1 00:00:03,960 --> 00:00:06,720 Speaker 1: Well, Elon Musk gives now the richest person on the planet. 2 00:00:07,200 --> 00:00:10,039 Speaker 1: More than half the satellites in space are owned and 3 00:00:10,119 --> 00:00:15,160 Speaker 1: controlled by one man, starting his own artificial intelligence company. 4 00:00:15,400 --> 00:00:18,400 Speaker 2: Well, he's a legitimate super genius, I mean legitimate. 5 00:00:18,560 --> 00:00:18,919 Speaker 1: He says. 6 00:00:18,920 --> 00:00:21,560 Speaker 3: He's always voted for Democrats, but this year it will 7 00:00:21,560 --> 00:00:23,000 Speaker 3: be different. He'll vote Republican. 8 00:00:23,280 --> 00:00:25,760 Speaker 1: There is a reason the US government is so reliant 9 00:00:25,800 --> 00:00:26,160 Speaker 1: on him. 10 00:00:26,360 --> 00:00:29,960 Speaker 4: Elon Musk is a scam artist and he's done nothing. 11 00:00:30,600 --> 00:00:43,360 Speaker 2: Anything he does is fascinating the people. Welcome to Elon, Inc. 12 00:00:43,479 --> 00:00:47,360 Speaker 2: Where we discuss Elon Musk's vast corporate empire, his latest 13 00:00:47,360 --> 00:00:50,279 Speaker 2: gambits and antics, and how to make sense of it all. 14 00:00:50,760 --> 00:00:53,440 Speaker 2: I'm Joe Webber, sitting in for David Papa Duplas for 15 00:00:53,479 --> 00:00:55,600 Speaker 2: the first time on Eloni. We're gonna get you ready 16 00:00:55,640 --> 00:00:59,160 Speaker 2: for Tesla's earnings called this Wednesday, and we have a 17 00:00:59,160 --> 00:01:02,680 Speaker 2: little surprise to take the fun factor to ludicrous mode. 18 00:01:03,280 --> 00:01:06,520 Speaker 2: To help us prep the popcorn will convene Dana Hole, 19 00:01:06,720 --> 00:01:09,199 Speaker 2: who's covered more Tesla earnings call than any other reporter 20 00:01:09,319 --> 00:01:14,119 Speaker 2: I know. Hi, Dana, Good morning. Sarah Fryar, who leads 21 00:01:14,120 --> 00:01:17,760 Speaker 2: the Big Tech team here at Bloomberg News. Hello, and 22 00:01:17,880 --> 00:01:22,080 Speaker 2: Max Chafkin, Senior reporter at Bloomberg BusinessWeek. Hey, but first, 23 00:01:22,560 --> 00:01:25,399 Speaker 2: some recent topics we've discussed on elon ink have been 24 00:01:25,440 --> 00:01:28,959 Speaker 2: back in the news. A big one anti Semitism. Both 25 00:01:29,280 --> 00:01:32,200 Speaker 2: X and Musk have been accused of amplifying hate speech 26 00:01:32,600 --> 00:01:35,880 Speaker 2: on the platform. On Monday, Musk got a private tour 27 00:01:35,959 --> 00:01:40,120 Speaker 2: of the Aschwitz concentration camp in southern Poland. Later he 28 00:01:40,160 --> 00:01:44,080 Speaker 2: participated in a discussion hosted by Ben Shapiro, a controversial 29 00:01:44,120 --> 00:01:47,080 Speaker 2: media pundit. Let's listen, why as. 30 00:01:47,000 --> 00:01:49,480 Speaker 4: Many Jewish friends as non Jewish friends. I'm like Jewish 31 00:01:49,480 --> 00:01:51,840 Speaker 4: by association, I'm aspirationally. 32 00:01:51,240 --> 00:01:54,000 Speaker 2: Jewish, Sarah, what do you make of this visit to 33 00:01:54,000 --> 00:01:58,240 Speaker 2: Aschwitz and sort of this Musk non apology apology. 34 00:01:58,880 --> 00:02:02,840 Speaker 4: It's extremely crn to go to a concentration camp to 35 00:02:03,000 --> 00:02:09,880 Speaker 4: try to launder your image and say that you are 36 00:02:10,080 --> 00:02:15,840 Speaker 4: aspirationally Jewish, basically like this happened to your people by association. 37 00:02:17,240 --> 00:02:19,000 Speaker 4: I mean, I can't believe it happened. 38 00:02:20,120 --> 00:02:23,239 Speaker 1: Just welcome to the tribe, brother, Thank you. As a 39 00:02:23,280 --> 00:02:27,120 Speaker 1: Jewish American, I'm just so happy to have encountered another 40 00:02:27,200 --> 00:02:31,160 Speaker 1: Jewish American. Elon Musk I also agree, why is doing 41 00:02:31,200 --> 00:02:36,800 Speaker 1: this cringe? So our editor Naomi joke yesterday that you know, normally, 42 00:02:37,000 --> 00:02:40,400 Speaker 1: if you commit an anti Semitic faux pa, you have 43 00:02:40,480 --> 00:02:43,160 Speaker 1: to go to the Holocaust Museum in Washington, d C. 44 00:02:43,800 --> 00:02:47,320 Speaker 1: If you commit a world historical anti Semitic foe pa, 45 00:02:47,360 --> 00:02:51,800 Speaker 1: if you allow hundreds or maybe even thousands of crazy 46 00:02:51,840 --> 00:02:55,160 Speaker 1: anti Semitic accounts, if you buy into crazy conspiracy theories, 47 00:02:55,200 --> 00:02:58,720 Speaker 1: if you, as the world's richest man, promote all this nonsense, 48 00:02:59,080 --> 00:03:02,520 Speaker 1: then you have to actually go to Ashwitz. And so yeah, 49 00:03:02,520 --> 00:03:06,040 Speaker 1: I mean, he's like attempting to make things right, I guess, 50 00:03:06,160 --> 00:03:09,960 Speaker 1: but of course doesn't seem to be really addressing any 51 00:03:10,040 --> 00:03:13,280 Speaker 1: of the normal stuff, and is claiming Jewish identity in 52 00:03:13,280 --> 00:03:16,799 Speaker 1: an extremely depressing and cringe worthy way. 53 00:03:16,800 --> 00:03:19,359 Speaker 3: In my opinion, just struck me as like a very 54 00:03:19,400 --> 00:03:23,320 Speaker 3: political It's like all optics. It's like there's Elon Musk 55 00:03:23,400 --> 00:03:26,280 Speaker 3: looking somber with let us note his three year old 56 00:03:26,320 --> 00:03:28,519 Speaker 3: toddler on his shoulders. 57 00:03:28,320 --> 00:03:31,040 Speaker 2: And an Auschwitz survivor standing next to him too, and an. 58 00:03:30,919 --> 00:03:33,040 Speaker 3: Oshwitz survivor standing next to him, and then like the 59 00:03:33,080 --> 00:03:35,800 Speaker 3: images that were released, it very much struck me. It 60 00:03:36,040 --> 00:03:37,960 Speaker 3: looked like a political campaign. 61 00:03:38,000 --> 00:03:38,280 Speaker 1: Stop. 62 00:03:38,320 --> 00:03:41,240 Speaker 3: Obviously, Musk is not running for any kind of office, 63 00:03:41,280 --> 00:03:43,680 Speaker 3: but it had all of the stage craft of a 64 00:03:43,680 --> 00:03:47,520 Speaker 3: political moment during the week when Tesla's reporting earnings and 65 00:03:47,560 --> 00:03:49,400 Speaker 3: so for him to sort of take the time to 66 00:03:49,520 --> 00:03:52,880 Speaker 3: fly to Poland and fly back. Why now it's all 67 00:03:52,920 --> 00:03:55,200 Speaker 3: because of X and advertising, right, Sarah, I mean, this 68 00:03:55,240 --> 00:03:57,760 Speaker 3: is still sort of an effort to lure the advertisers back. 69 00:03:57,920 --> 00:04:01,800 Speaker 4: It's wild because this is the only thing that Elon 70 00:04:01,920 --> 00:04:05,400 Speaker 4: Musk and Linda Yakarino are talking about. Linda's the CEO 71 00:04:05,720 --> 00:04:09,840 Speaker 4: when they speak publicly like they're just doubling down on 72 00:04:09,880 --> 00:04:13,320 Speaker 4: the how could you think we were ever anti semitic? Meanwhile, 73 00:04:13,320 --> 00:04:16,159 Speaker 4: if you look at what Elon Musk is tweeting, there's 74 00:04:16,320 --> 00:04:19,000 Speaker 4: there it runs the gamut. There's there's all sorts of 75 00:04:19,240 --> 00:04:22,880 Speaker 4: anti immigrant sentiments, all sorts of of you know, other 76 00:04:23,040 --> 00:04:26,160 Speaker 4: race t stuff, and he's promoting a lot of these 77 00:04:26,160 --> 00:04:28,200 Speaker 4: accounts that are the worst of the worst. It is 78 00:04:28,279 --> 00:04:31,560 Speaker 4: not just anti semitism. It is a wholesale embrace of 79 00:04:31,680 --> 00:04:37,560 Speaker 4: people who were banned before were considered dangerous to have 80 00:04:37,640 --> 00:04:40,400 Speaker 4: on the platform in the name of his his free 81 00:04:40,400 --> 00:04:42,280 Speaker 4: speech initiative and So. 82 00:04:41,920 --> 00:04:44,920 Speaker 1: The funniest thing about this about this event was that 83 00:04:45,360 --> 00:04:48,919 Speaker 1: Elon Musk and the European Jewish Association which hosted it, 84 00:04:48,960 --> 00:04:51,760 Speaker 1: started with this kind of thought experiment like what if 85 00:04:52,000 --> 00:04:55,000 Speaker 1: we had X during the late Weimar period in Germany, 86 00:04:55,000 --> 00:04:57,599 Speaker 1: maybe we would never have had the Holocaust? And Elon 87 00:04:57,680 --> 00:05:01,480 Speaker 1: Musk claimed essentially that, yes, if we'd only had Twitter, 88 00:05:01,800 --> 00:05:04,440 Speaker 1: you know, back in the late thirties, early nineteen forties, 89 00:05:04,440 --> 00:05:07,160 Speaker 1: a Holocaust never would have happened, which, like I guess, 90 00:05:07,200 --> 00:05:08,919 Speaker 1: you know, pay sign up for groc guys. 91 00:05:09,240 --> 00:05:13,640 Speaker 2: Okay. Elsewhere in current events, Florida Governor Ron DeSantis dropped 92 00:05:13,640 --> 00:05:16,400 Speaker 2: out of the Republican presidential race ahead of the New 93 00:05:16,440 --> 00:05:19,640 Speaker 2: Hampshire primary. One of our mini Elon in pilots was 94 00:05:19,680 --> 00:05:24,039 Speaker 2: all about DeSantis announcing his campaign on X with Elon, 95 00:05:24,560 --> 00:05:28,159 Speaker 2: which was glitchy, shall we say, I. 96 00:05:28,279 --> 00:05:28,960 Speaker 1: Think we've got. 97 00:05:30,720 --> 00:05:34,599 Speaker 2: Just a massive number of people online, so it's serve 98 00:05:34,600 --> 00:05:39,440 Speaker 2: as as training somewhat so very glitchy, Sarah Fryar Howell history, 99 00:05:39,720 --> 00:05:45,560 Speaker 2: remember that Musk DeSantis embrace just a you know. 100 00:05:45,480 --> 00:05:48,599 Speaker 4: A bad bet, right, but you know, it wasn't the 101 00:05:48,640 --> 00:05:52,640 Speaker 4: only candidate that he has aligned himself with. He also 102 00:05:52,680 --> 00:05:55,520 Speaker 4: did one of those for RFK Junior, the known anti 103 00:05:55,600 --> 00:06:00,760 Speaker 4: vax conspiracy theorist. He's also been in spaces with the 104 00:06:00,839 --> 00:06:05,359 Speaker 4: vik Ramaswami. This is is all kind of wishful thinking 105 00:06:05,400 --> 00:06:08,640 Speaker 4: on his part, because he would really like it if 106 00:06:08,680 --> 00:06:12,880 Speaker 4: he didn't have to cozy up to either Trump or Biden. 107 00:06:14,080 --> 00:06:17,000 Speaker 4: But sounds like that's what he's going to have to do. 108 00:06:17,720 --> 00:06:20,040 Speaker 4: And he's already We've already discussed on this podcast that 109 00:06:20,080 --> 00:06:22,280 Speaker 4: he has said he will not vote Biden. And so 110 00:06:22,320 --> 00:06:26,320 Speaker 4: we do see a lot more Trump aligned rhetoric these 111 00:06:26,400 --> 00:06:31,000 Speaker 4: days on X, including voting conspiracy theories. 112 00:06:31,680 --> 00:06:35,480 Speaker 2: Max if Runda Santis is out of the race. Who 113 00:06:35,839 --> 00:06:36,960 Speaker 2: is Elon's horse? Now? 114 00:06:37,040 --> 00:06:40,600 Speaker 1: I mean we've we're already seeing I looked at Elon's 115 00:06:41,440 --> 00:06:44,120 Speaker 1: X page earlier today, and you know, he's he's tweeting 116 00:06:44,160 --> 00:06:46,440 Speaker 1: about twenty twenty election stuff. We've talked about this on 117 00:06:46,440 --> 00:06:49,440 Speaker 1: the podcast before. He's been stopped to steal curious, you know, 118 00:06:49,520 --> 00:06:52,640 Speaker 1: for some time, and he is, you know, reiterating those concerns, 119 00:06:52,880 --> 00:06:56,919 Speaker 1: sort of rebroadcasting or or promoting what essentially amount to 120 00:06:57,000 --> 00:07:02,760 Speaker 1: like willful misreadings by Donald Trump and Donald Trump type 121 00:07:02,760 --> 00:07:06,040 Speaker 1: supporters of the twenty Tony voting results, among many other things. 122 00:07:06,080 --> 00:07:08,680 Speaker 1: I mean, he is essentially, you know, moving towards Trump. 123 00:07:08,760 --> 00:07:10,720 Speaker 1: As Sarah said, I mean, I think I think we're 124 00:07:11,040 --> 00:07:14,280 Speaker 1: already seeing it. I'm guessing we'll see it more like Frankly, 125 00:07:14,520 --> 00:07:17,559 Speaker 1: like many billionaires who affiliate with the right right, where 126 00:07:17,600 --> 00:07:20,360 Speaker 1: like there were other candidates that they might have preferred. 127 00:07:20,880 --> 00:07:23,320 Speaker 1: We have a new Hampshire primary today. It doesn't look 128 00:07:23,320 --> 00:07:25,120 Speaker 1: great for NICKI Haley, and I think we're gonna see 129 00:07:25,120 --> 00:07:29,280 Speaker 1: this same evolution playing out among other people, not just Elon. 130 00:07:29,080 --> 00:07:36,360 Speaker 2: Musk, Max any Musk Trump predictions. Obviously, Trump was a 131 00:07:36,360 --> 00:07:42,200 Speaker 2: prolific tweeter before Musk purchased the platform. Trump obviously has 132 00:07:42,240 --> 00:07:44,880 Speaker 2: not been active on X but there's sets up this 133 00:07:44,960 --> 00:07:47,440 Speaker 2: dynamic where you've got Musk and Trump. 134 00:07:48,000 --> 00:07:51,400 Speaker 1: Perhaps another first of all, to me, Musk supporting Trump 135 00:07:51,440 --> 00:07:53,560 Speaker 1: is like a four on conclusion. Basically, I think it's 136 00:07:53,600 --> 00:07:56,000 Speaker 1: it's going to happen. I think you could argue it is, 137 00:07:56,120 --> 00:07:58,800 Speaker 1: it has already happened. The real question to me is 138 00:07:58,840 --> 00:08:01,680 Speaker 1: like what happens. You know after twenty twenty, there were 139 00:08:01,720 --> 00:08:04,720 Speaker 1: all of these right leaning social networks that launched, you know, 140 00:08:04,800 --> 00:08:07,680 Speaker 1: one of which was truth Social, Donald Trump's company, and 141 00:08:08,040 --> 00:08:11,120 Speaker 1: then another one of them is X essentially, and it 142 00:08:11,440 --> 00:08:13,760 Speaker 1: does seem like X has a chance of like claiming 143 00:08:13,760 --> 00:08:16,120 Speaker 1: that market. You do sort of wonder, you know, this 144 00:08:16,200 --> 00:08:17,440 Speaker 1: is kind of a deep cut. I mean, you really 145 00:08:17,480 --> 00:08:19,560 Speaker 1: have to be the interested in sort of right wing 146 00:08:19,600 --> 00:08:21,880 Speaker 1: media and media landscape to care like what happens to 147 00:08:21,920 --> 00:08:25,520 Speaker 1: truth Social, parlor Getter, what's the right wing dating site 148 00:08:25,560 --> 00:08:26,440 Speaker 1: of the right stuff? 149 00:08:26,680 --> 00:08:27,720 Speaker 2: Like, they're all these sort of. 150 00:08:27,680 --> 00:08:32,200 Speaker 1: Media business media adjacent businesses that seem like their reason 151 00:08:32,280 --> 00:08:35,280 Speaker 1: for being doesn't quite doesn't exist as much now that 152 00:08:35,400 --> 00:08:38,800 Speaker 1: now that Elon Musk has so firmly turned X to 153 00:08:38,840 --> 00:08:39,440 Speaker 1: that market. 154 00:08:39,520 --> 00:08:42,000 Speaker 2: So in that sense, you know, it's like all gravy 155 00:08:42,000 --> 00:08:42,439 Speaker 2: for Elon. 156 00:08:42,520 --> 00:08:45,480 Speaker 1: I think I think the consolidation of the Republican Party 157 00:08:45,520 --> 00:08:48,960 Speaker 1: probably allows, you know, X honestly to consolidate this sort 158 00:08:49,000 --> 00:08:50,199 Speaker 1: of ecosystem. 159 00:08:50,480 --> 00:08:53,880 Speaker 4: But let's be clear, Trump does not need X the 160 00:08:53,920 --> 00:08:56,880 Speaker 4: way he needed Twitter in twenty twenty or twenty sixteen. 161 00:08:57,400 --> 00:09:01,480 Speaker 4: It is a completely different platform now and Trump has 162 00:09:01,559 --> 00:09:04,520 Speaker 4: managed to build up his own network that he has 163 00:09:04,559 --> 00:09:08,439 Speaker 4: control over of email addresses, of phone numbers, of people 164 00:09:08,520 --> 00:09:12,120 Speaker 4: who will hand him those five dollars donations when he 165 00:09:12,200 --> 00:09:15,080 Speaker 4: sends a scary thought. I think that that's really how 166 00:09:15,120 --> 00:09:19,560 Speaker 4: we are seeing political fundraising evolve, and the idea that 167 00:09:20,320 --> 00:09:24,120 Speaker 4: a politician needs to use X in order to rally 168 00:09:24,160 --> 00:09:27,240 Speaker 4: their base may still be in Musk's mind, which is 169 00:09:27,280 --> 00:09:30,400 Speaker 4: why he rewarded the candidates that we're leaning into it. 170 00:09:30,679 --> 00:09:33,840 Speaker 4: But as we've seen, that hasn't been successful for them. 171 00:09:34,200 --> 00:09:36,800 Speaker 4: And I think that what Trump is seeing with a 172 00:09:36,840 --> 00:09:39,280 Speaker 4: platform is the same thing we're all seeing. It is 173 00:09:39,320 --> 00:09:43,840 Speaker 4: not as effective anymore for driving results with an audience. 174 00:09:44,200 --> 00:09:47,040 Speaker 4: The followers that it shows you you have are not 175 00:09:47,320 --> 00:09:52,040 Speaker 4: all visiting the platform anymore. So I think in that sense, 176 00:09:52,200 --> 00:09:55,000 Speaker 4: maybe Musk needs Trump more than Trump needs Musk at 177 00:09:55,040 --> 00:09:55,439 Speaker 4: this point. 178 00:09:56,360 --> 00:09:58,200 Speaker 1: I one hundred percent agree with that, And I do 179 00:09:58,240 --> 00:10:01,560 Speaker 1: think there's some question about whether Trump ever needed Twitter, 180 00:10:01,679 --> 00:10:03,920 Speaker 1: like I think there was a there has been a 181 00:10:03,960 --> 00:10:07,880 Speaker 1: tendency to kind of like overstate the impact of social media, 182 00:10:07,920 --> 00:10:11,640 Speaker 1: and a lot of times media kind of chases like 183 00:10:12,080 --> 00:10:15,400 Speaker 1: whatever people are into rather than like setting the agenda. 184 00:10:15,800 --> 00:10:18,439 Speaker 1: But with Trump, it definitely is kind of has reached 185 00:10:18,440 --> 00:10:21,400 Speaker 1: a different dynamic where he's basically been absent from this 186 00:10:21,480 --> 00:10:23,640 Speaker 1: platform for a very long time, and it hasn't seemed 187 00:10:23,640 --> 00:10:24,880 Speaker 1: to make a huge difference. 188 00:10:25,720 --> 00:10:30,000 Speaker 2: One more fallout of DeSantis exiting the race, there was 189 00:10:30,360 --> 00:10:34,800 Speaker 2: a potential Few of the Week max from Elon Musk 190 00:10:34,840 --> 00:10:37,680 Speaker 2: trolling economist Paul Krugman, who's a commist at the New 191 00:10:37,760 --> 00:10:39,120 Speaker 2: York Times. What happened? 192 00:10:39,160 --> 00:10:42,800 Speaker 1: Well, I mean, I'm a little as an Elon feud connoisseur, 193 00:10:43,080 --> 00:10:46,319 Speaker 1: I am sad to report that Paul Krugman, the Nobel 194 00:10:46,360 --> 00:10:50,520 Speaker 1: Prize winning economists Princeton University, New York Times columnist, well 195 00:10:50,559 --> 00:10:54,439 Speaker 1: known progressive liberal commentator, did not take the bait. 196 00:10:54,320 --> 00:10:55,200 Speaker 2: So it's kind of a fake. 197 00:10:55,559 --> 00:10:59,920 Speaker 1: Krugman trolled DeSantis, essentially saying like, let's not forget how 198 00:11:00,080 --> 00:11:04,080 Speaker 1: wrong Rondistances was on covid, pointing to long standing criticisms 199 00:11:04,080 --> 00:11:07,160 Speaker 1: among the left that Dessant has kind of ignored you know, 200 00:11:07,200 --> 00:11:11,560 Speaker 1: basic CDC guidelines and so on, and Elon must responded like, 201 00:11:11,640 --> 00:11:15,360 Speaker 1: you are a disgrace to the economics profession, which Krugman 202 00:11:15,440 --> 00:11:17,600 Speaker 1: sadly did not take the bait. And I'm mostly sad 203 00:11:17,800 --> 00:11:20,000 Speaker 1: because it was sort of amusing to think of Elon 204 00:11:20,160 --> 00:11:24,920 Speaker 1: as a connoisseur of like academic economics, Like he's like, oh, man, 205 00:11:25,040 --> 00:11:28,120 Speaker 1: you know, you're just like disgracing canes or like like 206 00:11:28,200 --> 00:11:31,000 Speaker 1: Elon has no idea who like it does not follow 207 00:11:31,000 --> 00:11:34,560 Speaker 1: the deep nuances of economics, like he follows the Wait. 208 00:11:34,440 --> 00:11:37,520 Speaker 3: He's starting university and he has an economics degree from 209 00:11:37,640 --> 00:11:40,080 Speaker 3: or business degree from Wharton. Come on, he has like 210 00:11:40,160 --> 00:11:43,000 Speaker 3: some grounding in business as a point, But. 211 00:11:42,960 --> 00:11:45,400 Speaker 1: That doesn't mean he's like some kind of connoisseur of 212 00:11:45,440 --> 00:11:49,400 Speaker 1: who you know, like academic economic academia. Yeah, he doesn't 213 00:11:49,480 --> 00:11:53,120 Speaker 1: know what whether Krugman's or disgraced anything. He's just responded 214 00:11:53,160 --> 00:11:53,720 Speaker 1: to the vibes. 215 00:11:54,040 --> 00:11:56,560 Speaker 3: It's not a few if it's not what's below a feud? 216 00:11:56,679 --> 00:11:59,439 Speaker 3: Is it like a schoolyard scrap. It's like a it's like. 217 00:11:59,400 --> 00:12:01,360 Speaker 2: A we have I think it's a big nothing burger. 218 00:12:01,480 --> 00:12:03,160 Speaker 2: It's like it's like. 219 00:12:03,080 --> 00:12:05,200 Speaker 1: On the hurricue, you have the one to five on 220 00:12:05,200 --> 00:12:08,160 Speaker 1: the hurricane scale, but below that it's like a tropical storm. 221 00:12:08,320 --> 00:12:12,400 Speaker 1: This is like a tropical depression. Maybe I guess as 222 00:12:12,440 --> 00:12:13,199 Speaker 1: far as fews go. 223 00:12:13,480 --> 00:12:23,520 Speaker 2: Maybe watch yeah Witchery mix And now for our main 224 00:12:23,559 --> 00:12:28,320 Speaker 2: event earnings. Okay. Publicly traded companies like Tesla get to 225 00:12:28,320 --> 00:12:30,800 Speaker 2: do these quarterly meetings where they reveal how much money 226 00:12:30,880 --> 00:12:33,439 Speaker 2: they made or lost. It's to face the music moment 227 00:12:33,440 --> 00:12:37,319 Speaker 2: that gives investors, analysts, and journalists a better look at 228 00:12:37,360 --> 00:12:41,760 Speaker 2: what's happening in a given companies business. Dana, you are 229 00:12:41,840 --> 00:12:44,840 Speaker 2: the Tesla pro here. How many earnings have you done? 230 00:12:44,840 --> 00:12:47,920 Speaker 2: And can you give us a short history of Tesla's 231 00:12:48,360 --> 00:12:49,240 Speaker 2: earnings calls? 232 00:12:49,800 --> 00:12:53,280 Speaker 3: So I've covered Tesla since two thousand and nine. They 233 00:12:53,320 --> 00:12:56,400 Speaker 3: went public in June of twenty ten, and I don't 234 00:12:56,440 --> 00:12:58,600 Speaker 3: think I've ever missed an earnings call. So that's what 235 00:12:58,880 --> 00:13:01,160 Speaker 3: ten years, four times year, I don't know, I've done 236 00:13:01,240 --> 00:13:05,680 Speaker 3: like over forty maybe fifty that rest in my life. 237 00:13:06,040 --> 00:13:09,400 Speaker 3: And they're pretty wild because if you so, like, most 238 00:13:09,440 --> 00:13:12,400 Speaker 3: corporate earnings calls are pretty boring, frankly, like the CEO 239 00:13:13,160 --> 00:13:18,400 Speaker 3: reads scripted remarks, the CFO reads scripted remarks that basically 240 00:13:18,400 --> 00:13:20,560 Speaker 3: repeat what they've just put out in the shareholder letter, 241 00:13:20,600 --> 00:13:23,160 Speaker 3: and then they take questions from analysts that are like, 242 00:13:23,720 --> 00:13:26,559 Speaker 3: congratulations on a good quarter, Bob, like can you give 243 00:13:26,640 --> 00:13:29,080 Speaker 3: us some color about the YadA YadA? And it's just like, 244 00:13:29,280 --> 00:13:32,800 Speaker 3: very frankly, it's very boring. Like test Le earnings calls 245 00:13:32,840 --> 00:13:35,520 Speaker 3: are not like that at all, like Elon is not scripted. 246 00:13:35,960 --> 00:13:38,520 Speaker 3: He gets really pissed off at the analysts. He gets 247 00:13:38,559 --> 00:13:41,400 Speaker 3: bored and like a big question like when this call 248 00:13:41,440 --> 00:13:43,360 Speaker 3: starts is like ooh, what kind of mood is dear 249 00:13:43,440 --> 00:13:46,320 Speaker 3: leader in? And we've had some really wild earnings calls 250 00:13:46,360 --> 00:13:48,640 Speaker 3: over the years. I mean, at one point in twenty 251 00:13:48,679 --> 00:13:52,640 Speaker 3: eighteen he called Tony Secanagi, who's like a great analyst 252 00:13:52,640 --> 00:13:55,280 Speaker 3: at Bernstein, like you know that his question he like 253 00:13:55,280 --> 00:13:57,320 Speaker 3: didn't answer his question. He was like this is boring 254 00:13:57,360 --> 00:14:00,360 Speaker 3: and boneheaded. At one point he went to YouTube and 255 00:14:00,400 --> 00:14:03,400 Speaker 3: took spent twenty five minutes talking to like a millennial 256 00:14:03,520 --> 00:14:06,920 Speaker 3: who has like a YouTube channel, and they're just really 257 00:14:07,280 --> 00:14:09,920 Speaker 3: they're very interesting calls, and so much of like how 258 00:14:10,120 --> 00:14:14,000 Speaker 3: Stock reacts is to what does Elon say? And then 259 00:14:14,040 --> 00:14:16,280 Speaker 3: like what's the actual news from the call? Because it's 260 00:14:16,280 --> 00:14:18,680 Speaker 3: all about like what does he say about, you know, 261 00:14:18,760 --> 00:14:21,760 Speaker 3: things going forward? And the January call is always really 262 00:14:21,760 --> 00:14:23,920 Speaker 3: important because it kind of sets out the expectations for 263 00:14:24,000 --> 00:14:26,800 Speaker 3: the year. So tomorrow the question is like are we 264 00:14:26,880 --> 00:14:30,800 Speaker 3: going to get embulent, expansive, like wonderful Elon or is 265 00:14:30,800 --> 00:14:32,960 Speaker 3: he going to be like morose Elon talking about how 266 00:14:33,000 --> 00:14:35,720 Speaker 3: they're digging their own grave, Like that's the big question 267 00:14:35,760 --> 00:14:36,680 Speaker 3: on everybody's mind. 268 00:14:36,920 --> 00:14:40,080 Speaker 2: Elon is such a fixture here, right, Like how many 269 00:14:40,080 --> 00:14:44,720 Speaker 2: of these has he missed in your time covering the company? 270 00:14:44,560 --> 00:14:47,040 Speaker 3: He's only missed one. He made this big show a 271 00:14:47,080 --> 00:14:48,960 Speaker 3: couple of years ago about how he's like not going 272 00:14:49,040 --> 00:14:51,000 Speaker 3: to be in the earnings calls unless he has big 273 00:14:51,040 --> 00:14:53,840 Speaker 3: news to announce. And then there was one call that 274 00:14:53,920 --> 00:14:56,880 Speaker 3: he missed that the CFO led that was great, and 275 00:14:56,920 --> 00:15:00,360 Speaker 3: then Elon was back and he just can't stay out 276 00:15:00,360 --> 00:15:02,960 Speaker 3: of the limelight, like he's got to be the main character. Like, 277 00:15:03,160 --> 00:15:06,720 Speaker 3: so he's only he's been on every single call. 278 00:15:06,840 --> 00:15:09,800 Speaker 2: Okay, So because this is a January call, this one 279 00:15:09,920 --> 00:15:13,440 Speaker 2: is more year ahead leaning than a typical one, which 280 00:15:13,480 --> 00:15:17,600 Speaker 2: would typically look back at the previous quarter. Is that right? Yeah? 281 00:15:17,720 --> 00:15:21,080 Speaker 3: Yeah, especially because Tesla has not given guidance for twenty 282 00:15:21,080 --> 00:15:25,160 Speaker 3: twenty four. They have not said publicly how many vehicles 283 00:15:25,160 --> 00:15:28,080 Speaker 3: they expect to deliver in twenty twenty four. We know 284 00:15:28,160 --> 00:15:31,600 Speaker 3: that they delivered roughly one point eight million cars in 285 00:15:31,600 --> 00:15:33,960 Speaker 3: twenty twenty three through so like what is the forecast? 286 00:15:34,000 --> 00:15:36,880 Speaker 3: Like we're seeing kind of slowing growth in the EV sector. 287 00:15:37,000 --> 00:15:39,400 Speaker 3: Like evs are still growing as part of the overall 288 00:15:39,440 --> 00:15:42,600 Speaker 3: auto market, but it's not this like blockbuster fifty percent 289 00:15:42,680 --> 00:15:45,760 Speaker 3: year of year growth. So that's a big question for investors. 290 00:15:45,800 --> 00:15:47,880 Speaker 3: Are they going to say that they're going to deliver 291 00:15:48,160 --> 00:15:50,520 Speaker 3: two million or two point five? Like what is the 292 00:15:50,560 --> 00:15:54,040 Speaker 3: growth trajectory? And then how much does Musk talk about 293 00:15:54,080 --> 00:15:56,120 Speaker 3: all the other aspects of the business. Is he going 294 00:15:56,200 --> 00:15:58,280 Speaker 3: to be like, we're an AI company, or is he 295 00:15:58,320 --> 00:16:01,800 Speaker 3: going to talk about energy or insurance or you know, 296 00:16:02,360 --> 00:16:04,800 Speaker 3: the optimist robot. I mean, there's all these different sort 297 00:16:04,800 --> 00:16:07,800 Speaker 3: of aspects of the business that people are curious about. 298 00:16:08,560 --> 00:16:11,440 Speaker 2: And and what do everybody who's watching the company, what 299 00:16:11,680 --> 00:16:13,840 Speaker 2: do we think about what the twenty twenty four numbers 300 00:16:13,880 --> 00:16:16,760 Speaker 2: could look like, especially after all the price cuts of 301 00:16:17,200 --> 00:16:19,760 Speaker 2: last year but also possibly this slowing demand. 302 00:16:20,480 --> 00:16:23,360 Speaker 4: Well did they tend to over promise? You know, because 303 00:16:23,360 --> 00:16:26,840 Speaker 4: I know like a lot of companies have the opposite strategy, 304 00:16:26,840 --> 00:16:28,880 Speaker 4: where they underpromise and then they try to beat that. 305 00:16:28,960 --> 00:16:33,560 Speaker 4: But historically Tesla has said more good things are going 306 00:16:33,560 --> 00:16:35,760 Speaker 4: to happen than actually end up happening. So what do 307 00:16:35,840 --> 00:16:36,840 Speaker 4: we think? 308 00:16:37,800 --> 00:16:40,520 Speaker 3: It's kind of weird they'll give like a range where 309 00:16:40,680 --> 00:16:42,680 Speaker 3: a Musk will be like, well, like there could be 310 00:16:42,680 --> 00:16:45,480 Speaker 3: a force measure and things that I can't control, like 311 00:16:45,560 --> 00:16:48,840 Speaker 3: earthquakes and pandemics and tsunamis, and like, I don't really 312 00:16:48,880 --> 00:16:50,800 Speaker 3: know how things are going to go. But he actually 313 00:16:50,800 --> 00:16:53,640 Speaker 3: tends to blame the macroeconomic environment quite a bit, like 314 00:16:54,240 --> 00:16:57,120 Speaker 3: you know, on previous startings calls, he's he's really bashed 315 00:16:57,160 --> 00:16:59,480 Speaker 3: to FED and high interest rates and been like, look, 316 00:17:00,040 --> 00:17:02,320 Speaker 3: you could sell more cars if interest rates were lower. 317 00:17:02,360 --> 00:17:04,359 Speaker 3: The fact that interest rates are high is making the 318 00:17:04,400 --> 00:17:07,040 Speaker 3: cars too expensive for people, which is why they made 319 00:17:07,040 --> 00:17:10,320 Speaker 3: this big gambit to chase volume over profit margins. And 320 00:17:10,359 --> 00:17:12,080 Speaker 3: they could do that again. He could say we're going 321 00:17:12,160 --> 00:17:14,600 Speaker 3: to continue to dominate and we're going to cut prices, 322 00:17:15,280 --> 00:17:17,919 Speaker 3: and I mean that would be one strategy. It's just 323 00:17:17,960 --> 00:17:20,679 Speaker 3: that then the margins are much smaller. But you know, 324 00:17:20,760 --> 00:17:23,159 Speaker 3: Tesla has room to play with their margins, and we 325 00:17:23,200 --> 00:17:24,800 Speaker 3: could see that again in twenty twenty four. 326 00:17:25,160 --> 00:17:28,000 Speaker 1: It also seems like there's like a real guidance and 327 00:17:28,040 --> 00:17:30,680 Speaker 1: then an Elon Musk guidance. Elon will throw in hey, 328 00:17:30,920 --> 00:17:34,320 Speaker 1: like anything's possible, like full self drive. There's this whole 329 00:17:34,359 --> 00:17:37,080 Speaker 1: other category of stuff that doesn't relate to like number 330 00:17:37,119 --> 00:17:40,000 Speaker 1: of units sold or like margins where the guidance is 331 00:17:40,640 --> 00:17:43,720 Speaker 1: whacked out, and kind of everyone agreed. Everyone understands that 332 00:17:43,760 --> 00:17:46,000 Speaker 1: this is like, these are not real timelines. These are 333 00:17:46,040 --> 00:17:48,560 Speaker 1: Elon Musk timelines. You know. I was reading an analyst 334 00:17:48,640 --> 00:17:51,719 Speaker 1: note that Dana sent me just before we went on, 335 00:17:51,800 --> 00:17:55,160 Speaker 1: and they're talking about the question of robotaxis as anywhere 336 00:17:55,200 --> 00:17:58,080 Speaker 1: from three years to a decade out like that. That's 337 00:17:58,119 --> 00:18:01,080 Speaker 1: the timeline that this analyst was suggest which is like 338 00:18:01,160 --> 00:18:03,480 Speaker 1: a huge range, and it reflects the fact that Elon 339 00:18:03,560 --> 00:18:06,719 Speaker 1: Musk has essentially said robotaxis are just around the corner 340 00:18:07,000 --> 00:18:09,919 Speaker 1: basically every year for almost a decade. So it's like 341 00:18:10,040 --> 00:18:12,959 Speaker 1: very confusing and analysts have just learned to, I think, 342 00:18:13,359 --> 00:18:17,240 Speaker 1: just kind of ignore slash discount some of those wilder promises. 343 00:18:24,600 --> 00:18:28,520 Speaker 2: And now for the really fun part. The brains behind 344 00:18:28,560 --> 00:18:33,240 Speaker 2: Elon Inc. Have created a special bingo card which you 345 00:18:33,280 --> 00:18:36,960 Speaker 2: can download at Bloomberg dot com, slash Elon Inc. Or 346 00:18:37,000 --> 00:18:40,359 Speaker 2: on the Mini BusinessWeek social media accounts. Max, what is 347 00:18:40,400 --> 00:18:42,919 Speaker 2: earnings bingo and how do I play along? Okay, so 348 00:18:43,119 --> 00:18:45,439 Speaker 2: earnings bingo is this is a thing that exists. 349 00:18:45,440 --> 00:18:47,720 Speaker 1: We did not invent this you know, all credit to 350 00:18:47,840 --> 00:18:51,440 Speaker 1: the Elon stands out there on Twitter as well as 351 00:18:51,440 --> 00:18:54,719 Speaker 1: the Elon critics, the tesla Q army. Basically anyone who 352 00:18:54,760 --> 00:18:57,760 Speaker 1: has an opinion about this guy has been making these 353 00:18:57,760 --> 00:18:59,399 Speaker 1: bingo cards for years. 354 00:18:59,440 --> 00:19:01,159 Speaker 2: And the idea is you. 355 00:19:01,040 --> 00:19:02,800 Speaker 1: Have a card the one that we have, which you 356 00:19:02,840 --> 00:19:07,600 Speaker 1: can go to Bloomberg dot com slash Elon Inc. And 357 00:19:07,720 --> 00:19:11,239 Speaker 1: download and it's twenty four squares. It's plus I love it, 358 00:19:11,280 --> 00:19:14,760 Speaker 1: plus the free space. I'll just tell you, so, some 359 00:19:14,840 --> 00:19:17,119 Speaker 1: of these are are ones that I think are definitely 360 00:19:17,160 --> 00:19:19,439 Speaker 1: gonna happen like these are. This is like easy money 361 00:19:19,760 --> 00:19:23,000 Speaker 1: some of these. So so for instance, through the Okay, 362 00:19:23,000 --> 00:19:26,960 Speaker 1: so we've got B I n G G four that's 363 00:19:27,000 --> 00:19:27,680 Speaker 1: cyber truck. 364 00:19:27,760 --> 00:19:30,280 Speaker 2: That's come on. That's a gimme. That's practically a free space. 365 00:19:30,440 --> 00:19:34,360 Speaker 1: Now B one is definitely less likely, I would say, 366 00:19:34,480 --> 00:19:38,080 Speaker 1: and that one's audibly puffs joint. So, as I understand it, 367 00:19:38,160 --> 00:19:40,800 Speaker 1: like my job at Bloomberg now, at least part of 368 00:19:40,800 --> 00:19:42,639 Speaker 1: my job anyway, is I'm gonna be listening to that 369 00:19:42,920 --> 00:19:45,800 Speaker 1: podcast as the you know, person who has to listen 370 00:19:45,840 --> 00:19:49,600 Speaker 1: for the audible joint puffing. By the way, sorry, B 371 00:19:49,640 --> 00:19:52,440 Speaker 1: two because It's not enough to just puff a joint. 372 00:19:52,800 --> 00:19:54,560 Speaker 1: It has to be it has to be audible. 373 00:19:55,040 --> 00:19:56,600 Speaker 2: Dan, do you have a favorite on here that you're 374 00:19:56,640 --> 00:19:59,160 Speaker 2: gonna hopefully get to scratch out. 375 00:19:59,440 --> 00:20:01,560 Speaker 3: Yes. I think what's really interesting is, you know, Elon 376 00:20:01,680 --> 00:20:04,240 Speaker 3: just has this very peculiar way of speaking that all 377 00:20:04,280 --> 00:20:07,960 Speaker 3: of his fans then adopt. And I am a big fan. 378 00:20:08,000 --> 00:20:11,600 Speaker 3: I forget what the square is, but reasonably optimistic, because 379 00:20:11,680 --> 00:20:14,920 Speaker 3: you know, Elon will say I'm reasonably optimistic that we'll 380 00:20:14,920 --> 00:20:16,600 Speaker 3: have full self driving at the end of the year, 381 00:20:16,720 --> 00:20:19,480 Speaker 3: or I'm reasonably optimistic that we will, you know, sell 382 00:20:19,520 --> 00:20:23,520 Speaker 3: fifty thousand cyber trucks in twenty twenty four. And so 383 00:20:23,880 --> 00:20:26,960 Speaker 3: the Bingo card is really about Musk and his language 384 00:20:26,960 --> 00:20:29,800 Speaker 3: and his very unique use of language and how he 385 00:20:29,880 --> 00:20:33,280 Speaker 3: invents all these words that then enter the lexicon, like 386 00:20:33,800 --> 00:20:37,600 Speaker 3: gigafactory is not a real word, but Elon started calling 387 00:20:37,720 --> 00:20:41,320 Speaker 3: his battery plant a gigafactory, and now every automaker in 388 00:20:41,359 --> 00:20:44,119 Speaker 3: every battery company on the planet talks about how they 389 00:20:44,160 --> 00:20:47,440 Speaker 3: have a gigafactory. Similarly, megapack like what the hell is 390 00:20:47,480 --> 00:20:49,960 Speaker 3: a mega pack? It's Tesla's name for their like utility 391 00:20:49,960 --> 00:20:52,359 Speaker 3: scale battery products. So I mean, I would love to 392 00:20:52,359 --> 00:20:54,680 Speaker 3: get a like linguist on the phone someday to talk 393 00:20:54,720 --> 00:20:57,480 Speaker 3: to us all about this. But his use of language 394 00:20:57,520 --> 00:21:00,000 Speaker 3: is very unique and it is adopted by his life 395 00:21:00,040 --> 00:21:03,080 Speaker 3: regions of fans. And the other thing about the earnings 396 00:21:03,119 --> 00:21:06,800 Speaker 3: calls is that like tens of thousands of people listen 397 00:21:06,880 --> 00:21:10,080 Speaker 3: to them, like employees listen to them, fans listen to them, 398 00:21:10,200 --> 00:21:13,520 Speaker 3: like a lot of people dial in and listen. And 399 00:21:13,960 --> 00:21:16,760 Speaker 3: it's not just like Wall Street analysts. It's like hundreds 400 00:21:16,800 --> 00:21:17,600 Speaker 3: of thousands of people. 401 00:21:18,040 --> 00:21:19,960 Speaker 1: We talk a lot about on this podcast about like 402 00:21:19,960 --> 00:21:21,359 Speaker 1: how to think about Elon, and I think on the 403 00:21:21,400 --> 00:21:24,240 Speaker 1: last episode Davey Alba compared him to Donald Trump right 404 00:21:24,440 --> 00:21:27,280 Speaker 1: as as sort of a political influencer and so on. 405 00:21:27,520 --> 00:21:29,639 Speaker 1: But I was thinking, like with the looking at this 406 00:21:29,720 --> 00:21:31,920 Speaker 1: BNGO card and thinking about the way, as Dana says, 407 00:21:31,920 --> 00:21:35,080 Speaker 1: that Elon sort of remixes himself, like he has these 408 00:21:35,119 --> 00:21:38,239 Speaker 1: tropes that he repeats and reuses, And you know, I 409 00:21:38,280 --> 00:21:41,359 Speaker 1: think the sort of great oral poets, right, like the 410 00:21:41,840 --> 00:21:45,240 Speaker 1: way that I think linguists believe that oral poetry Homer 411 00:21:45,280 --> 00:21:46,840 Speaker 1: and so on was consumed was you'd have these like 412 00:21:47,080 --> 00:21:50,400 Speaker 1: little like couplets or little bits of language that would 413 00:21:50,440 --> 00:21:52,240 Speaker 1: then be arranged in order. And it's almost like that 414 00:21:52,280 --> 00:21:54,919 Speaker 1: with Elon, where he's got these like catchphrases that just 415 00:21:55,000 --> 00:21:57,919 Speaker 1: get tossed in and it becomes like a part of 416 00:21:58,000 --> 00:22:00,639 Speaker 1: the fandom. As Dana said, it's something that the that 417 00:22:00,760 --> 00:22:03,440 Speaker 1: his fans repeat and also that they look for, right 418 00:22:03,480 --> 00:22:06,280 Speaker 1: and it's it is like the experience of like one 419 00:22:06,280 --> 00:22:09,640 Speaker 1: of these Elon Musk Earnings calls. It's like not all 420 00:22:09,680 --> 00:22:12,600 Speaker 1: that different from the Taylor Swift concert right where you 421 00:22:12,640 --> 00:22:14,480 Speaker 1: see the snake and you're like, oh, the snake, Like 422 00:22:14,520 --> 00:22:17,520 Speaker 1: here it is, except here, it's like it's like optimists 423 00:22:17,560 --> 00:22:21,040 Speaker 1: or dojo or the phrase, you know, digging our own 424 00:22:21,080 --> 00:22:23,919 Speaker 1: grave or whatever. My other favorite square here is not 425 00:22:24,040 --> 00:22:26,600 Speaker 1: many people understand this, which is just like a thing 426 00:22:26,640 --> 00:22:29,520 Speaker 1: that he says all the time and just really speaks 427 00:22:29,560 --> 00:22:33,399 Speaker 1: to like this sense of inn Elon fandom, of that 428 00:22:33,440 --> 00:22:36,679 Speaker 1: you're given access to this truth or really it's like 429 00:22:36,680 --> 00:22:39,720 Speaker 1: a set of truths that are not well understood, and 430 00:22:39,760 --> 00:22:42,159 Speaker 1: if you understand them, you are going to have You're 431 00:22:42,160 --> 00:22:43,880 Speaker 1: gonna get rich, you're gonna you're gonna be a part 432 00:22:43,920 --> 00:22:45,320 Speaker 1: of this. It's not only gonna be a source of 433 00:22:45,359 --> 00:22:47,320 Speaker 1: cultural meaning, but it's gonna be a source of wealth. 434 00:22:47,320 --> 00:22:48,800 Speaker 4: Now, I was just gonna say, like, I love O 435 00:22:49,040 --> 00:22:52,840 Speaker 4: three refers to Earth as a market because that is 436 00:22:52,960 --> 00:22:57,520 Speaker 4: just so how he talks about everything, like are you 437 00:22:57,600 --> 00:23:01,160 Speaker 4: with humanity or against it? We're fighting for our consciousness, 438 00:23:01,160 --> 00:23:05,480 Speaker 4: We're fighting for the future of Earth, and referring to 439 00:23:05,520 --> 00:23:08,040 Speaker 4: Earth as a market implies that there will be future 440 00:23:08,160 --> 00:23:11,679 Speaker 4: planetary markets, which is of course his worldview. 441 00:23:11,800 --> 00:23:15,480 Speaker 3: And because he uses these tropes so often, part of 442 00:23:15,520 --> 00:23:18,159 Speaker 3: the challenge of listening to a Tesla earnings call as 443 00:23:18,160 --> 00:23:20,840 Speaker 3: a journalist or an analyst is that you almost get 444 00:23:20,840 --> 00:23:23,280 Speaker 3: like a focus hangover, because you have to listen very 445 00:23:23,280 --> 00:23:26,399 Speaker 3: carefully for any change in nuance because that's what the 446 00:23:26,440 --> 00:23:30,639 Speaker 3: news is like. Is he slightly altering these tropes in 447 00:23:30,680 --> 00:23:33,000 Speaker 3: a way that's either positive or negative for the future 448 00:23:33,000 --> 00:23:35,800 Speaker 3: of the company, And is he backing away from you know, 449 00:23:35,960 --> 00:23:39,679 Speaker 3: dojo or is he talking down cyber truck. I mean, 450 00:23:39,720 --> 00:23:42,040 Speaker 3: on the last call he very famously said that with 451 00:23:42,080 --> 00:23:44,040 Speaker 3: the cyber truck we dug our own grave. And it 452 00:23:44,119 --> 00:23:46,679 Speaker 3: was like, ooh, like what is that about, man? So 453 00:23:46,720 --> 00:23:48,960 Speaker 3: you have to listen very very carefully for any kind 454 00:23:48,960 --> 00:23:50,439 Speaker 3: of change in nuance or tone. 455 00:23:50,560 --> 00:23:51,600 Speaker 4: We dug our own grave. 456 00:23:53,160 --> 00:23:55,640 Speaker 1: Every big things are gray better than themselves, and. 457 00:23:55,600 --> 00:23:58,600 Speaker 3: It's kind of it's just really wild and usually but 458 00:23:58,680 --> 00:24:01,160 Speaker 3: I'm on the calls, I'm like blowug and I'm listening 459 00:24:01,200 --> 00:24:03,720 Speaker 3: and I've got my Bingo card. But I'm also like texting, 460 00:24:03,840 --> 00:24:08,280 Speaker 3: like employees and analysts and like fans, and everyone's like, 461 00:24:08,359 --> 00:24:09,920 Speaker 3: you know, did you catch when he said this? Did 462 00:24:09,960 --> 00:24:11,640 Speaker 3: you catch when he said that? And you if you've 463 00:24:11,680 --> 00:24:14,119 Speaker 3: never listened to an earnings call, we really encourage you 464 00:24:14,160 --> 00:24:15,680 Speaker 3: to join along. 465 00:24:15,880 --> 00:24:18,440 Speaker 2: And play and go to Bloomberg dot com slash elon 466 00:24:18,480 --> 00:24:22,320 Speaker 2: and to download our first Bengo card. I think we'll 467 00:24:22,320 --> 00:24:27,240 Speaker 2: do this again, will we? Okay, well we. 468 00:24:27,359 --> 00:24:29,919 Speaker 3: Have to the great thing about the Bingo card is 469 00:24:29,920 --> 00:24:32,840 Speaker 3: that everybody wins. We only made one card, so for 470 00:24:32,960 --> 00:24:33,800 Speaker 3: everyone loses. 471 00:24:35,080 --> 00:24:37,280 Speaker 2: Yeah, yeah, what happens if I win? Do I get 472 00:24:37,280 --> 00:24:38,800 Speaker 2: all that money that you were talking about? Max? 473 00:24:39,000 --> 00:24:41,440 Speaker 1: Well, I just want to say, like it sounds kind 474 00:24:41,440 --> 00:24:44,959 Speaker 1: of superficial to be Dana opened this conversation talking about 475 00:24:45,200 --> 00:24:47,680 Speaker 1: do we get dour elon? Do we get happy elon? 476 00:24:47,880 --> 00:24:50,639 Speaker 1: But like that actually matters, and like that's what what 477 00:24:50,720 --> 00:24:54,000 Speaker 1: Dan's kind of saying is that because he repeats these phrases, 478 00:24:54,040 --> 00:24:56,160 Speaker 1: like it sounds like we're just we're doing like theater 479 00:24:56,240 --> 00:24:59,080 Speaker 1: criticism or something. But like the fortunes of this company 480 00:24:59,720 --> 00:25:02,280 Speaker 1: hundred it's of billions of dollars, you know, hundreds of 481 00:25:02,280 --> 00:25:06,520 Speaker 1: thousands of jobs sort of depend on like what Elon projects, 482 00:25:06,600 --> 00:25:09,480 Speaker 1: and like does he sound depressed, you know when he's 483 00:25:09,520 --> 00:25:12,080 Speaker 1: talking to this audience of analysts and fans and like 484 00:25:12,160 --> 00:25:15,040 Speaker 1: it's such a weird It's just such a weird world 485 00:25:15,080 --> 00:25:17,000 Speaker 1: we live in, and like you can kind of understand. 486 00:25:17,640 --> 00:25:19,800 Speaker 1: I doubt this is a cause for Elon to believe 487 00:25:19,800 --> 00:25:23,280 Speaker 1: in the simulation, but it kind of makes me like 488 00:25:23,600 --> 00:25:27,679 Speaker 1: question our reality when so much depends on you know, 489 00:25:27,720 --> 00:25:31,480 Speaker 1: basically how Wall Street interprets Elon Musk's mood. 490 00:25:31,880 --> 00:25:32,080 Speaker 2: Yeah. 491 00:25:32,119 --> 00:25:34,520 Speaker 3: The other thing that's really fun fun to do if 492 00:25:34,520 --> 00:25:37,520 Speaker 3: you have a terminal is while he's talking, watch gip 493 00:25:37,560 --> 00:25:40,920 Speaker 3: post and you can see the stock trade after hours 494 00:25:40,960 --> 00:25:43,720 Speaker 3: in real time. And if he gets like dark, and 495 00:25:43,880 --> 00:25:45,800 Speaker 3: if we get like dark Elon, you will just see 496 00:25:45,800 --> 00:25:48,240 Speaker 3: the stock completely tank. And then if he's like embulent 497 00:25:48,359 --> 00:25:50,520 Speaker 3: and like very confident, you'll see the stock go up. 498 00:25:50,640 --> 00:25:52,720 Speaker 4: So that's like another trick of the trade that a 499 00:25:52,720 --> 00:25:55,359 Speaker 4: lot of people do. You all just learned to terminal function. 500 00:25:56,080 --> 00:25:57,919 Speaker 1: I love that Dan and I are going to be 501 00:25:57,960 --> 00:25:59,760 Speaker 1: live blogging on the terminal as well. 502 00:26:00,240 --> 00:26:01,679 Speaker 2: You know, get ready. 503 00:26:01,920 --> 00:26:04,760 Speaker 3: People are also already criticizing our Bingo card. We don't 504 00:26:04,760 --> 00:26:08,000 Speaker 3: have a square that says the limiting factor. We don't 505 00:26:08,000 --> 00:26:13,120 Speaker 3: have a square that says, you know, talks about population collapse. 506 00:26:13,160 --> 00:26:14,920 Speaker 3: But to be fair, like he doesn't really talk about 507 00:26:14,960 --> 00:26:17,240 Speaker 3: population collaps on the earnings calls. He talks about them 508 00:26:17,359 --> 00:26:18,840 Speaker 3: like all the rest of the time. 509 00:26:18,960 --> 00:26:22,240 Speaker 1: You know what, Listeners, tell us what's wrong with our 510 00:26:22,600 --> 00:26:23,240 Speaker 1: Bingo card? 511 00:26:23,240 --> 00:26:24,200 Speaker 2: Tell us what's missing? 512 00:26:24,280 --> 00:26:26,840 Speaker 1: Make your own Bengo cards. We you know, yeah, we'll 513 00:26:26,840 --> 00:26:31,840 Speaker 1: be back in qeqe Q one is only three months away, exactly. 514 00:26:33,440 --> 00:26:37,520 Speaker 2: All right, enough, let's call it. Thanks for listening to 515 00:26:37,600 --> 00:26:41,760 Speaker 2: Elon Inc. And thanks to our panel Dana, Sarah and Max, 516 00:26:42,119 --> 00:26:43,359 Speaker 2: thank you, Thank you. 517 00:26:44,200 --> 00:26:44,959 Speaker 3: Always a pleasure. 518 00:26:52,240 --> 00:26:55,000 Speaker 2: This episode was produced by Stacey Wong me and We 519 00:26:55,080 --> 00:26:58,800 Speaker 2: Shaven and Rayhan Hermanchi are our senior editors. The idea 520 00:26:58,840 --> 00:27:01,720 Speaker 2: for this very show came from Rayhan. Like Maple's handles 521 00:27:01,760 --> 00:27:04,560 Speaker 2: engineering and we get special editing assistants from Jeff crocott. 522 00:27:05,200 --> 00:27:09,000 Speaker 2: Our supervising producer is Magnus Hendrickson. The Elon Inc. Theme 523 00:27:09,040 --> 00:27:12,840 Speaker 2: is written and performed by Taka Yazazawa and Alex Sigira. 524 00:27:13,320 --> 00:27:15,520 Speaker 2: Sage Bauman is the head of Bloomberg Podcast and our 525 00:27:15,560 --> 00:27:19,080 Speaker 2: executive producer. I'm Joe Webber covering for David pop Douglass. 526 00:27:19,600 --> 00:27:22,160 Speaker 2: If you have a minute, please rate and review our show. 527 00:27:22,240 --> 00:27:25,879 Speaker 2: It'll help other listeners find us. Also, let us know 528 00:27:25,880 --> 00:27:29,160 Speaker 2: what you think of Tesla Bingo. See you next week.