1 00:00:12,880 --> 00:00:15,360 Speaker 1: And Caroline hired at Bloomberg's world headquarters in New York 2 00:00:15,720 --> 00:00:19,320 Speaker 1: and Imed Ludlow in San Francisco. This is Bloomberg Technology 3 00:00:19,440 --> 00:00:22,360 Speaker 1: coming up. Tens of thousands more tech employees are out 4 00:00:22,360 --> 00:00:25,760 Speaker 1: of a job after Microsoft and Amazon start downsize today 5 00:00:26,079 --> 00:00:29,800 Speaker 1: as employers reduced headcount. Is the pandemics moment of worker empowerment? 6 00:00:29,960 --> 00:00:35,120 Speaker 1: Over and universities in Texas are taking action against TikTok, 7 00:00:35,240 --> 00:00:39,080 Speaker 1: with Texas A and M and Texas University reportedly blocking 8 00:00:39,120 --> 00:00:41,640 Speaker 1: the app from its WiFi networks as part of the 9 00:00:41,640 --> 00:00:45,400 Speaker 1: governor's orders. Could this be the start of a greater trend? 10 00:00:45,920 --> 00:00:49,000 Speaker 1: And do you want one of Twitter's officers neon signs 11 00:00:49,280 --> 00:00:52,159 Speaker 1: or a white board or a giant refrigerator. Twitter has 12 00:00:52,200 --> 00:00:55,680 Speaker 1: resorted to auctioning office items will show you what was 13 00:00:55,760 --> 00:00:58,760 Speaker 1: up for grabs. But first we want to talk about 14 00:00:58,800 --> 00:01:01,560 Speaker 1: one of the key issues that off sometimes bring, certainly 15 00:01:01,600 --> 00:01:06,199 Speaker 1: for plenty of chief technology and chief security officers, because 16 00:01:06,240 --> 00:01:09,319 Speaker 1: there are security concerns around all of this. Disgruntled workers 17 00:01:09,319 --> 00:01:12,120 Speaker 1: for examples, maybe they take corporate secrets with them when 18 00:01:12,120 --> 00:01:14,320 Speaker 1: they get laid off, or maybe they get a bit 19 00:01:14,360 --> 00:01:17,240 Speaker 1: careless about them. Margie Murphy has a great story on 20 00:01:17,280 --> 00:01:19,280 Speaker 1: all of this, and just tell us a little bit, Margy, 21 00:01:19,360 --> 00:01:22,640 Speaker 1: about how companies are starting to prepare themselves as they 22 00:01:22,680 --> 00:01:24,720 Speaker 1: know they're going to make layoffs. How do they ensure 23 00:01:24,800 --> 00:01:28,560 Speaker 1: that the data that they have remained secure. Yeah, it's 24 00:01:28,600 --> 00:01:31,360 Speaker 1: a real problem because around the time that layoffs are happening, 25 00:01:31,400 --> 00:01:34,559 Speaker 1: obviously everyone's trying to tighten their belt, and that's when 26 00:01:34,680 --> 00:01:38,000 Speaker 1: security budgets might be cut a little bit. But companies 27 00:01:38,040 --> 00:01:40,520 Speaker 1: are looking at things like new software that's around to 28 00:01:40,640 --> 00:01:46,600 Speaker 1: track whether employees are are using data exfiltration methods um. 29 00:01:46,640 --> 00:01:49,320 Speaker 1: And they're also trying really hard to just give out 30 00:01:49,360 --> 00:01:52,280 Speaker 1: the messaging and reminding people that things that they're working 31 00:01:52,280 --> 00:01:55,920 Speaker 1: on belong to the company, not to themselves. Um I 32 00:01:55,960 --> 00:02:01,040 Speaker 1: mentioned data exfiltration, and that's when people toward emails to 33 00:02:01,080 --> 00:02:03,960 Speaker 1: their personal accounts or maybe a copy and paste of 34 00:02:04,040 --> 00:02:08,600 Speaker 1: spreadsheet with contact details or client contracts, and that puts 35 00:02:08,639 --> 00:02:11,639 Speaker 1: companies at risk of obviously losing business if that person 36 00:02:11,720 --> 00:02:14,880 Speaker 1: takes that with them to another company, but also regulatory 37 00:02:14,919 --> 00:02:19,079 Speaker 1: and confidentiality issues as well, Margie, we we've got to 38 00:02:19,080 --> 00:02:22,280 Speaker 1: ask ourselves why we're discussing this story. Fantastic work in 39 00:02:22,320 --> 00:02:26,280 Speaker 1: business Week magazine and an analysis of what happens when 40 00:02:26,280 --> 00:02:30,480 Speaker 1: we hear about jobs being cut layoffs in the technology 41 00:02:30,480 --> 00:02:33,040 Speaker 1: industry to the tunes of thousands, the kind of the 42 00:02:33,080 --> 00:02:35,440 Speaker 1: anecdote that you guys uses coin based right when it 43 00:02:35,440 --> 00:02:40,800 Speaker 1: announced its layoffs and Brian or Armstrong basically explaining that 44 00:02:40,880 --> 00:02:44,640 Speaker 1: by the end of the day, UH employees would receive 45 00:02:45,200 --> 00:02:49,320 Speaker 1: notifications their personal email addresses, and it was very abrupt 46 00:02:49,320 --> 00:02:50,880 Speaker 1: and he said, look, we kind of have to do 47 00:02:50,919 --> 00:02:54,400 Speaker 1: this because our customer information is key. We have to 48 00:02:54,440 --> 00:02:58,959 Speaker 1: protect that customer data. What kind of serious steps have 49 00:02:59,080 --> 00:03:01,600 Speaker 1: you report it on that companies are taking to kind 50 00:03:01,600 --> 00:03:04,520 Speaker 1: of safeguard themselves and their customers when they go through 51 00:03:04,520 --> 00:03:07,840 Speaker 1: a random lyoffside this. Well, yeah, as you mentioned the 52 00:03:08,320 --> 00:03:10,600 Speaker 1: case with coin base, it was, you know, employees were 53 00:03:10,639 --> 00:03:13,960 Speaker 1: waking up to already having their email shut off, which 54 00:03:14,200 --> 00:03:18,280 Speaker 1: sounds incredibly callous and was criticized, you know for kind 55 00:03:18,320 --> 00:03:21,160 Speaker 1: of having lacking that personal touch to realize that you've 56 00:03:21,160 --> 00:03:24,079 Speaker 1: been completely wiped from a company. But more and more 57 00:03:24,120 --> 00:03:26,360 Speaker 1: companies are having to do this because the risk is 58 00:03:26,400 --> 00:03:29,360 Speaker 1: just so high. It's so easy for employees to just 59 00:03:29,400 --> 00:03:32,080 Speaker 1: simply forward an email, you know, they if they've got 60 00:03:32,160 --> 00:03:35,560 Speaker 1: a few hours left, they'll just be kind of sending 61 00:03:35,640 --> 00:03:39,760 Speaker 1: stuff to to their own personal gmails and not. You know, 62 00:03:39,840 --> 00:03:42,320 Speaker 1: you've got some people who are obviously irritated that they're 63 00:03:42,320 --> 00:03:46,040 Speaker 1: fired and maybe want to do something really kind of 64 00:03:46,080 --> 00:03:48,320 Speaker 1: bad with that and leak it. But often people don't 65 00:03:48,360 --> 00:03:51,160 Speaker 1: even realize that what they're doing is it is against 66 00:03:51,160 --> 00:03:54,240 Speaker 1: their employment contract, and they simply want to kind of 67 00:03:54,440 --> 00:03:57,360 Speaker 1: better themselves and then next employment because they're scared they've 68 00:03:57,400 --> 00:04:01,040 Speaker 1: they've just been let go. Um. But largely we're seeing 69 00:04:01,080 --> 00:04:04,160 Speaker 1: companies just really cracked down and shut down from the 70 00:04:04,240 --> 00:04:09,040 Speaker 1: minute people are being laid off and being very very cold, 71 00:04:09,120 --> 00:04:13,960 Speaker 1: I guess, but but extremely secure just because the risks 72 00:04:13,960 --> 00:04:16,839 Speaker 1: are so high. We've seen issues with coin Base, um, 73 00:04:16,880 --> 00:04:19,760 Speaker 1: you know, it had that an employee was accused of 74 00:04:19,800 --> 00:04:23,400 Speaker 1: insider trading because they were sharing some information. That was 75 00:04:23,400 --> 00:04:29,400 Speaker 1: the famous case with Waymo, which was Google's driverless car 76 00:04:29,640 --> 00:04:35,200 Speaker 1: arm Anthony Levandarsky he famously went to ub was defected 77 00:04:35,680 --> 00:04:38,840 Speaker 1: and he took company's secrets with him and it was 78 00:04:38,880 --> 00:04:43,760 Speaker 1: an extremely long court case and had serious ramiflications for Google. 79 00:04:44,520 --> 00:04:48,760 Speaker 1: So the stakes are really really high, alright, Bloomberg Margie 80 00:04:48,800 --> 00:04:52,400 Speaker 1: Murphy just terrific reporting on air and also in business. 81 00:04:52,400 --> 00:04:55,200 Speaker 1: We check out that story on Bloomberg dot com. Now, 82 00:04:55,240 --> 00:04:58,920 Speaker 1: as we've been discussing, Amazon and Microsoft are laying off 83 00:04:59,080 --> 00:05:01,720 Speaker 1: some of their work force. This is part of a 84 00:05:01,760 --> 00:05:04,440 Speaker 1: wider wave of job cuts that are hitting the tech 85 00:05:04,520 --> 00:05:07,680 Speaker 1: industry right now, already facing of course, the chance of 86 00:05:07,720 --> 00:05:11,200 Speaker 1: recession globally. Here's what some of our guests across Bloomberg 87 00:05:11,200 --> 00:05:16,440 Speaker 1: Television have been talking about. We've not been positive on 88 00:05:16,560 --> 00:05:18,560 Speaker 1: Big Deck for I don't know a year, a year 89 00:05:18,640 --> 00:05:21,360 Speaker 1: and a half. We do believe that there is still 90 00:05:21,400 --> 00:05:23,880 Speaker 1: some deflation to come out of this market after the 91 00:05:23,920 --> 00:05:26,480 Speaker 1: exuberence of twenty one. I actually think that the tech 92 00:05:26,520 --> 00:05:29,239 Speaker 1: sector is one of the few sectors that is really 93 00:05:29,279 --> 00:05:33,520 Speaker 1: discounting a recession in its outlook. The longer the macro 94 00:05:33,600 --> 00:05:36,520 Speaker 1: volatility persists, we do expect early stage and seed to 95 00:05:36,960 --> 00:05:39,280 Speaker 1: start to see that crunch that the late stages is 96 00:05:39,320 --> 00:05:40,719 Speaker 1: seen right now, and there is going to be some 97 00:05:40,760 --> 00:05:44,800 Speaker 1: amount of normalization of the demand. Uh. Quite frankly, we 98 00:05:44,920 --> 00:05:47,400 Speaker 1: in the tech industry will also have to get efficient, right. 99 00:05:47,400 --> 00:05:49,919 Speaker 1: It's not about everyone else doing more with less. We 100 00:05:49,960 --> 00:05:53,280 Speaker 1: will have to do more with less. All right, So 101 00:05:53,360 --> 00:05:56,400 Speaker 1: let's bring in Bloomberg's Austin car who has been writing 102 00:05:56,400 --> 00:05:59,720 Speaker 1: about why behind the job cuts, and you heard their 103 00:05:59,720 --> 00:06:02,040 Speaker 1: auster and from a rural range of names across venture 104 00:06:02,080 --> 00:06:06,800 Speaker 1: capital public markets also sat in Adela on why we're 105 00:06:06,839 --> 00:06:10,520 Speaker 1: seeing these layoffs in the technology industry. Rapp all of 106 00:06:10,600 --> 00:06:13,400 Speaker 1: this together, because you've been writing about this also in 107 00:06:13,440 --> 00:06:17,480 Speaker 1: Business Week. So what we saw over the last year 108 00:06:17,560 --> 00:06:20,080 Speaker 1: or two was just as one of those analysts had 109 00:06:20,120 --> 00:06:23,120 Speaker 1: noticed at this exuberance coming out of the COVID nineteen pandemic, 110 00:06:23,200 --> 00:06:26,120 Speaker 1: where the tech sector, more than any other industry, had 111 00:06:26,120 --> 00:06:30,600 Speaker 1: really bet big on this new revenue acceleration being permanent. 112 00:06:31,160 --> 00:06:34,320 Speaker 1: And we've seen that course correction happened quite harshly in 113 00:06:34,360 --> 00:06:40,160 Speaker 1: more recent days and months, with Amazon and Microsoft announcing 114 00:06:40,160 --> 00:06:43,400 Speaker 1: sort of headline grabbing uh layoffs in the you know, 115 00:06:43,560 --> 00:06:47,720 Speaker 1: ten eighteen thousand range figures. And what's totally remarkable about 116 00:06:47,800 --> 00:06:49,360 Speaker 1: that is how it compares to the rest of the 117 00:06:49,440 --> 00:06:53,600 Speaker 1: US economy. Job cuts in two were actually up and 118 00:06:53,680 --> 00:06:57,919 Speaker 1: the tech sector six compared to the previous year, whereas 119 00:06:58,000 --> 00:07:00,000 Speaker 1: in the rest of the economy was only the layoffs 120 00:07:00,000 --> 00:07:02,760 Speaker 1: we're only up. So what you saw is all these 121 00:07:02,800 --> 00:07:05,599 Speaker 1: tech companies betting huge on that COVID error growth remaining 122 00:07:05,640 --> 00:07:08,720 Speaker 1: and and being a much longer term phenomenon, and we're 123 00:07:08,720 --> 00:07:10,800 Speaker 1: now seeing a sort of big course correction from other 124 00:07:10,840 --> 00:07:13,760 Speaker 1: big tech companies, and only is trying to understand firstly 125 00:07:13,760 --> 00:07:16,440 Speaker 1: whether it's a bell weather, but also also why it's 126 00:07:16,480 --> 00:07:18,520 Speaker 1: not really showing up in the big data at the moment. 127 00:07:18,520 --> 00:07:21,600 Speaker 1: Many hypothesize that these people are highly talented, highly skilled, 128 00:07:21,600 --> 00:07:24,160 Speaker 1: and people still want them in the other industries, so 129 00:07:24,200 --> 00:07:26,520 Speaker 1: they're snapped up very quickly rather than showing up in 130 00:07:26,560 --> 00:07:29,600 Speaker 1: the BLS data for example. But go to also where 131 00:07:29,600 --> 00:07:32,000 Speaker 1: they might add jobs, because I thought that was interesting 132 00:07:32,000 --> 00:07:35,360 Speaker 1: from SATI in a data and Amazon and Microsoft at large, 133 00:07:35,520 --> 00:07:39,160 Speaker 1: they are still going to be hiring in some spaces, right, Well, 134 00:07:39,160 --> 00:07:41,920 Speaker 1: that's the narrative that they sold Walter on this sort 135 00:07:41,920 --> 00:07:45,320 Speaker 1: of high growth, higher risk, big return investments. So they 136 00:07:45,320 --> 00:07:47,760 Speaker 1: can't just stop all of those moonshots that they've been 137 00:07:47,760 --> 00:07:49,880 Speaker 1: investing in for the last couple of years and let 138 00:07:49,880 --> 00:07:53,720 Speaker 1: go of that very high caliber, expensive engineering talent. So 139 00:07:53,760 --> 00:07:56,520 Speaker 1: you're seeing sort of this balance between tech companies trying 140 00:07:56,520 --> 00:07:58,640 Speaker 1: to give off some signal that they're going to be 141 00:07:58,640 --> 00:08:01,240 Speaker 1: a little bit more financially prudent and conservative while not 142 00:08:01,280 --> 00:08:04,280 Speaker 1: giving up on some of those longer term bets. With Microsoft, 143 00:08:04,280 --> 00:08:06,640 Speaker 1: for example, such an Adela saying we're going to double 144 00:08:06,640 --> 00:08:08,720 Speaker 1: down in core businesses, but we're going to continue to 145 00:08:08,760 --> 00:08:11,760 Speaker 1: invest in high growth areas like AI or with Amazon, 146 00:08:12,080 --> 00:08:15,880 Speaker 1: they're seeing layoffs happen in the retail division or in 147 00:08:15,960 --> 00:08:18,960 Speaker 1: some of their devices, sort of the risky or hardware 148 00:08:18,960 --> 00:08:21,280 Speaker 1: bets that they've been making with Alexa Advices. But at 149 00:08:21,320 --> 00:08:23,800 Speaker 1: the same time he's also saying Andy Jassy, the CEO, 150 00:08:23,920 --> 00:08:25,880 Speaker 1: has said we're still going to invest in high growth 151 00:08:25,880 --> 00:08:29,080 Speaker 1: areas like groceries or B two B services, third part 152 00:08:29,120 --> 00:08:31,480 Speaker 1: of party seller markets, So they're really trying to sort 153 00:08:31,480 --> 00:08:33,960 Speaker 1: of balance that that sort of risk and reward. Right now, 154 00:08:35,480 --> 00:08:37,640 Speaker 1: I want to bring up this terminal chart again which 155 00:08:37,640 --> 00:08:42,120 Speaker 1: we showed earlier in the show. Um Amazon's total global 156 00:08:42,200 --> 00:08:46,240 Speaker 1: head count, right so eighteen thousand jobs. It's it's a 157 00:08:46,280 --> 00:08:50,679 Speaker 1: striking headline, but it's one percent of its global workforce. 158 00:08:50,720 --> 00:08:53,599 Speaker 1: And I think there's discussion about this from market participants 159 00:08:53,600 --> 00:08:56,800 Speaker 1: in our in our reporting, right Austin, what does that 160 00:08:56,880 --> 00:09:00,479 Speaker 1: what does that tell us the difference between the headlines 161 00:09:00,679 --> 00:09:04,360 Speaker 1: and the reality and how this period, this economy of 162 00:09:05,080 --> 00:09:07,320 Speaker 1: three might be different to the dot com bubble, even 163 00:09:07,360 --> 00:09:11,280 Speaker 1: the two thousand and eight financial crisis. Yeah, it indicates 164 00:09:11,280 --> 00:09:13,920 Speaker 1: so far that just given how wired big tech is 165 00:09:14,000 --> 00:09:16,040 Speaker 1: into the rest of the global economy, it's a lot 166 00:09:16,040 --> 00:09:18,480 Speaker 1: different than it was during the dot com crash two 167 00:09:18,480 --> 00:09:21,400 Speaker 1: decades ago. Uh. You know, all these companies around the 168 00:09:21,400 --> 00:09:25,400 Speaker 1: world are much more dependent on Silicon Valley software, their hardware, 169 00:09:25,480 --> 00:09:30,080 Speaker 1: their chips, their cloud computing services, their enterprise services as well. 170 00:09:30,320 --> 00:09:32,480 Speaker 1: So it's not the case that as much as these 171 00:09:32,480 --> 00:09:35,320 Speaker 1: headline grabbing numbers are are pretty massive, there's still a 172 00:09:35,360 --> 00:09:38,840 Speaker 1: small percentage of of Amazon's overall workforce, or for Microsoft, 173 00:09:38,920 --> 00:09:41,000 Speaker 1: the ten thousand cuts that they announced, that's I think 174 00:09:41,000 --> 00:09:43,680 Speaker 1: only five percent of their overall workforce. So it's a 175 00:09:43,679 --> 00:09:46,560 Speaker 1: lot smaller. Uh. And it's also in specific areas, at 176 00:09:46,640 --> 00:09:49,240 Speaker 1: least from our sources we're hearing from HR and recruiting 177 00:09:49,840 --> 00:09:52,439 Speaker 1: rather than some of the high, high caliber, expensive talent 178 00:09:52,480 --> 00:09:55,240 Speaker 1: and engineering. They're sort of can double down for their 179 00:09:55,280 --> 00:09:58,040 Speaker 1: core businesses that they want to continue invest in. But 180 00:09:58,080 --> 00:10:00,640 Speaker 1: again it's always that balance. With Mark zucker Burke, he said, 181 00:10:00,640 --> 00:10:02,439 Speaker 1: you know what, we're still going to invest more. We're 182 00:10:02,440 --> 00:10:04,959 Speaker 1: going to reprioritize our adversis, but we're still going to 183 00:10:05,040 --> 00:10:08,079 Speaker 1: make that that bet in engineering talent, our metaverse, which 184 00:10:08,160 --> 00:10:11,439 Speaker 1: is that longer term bet that they're they're making. It's nuanced, 185 00:10:11,600 --> 00:10:13,320 Speaker 1: and you do it so well for us, Austin, thank 186 00:10:13,360 --> 00:10:16,439 Speaker 1: you very much. Indeed, Austin car it's a great read. Meanwhile, 187 00:10:16,440 --> 00:10:19,280 Speaker 1: we'll one sector in technology that's really been letting go 188 00:10:19,320 --> 00:10:21,320 Speaker 1: of a lot of staff is crypto, and in fact, 189 00:10:21,400 --> 00:10:23,680 Speaker 1: crypto firm Genesis is one of those, and it's also 190 00:10:23,679 --> 00:10:26,840 Speaker 1: said it's preparing now we understand for bankruptcy filing as 191 00:10:26,840 --> 00:10:29,240 Speaker 1: early as this week. They're lending unit of Barry Silbert's 192 00:10:29,240 --> 00:10:33,800 Speaker 1: Digital Currency Group. Genesis is in confidential negotiations with various 193 00:10:33,800 --> 00:10:38,200 Speaker 1: creditor groups amid a liquidity crunch. Genesis suspended withdrawals in November, 194 00:10:38,480 --> 00:10:41,640 Speaker 1: soon after ft X men bankrupt course. On Tuesday, Digital 195 00:10:41,640 --> 00:10:45,000 Speaker 1: Currency Group told shareholders that it's suspending quarterly dividends in 196 00:10:45,040 --> 00:10:48,880 Speaker 1: an effort to conserve cash. Coming up, Well, now, what 197 00:10:49,040 --> 00:10:53,120 Speaker 1: access to TikTok at universities in Texas? What impact could 198 00:10:53,120 --> 00:11:05,760 Speaker 1: that ban have? We'll discuss this Blue beg I think 199 00:11:05,800 --> 00:11:09,439 Speaker 1: the national security issue of our time is the technology 200 00:11:09,480 --> 00:11:13,480 Speaker 1: competition with China. We've already seen that around things like 201 00:11:13,600 --> 00:11:17,480 Speaker 1: five G Semiconductors were now looking at issues like new 202 00:11:17,600 --> 00:11:21,760 Speaker 1: energy and synthetic biology because in China we have a 203 00:11:21,760 --> 00:11:26,040 Speaker 1: competitor that's investing at a rate that's commensurate with what 204 00:11:26,160 --> 00:11:29,640 Speaker 1: we're investing. And uh, I'm all for innovation, but I've 205 00:11:29,640 --> 00:11:36,040 Speaker 1: been particularly concerned about TikTok. My fears one TikTok collects 206 00:11:36,080 --> 00:11:39,360 Speaker 1: more information about you as a user than virtually any 207 00:11:39,360 --> 00:11:44,040 Speaker 1: other site around your keystrokes, your facial expressions, and I'm 208 00:11:44,080 --> 00:11:47,160 Speaker 1: horribly afraid that that's being stored somewhere in besion. I'm 209 00:11:47,200 --> 00:11:51,520 Speaker 1: also concerned about it being an ability to manipulate um 210 00:11:51,559 --> 00:11:55,480 Speaker 1: the flow of information to you. I was Senator Mark 211 00:11:55,559 --> 00:11:59,200 Speaker 1: Warner that and look, speaking of TikTok, some universities in Texas, 212 00:11:59,280 --> 00:12:01,880 Speaker 1: like the University of North Texas among others, there are 213 00:12:01,960 --> 00:12:04,440 Speaker 1: said to be blocking access to the video sharing app 214 00:12:04,440 --> 00:12:08,480 Speaker 1: TikTok on its WiFi and wide networks to comply with 215 00:12:08,600 --> 00:12:12,360 Speaker 1: quote Governor Abbot's directive to all state agencies banning employees 216 00:12:12,400 --> 00:12:15,800 Speaker 1: from using or downloading TikTok on all state issued or 217 00:12:15,840 --> 00:12:19,120 Speaker 1: managed devices and environments. That's according to a statement shared 218 00:12:19,160 --> 00:12:22,080 Speaker 1: with Bloomberg. Now, Rumberg's Alex Brinka is here to break 219 00:12:22,120 --> 00:12:24,200 Speaker 1: it all down. And what's so interesting to me, Alex 220 00:12:24,280 --> 00:12:29,400 Speaker 1: is basically the university's front running the rest of the state. Oh, 221 00:12:29,440 --> 00:12:31,079 Speaker 1: I think we've got a technical glitch. We're gonna be 222 00:12:31,080 --> 00:12:32,720 Speaker 1: getting to Alex in a moment. Who's out in l A. 223 00:12:32,800 --> 00:12:35,800 Speaker 1: But ed, first, let's bring in what our audience said, 224 00:12:35,840 --> 00:12:38,760 Speaker 1: because yes, it seems as though we're seeing a front 225 00:12:38,840 --> 00:12:42,040 Speaker 1: running in certain institutions in a state versus the state 226 00:12:42,040 --> 00:12:45,640 Speaker 1: itself and states themselves front running the federal government. But 227 00:12:45,760 --> 00:12:48,000 Speaker 1: we are sked ultimately our audience as to whether this 228 00:12:48,080 --> 00:12:50,600 Speaker 1: is going to make any difference. And well, many seem 229 00:12:50,640 --> 00:12:52,800 Speaker 1: to think VPNs are going to come to the rescue 230 00:12:52,800 --> 00:12:56,080 Speaker 1: in some way. Yeah. Look, there's two sides to this debate. 231 00:12:56,160 --> 00:12:59,480 Speaker 1: Right in Texas, what you see is university is essentially 232 00:12:59,520 --> 00:13:04,040 Speaker 1: public entities enacting the directive from the governor and putting 233 00:13:04,080 --> 00:13:07,840 Speaker 1: that into practice. And that manifests itself by the universities 234 00:13:08,040 --> 00:13:11,480 Speaker 1: not allowing students or anyone on campus to access the 235 00:13:11,480 --> 00:13:14,840 Speaker 1: TikTok app through the WiFi network. Well, they can probably 236 00:13:14,840 --> 00:13:17,280 Speaker 1: still access if they turn off WiFi and use their 237 00:13:17,280 --> 00:13:20,960 Speaker 1: five G network. But it's it's the debate around who 238 00:13:20,960 --> 00:13:24,080 Speaker 1: should be acting on this, because the buying administration and 239 00:13:24,120 --> 00:13:27,160 Speaker 1: the federal government have been leading a security view of 240 00:13:27,160 --> 00:13:30,960 Speaker 1: TikTok and other cyber screty threats for some time. And 241 00:13:31,000 --> 00:13:33,360 Speaker 1: there's the answer, right Carol. For the eight percent of 242 00:13:33,360 --> 00:13:35,880 Speaker 1: the respondents we polled said this isn't going to fix 243 00:13:35,920 --> 00:13:39,920 Speaker 1: anything bank the specifics of not allowing TikTok access on 244 00:13:40,040 --> 00:13:44,160 Speaker 1: campus in Texas universities. But they are following through in 245 00:13:44,200 --> 00:13:47,240 Speaker 1: the governor's directive. They are, And let's get to Alex. Now, 246 00:13:47,320 --> 00:13:50,440 Speaker 1: I think technology issues solved on this technology show. Tell 247 00:13:50,559 --> 00:13:53,439 Speaker 1: us Alex about whether you're surprised that some of these 248 00:13:53,480 --> 00:13:57,480 Speaker 1: institutions are going headlong into these sorts of bands. It 249 00:13:57,559 --> 00:14:00,400 Speaker 1: is a little interesting how they're interpreting this right. They 250 00:14:00,440 --> 00:14:03,560 Speaker 1: actually University of Texas at Austin, for example, doesn't have 251 00:14:03,600 --> 00:14:06,680 Speaker 1: a specific directive to move forward in this um. In 252 00:14:06,720 --> 00:14:09,760 Speaker 1: this way, Texas Tech is waiting until they get more 253 00:14:09,800 --> 00:14:13,480 Speaker 1: guidance from the state. So it's interesting to see Texas, Georgia, 254 00:14:13,600 --> 00:14:17,520 Speaker 1: Alabama really being front footed here. And following along what 255 00:14:17,600 --> 00:14:21,200 Speaker 1: we heard at the top from Senator Warner basically saying, 256 00:14:21,240 --> 00:14:24,680 Speaker 1: we're worried about data sharing on TikTok and potential data 257 00:14:24,720 --> 00:14:27,080 Speaker 1: sharing with the Chinese government, and we're going to lock 258 00:14:27,160 --> 00:14:31,040 Speaker 1: this down now. Ed what you said, um about students 259 00:14:31,080 --> 00:14:35,560 Speaker 1: potentially looking for workarounds accessing TikTok on their cellular data 260 00:14:35,640 --> 00:14:39,200 Speaker 1: and not on the WiFi words band, That's exactly what's happening. 261 00:14:39,240 --> 00:14:41,080 Speaker 1: I've been chatting to folks who are on the ground 262 00:14:41,240 --> 00:14:43,920 Speaker 1: at u T Austin in particular, who have said, look, 263 00:14:44,000 --> 00:14:46,400 Speaker 1: we have other ways we're gonna access this app. I 264 00:14:46,480 --> 00:14:49,680 Speaker 1: use it for entertainment, I use it for educational content. 265 00:14:50,040 --> 00:14:53,080 Speaker 1: So they're on TikTok anyways, even if it's not on 266 00:14:53,120 --> 00:14:56,080 Speaker 1: the WiFi at the university. Yeah. Quite. I think it's 267 00:14:56,080 --> 00:14:59,200 Speaker 1: also an educational issue, right understanding why is this a 268 00:14:59,280 --> 00:15:02,800 Speaker 1: security skin and conveying that to uses of all social 269 00:15:02,840 --> 00:15:06,080 Speaker 1: media platforms. That seems to be what we're hearing from 270 00:15:06,240 --> 00:15:09,280 Speaker 1: users of that particular platform. Bloombergs, Alex Barrinka, thank you 271 00:15:09,360 --> 00:15:14,640 Speaker 1: very much. Now. Cisco CEO Chuck Robbins spoke to Bloombergs, 272 00:15:14,680 --> 00:15:17,440 Speaker 1: David Weston and Davos about tech layoffs, the impact of 273 00:15:17,480 --> 00:15:20,280 Speaker 1: COVID nineteen on the tech sector, but first about their 274 00:15:20,320 --> 00:15:25,560 Speaker 1: investment into software. Have a listen. Well, there's a few 275 00:15:25,560 --> 00:15:28,720 Speaker 1: things we've done. We've added a lot of our cybersecurity 276 00:15:28,800 --> 00:15:31,440 Speaker 1: technology is just pure software, right, I mean, that's just 277 00:15:31,520 --> 00:15:34,560 Speaker 1: the nature of the industry and how we're defending against threats. 278 00:15:35,320 --> 00:15:38,600 Speaker 1: Collaboration is a lot of software. Our web ex portfolio 279 00:15:38,680 --> 00:15:42,320 Speaker 1: and the meeting capability, so those are just natural software 280 00:15:43,240 --> 00:15:47,480 Speaker 1: products that we then also begun to sell subscriptions on 281 00:15:47,480 --> 00:15:51,320 Speaker 1: our hardware platforms, and that's been a transition we started 282 00:15:51,360 --> 00:15:54,880 Speaker 1: back in two thousand, seventeen or eighteen and uh last quarter, 283 00:15:55,320 --> 00:15:59,480 Speaker 1: recurring revenue including software and services represented our business, which 284 00:15:59,520 --> 00:16:03,080 Speaker 1: is significantly different than where we were five, six, seven 285 00:16:03,120 --> 00:16:06,080 Speaker 1: years ago. You mentioned earlier the pandemic and what that did. Intact, 286 00:16:06,080 --> 00:16:08,960 Speaker 1: I didn't change your business, and maybe over the longer term, 287 00:16:09,000 --> 00:16:11,320 Speaker 1: maybe not just over the short term, but we're their 288 00:16:11,320 --> 00:16:15,480 Speaker 1: fundamental shifts in the use of technology that fect Cisco. Well, 289 00:16:15,480 --> 00:16:19,160 Speaker 1: I think it became clear. I really believe that prior 290 00:16:19,200 --> 00:16:22,240 Speaker 1: to the pandemic, every executive believed technology was strategic. Right. 291 00:16:22,280 --> 00:16:24,520 Speaker 1: We've a decade or fifteen years ago, we moved from 292 00:16:24,520 --> 00:16:28,000 Speaker 1: it being an operational productivity driver to really being a 293 00:16:28,000 --> 00:16:32,680 Speaker 1: strategic enabler and a strategic differentiator for our customers. During 294 00:16:32,680 --> 00:16:36,360 Speaker 1: the pandemic, it just elevated. I mean everyone's eyes were 295 00:16:36,400 --> 00:16:39,920 Speaker 1: open as to I mean, we kept the world running 296 00:16:40,840 --> 00:16:43,240 Speaker 1: when everyone was at home, We kept everyone productive, and 297 00:16:43,240 --> 00:16:45,520 Speaker 1: I think no one believed that was possible, including a 298 00:16:45,560 --> 00:16:47,960 Speaker 1: lot of people in tech, and uh, we've never tried 299 00:16:48,000 --> 00:16:50,080 Speaker 1: it before, so doing it at that scale, and I 300 00:16:50,080 --> 00:16:52,920 Speaker 1: think what that did is it gave So I think 301 00:16:52,920 --> 00:16:56,560 Speaker 1: it gave our customers this incredible confidence and investing in 302 00:16:56,640 --> 00:16:59,360 Speaker 1: technology and listening to their teams who are bringing them 303 00:16:59,400 --> 00:17:02,360 Speaker 1: these new, crazy creative projects around what they want to 304 00:17:02,360 --> 00:17:04,840 Speaker 1: do with technology, and they go, well, I've seen this, 305 00:17:05,080 --> 00:17:06,879 Speaker 1: I've seen it work here, so I'm gonna trust you 306 00:17:06,920 --> 00:17:09,159 Speaker 1: and believe you. So I think that's one piece of it. 307 00:17:09,200 --> 00:17:11,800 Speaker 1: And I think from a cultural perspective, I think every 308 00:17:11,840 --> 00:17:15,640 Speaker 1: company's culture was magnified during the pandemic, and I think 309 00:17:15,680 --> 00:17:18,600 Speaker 1: that we'll never go back to the way we were. 310 00:17:18,640 --> 00:17:22,240 Speaker 1: I think, uh, we're gonna operate in environments where employees 311 00:17:22,800 --> 00:17:25,920 Speaker 1: want to have human, authentic conversations, just like we did 312 00:17:25,920 --> 00:17:27,960 Speaker 1: over video while everybody was at home, and so I 313 00:17:28,000 --> 00:17:30,280 Speaker 1: think it changed how we engage with our employees as well. 314 00:17:30,320 --> 00:17:33,120 Speaker 1: Tell you what new crazy creative projects in teach. There's 315 00:17:33,119 --> 00:17:35,680 Speaker 1: a lot of talk around here about open AI, artificial 316 00:17:35,680 --> 00:17:38,200 Speaker 1: intelligence and by the way, quantum computing. I'm not sure 317 00:17:38,200 --> 00:17:40,240 Speaker 1: exactly what those mean, but there's a lot of talk 318 00:17:40,280 --> 00:17:43,320 Speaker 1: about them. Is that going to fundamentally change your business? Well, 319 00:17:43,359 --> 00:17:46,520 Speaker 1: it's we're working on quantum networking today because when you 320 00:17:46,680 --> 00:17:49,119 Speaker 1: have big networks of quantum computers, you have to be 321 00:17:49,160 --> 00:17:51,840 Speaker 1: able to connect them and actually move the bits at 322 00:17:51,920 --> 00:17:53,960 Speaker 1: at rates that are commensurate with the speed of the computers. 323 00:17:53,960 --> 00:17:55,600 Speaker 1: So there's a lot of research that we're engaged in 324 00:17:55,720 --> 00:17:57,840 Speaker 1: right now. So we're in the early days. We have 325 00:17:57,880 --> 00:18:01,160 Speaker 1: team of people doing research on quantum networking, so absolutely 326 00:18:01,200 --> 00:18:02,639 Speaker 1: that will be a part of our business. And then 327 00:18:02,680 --> 00:18:05,160 Speaker 1: these open AI and things like chat, GPT, and then 328 00:18:05,920 --> 00:18:08,720 Speaker 1: that's something that I actually had a conversation with my 329 00:18:08,760 --> 00:18:11,080 Speaker 1: team about and I think it's it's amazing how fast 330 00:18:11,119 --> 00:18:14,119 Speaker 1: it went from sort of being exposed to all of 331 00:18:14,160 --> 00:18:16,720 Speaker 1: a sudden My guess is my next board meeting, we'll 332 00:18:16,760 --> 00:18:19,800 Speaker 1: have a conversation about chat GPT and open AI, and 333 00:18:20,240 --> 00:18:22,359 Speaker 1: we think we're there's lots of great use cases that 334 00:18:22,400 --> 00:18:25,119 Speaker 1: we can leverage that technology for, and there's also a 335 00:18:25,119 --> 00:18:29,560 Speaker 1: lot of uh under probably some understood and some not understood, 336 00:18:29,920 --> 00:18:34,280 Speaker 1: unintended consequences from that technology. Think you'd very fundamentally disruptive. Absolutely, 337 00:18:35,080 --> 00:18:45,560 Speaker 1: I don't think there's any doubt. Want to own a 338 00:18:45,600 --> 00:18:48,639 Speaker 1: piece of Silicon Valley history, Maybe you now do. Because 339 00:18:48,680 --> 00:18:51,760 Speaker 1: Twitter was opening off it's old office supplies at the 340 00:18:51,880 --> 00:18:54,760 Speaker 1: six thirty one lots on offer, and some of them 341 00:18:54,760 --> 00:18:58,040 Speaker 1: went the tens of thousands of dollars, for example, a 342 00:18:58,080 --> 00:19:01,160 Speaker 1: blue Neon light, the Twitter about itself, maybe an apt 343 00:19:01,240 --> 00:19:03,800 Speaker 1: sign that was a planter, all of these going for 344 00:19:03,920 --> 00:19:07,000 Speaker 1: big bucks. The auction house behind it says that this 345 00:19:07,040 --> 00:19:09,879 Speaker 1: isn't actually to secure the financing of Twitter itself, although 346 00:19:09,920 --> 00:19:13,119 Speaker 1: we do know is in a relatively precarious situation at 347 00:19:13,160 --> 00:19:15,520 Speaker 1: least to be paying down debt, and Elon Musk himself 348 00:19:15,680 --> 00:19:17,560 Speaker 1: is or not paying some of the rents over in 349 00:19:17,600 --> 00:19:21,000 Speaker 1: San Francisco. But for now the auction is done, and 350 00:19:21,040 --> 00:19:25,760 Speaker 1: maybe you now have a piece of corporate history and 351 00:19:25,880 --> 00:19:27,879 Speaker 1: you've checked it out, because I think it's stopped the 352 00:19:27,920 --> 00:19:31,240 Speaker 1: auction twenty seven hours long, finished at ten am Pacific time. 353 00:19:31,280 --> 00:19:34,159 Speaker 1: Your time ed right thirty two thousand or there or 354 00:19:34,200 --> 00:19:37,879 Speaker 1: thereabouts for that Neon sign. This is kind of trophy stuff, 355 00:19:37,920 --> 00:19:40,359 Speaker 1: particularly if you work there. So I love that you 356 00:19:40,480 --> 00:19:43,600 Speaker 1: focused on kind of more glitzy, shiny items. What I 357 00:19:43,640 --> 00:19:47,800 Speaker 1: loved was just the endless thumbnails showing tables and chairs 358 00:19:47,840 --> 00:19:50,159 Speaker 1: and if you really wanted, you could buy a full 359 00:19:50,400 --> 00:19:53,960 Speaker 1: office set to furnish your office from Twitter issue. I 360 00:19:53,960 --> 00:19:56,639 Speaker 1: actually know a guy who's been in that building a 361 00:19:56,680 --> 00:19:59,840 Speaker 1: few times, Bloomberg Newses Kurt Wagner, who joins us now. 362 00:20:00,000 --> 00:20:02,359 Speaker 1: Because on top of that news, the report by the 363 00:20:02,400 --> 00:20:05,920 Speaker 1: Information that Twitter's revenue dropped as much as thirty five 364 00:20:06,000 --> 00:20:10,800 Speaker 1: percent last quarter from the same period a year ago. Goss, Twitter, 365 00:20:11,160 --> 00:20:15,440 Speaker 1: You and I we've had a rough two navigating this. 366 00:20:15,600 --> 00:20:17,800 Speaker 1: Let's let's start with the financials and we'll get to 367 00:20:17,840 --> 00:20:22,320 Speaker 1: the furniture. A drop in revenue as reported by the Information, 368 00:20:23,040 --> 00:20:25,120 Speaker 1: I mean, what's happening in sure? Yeah, I mean we've 369 00:20:25,160 --> 00:20:27,040 Speaker 1: been talking about this for a while, right, which is 370 00:20:27,080 --> 00:20:31,560 Speaker 1: that roughly of Twitter's revenue comes from advertising, and if 371 00:20:31,560 --> 00:20:34,119 Speaker 1: you're an advertiser right now, Twitter is a sort of 372 00:20:34,119 --> 00:20:38,400 Speaker 1: a scary place. It's not a comfortable, a safe environment 373 00:20:38,440 --> 00:20:41,320 Speaker 1: to be spending your money. Um, And so I don't 374 00:20:41,359 --> 00:20:43,080 Speaker 1: think any of us are shocked. We've we've seen the 375 00:20:43,119 --> 00:20:45,720 Speaker 1: headlines and news about a lot of these big advertisers 376 00:20:45,720 --> 00:20:48,200 Speaker 1: who are step have stepped back from Twitter, and so 377 00:20:48,320 --> 00:20:50,399 Speaker 1: the reality that this hit them very hard, especially in 378 00:20:50,440 --> 00:20:52,600 Speaker 1: the fourth quarter, when you know a lot of the 379 00:20:52,640 --> 00:20:55,280 Speaker 1: marketing budgets usually come about, is not a super big 380 00:20:55,320 --> 00:21:00,280 Speaker 1: surprise to me. How is Twitter in chief dealing all 381 00:21:00,320 --> 00:21:03,159 Speaker 1: of this because many it sort of commented that the 382 00:21:03,160 --> 00:21:06,119 Speaker 1: tweets coming from Elon Musque have got a little less 383 00:21:06,720 --> 00:21:08,800 Speaker 1: well controversial, shall we say, of late, But how is 384 00:21:08,840 --> 00:21:10,600 Speaker 1: he trying to ensure that people are coming back to 385 00:21:10,600 --> 00:21:13,920 Speaker 1: the platform wanting to advertise for him. Yeah, it does 386 00:21:14,000 --> 00:21:17,520 Speaker 1: seem like he has sort of maybe redirected some of 387 00:21:17,560 --> 00:21:21,440 Speaker 1: his attention, right and and perhaps that means to Tesla. Obviously, 388 00:21:21,520 --> 00:21:23,200 Speaker 1: at the end of the year beginning of this year, 389 00:21:23,240 --> 00:21:26,359 Speaker 1: there was a lot of um frustration from Tesla investors 390 00:21:26,400 --> 00:21:29,280 Speaker 1: that that he was not paying closer attention to the company. 391 00:21:29,280 --> 00:21:31,600 Speaker 1: And so this may just be simply that you know, 392 00:21:31,720 --> 00:21:33,280 Speaker 1: he he got there, He did a lot of the 393 00:21:33,320 --> 00:21:35,639 Speaker 1: budget cuts that he was planned, the layoffs that were planned, 394 00:21:35,920 --> 00:21:38,120 Speaker 1: and now he feels like he can sort of redirect 395 00:21:38,119 --> 00:21:42,000 Speaker 1: attention elsewhere, right, But I think like bringing people back, unfortunately, 396 00:21:42,000 --> 00:21:44,080 Speaker 1: that just takes time and it takes trust if you're 397 00:21:44,119 --> 00:21:47,040 Speaker 1: an advertiser, and I'm not sure that Twitter has really 398 00:21:47,080 --> 00:21:50,800 Speaker 1: earned um the trust back from those big brand marketers. 399 00:21:51,000 --> 00:21:52,520 Speaker 1: And it's probably the kind of thing that will take 400 00:21:52,560 --> 00:21:54,320 Speaker 1: a while and they'll have to watch and say, like, 401 00:21:54,520 --> 00:21:56,520 Speaker 1: is Ellen just taking a couple of weeks off of 402 00:21:56,760 --> 00:21:59,720 Speaker 1: tweeting controversial things or is this the new normal for 403 00:21:59,800 --> 00:22:01,400 Speaker 1: him moving forward? And I don't think we can say 404 00:22:01,440 --> 00:22:05,200 Speaker 1: that just yet. I want to dig deep into financials. 405 00:22:05,240 --> 00:22:07,919 Speaker 1: I want to talk about the world's second richest man 406 00:22:08,640 --> 00:22:10,760 Speaker 1: and how he's going to bring advertise this back. But 407 00:22:10,800 --> 00:22:12,800 Speaker 1: I'm not going to ask you about that. You want 408 00:22:12,840 --> 00:22:14,800 Speaker 1: to know about the chairs chairs. I want to know 409 00:22:14,840 --> 00:22:17,399 Speaker 1: about this auction. You're right, did you buy anything or 410 00:22:17,480 --> 00:22:20,320 Speaker 1: I didn't buy anything? Um, maybe a high top table. 411 00:22:20,440 --> 00:22:22,840 Speaker 1: You and Caroline can come over for dinner sometime. That 412 00:22:22,880 --> 00:22:25,560 Speaker 1: would be quite fun. But no, I mean, like it's sad, 413 00:22:25,760 --> 00:22:28,480 Speaker 1: right it is. There is a serious point behind this, 414 00:22:28,560 --> 00:22:30,679 Speaker 1: which is that many people lost their jobs, that building 415 00:22:30,800 --> 00:22:34,520 Speaker 1: was once full. It makes sense from a logistic standpoint, right, 416 00:22:34,520 --> 00:22:39,080 Speaker 1: if your Twitter used to have close to thousand employees 417 00:22:39,400 --> 00:22:42,400 Speaker 1: um and you now have of that, you don't need 418 00:22:42,440 --> 00:22:45,160 Speaker 1: all this office space, you don't need all this office furniture. 419 00:22:45,160 --> 00:22:48,040 Speaker 1: You don't need five hundred white boards or five hundred 420 00:22:48,119 --> 00:22:50,240 Speaker 1: standing desks or whatever may be, right, So it makes 421 00:22:50,280 --> 00:22:52,040 Speaker 1: sense that they would get rid of this. But I 422 00:22:52,080 --> 00:22:53,800 Speaker 1: think you know what a lot of people are seeing, right, 423 00:22:54,040 --> 00:22:57,200 Speaker 1: is sort of like a yard sale from this company 424 00:22:57,240 --> 00:23:00,280 Speaker 1: that had UH for a long time, like a very 425 00:23:00,520 --> 00:23:03,520 Speaker 1: visible culture, probably more than most tech companies, right with 426 00:23:03,520 --> 00:23:05,560 Speaker 1: the AT symbol or the big neon bird, Like these 427 00:23:05,600 --> 00:23:07,720 Speaker 1: are the things we would see pictures of at like 428 00:23:07,760 --> 00:23:09,919 Speaker 1: the office holiday party or whatever. Right, So I just 429 00:23:09,960 --> 00:23:13,360 Speaker 1: feel like maybe there's a little bit more UH kind 430 00:23:13,359 --> 00:23:16,720 Speaker 1: of connection to Twitter's culture than most companies, just because 431 00:23:16,720 --> 00:23:18,720 Speaker 1: so much of it plays out on the service itself. 432 00:23:19,080 --> 00:23:22,040 Speaker 1: So looking at the stats, the bird statue went for 433 00:23:22,080 --> 00:23:25,520 Speaker 1: a hundred thousand in the auction, but Heritage Global Partners, 434 00:23:25,520 --> 00:23:27,280 Speaker 1: who is behind the auction, kept saying that this isn't 435 00:23:27,280 --> 00:23:29,600 Speaker 1: for the finances. Why do it then, and why do 436 00:23:29,640 --> 00:23:33,600 Speaker 1: it say publicly? Well, I think to do it is 437 00:23:33,760 --> 00:23:35,639 Speaker 1: again sort of like a space thing, right, Like if 438 00:23:35,640 --> 00:23:37,720 Speaker 1: you're really getting rid of offices in the way that 439 00:23:37,760 --> 00:23:40,320 Speaker 1: we've seen Twitter get rid of close the Seattle office. 440 00:23:40,720 --> 00:23:42,560 Speaker 1: I think there was a Singapore office as they that 441 00:23:42,600 --> 00:23:44,920 Speaker 1: they closed last week as well, Like you've got to 442 00:23:45,000 --> 00:23:47,439 Speaker 1: do something with all the stuff that's in those offices. Right, 443 00:23:47,480 --> 00:23:50,399 Speaker 1: there's a logistics part of this where they no longer 444 00:23:50,440 --> 00:23:52,840 Speaker 1: have the space to hold it, So I think that's 445 00:23:52,840 --> 00:23:54,920 Speaker 1: probably part of it. Now doing it publicly, I guess, 446 00:23:55,119 --> 00:23:56,480 Speaker 1: how are you going to get the most bang for 447 00:23:56,560 --> 00:23:58,800 Speaker 1: your buck? Right? You have to make sure people know 448 00:23:58,920 --> 00:24:01,040 Speaker 1: to go bid on this thing. So I'm sort of 449 00:24:01,040 --> 00:24:03,320 Speaker 1: speculating here, right because we haven't heard Eylan say here's 450 00:24:03,359 --> 00:24:05,800 Speaker 1: the strategy behind the auction, But my guests would be 451 00:24:05,800 --> 00:24:07,160 Speaker 1: that that's why they want to do it the way 452 00:24:07,160 --> 00:24:09,840 Speaker 1: that they've been doing it all right. Bloomberg's Curt Wagner 453 00:24:10,000 --> 00:24:13,439 Speaker 1: covering every twist and turn inside and outside of Twitter, 454 00:24:13,480 --> 00:24:15,600 Speaker 1: Thank you so much. I actually want to turn to 455 00:24:15,600 --> 00:24:19,320 Speaker 1: the latest on Apple because another day, another scoop, and 456 00:24:19,359 --> 00:24:22,920 Speaker 1: it's taking on Amazon and Google, according to sources, by 457 00:24:22,960 --> 00:24:27,399 Speaker 1: expanding their in home product lineup. Meanwhile, Apple still planning 458 00:24:27,400 --> 00:24:31,040 Speaker 1: to unveil its first mixed reality headset this year, but 459 00:24:31,119 --> 00:24:35,800 Speaker 1: the plans for a lightweight augmented reality glasses have been postponed, 460 00:24:35,840 --> 00:24:39,720 Speaker 1: sources say, due to technical challenges. Bloomberg's Mark German broke 461 00:24:39,800 --> 00:24:41,920 Speaker 1: both of those stories in the space of a single 462 00:24:41,960 --> 00:24:45,119 Speaker 1: working day, and I'm delighted to say, joys, Caroline and I. 463 00:24:45,200 --> 00:24:49,639 Speaker 1: Now let's let's start with the in home devices, because actually, 464 00:24:49,640 --> 00:24:52,280 Speaker 1: when I was at CS in Vegas recently, that was 465 00:24:52,320 --> 00:24:55,000 Speaker 1: a big theme for many of the consumer electronics companies. 466 00:24:55,119 --> 00:24:58,760 Speaker 1: What are the details you've reported? So, actually, this morning 467 00:24:58,800 --> 00:25:01,920 Speaker 1: Apple rolled out a new home pod, right, it's basically 468 00:25:01,960 --> 00:25:04,240 Speaker 1: the return of the original home Pod. It's a bit 469 00:25:04,320 --> 00:25:08,439 Speaker 1: lower cost because they've rolled it out at three instead 470 00:25:08,440 --> 00:25:11,280 Speaker 1: of the original three fifty of the home Pod, there's 471 00:25:11,320 --> 00:25:15,119 Speaker 1: a few there's fewer microphones and tweeters inside the device, 472 00:25:15,160 --> 00:25:17,600 Speaker 1: so not as powerful as the previous one. And it 473 00:25:17,720 --> 00:25:21,040 Speaker 1: uses an Apple Watch processor instead of an iPhone processor, 474 00:25:21,280 --> 00:25:23,119 Speaker 1: so you have a little bit of a shift there. 475 00:25:23,160 --> 00:25:26,640 Speaker 1: But for audio fans, you're really not getting better than 476 00:25:26,640 --> 00:25:28,800 Speaker 1: an Apple home Pod, the big one at three bucks 477 00:25:28,800 --> 00:25:30,680 Speaker 1: in your home. So I know a lot of people 478 00:25:30,680 --> 00:25:33,520 Speaker 1: are looking forward to that product. Now they're working on 479 00:25:33,560 --> 00:25:35,760 Speaker 1: a few other devices. They're working on a faster Apple 480 00:25:35,760 --> 00:25:39,040 Speaker 1: TV for next year, though it won't have eight K functionality, 481 00:25:39,400 --> 00:25:41,400 Speaker 1: and they're also working on a new low end iPad 482 00:25:41,480 --> 00:25:45,720 Speaker 1: for the home smart home appliances, using it to FaceTime, 483 00:25:45,800 --> 00:25:48,040 Speaker 1: using it to watch some video. So that's the smart 484 00:25:48,080 --> 00:25:52,520 Speaker 1: home strategy for Apple moving forward away from smart home 485 00:25:52,760 --> 00:25:55,239 Speaker 1: to a r VA. Just tell us a little bit 486 00:25:55,240 --> 00:25:57,520 Speaker 1: about the strategy there, because it looks as though they're 487 00:25:57,520 --> 00:26:00,719 Speaker 1: struggling with the glasses at least. So Apple has been 488 00:26:00,760 --> 00:26:03,720 Speaker 1: developing it's a r VR strategy for north of six years, 489 00:26:03,760 --> 00:26:06,000 Speaker 1: seven years, eight years at this point, and for a 490 00:26:06,000 --> 00:26:08,680 Speaker 1: long time the strategy was twofold. We're first going to 491 00:26:08,840 --> 00:26:11,320 Speaker 1: roll out this high end halo device that's going to 492 00:26:11,400 --> 00:26:14,600 Speaker 1: be a mixed reality had set merging virtual reality and 493 00:26:14,680 --> 00:26:18,720 Speaker 1: augmented reality, a very complex, expensive three thousand dollar device 494 00:26:18,800 --> 00:26:20,840 Speaker 1: that will cast a shadow over the a r v 495 00:26:21,000 --> 00:26:24,040 Speaker 1: R market, and then as early as a year later 496 00:26:24,520 --> 00:26:27,400 Speaker 1: we would roll out glasses. These are lightweight, a our 497 00:26:27,520 --> 00:26:31,360 Speaker 1: only glasses. That plan has changed. The mixed reality had 498 00:26:31,400 --> 00:26:34,240 Speaker 1: set the high end one the reality pro that's still 499 00:26:34,280 --> 00:26:37,000 Speaker 1: coming this year, but instead of the follow up product 500 00:26:37,040 --> 00:26:39,960 Speaker 1: being the AAR glasses. The successive product is going to 501 00:26:40,080 --> 00:26:42,400 Speaker 1: be a new low end version of the mixed reality 502 00:26:42,440 --> 00:26:45,000 Speaker 1: head set, so one that's probably about half the price. 503 00:26:45,200 --> 00:26:49,040 Speaker 1: Somewhere closer to the same price is the high end 504 00:26:49,240 --> 00:26:53,120 Speaker 1: Meta quest Pro head set. Now, the bigger news those 505 00:26:53,160 --> 00:26:56,720 Speaker 1: are glasses. Those are not coming anytime soon. Those have 506 00:26:56,840 --> 00:27:01,760 Speaker 1: been pushed back indefinitely. Lots of technical challenges, lots of difficulty, right, 507 00:27:02,080 --> 00:27:04,240 Speaker 1: the big headset is going to have about two hours 508 00:27:04,240 --> 00:27:06,800 Speaker 1: of battery life. The a R glasses or something you 509 00:27:06,840 --> 00:27:09,199 Speaker 1: need to wear all day without needing to recharge them 510 00:27:09,240 --> 00:27:12,000 Speaker 1: until nighttime, like a phone or an Apple Watch. If 511 00:27:12,000 --> 00:27:14,560 Speaker 1: the high end device is getting two hours, how could 512 00:27:14,560 --> 00:27:17,919 Speaker 1: a device with even less space for components inside be 513 00:27:18,000 --> 00:27:21,760 Speaker 1: worn all day. There's also challenges related to the displays, right. 514 00:27:21,880 --> 00:27:25,000 Speaker 1: That's called a wave guide technology. To be able to 515 00:27:25,040 --> 00:27:27,680 Speaker 1: see things in front of you while also being able 516 00:27:27,720 --> 00:27:30,520 Speaker 1: to glean information off and displays. They'll have to be 517 00:27:30,520 --> 00:27:34,120 Speaker 1: connected to cellular radios and WiFi, AM Bluetooth at all times. 518 00:27:34,160 --> 00:27:36,359 Speaker 1: So much complexity in that product. So it's gonna be 519 00:27:36,400 --> 00:27:39,840 Speaker 1: many years before we see augmented reality glasses from Apple. 520 00:27:40,600 --> 00:27:44,119 Speaker 1: Expertly analyze for our smart gum and thank you as always, 521 00:27:44,160 --> 00:27:46,480 Speaker 1: will let you get back to your scoop getting Meanwhile, 522 00:27:46,520 --> 00:27:49,760 Speaker 1: coming up, Netflix picks up steam ahead of its earnings 523 00:27:50,040 --> 00:28:03,560 Speaker 1: all important for tomorrow this bring back Netflix earnings as 524 00:28:03,640 --> 00:28:07,920 Speaker 1: usual will focus on the company's new subscribe account. Every quarter, 525 00:28:08,000 --> 00:28:11,440 Speaker 1: it is that way investors always thinking subscribeer growth, did 526 00:28:11,440 --> 00:28:12,880 Speaker 1: it go up? Did it go down? But this time 527 00:28:12,920 --> 00:28:16,600 Speaker 1: around they'll also be looking to initiatives to increase revenue, 528 00:28:16,720 --> 00:28:18,840 Speaker 1: and of course all the rage in the world of 529 00:28:18,920 --> 00:28:23,280 Speaker 1: streaming is add supported tiers, which Netflix launched in November. 530 00:28:23,400 --> 00:28:26,760 Speaker 1: Matt Spiegel, executive vice president of the Media and Entertainment 531 00:28:26,840 --> 00:28:31,200 Speaker 1: Verse call a TransUnion, joins us, it's that interesting dynamic. 532 00:28:31,280 --> 00:28:35,919 Speaker 1: We are obsessed with subscriber growth at all streaming platforms, 533 00:28:35,920 --> 00:28:39,880 Speaker 1: but particularly Netflix. Yet this quarter we're gonna ask ourselves 534 00:28:40,320 --> 00:28:44,120 Speaker 1: how successful as Netflix been in attracting US customers in 535 00:28:44,160 --> 00:28:47,840 Speaker 1: particular to an ad supported tier. Is that the focus 536 00:28:47,880 --> 00:28:51,040 Speaker 1: for you? Yeah? Well, thanks for having me. It certainly is, 537 00:28:51,040 --> 00:28:53,640 Speaker 1: and I quite frankly think it's going to be a challenge, 538 00:28:54,280 --> 00:28:57,320 Speaker 1: but that's ultimately because they're a victim over their own success. 539 00:28:57,840 --> 00:29:00,480 Speaker 1: Do you look at how big Netflix is in the US, 540 00:29:00,520 --> 00:29:03,040 Speaker 1: they really have more subscribers than really any of the 541 00:29:03,080 --> 00:29:06,360 Speaker 1: stream players in the country, and so really the hard 542 00:29:06,400 --> 00:29:09,400 Speaker 1: part is how do you get that penetration to shift 543 00:29:09,440 --> 00:29:11,440 Speaker 1: to add dollars or to an add experience. And the 544 00:29:11,720 --> 00:29:14,720 Speaker 1: likely reality is most of us who have Netflix accounts 545 00:29:14,720 --> 00:29:17,360 Speaker 1: aren't going to do that, and so my guess is 546 00:29:17,400 --> 00:29:19,400 Speaker 1: over the short term it's going to take a little 547 00:29:19,400 --> 00:29:21,440 Speaker 1: while for them to get to the numbers they expect 548 00:29:21,920 --> 00:29:23,720 Speaker 1: on the add model. I think over the long term 549 00:29:23,720 --> 00:29:25,760 Speaker 1: they'll be fine. But you know, if i'd make a 550 00:29:25,760 --> 00:29:29,040 Speaker 1: predictionable here tomorrow, my guests is less AD users than 551 00:29:29,080 --> 00:29:32,440 Speaker 1: they might have ultimately hoped for um, but we'll build 552 00:29:32,480 --> 00:29:37,800 Speaker 1: over time. I actually have some some sympathy to to Netflix, 553 00:29:37,800 --> 00:29:41,120 Speaker 1: so I know have frustrations about how micro focused we are. 554 00:29:41,240 --> 00:29:45,479 Speaker 1: Consensus is for new subscribe editions around four point five million. 555 00:29:45,800 --> 00:29:47,440 Speaker 1: But Karen, I have talked about this in the past. 556 00:29:47,440 --> 00:29:50,280 Speaker 1: There's a whole generation of user out there, many of 557 00:29:50,280 --> 00:29:53,520 Speaker 1: them young people who have never seen ads. They grew 558 00:29:53,600 --> 00:29:56,520 Speaker 1: up where they could stream whatever they wanted whenever they wanted, 559 00:29:56,640 --> 00:30:00,440 Speaker 1: add free, and I wonder if that is a big 560 00:30:00,480 --> 00:30:03,920 Speaker 1: factor here, not just for Netflix, for Disney the attractiveness 561 00:30:03,960 --> 00:30:08,160 Speaker 1: of an ad supportive product, many people just don't want it. Yeah, listen, 562 00:30:08,200 --> 00:30:10,960 Speaker 1: that's fair. Certainly, no consumers says they want ads. But 563 00:30:11,000 --> 00:30:13,320 Speaker 1: if you look at really what happens in the entirety 564 00:30:13,360 --> 00:30:15,800 Speaker 1: of the media ecosystem, there's always a balance there. The 565 00:30:16,320 --> 00:30:19,360 Speaker 1: practical reality as we as consumers can only spend money 566 00:30:19,440 --> 00:30:22,800 Speaker 1: on so many things to really subscribe to. But and 567 00:30:22,800 --> 00:30:26,920 Speaker 1: we're really willing to actually exchange our time, uh, in 568 00:30:27,040 --> 00:30:30,600 Speaker 1: exchange for really subsidizing that experience. Think about certainly the 569 00:30:30,680 --> 00:30:34,360 Speaker 1: cable experience and the linear experience will much less prevalent 570 00:30:34,360 --> 00:30:36,640 Speaker 1: than it was, there are still times we do that, 571 00:30:36,680 --> 00:30:39,040 Speaker 1: and so I don't think we have a question of 572 00:30:39,040 --> 00:30:41,640 Speaker 1: whether advertising is part of the ecosystem or not. You 573 00:30:41,720 --> 00:30:44,800 Speaker 1: really have You've got premium content creators, Netflix being one 574 00:30:44,840 --> 00:30:47,000 Speaker 1: of them, which is always going to look for an 575 00:30:47,040 --> 00:30:49,840 Speaker 1: opportunity to make additional revenue and by the way, very 576 00:30:49,920 --> 00:30:52,880 Speaker 1: highly a creative revenue when its scales in the form 577 00:30:52,920 --> 00:30:56,560 Speaker 1: of advertising. So that balance between subscribers and advertising, I 578 00:30:56,560 --> 00:30:59,640 Speaker 1: don't think it's going anywhere. And sure many consumers won't 579 00:31:00,040 --> 00:31:02,560 Speaker 1: always want advertising, but my guess is you'll you'll always 580 00:31:02,560 --> 00:31:05,000 Speaker 1: find a market somewhere one of those markets. I mean, 581 00:31:05,040 --> 00:31:07,080 Speaker 1: the only time I want advertising is when I'm watching 582 00:31:07,120 --> 00:31:09,840 Speaker 1: the Super Bowl, right, So Matt, to that end, what 583 00:31:09,960 --> 00:31:12,600 Speaker 1: about sport? What about live events? Is that how we're 584 00:31:12,600 --> 00:31:14,959 Speaker 1: going to see the actual product and some of the 585 00:31:14,960 --> 00:31:18,000 Speaker 1: content they provide its rate over a Netflix Listen, I 586 00:31:18,040 --> 00:31:20,360 Speaker 1: think that's exactly right. What we're really talking about is 587 00:31:20,400 --> 00:31:23,200 Speaker 1: netflixs becoming a media company much like the others, right, 588 00:31:23,240 --> 00:31:26,400 Speaker 1: different from a streaming content company that was all about subs. 589 00:31:26,440 --> 00:31:29,160 Speaker 1: You're now talking about having to attract the type of 590 00:31:29,160 --> 00:31:33,120 Speaker 1: eyeballs at one time in scale to balance that ad model. 591 00:31:33,240 --> 00:31:35,760 Speaker 1: And you're right. You're seeing them already invest in or 592 00:31:36,520 --> 00:31:39,160 Speaker 1: reportedly invest in the opportunity to take sports right, and 593 00:31:39,200 --> 00:31:41,120 Speaker 1: I think that will be something they'll continue to do. 594 00:31:41,200 --> 00:31:44,160 Speaker 1: You've clearly seen that work well with an Amazon crime 595 00:31:44,800 --> 00:31:46,800 Speaker 1: and ultimately, if you want to be in the media 596 00:31:46,840 --> 00:31:50,000 Speaker 1: business inclusive advertising, you need to have those quote water 597 00:31:50,040 --> 00:31:52,800 Speaker 1: cooler moments. It's not only sports, but it is clearly 598 00:31:52,840 --> 00:31:56,520 Speaker 1: heavily dominated by sports these days. Those live moments really 599 00:31:56,520 --> 00:31:59,240 Speaker 1: do matter, and that's really what all the media companies 600 00:31:59,240 --> 00:32:02,320 Speaker 1: are finding that right. Balance right, The linear model clearly 601 00:32:02,480 --> 00:32:05,280 Speaker 1: is shifting. Uh, it's not going to die for a while. 602 00:32:05,840 --> 00:32:08,560 Speaker 1: Streaming will be the dominant form of distribution those time 603 00:32:08,600 --> 00:32:11,480 Speaker 1: goes on, but that can be on demand, it can 604 00:32:11,480 --> 00:32:14,240 Speaker 1: still be what really feels like a linear TV ad 605 00:32:14,280 --> 00:32:17,360 Speaker 1: business just in a streaming environment, which is things like 606 00:32:17,440 --> 00:32:20,400 Speaker 1: live sports and balancing all those things is going to 607 00:32:20,440 --> 00:32:22,800 Speaker 1: be what companies like Netflix have to do to compete. 608 00:32:23,040 --> 00:32:26,280 Speaker 1: Listen that they are absolutely in competition for against the 609 00:32:26,360 --> 00:32:28,960 Speaker 1: Disneys and the Warner Brothers of the world, and that's 610 00:32:29,000 --> 00:32:31,560 Speaker 1: part of the ecosystem and kind of I wonder what 611 00:32:31,640 --> 00:32:33,959 Speaker 1: sets them apart from some of the competitors is that 612 00:32:34,080 --> 00:32:37,000 Speaker 1: they get they've got technology in their bones. They all 613 00:32:37,120 --> 00:32:39,320 Speaker 1: group of engineers. They're sort of a tech first but 614 00:32:39,400 --> 00:32:41,600 Speaker 1: also media player, and I'm interested as to whether that 615 00:32:41,640 --> 00:32:44,640 Speaker 1: sets them apart. Can they win the personalization race, if 616 00:32:44,640 --> 00:32:48,200 Speaker 1: perhaps at all putting back on content spend now, Yeah, 617 00:32:48,200 --> 00:32:50,000 Speaker 1: I think that's gonna have to be part of it. Um. 618 00:32:50,120 --> 00:32:52,960 Speaker 1: Certainly there are other companies that have good technology as well, 619 00:32:52,960 --> 00:32:55,120 Speaker 1: but Netflix, to your point, has that at their bones. 620 00:32:55,400 --> 00:32:57,800 Speaker 1: I think that's gonna impact not only their content strategy 621 00:32:57,840 --> 00:32:59,760 Speaker 1: but also their ad strategy. You really got to give 622 00:32:59,800 --> 00:33:02,200 Speaker 1: them credit for how they've approached the ad business so far, 623 00:33:02,840 --> 00:33:05,040 Speaker 1: some really gate you know, senior hires in that team 624 00:33:05,080 --> 00:33:07,560 Speaker 1: a partnership with Microsoft, which makes a ton of sense. 625 00:33:07,880 --> 00:33:10,080 Speaker 1: And so while we're certainly gonna, like I said, I 626 00:33:10,080 --> 00:33:12,560 Speaker 1: expected a short term, it's gonna be a harder ramp 627 00:33:13,040 --> 00:33:15,080 Speaker 1: for Netflix to get to the point where their ad 628 00:33:15,080 --> 00:33:18,040 Speaker 1: business is as scaled as they hope. Um. But having 629 00:33:18,080 --> 00:33:21,600 Speaker 1: that DNA, which is part content, part technology, I agree, 630 00:33:21,640 --> 00:33:23,960 Speaker 1: can only help. You know, certainly, the ad business is 631 00:33:24,040 --> 00:33:27,560 Speaker 1: one where the mix of scale and precision matters right. 632 00:33:27,640 --> 00:33:31,240 Speaker 1: Advertisers are absolutely absolutely looking for that balance and a 633 00:33:31,360 --> 00:33:34,120 Speaker 1: Netflix who can hopefully invent in the right ways to 634 00:33:34,160 --> 00:33:36,920 Speaker 1: provide both a scaled audience but doing that in a 635 00:33:36,960 --> 00:33:40,240 Speaker 1: real targeted way with interesting new add creative which again, 636 00:33:40,320 --> 00:33:42,560 Speaker 1: let's be clear, this is gonna take time. It will 637 00:33:42,640 --> 00:33:45,360 Speaker 1: it will be an evolutionary process, but there's a lot 638 00:33:45,400 --> 00:33:47,800 Speaker 1: to be excited about for Netflix over the long term. 639 00:33:47,960 --> 00:33:50,080 Speaker 1: Now speak of great to have you on that look 640 00:33:50,120 --> 00:33:54,000 Speaker 1: ahead to the earnings. Executive vice president at TransUnion, I mean, well, 641 00:33:54,120 --> 00:33:57,520 Speaker 1: let's pivot back to Switzerland the out to doubles because 642 00:33:57,960 --> 00:34:01,000 Speaker 1: HCl Tech the software giant and it's based in India 643 00:34:01,480 --> 00:34:03,680 Speaker 1: is coming over to talk about how they're focusing on 644 00:34:03,920 --> 00:34:06,800 Speaker 1: s G or nonlinear growth in the long term. Then 645 00:34:06,880 --> 00:34:09,960 Speaker 1: Bloomberg's has an endarm and discussed the company's priorities. When 646 00:34:10,000 --> 00:34:16,120 Speaker 1: the HCl Tech chairperson, that's what's yourn email Hotrah. So, 647 00:34:16,160 --> 00:34:19,399 Speaker 1: I think, you know, we've got a couple of five 648 00:34:19,440 --> 00:34:22,719 Speaker 1: strategic objectives, the first one being we are an IT 649 00:34:23,000 --> 00:34:27,279 Speaker 1: services group and we cover UM digital engineering, cloud as 650 00:34:27,280 --> 00:34:29,840 Speaker 1: well as software. So we want to be able to 651 00:34:30,719 --> 00:34:34,520 Speaker 1: lead with differentiated products and services. We want to be 652 00:34:34,560 --> 00:34:37,920 Speaker 1: able to be a preferred digital partner for global two 653 00:34:38,000 --> 00:34:43,120 Speaker 1: thousand companies in preferred geographies. UM. You know, I think 654 00:34:44,280 --> 00:34:46,760 Speaker 1: weaving E s G into a lot of the business 655 00:34:46,800 --> 00:34:50,480 Speaker 1: strategy is absolutely critical for us. UM A c L 656 00:34:50,520 --> 00:34:54,920 Speaker 1: Technologies hires two five thousand people across the world and 657 00:34:55,000 --> 00:34:57,200 Speaker 1: the average age is twenty seven and below. So I 658 00:34:57,239 --> 00:35:01,200 Speaker 1: think today their strategic priority for a company has to 659 00:35:01,239 --> 00:35:05,560 Speaker 1: be to be a preferred employer. And and then lastly, UM, 660 00:35:05,840 --> 00:35:08,600 Speaker 1: I think if we're able to meet these strategic objectives, 661 00:35:09,000 --> 00:35:13,960 Speaker 1: then you know, delivering the highest shareholder return in medium 662 00:35:14,000 --> 00:35:19,680 Speaker 1: to term it should be the ultimate strategic direction. There's 663 00:35:19,680 --> 00:35:25,239 Speaker 1: so much uncertainty there's so much disruption, what's use strategy 664 00:35:25,360 --> 00:35:28,840 Speaker 1: to stay relevant? So I think, like I said, I 665 00:35:28,880 --> 00:35:31,640 Speaker 1: think one part of it is technology, and what's the 666 00:35:31,800 --> 00:35:35,560 Speaker 1: growth strategy. I think the growth strategy is investing in 667 00:35:35,560 --> 00:35:39,839 Speaker 1: our people and investing in skills. Um you know, like 668 00:35:39,880 --> 00:35:43,200 Speaker 1: I said, we've got twenty five thousand people all over 669 00:35:43,239 --> 00:35:46,000 Speaker 1: the world. Technology is moving a lot faster than the 670 00:35:46,040 --> 00:35:49,480 Speaker 1: skills can keep up. We do about two million to 671 00:35:49,600 --> 00:35:53,719 Speaker 1: three million hours of training a quota, and that is 672 00:35:53,719 --> 00:35:56,879 Speaker 1: only going to grow. We also have to find non 673 00:35:57,000 --> 00:36:01,000 Speaker 1: linear ways of growth. So if you look at our 674 00:36:01,040 --> 00:36:05,040 Speaker 1: software portfolio, which is quite unique to eight sale technologies, 675 00:36:05,120 --> 00:36:08,399 Speaker 1: it's about two billion of our twelve billion revenue. It's 676 00:36:08,680 --> 00:36:13,440 Speaker 1: much more non linear. So while the balance ten billion 677 00:36:13,719 --> 00:36:16,440 Speaker 1: would have more than a hundred and fifty thousand people 678 00:36:16,600 --> 00:36:19,719 Speaker 1: working on it, are two billion in software will have 679 00:36:19,800 --> 00:36:23,120 Speaker 1: about five thousand. You talk about being a twelve billion 680 00:36:23,120 --> 00:36:25,600 Speaker 1: dollar company, how do you take it to thirty? How 681 00:36:25,600 --> 00:36:27,839 Speaker 1: do you take it to fifty? When can you get there? 682 00:36:28,760 --> 00:36:30,279 Speaker 1: I don't know when we're going to get there. I 683 00:36:30,280 --> 00:36:32,799 Speaker 1: think I can only talk about our strategic vision in 684 00:36:32,840 --> 00:36:37,279 Speaker 1: the next five to seven years. But the investment that 685 00:36:37,320 --> 00:36:39,960 Speaker 1: we made in eight c L software, which is a 686 00:36:39,960 --> 00:36:43,000 Speaker 1: two billion that we get from you know, five thousand 687 00:36:43,160 --> 00:36:46,680 Speaker 1: engineers is a non linear growth and I think that 688 00:36:46,760 --> 00:36:50,040 Speaker 1: as we go forward, we're going to want to grow 689 00:36:50,080 --> 00:36:56,040 Speaker 1: that business as well. Indian ID executives lament that there 690 00:36:56,120 --> 00:36:59,800 Speaker 1: is a gap between engineering education and what the industries 691 00:36:59,840 --> 00:37:02,960 Speaker 1: are actually need. How do you bridge that gap, especially 692 00:37:03,400 --> 00:37:06,120 Speaker 1: now that you're part of the thin M T. S 693 00:37:06,840 --> 00:37:16,160 Speaker 1: Dean of the Global Advisory come, thank you so. Um. 694 00:37:16,200 --> 00:37:18,040 Speaker 1: You know, there are a couple of experiments that we're 695 00:37:18,040 --> 00:37:20,120 Speaker 1: doing in eight c L. I of course talked about 696 00:37:21,200 --> 00:37:26,320 Speaker 1: investing in a lot of training, which you know, closes 697 00:37:26,360 --> 00:37:29,560 Speaker 1: the gap between fresh engineers and what they need to 698 00:37:29,600 --> 00:37:32,480 Speaker 1: do to deliver to customers all over the world. We're 699 00:37:32,520 --> 00:37:37,000 Speaker 1: also doing something very interesting, which is we've got eight 700 00:37:37,080 --> 00:37:41,640 Speaker 1: thousand UM fresh school graduates in the eighth c L 701 00:37:41,719 --> 00:37:45,600 Speaker 1: system and we've partnered with university so that on the 702 00:37:45,640 --> 00:37:50,040 Speaker 1: weekends they can pursue an undergraduate education, but Monday to Friday, 703 00:37:50,400 --> 00:37:53,920 Speaker 1: these are eighteen year olds, their digital natives. Um, you know, 704 00:37:54,000 --> 00:37:57,239 Speaker 1: we got them from grade twelve. We've got fifty percent women, 705 00:37:57,400 --> 00:38:01,839 Speaker 1: young women as part of this cohort and after you know, 706 00:38:02,200 --> 00:38:04,840 Speaker 1: eight to nine months of training, they're ready to deliver. 707 00:38:05,200 --> 00:38:10,080 Speaker 1: So it's also looking at alternate talent calls. And you know, um, 708 00:38:10,120 --> 00:38:11,960 Speaker 1: as you go forward in the world, you're going to 709 00:38:12,080 --> 00:38:15,480 Speaker 1: realize that there are more people who don't pursue higher 710 00:38:15,600 --> 00:38:19,040 Speaker 1: education than those who actually do, because higher education is 711 00:38:19,040 --> 00:38:22,879 Speaker 1: becoming prohibitively expensive. So how do we actually tap into 712 00:38:22,920 --> 00:38:26,320 Speaker 1: different talent calls? And so I think that's quite unique 713 00:38:26,320 --> 00:38:38,040 Speaker 1: to a cl tech going viral today? What was the 714 00:38:38,080 --> 00:38:41,400 Speaker 1: fear of opening your bank account to see a negative balance? 715 00:38:41,680 --> 00:38:44,680 Speaker 1: Dozens of Bank of America customers using the transaction service 716 00:38:44,719 --> 00:38:48,960 Speaker 1: zell they were tweeting about how their funds Sunday disappeared overnight, 717 00:38:49,040 --> 00:38:51,319 Speaker 1: although they had trouble logging into the Banking Apple tool. 718 00:38:51,440 --> 00:38:54,520 Speaker 1: Now the website down detector had hundreds of reports. Then 719 00:38:54,560 --> 00:38:56,319 Speaker 1: there were issues with zul. Take a look at this 720 00:38:56,400 --> 00:38:59,160 Speaker 1: chart and app similar of course, the vemmo built by 721 00:38:59,200 --> 00:39:01,720 Speaker 1: a group of news biggest banks like Bank of America. 722 00:39:02,000 --> 00:39:05,799 Speaker 1: Those reports they seem to have died down by Wednesday afternoon. Now, 723 00:39:05,840 --> 00:39:09,240 Speaker 1: according to American Banker, Zel runs more than one point 724 00:39:09,280 --> 00:39:13,480 Speaker 1: six billion dollars in transactions daily. This is heavily lent 725 00:39:13,600 --> 00:39:16,160 Speaker 1: upon an overall, I know that I use it. I 726 00:39:16,200 --> 00:39:19,320 Speaker 1: know that many many person uses to log on and 727 00:39:19,400 --> 00:39:23,200 Speaker 1: does see negative also freaked out. Yeah, I mean then 728 00:39:23,320 --> 00:39:25,680 Speaker 1: saying as P s A. I guess three pm Eastern 729 00:39:25,760 --> 00:39:29,080 Speaker 1: this was resolved. But you know, if if Instagram goes down, 730 00:39:29,120 --> 00:39:31,600 Speaker 1: Twitter goes down, whatever, your bank account goes down and 731 00:39:31,600 --> 00:39:35,040 Speaker 1: then shows missing funds, that's different and seeing social media 732 00:39:35,080 --> 00:39:38,000 Speaker 1: response savage. Yeah, I have to say some of the 733 00:39:38,080 --> 00:39:39,799 Speaker 1: memes are pretty great. Go and check them out on 734 00:39:39,840 --> 00:39:42,200 Speaker 1: Twitter or on Instagram. Meanwhile, that does it. From the 735 00:39:42,200 --> 00:39:45,560 Speaker 1: addition of technology, Thursday, we'll share new data about crypto 736 00:39:45,680 --> 00:39:51,640 Speaker 1: launderings from chain analysis. Don't forget the problem.