1 00:00:13,400 --> 00:00:16,400 Speaker 1: I'm Caroline Higher, Bloomberg's World headquarters in New York, and 2 00:00:16,400 --> 00:00:20,360 Speaker 1: I'm Ed Ludlow in San Francisco. This is Bloomberg Technology. Netflix, 3 00:00:20,400 --> 00:00:23,200 Speaker 1: a story of subscriber growth and succession. We dig into 4 00:00:23,239 --> 00:00:25,800 Speaker 1: the earnings and the executive changes. As we learned Read 5 00:00:25,840 --> 00:00:30,280 Speaker 1: Hastings will step down as CO as CEO. Plus emails 6 00:00:30,320 --> 00:00:33,360 Speaker 1: from Elon Musk's shed light on his involvement in a 7 00:00:33,440 --> 00:00:39,280 Speaker 1: twenty sixteen demo that exaggerated autopilot capabilities clus the hacks 8 00:00:39,320 --> 00:00:42,280 Speaker 1: to stop hackers taking your money, and they're working. New 9 00:00:42,320 --> 00:00:45,360 Speaker 1: research shows that the fewer companies are paying ransoms, that 10 00:00:45,440 --> 00:00:48,360 Speaker 1: doesn't mean the number of attacks is actually down. We're 11 00:00:48,360 --> 00:00:50,440 Speaker 1: going to discuss that, but first let's dig in on 12 00:00:50,479 --> 00:00:52,880 Speaker 1: all of that, because it's interesting. We're getting succession planning 13 00:00:52,960 --> 00:00:56,480 Speaker 1: and a chairman remaining over at Texas Instruments is similar 14 00:00:56,520 --> 00:00:59,720 Speaker 1: for Netflix succession planning, unlike over at Disney. For many 15 00:00:59,720 --> 00:01:02,680 Speaker 1: would argue, it's interesting, therefore, to bring on Lucas Shore, 16 00:01:02,800 --> 00:01:05,840 Speaker 1: who so intimately understands this company and indeed the in 17 00:01:05,920 --> 00:01:08,600 Speaker 1: the workings of Read Hastings. He himself, of course, a 18 00:01:08,600 --> 00:01:11,600 Speaker 1: man who's focused on culture in so many ways. Lucas, 19 00:01:11,760 --> 00:01:14,960 Speaker 1: what you make of who now steps up, Greg taking 20 00:01:14,959 --> 00:01:17,319 Speaker 1: the co CEO roans and ultimately who's going to have 21 00:01:17,640 --> 00:01:19,959 Speaker 1: the ultimate decision making power here because he remains as 22 00:01:20,040 --> 00:01:25,760 Speaker 1: executive chairman. Yeah. Look, my assumption is that that Read 23 00:01:25,880 --> 00:01:30,440 Speaker 1: will say that that Ted and Greg get ultimate authority 24 00:01:30,480 --> 00:01:33,720 Speaker 1: there now the CEO obviously as chairman of the board, 25 00:01:33,920 --> 00:01:37,280 Speaker 1: as co founder, as a major shareholder. If there were 26 00:01:37,360 --> 00:01:42,040 Speaker 1: some huge transaction Netflix decided to sell itself by something significant, 27 00:01:42,040 --> 00:01:44,640 Speaker 1: I imagine Read would get would get more involved. But 28 00:01:44,760 --> 00:01:47,080 Speaker 1: he has been gradually stepping back from the day to 29 00:01:47,120 --> 00:01:49,760 Speaker 1: day operations of the company already. You know, he delegated 30 00:01:49,800 --> 00:01:52,760 Speaker 1: most of the Hollywood stuff to to Ted. He's put 31 00:01:52,800 --> 00:01:55,840 Speaker 1: Gregg in charge of these two big initiative advertising and 32 00:01:55,880 --> 00:01:58,200 Speaker 1: password sharing, And I think that's how the company is 33 00:01:58,240 --> 00:02:00,920 Speaker 1: mostly going to keep operating with sort of Gregg looking 34 00:02:00,920 --> 00:02:03,320 Speaker 1: at a lot of the kind of product and strategic fit, 35 00:02:03,680 --> 00:02:05,560 Speaker 1: Ted looking at a lot of the you know, the 36 00:02:05,600 --> 00:02:10,919 Speaker 1: more entertainment focused and programming operations. Hey, Lucas, let's think 37 00:02:10,919 --> 00:02:13,160 Speaker 1: about the forward looking nature of this, you know, because 38 00:02:13,200 --> 00:02:16,120 Speaker 1: Greg Peters kind of was already leading the charge on 39 00:02:16,160 --> 00:02:18,640 Speaker 1: the ad supported tier, already leading the charge on the 40 00:02:18,639 --> 00:02:22,640 Speaker 1: crackdown for password shared Ted Surrandos, the man about Hollywood 41 00:02:22,880 --> 00:02:26,440 Speaker 1: doing the deals, thinking about content, given the numbers we 42 00:02:26,560 --> 00:02:29,360 Speaker 1: just got, which was strong beats right, subscribers and on 43 00:02:29,600 --> 00:02:32,440 Speaker 1: the top line is post bond line a bit weaker. 44 00:02:32,720 --> 00:02:37,120 Speaker 1: Where will they focus their energies going forward? Fore, you know, 45 00:02:37,240 --> 00:02:40,440 Speaker 1: it's going to be continued execution on advertising because remember 46 00:02:40,520 --> 00:02:42,520 Speaker 1: that that app here is only two months old, so 47 00:02:42,560 --> 00:02:44,640 Speaker 1: that will be kind of improving some of the technology 48 00:02:44,680 --> 00:02:47,560 Speaker 1: behind it, you know, targeting sales. You know, it got 49 00:02:47,560 --> 00:02:49,799 Speaker 1: off to a relatively slow start, but I think they 50 00:02:49,840 --> 00:02:52,040 Speaker 1: like what they're seeing. It will be rolling out this 51 00:02:52,080 --> 00:02:54,560 Speaker 1: whole password crackdown, which I think is going to start 52 00:02:54,560 --> 00:02:56,640 Speaker 1: this quarter and really ramp up in the middle of 53 00:02:56,639 --> 00:02:59,080 Speaker 1: the year. Um, it's going to be kind of continuing 54 00:02:59,120 --> 00:03:01,519 Speaker 1: to try to improve of programming in certain areas. You know, 55 00:03:01,560 --> 00:03:05,919 Speaker 1: they've they've done really well recently on English language television programming, 56 00:03:06,120 --> 00:03:09,200 Speaker 1: not quite as well on the foreign language television programming. 57 00:03:09,360 --> 00:03:11,200 Speaker 1: I think they want to keep improving their hit rate 58 00:03:11,200 --> 00:03:13,120 Speaker 1: on movies because they release a lot of movies people 59 00:03:13,120 --> 00:03:15,760 Speaker 1: don't know, you know, don't watch. And then there's all 60 00:03:15,800 --> 00:03:17,639 Speaker 1: gaming piece of it, which is still a small part 61 00:03:17,720 --> 00:03:20,519 Speaker 1: of the business, but they're investing a lot of money 62 00:03:20,560 --> 00:03:22,120 Speaker 1: into it. I don't think you're going to see a 63 00:03:22,120 --> 00:03:24,600 Speaker 1: ton of news around that in twenty three, but it 64 00:03:24,720 --> 00:03:26,799 Speaker 1: is something that they will continue to talk about as 65 00:03:26,840 --> 00:03:28,560 Speaker 1: with the future. The one point I do want to 66 00:03:28,560 --> 00:03:31,240 Speaker 1: make is while they had a really strong final quarter 67 00:03:31,320 --> 00:03:33,320 Speaker 1: of the year, at least in terms of subscriber growth, 68 00:03:33,440 --> 00:03:35,320 Speaker 1: the thing to keep in mind, just sort of big picture, 69 00:03:35,440 --> 00:03:37,360 Speaker 1: is that for the full year, they did still post 70 00:03:37,360 --> 00:03:41,440 Speaker 1: their worst subscriber growth since two thousand eleven. Alright, bloom 71 00:03:41,440 --> 00:03:44,160 Speaker 1: bags Luke is sure great reporting on what's changing at 72 00:03:44,160 --> 00:03:46,920 Speaker 1: Netflix and good analysis of the numbers, which I think 73 00:03:46,960 --> 00:03:49,240 Speaker 1: we need more of. So let's bring a branding cats 74 00:03:49,280 --> 00:03:53,520 Speaker 1: industry analysts at Para Analytics. It's interesting the third party 75 00:03:53,600 --> 00:03:56,520 Speaker 1: data suggested that actually in the final three months of 76 00:03:56,600 --> 00:04:00,640 Speaker 1: last year, Netflix is facing great competition from likes of Warned, 77 00:04:00,680 --> 00:04:04,080 Speaker 1: Discovery and Disney, and actually demand over rule is starting 78 00:04:04,120 --> 00:04:07,280 Speaker 1: to pull back. Well, when you saw those net subscribing 79 00:04:07,360 --> 00:04:10,480 Speaker 1: ads for Netflix seven point seven million, what's your reaction, Brandon, 80 00:04:12,120 --> 00:04:14,600 Speaker 1: I'm still not totally surprised. I mean, Netflix has always 81 00:04:14,600 --> 00:04:17,839 Speaker 1: been savvy with its guidance. It likes to set the 82 00:04:17,960 --> 00:04:21,719 Speaker 1: stage for positive narratives, which we are now very much 83 00:04:21,800 --> 00:04:24,760 Speaker 1: engaging in. And we look at Netflix from our angle, 84 00:04:24,920 --> 00:04:27,440 Speaker 1: is still the streaming industry leader. It's the most global 85 00:04:27,480 --> 00:04:31,280 Speaker 1: surprise subscribers, and is the most in demand overall catalog 86 00:04:31,320 --> 00:04:34,320 Speaker 1: with US audiences in all of two and that includes 87 00:04:34,360 --> 00:04:37,320 Speaker 1: original and licensed movies and TV shows. And it also 88 00:04:37,400 --> 00:04:41,040 Speaker 1: still leads the entire industry in global original demand and 89 00:04:41,279 --> 00:04:44,520 Speaker 1: US original demands. So yes, that is shrinking as it 90 00:04:44,600 --> 00:04:47,720 Speaker 1: seeds ground to you know, very hungry competitors that are 91 00:04:47,720 --> 00:04:51,280 Speaker 1: gaining market share, but they still have a massive, massive lead. 92 00:04:51,320 --> 00:04:54,040 Speaker 1: And I think all the doom and gloom around Netflix 93 00:04:54,120 --> 00:04:57,400 Speaker 1: last April perhaps a bit premature, as we're seeing now 94 00:04:57,480 --> 00:05:00,680 Speaker 1: even with what Lucas accurately pointed out there a subscriber 95 00:05:00,720 --> 00:05:04,200 Speaker 1: total in a year since two thousand. Later, Lucas at 96 00:05:04,200 --> 00:05:07,520 Speaker 1: the end of his Bloomberg News report, uh summed up 97 00:05:07,520 --> 00:05:09,920 Speaker 1: the comments about the ads supported tier and they were 98 00:05:09,960 --> 00:05:12,919 Speaker 1: a little bit muted from Netflix there. Please, with the 99 00:05:12,920 --> 00:05:15,760 Speaker 1: progress on the ad supported tier, they're saying that it's 100 00:05:15,760 --> 00:05:19,800 Speaker 1: bringing in cost conscious consumers. Do you think that that 101 00:05:19,920 --> 00:05:22,760 Speaker 1: was part of the beat when it came to subscribe 102 00:05:22,760 --> 00:05:26,039 Speaker 1: a growth in the last three months of last year. Yeah, 103 00:05:26,040 --> 00:05:28,560 Speaker 1: and they even pointed out in their letter to shareholders 104 00:05:28,600 --> 00:05:30,839 Speaker 1: that they aren't seeing a lot of conversions from premium 105 00:05:30,880 --> 00:05:33,440 Speaker 1: subscriptions to add tiers, which is exactly what they wanted. 106 00:05:33,640 --> 00:05:36,839 Speaker 1: They want the ad tire to expand the total addressable 107 00:05:36,880 --> 00:05:40,320 Speaker 1: market from Netflix, and particularly at a time when consumers 108 00:05:40,320 --> 00:05:43,360 Speaker 1: are weighing concession fears and maybe our recession fears, maybe 109 00:05:43,360 --> 00:05:47,160 Speaker 1: a little bit more cost conscious. Now, yes, not the fastest, 110 00:05:47,200 --> 00:05:50,120 Speaker 1: most explosive rollout, but this is going to be a 111 00:05:50,279 --> 00:05:53,120 Speaker 1: must have for all advertisers looking to get into connect 112 00:05:53,160 --> 00:05:55,599 Speaker 1: the TVs and digital TVs. So we are going to 113 00:05:55,640 --> 00:05:59,760 Speaker 1: see that grow throughout. And we gotta remember they turned 114 00:05:59,760 --> 00:06:03,720 Speaker 1: around and from years of an anti ad stance and 115 00:06:03,800 --> 00:06:06,280 Speaker 1: created their ads here and only six months so there 116 00:06:06,360 --> 00:06:10,160 Speaker 1: is a lot of upward potential and room to grow here. Yeah. 117 00:06:10,240 --> 00:06:13,359 Speaker 1: Interesting in that earning statement they said, the early results 118 00:06:13,600 --> 00:06:16,480 Speaker 1: they say they're pleased with, but there's much still to do. 119 00:06:17,240 --> 00:06:19,720 Speaker 1: Can you run in any way talk about sort of 120 00:06:19,720 --> 00:06:22,520 Speaker 1: the culture of innovation over a Netflix, because that is 121 00:06:22,560 --> 00:06:24,719 Speaker 1: of course what we've come to know having read the 122 00:06:24,800 --> 00:06:28,040 Speaker 1: No Rules Rules Book by read Hastings himself. I mean 123 00:06:28,040 --> 00:06:29,880 Speaker 1: it's all about the way in which they set apart 124 00:06:29,920 --> 00:06:32,919 Speaker 1: on different corporate culture. Now it's interesting to compare. Should 125 00:06:32,920 --> 00:06:35,960 Speaker 1: we see succession planning with Disney Plus versus Netflix and 126 00:06:36,240 --> 00:06:38,760 Speaker 1: the kind of operate opposite end of the spectrum. But 127 00:06:38,880 --> 00:06:42,840 Speaker 1: we are seeing Disney Plus and Warner Brothers Discovery taking 128 00:06:43,040 --> 00:06:46,240 Speaker 1: chunks perotentially out of market share from Netflix. How can 129 00:06:46,240 --> 00:06:49,480 Speaker 1: Netflix ensure that it's stunding in goodstead to keep on growing, 130 00:06:49,560 --> 00:06:54,200 Speaker 1: keep on innovating, keep on personalizing. It's funny because Netflix 131 00:06:54,200 --> 00:06:57,880 Speaker 1: has always fanacied itself as the greatest disruptor in Hollywood history, 132 00:06:58,360 --> 00:07:00,360 Speaker 1: and now it's really reached a point where it is 133 00:07:00,920 --> 00:07:04,679 Speaker 1: recreating the playbook of its linear rivals instead of vice versa. 134 00:07:04,760 --> 00:07:08,359 Speaker 1: So the market emphasis has shifted obviously towards profit and revenue, 135 00:07:08,400 --> 00:07:12,360 Speaker 1: which they are clearly emphasizing, and their strategy and public 136 00:07:12,440 --> 00:07:16,720 Speaker 1: narrative has followed suits. So clearly they are adjusting to 137 00:07:16,760 --> 00:07:19,800 Speaker 1: the times. And frankly, they're in a better financial situation 138 00:07:19,800 --> 00:07:22,280 Speaker 1: than every other major streamer. They're the only one really 139 00:07:22,400 --> 00:07:25,000 Speaker 1: that's profitable at the moment. With Disney Plus, HBO, Max, 140 00:07:25,000 --> 00:07:28,760 Speaker 1: Paramount Plus and Peacock all looking at or beyond to 141 00:07:28,880 --> 00:07:32,160 Speaker 1: finally get into black. But in terms of continuing to grow, 142 00:07:32,400 --> 00:07:35,960 Speaker 1: they're really really going to emphasize not only the ads 143 00:07:36,000 --> 00:07:40,080 Speaker 1: here as a more cost effective alternative to cost conscious consumers, 144 00:07:40,160 --> 00:07:42,880 Speaker 1: not only password sharing, where if they convert just ten 145 00:07:42,960 --> 00:07:46,320 Speaker 1: percent of the hundred million or so password shairs, that's 146 00:07:46,320 --> 00:07:48,920 Speaker 1: ten million new subs. But they also want to continue 147 00:07:48,960 --> 00:07:52,520 Speaker 1: investing in non English content and create not just regional 148 00:07:52,560 --> 00:07:56,080 Speaker 1: hits that resonate with the Latin American market or Asia Pacific, 149 00:07:56,160 --> 00:07:59,200 Speaker 1: but that travel globally and we're seeing that steadily increase 150 00:07:59,240 --> 00:08:02,560 Speaker 1: in frequency over the past several quarters. Yeah, really interesting 151 00:08:02,640 --> 00:08:04,960 Speaker 1: to bring up the global perspective of this business. And 152 00:08:05,000 --> 00:08:06,720 Speaker 1: I think what's really interesting is the way in which 153 00:08:06,760 --> 00:08:08,640 Speaker 1: investors have started to buy in. And it was you 154 00:08:08,720 --> 00:08:11,080 Speaker 1: that was on Twitter just saying what we're off more 155 00:08:12,200 --> 00:08:14,640 Speaker 1: up from the lows that Netflix's share price that hit 156 00:08:14,920 --> 00:08:17,360 Speaker 1: analysts looking more far more on the buy side than 157 00:08:17,400 --> 00:08:19,520 Speaker 1: they are on the cull side in terms of ratings. Brandon, 158 00:08:20,000 --> 00:08:23,240 Speaker 1: from your geographical perspective, which leaders can they pull where 159 00:08:23,240 --> 00:08:25,120 Speaker 1: it's going to be growing the most at the moment. 160 00:08:25,240 --> 00:08:29,960 Speaker 1: Is it Latin, is it Asia Visa v U S Europe. Yeah, 161 00:08:29,960 --> 00:08:34,559 Speaker 1: I mean Netflix has years long unrivaled investments in overseas 162 00:08:34,600 --> 00:08:36,880 Speaker 1: content regions, so they really do have a lead on 163 00:08:36,920 --> 00:08:39,880 Speaker 1: the competition in that regard. Asia Pacific has been their 164 00:08:39,920 --> 00:08:43,400 Speaker 1: biggest growth arena over the last five or six corners quarters, 165 00:08:43,440 --> 00:08:46,600 Speaker 1: so they're going to continue to hyper focus on that. Obviously, 166 00:08:46,640 --> 00:08:50,160 Speaker 1: we've seen South Korea become a major international hub for content, 167 00:08:50,240 --> 00:08:53,200 Speaker 1: both his regional hits and hits that can hopefully translate 168 00:08:53,360 --> 00:08:56,000 Speaker 1: to the domestic audience as well. And then there's the 169 00:08:56,280 --> 00:08:58,640 Speaker 1: big White whale and streaming. Everyone is trying to crack 170 00:08:58,720 --> 00:09:01,440 Speaker 1: the code in India. Now. Netflix has gotten off to 171 00:09:01,440 --> 00:09:04,160 Speaker 1: a slow start there. They're behind Amazon and Disney Plus 172 00:09:04,240 --> 00:09:07,199 Speaker 1: in terms of market share, but they continue to experiment 173 00:09:07,240 --> 00:09:10,280 Speaker 1: with morbil only plans with six months and annual plans 174 00:09:10,280 --> 00:09:13,160 Speaker 1: that offer discounts, and they continue to invest in original 175 00:09:13,200 --> 00:09:17,000 Speaker 1: content that covers a variety of regional dialects. So they're 176 00:09:17,000 --> 00:09:19,600 Speaker 1: going to hyper focus on on that area while still 177 00:09:19,640 --> 00:09:22,960 Speaker 1: serving the areas that have proved to be fruitful to them, 178 00:09:23,080 --> 00:09:29,120 Speaker 1: such as Latin America. Alright, Para analytics industry analyst Brandon Cats, 179 00:09:29,120 --> 00:09:30,760 Speaker 1: I think we're gonna have being this conversation about the 180 00:09:30,800 --> 00:09:33,360 Speaker 1: future of Netflix for many weeks to come. Thank you 181 00:09:33,440 --> 00:09:35,760 Speaker 1: so much for joining the program. I want to bring 182 00:09:35,800 --> 00:09:38,880 Speaker 1: a quick correction to our audience, Caroline, because earlier we 183 00:09:39,000 --> 00:09:40,959 Speaker 1: showed a board of the share price of a T 184 00:09:41,080 --> 00:09:43,439 Speaker 1: and T, and after ours when we meant to show 185 00:09:43,520 --> 00:09:46,880 Speaker 1: T mobile, which is disclosed, it will take a significant 186 00:09:46,960 --> 00:09:51,000 Speaker 1: charge from a cybersecurity vulnerability that it disclosed in a 187 00:09:51,040 --> 00:09:53,360 Speaker 1: regulatory filing. After the market close. You can see in 188 00:09:53,400 --> 00:09:56,079 Speaker 1: your screen te Mobile down one point five percent. I 189 00:09:56,080 --> 00:09:58,120 Speaker 1: got my Bloomberg terminal in front of me and I 190 00:09:58,160 --> 00:10:02,120 Speaker 1: can confirm it's down one point five in after hours. 191 00:10:02,320 --> 00:10:05,080 Speaker 1: Will continue to track that story. Now coming up some 192 00:10:05,200 --> 00:10:09,319 Speaker 1: emails shedding light on Elon Musk's involvement in a twenty 193 00:10:09,480 --> 00:10:12,240 Speaker 1: sixteen demo. What does that mean for the ev maker? 194 00:10:12,640 --> 00:10:15,959 Speaker 1: Could it face probes? How does it market its technology? 195 00:10:15,960 --> 00:10:28,040 Speaker 1: Will discuss all of that next. This is Bloomberg. Quite 196 00:10:28,080 --> 00:10:31,400 Speaker 1: the scoop coming from Bloomberg use today. Elon Musk oversaw 197 00:10:31,480 --> 00:10:35,240 Speaker 1: the creation of a video that exaggerated the abilities of 198 00:10:35,280 --> 00:10:39,360 Speaker 1: Tesla's driver assistant system called Autopilot. He even dictated the 199 00:10:39,360 --> 00:10:42,680 Speaker 1: opening text that was claiming the company's car drove itself. 200 00:10:43,040 --> 00:10:45,960 Speaker 1: This is all according to internal emails viewed by Bloomberg 201 00:10:46,280 --> 00:10:48,679 Speaker 1: and our own Dana Hall is here to join us 202 00:10:48,720 --> 00:10:52,600 Speaker 1: and discuss your scoop DNA. And it's extraordinary, really the 203 00:10:52,640 --> 00:10:55,840 Speaker 1: relevancy that this still has, because what's six years on, 204 00:10:56,200 --> 00:10:59,160 Speaker 1: we're still debating whether autopilot is really what it says 205 00:10:59,160 --> 00:11:02,880 Speaker 1: it is. Yeah, and this is pretty extraordinary. I mean, 206 00:11:02,880 --> 00:11:06,200 Speaker 1: this video that I'm referring to is still on Tesla's website. 207 00:11:06,280 --> 00:11:08,800 Speaker 1: It's a pretty seminal video in the history of the company. 208 00:11:08,920 --> 00:11:11,280 Speaker 1: It is this, you know, a great video sent to 209 00:11:11,320 --> 00:11:14,319 Speaker 1: the tune of the Rolling Stone song um Painted Black. 210 00:11:14,400 --> 00:11:16,880 Speaker 1: It's still on Tessel's website, and it purports that this 211 00:11:16,960 --> 00:11:19,280 Speaker 1: car is driving itself and that the only reason why 212 00:11:19,280 --> 00:11:22,400 Speaker 1: a drivers in the cars for legal reasons. But what 213 00:11:22,480 --> 00:11:25,440 Speaker 1: we learned via these emails and via deposition that we 214 00:11:25,440 --> 00:11:28,000 Speaker 1: got a hold of this week is that, you know, 215 00:11:28,120 --> 00:11:31,480 Speaker 1: Musk really oversaw the production of this video, and dozens 216 00:11:31,480 --> 00:11:34,760 Speaker 1: of Tesla staffers were involved, and Musk himself wrote that 217 00:11:34,880 --> 00:11:37,360 Speaker 1: language that you see when the video first starts playing. 218 00:11:38,440 --> 00:11:40,760 Speaker 1: I want to go back to the basics of this 219 00:11:40,880 --> 00:11:44,920 Speaker 1: story because it's important so we're talking about some emails 220 00:11:44,960 --> 00:11:48,120 Speaker 1: that Elon Musque sent to quite a large group of 221 00:11:48,400 --> 00:11:51,040 Speaker 1: the Autopilot team. We have some of it that we 222 00:11:51,080 --> 00:11:53,320 Speaker 1: can bring up that I'll read you. Since this is 223 00:11:53,360 --> 00:11:56,920 Speaker 1: a demo, it is fine to hardcore code some of 224 00:11:56,960 --> 00:12:00,880 Speaker 1: it since we will backfill with production code later in 225 00:12:00,920 --> 00:12:03,520 Speaker 1: an O tier update O T over the air update. 226 00:12:03,800 --> 00:12:06,480 Speaker 1: What is Elon must talking about here? So this was 227 00:12:06,600 --> 00:12:08,600 Speaker 1: so the first email that we got is an email 228 00:12:08,640 --> 00:12:11,920 Speaker 1: that went out to the entire Autopilot team in mid October, 229 00:12:12,920 --> 00:12:15,439 Speaker 1: and he's basically like, all hands on deck for this demo, 230 00:12:15,480 --> 00:12:18,199 Speaker 1: and he's basically saying, don't worry, like we're going to 231 00:12:18,280 --> 00:12:20,319 Speaker 1: continue to work on this code. We're going to continue 232 00:12:20,320 --> 00:12:22,120 Speaker 1: to refine it we find it through over the air 233 00:12:22,200 --> 00:12:25,600 Speaker 1: updates and over their updates. Is something that Tesla regularly does. 234 00:12:26,280 --> 00:12:30,199 Speaker 1: But what's important is that nine days later he in 235 00:12:30,559 --> 00:12:33,040 Speaker 1: a second email, He's basically like, this is the language 236 00:12:33,080 --> 00:12:36,280 Speaker 1: that I want in the video, and the language is 237 00:12:37,280 --> 00:12:41,720 Speaker 1: basically ultimately saying this is going to drive itself. I'm 238 00:12:41,760 --> 00:12:44,400 Speaker 1: telling you where we are going to be rather than 239 00:12:44,440 --> 00:12:47,160 Speaker 1: where we are in this here and now. The here 240 00:12:47,200 --> 00:12:50,080 Speaker 1: and now. Dana is also that there's loads of probes 241 00:12:50,120 --> 00:12:54,760 Speaker 1: into autopilot right from various authorities in the US. Yeah, 242 00:12:54,800 --> 00:12:56,160 Speaker 1: so this is the this is the thing that I 243 00:12:56,160 --> 00:12:58,320 Speaker 1: think will be interesting to watch going forward. You have 244 00:12:58,440 --> 00:13:02,160 Speaker 1: sort of two simultaneous things happening. The first is families 245 00:13:02,200 --> 00:13:06,319 Speaker 1: of drivers that died in crashes where autopilot was engaged 246 00:13:06,440 --> 00:13:09,200 Speaker 1: or may have been engaged, are suing the company. I mean, 247 00:13:09,200 --> 00:13:12,680 Speaker 1: there are several civil cases ongoing, including the case of 248 00:13:12,679 --> 00:13:15,400 Speaker 1: Walter Huang, which is here in the Bay Area, and 249 00:13:15,400 --> 00:13:18,680 Speaker 1: that trial begins in March. And then secondly, you have 250 00:13:18,920 --> 00:13:22,120 Speaker 1: you know, NITZA and the California Department of Motor Vehicles 251 00:13:22,360 --> 00:13:24,959 Speaker 1: looking you know, autopilot kind of on two fronts, both 252 00:13:25,600 --> 00:13:28,520 Speaker 1: the technology itself as well as how it was marketed 253 00:13:28,520 --> 00:13:32,800 Speaker 1: to customers. And you know, bureaucracy moves very slowly. I 254 00:13:32,800 --> 00:13:35,000 Speaker 1: don't know what the latest is on those investigations, but 255 00:13:35,040 --> 00:13:38,560 Speaker 1: this is technology that has been you know, under scrutiny 256 00:13:38,600 --> 00:13:40,600 Speaker 1: for quite some time now, and I think what's what's 257 00:13:40,640 --> 00:13:43,439 Speaker 1: relevant is that, Okay, yes, we got emails that are 258 00:13:43,440 --> 00:13:46,439 Speaker 1: frankly six years old, but they are they show just 259 00:13:46,559 --> 00:13:50,120 Speaker 1: how heavily involved Musk himself was in the creation of 260 00:13:50,120 --> 00:13:52,000 Speaker 1: the video, the production of the video, and what the 261 00:13:52,080 --> 00:13:54,559 Speaker 1: language of the video was going to say. Alright, bloom 262 00:13:54,600 --> 00:13:57,280 Speaker 1: bags down the hole. Just terrific reporting alongside Seawan O. 263 00:13:57,400 --> 00:14:07,840 Speaker 1: Kaine is at in Austin. Thank you so much. Just 264 00:14:07,880 --> 00:14:10,760 Speaker 1: a few years ago people were writing stories about how 265 00:14:10,800 --> 00:14:13,080 Speaker 1: this is stigmatized and how people should not talk to 266 00:14:13,280 --> 00:14:16,440 Speaker 1: AI and this is creepy and strange. Now this is 267 00:14:16,440 --> 00:14:19,240 Speaker 1: not a question anymore. But again, now the question is 268 00:14:19,240 --> 00:14:21,600 Speaker 1: it okay to have any golfer? Is it okay to 269 00:14:21,640 --> 00:14:27,160 Speaker 1: have that outwork? Jenni Acuta, CEO of the AI chatbot Replica, 270 00:14:27,360 --> 00:14:29,640 Speaker 1: which we spoke to last week, And now let's bring 271 00:14:29,640 --> 00:14:32,240 Speaker 1: in Andrew Straight for more context on all of this, 272 00:14:32,400 --> 00:14:35,080 Speaker 1: because he's an Associate director of Emergent Technology and Industry 273 00:14:35,080 --> 00:14:38,080 Speaker 1: Practice over at the Aida Lovelace Institute in London, focusing 274 00:14:38,120 --> 00:14:41,440 Speaker 1: on research on policy on practice. Basically, sure and the 275 00:14:41,520 --> 00:14:44,360 Speaker 1: data and AI work for people, work for society. But 276 00:14:44,400 --> 00:14:47,640 Speaker 1: what's so fascinating about your background is you've long worked 277 00:14:47,640 --> 00:14:50,440 Speaker 1: in AI ethics, you've long worked in content moderation. You 278 00:14:50,520 --> 00:14:53,440 Speaker 1: worked DeepMind, such a standout AI performer over in the 279 00:14:53,520 --> 00:14:57,240 Speaker 1: UK and which is ultimately bought by alphabets. Google talk 280 00:14:57,280 --> 00:14:59,640 Speaker 1: to us about when we're thinking of chat gypt, when 281 00:14:59,640 --> 00:15:02,280 Speaker 1: we're thinking of replica and all the innovations around chatbots 282 00:15:02,280 --> 00:15:05,160 Speaker 1: and AI, are we up to speed with the ethical 283 00:15:05,280 --> 00:15:10,120 Speaker 1: ramifications here. It's a very good question. I would say 284 00:15:10,160 --> 00:15:12,640 Speaker 1: that in general, we have continued to not do the 285 00:15:12,680 --> 00:15:16,000 Speaker 1: best of jobs of communicating limitations and risks. These kinds 286 00:15:16,040 --> 00:15:20,800 Speaker 1: of technologies can raise people society. Um, these are very 287 00:15:20,800 --> 00:15:23,040 Speaker 1: exciting technologies. I think there's no doubt to say that 288 00:15:23,240 --> 00:15:26,480 Speaker 1: general models are raising all kinds of exciting applications from 289 00:15:26,480 --> 00:15:29,960 Speaker 1: home writing to co generation. But they are not magic. 290 00:15:30,040 --> 00:15:35,840 Speaker 1: They're built on hidden a process of labor that are 291 00:15:35,840 --> 00:15:39,720 Speaker 1: oftentimes relying on exploited and underpaid workers, and they can 292 00:15:39,760 --> 00:15:42,000 Speaker 1: have very serious impacts and people's society that needs to 293 00:15:42,000 --> 00:15:45,880 Speaker 1: be better communicated. So it's critical as developers these technologies, 294 00:15:45,880 --> 00:15:49,600 Speaker 1: as policymakers that we create the conditions for these technologies 295 00:15:49,640 --> 00:15:53,120 Speaker 1: to be beneficial to people in society. That's something we 296 00:15:53,160 --> 00:15:55,080 Speaker 1: certainly can see these technic these tech companies doing a 297 00:15:55,120 --> 00:15:58,120 Speaker 1: bit more of. Yeah, it feels like open AI, for example, 298 00:15:58,440 --> 00:16:02,480 Speaker 1: which is behind chatching Piece, is very much trying to 299 00:16:02,560 --> 00:16:05,520 Speaker 1: have the narrative of how do we ethically introduces, how 300 00:16:05,520 --> 00:16:08,560 Speaker 1: do we ensure that it's iterating at a safe pace? 301 00:16:09,240 --> 00:16:11,320 Speaker 1: But what more can be done where in the world, 302 00:16:11,360 --> 00:16:16,160 Speaker 1: for example, is working well with private, with public, with academics, 303 00:16:16,160 --> 00:16:20,520 Speaker 1: with governments, for example. It's a very good question. Um. 304 00:16:20,760 --> 00:16:23,160 Speaker 1: One thing I think that we can see more of 305 00:16:23,440 --> 00:16:27,160 Speaker 1: is initial consideration of the potential uses or misuses of 306 00:16:27,200 --> 00:16:30,040 Speaker 1: these technologies and building in safeguards that are meaningful to 307 00:16:30,360 --> 00:16:33,000 Speaker 1: prevent those misuses. So, to give up a credit, they 308 00:16:33,080 --> 00:16:35,960 Speaker 1: when they release chat GPT, they were very careful to 309 00:16:35,960 --> 00:16:38,800 Speaker 1: put in place safeguards to prevent certain types of toxic 310 00:16:38,920 --> 00:16:43,320 Speaker 1: or harmful content or dangerous content from being shared. But 311 00:16:43,640 --> 00:16:46,200 Speaker 1: as you can see, these these kinds of safeguards could 312 00:16:46,240 --> 00:16:49,520 Speaker 1: very quickly be overcome. If you typed in the appropriate response. 313 00:16:49,560 --> 00:16:51,920 Speaker 1: You could get chat GPT to give you instructions now 314 00:16:51,960 --> 00:16:55,320 Speaker 1: to make a Molotov cocktail or how to hotwire a car. Um. 315 00:16:55,400 --> 00:16:57,440 Speaker 1: And even more concerning is that it's not just the 316 00:16:57,480 --> 00:17:00,920 Speaker 1: story of chat GPT. Even if Chat GPT and Opening 317 00:17:00,920 --> 00:17:03,600 Speaker 1: Eye put in these kinds of of safeguards, other types 318 00:17:04,000 --> 00:17:07,639 Speaker 1: of companies doing creating generative models may not do the same. 319 00:17:08,280 --> 00:17:10,439 Speaker 1: So I think a few things are needed. One is 320 00:17:10,480 --> 00:17:13,520 Speaker 1: there needs to be more cross industry collaboration, communication and 321 00:17:13,520 --> 00:17:17,000 Speaker 1: discussion about the kinds of risk these technologies pose. There 322 00:17:17,040 --> 00:17:19,640 Speaker 1: needs to be more discussion and clarity of the kinds 323 00:17:19,680 --> 00:17:21,560 Speaker 1: of standards need to be met in order for these 324 00:17:21,560 --> 00:17:24,440 Speaker 1: technologies to be released safely. And there also needs to 325 00:17:24,440 --> 00:17:26,760 Speaker 1: be a bit more clarification and communication with members the 326 00:17:26,760 --> 00:17:29,439 Speaker 1: public about those impacts. Um I'm thinking particularly there of 327 00:17:29,560 --> 00:17:32,760 Speaker 1: educators and creators who are most impacted by these technologies 328 00:17:32,760 --> 00:17:35,560 Speaker 1: at the moment. And lastly, we need really strong and 329 00:17:35,560 --> 00:17:38,159 Speaker 1: stringent regulation. We do a lot of public attitude surveying 330 00:17:38,200 --> 00:17:40,399 Speaker 1: and research in our in our institute, and one of 331 00:17:40,440 --> 00:17:43,800 Speaker 1: the most clear messages that comes across is people want 332 00:17:43,880 --> 00:17:46,880 Speaker 1: regulation for AI. They want to feel safe, They want 333 00:17:46,880 --> 00:17:49,240 Speaker 1: to have some sense that government has a grip on 334 00:17:49,280 --> 00:17:52,000 Speaker 1: these technologies and is putting in place the right safeguards, 335 00:17:52,040 --> 00:17:55,080 Speaker 1: keep them, keep them stick, Andrew. The big news headline 336 00:17:55,160 --> 00:17:57,760 Speaker 1: that that I saw on Thursday today in the world 337 00:17:57,760 --> 00:18:00,800 Speaker 1: of AI was the FBI Director Chris A. Ray saying 338 00:18:00,840 --> 00:18:05,080 Speaker 1: he's deeply concerned about China's research in the field of 339 00:18:05,080 --> 00:18:07,840 Speaker 1: AI and it in it's AI program and what it 340 00:18:07,920 --> 00:18:12,479 Speaker 1: might use AI for an independent research group like yours. 341 00:18:12,880 --> 00:18:15,359 Speaker 1: Do you see what China is doing in the field 342 00:18:15,400 --> 00:18:18,119 Speaker 1: of AI is a worry. I think what the FBI 343 00:18:18,280 --> 00:18:21,000 Speaker 1: talking about here and broadly what others have talked about, 344 00:18:21,040 --> 00:18:23,879 Speaker 1: is the military applications of AI that could pose a 345 00:18:23,880 --> 00:18:29,760 Speaker 1: threat to society. It's a it's a concerning development, certainly 346 00:18:29,840 --> 00:18:32,399 Speaker 1: whenever you are kind of rushing into the use of 347 00:18:32,440 --> 00:18:35,159 Speaker 1: AI from military and times weapons. But it's important to 348 00:18:35,160 --> 00:18:39,040 Speaker 1: acknowledge that's not simply a China problem. Many militaries around 349 00:18:39,040 --> 00:18:41,840 Speaker 1: the world are are developing these kinds of technologies, again 350 00:18:42,000 --> 00:18:46,080 Speaker 1: oftentimes without much consideration for the kinds of race dynamics 351 00:18:46,119 --> 00:18:49,480 Speaker 1: that doing so can create between other countries. So in 352 00:18:49,520 --> 00:18:52,320 Speaker 1: the UK and the US, in other parts of the 353 00:18:52,320 --> 00:18:55,120 Speaker 1: world these kinds of Tina's weapons systems are being developed. 354 00:18:55,119 --> 00:18:58,040 Speaker 1: There are as I understand, it's some some excellent work 355 00:18:58,040 --> 00:19:00,560 Speaker 1: being done to develop ethical codes of practice groups like 356 00:19:00,600 --> 00:19:04,240 Speaker 1: them defense, but it is important to acknowledge that those 357 00:19:04,280 --> 00:19:06,520 Speaker 1: are are nut risks unique to China, that those are 358 00:19:06,600 --> 00:19:09,320 Speaker 1: very serious ethical and legal considerations that need to be 359 00:19:10,119 --> 00:19:13,199 Speaker 1: seriously considering the dress on the international level. All Right. 360 00:19:13,280 --> 00:19:17,200 Speaker 1: Andrew Straight, Associate Director of Emerging Technology and industry practice 361 00:19:17,400 --> 00:19:19,439 Speaker 1: at the aid of loves Lace Institry. Thank you for 362 00:19:19,440 --> 00:19:29,840 Speaker 1: staying up so late for us. Welcome back to the technology. 363 00:19:29,840 --> 00:19:32,280 Speaker 1: I'm Caroline Hyde in New York and I made lovelow 364 00:19:32,320 --> 00:19:34,879 Speaker 1: back in San Francisco. And let's get straight back to 365 00:19:34,960 --> 00:19:37,760 Speaker 1: the Netflix news and the future of the company and 366 00:19:37,840 --> 00:19:40,879 Speaker 1: Read Hastings and bring in John Klein, the CEO of 367 00:19:40,960 --> 00:19:44,560 Speaker 1: hang and a former president of CNN. Let's be honest 368 00:19:44,600 --> 00:19:46,680 Speaker 1: while we've got you on the show. You know this industry, 369 00:19:46,840 --> 00:19:49,080 Speaker 1: you know the names in the industry. There was a 370 00:19:49,119 --> 00:19:52,440 Speaker 1: big stock reaction to the news that Read Hastings would 371 00:19:52,440 --> 00:19:56,520 Speaker 1: step back as co CEO go to the executive chair position. 372 00:19:56,920 --> 00:20:00,119 Speaker 1: Greg Peters, a name well known, steps up to cocy 373 00:20:00,240 --> 00:20:02,680 Speaker 1: uh Um, what do you make of all of that? 374 00:20:02,880 --> 00:20:08,000 Speaker 1: Is just an example of good succession planning well. At first, Blush, 375 00:20:08,040 --> 00:20:10,919 Speaker 1: it feels like the opposite of the Bob Eiger situation, 376 00:20:10,960 --> 00:20:15,080 Speaker 1: where he decided to dive back into a horribly messy 377 00:20:15,320 --> 00:20:19,960 Speaker 1: macro environment in the hope of fixing it. Whereas Reid 378 00:20:20,000 --> 00:20:24,240 Speaker 1: looks like he is stepping back a little bit. But 379 00:20:24,280 --> 00:20:26,439 Speaker 1: then as you think about it a little further, you know, 380 00:20:26,520 --> 00:20:30,159 Speaker 1: an executive chairman is very different than a non executive chairman. 381 00:20:30,240 --> 00:20:33,240 Speaker 1: Executive chairman sort of says, I still have to clear 382 00:20:33,280 --> 00:20:35,399 Speaker 1: everything if I want to. I've still got an office 383 00:20:35,480 --> 00:20:37,919 Speaker 1: right there, and you're gonna see me all the time. 384 00:20:38,480 --> 00:20:42,480 Speaker 1: Plus the fact that they brought in a co CEO. Still, 385 00:20:42,840 --> 00:20:46,399 Speaker 1: I thought when they elevated Ted Serrando's to co CEO 386 00:20:46,480 --> 00:20:49,760 Speaker 1: that that was the sign that he was the success 387 00:20:50,359 --> 00:20:55,040 Speaker 1: So imagine now the idea that he was not the successor, 388 00:20:55,119 --> 00:20:57,800 Speaker 1: and in fact he's sort of pemmed in on both sides. 389 00:20:58,119 --> 00:21:00,240 Speaker 1: I don't know how that's gonna play out. I don't 390 00:21:00,240 --> 00:21:04,280 Speaker 1: know any of the psychology involved among these people, but 391 00:21:05,160 --> 00:21:08,280 Speaker 1: I just wonder how that's going to play out. John, 392 00:21:08,320 --> 00:21:11,440 Speaker 1: You've led sort of legacy traditional media companies, You've led 393 00:21:11,480 --> 00:21:15,840 Speaker 1: digital media companies. Um, where does Netflix sit right now? 394 00:21:15,880 --> 00:21:18,720 Speaker 1: For you? And I guess the leadership of this growing 395 00:21:18,800 --> 00:21:23,040 Speaker 1: and increasingly competitive field, you know, Disney, Warner Brother, Discovery. 396 00:21:23,080 --> 00:21:26,239 Speaker 1: The data shows they're growing. They're also considering whether they 397 00:21:26,240 --> 00:21:29,760 Speaker 1: aggressively spend, whether they pull back. You know, is Netflix 398 00:21:29,800 --> 00:21:32,400 Speaker 1: a healthy company despite kind of the shake up they've 399 00:21:32,440 --> 00:21:36,800 Speaker 1: had it management? Well, Netflix transform the entire media industry, 400 00:21:36,960 --> 00:21:39,240 Speaker 1: not so much by the content choice as they made, 401 00:21:39,520 --> 00:21:44,760 Speaker 1: but by deploying AI data crunching to better understand what 402 00:21:45,040 --> 00:21:47,320 Speaker 1: content they ought to make and how to market it. 403 00:21:47,600 --> 00:21:50,040 Speaker 1: So they belong in the Hall of Fame for that. 404 00:21:50,440 --> 00:21:55,359 Speaker 1: But I think everybody's ignoring TikTok. TikTok has passed Netflix 405 00:21:55,560 --> 00:22:00,880 Speaker 1: as the number two streaming choice for US audiences under 406 00:22:00,960 --> 00:22:07,080 Speaker 1: thirty five, and that is huge. Gen Z is part 407 00:22:07,080 --> 00:22:11,240 Speaker 1: of They are a tsunami and they're making their choices 408 00:22:11,320 --> 00:22:16,120 Speaker 1: with short form video that doesn't cost barely anything. And 409 00:22:16,119 --> 00:22:20,280 Speaker 1: and meantime, everybody's obsessing over Netflix subscriber growth, but the 410 00:22:21,520 --> 00:22:24,679 Speaker 1: people are voting with their with their swipey fingers. And 411 00:22:24,760 --> 00:22:28,280 Speaker 1: you know, I run a gen Z platform. Hang enables 412 00:22:28,560 --> 00:22:33,800 Speaker 1: fans to watch sports on TV alongside athletes and entertainers 413 00:22:33,800 --> 00:22:37,920 Speaker 1: and celebrities, and we spend next to nothing on content, 414 00:22:38,359 --> 00:22:42,199 Speaker 1: but we've grown one thousand x primarily with gen Z 415 00:22:42,720 --> 00:22:46,520 Speaker 1: users because they care about the immediate, they care about 416 00:22:46,840 --> 00:22:50,040 Speaker 1: meaningful moments, and they want to be able to dip 417 00:22:50,080 --> 00:22:52,560 Speaker 1: in and out whenever they feel like it. That's very 418 00:22:52,640 --> 00:22:57,720 Speaker 1: different than the beautifully constructed content models that all the 419 00:22:57,800 --> 00:23:01,160 Speaker 1: streaming the traditional streaming platform are using so I think 420 00:23:01,160 --> 00:23:04,480 Speaker 1: everybody's ignoring the TikTok elephant in the room. I think 421 00:23:04,520 --> 00:23:07,639 Speaker 1: that's really interesting ring that, John, because I always remember 422 00:23:07,680 --> 00:23:11,400 Speaker 1: the surprising competitor that Netflix used to reference was gaining 423 00:23:11,600 --> 00:23:13,720 Speaker 1: and really felt that that was where the eyeballs were. 424 00:23:13,760 --> 00:23:16,119 Speaker 1: And of course now setting up their own sort of 425 00:23:16,160 --> 00:23:19,120 Speaker 1: gaming focus, you're saying, look, it's actually really social media, 426 00:23:19,160 --> 00:23:21,679 Speaker 1: but this new generation of social media, and John, to 427 00:23:21,720 --> 00:23:23,640 Speaker 1: that point, let's talk about culture for a minute, because 428 00:23:23,640 --> 00:23:25,480 Speaker 1: you're someone who's had to think about this long and hard. 429 00:23:25,480 --> 00:23:28,200 Speaker 1: Whether you've been in the original media this CNN roles, 430 00:23:28,240 --> 00:23:30,840 Speaker 1: whether you're now with hanging thinking about cultivating that that 431 00:23:30,920 --> 00:23:34,280 Speaker 1: innovative style and and that's really also what Read Hastings 432 00:23:34,359 --> 00:23:36,000 Speaker 1: was known for was shaking things up when it comes 433 00:23:36,000 --> 00:23:38,000 Speaker 1: to a corporate culture, when it comes to reinventing the 434 00:23:38,000 --> 00:23:41,520 Speaker 1: way in which people have responsibility but also freedom. Are 435 00:23:41,560 --> 00:23:43,920 Speaker 1: they able to innovative? Are they innovator? Are they able 436 00:23:43,960 --> 00:23:47,040 Speaker 1: to take over and take on the likes of TikTok 437 00:23:47,200 --> 00:23:49,320 Speaker 1: and there are other competitors at the space with this 438 00:23:49,359 --> 00:23:52,440 Speaker 1: new leadership, do you think it's very, very difficult for 439 00:23:52,760 --> 00:23:57,440 Speaker 1: established incumbens to to change a culture but we've seen 440 00:23:57,480 --> 00:24:01,160 Speaker 1: it happen at Microsoft, and you know here they are 441 00:24:01,359 --> 00:24:04,600 Speaker 1: still players and and so it can be done, but 442 00:24:04,680 --> 00:24:07,119 Speaker 1: it's a big mission. It's a little harder to do 443 00:24:07,200 --> 00:24:09,760 Speaker 1: with three people at the top, which is what Netflix 444 00:24:09,800 --> 00:24:12,520 Speaker 1: now has. And and maybe I'm a little colored by 445 00:24:12,560 --> 00:24:14,800 Speaker 1: the fact that I am the media consultant for the 446 00:24:14,800 --> 00:24:19,880 Speaker 1: show's succession where it's always uh, some sort of diabolical, 447 00:24:20,000 --> 00:24:22,960 Speaker 1: underhanded goings on in the corporate suite and the living room. 448 00:24:23,640 --> 00:24:27,119 Speaker 1: But it just looks to me, you know, culture starts 449 00:24:27,119 --> 00:24:29,960 Speaker 1: at the very top. That's how cultures are set. It's 450 00:24:29,960 --> 00:24:31,880 Speaker 1: a big part of why I think Eiger came back 451 00:24:31,920 --> 00:24:35,600 Speaker 1: in because he was one man who one person who 452 00:24:35,920 --> 00:24:40,600 Speaker 1: created the culture and can enforce it. Um trickier when 453 00:24:40,600 --> 00:24:42,840 Speaker 1: you've got three, especially when you've got the founder of 454 00:24:42,880 --> 00:24:45,600 Speaker 1: the company still there. You know, when I ran CNN, 455 00:24:46,000 --> 00:24:48,639 Speaker 1: Ted Turner had not been there for a good ten years, 456 00:24:49,119 --> 00:24:53,560 Speaker 1: and it would have been kind of awkward for the 457 00:24:53,680 --> 00:24:58,040 Speaker 1: management to make decisions with the founder still sitting there. 458 00:24:58,520 --> 00:25:01,679 Speaker 1: He had a good grace to exit. A lot of 459 00:25:01,960 --> 00:25:06,159 Speaker 1: CEOs and founders realized that it's time to move on. 460 00:25:06,440 --> 00:25:08,840 Speaker 1: You know, when you're done, you're done. I'm not sure 461 00:25:08,920 --> 00:25:11,400 Speaker 1: how this is going to play out. Interesting you talk 462 00:25:11,440 --> 00:25:13,480 Speaker 1: about and it is such a question of corporate governance, 463 00:25:13,640 --> 00:25:16,800 Speaker 1: is also a question of bench now and better Bajaria, 464 00:25:17,000 --> 00:25:19,280 Speaker 1: that was an interesting move that now she takes the 465 00:25:19,400 --> 00:25:22,440 Speaker 1: role of chief content officer, having been heading up the 466 00:25:22,440 --> 00:25:26,440 Speaker 1: global TV side of Netflix. Content is king. We all 467 00:25:26,480 --> 00:25:28,760 Speaker 1: know that. So from your perspective, what are the innovative 468 00:25:28,760 --> 00:25:30,639 Speaker 1: ways she can think about content? Is she going to 469 00:25:30,720 --> 00:25:33,520 Speaker 1: have to start to think about how she keeps people 470 00:25:33,640 --> 00:25:37,399 Speaker 1: glued amid the distraction of social media? Well, you know 471 00:25:37,520 --> 00:25:40,080 Speaker 1: they're they're lucky and neclix And I guess I shouldn't 472 00:25:40,080 --> 00:25:43,159 Speaker 1: say lucky because it was on purpose. They have built 473 00:25:43,600 --> 00:25:51,240 Speaker 1: an incredible data capture and analytics engine that eliminates the 474 00:25:51,280 --> 00:25:55,000 Speaker 1: gut instinct of executives which are so often wrong. They're 475 00:25:55,080 --> 00:25:57,240 Speaker 1: usually wrong. Look at how many news shows are launched 476 00:25:57,280 --> 00:26:00,119 Speaker 1: on a TVCs and versus how many last even a 477 00:26:00,160 --> 00:26:04,400 Speaker 1: month um And I think what she's probably doing even 478 00:26:04,440 --> 00:26:07,720 Speaker 1: as we speak, is doubling down on drilling into those 479 00:26:07,960 --> 00:26:12,000 Speaker 1: thousands of different taste clusters that tell them what you 480 00:26:12,080 --> 00:26:15,119 Speaker 1: like to watch versus what ED likes to watch versus 481 00:26:15,160 --> 00:26:17,680 Speaker 1: what d other people like to watch, and you're all 482 00:26:17,720 --> 00:26:21,960 Speaker 1: getting hit with different suggestions and each presented in a 483 00:26:22,040 --> 00:26:25,000 Speaker 1: different way because they also understand what kind of thumbnail 484 00:26:25,040 --> 00:26:28,840 Speaker 1: pictures you respond to, what kind of previews and highlights 485 00:26:28,960 --> 00:26:33,080 Speaker 1: you tend to click on, rather than anybody else. So 486 00:26:33,440 --> 00:26:35,919 Speaker 1: they say, everybody gets their own Netflix, and that's true. 487 00:26:36,800 --> 00:26:40,160 Speaker 1: That's an advantage for Netflix in in kind of figuring 488 00:26:40,160 --> 00:26:42,960 Speaker 1: their way through this desert. They've doubled down on the 489 00:26:43,000 --> 00:26:47,920 Speaker 1: amount of Korean programming that they're buying now, and that's 490 00:26:47,960 --> 00:26:51,440 Speaker 1: because their algorithms are telling them to do that. So 491 00:26:51,600 --> 00:26:54,160 Speaker 1: you were just talking a lot about AI. Another way 492 00:26:54,160 --> 00:26:57,240 Speaker 1: that a I impacts the entertainment industry and and will 493 00:26:57,280 --> 00:27:01,200 Speaker 1: do even more moving forward, is by its ability to 494 00:27:01,240 --> 00:27:05,200 Speaker 1: make us dumb humans have insights that we never could 495 00:27:05,200 --> 00:27:08,840 Speaker 1: have achieved. Do our own interesting all about the tech 496 00:27:08,840 --> 00:27:11,880 Speaker 1: backbone here and sendly something you're interweaving with Hang. I'm sure, 497 00:27:11,880 --> 00:27:14,480 Speaker 1: of course the sports streaming eventually you're now doing. Co 498 00:27:14,640 --> 00:27:17,800 Speaker 1: founder CEO of Hang, John Klein, we thank you so much. 499 00:27:18,359 --> 00:27:20,720 Speaker 1: Let's pivot here a little bit and let's talk about 500 00:27:21,240 --> 00:27:24,040 Speaker 1: well AI. We've already been worrying about its implications to 501 00:27:24,119 --> 00:27:27,399 Speaker 1: cyber let's talk about cyber attacks more generally now still 502 00:27:27,440 --> 00:27:29,959 Speaker 1: a big concern these days, but few of your companies 503 00:27:30,000 --> 00:27:33,520 Speaker 1: are actually infected with ransomware. Then they're actually having to 504 00:27:33,600 --> 00:27:36,560 Speaker 1: yield to the extortion payments demanded by the hackers. That's 505 00:27:36,560 --> 00:27:40,240 Speaker 1: according to new research on the blockchain forensics firm Chain Analysis. 506 00:27:40,600 --> 00:27:43,640 Speaker 1: What's the reason behind this? Jackie Cohen's with us, head 507 00:27:43,640 --> 00:27:46,960 Speaker 1: of cyber threat intelligence at Chain Analysis. Wonderful Jackie to 508 00:27:46,960 --> 00:27:48,800 Speaker 1: have you right here in the studio and just talk 509 00:27:48,840 --> 00:27:52,600 Speaker 1: to us a little bit. Ransomware, Well, ransom attacks haven't 510 00:27:53,440 --> 00:27:56,360 Speaker 1: died down, but it feels as though companies have some 511 00:27:56,440 --> 00:27:58,440 Speaker 1: of the protections in place to stop them having to 512 00:27:58,520 --> 00:28:01,520 Speaker 1: hand over crypto or whatever they're being asful exactly. It's 513 00:28:01,800 --> 00:28:04,720 Speaker 1: rare we get a good news story associated with ransomware, 514 00:28:04,720 --> 00:28:07,719 Speaker 1: and so it was encouraging to see our results this 515 00:28:07,800 --> 00:28:11,040 Speaker 1: year in terms of ransoms being dulled out, it has 516 00:28:11,160 --> 00:28:15,399 Speaker 1: decreased significantly, as much as forty However, that's not to 517 00:28:15,400 --> 00:28:18,600 Speaker 1: say that ransomware attacks are on the decline. They're actually 518 00:28:18,640 --> 00:28:22,760 Speaker 1: on par or maybe slightly depressed of since last year. 519 00:28:23,000 --> 00:28:27,119 Speaker 1: So what this means is that victims, representatives of victims 520 00:28:27,160 --> 00:28:30,280 Speaker 1: and their insurers are deciding not to pay, and that's 521 00:28:30,320 --> 00:28:34,120 Speaker 1: in part because of concern over sanctions whether they're paying 522 00:28:34,119 --> 00:28:38,120 Speaker 1: a sanctioned entity, but also they're better defended, most of 523 00:28:38,120 --> 00:28:41,520 Speaker 1: them to be able to recover without having to pay 524 00:28:41,560 --> 00:28:45,200 Speaker 1: the ransoms. Jackie, I want to go back some breaking 525 00:28:45,240 --> 00:28:48,160 Speaker 1: news we've got in the last hour or so, which 526 00:28:48,240 --> 00:28:52,320 Speaker 1: mood markets. T Mobile is disclosed that a hacker obtained 527 00:28:52,440 --> 00:28:56,800 Speaker 1: thirty seven million customer accounts data, but it did not 528 00:28:56,960 --> 00:29:00,920 Speaker 1: include payment or card information. The company saying that it 529 00:29:01,000 --> 00:29:04,880 Speaker 1: discovered the hack back on January five. It traced that 530 00:29:04,880 --> 00:29:07,960 Speaker 1: hack to the source and stopped it within a single day. 531 00:29:08,280 --> 00:29:12,960 Speaker 1: They're investigating, but saying early indications that this threat was 532 00:29:13,040 --> 00:29:15,960 Speaker 1: able to obtain the information through a single entry point 533 00:29:16,080 --> 00:29:20,080 Speaker 1: serving customer data. It did not breach the company systems 534 00:29:20,160 --> 00:29:23,040 Speaker 1: or network. I'll ask you, PAS for your reaction to that. 535 00:29:23,120 --> 00:29:25,800 Speaker 1: You know, the information that we have, How common is 536 00:29:25,840 --> 00:29:31,080 Speaker 1: that as a threat that Corporate America Global corporate space. Yeah, unfortunately, 537 00:29:31,120 --> 00:29:33,959 Speaker 1: we do see victims get re victimized. I believe this 538 00:29:34,000 --> 00:29:37,880 Speaker 1: is not the first data breach that has affected T Mobile, 539 00:29:38,000 --> 00:29:41,080 Speaker 1: And what this goes on to to prove is that 540 00:29:41,200 --> 00:29:46,600 Speaker 1: the underground economy that is fueling data breaches and including ransomware, 541 00:29:47,080 --> 00:29:49,840 Speaker 1: is still thriving. There are still threat actors out there 542 00:29:49,920 --> 00:29:52,800 Speaker 1: that are able to sell data for money, whether or 543 00:29:52,840 --> 00:29:57,720 Speaker 1: not they encrypt the victims systems, and there are vibrant 544 00:29:57,720 --> 00:30:02,400 Speaker 1: markets selling user credential as for various purposes. And we 545 00:30:02,440 --> 00:30:06,480 Speaker 1: cannot let our defenses down in despite the promising news 546 00:30:06,560 --> 00:30:09,680 Speaker 1: that we be uncovered in two can you tell us 547 00:30:09,680 --> 00:30:12,520 Speaker 1: about the defenses being used and how we're able to 548 00:30:12,600 --> 00:30:15,480 Speaker 1: keep up with ransomware? With these new threats that Corporate 549 00:30:15,520 --> 00:30:20,240 Speaker 1: America does face and individuals house insurance protecting us, now, 550 00:30:20,280 --> 00:30:22,160 Speaker 1: how are we able to ensure that we feel we 551 00:30:22,160 --> 00:30:24,640 Speaker 1: don't have to call full for the money every time. Well, 552 00:30:24,680 --> 00:30:27,520 Speaker 1: part of it is that insurance companies are now being 553 00:30:27,560 --> 00:30:31,200 Speaker 1: more stringent about the companies that they cover, and then 554 00:30:31,240 --> 00:30:36,000 Speaker 1: in order to cover them, they must incur some security practices. 555 00:30:36,040 --> 00:30:39,000 Speaker 1: They must have backups is a big one, so that 556 00:30:39,280 --> 00:30:42,320 Speaker 1: if the systems do go down, that the company can 557 00:30:42,440 --> 00:30:47,440 Speaker 1: very quickly recover and resume resume businesses. It's not full proof. Um, 558 00:30:47,520 --> 00:30:51,600 Speaker 1: there's no organization or company that is immune unfortunately, and 559 00:30:52,600 --> 00:30:55,200 Speaker 1: companies need to have a plan what happens if they 560 00:30:55,240 --> 00:30:57,040 Speaker 1: do get attacked, how are you going to handle it? 561 00:30:57,040 --> 00:31:01,240 Speaker 1: From illegal pr as well as a security standpoint, and 562 00:31:01,480 --> 00:31:03,720 Speaker 1: go back to the roots of how crypto is involved 563 00:31:03,760 --> 00:31:05,920 Speaker 1: in all of this, because much of the aggravation from 564 00:31:05,920 --> 00:31:08,480 Speaker 1: the crypto community is that it's always tarnished with money 565 00:31:08,520 --> 00:31:12,000 Speaker 1: and laundering speculation and used to drug money, etcetera. But 566 00:31:12,040 --> 00:31:13,800 Speaker 1: the whole beauty of crypto is that you're meant to 567 00:31:13,800 --> 00:31:16,160 Speaker 1: be able to see where it goes, how much is 568 00:31:16,160 --> 00:31:18,880 Speaker 1: the washing still happening, how we're able to ensure that 569 00:31:19,040 --> 00:31:21,480 Speaker 1: the money is moving, and we're able to see who's 570 00:31:21,520 --> 00:31:24,160 Speaker 1: actually at the bad acting front of this. Right, We've 571 00:31:24,200 --> 00:31:29,280 Speaker 1: actually calculated this year that of illicit cryptocurrency use reached 572 00:31:29,320 --> 00:31:33,440 Speaker 1: an all time high. However, only point to four percent 573 00:31:33,600 --> 00:31:37,920 Speaker 1: of all cryptocurrency activity was illicit, and so while the 574 00:31:38,040 --> 00:31:41,160 Speaker 1: raw numbers did increase overall, it is a very small 575 00:31:41,200 --> 00:31:43,960 Speaker 1: fraction and we're only able to calculate that because of 576 00:31:43,960 --> 00:31:48,840 Speaker 1: the transparency of cryptocurrency in the blockchain, and that also 577 00:31:49,080 --> 00:31:53,080 Speaker 1: enables us to track bad actors, to recover funds, to 578 00:31:53,520 --> 00:31:59,040 Speaker 1: pinpoint which cryptocurrency exchanges are our rogue or be able 579 00:31:59,040 --> 00:32:02,080 Speaker 1: to dismantle, and like we did UH like the bits 580 00:32:02,160 --> 00:32:05,960 Speaker 1: Latto exchange yesterday, which was taken down as far as 581 00:32:05,960 --> 00:32:12,200 Speaker 1: an international action dismantled by international law enforcement agencies UM 582 00:32:12,640 --> 00:32:15,720 Speaker 1: also the hydro darknet marketplace, which was taken down to 583 00:32:15,800 --> 00:32:19,640 Speaker 1: We're able to have these successes because of the traceability, 584 00:32:19,760 --> 00:32:24,960 Speaker 1: and it's only a small fraction of overall cryptocurrency activity today, Jackie. 585 00:32:24,960 --> 00:32:28,200 Speaker 1: We started the year with the fascinating conversation with Jenny Stilly, 586 00:32:28,520 --> 00:32:31,800 Speaker 1: the director of SISSA, and her message at CS was 587 00:32:32,120 --> 00:32:34,400 Speaker 1: the private sector has to do a lot more, right 588 00:32:34,440 --> 00:32:37,640 Speaker 1: from when you're designing your producted its origins through to 589 00:32:37,680 --> 00:32:40,360 Speaker 1: how you conduct business. That's why she was there in 590 00:32:40,440 --> 00:32:42,640 Speaker 1: Vegas to kind of get that message. Then, do you 591 00:32:42,680 --> 00:32:45,880 Speaker 1: see the private sector doing enough to ward off the 592 00:32:45,920 --> 00:32:49,280 Speaker 1: threats that you yourself are warning about Now? I really 593 00:32:49,400 --> 00:32:55,240 Speaker 1: view this year's findings of two's ransomware payments on the 594 00:32:55,280 --> 00:33:00,080 Speaker 1: decline as a representative of public and private sector for 595 00:33:00,120 --> 00:33:05,280 Speaker 1: it's working together. We have government entities doing takedowns and sanctions. 596 00:33:05,480 --> 00:33:10,000 Speaker 1: We have private sector partners, insurance companies tightening UH as 597 00:33:10,000 --> 00:33:13,680 Speaker 1: far as what they're willing to pay, being adhering to 598 00:33:14,040 --> 00:33:17,920 Speaker 1: to sanctions concerns as well. As the research community that 599 00:33:18,080 --> 00:33:22,360 Speaker 1: is actively finding vulnerabilities in the encryption that these ransomer 600 00:33:22,400 --> 00:33:25,800 Speaker 1: actors are using. And it really is that fine balance 601 00:33:26,280 --> 00:33:31,320 Speaker 1: of not um not penalizing victims, but being able to 602 00:33:31,360 --> 00:33:35,000 Speaker 1: help them when needed um. It's it's been a really 603 00:33:35,000 --> 00:33:38,800 Speaker 1: phenomenal public and private sector effort, and we rarely get 604 00:33:38,840 --> 00:33:43,400 Speaker 1: the opportunity to quantify what that impact is. Jackie, great 605 00:33:43,400 --> 00:33:45,480 Speaker 1: to have you here in the studio, Jackie Coven think 606 00:33:45,480 --> 00:33:48,640 Speaker 1: you had a slider threat intelligence hutch analysis. I mean 607 00:33:48,640 --> 00:33:52,000 Speaker 1: while coming up the tech behind fake neat and why 608 00:33:52,040 --> 00:33:55,320 Speaker 1: the industry but love by Silicon Body VCS. It's now 609 00:33:55,400 --> 00:34:08,000 Speaker 1: prestling back. We have got to talk about Bloomberg's Big 610 00:34:08,040 --> 00:34:10,560 Speaker 1: Take because it is one of the most read across 611 00:34:10,680 --> 00:34:13,440 Speaker 1: all of our platforms. But it's also about the big 612 00:34:13,560 --> 00:34:16,319 Speaker 1: fake meat a few years ago. Just remember lap made 613 00:34:16,360 --> 00:34:19,319 Speaker 1: meat seemed poised up in the world's one trillion dollar 614 00:34:19,360 --> 00:34:22,840 Speaker 1: meat industry, and now it's belling to look like a fads. 615 00:34:22,920 --> 00:34:26,400 Speaker 1: Tina Shanka wrote the Big Take, joining us, and ultimately 616 00:34:26,400 --> 00:34:28,520 Speaker 1: this is a technology story. It was an idea that 617 00:34:28,560 --> 00:34:30,799 Speaker 1: you could use laboratories, you could use innovation to be 618 00:34:30,840 --> 00:34:32,919 Speaker 1: able to get something that felt and tasted like meat, 619 00:34:32,960 --> 00:34:36,359 Speaker 1: but wasn't had a better footprint on the environment. Now 620 00:34:36,480 --> 00:34:38,919 Speaker 1: we're all putting that we hope that maybe actually lab 621 00:34:39,000 --> 00:34:41,400 Speaker 1: grown like from sells meat is going to be our answer, 622 00:34:41,520 --> 00:34:45,200 Speaker 1: because it feels like Beyond Meat, for example, didn't grow 623 00:34:45,239 --> 00:34:49,359 Speaker 1: in the right way. That's right. Basically, we had these 624 00:34:49,360 --> 00:34:53,120 Speaker 1: founders come out. Ethan Brown founded Beyond Meat in two 625 00:34:53,160 --> 00:34:55,200 Speaker 1: thousand nine, pet Brown in two thousand and eleven with 626 00:34:55,239 --> 00:34:59,080 Speaker 1: impassable Foods, and they made these really big premises. Ethan 627 00:34:59,120 --> 00:35:04,239 Speaker 1: Brown talked about basically copying the structure of meat, but 628 00:35:04,400 --> 00:35:07,840 Speaker 1: doing it with parts that he extracted from plants. Pat 629 00:35:07,840 --> 00:35:12,800 Speaker 1: Brown talked about him which is found in large concentrations 630 00:35:12,960 --> 00:35:14,839 Speaker 1: in in red meat, and he was going to make 631 00:35:14,880 --> 00:35:18,920 Speaker 1: it with a genetically modified yeast and soy and it 632 00:35:18,960 --> 00:35:21,879 Speaker 1: would be uh soily. He like hemoglobin, what he called 633 00:35:21,960 --> 00:35:24,480 Speaker 1: his magic ingredient, and this was going to give his 634 00:35:24,600 --> 00:35:30,160 Speaker 1: burgers this meaty taste. And both companies promised to basically 635 00:35:30,320 --> 00:35:33,439 Speaker 1: up end uh well impossible really went out and said 636 00:35:33,440 --> 00:35:36,640 Speaker 1: they were gonna upbend animal agriculture. Ethan Brown's promises were 637 00:35:36,640 --> 00:35:40,040 Speaker 1: more along the lines of saving uh the world from 638 00:35:40,400 --> 00:35:46,560 Speaker 1: health and environmental disasters. Yeah, just just just little things. UM. 639 00:35:46,680 --> 00:35:49,879 Speaker 1: And that they were going to create products that were 640 00:35:50,440 --> 00:35:54,279 Speaker 1: so identical to the meat that people love that people 641 00:35:54,320 --> 00:35:59,759 Speaker 1: would just swap them in um and sidestep the animal. UM. 642 00:35:59,760 --> 00:36:02,480 Speaker 1: It really hasn't worked out that way. And I think 643 00:36:02,520 --> 00:36:06,000 Speaker 1: most people would agree that these burghers are more a 644 00:36:06,000 --> 00:36:09,840 Speaker 1: lot closer to beef than you know, their forebears, UM, 645 00:36:09,920 --> 00:36:13,439 Speaker 1: like say like morning Star farms from Kellogg. But they're 646 00:36:13,560 --> 00:36:18,000 Speaker 1: they're certainly not close enough and meat eaters just aren't 647 00:36:18,000 --> 00:36:20,600 Speaker 1: that interested or as one expert told me, he said, 648 00:36:20,800 --> 00:36:24,040 Speaker 1: they're just not that into it. Adina. You know, Ethan 649 00:36:24,080 --> 00:36:27,959 Speaker 1: Brown likened it to technology doing away with the horse 650 00:36:28,000 --> 00:36:30,360 Speaker 1: pawn cart, right, that what they were doing in the 651 00:36:30,480 --> 00:36:33,160 Speaker 1: lab would change what's on our plate, do away with 652 00:36:33,160 --> 00:36:35,120 Speaker 1: the meat. I guess it's not quite materialized. We ask 653 00:36:35,160 --> 00:36:38,439 Speaker 1: our own audience, you know what their attitude is too, 654 00:36:39,080 --> 00:36:43,520 Speaker 1: lab grown, lab generated meat. That's the answer for center. 655 00:36:43,560 --> 00:36:48,440 Speaker 1: Respondents said, you know, uh, does you're reporting back up 656 00:36:48,600 --> 00:36:51,239 Speaker 1: the findings of that poll. What is the attitude of 657 00:36:51,280 --> 00:36:56,200 Speaker 1: consumers right now? So basically the market for these products 658 00:36:56,360 --> 00:36:59,879 Speaker 1: is is declining. People UM. A lot of people tried 659 00:37:00,000 --> 00:37:01,800 Speaker 1: them when they first came out, they were really excited 660 00:37:01,840 --> 00:37:05,719 Speaker 1: about them um. And now people are moving away and 661 00:37:05,760 --> 00:37:08,520 Speaker 1: they're going either to maybe they maybe they want to 662 00:37:08,600 --> 00:37:10,720 Speaker 1: keep their meat consumption down, so they're going to something 663 00:37:10,719 --> 00:37:14,319 Speaker 1: like say like lentils or beans um, or maybe they 664 00:37:14,360 --> 00:37:16,960 Speaker 1: are going to chicken, which is less expensive than these 665 00:37:16,960 --> 00:37:20,359 Speaker 1: products and certainly less expensive than beef. A lot of 666 00:37:20,360 --> 00:37:23,560 Speaker 1: people try the products once or maybe even twice, but 667 00:37:23,600 --> 00:37:26,080 Speaker 1: they just don't stick with it. It just does not 668 00:37:26,440 --> 00:37:29,319 Speaker 1: stay as part of their normal routine. The people that 669 00:37:29,360 --> 00:37:32,399 Speaker 1: eat the most of this stuff are vegans and vegetarians, 670 00:37:32,400 --> 00:37:36,400 Speaker 1: who were very much not the target. Interesting and I 671 00:37:36,400 --> 00:37:39,480 Speaker 1: have to say, like from a lincdotal evidence, you know, 672 00:37:39,719 --> 00:37:42,640 Speaker 1: we pour in a lot of beyond meat and impossible 673 00:37:42,680 --> 00:37:44,520 Speaker 1: into our family home, largely because we've got an a 674 00:37:44,680 --> 00:37:47,480 Speaker 1: pair who's vegetarian. But also my husband really didn't like 675 00:37:47,520 --> 00:37:49,400 Speaker 1: the way that meat made him feel. He wanted to 676 00:37:49,440 --> 00:37:52,600 Speaker 1: cut down. But ultimately they weren't kind of healthy enough, 677 00:37:52,760 --> 00:37:55,960 Speaker 1: like they didn't felt like lentils was a better option. Ultimately, 678 00:37:56,360 --> 00:38:00,960 Speaker 1: all these companies trying to uproot themselves, are they trying 679 00:38:01,040 --> 00:38:04,160 Speaker 1: to change, to listen to feedback, to innovate that little 680 00:38:04,160 --> 00:38:07,240 Speaker 1: bit more or is it all just going to go quat? Well, 681 00:38:07,400 --> 00:38:10,920 Speaker 1: yes they do. They do talk about more innovation um, 682 00:38:11,000 --> 00:38:15,480 Speaker 1: and they constantly release new versions. Um. You know the 683 00:38:15,480 --> 00:38:18,040 Speaker 1: Beyond Burger. There's a new Beyond Burger almost every year 684 00:38:18,080 --> 00:38:21,520 Speaker 1: that they say, Um, this one's gonna be juicier, moister, 685 00:38:21,600 --> 00:38:25,719 Speaker 1: or more meat like. Um. And Impossible also does the 686 00:38:25,760 --> 00:38:29,360 Speaker 1: same thing. They also say by doing some of the improvements, 687 00:38:29,400 --> 00:38:32,480 Speaker 1: were also going to make it healthier. But I think 688 00:38:32,520 --> 00:38:35,840 Speaker 1: for a lot of people the the idea of further 689 00:38:35,920 --> 00:38:39,759 Speaker 1: processing is sort of antithetical to making it healthier and 690 00:38:39,840 --> 00:38:43,400 Speaker 1: so um that they might say lower the calories or 691 00:38:43,440 --> 00:38:46,000 Speaker 1: lower the sodium, but that doesn't change whether or not 692 00:38:46,040 --> 00:38:50,320 Speaker 1: it's an ultra processed food. Dana, such great reporting. Fascinating 693 00:38:50,360 --> 00:38:52,480 Speaker 1: to see how the McDonald's deals and this, that and 694 00:38:52,480 --> 00:39:03,960 Speaker 1: the other. Go to Dana Shanka, thank you. Going viral 695 00:39:04,000 --> 00:39:07,240 Speaker 1: today is the influx of venture countal money flowing towards 696 00:39:07,280 --> 00:39:10,800 Speaker 1: social media apps focus on happiness. Take the Berlin based 697 00:39:10,840 --> 00:39:14,200 Speaker 1: Slay app, which lets users send anonymous compliments just raise 698 00:39:14,239 --> 00:39:16,880 Speaker 1: two point six million dollars. Slave reached number one on 699 00:39:16,920 --> 00:39:20,319 Speaker 1: the German iOS app store four days after launch. Then 700 00:39:20,400 --> 00:39:23,080 Speaker 1: there's Gas, an app bought by Discord this week. It's 701 00:39:23,080 --> 00:39:25,279 Speaker 1: here in the US, and it uses anonymous polling to 702 00:39:25,400 --> 00:39:29,279 Speaker 1: send compliments and boost components of users. These apps, said, 703 00:39:29,320 --> 00:39:31,879 Speaker 1: are just putting kind of a spin on social media 704 00:39:31,960 --> 00:39:35,480 Speaker 1: landscape that has kind of improven to thrive on toxicity. 705 00:39:35,520 --> 00:39:38,120 Speaker 1: And I think it's gonna be so fascinating to see 706 00:39:38,160 --> 00:39:41,799 Speaker 1: how basically the science of happiness of kindness, it makes 707 00:39:41,840 --> 00:39:44,359 Speaker 1: you feel good as well as the other person feel good. 708 00:39:44,640 --> 00:39:47,600 Speaker 1: And I wonder if advertisers get into that too. Yeah, 709 00:39:47,640 --> 00:39:50,600 Speaker 1: I mean, Discord's ownership of it is being debated out there. 710 00:39:50,640 --> 00:39:52,400 Speaker 1: But the data behind this was staggering. So in the 711 00:39:52,400 --> 00:39:55,960 Speaker 1: month of October, apparently the app was adding thirty thousand 712 00:39:55,960 --> 00:39:59,000 Speaker 1: new users per hour in October, you know, it's reaching 713 00:39:59,040 --> 00:40:01,319 Speaker 1: a million. And I think everyone needed some feel good 714 00:40:01,320 --> 00:40:03,200 Speaker 1: at that time, right when Twitter was kind of all 715 00:40:03,280 --> 00:40:05,799 Speaker 1: up in the air. And yeah, an interesting one. As 716 00:40:05,800 --> 00:40:07,840 Speaker 1: I say, the kids are going to save us. Keep 717 00:40:07,920 --> 00:40:12,360 Speaker 1: on growing those sorts of happiness and positive apps out there. Meanwhile, 718 00:40:13,160 --> 00:40:15,839 Speaker 1: that does it for this edition of Boomberg Technology. Yes, 719 00:40:15,880 --> 00:40:18,280 Speaker 1: so something very special coming up Friday at noon, Easton 720 00:40:18,320 --> 00:40:20,480 Speaker 1: will round up the biggest tech news of the week 721 00:40:20,520 --> 00:40:23,759 Speaker 1: on our weekly Twitter spaces, Carrol, We've loved doing this, 722 00:40:24,040 --> 00:40:28,040 Speaker 1: hosting our biggest names across Bloomberg News, Bloomberg Intelligence, and 723 00:40:28,160 --> 00:40:31,480 Speaker 1: if you're lucky, if you're lucky, this Friday, we will 724 00:40:31,520 --> 00:40:34,920 Speaker 1: have a special surprise guest in our Twitter spaces. Yeah, 725 00:40:34,960 --> 00:40:36,560 Speaker 1: we're going to talk a bit of VC, the biggest 726 00:40:36,560 --> 00:40:41,680 Speaker 1: stories and let's also interesting anything I'm not promising, I'm promising. 727 00:40:42,280 --> 00:40:44,279 Speaker 1: Tune in and you'll find out this is Bloomberg