1 00:00:02,480 --> 00:00:07,080 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. 2 00:00:07,920 --> 00:00:09,520 Speaker 2: Let me tell you we have a new star. 3 00:00:10,520 --> 00:00:14,080 Speaker 3: A star is born Elon puns up On Mars Youth 4 00:00:14,160 --> 00:00:14,680 Speaker 3: and Kennemy. 5 00:00:14,880 --> 00:00:18,480 Speaker 1: He is the Thomas Edison plus plus plus of our age. 6 00:00:18,640 --> 00:00:21,040 Speaker 2: Probably his whole life is from a position of insecurity. 7 00:00:21,040 --> 00:00:23,200 Speaker 2: I feel for the guy. I would say ninety eight 8 00:00:23,239 --> 00:00:26,560 Speaker 2: percent really appreciate what he does. But those two percent 9 00:00:26,640 --> 00:00:30,400 Speaker 2: that are nasty, they are update in four folds. We 10 00:00:30,400 --> 00:00:32,919 Speaker 2: were meant for great things in the United States of America, 11 00:00:33,360 --> 00:00:36,720 Speaker 2: and Elon reminds us of that we don't have a 12 00:00:36,880 --> 00:00:46,920 Speaker 2: fourth branch of governments called Elon Musk. Welcome to Elon, 13 00:00:46,960 --> 00:00:51,120 Speaker 2: a Bloomberg's weekly podcast about Elon Musk. It's Tuesday, February eighteenth. 14 00:00:51,200 --> 00:00:54,360 Speaker 2: I'm your host, David Papatoppos, sitting here in the studio 15 00:00:54,560 --> 00:00:57,880 Speaker 2: in New York with stalwart of the podcast, an intrepid 16 00:00:57,960 --> 00:01:02,240 Speaker 2: Elon Musk reporter Dana Hull. Dana, Welcome to New York. David. 17 00:01:02,280 --> 00:01:03,360 Speaker 1: Great to see you in person. 18 00:01:03,760 --> 00:01:06,240 Speaker 2: Yeah, great to have you here. But what the hell 19 00:01:06,600 --> 00:01:09,280 Speaker 2: did you leave sixty degrees in a light breeze in 20 00:01:09,319 --> 00:01:12,880 Speaker 2: San Francisco for this godforsaken Polar Vortex for. 21 00:01:13,400 --> 00:01:16,440 Speaker 1: Well, David. We are doing a live taping of Elon Inc. 22 00:01:16,600 --> 00:01:19,479 Speaker 1: This Thursday at the on Air Fest. It's this big 23 00:01:19,520 --> 00:01:23,080 Speaker 1: festival of like audio, storytelling and podcasting and Elon Inc. 24 00:01:23,200 --> 00:01:27,840 Speaker 1: Is doing a live taping Thursday afternoon, two thirty Thursday, 25 00:01:28,000 --> 00:01:29,520 Speaker 1: Brooklyn at the Wife Hotel. 26 00:01:29,760 --> 00:01:32,760 Speaker 2: Be there or b Square yep. So it's Dana, Me, 27 00:01:33,080 --> 00:01:37,240 Speaker 2: Max Chafkin, and a special guest from Wired Magazine McKenna Kelly. 28 00:01:37,600 --> 00:01:40,600 Speaker 2: Come join us in the afternoon. If you're in the neighborhood, Dana, 29 00:01:41,200 --> 00:01:45,320 Speaker 2: we'll be signing autographs, at least one for me. Okay, Dani. 30 00:01:45,360 --> 00:01:47,840 Speaker 2: So we're going to keep it tight today. You've been 31 00:01:47,960 --> 00:01:52,120 Speaker 2: busier than ever cranking out story after story on Musk's 32 00:01:52,320 --> 00:01:55,120 Speaker 2: band of Doge costcutters. I'm just going to hit you 33 00:01:55,160 --> 00:01:58,760 Speaker 2: with some storylines on Doze or other related Musk topics, 34 00:01:58,760 --> 00:02:01,000 Speaker 2: and you're gonna give me the skinny in the Dana 35 00:02:01,040 --> 00:02:06,560 Speaker 2: Hall take rapid fire Lightning rounde. So Doge Musk those 36 00:02:06,640 --> 00:02:09,800 Speaker 2: team indeed has been added fast and furious. Tell me 37 00:02:10,280 --> 00:02:15,440 Speaker 2: for starters about layoffs. I believe there's been some layoffs 38 00:02:15,480 --> 00:02:19,400 Speaker 2: of probationary employees, some layoffs at the DOE, the Department. 39 00:02:20,240 --> 00:02:22,920 Speaker 1: They have been layoffs everywhere, so does appears to be 40 00:02:22,960 --> 00:02:26,760 Speaker 1: starting with what's called probationary employees, which are either people 41 00:02:26,800 --> 00:02:28,960 Speaker 1: that are relatively new to the agency and have been 42 00:02:29,000 --> 00:02:31,320 Speaker 1: there for less than a year. But if you change the. 43 00:02:31,440 --> 00:02:33,400 Speaker 2: Job, that's the definition of a probationary. 44 00:02:33,600 --> 00:02:35,560 Speaker 1: Yeah, it's just performance. It has nothing to do with 45 00:02:35,840 --> 00:02:38,040 Speaker 1: it has nothing to do with performance. It's if you're 46 00:02:38,120 --> 00:02:40,920 Speaker 1: if you're relatively new at an agency, or if you 47 00:02:40,960 --> 00:02:44,480 Speaker 1: have switched positions within an agency, your probationary clock kind 48 00:02:44,520 --> 00:02:48,760 Speaker 1: of restarts. Scores of people have been sacked at Department 49 00:02:48,760 --> 00:02:51,880 Speaker 1: of Energy, the EPA, like HHS. 50 00:02:51,960 --> 00:02:52,720 Speaker 2: It's widespread. 51 00:02:52,800 --> 00:02:55,760 Speaker 1: It's like every agency is getting hit park rangers for a. 52 00:02:55,840 --> 00:02:59,960 Speaker 2: Success and the reason why they're going after these people 53 00:03:00,040 --> 00:03:02,200 Speaker 2: but was so little tenures Because it's easier. 54 00:03:02,400 --> 00:03:05,160 Speaker 1: I imagine that it's easier. I'm not entirely sure what 55 00:03:05,240 --> 00:03:09,760 Speaker 1: the federal union protections are, but at any company, usually 56 00:03:09,800 --> 00:03:11,480 Speaker 1: the people that are the newest are the easiest to 57 00:03:11,480 --> 00:03:11,960 Speaker 1: get rid of. 58 00:03:12,520 --> 00:03:16,680 Speaker 2: I see. Also, we have a final number, it appears 59 00:03:16,919 --> 00:03:21,480 Speaker 2: on how many federal employees took the DOGE buyout offer. 60 00:03:21,560 --> 00:03:22,600 Speaker 2: What was that final number? 61 00:03:23,400 --> 00:03:26,520 Speaker 1: The last i've read was roughly seventy five thousand, which 62 00:03:26,560 --> 00:03:28,720 Speaker 1: is really low. I mean, I think that the vast 63 00:03:28,760 --> 00:03:31,480 Speaker 1: majority of federal workers have dug in and are not 64 00:03:31,600 --> 00:03:34,400 Speaker 1: taking quote unquote the fork in the road because they 65 00:03:34,440 --> 00:03:36,560 Speaker 1: don't trust it. They don't trust that they're going to 66 00:03:36,600 --> 00:03:39,800 Speaker 1: get paid through September. They don't see a reason to 67 00:03:39,840 --> 00:03:42,240 Speaker 1: take it. They've been advised in many cases by their 68 00:03:42,320 --> 00:03:44,800 Speaker 1: unions not to take it. So the number of people 69 00:03:44,800 --> 00:03:47,680 Speaker 1: who took the quote unquote buyout is low. Now they 70 00:03:47,720 --> 00:03:50,800 Speaker 1: are getting rid of a lot of probationary employees and 71 00:03:50,840 --> 00:03:52,880 Speaker 1: then the next phase will just be like a reduction 72 00:03:52,960 --> 00:03:53,400 Speaker 1: in force. 73 00:03:53,720 --> 00:04:00,760 Speaker 2: Gotcha? Okay? Moving on, DOGE is inside the Social Security Administration? 74 00:04:01,560 --> 00:04:02,320 Speaker 2: What do we know there? 75 00:04:02,520 --> 00:04:05,080 Speaker 1: Okay, So to be clear, DOGE is everywhere, Like I think. 76 00:04:04,920 --> 00:04:06,120 Speaker 2: That they're everywhere. 77 00:04:06,360 --> 00:04:11,000 Speaker 1: Yeah, I think that there's this perception that DOGE is 78 00:04:11,040 --> 00:04:13,720 Speaker 1: going agency by agency, and the truth is that they 79 00:04:13,720 --> 00:04:15,320 Speaker 1: will eventually be everywhere. 80 00:04:15,520 --> 00:04:18,120 Speaker 2: They're everywhere all the time. But are there not like 81 00:04:18,520 --> 00:04:21,480 Speaker 2: specific focal points where they're really bearing down on. 82 00:04:21,600 --> 00:04:24,080 Speaker 1: Well, I think so they have gone to the Department 83 00:04:24,120 --> 00:04:27,360 Speaker 1: of Energy, the EPA, They're apparently they're going into the 84 00:04:27,400 --> 00:04:31,440 Speaker 1: SEC we're keeping an eye out today for NASA. The 85 00:04:31,440 --> 00:04:34,440 Speaker 1: Social Security Administration and the I R S and the 86 00:04:34,480 --> 00:04:37,880 Speaker 1: Treasury have been key agencies. Obviously, SSA is a big 87 00:04:37,880 --> 00:04:40,880 Speaker 1: concern because it's like everyone has a Social Security number, right, 88 00:04:40,920 --> 00:04:43,080 Speaker 1: I mean, And there was some reporting out of the 89 00:04:43,160 --> 00:04:46,760 Speaker 1: Washington Post over the weekend about how the Commissioner of 90 00:04:46,800 --> 00:04:50,720 Speaker 1: the Social Security Administration either fired or was resigned, and so, yes, 91 00:04:50,839 --> 00:04:53,200 Speaker 1: Doge appears to be very active there this week. 92 00:04:53,839 --> 00:04:57,440 Speaker 2: And Musk, indeed, there's been a lot of commentary and 93 00:04:57,839 --> 00:05:02,479 Speaker 2: pushback on the push into the Social Security Administration. My 94 00:05:02,680 --> 00:05:06,360 Speaker 2: understanding is that he's saying a couple of things. One, well, heck, 95 00:05:06,400 --> 00:05:09,680 Speaker 2: if it was just about me rummaging around and taking 96 00:05:09,720 --> 00:05:13,560 Speaker 2: your information, I could have done that years ago at PayPal, okay. 97 00:05:13,960 --> 00:05:16,320 Speaker 2: And Then b I think he's saying, well, listen, there 98 00:05:16,320 --> 00:05:19,640 Speaker 2: are all sorts of people still receiving quote unquote social 99 00:05:19,680 --> 00:05:22,480 Speaker 2: Security who are one hundred and eighty years old. We're 100 00:05:22,520 --> 00:05:24,920 Speaker 2: just trying to scrub them from the system. 101 00:05:25,120 --> 00:05:28,760 Speaker 1: And I worry about people amplifying what Musk is saying 102 00:05:28,880 --> 00:05:32,239 Speaker 1: because there's no proof that what Musk is saying is real. 103 00:05:32,720 --> 00:05:33,880 Speaker 2: So and he. 104 00:05:33,920 --> 00:05:37,040 Speaker 1: Has like, you know, two hundred or two hundred million followers, 105 00:05:37,080 --> 00:05:40,080 Speaker 1: but like it could be a coding thing that we 106 00:05:40,120 --> 00:05:41,760 Speaker 1: can't take what he's saying at face value. 107 00:05:43,000 --> 00:05:47,160 Speaker 2: He has pledged radical transparency on all of this and 108 00:05:47,560 --> 00:05:53,119 Speaker 2: all their endeavors that transparency was lacking initially, any further 109 00:05:53,160 --> 00:05:54,839 Speaker 2: greater transparency into what they're doing. 110 00:05:54,880 --> 00:05:57,839 Speaker 1: There is zero transparency. And here's why. First of all, 111 00:05:59,040 --> 00:06:02,120 Speaker 1: there is there's no website that shows you who is 112 00:06:02,200 --> 00:06:05,000 Speaker 1: working for DOGE. We have been able to piece together 113 00:06:05,160 --> 00:06:08,960 Speaker 1: which people from DOGE are working with each agency thanks 114 00:06:09,000 --> 00:06:14,400 Speaker 1: to reporting by wireds screenshots, legal declarations, legal filings, but 115 00:06:14,480 --> 00:06:16,880 Speaker 1: like it has been this massive effort just to figure 116 00:06:16,880 --> 00:06:20,680 Speaker 1: out who works with DOGE, who's running DOGE, who is 117 00:06:20,720 --> 00:06:23,320 Speaker 1: embedded at which agency. Like, if you want to start 118 00:06:23,360 --> 00:06:25,640 Speaker 1: with transparency, you'd start with the actual people. And the 119 00:06:25,680 --> 00:06:29,559 Speaker 1: other bit is Musk's financial disclosures with the White House. 120 00:06:29,720 --> 00:06:31,479 Speaker 1: Like I filed a FOYA to try to get a 121 00:06:31,480 --> 00:06:34,360 Speaker 1: copy of it, it was denied. Like we have zero 122 00:06:34,880 --> 00:06:37,120 Speaker 1: transparency around the conflicx of interest. 123 00:06:37,320 --> 00:06:41,080 Speaker 2: The denying of the FOYA is because Mosque is technically 124 00:06:41,120 --> 00:06:42,360 Speaker 2: part of. 125 00:06:42,000 --> 00:06:45,480 Speaker 1: The White House, the White House and a special government employee. 126 00:06:45,600 --> 00:06:48,320 Speaker 2: Yeah, I see, And as a result, those are in 127 00:06:48,400 --> 00:06:49,560 Speaker 2: all cases off limits. 128 00:06:50,120 --> 00:06:52,560 Speaker 1: Well they're saying that they're off limits. Yeah, I mean, 129 00:06:52,600 --> 00:06:55,240 Speaker 1: we can keep pushing the effort, but I mean this, 130 00:06:55,560 --> 00:06:59,000 Speaker 1: so DOGE has a website, they're claiming to be transparent, 131 00:06:59,520 --> 00:07:02,039 Speaker 1: but you have to start with the basics, which is 132 00:07:02,520 --> 00:07:06,760 Speaker 1: Musk's financial disclosures and the conflicts of interest and releasing 133 00:07:06,800 --> 00:07:09,200 Speaker 1: that which the White House is not seem inclined to do, 134 00:07:09,320 --> 00:07:11,640 Speaker 1: and then be who are the people Like at the 135 00:07:11,640 --> 00:07:14,800 Speaker 1: Department of Education, for example, there are six people that 136 00:07:14,880 --> 00:07:18,640 Speaker 1: are affiliated with DOGE embedded at the department. To our 137 00:07:18,920 --> 00:07:22,120 Speaker 1: kind of Department of Education employees for are detail to 138 00:07:22,200 --> 00:07:24,800 Speaker 1: the agency. We don't know who those six people are. 139 00:07:24,840 --> 00:07:28,480 Speaker 1: We know one of them because he's filed several legal declarations, 140 00:07:28,480 --> 00:07:31,000 Speaker 1: but there's no transparency about the others, and we've had 141 00:07:31,040 --> 00:07:33,560 Speaker 1: to spend an enormous amount of time just piecing that together. 142 00:07:34,400 --> 00:07:37,160 Speaker 2: You said there's a DOGE website. I actually didn't realize that. 143 00:07:37,240 --> 00:07:38,560 Speaker 2: What appears on. 144 00:07:38,480 --> 00:07:41,679 Speaker 1: The like a lot of graphics that purport to show 145 00:07:42,000 --> 00:07:45,360 Speaker 1: cost savings and a live stream from X and now 146 00:07:45,480 --> 00:07:47,800 Speaker 1: the DOGE teams that all these different departments have their 147 00:07:47,840 --> 00:07:50,440 Speaker 1: own X feeds, So it's kind of like a hybrid 148 00:07:50,560 --> 00:07:52,440 Speaker 1: of like an X feed and a website. 149 00:07:52,680 --> 00:07:55,080 Speaker 2: Okay, we're going to move on from Doge in a second. 150 00:07:55,240 --> 00:07:57,360 Speaker 2: Anything else in the Doge push though, that we should 151 00:07:57,360 --> 00:07:58,120 Speaker 2: know about before we do. 152 00:07:58,400 --> 00:07:59,760 Speaker 1: I think one point that I just want to keep 153 00:07:59,800 --> 00:08:02,080 Speaker 1: him ring for our listeners is that I've read a 154 00:08:02,080 --> 00:08:05,200 Speaker 1: lot of reporting that you know, Trump has tapped Musk 155 00:08:05,360 --> 00:08:09,600 Speaker 1: to lead this government efficiency effort, and to just remind 156 00:08:09,600 --> 00:08:13,960 Speaker 1: everyone that this was Musk's idea. Musk pitched this idea 157 00:08:14,160 --> 00:08:17,760 Speaker 1: to Trump in August when he hosted Trump on X 158 00:08:17,880 --> 00:08:22,120 Speaker 1: for that long two hour plus spaces and he has 159 00:08:22,160 --> 00:08:24,000 Speaker 1: had a real plan to do this, and it's not 160 00:08:24,320 --> 00:08:28,200 Speaker 1: so much about efficiency as it is about really gutting 161 00:08:28,240 --> 00:08:31,480 Speaker 1: a lot of these agencies, getting rid of DEI efforts. 162 00:08:31,560 --> 00:08:35,040 Speaker 1: And you know, we have to remind everyone that Musk's 163 00:08:35,080 --> 00:08:39,800 Speaker 1: companies have a lot of regulatory investigative stuff happening at 164 00:08:40,000 --> 00:08:41,480 Speaker 1: the agencies that he's now gutting. 165 00:08:42,200 --> 00:08:44,400 Speaker 2: Now you have at the real lease stand and drilled 166 00:08:44,400 --> 00:08:47,559 Speaker 2: it into my head. I'm going around saying Trump didn't 167 00:08:47,600 --> 00:08:50,240 Speaker 2: tap Musk for this position, but as you say, Musk 168 00:08:50,280 --> 00:08:53,880 Speaker 2: effectively tap Musk for this position. And Donald Trump said, 169 00:08:53,880 --> 00:08:55,719 Speaker 2: I think that's a great idea, and off to the 170 00:08:55,800 --> 00:09:02,600 Speaker 2: race as they are. Okay, moving on to a very 171 00:09:02,640 --> 00:09:05,240 Speaker 2: nice piece, an in depth piece of reporting that Business 172 00:09:05,240 --> 00:09:09,120 Speaker 2: Week had on Musk's factories in Texas and how they 173 00:09:09,200 --> 00:09:13,680 Speaker 2: are employing undocumented workers. Yeah. 174 00:09:13,720 --> 00:09:16,600 Speaker 1: So this was a wonderful feature by our colleague Julia Love, 175 00:09:16,640 --> 00:09:20,480 Speaker 1: who's based in San Francisco. Julia is fluent in Spanish, 176 00:09:21,160 --> 00:09:25,960 Speaker 1: spent an enormous amount of time in Texas interviewing undocumented 177 00:09:25,960 --> 00:09:30,080 Speaker 1: workers who helped build the gigafactory in Austin. And to 178 00:09:30,120 --> 00:09:32,920 Speaker 1: be clear, so far, it shows that most of the 179 00:09:33,040 --> 00:09:36,280 Speaker 1: workers were not employed directly by Tesla. They were construction 180 00:09:36,400 --> 00:09:40,920 Speaker 1: workers that were employed by contractors and subcontractors, and that 181 00:09:41,120 --> 00:09:43,559 Speaker 1: is like widespread in the United States. If you want 182 00:09:43,600 --> 00:09:49,000 Speaker 1: to look at the undocumented workforce, they are in construction, meatpacking, agriculture, 183 00:09:49,200 --> 00:09:53,599 Speaker 1: and the gigafactory was a massive project and one of 184 00:09:53,640 --> 00:09:56,440 Speaker 1: the largest buildings in the world, and it's not a 185 00:09:56,480 --> 00:09:59,720 Speaker 1: surprise that undocumented workers were part of those contractors and 186 00:09:59,679 --> 00:10:01,760 Speaker 1: sub contractors that helped to build that factory. 187 00:10:02,240 --> 00:10:04,920 Speaker 2: Yeah. So, more than it being oh my god, look 188 00:10:04,960 --> 00:10:08,560 Speaker 2: at all the undocumented workers that these Musk companies or 189 00:10:08,600 --> 00:10:13,120 Speaker 2: these companies that worked with Musk, companies hired. It's sort 190 00:10:13,120 --> 00:10:16,400 Speaker 2: of an emblematic of the world we live in here 191 00:10:16,400 --> 00:10:20,640 Speaker 2: in the US, but it does it's certainly a little uncomfortable, 192 00:10:20,880 --> 00:10:23,960 Speaker 2: one would say, for Elon Musk as he pushes hard 193 00:10:23,480 --> 00:10:26,600 Speaker 2: to drive these people out of the country. 194 00:10:27,040 --> 00:10:28,880 Speaker 1: Yeah, I mean, I think that the contrast between the 195 00:10:28,880 --> 00:10:32,880 Speaker 1: fact that you know, so many people worked on building 196 00:10:33,000 --> 00:10:37,920 Speaker 1: these massive industrial projects for him and his rhetoric around immigration. 197 00:10:38,080 --> 00:10:40,800 Speaker 1: You know, this is a CEO who has live streamed 198 00:10:40,840 --> 00:10:43,400 Speaker 1: from the border and has used X to amplify a 199 00:10:43,400 --> 00:10:46,120 Speaker 1: lot of anti immigrant rhetoric, and he's you know, always 200 00:10:46,200 --> 00:10:49,160 Speaker 1: drawn a line between h one B visas and not. 201 00:10:49,520 --> 00:10:53,160 Speaker 1: But yeah, the truth is that, I mean, the undocumented 202 00:10:53,200 --> 00:10:55,160 Speaker 1: workforce is a key part of the labor market in 203 00:10:55,200 --> 00:10:59,079 Speaker 1: the United States, and multiple industries, including construction his empire 204 00:10:59,120 --> 00:11:01,680 Speaker 1: has benefited from their labor. I think it's interesting that 205 00:11:01,800 --> 00:11:05,840 Speaker 1: Musk went super anti immigrant after the factory was already finished. 206 00:11:06,280 --> 00:11:08,520 Speaker 1: But yeah, Julia's piece is well worth reading. 207 00:11:09,040 --> 00:11:11,319 Speaker 2: That is a that is a good point. Okay, Now 208 00:11:11,360 --> 00:11:14,160 Speaker 2: tipping back a little bit towards Doge or some of 209 00:11:14,200 --> 00:11:18,400 Speaker 2: the ripple effects from the Doge Committee and all the 210 00:11:18,440 --> 00:11:21,559 Speaker 2: cost cutting it's been doing inside the government. There were 211 00:11:21,559 --> 00:11:25,160 Speaker 2: protests this weekend outside Tesla's store rooms. Danna, you were 212 00:11:25,240 --> 00:11:26,760 Speaker 2: at one of them. Tell us about that. 213 00:11:27,240 --> 00:11:30,080 Speaker 1: Yeah, So I went to the showroom in Berkeley, California. 214 00:11:30,280 --> 00:11:33,120 Speaker 1: And you know, Berkeley is like ground zero for people 215 00:11:33,120 --> 00:11:36,439 Speaker 1: who care deeply about climate change and science. It is 216 00:11:36,480 --> 00:11:40,960 Speaker 1: a university town. It is an older, fairly wealthy community. 217 00:11:41,200 --> 00:11:43,800 Speaker 1: When you know, the Model three came out, or when 218 00:11:43,800 --> 00:11:46,120 Speaker 1: Tesla came out with the solar roof and the power wall, like, 219 00:11:46,240 --> 00:11:50,000 Speaker 1: these are the consumers that buy Tesla products. And so 220 00:11:50,000 --> 00:11:52,800 Speaker 1: it was super interesting to just be there at this 221 00:11:53,000 --> 00:11:56,240 Speaker 1: rally where you had, you know, well over one hundred 222 00:11:56,240 --> 00:11:58,439 Speaker 1: people screaming all kinds of things. 223 00:11:58,640 --> 00:12:01,480 Speaker 2: Yeah, and actually, Dana, you recorded some of that, right, 224 00:12:01,559 --> 00:12:02,640 Speaker 2: So let's give it a listen. 225 00:12:02,960 --> 00:12:10,240 Speaker 3: Hey home, Elon lass Hey, Hey, hell Home, Elon last 226 00:12:10,240 --> 00:12:15,880 Speaker 3: tss hey ho home Ela las Hey. 227 00:12:17,000 --> 00:12:19,959 Speaker 2: Now I saw the ft report kind of a similar 228 00:12:19,960 --> 00:12:21,880 Speaker 2: scene in Portland and in Chicago. 229 00:12:22,280 --> 00:12:24,920 Speaker 1: They were at showrooms all across the United States, for sure. 230 00:12:25,200 --> 00:12:29,560 Speaker 1: And you know, it's interesting because it's the first time 231 00:12:29,600 --> 00:12:33,120 Speaker 1: that we've really seen people who are frustrated about Musk's 232 00:12:33,200 --> 00:12:36,720 Speaker 1: role in the Trump administration take their anger out on 233 00:12:37,000 --> 00:12:40,680 Speaker 1: Tesla's show rooms. When I was there, you know, I 234 00:12:40,720 --> 00:12:42,800 Speaker 1: had a lot of conversations with people that they're very 235 00:12:42,840 --> 00:12:46,520 Speaker 1: upset about Musk and Doge, and there were flyers being 236 00:12:46,520 --> 00:12:49,040 Speaker 1: handed out really urging people to do two things. One 237 00:12:49,120 --> 00:12:53,200 Speaker 1: is to call on Tesla's board to fire Musk as 238 00:12:53,280 --> 00:12:56,600 Speaker 1: the Cudio as the CEO of doesn't that seems unlikely. 239 00:12:56,720 --> 00:12:59,600 Speaker 1: The other is to pressure CalPERS, which is the huge 240 00:12:59,600 --> 00:13:03,400 Speaker 1: Californi pension fund, to divest from Tesla. CalPERS is a 241 00:13:03,480 --> 00:13:07,240 Speaker 1: huge shareholder billions of dollars for sure, So that's an 242 00:13:07,240 --> 00:13:10,199 Speaker 1: interesting pressure point. They're a big shareholder and always happen. 243 00:13:10,679 --> 00:13:12,520 Speaker 1: And then I you know, interviewed a lot of people. 244 00:13:12,520 --> 00:13:15,080 Speaker 1: I mean, there's one woman she bought a Tesla during 245 00:13:15,120 --> 00:13:19,400 Speaker 1: the pandemic. As soon as the election happened, she and 246 00:13:19,480 --> 00:13:21,720 Speaker 1: her partner decided to sell it. It took them a 247 00:13:21,760 --> 00:13:24,760 Speaker 1: while to actually sell it. They ultimately sold it on Kriigslist, 248 00:13:24,760 --> 00:13:26,359 Speaker 1: and she was holding up the side. 249 00:13:26,040 --> 00:13:29,080 Speaker 2: Why why is that? Why was there no demand for it? 250 00:13:29,600 --> 00:13:33,520 Speaker 1: I just think that, especially particularly in California, like I 251 00:13:33,520 --> 00:13:35,640 Speaker 1: don't think that there is a huge demand for use 252 00:13:35,679 --> 00:13:38,520 Speaker 1: Tesla's now and also just in general, like electric cars, 253 00:13:38,559 --> 00:13:41,080 Speaker 1: like people tend to want to buy them new. You 254 00:13:41,080 --> 00:13:43,960 Speaker 1: don't know what the battery degradation is. So just took 255 00:13:43,960 --> 00:13:45,679 Speaker 1: her longer to sell than she had thought. 256 00:13:46,040 --> 00:13:48,360 Speaker 2: So on those things, on the pressure points that they 257 00:13:48,360 --> 00:13:50,760 Speaker 2: can put on musk and you said you heard, or 258 00:13:50,840 --> 00:13:52,720 Speaker 2: you know, the flyers that were hanging out said, hey 259 00:13:52,760 --> 00:13:57,280 Speaker 2: the board, pressure the board to sack Elon. Tricky, Hey 260 00:13:57,360 --> 00:14:00,880 Speaker 2: CalPERS sell all your Tesla shares. Maybe that could happen. 261 00:14:01,320 --> 00:14:04,480 Speaker 2: I saw in an FT report that up in Portland, 262 00:14:04,720 --> 00:14:07,680 Speaker 2: one of the leaders there at the protest was saying, 263 00:14:07,920 --> 00:14:10,760 Speaker 2: the greatest leverage we have on him is indeed to 264 00:14:10,800 --> 00:14:16,400 Speaker 2: simply boycott Tesla cars, do not buy them. Every sale 265 00:14:16,640 --> 00:14:19,880 Speaker 2: that we block is a small dent on his wealth. 266 00:14:20,080 --> 00:14:21,640 Speaker 2: We shall see. Well, I don't really. 267 00:14:21,560 --> 00:14:23,880 Speaker 1: Agree with that. I mean, so his wealth is based 268 00:14:23,880 --> 00:14:27,720 Speaker 1: on the stock price, right, so if you want to 269 00:14:27,720 --> 00:14:31,640 Speaker 1: have leverage over Elon, it's really driving down the share price. 270 00:14:31,960 --> 00:14:35,120 Speaker 1: And you know, sales are a big part of Tesla's revenue, 271 00:14:35,160 --> 00:14:37,600 Speaker 1: but like forty percent of the sales come from China. 272 00:14:37,760 --> 00:14:40,520 Speaker 1: So even though people in the United States, particularly in 273 00:14:40,600 --> 00:14:43,760 Speaker 1: left leaning places like Berkeley and Portland, are very upset, 274 00:14:43,840 --> 00:14:45,920 Speaker 1: Like you have to kind of realize that we live 275 00:14:45,960 --> 00:14:48,640 Speaker 1: in a global auto market and there are still customers. 276 00:14:48,640 --> 00:14:49,960 Speaker 2: I guess just the argument is this, I mean, you're 277 00:14:49,960 --> 00:14:51,920 Speaker 2: absolutely right. At the end of the day, Elon derives 278 00:14:51,960 --> 00:14:55,240 Speaker 2: his well from the stock, not from sales. But it's 279 00:14:55,280 --> 00:14:56,840 Speaker 2: this thing that we've been talking about for a while, 280 00:14:56,840 --> 00:14:58,880 Speaker 2: and we're going to continue to talk on this show 281 00:14:58,920 --> 00:15:01,640 Speaker 2: for weeks and weeks, which is is, at some point, 282 00:15:01,720 --> 00:15:06,080 Speaker 2: if there is a the divergence between the underlying fundamentals 283 00:15:06,120 --> 00:15:08,520 Speaker 2: of the company and the stock price keep growing and growing, 284 00:15:09,280 --> 00:15:11,800 Speaker 2: something's got to give. And so I think that is 285 00:15:11,880 --> 00:15:16,120 Speaker 2: the argument, and we shall see. But okay, So in 286 00:15:16,200 --> 00:15:21,760 Speaker 2: another corner of the Musk universe, Xai his Ai company 287 00:15:22,240 --> 00:15:24,880 Speaker 2: is seeking a fresh round of funding ten billion dollars 288 00:15:24,960 --> 00:15:27,840 Speaker 2: that would value it at seventy five billion dollars. At 289 00:15:27,880 --> 00:15:30,280 Speaker 2: last blush Dawn of the company's worth fifty one billion. 290 00:15:30,360 --> 00:15:33,600 Speaker 2: So it's up, up, up and up for Xai Deep 291 00:15:33,680 --> 00:15:35,120 Speaker 2: Seek be damned right. 292 00:15:35,280 --> 00:15:39,000 Speaker 1: So and last night Xai like released a new and 293 00:15:39,040 --> 00:15:42,320 Speaker 1: improved version of groc and Elon says it's great and 294 00:15:42,360 --> 00:15:45,640 Speaker 1: like they're coming for chat GBC and deep Seek and 295 00:15:45,680 --> 00:15:48,520 Speaker 1: like they really want to be a player in this space. 296 00:15:48,600 --> 00:15:52,360 Speaker 1: And the valuation continues to climb, so obviously there's a 297 00:15:52,400 --> 00:15:53,840 Speaker 1: crew of investors that believe it. 298 00:15:54,080 --> 00:15:56,920 Speaker 2: Yeah, and that continuing climb and valuation. Of course, we 299 00:15:56,960 --> 00:15:58,520 Speaker 2: do need to see if they indeed get that ten 300 00:15:58,520 --> 00:16:00,440 Speaker 2: billion dollars in well. 301 00:16:00,080 --> 00:16:02,800 Speaker 1: They were, I mean, come on, Elon does not have 302 00:16:02,840 --> 00:16:03,800 Speaker 1: trouble too money. 303 00:16:04,040 --> 00:16:07,160 Speaker 2: Okay, Then I want to move on to a story 304 00:16:07,240 --> 00:16:09,960 Speaker 2: that kind of popped up mysteriously last week and then 305 00:16:10,080 --> 00:16:11,800 Speaker 2: it was up and then it went down, which is 306 00:16:11,840 --> 00:16:17,600 Speaker 2: that the government had plans to buy armored Tesla's. The 307 00:16:17,680 --> 00:16:19,200 Speaker 2: story said what became of this? 308 00:16:19,920 --> 00:16:22,000 Speaker 1: So this was Yeah, this was like a weird news cycle. 309 00:16:22,320 --> 00:16:24,880 Speaker 1: Like there's so many Elon stories every day. So that 310 00:16:25,040 --> 00:16:29,120 Speaker 1: the State Department has a procurement website with a spreadsheet 311 00:16:29,480 --> 00:16:34,800 Speaker 1: that's regularly updated, and like the spreadsheet was updated in December, 312 00:16:35,200 --> 00:16:38,320 Speaker 1: after the election, but before Trump's inauguration, and there was 313 00:16:38,360 --> 00:16:40,960 Speaker 1: like a line item on the spreadsheet that said armored 314 00:16:41,000 --> 00:16:45,400 Speaker 1: Tesla vehicles four hundred million dollars, And it seemed like 315 00:16:45,440 --> 00:16:47,960 Speaker 1: there was an idea that the State Department was going 316 00:16:48,000 --> 00:16:50,920 Speaker 1: to buy armored Tesla vehicles. Now I'm assuming that that's 317 00:16:50,960 --> 00:16:53,360 Speaker 1: a cyber truck, but maybe it was an armored plated 318 00:16:53,400 --> 00:16:56,560 Speaker 1: model Y. I have no idea. Obviously, the State Department 319 00:16:56,600 --> 00:16:58,480 Speaker 1: and the Foreign Service, there's a lot of diplomats and 320 00:16:58,520 --> 00:17:01,960 Speaker 1: foreign countries that use armored vehicles, and there were also 321 00:17:02,000 --> 00:17:05,520 Speaker 1: other vehicles listed, like you know, BMW was on there, 322 00:17:05,600 --> 00:17:07,679 Speaker 1: and this was just sort of like a forecast for 323 00:17:07,760 --> 00:17:11,119 Speaker 1: what they were planning to spend. So I downloaded the spreadsheet, 324 00:17:11,560 --> 00:17:13,920 Speaker 1: put it on my to do list. By the time 325 00:17:14,000 --> 00:17:17,600 Speaker 1: I went to sit and write about it, Tesla was gone, 326 00:17:17,720 --> 00:17:20,680 Speaker 1: and thank god I'd save the spreadsheet. So the name 327 00:17:20,720 --> 00:17:23,480 Speaker 1: TESLA was removed, and now it just says like armored 328 00:17:23,480 --> 00:17:26,080 Speaker 1: electric vehicles, And you could look at the spreadsheet you 329 00:17:26,080 --> 00:17:27,960 Speaker 1: could see that they changed it the same day that 330 00:17:28,000 --> 00:17:31,120 Speaker 1: a lot of this reporting came out. Then the story is, well, 331 00:17:31,119 --> 00:17:33,560 Speaker 1: why did they remove it? Like is this thing going 332 00:17:33,600 --> 00:17:35,880 Speaker 1: forward or not? Eventually I got like a very bland 333 00:17:36,040 --> 00:17:38,840 Speaker 1: statement from an unnamed State Department person that was like, 334 00:17:38,880 --> 00:17:41,240 Speaker 1: we're not this is not going forward. But it begs 335 00:17:41,240 --> 00:17:43,359 Speaker 1: the question as to why it was there in the 336 00:17:43,359 --> 00:17:45,720 Speaker 1: first place, because Trump was not in office yet, and 337 00:17:45,760 --> 00:17:48,560 Speaker 1: so it's just a reminder that you know, when we 338 00:17:48,560 --> 00:17:52,080 Speaker 1: think of government contracts. We think of SpaceX, but Tesla 339 00:17:52,160 --> 00:17:56,240 Speaker 1: itself also has government contracts for fleet vehicles with the government, 340 00:17:56,600 --> 00:17:57,919 Speaker 1: and that's something to keep an eye on. 341 00:17:58,240 --> 00:18:02,320 Speaker 2: All right, Well, listen, Dana, bundle up, it's brutally cold 342 00:18:02,320 --> 00:18:05,120 Speaker 2: out there. Get yourself to Brooklyn on Thursday. I will 343 00:18:05,160 --> 00:18:08,040 Speaker 2: be there as well. The on air fest in the 344 00:18:08,080 --> 00:18:12,800 Speaker 2: afternoon me Dana, Max Chafkin and McKenna Kelly from Wired 345 00:18:12,880 --> 00:18:15,040 Speaker 2: and it's going to be a hoop. I'll see you there, Dana, 346 00:18:15,160 --> 00:18:20,520 Speaker 2: can't wait. This episode was produced by Stacy Wang. Anna 347 00:18:20,560 --> 00:18:23,840 Speaker 2: Maserakas is our editor, and Rayhan Harmanci our senior editor. 348 00:18:24,200 --> 00:18:27,560 Speaker 2: Blake Maples handles engineering, and Dave Purcell fact checks. Our 349 00:18:27,680 --> 00:18:31,560 Speaker 2: supervising producer is Magnus Hendrickson. The elon Ink theme is 350 00:18:31,600 --> 00:18:35,959 Speaker 2: written and performed by Taka Yasuzawa and Alex Sugiira. Brendan 351 00:18:36,040 --> 00:18:38,920 Speaker 2: Francis Newnham is our executive producer, and Sage Bauman is 352 00:18:38,920 --> 00:18:42,520 Speaker 2: the head of Bloomberg Podcasts. A big thanks as always 353 00:18:42,520 --> 00:18:45,639 Speaker 2: to our supporters Joel Weber and Brad Stone. I am 354 00:18:45,720 --> 00:18:48,760 Speaker 2: David Papadopoulos. If you have a minute, rate and review 355 00:18:48,800 --> 00:18:51,760 Speaker 2: our show, it'll help other listeners find us see you 356 00:18:51,840 --> 00:19:00,520 Speaker 2: Thursday in Brooklyn, f