1 00:00:00,080 --> 00:00:01,599 Speaker 1: Hey, guys, Saga and Crystal here. 2 00:00:01,680 --> 00:00:05,240 Speaker 2: Independent media just played a truly massive role in this election, 3 00:00:05,360 --> 00:00:07,840 Speaker 2: and we are so excited about what that means for 4 00:00:07,880 --> 00:00:08,720 Speaker 2: the future of this show. 5 00:00:08,880 --> 00:00:10,760 Speaker 1: This is the only place where you can find honest 6 00:00:10,760 --> 00:00:13,239 Speaker 1: perspectives from the left and the right that simply does 7 00:00:13,320 --> 00:00:14,640 Speaker 1: not exist anywhere else. 8 00:00:14,720 --> 00:00:17,080 Speaker 2: So if that is something that's important to you, please 9 00:00:17,120 --> 00:00:19,599 Speaker 2: go to Breakingpoints dot com. Become a member today and 10 00:00:19,640 --> 00:00:22,800 Speaker 2: you'll get access to our full shows, unedited, ad free, 11 00:00:22,800 --> 00:00:25,600 Speaker 2: and all put together for you every morning in your inbox. 12 00:00:25,680 --> 00:00:27,560 Speaker 1: We need your help to build the future of independent 13 00:00:27,560 --> 00:00:29,920 Speaker 1: news media, and we hope to see you at Breakingpoints 14 00:00:29,960 --> 00:00:33,239 Speaker 1: dot com. 15 00:00:33,760 --> 00:00:37,120 Speaker 2: Some very interesting comments from Dave Portnoy yesterday on Fox 16 00:00:37,159 --> 00:00:40,480 Speaker 2: News with Jesse Waters. Apparently he is considering a run 17 00:00:40,520 --> 00:00:44,040 Speaker 2: for office and maybe even would run against Zoronmmdani for 18 00:00:44,120 --> 00:00:45,080 Speaker 2: mayor of New York City. 19 00:00:45,120 --> 00:00:47,960 Speaker 3: List Saigalism, losing my mind on what's happening right now, 20 00:00:48,080 --> 00:00:50,080 Speaker 3: especially in New York. I mean, you get the Nazi 21 00:00:50,080 --> 00:00:53,720 Speaker 3: and Maine. This really really really worries me. 22 00:00:53,840 --> 00:00:56,480 Speaker 4: So I'm while you're run against Mondani. 23 00:00:56,280 --> 00:00:57,960 Speaker 3: I would love to run against If I was going 24 00:00:58,040 --> 00:00:59,720 Speaker 3: to run, I would be here. Can I win here. 25 00:01:00,040 --> 00:01:00,880 Speaker 5: I have no idea. 26 00:01:01,000 --> 00:01:03,360 Speaker 3: I don't know the demographics, whether they get enough votes. 27 00:01:03,720 --> 00:01:06,080 Speaker 3: There's a lot of people who like me in New 28 00:01:06,160 --> 00:01:08,399 Speaker 3: York City. I know that I've done a lot of 29 00:01:08,400 --> 00:01:11,240 Speaker 3: good in New York City when I wasn't thinking about politics, 30 00:01:11,240 --> 00:01:14,400 Speaker 3: whether it's the barstool fund, pizza places, so it wasn't 31 00:01:14,400 --> 00:01:14,880 Speaker 3: for show. 32 00:01:15,200 --> 00:01:15,759 Speaker 5: I've had a. 33 00:01:15,720 --> 00:01:19,280 Speaker 3: Real job, I've done real things, unlike these clown politicians 34 00:01:19,360 --> 00:01:21,240 Speaker 3: who have never had a job and never been in 35 00:01:21,240 --> 00:01:26,160 Speaker 3: the real world for a day. But the people voting 36 00:01:26,760 --> 00:01:30,119 Speaker 3: for these people, there's one are like these young white. 37 00:01:30,480 --> 00:01:30,640 Speaker 4: Like. 38 00:01:32,360 --> 00:01:35,400 Speaker 3: Ivy league ish, elitish women. 39 00:01:35,480 --> 00:01:37,080 Speaker 5: It's like, who are they like? 40 00:01:37,120 --> 00:01:39,680 Speaker 3: They'll never vote for me, They'll never believe in common sense. 41 00:01:39,720 --> 00:01:40,560 Speaker 6: When you're unhappy, you. 42 00:01:40,480 --> 00:01:42,080 Speaker 4: Want to make other people unhappy in that. 43 00:01:42,680 --> 00:01:44,399 Speaker 2: I don't think your election is going to go too 44 00:01:44,440 --> 00:01:47,360 Speaker 2: well if you start off by insulting the voting base. 45 00:01:47,680 --> 00:01:52,160 Speaker 2: You know, and I just I mean mysteriously, But we 46 00:01:52,240 --> 00:01:55,480 Speaker 2: talked yesterday about how Zorn's favorability just keeps going up. 47 00:01:55,560 --> 00:01:57,680 Speaker 2: Yes in the state at large, but in the City 48 00:01:57,720 --> 00:02:01,800 Speaker 2: of New York specifically, which is obviously his constientt Also 49 00:02:02,000 --> 00:02:06,400 Speaker 2: fantastic analysis from Michael Lang who looks at New York 50 00:02:06,520 --> 00:02:10,120 Speaker 2: politics like block by block of the coalitions that not 51 00:02:10,160 --> 00:02:13,840 Speaker 2: only elected Zoran, but now have elected Daria Liza and 52 00:02:13,960 --> 00:02:15,240 Speaker 2: elected Clare Valdas. 53 00:02:15,320 --> 00:02:16,200 Speaker 4: And so this. 54 00:02:16,160 --> 00:02:19,840 Speaker 2: Notion that Portnoy is offering here that oh, the voters 55 00:02:19,840 --> 00:02:22,960 Speaker 2: are just like the young college educated white girls. You 56 00:02:23,000 --> 00:02:27,400 Speaker 2: are not going to win a Harlem congressional district on 57 00:02:27,480 --> 00:02:30,760 Speaker 2: the backing of like young delusional white girls. Nor are 58 00:02:30,840 --> 00:02:33,000 Speaker 2: you going to win you know, in really anywhere in 59 00:02:33,000 --> 00:02:36,080 Speaker 2: the city, which is highly diverse, multi racial. And so 60 00:02:36,280 --> 00:02:38,639 Speaker 2: when you're talking about, you know, these coalitions that have 61 00:02:38,680 --> 00:02:43,680 Speaker 2: come together, it is a young, multi racial coalition that 62 00:02:43,760 --> 00:02:46,720 Speaker 2: is backing these candidates, and they really have grown beyond, 63 00:02:46,919 --> 00:02:51,280 Speaker 2: significantly beyond that like initial white activist base. And I 64 00:02:51,280 --> 00:02:54,680 Speaker 2: think that is why there's a big freak out, because 65 00:02:54,680 --> 00:02:56,280 Speaker 2: you could no longer just say well, you can only 66 00:02:56,280 --> 00:02:58,160 Speaker 2: win this type of district, or you only can win 67 00:02:58,160 --> 00:02:59,280 Speaker 2: this with this type of voter. 68 00:02:59,760 --> 00:03:01,400 Speaker 4: When you have that level of. 69 00:03:01,360 --> 00:03:04,080 Speaker 2: Broad based support, it becomes much more of a threat. 70 00:03:04,080 --> 00:03:05,480 Speaker 2: But in any case, I mean, I don't really take 71 00:03:05,480 --> 00:03:07,200 Speaker 2: this seriously, but I do think it'd be funny if 72 00:03:07,200 --> 00:03:08,799 Speaker 2: he ran against Oran. And I think he would run 73 00:03:08,800 --> 00:03:10,760 Speaker 2: into a brick wall. Because right now New Yorkers are 74 00:03:10,800 --> 00:03:11,840 Speaker 2: absolutely loving Zoran. 75 00:03:12,200 --> 00:03:17,160 Speaker 5: It's the toxic millennial election that we deserve. Really, yeah, 76 00:03:17,200 --> 00:03:19,360 Speaker 5: that's it's I mean, I wouldn't be surprised if he 77 00:03:19,360 --> 00:03:21,880 Speaker 5: tries to do something like that one day. Really, I 78 00:03:21,880 --> 00:03:22,600 Speaker 5: wouldn't surprise me. 79 00:03:22,720 --> 00:03:23,120 Speaker 4: I don't know. 80 00:03:23,200 --> 00:03:25,440 Speaker 2: I'm not that much of a student of Dave Boorden. Yeah, 81 00:03:25,480 --> 00:03:27,600 Speaker 2: I just I just do think it's funny because he's 82 00:03:27,800 --> 00:03:29,400 Speaker 2: you know, there was this whole like saga we talk 83 00:03:29,440 --> 00:03:32,400 Speaker 2: about the barstool Conservatism, which is the core theme of 84 00:03:32,440 --> 00:03:35,360 Speaker 2: which was being like anti woke, and but when it 85 00:03:35,400 --> 00:03:39,280 Speaker 2: comes to you know, anything regarding Israel and quote unquote 86 00:03:39,320 --> 00:03:42,040 Speaker 2: anti Semitism, and suddenly you know, oh my god, Graham 87 00:03:42,080 --> 00:03:45,160 Speaker 2: Plattner's tattoo and his Ruddit post, he's a Nazi quote. 88 00:03:45,000 --> 00:03:46,040 Speaker 5: Nazi running in Maine. 89 00:03:46,080 --> 00:03:47,640 Speaker 4: He just yes, exactly. 90 00:03:47,760 --> 00:03:50,560 Speaker 2: So I do find it it's kind of laughable from him. 91 00:03:50,360 --> 00:03:52,880 Speaker 5: But you tard like some of the megalomania. Even in 92 00:03:52,880 --> 00:03:55,200 Speaker 5: that interview, He's like, all the good that I've done, 93 00:03:55,520 --> 00:03:57,160 Speaker 5: That's what I'm saying. Like I could actually see that. 94 00:03:57,240 --> 00:03:59,760 Speaker 5: I could see him at some point maybe even like 95 00:03:59,840 --> 00:04:03,000 Speaker 5: just dipping his toe in. He hires a consultant, tries 96 00:04:03,080 --> 00:04:05,400 Speaker 5: to test the waters, allow them. But you know, it 97 00:04:05,440 --> 00:04:07,920 Speaker 5: is interesting because in New York, that Black by Block 98 00:04:07,960 --> 00:04:10,040 Speaker 5: analysis from Michael Lang, I was looking at it yesterday 99 00:04:10,120 --> 00:04:12,320 Speaker 5: and I was comparing it with what just happened here 100 00:04:12,360 --> 00:04:14,800 Speaker 5: in DC, where a lot of the polling did indeed 101 00:04:14,880 --> 00:04:18,360 Speaker 5: find you could call it like gentrifiers. We're more attractive 102 00:04:18,360 --> 00:04:21,120 Speaker 5: to Genie Silas George the Democratic Socialist, the DSA candidate 103 00:04:21,160 --> 00:04:22,960 Speaker 5: here in DC. But what I think is interesting when 104 00:04:22,960 --> 00:04:25,040 Speaker 5: you look at New York is that this is where 105 00:04:25,040 --> 00:04:28,160 Speaker 5: the DSA is seeing it as I don't want to 106 00:04:28,200 --> 00:04:30,320 Speaker 5: say laboratory because that kind of downplays it, but like 107 00:04:30,360 --> 00:04:34,880 Speaker 5: a political laboratory to build a coalition. Yeah, and it's 108 00:04:35,400 --> 00:04:39,680 Speaker 5: actually working. It's picking up working class voters, non sort 109 00:04:39,680 --> 00:04:42,240 Speaker 5: of like elite voters to borrow the Portnoy word, which 110 00:04:42,279 --> 00:04:44,200 Speaker 5: is true in other cases, like you have seen that 111 00:04:44,520 --> 00:04:47,320 Speaker 5: happen in other cases. But that's what's happening right now 112 00:04:47,400 --> 00:04:49,280 Speaker 5: under the nose of people like Dave Portnoy, is that 113 00:04:49,320 --> 00:04:52,800 Speaker 5: they're actually building a coalition that's different from what we've 114 00:04:52,800 --> 00:04:56,440 Speaker 5: seen in the past, where they're bringing a board, working 115 00:04:56,480 --> 00:05:00,680 Speaker 5: class people looking at even like the Espalot result was 116 00:05:00,720 --> 00:05:03,520 Speaker 5: fascinating because it's was different actually from what we just 117 00:05:03,560 --> 00:05:05,760 Speaker 5: saw here in d C. It was not clear cut 118 00:05:05,920 --> 00:05:06,440 Speaker 5: in any way. 119 00:05:06,520 --> 00:05:11,360 Speaker 2: Well in DC, if memory serves Jeaniez Louis George, first 120 00:05:11,360 --> 00:05:14,320 Speaker 2: of all, she won quite convincing, yeah, overwhelmingly, and again 121 00:05:14,480 --> 00:05:16,920 Speaker 2: in DC, you can't do that if you have an 122 00:05:16,920 --> 00:05:20,080 Speaker 2: all white coalition. You know, there continues to be a 123 00:05:20,200 --> 00:05:24,200 Speaker 2: very divers city, and she the only areas that she 124 00:05:24,360 --> 00:05:27,719 Speaker 2: lost were the wealthiest, whitest areas of the city. So 125 00:05:27,760 --> 00:05:32,120 Speaker 2: she also was able to really defy the conventional wisdom 126 00:05:32,160 --> 00:05:35,200 Speaker 2: about what her campaign was and what it was appealing to, 127 00:05:35,720 --> 00:05:38,960 Speaker 2: and did win on a multiracial, multi class, you know, 128 00:05:39,000 --> 00:05:43,000 Speaker 2: class diverse also coalition, which is something similar to what 129 00:05:43,000 --> 00:05:46,000 Speaker 2: we saw here. Let's go and put up this is 130 00:05:46,200 --> 00:05:48,560 Speaker 2: another sort of shot across the bow for I think 131 00:05:48,640 --> 00:05:52,040 Speaker 2: mainstream analysis and also Fox News analysis, but d one 132 00:05:52,120 --> 00:05:52,800 Speaker 2: up on this screen. 133 00:05:53,320 --> 00:05:53,400 Speaker 7: This. 134 00:05:54,279 --> 00:05:55,719 Speaker 4: I noted this with great interest. 135 00:05:56,080 --> 00:06:00,960 Speaker 2: So they did this poll where they ask people there's 136 00:06:01,040 --> 00:06:05,400 Speaker 2: zero to one hundred feeling of various politicians, and so 137 00:06:05,520 --> 00:06:07,719 Speaker 2: this is like your vibe do you like these people? 138 00:06:07,839 --> 00:06:07,919 Speaker 5: Not? 139 00:06:08,120 --> 00:06:10,159 Speaker 2: You know their policies are what just like, do you 140 00:06:10,240 --> 00:06:14,200 Speaker 2: like these people? The most popular politician among the Democratic 141 00:06:14,279 --> 00:06:19,040 Speaker 2: base continues to be Barack Obama by a pretty decent margin. 142 00:06:19,080 --> 00:06:23,280 Speaker 2: He's at fifty four. Next up, you've got Bernie Sanders. 143 00:06:23,880 --> 00:06:26,640 Speaker 2: You see this becoming very relevant in a lot of 144 00:06:26,680 --> 00:06:30,120 Speaker 2: these primaries, so he is endorsed for example Abdullah Sayad 145 00:06:30,200 --> 00:06:34,400 Speaker 2: in Michigan, and Abdul has been running ads touting his 146 00:06:34,560 --> 00:06:38,680 Speaker 2: Bernie Sanders endorsement, and from this poll you can understand 147 00:06:38,680 --> 00:06:39,440 Speaker 2: why he's doing that. 148 00:06:39,760 --> 00:06:41,000 Speaker 4: Guess who comes in. 149 00:06:41,120 --> 00:06:44,480 Speaker 2: Right on the heels of the goat, Bernie Sanders himself, 150 00:06:44,720 --> 00:06:48,760 Speaker 2: Zora Mamdanni. And then you have the Democratic Party overall. 151 00:06:48,800 --> 00:06:52,120 Speaker 2: I'm actually surprised that it scores that well. Pete Bootage, 152 00:06:52,279 --> 00:06:55,400 Speaker 2: John Ossoff. And then you have AOC. So some of 153 00:06:55,440 --> 00:07:00,440 Speaker 2: the most popular Democratic figures for Democratic voters are Bernie's 154 00:07:00,000 --> 00:07:04,360 Speaker 2: and zoron mom Donnie. I think that is pretty noteworthy. 155 00:07:04,360 --> 00:07:06,840 Speaker 2: And the Republican ones here too are interesting. First of all, 156 00:07:06,880 --> 00:07:09,640 Speaker 2: no Republican really rates all that high. It's just kind 157 00:07:09,640 --> 00:07:14,280 Speaker 2: of noteworthy. Marco Rubio comes in first, right around the 158 00:07:14,320 --> 00:07:17,320 Speaker 2: AOC mark. Then you've got the Republican Party. Then you've 159 00:07:17,360 --> 00:07:21,280 Speaker 2: got JD. Vance and Donald Trump. Then you've got Elon Musk, 160 00:07:21,560 --> 00:07:26,240 Speaker 2: Mike Johnson, John Thune, and this is one is worth well, 161 00:07:26,360 --> 00:07:29,480 Speaker 2: we should do another segment on the Tucker Carlson like 162 00:07:29,560 --> 00:07:32,040 Speaker 2: the theory that he could win a Republican primary, because 163 00:07:32,040 --> 00:07:34,720 Speaker 2: he comes in pretty low here at twenty five percent, 164 00:07:34,880 --> 00:07:38,840 Speaker 2: and I think that's our twenty eight percent rather and 165 00:07:38,920 --> 00:07:40,680 Speaker 2: I do think there is a little bit of delusion 166 00:07:40,960 --> 00:07:44,120 Speaker 2: about what the Republican base would be looking for in 167 00:07:44,160 --> 00:07:47,160 Speaker 2: a candidate. And since Tucker has gotten crosswise with Trump, 168 00:07:47,360 --> 00:07:48,880 Speaker 2: you know, that's something that matters a lot to the 169 00:07:48,880 --> 00:07:50,680 Speaker 2: Republican base. So I think he would have a hard 170 00:07:50,680 --> 00:07:52,560 Speaker 2: time winning a Republican primary at this point. 171 00:07:53,480 --> 00:07:56,720 Speaker 5: This is so so interesting, Like these results are really 172 00:07:56,720 --> 00:07:58,880 Speaker 5: really interesting for a number of reasons, one of which is, 173 00:07:58,920 --> 00:08:01,520 Speaker 5: to your point, not a lot of Republicans rating up there. 174 00:08:01,880 --> 00:08:07,520 Speaker 5: Marco Rubio back in twenty fifteen, I think there was polling. 175 00:08:07,560 --> 00:08:09,200 Speaker 5: I have to remember a specific poll I remember being 176 00:08:09,200 --> 00:08:11,760 Speaker 5: written up in the Washington Post, but he was of 177 00:08:11,840 --> 00:08:14,040 Speaker 5: all of the Republican candidates who were flirting with the 178 00:08:14,080 --> 00:08:16,720 Speaker 5: nomination at that point. They pulled him against Hillary Clinton, 179 00:08:17,160 --> 00:08:20,040 Speaker 5: and Rubio was the only one of all the Republican 180 00:08:20,040 --> 00:08:23,080 Speaker 5: candidates that was actually beating Clinton with millennials at the time, 181 00:08:23,160 --> 00:08:26,800 Speaker 5: the young voting demo back into twenty fifteen. And that's 182 00:08:26,800 --> 00:08:30,840 Speaker 5: actually like, I think there is something about Rubio that 183 00:08:30,880 --> 00:08:34,360 Speaker 5: gets underestimated both in the MAGA right and sometimes on 184 00:08:34,400 --> 00:08:39,119 Speaker 5: the left, which is that his ability to communicate conservative 185 00:08:39,160 --> 00:08:41,960 Speaker 5: ideas in a way that feels i would say, almost 186 00:08:42,040 --> 00:08:44,640 Speaker 5: non political when he's really cooking, when he's giving like 187 00:08:44,679 --> 00:08:48,440 Speaker 5: a stump speech. That's also what caused him problems in 188 00:08:48,480 --> 00:08:52,080 Speaker 5: the twenty sixteen election because Chris Christy called him out 189 00:08:52,080 --> 00:08:54,760 Speaker 5: for like sticking to his talking points. So anyway, all 190 00:08:54,800 --> 00:08:57,560 Speaker 5: that is to say, I understand why Rubio was up there. Further, 191 00:08:57,640 --> 00:09:00,880 Speaker 5: I think, especially when you have a more like multi 192 00:09:01,000 --> 00:09:05,400 Speaker 5: racial voting electorate or electorate in. 193 00:09:05,360 --> 00:09:07,080 Speaker 4: General, with the Republican base. 194 00:09:07,000 --> 00:09:10,680 Speaker 5: Yeah, there's something about Rubio that clicks with more people. 195 00:09:11,000 --> 00:09:13,000 Speaker 5: So I don't know. I mean, it doesn't surprise me 196 00:09:13,040 --> 00:09:15,240 Speaker 5: that he's the one Republican that's up there a little 197 00:09:15,240 --> 00:09:17,200 Speaker 5: bit higher. It's not to say that he's like a 198 00:09:17,280 --> 00:09:19,480 Speaker 5: shoe in, but it is to say the reason that Trump, 199 00:09:19,720 --> 00:09:22,719 Speaker 5: who is pretty like one thing he understands is television 200 00:09:22,880 --> 00:09:27,720 Speaker 5: and social media and understands is an interesting way to 201 00:09:27,720 --> 00:09:31,600 Speaker 5: put it, but as a knack will put it back. 202 00:09:31,760 --> 00:09:32,720 Speaker 4: An animal instinct. 203 00:09:32,800 --> 00:09:35,880 Speaker 5: It's an animal instinct. That's well said. I think it's 204 00:09:35,880 --> 00:09:38,559 Speaker 5: one of the reasons he gravitates towards Rubio is that 205 00:09:38,640 --> 00:09:42,040 Speaker 5: he sees there's a telegenic element of him that Jadie 206 00:09:42,080 --> 00:09:45,720 Speaker 5: Vance doesn't have. So that was interesting. But these DSA 207 00:09:45,800 --> 00:09:49,720 Speaker 5: people have become pop culture figures. Yeah, truly. It doesn't 208 00:09:49,760 --> 00:09:52,320 Speaker 5: mean that that's gonna be enough to win a primary 209 00:09:52,320 --> 00:09:54,800 Speaker 5: and then a presidential election, but it does speak to 210 00:09:55,080 --> 00:09:58,320 Speaker 5: something in the culture that is much more favorable to 211 00:09:58,440 --> 00:09:59,000 Speaker 5: that right now. 212 00:09:59,160 --> 00:10:02,840 Speaker 2: Yeah, well, with so Zorn really at this point, because 213 00:10:02,880 --> 00:10:06,800 Speaker 2: Bernie is is older and Zoran is the one who's 214 00:10:06,840 --> 00:10:08,520 Speaker 2: really out there in the face of the movement at 215 00:10:08,520 --> 00:10:11,440 Speaker 2: this point and putting the principles into practice and notching 216 00:10:11,480 --> 00:10:13,119 Speaker 2: wins in New York City. 217 00:10:13,360 --> 00:10:16,760 Speaker 4: And there was such an incredible freak out about him. 218 00:10:17,040 --> 00:10:19,400 Speaker 2: We talked a little bit about yesterday that I also 219 00:10:19,440 --> 00:10:22,080 Speaker 2: think that the critics of not just him, but by 220 00:10:22,120 --> 00:10:25,320 Speaker 2: proxy the candidates that he supports, end up looking very 221 00:10:25,440 --> 00:10:28,840 Speaker 2: hysterical and disconnected from the impression that most people have 222 00:10:28,960 --> 00:10:31,360 Speaker 2: of them that comes through with you know, Portnoy here 223 00:10:31,480 --> 00:10:33,880 Speaker 2: calling Graham Platner a Nazi. I mean, if you genuinely 224 00:10:33,880 --> 00:10:36,200 Speaker 2: think this guy is a Nazi like that is completely insane. 225 00:10:36,240 --> 00:10:38,240 Speaker 2: You're gonna have issues with you know, things he said, 226 00:10:38,240 --> 00:10:39,160 Speaker 2: and don blah blah blah. 227 00:10:39,160 --> 00:10:41,160 Speaker 5: Are you control based on it? Be like, oh, you 228 00:10:41,160 --> 00:10:44,000 Speaker 5: guys have a Nazi. But if you're genuine if you really. 229 00:10:43,840 --> 00:10:46,640 Speaker 2: Genuinely think that this man is a Nazi like that 230 00:10:46,880 --> 00:10:51,160 Speaker 2: is just completely delusional and no, not serious your argument. 231 00:10:51,480 --> 00:10:53,920 Speaker 4: Read through his Reddit posts right, you can see all of. 232 00:10:53,800 --> 00:10:58,000 Speaker 2: His political views there plaina's day and they have nothing, 233 00:10:58,080 --> 00:11:02,040 Speaker 2: no bearing, you know, no relationship to any sort of 234 00:11:02,120 --> 00:11:04,920 Speaker 2: Nazi ideology. At the same time, Bruce Blakeman, who's the 235 00:11:05,000 --> 00:11:07,480 Speaker 2: county executive for NASA County in New York, which is 236 00:11:07,480 --> 00:11:10,680 Speaker 2: a large county, he went on Fox News and called 237 00:11:11,040 --> 00:11:16,839 Speaker 2: Brad Lander a Jewish man who happens to oppose war 238 00:11:16,880 --> 00:11:20,200 Speaker 2: crimes being committed by Israel with the consent and assistance 239 00:11:20,240 --> 00:11:24,040 Speaker 2: of our government. Compares him to a Nazi guard and 240 00:11:24,160 --> 00:11:31,480 Speaker 2: a quote unquote collaborator. Utterly insane, delusional, absurd reading of things. 241 00:11:31,520 --> 00:11:33,480 Speaker 2: Let's go ahead and take a listen to Bruce Blakeman. 242 00:11:34,000 --> 00:11:37,400 Speaker 8: I think the question, though, talks about genocide in Gaza, 243 00:11:37,440 --> 00:11:40,280 Speaker 8: when there was no genocide in Gaza. What you had 244 00:11:40,559 --> 00:11:43,800 Speaker 8: was you had a military action with military objectives on 245 00:11:43,840 --> 00:11:48,640 Speaker 8: the part of Israel. Take that in contrast to Hamas 246 00:11:49,200 --> 00:11:54,040 Speaker 8: which raped women, cut off babies heads, and shot people 247 00:11:54,080 --> 00:11:57,120 Speaker 8: that were unarmed. So when you compare that, there's no more. 248 00:11:57,360 --> 00:11:59,800 Speaker 9: But in what instance, Well, I mean talk about a 249 00:11:59,800 --> 00:12:04,559 Speaker 9: more equivalency. You're likening him to a Nazi guard. Well, 250 00:12:04,559 --> 00:12:06,760 Speaker 9: he's a cut That seems like a false moral equivalency. 251 00:12:06,760 --> 00:12:09,640 Speaker 8: No, he's a collaborator. There's no question in my mind. 252 00:12:09,679 --> 00:12:11,160 Speaker 8: He's either a collaborator or a coward. 253 00:12:11,240 --> 00:12:13,760 Speaker 9: And what is that distinction? I saw you said that 254 00:12:13,760 --> 00:12:16,600 Speaker 9: to the New York Times. What is the distinction and likening 255 00:12:16,640 --> 00:12:17,120 Speaker 9: someone who. 256 00:12:18,840 --> 00:12:20,640 Speaker 8: Well, you have to know your World War two history, 257 00:12:20,679 --> 00:12:22,840 Speaker 8: you have to know about the Holocaust. And the collaborators 258 00:12:22,840 --> 00:12:27,440 Speaker 8: were people who were Jewish, who would identify Jewish families. 259 00:12:27,760 --> 00:12:28,600 Speaker 4: I understand that. 260 00:12:28,960 --> 00:12:29,880 Speaker 5: I understand the facts. 261 00:12:29,880 --> 00:12:32,040 Speaker 9: I'm saying, what is the difference in the distinction you're 262 00:12:32,080 --> 00:12:33,880 Speaker 9: making in terms of mister Bradlander. 263 00:12:34,840 --> 00:12:37,120 Speaker 8: The distinction I'm making is that the fact that he 264 00:12:37,240 --> 00:12:40,959 Speaker 8: is Jewish, has nothing to do with his positions. As 265 00:12:41,000 --> 00:12:44,280 Speaker 8: a matter of fact, being a Jewish person, a Jewish 266 00:12:44,320 --> 00:12:49,000 Speaker 8: public official, it should be his responsibility to speak out 267 00:12:49,080 --> 00:12:50,040 Speaker 8: and speak the truth. 268 00:12:50,600 --> 00:12:52,240 Speaker 4: This is just beyond disgusting. 269 00:12:52,320 --> 00:12:56,840 Speaker 2: And of course he has to repeat the atrocity propagandaize 270 00:12:57,000 --> 00:12:59,320 Speaker 2: the babies that are beheaded, you know, the mass rape 271 00:12:59,320 --> 00:13:01,400 Speaker 2: which is contest did and then I love this he 272 00:13:01,520 --> 00:13:03,800 Speaker 2: says that Hamas shot people who were unarmed. 273 00:13:05,160 --> 00:13:07,000 Speaker 4: Do you know what Israel. 274 00:13:06,720 --> 00:13:10,160 Speaker 2: Has been doing for these past not just post October seventh, 275 00:13:10,240 --> 00:13:13,640 Speaker 2: but for literally decades. I mean, we can show you 276 00:13:13,720 --> 00:13:19,679 Speaker 2: the babies who have been executed by Israel, by the IDF. 277 00:13:20,040 --> 00:13:24,680 Speaker 2: And furthermore, we are not funding and arming Hamas, We 278 00:13:24,800 --> 00:13:29,079 Speaker 2: are funding and arming Israel. So but to smear Bradlander 279 00:13:29,120 --> 00:13:31,520 Speaker 2: this way is just utterly discussing. Let's put the next 280 00:13:31,520 --> 00:13:33,920 Speaker 2: images up on the screen. This is the man at 281 00:13:33,920 --> 00:13:38,040 Speaker 2: the Pride parade that you were saying is a Nazi collaborator, 282 00:13:38,480 --> 00:13:41,200 Speaker 2: that he's like the equivalent of a Nazi guard here. 283 00:13:41,640 --> 00:13:43,839 Speaker 4: It's just and so this is where. 284 00:13:44,120 --> 00:13:47,400 Speaker 2: Any normal person, like I know, there are Fox News 285 00:13:47,400 --> 00:13:49,439 Speaker 2: grandmas that will eat this kind of thing up right, 286 00:13:51,400 --> 00:13:53,960 Speaker 2: and maybe some Sian and Grandma's, but not that many, 287 00:13:53,960 --> 00:13:56,600 Speaker 2: I don't think at this point, given how not just 288 00:13:56,679 --> 00:13:58,679 Speaker 2: young people and Democratic Party, but there has been a 289 00:13:58,760 --> 00:14:01,800 Speaker 2: whole shift of viewpoint and the Democratic Party top to bottom, 290 00:14:01,920 --> 00:14:04,840 Speaker 2: and very much among Independence as well. But you know, 291 00:14:04,960 --> 00:14:08,800 Speaker 2: outside of the Fox News and right wing echo chamber, 292 00:14:09,160 --> 00:14:11,600 Speaker 2: people are going to look at Brad Lander and listen 293 00:14:11,640 --> 00:14:14,360 Speaker 2: to him talk and his viewpoints, and here you compare 294 00:14:14,440 --> 00:14:17,240 Speaker 2: him to a Nazi and think you are completely insane 295 00:14:17,280 --> 00:14:18,000 Speaker 2: and out to lunch. 296 00:14:18,120 --> 00:14:20,960 Speaker 5: Yeah, and that propaganda used to work, including on people 297 00:14:20,960 --> 00:14:24,200 Speaker 5: like me until like what five years ago, especially the 298 00:14:24,280 --> 00:14:28,240 Speaker 5: last several years. But it's really powerful, and it's really gross, 299 00:14:28,720 --> 00:14:31,280 Speaker 5: and it's also I'll just argue, if you're a proponent 300 00:14:31,360 --> 00:14:34,040 Speaker 5: of a robust alliance with Israel, it's not helpful to 301 00:14:34,080 --> 00:14:37,680 Speaker 5: your cause whatsoever because you are totally first of all, 302 00:14:37,720 --> 00:14:39,840 Speaker 5: this the smear has lost its power. But second of all, 303 00:14:39,880 --> 00:14:43,720 Speaker 5: you are totally misreading and arguing against a straw man, 304 00:14:44,000 --> 00:14:46,160 Speaker 5: which is not again going to be helpful for you 305 00:14:46,280 --> 00:14:49,480 Speaker 5: to defeat the arguments of people who are saying, look 306 00:14:49,600 --> 00:14:52,680 Speaker 5: at what Israel did in Gaza over the last several years. 307 00:14:52,720 --> 00:14:56,320 Speaker 5: Continuing to just say you're a Nazi collaborator doesn't prepare 308 00:14:56,440 --> 00:14:59,560 Speaker 5: you to defeat that argument in the court of public opinion, 309 00:14:59,640 --> 00:15:01,800 Speaker 5: especially now that people have so much more access to 310 00:15:01,800 --> 00:15:05,840 Speaker 5: information bypassing the traditional gatekeepers. That argument actually is very 311 00:15:05,880 --> 00:15:08,680 Speaker 5: interesting as like a media artifact, because what you see 312 00:15:08,760 --> 00:15:12,440 Speaker 5: him almost coasting on, thinking he can coast on is 313 00:15:12,560 --> 00:15:14,520 Speaker 5: just saying, well, you got to know the history of 314 00:15:14,600 --> 00:15:21,800 Speaker 5: World War two and the babies, and Caitlin is pushing back. Yeah, 315 00:15:21,880 --> 00:15:24,640 Speaker 5: he wasn't. I honestly think he was surprised to get 316 00:15:24,640 --> 00:15:26,320 Speaker 5: that level of pushback. 317 00:15:25,800 --> 00:15:29,000 Speaker 2: On I'm on with Jake Tapper or Dana Bash. 318 00:15:29,040 --> 00:15:32,600 Speaker 5: You might have gone different. Those were basic questions, right, 319 00:15:32,880 --> 00:15:35,440 Speaker 5: Those are totally fair basic questions, And again I think 320 00:15:35,560 --> 00:15:38,880 Speaker 5: you could reflexively kind of coast on the goodwill a 321 00:15:38,880 --> 00:15:42,920 Speaker 5: lot of Americans had towards Israel and the genuine opposition. 322 00:15:43,000 --> 00:15:44,760 Speaker 5: This is what I think is so disgusting about it, 323 00:15:44,880 --> 00:15:48,359 Speaker 5: the genuine opposition. A lot of Americans have too actual bigotry, 324 00:15:48,360 --> 00:15:51,160 Speaker 5: Like we have made enormous strides if you look at 325 00:15:51,160 --> 00:15:54,560 Speaker 5: public polling on anti Semitic sentiments, like what we have 326 00:15:55,000 --> 00:15:57,760 Speaker 5: made in terms of progress on that in this country 327 00:15:57,840 --> 00:16:00,440 Speaker 5: very quickly, by the way, is a really good thing 328 00:16:00,440 --> 00:16:02,720 Speaker 5: that we actually should be proud of, and to prey 329 00:16:02,840 --> 00:16:07,280 Speaker 5: on the discust the average American has towards bigotry is 330 00:16:07,440 --> 00:16:09,680 Speaker 5: so like, that's what I find to be the most 331 00:16:09,680 --> 00:16:12,960 Speaker 5: disgusting element of all of it, because it's exploitation of 332 00:16:13,240 --> 00:16:15,960 Speaker 5: something that we've worked really noble impulse. 333 00:16:16,080 --> 00:16:18,360 Speaker 2: Yeah, well, I think he should listen to Vice President 334 00:16:18,400 --> 00:16:20,960 Speaker 2: jad Vance, who says that if everything is true hatred, 335 00:16:21,040 --> 00:16:23,360 Speaker 2: then nothing is jeue hatred. I think maybe he should 336 00:16:23,360 --> 00:16:24,800 Speaker 2: take a listen to some of the things the Vice 337 00:16:24,840 --> 00:16:29,200 Speaker 2: President has said recently. We've got some really significant, you know, 338 00:16:29,440 --> 00:16:32,760 Speaker 2: primary challenges happening today in the state of Colorado. Emily 339 00:16:32,760 --> 00:16:34,720 Speaker 2: and Ryan will of course cover the results tomorrow, but 340 00:16:34,840 --> 00:16:39,040 Speaker 2: d five up on the screen. We interviewed yesterday two 341 00:16:39,040 --> 00:16:41,920 Speaker 2: of the candidates in these races, malt Kiros who is 342 00:16:42,000 --> 00:16:46,280 Speaker 2: challenging Diana to get in a Denver congressional district, and 343 00:16:46,680 --> 00:16:48,720 Speaker 2: that one really. 344 00:16:48,560 --> 00:16:49,880 Speaker 4: Looks very possible. 345 00:16:50,920 --> 00:16:53,920 Speaker 2: Mi Lot is a DSA candidate, Hassan Piker is there 346 00:16:53,920 --> 00:16:56,240 Speaker 2: in the state today to rally for her. There's been 347 00:16:57,000 --> 00:16:59,640 Speaker 2: a surge of enthusiasm for her race and the possibility 348 00:16:59,640 --> 00:17:03,280 Speaker 2: there after the DSA victories last week. Diana de Gett 349 00:17:03,440 --> 00:17:06,560 Speaker 2: is in the Congressional Progressive Caucus, but there's all this 350 00:17:06,920 --> 00:17:09,479 Speaker 2: dark money that's flowing in from a variety of shady 351 00:17:09,480 --> 00:17:13,159 Speaker 2: sources to back her and keep her in because she is, 352 00:17:13,520 --> 00:17:16,399 Speaker 2: you know, she is progressive on certain issues, but she 353 00:17:16,520 --> 00:17:18,240 Speaker 2: also at the end of the day, is quite corporate. 354 00:17:18,320 --> 00:17:20,680 Speaker 2: And you know, on a key issue for a lot 355 00:17:20,680 --> 00:17:22,960 Speaker 2: of young voters in particular, really voters at large in 356 00:17:23,000 --> 00:17:25,960 Speaker 2: the Democratic Party on Israel, she has not been good. 357 00:17:26,240 --> 00:17:29,239 Speaker 2: You also have a you know, a primary challenge in 358 00:17:29,320 --> 00:17:33,280 Speaker 2: the in the governor's race as well. That Michael Bennett 359 00:17:33,280 --> 00:17:37,240 Speaker 2: is running in the Democratic governatorial primary, and he is 360 00:17:37,440 --> 00:17:39,359 Speaker 2: very well known in the state senator all of that 361 00:17:39,400 --> 00:17:41,159 Speaker 2: sort of stuff, and it looks like he has a 362 00:17:41,200 --> 00:17:44,720 Speaker 2: stronger challenge than was anticipated. And then Julie Gonzalez, who 363 00:17:44,720 --> 00:17:48,760 Speaker 2: we also interviewed yesterday, is challenging challenging John Hickenlooper in 364 00:17:48,840 --> 00:17:51,600 Speaker 2: the Senate, and you know, there are some indications that 365 00:17:51,640 --> 00:17:54,280 Speaker 2: her campaign has a lot of energy as well, although 366 00:17:54,280 --> 00:17:56,560 Speaker 2: I think the statewide lift is more difficult than what 367 00:17:56,720 --> 00:17:59,920 Speaker 2: a lot would be pulling off the in that debt 368 00:18:00,119 --> 00:18:02,639 Speaker 2: for congressional district. So in any case, The piece that 369 00:18:02,680 --> 00:18:04,800 Speaker 2: I just had up there from Politico was just basically 370 00:18:04,880 --> 00:18:07,520 Speaker 2: like an accounting of how the dem establishment is completely 371 00:18:07,520 --> 00:18:10,639 Speaker 2: freaking out, but Colorado is not the only place that 372 00:18:10,680 --> 00:18:13,480 Speaker 2: they have to be concerned about. We have another left 373 00:18:13,560 --> 00:18:16,040 Speaker 2: leaning candidate, more in the progressive lane than in the 374 00:18:16,119 --> 00:18:20,120 Speaker 2: DSA wing, that is leading in the Minnesota Senate primary. 375 00:18:20,240 --> 00:18:24,200 Speaker 2: We can put the D four element up on the screen. 376 00:18:24,240 --> 00:18:27,280 Speaker 2: This is the latest poll out of Minnesota finds Peggy 377 00:18:27,280 --> 00:18:29,520 Speaker 2: flan Again, who is the current lieutenant governor, at forty 378 00:18:29,520 --> 00:18:33,240 Speaker 2: three percent. Angie Craig, who is a very moderate Democrat. 379 00:18:33,240 --> 00:18:37,399 Speaker 2: I think she was like healthcare, medical device something. Anyway, 380 00:18:37,520 --> 00:18:40,280 Speaker 2: she comes from a corporate job into Congress. She's at 381 00:18:40,280 --> 00:18:42,560 Speaker 2: thirty six percent, and you've got twenty two percent who 382 00:18:42,640 --> 00:18:43,600 Speaker 2: are unsure. 383 00:18:43,640 --> 00:18:44,639 Speaker 4: So that one is close. 384 00:18:44,680 --> 00:18:46,840 Speaker 2: But according to this pole, at least Peggy flan Again, 385 00:18:47,000 --> 00:18:49,400 Speaker 2: who is more of the left wing candidate, has the lead. 386 00:18:49,520 --> 00:18:52,919 Speaker 2: And you know there's others as well that after Colorado 387 00:18:53,040 --> 00:18:55,040 Speaker 2: you're going to be, you know, seeing more of these 388 00:18:55,080 --> 00:18:58,440 Speaker 2: primary challenges rise to the surface and get online support 389 00:18:58,560 --> 00:19:01,919 Speaker 2: and traction and national attention. Because it truly is a 390 00:19:01,960 --> 00:19:06,080 Speaker 2: party reckoning. Espayat is the perfect example. Again, this is 391 00:19:06,119 --> 00:19:11,760 Speaker 2: a guy who and Goldman in the Bradlander's opponent who 392 00:19:11,920 --> 00:19:14,840 Speaker 2: took out Dan Goldman in Manhattan is another perfect example. 393 00:19:15,240 --> 00:19:16,320 Speaker 5: Very Jewish district. 394 00:19:16,480 --> 00:19:18,760 Speaker 2: So if you're saying one of the Nazi collaborates one 395 00:19:18,800 --> 00:19:20,960 Speaker 2: of the most Jewish districts in the country. 396 00:19:20,720 --> 00:19:23,800 Speaker 5: You're calling a lot of Jews in New York Nazi collaborators, 397 00:19:23,840 --> 00:19:25,480 Speaker 5: the supporters of a Nazi collaborator. 398 00:19:25,520 --> 00:19:28,399 Speaker 2: Yes, and Lander didn't win by a slim margin, No, 399 00:19:28,640 --> 00:19:32,480 Speaker 2: it was called immediately it was an overwhelming landslide. And 400 00:19:32,560 --> 00:19:35,919 Speaker 2: Goldman again was kind of a resistance dem figure, you know, 401 00:19:35,960 --> 00:19:38,320 Speaker 2: involved in impeachment hearings and all this sort of stuff 402 00:19:38,320 --> 00:19:42,080 Speaker 2: on certain issues, positioned himself as progressive. But the end 403 00:19:42,119 --> 00:19:44,359 Speaker 2: of the day, voter said, yeah, but you support moral 404 00:19:44,880 --> 00:19:47,320 Speaker 2: atrocities that are unacceptable to us, and we want nothing 405 00:19:47,320 --> 00:19:49,439 Speaker 2: to do with you. And so that's you know why 406 00:19:49,640 --> 00:19:52,200 Speaker 2: they overwhelmingly voted for a guy they felt a lot 407 00:19:52,200 --> 00:19:52,879 Speaker 2: more comfortable with. 408 00:19:52,960 --> 00:19:53,560 Speaker 4: Bradlander. 409 00:19:53,680 --> 00:19:55,160 Speaker 5: Yea, and Ryan will be here of course to break 410 00:19:55,160 --> 00:19:58,280 Speaker 5: down those As you mentioned Colorado results tomorrow. Also should 411 00:19:58,400 --> 00:20:01,560 Speaker 5: mention that Angie Craig got the the endorsement yesterday of 412 00:20:01,560 --> 00:20:05,560 Speaker 5: Minneapolis Mayor Jacob Frye, who has been obviously out front 413 00:20:05,680 --> 00:20:09,520 Speaker 5: of the conflict between the State of Minnesota the City 414 00:20:09,520 --> 00:20:13,120 Speaker 5: of Minneapolis between them and Donald Trump, But he went 415 00:20:13,160 --> 00:20:16,920 Speaker 5: with Angie Craig in that endorsement yesterday, who was lagging 416 00:20:17,000 --> 00:20:20,119 Speaker 5: according to the polls that Crystal just showed. So a 417 00:20:20,160 --> 00:20:22,520 Speaker 5: lot to watch, and I think one big takeaway if 418 00:20:22,520 --> 00:20:26,000 Speaker 5: you're somebody that is sympathetic to the political establishment maybe 419 00:20:26,080 --> 00:20:28,320 Speaker 5: or even like a left of center Democrat who doesn't 420 00:20:28,359 --> 00:20:31,960 Speaker 5: like what you see from DSA candidates, or someone on 421 00:20:32,000 --> 00:20:34,119 Speaker 5: the right who's horrified by what you see from some 422 00:20:34,240 --> 00:20:37,280 Speaker 5: DSA candidates, I think one of the big themes just 423 00:20:37,359 --> 00:20:40,119 Speaker 5: looking at all these races is how, to your point, Crystal, 424 00:20:40,200 --> 00:20:43,800 Speaker 5: there is a new coalition building happening on the ground. 425 00:20:43,880 --> 00:20:47,639 Speaker 5: It is not just true that this is a totally 426 00:20:47,680 --> 00:20:50,120 Speaker 5: elite movement anymore. I mean, you and I can debate 427 00:20:50,160 --> 00:20:52,320 Speaker 5: about whether that was always the case whatever, but a 428 00:20:52,359 --> 00:20:54,800 Speaker 5: lot of people coming out of you know, academia, and 429 00:20:54,880 --> 00:20:56,639 Speaker 5: Chavali is a good example. I think she's like in 430 00:20:56,680 --> 00:20:58,600 Speaker 5: her seventh year of a PhD. You know, It's like 431 00:20:59,400 --> 00:21:01,520 Speaker 5: people who are upwardly mobile but spend a lot of 432 00:21:01,520 --> 00:21:04,840 Speaker 5: time in academia, at elite schools. That's Columbia in every case. 433 00:21:04,880 --> 00:21:07,800 Speaker 5: So obviously there's appeal there. But what's happening right now 434 00:21:08,000 --> 00:21:10,840 Speaker 5: is a broadening of the coalition or attempt to broaden 435 00:21:10,840 --> 00:21:14,280 Speaker 5: the coalition, and we're seeing some successes at that. Yeah. 436 00:21:14,320 --> 00:21:17,040 Speaker 5: So that's a I think a pretty significant development and 437 00:21:17,040 --> 00:21:19,200 Speaker 5: takeaway for people who are watching this race and these 438 00:21:19,280 --> 00:21:21,440 Speaker 5: races and are maybe not happy with the DSA candidate. 439 00:21:21,480 --> 00:21:25,040 Speaker 2: Look, here's the thing the establishment sold people, successfully sold 440 00:21:25,080 --> 00:21:27,000 Speaker 2: people on one model. If here's how we're going to 441 00:21:27,040 --> 00:21:30,000 Speaker 2: defeat Trump, yep, right, And it was and it was 442 00:21:30,080 --> 00:21:33,120 Speaker 2: Dan Goldman, and it was Hillary Clinton, and it was 443 00:21:33,480 --> 00:21:36,640 Speaker 2: Joe Biden who was able to ecount of victory yes 444 00:21:36,680 --> 00:21:38,960 Speaker 2: in twenty twenty. But the idea that that was going 445 00:21:39,040 --> 00:21:40,800 Speaker 2: to be the end of things and we're going to 446 00:21:40,880 --> 00:21:43,439 Speaker 2: quote unquote go back to normal that obviously didn't happen. 447 00:21:43,840 --> 00:21:46,600 Speaker 2: So their credibility is in the toilet, even just on 448 00:21:46,680 --> 00:21:49,880 Speaker 2: a basic like pragmatic Okay, how are we going to win? 449 00:21:50,400 --> 00:21:53,040 Speaker 2: And so people are no longer cowed by this. Well, 450 00:21:53,080 --> 00:21:56,280 Speaker 2: you may like this candidate, but you can't have them. 451 00:21:56,400 --> 00:21:58,080 Speaker 2: You got to do this other you gotta go with 452 00:21:58,119 --> 00:22:00,800 Speaker 2: Angie Craig, you got to go with Hailey Steve. And 453 00:22:01,240 --> 00:22:05,000 Speaker 2: speaking of Hailey Stevens and the Michigan race, the latest 454 00:22:05,000 --> 00:22:07,720 Speaker 2: pulls out have Abdullah Sayah doing better in the general 455 00:22:07,760 --> 00:22:11,840 Speaker 2: election against Mike Rogers, who's relatively strong for a Republican candidate, 456 00:22:12,680 --> 00:22:16,240 Speaker 2: doing better and winning that general election race in a 457 00:22:16,240 --> 00:22:18,560 Speaker 2: way that Hailey Stephens and Meller mcmorro are not. 458 00:22:19,240 --> 00:22:20,280 Speaker 4: So all of. 459 00:22:20,200 --> 00:22:22,760 Speaker 2: Those adages that have been sold to people about what 460 00:22:22,840 --> 00:22:24,680 Speaker 2: you have, the way you've got to suck up and 461 00:22:24,760 --> 00:22:28,480 Speaker 2: accept from your Democratic representatives, they're just not buying it anymore. 462 00:22:28,520 --> 00:22:28,600 Speaker 5: So. 463 00:22:28,680 --> 00:22:33,040 Speaker 2: The base has always been more aligned ideologically with the 464 00:22:33,080 --> 00:22:36,439 Speaker 2: Bernie wing of the party, and now that the establishment 465 00:22:36,440 --> 00:22:40,560 Speaker 2: wing has destroyed their credibility on their electability argument, it 466 00:22:40,600 --> 00:22:42,720 Speaker 2: has opened up a lot of possibilities. 467 00:22:42,760 --> 00:22:44,360 Speaker 4: And obviously there's been deep. 468 00:22:44,119 --> 00:22:47,159 Speaker 2: Disappointment too with the way that the Democratic establishment has 469 00:22:47,200 --> 00:22:50,199 Speaker 2: handled themselves in the Trump two point zero era. And 470 00:22:50,240 --> 00:22:52,520 Speaker 2: then you add Israel to that mix and it becomes 471 00:22:52,680 --> 00:22:54,560 Speaker 2: quite a powerful force to be reckoned with. 472 00:22:54,760 --> 00:22:57,560 Speaker 5: Final thought for me, we'll tie this all together with 473 00:22:57,600 --> 00:23:01,159 Speaker 5: your Utah segment, Crystal, But if you are are unhappy 474 00:23:01,520 --> 00:23:04,399 Speaker 5: with either left populism or right populism, because a lot 475 00:23:04,400 --> 00:23:07,040 Speaker 5: of right populists are very unhappy now with left populism 476 00:23:07,160 --> 00:23:10,240 Speaker 5: finding less kind of like handshakes as there was previously 477 00:23:10,280 --> 00:23:12,399 Speaker 5: on anti trust and big tech, and to the extent 478 00:23:12,480 --> 00:23:16,320 Speaker 5: that was real. There's clear division now. But if you're 479 00:23:16,400 --> 00:23:19,919 Speaker 5: unhappy with that, obviously, I think we can all agree 480 00:23:20,000 --> 00:23:22,680 Speaker 5: on exactly what Crystal just said about the establishment, whether 481 00:23:22,680 --> 00:23:26,159 Speaker 5: it's the Republican establishment or the Democratic establishment. And Trump, 482 00:23:26,200 --> 00:23:29,080 Speaker 5: as somebody who's anti establishment or wanted to tell people 483 00:23:29,160 --> 00:23:33,600 Speaker 5: was anti establishment, is currently tabling the housing bill, the 484 00:23:33,600 --> 00:23:37,159 Speaker 5: bipartisan Housing Bill between Tim Scott and Elizabeth Warren because 485 00:23:37,359 --> 00:23:39,359 Speaker 5: they won't pass the Save Act, which again is a 486 00:23:39,400 --> 00:23:43,240 Speaker 5: huge priority for the conservative grassroots. I think it's crazy 487 00:23:43,320 --> 00:23:45,520 Speaker 5: what Republican senators have done to try to block that 488 00:23:45,520 --> 00:23:49,040 Speaker 5: from getting to the floor. But holy smokes, your boomer 489 00:23:49,119 --> 00:23:51,760 Speaker 5: president is tabling a housing bill to get the Save 490 00:23:52,000 --> 00:23:54,800 Speaker 5: like you can maybe try to do both of these things. 491 00:23:55,000 --> 00:23:57,200 Speaker 5: And he's trying to say that without the Save Act, 492 00:23:57,200 --> 00:23:59,240 Speaker 5: you end up not having a country. But I'm just saying, 493 00:23:59,520 --> 00:24:02,960 Speaker 5: the option that you are offering young people, the more 494 00:24:03,000 --> 00:24:06,600 Speaker 5: insufficient those options are, the more people are going to 495 00:24:06,720 --> 00:24:10,400 Speaker 5: continue finding the better option to be an imperfect option. 496 00:24:10,480 --> 00:24:13,000 Speaker 5: In many cases, they won't say there was a crazy 497 00:24:13,040 --> 00:24:15,479 Speaker 5: poll recently. I think it was Zoron. I think it 498 00:24:15,520 --> 00:24:18,080 Speaker 5: was exit polls from Zorn that found people were voting 499 00:24:18,080 --> 00:24:20,320 Speaker 5: for him even though they don't like socialism. They're voting 500 00:24:20,359 --> 00:24:23,080 Speaker 5: for democratic socialists even though they weren't supportive of socialism 501 00:24:23,280 --> 00:24:26,879 Speaker 5: because the other options are so terrible. So start offering 502 00:24:26,920 --> 00:24:31,080 Speaker 5: better options and stop blaming China for running an op 503 00:24:31,240 --> 00:24:34,280 Speaker 5: that's making people favorable to socialism and humrism like, just 504 00:24:34,320 --> 00:24:35,680 Speaker 5: start offering better options. 505 00:24:36,040 --> 00:24:37,600 Speaker 2: We'll come back to that when we get to the 506 00:24:37,920 --> 00:24:42,280 Speaker 2: Utah block and the Chinese alleged misinformation angle here, indeed 507 00:24:42,280 --> 00:24:49,520 Speaker 2: we will. We are tracking escalating warnings that AI is 508 00:24:49,600 --> 00:24:53,440 Speaker 2: a giant bubble that may collapse and take the entire 509 00:24:53,520 --> 00:24:56,600 Speaker 2: economy down with it. One of the people making that 510 00:24:56,800 --> 00:24:59,800 Speaker 2: argument is Jeremy Grantham, sometimes these characterized as like a 511 00:24:59,800 --> 00:25:02,399 Speaker 2: per A Barr. He is very skeptical of the idea 512 00:25:02,440 --> 00:25:04,520 Speaker 2: that the line will just continue to go up forever, 513 00:25:04,800 --> 00:25:07,240 Speaker 2: has been for a while, but certainly thinks that AI 514 00:25:07,480 --> 00:25:10,440 Speaker 2: is a bubble that could prove calamitous for the global economy. 515 00:25:10,680 --> 00:25:13,680 Speaker 2: Here he is fighting with Joe Kernin on CNBC about 516 00:25:13,680 --> 00:25:14,560 Speaker 2: exactly this topic. 517 00:25:14,560 --> 00:25:17,600 Speaker 10: Listic listen other far fact that some people have made 518 00:25:17,600 --> 00:25:21,000 Speaker 10: a lot of money like a chain letter. What does 519 00:25:21,119 --> 00:25:21,720 Speaker 10: crypto do? 520 00:25:22,640 --> 00:25:24,920 Speaker 6: I don't understand the question what what. 521 00:25:24,880 --> 00:25:26,320 Speaker 4: Does the use of crypto? 522 00:25:26,640 --> 00:25:30,119 Speaker 10: It pays no dividend, It doesn't represent an asset you 523 00:25:30,160 --> 00:25:33,080 Speaker 10: can put your fingers on. There is nothing there there. 524 00:25:33,480 --> 00:25:36,399 Speaker 10: It is just an idea that it will go up 525 00:25:36,440 --> 00:25:38,520 Speaker 10: in price if you trust me, it will go up. 526 00:25:38,680 --> 00:25:42,360 Speaker 6: When when in the on an Island, when shells were 527 00:25:42,440 --> 00:25:45,080 Speaker 6: used to represent an hour of work that you got 528 00:25:45,160 --> 00:25:45,879 Speaker 6: one hundred. 529 00:25:45,600 --> 00:25:50,840 Speaker 10: People comparison, this is completely faith based shape. 530 00:25:50,960 --> 00:25:54,280 Speaker 6: It represents proof of work. It represents proof of work. 531 00:25:54,560 --> 00:25:57,640 Speaker 6: You're going to be totally wrong on bitcoin too. You're 532 00:25:57,680 --> 00:25:59,040 Speaker 6: going to be wrong on everything. 533 00:25:58,960 --> 00:25:59,960 Speaker 5: Proof of unnecesed. 534 00:26:00,080 --> 00:26:02,920 Speaker 10: Sorry, work shouldn't be worth a bucket of warm spit. 535 00:26:03,040 --> 00:26:06,560 Speaker 10: The point is it hasn't outlived a general bullmarket. We've 536 00:26:06,600 --> 00:26:08,480 Speaker 10: been in a bullmarket since. 537 00:26:11,000 --> 00:26:11,240 Speaker 6: March. 538 00:26:11,320 --> 00:26:15,119 Speaker 10: You're nine, right, And when we get into a bad market, 539 00:26:15,160 --> 00:26:17,680 Speaker 10: which Joe may may think, we never will be again 540 00:26:17,800 --> 00:26:21,800 Speaker 10: a serious it's just a down fifty sixty seven. Do 541 00:26:21,880 --> 00:26:23,760 Speaker 10: we think it will pross a lot of that arm. 542 00:26:23,840 --> 00:26:27,080 Speaker 6: Anybody that listened to you from twenty ten is you've 543 00:26:27,119 --> 00:26:30,800 Speaker 6: done a grave disservice to them. So if you feel 544 00:26:30,800 --> 00:26:33,199 Speaker 6: fine with that, that's that's that's what you do for 545 00:26:33,240 --> 00:26:33,560 Speaker 6: a living. 546 00:26:33,600 --> 00:26:34,040 Speaker 5: That's fine. 547 00:26:34,080 --> 00:26:37,240 Speaker 6: I don't have a pump. Andrew invited you on. I'm 548 00:26:37,280 --> 00:26:41,960 Speaker 6: just pointing out the facts of the situation. If someday 549 00:26:41,960 --> 00:26:43,600 Speaker 6: you might be right like a broken. 550 00:26:43,280 --> 00:26:46,919 Speaker 2: Clock Andrew Russ organ takeing some strays there. Well, Andrew 551 00:26:46,960 --> 00:26:49,240 Speaker 2: wanted you. It was really not I got to start. 552 00:26:49,040 --> 00:26:51,840 Speaker 5: Doing that with the DSA. People are like Crystal invited 553 00:26:51,880 --> 00:26:53,320 Speaker 5: you on. I'm just saying, I'm. 554 00:26:53,160 --> 00:26:56,240 Speaker 4: Just like, I like that. 555 00:26:56,480 --> 00:26:58,679 Speaker 2: Yeah, we should definitely go in that direction. Okay, you 556 00:26:58,720 --> 00:27:02,720 Speaker 2: want breadlines, CRISTI invited you here, but you know that's 557 00:27:02,840 --> 00:27:05,280 Speaker 2: your business between you and her. Enjoy your redline. 558 00:27:05,320 --> 00:27:05,679 Speaker 5: I like it. 559 00:27:05,680 --> 00:27:08,160 Speaker 2: And your Central Park executions or whatever else in Sanit 560 00:27:08,240 --> 00:27:10,640 Speaker 2: and your Nazi collaborators. Anyway, as I. 561 00:27:10,600 --> 00:27:12,280 Speaker 5: Told you last week, all I asked is that the 562 00:27:12,320 --> 00:27:13,400 Speaker 5: execution is painless. 563 00:27:13,680 --> 00:27:14,400 Speaker 4: I got you, girl. 564 00:27:14,440 --> 00:27:14,720 Speaker 11: Thank you. 565 00:27:14,760 --> 00:27:15,280 Speaker 4: Don't worry. 566 00:27:15,560 --> 00:27:19,160 Speaker 2: Okay, we've got a central bank who is giving Grantham 567 00:27:19,200 --> 00:27:21,960 Speaker 2: some backup here in his analysis, we could put this 568 00:27:22,080 --> 00:27:24,720 Speaker 2: up on the screen. The AI boom propic up markets 569 00:27:24,720 --> 00:27:27,639 Speaker 2: could trigger the next crash. Central banks worn In its 570 00:27:27,640 --> 00:27:30,320 Speaker 2: annual economic report published on Sunday, the Bank for International 571 00:27:30,320 --> 00:27:33,960 Speaker 2: Settlements known as the Central Bank for Central Banks, warned 572 00:27:34,000 --> 00:27:38,120 Speaker 2: that the enormous spending on AI is accumulating financial vulnerabilities 573 00:27:38,160 --> 00:27:41,520 Speaker 2: that could amplify any future shock and spread from markets 574 00:27:41,560 --> 00:27:45,600 Speaker 2: into the wider economy. If memory serves, this is just 575 00:27:45,680 --> 00:27:48,679 Speaker 2: the latest of there have been several central banks that 576 00:27:48,760 --> 00:27:53,960 Speaker 2: have sounded similar warnings as insane amounts of money flow 577 00:27:54,080 --> 00:27:58,439 Speaker 2: into this hyperscaling data center bill out. And you know, 578 00:27:58,480 --> 00:28:00,680 Speaker 2: you've got companies, and we had ads thron On who 579 00:28:00,760 --> 00:28:04,119 Speaker 2: laid this dound very clearly. You have companies that are 580 00:28:04,240 --> 00:28:06,720 Speaker 2: assuming that the revenue will catch up with the spend 581 00:28:06,800 --> 00:28:09,480 Speaker 2: at some point in the future if they are able 582 00:28:09,560 --> 00:28:13,199 Speaker 2: to make good on their promises of effectively replacing all 583 00:28:13,240 --> 00:28:15,280 Speaker 2: of human labor, and that would be a very valuable 584 00:28:15,280 --> 00:28:17,840 Speaker 2: proposition to a lot of companies. That is what this 585 00:28:17,960 --> 00:28:21,200 Speaker 2: is all being bet on. And at this point you 586 00:28:21,640 --> 00:28:24,800 Speaker 2: have had some displacement, but certainly you haven't had the 587 00:28:24,920 --> 00:28:28,560 Speaker 2: wave of mass displacement that would be required for these 588 00:28:29,040 --> 00:28:32,359 Speaker 2: numbers these valuation numbers and the level of spend to 589 00:28:32,480 --> 00:28:34,560 Speaker 2: really add up and make sense. Now on the other 590 00:28:34,600 --> 00:28:38,680 Speaker 2: side of it, you have the, I think in controvertible 591 00:28:38,760 --> 00:28:41,200 Speaker 2: fact that these companies are effectively too big to fail. 592 00:28:41,240 --> 00:28:44,200 Speaker 2: At this point, they are effectively sort of de facto 593 00:28:44,280 --> 00:28:47,240 Speaker 2: government entities whenever government wants to intervene and tell them 594 00:28:47,240 --> 00:28:49,680 Speaker 2: what to do, as has happened in recent cases, most 595 00:28:49,720 --> 00:28:52,640 Speaker 2: notably with claude Ananthropic. We have another case now with 596 00:28:52,960 --> 00:28:56,600 Speaker 2: Open AI and chatch BT's release also being limited. In 597 00:28:56,640 --> 00:28:59,040 Speaker 2: any case, the government has a great interest in what 598 00:28:59,160 --> 00:29:01,960 Speaker 2: happens here is also going to be very interested making 599 00:29:01,960 --> 00:29:04,640 Speaker 2: sure the line does continue to go up. The size 600 00:29:04,680 --> 00:29:06,920 Speaker 2: of the buildoun and then sheer amount of cash that 601 00:29:07,000 --> 00:29:08,880 Speaker 2: is being spent here might be too much for them 602 00:29:08,920 --> 00:29:11,400 Speaker 2: to ultimately backstop, but I think we both know that 603 00:29:11,440 --> 00:29:12,400 Speaker 2: they will certainly try. 604 00:29:12,600 --> 00:29:14,320 Speaker 5: Yeah. So, when you have what thirty percent of the 605 00:29:14,320 --> 00:29:16,440 Speaker 5: mag seven we've talked about this a ton tied up 606 00:29:16,560 --> 00:29:20,120 Speaker 5: in basically AI stocks at this point such a sugar high, 607 00:29:20,200 --> 00:29:22,720 Speaker 5: you end up in this trap where you either have 608 00:29:22,760 --> 00:29:25,000 Speaker 5: to and we talked to ry Tropra about this, you 609 00:29:25,080 --> 00:29:27,920 Speaker 5: either have to deliver on wiping out fifty percent of 610 00:29:27,960 --> 00:29:32,640 Speaker 5: white collar jobs, as Dariamide said that AI would or 611 00:29:32,880 --> 00:29:34,640 Speaker 5: you end up with a market crash. So you're in 612 00:29:34,680 --> 00:29:38,040 Speaker 5: a trap. You either end up with mass job displacement 613 00:29:38,320 --> 00:29:40,520 Speaker 5: or a crash. Basically, I mean, I don't see a 614 00:29:40,560 --> 00:29:42,520 Speaker 5: real way out of either of those options. 615 00:29:42,600 --> 00:29:44,040 Speaker 4: In fact, even you may get both. 616 00:29:44,480 --> 00:29:46,920 Speaker 2: You could get both, And that is kind of where 617 00:29:46,960 --> 00:29:50,200 Speaker 2: I am leaning as a likely outcome of this thing. 618 00:29:50,520 --> 00:29:52,440 Speaker 2: I think it is definitely a bubble. I think it 619 00:29:52,480 --> 00:29:55,200 Speaker 2: is going to crash. I also think the technology is 620 00:29:55,280 --> 00:29:57,520 Speaker 2: very disruptive, and I don't think they're going to replace 621 00:29:57,520 --> 00:29:59,480 Speaker 2: all of human labor, but I do think it is 622 00:29:59,520 --> 00:30:02,280 Speaker 2: going to a lot of economic dislocation. 623 00:30:02,360 --> 00:30:03,720 Speaker 5: Well, see, this is interesting. I was going to ask 624 00:30:03,720 --> 00:30:05,400 Speaker 5: you to even flush that out, because I guess it 625 00:30:05,400 --> 00:30:07,440 Speaker 5: would be the magnitude of the bubble crash and the 626 00:30:07,480 --> 00:30:11,200 Speaker 5: magnitude of the job displacement, or even just the longevity 627 00:30:11,320 --> 00:30:15,640 Speaker 5: of how for what period of time there is displacement, 628 00:30:15,640 --> 00:30:17,520 Speaker 5: because it is possible that there are a lot of 629 00:30:17,560 --> 00:30:20,360 Speaker 5: new jobs created because of AI, Like we'll just say 630 00:30:20,400 --> 00:30:22,880 Speaker 5: that that's a possibility, and maybe it takes there's a 631 00:30:22,960 --> 00:30:25,240 Speaker 5: lag in the market, it takes five years or something 632 00:30:25,280 --> 00:30:27,240 Speaker 5: like that for those job to be replaced. So you 633 00:30:27,320 --> 00:30:31,800 Speaker 5: could have this weird position where the markets are reacting 634 00:30:31,800 --> 00:30:34,160 Speaker 5: to both of those things of bubble crash like that. Actually, 635 00:30:34,240 --> 00:30:36,600 Speaker 5: I'm curious when you say you think both of those 636 00:30:36,640 --> 00:30:38,640 Speaker 5: could happen, You mean, like simultaneously both of those. 637 00:30:38,800 --> 00:30:41,680 Speaker 2: Yeah, well, I mean the classic example people in offering 638 00:30:41,880 --> 00:30:42,840 Speaker 2: is the railroad boom. 639 00:30:42,920 --> 00:30:43,400 Speaker 5: Yeah right. 640 00:30:43,520 --> 00:30:45,880 Speaker 2: It was a genuine bubble. It crashed, It was calamitous 641 00:30:45,880 --> 00:30:49,600 Speaker 2: for the economy. It also was a genuinely transformational technology Internet, 642 00:30:49,680 --> 00:30:53,680 Speaker 2: same thing. I don't really buy into the happy talk 643 00:30:53,840 --> 00:30:57,600 Speaker 2: about well, there's some unspecified jobs that we can't really 644 00:30:57,600 --> 00:30:59,320 Speaker 2: tell you what they are, and I have no idea 645 00:30:59,360 --> 00:31:01,600 Speaker 2: what they would be that will emerge from the ether 646 00:31:02,080 --> 00:31:04,440 Speaker 2: as they have in the past, because past technologies have 647 00:31:04,520 --> 00:31:08,000 Speaker 2: not been meant to directly replace humans, you know, They've 648 00:31:08,040 --> 00:31:11,040 Speaker 2: been meant to replace like horses. This is meant to 649 00:31:11,120 --> 00:31:13,680 Speaker 2: replace the intellect of the human, which is like the 650 00:31:14,040 --> 00:31:16,400 Speaker 2: last thing that we can really hold on to as 651 00:31:16,480 --> 00:31:19,960 Speaker 2: being like, you know, having a superior position vis a vi. 652 00:31:20,200 --> 00:31:23,560 Speaker 2: The mechanical world. So you know, I need to see 653 00:31:23,600 --> 00:31:26,960 Speaker 2: some evidence of what these jobs would ultimately be. Now, 654 00:31:27,120 --> 00:31:29,240 Speaker 2: let me put on the other side on put E 655 00:31:29,320 --> 00:31:31,400 Speaker 2: three up on the screen. People took a lot of 656 00:31:31,440 --> 00:31:35,040 Speaker 2: notice of this yesterday. Ford has rehired hundreds of human 657 00:31:35,080 --> 00:31:40,200 Speaker 2: engineers after the AI that they implemented failed to match 658 00:31:40,280 --> 00:31:45,440 Speaker 2: the human driven quality checks, and Ford is touting this. 659 00:31:46,120 --> 00:31:47,959 Speaker 4: They also a lot of these companies. 660 00:31:47,960 --> 00:31:50,400 Speaker 2: So Ford has done, like, I don't know, fifty three 661 00:31:50,480 --> 00:31:53,400 Speaker 2: hundred people have lost their jobs at Ford over just 662 00:31:53,680 --> 00:31:57,440 Speaker 2: the past recent period, so they're bringing back a few hundred. 663 00:31:57,680 --> 00:31:59,480 Speaker 2: Now that doesn't mean that all of those jobs were 664 00:31:59,520 --> 00:32:01,720 Speaker 2: lost due to automation, but I think it's just worth 665 00:32:01,960 --> 00:32:04,960 Speaker 2: keeping in mind that the workforce there is still shrinking. 666 00:32:05,400 --> 00:32:07,200 Speaker 2: And then the other thing when you dig into this 667 00:32:07,440 --> 00:32:10,040 Speaker 2: is they say they're bringing these people back not to 668 00:32:10,160 --> 00:32:13,000 Speaker 2: permanently serve as okay, we're just sticking with humans. They 669 00:32:13,040 --> 00:32:16,000 Speaker 2: say they want the humans to better train the AI 670 00:32:16,920 --> 00:32:20,760 Speaker 2: on the systems and so, which reminds me very much 671 00:32:20,760 --> 00:32:23,200 Speaker 2: of what Meta is doing. They're using some of their 672 00:32:23,240 --> 00:32:27,520 Speaker 2: top engineers not to necessarily do the engineer work permanently, 673 00:32:28,040 --> 00:32:32,000 Speaker 2: but to train the AI that is intended to ultimately 674 00:32:32,120 --> 00:32:35,320 Speaker 2: replace them. Now, maybe because of the nature of AI, 675 00:32:35,560 --> 00:32:38,840 Speaker 2: and this is what plenty of people argue it's just 676 00:32:38,880 --> 00:32:41,200 Speaker 2: reading a New York Times aped this morning that makes 677 00:32:41,200 --> 00:32:43,400 Speaker 2: this argument. Because of the nature of AI, maybe it 678 00:32:43,440 --> 00:32:48,360 Speaker 2: never can really replace the human quality checkers, you know. 679 00:32:48,400 --> 00:32:51,240 Speaker 2: Maybe it hallucinates too much. Maybe it sort of loses 680 00:32:51,240 --> 00:32:54,080 Speaker 2: its train of thought as your conversation goes on and on, 681 00:32:54,200 --> 00:32:55,520 Speaker 2: or it continues to do the same. 682 00:32:55,400 --> 00:32:56,720 Speaker 4: Work process over and over. 683 00:32:57,080 --> 00:33:00,880 Speaker 2: It's possible that there are hard and fast limitations where 684 00:33:00,960 --> 00:33:04,760 Speaker 2: AI development kind of peaks and pewters ound. I will 685 00:33:04,760 --> 00:33:07,760 Speaker 2: just say that we have not found that wall for 686 00:33:07,840 --> 00:33:08,560 Speaker 2: AI to hit. 687 00:33:08,720 --> 00:33:08,960 Speaker 5: Yet. 688 00:33:09,080 --> 00:33:13,000 Speaker 2: It continues to improve. It continues to achieve more and 689 00:33:13,000 --> 00:33:16,200 Speaker 2: more impressive numbers on benchmarks. It continues to achieve things 690 00:33:16,200 --> 00:33:18,840 Speaker 2: like being able to solve mathematical puzzles that humans were 691 00:33:19,000 --> 00:33:22,280 Speaker 2: unable to solve, you know, for eighty plus years. It 692 00:33:22,320 --> 00:33:27,040 Speaker 2: continues to be able to write better works of even 693 00:33:27,280 --> 00:33:31,440 Speaker 2: literary fiction that is winning prizes, and even discerning people 694 00:33:31,520 --> 00:33:33,960 Speaker 2: are having trouble telling the difference between the human output 695 00:33:33,960 --> 00:33:37,160 Speaker 2: and the AI output. So if there is a wall, 696 00:33:37,280 --> 00:33:40,280 Speaker 2: it does not appear that we have hit that wall yet. 697 00:33:40,400 --> 00:33:43,360 Speaker 5: The railroad comparison, I think is really helpful too, because 698 00:33:43,480 --> 00:33:47,080 Speaker 5: it's not like when you're just smeared as a doomer, 699 00:33:47,320 --> 00:33:50,160 Speaker 5: which I kind of like, happily will take that label. Yeah, 700 00:33:50,280 --> 00:33:53,160 Speaker 5: call me a doomer by all means, I'll take it. 701 00:33:53,200 --> 00:33:55,560 Speaker 5: But when you call a doomer, it doesn't necessarily just 702 00:33:55,680 --> 00:33:58,920 Speaker 5: mean that you're saying the technology isn't transformational, that it 703 00:33:58,960 --> 00:34:02,040 Speaker 5: won't have some good benefits. Everyone is smeared as a 704 00:34:02,080 --> 00:34:05,520 Speaker 5: duomer if they're like, well, hey, maybe we should adjust 705 00:34:05,680 --> 00:34:07,520 Speaker 5: in a way that makes more sense. Maybe we shouldn't 706 00:34:07,600 --> 00:34:11,400 Speaker 5: ban all states from regulating AI. You would called a 707 00:34:11,480 --> 00:34:14,000 Speaker 5: doomer a year ago if you were opposing that. I mean, 708 00:34:14,040 --> 00:34:16,480 Speaker 5: think about it. Basically, Now it's a consensus position that 709 00:34:16,480 --> 00:34:20,040 Speaker 5: that was insane and was an industry handout, which is 710 00:34:20,040 --> 00:34:24,160 Speaker 5: why ultimately it got defeated. But at the time that 711 00:34:24,320 --> 00:34:27,400 Speaker 5: was a real claim coming from people in the industry, 712 00:34:27,480 --> 00:34:29,759 Speaker 5: is that you are a doomer if you don't want this. 713 00:34:29,800 --> 00:34:31,919 Speaker 5: It was like a ted cruise provision in the big 714 00:34:31,960 --> 00:34:36,440 Speaker 5: beautiful Bill. And think of just a year's time, how 715 00:34:36,600 --> 00:34:39,279 Speaker 5: the Overton window has shifted on that debate. It's a 716 00:34:39,320 --> 00:34:41,520 Speaker 5: basically a consensus position now that it would be insane 717 00:34:41,520 --> 00:34:45,280 Speaker 5: to ban states from their own AI regulation for good reason, 718 00:34:45,400 --> 00:34:48,719 Speaker 5: and so anyway, all that is to say is there's 719 00:34:49,000 --> 00:34:51,720 Speaker 5: a process of implementing things that you can learn from history, 720 00:34:51,719 --> 00:34:53,840 Speaker 5: even if we accept that this is an inevitability. And 721 00:34:53,880 --> 00:34:57,040 Speaker 5: then the I think kind of ridiculous question of whether 722 00:34:57,120 --> 00:35:00,880 Speaker 5: or not we're beating China, which nobody can define. Literally, 723 00:35:00,880 --> 00:35:03,080 Speaker 5: nobody can define what it means to beat China, right 724 00:35:03,200 --> 00:35:05,359 Speaker 5: AI that this is kind of a meaningless thing to say. 725 00:35:05,400 --> 00:35:06,839 Speaker 5: You're going to talk about this later in the show, 726 00:35:06,880 --> 00:35:09,320 Speaker 5: and we actually can put E four up on the screen. 727 00:35:09,400 --> 00:35:12,920 Speaker 5: This is a piece from Axios China's AI advances collide 728 00:35:12,960 --> 00:35:15,840 Speaker 5: with US safety debate. One of the biggest unknowns in 729 00:35:15,880 --> 00:35:18,200 Speaker 5: AI security is also one of the most consequential Axios 730 00:35:18,280 --> 00:35:23,000 Speaker 5: rights China's progress toward frontier AI models. A new Chinese 731 00:35:23,040 --> 00:35:25,960 Speaker 5: open source model, GLM five point two rock the Internet 732 00:35:26,000 --> 00:35:28,520 Speaker 5: this weekend with its ability to match the eugenic capabilities 733 00:35:28,560 --> 00:35:31,439 Speaker 5: of models like anthropics Opus four point eight, guarding praise 734 00:35:31,480 --> 00:35:33,880 Speaker 5: from Silicon Valley elites and raising questions about just how 735 00:35:33,960 --> 00:35:37,360 Speaker 5: quickly China will close the gap. And this comes Acxious 736 00:35:37,400 --> 00:35:39,480 Speaker 5: points out as the Trump admin is still debating the 737 00:35:39,480 --> 00:35:42,600 Speaker 5: best way to release Fable five and Methos five models. 738 00:35:42,640 --> 00:35:45,760 Speaker 5: From anthropic over safety and national security concerns, and again 739 00:35:45,800 --> 00:35:48,759 Speaker 5: it just gets back to crystal. Nobody is well, some 740 00:35:48,800 --> 00:35:52,600 Speaker 5: people probably would say block all of the models, moratorium 741 00:35:52,719 --> 00:35:54,879 Speaker 5: something like that. It's not what most people are saying. 742 00:35:54,880 --> 00:36:00,120 Speaker 5: Most people are saying, you can't destroy society for this 743 00:36:00,200 --> 00:36:03,400 Speaker 5: abstract goal of beating China, like literally whatever that means, 744 00:36:03,440 --> 00:36:06,000 Speaker 5: and whatever you could possibly do to even prevent it 745 00:36:06,040 --> 00:36:06,640 Speaker 5: at this point. 746 00:36:06,719 --> 00:36:08,880 Speaker 2: Yeah, well, and this is the talking point that you 747 00:36:08,960 --> 00:36:13,840 Speaker 2: hear from the biggest AI hyper scale boosters that everything 748 00:36:13,920 --> 00:36:16,879 Speaker 2: is justified because of China, right, And it's always left, 749 00:36:17,040 --> 00:36:20,560 Speaker 2: this threat is always left very ambiguous because you know, 750 00:36:20,600 --> 00:36:22,360 Speaker 2: it's meant to be it's meant to just be scary. 751 00:36:22,440 --> 00:36:23,040 Speaker 4: Oh my god. 752 00:36:23,120 --> 00:36:23,319 Speaker 5: You know. 753 00:36:23,480 --> 00:36:24,920 Speaker 4: Kevin Leary, I'm going to. 754 00:36:24,840 --> 00:36:26,520 Speaker 2: Talk about it a minute, was saying like they're gonna 755 00:36:26,520 --> 00:36:28,480 Speaker 2: be telling your kids, we'll eat from breakfast, and like, 756 00:36:28,640 --> 00:36:31,520 Speaker 2: I don't think they actually care what my kids for breakfast. 757 00:36:31,560 --> 00:36:34,600 Speaker 2: I need no evidence of that in any case. But 758 00:36:35,120 --> 00:36:38,480 Speaker 2: I will say on the economic front, I think China 759 00:36:38,600 --> 00:36:42,480 Speaker 2: poses quite a threat to anthropic and open AI and 760 00:36:42,680 --> 00:36:46,520 Speaker 2: to all of the American to GROCK, to all of 761 00:36:46,560 --> 00:36:50,719 Speaker 2: the American you know, frontier AI models, and we can 762 00:36:50,760 --> 00:36:52,960 Speaker 2: put you know, in E four they talk about the 763 00:36:53,000 --> 00:36:56,600 Speaker 2: fact that you have a new Chinese release that is 764 00:36:56,680 --> 00:37:03,320 Speaker 2: matching the agentic capabilities of the foremost American AI models. 765 00:37:03,320 --> 00:37:06,680 Speaker 2: So this new Chinese open source model GLM five point 766 00:37:06,719 --> 00:37:09,120 Speaker 2: two rocked the internet this weekend with its ability to 767 00:37:09,160 --> 00:37:12,520 Speaker 2: match the agentic capabilities models like Anthropics Opus four point eight, 768 00:37:12,800 --> 00:37:15,839 Speaker 2: garnering praise from Silicon Valley elites and raising questions about 769 00:37:15,840 --> 00:37:19,640 Speaker 2: just how quickly China will close the gap. We showed 770 00:37:19,640 --> 00:37:23,880 Speaker 2: you before on this show a chart that of companies 771 00:37:23,920 --> 00:37:27,600 Speaker 2: that use AI in their businesses startups in particular, they 772 00:37:27,680 --> 00:37:31,560 Speaker 2: overwhelmingly use Chinese models. Not because they're maybe the best 773 00:37:31,560 --> 00:37:33,600 Speaker 2: of the best, although they are pretty darn close, and 774 00:37:33,640 --> 00:37:37,040 Speaker 2: even David's ax admits that they're probably six months behind 775 00:37:37,200 --> 00:37:39,920 Speaker 2: maybe the leading models here, so that is a tiny 776 00:37:39,960 --> 00:37:43,280 Speaker 2: difference at this point, but because they are way cheaper 777 00:37:43,680 --> 00:37:46,000 Speaker 2: and they're complete, you know, they're open source, so they're 778 00:37:46,040 --> 00:37:50,200 Speaker 2: completely accessible, and they are vastly cheaper to build and 779 00:37:50,239 --> 00:37:53,879 Speaker 2: then also to run. So when you have that kind 780 00:37:53,920 --> 00:37:58,239 Speaker 2: of an advantage in terms of the economics, how is 781 00:37:58,320 --> 00:38:01,360 Speaker 2: anthropic how are open AI to compete with that? That 782 00:38:01,440 --> 00:38:07,040 Speaker 2: seems increasingly like the most likely, in my layman's view, 783 00:38:07,160 --> 00:38:08,919 Speaker 2: the most likely way the bubble is going to pop 784 00:38:09,000 --> 00:38:12,040 Speaker 2: is going to be from this realization of like, oh, 785 00:38:12,640 --> 00:38:15,640 Speaker 2: most companies don't need the very very very best of 786 00:38:15,680 --> 00:38:17,799 Speaker 2: the best AI. They can settle for the one that 787 00:38:17,960 --> 00:38:22,040 Speaker 2: is a few months for now behind, but is way 788 00:38:22,160 --> 00:38:26,719 Speaker 2: cheaper than the frontier models, and so that, you know, 789 00:38:26,920 --> 00:38:31,239 Speaker 2: really changes your multi trillion dollar valuations or whatever these 790 00:38:31,239 --> 00:38:34,480 Speaker 2: companies are garnering. That really puts a major dent in 791 00:38:34,520 --> 00:38:37,640 Speaker 2: those to be to be. I guess kind I. 792 00:38:37,600 --> 00:38:39,040 Speaker 5: Mentioned this on the show a couple of weeks ago, 793 00:38:39,040 --> 00:38:41,960 Speaker 5: but I was asking Senator Rick Scott, Republican of Florida 794 00:38:42,040 --> 00:38:45,360 Speaker 5: about this during like a pen and pad conversation recently, 795 00:38:45,719 --> 00:38:48,080 Speaker 5: and like about that question of whether we find ourselves 796 00:38:48,120 --> 00:38:51,439 Speaker 5: in a trap, and he didn't seem to really want 797 00:38:51,480 --> 00:38:53,560 Speaker 5: to give an explicit answer to that, but he was 798 00:38:53,560 --> 00:38:56,400 Speaker 5: basically saying that some of these investments are like what 799 00:38:56,800 --> 00:38:58,640 Speaker 5: I read into it is, some of these investments are 800 00:38:58,680 --> 00:39:02,720 Speaker 5: absolutely ridiculous, wildly overvalid. He was talking about SpaceX, for example, 801 00:39:02,800 --> 00:39:04,640 Speaker 5: and he was like, look at the cash flow versus 802 00:39:04,680 --> 00:39:09,399 Speaker 5: the that's a Republican senator a Republican senator saying that, 803 00:39:09,480 --> 00:39:15,520 Speaker 5: like a Trump friendly Republican senator about the administration's policy, 804 00:39:15,560 --> 00:39:18,719 Speaker 5: I asked specifically about Trump bear hugging the AI industry, 805 00:39:19,160 --> 00:39:21,160 Speaker 5: and I think that's probably why I didn't get a 806 00:39:21,200 --> 00:39:24,440 Speaker 5: more direct answer. But that to me was like actually 807 00:39:24,560 --> 00:39:28,600 Speaker 5: very frightening to say, this is the administration, you're friendly 808 00:39:28,640 --> 00:39:30,799 Speaker 5: to bear hugging the AI industry, and then to have 809 00:39:31,200 --> 00:39:35,040 Speaker 5: some like, yeah, these valuations are pretty like they look 810 00:39:35,120 --> 00:39:36,480 Speaker 5: really sugary. Yeah. 811 00:39:36,560 --> 00:39:40,800 Speaker 2: Well, and so that's on the economic the bubble front. 812 00:39:41,160 --> 00:39:46,040 Speaker 2: Let's put E five up on the screen. So even 813 00:39:46,040 --> 00:39:50,399 Speaker 2: the Trump administration, which is very famously no holds barred 814 00:39:50,440 --> 00:39:53,560 Speaker 2: off to the races, anything goes in terms of AI development, 815 00:39:53,920 --> 00:39:56,359 Speaker 2: is getting a little nervous about where these models are 816 00:39:56,480 --> 00:39:58,719 Speaker 2: at this point, what the potential impact could be. So 817 00:39:58,760 --> 00:40:03,680 Speaker 2: we covered before their limit tation of anthropicsatest model Mythos 818 00:40:03,719 --> 00:40:06,560 Speaker 2: and Fable, which was like the version that was supposed 819 00:40:06,600 --> 00:40:09,000 Speaker 2: to be safe and have the guardrails put on. They're 820 00:40:09,040 --> 00:40:12,959 Speaker 2: now also limiting the release of open Ayes latest model, 821 00:40:13,000 --> 00:40:15,640 Speaker 2: at least for now, GBT five point six only in 822 00:40:15,680 --> 00:40:18,920 Speaker 2: a limited preview to a small group of partners. The 823 00:40:18,960 --> 00:40:23,920 Speaker 2: government themselves is personally picking and choosing who gets access 824 00:40:24,000 --> 00:40:27,440 Speaker 2: to this latest model. This person opines as a de 825 00:40:27,520 --> 00:40:31,279 Speaker 2: facto licensing regime. This is happening, So in terms of 826 00:40:31,280 --> 00:40:35,279 Speaker 2: the capabilities, the Trump administration is getting nerves. We also 827 00:40:35,320 --> 00:40:37,520 Speaker 2: have an update on the Anthropic front. We can put 828 00:40:37,560 --> 00:40:39,600 Speaker 2: E six up on the screen. They have struck a 829 00:40:39,640 --> 00:40:43,440 Speaker 2: deal with Anthropic grants the company permission to release its 830 00:40:43,520 --> 00:40:45,720 Speaker 2: Mythos five model to a group of about one hundred 831 00:40:45,719 --> 00:40:50,160 Speaker 2: companies and federal agencies. So again similar dynamic with the 832 00:40:50,760 --> 00:40:53,839 Speaker 2: government stepping in and saying okay, we will approve who 833 00:40:53,880 --> 00:40:57,279 Speaker 2: gets access to this. Senior Anthropic staffers could be ce 834 00:40:57,440 --> 00:40:59,760 Speaker 2: letter rights to FLU to DC to meet with members 835 00:40:59,760 --> 00:41:01,799 Speaker 2: of the tr Trump Admin, Anthropics ceter earlier this month 836 00:41:01,800 --> 00:41:04,359 Speaker 2: that disabled access to Fable five and mid Those five 837 00:41:04,400 --> 00:41:07,440 Speaker 2: to comply with an export control directive. Trump admin and 838 00:41:07,560 --> 00:41:09,759 Speaker 2: Thropic have been in a two week long standoff over 839 00:41:09,840 --> 00:41:12,799 Speaker 2: its latest models. Deal will have industry wide implications, So 840 00:41:13,680 --> 00:41:16,560 Speaker 2: you know, look to be fair to this administration. When 841 00:41:16,560 --> 00:41:21,440 Speaker 2: they slapped this onto Anthropic, it looks very political because 842 00:41:21,440 --> 00:41:23,880 Speaker 2: they've been in these fights, especially with at the Department 843 00:41:23,920 --> 00:41:26,479 Speaker 2: of War with Anthropic. Now that they're doing the same 844 00:41:26,520 --> 00:41:29,400 Speaker 2: thing to open AI. I think you have to say, like, okay, 845 00:41:29,400 --> 00:41:33,400 Speaker 2: well they are genuinely concerned, yeah about the safety considerations 846 00:41:33,440 --> 00:41:35,440 Speaker 2: here of these bleeding edge models. 847 00:41:35,520 --> 00:41:37,719 Speaker 5: Yeah, as they should be. And it did. You're right, 848 00:41:37,760 --> 00:41:39,920 Speaker 5: it did. The Pentagon thing, reporting from inside of the 849 00:41:39,920 --> 00:41:42,319 Speaker 5: Pentagon made it sound really political. But even if it 850 00:41:42,400 --> 00:41:45,319 Speaker 5: was political at the time, I was thinking, actually, this 851 00:41:45,360 --> 00:41:48,360 Speaker 5: is not that easy or clear cut of a decision 852 00:41:48,520 --> 00:41:51,040 Speaker 5: on their behalf, Like there's a It almost gets into 853 00:41:51,239 --> 00:41:54,520 Speaker 5: the question of have we seen conservatives and we have 854 00:41:54,560 --> 00:41:57,440 Speaker 5: seen a couple of candidates flirt with the Bernie Sanders 855 00:41:57,640 --> 00:42:01,879 Speaker 5: idea of data center moratoriums and starting to really crack 856 00:42:01,960 --> 00:42:05,200 Speaker 5: down and have a more concerted industrial policy on all 857 00:42:05,239 --> 00:42:09,000 Speaker 5: of this, which again gets you smeared and stigmatized as 858 00:42:09,120 --> 00:42:12,400 Speaker 5: a doomer in politics and in the Silicon Valley. But 859 00:42:13,360 --> 00:42:15,840 Speaker 5: it's not the easiest, Like, even if you are a 860 00:42:15,960 --> 00:42:20,280 Speaker 5: pro accelerationist type person, an EAC person, when you're looking 861 00:42:20,360 --> 00:42:23,080 Speaker 5: internally at the security questions from the vantage point of 862 00:42:23,120 --> 00:42:27,320 Speaker 5: the Pentagon, those questions get really complicated for obvious reasons. 863 00:42:28,000 --> 00:42:30,520 Speaker 2: Yeah, that's absolutely right. All right, Well, let's talk about 864 00:42:31,000 --> 00:42:34,919 Speaker 2: this idea that China is behind any opposition to what's 865 00:42:34,920 --> 00:42:38,520 Speaker 2: going on. If you see an American who believes that 866 00:42:38,560 --> 00:42:40,920 Speaker 2: maybe we should slow down the data center construction, maybe 867 00:42:40,920 --> 00:42:43,839 Speaker 2: we should have a democratic check on this AI development, 868 00:42:44,120 --> 00:42:45,920 Speaker 2: you need to ask them who is paying them. 869 00:42:45,960 --> 00:42:47,160 Speaker 4: According to Kevin. 870 00:42:46,920 --> 00:42:54,760 Speaker 2: O'Leary, Kevin O'Leary is eating crow after wildly accusing activists 871 00:42:54,760 --> 00:42:58,200 Speaker 2: opposing his hyper scale Utah based data center of being 872 00:42:58,360 --> 00:43:01,440 Speaker 2: backed by the OsO scary Chinese in a number of 873 00:43:01,480 --> 00:43:04,600 Speaker 2: media appearances on Fox News and also with Tucker Carlson, 874 00:43:04,760 --> 00:43:08,000 Speaker 2: Kevin O'Leary claimed his data guys they had dug into 875 00:43:08,000 --> 00:43:10,799 Speaker 2: the social media opposition his big Utah data center, and 876 00:43:10,840 --> 00:43:15,040 Speaker 2: they uncovered smoking gun definitive proof that this opposition was 877 00:43:15,080 --> 00:43:18,720 Speaker 2: being funded by the scary Chinese Communist Party. He could 878 00:43:18,719 --> 00:43:23,880 Speaker 2: not possibly conceive of Americans organically opposing his energy price spiking, 879 00:43:24,000 --> 00:43:28,000 Speaker 2: water sucking, noise polluting data center. Such sentiments must have 880 00:43:28,040 --> 00:43:31,560 Speaker 2: been invented by China or maybe even possibly Cuba, but 881 00:43:31,760 --> 00:43:36,160 Speaker 2: definitely not Republican voters in rural Utah. One of the 882 00:43:36,160 --> 00:43:38,719 Speaker 2: groups that he accused, Elevates Strategies, had a lot of 883 00:43:38,719 --> 00:43:40,759 Speaker 2: fun with these baseless accusations. 884 00:43:41,160 --> 00:43:44,960 Speaker 7: Elevates Strategies. Also a cell operating inside of Utah. Gabby 885 00:43:45,000 --> 00:43:47,960 Speaker 7: Finlish said, Gabby, what are you doing and who's paying you? 886 00:43:48,080 --> 00:43:52,439 Speaker 5: Well? Hi, Hello, it's me Gabby Finlayson. What am I doing? 887 00:43:52,600 --> 00:43:54,919 Speaker 11: Fairly, we've reached the part of the Strata's data center 888 00:43:55,040 --> 00:43:58,200 Speaker 11: journey where Kevin O'Leary goes on National Fox News. Do 889 00:43:58,200 --> 00:44:01,600 Speaker 11: you accuse us of being a sells for the Chinese 890 00:44:01,640 --> 00:44:02,439 Speaker 11: Communist Party? 891 00:44:02,560 --> 00:44:04,600 Speaker 7: Yeah, because at the end of the day, who would 892 00:44:04,600 --> 00:44:08,640 Speaker 7: want us to stop building our electrical grid? Which adversary 893 00:44:08,760 --> 00:44:11,520 Speaker 7: would want that? There's only one, It's China. So what 894 00:44:11,600 --> 00:44:13,480 Speaker 7: I think is happening? And I got my guys to 895 00:44:13,520 --> 00:44:15,759 Speaker 7: go a deep dig into the IP addresses and here's 896 00:44:15,800 --> 00:44:16,399 Speaker 7: what we found out. 897 00:44:16,440 --> 00:44:17,279 Speaker 5: This is fascinating. 898 00:44:17,320 --> 00:44:22,560 Speaker 7: We found two cells inside of Utah Elevates Strategy. Gabby finlissen, Gabby, 899 00:44:22,600 --> 00:44:25,000 Speaker 7: what are you doing and who's paying you? So what 900 00:44:25,040 --> 00:44:27,239 Speaker 7: I'm doing right now is, after getting this data, I'm 901 00:44:27,280 --> 00:44:29,200 Speaker 7: calling out Gabby operating in Utah. 902 00:44:29,360 --> 00:44:32,080 Speaker 4: So Hi, Kevin, we are Elevate Strategies. 903 00:44:32,400 --> 00:44:33,160 Speaker 5: This is Utah. 904 00:44:33,280 --> 00:44:35,600 Speaker 11: You might not know it because you're from Canada, is 905 00:44:35,600 --> 00:44:36,160 Speaker 11: where we live. 906 00:44:36,280 --> 00:44:37,800 Speaker 5: Before we're going to do anything too serious. 907 00:44:37,840 --> 00:44:40,840 Speaker 11: This is the very scary, very intimidating man that is 908 00:44:40,880 --> 00:44:43,680 Speaker 11: threatening us. We are not taking the criticism of anyone 909 00:44:43,800 --> 00:44:47,000 Speaker 11: who is wearing flip flops and a suit on national television. 910 00:44:47,120 --> 00:44:49,160 Speaker 11: You know, it's not every day you get called out 911 00:44:49,200 --> 00:44:51,759 Speaker 11: by first and last name on Fox News by a 912 00:44:51,800 --> 00:44:53,680 Speaker 11: Canadian billionaire trying to ruin my state. 913 00:44:54,160 --> 00:44:56,400 Speaker 2: Well, Kevin has in a sense won the war. It 914 00:44:56,440 --> 00:44:58,719 Speaker 2: looks like his data center is going in after the 915 00:44:59,000 --> 00:45:01,799 Speaker 2: Box Elder County Mission voted three to zero to clear 916 00:45:01,880 --> 00:45:05,319 Speaker 2: the local regulatory hurdles. But at the very least, these 917 00:45:05,400 --> 00:45:07,960 Speaker 2: ladies and the others Kevin baselessly smeared are getting a 918 00:45:07,960 --> 00:45:10,040 Speaker 2: little bit of satisfaction this week, as he had to 919 00:45:10,120 --> 00:45:13,800 Speaker 2: fully retract his claims and Fox News had to issue 920 00:45:13,920 --> 00:45:15,680 Speaker 2: multiple on air corrections. 921 00:45:16,040 --> 00:45:19,520 Speaker 12: Kevin O'Leary appeared as a guest on this program back 922 00:45:19,560 --> 00:45:23,120 Speaker 12: on May eleventh and discussed the ongoing controversy surrounding his 923 00:45:23,280 --> 00:45:26,560 Speaker 12: planned data center project in Utah. Now he made certain 924 00:45:26,600 --> 00:45:30,320 Speaker 12: claims relating to the opponents of that project. Mister O'Leary 925 00:45:30,360 --> 00:45:32,799 Speaker 12: has now corrected the record and explained that he has 926 00:45:32,800 --> 00:45:36,080 Speaker 12: no evidence that the Alliance for a Better Utah elevates 927 00:45:36,080 --> 00:45:41,680 Speaker 12: strategies Josh Kanter, Taylor Kanuth, or Gabrielle Finlayson are funded 928 00:45:41,760 --> 00:45:43,760 Speaker 12: by China or the Chinese Communist Party. 929 00:45:44,000 --> 00:45:45,160 Speaker 5: Fox News Media is. 930 00:45:45,239 --> 00:45:49,279 Speaker 12: Likely likewise aware of no evidence that they are funded by, 931 00:45:49,400 --> 00:45:52,719 Speaker 12: or acting at the direction of, or in coordination with, 932 00:45:52,840 --> 00:45:56,919 Speaker 12: Chinese interests in opposing Kevin O'Leary's projects. Fox News Media 933 00:45:56,920 --> 00:45:59,360 Speaker 12: apologizes for the error We'll be right. 934 00:45:59,239 --> 00:46:02,399 Speaker 2: Back feeling when the lawyer call hits. But this Kevin 935 00:46:02,440 --> 00:46:05,239 Speaker 2: O'Leary back data Center is leaving its mark not only 936 00:46:05,280 --> 00:46:08,759 Speaker 2: on Fox News programming, but on the entire political landscape 937 00:46:08,800 --> 00:46:12,880 Speaker 2: of Utah. In a series of shocking upsets, two of 938 00:46:12,920 --> 00:46:15,840 Speaker 2: those Box Elder County commissioners who okayed the data center 939 00:46:15,880 --> 00:46:19,359 Speaker 2: over the fear subjections of their constituents have now been 940 00:46:19,440 --> 00:46:20,440 Speaker 2: tossed from office. 941 00:46:20,480 --> 00:46:22,040 Speaker 4: The third one was not on the ballot. 942 00:46:22,440 --> 00:46:25,520 Speaker 2: One of those losing incumbents told the Salt Lake Tribune quote, 943 00:46:25,680 --> 00:46:27,600 Speaker 2: do I think that the data center vote cost me 944 00:46:27,600 --> 00:46:28,040 Speaker 2: the election? 945 00:46:28,360 --> 00:46:28,600 Speaker 4: Yes? 946 00:46:28,640 --> 00:46:28,960 Speaker 8: I do. 947 00:46:29,400 --> 00:46:31,680 Speaker 4: Would I do anything different? I would not. 948 00:46:31,640 --> 00:46:34,520 Speaker 2: Vote differently, but I would push back against the state 949 00:46:34,760 --> 00:46:37,399 Speaker 2: and make them come out publicly and tell everybody why 950 00:46:37,400 --> 00:46:38,439 Speaker 2: they're forcing it down. 951 00:46:38,520 --> 00:46:39,080 Speaker 4: Our throat. 952 00:46:39,640 --> 00:46:42,160 Speaker 2: So apparently even the guys who voted this thing through 953 00:46:42,200 --> 00:46:44,839 Speaker 2: in the County Commission are not feeling too enthusiastic about 954 00:46:44,840 --> 00:46:48,080 Speaker 2: the project at this point. The real political earthquake for 955 00:46:48,120 --> 00:46:50,840 Speaker 2: the state, though, came from the loss of the president 956 00:46:50,960 --> 00:46:53,759 Speaker 2: of the state Senate, guy by the name of j. 957 00:46:53,920 --> 00:46:57,320 Speaker 2: Stewart Adams, was tossed down in favor of a political newcomer, 958 00:46:57,600 --> 00:47:00,839 Speaker 2: largely over his role in pushing the so called Stratos 959 00:47:00,960 --> 00:47:04,080 Speaker 2: data center. Adams was one of the longest serving politicians 960 00:47:04,080 --> 00:47:06,040 Speaker 2: in the state and also one of the most powerful. 961 00:47:06,280 --> 00:47:08,960 Speaker 2: He became the focus of anti data center iire for 962 00:47:09,000 --> 00:47:11,960 Speaker 2: his role as chairman of a state commission that greenlit 963 00:47:12,080 --> 00:47:15,279 Speaker 2: that installation to begin with. Now, sometimes it's easy to 964 00:47:15,320 --> 00:47:18,080 Speaker 2: project your national political lens onto a local race and 965 00:47:18,080 --> 00:47:20,600 Speaker 2: miss some other local dynamics that played a larger role, 966 00:47:20,719 --> 00:47:22,439 Speaker 2: but that does not appear to be the case here. 967 00:47:22,680 --> 00:47:25,880 Speaker 2: Adam's chief opponent a former university lawyer that they attempted 968 00:47:25,920 --> 00:47:27,520 Speaker 2: to brand as a DEI lawyer. 969 00:47:27,880 --> 00:47:29,399 Speaker 4: She was very explicit that. 970 00:47:29,400 --> 00:47:31,799 Speaker 2: Much of the energy for her campaign came from this 971 00:47:31,960 --> 00:47:35,640 Speaker 2: data center opposition. Here's how Local News framed the focus 972 00:47:35,719 --> 00:47:37,520 Speaker 2: of one of their candidate forums. 973 00:47:37,520 --> 00:47:40,920 Speaker 13: The Stratos Data Center in Boxelder County is dominating a 974 00:47:41,000 --> 00:47:45,080 Speaker 13: high profile Utah Senate race. Senate President Stuart Adams is 975 00:47:45,120 --> 00:47:49,440 Speaker 13: being challenged by fellow Republicans Braden hess and Stephanie Hollist, 976 00:47:49,840 --> 00:47:52,720 Speaker 13: all of them asked at this forum about their vision 977 00:47:52,800 --> 00:47:54,360 Speaker 13: for the state's water policy. 978 00:47:54,920 --> 00:47:58,680 Speaker 11: I might start by not proposing the largest data center 979 00:47:58,840 --> 00:47:59,560 Speaker 11: in the country. 980 00:48:00,960 --> 00:48:05,160 Speaker 13: Hollis repeatedly criticized the data center being pushed by celebrity 981 00:48:05,200 --> 00:48:09,240 Speaker 13: businessman Kevin O'Leary. Adams has helped move the project along 982 00:48:09,560 --> 00:48:14,280 Speaker 13: as chair of the state's Military Installation Development Authority or MIDA. 983 00:48:15,000 --> 00:48:19,399 Speaker 5: The people spoke, we need clarity, so I pushed back. 984 00:48:19,640 --> 00:48:23,360 Speaker 13: Adams recently softened his public support of the project, pushing 985 00:48:23,400 --> 00:48:26,920 Speaker 13: the developer to reduce its size and make other changes. 986 00:48:27,239 --> 00:48:27,880 Speaker 5: Try to listen. 987 00:48:27,960 --> 00:48:30,880 Speaker 7: I think we're getting We're actually got a better project. 988 00:48:30,880 --> 00:48:31,400 Speaker 5: Because of that. 989 00:48:31,719 --> 00:48:34,880 Speaker 13: Has told this crowd the government shouldn't treat data centers 990 00:48:34,920 --> 00:48:36,920 Speaker 13: differently than any other business. 991 00:48:37,400 --> 00:48:41,920 Speaker 14: As long as they are not harming society and our 992 00:48:41,960 --> 00:48:44,440 Speaker 14: resources in measurable ways, I think we should allow them 993 00:48:44,480 --> 00:48:45,120 Speaker 14: to go forward. 994 00:48:45,239 --> 00:48:48,600 Speaker 13: But to this audience, the data center is a non starter. 995 00:48:48,840 --> 00:48:50,879 Speaker 15: When you talk about a project that's going to sap 996 00:48:50,960 --> 00:48:53,879 Speaker 15: tax dollars away from residents, that's going to take water, 997 00:48:54,040 --> 00:48:57,120 Speaker 15: that's going to endanger our way of life, or because 998 00:48:57,120 --> 00:49:00,640 Speaker 15: it's all a red flag, it's all feels pretty dangerous 999 00:49:00,680 --> 00:49:01,800 Speaker 15: for where we're at right now. 1000 00:49:02,320 --> 00:49:04,759 Speaker 2: It is no accident that the one candidate you heard 1001 00:49:04,800 --> 00:49:07,720 Speaker 2: there who was clear in her opposition to this data center, 1002 00:49:07,920 --> 00:49:10,080 Speaker 2: won the day. And I will remind you this is 1003 00:49:10,120 --> 00:49:13,239 Speaker 2: a Republican primary in Utah. This is also not an 1004 00:49:13,239 --> 00:49:16,719 Speaker 2: isolated political phenomenon either. NBC News recently spoke with a 1005 00:49:16,840 --> 00:49:20,880 Speaker 2: diehard Trump voter set to vote tallar Ico over Paxton 1006 00:49:20,960 --> 00:49:25,000 Speaker 2: in Texas solely over her disgust at data centers. 1007 00:49:25,000 --> 00:49:28,239 Speaker 16: Quarter mile away. Cheryl Shadden says, the noise is as 1008 00:49:28,280 --> 00:49:28,919 Speaker 16: loud as ever. 1009 00:49:29,280 --> 00:49:32,240 Speaker 14: It's like living on the edge of Niagara Falls, or 1010 00:49:32,280 --> 00:49:35,279 Speaker 14: you're on a runway next to a jet that's taking off, 1011 00:49:35,280 --> 00:49:36,520 Speaker 14: but this jet doesn't take. 1012 00:49:36,320 --> 00:49:39,600 Speaker 16: Off morning, noon, night. She even hears it in her bedroom, 1013 00:49:39,680 --> 00:49:42,360 Speaker 16: and it's changed everything about how she lives her life, 1014 00:49:42,480 --> 00:49:44,839 Speaker 16: including her politics. 1015 00:49:44,560 --> 00:49:45,320 Speaker 5: Red or blue. 1016 00:49:45,400 --> 00:49:47,799 Speaker 14: If you vote against data centers, we vote for you. 1017 00:49:48,000 --> 00:49:51,760 Speaker 16: A lifelong conservative, she's so angry she refuses to vote 1018 00:49:51,760 --> 00:49:55,000 Speaker 16: for Trump backed Attorney General Ken Paxton, who clinched the 1019 00:49:55,040 --> 00:49:58,920 Speaker 16: GOP nomination for Senate Tuesday. Instead, she's all in for 1020 00:49:59,040 --> 00:50:02,680 Speaker 16: James Tallerika, a Democrat seeking to flip a seat controlled 1021 00:50:02,680 --> 00:50:06,840 Speaker 16: by Republicans since nineteen ninety three. You're willing at this 1022 00:50:07,000 --> 00:50:14,400 Speaker 16: point to forego basically every conservative issue and let the 1023 00:50:14,440 --> 00:50:16,560 Speaker 16: Senate fall into the hands of Democrats if that's what 1024 00:50:16,600 --> 00:50:17,960 Speaker 16: it takes to kill data centers. 1025 00:50:18,040 --> 00:50:18,279 Speaker 11: Yep. 1026 00:50:18,880 --> 00:50:22,359 Speaker 14: My entire community is going to break rank. Everybody, all 1027 00:50:22,360 --> 00:50:22,680 Speaker 14: of us. 1028 00:50:22,840 --> 00:50:25,800 Speaker 16: We've had enough breaking rank and coming together. 1029 00:50:26,200 --> 00:50:28,720 Speaker 4: My entire community is going to break rank. 1030 00:50:29,400 --> 00:50:33,000 Speaker 2: Not just there in Tennessee HBCU Fisk University, they are 1031 00:50:33,040 --> 00:50:36,120 Speaker 2: facing massive backlash over a plan they announced to build 1032 00:50:36,120 --> 00:50:39,360 Speaker 2: a data center right on the campus. Also in Tennessee, 1033 00:50:39,400 --> 00:50:42,480 Speaker 2: another planned data center near the Nashville Zoo has led 1034 00:50:42,480 --> 00:50:45,680 Speaker 2: to a political freak ount among elected Democrats and Republicans, 1035 00:50:45,760 --> 00:50:48,000 Speaker 2: some of who have come together to oppose that build 1036 00:50:48,200 --> 00:50:50,399 Speaker 2: in hopes of avoiding the political fate of. 1037 00:50:50,320 --> 00:50:51,960 Speaker 4: Their Utah compatriots here. 1038 00:50:52,040 --> 00:50:55,680 Speaker 2: Presumably, the more Americans learn about the immediate impact of 1039 00:50:55,760 --> 00:50:58,040 Speaker 2: data centers on communities, and the more they learn about 1040 00:50:58,080 --> 00:51:01,440 Speaker 2: the ai that the data centers are to support, the 1041 00:51:01,480 --> 00:51:05,240 Speaker 2: more adamant they grow in their opposition. Bipartisan data center 1042 00:51:05,320 --> 00:51:08,839 Speaker 2: Nindiism apparently is our first line of defense against the 1043 00:51:08,880 --> 00:51:13,200 Speaker 2: AI fueled tech oligarch global takeover. And that really is 1044 00:51:13,400 --> 00:51:16,040 Speaker 2: how I see it, Emily, that like, the data centers 1045 00:51:16,080 --> 00:51:21,520 Speaker 2: are this very important locus of organizing around opposition to 1046 00:51:21,640 --> 00:51:26,440 Speaker 2: the broader AI project, and it truly is bipartisan. You know, 1047 00:51:26,480 --> 00:51:28,400 Speaker 2: I think polls show Democrats are a little bit more 1048 00:51:28,440 --> 00:51:32,120 Speaker 2: imposed and independence than Republicans, but you can see clearly 1049 00:51:32,320 --> 00:51:34,319 Speaker 2: by what's going on in Utah there's a lot of 1050 00:51:34,360 --> 00:51:37,200 Speaker 2: Republican opposition as well, and this really is a coalition 1051 00:51:37,239 --> 00:51:38,560 Speaker 2: that is up for grabs at this point. 1052 00:51:38,680 --> 00:51:40,759 Speaker 5: It's fascinating because in the last couple of weeks I've 1053 00:51:40,760 --> 00:51:43,400 Speaker 5: started to see people THEO von Klipp, for example, went 1054 00:51:43,480 --> 00:51:45,319 Speaker 5: viral of him being like you probably saw them, yes, 1055 00:51:45,440 --> 00:51:46,879 Speaker 5: Like wow, what do we you know all these data 1056 00:51:46,880 --> 00:51:50,600 Speaker 5: centers for and some of the accelerations. People were quote 1057 00:51:50,600 --> 00:51:52,440 Speaker 5: tweeting him and being like, bro, how do you think 1058 00:51:52,440 --> 00:51:55,720 Speaker 5: people are watching your podcast? Like we use so much 1059 00:51:55,920 --> 00:51:58,000 Speaker 5: data because of I mean, first of all, a lot 1060 00:51:58,040 --> 00:52:01,360 Speaker 5: of that is coming from just AI, not just podcasting 1061 00:52:01,440 --> 00:52:03,640 Speaker 5: for the record, but secondly. 1062 00:52:03,480 --> 00:52:06,120 Speaker 4: Very convenient thing for us to say it is also true. 1063 00:52:06,000 --> 00:52:08,319 Speaker 5: It's true. But I think this is this tension is 1064 00:52:08,360 --> 00:52:11,640 Speaker 5: actually really important because they think this is a dunk 1065 00:52:11,719 --> 00:52:14,319 Speaker 5: and an own on the average person, who is like, well, 1066 00:52:14,320 --> 00:52:16,440 Speaker 5: what do we need all of this for? But the 1067 00:52:16,520 --> 00:52:19,279 Speaker 5: tension is real because a lot of people are now 1068 00:52:19,280 --> 00:52:21,920 Speaker 5: looking around and being like, well, we have adopted to 1069 00:52:21,960 --> 00:52:25,279 Speaker 5: these lifestyles that maybe we actually don't even agree with, right, 1070 00:52:25,280 --> 00:52:29,000 Speaker 5: Like there's a fundamental question happening, which is why do 1071 00:52:29,040 --> 00:52:32,960 Speaker 5: we need to use this much internet? Like do we? 1072 00:52:33,560 --> 00:52:35,680 Speaker 5: And now you're going to like, if it's a choice 1073 00:52:35,680 --> 00:52:38,279 Speaker 5: between if you're gonna put this choice between me living 1074 00:52:38,320 --> 00:52:40,600 Speaker 5: next to this plane that never takes off, which was 1075 00:52:40,640 --> 00:52:44,600 Speaker 5: a beautiful quote from that woman, Yeah, and cutting back 1076 00:52:44,640 --> 00:52:48,600 Speaker 5: on large language model usage, which if you're like sixty 1077 00:52:48,600 --> 00:52:50,880 Speaker 5: five years old, maybe you're not even using to begin with. 1078 00:52:52,040 --> 00:52:54,799 Speaker 5: That choice is going to be actually increasingly clear. And 1079 00:52:54,840 --> 00:52:57,759 Speaker 5: the people like Kevin O'Leary, who I think, I mean, 1080 00:52:58,160 --> 00:53:00,000 Speaker 5: if it's cynical, it was really pathetic. But I think 1081 00:53:00,000 --> 00:53:02,960 Speaker 5: think he sincerely had people telling him this is all China. 1082 00:53:03,160 --> 00:53:05,960 Speaker 5: It's all China, and he really thought it because shows 1083 00:53:06,000 --> 00:53:08,440 Speaker 5: you how ountouched exactly because if you go back to 1084 00:53:08,440 --> 00:53:11,799 Speaker 5: his Tucker Carlson interview, he was hyping up these like 1085 00:53:11,840 --> 00:53:14,719 Speaker 5: this research that he found about all of it coming 1086 00:53:14,760 --> 00:53:17,600 Speaker 5: from China, and we covered here. Open Ai put out 1087 00:53:17,600 --> 00:53:20,360 Speaker 5: a survey that was covered by the industry as like 1088 00:53:20,440 --> 00:53:24,920 Speaker 5: damning proof of Chinese ops whipping up opposition to data centers, 1089 00:53:25,160 --> 00:53:27,440 Speaker 5: and it actually came out and said in the like 1090 00:53:27,520 --> 00:53:31,560 Speaker 5: fine print towards the second page or something of the study, well, 1091 00:53:31,719 --> 00:53:33,839 Speaker 5: you know, this didn't have much effect. The posts that 1092 00:53:33,880 --> 00:53:36,920 Speaker 5: the Chinese operatives were planting on social media were barely 1093 00:53:36,960 --> 00:53:39,719 Speaker 5: seen by many people, and it's like they pulled the 1094 00:53:39,719 --> 00:53:41,600 Speaker 5: headline from that and they were like, China is whipping 1095 00:53:41,640 --> 00:53:44,520 Speaker 5: up opposition. It's like, no, No, China might be trying 1096 00:53:44,880 --> 00:53:48,240 Speaker 5: to whip up opposition, but it's not really being effective. 1097 00:53:48,280 --> 00:53:50,360 Speaker 5: It reminds me so much of twenty seventeen. 1098 00:53:49,960 --> 00:53:51,680 Speaker 4: Like Russia Corrush, like the memes. 1099 00:53:51,760 --> 00:53:55,279 Speaker 5: Yeah, it's so similar to that, and it's just a 1100 00:53:55,280 --> 00:53:58,680 Speaker 5: way to kind of either stigmatize people into suppressing their 1101 00:53:58,760 --> 00:54:02,600 Speaker 5: voices or the argument completely because you're just making them 1102 00:54:02,640 --> 00:54:05,759 Speaker 5: so much angrier about what you're doing to their communities 1103 00:54:05,760 --> 00:54:08,080 Speaker 5: by not meeting them where they are and hearing their 1104 00:54:08,080 --> 00:54:10,680 Speaker 5: honest and sincere concerns with this, some of which aren't 1105 00:54:10,680 --> 00:54:12,680 Speaker 5: even what you think. You think, Oh, this is a 1106 00:54:12,680 --> 00:54:15,759 Speaker 5: big slam dunk own. You don't want your data, you 1107 00:54:15,760 --> 00:54:17,719 Speaker 5: don't want more data. What are you talking about. We 1108 00:54:17,760 --> 00:54:19,920 Speaker 5: need all this data for these lms. And that's what 1109 00:54:19,960 --> 00:54:22,600 Speaker 5: they're not getting, is that people are actually now reckoning 1110 00:54:22,840 --> 00:54:24,400 Speaker 5: with whether we do need to live like that. 1111 00:54:24,719 --> 00:54:28,680 Speaker 2: Well, the Utah State Senate president losing in this fashion 1112 00:54:29,600 --> 00:54:32,680 Speaker 2: should be I hope politicians are paying attention because it 1113 00:54:32,840 --> 00:54:35,319 Speaker 2: should be a wake up call. One of the most 1114 00:54:35,360 --> 00:54:36,919 Speaker 2: powerful politicians in the state. 1115 00:54:37,440 --> 00:54:38,080 Speaker 4: He faced. 1116 00:54:38,160 --> 00:54:40,040 Speaker 2: You know, they thought he would pull through because he 1117 00:54:40,080 --> 00:54:43,319 Speaker 2: had a divided opposition. You had two candidates opposing him, right, 1118 00:54:43,719 --> 00:54:46,160 Speaker 2: but because the oppositions data. 1119 00:54:45,960 --> 00:54:46,880 Speaker 4: Center was so strong. 1120 00:54:47,080 --> 00:54:48,880 Speaker 2: There were some other issues with him as well, but 1121 00:54:48,960 --> 00:54:52,520 Speaker 2: this genuinely was the primary complaint about him at this point. 1122 00:54:52,760 --> 00:54:53,960 Speaker 4: It was enough to topple him. 1123 00:54:54,120 --> 00:54:57,360 Speaker 2: Long time incumbent political newcomer comes in just kind of 1124 00:54:57,719 --> 00:54:59,800 Speaker 2: lib coded in a very conservative state. 1125 00:55:00,120 --> 00:55:01,359 Speaker 4: They tried to tie her. 1126 00:55:02,080 --> 00:55:04,880 Speaker 2: I read her website and she had this FAQ that 1127 00:55:05,040 --> 00:55:08,400 Speaker 2: was like people say that you're backed by George Soros, 1128 00:55:08,880 --> 00:55:11,399 Speaker 2: and like I'm not backed by George. People say you're 1129 00:55:11,440 --> 00:55:13,759 Speaker 2: a Dei lawyer. I'm not a Dei lawyer. But in 1130 00:55:13,800 --> 00:55:15,600 Speaker 2: any case, that was trying how they were trying to 1131 00:55:15,600 --> 00:55:17,640 Speaker 2: defeat ers, like look at this Dei lib. 1132 00:55:17,680 --> 00:55:18,759 Speaker 4: You don't want this for you jab. 1133 00:55:19,040 --> 00:55:21,560 Speaker 2: Obviously it failed because the concern over the data center 1134 00:55:21,600 --> 00:55:24,440 Speaker 2: was greater than the fear mongering about whatever her ideological 1135 00:55:24,520 --> 00:55:24,960 Speaker 2: leanings are. 1136 00:55:25,040 --> 00:55:26,600 Speaker 5: They tried to do the many people are saying. 1137 00:55:26,840 --> 00:55:31,120 Speaker 4: Many people are saying, yes, Trump move, Many people are saying. 1138 00:55:31,360 --> 00:55:33,759 Speaker 2: All right, guys, that does it for us today Again, 1139 00:55:33,880 --> 00:55:35,799 Speaker 2: go sign up for the newsletter, the free version, the 1140 00:55:35,800 --> 00:55:37,279 Speaker 2: premium version, whatever you want to do. 1141 00:55:37,360 --> 00:55:38,399 Speaker 4: Breakingpoints dot com. 1142 00:55:38,400 --> 00:55:40,520 Speaker 2: Thank you for the feedback, Thank you as always for 1143 00:55:40,560 --> 00:55:43,680 Speaker 2: your support. And there will be big election results to 1144 00:55:43,760 --> 00:55:46,120 Speaker 2: cover tomorrow, and of course we have Emily will do 1145 00:55:46,160 --> 00:55:46,640 Speaker 2: a great job. 1146 00:55:46,680 --> 00:55:48,960 Speaker 4: But we're very lucky to have Rye Rent pays very 1147 00:55:48,960 --> 00:55:50,400 Speaker 4: close attention the squad. 1148 00:55:50,440 --> 00:55:52,960 Speaker 2: It's like the guy, yeah for these sorts of things. 1149 00:55:53,000 --> 00:55:54,960 Speaker 2: So I will be watching with great interest personally. 1150 00:55:55,040 --> 00:55:56,640 Speaker 5: Yeah, it's gonna be fun. So we'll be here. We'll 1151 00:55:56,640 --> 00:55:57,880 Speaker 5: see you. Then, make sure you sign up for the 1152 00:55:57,880 --> 00:56:16,880 Speaker 5: newsletter breakingpoints dot com. Have a great day, everyone, HM,