1 00:00:02,520 --> 00:00:08,560 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. Let me tell you 2 00:00:08,600 --> 00:00:09,520 Speaker 1: we have a new star. 3 00:00:10,520 --> 00:00:14,640 Speaker 2: A star is born Elon pumped up on Mars Juson Kennemy. 4 00:00:14,880 --> 00:00:18,520 Speaker 3: He is the Thomas Edison plus plus plus of our age. 5 00:00:18,640 --> 00:00:21,040 Speaker 1: Probably his whole life is from a position of insecurity. 6 00:00:21,079 --> 00:00:21,800 Speaker 4: I feel for the guy. 7 00:00:22,040 --> 00:00:25,320 Speaker 5: I would say ninety eight percent really appreciate what he does. 8 00:00:25,400 --> 00:00:28,480 Speaker 5: But those two percent that are nasty, they are I'll 9 00:00:28,560 --> 00:00:30,040 Speaker 5: be in four fos. 10 00:00:30,280 --> 00:00:32,320 Speaker 1: We are meant for great things in the United States 11 00:00:32,360 --> 00:00:36,400 Speaker 1: of America, and Elon reminds us of that we don't 12 00:00:36,440 --> 00:00:45,479 Speaker 1: have a fourth branch of governments called Elon Musk. Welcome 13 00:00:45,520 --> 00:00:50,040 Speaker 1: to Elon, Inc. Bloomberg's weekly podcast about Elon Musk. It's Tuesday, 14 00:00:50,200 --> 00:00:53,240 Speaker 1: April fifteenth. I'm your host, Joel Webber, sitting in for 15 00:00:53,360 --> 00:00:56,680 Speaker 1: David Papadopolos. It's tax day in the US, when the 16 00:00:56,680 --> 00:00:59,480 Speaker 1: federal government is supposed to collect its money, and maybe 17 00:00:59,520 --> 00:01:02,080 Speaker 1: it still came. But of course DIRS has been gutted 18 00:01:02,320 --> 00:01:05,800 Speaker 1: alongside many other federal agencies thanks to Elon and DOGE. 19 00:01:06,200 --> 00:01:08,680 Speaker 1: But the news stories about DOGE seems to have been 20 00:01:08,680 --> 00:01:11,520 Speaker 1: slowing down a little bit. What exactly is Elon and 21 00:01:11,600 --> 00:01:14,319 Speaker 1: his young team up to these days, We've got lots 22 00:01:14,319 --> 00:01:17,280 Speaker 1: to discuss, so we'll talk about that, and then we're 23 00:01:17,280 --> 00:01:20,840 Speaker 1: gonna dig back into the roller coaster of Tesla's share 24 00:01:20,880 --> 00:01:25,000 Speaker 1: price as investors await next week's earnings call. Okay, let's 25 00:01:25,000 --> 00:01:28,040 Speaker 1: start with doche and to unpack the leaders. We're joined 26 00:01:28,080 --> 00:01:31,880 Speaker 1: by Elon Inc. Regular Dana Hole, Hi, Dana, Hey, Joel, 27 00:01:32,360 --> 00:01:34,840 Speaker 1: and a special guest and now friend of the podcast, 28 00:01:35,360 --> 00:01:38,960 Speaker 1: McKenna Kelly from Wired Magazine. Hello, McKenna, it's good to 29 00:01:39,000 --> 00:01:39,319 Speaker 1: be back. 30 00:01:39,400 --> 00:01:39,600 Speaker 4: Hey. 31 00:01:39,840 --> 00:01:42,960 Speaker 1: Okay, I'm gonna start with a clip from October twenty 32 00:01:43,000 --> 00:01:45,840 Speaker 1: twenty four at Maniston's Quare Garden, where Elon is on 33 00:01:45,920 --> 00:01:48,480 Speaker 1: stage with Howard Luttnik out of Trump rally. 34 00:01:48,640 --> 00:01:50,560 Speaker 2: Oh, I got one question for it that I'm getting 35 00:01:50,600 --> 00:01:50,960 Speaker 2: out of here. 36 00:01:51,280 --> 00:01:55,000 Speaker 1: This is your stage. But we set up doge. Yes, 37 00:01:55,120 --> 00:01:58,280 Speaker 1: how much do you think we can rip out of 38 00:01:58,360 --> 00:02:04,400 Speaker 1: this wasted six two point five trillion dollar Harris Biden budget? Well, 39 00:02:04,560 --> 00:02:09,040 Speaker 1: I think we could do at least two trillion. Yeah, yes, 40 00:02:09,400 --> 00:02:13,840 Speaker 1: two trillion. And now here's Elon just a few days 41 00:02:13,840 --> 00:02:15,120 Speaker 1: ago with a new number. 42 00:02:15,280 --> 00:02:19,240 Speaker 5: I'm excited to announce that we anticipate savings in FY 43 00:02:19,320 --> 00:02:21,840 Speaker 5: twenty six from reduction of waste and broad by one 44 00:02:21,919 --> 00:02:23,400 Speaker 5: hundred and fifty billion dollars. 45 00:02:23,960 --> 00:02:26,920 Speaker 1: McKennon, we had two trillion. Now it's one hundred and 46 00:02:26,960 --> 00:02:31,760 Speaker 1: fifty billion, dramatically smaller. How realistic is even that number? 47 00:02:32,320 --> 00:02:35,280 Speaker 4: Yeah, I mean when you look at the kinds of 48 00:02:35,320 --> 00:02:38,360 Speaker 4: areas that Dough just targeting right now, one of the 49 00:02:38,400 --> 00:02:41,760 Speaker 4: biggest cost savings I think you can argue is all 50 00:02:41,840 --> 00:02:44,760 Speaker 4: of these layoffs and riffs that have been happening across 51 00:02:44,760 --> 00:02:47,799 Speaker 4: the federal government. But at a lot of agencies, the 52 00:02:48,160 --> 00:02:52,760 Speaker 4: workforce is one of the cheapest parts of these agencies, right, 53 00:02:52,960 --> 00:02:56,320 Speaker 4: And so again it's like it's hard to predict, right, 54 00:02:56,800 --> 00:02:59,959 Speaker 4: if one hundred and fifty billion will actually be the number, 55 00:03:00,080 --> 00:03:03,000 Speaker 4: or if it'll be even less. It's hard to even 56 00:03:03,080 --> 00:03:05,720 Speaker 4: just trust Elon's goals here because even when we go 57 00:03:05,760 --> 00:03:08,000 Speaker 4: to like doache dot gov, where all of these savings 58 00:03:08,040 --> 00:03:09,720 Speaker 4: are supposed to be listed, a lot of these end 59 00:03:09,840 --> 00:03:13,160 Speaker 4: up being incorrect and even removed from the website. So 60 00:03:13,840 --> 00:03:16,560 Speaker 4: even if he claims to have met one hundred and 61 00:03:16,600 --> 00:03:19,480 Speaker 4: fifty billions, say like six months from now, I have 62 00:03:19,480 --> 00:03:22,080 Speaker 4: a hard time believing that number would even be accurate. 63 00:03:22,360 --> 00:03:25,320 Speaker 1: And Dana how Active, I know this is an ongoing 64 00:03:25,560 --> 00:03:30,079 Speaker 1: current on the show. How Active is. DOZE currently feels 65 00:03:30,080 --> 00:03:31,400 Speaker 1: like it's winded down a little bit. 66 00:03:31,800 --> 00:03:33,480 Speaker 3: I think I'm going to push back on that. I 67 00:03:33,520 --> 00:03:36,280 Speaker 3: think that people think it's winded down because everyone is 68 00:03:36,280 --> 00:03:39,320 Speaker 3: focused on tariffs and what tariffs are doing to the economy. 69 00:03:39,360 --> 00:03:42,200 Speaker 3: But DOZE is still very much embedded in all of 70 00:03:42,200 --> 00:03:46,280 Speaker 3: these agencies. You know, Antonio Gracias, one of Elon's best 71 00:03:46,320 --> 00:03:49,680 Speaker 3: friends who's on the board of SpaceX, is like at 72 00:03:49,680 --> 00:03:52,680 Speaker 3: the Social Security Administration, They're doing all this stuff with 73 00:03:52,800 --> 00:03:58,080 Speaker 3: Social Security and immigration and putting people on these dead lists. 74 00:03:58,320 --> 00:04:00,920 Speaker 3: And I just think that just because there's not maybe 75 00:04:00,960 --> 00:04:03,120 Speaker 3: the wall to wall media coverage that there was in 76 00:04:03,160 --> 00:04:05,800 Speaker 3: dozes early these days, does not mean that DOGE is 77 00:04:05,840 --> 00:04:10,520 Speaker 3: winding down. Elon's time as a special government employee will 78 00:04:10,680 --> 00:04:14,600 Speaker 3: sunset after the one hundred and thirty day mark. But like, 79 00:04:15,000 --> 00:04:18,040 Speaker 3: DOGE is very much embedded in all of these agencies. 80 00:04:18,080 --> 00:04:20,800 Speaker 3: And what McKenna has written about a lot is and 81 00:04:21,200 --> 00:04:23,080 Speaker 3: what we've seen too is like, you know, it's not 82 00:04:23,200 --> 00:04:26,080 Speaker 3: just this like band of kids that like got a 83 00:04:26,080 --> 00:04:28,480 Speaker 3: lot of publicity in the early days. People are now 84 00:04:28,480 --> 00:04:32,560 Speaker 3: in CIO roles, people have access to wide swaths of information, 85 00:04:32,880 --> 00:04:36,000 Speaker 3: and so I just think it's incorrect to sort of 86 00:04:36,680 --> 00:04:38,719 Speaker 3: act like DOGE is sort of on the way out. 87 00:04:38,839 --> 00:04:41,320 Speaker 3: It is not like they have more access to more 88 00:04:41,400 --> 00:04:45,760 Speaker 3: databases and more agency functions than ever, and there's like, 89 00:04:46,200 --> 00:04:48,679 Speaker 3: you know, easily I don't know. I think our internal 90 00:04:48,720 --> 00:04:51,960 Speaker 3: list is over eighty people, but there are people like 91 00:04:52,600 --> 00:04:56,640 Speaker 3: at every agency accessing all kinds of data, and they're 92 00:04:56,640 --> 00:04:59,040 Speaker 3: still doing a lot of work, like hand in hand 93 00:04:59,080 --> 00:05:00,920 Speaker 3: with omb the Trump administration. 94 00:05:01,640 --> 00:05:04,200 Speaker 4: I also think part of what's happening here too, and 95 00:05:04,240 --> 00:05:05,800 Speaker 4: a lot from my reporting and the reporting that you 96 00:05:05,839 --> 00:05:08,760 Speaker 4: guys have been doing at Bloomberg, is that at the beginning, 97 00:05:08,880 --> 00:05:12,200 Speaker 4: there are these stories of these teenage strike forces of 98 00:05:12,240 --> 00:05:16,000 Speaker 4: engineers descending on these agencies, and then also, I mean, 99 00:05:16,040 --> 00:05:19,599 Speaker 4: it was every single agency. And now I mean, the 100 00:05:19,640 --> 00:05:21,240 Speaker 4: way that I've been describing this to folks that I've 101 00:05:21,240 --> 00:05:23,560 Speaker 4: been speaking with recently is that we've kind of we're 102 00:05:23,600 --> 00:05:26,919 Speaker 4: kind of exiting the stage one of DOGE right and 103 00:05:27,120 --> 00:05:29,679 Speaker 4: entering what I've been kind of calling stage two, although 104 00:05:29,680 --> 00:05:31,920 Speaker 4: I don't have a proper definition for it yet because 105 00:05:31,960 --> 00:05:34,279 Speaker 4: I don't think we're in it all the way, but 106 00:05:34,320 --> 00:05:36,760 Speaker 4: a lot of what's happening at agencies specifically, I'm thinking 107 00:05:36,800 --> 00:05:39,680 Speaker 4: about the IRS. It's tax day. There's a lot of 108 00:05:39,839 --> 00:05:43,920 Speaker 4: very technical things happening. Groups of engineers you know at 109 00:05:43,920 --> 00:05:47,800 Speaker 4: the IRS, and groups of doge folks and palenteer coming 110 00:05:47,839 --> 00:05:50,880 Speaker 4: in and trying to build these like unified API layers. 111 00:05:50,920 --> 00:05:53,560 Speaker 4: That doesn't really make a great headline. And then you 112 00:05:53,600 --> 00:05:55,320 Speaker 4: have to also have to think like does your audience, 113 00:05:55,520 --> 00:05:58,440 Speaker 4: you know, even understand fully what all of this means. 114 00:05:58,480 --> 00:06:01,400 Speaker 4: And so there's a lot going on, right, but it's 115 00:06:01,520 --> 00:06:07,520 Speaker 4: not this like headline grabbing, attention grabbing teenagers storming through 116 00:06:07,800 --> 00:06:12,320 Speaker 4: the State Department right Instead, they're sitting at computers at hackathons. 117 00:06:13,000 --> 00:06:15,800 Speaker 3: I feel like we've gone from like feeding a USAID 118 00:06:15,960 --> 00:06:19,880 Speaker 3: into the wood chipper on a Saturday to like, let's 119 00:06:19,960 --> 00:06:23,880 Speaker 3: remake like technical infrastructure of how this agency actually operates 120 00:06:24,000 --> 00:06:27,800 Speaker 3: and like strip out all of the legacy software contracts 121 00:06:27,920 --> 00:06:31,560 Speaker 3: and make a new database that is run by like 122 00:06:31,640 --> 00:06:32,480 Speaker 3: private enterprise. 123 00:06:32,800 --> 00:06:35,400 Speaker 1: And McKennon, tell us more about your reporting on this, 124 00:06:35,440 --> 00:06:38,760 Speaker 1: because you have written about specifically at the IRS and 125 00:06:38,800 --> 00:06:42,920 Speaker 1: this API that you mentioned ANDEER Palenteer, of course, private 126 00:06:42,920 --> 00:06:47,200 Speaker 1: company that's legendary for being able to like create layers 127 00:06:47,200 --> 00:06:49,640 Speaker 1: of software that can kind of reach across databases. What 128 00:06:50,000 --> 00:06:51,960 Speaker 1: is the end goal here and what could this end 129 00:06:52,040 --> 00:06:52,599 Speaker 1: up looking like? 130 00:06:52,960 --> 00:06:54,599 Speaker 4: Yeah, I mean it's really hard to predict what the 131 00:06:54,680 --> 00:06:58,440 Speaker 4: end goal is. But what's happening at the IRS specifically 132 00:06:59,000 --> 00:07:02,760 Speaker 4: is that doge specifically the doge lead at IRS, his 133 00:07:02,839 --> 00:07:06,160 Speaker 4: name is Sam forcos, he wants to build this unified 134 00:07:06,160 --> 00:07:10,360 Speaker 4: API layer across all of the IRS systems, basically creating 135 00:07:10,400 --> 00:07:14,640 Speaker 4: one kind of one cloud platform using Palentti your foundry 136 00:07:14,640 --> 00:07:18,280 Speaker 4: software right to be able to create different apps to 137 00:07:18,360 --> 00:07:22,040 Speaker 4: run LLLMS over IRS data. And he thinks he can 138 00:07:22,080 --> 00:07:24,920 Speaker 4: do this in thirty days. This is happening at other 139 00:07:24,960 --> 00:07:27,160 Speaker 4: agencies as well. A couple of weeks ago, I reported 140 00:07:27,160 --> 00:07:30,320 Speaker 4: that at the SOID Security Administration, for example, there were 141 00:07:30,480 --> 00:07:34,640 Speaker 4: doge folks who wanted to completely rebuild the SSA code base. 142 00:07:34,680 --> 00:07:36,040 Speaker 4: And again they think that they can do this in 143 00:07:36,080 --> 00:07:38,920 Speaker 4: like thirty days or a month, completely build it up 144 00:07:39,080 --> 00:07:42,320 Speaker 4: from the ground up and removing all of this legacy code, 145 00:07:42,320 --> 00:07:47,640 Speaker 4: whether that is assembly or coball, and get it ready 146 00:07:47,720 --> 00:07:52,280 Speaker 4: for whatever. You know, This next phase of doges and Danna. 147 00:07:52,720 --> 00:07:54,800 Speaker 1: When you think about what that looks like and how 148 00:07:54,840 --> 00:07:57,800 Speaker 1: it's currently used in tech, like, what do you think 149 00:07:57,840 --> 00:07:59,160 Speaker 1: it could end up looking like? 150 00:07:59,520 --> 00:08:02,440 Speaker 3: Well, there's just like a lot of great privacy concerns 151 00:08:02,560 --> 00:08:05,679 Speaker 3: because the federal government always had like really strict rules 152 00:08:05,680 --> 00:08:08,920 Speaker 3: about who had access to what, and the inability to 153 00:08:09,000 --> 00:08:11,320 Speaker 3: kind of cross agencies was sort of by design, right, 154 00:08:11,360 --> 00:08:14,680 Speaker 3: because the federal government took very seriously the privacy of 155 00:08:14,760 --> 00:08:18,680 Speaker 3: American citizens. But if databases can talk to each other, 156 00:08:18,720 --> 00:08:22,720 Speaker 3: there's nothing that's stopping dose from like using IRS data 157 00:08:22,760 --> 00:08:26,360 Speaker 3: to coordinate with like it, you know, with ICE or 158 00:08:26,440 --> 00:08:28,520 Speaker 3: the student loan program, and like that is the big 159 00:08:28,560 --> 00:08:32,240 Speaker 3: fear of a lot of civil rights experts and student groups. 160 00:08:32,240 --> 00:08:34,319 Speaker 3: And you're seeing in a lot of the lawsuits that 161 00:08:34,360 --> 00:08:37,400 Speaker 3: have been filed great concern about the lack of privacy 162 00:08:37,400 --> 00:08:40,920 Speaker 3: controls that are going into these new systems. And you know, 163 00:08:41,040 --> 00:08:44,720 Speaker 3: Musk is very focused on immigration and they you know, 164 00:08:44,760 --> 00:08:48,640 Speaker 3: they are talking a lot about social security and what 165 00:08:48,640 --> 00:08:52,240 Speaker 3: they're doing with social security and how so many immigrants 166 00:08:52,360 --> 00:08:54,680 Speaker 3: have Social Security numbers, and so I think that there's 167 00:08:54,720 --> 00:08:58,480 Speaker 3: just great concern about the lack of privacy controls as 168 00:08:58,640 --> 00:09:02,360 Speaker 3: these new databases are being built and like cross reference 169 00:09:02,400 --> 00:09:05,520 Speaker 3: across the government, you can argue that doing that is 170 00:09:05,600 --> 00:09:08,800 Speaker 3: more efficient and will help find waste and fraud. But 171 00:09:08,920 --> 00:09:12,439 Speaker 3: on the flip side, like the government now has unfettered 172 00:09:12,720 --> 00:09:15,600 Speaker 3: more and more access to everyone's information than it ever 173 00:09:15,640 --> 00:09:16,200 Speaker 3: had before. 174 00:09:21,760 --> 00:09:25,600 Speaker 1: Mc kenneth, where else has your reporting on DOGE taking 175 00:09:25,679 --> 00:09:28,320 Speaker 1: you here? We've got the IRS stuff, We've got HHS, 176 00:09:28,760 --> 00:09:31,760 Speaker 1: the Palanteer stuff is really interesting, Like what's the signal 177 00:09:31,800 --> 00:09:34,280 Speaker 1: through all of this, especially as you know, we start 178 00:09:34,280 --> 00:09:37,439 Speaker 1: to look forward to perhaps Elon maybe stepping back a 179 00:09:37,480 --> 00:09:39,240 Speaker 1: little bit at the end of his one hundred and 180 00:09:39,240 --> 00:09:41,440 Speaker 1: thirty days as a special government employee. 181 00:09:41,679 --> 00:09:44,240 Speaker 4: Yeah, to piggyback off of what Dana was saying. I mean, 182 00:09:44,280 --> 00:09:46,480 Speaker 4: that's the fear right now, is that one of the 183 00:09:46,480 --> 00:09:49,840 Speaker 4: administration's biggest goals right now is immigration and immigration reform, 184 00:09:49,880 --> 00:09:53,520 Speaker 4: if that's what you want to call disappearing people immigration reform. 185 00:09:53,840 --> 00:09:57,480 Speaker 4: But if you look at the agencies that DOGE has targeted. 186 00:09:57,600 --> 00:10:00,800 Speaker 4: You mentioned AJHS, which has a database of unac companied minors. 187 00:10:01,200 --> 00:10:04,000 Speaker 4: You look at IRS. Despite as much as what Elon 188 00:10:04,040 --> 00:10:08,320 Speaker 4: wants to say, immigrants in our country do pay taxes. 189 00:10:08,520 --> 00:10:11,079 Speaker 4: In a variety of ways billions and billions of dollars 190 00:10:11,120 --> 00:10:13,960 Speaker 4: every year, and then ssay as well, they end up 191 00:10:13,960 --> 00:10:16,200 Speaker 4: paying into Social Security as well. And so when you 192 00:10:16,240 --> 00:10:19,600 Speaker 4: look at the agencies that DOT has targeted, with the 193 00:10:19,679 --> 00:10:24,479 Speaker 4: priority of the administration being immigration, by building these APIs 194 00:10:24,480 --> 00:10:28,120 Speaker 4: and modernizing these systems, allowing them to talk would make 195 00:10:28,120 --> 00:10:30,680 Speaker 4: it a lot easier. And what sources that I've been 196 00:10:30,720 --> 00:10:34,080 Speaker 4: speaking to the fears, you know, building this gigantic migrant 197 00:10:34,120 --> 00:10:37,280 Speaker 4: surveillance system. And the scariest part of this to me 198 00:10:37,480 --> 00:10:39,600 Speaker 4: is the idea that you can do this in thirty days. 199 00:10:39,880 --> 00:10:42,160 Speaker 4: I mean, just identifying where all the databases are, what 200 00:10:42,200 --> 00:10:45,360 Speaker 4: they do, who accesses them, and for what purpose would 201 00:10:45,400 --> 00:10:47,560 Speaker 4: take longer than thirty days, you know, according to everyone 202 00:10:47,640 --> 00:10:50,760 Speaker 4: that I've spoken to. And so even if you want 203 00:10:50,760 --> 00:10:53,720 Speaker 4: to even beyond that thirty day limit, is there even 204 00:10:53,800 --> 00:10:57,000 Speaker 4: time to test how these were built? Are things going 205 00:10:57,040 --> 00:10:59,719 Speaker 4: to go wrong? I think you know. The big thing too, 206 00:10:59,840 --> 00:11:01,679 Speaker 4: is like who is going to get trapped in this? 207 00:11:01,800 --> 00:11:05,000 Speaker 4: We had over the weekend an immigration lawyer in Massachusetts 208 00:11:05,080 --> 00:11:08,240 Speaker 4: right who received an email saying that she needed to 209 00:11:08,280 --> 00:11:12,080 Speaker 4: self deport immediately. That was a mistake. There's just so 210 00:11:12,200 --> 00:11:15,439 Speaker 4: many mistakes that could happen, and when the administration is 211 00:11:15,520 --> 00:11:18,120 Speaker 4: sending so many of these folks down to the seacop 212 00:11:18,160 --> 00:11:21,600 Speaker 4: prison in Al Salvador and not really fighting to bring 213 00:11:21,640 --> 00:11:24,640 Speaker 4: them back when mistakes are made. That adds another layer 214 00:11:24,840 --> 00:11:27,120 Speaker 4: to this, all right, because maybe they don't even care 215 00:11:27,640 --> 00:11:30,280 Speaker 4: if you know, the system is one hundred percent perfect 216 00:11:30,360 --> 00:11:32,240 Speaker 4: and doesn't make these horrible mistakes. 217 00:11:32,960 --> 00:11:36,400 Speaker 1: Dana, that is all kind of scary. We're starting to 218 00:11:36,440 --> 00:11:39,600 Speaker 1: think about, you know, what a complete kind of surveillance 219 00:11:39,640 --> 00:11:43,000 Speaker 1: state might look like here in the US. It's also 220 00:11:43,800 --> 00:11:47,760 Speaker 1: I'm curious, you know, I mentioned how Elon could be 221 00:11:47,840 --> 00:11:49,800 Speaker 1: stepping away here At the end of a one hundred 222 00:11:49,800 --> 00:11:53,920 Speaker 1: and thirty day period, he will have this special government 223 00:11:53,960 --> 00:11:57,880 Speaker 1: employee status that will sort of supposedly end. Is he 224 00:11:57,960 --> 00:11:58,800 Speaker 1: actually going to leave? 225 00:11:59,440 --> 00:12:02,360 Speaker 3: So has discretion in terms of how the one hundred 226 00:12:02,360 --> 00:12:04,959 Speaker 3: and thirty days are counted. Like if if you think 227 00:12:05,559 --> 00:12:08,480 Speaker 3: if the inauguration on January twentieth was day one, then 228 00:12:08,480 --> 00:12:10,679 Speaker 3: the one hundred and thirty days brings you till roughly 229 00:12:10,720 --> 00:12:12,960 Speaker 3: the end of May. I mean, there have definitely been 230 00:12:13,000 --> 00:12:16,679 Speaker 3: signals that Elon is going to like sort of step back. 231 00:12:17,000 --> 00:12:19,599 Speaker 3: Trump has said basically at some point, Elon's going to 232 00:12:19,679 --> 00:12:23,280 Speaker 3: have to leave. Elon talked very publicly at that Wisconsin 233 00:12:23,360 --> 00:12:25,760 Speaker 3: rally about the sort of hit that he's taking in 234 00:12:25,880 --> 00:12:29,400 Speaker 3: terms of the backlash against Tesla and how that's impacted 235 00:12:29,520 --> 00:12:33,120 Speaker 3: his stock and the stock of everyone who owns Tesla frankly, 236 00:12:33,559 --> 00:12:35,439 Speaker 3: and so there is a sense that, like he might 237 00:12:35,520 --> 00:12:38,120 Speaker 3: pull back, but I think that he has unfettered access 238 00:12:38,120 --> 00:12:40,840 Speaker 3: to the Trump administration. And we have to remind everyone 239 00:12:41,000 --> 00:12:44,600 Speaker 3: he bankrolled this electoral comeback. He spent more than two 240 00:12:44,679 --> 00:12:48,240 Speaker 3: hundred and fifty million dollars to get Trump reelected. He 241 00:12:48,400 --> 00:12:51,240 Speaker 3: campaigned for Trump all across Pennsylvania. And he's not just 242 00:12:51,320 --> 00:12:53,800 Speaker 3: going to like take his ball and go back to 243 00:12:53,880 --> 00:12:58,600 Speaker 3: Tesla like he He funded this campaign for a reason. 244 00:12:59,160 --> 00:13:02,319 Speaker 3: I think he is very proud of what Doge has accomplished, 245 00:13:02,320 --> 00:13:05,640 Speaker 3: but frustrated that the public isn't giving him accolades like 246 00:13:05,720 --> 00:13:09,040 Speaker 3: he is. He is frustrated by the backlash his companies 247 00:13:09,080 --> 00:13:11,440 Speaker 3: are in. I mean, Tesla at least is in trouble, 248 00:13:11,440 --> 00:13:14,400 Speaker 3: like they have a stale lineup byd is eating their 249 00:13:14,480 --> 00:13:17,400 Speaker 3: starting to eat their lunch. Globally, the cyber truck is 250 00:13:17,440 --> 00:13:20,080 Speaker 3: a disaster. In terms of sales, So like at some 251 00:13:20,120 --> 00:13:22,160 Speaker 3: point he's going to have to reckon with like what 252 00:13:22,400 --> 00:13:24,880 Speaker 3: is his company? But I don't think he's gonna like 253 00:13:25,200 --> 00:13:26,880 Speaker 3: not be in touch with Trump. I mean, the two 254 00:13:26,880 --> 00:13:28,400 Speaker 3: of them text each other all the time. 255 00:13:28,760 --> 00:13:30,520 Speaker 4: The thing I would add to that too is that 256 00:13:30,559 --> 00:13:34,839 Speaker 4: he owns x Twitter. I mean, his formal role is 257 00:13:34,880 --> 00:13:37,360 Speaker 4: as a senior advisor. I think maybe you can just 258 00:13:37,360 --> 00:13:41,080 Speaker 4: do that by competing. Unfortunately as well, because it's very unclear. 259 00:13:41,120 --> 00:13:45,680 Speaker 4: You know that the administration Republican influencers everyone in this ecosystem, 260 00:13:45,720 --> 00:13:49,840 Speaker 4: this Trump administration ecosystem is on there and probably you know, 261 00:13:49,880 --> 00:13:52,960 Speaker 4: gets push notifications like we all do when Elon Musk tweets, 262 00:13:52,960 --> 00:13:54,200 Speaker 4: whether we turn them on or not. 263 00:13:55,280 --> 00:13:57,520 Speaker 1: Mcinnay. You know, that's an interesting idea and it does 264 00:13:57,559 --> 00:13:59,800 Speaker 1: speak to maybe what the future it looks like. But 265 00:13:59,840 --> 00:14:03,280 Speaker 1: like back just in the present, since he's still in DOGE, 266 00:14:03,320 --> 00:14:06,560 Speaker 1: how involved is he on a day to day basis 267 00:14:06,559 --> 00:14:09,760 Speaker 1: Based on what you've learned through your reporting. 268 00:14:10,000 --> 00:14:11,760 Speaker 4: Yeah, I mean it sounds like the way that he 269 00:14:11,800 --> 00:14:13,520 Speaker 4: operates with a ton of his companies, right, he has 270 00:14:13,559 --> 00:14:17,559 Speaker 4: these grand ideas or whatever, and he builds a group 271 00:14:17,600 --> 00:14:21,040 Speaker 4: of people who he knows can execute. Steve Davis seems 272 00:14:21,040 --> 00:14:23,000 Speaker 4: to be heavily involved, who you know, is now the 273 00:14:23,000 --> 00:14:26,000 Speaker 4: president of the Waring Company. He was brought in for 274 00:14:26,080 --> 00:14:28,280 Speaker 4: the Twitter acquisition and did a lot of the cost 275 00:14:28,320 --> 00:14:30,800 Speaker 4: cutting there. It seems like it's the same playbook, right, 276 00:14:31,120 --> 00:14:33,240 Speaker 4: and then it gives Elan a lot of time to go, 277 00:14:33,440 --> 00:14:36,040 Speaker 4: you know, do some of his company stuff. Played Diablo 278 00:14:36,120 --> 00:14:38,960 Speaker 4: four at three o'clock in the morning to test his 279 00:14:39,000 --> 00:14:41,640 Speaker 4: starlink or whatever. But yeah, I think it's still you know, 280 00:14:41,760 --> 00:14:45,400 Speaker 4: him operating as this like philosopher King more than this 281 00:14:45,880 --> 00:14:49,600 Speaker 4: everyday roll of like getting his hands dirty with firings 282 00:14:49,760 --> 00:14:51,840 Speaker 4: or you know, doing specific stuffs. 283 00:14:52,200 --> 00:14:54,080 Speaker 3: When I thought it was super interesting that Musk and 284 00:14:54,120 --> 00:14:56,400 Speaker 3: a bunch of Doge people went on Fox News with 285 00:14:56,480 --> 00:14:58,760 Speaker 3: Brett Baher to kind of talk about their work, and 286 00:14:58,800 --> 00:15:01,040 Speaker 3: Steve Davis was sitting right next to Elon and he 287 00:15:01,120 --> 00:15:04,880 Speaker 3: was introduced by the Fox host as the COO. 288 00:15:04,640 --> 00:15:05,840 Speaker 1: Of Doge of Doge. 289 00:15:05,880 --> 00:15:09,320 Speaker 3: Yeah, so did A McKenna's point, like it is Elon's philosophy, 290 00:15:09,320 --> 00:15:11,840 Speaker 3: but he has kind of deputized some of his most 291 00:15:11,880 --> 00:15:15,240 Speaker 3: loyal lieutenants to actually carry out the work. But Musk 292 00:15:15,360 --> 00:15:18,040 Speaker 3: is still very much in DC. I mean, he was 293 00:15:18,080 --> 00:15:21,000 Speaker 3: with Trump in Florida over the weekend at the UFC fight. 294 00:15:21,600 --> 00:15:26,440 Speaker 3: Like he is not in Fremont, he's still very much 295 00:15:26,440 --> 00:15:27,320 Speaker 3: in the nation's capital. 296 00:15:27,600 --> 00:15:30,120 Speaker 1: So when he's not there, let's say, after these one 297 00:15:30,200 --> 00:15:32,400 Speaker 1: hundred and thirty days or whatever, who are some of 298 00:15:32,440 --> 00:15:35,320 Speaker 1: the key people you mentioned Steve Davis, who are some 299 00:15:35,360 --> 00:15:38,400 Speaker 1: of the key people in DOGE that will be kind 300 00:15:38,400 --> 00:15:42,800 Speaker 1: of primary reporting targets as you kind of look ahead, Dana, 301 00:15:42,840 --> 00:15:43,600 Speaker 1: I'm gonna start with you. 302 00:15:43,960 --> 00:15:44,520 Speaker 5: Yeah, I think I. 303 00:15:44,480 --> 00:15:46,400 Speaker 3: Think it's super interesting to look at who's taken on 304 00:15:46,480 --> 00:15:49,400 Speaker 3: the CIO roles so. At Social Security, for instance, the 305 00:15:49,960 --> 00:15:53,760 Speaker 3: sort of interim CIO was this guy Mike Russo, but 306 00:15:53,840 --> 00:15:57,520 Speaker 3: now it's Scott Colter, who's like a young kind of 307 00:15:57,960 --> 00:16:01,000 Speaker 3: private equity VC kind of person and who is now 308 00:16:01,600 --> 00:16:04,800 Speaker 3: like in that job. And so that's that's like one 309 00:16:04,800 --> 00:16:07,320 Speaker 3: example of like an agency person that I would look 310 00:16:07,360 --> 00:16:09,960 Speaker 3: closely at. I think it's super interesting to see how 311 00:16:10,040 --> 00:16:12,800 Speaker 3: long Antonio Gracias, who's on the board of SpaceX, and 312 00:16:12,920 --> 00:16:15,320 Speaker 3: Joe Gebia who's on the board of Tesla, stay involved 313 00:16:15,320 --> 00:16:19,240 Speaker 3: in DOGE. They have both been very active Antonio at 314 00:16:19,280 --> 00:16:22,520 Speaker 3: Social Security and Gebia's like at oh PM looking at 315 00:16:22,520 --> 00:16:25,000 Speaker 3: the retirement system. You know, some of these people kind 316 00:16:25,000 --> 00:16:28,160 Speaker 3: of cycled through and have cycled back to their companies already, 317 00:16:28,200 --> 00:16:30,520 Speaker 3: Like some of the young like engineers who worked at 318 00:16:30,520 --> 00:16:33,240 Speaker 3: Tesla and SpaceX, are already sort of done with their 319 00:16:33,400 --> 00:16:35,440 Speaker 3: with their work and are now back at the companies. 320 00:16:35,480 --> 00:16:38,600 Speaker 3: But to McKenna's point, like as we enter sort of 321 00:16:38,720 --> 00:16:42,720 Speaker 3: DOGE two point zero or like the second phase, I 322 00:16:42,960 --> 00:16:44,760 Speaker 3: think it's going to be some of the less lesser 323 00:16:44,880 --> 00:16:47,720 Speaker 3: known names that will actually have the most influence. And 324 00:16:47,720 --> 00:16:49,920 Speaker 3: they're lesser known by design. I mean a lot of 325 00:16:49,920 --> 00:16:51,920 Speaker 3: what we know of the about these people is based 326 00:16:51,960 --> 00:16:56,880 Speaker 3: on court filings and tros and declarations from the government 327 00:16:57,000 --> 00:16:59,320 Speaker 3: in these various lawsuits. A lot of these people have 328 00:16:59,320 --> 00:17:02,440 Speaker 3: scrubbed their social media. They don't have a LinkedIn page, 329 00:17:02,520 --> 00:17:05,919 Speaker 3: like they are being very low key on purpose and 330 00:17:06,000 --> 00:17:06,680 Speaker 3: worth mentioning. 331 00:17:06,760 --> 00:17:09,600 Speaker 1: Dana, you work with Sophie Alexander and our graphics team 332 00:17:09,600 --> 00:17:11,919 Speaker 1: here at Bloomberg to sort of map out what that 333 00:17:12,320 --> 00:17:15,120 Speaker 1: universe of people really look like. That will be kind 334 00:17:15,119 --> 00:17:18,920 Speaker 1: of leading DOGE potentially forward after elon the steps aside, Yeah, 335 00:17:18,960 --> 00:17:22,439 Speaker 1: for sure, any particular names that we haven't mentioned yet 336 00:17:22,520 --> 00:17:23,520 Speaker 1: that are of interest to you. 337 00:17:24,200 --> 00:17:27,560 Speaker 4: Yeah, The people that I'm most interested in are the 338 00:17:27,600 --> 00:17:32,680 Speaker 4: DOGE people who are within that USDs system, which was 339 00:17:32,720 --> 00:17:35,320 Speaker 4: formerly of course called the US Digital Services created under 340 00:17:35,320 --> 00:17:39,119 Speaker 4: Obama to resolve the whole healthcare dot gov debacle, now 341 00:17:39,160 --> 00:17:42,840 Speaker 4: called the US Doage Services, seemingly led by a woman 342 00:17:42,920 --> 00:17:45,800 Speaker 4: named Amy Gleason, who you don't hear a lot from. 343 00:17:46,080 --> 00:17:49,760 Speaker 4: So I'm definitely interested in seeing what Amy's role evolves into. 344 00:17:50,080 --> 00:17:54,120 Speaker 4: Does she end up making more decisions, does she take 345 00:17:54,160 --> 00:17:57,200 Speaker 4: the reins on a lot of this. I have my doubts, 346 00:17:57,320 --> 00:18:01,440 Speaker 4: of course, but definitely the leadership there and at GSA 347 00:18:01,480 --> 00:18:04,640 Speaker 4: as well, because the General Services Administration to me has 348 00:18:04,760 --> 00:18:08,080 Speaker 4: kind of seemed with all the layoffs happening and getting 349 00:18:08,160 --> 00:18:10,000 Speaker 4: rid of so many folks at these agencies, it seems 350 00:18:10,000 --> 00:18:12,520 Speaker 4: like they're building like this group of technologists that they 351 00:18:12,560 --> 00:18:15,440 Speaker 4: can then just like deploy anywhere to you know, quote 352 00:18:15,520 --> 00:18:17,680 Speaker 4: unquote like fix issues or do things. 353 00:18:18,000 --> 00:18:19,960 Speaker 1: All Right, McKenna, we're going to have you back on 354 00:18:19,960 --> 00:18:22,840 Speaker 1: the pod. Thanks for joining us today. Really appreciate you 355 00:18:22,880 --> 00:18:23,440 Speaker 1: in your time. 356 00:18:23,720 --> 00:18:24,760 Speaker 4: Great great to be here. 357 00:18:30,640 --> 00:18:32,600 Speaker 1: Oh right now we're going to talk about the other 358 00:18:32,640 --> 00:18:35,280 Speaker 1: most exciting thing in the world, which is Tesla as 359 00:18:35,320 --> 00:18:37,520 Speaker 1: a stock. And we're going to be joined by Esha Day, 360 00:18:37,880 --> 00:18:41,320 Speaker 1: a Bloomberg Stock reporter. Esha, thanks for joining us. Thank 361 00:18:41,359 --> 00:18:45,560 Speaker 1: you for having me, Dana, you're still here, of course, Escha. 362 00:18:45,600 --> 00:18:48,200 Speaker 1: What is going on with this stock right now? 363 00:18:48,320 --> 00:18:49,600 Speaker 2: Well, that's an interesting question. 364 00:18:49,720 --> 00:18:53,520 Speaker 5: As always, it's very volatile and it's mostly going down now. 365 00:18:54,200 --> 00:18:56,119 Speaker 5: You would say that about the US stock market as 366 00:18:56,160 --> 00:18:57,480 Speaker 5: better right now and. 367 00:18:57,280 --> 00:18:59,960 Speaker 1: For numbers like about seven and a half percent down, 368 00:19:00,240 --> 00:19:01,520 Speaker 1: And I. 369 00:19:01,520 --> 00:19:03,840 Speaker 5: Just want to set up like you know, yes, Tesla 370 00:19:03,960 --> 00:19:06,520 Speaker 5: is moving a lot like the US stock market, which 371 00:19:06,520 --> 00:19:08,800 Speaker 5: itself is moving like a memestock these days. 372 00:19:09,000 --> 00:19:11,600 Speaker 2: But I want to tell you, like how much wilder 373 00:19:11,640 --> 00:19:12,159 Speaker 2: Tesla is. 374 00:19:12,200 --> 00:19:15,440 Speaker 5: For example, Tesla shares are down last I checked, about 375 00:19:15,440 --> 00:19:18,439 Speaker 5: thirty eight percent this year. Sm P five hundred is 376 00:19:18,480 --> 00:19:21,200 Speaker 5: down about eight percent of over that time. The NaSTA 377 00:19:21,320 --> 00:19:23,960 Speaker 5: one hundred, which is I would say usually a little 378 00:19:24,000 --> 00:19:26,480 Speaker 5: more volatile than sm P five hundred index because it's 379 00:19:26,480 --> 00:19:30,000 Speaker 5: so technology heavy, is down about ten percent. 380 00:19:30,480 --> 00:19:31,800 Speaker 2: So you know that's a comparison. 381 00:19:31,880 --> 00:19:35,880 Speaker 5: Now since April second, when this recent tariff turmoil really started, 382 00:19:35,960 --> 00:19:39,919 Speaker 5: and by many accounts, some people would say that Tesla 383 00:19:40,080 --> 00:19:43,520 Speaker 5: is relatively insulated maybe from some of the tariffs. Yet 384 00:19:43,680 --> 00:19:48,600 Speaker 5: Tesla shares are down about eleven person since April seconds close. 385 00:19:48,920 --> 00:19:51,480 Speaker 5: Sembly five hundred is down about four point five percent 386 00:19:51,600 --> 00:19:54,119 Speaker 5: over that time, NaSTA one hundred down four persons. So 387 00:19:54,359 --> 00:19:56,719 Speaker 5: you know, by whichever way you look at this, Tesla 388 00:19:56,760 --> 00:19:58,000 Speaker 5: chees are doing very badly. 389 00:19:58,119 --> 00:20:00,760 Speaker 1: Okay, there's this thing called the death crow that's come 390 00:20:00,840 --> 00:20:03,840 Speaker 1: up a little bit and Tesla seems to be maybe 391 00:20:03,880 --> 00:20:08,240 Speaker 1: flirting with it. How significant is this death cross? 392 00:20:09,359 --> 00:20:11,240 Speaker 2: Yeah, it's significant for sure. 393 00:20:11,320 --> 00:20:14,480 Speaker 5: It's a death cross is basically a chart pattern, but 394 00:20:14,560 --> 00:20:17,719 Speaker 5: it's one of the more prominent ones out there, something 395 00:20:17,800 --> 00:20:21,760 Speaker 5: that technical analysts and chart analysts kind of look at 396 00:20:21,840 --> 00:20:25,040 Speaker 5: very closely. Now, any chart pattern we would not look 397 00:20:25,080 --> 00:20:28,000 Speaker 5: at them in isolation. We usually want to see what 398 00:20:28,040 --> 00:20:30,119 Speaker 5: else is happening with the stock at the same point. 399 00:20:30,560 --> 00:20:34,440 Speaker 5: But overall, what this death cross is is it's basically 400 00:20:34,600 --> 00:20:38,159 Speaker 5: is giving you a sense of what the momentum of 401 00:20:38,240 --> 00:20:39,240 Speaker 5: the share price is doing. 402 00:20:39,320 --> 00:20:39,520 Speaker 2: Right. 403 00:20:39,960 --> 00:20:43,480 Speaker 5: So, the way it's defined is when a short term 404 00:20:44,040 --> 00:20:48,040 Speaker 5: price support line of a stock or an asset falls 405 00:20:48,080 --> 00:20:51,080 Speaker 5: below the long term support line or trend line. The 406 00:20:51,160 --> 00:20:54,639 Speaker 5: way it's usually defined is by the fifty day moving 407 00:20:54,680 --> 00:20:57,560 Speaker 5: average and the two hundred day moving average, and Tesla 408 00:20:57,600 --> 00:21:02,399 Speaker 5: Shayes just cross that kind of ominous milestone yesterday. What 409 00:21:02,440 --> 00:21:04,840 Speaker 5: it usually means is that you know that, like the 410 00:21:04,880 --> 00:21:07,000 Speaker 5: momentum is now on the lower side. 411 00:21:07,160 --> 00:21:08,720 Speaker 2: The last time it happened was in. 412 00:21:08,800 --> 00:21:12,960 Speaker 5: Early I would say late January early February twenty twenty four, 413 00:21:13,440 --> 00:21:16,240 Speaker 5: and right after that happened, Tesla Chez fell about twenty 414 00:21:16,240 --> 00:21:18,360 Speaker 5: five person over fifty five trading sessions. 415 00:21:18,400 --> 00:21:20,439 Speaker 2: So you know that's how it goes. 416 00:21:20,680 --> 00:21:23,159 Speaker 1: Okay, Dana, I'm gonna turn to you. What's the word 417 00:21:23,440 --> 00:21:27,360 Speaker 1: among investors and analysts? Were a week away from from 418 00:21:27,480 --> 00:21:30,600 Speaker 1: earnings here, it seems like a pretty significant earning. So 419 00:21:30,600 --> 00:21:33,920 Speaker 1: so what are we hearing as we march towards that day? 420 00:21:34,160 --> 00:21:36,439 Speaker 3: Yeah, I mean you're seeing a lot of analysts cutting 421 00:21:36,480 --> 00:21:40,720 Speaker 3: their price targets for volume because Tesla just does not 422 00:21:40,840 --> 00:21:44,119 Speaker 3: really have a robust lineup, and people want to hear like, 423 00:21:44,600 --> 00:21:46,919 Speaker 3: what is up with? Is Tesla ever going to make 424 00:21:46,920 --> 00:21:49,199 Speaker 3: a cheaper car? Is the cheaper car like really a 425 00:21:49,240 --> 00:21:50,960 Speaker 3: new car or is it just like a de content 426 00:21:51,040 --> 00:21:53,600 Speaker 3: and Model three and Model Y. I've written about this 427 00:21:53,760 --> 00:21:56,000 Speaker 3: that like Elon Musk is basically. 428 00:21:55,600 --> 00:21:56,840 Speaker 4: Bored with making cars. 429 00:21:56,920 --> 00:21:58,920 Speaker 3: He kind of proved to the world that you could 430 00:21:58,960 --> 00:22:01,639 Speaker 3: make a high performance like car and he accomplished that, 431 00:22:01,760 --> 00:22:05,359 Speaker 3: and now he's all about AI and the robotaxi. The 432 00:22:05,400 --> 00:22:08,640 Speaker 3: company is supposed to launch some kind of soft robotaxi 433 00:22:08,720 --> 00:22:11,920 Speaker 3: service in Austin in June. So I think the earnings 434 00:22:11,960 --> 00:22:14,719 Speaker 3: call is all going to be about like the catalysts 435 00:22:14,720 --> 00:22:17,280 Speaker 3: for the future. Everyone knows it was a really bad 436 00:22:17,359 --> 00:22:21,359 Speaker 3: quarter in terms of sales. How testy analysts get with 437 00:22:21,480 --> 00:22:23,960 Speaker 3: Elon on the call, I think will be interesting. I'm 438 00:22:24,000 --> 00:22:27,080 Speaker 3: hoping that analysts will ask him like when are you 439 00:22:27,119 --> 00:22:29,520 Speaker 3: stepping back from doge? When are you going to actually 440 00:22:29,600 --> 00:22:31,760 Speaker 3: run the company? Like he has always been a part 441 00:22:31,800 --> 00:22:35,000 Speaker 3: time CEO, and I think you're really seeing frustration about 442 00:22:35,000 --> 00:22:38,480 Speaker 3: that spillover. Like Dan Ives, who's you know, always been 443 00:22:38,520 --> 00:22:41,000 Speaker 3: one of the most boolish analysts on Wall Street, sort 444 00:22:41,000 --> 00:22:44,480 Speaker 3: of talked about the brand destruction and that like Elon 445 00:22:44,560 --> 00:22:47,040 Speaker 3: has to kind of make a decision and read the room. 446 00:22:47,440 --> 00:22:49,360 Speaker 3: So yeah, it'll be as always, it'll be a very 447 00:22:49,359 --> 00:22:49,880 Speaker 3: interesting call. 448 00:22:49,960 --> 00:22:50,439 Speaker 2: Next week. 449 00:22:50,800 --> 00:22:54,040 Speaker 1: Dan just brought up Dan Ives, who, as she also mentioned, 450 00:22:54,560 --> 00:22:58,040 Speaker 1: is has been a huge test lablele. How significant was 451 00:22:58,200 --> 00:23:00,000 Speaker 1: him changing his tune a little bit. 452 00:23:00,600 --> 00:23:04,040 Speaker 5: Yeah, So, Dan Ives, I would say, is one of 453 00:23:04,080 --> 00:23:08,720 Speaker 5: the strongest bullish voices among seal side analysts on Tesla. 454 00:23:09,040 --> 00:23:12,040 Speaker 5: You know, he's very rarely criticized as a stock or 455 00:23:12,480 --> 00:23:16,440 Speaker 5: Elon Musk himself. So him coming out was really kind 456 00:23:16,440 --> 00:23:18,360 Speaker 5: of emblematic of how a. 457 00:23:18,320 --> 00:23:19,680 Speaker 2: Lot of the bullish voices. 458 00:23:20,280 --> 00:23:23,240 Speaker 5: And I speak to investors, both retail investors and institutional 459 00:23:23,240 --> 00:23:26,879 Speaker 5: investors on Tesla all the time, and when around the 460 00:23:26,920 --> 00:23:29,320 Speaker 5: time when Dan kind of came out and said that Okay, 461 00:23:29,440 --> 00:23:32,000 Speaker 5: this is time for the company to really kind of 462 00:23:32,040 --> 00:23:34,920 Speaker 5: you know, listen to the room really and for Elon 463 00:23:35,000 --> 00:23:37,800 Speaker 5: Musk to take a step back and kind of refocus 464 00:23:37,960 --> 00:23:40,840 Speaker 5: on the company again, that was really emblematic of the 465 00:23:40,920 --> 00:23:43,440 Speaker 5: mood of the street. Like when I talked to investors 466 00:23:43,480 --> 00:23:47,280 Speaker 5: that they're very cautious, like there are I mean, there 467 00:23:47,359 --> 00:23:50,560 Speaker 5: is this thing about Tesla bullish investors where they're almost 468 00:23:50,600 --> 00:23:54,040 Speaker 5: always optimistic in some way, but the mood of caution, 469 00:23:54,200 --> 00:23:56,920 Speaker 5: the mood of concern is really coming out right now 470 00:23:56,960 --> 00:24:00,560 Speaker 5: and that is like becoming really intense after the squatter 471 00:24:00,600 --> 00:24:03,760 Speaker 5: deliver numbers and the results that we are expecting next week. 472 00:24:03,960 --> 00:24:07,960 Speaker 1: Okay, so there's caution, but then there's short shorts. No 473 00:24:08,040 --> 00:24:10,320 Speaker 1: one seems to be willing to short it though, right 474 00:24:10,480 --> 00:24:14,000 Speaker 1: and Dana, You've written about shorts and how devastated they've 475 00:24:14,000 --> 00:24:17,040 Speaker 1: been by this stock. So even though there's caution, like, 476 00:24:17,320 --> 00:24:19,720 Speaker 1: is anybody really willing to bet against Elon? 477 00:24:20,240 --> 00:24:21,600 Speaker 3: You know, that's a really good question. I mean, the 478 00:24:21,920 --> 00:24:24,159 Speaker 3: sort of a group of short sellers known as Tesla 479 00:24:24,280 --> 00:24:26,480 Speaker 3: Hugh really got blown out of the water when the 480 00:24:26,520 --> 00:24:29,119 Speaker 3: Model Y became like the best selling vehicle and the 481 00:24:29,160 --> 00:24:32,600 Speaker 3: company kind of very definitely navigated the pandemic and hit 482 00:24:32,600 --> 00:24:35,280 Speaker 3: a trillion dollar valuation. I actually don't know if the 483 00:24:35,320 --> 00:24:37,560 Speaker 3: shorts have come back. I mean, some of the biggest 484 00:24:37,600 --> 00:24:40,959 Speaker 3: and loudest short sellers like David Einhorn and Jim Chanos, 485 00:24:41,000 --> 00:24:44,400 Speaker 3: I don't actually know if they still are actively shortening 486 00:24:44,400 --> 00:24:47,320 Speaker 3: stocks like ESHA. I haven't caught up with them recently, 487 00:24:47,320 --> 00:24:48,000 Speaker 3: have you. 488 00:24:48,000 --> 00:24:48,640 Speaker 2: No, You're right. 489 00:24:48,720 --> 00:24:52,359 Speaker 5: I mean, there are almost no prominent bearish voices or 490 00:24:52,400 --> 00:24:55,480 Speaker 5: short seller voices to be found in Tesla at this point. 491 00:24:55,560 --> 00:24:58,800 Speaker 5: And you know, they kind of absolutely got blown apart 492 00:24:58,880 --> 00:25:01,920 Speaker 5: during the twenty twenty two an even rally in the stock, 493 00:25:02,160 --> 00:25:04,960 Speaker 5: so I just again numbers a little bit for context. 494 00:25:05,320 --> 00:25:07,600 Speaker 5: There was a time, I would say around twenty eighteen 495 00:25:07,640 --> 00:25:11,840 Speaker 5: twenty nineteen, the short seller percentage of Tesla stock was 496 00:25:11,840 --> 00:25:14,200 Speaker 5: around thirty three percent of its free flow. Right now, 497 00:25:14,240 --> 00:25:16,760 Speaker 5: it's hovering around three percent for the past four or 498 00:25:16,760 --> 00:25:19,359 Speaker 5: five years. So that's where we are. Even when we 499 00:25:19,400 --> 00:25:21,960 Speaker 5: see a slight bit of uptick in the stock, we 500 00:25:22,040 --> 00:25:25,560 Speaker 5: haven't really seen anything that's you know, worth really noting. 501 00:25:25,680 --> 00:25:28,000 Speaker 5: When I talk to people on the street, there are 502 00:25:28,200 --> 00:25:31,520 Speaker 5: enough people out there who think this stock should be shorted, 503 00:25:31,960 --> 00:25:34,199 Speaker 5: but they're just not willing to stand in front of it, 504 00:25:34,359 --> 00:25:38,119 Speaker 5: just because it's such an immense beast of a momentum 505 00:25:38,160 --> 00:25:40,960 Speaker 5: stock right just when the stock turns, it's very hard 506 00:25:41,000 --> 00:25:43,600 Speaker 5: to tell when it might turn, and especially with Lan's 507 00:25:44,280 --> 00:25:48,119 Speaker 5: kind of political prominence and everything, the understanding is that 508 00:25:48,160 --> 00:25:50,440 Speaker 5: once the stock really turns, it's very hard to stand 509 00:25:50,480 --> 00:25:52,520 Speaker 5: in front of that momentum, and nobody wants to get 510 00:25:52,800 --> 00:25:53,720 Speaker 5: kind of blown part by that. 511 00:25:54,320 --> 00:25:56,840 Speaker 1: Esha Danna, thanks for joining us on Elennik. 512 00:25:57,160 --> 00:25:58,560 Speaker 4: Thank you always a pleasure. 513 00:26:05,000 --> 00:26:08,080 Speaker 1: This episode was produced by Stacy Wong. Anna Maserakas is 514 00:26:08,119 --> 00:26:12,480 Speaker 1: our editor, and Rayhan Harmanci, our senior editor. The idea 515 00:26:12,520 --> 00:26:14,960 Speaker 1: for this very show also came from Rayhan, and she 516 00:26:15,080 --> 00:26:17,119 Speaker 1: deserves a special shout out since this will be our 517 00:26:17,119 --> 00:26:21,680 Speaker 1: final episode. Blake Maples handles engineering and David Purcell fact checks. 518 00:26:22,040 --> 00:26:25,800 Speaker 1: Our supervising producer is Magnus Henrickson. The elon Ink theme 519 00:26:26,200 --> 00:26:30,560 Speaker 1: is written and performed by Taka Yazozawa and Alex Sugahira. 520 00:26:31,200 --> 00:26:35,160 Speaker 1: Brendan Francis Newnan is our executive producer, and Sage Bauman 521 00:26:35,359 --> 00:26:37,879 Speaker 1: is the head of Bloomberg Podcasts. A big thanks to 522 00:26:37,880 --> 00:26:40,439 Speaker 1: our supporter Brad Stone. I'm Joel Weber. If you have 523 00:26:40,480 --> 00:26:43,040 Speaker 1: a minute, rate and review our show, it'll help other 524 00:26:43,119 --> 00:26:47,320 Speaker 1: listeners find us. See you next week.