1 00:00:02,720 --> 00:00:09,040 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Okay, it's the last 2 00:00:09,119 --> 00:00:09,640 Speaker 1: DAP school. 3 00:00:10,240 --> 00:00:12,720 Speaker 2: I know. I saw on your Instagram. 4 00:00:13,000 --> 00:00:19,400 Speaker 1: It was just of course, so that's the school for 5 00:00:19,520 --> 00:00:23,200 Speaker 1: my children. And I've lived, you know, much of my 6 00:00:23,239 --> 00:00:25,400 Speaker 1: adult life no longer and like the rhythms of the 7 00:00:25,400 --> 00:00:27,680 Speaker 1: school year. But like then you have kids and you're like, 8 00:00:27,760 --> 00:00:30,360 Speaker 1: why am I not on summer vacation. It just feels 9 00:00:30,360 --> 00:00:31,240 Speaker 1: like it's summer vacation. 10 00:00:31,400 --> 00:00:33,360 Speaker 2: Now they're gonna wake up tomorrow and what are they 11 00:00:33,360 --> 00:00:33,720 Speaker 2: going to do? 12 00:00:34,320 --> 00:00:38,360 Speaker 1: To the pool? Watch TV, watch the World Cup. Watching 13 00:00:38,400 --> 00:00:39,200 Speaker 1: so much World Cup. 14 00:00:39,680 --> 00:00:43,560 Speaker 2: That sounds so fun, but you haven't watched any World Cup. 15 00:00:43,640 --> 00:00:44,680 Speaker 1: Oh my gosh, it's so good. 16 00:00:45,080 --> 00:00:46,599 Speaker 2: Apparently the ratings are pretty good. 17 00:00:46,720 --> 00:00:48,800 Speaker 1: It's great, Like it's it's been a good World Cup. 18 00:00:48,840 --> 00:00:51,240 Speaker 1: And dang, I think I complained to you last week 19 00:00:51,280 --> 00:00:54,160 Speaker 1: that like I think the USA placed tonight at. 20 00:00:54,040 --> 00:00:57,360 Speaker 2: Ten o'clock, Like that's that's what happening. 21 00:00:57,440 --> 00:00:59,600 Speaker 1: I prefer like a European World Cup where like I 22 00:00:59,600 --> 00:01:02,920 Speaker 1: can watch all the games during the day. But it's 23 00:01:02,960 --> 00:01:05,160 Speaker 1: been really good. It's been really fun to watch the kids, 24 00:01:05,200 --> 00:01:07,200 Speaker 1: and I really feel like it is time for my 25 00:01:07,319 --> 00:01:10,319 Speaker 1: summer vacation and I'll see you and I'll see you. 26 00:01:10,240 --> 00:01:13,280 Speaker 2: And yeah, I mean i'll see you next week. 27 00:01:13,400 --> 00:01:16,200 Speaker 1: Yeah, if I were taking a vacation a lot of time, because. 28 00:01:16,040 --> 00:01:19,679 Speaker 2: You're that's true, a sabbatical of sorts, of a child 29 00:01:19,720 --> 00:01:25,759 Speaker 2: bearing sabbatical, to be clearly. Yeah, I plan to have 30 00:01:25,800 --> 00:01:26,520 Speaker 2: a great time. 31 00:01:28,280 --> 00:01:29,720 Speaker 1: Everyone everyone plans that. 32 00:01:30,080 --> 00:01:30,280 Speaker 3: Yeah. 33 00:01:30,319 --> 00:01:32,400 Speaker 2: I know, as someone who's never had a baby before, 34 00:01:32,400 --> 00:01:35,120 Speaker 2: I think I'm going to have a fanta time. It 35 00:01:35,160 --> 00:01:41,160 Speaker 2: seems easy. I mean one seems easy, right as a 36 00:01:41,160 --> 00:01:44,640 Speaker 2: parent of multiples. I don't know if I've talked about 37 00:01:44,680 --> 00:01:46,400 Speaker 2: it on this podcast, but I really have just been 38 00:01:46,440 --> 00:01:49,120 Speaker 2: approaching it like I'm getting a new puppy. I don't 39 00:01:49,160 --> 00:01:51,240 Speaker 2: know how to take care of a human, but I 40 00:01:51,240 --> 00:01:52,320 Speaker 2: can do a small animal. 41 00:01:52,920 --> 00:01:57,279 Speaker 1: I mean, look, obviously it's not like getting a new puppy, 42 00:01:57,320 --> 00:01:59,320 Speaker 1: but there's some continuum. 43 00:01:59,400 --> 00:02:03,680 Speaker 2: Yeah, I mean, she can't talk, she can't really do much. 44 00:02:04,320 --> 00:02:08,720 Speaker 2: She's like a little forest creature, right right. 45 00:02:10,240 --> 00:02:12,080 Speaker 1: I feel like it's very possible that your child would 46 00:02:12,080 --> 00:02:13,680 Speaker 1: turn into a little forest creature. 47 00:02:13,720 --> 00:02:15,799 Speaker 2: I hope. So I'm really excited. 48 00:02:16,160 --> 00:02:18,200 Speaker 1: In a month or so, you'll know, like or be pregnant. 49 00:02:18,200 --> 00:02:20,760 Speaker 1: But like, yeah, we won't be like that was a 50 00:02:20,800 --> 00:02:21,320 Speaker 1: weird dream. 51 00:02:21,520 --> 00:02:26,040 Speaker 2: Then the party really start anyway, I guess we should 52 00:02:26,040 --> 00:02:27,040 Speaker 2: do the podcast. 53 00:02:27,919 --> 00:02:30,840 Speaker 1: Hello and welcome to the Money Stuff Podcast, your weekly 54 00:02:30,880 --> 00:02:35,359 Speaker 1: podcast where we talk about stuff related to money. I'm 55 00:02:35,400 --> 00:02:37,239 Speaker 1: Matt Levine and I write The Money Stuff com for 56 00:02:37,360 --> 00:02:38,560 Speaker 1: Bloomberg Opinion. 57 00:02:39,200 --> 00:02:41,760 Speaker 2: And I'm Katie Greifeld, a reporter for Bloomberg News and 58 00:02:41,760 --> 00:02:43,280 Speaker 2: an anchor for Bloomberg Television. 59 00:02:52,280 --> 00:02:54,640 Speaker 1: We almost had a vacation from SpaceX last week. I 60 00:02:54,639 --> 00:02:56,160 Speaker 1: feel like we snuck in a little SpaceX. 61 00:02:56,200 --> 00:02:57,160 Speaker 2: Yeah, I mean you have to. 62 00:02:57,360 --> 00:03:00,280 Speaker 1: Write hardly a noticeable amount of SpaceX. 63 00:03:00,000 --> 00:03:01,799 Speaker 2: And now we're going to talk about SpaceX a. 64 00:03:01,720 --> 00:03:05,760 Speaker 1: Lot, well a medium that way too much a good amount. Yeah, 65 00:03:05,880 --> 00:03:08,839 Speaker 1: like a segment of the show, did you know it's 66 00:03:08,840 --> 00:03:10,960 Speaker 1: below two trillion dollars today? Or it was as of 67 00:03:11,040 --> 00:03:12,680 Speaker 1: like one thirty or something. 68 00:03:12,880 --> 00:03:16,720 Speaker 2: Yeah, it's been interesting to see the back and forth 69 00:03:17,240 --> 00:03:20,440 Speaker 2: in these shares. I mean, who knows. I mean as 70 00:03:20,440 --> 00:03:23,120 Speaker 2: someone who watches lines all day, yeah, talks about them 71 00:03:23,120 --> 00:03:23,400 Speaker 2: for a. 72 00:03:23,320 --> 00:03:26,080 Speaker 1: Living, right, I did sort of like you know, you 73 00:03:26,120 --> 00:03:28,160 Speaker 1: have an ipo then like it's supposed to go up 74 00:03:28,200 --> 00:03:31,440 Speaker 1: from there, and like this one has gotten up a 75 00:03:31,440 --> 00:03:33,160 Speaker 1: little and now it's kind of done. It's not below 76 00:03:33,200 --> 00:03:35,119 Speaker 1: the IPO price, but it's below like the first day, 77 00:03:35,200 --> 00:03:36,080 Speaker 1: you know, closing price. 78 00:03:36,280 --> 00:03:38,200 Speaker 2: Yeah, big time. I mean the IPO price is like 79 00:03:38,240 --> 00:03:39,120 Speaker 2: one hundred and thirty. 80 00:03:38,960 --> 00:03:41,800 Speaker 1: Five dollars fifty Thursday afternoon. 81 00:03:42,000 --> 00:03:44,840 Speaker 2: Yeah, who knows what it'll be when we published this podcast. 82 00:03:44,880 --> 00:03:47,800 Speaker 2: But there was a good thought experiment in the New 83 00:03:47,840 --> 00:03:48,360 Speaker 2: York Times this. 84 00:03:48,440 --> 00:03:52,080 Speaker 1: Week about SpaceX and Tesla merging big times. I don't 85 00:03:52,120 --> 00:03:54,200 Speaker 1: think that's a New York Times thought exper No, I've 86 00:03:54,240 --> 00:03:56,840 Speaker 1: seen that everywhere, and I don't want to say that 87 00:03:56,920 --> 00:03:59,520 Speaker 1: like it was planted by SpaceX, but I do feel 88 00:03:59,520 --> 00:04:01,880 Speaker 1: a little bit like they are dusting the waters for 89 00:04:01,960 --> 00:04:05,040 Speaker 1: like that's the next thing that's going to happen the president. 90 00:04:05,160 --> 00:04:09,280 Speaker 2: She's not the CEO. She was on CNBC on IPO 91 00:04:09,400 --> 00:04:10,400 Speaker 2: Day talking about it. 92 00:04:10,480 --> 00:04:14,320 Speaker 3: So there's no question that there's synergies between Tesla and 93 00:04:14,400 --> 00:04:19,040 Speaker 3: SpaceX and our futures. Definitely, there's a convergence of kind 94 00:04:19,080 --> 00:04:21,359 Speaker 3: of what we're all trying to accomplish in the future. 95 00:04:21,520 --> 00:04:26,040 Speaker 2: That's our next president, Yeah, pretty much. It is really crazy. 96 00:04:26,520 --> 00:04:27,479 Speaker 2: What are they waiting for? 97 00:04:30,400 --> 00:04:32,240 Speaker 1: I think in general, if you work for Elon Musk, 98 00:04:32,320 --> 00:04:35,440 Speaker 1: that need to like sleep and live a nice life 99 00:04:35,520 --> 00:04:37,320 Speaker 1: as not you know, no one cares. 100 00:04:37,600 --> 00:04:41,239 Speaker 2: But yeah, they did just do it. That's true. 101 00:04:41,360 --> 00:04:42,960 Speaker 1: But just a minute ago. 102 00:04:43,680 --> 00:04:46,520 Speaker 2: It's just funny because we're going to talk about this 103 00:04:46,640 --> 00:04:48,520 Speaker 2: until it happens, and it. 104 00:04:48,560 --> 00:04:51,359 Speaker 1: Feels like it's going to happen. Yeah, I feel like 105 00:04:51,520 --> 00:04:53,120 Speaker 1: six months ago people were talking about this as a 106 00:04:53,200 --> 00:04:55,000 Speaker 1: joke and now it's like, yep, that'll be next week. 107 00:04:55,120 --> 00:04:57,440 Speaker 2: Well I feel like six months ago. I mean, who 108 00:04:57,480 --> 00:05:01,479 Speaker 2: knows how fuzzy my timeline is, but the idea of 109 00:05:01,480 --> 00:05:03,600 Speaker 2: them ipoing was still a who knows. 110 00:05:04,120 --> 00:05:07,640 Speaker 1: I guess that's right. Yes, it was not fast. Yeah, 111 00:05:07,680 --> 00:05:09,960 Speaker 1: they move on the merger thing. I mean, one answer 112 00:05:10,000 --> 00:05:11,520 Speaker 1: to the question what are they waiting for is like, 113 00:05:11,960 --> 00:05:16,320 Speaker 1: presumably a merger means an all stock merger because no 114 00:05:16,360 --> 00:05:19,479 Speaker 1: one's gonna buy it. It's Tesla for cash. But like 115 00:05:20,440 --> 00:05:23,880 Speaker 1: one answer that question might be that if SpaceX went 116 00:05:23,920 --> 00:05:27,760 Speaker 1: public and its evaluation quickly rose to like, you know, 117 00:05:28,000 --> 00:05:31,800 Speaker 1: four trillion dollars, then I think that would be more 118 00:05:31,800 --> 00:05:34,800 Speaker 1: convenient for Musk. Like I think from Elon Musk's perspective, 119 00:05:34,800 --> 00:05:38,119 Speaker 1: the more SpaceX is worth relative to Tesla, the better 120 00:05:38,200 --> 00:05:42,839 Speaker 1: this is because he owns more of SpaceX, you know, yeah, 121 00:05:42,880 --> 00:05:46,120 Speaker 1: and SpaceX is kind of the new baby, and so 122 00:05:46,160 --> 00:05:49,920 Speaker 1: the more that like SpaceX is represented in the combined company, 123 00:05:49,960 --> 00:05:52,160 Speaker 1: like probably that's more convenient for him. I don't know 124 00:05:52,200 --> 00:05:54,880 Speaker 1: how much that matters, because he has super voting stock 125 00:05:54,920 --> 00:05:57,560 Speaker 1: of SpaceX, and like he doesn't necessarily care that much 126 00:05:57,560 --> 00:06:00,480 Speaker 1: about the charlder, so like it's probably fine to merge them. 127 00:06:00,680 --> 00:06:03,520 Speaker 1: You know. Right now, SpaceX weth like two trillion dollars 128 00:06:03,600 --> 00:06:06,840 Speaker 1: and Tesla's worth like one point four trillion dollars, so 129 00:06:07,600 --> 00:06:10,160 Speaker 1: like almost a merger of equals, Like that's fine for him, 130 00:06:10,200 --> 00:06:12,000 Speaker 1: But it would have been probably a little bit nicer 131 00:06:12,040 --> 00:06:13,880 Speaker 1: if it was like, you know, three to one or something. 132 00:06:13,960 --> 00:06:15,480 Speaker 2: So that's the Elon Musk perspective. 133 00:06:15,560 --> 00:06:16,479 Speaker 1: Oh, which is the only person. 134 00:06:16,880 --> 00:06:17,120 Speaker 3: Well. 135 00:06:18,080 --> 00:06:22,120 Speaker 2: The New York Times did include some quotes from Tasha Kini. 136 00:06:22,240 --> 00:06:26,000 Speaker 2: She's at ARC Investment Management, which obviously really likes Elon Musk. 137 00:06:26,360 --> 00:06:29,440 Speaker 2: She made the point that ARC would prefer the merger 138 00:06:29,480 --> 00:06:32,279 Speaker 2: took place after Tesla had become the dominant company in 139 00:06:32,279 --> 00:06:35,240 Speaker 2: self driving taxis. She said it would be good for 140 00:06:35,320 --> 00:06:38,960 Speaker 2: shareholders to see that take off before the merger. Theoretically, 141 00:06:38,960 --> 00:06:41,760 Speaker 2: because I and she didn't say this, but I'm assuming 142 00:06:41,800 --> 00:06:43,880 Speaker 2: because Tesla shares would be worth more. 143 00:06:44,480 --> 00:06:46,440 Speaker 1: Yeah, I mean, like one thing that's going to happen 144 00:06:46,520 --> 00:06:48,480 Speaker 1: is like SpaceX will do it's business, Tesla will do 145 00:06:48,560 --> 00:06:51,360 Speaker 1: it's business. You know, ideally they would both go up, right, 146 00:06:52,480 --> 00:06:55,000 Speaker 1: But one question you might have is like, in the 147 00:06:55,040 --> 00:06:59,560 Speaker 1: next few months, a few years, whatever, will SpaceX go 148 00:06:59,720 --> 00:07:04,039 Speaker 1: up more than Tesla? Will it become more valuable faster 149 00:07:04,200 --> 00:07:07,760 Speaker 1: or slower than Tesla? Not the answer, But doesn't it 150 00:07:07,800 --> 00:07:12,240 Speaker 1: feel like Elon Musk's attention and the world's attention is 151 00:07:12,280 --> 00:07:16,280 Speaker 1: on SpaceX and data centers, AI and whatever, and not 152 00:07:16,520 --> 00:07:20,560 Speaker 1: so much on the cars. Now there's AI stuff at 153 00:07:20,560 --> 00:07:24,120 Speaker 1: Tesla there, they're making the robots, they're making the cars, 154 00:07:24,200 --> 00:07:26,960 Speaker 1: all sorts of stuff, right, and so in you know, 155 00:07:27,000 --> 00:07:28,920 Speaker 1: in a year, like could this have reversed? Then could 156 00:07:29,000 --> 00:07:32,360 Speaker 1: Tesla be the hot company because it's the human ide robots. Maybe? 157 00:07:32,680 --> 00:07:34,480 Speaker 1: But doesn't it kind of feel like his attention on 158 00:07:34,520 --> 00:07:39,080 Speaker 1: SpaceX now and like the longer you wait, the verse 159 00:07:39,160 --> 00:07:41,800 Speaker 1: it is for Tesla. Yeah, this is just a cosma. 160 00:07:42,120 --> 00:07:44,960 Speaker 2: Yeah, No, it's a good point. He's always been the 161 00:07:45,000 --> 00:07:47,480 Speaker 2: CEO of multiple companies, et cetera, et cetera. But it 162 00:07:47,480 --> 00:07:49,480 Speaker 2: does feel like there's a clear favorite in his mind 163 00:07:49,560 --> 00:07:49,920 Speaker 2: right now. 164 00:07:50,080 --> 00:07:53,360 Speaker 1: Yeah. The only thing is that like Grock, but Crock is. 165 00:07:54,280 --> 00:07:57,640 Speaker 2: That's also yes, how do I keep forgetting that that 166 00:07:57,640 --> 00:08:00,680 Speaker 2: it's understandable X is XAI, which is, yeah, that's what 167 00:08:00,760 --> 00:08:01,320 Speaker 2: it says. 168 00:08:01,520 --> 00:08:04,120 Speaker 1: The other other thing is like I've gotten used for 169 00:08:04,160 --> 00:08:06,200 Speaker 1: a long time to this model of Elon Musk of 170 00:08:06,320 --> 00:08:09,000 Speaker 1: like he has a bunch of different companies and a 171 00:08:09,040 --> 00:08:14,040 Speaker 1: bunch of different independent bats and he's somehow multitasking between 172 00:08:14,080 --> 00:08:17,840 Speaker 1: all of them. But like that is changing, right, I mean, 173 00:08:17,880 --> 00:08:21,120 Speaker 1: like he had Twitter and an AI company and a 174 00:08:21,160 --> 00:08:24,160 Speaker 1: space company and now they're all one company. And then 175 00:08:24,240 --> 00:08:26,400 Speaker 1: you know, you combine that with his car company, which 176 00:08:26,440 --> 00:08:28,840 Speaker 1: is also like a separate solar company that he mushed 177 00:08:28,840 --> 00:08:33,880 Speaker 1: into Tesla years ago, then like basically all of his 178 00:08:34,800 --> 00:08:38,880 Speaker 1: scaled commercial stuff would be in one giant company, which 179 00:08:38,920 --> 00:08:41,960 Speaker 1: is a real change from how he's been operating and 180 00:08:42,040 --> 00:08:44,920 Speaker 1: also makes me wonder about like Neuralink and the boring 181 00:08:45,000 --> 00:08:48,360 Speaker 1: company could be the only ones left probably missing one, 182 00:08:48,840 --> 00:08:50,680 Speaker 1: but like I think those are the only big Elon 183 00:08:50,760 --> 00:08:52,720 Speaker 1: Musk companies that would not be rolled up into this 184 00:08:53,160 --> 00:08:57,400 Speaker 1: and so how long beforeror Neuralink gets folded into the 185 00:08:57,440 --> 00:09:02,960 Speaker 1: AI behemoth. It's all change from back in the day. 186 00:09:04,280 --> 00:09:07,080 Speaker 1: I had like a whole shpiel about like how Elon Musk, 187 00:09:07,080 --> 00:09:09,959 Speaker 1: by keeping all of his businesses in separate bets, was 188 00:09:10,040 --> 00:09:14,800 Speaker 1: somehow doing this like seven dimensional chess mastermind fundraising thing 189 00:09:14,840 --> 00:09:17,439 Speaker 1: where he could tap the same investors for every company 190 00:09:17,520 --> 00:09:20,720 Speaker 1: and like shift resources around without the constraints of running 191 00:09:20,720 --> 00:09:22,280 Speaker 1: a public company. But like now it's just like, yeah, 192 00:09:22,280 --> 00:09:25,240 Speaker 1: he's going to run a giant company, and that giant 193 00:09:25,240 --> 00:09:29,120 Speaker 1: company will be a conglomerate, and he will allocate capital 194 00:09:29,240 --> 00:09:32,240 Speaker 1: to whatever he thinks is the best business to have 195 00:09:32,280 --> 00:09:36,040 Speaker 1: that capital, whether it's cars or humanoid robots, or space 196 00:09:36,120 --> 00:09:39,280 Speaker 1: data centers or social media, yeah, ninos other things. 197 00:09:39,480 --> 00:09:41,280 Speaker 2: When you put it like that, it is a sea change. 198 00:09:41,640 --> 00:09:43,600 Speaker 1: Yeah, it's just like in many ways more. 199 00:09:43,440 --> 00:09:47,200 Speaker 2: Normal, right, Like maybe he got tired. I don't know, that. 200 00:09:47,240 --> 00:09:50,880 Speaker 1: Doesn't sound right given my general theory that sleep is 201 00:09:50,920 --> 00:09:53,480 Speaker 1: not an impediment to doing anything in SpaceX. But yeah, 202 00:09:53,520 --> 00:09:56,200 Speaker 1: it is more sensible, Yeah, to have one guy running 203 00:09:56,200 --> 00:09:59,200 Speaker 1: one company or other than seven different companies. 204 00:09:58,800 --> 00:10:00,000 Speaker 2: And one guy seven company. 205 00:10:00,160 --> 00:10:05,199 Speaker 1: You know, every time he's like moving GPUs from Tesla 206 00:10:05,280 --> 00:10:08,400 Speaker 1: to Xai or whatever dopes like me or writing columns 207 00:10:08,400 --> 00:10:10,520 Speaker 1: being like you shouldn't be doing that, Like very good 208 00:10:10,520 --> 00:10:12,880 Speaker 1: corporate governance, Like now it's fixed, right, if they're all 209 00:10:12,960 --> 00:10:15,560 Speaker 1: one company, it's great corporate governance for him to move. 210 00:10:17,120 --> 00:10:19,440 Speaker 2: Yeah, I want to talk a little bit about what 211 00:10:19,480 --> 00:10:21,440 Speaker 2: could stop this merger. It's not going to be a 212 00:10:21,440 --> 00:10:22,920 Speaker 2: shareholder lawsuit. 213 00:10:23,640 --> 00:10:28,880 Speaker 1: Although I've written about like it's like a slim outside 214 00:10:29,600 --> 00:10:32,319 Speaker 1: path to maybe a shareholder. 215 00:10:31,880 --> 00:10:36,320 Speaker 2: Maybe Vanguard, maybe Fidelity. No, probably not. But the New 216 00:10:36,400 --> 00:10:40,360 Speaker 2: York Times did raise the question, which it then batted down. 217 00:10:40,440 --> 00:10:44,680 Speaker 2: But couldn't there be some sort of antitrust concern here 218 00:10:44,840 --> 00:10:47,520 Speaker 2: that could be raised? They're both AI companies. 219 00:10:48,480 --> 00:10:50,760 Speaker 1: I feel like in traditional anti trust you would say 220 00:10:50,920 --> 00:10:55,080 Speaker 1: not really. I don't think that SpaceX is exactly in 221 00:10:55,200 --> 00:10:57,960 Speaker 1: a real market power position in the sort of broad 222 00:10:58,040 --> 00:11:01,480 Speaker 1: like AI lab business, and like it has a very 223 00:11:01,600 --> 00:11:05,520 Speaker 1: leading position in launching rockets into space for sure, and Tesla, 224 00:11:05,600 --> 00:11:09,000 Speaker 1: you know, arguably it's something in electric cars in the US, 225 00:11:09,160 --> 00:11:11,520 Speaker 1: it's not you know, it's not a monopolist and electric cars, 226 00:11:11,559 --> 00:11:13,560 Speaker 1: but like those are not overlapping businesses, right, Like the 227 00:11:13,600 --> 00:11:16,560 Speaker 1: question is like are you combining to overlapping businesses in 228 00:11:16,600 --> 00:11:19,880 Speaker 1: the same area. And I don't really think that's true. 229 00:11:19,880 --> 00:11:22,200 Speaker 1: I think there's like beginning to be some rumblings of 230 00:11:22,240 --> 00:11:26,280 Speaker 1: worry about like space access position and AI infrastructure, like 231 00:11:26,360 --> 00:11:28,640 Speaker 1: especially they put the data centers in space, but like 232 00:11:28,720 --> 00:11:30,920 Speaker 1: that doesn't really seem like an anti trust concern for 233 00:11:30,960 --> 00:11:33,840 Speaker 1: combining the companies. But like, I think if this were 234 00:11:33,960 --> 00:11:38,080 Speaker 1: a democratic administration, the combination of like two giant tech 235 00:11:38,200 --> 00:11:41,400 Speaker 1: companies merging and like it being Elon Musk might raise 236 00:11:41,880 --> 00:11:46,079 Speaker 1: anti trust concerns. But guess what, Yeah, so I don't 237 00:11:46,440 --> 00:11:49,240 Speaker 1: see it. But you know, if Donald Trump is mad 238 00:11:49,240 --> 00:11:51,200 Speaker 1: at Elon Musk that week, will there be an antro 239 00:11:51,240 --> 00:11:54,280 Speaker 1: trust problem? Like maybe, and then like European. 240 00:11:53,880 --> 00:11:55,800 Speaker 2: Anti trust you know, yeah, they're going to be all 241 00:11:55,880 --> 00:11:56,200 Speaker 2: over it. 242 00:11:56,400 --> 00:12:00,160 Speaker 1: They'll be all over it. He's not winning any friends there. 243 00:12:00,200 --> 00:12:03,280 Speaker 1: And again, like a traditional anti dress analysis, like it 244 00:12:03,320 --> 00:12:07,160 Speaker 1: doesn't seem like they're really emerging competing businesses, but who knows. 245 00:12:07,320 --> 00:12:10,080 Speaker 2: I did want to also call out this nugget from 246 00:12:10,080 --> 00:12:12,080 Speaker 2: the New York Times report because I didn't appreciate this. 247 00:12:12,160 --> 00:12:16,640 Speaker 2: I knew about terrorfab so SpaceX and Tesla plan to 248 00:12:16,679 --> 00:12:20,880 Speaker 2: jointly produce AI chips at a proposed factory called TERRAFAB 249 00:12:21,080 --> 00:12:25,840 Speaker 2: also develop AI software through another project called macro Hard, 250 00:12:25,920 --> 00:12:29,280 Speaker 2: which is obviously the opposite of Microsoft. 251 00:12:30,600 --> 00:12:32,760 Speaker 1: I think he's been making that joke for years. 252 00:12:32,760 --> 00:12:38,000 Speaker 2: Sounds stupid, I mean it's funny. It is funny. I 253 00:12:38,040 --> 00:12:40,600 Speaker 2: didn't know about what I didn't know about macro Hard. 254 00:12:42,040 --> 00:12:45,760 Speaker 1: I hate to give him one, but yeah, macro Hard, 255 00:12:47,080 --> 00:12:48,400 Speaker 1: nodding wistfully. 256 00:12:50,280 --> 00:12:52,120 Speaker 2: Oh, do you want to talk about Triller? 257 00:12:52,360 --> 00:12:54,280 Speaker 1: Triller? Yeah? I do kind of want to talk about Trailler. 258 00:12:54,440 --> 00:12:57,320 Speaker 2: So I went to your bio just before this podcast 259 00:12:57,320 --> 00:12:58,679 Speaker 2: because I was like, all right, I have all my notes. 260 00:12:58,840 --> 00:13:02,400 Speaker 2: Christmas guy again, matt Levine. No, I was like, I 261 00:13:02,400 --> 00:13:04,120 Speaker 2: have all my notes. I just want to check one 262 00:13:04,120 --> 00:13:06,840 Speaker 2: more thing. And then I saw you published about the 263 00:13:06,880 --> 00:13:08,240 Speaker 2: SpaceX Treasury. 264 00:13:07,880 --> 00:13:09,559 Speaker 1: Company treasury company. Baby. 265 00:13:09,679 --> 00:13:12,600 Speaker 2: I audibly growned because I had completely forgotten about it. Yeah, 266 00:13:12,760 --> 00:13:13,720 Speaker 2: but tell me about Triller. 267 00:13:13,760 --> 00:13:17,760 Speaker 1: There's a SpaceX Treasury company, of course, called Triller. Yeah, yesterday, 268 00:13:17,800 --> 00:13:20,760 Speaker 1: it's a fifteen million dollar market cap. It's like it's 269 00:13:20,800 --> 00:13:23,400 Speaker 1: like one of these things where they're like, we provide 270 00:13:23,440 --> 00:13:26,199 Speaker 1: social media stuff for such celebrities as and then it 271 00:13:26,280 --> 00:13:29,400 Speaker 1: names three people who I'm like, yes, these people are 272 00:13:29,480 --> 00:13:33,240 Speaker 1: all actually famous, but I don't know anything about them, 273 00:13:33,480 --> 00:13:36,200 Speaker 1: nor do I remember their names. No, they do like 274 00:13:36,240 --> 00:13:38,880 Speaker 1: some something in social media. And also they sell insurance 275 00:13:38,920 --> 00:13:39,559 Speaker 1: in Hong Kong. 276 00:13:39,880 --> 00:13:42,560 Speaker 2: Why who knows natural But so. 277 00:13:42,559 --> 00:13:46,599 Speaker 1: A teeny company, teeny public lesi company, and they announced 278 00:13:46,640 --> 00:13:51,200 Speaker 1: that they are acquiring a four hundred million dollar slug 279 00:13:51,240 --> 00:13:55,440 Speaker 1: of SpaceX through like three layers of SPVs, so like 280 00:13:55,480 --> 00:13:58,959 Speaker 1: they're acquiring shares in an SPV that on shares in 281 00:13:59,000 --> 00:14:01,720 Speaker 1: an SPV that owns some like pre appo locked up 282 00:14:01,760 --> 00:14:06,160 Speaker 1: SpaceX stock excellent, yes, and they're like, this will be 283 00:14:06,200 --> 00:14:10,240 Speaker 1: a fun new treasury asset for US. Okay, it's like 284 00:14:10,280 --> 00:14:12,920 Speaker 1: the digital asset treasury trade, which you know, at its 285 00:14:12,960 --> 00:14:14,920 Speaker 1: peak last year. I was like, we talked. 286 00:14:14,720 --> 00:14:15,880 Speaker 2: About this definitely. 287 00:14:16,040 --> 00:14:19,320 Speaker 1: I was like, sure, you can like sell some stock 288 00:14:19,400 --> 00:14:21,480 Speaker 1: to buy bitcoin, but like, there's so many other things 289 00:14:21,480 --> 00:14:24,080 Speaker 1: in the world besides bitcoin. Someone did a game stop treasure. 290 00:14:24,120 --> 00:14:27,000 Speaker 1: We talked about an art market treasure all sorts of stuff. 291 00:14:27,040 --> 00:14:29,720 Speaker 1: Dinosaur bone, dinosaur bone treasury company. That was the title 292 00:14:29,720 --> 00:14:34,520 Speaker 1: of an episode. And right, so instead of a dinosaur 293 00:14:34,600 --> 00:14:37,800 Speaker 1: bone treasury company, they're doing a SpaceX treasury company. I 294 00:14:37,840 --> 00:14:38,160 Speaker 1: love it. 295 00:14:38,280 --> 00:14:40,800 Speaker 2: As you've already pointed out, they're at least a year 296 00:14:40,880 --> 00:14:42,880 Speaker 2: late here, they're a year late. 297 00:14:42,760 --> 00:14:46,280 Speaker 1: To the Dinosaur Bone treasury company trade. And also they're 298 00:14:46,360 --> 00:14:50,560 Speaker 1: late to ooh, would you like access to SpaceX stock 299 00:14:50,640 --> 00:14:52,720 Speaker 1: because you can just buy SpaceX. 300 00:14:52,160 --> 00:14:54,320 Speaker 2: Stock seems easy. 301 00:14:54,400 --> 00:14:57,000 Speaker 1: It's like not a very good reason, but a reason 302 00:14:57,040 --> 00:14:58,840 Speaker 1: to have a bitcoin treasury company is that, like it 303 00:14:58,920 --> 00:15:02,320 Speaker 1: is somewhat more difficult for a normal person to buy 304 00:15:02,400 --> 00:15:05,000 Speaker 1: bitcoin than it is for a normal person to buy 305 00:15:05,520 --> 00:15:08,040 Speaker 1: stock of strategy, Like not really you can buy bitcoin. 306 00:15:08,080 --> 00:15:09,600 Speaker 1: You're robbing it app. But like maybe you don't have 307 00:15:09,680 --> 00:15:13,160 Speaker 1: robbing It. Maybe maybe you had an old timey record 308 00:15:13,160 --> 00:15:15,480 Speaker 1: where you can only buy stock and you're like, oh yeah, strategy, 309 00:15:15,480 --> 00:15:17,720 Speaker 1: that's a good way to get bitcoin. But like anyone 310 00:15:17,760 --> 00:15:21,000 Speaker 1: who can buy Triller can buy SpaceX because they're on 311 00:15:21,040 --> 00:15:22,240 Speaker 1: the same stock exchange. 312 00:15:22,320 --> 00:15:26,680 Speaker 2: Yes, well, it will be a fun experiment. It's a 313 00:15:26,720 --> 00:15:43,720 Speaker 2: line I'll watch when on All, right, do we get 314 00:15:43,720 --> 00:15:50,160 Speaker 2: this SpaceX out of our system? Meta Meta? Another New 315 00:15:50,280 --> 00:15:54,680 Speaker 2: York Times article, Mark Zuckerberg, Now he's looking at prediction markets, 316 00:15:55,800 --> 00:15:58,840 Speaker 2: specifically an app. There wouldn't be real money. 317 00:15:58,640 --> 00:16:01,760 Speaker 1: Involved, right, I think it's called arena that has been 318 00:16:01,800 --> 00:16:06,320 Speaker 1: reported from meta slash Facebook that allows you to do 319 00:16:06,520 --> 00:16:09,120 Speaker 1: prediction markets, and I think the way it's supposed to 320 00:16:09,160 --> 00:16:12,520 Speaker 1: work is like from NPR reporting, is that AI would 321 00:16:12,560 --> 00:16:15,360 Speaker 1: come up with the markets and then also resolve the markets. 322 00:16:15,560 --> 00:16:18,400 Speaker 1: Some meta AI model would be like, you know what 323 00:16:18,440 --> 00:16:20,960 Speaker 1: we need as a prediction market on you know, Vner 324 00:16:21,080 --> 00:16:24,400 Speaker 1: Taylor Slip get married and then here are the options 325 00:16:24,440 --> 00:16:26,760 Speaker 1: and then like it'll take bets and then like it'll 326 00:16:26,800 --> 00:16:29,200 Speaker 1: notice when she gets married and resolved bets or something 327 00:16:29,280 --> 00:16:29,600 Speaker 1: like that. 328 00:16:29,680 --> 00:16:30,000 Speaker 2: Fun. 329 00:16:30,200 --> 00:16:33,400 Speaker 1: Yeah, So when you think about like Calshier poly market, 330 00:16:33,520 --> 00:16:35,920 Speaker 1: like they're like, we're you know, building truth machines. We're 331 00:16:35,920 --> 00:16:39,320 Speaker 1: building a financial market that real world companies can use 332 00:16:39,360 --> 00:16:41,800 Speaker 1: the hedge their risk of things like you know, the 333 00:16:41,840 --> 00:16:42,560 Speaker 1: next winning. 334 00:16:42,360 --> 00:16:46,320 Speaker 2: A playoff game with a very straight But then like. 335 00:16:46,320 --> 00:16:49,400 Speaker 1: The alternative model is like you have built a gambling platform. 336 00:16:49,560 --> 00:16:52,440 Speaker 1: What is gambling? Gambling is providing entertainment for money, right, 337 00:16:52,440 --> 00:16:55,840 Speaker 1: It's like, yeah, it's like giving people like the rush 338 00:16:55,960 --> 00:16:58,720 Speaker 1: of excitement from betting on a game, and like in expectation, 339 00:16:58,760 --> 00:17:03,200 Speaker 1: they lose money. But I've gone to Vegas and gambled 340 00:17:03,320 --> 00:17:06,440 Speaker 1: and felt and lost money and felt fine because I 341 00:17:06,480 --> 00:17:09,840 Speaker 1: was like, I'm spending this money on entertainment, right, and 342 00:17:09,880 --> 00:17:13,760 Speaker 1: that's like that's the good case for gambling. And if 343 00:17:13,760 --> 00:17:15,560 Speaker 1: that's your model, then it's like, well yeah, like if 344 00:17:15,600 --> 00:17:17,800 Speaker 1: it's entertaining, maybe you can do it without the money. 345 00:17:18,320 --> 00:17:24,879 Speaker 1: And Facebook are really good at harnessing human behavior to 346 00:17:25,119 --> 00:17:28,680 Speaker 1: like give people dopamine heads in exchange for spending all 347 00:17:28,680 --> 00:17:31,639 Speaker 1: of their time on Facebook's apps, and like that's what 348 00:17:31,680 --> 00:17:34,200 Speaker 1: this is, right, That's why Calshei and polymarket have proved 349 00:17:34,200 --> 00:17:37,440 Speaker 1: out a new form of human social behavior that keeps 350 00:17:37,480 --> 00:17:39,199 Speaker 1: them glued to their apps all the time. And like, 351 00:17:39,200 --> 00:17:40,840 Speaker 1: we want them to be glued to ara apps, so 352 00:17:40,840 --> 00:17:42,720 Speaker 1: we're gonna launch an app that they'll be glued to, 353 00:17:43,040 --> 00:17:44,119 Speaker 1: you know, making predictions. 354 00:17:44,440 --> 00:17:47,000 Speaker 2: It is a smart move by Meta. 355 00:17:47,119 --> 00:17:48,760 Speaker 1: It's quite grim in some ways. 356 00:17:48,840 --> 00:17:50,760 Speaker 2: I mean for sure, I mean, like it better. 357 00:17:50,600 --> 00:17:52,840 Speaker 1: To have fake money gambling than real money gambling. But 358 00:17:52,880 --> 00:17:53,359 Speaker 1: like I don't know. 359 00:17:54,240 --> 00:17:56,800 Speaker 2: The company is not ruled out eventual use of money 360 00:17:56,840 --> 00:17:58,840 Speaker 2: according to the New Time, so that could be coming. 361 00:17:59,320 --> 00:18:03,200 Speaker 2: I do like how how you wrote that Zuckerberg's point 362 00:18:03,240 --> 00:18:05,840 Speaker 2: that this could be all thought of as addictive phone 363 00:18:05,880 --> 00:18:10,919 Speaker 2: apps rather than financial instruments is true. Like again, like 364 00:18:11,080 --> 00:18:13,399 Speaker 2: Calci and Polymarket will tell you with a straight face 365 00:18:13,440 --> 00:18:16,960 Speaker 2: that you know we're creating or we're helping to find 366 00:18:17,040 --> 00:18:21,199 Speaker 2: true economic information here, but it does feel like a 367 00:18:21,240 --> 00:18:24,919 Speaker 2: sort of emperor has no close moment of Facebook is 368 00:18:24,960 --> 00:18:26,359 Speaker 2: creating a prediction markets app. 369 00:18:26,480 --> 00:18:28,800 Speaker 1: Yeah, a couple of things. One, everything's on a continuum, right, 370 00:18:28,800 --> 00:18:32,800 Speaker 1: I mean, Robinhood. Their essential insight is stock trading is 371 00:18:32,800 --> 00:18:35,200 Speaker 1: also like an addictive phone behavior, and so we can 372 00:18:35,200 --> 00:18:37,160 Speaker 1: make it more addictive and more on your phone. True, 373 00:18:37,400 --> 00:18:40,040 Speaker 1: everything is both right. The other thing I'll say is 374 00:18:40,080 --> 00:18:44,520 Speaker 1: like the fact that Meta is in essentially the addictive 375 00:18:44,520 --> 00:18:48,160 Speaker 1: phone apps business and looks at this and has big 376 00:18:48,200 --> 00:18:50,000 Speaker 1: dollar signs in its thighs of like, ooh, this is 377 00:18:50,040 --> 00:18:52,280 Speaker 1: an addictive phone app. That doesn't mean that it's not 378 00:18:52,440 --> 00:18:54,720 Speaker 1: also serving some truth seeking function. 379 00:18:55,080 --> 00:18:58,240 Speaker 2: Maybe maybe just thinking about Farmville, you know, which was 380 00:18:59,280 --> 00:19:00,840 Speaker 2: another face book app that I. 381 00:19:00,760 --> 00:19:02,640 Speaker 1: Loved, right and Farmville. 382 00:19:03,240 --> 00:19:05,080 Speaker 2: I don't know if there was any truth seeking going 383 00:19:05,080 --> 00:19:06,920 Speaker 2: on in the hours I spent on Farmville. 384 00:19:06,960 --> 00:19:09,320 Speaker 1: Well, I hear you, But like, also I'm reading the 385 00:19:09,359 --> 00:19:13,560 Speaker 1: Sebastian Malaby biography of Demi Sasabis. The Google Deep Mind 386 00:19:13,560 --> 00:19:17,840 Speaker 1: Fighter and deep Mind spent a lot of time trying 387 00:19:17,880 --> 00:19:21,960 Speaker 1: to solve games like early Atari, arcade games and StarCraft, 388 00:19:22,119 --> 00:19:26,040 Speaker 1: all these like video games because and the reason they 389 00:19:26,040 --> 00:19:28,240 Speaker 1: were doing that is partly because like they're gamers and 390 00:19:28,240 --> 00:19:32,159 Speaker 1: they like video games, but partly it was like the 391 00:19:32,200 --> 00:19:35,719 Speaker 1: way to develop and also prove the concept of like 392 00:19:36,119 --> 00:19:39,840 Speaker 1: reinforcement learning machine artificial intelligence is to have it learned 393 00:19:39,840 --> 00:19:41,960 Speaker 1: to play games at better than human standard, and so 394 00:19:42,359 --> 00:19:44,800 Speaker 1: there's like real lessons to be learned from those games. 395 00:19:45,200 --> 00:19:48,480 Speaker 1: That's probably not true of Farmville, but I do think 396 00:19:48,520 --> 00:19:51,240 Speaker 1: that one thing that is probably going on, and Berne 397 00:19:51,240 --> 00:19:54,000 Speaker 1: Hobart has a good newsletter about it on Thursday of 398 00:19:54,040 --> 00:19:58,720 Speaker 1: this week is Meta is one an addictive phone app 399 00:19:58,760 --> 00:20:03,640 Speaker 1: company and two AI company, and prediction markets in AI 400 00:20:03,720 --> 00:20:07,240 Speaker 1: are pretty interesting. Wen't have our rights. Prediction markets try 401 00:20:07,240 --> 00:20:10,000 Speaker 1: to quantify the magnitude of interesting uncertainty. That means they're 402 00:20:10,000 --> 00:20:12,119 Speaker 1: implicitly taking the same form as the input to an 403 00:20:12,119 --> 00:20:15,199 Speaker 1: AI model, given some information, make a prediction about what 404 00:20:15,280 --> 00:20:17,960 Speaker 1: comes next. And so, like other people have talked to 405 00:20:17,960 --> 00:20:22,359 Speaker 1: me about this, like using prediction markets to train AI 406 00:20:22,560 --> 00:20:25,080 Speaker 1: is just sort of like an interesting corner of the world. 407 00:20:25,119 --> 00:20:28,480 Speaker 1: It's like, we want to make computers that are good 408 00:20:28,560 --> 00:20:32,360 Speaker 1: at understanding the world. We want to make computers that 409 00:20:32,640 --> 00:20:34,040 Speaker 1: can buy stocks that I'll go up. We want to 410 00:20:34,040 --> 00:20:39,080 Speaker 1: make computers that can cure cancer whatever. Like if the 411 00:20:39,080 --> 00:20:42,040 Speaker 1: computers get better at like predicting the future based on 412 00:20:42,680 --> 00:20:45,720 Speaker 1: the current data, like that is good for artificial intelligence. 413 00:20:46,000 --> 00:20:48,000 Speaker 1: And maybe one way to give them training data is 414 00:20:48,040 --> 00:20:51,479 Speaker 1: to have a bunch of Facebook people on their phone saying, 415 00:20:51,560 --> 00:20:53,040 Speaker 1: you know, this is where I think Taylor shots will 416 00:20:53,040 --> 00:20:55,080 Speaker 1: get married, and like the computers will learn from that. 417 00:20:55,119 --> 00:20:57,600 Speaker 1: So like one possibility is that this is not so 418 00:20:57,720 --> 00:21:01,440 Speaker 1: much a like engagement bait play from Meta and more 419 00:21:01,480 --> 00:21:04,760 Speaker 1: a like AI training play. But it's really both. Yeah, 420 00:21:05,080 --> 00:21:08,840 Speaker 1: Open AI, like four years ago, like their rhetoric was 421 00:21:08,920 --> 00:21:13,919 Speaker 1: so utopian and ambitious, and then they're like, we're going 422 00:21:14,000 --> 00:21:15,600 Speaker 1: to do ads and we're going to turn the dial 423 00:21:15,640 --> 00:21:17,720 Speaker 1: on chat chpt to make it more engaging so that 424 00:21:17,760 --> 00:21:20,040 Speaker 1: people spend more time with chat chpt. It's like everything 425 00:21:20,040 --> 00:21:22,160 Speaker 1: is a social media company. Everything is an addictive phone 426 00:21:22,160 --> 00:21:24,800 Speaker 1: app company, and so like you know, yeah, you make 427 00:21:24,840 --> 00:21:29,120 Speaker 1: your AI better so that you can keep people engaged 428 00:21:29,160 --> 00:21:30,720 Speaker 1: with your app for longer, so you can serve the 429 00:21:30,760 --> 00:21:31,240 Speaker 1: more ads. 430 00:21:31,440 --> 00:21:41,040 Speaker 2: Yeah, it's also grim, even more grim than Arena potentially. 431 00:21:41,240 --> 00:21:43,919 Speaker 2: The New York Times reporting that Arena is just one 432 00:21:43,960 --> 00:21:46,680 Speaker 2: of a handful of apps that Meta is currently trying out. 433 00:21:46,760 --> 00:21:48,040 Speaker 1: The other one is macro hard. 434 00:21:48,680 --> 00:21:55,640 Speaker 2: Macro Hard also Meta photos it photos. Yeah, it would 435 00:21:55,680 --> 00:21:59,360 Speaker 2: create new types of media using AI. According to people 436 00:21:59,400 --> 00:22:02,520 Speaker 2: familiar that, I don't think so. 437 00:22:03,880 --> 00:22:06,800 Speaker 1: Great. It's like Sora, No it's not. I don't care. 438 00:22:07,320 --> 00:22:08,840 Speaker 2: Yeah, no, I think SOA is a thing, but I don't. 439 00:22:08,880 --> 00:22:10,600 Speaker 1: Sorry, it was the open A version of it. Yeah, 440 00:22:10,960 --> 00:22:12,640 Speaker 1: you look at AI videos that we had. 441 00:22:12,720 --> 00:22:15,840 Speaker 2: Made for you, similar to Macrohart. Something that I didn't 442 00:22:15,880 --> 00:22:18,960 Speaker 2: appreciate until the New York Times told me about it 443 00:22:19,080 --> 00:22:24,159 Speaker 2: was Meta has tried prediction markets. They were so early 444 00:22:24,760 --> 00:22:28,320 Speaker 2: twenty twenty they released Forecast, which was a crowdsource prediction 445 00:22:28,400 --> 00:22:31,000 Speaker 2: market app. It prompted people to make guesses about the 446 00:22:31,000 --> 00:22:34,680 Speaker 2: world in the early days of covid. It eventually shut 447 00:22:34,720 --> 00:22:38,440 Speaker 2: down in twenty twenty two. But man, this could have 448 00:22:38,480 --> 00:22:39,320 Speaker 2: been Meta's market. 449 00:22:39,760 --> 00:22:41,760 Speaker 1: Yeah, that wasn't even that early. Like I mean, people 450 00:22:41,800 --> 00:22:44,320 Speaker 1: have been playing around with many prediction markets for a 451 00:22:44,359 --> 00:22:46,920 Speaker 1: long time, but nobody really. 452 00:22:46,800 --> 00:22:49,840 Speaker 2: Similar to AI. Like we've been talking about AI forever, 453 00:22:49,880 --> 00:22:51,760 Speaker 2: but it feels like it only really exploded in the 454 00:22:51,800 --> 00:22:52,639 Speaker 2: last couple of years. 455 00:22:53,119 --> 00:22:55,360 Speaker 1: I do think that, like kylsh and Poling Market, did 456 00:22:55,400 --> 00:22:57,480 Speaker 1: corrareck the good of making it more exciting? And do 457 00:22:57,520 --> 00:22:58,600 Speaker 1: you know how they cracked that good? 458 00:22:59,080 --> 00:23:03,080 Speaker 2: Tell me sports sports? There you go. I do just 459 00:23:03,080 --> 00:23:05,880 Speaker 2: think it makes sense for meta. Good on you, Mark Zuckerberg. 460 00:23:05,960 --> 00:23:08,840 Speaker 2: Maybe it'll work out better than Threads. 461 00:23:10,400 --> 00:23:13,200 Speaker 1: I still look at Threads a lot. Really, I was 462 00:23:13,240 --> 00:23:14,959 Speaker 1: constantly trying to push you to it, and then like 463 00:23:15,000 --> 00:23:17,800 Speaker 1: it's like clean accent and it just serves you a 464 00:23:17,840 --> 00:23:22,320 Speaker 1: series of rage bait and it's just like it's Mark Zuckerberg. 465 00:23:22,840 --> 00:23:26,280 Speaker 1: There's the gap between like what you want to want 466 00:23:26,680 --> 00:23:29,919 Speaker 1: and like your base revealed. The preferences is like the 467 00:23:29,960 --> 00:23:34,720 Speaker 1: world's greatest exploiter of that gap, where like do I 468 00:23:34,840 --> 00:23:38,359 Speaker 1: want to be looking at stuff on threads that makes 469 00:23:38,359 --> 00:23:40,159 Speaker 1: me angry but just because it's stupid? 470 00:23:40,359 --> 00:23:40,600 Speaker 3: Yeah? 471 00:23:40,840 --> 00:23:43,639 Speaker 1: No, do I want that? No? Do I do it? Yes? 472 00:23:43,800 --> 00:23:47,080 Speaker 1: And yet and like you know, is he making billions 473 00:23:47,080 --> 00:24:06,000 Speaker 1: off of that? Probably private credit is to the thing. 474 00:24:06,320 --> 00:24:09,679 Speaker 2: Yeah, there's been a ton of redemption requests. There is 475 00:24:09,760 --> 00:24:15,199 Speaker 2: Morgan Stanley all, Yeah, they're all getting capped at five percent. Yea, 476 00:24:15,720 --> 00:24:18,240 Speaker 2: it seems like that was working is design, Yeah, as 477 00:24:18,320 --> 00:24:18,800 Speaker 2: the signs. 478 00:24:19,240 --> 00:24:20,800 Speaker 1: But yeah, it's like what we talked about with buzz, 479 00:24:20,800 --> 00:24:24,280 Speaker 1: which is that when you gate these or when you 480 00:24:24,320 --> 00:24:26,960 Speaker 1: can't redemption to five percent, you create a backlog in 481 00:24:27,000 --> 00:24:28,840 Speaker 1: the next quarter. People are like, I want want me. 482 00:24:29,359 --> 00:24:31,399 Speaker 1: You have all the redemptions from that quarter plus all 483 00:24:31,440 --> 00:24:33,240 Speaker 1: the like leftover runs in the previous quarter, and it 484 00:24:33,280 --> 00:24:35,440 Speaker 1: keeps snowballing from there. So that's kind of happening. 485 00:24:35,640 --> 00:24:37,719 Speaker 2: Yeah, so this will be a story that's with us 486 00:24:37,760 --> 00:24:38,320 Speaker 2: for some time. 487 00:24:38,560 --> 00:24:41,879 Speaker 1: Yeah, unless like people start being more confident about private 488 00:24:41,920 --> 00:24:44,840 Speaker 1: credit or I don't know, man, I don't know. 489 00:24:45,240 --> 00:24:49,199 Speaker 2: But JP Morgan says, Hey, so you don't like the 490 00:24:49,240 --> 00:24:52,480 Speaker 2: whole quarterly redemption schedule, how about monthly liquidity? 491 00:24:52,520 --> 00:24:54,720 Speaker 1: I love it. First of all, love is a strong word, 492 00:24:54,800 --> 00:24:55,040 Speaker 1: like this. 493 00:24:55,000 --> 00:24:57,560 Speaker 2: Is boring technical, you know, you love it. 494 00:24:57,720 --> 00:24:58,200 Speaker 1: I love it. 495 00:24:58,520 --> 00:25:00,879 Speaker 2: The sec also, I'm I mean, maybe they don't love it, 496 00:25:00,920 --> 00:25:01,760 Speaker 2: but they're okay with it. 497 00:25:01,800 --> 00:25:03,760 Speaker 1: Of course they're okay with it. Like the point is, like, 498 00:25:03,800 --> 00:25:06,160 Speaker 1: there's a rule that says you have to offer quarterly liquidity. 499 00:25:06,680 --> 00:25:10,040 Speaker 1: Jerry Morgan is like, well, monthly liquidity is not quarterly liquidity, 500 00:25:10,240 --> 00:25:12,919 Speaker 1: but it is strictly better than quarterly liquidity. And this 501 00:25:13,160 --> 00:25:15,399 Speaker 1: is like, yes, that's true, it's strictly better. And so 502 00:25:15,480 --> 00:25:17,280 Speaker 1: they like waived the rule to how you can have 503 00:25:17,400 --> 00:25:18,200 Speaker 1: monthly liquidity. 504 00:25:18,400 --> 00:25:18,800 Speaker 2: Yeah. 505 00:25:18,960 --> 00:25:21,159 Speaker 1: What I love about it is like one reason to 506 00:25:21,200 --> 00:25:24,520 Speaker 1: do this is to like give people more liquidity, but 507 00:25:25,240 --> 00:25:28,000 Speaker 1: I think another reason to do it is to actually 508 00:25:29,119 --> 00:25:31,800 Speaker 1: have people demand less liquidity, right, and so, like one 509 00:25:31,800 --> 00:25:35,480 Speaker 1: thing I was thinking is like if you offer quarterly redemptions, 510 00:25:35,920 --> 00:25:37,880 Speaker 1: like that's like a big decision, right, If you don't 511 00:25:37,880 --> 00:25:39,080 Speaker 1: get your money out of this quarter, you have to 512 00:25:39,080 --> 00:25:40,880 Speaker 1: wait a whole quarter for like us to get worse 513 00:25:40,920 --> 00:25:42,560 Speaker 1: before you can get your money out again. But if 514 00:25:42,560 --> 00:25:46,159 Speaker 1: you offered like real time liquidity, then no one has 515 00:25:46,200 --> 00:25:49,040 Speaker 1: any incentive to take money out now because in five 516 00:25:49,080 --> 00:25:50,960 Speaker 1: seconds they can take money out. Right. Yeah, It's like 517 00:25:51,480 --> 00:25:53,080 Speaker 1: monthly is somewhere in between. 518 00:25:53,320 --> 00:25:56,359 Speaker 2: Yeah, perhaps I'm comfortable waiting a month to get my money, 519 00:25:56,359 --> 00:25:58,280 Speaker 2: but I'm not comfortable waiting three months. 520 00:25:58,520 --> 00:26:01,840 Speaker 1: It sounds less dire, you know. Yeah, there was all 521 00:26:01,880 --> 00:26:05,520 Speaker 1: this worry for years, been worry about mutual funds which 522 00:26:05,520 --> 00:26:09,679 Speaker 1: have daily liquidity, and about like the idea that you know, 523 00:26:09,760 --> 00:26:11,840 Speaker 1: if stocks go down or bonds go down or whatever, 524 00:26:11,920 --> 00:26:14,960 Speaker 1: then mutual fund investors will to add all their money back. 525 00:26:15,640 --> 00:26:17,920 Speaker 1: And I was writing for years people are worried about 526 00:26:17,960 --> 00:26:20,080 Speaker 1: bomb market liquidity. That's the one point to me. Like 527 00:26:20,160 --> 00:26:22,920 Speaker 1: John Brooks in the sixties was writing about like there 528 00:26:22,960 --> 00:26:24,879 Speaker 1: was a stock market crash, and everyone's like, oh, no, 529 00:26:25,320 --> 00:26:28,239 Speaker 1: mutual funds, which were like newly invented will sell out 530 00:26:28,280 --> 00:26:31,320 Speaker 1: their stock if there's a crash, And then it was fine. Yeah, 531 00:26:31,359 --> 00:26:33,040 Speaker 1: And I think that like some of that is like 532 00:26:33,840 --> 00:26:36,720 Speaker 1: people who know they can get daily liquidity or not, 533 00:26:36,880 --> 00:26:38,720 Speaker 1: you know, they're not rushing for the exits, whereas like 534 00:26:38,720 --> 00:26:40,879 Speaker 1: you know, people in these private credit funds are rushing 535 00:26:40,920 --> 00:26:44,160 Speaker 1: for the exits. So maybe maybe monthly liquidity will solve 536 00:26:44,200 --> 00:26:44,879 Speaker 1: that problem. 537 00:26:45,000 --> 00:26:47,760 Speaker 2: I mean, they're all tailored towards retail investors, but this 538 00:26:47,840 --> 00:26:52,080 Speaker 2: specific one is tailored towards retail investors. There was this 539 00:26:52,240 --> 00:26:55,200 Speaker 2: shift after what happened in the first quarter. You saw 540 00:26:55,200 --> 00:26:58,679 Speaker 2: a lot of firms talking up institutional investors like that's 541 00:26:58,680 --> 00:27:01,760 Speaker 2: where the focus is. But JP Morgan still seems interested 542 00:27:01,800 --> 00:27:03,760 Speaker 2: in courting that retail investor, right. 543 00:27:03,600 --> 00:27:05,960 Speaker 1: I mean, you have to court the retail investor, right, like, 544 00:27:06,000 --> 00:27:07,960 Speaker 1: the long game has to be we're going to sell 545 00:27:07,960 --> 00:27:10,320 Speaker 1: privates to people with trillions of dollars in their prof 546 00:27:10,320 --> 00:27:11,800 Speaker 1: one case. It just has to be the long game 547 00:27:12,040 --> 00:27:17,000 Speaker 1: of everything correct. And so right now that retail capital 548 00:27:17,040 --> 00:27:19,959 Speaker 1: seems to flighty and you're like, yeah, institutions still love us. 549 00:27:19,960 --> 00:27:21,920 Speaker 1: It's great, but like you still have to be thinking 550 00:27:21,960 --> 00:27:25,560 Speaker 1: about that long game. Also, there's an article this week 551 00:27:25,680 --> 00:27:29,919 Speaker 1: in bloomerket about the private credit arbitrage trade getting momentum 552 00:27:30,280 --> 00:27:34,680 Speaker 1: from advisors. Did you see this? No, so the arbitrage chairs. 553 00:27:34,720 --> 00:27:38,919 Speaker 1: I don't love that phrase, but whatever the trade is. Like, 554 00:27:38,960 --> 00:27:43,600 Speaker 1: if you are in a private, non traded business development 555 00:27:43,600 --> 00:27:47,200 Speaker 1: company credit fund, you can ask for your money back 556 00:27:47,280 --> 00:27:49,080 Speaker 1: and if you get it, which you know, there's caps 557 00:27:49,119 --> 00:27:51,120 Speaker 1: on redumptions or whatever. But any money you do get back, 558 00:27:51,160 --> 00:27:52,640 Speaker 1: you get back on one hundred cents on the dollar. 559 00:27:52,680 --> 00:27:55,080 Speaker 1: You get it back at net asset value. That's the 560 00:27:55,200 --> 00:27:59,640 Speaker 1: rule how these things work. Meanwhile, they are publicly traded BBCs, 561 00:28:00,160 --> 00:28:03,440 Speaker 1: which are essentially the same assets the same fund as 562 00:28:03,480 --> 00:28:05,840 Speaker 1: the private BDCs, or like, they're at least run by 563 00:28:05,880 --> 00:28:08,760 Speaker 1: the same managers that have overlapping assets, and those trade 564 00:28:08,760 --> 00:28:11,520 Speaker 1: it like you know, eighty seventy five cents on the dollar, 565 00:28:12,040 --> 00:28:14,879 Speaker 1: and so a lot of people think, well, I should 566 00:28:14,920 --> 00:28:17,359 Speaker 1: take my money out from the public one a one 567 00:28:17,400 --> 00:28:18,840 Speaker 1: hundred cents on the dollar and then put it back 568 00:28:18,880 --> 00:28:21,040 Speaker 1: into the same assets that's seventy five cents on the dollar. 569 00:28:21,280 --> 00:28:24,840 Speaker 1: Because that's just saves me twenty five cents. That's a 570 00:28:24,880 --> 00:28:27,320 Speaker 1: trade that, like was Winstein talked up on this podcast, 571 00:28:27,440 --> 00:28:30,440 Speaker 1: that people have talked about and written about. But the 572 00:28:30,440 --> 00:28:35,080 Speaker 1: Bloomer article this week is about financial advisors telling their 573 00:28:35,119 --> 00:28:38,000 Speaker 1: clients to do that, which is fascinating to me because 574 00:28:38,040 --> 00:28:41,120 Speaker 1: on the one hand, I don't want to give investment advice. 575 00:28:41,160 --> 00:28:43,560 Speaker 1: I don't want to say that's good investing advice. There 576 00:28:43,560 --> 00:28:47,560 Speaker 1: are pluses and minuses. The discount might get wider. You 577 00:28:47,640 --> 00:28:50,880 Speaker 1: can't know that this is not really an arbitrage. The 578 00:28:50,880 --> 00:28:53,920 Speaker 1: liber Artical quotes a guy named John Scott at CF 579 00:28:53,920 --> 00:28:56,680 Speaker 1: Advisers saying, when the same credit manager is running a 580 00:28:56,680 --> 00:28:59,160 Speaker 1: non traded BBC at its net asset value and I 581 00:28:59,240 --> 00:29:01,760 Speaker 1: listed sibling fund it a twenty four to twenty seven 582 00:29:01,800 --> 00:29:05,440 Speaker 1: percent discount, that's not a philosophical debate. That's a math problem. 583 00:29:05,720 --> 00:29:07,880 Speaker 1: That's kind of right, right, like it's kind of a 584 00:29:07,920 --> 00:29:10,120 Speaker 1: good trade, and so as a financial advisor, it kind 585 00:29:10,160 --> 00:29:11,840 Speaker 1: of be who's you to tell people who do this trade? 586 00:29:12,000 --> 00:29:17,000 Speaker 1: But also the whole non traded BDC product is kind 587 00:29:17,040 --> 00:29:20,560 Speaker 1: of like financial advisors putting people into it, right, yeah, 588 00:29:20,680 --> 00:29:23,840 Speaker 1: by financial advisor putting me into one, right, et cetera. 589 00:29:24,360 --> 00:29:27,720 Speaker 1: And like, you know, you might ask why can non 590 00:29:27,760 --> 00:29:30,560 Speaker 1: traded BDCs sell shares a one hundred cents on the 591 00:29:30,560 --> 00:29:33,120 Speaker 1: dollar when traded ones sell shares it, you know, they 592 00:29:33,240 --> 00:29:35,600 Speaker 1: traded eighty cents on the dollar. The answer is because 593 00:29:35,640 --> 00:29:37,120 Speaker 1: your advisor is putting you into it. Why is your 594 00:29:37,120 --> 00:29:44,000 Speaker 1: advisor putting into it? You know? Whyto advisors do anything? Right? 595 00:29:44,560 --> 00:29:46,880 Speaker 1: And so it is funny to see this like the 596 00:29:46,960 --> 00:29:50,040 Speaker 1: advisors shifting their clients out of the advisor product and 597 00:29:50,080 --> 00:29:53,040 Speaker 1: into the like publicly available product. It's just like, yeah, 598 00:29:53,200 --> 00:29:55,000 Speaker 1: at some point in the cycle, you're like, wait a minute, 599 00:29:55,000 --> 00:29:56,280 Speaker 1: I'm going to give people good advice. 600 00:29:56,560 --> 00:30:00,560 Speaker 2: Yeah right, Oh god, well that's cool. 601 00:30:00,600 --> 00:30:02,440 Speaker 1: It's not investing advice. I'm not thinking it's good advice. 602 00:30:02,440 --> 00:30:03,400 Speaker 1: I'm just saying a lot. 603 00:30:03,360 --> 00:30:06,040 Speaker 2: Of it's on investing advices. Just the problem until a 604 00:30:06,040 --> 00:30:08,880 Speaker 2: financial advisor tells you so not this podcast, right, that's right, 605 00:30:08,960 --> 00:30:11,440 Speaker 2: that's right. Run it by your financial advisor. 606 00:30:11,680 --> 00:30:24,280 Speaker 1: See what she says. And that was the Money Stuff Podcast. 607 00:30:24,560 --> 00:30:26,680 Speaker 2: I'm Matt Levine and I'm Katie Greifeld. 608 00:30:26,880 --> 00:30:29,040 Speaker 1: You can find my work by subscribeing to the Money 609 00:30:29,040 --> 00:30:31,040 Speaker 1: Stuff newsletter on Bloomberg dot. 610 00:30:30,880 --> 00:30:33,640 Speaker 2: Com, and you can find me on Bloomberg TV every 611 00:30:33,680 --> 00:30:36,840 Speaker 2: day on the Clothes between three and five pm Eastern. 612 00:30:37,600 --> 00:30:39,560 Speaker 1: We'd love to hear from you. You can send an 613 00:30:39,600 --> 00:30:42,840 Speaker 1: email to money Pod at Bloomberg dot net, ask us 614 00:30:42,840 --> 00:30:44,840 Speaker 1: a question and we might answer it on the air. 615 00:30:45,200 --> 00:30:47,800 Speaker 2: You can also subscribe to our show wherever you're listening 616 00:30:47,880 --> 00:30:49,720 Speaker 2: right now and leave us a review. It helps more 617 00:30:49,720 --> 00:30:50,600 Speaker 2: people find the show. 618 00:30:51,320 --> 00:30:55,160 Speaker 1: The Money Stuff Podcast is produced by annam Aserakas, Moses 619 00:30:55,200 --> 00:30:57,200 Speaker 1: onm and Alexis Hot. 620 00:30:57,880 --> 00:31:00,320 Speaker 2: Our theme music was composed by Blake Mabel. 621 00:31:00,880 --> 00:31:04,320 Speaker 1: Amy Keen is our executive producer. Thanks for listening to 622 00:31:04,360 --> 00:31:06,800 Speaker 1: The Money Stuff Podcast. We'll be back next week with 623 00:31:07,000 --> 00:31:07,560 Speaker 1: more stuff.