1 00:00:03,120 --> 00:00:09,560 Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. 2 00:00:11,400 --> 00:00:14,360 Speaker 2: Hello and welcome to The Money Stuff Podcast, your weekly 3 00:00:14,360 --> 00:00:18,680 Speaker 2: podcast where we talk about stuff related to money. I'm 4 00:00:18,680 --> 00:00:20,639 Speaker 2: Matt Levine and I write the Money Stuff column for 5 00:00:20,680 --> 00:00:21,480 Speaker 2: Bloomberg Opinion. 6 00:00:21,640 --> 00:00:24,040 Speaker 1: And I'm Katie Greifeld, a reporter for Bloomberg News and 7 00:00:24,079 --> 00:00:25,640 Speaker 1: an anchor for Bloomberg Television. 8 00:00:25,840 --> 00:00:26,920 Speaker 2: What do we got today, Kitty? 9 00:00:27,400 --> 00:00:30,560 Speaker 1: We're going to talk about SEC raids and how to 10 00:00:30,600 --> 00:00:33,800 Speaker 1: trade them. We're going to talk about private credit ETFs, 11 00:00:33,880 --> 00:00:36,840 Speaker 1: question mark, and we're going to talk about Spotify arbitrage. 12 00:00:37,000 --> 00:00:45,320 Speaker 2: Yes, excellent, sec raids. 13 00:00:46,000 --> 00:00:48,400 Speaker 1: I'm a little bit surprised. Were your SEC jacket? 14 00:00:48,520 --> 00:00:50,440 Speaker 2: I know I should have. I wrote about this, like 15 00:00:50,920 --> 00:00:54,640 Speaker 2: amazing paper about what happens on the SEC visits a company, 16 00:00:54,840 --> 00:00:57,200 Speaker 2: and I mentioned that I own an SEC raid jacket 17 00:00:57,200 --> 00:00:59,920 Speaker 2: that I joke about wearing but never wear. 18 00:01:00,120 --> 00:01:01,440 Speaker 1: And you didn't even bring. 19 00:01:01,240 --> 00:01:02,680 Speaker 2: It in, did you know? I read about this and 20 00:01:02,720 --> 00:01:05,880 Speaker 2: then reader emailed to say that an sec red jacket 21 00:01:05,959 --> 00:01:09,240 Speaker 2: was his Halloween costume. That's funny, but he didn't have one, 22 00:01:09,319 --> 00:01:11,520 Speaker 2: and he didn't actually know that they even existed, so 23 00:01:11,560 --> 00:01:14,280 Speaker 2: he just like got a blue windpricker and like got 24 00:01:14,400 --> 00:01:17,240 Speaker 2: yellow tape and put SEC on the back and wore 25 00:01:17,280 --> 00:01:20,040 Speaker 2: it to like Halloween parties with the finance pro friends, 26 00:01:20,080 --> 00:01:22,440 Speaker 2: to be scary. Yeah, that was a really good idea. 27 00:01:22,840 --> 00:01:23,520 Speaker 2: I have a real one. 28 00:01:23,560 --> 00:01:25,759 Speaker 1: It'd be kind of funny if he paired that with 29 00:01:25,880 --> 00:01:29,959 Speaker 1: just fangs. That's it's the whole costume. That's cool. Well, 30 00:01:31,200 --> 00:01:33,760 Speaker 1: if you used the fact that you own an SEC 31 00:01:33,840 --> 00:01:36,320 Speaker 1: jacket is like a cute little setup to talk about. 32 00:01:36,840 --> 00:01:40,160 Speaker 1: Maybe a tradable signal. Maybe not that amazing paper. Watching 33 00:01:40,160 --> 00:01:41,720 Speaker 1: the Watchdogs, yes. 34 00:01:41,560 --> 00:01:45,000 Speaker 2: Watching the watch dogs tracking SEC increase using geolocation data. 35 00:01:45,040 --> 00:01:47,720 Speaker 2: What they find is that when the SEC visits a 36 00:01:47,720 --> 00:01:50,920 Speaker 2: public company, that probably means there's something bad, right, and 37 00:01:50,920 --> 00:01:52,080 Speaker 2: so it's a negative signal. 38 00:01:52,160 --> 00:01:54,640 Speaker 1: It's not just a chat, like just a coffee meeting. 39 00:01:55,320 --> 00:01:58,400 Speaker 2: In aggregate, statistically it's a bad sign And so like 40 00:01:58,640 --> 00:02:01,960 Speaker 2: you get Adnorrambal returns of like negative one point four 41 00:02:02,000 --> 00:02:04,040 Speaker 2: to one point nine percent over the three months after 42 00:02:04,040 --> 00:02:08,040 Speaker 2: the SEC visit, whether or not there's a subsequent announcement 43 00:02:08,080 --> 00:02:11,080 Speaker 2: of an SEC investigation, even if they just do an 44 00:02:11,120 --> 00:02:14,120 Speaker 2: informal visit, there's some negative signal there. They also have 45 00:02:14,480 --> 00:02:18,600 Speaker 2: some really fun findings about the insider trading around. Yeah, 46 00:02:18,760 --> 00:02:20,639 Speaker 2: usually the sea of a company and the SEC comes 47 00:02:20,639 --> 00:02:23,799 Speaker 2: and visits. Should you sell the stock because that's bad 48 00:02:23,840 --> 00:02:26,079 Speaker 2: news or should you not sell the stock because that's 49 00:02:26,120 --> 00:02:29,440 Speaker 2: insider trading? Yeah, the answer is most people. There seems 50 00:02:29,440 --> 00:02:31,520 Speaker 2: to be a chilling effect where people tend not to sell, 51 00:02:31,800 --> 00:02:33,639 Speaker 2: but like when it's really bad news, they do sell. 52 00:02:33,720 --> 00:02:35,400 Speaker 1: Well, I have to say so. The stat that you 53 00:02:35,480 --> 00:02:38,800 Speaker 1: highlighted was that insiders are sixteen percent less likely to 54 00:02:38,840 --> 00:02:41,920 Speaker 1: sell into two weeks surrounding an SEC visit relative to 55 00:02:41,960 --> 00:02:44,840 Speaker 1: periods with no visits. I'm kind of surprised it wasn't 56 00:02:44,880 --> 00:02:47,679 Speaker 1: like eighty five percent or ninety percent. I feel like 57 00:02:47,720 --> 00:02:49,639 Speaker 1: if the SEC was knocking on the door, maybe I 58 00:02:49,639 --> 00:02:51,800 Speaker 1: would just sit tight for a moment, right. 59 00:02:51,880 --> 00:02:55,640 Speaker 2: Because like, arguably that's material news, and if you're still selling, 60 00:02:56,040 --> 00:02:59,120 Speaker 2: it does seem risky. But you know it's not that 61 00:02:59,520 --> 00:03:01,720 Speaker 2: because like maybe they're coming to chat, right, like you don't. 62 00:03:01,520 --> 00:03:04,000 Speaker 1: Know, Ye, well, maybe it's that pension for risk that 63 00:03:04,240 --> 00:03:06,640 Speaker 1: got you from the SEC in the first place. 64 00:03:06,760 --> 00:03:09,160 Speaker 2: Right, The causation can go their way, right, You're you're 65 00:03:09,200 --> 00:03:12,080 Speaker 2: the person who's you know, they're visiting you about your 66 00:03:12,080 --> 00:03:16,520 Speaker 2: insider training. Keep insider trading. Yeah, so that is interesting, 67 00:03:16,560 --> 00:03:19,000 Speaker 2: but it's not as interesting as the methodology of the paper, 68 00:03:19,000 --> 00:03:22,440 Speaker 2: which is like amazing. It's basically like, your phone has 69 00:03:22,440 --> 00:03:24,320 Speaker 2: two hundred as on it, and like one hundred of 70 00:03:24,360 --> 00:03:27,760 Speaker 2: them track your location, and ninety nine of all times, 71 00:03:27,800 --> 00:03:30,440 Speaker 2: at all times, and ninety nine of those sell your 72 00:03:30,560 --> 00:03:34,280 Speaker 2: location to a data vendor that then sells it to 73 00:03:34,320 --> 00:03:35,880 Speaker 2: hedge funds. Right, that's what your phone is. 74 00:03:35,880 --> 00:03:38,440 Speaker 1: That's all right, that's why I never turn off my location. 75 00:03:38,520 --> 00:03:40,360 Speaker 1: I want the hedge funds to have that data. 76 00:03:40,400 --> 00:03:43,520 Speaker 2: And the hedge funds appreciate that. Yeah, and they use 77 00:03:43,560 --> 00:03:47,760 Speaker 2: it as far as I can tell, mostly to figure 78 00:03:47,800 --> 00:03:50,960 Speaker 2: out what stores are getting a lot of foot traffic. Yeah, 79 00:03:51,160 --> 00:03:53,560 Speaker 2: that's the main U said. This sort of thing is like, oh, 80 00:03:53,560 --> 00:03:55,800 Speaker 2: there's a lot of foot traffic to this retailer, so 81 00:03:55,840 --> 00:03:56,720 Speaker 2: we should buy that retailer. 82 00:03:56,960 --> 00:03:59,520 Speaker 1: Not just like Katie want to just salad today and 83 00:03:59,600 --> 00:04:01,040 Speaker 1: usually she goes to Sweet Green. 84 00:04:01,560 --> 00:04:06,680 Speaker 2: You know, my understanding is that this data is pretty anonymized. 85 00:04:06,880 --> 00:04:09,280 Speaker 2: They don't have like Katie's data. They have like you know, 86 00:04:09,320 --> 00:04:14,000 Speaker 2: how many people went to Sweet Green. But if you 87 00:04:14,080 --> 00:04:17,240 Speaker 2: get all the data, you can like kind of recreate 88 00:04:17,279 --> 00:04:19,840 Speaker 2: who's Katie. Right. You can look at the phone that 89 00:04:19,920 --> 00:04:22,240 Speaker 2: is at the Bloomberg headquarters a lot and at the 90 00:04:22,279 --> 00:04:25,760 Speaker 2: horse Barn a lot. Yeah, I'm pretty sure this is Katy. 91 00:04:25,960 --> 00:04:27,640 Speaker 1: It never goes downtown, right. 92 00:04:28,080 --> 00:04:31,200 Speaker 2: And so what the authors here did is they got 93 00:04:31,240 --> 00:04:33,560 Speaker 2: all the cell phone data and they figured out which 94 00:04:33,640 --> 00:04:37,520 Speaker 2: phones belonged to SEC employees by looking at like which 95 00:04:37,520 --> 00:04:40,120 Speaker 2: phones were like mostly in SEC offices. And if your 96 00:04:40,160 --> 00:04:42,440 Speaker 2: phone is mostly in SC office, you're an SECM player. 97 00:04:42,520 --> 00:04:45,120 Speaker 2: And then if it goes to a corporate headquarters, then 98 00:04:45,400 --> 00:04:47,040 Speaker 2: the SEC was visiting that company. 99 00:04:47,160 --> 00:04:49,560 Speaker 1: Well, let's just read this. You put it in the column. 100 00:04:49,600 --> 00:04:52,960 Speaker 1: But for listeners who haven't read the newsletter, a smartphone 101 00:04:53,000 --> 00:04:55,159 Speaker 1: is assumed to belong to an SEC employee if it 102 00:04:55,240 --> 00:04:58,080 Speaker 1: is quote paying for at least twenty unique workday hours 103 00:04:58,120 --> 00:05:01,480 Speaker 1: within one SEC location during the month, and the accumulated 104 00:05:01,520 --> 00:05:04,239 Speaker 1: time in the SEC building is greater than in any 105 00:05:04,279 --> 00:05:07,120 Speaker 1: other buildings in the respective month. I do like to 106 00:05:07,160 --> 00:05:09,440 Speaker 1: imagine though, that they're tracking janitors. 107 00:05:09,680 --> 00:05:11,680 Speaker 2: Maybe, but the point is that those genders are then 108 00:05:11,720 --> 00:05:12,440 Speaker 2: also visiting. 109 00:05:12,640 --> 00:05:14,480 Speaker 1: Maybe they're freelancing. I don't know. 110 00:05:14,800 --> 00:05:18,839 Speaker 2: Okay, okay, but statistically it works out to be a 111 00:05:18,920 --> 00:05:19,640 Speaker 2: useful signal. 112 00:05:19,760 --> 00:05:20,320 Speaker 1: This is true. 113 00:05:20,600 --> 00:05:23,479 Speaker 2: So there's so much to say about this methodology. One 114 00:05:23,480 --> 00:05:26,000 Speaker 2: thing about it is I've talked a lot of my 115 00:05:26,080 --> 00:05:30,120 Speaker 2: column about how the SEC gets mad at banks for 116 00:05:30,240 --> 00:05:33,120 Speaker 2: letting their employees talk about business on their personal cell 117 00:05:33,120 --> 00:05:36,760 Speaker 2: phones and finds them hundreds of millions of dollars. This 118 00:05:37,000 --> 00:05:41,560 Speaker 2: is like SEC employees' personal cell phones providing tradable signals 119 00:05:41,600 --> 00:05:44,800 Speaker 2: too well to academics, right, And I think this is 120 00:05:44,880 --> 00:05:47,560 Speaker 2: very funny that the personal cell phone usage of the 121 00:05:47,600 --> 00:05:51,000 Speaker 2: SAC continues to be like a funny little mindfield an 122 00:05:51,040 --> 00:05:53,520 Speaker 2: agency that is finding banks for using personal cell phones. 123 00:05:53,560 --> 00:05:56,480 Speaker 1: I will note that it was a pretty short time period. 124 00:05:56,480 --> 00:06:00,280 Speaker 1: They were collecting data from January twenty nineteen to your 125 00:06:00,279 --> 00:06:02,560 Speaker 1: A twenty twenty, so you know, a little bit over 126 00:06:02,600 --> 00:06:02,920 Speaker 1: a year. 127 00:06:03,000 --> 00:06:05,920 Speaker 2: But still, Yeah, the paper came out like this year, 128 00:06:05,960 --> 00:06:08,599 Speaker 2: and the data is from four or five years ago, 129 00:06:08,960 --> 00:06:11,600 Speaker 2: so it's not tradable in the sense. 130 00:06:11,400 --> 00:06:13,920 Speaker 1: That you know, well, the data is. 131 00:06:14,920 --> 00:06:17,400 Speaker 2: If you had it in real time, and like these vendors, 132 00:06:17,480 --> 00:06:20,000 Speaker 2: they do sell the data in kind of real time. 133 00:06:20,040 --> 00:06:24,960 Speaker 2: So like my question was, can and do hedge funds 134 00:06:25,600 --> 00:06:26,800 Speaker 2: trade on this signal? 135 00:06:27,040 --> 00:06:27,720 Speaker 1: Yeah? 136 00:06:27,760 --> 00:06:30,680 Speaker 2: And I asked people the email if they did, and 137 00:06:30,720 --> 00:06:34,280 Speaker 2: no one said they did. My impression is that most 138 00:06:34,279 --> 00:06:36,159 Speaker 2: hedge funds do not try to get the data that 139 00:06:36,240 --> 00:06:38,359 Speaker 2: is like can we identify Katie? Or like can we 140 00:06:38,400 --> 00:06:41,720 Speaker 2: identify SEC employees? They get like how many people were 141 00:06:41,720 --> 00:06:43,880 Speaker 2: in the building? Right, Like they're getting you know, aggregated 142 00:06:43,920 --> 00:06:47,440 Speaker 2: data and not like trying to individual identify people. And 143 00:06:47,480 --> 00:06:50,320 Speaker 2: so this is probably not a signal that anyone actually 144 00:06:50,360 --> 00:06:53,279 Speaker 2: trades on. Also, it's not like so strong a signal. 145 00:06:53,279 --> 00:06:55,560 Speaker 2: You know, it's like one point nine percent of normally 146 00:06:55,600 --> 00:06:57,799 Speaker 2: turns over three months. That's like, yeah, it's. 147 00:06:57,680 --> 00:07:01,360 Speaker 1: Like, well, I was wondering why that would exist at 148 00:07:01,360 --> 00:07:03,920 Speaker 1: all if people weren't trading on it. You know, it's 149 00:07:03,960 --> 00:07:04,960 Speaker 1: like a chicken in the egg thing. 150 00:07:05,040 --> 00:07:05,880 Speaker 2: Why what the data? 151 00:07:06,040 --> 00:07:08,359 Speaker 1: No, Like, why you would get those abnormal returns? Like 152 00:07:08,400 --> 00:07:13,040 Speaker 1: why is an informal SEC visit bad? Necessarily? What was interesting, 153 00:07:13,080 --> 00:07:15,040 Speaker 1: if I understanding this correctly, is that you had that 154 00:07:15,640 --> 00:07:19,600 Speaker 1: abnormal performance even if no formal investigation was announced. 155 00:07:19,920 --> 00:07:24,000 Speaker 2: You could tell stories, right like they're aggressive with their accounting, 156 00:07:24,240 --> 00:07:26,280 Speaker 2: and the SEC says, knock it off, and they stop 157 00:07:26,320 --> 00:07:28,160 Speaker 2: being aggressive with their accounting, and their next you know, 158 00:07:28,200 --> 00:07:31,160 Speaker 2: earnings report is disappointing because they've stopped fudging them right 159 00:07:31,280 --> 00:07:33,160 Speaker 2: right right, you know stuff like that, or just like 160 00:07:33,240 --> 00:07:35,720 Speaker 2: they're a badly managed company, which is why the SEC 161 00:07:35,840 --> 00:07:38,120 Speaker 2: is visiting them, and that bad management ends up affecting 162 00:07:38,120 --> 00:07:41,360 Speaker 2: their performance. Yeah, but like I don't think people trade 163 00:07:41,400 --> 00:07:44,160 Speaker 2: on this, lagneal, but it is interesting because heedgehones. Do 164 00:07:44,200 --> 00:07:46,840 Speaker 2: you use self one location data and self on location 165 00:07:46,920 --> 00:07:49,600 Speaker 2: data is like controversial, right, people get mad. You know, 166 00:07:49,680 --> 00:07:52,040 Speaker 2: your phone now probably asks you like will you left 167 00:07:52,040 --> 00:07:53,680 Speaker 2: this app track you which you didn't like used to 168 00:07:53,680 --> 00:07:56,240 Speaker 2: do as much like now it's like Apple is trying 169 00:07:56,280 --> 00:07:58,320 Speaker 2: to provide apps from tracking you unless you want them to, 170 00:07:58,960 --> 00:07:59,880 Speaker 2: which you do for them. 171 00:08:00,800 --> 00:08:01,840 Speaker 1: I always say yes. 172 00:08:02,240 --> 00:08:06,480 Speaker 2: And you know, people have like periodic like privacy worries 173 00:08:06,480 --> 00:08:10,120 Speaker 2: about these things, And if you're a hedge fund, you're 174 00:08:10,160 --> 00:08:12,520 Speaker 2: putting a lot of effort into compliance. You're putting a 175 00:08:12,560 --> 00:08:14,120 Speaker 2: lot of effort into making sure that you're buying this 176 00:08:14,240 --> 00:08:17,400 Speaker 2: data from vendors who sort of ultimately have user permission 177 00:08:17,640 --> 00:08:20,480 Speaker 2: to track their location, and that it's anonymized in the 178 00:08:20,520 --> 00:08:22,120 Speaker 2: right way so that no one is going to later 179 00:08:22,160 --> 00:08:24,760 Speaker 2: accuse you of insider trading or of like violating people's 180 00:08:24,760 --> 00:08:27,800 Speaker 2: privacy or anything like that. And you know, you worry 181 00:08:27,840 --> 00:08:31,600 Speaker 2: about the regulatory environment changing and like getting harder to 182 00:08:31,760 --> 00:08:34,640 Speaker 2: use this sort of self in location data. And one 183 00:08:34,640 --> 00:08:37,200 Speaker 2: thing that might cause that to happen is the SEC 184 00:08:37,920 --> 00:08:41,040 Speaker 2: finding their personal phones being tracked to like create a 185 00:08:41,040 --> 00:08:43,400 Speaker 2: tradable signal. The SEC might get annoyed by that. They 186 00:08:43,440 --> 00:08:45,760 Speaker 2: might say, so we should cry down on these vendors. 187 00:08:45,880 --> 00:08:49,520 Speaker 1: Maybe don't build your whole business on the availability of 188 00:08:49,559 --> 00:08:50,200 Speaker 1: this data. 189 00:08:50,320 --> 00:08:52,160 Speaker 2: Yeah, but I think that this is a big source 190 00:08:52,200 --> 00:08:54,760 Speaker 2: of like alternative data for chants to actually trade on. 191 00:08:55,200 --> 00:08:58,120 Speaker 2: And now, obviously don't build your business on like SEC 192 00:08:58,200 --> 00:09:00,280 Speaker 2: self fund data, but nobody does. That's just going to 193 00:09:00,280 --> 00:09:03,160 Speaker 2: make paper. But the academic paper that draws awkward attention 194 00:09:03,240 --> 00:09:04,520 Speaker 2: to location. 195 00:09:04,240 --> 00:09:07,000 Speaker 1: Data, well, I was going to say, even if no 196 00:09:07,040 --> 00:09:08,920 Speaker 1: one has emailed you to say that they're trading on it, 197 00:09:08,960 --> 00:09:12,760 Speaker 1: there's an ETF for everything, Like some interesting indie little 198 00:09:12,800 --> 00:09:13,880 Speaker 1: issuer might be reading this. 199 00:09:14,080 --> 00:09:17,000 Speaker 2: People do try to trade on SEC investigations, people like 200 00:09:17,040 --> 00:09:20,280 Speaker 2: FOIA SEC investigations to sort of see what's being investigated 201 00:09:20,280 --> 00:09:22,240 Speaker 2: and use that as a signal. And this is like 202 00:09:22,840 --> 00:09:27,120 Speaker 2: a sort of much more real time, weaker but interesting signal. 203 00:09:27,160 --> 00:09:28,920 Speaker 1: It's just still Yeah. 204 00:09:28,960 --> 00:09:31,120 Speaker 2: I don't see people paying for this data. 205 00:09:31,160 --> 00:09:33,320 Speaker 1: I want to see it. How much does this state 206 00:09:33,360 --> 00:09:36,000 Speaker 1: of cost? We should do like the money stuff investigation 207 00:09:36,600 --> 00:09:40,360 Speaker 1: and try to trade on it. It sounds fun. It's 208 00:09:40,400 --> 00:09:57,960 Speaker 1: still out there. Moving on, moving on to ETFs that 209 00:09:58,360 --> 00:10:00,520 Speaker 1: don't exist but might one day. At least this one 210 00:10:00,559 --> 00:10:05,320 Speaker 1: has plans private credit ETF. So we were just talking 211 00:10:05,360 --> 00:10:08,240 Speaker 1: about this sort of in a thought experiment type of way. 212 00:10:08,280 --> 00:10:09,200 Speaker 1: How would this work? 213 00:10:09,520 --> 00:10:09,960 Speaker 2: That's real. 214 00:10:10,120 --> 00:10:14,480 Speaker 1: The big brains that Apollo have a plan, Apollo. Yeah, 215 00:10:14,480 --> 00:10:18,360 Speaker 1: I did, the big brains at Apollo. I'm screaming. The 216 00:10:18,400 --> 00:10:20,600 Speaker 1: big brains at Apollo have a plan. 217 00:10:20,880 --> 00:10:24,000 Speaker 2: We're keeping all better. Yeah, they have a plan. And 218 00:10:24,000 --> 00:10:25,760 Speaker 2: the plan is they're a teammate with State Street to 219 00:10:25,880 --> 00:10:29,200 Speaker 2: make an ETF that is like mostly public credit and 220 00:10:29,240 --> 00:10:31,760 Speaker 2: some private credit. I think, yeah, I understanding. 221 00:10:32,200 --> 00:10:36,680 Speaker 1: There's many interesting things about this. I find the partnership 222 00:10:36,840 --> 00:10:40,280 Speaker 1: interesting just from like an ETF industry perspective. It's public 223 00:10:40,320 --> 00:10:42,800 Speaker 1: and private credit. We had talked a bit about how 224 00:10:42,880 --> 00:10:47,400 Speaker 1: there was that fifteen percent limit on a liquid securities 225 00:10:47,679 --> 00:10:50,199 Speaker 1: Give me the mount Levine take, because you actually haven't 226 00:10:50,200 --> 00:10:51,240 Speaker 1: written about this yet. 227 00:10:51,400 --> 00:10:54,520 Speaker 2: I haven't this is a podcast exclusive. Okay, So you 228 00:10:54,520 --> 00:10:58,040 Speaker 2: mentioned the liquidity role, right, Like typical sec mutual funds, 229 00:10:58,080 --> 00:11:00,680 Speaker 2: including ETFs I have to have none more than fifteen 230 00:11:00,679 --> 00:11:04,320 Speaker 2: percent of their assets in I liquid securities, and private 231 00:11:04,360 --> 00:11:08,800 Speaker 2: credit is probably an ill liquid security, probably in the 232 00:11:08,880 --> 00:11:11,000 Speaker 2: sense that like you can't it doesn't. It's not like 233 00:11:11,040 --> 00:11:14,240 Speaker 2: a really you know, liquid trading market. I liquid means 234 00:11:14,240 --> 00:11:15,840 Speaker 2: something like we take you more than seven days to 235 00:11:15,840 --> 00:11:17,960 Speaker 2: sell it without affecting the market, and we just have 236 00:11:18,040 --> 00:11:19,719 Speaker 2: like an initial filing. And so I don't exactly know 237 00:11:19,760 --> 00:11:21,600 Speaker 2: what they're doing, but what they've done here is they've 238 00:11:21,640 --> 00:11:25,320 Speaker 2: said Apollo has a trading desk to trade private credit. 239 00:11:25,559 --> 00:11:27,320 Speaker 2: Just first of all, fascinating, like that's kind of a 240 00:11:27,320 --> 00:11:29,800 Speaker 2: new thing. But their trading desk is going to give 241 00:11:30,360 --> 00:11:36,240 Speaker 2: firm inter day bids to the ETF. So all the 242 00:11:36,320 --> 00:11:39,040 Speaker 2: private credit stuff that the ETF holds, which it presumably 243 00:11:39,080 --> 00:11:42,120 Speaker 2: bought from Apollo, apollow will buy it back and they 244 00:11:42,280 --> 00:11:44,120 Speaker 2: give you a bid to buy it back every day, 245 00:11:44,320 --> 00:11:45,800 Speaker 2: and the ATF can sell it every day. And I 246 00:11:45,840 --> 00:11:48,800 Speaker 2: think that the point of that is to satisfy the 247 00:11:48,840 --> 00:11:52,079 Speaker 2: liquidity requirement, because what they say is, you know, if 248 00:11:52,080 --> 00:11:55,000 Speaker 2: apollow will buy it back instantly. Then that makes it 249 00:11:55,040 --> 00:11:58,200 Speaker 2: liquid and so we can fulfill the ETF rules. I 250 00:11:58,200 --> 00:12:01,240 Speaker 2: think that works. It's a little cheap, it's a little weird. 251 00:12:01,520 --> 00:12:03,880 Speaker 1: Well, I have to say, I'm really interested to see. 252 00:12:04,080 --> 00:12:06,079 Speaker 1: First of all, we have to get at the launch. 253 00:12:06,280 --> 00:12:09,200 Speaker 1: Talking to people, it seems like there is confidence that 254 00:12:09,440 --> 00:12:12,440 Speaker 1: this will launch, but with any first of its kind 255 00:12:12,559 --> 00:12:15,400 Speaker 1: sort of fund, there's always questions over whether the sec 256 00:12:15,480 --> 00:12:17,960 Speaker 1: will green lighted. I am curious to see if it launches, 257 00:12:18,040 --> 00:12:20,640 Speaker 1: how much of it actually is private credit, because you 258 00:12:20,679 --> 00:12:22,920 Speaker 1: take a look at some of the details, at least 259 00:12:22,920 --> 00:12:26,319 Speaker 1: eighty percent of the fund's assets will be in investment 260 00:12:26,360 --> 00:12:30,000 Speaker 1: grade either public or private securities. As much as twenty 261 00:12:30,000 --> 00:12:32,280 Speaker 1: percent may be allocated to high yield bond. So it 262 00:12:32,320 --> 00:12:37,319 Speaker 1: could just turn into it's eighty percent high grade public 263 00:12:37,520 --> 00:12:40,080 Speaker 1: debt and then twenty percent high old bonds. 264 00:12:40,280 --> 00:12:43,280 Speaker 2: Yeah, there are a lot of regular stock mutual funds 265 00:12:43,280 --> 00:12:46,320 Speaker 2: that are tech focused then like have one little holding 266 00:12:46,360 --> 00:12:49,199 Speaker 2: of like a startup private stock. But yeah, ninety nine 267 00:12:49,200 --> 00:12:51,560 Speaker 2: percent public equities. You can imagine this being that there's 268 00:12:51,600 --> 00:12:53,319 Speaker 2: like a little sprinkling of private credit. 269 00:12:53,400 --> 00:12:55,600 Speaker 1: Yeah. Ron Barren's funds, I mean he has at least 270 00:12:55,640 --> 00:12:58,880 Speaker 1: one mutual fund that's all public and then just SpaceX. 271 00:13:00,040 --> 00:13:02,240 Speaker 2: Two points with this one is it's easy to get 272 00:13:02,559 --> 00:13:07,240 Speaker 2: retail exchange traded private credit exposure. It is called the BDC. 273 00:13:07,679 --> 00:13:09,880 Speaker 1: Right, people find that unsatisfying, though I. 274 00:13:11,640 --> 00:13:14,839 Speaker 2: Know that I don't enough. A BDC is a business 275 00:13:14,880 --> 00:13:18,000 Speaker 2: development company. It's a publicly traded pot of private credit. 276 00:13:18,440 --> 00:13:21,360 Speaker 1: It feels like buying micro strategy for bitcoin exposure. 277 00:13:21,360 --> 00:13:25,800 Speaker 2: Though no, it's not feel like it. I don't think 278 00:13:25,800 --> 00:13:28,600 Speaker 2: it does. That's what the BBC is a private credit fund. 279 00:13:29,000 --> 00:13:32,560 Speaker 2: That's what it is. It's a exchange traded, publicly available 280 00:13:33,280 --> 00:13:36,960 Speaker 2: fund that holds private credit loans. The difference between it 281 00:13:37,040 --> 00:13:39,520 Speaker 2: and an ETF is that a BDC is closed. Then 282 00:13:39,720 --> 00:13:42,200 Speaker 2: you can't take your money out. There's an arbitraged mechanism, 283 00:13:42,360 --> 00:13:44,640 Speaker 2: and so it won't necessarily trade it net asset value, 284 00:13:44,679 --> 00:13:48,680 Speaker 2: but mostly pretty close. I think. So I don't fully 285 00:13:48,760 --> 00:13:50,880 Speaker 2: understand the need for an ETF. I think some of 286 00:13:50,920 --> 00:13:53,600 Speaker 2: it is like feed driven. Some of it is like 287 00:13:53,720 --> 00:13:56,520 Speaker 2: just the ETF is the popular product, and so a 288 00:13:56,559 --> 00:13:59,120 Speaker 2: BDC feels weird and different than the ETF, feels like 289 00:13:59,600 --> 00:14:02,520 Speaker 2: an easy thing to hold in your portfolio. I suspect 290 00:14:02,520 --> 00:14:06,040 Speaker 2: part of it is like market maker driven. I suspect 291 00:14:06,080 --> 00:14:08,600 Speaker 2: that like the jan Straits of the world love trade 292 00:14:08,600 --> 00:14:10,400 Speaker 2: and ETFs and so like they're like, hey, we could 293 00:14:10,400 --> 00:14:12,400 Speaker 2: do it private credit ETF and so there's some like 294 00:14:13,000 --> 00:14:15,240 Speaker 2: desire for that, whereas the BDC is more of an 295 00:14:15,320 --> 00:14:18,560 Speaker 2: orphan in like the market structure world. But still it's weird, 296 00:14:18,600 --> 00:14:20,840 Speaker 2: right because like a BDC is this it's a retail 297 00:14:21,160 --> 00:14:22,040 Speaker 2: private credit fund. 298 00:14:22,120 --> 00:14:24,440 Speaker 1: Yeah, I don't know. It does feel unsatisfying. Well, I 299 00:14:24,440 --> 00:14:27,160 Speaker 1: mean maybe because I'm totally ETF pilled, But why. 300 00:14:26,960 --> 00:14:28,760 Speaker 2: Does it feel like? Tell me what is wrong with 301 00:14:28,760 --> 00:14:29,800 Speaker 2: a BDC in your mind? 302 00:14:29,960 --> 00:14:31,160 Speaker 1: It just feels like a proxy. 303 00:14:31,240 --> 00:14:33,360 Speaker 2: It just feels not as lot a proxy. It's a 304 00:14:33,400 --> 00:14:36,080 Speaker 2: pot of private credit assets. There's nothing proxy about it. 305 00:14:36,080 --> 00:14:38,280 Speaker 2: It is simply a pot of private credit assets. 306 00:14:38,320 --> 00:14:42,240 Speaker 1: I know I'm not alone because if that answered the 307 00:14:42,320 --> 00:14:45,200 Speaker 1: desire in the marketplace. And maybe it's a misguide. 308 00:14:45,280 --> 00:14:49,360 Speaker 2: Because the name is business development company. Yeah, sounds like 309 00:14:49,400 --> 00:14:51,120 Speaker 2: a need a company that develops businesses. 310 00:14:51,240 --> 00:14:53,520 Speaker 1: It needs a rebrand, right if you just call it 311 00:14:53,560 --> 00:14:56,040 Speaker 1: a exchange would you be happier if you called it 312 00:14:56,080 --> 00:14:58,880 Speaker 1: an exchange traded closed end private credit fund? 313 00:14:58,920 --> 00:15:01,800 Speaker 2: Are you still just allergic to the words closed in Yeah. 314 00:15:01,960 --> 00:15:05,080 Speaker 1: I mean again, like I'm ETF pilled, so I need 315 00:15:05,120 --> 00:15:06,520 Speaker 1: the I need the openness. 316 00:15:06,880 --> 00:15:08,760 Speaker 2: Okay, here's the other thing I want to say about 317 00:15:08,760 --> 00:15:10,520 Speaker 2: this though. You know, we talked about Bill Ackman's closed 318 00:15:10,560 --> 00:15:12,680 Speaker 2: then fund he should do an ETF, and I was like, well, 319 00:15:12,720 --> 00:15:16,080 Speaker 2: you know, he wants long term capital so that he 320 00:15:16,080 --> 00:15:18,880 Speaker 2: can make long term investments. I've written a lot about 321 00:15:18,880 --> 00:15:22,000 Speaker 2: private credit in the last few months, and one thing 322 00:15:22,000 --> 00:15:24,520 Speaker 2: I always say about private credit is that it is 323 00:15:25,440 --> 00:15:28,560 Speaker 2: a better funding structure for making loans than banking, is right, 324 00:15:28,680 --> 00:15:31,760 Speaker 2: Banks have short term funding, Banks take deposits. It's possible 325 00:15:31,760 --> 00:15:34,360 Speaker 2: to have a run on bank deposits, and so if 326 00:15:34,360 --> 00:15:36,920 Speaker 2: banks are making like liquid long term loans and funding 327 00:15:36,920 --> 00:15:39,320 Speaker 2: them with short term deposits, that's a risky business to 328 00:15:39,320 --> 00:15:42,160 Speaker 2: be in private credit. Meanwhile, Like raises money from insurance 329 00:15:42,160 --> 00:15:44,800 Speaker 2: companies and it's a fairly safe funding source for their 330 00:15:44,840 --> 00:15:48,400 Speaker 2: long term loans. If Apollo is offering to buy back 331 00:15:48,440 --> 00:15:51,480 Speaker 2: it's private credit assets every day, like that turns it 332 00:15:51,600 --> 00:15:54,320 Speaker 2: into runnable funding. If they have an ETF that they 333 00:15:54,320 --> 00:15:57,000 Speaker 2: promise to buy back the assets every day, like if 334 00:15:57,000 --> 00:15:59,640 Speaker 2: something goes wrong, investors can take all their money out 335 00:15:59,760 --> 00:16:01,760 Speaker 2: and follow off the buio back and then have to 336 00:16:01,840 --> 00:16:05,520 Speaker 2: raise new funding. So it is a much riskier funding 337 00:16:05,520 --> 00:16:08,680 Speaker 2: source for private credit than like the traditional you know, 338 00:16:08,760 --> 00:16:13,280 Speaker 2: insurance company, wealthy individual, whatever, like long term locked up money. 339 00:16:13,600 --> 00:16:16,080 Speaker 2: So in that sense, it's very different from a BDC. 340 00:16:16,200 --> 00:16:20,080 Speaker 2: R BDC's are permanent capital. ETFs are kind of runnable capital. 341 00:16:20,400 --> 00:16:22,280 Speaker 1: I just got a buzz. Maybe we don't want to 342 00:16:22,280 --> 00:16:24,080 Speaker 1: include this, but I just got a buzz because there 343 00:16:24,160 --> 00:16:27,480 Speaker 1: was just another filing for private credit ETF. Oh there 344 00:16:27,480 --> 00:16:31,720 Speaker 1: you get at like two thirty pm on Thursday for 345 00:16:32,160 --> 00:16:36,400 Speaker 1: the bond blocks Private Credit Clo ETF. Reading from the prospectus, 346 00:16:36,800 --> 00:16:40,320 Speaker 1: the fund is a newly organized, actively managed exchange traded 347 00:16:40,360 --> 00:16:44,320 Speaker 1: fund that will invest, under normal circumstances, at least eighty 348 00:16:44,360 --> 00:16:46,960 Speaker 1: percent of its net assets in private credit. Colos may 349 00:16:46,960 --> 00:16:48,880 Speaker 1: invest up to twenty percent of its net assets in 350 00:16:48,920 --> 00:16:53,240 Speaker 1: broadly syndicated bank loans, broadly syndicated bank loans clos, high 351 00:16:53,280 --> 00:16:57,120 Speaker 1: yield bonds, investment grade bonds, and cash. So the demand 352 00:16:57,360 --> 00:17:01,000 Speaker 1: at least from issuers is out there. Even know BDCs, 353 00:17:01,080 --> 00:17:04,800 Speaker 1: to your very real point, they exist. They exist also 354 00:17:05,160 --> 00:17:07,439 Speaker 1: it feels like there was a response. There's clearly a 355 00:17:07,480 --> 00:17:09,439 Speaker 1: response from this issue or bond blocks. It feels like 356 00:17:09,440 --> 00:17:12,080 Speaker 1: there was a response from Blackrock to this because people 357 00:17:12,119 --> 00:17:13,840 Speaker 1: have been waiting for this. I mean, we've talked about 358 00:17:13,840 --> 00:17:15,920 Speaker 1: it on the podcast before, like who's going to figure 359 00:17:15,920 --> 00:17:19,040 Speaker 1: out how to put private assets in a publicly traded ETF. 360 00:17:19,080 --> 00:17:23,440 Speaker 1: We know that black we know that Blackrock has ambitions there, 361 00:17:23,480 --> 00:17:26,879 Speaker 1: and on Thursday they didn't announce that they were doing that, 362 00:17:26,960 --> 00:17:31,280 Speaker 1: but they did announce this partnership with partners Group. They're 363 00:17:31,320 --> 00:17:34,719 Speaker 1: teaming up to offer retail investors a variety of private 364 00:17:34,760 --> 00:17:37,320 Speaker 1: markets through a single portfolio. They want to create like 365 00:17:37,359 --> 00:17:42,600 Speaker 1: this one stop portfolio to private equity, private credit, real assets. 366 00:17:42,840 --> 00:17:45,800 Speaker 1: It's not an ETF though, no, because it's not true retail. 367 00:17:45,840 --> 00:17:48,960 Speaker 2: It's like a model portfolio for like qualified investors. Yeah, 368 00:17:49,040 --> 00:17:50,760 Speaker 2: you need to have like, no, for two million dollars 369 00:17:50,760 --> 00:17:51,159 Speaker 2: in assets. 370 00:17:51,400 --> 00:17:52,120 Speaker 1: Yeah. 371 00:17:52,160 --> 00:17:54,800 Speaker 2: And it's just like if you are a customer of 372 00:17:54,800 --> 00:17:57,880 Speaker 2: a wealth manager, like they'll happily put you into like 373 00:17:58,240 --> 00:18:02,120 Speaker 2: a private credit fund or whatever, and now black Rock 374 00:18:02,200 --> 00:18:05,240 Speaker 2: will help them put you into a portfolio of private 375 00:18:05,240 --> 00:18:07,520 Speaker 2: credit funds in one place. But it's not an ETF. 376 00:18:07,560 --> 00:18:09,679 Speaker 2: It's not like it's not like a true retail thing 377 00:18:09,720 --> 00:18:10,160 Speaker 2: you can buy. 378 00:18:11,040 --> 00:18:13,600 Speaker 1: Yeah, you can't click a button and then buy it. 379 00:18:13,600 --> 00:18:16,240 Speaker 1: It is interesting though, just it feels like, especially in 380 00:18:16,280 --> 00:18:19,600 Speaker 1: the ETF market, there's an arms race for any white space, 381 00:18:19,680 --> 00:18:22,880 Speaker 1: and here's the white space. I do think it's interesting 382 00:18:22,880 --> 00:18:27,760 Speaker 1: that Apollo partnered with State Street. It does suggest that 383 00:18:27,880 --> 00:18:31,399 Speaker 1: like Apollo isn't all in on the ETF industry, that 384 00:18:31,440 --> 00:18:33,400 Speaker 1: they didn't want to you know, high. 385 00:18:34,040 --> 00:18:37,600 Speaker 2: They don't care, I know, I know, but they want 386 00:18:37,600 --> 00:18:41,679 Speaker 2: like funding for their giant portfolio of private credit. Right 387 00:18:42,080 --> 00:18:43,639 Speaker 2: if State Street comes to them is like, we'll give 388 00:18:43,680 --> 00:18:46,000 Speaker 2: you retail funding, they'll take it, right. They're not like, ooh, 389 00:18:46,080 --> 00:18:49,680 Speaker 2: the one basis point fees on ets we want that juicy. 390 00:18:49,720 --> 00:18:51,600 Speaker 1: It is interesting that they went with State Street though, 391 00:18:51,600 --> 00:18:53,760 Speaker 1: I mean, State Street obviously is home to the oldest 392 00:18:53,800 --> 00:18:56,560 Speaker 1: and biggest ETF, but they've kind of been slipping in 393 00:18:56,600 --> 00:19:02,040 Speaker 1: the ranks. Who does States But Goldman Sachs, for example, 394 00:19:02,160 --> 00:19:06,200 Speaker 1: has this accelerator platform which is like a white label, 395 00:19:06,240 --> 00:19:08,520 Speaker 1: but not quite a white label that some big names 396 00:19:08,520 --> 00:19:11,119 Speaker 1: have come through. I'm kind of surprised that Apollo went 397 00:19:11,160 --> 00:19:13,680 Speaker 1: with State Street versus going through a white label such 398 00:19:13,720 --> 00:19:16,920 Speaker 1: as Goldman's. But that's just a little bit of industry chatter. 399 00:19:17,119 --> 00:19:35,119 Speaker 1: Does not it does not matter to end investors at all. 400 00:19:35,200 --> 00:19:39,159 Speaker 1: Let me just check my phone. I'm sorry, everything's so 401 00:19:39,240 --> 00:19:42,800 Speaker 1: stupid all the time. I'm excited to talk about Spotify. 402 00:19:43,119 --> 00:19:43,480 Speaker 2: That's a. 403 00:19:46,080 --> 00:19:49,320 Speaker 1: I've never thought about the economics of Spotify before we 404 00:19:49,359 --> 00:19:52,320 Speaker 1: get to Michael Smith, which we might. We might get 405 00:19:52,359 --> 00:19:54,800 Speaker 1: to him. He sounds like a fake person. Maybe he's 406 00:19:54,840 --> 00:19:55,359 Speaker 1: a robot. 407 00:19:55,600 --> 00:20:01,560 Speaker 2: Oh could be yeah, Michael calm Knuckles Smith anyway, Spotify, Yeah, 408 00:20:01,800 --> 00:20:02,960 Speaker 2: I mean the way it works is like you pay 409 00:20:03,000 --> 00:20:03,480 Speaker 2: like twelve. 410 00:20:03,320 --> 00:20:05,000 Speaker 1: Bucks a month every month I do. 411 00:20:05,600 --> 00:20:06,879 Speaker 2: It doesn't matter how much you listen to it. You 412 00:20:06,920 --> 00:20:07,800 Speaker 2: pay twelve bucks a month. 413 00:20:07,960 --> 00:20:11,400 Speaker 1: Yeah. I have never thought about that, and it would 414 00:20:11,440 --> 00:20:13,680 Speaker 1: really suck if they did a sliding scale based on 415 00:20:13,920 --> 00:20:16,080 Speaker 1: use because I listened to so much. 416 00:20:16,280 --> 00:20:17,800 Speaker 2: Yeah, I mean, the whole point of it is like 417 00:20:17,840 --> 00:20:19,600 Speaker 2: you don't, right, Like, the whole point of it is like, 418 00:20:19,680 --> 00:20:21,320 Speaker 2: you know, it used to be paid for albums and 419 00:20:21,320 --> 00:20:24,200 Speaker 2: now you pay for unlimited streaming and you pay twelve 420 00:20:24,200 --> 00:20:28,160 Speaker 2: bucks a month, but Spotify still pays the artists based 421 00:20:28,200 --> 00:20:31,480 Speaker 2: on how much they stream, sort of like takes all 422 00:20:31,480 --> 00:20:34,720 Speaker 2: the money that it gets, yeah, and it signs seventy 423 00:20:34,720 --> 00:20:37,600 Speaker 2: five percent to the rights holders, artists and record labels, 424 00:20:38,240 --> 00:20:40,720 Speaker 2: and then it divides up that pot of money based 425 00:20:40,760 --> 00:20:43,600 Speaker 2: on relatively how much you stream. It's not like Spotify 426 00:20:43,640 --> 00:20:45,240 Speaker 2: is like we pay one cent to stream, and like 427 00:20:45,280 --> 00:20:47,040 Speaker 2: if they're more streams and they expected, they pay more 428 00:20:47,080 --> 00:20:49,320 Speaker 2: money thany expected. They pay a fixed amount of money, 429 00:20:49,640 --> 00:20:51,840 Speaker 2: but they divide it up based on how much your 430 00:20:51,920 --> 00:20:54,600 Speaker 2: songs get streamed. And so what that means is that 431 00:20:54,640 --> 00:20:57,280 Speaker 2: if you are a customer and you pay twelve bucks 432 00:20:57,280 --> 00:20:59,639 Speaker 2: a month and you stream music twenty four to seven 433 00:20:59,720 --> 00:21:03,320 Speaker 2: from month, you will allocate more money than the twelve 434 00:21:03,359 --> 00:21:06,359 Speaker 2: bucks you put in. Spotify will send more than twelve 435 00:21:06,440 --> 00:21:09,560 Speaker 2: dollars to the people you are listening to. Meanwhile, if 436 00:21:09,600 --> 00:21:11,520 Speaker 2: I sign up for Spotify and I listened to one 437 00:21:11,520 --> 00:21:14,280 Speaker 2: song a month, Spotify will send like one penny to 438 00:21:14,359 --> 00:21:16,280 Speaker 2: the artist of the song that I listened to. And 439 00:21:16,320 --> 00:21:20,160 Speaker 2: once you know that, like the answer is obvious, which 440 00:21:20,200 --> 00:21:23,520 Speaker 2: is like you sign up for Spotify, you know you 441 00:21:23,520 --> 00:21:26,720 Speaker 2: have a computer on mute playing twenty four to seven 442 00:21:26,920 --> 00:21:28,719 Speaker 2: the songs that you want to send money to, and 443 00:21:28,720 --> 00:21:29,720 Speaker 2: then like you get the money. 444 00:21:29,880 --> 00:21:33,760 Speaker 1: Yeah, And that's what I do money, That's what I 445 00:21:33,800 --> 00:21:36,560 Speaker 1: do with money stuff. Absolutely, I have my laptop at 446 00:21:36,560 --> 00:21:37,760 Speaker 1: home just listening on. 447 00:21:37,720 --> 00:21:42,240 Speaker 2: Loose and I make three tenths of a penny. 448 00:21:42,480 --> 00:21:48,239 Speaker 1: I make nothing. I'm doing this for fun. I'm just 449 00:21:48,240 --> 00:21:49,560 Speaker 1: doing it for the good of the company. 450 00:21:52,600 --> 00:21:55,720 Speaker 2: He gets all of it. There's not that much money 451 00:21:55,760 --> 00:21:58,479 Speaker 2: in this unless you have a thousand computers saying it. 452 00:21:58,640 --> 00:22:01,920 Speaker 2: And so there's this guy, Michael Smith who had a 453 00:22:02,000 --> 00:22:05,359 Speaker 2: thousand computers which I mean actually like virtual computers on 454 00:22:05,400 --> 00:22:07,600 Speaker 2: like a cloud service. Right, He had like a thousand 455 00:22:07,640 --> 00:22:11,800 Speaker 2: cloud computers streaming songs twenty four to seven to send 456 00:22:11,880 --> 00:22:16,160 Speaker 2: him the money. And because Spotify pays attention to this stuff, 457 00:22:16,160 --> 00:22:18,440 Speaker 2: he couldn't just like stream one song over and over again. 458 00:22:18,960 --> 00:22:21,320 Speaker 2: He had to set up a bunch of fake bands 459 00:22:21,760 --> 00:22:24,200 Speaker 2: and have the fake bands record a bunch of fake songs. 460 00:22:24,320 --> 00:22:25,960 Speaker 2: And even the fake songs, he can't just be like 461 00:22:26,240 --> 00:22:28,400 Speaker 2: him tapping on a table, you know, has to like 462 00:22:28,600 --> 00:22:31,679 Speaker 2: sound song like. And so he had like hired an 463 00:22:31,680 --> 00:22:34,359 Speaker 2: AI company to write like AI music and it is 464 00:22:34,400 --> 00:22:37,240 Speaker 2: so disappointing. The prosecutors list a couple of dozen band 465 00:22:37,280 --> 00:22:38,679 Speaker 2: names and a couple of dozen. 466 00:22:39,520 --> 00:22:41,280 Speaker 1: A lot of them titles, I mean, many of them 467 00:22:41,280 --> 00:22:42,439 Speaker 1: are kind of cool sounding. 468 00:22:43,080 --> 00:22:47,119 Speaker 2: They're so good. This is like an alphabetical list, like 469 00:22:47,119 --> 00:22:49,959 Speaker 2: there's like a randomly selected bit of the alphabet. So 470 00:22:50,680 --> 00:22:54,800 Speaker 2: the song titles are mostly words starting with zy that 471 00:22:54,840 --> 00:22:59,600 Speaker 2: don't make any sense. But they include zygotes, zygotic, zygotic, 472 00:22:59,800 --> 00:23:05,280 Speaker 2: wash stands, zime, doing zimes, zimite, zimo, fight, zimo, jens. 473 00:23:05,480 --> 00:23:12,040 Speaker 2: And the artist names include Calliope, Bloom, Callous, Humane, calm Knuckles, good, 474 00:23:12,200 --> 00:23:17,280 Speaker 2: calm Market, calvinistic dust. Yeah. Anyway, it's like a lot 475 00:23:17,320 --> 00:23:22,399 Speaker 2: of great, randomly chosen names for fakes. But my point 476 00:23:22,440 --> 00:23:26,760 Speaker 2: is that all of this stuff presumably lived on like Spotify. 477 00:23:26,760 --> 00:23:29,000 Speaker 2: I say, we say Spotify, it's like all the streaming 478 00:23:29,000 --> 00:23:31,280 Speaker 2: services he apparently took for money, so Spotify, Apple, and 479 00:23:31,320 --> 00:23:34,439 Speaker 2: he's like YouTube. All these songs apparently lived on these services, 480 00:23:34,800 --> 00:23:36,880 Speaker 2: and they all seem to have been taken down because 481 00:23:36,920 --> 00:23:39,000 Speaker 2: I've been searching for them, I can't find any because 482 00:23:39,000 --> 00:23:39,920 Speaker 2: like if we could play. 483 00:23:39,720 --> 00:23:43,400 Speaker 1: Them, I know, like asking from the perspective of the listener, 484 00:23:43,600 --> 00:23:45,080 Speaker 1: like we should be playing this music. 485 00:23:45,160 --> 00:23:48,639 Speaker 2: These songs good, were they best? Were they mediocre? They 486 00:23:48,680 --> 00:23:51,320 Speaker 2: were generated? By AI, so they weren't like just nothing, 487 00:23:51,920 --> 00:23:54,920 Speaker 2: they were an AI trained in music. There he composed 488 00:23:54,960 --> 00:23:55,359 Speaker 2: the song. 489 00:23:55,720 --> 00:23:59,560 Speaker 1: So how much money did he make? That's insane? So 490 00:23:59,640 --> 00:24:02,400 Speaker 1: it was about this, like when people just get too greedy, 491 00:24:02,480 --> 00:24:06,760 Speaker 1: Like what if he had pretended to be a real 492 00:24:06,880 --> 00:24:10,119 Speaker 1: artist and like made his own music, and like maybe 493 00:24:10,840 --> 00:24:13,080 Speaker 1: I don't know, he had a hundred songs under his name, 494 00:24:13,119 --> 00:24:14,840 Speaker 1: but he had all these robots listening to it, and 495 00:24:14,880 --> 00:24:17,119 Speaker 1: maybe he only made a million dollars. Would that have 496 00:24:17,200 --> 00:24:18,160 Speaker 1: been less detectable? 497 00:24:18,240 --> 00:24:19,879 Speaker 2: I think it would have been more detectable, because like 498 00:24:20,040 --> 00:24:23,280 Speaker 2: the reason he had all these songs was that no 499 00:24:23,320 --> 00:24:26,720 Speaker 2: one was streaming these songs millions of times. Each of 500 00:24:26,760 --> 00:24:29,520 Speaker 2: these songs was strained a small number of times, and 501 00:24:29,600 --> 00:24:31,920 Speaker 2: like in the aggregate, all of his songs were streamed 502 00:24:31,920 --> 00:24:33,600 Speaker 2: a lot, and he made a lot of money. But 503 00:24:34,200 --> 00:24:36,520 Speaker 2: if he had just one song and it was streamed 504 00:24:36,560 --> 00:24:39,280 Speaker 2: ten million times, someone might say, but what if. 505 00:24:39,200 --> 00:24:40,600 Speaker 1: He had a hundred songs? You know? 506 00:24:40,840 --> 00:24:44,840 Speaker 2: Like what he decided that the best way to avoid 507 00:24:44,920 --> 00:24:48,520 Speaker 2: detection and to avoid getting caught by Spotify's cheating checking 508 00:24:48,560 --> 00:24:50,679 Speaker 2: algorithms was to have a lot of songs so that 509 00:24:50,720 --> 00:24:53,240 Speaker 2: it's spreading out among a bunch of different songs. 510 00:24:53,320 --> 00:24:57,560 Speaker 1: I respect his hustle. Should I say that I respect okay, good, 511 00:24:57,640 --> 00:25:00,399 Speaker 1: I respect its hustle too. All say I wish that 512 00:25:00,440 --> 00:25:03,439 Speaker 1: he had actually made music. 513 00:25:03,800 --> 00:25:05,560 Speaker 2: He had a robot makings and I probably I know he 514 00:25:05,600 --> 00:25:07,560 Speaker 2: made music, and then he decided that it was more. 515 00:25:07,560 --> 00:25:10,359 Speaker 1: Like I would have appreciated the vulnerability if he put 516 00:25:10,440 --> 00:25:13,960 Speaker 1: his music online and then he. 517 00:25:13,960 --> 00:25:17,560 Speaker 2: Needed thouands of songs. Yeah, he was busy running a scam. 518 00:25:17,600 --> 00:25:19,800 Speaker 1: That's true. It does sound. It does sound draining. 519 00:25:19,920 --> 00:25:21,640 Speaker 2: People email me a lot to be like, your math 520 00:25:21,760 --> 00:25:24,520 Speaker 2: is wrong. And in fact, when like Spotify gives this 521 00:25:24,640 --> 00:25:26,640 Speaker 2: money to the rights holder, like most of that goes 522 00:25:26,680 --> 00:25:28,800 Speaker 2: to the record labels, it doesn't really go to the artists, 523 00:25:28,880 --> 00:25:32,119 Speaker 2: and the artists are getting stewed. Fine point taken. I'm 524 00:25:32,160 --> 00:25:35,159 Speaker 2: writing about this guy scam like in fact, the streaming 525 00:25:35,200 --> 00:25:38,200 Speaker 2: services are worse for musicians than I implied when I 526 00:25:38,200 --> 00:25:41,240 Speaker 2: read about the arbitrage. But secondly, like twenty people emailed 527 00:25:41,280 --> 00:25:45,080 Speaker 2: me about Wolfpeck, which is a funk band that you 528 00:25:45,160 --> 00:25:48,919 Speaker 2: might know as one of my like notes for what 529 00:25:48,960 --> 00:25:50,120 Speaker 2: I wanted the money instead. 530 00:25:49,880 --> 00:25:53,720 Speaker 1: Of theme song to sound like oh right now, I. 531 00:25:53,600 --> 00:25:56,719 Speaker 2: Was like describing the vibe I wanted and a brilliant 532 00:25:56,800 --> 00:25:58,440 Speaker 2: art director Jackie was like, oh, you should listen to 533 00:25:58,480 --> 00:25:59,280 Speaker 2: this Voltpec song. 534 00:25:59,320 --> 00:26:02,960 Speaker 1: I was like, yes, now we're going to see stream skyrocket. 535 00:26:02,880 --> 00:26:06,880 Speaker 2: For full Yeah. Yeah. In twenty fourteen, they put out 536 00:26:06,920 --> 00:26:13,080 Speaker 2: an album that was like twelve thirty second tracks of silence. 537 00:26:12,800 --> 00:26:14,040 Speaker 1: And they were like that, so what you wanted our 538 00:26:14,119 --> 00:26:15,720 Speaker 1: music to sound like? No? 539 00:26:15,720 --> 00:26:18,840 Speaker 2: No, Otherwise they put out music that is louder. They 540 00:26:18,920 --> 00:26:23,679 Speaker 2: asked their fans to stream it on Loop as they slept. Yeah, 541 00:26:23,760 --> 00:26:26,080 Speaker 2: because the fans weren't paying for that, right, they didn't 542 00:26:26,160 --> 00:26:28,720 Speaker 2: didn't affect them right as quiet, and they weren't paying 543 00:26:28,720 --> 00:26:31,280 Speaker 2: because they paid a flat feet of Spotify. Spotify counts 544 00:26:31,320 --> 00:26:33,080 Speaker 2: to stream if it's like more than thirty seconds, So 545 00:26:33,200 --> 00:26:35,560 Speaker 2: you stream like twelve thirty one second songs on loop 546 00:26:35,600 --> 00:26:36,840 Speaker 2: all night, then that's like, you. 547 00:26:36,840 --> 00:26:38,240 Speaker 1: Know, that's great. 548 00:26:40,200 --> 00:26:43,879 Speaker 2: Eighty stream right now, eight hundred streams. 549 00:26:44,000 --> 00:26:46,199 Speaker 1: I'm certainly not going to fact check you. 550 00:26:46,440 --> 00:26:48,880 Speaker 2: Yeah, it's like thousands streams for them, right, So that's 551 00:26:48,920 --> 00:26:51,520 Speaker 2: like thousands streams that like a fraction of a penny 552 00:26:51,560 --> 00:26:54,160 Speaker 2: per stream is like ten bucks or something. Not bad 553 00:26:54,200 --> 00:26:56,439 Speaker 2: if all of their fans do that. They were like, 554 00:26:56,440 --> 00:26:58,800 Speaker 2: we're going to put on a free concert funded by. 555 00:26:58,720 --> 00:26:59,920 Speaker 1: The that's hysterical. 556 00:27:00,280 --> 00:27:03,000 Speaker 2: They ended up raising about twenty thousand dollars and the 557 00:27:03,040 --> 00:27:05,800 Speaker 2: album was removed for violting Spotify's terms of service. Oh, 558 00:27:05,800 --> 00:27:09,280 Speaker 2: come on, nobody thought it was a crime. Somebody emailed 559 00:27:09,280 --> 00:27:11,000 Speaker 2: me to say that they handed out twenty dollars bills 560 00:27:11,000 --> 00:27:13,640 Speaker 2: at this free concert to thank their fans for doing it. 561 00:27:13,760 --> 00:27:14,520 Speaker 2: I don't know that's true. 562 00:27:14,560 --> 00:27:15,320 Speaker 1: That's so fun. 563 00:27:15,119 --> 00:27:17,399 Speaker 2: Anyways, really cool. The other thing that people pointed me 564 00:27:17,440 --> 00:27:22,359 Speaker 2: too is that in twenty sixteen, Nelly owed two point 565 00:27:22,359 --> 00:27:24,879 Speaker 2: four million dollars in back taxes, and so there is 566 00:27:24,920 --> 00:27:28,479 Speaker 2: a hashtag campaign Saved Nelly to get people to stream 567 00:27:28,560 --> 00:27:32,600 Speaker 2: hot in Here some number of times that was estimated 568 00:27:32,600 --> 00:27:34,680 Speaker 2: between like two hundred and eighty and four hundred million 569 00:27:34,960 --> 00:27:36,080 Speaker 2: in order to raise the money. 570 00:27:36,119 --> 00:27:38,800 Speaker 1: It's insane. I don't know if it were it worked 571 00:27:38,800 --> 00:27:40,520 Speaker 1: to put I'm sure Nelly's fine. 572 00:27:40,640 --> 00:27:41,200 Speaker 2: He's fine. 573 00:27:41,280 --> 00:27:44,080 Speaker 1: Yeah, he's fine. I will say I listened to podcasts 574 00:27:44,119 --> 00:27:46,919 Speaker 1: when I sleep, which got me thinking about the sliding scale, 575 00:27:46,920 --> 00:27:49,480 Speaker 1: Like most of my Spotify usage comes at night when 576 00:27:49,520 --> 00:27:50,080 Speaker 1: I'm asleep. 577 00:27:50,160 --> 00:27:51,800 Speaker 2: Oh, I mean, people were like, is this a crime 578 00:27:51,840 --> 00:27:52,520 Speaker 2: should be a crime. 579 00:27:52,560 --> 00:27:53,600 Speaker 1: I know it's in the code. 580 00:27:53,960 --> 00:27:56,159 Speaker 2: It's in the code, right. I mean, like there's a 581 00:27:56,200 --> 00:27:58,400 Speaker 2: lot of stuff like this, where like there's a company 582 00:27:58,520 --> 00:28:00,720 Speaker 2: that has some terms of service and people violate the 583 00:28:00,800 --> 00:28:03,399 Speaker 2: terms of service and then people are, yeah, shouldn't they 584 00:28:03,440 --> 00:28:04,160 Speaker 2: just sue them? 585 00:28:04,400 --> 00:28:04,600 Speaker 1: Right? 586 00:28:05,280 --> 00:28:07,119 Speaker 2: And I think here it's a little different because this 587 00:28:07,160 --> 00:28:10,600 Speaker 2: doesn't actually cost Spotify any money. Spotify doesn't care. Yeah, 588 00:28:10,760 --> 00:28:14,639 Speaker 2: Spotify is like, we get all this money and we 589 00:28:14,680 --> 00:28:16,960 Speaker 2: set aside three quarters of it the people who own 590 00:28:16,960 --> 00:28:19,439 Speaker 2: the songs, and we just don't care what happens. Yeah, 591 00:28:19,480 --> 00:28:21,639 Speaker 2: whoever wants that, you know, we don't care. We have 592 00:28:21,680 --> 00:28:24,240 Speaker 2: like some reasonably fair formula as a matter of like 593 00:28:24,280 --> 00:28:27,520 Speaker 2: pr and as a matter of like you know, getting 594 00:28:27,520 --> 00:28:29,359 Speaker 2: the record labels to sign up to put their music 595 00:28:29,400 --> 00:28:32,560 Speaker 2: on Spotify, but like, they don't care. It's not their money. 596 00:28:32,720 --> 00:28:35,360 Speaker 1: So it doesn't get worse for artists. Is Spotify g's 597 00:28:35,359 --> 00:28:37,360 Speaker 1: more popular, that's. 598 00:28:38,240 --> 00:28:41,040 Speaker 2: What compared to I don't know. I guess it gets 599 00:28:41,280 --> 00:28:44,760 Speaker 2: good for artists because Spotify has more money and it 600 00:28:45,000 --> 00:28:48,360 Speaker 2: sets aside a fixed fraction of that money for artists. 601 00:28:48,880 --> 00:28:51,640 Speaker 2: It's bad for artists if the alternative Spotify was like 602 00:28:51,720 --> 00:28:55,600 Speaker 2: buying CDs. Oh yeah, But what's really bad for artists 603 00:28:55,720 --> 00:28:57,000 Speaker 2: is like if there's a lot of guys like this, 604 00:28:57,760 --> 00:29:00,160 Speaker 2: that is people's reaction to the story is like, yeah, okay, 605 00:29:00,160 --> 00:29:03,040 Speaker 2: here's this one guy with a bunch of bots and 606 00:29:03,080 --> 00:29:05,480 Speaker 2: a very sort of sophisticated way to do this arbitrage 607 00:29:05,560 --> 00:29:08,880 Speaker 2: who made ten million dollars? But like, are there hundreds 608 00:29:08,880 --> 00:29:11,720 Speaker 2: of those guys? Is a lot of the money on 609 00:29:11,760 --> 00:29:14,160 Speaker 2: Spotify going to people who are somewhere faking their streams, 610 00:29:14,440 --> 00:29:16,440 Speaker 2: and obviously Spotify like puts a lot of effort into 611 00:29:16,480 --> 00:29:19,360 Speaker 2: stopping that, and they did keep stopping this guy, and 612 00:29:19,400 --> 00:29:21,840 Speaker 2: you had to go to some lengths to get back on. 613 00:29:21,920 --> 00:29:24,840 Speaker 2: But you know, people are suspecious and they did extract 614 00:29:24,920 --> 00:29:26,280 Speaker 2: ten million dollars from Spotify. 615 00:29:26,360 --> 00:29:30,560 Speaker 1: That's so funny. That's so much money. Sounds like a good, 616 00:29:30,680 --> 00:29:33,040 Speaker 1: good gig. Oh. One thing I wanted to add he 617 00:29:33,120 --> 00:29:35,320 Speaker 1: paired up with that AI company in like twenty eighteen 618 00:29:35,400 --> 00:29:37,520 Speaker 1: or twenty nineteen, Right, they were early. 619 00:29:37,600 --> 00:29:41,080 Speaker 2: That's true. Yeah, yeah, I don't know what AI means, right, 620 00:29:41,120 --> 00:29:43,959 Speaker 2: Like you can get pretty sophisticated with chenerative AI models 621 00:29:43,960 --> 00:29:45,640 Speaker 2: now that will like make a song in the style 622 00:29:45,640 --> 00:29:48,360 Speaker 2: of blah blah blah, but like like if you. 623 00:29:48,480 --> 00:29:51,400 Speaker 1: Randomly, I do wonder like when it's together, you might 624 00:29:51,480 --> 00:29:52,040 Speaker 1: have enough. 625 00:29:51,880 --> 00:29:53,600 Speaker 2: To full Spotify in twenty eighteen. 626 00:29:53,680 --> 00:29:56,240 Speaker 1: Yeah, that's true. I do wonder though, when AI artists 627 00:29:56,360 --> 00:30:00,560 Speaker 1: are going to become big on Spotify, because I'm TikTok 628 00:30:00,600 --> 00:30:04,880 Speaker 1: a lot and there's all these silly AI songs of 629 00:30:05,040 --> 00:30:09,760 Speaker 1: Drake singing like Alana del Rey cover and some of 630 00:30:09,800 --> 00:30:12,360 Speaker 1: them we're good, some of them sound like real music, 631 00:30:12,480 --> 00:30:15,320 Speaker 1: So that'll be an interesting ethical question for Spotify. And 632 00:30:15,400 --> 00:30:17,320 Speaker 1: like a couple of years, probably I wish I had 633 00:30:17,320 --> 00:30:19,280 Speaker 1: a snapperod turn. That's fine, that's fine. 634 00:30:21,800 --> 00:30:23,280 Speaker 2: And that was the Money Stuff Podcast. 635 00:30:23,400 --> 00:30:25,600 Speaker 1: I'm Matt Levy and I'm Katie Greifeld. 636 00:30:25,800 --> 00:30:27,840 Speaker 2: You can find my work by subscribing to the Money 637 00:30:27,840 --> 00:30:30,160 Speaker 2: Stuff newsletter on Bloomberg dot com. 638 00:30:29,880 --> 00:30:32,320 Speaker 1: And you can find me on Bloomberg TV every day 639 00:30:32,400 --> 00:30:35,480 Speaker 1: on Open Interest between nine to eleven am Eastern. 640 00:30:35,880 --> 00:30:37,560 Speaker 2: We'd love to hear from you. You can send an 641 00:30:37,600 --> 00:30:40,560 Speaker 2: email to you money pot at Bloomberg dot net, ask 642 00:30:40,640 --> 00:30:42,280 Speaker 2: us a question and we might answer it on air. 643 00:30:42,960 --> 00:30:45,120 Speaker 1: You can also subscribe to our show wherever you're listening 644 00:30:45,240 --> 00:30:47,240 Speaker 1: right now. And leave us a review. It helps more 645 00:30:47,280 --> 00:30:48,160 Speaker 1: people find the show. 646 00:30:48,640 --> 00:30:51,440 Speaker 2: The Money Stuff Podcast is produced by Anna Maserakus and 647 00:30:51,480 --> 00:30:52,600 Speaker 2: Moses on Them. 648 00:30:52,720 --> 00:30:54,520 Speaker 1: Our theme music was composed by Blake. 649 00:30:54,400 --> 00:30:57,600 Speaker 2: Maples, Brandon, Francis nunim As our executive producer. 650 00:30:57,520 --> 00:30:59,640 Speaker 1: And Stage Bauman is Bloomberg's Head of podcasts. 651 00:31:00,120 --> 00:31:02,360 Speaker 2: For listening to The Money Stuff Podcast. We'll be back 652 00:31:02,400 --> 00:31:03,680 Speaker 2: next week with more stuff