1 00:00:01,600 --> 00:00:06,880 Speaker 1: From Mahard where Innovation, Money and Power Collie in Silicon Valley, NBN. 2 00:00:07,240 --> 00:00:11,760 Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 3 00:00:24,720 --> 00:00:26,920 Speaker 3: And Caroline Heind of Bloomberg's world headquarters in New York, 4 00:00:27,280 --> 00:00:28,880 Speaker 3: and I'm Ed Ludlow in San Francisco. 5 00:00:29,000 --> 00:00:31,280 Speaker 4: This is Bloomberg Technology coming up Apple. 6 00:00:31,400 --> 00:00:32,800 Speaker 5: It hits an intra day record. 7 00:00:32,800 --> 00:00:35,640 Speaker 3: The SMP closes in on its all time highs after 8 00:00:35,680 --> 00:00:38,040 Speaker 3: a dovish FED signal. What does it mean for the 9 00:00:38,080 --> 00:00:38,600 Speaker 3: tech rally? 10 00:00:38,600 --> 00:00:43,200 Speaker 6: We discussed plus as GM's cruise slashes twenty four percent 11 00:00:43,240 --> 00:00:45,680 Speaker 6: of its workforce. We'll talk the state of the tech 12 00:00:45,760 --> 00:00:48,440 Speaker 6: labor market as more than two hundred and fifty thousand 13 00:00:48,479 --> 00:00:51,639 Speaker 6: workers at tech companies of all sizes have been let 14 00:00:51,680 --> 00:00:52,320 Speaker 6: go this year. 15 00:00:52,560 --> 00:00:56,840 Speaker 3: Meanwhile, Adobe facing regulatory scrutiny and for the company subscription practices, 16 00:00:56,920 --> 00:00:59,480 Speaker 3: we'll take a look at the FTC investigation and why 17 00:00:59,520 --> 00:01:03,480 Speaker 3: the company AI ambissions well may take longer than expected. 18 00:01:03,920 --> 00:01:06,520 Speaker 6: The other big mover this morning is Intel. It has 19 00:01:06,560 --> 00:01:09,200 Speaker 6: given us details on its AI strategy. The stock up 20 00:01:09,200 --> 00:01:11,120 Speaker 6: three percent or two and a half percent now it 21 00:01:11,160 --> 00:01:13,200 Speaker 6: had been up as much as five and a half percent. 22 00:01:13,440 --> 00:01:16,600 Speaker 6: You have new zeon server chips. Those chips have been 23 00:01:16,640 --> 00:01:19,680 Speaker 6: through two redesigns in the last year already. You also 24 00:01:19,760 --> 00:01:23,319 Speaker 6: have details around Goudi three, the AI accelerator. This is 25 00:01:23,400 --> 00:01:27,280 Speaker 6: the GPU CPU hybrid that basically is going to take 26 00:01:27,360 --> 00:01:29,840 Speaker 6: on in video's h one hundred. And you have Intel 27 00:01:29,880 --> 00:01:33,440 Speaker 6: with this fighting talk saying that Galdi three is going 28 00:01:33,440 --> 00:01:36,319 Speaker 6: to be as good, if not better than in videos 29 00:01:36,440 --> 00:01:38,640 Speaker 6: H one hundred, but it's nowhere to be seen yet, 30 00:01:38,680 --> 00:01:40,880 Speaker 6: doesn't come out until twenty twenty four, Caroline. But the 31 00:01:40,920 --> 00:01:44,240 Speaker 6: battleground for AI we were at AMD last week is 32 00:01:44,280 --> 00:01:45,000 Speaker 6: heating up now. 33 00:01:45,040 --> 00:01:46,840 Speaker 3: It really feels this end of the year as just 34 00:01:46,920 --> 00:01:49,840 Speaker 3: a florry to get as much of the innovation out 35 00:01:49,840 --> 00:01:51,800 Speaker 3: the door as they possibly can, to steal some of 36 00:01:51,840 --> 00:01:54,600 Speaker 3: the market share. The overall hype that there's been around AI, 37 00:01:54,720 --> 00:01:57,800 Speaker 3: and as you say, some fighting talk coming from Pat Gelsinger, 38 00:01:57,800 --> 00:02:00,000 Speaker 3: and they're like the Galdi three has the most approved, 39 00:02:00,080 --> 00:02:02,160 Speaker 3: But ultimately how much of a win do you think 40 00:02:02,280 --> 00:02:04,480 Speaker 3: is the fact that they'll be more efficient with some 41 00:02:04,520 --> 00:02:06,040 Speaker 3: of these chips going to the data centers. 42 00:02:06,680 --> 00:02:09,880 Speaker 6: What's interesting in Intel's case is that they basically are 43 00:02:09,919 --> 00:02:11,920 Speaker 6: saying the market's going to be so big there'll be 44 00:02:11,960 --> 00:02:15,000 Speaker 6: something for everyone. Arsn Vidia and AMD, but they're also 45 00:02:15,080 --> 00:02:17,840 Speaker 6: focusing on PCs. Remember that's their bread and butter. They're 46 00:02:17,880 --> 00:02:21,880 Speaker 6: the biggest PC processor maker. And we're increasingly talking about 47 00:02:21,880 --> 00:02:24,920 Speaker 6: on device right, the idea that the processing power of 48 00:02:24,960 --> 00:02:28,200 Speaker 6: our laptops and our phones needs to be able to 49 00:02:28,360 --> 00:02:32,120 Speaker 6: handle the inference side of a generative AI product. It 50 00:02:32,160 --> 00:02:34,519 Speaker 6: needs to have the compute to perform whether it's in 51 00:02:34,560 --> 00:02:36,440 Speaker 6: aeroplane mode or not. And that seems to be a 52 00:02:36,440 --> 00:02:37,960 Speaker 6: part of what Intel's discussing today. 53 00:02:38,000 --> 00:02:39,600 Speaker 3: And I think you're so right to say this is 54 00:02:39,600 --> 00:02:42,359 Speaker 3: about the rest of the ecosystem of chips. We keep 55 00:02:42,400 --> 00:02:44,480 Speaker 3: talking about H one hundreds and keep sort of talking 56 00:02:44,480 --> 00:02:46,680 Speaker 3: about the Gowdy threes, but there's an awful lot of 57 00:02:46,760 --> 00:02:50,720 Speaker 3: infrastructure play, an awful lot of other ultimately picks and shovers, 58 00:02:50,800 --> 00:02:52,760 Speaker 3: bolts and chips that need to go into all of 59 00:02:52,800 --> 00:02:55,440 Speaker 3: this to ensure that the AI becomes reality. Let's dig 60 00:02:55,440 --> 00:02:56,880 Speaker 3: into that a little bit more with our next guest, 61 00:02:57,000 --> 00:03:01,079 Speaker 3: ed Silvia Geblonski Ceocio Defiance ETS, who you know has 62 00:03:01,120 --> 00:03:03,359 Speaker 3: been coming off what is an extraordinary day in terms 63 00:03:03,400 --> 00:03:05,880 Speaker 3: of macro policy, and I'm interested if you can put 64 00:03:05,919 --> 00:03:08,720 Speaker 3: that onto the micro of whether or not technology and 65 00:03:08,760 --> 00:03:10,640 Speaker 3: the AI HiPE was still something to buy into at 66 00:03:10,680 --> 00:03:11,200 Speaker 3: these points. 67 00:03:11,600 --> 00:03:14,840 Speaker 2: Absolutely, So you know, I think yesterday felt like Santa 68 00:03:14,880 --> 00:03:16,160 Speaker 2: Claus came to town a little bit. 69 00:03:16,240 --> 00:03:16,360 Speaker 7: Right. 70 00:03:16,440 --> 00:03:18,800 Speaker 2: The market's like the outlook for next year, we're finally 71 00:03:18,840 --> 00:03:21,000 Speaker 2: done with ray hikes. I think, you know, we'll have 72 00:03:21,040 --> 00:03:23,640 Speaker 2: some restrictive policy, but at least no more kind of 73 00:03:23,680 --> 00:03:26,040 Speaker 2: like pounding on the gas to hike rates and you know, 74 00:03:26,080 --> 00:03:28,200 Speaker 2: kind of break the economy. So I think that sets 75 00:03:28,240 --> 00:03:30,760 Speaker 2: tech up very well. And I do think that the 76 00:03:30,800 --> 00:03:32,960 Speaker 2: next five to ten years of technology are going to 77 00:03:32,960 --> 00:03:36,320 Speaker 2: be super computing, quantum computing, and AI. And we've talked 78 00:03:36,320 --> 00:03:38,200 Speaker 2: so much this year about Navidia. You and I have 79 00:03:38,200 --> 00:03:40,080 Speaker 2: talked so much about Navidia and a MD this year, 80 00:03:40,120 --> 00:03:42,040 Speaker 2: and I think they're kind of the clear leaders and chips. 81 00:03:42,080 --> 00:03:44,280 Speaker 2: But what I think will happen next year is now 82 00:03:44,440 --> 00:03:46,840 Speaker 2: all of the floodgates will open, So Intel's going to 83 00:03:46,840 --> 00:03:50,240 Speaker 2: come in, companies like ion cbe like IBM, and you know, 84 00:03:50,280 --> 00:03:54,560 Speaker 2: the bigger components around AI, like data processing, super computing 85 00:03:55,040 --> 00:03:58,080 Speaker 2: five G for the speed to make AI work will 86 00:03:58,120 --> 00:04:00,400 Speaker 2: all become tradable. Themes and you're going to start getting, 87 00:04:00,640 --> 00:04:03,160 Speaker 2: you know, beyond the magnific magnificent. 88 00:04:02,680 --> 00:04:03,960 Speaker 5: Seven for AI place. 89 00:04:04,320 --> 00:04:08,280 Speaker 3: What's interesting though, is how much the exuberance becomes real 90 00:04:08,320 --> 00:04:09,040 Speaker 3: revenue now. 91 00:04:09,080 --> 00:04:10,440 Speaker 5: And Vidia has managed to prove. 92 00:04:10,240 --> 00:04:11,840 Speaker 3: That point to certain extent, but I think of Adobe, 93 00:04:11,920 --> 00:04:13,680 Speaker 3: We're going to dig into that story later in the show. 94 00:04:13,720 --> 00:04:16,440 Speaker 3: But ultimately it's earnings not living up to the hype 95 00:04:16,440 --> 00:04:18,279 Speaker 3: in terms of how quickly it can turn it into 96 00:04:18,320 --> 00:04:20,919 Speaker 3: revenue into the bottom line. How much can an IBM, 97 00:04:20,920 --> 00:04:23,640 Speaker 3: how much can quantum computing finally becomes the sort of 98 00:04:23,640 --> 00:04:24,240 Speaker 3: revenue driver. 99 00:04:24,520 --> 00:04:25,599 Speaker 5: Yeah, and that's a great point. 100 00:04:25,600 --> 00:04:28,000 Speaker 2: And I think what happened a little bit with AI 101 00:04:28,200 --> 00:04:30,360 Speaker 2: is that AI has been around for such a long time, 102 00:04:30,440 --> 00:04:32,920 Speaker 2: and there was a news flash around Microsoft this year 103 00:04:32,920 --> 00:04:34,960 Speaker 2: and then AI became kind of the word of the year. 104 00:04:35,320 --> 00:04:37,520 Speaker 2: But then it started to feel like a bubble and 105 00:04:37,560 --> 00:04:40,760 Speaker 2: people started to equate it to you know, memestock media 106 00:04:40,800 --> 00:04:43,440 Speaker 2: and you know, kind of kind of that world that happened, 107 00:04:43,440 --> 00:04:44,720 Speaker 2: and then it was kind of like thrown to the 108 00:04:44,760 --> 00:04:46,760 Speaker 2: side a little bit. But now it's back because to 109 00:04:46,839 --> 00:04:49,440 Speaker 2: your point, they're starting to show revenues. So I do 110 00:04:49,480 --> 00:04:51,719 Speaker 2: think that everyone who's sort of hitched the word AI 111 00:04:51,960 --> 00:04:53,960 Speaker 2: onto their wagon, is now going to have to prove 112 00:04:54,000 --> 00:04:56,400 Speaker 2: it in you know, Q one, Q two, probably actually 113 00:04:56,480 --> 00:04:59,240 Speaker 2: Q four as well earning. So I do think that 114 00:04:59,279 --> 00:05:01,359 Speaker 2: you're going to start that play out, and it is 115 00:05:01,400 --> 00:05:04,000 Speaker 2: super important for companies like IBM. So IBM is a 116 00:05:04,040 --> 00:05:07,520 Speaker 2: company that has a formed you know, quantum computer, and 117 00:05:07,560 --> 00:05:10,960 Speaker 2: they're actually working with companies like Cleveland Clinic to have 118 00:05:11,279 --> 00:05:13,400 Speaker 2: better outcomes and drug trials and things like this. So 119 00:05:13,480 --> 00:05:15,840 Speaker 2: quantum computing is now actually becoming more of a. 120 00:05:15,800 --> 00:05:17,120 Speaker 5: Reality and it exists. 121 00:05:17,160 --> 00:05:18,880 Speaker 2: Granted, you need a whole room the size of this 122 00:05:18,960 --> 00:05:20,560 Speaker 2: and a lot of air conditioners torovite it for a 123 00:05:20,600 --> 00:05:24,160 Speaker 2: process now, but it's there, right, So five to ten 124 00:05:24,240 --> 00:05:26,159 Speaker 2: years from now, you know, Sky's limit. 125 00:05:27,400 --> 00:05:32,480 Speaker 6: Taking a look at the defiance quantum etf TICKA, qt M, 126 00:05:32,520 --> 00:05:35,000 Speaker 6: with which of course you are familiar, it's yours and 127 00:05:35,040 --> 00:05:37,200 Speaker 6: all of the names you've just mentioned are in there. 128 00:05:37,640 --> 00:05:40,800 Speaker 6: What I find so interesting is how long does this 129 00:05:40,880 --> 00:05:41,480 Speaker 6: keep going? 130 00:05:41,800 --> 00:05:41,960 Speaker 4: Right? 131 00:05:42,000 --> 00:05:44,679 Speaker 6: You look at the performance year today up almost forty percent. 132 00:05:45,000 --> 00:05:48,400 Speaker 6: You have Lisa Sewer of AMD saying that the market 133 00:05:48,520 --> 00:05:50,560 Speaker 6: she saw as being one hundred and fifty billion dollars 134 00:05:50,640 --> 00:05:52,640 Speaker 6: just in August is actually going to be four hundred 135 00:05:52,680 --> 00:05:54,480 Speaker 6: billion in twenty twenty seven. 136 00:05:56,080 --> 00:05:59,000 Speaker 4: How do you keep this going? It's been ridiculous. 137 00:05:59,400 --> 00:06:00,880 Speaker 5: Yeah, that's a great point. 138 00:06:00,920 --> 00:06:04,120 Speaker 2: And I think what's interesting about you know, the CTF 139 00:06:04,160 --> 00:06:07,200 Speaker 2: and you know, some of the names I mentioned is IBM, 140 00:06:07,440 --> 00:06:10,200 Speaker 2: I ONQ, you know, Arabella. Some of the smaller types 141 00:06:10,240 --> 00:06:12,440 Speaker 2: of companies in this index are not companies that we're 142 00:06:12,440 --> 00:06:14,720 Speaker 2: talking about right now. We're talking about Microsoft. We're talking 143 00:06:14,760 --> 00:06:17,839 Speaker 2: about Google, Navidia, A m D, maybe a little bit 144 00:06:17,880 --> 00:06:20,080 Speaker 2: of other names in there. But what's going to happen 145 00:06:20,120 --> 00:06:21,800 Speaker 2: is that you have a whole lot of small cap 146 00:06:21,880 --> 00:06:23,720 Speaker 2: names in there. So you know, why aren't we up 147 00:06:23,720 --> 00:06:25,960 Speaker 2: two hundred percent? Where you're getting the performance of the 148 00:06:26,040 --> 00:06:28,719 Speaker 2: large caps, the small caps because of rates and you know, 149 00:06:28,800 --> 00:06:32,280 Speaker 2: kind of new to the scene types of conditions are 150 00:06:32,360 --> 00:06:35,720 Speaker 2: going to probably start to rally and pick up performance. 151 00:06:35,760 --> 00:06:37,400 Speaker 2: So I do think that this is going to ride 152 00:06:37,440 --> 00:06:40,679 Speaker 2: out as those smaller companies start to you know, generate 153 00:06:40,760 --> 00:06:43,400 Speaker 2: revenues and then are kind of cushioned by the Google 154 00:06:43,440 --> 00:06:45,440 Speaker 2: and the Microsoft might end up returning five to ten 155 00:06:45,440 --> 00:06:47,920 Speaker 2: percent next year and not you know, triple digit for 156 00:06:48,000 --> 00:06:50,040 Speaker 2: Navidias and things like that. So I think having that 157 00:06:50,120 --> 00:06:53,240 Speaker 2: well balanced mix of names you're not thinking about gives 158 00:06:53,279 --> 00:06:55,600 Speaker 2: it room to run Sylvie. 159 00:06:55,440 --> 00:06:58,360 Speaker 6: In the context of yesterday's FED meeting, the ECO data 160 00:06:58,440 --> 00:07:03,000 Speaker 6: we've had of late, how isolated is what's happening in 161 00:07:03,040 --> 00:07:07,119 Speaker 6: the AI industry for want of a better descriptor, from 162 00:07:07,160 --> 00:07:09,280 Speaker 6: what's actually happening in the global economy. 163 00:07:10,080 --> 00:07:10,680 Speaker 5: Yeah, So I. 164 00:07:10,640 --> 00:07:13,160 Speaker 2: Think I think that, you know, the AI industry is 165 00:07:13,240 --> 00:07:16,080 Speaker 2: kind of almost being you know, pigeonholed again into some 166 00:07:16,200 --> 00:07:19,520 Speaker 2: of the Magnificent seven and a couple of specific you know, 167 00:07:19,600 --> 00:07:23,440 Speaker 2: themes that are very sort of just like straight technology 168 00:07:23,520 --> 00:07:26,400 Speaker 2: focused AI data processing things like that, but we don't 169 00:07:26,440 --> 00:07:29,120 Speaker 2: really talk about, you know, the macro impact and how 170 00:07:29,240 --> 00:07:31,560 Speaker 2: you know that will play into what's going on. So, 171 00:07:31,720 --> 00:07:34,480 Speaker 2: you know, first, the government is going to increase AI 172 00:07:34,600 --> 00:07:36,960 Speaker 2: spending by you know, billions of dollars next year, so 173 00:07:37,000 --> 00:07:39,760 Speaker 2: there's going to be sort of money going into AI, supercomputing, 174 00:07:39,840 --> 00:07:43,400 Speaker 2: artificial intelligence. The growth of AI is creating this need 175 00:07:43,440 --> 00:07:46,600 Speaker 2: for cloud and cybersecurity, so that's going to increase in 176 00:07:46,920 --> 00:07:49,160 Speaker 2: you know, kind of macro business spending and things like this. 177 00:07:49,640 --> 00:07:51,440 Speaker 2: And then I think just overall, you're finally going to 178 00:07:51,440 --> 00:07:54,440 Speaker 2: start talking about how AI impacts different sectors, you know, 179 00:07:54,520 --> 00:07:57,720 Speaker 2: healthcare for example, are we getting those better kind of 180 00:07:57,720 --> 00:08:01,280 Speaker 2: like drug outcomes, surgical proceeds, your outcomes, things like this. 181 00:08:01,480 --> 00:08:03,200 Speaker 2: So I think the story is going to change more 182 00:08:03,200 --> 00:08:06,120 Speaker 2: about how is AI making companies more efficient? And that 183 00:08:06,200 --> 00:08:10,000 Speaker 2: started a little bit with digitalization of factories for missing workers, 184 00:08:10,160 --> 00:08:12,240 Speaker 2: but I think that'll really continue to play out. 185 00:08:12,480 --> 00:08:15,840 Speaker 3: I'm interested in well Ed says, maybe the rally's been 186 00:08:15,920 --> 00:08:19,000 Speaker 3: ridiculous when it comes to AI. Some might have said 187 00:08:19,040 --> 00:08:21,120 Speaker 3: that crypto is starting to look a bit ridiculous again, 188 00:08:21,320 --> 00:08:24,400 Speaker 3: but it's managed to top out about shot forty five thousand. 189 00:08:24,560 --> 00:08:27,120 Speaker 5: There has been a rule in twenty twenty three? Will 190 00:08:27,120 --> 00:08:29,640 Speaker 5: that continue? Is that all about Matt Crow? Is there 191 00:08:29,640 --> 00:08:32,240 Speaker 5: anything it is in classic that you like that? Yeah? 192 00:08:32,280 --> 00:08:35,679 Speaker 2: So I think that there's a little more room to 193 00:08:35,760 --> 00:08:38,400 Speaker 2: run in crypto. And that's because I suspect I can't 194 00:08:38,400 --> 00:08:40,440 Speaker 2: predict the future. If I suspect that these approvals that 195 00:08:40,480 --> 00:08:43,040 Speaker 2: we've kind of longer waited for are probably going to 196 00:08:43,040 --> 00:08:44,719 Speaker 2: play out, and then you know, so you're just going 197 00:08:44,760 --> 00:08:47,240 Speaker 2: to have kind of a shift of assets and you know, 198 00:08:47,280 --> 00:08:49,079 Speaker 2: in some regard and new products are going to have 199 00:08:49,120 --> 00:08:53,000 Speaker 2: to buy more bitcoin to create themselves, so you know, 200 00:08:53,040 --> 00:08:56,240 Speaker 2: you'll really have that demand need. But then once that 201 00:08:56,280 --> 00:08:58,560 Speaker 2: comes out, now all of a sudden, these products become 202 00:08:58,600 --> 00:09:02,040 Speaker 2: more institutionalized, easier to trade, you get better spreads, you 203 00:09:02,040 --> 00:09:05,040 Speaker 2: don't have to worry about kind of futures, rolling contangle 204 00:09:05,160 --> 00:09:08,040 Speaker 2: or managing digital wallets. And then I think the efficiency 205 00:09:08,040 --> 00:09:10,960 Speaker 2: of all that starts to play out more. So, you know, 206 00:09:11,040 --> 00:09:12,640 Speaker 2: do I think it's going to double or triple? I 207 00:09:12,679 --> 00:09:15,240 Speaker 2: don't know, but I do think that there's some solid 208 00:09:15,320 --> 00:09:18,640 Speaker 2: runway left, particularly around that once we get that stamp 209 00:09:18,720 --> 00:09:20,480 Speaker 2: like this is approved, I think, you know, I think 210 00:09:20,520 --> 00:09:21,400 Speaker 2: bitcoin's going. 211 00:09:21,240 --> 00:09:25,000 Speaker 3: To run all lies on that spot Bitcoin ETF. Sylvia Jamansky, 212 00:09:25,080 --> 00:09:26,599 Speaker 3: who knows a thing or two about ets, she just 213 00:09:26,640 --> 00:09:27,800 Speaker 3: of course the defiance ETF. 214 00:09:27,920 --> 00:09:29,319 Speaker 5: And we thank you so much for being in the studio. 215 00:09:29,559 --> 00:09:32,840 Speaker 6: Ed all right. Coming up on the program, twenty twenty 216 00:09:32,840 --> 00:09:35,679 Speaker 6: three was a brutal year for tech workers. We're going 217 00:09:35,720 --> 00:09:38,000 Speaker 6: to be joined by the creator of the tech Layoffs 218 00:09:38,040 --> 00:09:40,079 Speaker 6: Tracker Layoffs dot FYI. 219 00:09:40,320 --> 00:09:41,400 Speaker 4: That's coming up next. 220 00:09:50,040 --> 00:09:53,040 Speaker 3: Let's talk layoffs. Because Cruise, the autonomous vehicle unit of 221 00:09:53,080 --> 00:09:56,280 Speaker 3: General Motors, will lay off twenty four percent of its workforce. 222 00:09:56,280 --> 00:09:58,760 Speaker 3: Around nine hundred people, it'scording to a blog post by 223 00:09:58,760 --> 00:10:01,680 Speaker 3: the company. These layoffs come just one day after Cruise 224 00:10:01,720 --> 00:10:04,200 Speaker 3: also just missed nine of its top executives. Now, the 225 00:10:04,200 --> 00:10:07,000 Speaker 3: company has been trying to cut its costs in an 226 00:10:07,000 --> 00:10:10,680 Speaker 3: effort to revamp the company after, of course, that crucial 227 00:10:10,840 --> 00:10:13,720 Speaker 3: accident in which a Cruise card dragged a pedestrian in 228 00:10:13,800 --> 00:10:14,960 Speaker 3: October ed. 229 00:10:16,600 --> 00:10:18,520 Speaker 6: Yeah, it's been a big story here in this city 230 00:10:18,559 --> 00:10:21,280 Speaker 6: and for that company and Cruises. Layoffs are just the 231 00:10:21,400 --> 00:10:25,800 Speaker 6: latest occurrence piling onto this year's wave of layoffs. 232 00:10:25,280 --> 00:10:26,200 Speaker 4: In the tech sector. 233 00:10:26,400 --> 00:10:29,239 Speaker 6: In total, more than two hundred and fifty thousand workers 234 00:10:29,480 --> 00:10:32,280 Speaker 6: at tech companies big and small have been let go 235 00:10:32,440 --> 00:10:33,360 Speaker 6: in twenty twenty three. 236 00:10:33,440 --> 00:10:34,160 Speaker 4: That's according to. 237 00:10:34,160 --> 00:10:37,920 Speaker 6: The job tracker Layoffs dot FYI. Let's bring in the 238 00:10:38,000 --> 00:10:41,920 Speaker 6: job trackers creator Roger Lee to dig into that data. 239 00:10:42,040 --> 00:10:44,360 Speaker 6: I think a good starting point is to ask, well, 240 00:10:44,360 --> 00:10:47,600 Speaker 6: two hundred and fifty thousand, what does that look like 241 00:10:47,720 --> 00:10:51,679 Speaker 6: relative to prior years, prior periods of recession, prior periods 242 00:10:51,720 --> 00:10:53,559 Speaker 6: of economic pain. 243 00:10:55,320 --> 00:10:58,960 Speaker 7: Well, the number is very high, even compared to last 244 00:10:59,000 --> 00:11:01,800 Speaker 7: year when we first started seeing a wave of tech layoffs. 245 00:11:02,080 --> 00:11:04,959 Speaker 7: Twenty twenty three is the highest number that we've seen today. 246 00:11:05,160 --> 00:11:08,320 Speaker 7: So two hundred fifty thousand tech employees have been laid 247 00:11:08,320 --> 00:11:11,400 Speaker 7: off so far this year. That's up from one hundred 248 00:11:11,400 --> 00:11:15,720 Speaker 7: and sixty five and twenty twenty two. And even in 249 00:11:15,800 --> 00:11:18,760 Speaker 7: twenty twenty, when we saw a wave of pandemic related 250 00:11:18,800 --> 00:11:22,120 Speaker 7: tech layoffs, that number was only about eighty thousand. So 251 00:11:22,280 --> 00:11:25,600 Speaker 7: this year has been the worst time in the past 252 00:11:25,640 --> 00:11:26,520 Speaker 7: few years. 253 00:11:27,320 --> 00:11:31,959 Speaker 6: What if anything to the companies that have done layoffs 254 00:11:32,000 --> 00:11:35,280 Speaker 6: and cuts got in common, be it their reasons for 255 00:11:35,360 --> 00:11:39,120 Speaker 6: doing so or their reaction into something. 256 00:11:41,000 --> 00:11:44,240 Speaker 7: Well, the biggest reason for these tech layoffs has been 257 00:11:44,840 --> 00:11:48,120 Speaker 7: an over correction of the over hiring that they did 258 00:11:48,200 --> 00:11:51,319 Speaker 7: during the pandemic search. You know, back in twenty twenty 259 00:11:51,320 --> 00:11:53,800 Speaker 7: to twenty twenty one, we were in a very low 260 00:11:53,840 --> 00:11:58,000 Speaker 7: industrate environment. Tech companies were booming in demand from people 261 00:11:58,040 --> 00:12:01,560 Speaker 7: staying at home and turning to tech services more and more, 262 00:12:02,000 --> 00:12:03,880 Speaker 7: and so these companies are able to go on a 263 00:12:03,920 --> 00:12:07,080 Speaker 7: hiring spree and they invested aggressively in initiatives that were 264 00:12:07,080 --> 00:12:09,640 Speaker 7: respeculative or wouldn't payoff for several years. 265 00:12:10,320 --> 00:12:12,480 Speaker 4: Now, of course, the climate is entally different. 266 00:12:12,520 --> 00:12:16,240 Speaker 7: We're in a period of interest rate hikes, and so 267 00:12:16,280 --> 00:12:19,400 Speaker 7: that's causing these same companies now cut back and correct 268 00:12:19,480 --> 00:12:21,040 Speaker 7: for that over hiring from before. 269 00:12:21,600 --> 00:12:24,080 Speaker 5: Okay, so now we're in talk of cups. So does 270 00:12:24,120 --> 00:12:25,000 Speaker 5: twenty twenty. 271 00:12:24,720 --> 00:12:28,360 Speaker 3: Four mean we rectify that we see more exuberant, more 272 00:12:28,440 --> 00:12:32,360 Speaker 3: hiring or is it actually slower and more specific. 273 00:12:31,960 --> 00:12:33,680 Speaker 5: Like, yes, we'll hire, but only in AI. 274 00:12:35,240 --> 00:12:38,839 Speaker 7: Yeah. Well, you know, after seeing actually some declines and 275 00:12:38,920 --> 00:12:41,720 Speaker 7: layoffs over the course of this year, the past two 276 00:12:41,760 --> 00:12:46,120 Speaker 7: months have marked an uptick in the tech layoffs and 277 00:12:46,720 --> 00:12:49,600 Speaker 7: I do expect that to carry over to early next 278 00:12:49,720 --> 00:12:52,240 Speaker 7: year because the year end is a natural time that 279 00:12:52,559 --> 00:12:55,200 Speaker 7: connoct a layoff as a coin science and annual budgeting. 280 00:12:55,320 --> 00:12:58,840 Speaker 7: So as companies in tech are taking stock of their 281 00:12:59,160 --> 00:13:02,280 Speaker 7: full year perform and are looking ahead to twenty twenty four, 282 00:13:02,679 --> 00:13:05,960 Speaker 7: they're finding our job cuts aren't necessary to improve profitability. 283 00:13:06,520 --> 00:13:08,160 Speaker 4: So I do expect. 284 00:13:07,800 --> 00:13:11,040 Speaker 7: That this remains slow for the next few months, although 285 00:13:11,360 --> 00:13:14,080 Speaker 7: as we look ahead to the rest of twenty twenty four, 286 00:13:15,280 --> 00:13:19,120 Speaker 7: hopefully if interest rates do come down as is expected, 287 00:13:19,480 --> 00:13:21,520 Speaker 7: that would mean that the tech where they are will 288 00:13:21,559 --> 00:13:23,199 Speaker 7: finally subside Roger. 289 00:13:23,240 --> 00:13:26,160 Speaker 3: It's interesting, isn't it that much of the so called 290 00:13:26,160 --> 00:13:28,800 Speaker 3: silver lining when everyone was really talking about the big 291 00:13:28,880 --> 00:13:31,480 Speaker 3: cuts that were being executed. At the beginning of the year, 292 00:13:31,520 --> 00:13:33,680 Speaker 3: everyone was like, well, more startups will be formed, while 293 00:13:33,679 --> 00:13:35,880 Speaker 3: people will leave. They've got some cash in the bank 294 00:13:35,880 --> 00:13:37,960 Speaker 3: that we be able to start up some other companies. 295 00:13:38,360 --> 00:13:41,560 Speaker 3: Now it feels also that we're talking of well, technology 296 00:13:41,559 --> 00:13:44,520 Speaker 3: being necessary everywhere, particularly in the field of AI, and 297 00:13:44,559 --> 00:13:48,000 Speaker 3: people going into banking sectors, going into different industry groups 298 00:13:48,000 --> 00:13:49,640 Speaker 3: that need that sort of technology retail. 299 00:13:49,679 --> 00:13:51,480 Speaker 5: I mean there is an industry that it doesn't affect. 300 00:13:51,720 --> 00:13:53,200 Speaker 5: Are people leaving tech more broadly? 301 00:13:54,880 --> 00:13:58,480 Speaker 7: Yeah, you know, the rate of startupformation is affected because 302 00:13:59,120 --> 00:14:02,520 Speaker 7: fundraising is so hard to come by. We're in an era 303 00:14:02,640 --> 00:14:05,280 Speaker 7: now where the cost of capital is much higher than 304 00:14:05,360 --> 00:14:08,120 Speaker 7: in years past, and so startups are finding it harder 305 00:14:08,160 --> 00:14:12,040 Speaker 7: to raise money compared to before. And that does make 306 00:14:12,080 --> 00:14:15,280 Speaker 7: it harder for these late off tech employees to stay 307 00:14:15,320 --> 00:14:17,960 Speaker 7: in tech or to go start their own company. 308 00:14:18,559 --> 00:14:19,800 Speaker 4: And so you are finding that. 309 00:14:19,680 --> 00:14:23,520 Speaker 7: More and more folks are going to other industries either, 310 00:14:23,640 --> 00:14:25,840 Speaker 7: you know, folks on the business side, like sales people 311 00:14:25,880 --> 00:14:29,080 Speaker 7: recruiters are leaving tech entirely because they can find better 312 00:14:29,160 --> 00:14:32,480 Speaker 7: roles in other industries. Since the tech downtern has not 313 00:14:32,600 --> 00:14:35,920 Speaker 7: broadly affected what's going on in the rest of the 314 00:14:35,960 --> 00:14:40,680 Speaker 7: economy where unemployment is still very, very favorable. And then 315 00:14:40,720 --> 00:14:44,080 Speaker 7: even engineers are also looking at other options too outside 316 00:14:44,080 --> 00:14:47,200 Speaker 7: of tech because of the reasons you said, tech is everywhere, 317 00:14:47,400 --> 00:14:51,720 Speaker 7: and they industry now has a need to invest in technology. 318 00:14:52,440 --> 00:14:56,120 Speaker 6: Yeah, Roger, I found it fascinating that the principal driver 319 00:14:56,360 --> 00:14:59,360 Speaker 6: is undoing the over exuberance of hiring from the pandemic 320 00:14:59,360 --> 00:15:02,600 Speaker 6: period about Caroline, But I just had this weird deja 321 00:15:02,680 --> 00:15:04,640 Speaker 6: vu because I felt like we told that story a 322 00:15:04,720 --> 00:15:05,200 Speaker 6: year ago. 323 00:15:05,520 --> 00:15:06,680 Speaker 4: Yeah, so why are these. 324 00:15:06,560 --> 00:15:10,040 Speaker 6: Companies doing cuts twelve months later on the same rationale. 325 00:15:10,080 --> 00:15:11,320 Speaker 4: Anyway, I just thought that was interesting. 326 00:15:11,360 --> 00:15:14,160 Speaker 3: Yeah, and the pendulum and we hope doesn't swing completely 327 00:15:14,200 --> 00:15:16,400 Speaker 3: the other direction once more. Roger, lee'fraid we have to 328 00:15:16,480 --> 00:15:18,200 Speaker 3: leave it there, but thank you so much. Creator of 329 00:15:18,320 --> 00:15:23,040 Speaker 3: layoffs dot f Yi. French billionaire Vizant Belori, Well, he's 330 00:15:23,040 --> 00:15:26,240 Speaker 3: considering a split of the media and entertainment empire he 331 00:15:26,240 --> 00:15:29,360 Speaker 3: effectively controls Blue Meg. Sources say the breakup would allow 332 00:15:29,400 --> 00:15:32,040 Speaker 3: for the venue to split into several companies in order 333 00:15:32,080 --> 00:15:34,320 Speaker 3: to better take advantage of each unit's strength. 334 00:15:34,880 --> 00:15:37,080 Speaker 5: Cheers. They've been searching on the hills of that report. 335 00:15:37,200 --> 00:15:41,800 Speaker 6: Edd Let's continue that story with Bloomberg's Benoir Bertilo out 336 00:15:41,840 --> 00:15:44,160 Speaker 6: in Paris. Go out to Paris with our correspondent who 337 00:15:44,160 --> 00:15:46,920 Speaker 6: in the last two hours has broken even more news 338 00:15:46,960 --> 00:15:50,880 Speaker 6: on the Vendi. Let's start with the basic premise of 339 00:15:50,880 --> 00:15:52,760 Speaker 6: what Vincen Belaure is doing here. 340 00:15:53,360 --> 00:15:59,720 Speaker 8: Yeah, a very unexpected move. Really announced late yes of 341 00:15:59,760 --> 00:16:03,200 Speaker 8: the that it would break up or explore the breaking 342 00:16:03,320 --> 00:16:07,320 Speaker 8: up of his company in three different units. That would 343 00:16:07,360 --> 00:16:10,560 Speaker 8: be one unit for PATV is building a sort of 344 00:16:10,720 --> 00:16:16,240 Speaker 8: Netflix like streaming service out of PATV operators across Europe, 345 00:16:16,320 --> 00:16:19,640 Speaker 8: so that's can help. Plus another unit would be the 346 00:16:19,680 --> 00:16:24,320 Speaker 8: ad group Avers. It's a good ad group valued between 347 00:16:24,360 --> 00:16:27,640 Speaker 8: two billion and three billion euros by investors, but it's 348 00:16:27,720 --> 00:16:32,040 Speaker 8: much smaller than Peers publicis WPP on income, So that 349 00:16:32,080 --> 00:16:35,920 Speaker 8: would be a stand alone company as well, both listed company, 350 00:16:36,200 --> 00:16:39,440 Speaker 8: and that would be a third company, an investment company 351 00:16:39,520 --> 00:16:43,640 Speaker 8: holding the shares of various groups that the Boloy and 352 00:16:43,720 --> 00:16:48,160 Speaker 8: Vivandi group has, including La Gardeer, which is a publishing 353 00:16:48,280 --> 00:16:53,480 Speaker 8: giant encompassing the third book publisher in the world, which 354 00:16:53,560 --> 00:16:58,160 Speaker 8: is Ashet. It also has travel retail operations, so a 355 00:16:58,200 --> 00:17:01,680 Speaker 8: complete split of the group he tried to build for 356 00:17:01,720 --> 00:17:04,880 Speaker 8: the past decade would be explored now. 357 00:17:05,560 --> 00:17:06,760 Speaker 5: I mean volsobroken news. 358 00:17:06,800 --> 00:17:09,199 Speaker 3: So maybe he thinks about selling a steak in the 359 00:17:09,240 --> 00:17:13,320 Speaker 3: former poem Monopolia Italy Telecometalia. It's also fascinating and paint 360 00:17:13,320 --> 00:17:15,560 Speaker 3: a picture of Beloray for us, because what's so interesting 361 00:17:15,560 --> 00:17:20,280 Speaker 3: in your story is basically on the Puifontein. The CEO 362 00:17:20,520 --> 00:17:23,159 Speaker 3: of Ivandi almost doesn't get a mention here. This is 363 00:17:23,200 --> 00:17:26,560 Speaker 3: about a lot sort of a Rupot Murdoch figure of France. 364 00:17:28,880 --> 00:17:33,159 Speaker 8: Yeah, we've compared him to report Murdoc in the past, 365 00:17:33,440 --> 00:17:37,679 Speaker 8: is like him a media mogul, is pretty conservative in 366 00:17:37,760 --> 00:17:42,240 Speaker 8: the line of his media outlets. He's seventy one years old. 367 00:17:42,480 --> 00:17:46,440 Speaker 8: Officially he retired last year, so he's not the CEO 368 00:17:46,880 --> 00:17:49,439 Speaker 8: of all chairman of the groups we're talking about, but 369 00:17:49,600 --> 00:17:52,720 Speaker 8: he is said to be really calling the shots for 370 00:17:53,400 --> 00:17:56,720 Speaker 8: any big moves like this, and it's really the writing 371 00:17:56,800 --> 00:18:00,280 Speaker 8: on the walls. Being known as a corporate Rado has 372 00:18:00,280 --> 00:18:03,560 Speaker 8: made a lot of surprising moves with his companies. For 373 00:18:03,560 --> 00:18:07,920 Speaker 8: example the listing of Universal Group two years ago, which 374 00:18:08,320 --> 00:18:11,600 Speaker 8: really is sort of an inspiration for what's happening now. 375 00:18:11,359 --> 00:18:14,160 Speaker 8: So so yeah, is at the age of a try 376 00:18:14,280 --> 00:18:17,439 Speaker 8: of free daring. Clearly he has his two older sons, 377 00:18:17,840 --> 00:18:24,840 Speaker 8: Sel and Yannique, both heads of Boloy and Vivandi. But clearly, 378 00:18:24,920 --> 00:18:27,879 Speaker 8: like it seems, he has a lot of some moves 379 00:18:28,000 --> 00:18:28,439 Speaker 8: still to. 380 00:18:28,480 --> 00:18:30,240 Speaker 4: Make all right. 381 00:18:30,280 --> 00:18:33,600 Speaker 6: Bloomberg's Ben wor Betterlow out in Paris on that story. 382 00:18:33,840 --> 00:18:35,479 Speaker 6: Coming up here on the program, we're gonna have all 383 00:18:35,520 --> 00:18:40,600 Speaker 6: the details on the FTC's probe into Adobe subscription cancelation rules. 384 00:18:40,880 --> 00:18:43,040 Speaker 6: Drives a lot to be mad I see on social media. 385 00:18:43,080 --> 00:18:45,479 Speaker 6: We're gonna be joined by Tech Policy Institute's senior fellow, 386 00:18:45,880 --> 00:18:49,800 Speaker 6: Sarah o'lamb. That's coming up next from San Francisco. This 387 00:18:50,080 --> 00:19:12,040 Speaker 6: is Bloomberg Technology. Welcome back to Bloomberg Technology, Ed Lovelow. 388 00:19:11,760 --> 00:19:14,000 Speaker 5: In San Francisco, and Carolin Hyde right here in New York. 389 00:19:14,040 --> 00:19:15,600 Speaker 3: Let's get a check on these markets because we are 390 00:19:15,640 --> 00:19:17,639 Speaker 3: dictated by macro polls at the moment. We had the 391 00:19:17,640 --> 00:19:20,880 Speaker 3: Federal Reserve yesterday, of course, signaling that rake carts say 392 00:19:20,880 --> 00:19:22,920 Speaker 3: are to come in twenty twenty four. The retail sales 393 00:19:23,040 --> 00:19:26,040 Speaker 3: number also just showing the resilience of this US economy. 394 00:19:26,080 --> 00:19:27,840 Speaker 3: The fact that we saw not only three percent growth 395 00:19:27,840 --> 00:19:30,600 Speaker 3: in retail sales in November rather than a contraction who also 396 00:19:30,600 --> 00:19:31,120 Speaker 3: of course got. 397 00:19:31,119 --> 00:19:33,800 Speaker 5: Jobless claims looking good. This is a strong economy. Do 398 00:19:33,840 --> 00:19:34,639 Speaker 5: we see a rotation? 399 00:19:34,760 --> 00:19:37,440 Speaker 3: Therefore, money is still going into the NASAC, though tentatively, 400 00:19:37,640 --> 00:19:39,639 Speaker 3: and this is the Nawstak one hundred. What's interesting is 401 00:19:39,680 --> 00:19:42,520 Speaker 3: you're getting more love for the well, let's more left 402 00:19:42,520 --> 00:19:44,399 Speaker 3: out in what has been a ramp in twenty twenty 403 00:19:44,400 --> 00:19:46,439 Speaker 3: three the small caps, of course, and let's move on 404 00:19:46,440 --> 00:19:48,600 Speaker 3: and to see what's happening on the individual stock movers 405 00:19:48,600 --> 00:19:50,600 Speaker 3: of choice though at the moment, and Apple did hit 406 00:19:50,640 --> 00:19:52,600 Speaker 3: an intra day high at one point, so there was 407 00:19:52,600 --> 00:19:54,440 Speaker 3: some love some of the big tech and still those 408 00:19:54,480 --> 00:19:55,719 Speaker 3: magnificent seven names. 409 00:19:55,880 --> 00:19:56,560 Speaker 5: Adobe. 410 00:19:56,840 --> 00:20:00,440 Speaker 3: Interesting outline, we're off by almost six percent. Big move 411 00:20:00,640 --> 00:20:02,280 Speaker 3: was Perform and then as that one hundred ed and 412 00:20:02,320 --> 00:20:03,960 Speaker 3: a lot of this to do with numbers that didn't 413 00:20:04,000 --> 00:20:06,000 Speaker 3: live up to expectations in terms of their earnings. 414 00:20:06,160 --> 00:20:07,160 Speaker 5: Still going to see double. 415 00:20:07,000 --> 00:20:09,159 Speaker 3: Digit growth in revenue, but the market wanted to see more, 416 00:20:09,200 --> 00:20:11,560 Speaker 3: particularly in the digital media. But I think also the 417 00:20:11,600 --> 00:20:14,199 Speaker 3: ongoing narrative around the fact that they're getting pushed back 418 00:20:14,240 --> 00:20:16,960 Speaker 3: on the Pigma deal and of course, the FTC the 419 00:20:17,000 --> 00:20:20,160 Speaker 3: fact that they are too difficult to cancel, and that's 420 00:20:20,160 --> 00:20:21,359 Speaker 3: a key story that want to dig in on. 421 00:20:22,400 --> 00:20:25,800 Speaker 6: Yeah, so Adobe has been cooperating with the FTC in 422 00:20:25,880 --> 00:20:29,720 Speaker 6: a civil investigation since twenty twenty two, which looks exactly 423 00:20:29,760 --> 00:20:34,080 Speaker 6: that policies around canceling subscriptions. It's driven many of you mad. 424 00:20:34,160 --> 00:20:36,880 Speaker 6: We know you tell us on social media, but it's 425 00:20:36,920 --> 00:20:39,760 Speaker 6: an interesting disclosure that Adobe had in that filing last night. 426 00:20:39,840 --> 00:20:43,480 Speaker 6: Let's bring in Tech Policy Institute Senior fellow Sarah o'lam 427 00:20:43,600 --> 00:20:46,600 Speaker 6: joining us here on the program. Your reaction to that 428 00:20:46,760 --> 00:20:51,240 Speaker 6: FTC look at Adobe's practices around canceling subscriptions and products 429 00:20:51,240 --> 00:20:52,159 Speaker 6: and services. 430 00:20:53,040 --> 00:20:55,720 Speaker 9: Sure, well, it was news yesterday and Adobe's eight K 431 00:20:55,960 --> 00:20:59,280 Speaker 9: filing that it wasn't really known before that that the 432 00:20:59,359 --> 00:21:03,280 Speaker 9: FTC as an investigative demand before it, and that in 433 00:21:03,359 --> 00:21:06,240 Speaker 9: November twenty twenty three last month, that it would enter 434 00:21:06,280 --> 00:21:12,639 Speaker 9: into negotiations to possibly impose penalties. They're using this authority 435 00:21:12,680 --> 00:21:16,720 Speaker 9: under Section five A Unfair and Deceptive Practices, but also 436 00:21:16,800 --> 00:21:20,080 Speaker 9: the Restore Online Shoppers Confidence Act RASCA. 437 00:21:20,280 --> 00:21:21,840 Speaker 10: It's a statute from twenty ten. 438 00:21:22,880 --> 00:21:27,280 Speaker 9: Folks might be reminded of the use of this law 439 00:21:27,320 --> 00:21:31,119 Speaker 9: as well when FTC sued Amazon earlier this year also 440 00:21:31,240 --> 00:21:35,800 Speaker 9: for prime subscription practices, So this is ongoing. It's also 441 00:21:35,880 --> 00:21:40,200 Speaker 9: related to rulemaking proceding a click to cancel negative option 442 00:21:40,359 --> 00:21:43,320 Speaker 9: rule proceeding from earlier this year as well. So the 443 00:21:43,359 --> 00:21:48,880 Speaker 9: FTC seems to be ramping up its investigations of subscription practices. 444 00:21:49,080 --> 00:21:52,080 Speaker 3: And maybe we shouldn't be surprised President Joe Biden. Of 445 00:21:52,080 --> 00:21:54,000 Speaker 3: course it was back in March. I think really talking 446 00:21:54,000 --> 00:21:56,840 Speaker 3: about the need for the ability to sign on to 447 00:21:56,840 --> 00:22:00,000 Speaker 3: be as easy as the ability to cancel these online subscriptions, 448 00:22:00,040 --> 00:22:03,200 Speaker 3: digital subscriptions, and you know when it costs up to 449 00:22:03,240 --> 00:22:05,800 Speaker 3: seven hundred dollars a year for an Adobe subscription, no 450 00:22:05,880 --> 00:22:08,199 Speaker 3: wonder they want to ensure that it doesn't drag on 451 00:22:08,320 --> 00:22:11,560 Speaker 3: in some way. Do you think the FTC is sort 452 00:22:11,560 --> 00:22:14,000 Speaker 3: of going to get more broad with this? Is it 453 00:22:14,040 --> 00:22:16,159 Speaker 3: always going to be looking for settlements, for example that 454 00:22:16,680 --> 00:22:19,520 Speaker 3: could have significant monetary costs Adobe lines? 455 00:22:20,840 --> 00:22:24,359 Speaker 9: Well, in January, in the new year, it's going to 456 00:22:24,400 --> 00:22:28,240 Speaker 9: have a hearing, a public hearing about this negative option rule, 457 00:22:28,320 --> 00:22:31,639 Speaker 9: which is its way of saying that they want to 458 00:22:31,680 --> 00:22:34,159 Speaker 9: make a rule across all companies that to make it 459 00:22:34,240 --> 00:22:36,280 Speaker 9: easier to cancel the click to cancel. 460 00:22:37,240 --> 00:22:41,439 Speaker 10: So it's an ongoing open proceeding for the FTC. 461 00:22:42,000 --> 00:22:46,120 Speaker 9: What's interesting about yesterday's announcement was that they are sending 462 00:22:46,160 --> 00:22:50,879 Speaker 9: these investigative demands to public companies with outside of the 463 00:22:50,960 --> 00:22:55,360 Speaker 9: rulemaking process, and so I think that might be causing 464 00:22:55,440 --> 00:23:00,840 Speaker 9: some stock market effects that that they're. 465 00:23:00,560 --> 00:23:02,560 Speaker 10: Getting all these investigative demands. 466 00:23:03,760 --> 00:23:08,280 Speaker 9: And what's interesting is, yeah, the Figma acquisition is still 467 00:23:08,920 --> 00:23:11,400 Speaker 9: up in the air. It's still under review at the 468 00:23:11,480 --> 00:23:15,480 Speaker 9: Justice Department. So Adobe has a few things happening with 469 00:23:15,520 --> 00:23:18,560 Speaker 9: the FTC right now and Justice Department. 470 00:23:18,280 --> 00:23:20,280 Speaker 5: And so does some other companies. 471 00:23:20,440 --> 00:23:22,720 Speaker 3: I mean, the FDC has got busy, so have actually 472 00:23:23,119 --> 00:23:26,159 Speaker 3: global regulators towards the end of the year. You're seeing 473 00:23:26,400 --> 00:23:29,159 Speaker 3: what the FTC looking at the nature of Microsoft's investment 474 00:23:29,200 --> 00:23:31,480 Speaker 3: in open Ai. The EU is looking at a similar 475 00:23:31,760 --> 00:23:33,879 Speaker 3: So while the UK indeed is looking at a similar 476 00:23:33,920 --> 00:23:36,600 Speaker 3: sort of relationship between Microsoft and open Ai, we've got 477 00:23:36,720 --> 00:23:40,080 Speaker 3: the EU looking in many a field of regulatory oversight. 478 00:23:40,160 --> 00:23:44,160 Speaker 3: We think of alphabets impact earlier this week, Sarah, our 479 00:23:44,200 --> 00:23:46,520 Speaker 3: regulators just having enough with some of these business models. 480 00:23:47,960 --> 00:23:50,800 Speaker 9: Well, what makes it easy for regulators is that they 481 00:23:50,840 --> 00:23:55,160 Speaker 9: can send a letter an investigative demand pretty costlessly. I mean, 482 00:23:55,200 --> 00:23:57,760 Speaker 9: they prepare it, but they send it, and then the 483 00:23:57,880 --> 00:24:00,360 Speaker 9: companies have to do a lot to respond. 484 00:24:00,080 --> 00:24:01,480 Speaker 10: And beyond noticed. 485 00:24:01,520 --> 00:24:04,800 Speaker 9: And so it depends on your view of how broad 486 00:24:04,880 --> 00:24:09,280 Speaker 9: the FTC's Section five a Unfair and Deceptive Practices authority is, 487 00:24:09,720 --> 00:24:12,800 Speaker 9: if they're going beyond the scope of their authority or 488 00:24:13,760 --> 00:24:16,720 Speaker 9: if they're within the scope of their authority. And so 489 00:24:17,280 --> 00:24:20,600 Speaker 9: that's a really good question right now about this FTC 490 00:24:20,760 --> 00:24:25,919 Speaker 9: in particular, which is a little bit more active than 491 00:24:26,000 --> 00:24:27,440 Speaker 9: prior FTCs. 492 00:24:27,840 --> 00:24:31,919 Speaker 6: Well, I appreciate Caroline's question too, because throughout the cadence 493 00:24:31,960 --> 00:24:33,959 Speaker 6: of this year, I think the only thing we can 494 00:24:34,000 --> 00:24:36,679 Speaker 6: agree on is there's been a lot of antitrust and 495 00:24:36,720 --> 00:24:40,800 Speaker 6: regulatory news when it comes to big tech net net Sarah, 496 00:24:40,960 --> 00:24:42,920 Speaker 6: who came out on top at the end of twenty 497 00:24:42,920 --> 00:24:46,040 Speaker 6: twenty three. The regulators or the technology companies. 498 00:24:46,880 --> 00:24:50,879 Speaker 9: Well, you know, it slows down business, but folks would 499 00:24:50,880 --> 00:24:53,960 Speaker 9: say that maybe it is the job of these government 500 00:24:54,040 --> 00:24:58,679 Speaker 9: regulators to make it harder or to protect consumers. And so, 501 00:24:59,720 --> 00:25:02,760 Speaker 9: you know, you hope that there is that balance of 502 00:25:02,800 --> 00:25:07,800 Speaker 9: being pro consumer and pro investment, especially for American companies, 503 00:25:07,880 --> 00:25:11,200 Speaker 9: which are you making up the most of big tech 504 00:25:12,320 --> 00:25:16,679 Speaker 9: in the markets and in globally, and so hopefully that 505 00:25:17,040 --> 00:25:18,520 Speaker 9: they can strike that balance. 506 00:25:20,119 --> 00:25:22,480 Speaker 6: Some of that is, Kurry pointed out, was in the 507 00:25:22,480 --> 00:25:24,119 Speaker 6: context of M and A, right, you think about like 508 00:25:24,160 --> 00:25:27,760 Speaker 6: Microsoft Activision or maybe now Microsoft's stake and open AI 509 00:25:28,680 --> 00:25:32,639 Speaker 6: in twenty twenty four, what will be the area of 510 00:25:32,680 --> 00:25:35,760 Speaker 6: focus do you think from regulators is they look at 511 00:25:35,840 --> 00:25:37,040 Speaker 6: technology companies? 512 00:25:38,840 --> 00:25:41,960 Speaker 9: Well, I think what's really important for you know, us 513 00:25:41,960 --> 00:25:46,439 Speaker 9: in the policy arena and Congress and the regulators is 514 00:25:46,480 --> 00:25:52,480 Speaker 9: to know that any government involvement slows things down. And 515 00:25:52,560 --> 00:25:55,879 Speaker 9: so there might be you know, good good reasons for 516 00:25:56,000 --> 00:26:00,080 Speaker 9: caution and for policy making, but there's also you know, 517 00:26:00,160 --> 00:26:05,439 Speaker 9: a big important value in letting companies innovate and merge 518 00:26:05,560 --> 00:26:08,959 Speaker 9: and acquire each other and then learn that way see 519 00:26:09,040 --> 00:26:13,119 Speaker 9: what happens in the market. So you know, I would 520 00:26:13,160 --> 00:26:18,159 Speaker 9: recommend the regulators to make sure that we're accelerating growth 521 00:26:18,160 --> 00:26:19,960 Speaker 9: and not slowing it down. 522 00:26:20,840 --> 00:26:23,200 Speaker 3: Well, said Sarah Alamb's great to have you, senior fellow 523 00:26:23,200 --> 00:26:26,600 Speaker 3: at the Technology Policy Institute. Great to have your expertise. Meanwhile, 524 00:26:26,720 --> 00:26:29,320 Speaker 3: let's just stick with it Obie for a moment, because well, 525 00:26:29,560 --> 00:26:32,000 Speaker 3: we know that the shares runner pressure. Also because of 526 00:26:32,040 --> 00:26:34,520 Speaker 3: its warm outlook for sales in twenty twenty four, it 527 00:26:34,520 --> 00:26:37,479 Speaker 3: seemed to signal a potential that the AI boosts, particularly 528 00:26:37,480 --> 00:26:39,480 Speaker 3: when it comes to its product far Fly, It's going 529 00:26:39,520 --> 00:26:42,040 Speaker 3: to take longer than expected to actually boost the bottom line. 530 00:26:42,080 --> 00:26:44,199 Speaker 3: Wall Street still expects Adobie to be one of the 531 00:26:44,280 --> 00:26:46,040 Speaker 3: first software chants to benefit. 532 00:26:45,680 --> 00:26:48,919 Speaker 5: From general to AI. Now that's stick with AI and look. 533 00:26:48,800 --> 00:26:50,920 Speaker 3: At just all the industries that it's going to transform, 534 00:26:51,000 --> 00:26:54,360 Speaker 3: most likely healthcare. I sat down with Best Some Adventures 535 00:26:54,400 --> 00:26:57,600 Speaker 3: partners Steve Kraus just talk about what he's seeing in 536 00:26:58,040 --> 00:26:58,879 Speaker 3: the impact so far. 537 00:26:58,960 --> 00:26:59,520 Speaker 5: Take a listen. 538 00:27:00,520 --> 00:27:03,840 Speaker 11: We at Bessemer have made a really large commitment to AI, 539 00:27:04,080 --> 00:27:06,040 Speaker 11: and I would say that healthcare is actually one of 540 00:27:06,080 --> 00:27:09,080 Speaker 11: the industries where it's going to be most relevant, most impactful. 541 00:27:09,119 --> 00:27:11,560 Speaker 11: And the reason for that is, you know, healthcare is 542 00:27:11,840 --> 00:27:15,920 Speaker 11: the most laborious, inefficient industry in the economy. It's also 543 00:27:16,160 --> 00:27:18,439 Speaker 11: thirty percent of the world's data and so if you 544 00:27:18,480 --> 00:27:21,840 Speaker 11: think about that combination, what is artificial intelligence good at. 545 00:27:21,920 --> 00:27:25,600 Speaker 11: It's good at automating tasks in all parts of our economy, 546 00:27:25,640 --> 00:27:27,120 Speaker 11: but we think in healthcare it's going to be really 547 00:27:27,160 --> 00:27:32,359 Speaker 11: impactful basically everywhere, from how the payment in healthcare is 548 00:27:32,400 --> 00:27:36,159 Speaker 11: administered on the back end, how healthcare is delivered you know, 549 00:27:36,280 --> 00:27:39,080 Speaker 11: by clinicians. In terms of clinical decision support, for instance, 550 00:27:39,200 --> 00:27:42,280 Speaker 11: AI can be very impactful, and then also how drugs 551 00:27:42,280 --> 00:27:45,080 Speaker 11: are designed and delivered to improve human health. And so 552 00:27:45,160 --> 00:27:47,159 Speaker 11: we think all aspects of the healthcare economy are going 553 00:27:47,200 --> 00:27:48,399 Speaker 11: to be revolutionized by AI. 554 00:27:48,680 --> 00:27:51,280 Speaker 3: The problem with twenty twenty three is suddenly we felt 555 00:27:51,280 --> 00:27:53,679 Speaker 3: that AI was this bright, new shiny object, and ultimately 556 00:27:53,760 --> 00:27:57,600 Speaker 3: artificial intelligen has been in an un sexy topic for years, 557 00:27:57,800 --> 00:27:59,760 Speaker 3: and I'm interested as to therefore for you, was it 558 00:27:59,760 --> 00:28:03,879 Speaker 3: a out starting to amplify the AI story amid the 559 00:28:03,880 --> 00:28:06,520 Speaker 3: companies you've already backed, or is it finding new companies 560 00:28:06,560 --> 00:28:08,720 Speaker 3: that are being built out of this sudden nique change 561 00:28:08,720 --> 00:28:10,160 Speaker 3: that we did see with generative AI. 562 00:28:10,600 --> 00:28:11,320 Speaker 1: I think it's both. 563 00:28:12,040 --> 00:28:14,359 Speaker 11: We've actually been investing You're right, it's been around for 564 00:28:14,359 --> 00:28:16,639 Speaker 11: a long time these technologies, and we've actually been investing 565 00:28:16,680 --> 00:28:19,359 Speaker 11: in five plus years in terms of how these technologies 566 00:28:19,359 --> 00:28:21,000 Speaker 11: are applied to healthcare, and I mentioned some of the 567 00:28:21,000 --> 00:28:23,239 Speaker 11: ways that could be and so I think there's going 568 00:28:23,280 --> 00:28:26,240 Speaker 11: to be plenty of new opportunities that come out over 569 00:28:26,280 --> 00:28:28,920 Speaker 11: the next decade of how AI can be applied to healthcare. 570 00:28:29,000 --> 00:28:31,400 Speaker 11: But I also think for existing companies. As you point out, 571 00:28:31,880 --> 00:28:34,800 Speaker 11: I talked about how healthcare is a very laborious industry. 572 00:28:34,840 --> 00:28:36,280 Speaker 1: A lot of its services you. 573 00:28:36,240 --> 00:28:39,240 Speaker 11: Know, humans doing tasks that actually can be automated, that 574 00:28:39,280 --> 00:28:41,720 Speaker 11: are pretty mundane but are very important. And so we 575 00:28:41,880 --> 00:28:45,520 Speaker 11: actually think AI is going to shift services to more software, 576 00:28:45,680 --> 00:28:48,920 Speaker 11: like in their administration and how care is delivered. 577 00:28:49,040 --> 00:28:51,320 Speaker 3: What has it done to the valuation story of the 578 00:28:51,320 --> 00:28:53,200 Speaker 3: companies that you're looking to invest it or already have that. 579 00:28:53,360 --> 00:28:56,440 Speaker 11: Well, AI is a hot space and I think, you know, 580 00:28:56,840 --> 00:29:00,440 Speaker 11: the valuations the AI sector are pretty robust, but we 581 00:29:00,560 --> 00:29:02,880 Speaker 11: also think the opportunities equally as robust. 582 00:29:02,880 --> 00:29:04,160 Speaker 1: And it's if you think about it. 583 00:29:04,280 --> 00:29:05,840 Speaker 11: You know, in my career and venture there have been 584 00:29:05,880 --> 00:29:09,280 Speaker 11: several platform changes, you know, moving from on premise software 585 00:29:09,320 --> 00:29:11,880 Speaker 11: to the cloud. That was a huge, huge shift in 586 00:29:11,920 --> 00:29:15,520 Speaker 11: our economy and created hundreds of billions, trillions of dollars 587 00:29:15,000 --> 00:29:19,840 Speaker 11: of innovation entrepreneurship. The iPhone right huge moment. AI is 588 00:29:19,880 --> 00:29:23,200 Speaker 11: also a huge moment. So while you know valuations are heady, 589 00:29:23,320 --> 00:29:25,800 Speaker 11: I actually think the opportunity is almost uncapped, and so 590 00:29:25,880 --> 00:29:29,360 Speaker 11: we're excited. And that applies to healthcare AI companies. 591 00:29:29,080 --> 00:29:31,000 Speaker 5: Too, and maybe even some exits. 592 00:29:31,160 --> 00:29:33,000 Speaker 3: I mean, talk to us about when you're thinking of 593 00:29:33,160 --> 00:29:35,880 Speaker 3: best some adventures, I think of, well, some of the 594 00:29:36,080 --> 00:29:39,000 Speaker 3: unbelievable companies that have been gone public through the pinterest 595 00:29:39,040 --> 00:29:41,400 Speaker 3: that you've been from early days and some of the 596 00:29:41,440 --> 00:29:42,680 Speaker 3: other Twilios linkedins. 597 00:29:42,880 --> 00:29:44,560 Speaker 5: Who are they in your current. 598 00:29:45,000 --> 00:29:48,240 Speaker 3: Source of portfolio companies that you've invested in. Where do 599 00:29:48,280 --> 00:29:49,560 Speaker 3: we see those exits coming from. 600 00:29:49,680 --> 00:29:53,080 Speaker 1: Yeah, I think it's the AI wave. 601 00:29:53,080 --> 00:29:55,040 Speaker 11: We're early in it, and so I think those exits 602 00:29:55,040 --> 00:29:58,200 Speaker 11: will come in you know, five ten years. But right now, 603 00:29:58,600 --> 00:30:02,560 Speaker 11: one area that we've invested in is the digitization of 604 00:30:02,600 --> 00:30:06,680 Speaker 11: the healthcare economy. Again, it's often been experienced where people 605 00:30:06,840 --> 00:30:09,160 Speaker 11: get their healthcare in person, but we saw that covid 606 00:30:09,680 --> 00:30:11,960 Speaker 11: actually unlock the whole idea of telehealth, and so we 607 00:30:12,040 --> 00:30:14,720 Speaker 11: think all throughout the delivery of care, whether it be 608 00:30:14,800 --> 00:30:18,160 Speaker 11: companies like Headspace Health or Hinge Health. Headspace is obviously 609 00:30:18,200 --> 00:30:20,640 Speaker 11: focused on mental health care, which as you know, has 610 00:30:20,680 --> 00:30:23,080 Speaker 11: been an area where frankly, there's a lack of supply 611 00:30:23,120 --> 00:30:25,640 Speaker 11: of clinicians in the US and in the world. And 612 00:30:25,680 --> 00:30:30,160 Speaker 11: so a company like Headspace that delivers really important, high 613 00:30:30,240 --> 00:30:33,240 Speaker 11: quality mental health care to all if you have a phone, 614 00:30:33,320 --> 00:30:37,400 Speaker 11: an iPhone. Again, that's a huge, huge opportunity that we 615 00:30:37,400 --> 00:30:38,800 Speaker 11: think is going to run for a long time. And 616 00:30:39,280 --> 00:30:41,040 Speaker 11: also a company like Hinge, which is doing the same 617 00:30:41,080 --> 00:30:43,640 Speaker 11: when it comes to back or knee or hip pain, 618 00:30:43,840 --> 00:30:46,160 Speaker 11: being able to deliver that care like physical therapy, you 619 00:30:46,200 --> 00:30:48,520 Speaker 11: don't have to go to see your physical therapist in person. 620 00:30:48,560 --> 00:30:51,480 Speaker 11: You can actually do that care via your iPhone. Again, 621 00:30:51,520 --> 00:30:53,320 Speaker 11: a huge market opportunity, and we think those are the 622 00:30:53,320 --> 00:30:56,600 Speaker 11: companies that we're going to see you get to exits 623 00:30:56,800 --> 00:30:57,440 Speaker 11: in the near term. 624 00:30:57,800 --> 00:31:00,440 Speaker 3: We'll based on the East Coast, but up in Boston. Yes, 625 00:31:00,880 --> 00:31:03,880 Speaker 3: where are those opportunities coming from? Where are these companies 626 00:31:04,320 --> 00:31:05,920 Speaker 3: being built at the moment? Do you think that you're 627 00:31:05,920 --> 00:31:07,440 Speaker 3: going to be looking to in twenty twenty four? 628 00:31:07,960 --> 00:31:10,480 Speaker 11: I You know, we at Bessemer have lawn believed that 629 00:31:10,600 --> 00:31:13,800 Speaker 11: you know, Silicon Valley is obviously a hotbed of innovation, 630 00:31:13,960 --> 00:31:17,840 Speaker 11: but Boston's a hot bed of biopharmacutical and healthcare innovation. 631 00:31:17,920 --> 00:31:21,040 Speaker 11: New York but frankly, we've invested all over the United 632 00:31:21,080 --> 00:31:21,480 Speaker 11: States and. 633 00:31:21,480 --> 00:31:22,200 Speaker 1: All over the globe. 634 00:31:22,200 --> 00:31:25,080 Speaker 11: We have offices worldwide, and so we actually think entrepreneurs 635 00:31:25,320 --> 00:31:28,000 Speaker 11: can be anywhere. And frankly, one of the things that 636 00:31:28,280 --> 00:31:31,680 Speaker 11: you know remote work taught us is that you you know, entrepreneurs. 637 00:31:31,240 --> 00:31:32,840 Speaker 1: Are everywhere and people can work everywhere. 638 00:31:32,840 --> 00:31:36,600 Speaker 11: And so we've invested in everywhere from Minneapolis to North 639 00:31:36,640 --> 00:31:40,160 Speaker 11: Carolina to London, Israel, and all. 640 00:31:40,120 --> 00:31:40,800 Speaker 1: Across the globe. 641 00:31:40,800 --> 00:31:43,080 Speaker 11: And so I don't think entrepreneurs are limited and where 642 00:31:43,120 --> 00:31:46,640 Speaker 11: they live. I think it's that spirit lives everywhere and 643 00:31:46,880 --> 00:31:48,200 Speaker 11: the possibilities are everywhere. 644 00:31:49,000 --> 00:31:51,040 Speaker 5: Steve Krausser from Bessemer Venture. 645 00:31:50,880 --> 00:31:54,480 Speaker 6: Partners venture back startups in the lab grown meat space 646 00:31:54,880 --> 00:31:57,880 Speaker 6: have received billions of dollars in the last decade, but 647 00:31:57,920 --> 00:32:01,440 Speaker 6: the companies aren't actually delivering and what was initially forecast 648 00:32:01,640 --> 00:32:05,040 Speaker 6: for mainstream adoption. In the latest edition of Bloomberg Business Week, 649 00:32:05,080 --> 00:32:07,880 Speaker 6: we dive deep into one of the industry's biggest players, 650 00:32:08,080 --> 00:32:10,840 Speaker 6: Upside Foods. Joining us now is one of the co 651 00:32:10,920 --> 00:32:14,760 Speaker 6: authors of that piece. Bloomberg's preer and end. There is 652 00:32:14,800 --> 00:32:18,400 Speaker 6: a stigma we've grab loan lab grown anything. 653 00:32:19,640 --> 00:32:22,480 Speaker 4: Tell us about your article in the magazine. What is 654 00:32:22,520 --> 00:32:24,600 Speaker 4: the main conclusion that it comes to. 655 00:32:25,240 --> 00:32:29,280 Speaker 12: These companies, especially Upside Foods, promised that they would deliver 656 00:32:29,600 --> 00:32:32,920 Speaker 12: a center of plate sort of style of meat that 657 00:32:33,280 --> 00:32:36,880 Speaker 12: is an equal substitute to folks who enjoy eating meat, 658 00:32:37,560 --> 00:32:41,240 Speaker 12: different from the plant based industry, where you know, it's 659 00:32:41,280 --> 00:32:44,800 Speaker 12: not necessarily one to one and it's not necessarily real meat. 660 00:32:44,840 --> 00:32:47,920 Speaker 12: There are other components of vegetables and other things like 661 00:32:47,960 --> 00:32:52,000 Speaker 12: that mixed in. And what has become clear is over 662 00:32:52,040 --> 00:32:54,720 Speaker 12: the years, after hundreds of millions of dollars in funding 663 00:32:55,000 --> 00:32:57,680 Speaker 12: for Upside Foods in particular, the company is still struggling 664 00:32:58,000 --> 00:33:01,360 Speaker 12: to make the technology work to actually deliver what it 665 00:33:01,400 --> 00:33:02,760 Speaker 12: has promised over the years. 666 00:33:02,920 --> 00:33:06,080 Speaker 6: Well like that one basics, so is it even revenue generating? 667 00:33:06,120 --> 00:33:08,880 Speaker 6: In other words, does it have a meat product lab 668 00:33:08,920 --> 00:33:10,320 Speaker 6: grown that it sells anywhere? 669 00:33:10,680 --> 00:33:13,880 Speaker 12: Right now, the company is supplying small amounts to a restaurant, 670 00:33:14,280 --> 00:33:17,320 Speaker 12: very very very small amounts, and it's far away from 671 00:33:17,360 --> 00:33:21,320 Speaker 12: being able to make a similar product at scale for supermarkets. 672 00:33:21,360 --> 00:33:22,800 Speaker 5: And for years, the promise. 673 00:33:22,480 --> 00:33:25,320 Speaker 12: Has been that these kinds of companies will by twenty 674 00:33:25,360 --> 00:33:29,680 Speaker 12: twenty one under supermarkets like Costco. And the idea was 675 00:33:30,320 --> 00:33:33,360 Speaker 12: people love eating meat, why should we slaughter more animals 676 00:33:33,360 --> 00:33:33,680 Speaker 12: for it? 677 00:33:34,760 --> 00:33:40,480 Speaker 3: What ultimately has been perhaps an exuberance about the timetable here, 678 00:33:40,640 --> 00:33:43,160 Speaker 3: is there any reality that we'll start to see lab 679 00:33:43,200 --> 00:33:45,640 Speaker 3: grown meat on our plates more quickly? Because you have 680 00:33:45,680 --> 00:33:47,960 Speaker 3: this beautiful part of the story where you're saying that 681 00:33:47,960 --> 00:33:48,960 Speaker 3: basically people are having. 682 00:33:48,800 --> 00:33:50,920 Speaker 5: To sort of dig out of test tubes. 683 00:33:50,600 --> 00:33:52,800 Speaker 3: And make one very small, tiny piece of chicken in 684 00:33:52,840 --> 00:33:55,240 Speaker 3: the metal when they should be making much larger amounts 685 00:33:55,240 --> 00:33:57,240 Speaker 3: of a feel that's right. 686 00:33:57,360 --> 00:33:59,080 Speaker 12: There are a number of companies in the space that 687 00:33:59,120 --> 00:34:01,800 Speaker 12: say they're going to use animal cells to create sort. 688 00:34:01,640 --> 00:34:03,160 Speaker 5: Of hybrid products that are. 689 00:34:03,160 --> 00:34:06,520 Speaker 12: Partly plant based, partly this lab grown meat to make 690 00:34:06,640 --> 00:34:10,560 Speaker 12: a plant based meat while tastes more like actual chicken 691 00:34:10,680 --> 00:34:13,880 Speaker 12: or actual beef, for example. And these companies say that 692 00:34:13,920 --> 00:34:17,000 Speaker 12: they will in a couple of years have these sort 693 00:34:17,040 --> 00:34:20,200 Speaker 12: of hybrid products available. But at the same time, the 694 00:34:20,239 --> 00:34:23,080 Speaker 12: process has been slow moving, and many of the largest 695 00:34:23,080 --> 00:34:25,960 Speaker 12: companies are still only in these small scale tasting events. 696 00:34:26,040 --> 00:34:28,040 Speaker 12: And in the case of Upside Foods, we've learned that 697 00:34:28,080 --> 00:34:31,920 Speaker 12: the company has yet to actually be producing at scale. 698 00:34:32,640 --> 00:34:35,520 Speaker 5: Preer fascinating. Go read the story. 699 00:34:35,719 --> 00:34:40,200 Speaker 3: It's really sort of a history of what is exuberant 700 00:34:40,200 --> 00:34:41,160 Speaker 3: in this particular space. 701 00:34:41,200 --> 00:34:43,800 Speaker 5: Faced with reality priann end. We thank you so much. 702 00:34:43,880 --> 00:34:47,520 Speaker 3: Meanwhile, look we're turning to another story, this gone viral. 703 00:34:47,440 --> 00:34:49,560 Speaker 5: That we haven't had a chance to discuss yet. 704 00:34:49,920 --> 00:34:52,960 Speaker 3: Ail Musk is starting his own university of wanting to 705 00:34:53,040 --> 00:34:55,520 Speaker 3: tax filings for the billionaire's latest charity, called the Foundation 706 00:34:55,920 --> 00:34:57,719 Speaker 3: Now as a CEO is planning. 707 00:34:57,400 --> 00:34:59,120 Speaker 5: To start a university in Austin. 708 00:34:59,400 --> 00:35:02,240 Speaker 3: New institution, inceeded with roughly one hundred million dollars gift 709 00:35:02,360 --> 00:35:05,880 Speaker 3: from Musk, will start with a STEM focused primary and 710 00:35:05,960 --> 00:35:09,160 Speaker 3: secondary school and then move on to late style education. 711 00:35:09,320 --> 00:35:10,880 Speaker 5: He's actually not the first time. 712 00:35:10,760 --> 00:35:14,080 Speaker 6: He started education for kids, right, Yeah, He's actually set 713 00:35:14,120 --> 00:35:16,760 Speaker 6: up a school for his children years ago in Texas. 714 00:35:16,760 --> 00:35:19,160 Speaker 4: This is the next level Elon Muster, the philanthropist. 715 00:35:19,840 --> 00:35:21,359 Speaker 5: What's he gonna call it? Is that have has something 716 00:35:21,400 --> 00:35:21,759 Speaker 5: to do with. 717 00:35:21,840 --> 00:35:24,680 Speaker 6: X Maybe it has something to do with being proximity 718 00:35:24,719 --> 00:35:25,560 Speaker 6: to his space company. 719 00:35:25,600 --> 00:35:28,640 Speaker 3: I think convenience is everything. Meanwhile, that does it. For 720 00:35:28,640 --> 00:35:31,359 Speaker 3: this edition of Bluebog Technology, check 721 00:35:31,360 --> 00:35:33,680 Speaker 6: Out the pod wherever you get your pods Apple and 722 00:35:33,680 --> 00:35:34,360 Speaker 6: on Bloomberg