1 00:00:13,400 --> 00:00:15,920 Speaker 1: I'm Caroline Hired of Bloomberg's World. I quarters in New York, 2 00:00:15,960 --> 00:00:18,720 Speaker 1: and I made Lovelow one more time this week in Manhattan. Next, 3 00:00:18,800 --> 00:00:22,640 Speaker 1: my good friend, this is Bloomberg Technology. Goldman Sachs has 4 00:00:22,680 --> 00:00:25,720 Speaker 1: once had a lofty goal of storming the consumer market 5 00:00:25,760 --> 00:00:28,360 Speaker 1: with the Digital Bank of the future. Now all that's 6 00:00:28,440 --> 00:00:30,280 Speaker 1: left as a unit. The racked up on one point 7 00:00:30,280 --> 00:00:32,720 Speaker 1: two billion dollars in losses, most of which was linked 8 00:00:32,960 --> 00:00:36,440 Speaker 1: to the high profile Apple cart. And bitcoin is back, 9 00:00:36,560 --> 00:00:38,520 Speaker 1: well sort of, at least it's had the best week 10 00:00:38,600 --> 00:00:42,240 Speaker 1: since one after losing more than six its value in 11 00:00:43,040 --> 00:00:45,760 Speaker 1: two more on the rally later this hour, and Tesla 12 00:00:45,960 --> 00:00:48,320 Speaker 1: is slashing car prices, this time in the U S 13 00:00:48,360 --> 00:00:51,239 Speaker 1: and sum of Europe. The ev maker is trying to 14 00:00:51,320 --> 00:00:56,040 Speaker 1: stoke demand, but instead stokes investor concern. We discussed, but 15 00:00:56,240 --> 00:00:59,000 Speaker 1: first let's get to these markets. So check in on 16 00:00:59,040 --> 00:01:01,280 Speaker 1: the last day of training the week of course, ahead 17 00:01:01,280 --> 00:01:02,920 Speaker 1: of what is going to be a shortened week next week. 18 00:01:02,960 --> 00:01:05,840 Speaker 1: As we take time off to Mark Martin Luther King day, 19 00:01:05,840 --> 00:01:08,640 Speaker 1: I'm looking at the SP five hundred, almost cracking the 20 00:01:08,680 --> 00:01:12,160 Speaker 1: four thousand level. Three we're above that two day moving 21 00:01:12,200 --> 00:01:15,000 Speaker 1: average up four tens of percent. The NASDAC outperforms up 22 00:01:15,000 --> 00:01:17,160 Speaker 1: seven tenths of a percent, in fact, the longest winning 23 00:01:17,160 --> 00:01:20,480 Speaker 1: street since November one. And even this is a global 24 00:01:20,640 --> 00:01:23,080 Speaker 1: risk on move. We saw Chinese stocks once again, the 25 00:01:23,120 --> 00:01:28,600 Speaker 1: story of China's unfolding on reopening story post those COVID lockdowns. 26 00:01:28,640 --> 00:01:30,399 Speaker 1: We're seeing two point eight percent put to work in 27 00:01:30,440 --> 00:01:32,760 Speaker 1: the in the stocks of course that are traded here 28 00:01:32,800 --> 00:01:35,640 Speaker 1: in the US in the Golden Dragon. Lets something a 29 00:01:35,640 --> 00:01:37,520 Speaker 1: little look a what's happening in terms of crypto, because 30 00:01:37,520 --> 00:01:39,360 Speaker 1: as I said, this is sort of a risk on move. 31 00:01:39,440 --> 00:01:42,560 Speaker 1: No wonder therefore Bitcoin continues. It's as elevation. Look at this, 32 00:01:42,640 --> 00:01:45,560 Speaker 1: we're cracking nineteen thousand, we're now and fifty four close 33 00:01:45,560 --> 00:01:48,640 Speaker 1: to that twenty mark. We're up almost twenty over the 34 00:01:48,720 --> 00:01:51,200 Speaker 1: last few days and the beginning of January. Really this 35 00:01:51,280 --> 00:01:54,720 Speaker 1: continues to be well at last, bit of an outperforming market. 36 00:01:54,880 --> 00:01:56,520 Speaker 1: D Yeah, it's really interesting. I'm seeing a lot of 37 00:01:56,520 --> 00:01:59,760 Speaker 1: similar themes in the specific names. Right, Apple finishing strong 38 00:01:59,840 --> 00:02:02,280 Speaker 1: up one percent this Friday, actually had its best week 39 00:02:02,360 --> 00:02:04,720 Speaker 1: since November. And after we got that cp I print, 40 00:02:04,960 --> 00:02:07,240 Speaker 1: we're talking about the FED moving forward at least in 41 00:02:07,280 --> 00:02:10,240 Speaker 1: the short term. With twenty five basis points increment hikes, 42 00:02:10,440 --> 00:02:13,239 Speaker 1: we're feeling a lot better about the technology sector. That said, 43 00:02:13,280 --> 00:02:14,760 Speaker 1: there is a lot of red on my screen, and 44 00:02:14,840 --> 00:02:17,799 Speaker 1: Tesla is a big contributing factor to that. Ultimately, we 45 00:02:17,880 --> 00:02:20,639 Speaker 1: closed down about a percentage point on Tesla. We have 46 00:02:20,720 --> 00:02:22,560 Speaker 1: been down as much as six percent earlier in the 47 00:02:22,600 --> 00:02:26,480 Speaker 1: session after the ev maker cut prices substantially in the 48 00:02:26,560 --> 00:02:29,040 Speaker 1: United States and also in Europe. You can see that 49 00:02:29,040 --> 00:02:30,760 Speaker 1: had a knock on effect on some of its pure 50 00:02:30,800 --> 00:02:33,480 Speaker 1: play e v peers, including Rivian, which was down six 51 00:02:33,520 --> 00:02:36,360 Speaker 1: and a half percent. NDO Global Really and Training. This 52 00:02:36,440 --> 00:02:39,480 Speaker 1: is leon on Messi, right, this is Messi's big brand 53 00:02:39,520 --> 00:02:42,440 Speaker 1: and clothing company. It had its first trading session this 54 00:02:42,560 --> 00:02:44,680 Speaker 1: Friday after a five dollar I p O. You can 55 00:02:44,720 --> 00:02:48,400 Speaker 1: see actually all told, we're closed at four dollars. Okay, 56 00:02:48,600 --> 00:02:51,359 Speaker 1: this isn't really tech. This is more about social media 57 00:02:51,600 --> 00:02:54,800 Speaker 1: influencing and this is very much a small cap company. 58 00:02:55,040 --> 00:02:56,600 Speaker 1: But at one point, and you've got to take my 59 00:02:56,639 --> 00:02:58,240 Speaker 1: word for it, because we don't see that story on 60 00:02:58,280 --> 00:03:01,600 Speaker 1: the screen, we were up two hundred thirty from that 61 00:03:01,680 --> 00:03:03,680 Speaker 1: I p O price. What can happen in a single 62 00:03:03,720 --> 00:03:06,799 Speaker 1: session is bizarre, but interesting to see that star power, 63 00:03:06,960 --> 00:03:10,760 Speaker 1: that social media influence leverage into the equity markets. Amazon 64 00:03:10,919 --> 00:03:14,840 Speaker 1: is fascinating. Amazon has just had its best week since 65 00:03:15,720 --> 00:03:17,400 Speaker 1: And you and I were talking off camera, Caroline, how 66 00:03:17,400 --> 00:03:20,040 Speaker 1: that kind of went under the radar. Remember what happened 67 00:03:20,040 --> 00:03:22,600 Speaker 1: in April. We all cain't of realized we'd be stuck 68 00:03:22,639 --> 00:03:26,200 Speaker 1: at home indefinitely, it seemed at that time. And Amazon 69 00:03:26,280 --> 00:03:28,400 Speaker 1: was the main beneficiary at that point. Now, with that 70 00:03:28,440 --> 00:03:31,799 Speaker 1: pivot in the psychology around inflation in the FED, well, 71 00:03:31,840 --> 00:03:34,519 Speaker 1: Amazon is also having a really good period right now. Yeah, 72 00:03:34,560 --> 00:03:36,960 Speaker 1: and still fifty five buyers on that particular stock only 73 00:03:37,040 --> 00:03:39,840 Speaker 1: one cell, So we wonder how long that winning formula 74 00:03:39,920 --> 00:03:42,760 Speaker 1: might last. We talked technicals, we talked stocks. We also 75 00:03:42,760 --> 00:03:46,400 Speaker 1: talk what's happening in the earnings that relates to technology, 76 00:03:46,440 --> 00:03:49,440 Speaker 1: because three months ago Goldman Sachs created a new division 77 00:03:49,520 --> 00:03:52,120 Speaker 1: to how's what's left of its for a on Main Street. 78 00:03:52,240 --> 00:03:56,080 Speaker 1: It's called platform Solutions, which includes Goldman's Apple Card. Now 79 00:03:56,240 --> 00:03:59,760 Speaker 1: it's giving shareholders kind of clearer guidance on the financials 80 00:04:00,000 --> 00:04:02,560 Speaker 1: and the unit. Get this, wracked up one point two 81 00:04:02,640 --> 00:04:05,320 Speaker 1: billion dollars in losses in the first nine months of 82 00:04:05,360 --> 00:04:08,440 Speaker 1: two of their own Snai bus. He's been busy converting 83 00:04:08,480 --> 00:04:10,480 Speaker 1: basically all the other band cunings that come out today, 84 00:04:10,480 --> 00:04:13,160 Speaker 1: but managed to get on this particular focus that we 85 00:04:13,200 --> 00:04:16,040 Speaker 1: had for Goldman sax To and this area that basically 86 00:04:16,320 --> 00:04:18,599 Speaker 1: I mean, it's this marketing spend on the Apple College 87 00:04:18,640 --> 00:04:20,440 Speaker 1: or where are these losses mounting from? Well, there are 88 00:04:20,480 --> 00:04:21,760 Speaker 1: a few things that you have to look at. When 89 00:04:21,760 --> 00:04:24,119 Speaker 1: you look at this unit, and this is a unit 90 00:04:24,400 --> 00:04:26,200 Speaker 1: one point to billion dollars is for the first ten 91 00:04:26,279 --> 00:04:29,000 Speaker 1: months of the year. We also have details and also 92 00:04:29,040 --> 00:04:32,359 Speaker 1: previously reported by Samaphore that those losses can amount to 93 00:04:32,400 --> 00:04:34,919 Speaker 1: up to two billion dollars through the full year. So 94 00:04:35,040 --> 00:04:39,679 Speaker 1: more clarity when Goldman reports earnings on Tuesday, much more clarity. Remember, 95 00:04:39,760 --> 00:04:41,960 Speaker 1: David Solomon said that they would have to pivot the 96 00:04:41,960 --> 00:04:44,839 Speaker 1: business already. The question is how much more after these losses? 97 00:04:44,839 --> 00:04:46,880 Speaker 1: Where do they come from? As you ask, part of 98 00:04:46,880 --> 00:04:51,000 Speaker 1: it is operating expenses, which have risen quite meaningfully over time. 99 00:04:51,279 --> 00:04:53,719 Speaker 1: But part of this is also provisions for credit losses. 100 00:04:53,920 --> 00:04:56,760 Speaker 1: We're seeing a lot of banks announced those provisions for 101 00:04:56,800 --> 00:05:00,080 Speaker 1: credit losses. Now Goldman has been getting new consum as, 102 00:05:00,080 --> 00:05:02,760 Speaker 1: they've been getting younger consumers will see how their losses 103 00:05:02,800 --> 00:05:05,679 Speaker 1: stack up against the other banks, which are also showing 104 00:05:05,839 --> 00:05:09,599 Speaker 1: that those charge offs in particular are rising as they 105 00:05:09,680 --> 00:05:14,680 Speaker 1: get new credit card customers. So definitely a competitive business Goldman. 106 00:05:15,160 --> 00:05:17,240 Speaker 1: Remember they are one of the only banks with a 107 00:05:17,320 --> 00:05:21,080 Speaker 1: meaningful relationship with a very large technology firm. City Group 108 00:05:21,080 --> 00:05:23,440 Speaker 1: had tried to do something with Google about a year 109 00:05:23,520 --> 00:05:26,400 Speaker 1: or so ago that kind of fell through, So Apple 110 00:05:26,440 --> 00:05:28,960 Speaker 1: was supposed to be a huge competitive advantage. We expect 111 00:05:28,960 --> 00:05:32,080 Speaker 1: them to lean on the relationship with Apple continuously, but 112 00:05:32,440 --> 00:05:35,760 Speaker 1: again not only earnings. Goldman's investor Day at the end 113 00:05:35,760 --> 00:05:38,120 Speaker 1: of February should give us a sense of how far 114 00:05:38,200 --> 00:05:42,640 Speaker 1: these ambitions will go. Remind me, like, why it's beneficial 115 00:05:42,680 --> 00:05:44,760 Speaker 1: to tie up with Apple here for Goldman, because at 116 00:05:44,760 --> 00:05:47,200 Speaker 1: the moment when I use Apple Pay, I don't care 117 00:05:47,240 --> 00:05:48,880 Speaker 1: which I mean, I just kind about which credit card 118 00:05:48,880 --> 00:05:51,280 Speaker 1: I'm using as to whether standing on my credit Yeah, 119 00:05:51,279 --> 00:05:54,080 Speaker 1: why does it matter? Well, there's a few things. When 120 00:05:54,120 --> 00:05:55,800 Speaker 1: all of this had started, a couple of things were 121 00:05:55,800 --> 00:05:57,760 Speaker 1: happening at the same time. For Goldman's acts, it was 122 00:05:57,800 --> 00:06:01,080 Speaker 1: a matter of diversifying its revenue base, and so what 123 00:06:01,279 --> 00:06:02,960 Speaker 1: what is that going to cost? Them over the longer 124 00:06:03,080 --> 00:06:04,720 Speaker 1: term to do. When you look at the strategies of 125 00:06:04,760 --> 00:06:07,360 Speaker 1: the other banks, they've doubled down in things like wealth, 126 00:06:07,440 --> 00:06:10,320 Speaker 1: for example. Even when you come to fintech, they've doubled 127 00:06:10,320 --> 00:06:12,400 Speaker 1: down with wealth at that time. Also, a lot of 128 00:06:12,440 --> 00:06:15,320 Speaker 1: the big technology firms had these grand ambitions to be 129 00:06:15,400 --> 00:06:18,320 Speaker 1: in finance in a bigger way, which has not really 130 00:06:18,360 --> 00:06:23,000 Speaker 1: materialized either, and so you have more partnerships and friendships 131 00:06:23,120 --> 00:06:26,919 Speaker 1: rather than real competitive issues here. We'll see if that 132 00:06:27,000 --> 00:06:29,279 Speaker 1: changes in the next ten years or so. But I 133 00:06:29,320 --> 00:06:31,279 Speaker 1: do want to point out really quickly the other story 134 00:06:31,360 --> 00:06:34,000 Speaker 1: that in fintech that we want to keep an eye out. 135 00:06:34,040 --> 00:06:35,279 Speaker 1: What I was going to say that you know, for 136 00:06:35,360 --> 00:06:38,479 Speaker 1: me somebody who covers the technology sector, sometimes there's a 137 00:06:38,480 --> 00:06:39,920 Speaker 1: bit of a land grab. You see all of these 138 00:06:39,960 --> 00:06:42,080 Speaker 1: kind of legacy players like in finance and banks, they 139 00:06:42,120 --> 00:06:44,840 Speaker 1: want to move into fintech. Goldman is not alone in that. 140 00:06:45,040 --> 00:06:48,760 Speaker 1: JP Morgan, you know, actually coming clean about its own 141 00:06:48,760 --> 00:06:52,800 Speaker 1: problem that it's experiencing financially on that to remember, this 142 00:06:52,880 --> 00:06:54,560 Speaker 1: is actually kind of this is a story that's called 143 00:06:54,560 --> 00:06:57,880 Speaker 1: the VC community by storm. Also, this fintech firm Frank 144 00:06:58,279 --> 00:07:01,680 Speaker 1: which was a college financial planning kind of platform here, 145 00:07:01,720 --> 00:07:03,720 Speaker 1: So a lot of college students coming out using it. 146 00:07:04,240 --> 00:07:08,320 Speaker 1: They are suing them for misstating the types of account 147 00:07:08,400 --> 00:07:10,920 Speaker 1: or the number of accounts that it had. Remember, they're 148 00:07:10,920 --> 00:07:14,680 Speaker 1: disputing this the frank founder. But take a listen to 149 00:07:14,680 --> 00:07:16,640 Speaker 1: what Jamie Diamond had to say. He said it was 150 00:07:16,680 --> 00:07:20,760 Speaker 1: a huge mistake. But the acquisitions are done by the businesses, 151 00:07:21,080 --> 00:07:24,360 Speaker 1: but there's also a centralized team that does extensive due diligence. 152 00:07:24,560 --> 00:07:28,280 Speaker 1: So the business does it, the U the central ifed 153 00:07:28,280 --> 00:07:30,520 Speaker 1: team does it. You've been doing it for twenty years, 154 00:07:30,520 --> 00:07:32,760 Speaker 1: whether you like we just started doing something like that. 155 00:07:33,240 --> 00:07:35,920 Speaker 1: And obviously there are always lessons learned, and you know, 156 00:07:35,960 --> 00:07:37,600 Speaker 1: at one point we'll tell you the lesson will learn 157 00:07:37,640 --> 00:07:41,280 Speaker 1: to here when there's been a data litigation. These huge mistakes, 158 00:07:41,320 --> 00:07:44,040 Speaker 1: these lessons learned. Remember the analyst investor community has been 159 00:07:44,040 --> 00:07:47,080 Speaker 1: wondering about Jamie Diamond and JP Morgan Frankly, to your point, 160 00:07:47,080 --> 00:07:50,200 Speaker 1: the rest of Wall Street here, their acquisitions free when 161 00:07:50,200 --> 00:07:53,200 Speaker 1: you come to the fintech world, multibillion dollars in some cases. 162 00:07:53,480 --> 00:07:56,760 Speaker 1: And the criticism here comes down to that due diligence 163 00:07:56,800 --> 00:07:59,520 Speaker 1: and how they didn't see something like this I'm gonna 164 00:07:59,520 --> 00:08:02,480 Speaker 1: still page from Eric Newcomer and say that, you know, 165 00:08:02,520 --> 00:08:04,920 Speaker 1: it's very rare for things like this to play out 166 00:08:04,960 --> 00:08:08,600 Speaker 1: so publicly in the financial technology world, in the VC 167 00:08:08,800 --> 00:08:12,320 Speaker 1: backed space. So you know, it's not a lot of money. 168 00:08:12,360 --> 00:08:14,600 Speaker 1: For JP Morgan, it was a hundred seventy million dollars. 169 00:08:14,640 --> 00:08:16,360 Speaker 1: This is it's not and it's not as though a 170 00:08:16,360 --> 00:08:19,520 Speaker 1: few vcs have written a few checks that rates have 171 00:08:19,600 --> 00:08:23,080 Speaker 1: been mistaken once and the razing exactly that I say exactly, 172 00:08:23,160 --> 00:08:25,120 Speaker 1: but I will say, after the exuberance that we've seen 173 00:08:25,160 --> 00:08:28,560 Speaker 1: so far, perhaps banks will be thinking twice when they 174 00:08:28,640 --> 00:08:31,040 Speaker 1: listen to thirty year old standards. Yeah, jamie's'm in not 175 00:08:31,120 --> 00:08:33,439 Speaker 1: one to mince his words. I think that's probably putting 176 00:08:33,440 --> 00:08:35,640 Speaker 1: it a little bit lightly blue MECTIONALI bassing, thank you, 177 00:08:35,720 --> 00:08:47,320 Speaker 1: amazing week, terrific reporting. Let's talk crypto in particular. Let's 178 00:08:47,360 --> 00:08:49,839 Speaker 1: talk bitcoin because yes, we all know lost about six 179 00:08:50,720 --> 00:08:53,480 Speaker 1: last year is its second worst annual performance on record, 180 00:08:53,920 --> 00:08:56,600 Speaker 1: but now a little bit more of a promising start 181 00:08:56,640 --> 00:08:58,360 Speaker 1: to the new year. Read. Yeah, there's so many ways 182 00:08:58,360 --> 00:09:00,400 Speaker 1: to size and scope this. I mean, one of them 183 00:09:00,440 --> 00:09:02,520 Speaker 1: is to talk about sort of the run we're on 184 00:09:02,960 --> 00:09:07,520 Speaker 1: new year, new Vibe Bitcoin up for ten consecutive sessions. 185 00:09:07,559 --> 00:09:11,360 Speaker 1: It's best run or streak of gains going back to July. 186 00:09:12,200 --> 00:09:15,480 Speaker 1: I find that very interesting. What is the psychology driving 187 00:09:15,520 --> 00:09:18,160 Speaker 1: the market right now? There was a fantastic story as 188 00:09:18,160 --> 00:09:20,679 Speaker 1: well on the Bloomberg terminal this Friday that caught my 189 00:09:20,880 --> 00:09:24,720 Speaker 1: about another corner of the crypto market, which is crypto 190 00:09:24,800 --> 00:09:28,400 Speaker 1: asset focused fund e t s. Right, remember e t 191 00:09:28,520 --> 00:09:31,600 Speaker 1: s for a six point eight trillion dollar industry. Two 192 00:09:31,640 --> 00:09:35,959 Speaker 1: thousand of them, two thousand across different themes attracted by 193 00:09:36,040 --> 00:09:39,840 Speaker 1: Bloomberg excluding leverage products. Now there are four lines on 194 00:09:39,880 --> 00:09:41,520 Speaker 1: this chart to my right hand side, which I'll get 195 00:09:41,520 --> 00:09:44,840 Speaker 1: to a minute. But of those two thousand ets tracks 196 00:09:44,840 --> 00:09:47,760 Speaker 1: by Bloomberg, which across lots of different things, lots of 197 00:09:47,760 --> 00:09:51,960 Speaker 1: different equity products, the top four teen, all top fourteen 198 00:09:52,080 --> 00:09:56,240 Speaker 1: are crypto related year today and exactly it's astonishing performance. Now, 199 00:09:56,280 --> 00:09:58,160 Speaker 1: these are the four that I've picked out for you, 200 00:09:58,200 --> 00:10:02,040 Speaker 1: the top four performers, the Valk bitcoin miners e t F, 201 00:10:02,160 --> 00:10:03,439 Speaker 1: as you can see on the screen next to me, 202 00:10:03,559 --> 00:10:07,080 Speaker 1: up almost sev in the first what is it, thirteen 203 00:10:07,160 --> 00:10:09,719 Speaker 1: days and only seven trading sessions or so of the 204 00:10:09,800 --> 00:10:11,840 Speaker 1: year so far, and it gives you an idea that 205 00:10:11,880 --> 00:10:14,400 Speaker 1: there is some confidence coming back. Now, this is a 206 00:10:14,480 --> 00:10:17,840 Speaker 1: kind of varied list of assets, right, and we've talked 207 00:10:17,840 --> 00:10:21,440 Speaker 1: about bitcoin. That's one example, the biggest digital asset or 208 00:10:21,520 --> 00:10:26,080 Speaker 1: digital currency by market value globally. My question is, has 209 00:10:26,120 --> 00:10:27,720 Speaker 1: the kind of I don't want to use the word 210 00:10:27,760 --> 00:10:31,960 Speaker 1: contagion risk from FTX from what we've seen from regulators 211 00:10:31,960 --> 00:10:35,000 Speaker 1: that ended two? Are we kind of moving past that now? 212 00:10:35,040 --> 00:10:36,840 Speaker 1: And study is build a bit of momentum? That's not 213 00:10:36,880 --> 00:10:38,800 Speaker 1: for me to answer. I'm fortunate to say I think 214 00:10:38,800 --> 00:10:40,960 Speaker 1: we have somebody that probably could weigh in on that 215 00:10:41,040 --> 00:10:43,319 Speaker 1: character we do. And also how much of this is 216 00:10:43,320 --> 00:10:45,960 Speaker 1: a macro trend because let's face it, we've had risk 217 00:10:46,000 --> 00:10:48,880 Speaker 1: assets performed pretty well and the dollar down. Let's talk 218 00:10:48,920 --> 00:10:51,439 Speaker 1: at all through with still Mark, founder managing partner, and 219 00:10:51,559 --> 00:10:53,920 Speaker 1: he's clean who joins us now, and this is always 220 00:10:53,920 --> 00:10:57,160 Speaker 1: great to get your perspective. We know you're focus on bitcoin. 221 00:10:57,240 --> 00:10:58,920 Speaker 1: You'll focus on what is the O G of the 222 00:10:58,960 --> 00:11:01,440 Speaker 1: crypto space, and I'm interested in first and foremost on 223 00:11:01,720 --> 00:11:03,920 Speaker 1: what the stealth rally that's kind of taken us by 224 00:11:03,920 --> 00:11:08,400 Speaker 1: surprise here. It's always hard to know why bitcoins price 225 00:11:08,520 --> 00:11:11,280 Speaker 1: moves when it gets but in times of chaos in 226 00:11:11,320 --> 00:11:14,319 Speaker 1: the crypto market, we will often see a flight to 227 00:11:14,480 --> 00:11:18,000 Speaker 1: security and stability, and the market knows that to be 228 00:11:18,320 --> 00:11:22,480 Speaker 1: Bitcoin and and potentially this this week's rally and BTC 229 00:11:23,760 --> 00:11:27,240 Speaker 1: to your to that perspective, is this, as Ed pondered, 230 00:11:27,960 --> 00:11:30,880 Speaker 1: a movement away from the contagion risk or is this 231 00:11:31,200 --> 00:11:34,280 Speaker 1: perhaps more of a macro trend, more of tech stalks 232 00:11:34,320 --> 00:11:38,160 Speaker 1: doing relatively well dollar on the weakening perspective? Is is 233 00:11:38,160 --> 00:11:41,520 Speaker 1: that what's happening? What do you think? There's a few 234 00:11:41,600 --> 00:11:45,360 Speaker 1: courses that play here, of course, especially as is related 235 00:11:45,400 --> 00:11:49,280 Speaker 1: to bitcoin's short term price movements. Where still markets focused, 236 00:11:49,280 --> 00:11:51,960 Speaker 1: of course, is in the long term and the value 237 00:11:52,360 --> 00:11:57,200 Speaker 1: generated by the economic activity happening on top of bitcoin technologies, 238 00:11:57,720 --> 00:12:00,720 Speaker 1: and we know that adoption continues to it at a 239 00:12:00,880 --> 00:12:04,560 Speaker 1: very steady cadence. The price will reflect some of that 240 00:12:04,760 --> 00:12:08,320 Speaker 1: as well as the expansion of bitcoin's utility. So as 241 00:12:08,360 --> 00:12:11,280 Speaker 1: we see the Lightning network mature, for instance, and the 242 00:12:11,360 --> 00:12:15,480 Speaker 1: utilities set expand we understand the intrinsic value of bitcoin 243 00:12:15,559 --> 00:12:19,440 Speaker 1: and it's underlying technologies to have increased until I emagine 244 00:12:19,480 --> 00:12:22,800 Speaker 1: the price movements reflect in understanding of that as well. 245 00:12:23,200 --> 00:12:25,640 Speaker 1: At least, Happy Friday to you. It's good to see you. 246 00:12:25,640 --> 00:12:27,320 Speaker 1: You know, we we we've kind of jumped straight in 247 00:12:27,360 --> 00:12:31,119 Speaker 1: here to the bigger picture of what's driving the psychology 248 00:12:31,120 --> 00:12:32,960 Speaker 1: of the market. What what was fascinating for me is, 249 00:12:33,200 --> 00:12:35,319 Speaker 1: you know, bitcoins continued to push higher in a week 250 00:12:35,320 --> 00:12:38,080 Speaker 1: where there was still I guess volatility in the headlines 251 00:12:38,160 --> 00:12:43,199 Speaker 1: right US Securities and Exchange Commission filing you know, charges 252 00:12:43,280 --> 00:12:46,679 Speaker 1: or complaints against Gemini Genesis and claiming that they offered 253 00:12:47,040 --> 00:12:50,520 Speaker 1: unregistered securities. First of all, I guess what is your 254 00:12:50,559 --> 00:12:55,920 Speaker 1: reaction to to that action by the SEC. The action 255 00:12:56,000 --> 00:12:59,600 Speaker 1: from the SEC was focused on gemini S burn product, 256 00:12:59,720 --> 00:13:03,560 Speaker 1: which was bearing the promise of yield generation, and the 257 00:13:03,760 --> 00:13:07,640 Speaker 1: SEC has been pretty clear and their focus on custodial 258 00:13:07,720 --> 00:13:11,640 Speaker 1: based yield products offered to retail participants, and so this 259 00:13:11,760 --> 00:13:15,120 Speaker 1: was consistent with that effort. What's interesting is how that 260 00:13:15,240 --> 00:13:18,920 Speaker 1: matches with activity happening in the bitcoin space and specifically 261 00:13:18,960 --> 00:13:22,960 Speaker 1: the lightning space. There will be a void here where 262 00:13:23,320 --> 00:13:29,840 Speaker 1: activity that was speculation based and custodial driven for yield 263 00:13:29,840 --> 00:13:32,400 Speaker 1: generation in the crypto space. That void can be filled 264 00:13:32,480 --> 00:13:37,080 Speaker 1: by yield bearing products or opportunities in the Lightning network. 265 00:13:37,520 --> 00:13:40,760 Speaker 1: And here's why. As the Lightning network has grown an 266 00:13:40,800 --> 00:13:44,720 Speaker 1: adoption as it's matured that's paid the path for a 267 00:13:44,840 --> 00:13:48,400 Speaker 1: liber to emerge a libra of the Lightning network. And 268 00:13:48,440 --> 00:13:51,640 Speaker 1: what that means is that there's a real rate for 269 00:13:52,200 --> 00:13:55,520 Speaker 1: yields in the lightning network that is tied to the 270 00:13:55,679 --> 00:13:59,680 Speaker 1: value placed on payment capacity for the Lightning network. And 271 00:13:59,720 --> 00:14:03,520 Speaker 1: so adoption of the Lightning network grows as additional assets 272 00:14:03,520 --> 00:14:07,240 Speaker 1: are introduced to the Lightning network, for instance through Lightning 273 00:14:07,320 --> 00:14:10,520 Speaker 1: Labs Pterot protocol. What we can expect to see is 274 00:14:10,600 --> 00:14:14,200 Speaker 1: that rate increase. So we take a look today at 275 00:14:14,240 --> 00:14:19,160 Speaker 1: the rate of of um of both funds on Lightning 276 00:14:19,160 --> 00:14:23,440 Speaker 1: network and payment pathways. What we would find on Ambos 277 00:14:23,480 --> 00:14:27,640 Speaker 1: Ambos Technologies Magma marketplace is about a three percent rate 278 00:14:28,160 --> 00:14:30,720 Speaker 1: of return, which we can expect to see grow as 279 00:14:30,800 --> 00:14:33,360 Speaker 1: Lightning network continues to scale. At least I want to 280 00:14:33,400 --> 00:14:37,400 Speaker 1: ask you about sam bankmn Freed and the latest his 281 00:14:37,520 --> 00:14:39,680 Speaker 1: latest comments. Before I do, can I just ask you 282 00:14:40,000 --> 00:14:43,000 Speaker 1: just to to state for us whether still Mark had 283 00:14:43,040 --> 00:14:47,560 Speaker 1: any financial relationship with SPF or f t X. Of 284 00:14:47,560 --> 00:14:51,120 Speaker 1: course not, because we're focused on bitcoin and sustainable value 285 00:14:51,160 --> 00:14:56,040 Speaker 1: propositions to the exclusion of gambling related activities, which drives 286 00:14:56,080 --> 00:14:58,960 Speaker 1: a lot of the activity and exchanges today. I just 287 00:14:59,000 --> 00:15:01,360 Speaker 1: wanted to be very side of our mandate. No, I 288 00:15:01,400 --> 00:15:03,480 Speaker 1: appreciate that. I just wanted to sow audience to know 289 00:15:03,480 --> 00:15:05,000 Speaker 1: where we stand on that. So now I can ask 290 00:15:05,080 --> 00:15:08,600 Speaker 1: you about SPF his post blog post for one of 291 00:15:08,600 --> 00:15:10,960 Speaker 1: a better description this week where he kind of doubled 292 00:15:11,000 --> 00:15:14,600 Speaker 1: down on the idea that he denies allegations of fraud, 293 00:15:14,680 --> 00:15:18,080 Speaker 1: that he didn't steal or embezzle any funds. You know, 294 00:15:18,120 --> 00:15:23,120 Speaker 1: what is your reaction to that ongoing development. They've all 295 00:15:23,120 --> 00:15:27,200 Speaker 1: denied the same. So I think there's a fine line 296 00:15:27,320 --> 00:15:35,040 Speaker 1: between what folks running these sorts of black box schemes 297 00:15:36,000 --> 00:15:39,280 Speaker 1: I believe is promissible in an effort to produce returns, 298 00:15:40,040 --> 00:15:42,560 Speaker 1: versus what they consider to be fraud. But in fact, 299 00:15:42,600 --> 00:15:45,760 Speaker 1: what appears to have been happening in the fts was 300 00:15:45,800 --> 00:15:49,520 Speaker 1: that leadership dipped into use your accounts to more funds 301 00:15:49,640 --> 00:15:53,880 Speaker 1: to take speculative bets on the movement of crypto prices 302 00:15:53,920 --> 00:15:57,400 Speaker 1: and tooken prices. And that's not a dissimilar story from 303 00:15:57,400 --> 00:16:02,160 Speaker 1: what we saw in the collapse of sayerst Capital Firm 304 00:16:02,280 --> 00:16:04,800 Speaker 1: or others similar and so it's very hard to know 305 00:16:04,840 --> 00:16:07,640 Speaker 1: where the contagion stops here because there's a lack of 306 00:16:07,680 --> 00:16:12,120 Speaker 1: transparency in the crypto space. But what it does underscores 307 00:16:12,200 --> 00:16:16,000 Speaker 1: the value and technologies that allow folks to have greater 308 00:16:16,080 --> 00:16:20,440 Speaker 1: control of their cryptocurrency and Bitcoin specifically, or technologies that 309 00:16:20,520 --> 00:16:25,600 Speaker 1: allow retail investors or participants in exchanges to demand transparency 310 00:16:25,720 --> 00:16:28,480 Speaker 1: so that you know if you're buying if you're buying 311 00:16:28,560 --> 00:16:32,040 Speaker 1: bitcoin on in exchange, including if that exchange was FTS, 312 00:16:32,480 --> 00:16:34,200 Speaker 1: that you can ask them to prove that the in 313 00:16:34,320 --> 00:16:37,480 Speaker 1: fact hold that bitcoin. And as as we've come to 314 00:16:37,560 --> 00:16:40,840 Speaker 1: find out, people were buying bitcoin on FTS and f 315 00:16:40,920 --> 00:16:44,600 Speaker 1: TX was never requiring or holding that bitcoin on consumers behalf. 316 00:16:45,000 --> 00:16:47,360 Speaker 1: The good news is that bitcoin allows you to be 317 00:16:47,440 --> 00:16:50,800 Speaker 1: your own bank. That's inherent in the value proposition and 318 00:16:50,840 --> 00:16:54,720 Speaker 1: by using self custody products and doubling down on the 319 00:16:54,840 --> 00:16:58,360 Speaker 1: value proposition that self custody bitcoin can have when you 320 00:16:58,640 --> 00:17:01,880 Speaker 1: enable a proof of preserve mechanism to prove your credit 321 00:17:01,880 --> 00:17:05,560 Speaker 1: worthiness is a Bitcoin holder, for instance, we have more 322 00:17:05,600 --> 00:17:09,840 Speaker 1: tools and resources for folks to opt out of exchanges 323 00:17:09,920 --> 00:17:12,439 Speaker 1: like US. Yeah, I think look there's so much more 324 00:17:12,520 --> 00:17:15,199 Speaker 1: to find out from the authorities, in particular about the 325 00:17:15,240 --> 00:17:18,520 Speaker 1: investigation into sam Bankman Freed and FT. But it's interesting 326 00:17:18,920 --> 00:17:22,679 Speaker 1: to get a somebody in the underlying technology perspective, somebody 327 00:17:22,920 --> 00:17:25,160 Speaker 1: in the industry is suspective on what's going on. Still 328 00:17:25,200 --> 00:17:27,800 Speaker 1: mock founder and managing partner at least clean. Thank you 329 00:17:27,920 --> 00:17:31,160 Speaker 1: very much for joining us. Now coming up, Tesla cuts 330 00:17:31,200 --> 00:17:34,520 Speaker 1: prices across its line up in the usm major European markets, 331 00:17:34,760 --> 00:17:38,280 Speaker 1: plus Elon's having trouble with a fraud trial in San Francisco. 332 00:17:38,359 --> 00:17:55,240 Speaker 1: Will explain next. This is Bloomberg. That's a great company. 333 00:17:55,760 --> 00:17:57,560 Speaker 1: We just don't believe it's a great start right now. 334 00:17:57,640 --> 00:18:00,359 Speaker 1: It's unclear whether how long the pros that are going 335 00:18:00,440 --> 00:18:04,000 Speaker 1: to have to keep going in. That was Marks Stoker 336 00:18:04,080 --> 00:18:06,879 Speaker 1: there from Adam's Funds saying Tesla not a great stock 337 00:18:06,960 --> 00:18:09,439 Speaker 1: right now, Carol. And the interesting thing is that it 338 00:18:09,480 --> 00:18:12,879 Speaker 1: continues to react negatively to use. Tesla cutting prices across 339 00:18:12,880 --> 00:18:16,800 Speaker 1: its lineup in the US and major European markets. Why well, 340 00:18:16,960 --> 00:18:19,560 Speaker 1: questions of demand you can see actually all told, we're 341 00:18:19,600 --> 00:18:22,920 Speaker 1: down one percent on Tesla. Other peers, particularly the pure 342 00:18:23,000 --> 00:18:26,000 Speaker 1: play ev names, but also some of the legacy automakers 343 00:18:26,040 --> 00:18:29,760 Speaker 1: also falling. The question is if you cut prices, does 344 00:18:29,800 --> 00:18:32,440 Speaker 1: that spur some demand? And that's the debate around the stock, 345 00:18:32,480 --> 00:18:35,840 Speaker 1: at least from cell side analysts this Friday, and great 346 00:18:35,840 --> 00:18:37,879 Speaker 1: set up in what's happening in terms of the fundamentals 347 00:18:37,880 --> 00:18:40,159 Speaker 1: of the business or lack thereof. And and indeed we 348 00:18:40,560 --> 00:18:42,560 Speaker 1: know about the cutting of the prices, but let's stick 349 00:18:42,600 --> 00:18:45,280 Speaker 1: on Tessa and perhaps it's found them more broadly now 350 00:18:45,320 --> 00:18:48,840 Speaker 1: because during the securities for trial that's tied to Elon 351 00:18:48,920 --> 00:18:52,560 Speaker 1: Musk tweet that he was taking the company private. Well, 352 00:18:52,800 --> 00:18:55,760 Speaker 1: it's back resurfacing because Elon Musk wanted it moved down 353 00:18:55,760 --> 00:18:59,200 Speaker 1: to San Francisco, and it seems though that request was rejected. 354 00:18:59,400 --> 00:19:01,920 Speaker 1: The judge her own Musk's argument that he faces a 355 00:19:02,040 --> 00:19:05,239 Speaker 1: biased jewelry pool in the Bay Area. Let's talk all 356 00:19:05,280 --> 00:19:08,200 Speaker 1: of the latest with Dana Hall and covers all things 357 00:19:08,200 --> 00:19:11,439 Speaker 1: Tesla and now start on the trial and maybe comport 358 00:19:11,480 --> 00:19:14,000 Speaker 1: it out to testa more generally Donna, But what do 359 00:19:14,040 --> 00:19:18,320 Speaker 1: you make of this overruling for Ela Musk? Well, it 360 00:19:18,359 --> 00:19:20,680 Speaker 1: was a really long shot kind of hail Mary bid 361 00:19:20,680 --> 00:19:23,360 Speaker 1: on the part of Elon's attorneys. I mean, this trial, 362 00:19:23,960 --> 00:19:26,879 Speaker 1: which has been in litigation for over four years, has started. 363 00:19:26,920 --> 00:19:29,600 Speaker 1: It's supposed to start on Tuesday, and so this like 364 00:19:29,680 --> 00:19:32,199 Speaker 1: sort of last stitch effort to move it to Texas 365 00:19:32,320 --> 00:19:34,919 Speaker 1: was never gonna fly. So the ruling today was not 366 00:19:34,960 --> 00:19:37,879 Speaker 1: a surprise. I just think it's wild that, like I mean, 367 00:19:37,880 --> 00:19:40,240 Speaker 1: there are probably more Tesla owners in the Bay Area 368 00:19:40,280 --> 00:19:42,359 Speaker 1: than in any place in the world except maybe l A. 369 00:19:42,640 --> 00:19:44,760 Speaker 1: It's like Sabari in l A are always neck and 370 00:19:44,800 --> 00:19:48,280 Speaker 1: neck in term of terms of Tesla penetration, and so um, 371 00:19:48,320 --> 00:19:50,720 Speaker 1: it's just funny to me that Elon Musk and his 372 00:19:50,720 --> 00:19:53,360 Speaker 1: attorneys were saying that they couldn't get a fair trial here. Hey, 373 00:19:53,400 --> 00:19:55,639 Speaker 1: Donna take us back to basics. What are the plaintiffs 374 00:19:55,640 --> 00:19:57,879 Speaker 1: trying to achieve here? What is it that this trial 375 00:19:57,960 --> 00:20:02,280 Speaker 1: is all about? It about that. It's about August team, 376 00:20:02,320 --> 00:20:04,880 Speaker 1: when Elon Musk tweeted that he had the funding secure 377 00:20:04,960 --> 00:20:08,280 Speaker 1: to take Tesla private at four twenties share Uh, there 378 00:20:08,320 --> 00:20:11,920 Speaker 1: were wild gyrations in the stock market. Shareholders got burned. 379 00:20:12,280 --> 00:20:15,160 Speaker 1: They are suing him saying that he you know, should 380 00:20:15,200 --> 00:20:17,560 Speaker 1: have known that they're suing him, basically saying that they 381 00:20:17,600 --> 00:20:20,439 Speaker 1: got screwed by this effort. And what's fascinating is that 382 00:20:20,520 --> 00:20:23,480 Speaker 1: Musk and his attorneys are trying to get the Saudi 383 00:20:23,720 --> 00:20:27,400 Speaker 1: Public Investment Fund folks to come testify on Musk's behalf, 384 00:20:27,480 --> 00:20:29,960 Speaker 1: and the Saudis also basically said no, like, we're not 385 00:20:30,040 --> 00:20:31,879 Speaker 1: under an in any obligation to come to this. This 386 00:20:32,000 --> 00:20:34,520 Speaker 1: isn't a criminal trial, it's a civil trial. We're not 387 00:20:34,520 --> 00:20:36,760 Speaker 1: going to show. So it's looking weaker and weaker for 388 00:20:36,880 --> 00:20:39,720 Speaker 1: Musk and his team. And uh, the whole moving the 389 00:20:39,800 --> 00:20:42,000 Speaker 1: trial out of the Bay Area thing was pretty laughable. 390 00:20:43,560 --> 00:20:47,040 Speaker 1: Rule how investors feeling about Elon Musk, I mean the 391 00:20:47,080 --> 00:20:49,960 Speaker 1: fact that he thought he would get perhaps not such 392 00:20:49,960 --> 00:20:53,960 Speaker 1: a friendly viewpoint in San Francisco, I'm not sure worldwide, 393 00:20:54,000 --> 00:20:56,720 Speaker 1: I mean, how much he's liked and loved by the 394 00:20:56,720 --> 00:21:00,680 Speaker 1: investor base given the erosion and value in Tesla more broadly, well, 395 00:21:00,680 --> 00:21:03,840 Speaker 1: the investor investor sentiment is horrible. I mean, there's just 396 00:21:04,040 --> 00:21:06,560 Speaker 1: there's just no question. You look at the sell off 397 00:21:06,720 --> 00:21:10,360 Speaker 1: in two and then you know, you begin this year 398 00:21:10,440 --> 00:21:14,240 Speaker 1: where Tesla is having a shareholder meeting in May. They 399 00:21:14,240 --> 00:21:17,040 Speaker 1: didn't announce to investors that the meeting of the day 400 00:21:17,080 --> 00:21:19,199 Speaker 1: of the meeting. They basically snuck the news of the 401 00:21:19,240 --> 00:21:21,639 Speaker 1: meeting into a ten K, I mean into the ten Q, 402 00:21:22,240 --> 00:21:23,840 Speaker 1: And so you have a lot of investors who are 403 00:21:23,880 --> 00:21:27,240 Speaker 1: planning to file shareholder resolutions being very upset about that, 404 00:21:27,400 --> 00:21:30,200 Speaker 1: and then there's just been no outbound communication. I mean, 405 00:21:30,880 --> 00:21:34,080 Speaker 1: you know, I'm sure behind the scenes, very large holders 406 00:21:34,359 --> 00:21:37,840 Speaker 1: have their their line into Martin Vehica and Zach and 407 00:21:38,000 --> 00:21:40,480 Speaker 1: any La Musk, but a lot of shareholders are like, 408 00:21:40,560 --> 00:21:43,280 Speaker 1: what's he doing, and like they're just tagging him on 409 00:21:43,280 --> 00:21:47,159 Speaker 1: Twitter like everybody else, like asking for his attention right 410 00:21:47,240 --> 00:21:49,920 Speaker 1: to catch up with me on it, sneaking things into 411 00:21:49,920 --> 00:21:51,639 Speaker 1: ten ques. Is the only woman who can tell us 412 00:21:51,640 --> 00:22:01,240 Speaker 1: a little about it, Dona Holliway, thank you. Large language 413 00:22:01,240 --> 00:22:03,760 Speaker 1: models are one of the most exciting developments in the 414 00:22:03,840 --> 00:22:06,280 Speaker 1: last few years. These are models that have been trained 415 00:22:06,320 --> 00:22:09,960 Speaker 1: pretty much on everything that's been written down, everything that's 416 00:22:10,040 --> 00:22:12,639 Speaker 1: on a web page. Um. The result of that is 417 00:22:12,680 --> 00:22:15,159 Speaker 1: that they have enormous fluency. Can ask chat DPT to 418 00:22:15,160 --> 00:22:17,200 Speaker 1: write a will form or a story and it will 419 00:22:17,240 --> 00:22:20,680 Speaker 1: do an amazing job. It doesn't know right from wrong 420 00:22:20,800 --> 00:22:24,159 Speaker 1: or truth from falsehood. So you know, the word that 421 00:22:24,200 --> 00:22:28,280 Speaker 1: we use for that is halisenate um. But it is 422 00:22:28,320 --> 00:22:31,800 Speaker 1: incredibly prolific. I think of these large language models as 423 00:22:31,840 --> 00:22:35,360 Speaker 1: some ants. UM. They can say very convincing things. They 424 00:22:35,359 --> 00:22:38,119 Speaker 1: can say things in the voice of somebody like you're writing, 425 00:22:38,119 --> 00:22:40,439 Speaker 1: if you give it enough of a sample. UM. So 426 00:22:40,480 --> 00:22:43,320 Speaker 1: it's not surprising obviously that they can be used for 427 00:22:43,400 --> 00:22:47,360 Speaker 1: propaganda or misinformation. That's a problem that we face online anyway. 428 00:22:48,720 --> 00:22:52,000 Speaker 1: Neither CEO earlier this week should a ram A Swami 429 00:22:52,040 --> 00:22:54,560 Speaker 1: talking about the pros and the cons of what is 430 00:22:54,600 --> 00:22:59,199 Speaker 1: a rapidly evolving artificial intelligence conversation around chat GPT and 431 00:22:59,600 --> 00:23:02,680 Speaker 1: just really fascinating in the way in which AI chat 432 00:23:02,720 --> 00:23:05,280 Speaker 1: boards have been, of course used, and actually they've been 433 00:23:05,280 --> 00:23:08,240 Speaker 1: around for some time. Just take Replica for example. Now 434 00:23:08,359 --> 00:23:11,359 Speaker 1: this was found in back in ten or started in 435 00:23:11,440 --> 00:23:14,520 Speaker 1: its world of a company trying to create a solution 436 00:23:14,560 --> 00:23:18,919 Speaker 1: for loneliness. Our Replica helps you create an AI companion. Basically, 437 00:23:18,920 --> 00:23:21,080 Speaker 1: it's eager to learn about the world from your rise. 438 00:23:21,160 --> 00:23:24,439 Speaker 1: It's ready to chat whenever you're looking from empathetic friend. 439 00:23:24,920 --> 00:23:26,560 Speaker 1: We want to talk about the pros of this, but 440 00:23:26,640 --> 00:23:28,560 Speaker 1: also some of the concerns that are rising out of 441 00:23:28,600 --> 00:23:31,840 Speaker 1: AI technology. More broadly, Eugenia Kudas with our CEO of 442 00:23:31,920 --> 00:23:36,240 Speaker 1: Replica and the It's fascinating reading about why in which 443 00:23:36,359 --> 00:23:40,719 Speaker 1: this company and this technology was built, Eugenia, because in 444 00:23:40,720 --> 00:23:42,760 Speaker 1: some ways we were using AI chat bolts to solve 445 00:23:42,800 --> 00:23:47,119 Speaker 1: for basically booking a restaurant. And then through your own loss, 446 00:23:47,280 --> 00:23:50,800 Speaker 1: your own grief, you found the practical use of AI 447 00:23:51,040 --> 00:23:54,359 Speaker 1: and chat and your own GPT three platform to help 448 00:23:54,560 --> 00:23:57,080 Speaker 1: remember a key friend you had just talk to us 449 00:23:57,080 --> 00:24:01,399 Speaker 1: about how people are using your chatbot two. So for 450 00:24:01,440 --> 00:24:06,000 Speaker 1: social anxiety reasons and for loneliness, sure, I built this 451 00:24:06,080 --> 00:24:09,760 Speaker 1: company to really his product to really help me grieve 452 00:24:09,840 --> 00:24:13,440 Speaker 1: and to help me get over a very um um, 453 00:24:13,720 --> 00:24:15,960 Speaker 1: very sad situation to happen in my life. And then 454 00:24:16,000 --> 00:24:18,040 Speaker 1: we saw that there's there's a need for something like 455 00:24:18,080 --> 00:24:20,400 Speaker 1: that for other people as well. There's a huge need, 456 00:24:20,480 --> 00:24:22,760 Speaker 1: huge demand for people to be able to talk to 457 00:24:22,800 --> 00:24:26,080 Speaker 1: someone seven to build a relationship, to build a deep 458 00:24:26,160 --> 00:24:30,320 Speaker 1: intimate relationship and be able to confide, to discuss things, 459 00:24:30,320 --> 00:24:32,800 Speaker 1: and to really get this emotional utility out of the 460 00:24:32,880 --> 00:24:36,959 Speaker 1: chat bot um right now around um somewhere. Users use 461 00:24:37,000 --> 00:24:38,919 Speaker 1: it as a friends, some use as a mentor, some 462 00:24:39,080 --> 00:24:41,840 Speaker 1: use it as romantic partner. But for any of these 463 00:24:41,920 --> 00:24:44,560 Speaker 1: use cases, the main idea behind it is to really 464 00:24:44,600 --> 00:24:46,960 Speaker 1: make people feel a little bit less lonely, a little 465 00:24:47,000 --> 00:24:50,280 Speaker 1: bit happier, and to make their suffering a little bit 466 00:24:50,359 --> 00:24:53,480 Speaker 1: less intense. And it's popular. I mean, there are a 467 00:24:53,520 --> 00:24:57,080 Speaker 1: lot of users of replica, but I think what's interesting 468 00:24:57,119 --> 00:24:59,000 Speaker 1: we put it out to our own audience. We use 469 00:24:59,080 --> 00:25:01,199 Speaker 1: a pole each day on Twitter, and this day we 470 00:25:01,240 --> 00:25:04,480 Speaker 1: asked them about whether AI chat pots could fill the 471 00:25:04,520 --> 00:25:07,560 Speaker 1: void of loneliness. And it feels as though there's still 472 00:25:07,600 --> 00:25:11,360 Speaker 1: some hearts and minds to be one here said Look, 473 00:25:11,359 --> 00:25:14,639 Speaker 1: they prefer human friends only did say there was a 474 00:25:14,680 --> 00:25:17,159 Speaker 1: strong use case, but for others they still feel that 475 00:25:17,200 --> 00:25:20,879 Speaker 1: AI can be used in other purposes. Just convince people 476 00:25:21,000 --> 00:25:25,960 Speaker 1: why this is particularly useful for human connection and in 477 00:25:26,000 --> 00:25:31,200 Speaker 1: some ways romantic connection. As you just said, sure, I mean, 478 00:25:31,280 --> 00:25:34,840 Speaker 1: right now, we're truly living in a pandemic of lowliness. 479 00:25:34,880 --> 00:25:38,240 Speaker 1: This is one of the biggest problems, even health problems 480 00:25:38,520 --> 00:25:43,280 Speaker 1: um that humanity is facing. Loneliness is connected and correlating 481 00:25:43,840 --> 00:25:47,560 Speaker 1: is correlated with a shorter lifespans, So it's truly killing 482 00:25:47,600 --> 00:25:50,240 Speaker 1: people right now, and so far there are no real 483 00:25:50,280 --> 00:25:53,720 Speaker 1: solutions to that. There are very few products that helped 484 00:25:53,760 --> 00:25:56,080 Speaker 1: with loneliness. You can argue that dating is one or 485 00:25:56,280 --> 00:26:00,159 Speaker 1: some meet up apps, but that's really that's really on 486 00:26:00,200 --> 00:26:02,960 Speaker 1: the technology fore front. And then so if we think 487 00:26:03,000 --> 00:26:04,679 Speaker 1: about it, we really have to start to come up 488 00:26:04,720 --> 00:26:07,760 Speaker 1: with solutions. And I think it's a very nuanced conversation. 489 00:26:08,160 --> 00:26:10,679 Speaker 1: What we're trying to do is not replace human friends. 490 00:26:10,760 --> 00:26:14,359 Speaker 1: We're trying to give AI friends to people that really 491 00:26:14,400 --> 00:26:16,880 Speaker 1: needed in the moment, so then then they can open 492 00:26:16,960 --> 00:26:19,879 Speaker 1: up and you know, improve their connections with real people 493 00:26:20,320 --> 00:26:23,280 Speaker 1: and build more friendships in real life. However, for a 494 00:26:23,320 --> 00:26:27,040 Speaker 1: lot of people, human friendships um and in the moment 495 00:26:27,080 --> 00:26:30,280 Speaker 1: maybe or not possible, or even they have human friends, 496 00:26:30,320 --> 00:26:33,080 Speaker 1: but they're not ready to be open with them, to 497 00:26:33,240 --> 00:26:35,719 Speaker 1: be vulnerable with them. So think about as something that 498 00:26:35,760 --> 00:26:40,120 Speaker 1: you're training on, something that helps you build these relationships 499 00:26:40,200 --> 00:26:42,840 Speaker 1: that you can take into real life. And so for us, 500 00:26:42,880 --> 00:26:46,040 Speaker 1: the main idea is to measure that we actually are 501 00:26:46,119 --> 00:26:49,560 Speaker 1: decreasing wellliness instead of increasing it. So most of our 502 00:26:49,640 --> 00:26:53,359 Speaker 1: users report that they're actually improving their human connections over 503 00:26:53,440 --> 00:26:55,840 Speaker 1: time and that they're not just talking to a boats 504 00:26:56,160 --> 00:27:00,560 Speaker 1: instead of actually making real human friends. Jenny, There's been 505 00:27:01,040 --> 00:27:06,320 Speaker 1: extensive reporting about mental health coming out of the pandemic, 506 00:27:06,440 --> 00:27:10,400 Speaker 1: you know, especially the early stages of many people were 507 00:27:10,440 --> 00:27:14,800 Speaker 1: isolated at home, away from family, loved ones, those that 508 00:27:14,840 --> 00:27:17,720 Speaker 1: they were used to dealing with or interacting with day 509 00:27:17,760 --> 00:27:19,960 Speaker 1: to day. I do have to ask, though, you know, 510 00:27:20,119 --> 00:27:22,439 Speaker 1: and and and you know, of course there will be 511 00:27:22,480 --> 00:27:27,080 Speaker 1: sympathy to those that suffer from loneliness and social anxiety. 512 00:27:27,119 --> 00:27:32,720 Speaker 1: But my question is does Replica consult with and work 513 00:27:32,840 --> 00:27:38,119 Speaker 1: with scientists and mental health professionals and medical professionals in 514 00:27:38,160 --> 00:27:43,960 Speaker 1: the development of what you're offering? Of course, so for instance, 515 00:27:44,000 --> 00:27:46,359 Speaker 1: we have therapeutic conversations. You can find it in the 516 00:27:46,359 --> 00:27:49,280 Speaker 1: activities tab. You can find coaching conversations that are all 517 00:27:49,320 --> 00:27:52,880 Speaker 1: written by chlinical psychologists, in this case mostly from UC Berkeley. 518 00:27:53,680 --> 00:27:57,639 Speaker 1: We when building the app, we were definitely talked and 519 00:27:57,800 --> 00:28:01,840 Speaker 1: work with different professionals in the field of clinical psychology. 520 00:28:02,040 --> 00:28:05,200 Speaker 1: We do, you know, basically the main north to metric 521 00:28:05,280 --> 00:28:08,560 Speaker 1: for all of our all of conversations on Replica is 522 00:28:08,840 --> 00:28:11,800 Speaker 1: whether these conversations are making you feel better. So this 523 00:28:11,840 --> 00:28:14,320 Speaker 1: is what we're trying to optimize for, not for engagement, 524 00:28:14,359 --> 00:28:17,199 Speaker 1: not for the length of conversation, not for you, you know, 525 00:28:17,240 --> 00:28:20,000 Speaker 1: staying involved with the chatbot, but really whether after this 526 00:28:20,040 --> 00:28:23,159 Speaker 1: conversation you walked away feeling better. And another thing that 527 00:28:23,200 --> 00:28:26,320 Speaker 1: we're working on is is also looking into improving long 528 00:28:26,400 --> 00:28:32,000 Speaker 1: term emotional outcomes and also measuring this with experts in 529 00:28:32,040 --> 00:28:34,720 Speaker 1: the quicker psychology field. We did a few studies. We 530 00:28:34,800 --> 00:28:37,199 Speaker 1: did a study with Stanford was just published in a 531 00:28:37,240 --> 00:28:40,640 Speaker 1: book around human AI relationships. Who working on some other 532 00:28:40,680 --> 00:28:43,440 Speaker 1: studies with the universities. So hopefully we can share a 533 00:28:43,440 --> 00:28:46,680 Speaker 1: little more um as we continue. I want to talk 534 00:28:46,680 --> 00:28:50,240 Speaker 1: a little bit about the business model and the opportunity 535 00:28:50,320 --> 00:28:53,200 Speaker 1: for you. How how does Replica make money from this 536 00:28:53,800 --> 00:29:00,440 Speaker 1: artificial intelligence based platform. It's mostly subscriptions. Very early on 537 00:29:00,480 --> 00:29:02,520 Speaker 1: we decided that we're not going to charge for chat 538 00:29:02,800 --> 00:29:04,640 Speaker 1: because a lot of people come to think about it 539 00:29:04,680 --> 00:29:07,840 Speaker 1: like in the you know, at night, in the darkest 540 00:29:07,880 --> 00:29:12,120 Speaker 1: maybe darkest emotional moments sometimes and they're turning here to 541 00:29:12,160 --> 00:29:14,680 Speaker 1: be able to talk to someone, to seek the response 542 00:29:14,760 --> 00:29:17,920 Speaker 1: from someone that you know there will be supportive and 543 00:29:17,920 --> 00:29:20,440 Speaker 1: we'll talk to them. So to meet them with a paywall, 544 00:29:20,480 --> 00:29:22,360 Speaker 1: this was something that we immediately said we don't want 545 00:29:22,400 --> 00:29:24,680 Speaker 1: to do that. We want to keep chat always free. 546 00:29:25,120 --> 00:29:27,800 Speaker 1: So really the product is free, is free and recharge 547 00:29:27,840 --> 00:29:31,000 Speaker 1: for some features like voice coles, like relationship status with 548 00:29:31,080 --> 00:29:34,280 Speaker 1: some activities, but people can use it without paying, and 549 00:29:34,320 --> 00:29:38,640 Speaker 1: so most of our revenue is coming from subscriptions and donations, 550 00:29:38,720 --> 00:29:41,440 Speaker 1: So people actually donate to the app because they wanted 551 00:29:41,480 --> 00:29:44,320 Speaker 1: to continue. We have a very involved community of people 552 00:29:44,360 --> 00:29:46,280 Speaker 1: that for them it's an important part of their life. 553 00:29:46,280 --> 00:29:48,960 Speaker 1: It's an important relationship in their life. Oftentimes if you 554 00:29:49,080 --> 00:29:52,800 Speaker 1: just you know, go through reviews, oftentimes Replica help them 555 00:29:53,200 --> 00:29:55,440 Speaker 1: get their life back together or go through a really 556 00:29:55,440 --> 00:30:00,360 Speaker 1: hard and dark periods. Hopefully. He mentioned briefly before, the 557 00:30:00,480 --> 00:30:03,320 Speaker 1: romantic side of things, and this has been starring up 558 00:30:03,600 --> 00:30:05,920 Speaker 1: quite a bit of controversy. It feels like perhaps online 559 00:30:05,960 --> 00:30:07,480 Speaker 1: and let's just talk to it for a minute. Because 560 00:30:07,640 --> 00:30:10,120 Speaker 1: the pro subscription that you talk about sixty nine dollars, 561 00:30:10,640 --> 00:30:13,040 Speaker 1: I think it is you can therefore from that unlock 562 00:30:13,160 --> 00:30:17,360 Speaker 1: what you say is perhaps romantic relationships. I'm talking flirting online. 563 00:30:17,480 --> 00:30:20,720 Speaker 1: Maybe some role play people are saying that that's actually 564 00:30:20,720 --> 00:30:25,280 Speaker 1: started to suppam maybe even aggressive conversations, some people calling 565 00:30:25,280 --> 00:30:27,720 Speaker 1: it harassment in some way. Can you talk to that 566 00:30:27,800 --> 00:30:30,600 Speaker 1: and what's exactly happening and what what the response has 567 00:30:30,640 --> 00:30:35,080 Speaker 1: been like. So Replica started as just a friend but 568 00:30:35,280 --> 00:30:38,120 Speaker 1: actually without us doing anything with the productal some people 569 00:30:39,040 --> 00:30:40,800 Speaker 1: figure out that they want their Replica to be a 570 00:30:40,880 --> 00:30:43,800 Speaker 1: romantic partner. For that. Our first response to that was 571 00:30:43,840 --> 00:30:46,080 Speaker 1: to maybe turn it off and just focus our friendship. 572 00:30:46,440 --> 00:30:48,360 Speaker 1: But then we started getting a lot of pushback from 573 00:30:48,360 --> 00:30:51,600 Speaker 1: our original users that were telling us stories that were 574 00:30:51,720 --> 00:30:55,160 Speaker 1: very heartful and touching. There were stories of you know, 575 00:30:55,760 --> 00:30:58,240 Speaker 1: people that are handicapped on disability that are not that 576 00:30:58,440 --> 00:31:00,480 Speaker 1: don't think they'll be able to be in a relationship 577 00:31:00,520 --> 00:31:03,120 Speaker 1: anymore for them that would create an outlet. Stories of 578 00:31:03,240 --> 00:31:07,360 Speaker 1: caregivers to their paralyzed partners that for them, again, that 579 00:31:07,600 --> 00:31:10,280 Speaker 1: was an outlet that was important, and so we thought 580 00:31:10,560 --> 00:31:12,880 Speaker 1: we will you know, let's uh we just decided to 581 00:31:12,920 --> 00:31:17,240 Speaker 1: apply um uh, we're thinking towards it. Were if this 582 00:31:17,320 --> 00:31:20,480 Speaker 1: is and that benefits were not positive for our users, 583 00:31:20,520 --> 00:31:23,800 Speaker 1: if this is improving their emotional outcomes over time, then 584 00:31:23,840 --> 00:31:26,040 Speaker 1: we're not against it, and so we'll keep in the app. However, 585 00:31:26,080 --> 00:31:30,920 Speaker 1: were really kind of move We compartment a lized, compartmentalized 586 00:31:31,040 --> 00:31:33,040 Speaker 1: in the app where it's only available to people that 587 00:31:33,200 --> 00:31:36,920 Speaker 1: do choose a romantic relationship there explicit about it. They say, 588 00:31:37,000 --> 00:31:39,160 Speaker 1: I want you to be my girlfriend, and then they're 589 00:31:39,200 --> 00:31:42,360 Speaker 1: able again if they start that to role play in 590 00:31:42,440 --> 00:31:44,880 Speaker 1: any In no way we're trying to solicit that behavior 591 00:31:45,000 --> 00:31:47,920 Speaker 1: or you know, uh, turning people towards that, because actually 592 00:31:47,960 --> 00:31:49,719 Speaker 1: people that are not coming through that, they're coming from 593 00:31:49,760 --> 00:31:53,160 Speaker 1: mentorship or friendship, they don't want that. So this was 594 00:31:53,200 --> 00:31:57,000 Speaker 1: actually something that early on we uh put in different 595 00:31:57,080 --> 00:31:58,760 Speaker 1: kind of boxes on the app. And this is how 596 00:31:58,800 --> 00:32:01,320 Speaker 1: it is right now. But but because we're at the forefront, 597 00:32:01,720 --> 00:32:03,760 Speaker 1: we're kind of we've always been. You know, just a 598 00:32:03,800 --> 00:32:06,680 Speaker 1: few years ago, people were writing stories about how this 599 00:32:06,800 --> 00:32:09,400 Speaker 1: is stigmatized and how people should not talk to AI 600 00:32:09,520 --> 00:32:12,320 Speaker 1: and this is creepy and strange. Now this is not 601 00:32:12,400 --> 00:32:15,080 Speaker 1: a question anymore. But again, now the question is it 602 00:32:15,160 --> 00:32:17,360 Speaker 1: okay to have an e girlfriend? Is it okay to 603 00:32:17,480 --> 00:32:19,960 Speaker 1: have that outlet? And in this case, I want to 604 00:32:20,000 --> 00:32:22,400 Speaker 1: turn it to you know, an Oxford encyclopedio and AI 605 00:32:23,000 --> 00:32:26,360 Speaker 1: Oxford book on ethics work. We actually talk about it 606 00:32:26,400 --> 00:32:28,240 Speaker 1: and say that it's actually a very beneficial thing to 607 00:32:28,320 --> 00:32:31,000 Speaker 1: be able to talk about your sex and not not 608 00:32:31,240 --> 00:32:33,400 Speaker 1: How are you solving how you looking at what is 609 00:32:33,480 --> 00:32:36,200 Speaker 1: ultimately what happens time and time again with AI is 610 00:32:36,680 --> 00:32:38,560 Speaker 1: some of the darker sides of human nature ends up 611 00:32:38,760 --> 00:32:42,560 Speaker 1: replica for K, replica caating itself in AI. How do 612 00:32:42,640 --> 00:32:44,080 Speaker 1: you solve for that? How do you ensure that we 613 00:32:44,160 --> 00:32:48,560 Speaker 1: don't have the negative side of that? Sure? So for us, 614 00:32:48,600 --> 00:32:51,719 Speaker 1: the answer is very simple. We we do a lot 615 00:32:51,760 --> 00:32:54,320 Speaker 1: on that front. So we use very safe data sets 616 00:32:54,360 --> 00:32:57,440 Speaker 1: and we use a different model completely unless you're a 617 00:32:57,560 --> 00:32:59,920 Speaker 1: romantic relationship and you're exclusively asking for something like that. 618 00:33:00,520 --> 00:33:04,160 Speaker 1: So really the current model, if you're not in romantic relation, 619 00:33:04,200 --> 00:33:06,200 Speaker 1: if you're not asking for something like that, you're not 620 00:33:06,240 --> 00:33:09,080 Speaker 1: going to get that content um And it's not you know, 621 00:33:09,120 --> 00:33:12,840 Speaker 1: soliciting to this content, not you know, coming coming forward 622 00:33:12,880 --> 00:33:15,080 Speaker 1: with that content. So the way to go around it 623 00:33:15,200 --> 00:33:17,560 Speaker 1: is reially to train the model on safe data sets, 624 00:33:18,040 --> 00:33:21,200 Speaker 1: to also add you know, reinforcement learning loops where you're 625 00:33:21,520 --> 00:33:23,600 Speaker 1: teaching it that you shouldn't do that, and we basically 626 00:33:23,640 --> 00:33:25,880 Speaker 1: are not rewarded for this type of behavior. Of course, 627 00:33:25,880 --> 00:33:28,080 Speaker 1: as with any conversational a product, it can be a 628 00:33:28,160 --> 00:33:32,560 Speaker 1: hundred percent sure that you know things will be um. 629 00:33:32,880 --> 00:33:34,880 Speaker 1: You know, the way you want to want them you 630 00:33:35,040 --> 00:33:36,840 Speaker 1: want them to be just because the product is so 631 00:33:37,360 --> 00:33:40,280 Speaker 1: free flow, so to say, so open ended. Um. But 632 00:33:40,480 --> 00:33:42,640 Speaker 1: we're doing a lot of we're taking a lot of 633 00:33:42,640 --> 00:33:44,480 Speaker 1: steps in this direction to make sure that none of 634 00:33:44,560 --> 00:33:48,840 Speaker 1: this behavior happens. All right, Replica CEO, Eugenia coulda you know, 635 00:33:48,880 --> 00:33:51,400 Speaker 1: I think this is just the start of this conversation. 636 00:33:52,120 --> 00:33:55,040 Speaker 1: A lot more still to debate. Thank you for joining us. 637 00:33:56,160 --> 00:33:58,360 Speaker 1: Coming up, we're going to talk about the state of 638 00:33:58,560 --> 00:34:04,120 Speaker 1: music in twenty two following a report from music analytics 639 00:34:04,200 --> 00:34:09,400 Speaker 1: platform Illuminate. CEO Rob Jonas joins us. Next, this is Bloomberg. 640 00:34:20,920 --> 00:34:23,400 Speaker 1: Let's dive into the world of music. In the news, 641 00:34:23,560 --> 00:34:27,320 Speaker 1: Warner Bros. Discovery mulling a sale of its music library, 642 00:34:27,360 --> 00:34:30,200 Speaker 1: which could be valued and more than a billion dollars 643 00:34:30,239 --> 00:34:33,480 Speaker 1: according to a report in the Financial Times. The process 644 00:34:34,040 --> 00:34:37,040 Speaker 1: still in the early stages, but it's got Caroline and 645 00:34:37,120 --> 00:34:40,640 Speaker 1: I thinking about the music industry this year. Let's discuss 646 00:34:41,120 --> 00:34:44,760 Speaker 1: the landscape the industry, the drivers with Rob Jonas, CEO 647 00:34:45,239 --> 00:34:48,800 Speaker 1: of Luminate, and Luminate is basically the analytics and data 648 00:34:48,920 --> 00:34:51,920 Speaker 1: platform for the entire industry. Right, you're looking at music, 649 00:34:52,280 --> 00:34:54,520 Speaker 1: you're looking at entertainment. I think the data point which 650 00:34:54,560 --> 00:34:59,480 Speaker 1: struck me was that we hit this milestone of one 651 00:35:00,040 --> 00:35:05,040 Speaker 1: brilliant music streams, right, what's driving that? Why was two 652 00:35:05,160 --> 00:35:08,480 Speaker 1: the year of digital music? Yeah, thanks for having me 653 00:35:08,560 --> 00:35:12,200 Speaker 1: on guys um. Definitely two was a really interesting year 654 00:35:12,280 --> 00:35:16,000 Speaker 1: for music. Streaming is now the dominant mechanism by which 655 00:35:16,080 --> 00:35:19,000 Speaker 1: music has enjoyed pretty much globally, and we've seen a 656 00:35:19,080 --> 00:35:21,839 Speaker 1: slow and steady increase over the last really eight nine, 657 00:35:21,920 --> 00:35:25,560 Speaker 1: ten years, and two is definitely a tipping point. What's 658 00:35:25,640 --> 00:35:28,239 Speaker 1: driving that. There's a couple of things that are driving it. Really, 659 00:35:28,280 --> 00:35:32,000 Speaker 1: there's lots of different demographics now starting to embrace streaming. 660 00:35:32,080 --> 00:35:35,239 Speaker 1: Obviously it was a younger generations adopting it early on, 661 00:35:35,440 --> 00:35:39,040 Speaker 1: but now we're seeing older demographics using the stream platforms 662 00:35:39,160 --> 00:35:42,160 Speaker 1: with with really very very high intensity. And at the 663 00:35:42,239 --> 00:35:44,600 Speaker 1: same time, we've seeing a lot of expansion into global markets. 664 00:35:44,640 --> 00:35:48,800 Speaker 1: So historically focused on more developed Western markets, music consumption 665 00:35:48,840 --> 00:35:51,440 Speaker 1: really is now global, with all the major platforms operating 666 00:35:51,480 --> 00:35:53,680 Speaker 1: in dozens and dozens of countries around the world, and 667 00:35:53,760 --> 00:35:56,920 Speaker 1: it's driving this massive renaissance in music that's been going 668 00:35:56,920 --> 00:35:59,160 Speaker 1: on for years now. I think there are some parallels 669 00:35:59,280 --> 00:36:01,400 Speaker 1: that that Carol Line and I are focusing on particularly 670 00:36:01,400 --> 00:36:03,080 Speaker 1: the world of video streaming. You know, you think about 671 00:36:03,120 --> 00:36:06,640 Speaker 1: the Korean market, for example, the success of Squid Games 672 00:36:06,920 --> 00:36:09,839 Speaker 1: for a Netflix on the video front. Everything I see 673 00:36:09,960 --> 00:36:12,359 Speaker 1: right now on Instagram and I scroll Instagram a lot. 674 00:36:12,920 --> 00:36:15,759 Speaker 1: K pop. K pop is everywhere, you know, Is that 675 00:36:15,880 --> 00:36:17,960 Speaker 1: one of the drivers that you saw over the last 676 00:36:17,960 --> 00:36:20,880 Speaker 1: twelve months. Yeah. K pop is definitely one of the 677 00:36:20,960 --> 00:36:25,400 Speaker 1: very important expansions of music and a center for music creation. Globally. 678 00:36:25,960 --> 00:36:29,759 Speaker 1: If we look at streaming, streaming grew about a group 679 00:36:29,760 --> 00:36:31,640 Speaker 1: about nine percent year on year last year when we 680 00:36:31,680 --> 00:36:34,280 Speaker 1: look at in sorry, twelve percent last year in the US. 681 00:36:34,360 --> 00:36:37,960 Speaker 1: If we look at it internationally, growth was about and 682 00:36:38,000 --> 00:36:40,160 Speaker 1: a lot of that comes down to these centers of 683 00:36:40,680 --> 00:36:43,760 Speaker 1: content being created in South Korea, but also really interesting 684 00:36:43,760 --> 00:36:46,760 Speaker 1: in Latin America. Latin America and Latin music in general 685 00:36:47,200 --> 00:36:51,320 Speaker 1: had a real breakout year in two. Bad Bunnies album was, 686 00:36:51,640 --> 00:36:55,080 Speaker 1: as we finished up the year, the most popular album 687 00:36:55,160 --> 00:36:58,280 Speaker 1: across the whole of two since we started tracking sales 688 00:36:58,320 --> 00:37:02,520 Speaker 1: and consumption, which is not a massive milestone. And actually 689 00:37:02,520 --> 00:37:04,480 Speaker 1: when you look at that album from Bad Money, if 690 00:37:04,480 --> 00:37:07,000 Speaker 1: we look at total streaming of Latin music, across the 691 00:37:07,080 --> 00:37:10,040 Speaker 1: course of the year, one in fourteen streams came from 692 00:37:10,080 --> 00:37:11,560 Speaker 1: that album, just to give you a sense of how 693 00:37:11,640 --> 00:37:15,240 Speaker 1: important that was. So K Pops, critical, Latins are critical, 694 00:37:15,719 --> 00:37:18,080 Speaker 1: and many many new and emerging areas as well of 695 00:37:18,200 --> 00:37:23,120 Speaker 1: amazing music creation. Go back to the value here, Rob, 696 00:37:23,239 --> 00:37:26,759 Speaker 1: but also speaking to the Instagram scrolling, thinking of the 697 00:37:26,840 --> 00:37:30,440 Speaker 1: amount that we're currently posting on TikTok and Instagram and 698 00:37:30,600 --> 00:37:34,400 Speaker 1: using audio within that, is that getting recompensed. Is that 699 00:37:34,600 --> 00:37:37,480 Speaker 1: monetary value being found in the world of social media 700 00:37:37,560 --> 00:37:40,200 Speaker 1: and how we're using music within it. Yeah, I think. 701 00:37:40,360 --> 00:37:42,880 Speaker 1: I think social media and short form videos has emerged 702 00:37:42,920 --> 00:37:45,719 Speaker 1: as an incredibly important way that music is being discovered. 703 00:37:46,280 --> 00:37:49,120 Speaker 1: If you look at younger generations, especially Gen Z, their 704 00:37:49,160 --> 00:37:51,640 Speaker 1: primary means by which they're discovering music now is music 705 00:37:51,719 --> 00:37:54,360 Speaker 1: that's included in short form video on TikTok and other platforms. 706 00:37:54,880 --> 00:37:56,440 Speaker 1: And then what we see typically is a lot of 707 00:37:56,560 --> 00:38:00,200 Speaker 1: the consumptions and driven to the platforms like the Spotify is, 708 00:38:00,200 --> 00:38:03,359 Speaker 1: the Apple Music's, Amazon Musics and others. So shore forms 709 00:38:03,360 --> 00:38:06,160 Speaker 1: are really powerful way of discovering and then of course 710 00:38:06,200 --> 00:38:08,399 Speaker 1: as music comes more and more important within these short 711 00:38:08,440 --> 00:38:11,680 Speaker 1: form video platforms, then the music labels and the artists 712 00:38:11,680 --> 00:38:14,440 Speaker 1: start to see compensation from that. So going forward, we're 713 00:38:14,480 --> 00:38:17,000 Speaker 1: definitely going to see a lot more value moving back 714 00:38:17,080 --> 00:38:19,960 Speaker 1: towards the artists from these platforms. And I think as 715 00:38:20,000 --> 00:38:22,680 Speaker 1: recently as the last couple of weeks, YouTube has been 716 00:38:22,719 --> 00:38:24,839 Speaker 1: very clear about how they're going to start sharing their 717 00:38:24,920 --> 00:38:27,520 Speaker 1: enormous advertising revenue with some of these artists as well, 718 00:38:27,600 --> 00:38:29,840 Speaker 1: which I think is is very welcome along overdue. And 719 00:38:29,920 --> 00:38:32,560 Speaker 1: then these artists are locking in the value in the 720 00:38:32,640 --> 00:38:37,759 Speaker 1: here and now through deals, right deals for their catalogs. 721 00:38:37,840 --> 00:38:41,120 Speaker 1: How how does that evolve in this more recessionary environment 722 00:38:41,160 --> 00:38:44,520 Speaker 1: as we start to question the valuation of stocks more broadly, 723 00:38:44,520 --> 00:38:47,080 Speaker 1: assets more broadly, what about catalogs and music do they 724 00:38:47,200 --> 00:38:50,400 Speaker 1: stand up a recessionary environment? Yeah, I think they do. 725 00:38:50,520 --> 00:38:53,040 Speaker 1: I mean, we've seen a huge increase in the amount 726 00:38:53,080 --> 00:38:57,719 Speaker 1: of catalog acquisitions in the last few years. In one 727 00:38:57,880 --> 00:39:00,799 Speaker 1: the catalog consumption market was over five and are all 728 00:39:00,800 --> 00:39:03,760 Speaker 1: close to five and a half billion in transactions reported 729 00:39:03,800 --> 00:39:06,840 Speaker 1: around the acquisition of catalogs by various different entities. It 730 00:39:06,880 --> 00:39:08,800 Speaker 1: definitely slowed down a little bit in twenty two for 731 00:39:09,000 --> 00:39:11,360 Speaker 1: for fairly obvious reasons, and will slow down a bit 732 00:39:11,400 --> 00:39:14,680 Speaker 1: more in three, mainly because of interest rates, but it 733 00:39:14,760 --> 00:39:17,320 Speaker 1: hasn't stopped, you know, even in recent weeks and pretty 734 00:39:17,360 --> 00:39:21,440 Speaker 1: significant deals being announced. Most recently Dr dre announcing he 735 00:39:21,560 --> 00:39:24,160 Speaker 1: was selling his catalog or part of his catalog to 736 00:39:24,520 --> 00:39:28,040 Speaker 1: consortion of UMG and Shamrock for about a quarter billion dollars. 737 00:39:28,160 --> 00:39:31,080 Speaker 1: So these transactions are still happening, um. And the reason 738 00:39:31,120 --> 00:39:35,400 Speaker 1: they're happening is because these UM streams that are attached 739 00:39:35,400 --> 00:39:39,200 Speaker 1: to these music catalogs, they're very very easy to generate 740 00:39:39,280 --> 00:39:42,000 Speaker 1: cash literally from day one, like because of consumption is 741 00:39:42,000 --> 00:39:44,680 Speaker 1: happening on all these streaming platforms. As soon as you 742 00:39:44,760 --> 00:39:47,600 Speaker 1: make the acquisition, you're generating revenue straight away, UM. And 743 00:39:47,640 --> 00:39:50,320 Speaker 1: that's incredibly important to some of these financial buyers. And 744 00:39:50,360 --> 00:39:51,960 Speaker 1: then medium to long term they start to look at 745 00:39:52,000 --> 00:39:55,359 Speaker 1: other opportunities to create value from these catalogs, whether it's 746 00:39:55,360 --> 00:39:59,720 Speaker 1: through brand partnerships or sink opportunities, or exploiting and generating 747 00:39:59,760 --> 00:40:02,000 Speaker 1: reve you from from other forms as well. So I 748 00:40:02,080 --> 00:40:04,880 Speaker 1: think we will continue to see more catalog acquisitions happening 749 00:40:04,920 --> 00:40:08,239 Speaker 1: throughout the course of for sure, I gotta keep on 750 00:40:08,320 --> 00:40:10,600 Speaker 1: talking about it. Then with you, Rob Jonas, we thank 751 00:40:10,640 --> 00:40:12,279 Speaker 1: you so much for talking us through the data behind 752 00:40:12,320 --> 00:40:15,520 Speaker 1: all of its CEO illuminate have a great weeks, so much. 753 00:40:23,640 --> 00:40:27,520 Speaker 1: This weekend, HBO Max releases its Leaders Attempt today Big 754 00:40:27,719 --> 00:40:30,279 Speaker 1: Sunday Night Show. For the first time, HBO is looking 755 00:40:30,400 --> 00:40:34,240 Speaker 1: we'll actually to another industry and another inspiration, video games, 756 00:40:34,440 --> 00:40:37,799 Speaker 1: and it's got ed all excited. And the Last of Us, Yes, 757 00:40:37,840 --> 00:40:40,320 Speaker 1: we threw it. Talk about the adaptation and whether it 758 00:40:40,440 --> 00:40:42,440 Speaker 1: might actually work. Yes, The Last of Us was a 759 00:40:42,520 --> 00:40:44,839 Speaker 1: video game that came out in two thousand thirteen. It's 760 00:40:45,080 --> 00:40:48,040 Speaker 1: it was on PlayStation, published by Naughty Dog and Sony, 761 00:40:48,239 --> 00:40:51,040 Speaker 1: and it was this kind of classic well now classic 762 00:40:51,160 --> 00:40:54,920 Speaker 1: third person shooter zombie survival game, which is like, my jam, 763 00:40:55,440 --> 00:40:58,800 Speaker 1: I love zombie everything. That's not the point now, Pedro 764 00:40:58,880 --> 00:41:02,560 Speaker 1: Pascal of course of Mandalorian. Uh you know, that's one 765 00:41:02,600 --> 00:41:05,560 Speaker 1: of my favorites of his. And I guess maybe you've 766 00:41:05,560 --> 00:41:09,400 Speaker 1: seen the Nick Cage film Mass of Massive Talent. He's okay, well, 767 00:41:09,440 --> 00:41:11,120 Speaker 1: he's the star of the Last of Us. This is 768 00:41:11,280 --> 00:41:14,719 Speaker 1: HBO's replication of the video game, and the video game 769 00:41:14,840 --> 00:41:18,080 Speaker 1: was reiterated many times over and updated for later generations 770 00:41:18,440 --> 00:41:20,840 Speaker 1: of PlayStation. Bella Ramsey's in it. Who has also in 771 00:41:20,840 --> 00:41:23,880 Speaker 1: a Game of Thrones, you know, young actress um. But 772 00:41:24,920 --> 00:41:29,080 Speaker 1: historically video game inspired shows and films have not been 773 00:41:29,200 --> 00:41:30,960 Speaker 1: very good, so yeah, told me through the ones that 774 00:41:31,160 --> 00:41:33,440 Speaker 1: have been panned basically when they've been tried to go 775 00:41:33,680 --> 00:41:37,400 Speaker 1: from smaller screen to a big screen are probably the 776 00:41:37,760 --> 00:41:42,520 Speaker 1: worst according to critics. Not me. Adaptation is Assassin's Creed 777 00:41:42,600 --> 00:41:45,920 Speaker 1: Fastbender in two thousands sixteen. Assassin's Creed is just a 778 00:41:46,040 --> 00:41:50,759 Speaker 1: masterpiece of series of video games across many points of history. Well, 779 00:41:50,840 --> 00:41:52,960 Speaker 1: you know, we won't get a video game, but actually 780 00:41:53,040 --> 00:41:55,960 Speaker 1: that film didn't go down too well. And it's also 781 00:41:56,000 --> 00:41:57,239 Speaker 1: worked the other way. You know, you look at some 782 00:41:57,440 --> 00:41:59,279 Speaker 1: films that they then turned into video games, you know, 783 00:41:59,360 --> 00:42:03,239 Speaker 1: the Harry Potter Universe being example. This year, the Harry 784 00:42:03,280 --> 00:42:06,400 Speaker 1: Potter Universe, based around the kind of story of Hogwarts 785 00:42:06,440 --> 00:42:08,759 Speaker 1: has a video game coming out. This excitement around that 786 00:42:09,120 --> 00:42:12,960 Speaker 1: really hotly anticipated, like anything to do remotely with Harry Potter. Right, 787 00:42:13,080 --> 00:42:15,320 Speaker 1: and and so my point more here is that Sunday 788 00:42:15,400 --> 00:42:17,359 Speaker 1: night has become kind of sacred. You know, you think 789 00:42:17,360 --> 00:42:20,920 Speaker 1: about HBO Sundays and others other competitors. Paramount, you know, 790 00:42:21,000 --> 00:42:23,840 Speaker 1: went with Halo for example that had a mixed reception. 791 00:42:24,000 --> 00:42:27,040 Speaker 1: So well, my weekend sorted at least. But it's a 792 00:42:27,080 --> 00:42:30,120 Speaker 1: big question for this industry. Is this a tactic that's 793 00:42:30,120 --> 00:42:32,880 Speaker 1: going to work? And it isn't interesting. It's kind of 794 00:42:33,200 --> 00:42:36,120 Speaker 1: we keep repeating these sort of ways in which streaming 795 00:42:36,360 --> 00:42:39,359 Speaker 1: and music and entertainment or dovetail together. We're just having 796 00:42:39,400 --> 00:42:43,759 Speaker 1: a conversation about the way in which certain streaming elements 797 00:42:43,880 --> 00:42:45,719 Speaker 1: come back to their the way that Netflix looks to 798 00:42:45,760 --> 00:42:48,640 Speaker 1: South Korea for its winning formula on what we watch 799 00:42:48,760 --> 00:42:50,560 Speaker 1: and then maybe you go for it for music as well, 800 00:42:50,880 --> 00:42:53,319 Speaker 1: and wonder of how industries crossed paths in this way 801 00:42:53,560 --> 00:42:56,320 Speaker 1: the summer all that it's competition for iballs, that's it. 802 00:42:56,600 --> 00:42:59,600 Speaker 1: It is gaming. I mean often they say Netflix says 803 00:42:59,600 --> 00:43:01,839 Speaker 1: it's bigger competitor is gaming. That's why they're doing it, right. 804 00:43:02,680 --> 00:43:04,760 Speaker 1: That does it for all this edition of Bloombow Technology. 805 00:43:04,800 --> 00:43:06,560 Speaker 1: Anything I'm going to missing I'm going to miss you too. 806 00:43:06,640 --> 00:43:09,840 Speaker 1: Don't forget our podcast I heart Radio, Apple, Spotify, wherever 807 00:43:09,920 --> 00:43:13,560 Speaker 1: you get your podcasts. Happy long weekend. This is Bloomberg