this case, 551 00:26:54,160 --> 00:26:56,679 Speaker 15: we just felt it was really important because this is 552 00:26:56,760 --> 00:27:00,359 Speaker 15: sort of feeling confident that you're making the right choice 553 00:27:00,359 --> 00:27:02,679 Speaker 15: about your system and that you that you're that you 554 00:27:02,680 --> 00:27:04,560 Speaker 15: can put it out in production when trust that it 555 00:27:04,600 --> 00:27:07,440 Speaker 15: goes well is a major impediment for a lot of people, 556 00:27:07,440 --> 00:27:09,439 Speaker 15: and we really saw it slowing down people in the 557 00:27:09,440 --> 00:27:11,639 Speaker 15: process of the point. And ultimately what's good for us 558 00:27:11,800 --> 00:27:14,119 Speaker 15: is for as many people as possible to put these 559 00:27:14,160 --> 00:27:17,200 Speaker 15: systems into production so that we can protect and monitor 560 00:27:17,240 --> 00:27:19,800 Speaker 15: them and so Uh, the reason we open source this 561 00:27:19,920 --> 00:27:22,600 Speaker 15: is just to sort of accelerate people's ability to deploy 562 00:27:22,680 --> 00:27:25,480 Speaker 15: these these tools in a in a responsible way in 563 00:27:25,720 --> 00:27:26,360 Speaker 15: an enterprise. 564 00:27:27,119 --> 00:27:30,480 Speaker 3: Ultimately, though your own business of Arthur is about making 565 00:27:30,520 --> 00:27:33,800 Speaker 3: money and you're backed by what index ventures include Capital, 566 00:27:33,880 --> 00:27:37,080 Speaker 3: gray Craft and the like. I'm interested is to the 567 00:27:37,200 --> 00:27:41,200 Speaker 3: groundswell of hype and reality. How much are you seeing 568 00:27:41,200 --> 00:27:43,960 Speaker 3: companies coming to you wanting to get your services, wanting 569 00:27:44,000 --> 00:27:46,040 Speaker 3: to understand where they put their money to work in 570 00:27:46,080 --> 00:27:46,960 Speaker 3: AI l LMS. 571 00:27:48,080 --> 00:27:48,640 Speaker 1: Yeah, it's been. 572 00:27:48,680 --> 00:27:50,600 Speaker 15: It's been unlike anything I've ever seen in my career, 573 00:27:50,600 --> 00:27:53,000 Speaker 15: and I think it's because, you know, chat GPTD is. 574 00:27:52,960 --> 00:27:54,920 Speaker 1: Just so accessible. Anyone can go in and play a 575 00:27:55,000 --> 00:27:55,560 Speaker 1: round and. 576 00:27:55,560 --> 00:27:58,639 Speaker 15: See the the just the power of it, and so 577 00:27:58,680 --> 00:28:01,000 Speaker 15: it's become a bordit levelation to it and everything from 578 00:28:01,000 --> 00:28:03,639 Speaker 15: fortun one hundreds down to every single startup where boards 579 00:28:03,640 --> 00:28:06,120 Speaker 15: are asking the CEO and the CIO what is our 580 00:28:06,160 --> 00:28:08,840 Speaker 15: generative AI strategy and how is it going to transform 581 00:28:08,880 --> 00:28:12,520 Speaker 15: our business? And so that that that sense of urgency 582 00:28:12,560 --> 00:28:15,360 Speaker 15: has cascaded through the organization and so we're seeing all 583 00:28:15,359 --> 00:28:20,320 Speaker 15: sorts of project teams mobilizing and working on you know 584 00:28:20,520 --> 00:28:22,600 Speaker 15: ways to transform some of the key leverage points in 585 00:28:22,640 --> 00:28:25,080 Speaker 15: their business using l MS, and it's been it's just 586 00:28:25,119 --> 00:28:27,040 Speaker 15: been breathtaking to see. And so that's what we you know, 587 00:28:27,080 --> 00:28:29,320 Speaker 15: we we come in a lot of times they call 588 00:28:29,440 --> 00:28:30,280 Speaker 15: us to help them. 589 00:28:30,160 --> 00:28:32,560 Speaker 1: Because it's you know, there is a lot of little 590 00:28:32,560 --> 00:28:35,000 Speaker 1: details that are that collectively are a little bit daunting 591 00:28:35,000 --> 00:28:36,800 Speaker 1: when you're trying to stand up one of these systems. 592 00:28:36,840 --> 00:28:40,120 Speaker 1: Even though ultimately it's pretty accessible. 593 00:28:39,720 --> 00:28:42,280 Speaker 15: Technology, there's certainly a learning curve and we help a 594 00:28:42,320 --> 00:28:44,280 Speaker 15: lot of our customers with that as well as solving 595 00:28:44,520 --> 00:28:48,880 Speaker 15: some of the common deployment challenges around you know, hallucinations 596 00:28:48,880 --> 00:28:51,600 Speaker 15: and prompt injection and leaking sensitive data, things like that. 597 00:28:52,800 --> 00:28:55,640 Speaker 4: Adam Winshew, author see thank you very much for joining 598 00:28:55,720 --> 00:28:59,520 Speaker 4: us here on Bloomboat Technology Now. After his world tour 599 00:28:59,600 --> 00:29:02,960 Speaker 4: to discus us the latest developments in artificial intelligence, open 600 00:29:03,000 --> 00:29:07,280 Speaker 4: AI CEO Sam Altman reflected on the emotional powers of 601 00:29:07,280 --> 00:29:11,040 Speaker 4: the technology and the need for global regulation. Bloomberger Regional's 602 00:29:11,080 --> 00:29:14,760 Speaker 4: host and executive producer Emily Chang spoke to him for 603 00:29:14,840 --> 00:29:16,760 Speaker 4: the latest episode of the circuit. 604 00:29:17,720 --> 00:29:20,040 Speaker 6: Was the goal more listening or explaining. 605 00:29:20,000 --> 00:29:22,680 Speaker 12: The goal was more listening? It ended up with more 606 00:29:22,720 --> 00:29:27,120 Speaker 12: explaining than we expected. We ended up meaning like many 607 00:29:27,160 --> 00:29:30,360 Speaker 12: many war leaders and talked about the sort of the 608 00:29:30,400 --> 00:29:33,160 Speaker 12: need for global regulation, and that was like more explaining 609 00:29:33,880 --> 00:29:35,719 Speaker 12: the listen was provided. I came back with like one 610 00:29:35,760 --> 00:29:37,320 Speaker 12: hundred handwritten pages of notes. 611 00:29:37,440 --> 00:29:40,040 Speaker 5: I heard that you do handwritten what happens to the 612 00:29:40,080 --> 00:29:40,840 Speaker 5: hand written notes? 613 00:29:40,880 --> 00:29:42,520 Speaker 12: But in this case, like I distilled it into like 614 00:29:42,520 --> 00:29:44,720 Speaker 12: here are the top fifty pieces of like feedback from 615 00:29:44,720 --> 00:29:47,080 Speaker 12: our users and what we need to go off and do. 616 00:29:47,520 --> 00:29:49,080 Speaker 12: But there's like a lot of things when you like 617 00:29:49,160 --> 00:29:51,560 Speaker 12: get people in person, like face to face or over 618 00:29:51,560 --> 00:29:53,760 Speaker 12: a drink or whatever what where people really would just 619 00:29:53,840 --> 00:29:56,520 Speaker 12: like say, you know, here is like my very harsh 620 00:29:56,560 --> 00:29:58,320 Speaker 12: feedback on what you're going wrong. And I don't want 621 00:29:58,360 --> 00:29:58,880 Speaker 12: to be different. 622 00:29:59,240 --> 00:30:00,760 Speaker 5: You didn't go to China or Russia. 623 00:30:00,840 --> 00:30:03,040 Speaker 12: I spoke remotely in China, but not Russia. 624 00:30:04,560 --> 00:30:11,080 Speaker 6: Should we be worried about them. 625 00:30:08,480 --> 00:30:11,400 Speaker 5: And where they are on AD or what they're all doing. 626 00:30:11,400 --> 00:30:14,160 Speaker 12: I'd love to know more precisely where they are. That 627 00:30:14,200 --> 00:30:17,960 Speaker 12: would be hopeful. We have I think very imperfect information there. 628 00:30:18,720 --> 00:30:21,560 Speaker 5: So how has chat gipt changed your own behavior? 629 00:30:22,960 --> 00:30:25,280 Speaker 12: There's like a lot of like little ways and then 630 00:30:25,360 --> 00:30:27,760 Speaker 12: kind of like one big thought. The little ways are, 631 00:30:28,040 --> 00:30:31,280 Speaker 12: you know, like on this trip for example, the translation. 632 00:30:30,960 --> 00:30:31,800 Speaker 1: Was like a lifesaver. 633 00:30:32,720 --> 00:30:35,720 Speaker 12: I also use it if I'm trying to like write 634 00:30:35,760 --> 00:30:38,000 Speaker 12: something which I write a lot to never publish, just 635 00:30:38,120 --> 00:30:40,080 Speaker 12: like for my own thinking, and I find that I 636 00:30:40,120 --> 00:30:43,680 Speaker 12: like write faster and can think more somehow, So it's 637 00:30:43,720 --> 00:30:46,120 Speaker 12: like a great unsticking tool. 638 00:30:46,360 --> 00:30:48,840 Speaker 1: But then the big ways I am, I am, I 639 00:30:49,240 --> 00:30:50,280 Speaker 1: see the path. 640 00:30:50,120 --> 00:30:53,320 Speaker 12: Towards like this, just being like my super assistant for 641 00:30:53,400 --> 00:30:55,200 Speaker 12: all of my cognitive. 642 00:30:54,680 --> 00:30:58,760 Speaker 5: Work super assistant. You know, we've talked about relationships with chatbots. 643 00:30:58,840 --> 00:31:01,000 Speaker 6: Did you see this as something people could. 644 00:31:00,760 --> 00:31:02,000 Speaker 1: Get emotionally attached to? 645 00:31:02,200 --> 00:31:03,440 Speaker 5: And how do you feel about that? 646 00:31:03,760 --> 00:31:06,720 Speaker 12: I think language models in general are something that people 647 00:31:07,600 --> 00:31:12,040 Speaker 12: are getting emotionally attached to, And you know, I have 648 00:31:12,160 --> 00:31:14,760 Speaker 12: like a complex set of thoughts about that. I personally 649 00:31:14,800 --> 00:31:18,600 Speaker 12: find it strange. I don't want it for myself. I 650 00:31:18,640 --> 00:31:20,360 Speaker 12: have a lot of concerns. I don't want to be 651 00:31:20,480 --> 00:31:22,400 Speaker 12: like the kind of like people telling other people what 652 00:31:22,440 --> 00:31:24,600 Speaker 12: they can do with tech. But it seems to me 653 00:31:24,680 --> 00:31:26,360 Speaker 12: like something you need to be careful with. 654 00:31:27,320 --> 00:31:30,160 Speaker 3: Open Ai CEO some album with their own Emily Chang, 655 00:31:30,200 --> 00:31:31,640 Speaker 3: you can watch the rest of the interview on the 656 00:31:31,640 --> 00:31:34,320 Speaker 3: circuit with Emily Chang. She also sat down with Microsoft 657 00:31:34,320 --> 00:31:37,000 Speaker 3: CEO sat In Adella on Bluembergo reginals. 658 00:31:36,600 --> 00:31:38,600 Speaker 5: I meanwhile coming up, look, we're going to be breaking down. 659 00:31:38,600 --> 00:31:43,040 Speaker 3: Synopsis is third quarter earnings the CEO after goose from 660 00:31:43,040 --> 00:31:44,280 Speaker 3: New York, from San Francisco. 661 00:31:44,720 --> 00:31:52,480 Speaker 5: This is Broomberg Technology. 662 00:31:59,160 --> 00:31:59,960 Speaker 6: Let's get to snap. 663 00:32:00,400 --> 00:32:03,840 Speaker 4: This is the chip design software maker reporting third quarter results. 664 00:32:04,120 --> 00:32:08,040 Speaker 4: The top testaments raised guidance AI a big part of performance, 665 00:32:08,240 --> 00:32:11,560 Speaker 4: but also announcing that Sezin Ghazi will assume the role 666 00:32:11,600 --> 00:32:15,920 Speaker 4: of president and CEO effective January first, twenty twenty more 667 00:32:16,560 --> 00:32:19,760 Speaker 4: four for more, let's bring in Art to gus synopsis, 668 00:32:19,880 --> 00:32:24,240 Speaker 4: current but outgoing CEO Art Welcome to Bloombog Technology. It 669 00:32:24,320 --> 00:32:28,440 Speaker 4: was interesting, real growth in China. That's where I want 670 00:32:28,480 --> 00:32:30,959 Speaker 4: to start, and I want to start there because no 671 00:32:30,960 --> 00:32:34,680 Speaker 4: one asked you about it on the earnings call. Why 672 00:32:34,720 --> 00:32:36,320 Speaker 4: and how are you growing in China? 673 00:32:37,360 --> 00:32:40,200 Speaker 16: Well, the other the growth was strongest quarter, partially also 674 00:32:40,240 --> 00:32:44,440 Speaker 16: because it was the post COVID quarter and in general 675 00:32:44,520 --> 00:32:48,640 Speaker 16: China has remained strong for US for the last ten years, 676 00:32:49,480 --> 00:32:52,760 Speaker 16: while simultaneously, of course, having certain restrictions on what we 677 00:32:52,800 --> 00:32:57,200 Speaker 16: can do there. But you know, design of chips continues 678 00:32:57,280 --> 00:32:59,440 Speaker 16: everywhere in the world at a very high speed, and 679 00:32:59,680 --> 00:33:02,640 Speaker 16: that's many different in China. 680 00:33:02,840 --> 00:33:06,160 Speaker 4: You booked so far in the year, I think I'm 681 00:33:06,200 --> 00:33:09,840 Speaker 4: right in saying about five hundred million dollars of AI 682 00:33:10,040 --> 00:33:13,280 Speaker 4: sales as well. What is the main driver of that, 683 00:33:13,880 --> 00:33:17,720 Speaker 4: because you essentially create software that is used in the 684 00:33:17,800 --> 00:33:20,840 Speaker 4: chip design process. What is it that's driving the AI 685 00:33:20,920 --> 00:33:21,959 Speaker 4: specific growth. 686 00:33:22,680 --> 00:33:24,880 Speaker 16: Well, you know already for the last few years, if 687 00:33:24,880 --> 00:33:28,080 Speaker 16: you look at the most advanced, most hard driving chips, 688 00:33:28,480 --> 00:33:30,880 Speaker 16: AI is in the middle of that, right, And actually 689 00:33:30,960 --> 00:33:34,160 Speaker 16: in your own reporting you have so much on AI, 690 00:33:34,280 --> 00:33:39,560 Speaker 16: which is all driving the need for much faster, lower power, 691 00:33:39,920 --> 00:33:44,400 Speaker 16: much higher capacity chips, and so the competitiveness of those 692 00:33:44,680 --> 00:33:48,200 Speaker 16: is absolutely essential. That's another way of saying is they 693 00:33:48,200 --> 00:33:51,040 Speaker 16: are the ones that use the most advanced silicon technologies 694 00:33:51,280 --> 00:33:54,720 Speaker 16: and also the most complex architectures. And this is where 695 00:33:54,800 --> 00:33:58,920 Speaker 16: Synopsis is the leader. We do the most advanced chips, 696 00:33:58,960 --> 00:34:01,360 Speaker 16: or we I should say, we support our customers in 697 00:34:01,400 --> 00:34:04,000 Speaker 16: doing the most advanced chips in the world for our 698 00:34:04,160 --> 00:34:07,560 Speaker 16: entire existence. And here's a wave that is just continued 699 00:34:07,600 --> 00:34:07,920 Speaker 16: to grow. 700 00:34:09,560 --> 00:34:12,880 Speaker 4: Caroline, I express surprise that no one asked about China 701 00:34:13,320 --> 00:34:15,520 Speaker 4: because Art is talking about the cutting edge of chip 702 00:34:15,560 --> 00:34:19,480 Speaker 4: technology in that context, which is in a very hard 703 00:34:19,600 --> 00:34:20,440 Speaker 4: environment right now. 704 00:34:20,520 --> 00:34:25,160 Speaker 3: Yeah, that feels politically charged as well. Art, can you 705 00:34:25,200 --> 00:34:28,719 Speaker 3: just talk us through sort of the restrictions, the concerns 706 00:34:28,760 --> 00:34:32,959 Speaker 3: about allowing your very specific software to be used by China. 707 00:34:33,000 --> 00:34:35,960 Speaker 3: When we're worried about the tensions and the AI races, 708 00:34:36,000 --> 00:34:36,840 Speaker 3: it seems to be deemed. 709 00:34:37,520 --> 00:34:40,120 Speaker 16: Yeah, I didn't say that the most advanced AI chips 710 00:34:40,200 --> 00:34:44,120 Speaker 16: come from China. There are many companies that are investing 711 00:34:44,120 --> 00:34:47,000 Speaker 16: in that. Some of the very large semicroductor companies are 712 00:34:47,000 --> 00:34:49,600 Speaker 16: absolutely driving the state of the art, and some of 713 00:34:49,640 --> 00:34:52,480 Speaker 16: the most advanced startups in the world are driving the 714 00:34:52,520 --> 00:34:56,120 Speaker 16: state of the art. And those are the customers that 715 00:34:56,200 --> 00:34:59,440 Speaker 16: first come to us because they very much rely on 716 00:34:59,480 --> 00:35:02,640 Speaker 16: the ability to differentiate. And you know, if there's one 717 00:35:02,680 --> 00:35:04,560 Speaker 16: word I would put on that, the speed of the 718 00:35:04,640 --> 00:35:07,640 Speaker 16: chip determines everything. And if you look at at the 719 00:35:07,680 --> 00:35:12,000 Speaker 16: wave of opportunity that's now coming in via gen AI, 720 00:35:12,600 --> 00:35:15,480 Speaker 16: all of these algorithms initially take a lot of computation, 721 00:35:16,320 --> 00:35:20,160 Speaker 16: and the faster you can make those, the more opportunities 722 00:35:20,200 --> 00:35:22,520 Speaker 16: there are there and so it's one of those those 723 00:35:22,560 --> 00:35:27,560 Speaker 16: wonderful situations where the demand continually exceeds what our customers 724 00:35:27,560 --> 00:35:31,120 Speaker 16: can deliver. I either race is on, and in that race, 725 00:35:31,200 --> 00:35:33,400 Speaker 16: we are key ingredients to the success. 726 00:35:33,719 --> 00:35:37,280 Speaker 3: But many of your I'm sure peers over in Silicon 727 00:35:37,360 --> 00:35:40,200 Speaker 3: Valley are really nervous about that race, and boys the 728 00:35:40,239 --> 00:35:43,480 Speaker 3: administration worried about that race. What sort of contracts do 729 00:35:43,520 --> 00:35:45,799 Speaker 3: you have with China and are you getting any pushback 730 00:35:46,239 --> 00:35:47,839 Speaker 3: in terms of your ability to. 731 00:35:48,040 --> 00:35:52,759 Speaker 16: Sell that Well, you know, it's actually fairly straightforward. The 732 00:35:53,160 --> 00:35:56,919 Speaker 16: rules of engagement are very clear. We follow those absolutely 733 00:35:57,600 --> 00:36:02,080 Speaker 16: to a t. And the issue is more for China 734 00:36:02,120 --> 00:36:05,040 Speaker 16: itself than for us. It's a certain percentage of our 735 00:36:05,080 --> 00:36:08,600 Speaker 16: business that is growing well over time. But notice also 736 00:36:08,680 --> 00:36:12,000 Speaker 16: that we had a very strong quarter in Korea for example. 737 00:36:12,360 --> 00:36:15,600 Speaker 16: We over the year have done well in all parts 738 00:36:15,920 --> 00:36:19,400 Speaker 16: of the world, and so yeah, if there were no restrictions, 739 00:36:19,400 --> 00:36:22,399 Speaker 16: we could probably sell more to China, but we live 740 00:36:22,480 --> 00:36:26,040 Speaker 16: up to those restrictions. What I want to highlight though, 741 00:36:26,160 --> 00:36:31,080 Speaker 16: is every nation today is investing in chip design. And 742 00:36:31,320 --> 00:36:33,880 Speaker 16: I'm sure you're familiar with the US Chips Act, but 743 00:36:34,360 --> 00:36:37,319 Speaker 16: really what's happening is all other nations have followed up 744 00:36:37,360 --> 00:36:40,640 Speaker 16: with their own Chips Act, and while we're not counting 745 00:36:40,719 --> 00:36:43,640 Speaker 16: at all on these, what is clear is the whole 746 00:36:43,640 --> 00:36:47,480 Speaker 16: world now understand chips are absolutely central to this whole 747 00:36:47,520 --> 00:36:50,919 Speaker 16: new wave of what we call smart everything in the world. 748 00:36:52,520 --> 00:36:55,279 Speaker 4: You've been at this company since the late nineties. I 749 00:36:55,320 --> 00:36:57,799 Speaker 4: know that you're stepping down at the beginning of next year. 750 00:36:58,239 --> 00:37:00,680 Speaker 4: I wanted to get your reaction to the Hour Semi 751 00:37:00,680 --> 00:37:03,360 Speaker 4: and Intel news this week and how you think it 752 00:37:03,360 --> 00:37:06,080 Speaker 4: will impact the m and a landscape in tech and 753 00:37:06,120 --> 00:37:07,279 Speaker 4: in chips in particular. 754 00:37:08,400 --> 00:37:10,920 Speaker 16: Well, I actually have been there since the mid eighties 755 00:37:11,040 --> 00:37:13,920 Speaker 16: because I started the County in mid eighties. No, no problem. 756 00:37:14,160 --> 00:37:17,799 Speaker 16: But it gives a very interesting perspective, right because many 757 00:37:17,840 --> 00:37:20,399 Speaker 16: of these companies that you're reporting on, some of them 758 00:37:20,480 --> 00:37:23,600 Speaker 16: existing existed already at that time, and they all have 759 00:37:23,680 --> 00:37:27,400 Speaker 16: had the same passway, which is how to stay close 760 00:37:27,440 --> 00:37:29,600 Speaker 16: to the leading edge. And Intel is a great example 761 00:37:29,600 --> 00:37:35,120 Speaker 16: of that. Right now, they're substantially investing in their next edge. 762 00:37:35,360 --> 00:37:37,560 Speaker 16: And you may have seen the agreement that we did 763 00:37:37,600 --> 00:37:40,480 Speaker 16: earlier this week, which is very important, where we provide 764 00:37:40,560 --> 00:37:44,160 Speaker 16: many of the building blocks that are necessary for them 765 00:37:44,280 --> 00:37:47,640 Speaker 16: to be successful in the foundry world, and these building 766 00:37:47,640 --> 00:37:51,799 Speaker 16: blocks get specifically designed for their most advanced technology. And 767 00:37:52,320 --> 00:37:56,120 Speaker 16: in general, part of our role is to support all 768 00:37:56,160 --> 00:37:59,040 Speaker 16: the founderies to go to market. And what they need 769 00:37:59,120 --> 00:38:01,960 Speaker 16: is they need the design tools to be ready for them, 770 00:38:02,000 --> 00:38:05,280 Speaker 16: and they need the IP blocks to be ready from them, 771 00:38:05,560 --> 00:38:08,200 Speaker 16: and we provide both of those. We provide those for 772 00:38:08,280 --> 00:38:11,520 Speaker 16: a broad set of foundries, and that race is on too. 773 00:38:12,000 --> 00:38:15,400 Speaker 16: And so this notion of the race forward is actually 774 00:38:15,400 --> 00:38:19,279 Speaker 16: a big positive. You're certainly familiar with fifty years of 775 00:38:19,360 --> 00:38:24,200 Speaker 16: More's law that was an exponential of unseen magnitude. We've 776 00:38:24,320 --> 00:38:26,480 Speaker 16: entered a new one of these, and the new one 777 00:38:26,560 --> 00:38:29,400 Speaker 16: is that multiple chips are going to get together in 778 00:38:29,600 --> 00:38:32,400 Speaker 16: very high proximity, including or stacking them. It's almost like 779 00:38:32,680 --> 00:38:36,120 Speaker 16: moving from building houses to moving hotel to building hotels now. 780 00:38:36,719 --> 00:38:39,320 Speaker 16: And this is all driven by smart everything. 781 00:38:40,360 --> 00:38:43,000 Speaker 3: The focus focus for you, the focus for the person 782 00:38:43,040 --> 00:38:45,319 Speaker 3: who picks up from you in January twenty and twenty four. 783 00:38:45,360 --> 00:38:47,040 Speaker 3: You found in the business co found and back in 784 00:38:47,080 --> 00:38:49,600 Speaker 3: nineteen eighty six, and an amazing time to be handing 785 00:38:49,640 --> 00:38:50,600 Speaker 3: over to secceine me. 786 00:38:50,640 --> 00:38:54,120 Speaker 5: Thank you so much. Ardgius is synopsist CEO. 787 00:38:55,400 --> 00:38:57,120 Speaker 3: So let's add New York City to the list of 788 00:38:57,120 --> 00:39:00,640 Speaker 3: places TikTok access will be banned on government owned This 789 00:39:00,760 --> 00:39:03,680 Speaker 3: comes well in California moves ahead a stronger language in 790 00:39:03,719 --> 00:39:07,320 Speaker 3: its own TikTok van bill. In Meg's technology Alex Birinka 791 00:39:07,400 --> 00:39:09,560 Speaker 3: is ever a busy woman, so it tooks through it. 792 00:39:10,760 --> 00:39:11,040 Speaker 6: Yeah. 793 00:39:11,080 --> 00:39:13,360 Speaker 17: In New York City, that security threat that a lot 794 00:39:13,400 --> 00:39:15,719 Speaker 17: of legislators have talked about has come to the forefront. 795 00:39:15,920 --> 00:39:18,040 Speaker 17: The New York City Cyber Command called it a quote 796 00:39:18,040 --> 00:39:21,040 Speaker 17: threat to the city's technical networks and made the decision 797 00:39:21,160 --> 00:39:24,279 Speaker 17: this week to give the city thirty days to get 798 00:39:24,320 --> 00:39:27,840 Speaker 17: it off of government phones. The state of California that 799 00:39:27,920 --> 00:39:29,680 Speaker 17: was taking a little bit of a longer approach. There's 800 00:39:29,719 --> 00:39:33,439 Speaker 17: a bill that was authored by Senator Bill Dodd, that's 801 00:39:33,480 --> 00:39:37,480 Speaker 17: California SB seventy four. That bill has been approved by 802 00:39:37,480 --> 00:39:41,560 Speaker 17: the Assembly Committee to basically ban it on all government 803 00:39:41,640 --> 00:39:42,640 Speaker 17: phones as well. 804 00:39:42,880 --> 00:39:43,880 Speaker 6: That bill still. 805 00:39:43,640 --> 00:39:46,279 Speaker 17: Has to go to the Appropriations Committee and then to 806 00:39:46,360 --> 00:39:49,360 Speaker 17: Governor Gavin Newsom's desk. But there was one point in 807 00:39:49,400 --> 00:39:53,120 Speaker 17: our reporting from that passage and Assembly committee today that 808 00:39:53,160 --> 00:39:56,440 Speaker 17: I thought was interesting. Dodd said that TikTok has been 809 00:39:56,520 --> 00:39:59,680 Speaker 17: running interference, so while this app is getting banned on 810 00:39:59,680 --> 00:40:02,960 Speaker 17: government devices and states and municipalities more than thirty five 811 00:40:03,000 --> 00:40:06,200 Speaker 17: states in the US, it's still running quote unquote interference 812 00:40:06,280 --> 00:40:09,880 Speaker 17: and trying to convince lawmakers to not just ban TikTok, 813 00:40:09,960 --> 00:40:12,640 Speaker 17: but to widen up the band to all entertainment devices. 814 00:40:12,960 --> 00:40:16,680 Speaker 17: It hasn't quite worked that argument. In TikTok's defense. We're 815 00:40:16,719 --> 00:40:20,120 Speaker 17: still seeing these bands roll on, but California is probably 816 00:40:20,120 --> 00:40:21,720 Speaker 17: the next one that we'll be watching closely. 817 00:40:22,120 --> 00:40:25,160 Speaker 3: All the inside track, Alister, and can we thank you meanwhile? 818 00:40:25,360 --> 00:40:25,719 Speaker 6: That does it? 819 00:40:25,760 --> 00:40:27,759 Speaker 5: From this edition of Bluebog Technology. 820 00:40:27,880 --> 00:40:31,240 Speaker 4: Yeah, just the earning season, Relentless continues to recap everything 821 00:40:31,239 --> 00:40:33,880 Speaker 4: from the show in our podcast apples, Spotify, and the 822 00:40:33,880 --> 00:40:36,959 Speaker 4: Bloomberg platforms for here in SF and out New York. 823 00:40:37,239 --> 00:40:42,520 Speaker 6: This is Bloomberg Technology.