1 00:00:01,080 --> 00:00:04,640 Speaker 1: So the super Bowl happened, and we decided we were 2 00:00:04,640 --> 00:00:06,760 Speaker 1: going to talk about it center. Cruz and I were 3 00:00:06,800 --> 00:00:09,479 Speaker 1: deciding how we were going to talk about bad Bunny. 4 00:00:09,480 --> 00:00:11,520 Speaker 1: And if you didn't understand a word he was saying 5 00:00:11,520 --> 00:00:14,040 Speaker 1: in the Super Bowl, yeah, that was on purpose. It's 6 00:00:14,120 --> 00:00:18,919 Speaker 1: part of his anti America, hate us all agenda. Now 7 00:00:19,000 --> 00:00:21,920 Speaker 1: that's just the reality of the situation. But how angry 8 00:00:22,000 --> 00:00:24,919 Speaker 1: should you be? And also there was an alternative, which 9 00:00:24,960 --> 00:00:27,200 Speaker 1: is what I love about this country. We're going to 10 00:00:27,240 --> 00:00:29,680 Speaker 1: break all that down for you as Senator Cruse and 11 00:00:29,760 --> 00:00:32,839 Speaker 1: I talk about that and much more. Welcome in his 12 00:00:32,960 --> 00:00:36,400 Speaker 1: verdical center. Ted Cruz post Super Bowl show. Nice to 13 00:00:36,440 --> 00:00:38,559 Speaker 1: have you with us, Senator, I know you were just 14 00:00:38,960 --> 00:00:41,479 Speaker 1: really excited about the halftime show. You didn't watch any 15 00:00:41,479 --> 00:00:43,320 Speaker 1: of the game, though, but that part you were there 16 00:00:43,320 --> 00:00:44,479 Speaker 1: for I'm assuming right. 17 00:00:45,240 --> 00:00:47,760 Speaker 2: Well, it's Super Bowl Sunday. I will say I had 18 00:00:47,800 --> 00:00:49,920 Speaker 2: the great joy of being on an airplane during a 19 00:00:49,920 --> 00:00:52,559 Speaker 2: lot of the Super Bowl, so I caught part of 20 00:00:52,600 --> 00:00:57,440 Speaker 2: the third quarter and that was about it. So I 21 00:00:58,000 --> 00:01:00,840 Speaker 2: didn't watch much. I got a field goal that that 22 00:01:00,520 --> 00:01:03,160 Speaker 2: that that was about it. I did not see the 23 00:01:03,200 --> 00:01:05,120 Speaker 2: halftime show, although I did pull it up on my 24 00:01:05,160 --> 00:01:09,160 Speaker 2: phone and watch part of it afterwards. But you know, look, 25 00:01:09,200 --> 00:01:10,720 Speaker 2: I mean the super Bowl. A lot of times I'll 26 00:01:10,760 --> 00:01:12,520 Speaker 2: go to a super Bowl party. In this case, I 27 00:01:12,600 --> 00:01:15,000 Speaker 2: was just just flying and landing in Texas. So got 28 00:01:15,160 --> 00:01:21,039 Speaker 2: got on the got home midway through. You know, I 29 00:01:21,080 --> 00:01:23,840 Speaker 2: got to say a lot of the noise about the 30 00:01:24,200 --> 00:01:29,039 Speaker 2: halftime show. Yeah, I just found myself not caring. But like, 31 00:01:29,360 --> 00:01:32,160 Speaker 2: I know that there were a lot of folks all 32 00:01:32,200 --> 00:01:34,360 Speaker 2: worked up and on the right. You're supposed to be 33 00:01:34,400 --> 00:01:38,040 Speaker 2: outraged that that that, you know, bad bunny. 34 00:01:38,120 --> 00:01:40,760 Speaker 1: There was like no English spoken I could have The 35 00:01:40,760 --> 00:01:44,240 Speaker 1: guy was saying yeah, yeah, yeah, So I'm not bilingual 36 00:01:44,360 --> 00:01:46,760 Speaker 1: like you are those center There's a difference. See you 37 00:01:46,840 --> 00:01:47,520 Speaker 1: understood it. 38 00:01:47,560 --> 00:01:50,720 Speaker 2: I didn't know, Benjamin. Look we Latino was a little 39 00:01:50,720 --> 00:01:52,840 Speaker 2: bit of Spanish. Won't kill you like I I just 40 00:01:53,560 --> 00:01:56,520 Speaker 2: you know, I don't like the guy's politics. At the Grammys. 41 00:01:56,520 --> 00:01:58,880 Speaker 2: I thought he made an ass of himself, like like 42 00:01:58,960 --> 00:02:02,320 Speaker 2: saying ice out that I don't like. And I don't 43 00:02:02,360 --> 00:02:07,279 Speaker 2: really like when celebrities try to force their politics on us. 44 00:02:07,800 --> 00:02:10,880 Speaker 2: But you know, I watched him like in the sugarcane 45 00:02:10,919 --> 00:02:13,200 Speaker 2: and dancing around. I think most of the halftime shows 46 00:02:13,200 --> 00:02:16,600 Speaker 2: are kind of silly, so like this didn't strike me 47 00:02:16,639 --> 00:02:21,640 Speaker 2: as particularly unusual in more or less silliness. You know, 48 00:02:21,720 --> 00:02:24,440 Speaker 2: the guy falls through a roof and lands in someone's apartment. 49 00:02:24,480 --> 00:02:26,440 Speaker 2: That was just kind of strange, But I you know, 50 00:02:26,600 --> 00:02:30,200 Speaker 2: it was I found myself. 51 00:02:30,200 --> 00:02:32,079 Speaker 1: We at the level now where you just say, if 52 00:02:32,080 --> 00:02:33,840 Speaker 1: you're like a singer and this is what you do, 53 00:02:33,919 --> 00:02:35,120 Speaker 1: you just don't give a crap. 54 00:02:36,200 --> 00:02:36,399 Speaker 3: Oh. 55 00:02:36,600 --> 00:02:41,079 Speaker 2: Look, I mean I my girls like that bunny. I 56 00:02:42,320 --> 00:02:46,680 Speaker 2: have to admit he's not on my playlist, but I 57 00:02:46,680 --> 00:02:49,560 Speaker 2: have a fifteen and seventeen year old daughter and they 58 00:02:49,639 --> 00:02:54,440 Speaker 2: listen to him some. Most of the halftime shows don't 59 00:02:54,440 --> 00:02:57,079 Speaker 2: do much for me. I mean it's they're fine. They're 60 00:02:57,120 --> 00:03:01,120 Speaker 2: typically typically like pop singer and and you know I 61 00:03:01,200 --> 00:03:03,480 Speaker 2: tend to listen to more more country music or or 62 00:03:03,639 --> 00:03:09,760 Speaker 2: or classic rock anyway. So look, it was We've already 63 00:03:09,840 --> 00:03:12,639 Speaker 2: talked more in the last four minutes about it that 64 00:03:12,760 --> 00:03:14,639 Speaker 2: I've probably thought about the whole thing. 65 00:03:15,080 --> 00:03:17,200 Speaker 1: I love and I love it. By the way, did 66 00:03:17,200 --> 00:03:19,160 Speaker 1: you see that turning point? I had like five million 67 00:03:19,240 --> 00:03:21,399 Speaker 1: live watching on YouTube. 68 00:03:21,560 --> 00:03:23,519 Speaker 2: Yeah, and look, kid Rock, you know, I mean Kid 69 00:03:23,600 --> 00:03:27,880 Speaker 2: Rock testified before the Senate uh, this past week, and 70 00:03:27,919 --> 00:03:29,560 Speaker 2: so I got to visit with Kid Rock. He came 71 00:03:29,600 --> 00:03:33,200 Speaker 2: by my office. He was a nice guy. I liked him. 72 00:03:33,600 --> 00:03:35,680 Speaker 2: But to be honest, I I was on a plane 73 00:03:35,720 --> 00:03:38,080 Speaker 2: for Kid Rocks too, so I didn't didn't watch either 74 00:03:38,160 --> 00:03:43,440 Speaker 2: one of them. You know it it uh, I will say. Also, 75 00:03:44,200 --> 00:03:46,360 Speaker 2: you know, as we talked about last week on the pod, 76 00:03:49,680 --> 00:03:52,880 Speaker 2: on Friday, I was at at my tia Sonya's funeral 77 00:03:53,400 --> 00:03:56,440 Speaker 2: and and that was up in Dallas, and uh, and 78 00:03:56,720 --> 00:04:00,520 Speaker 2: I spoke at the funeral, and my sister spoke at 79 00:04:00,560 --> 00:04:03,320 Speaker 2: the funeral, and and my cousin Biebie spoke, and my 80 00:04:03,400 --> 00:04:08,720 Speaker 2: dad spoke, and both my niece and nephew spoke, and 81 00:04:10,280 --> 00:04:12,200 Speaker 2: you know, it was it was a real celebration of 82 00:04:12,280 --> 00:04:16,040 Speaker 2: my theos Oni. And actually played a lot of Spanish 83 00:04:16,120 --> 00:04:19,159 Speaker 2: music because my theoso Onia liked it. So you know, 84 00:04:19,200 --> 00:04:21,520 Speaker 2: I enjoyed that more than the snippet I saw a 85 00:04:21,520 --> 00:04:26,240 Speaker 2: bad bunny, but you know that's fine. But I have 86 00:04:26,320 --> 00:04:28,640 Speaker 2: to say I was just kind of like it was 87 00:04:28,680 --> 00:04:30,080 Speaker 2: a beautiful funeral. 88 00:04:30,880 --> 00:04:31,320 Speaker 3: Uh. 89 00:04:31,800 --> 00:04:35,640 Speaker 2: She was eighty two. As as folks know, we put 90 00:04:35,680 --> 00:04:39,080 Speaker 2: out last week the interview that we did, we had 91 00:04:39,160 --> 00:04:43,640 Speaker 2: my theos on Onya on on Verdict and interviewed her 92 00:04:44,160 --> 00:04:46,640 Speaker 2: and and she was she was a freedom fighter, you know, 93 00:04:46,760 --> 00:04:51,520 Speaker 2: she fought against Fidel Castro in Cuba. She was thrown 94 00:04:51,560 --> 00:04:54,760 Speaker 2: in prison, she was tortured. Actually she was along with 95 00:04:54,800 --> 00:04:58,200 Speaker 2: my Ta Medium and my Tamela. Neither of them were 96 00:04:58,240 --> 00:05:01,080 Speaker 2: my aunts, but just in and culture, you just kind 97 00:05:01,080 --> 00:05:05,360 Speaker 2: of collect ds. By the way, if your kid, every 98 00:05:05,360 --> 00:05:07,520 Speaker 2: one of the theas will spank every one of the kids. 99 00:05:07,560 --> 00:05:09,960 Speaker 2: I mean, it's literally just kind of like if. 100 00:05:09,800 --> 00:05:12,240 Speaker 1: That's that's law in order, that's just how it yes 101 00:05:12,320 --> 00:05:12,520 Speaker 1: to go. 102 00:05:13,120 --> 00:05:16,640 Speaker 2: If there's a kid misbehaving, anyone will do that. You 103 00:05:16,680 --> 00:05:19,839 Speaker 2: whack the kid. And I got a lot of uh. 104 00:05:19,800 --> 00:05:21,560 Speaker 1: That you had a lot of those that in your 105 00:05:21,600 --> 00:05:22,760 Speaker 1: life when you were a kid. 106 00:05:22,880 --> 00:05:26,480 Speaker 2: Well, and their weapon of choice was the chankleta, which 107 00:05:26,560 --> 00:05:27,719 Speaker 2: which is this. 108 00:05:27,400 --> 00:05:30,640 Speaker 1: This translate this for me? What does that mean? 109 00:05:30,839 --> 00:05:31,080 Speaker 3: Okay? 110 00:05:31,279 --> 00:05:36,159 Speaker 2: Schank Cletta was this like heavy rubber sandal she would wear. Yeah, 111 00:05:36,200 --> 00:05:38,679 Speaker 2: and my thea Sonya could pull off the shank gletta 112 00:05:38,720 --> 00:05:41,320 Speaker 2: off of her foot, throw it and she'd whack you 113 00:05:41,360 --> 00:05:43,440 Speaker 2: in the back of the head and it was like 114 00:05:43,520 --> 00:05:46,760 Speaker 2: this you know, boomerang wow, and you'd be like wow, 115 00:05:46,800 --> 00:05:51,240 Speaker 2: that hurt. She was very good at it. But you know, 116 00:05:51,320 --> 00:05:54,760 Speaker 2: look what I liked about the funeral is it captured 117 00:05:54,760 --> 00:05:59,599 Speaker 2: her spirit. She was loud, she was fiery. She was 118 00:05:59,680 --> 00:06:04,400 Speaker 2: a warrior for liberty. Listen, when you've been thrown in, prisoned, 119 00:06:04,440 --> 00:06:10,839 Speaker 2: and tortured by communists, freedom matters a lot to you. 120 00:06:10,839 --> 00:06:15,080 Speaker 2: You know. I told the story at the funeral about 121 00:06:15,120 --> 00:06:19,360 Speaker 2: how when when I first ran for Senate back in 122 00:06:19,640 --> 00:06:22,320 Speaker 2: twenty twelve, and I was running against the lieutenant governor 123 00:06:22,360 --> 00:06:25,320 Speaker 2: who was worth a couple of hundred million dollars and 124 00:06:25,360 --> 00:06:27,520 Speaker 2: so put thirty five million dollars of his own money 125 00:06:27,560 --> 00:06:31,640 Speaker 2: in the race and just ran saturation attack ads and 126 00:06:31,720 --> 00:06:35,479 Speaker 2: my Theos Sonya. She was furious, and she she she 127 00:06:35,800 --> 00:06:39,680 Speaker 2: cursed like a sailor, and she was like, I will 128 00:06:39,720 --> 00:06:44,200 Speaker 2: black any black geek his ass. And I'm like it, yeah, Golement, 129 00:06:44,240 --> 00:06:49,360 Speaker 2: that just means calm down. It's not good politically for 130 00:06:49,440 --> 00:06:51,560 Speaker 2: you to be beating up my opponents. And by the way, 131 00:06:52,240 --> 00:06:54,080 Speaker 2: you would not want to fight my Theos Sonya. She 132 00:06:54,279 --> 00:07:00,280 Speaker 2: she would, I believe you, she was ferocious. I did 133 00:07:00,360 --> 00:07:03,280 Speaker 2: have a rule. My Theo Sonya was not allowed uh 134 00:07:03,440 --> 00:07:05,640 Speaker 2: to talk to reporters, and she'd be like, why not, 135 00:07:06,520 --> 00:07:09,840 Speaker 2: And I'm I'm like THEA because I know you, and 136 00:07:09,920 --> 00:07:12,520 Speaker 2: you're liable to say anything, You're liable to curse them out. 137 00:07:13,400 --> 00:07:16,800 Speaker 2: It's from the heart. But but let's just let's just 138 00:07:17,040 --> 00:07:20,200 Speaker 2: figure those sentiments within the family. There's a reason I 139 00:07:20,320 --> 00:07:24,640 Speaker 2: called her my Ta looka my crazy aunt. But I 140 00:07:24,760 --> 00:07:30,520 Speaker 2: got to tell the story about just a couple of 141 00:07:30,600 --> 00:07:32,800 Speaker 2: weeks ago when I went up to see her and 142 00:07:32,880 --> 00:07:35,200 Speaker 2: she was she was in the hospital, and she was 143 00:07:35,280 --> 00:07:37,520 Speaker 2: fading fast. She was kind of coming in and out 144 00:07:37,560 --> 00:07:41,880 Speaker 2: of consciousness, and she wasn't entirely able to to understand 145 00:07:41,920 --> 00:07:44,920 Speaker 2: everything that was happening. Yeah, and you could tell it 146 00:07:45,120 --> 00:07:48,720 Speaker 2: was we were in the last days. But but I 147 00:07:48,800 --> 00:07:52,640 Speaker 2: had the chance to tell her, I said THEA, they 148 00:07:52,720 --> 00:07:56,800 Speaker 2: got Maduro, they arrested Maduro and I and I told her, Tia, 149 00:07:58,200 --> 00:08:01,880 Speaker 2: the Cuban Communist government is going to fall soon. And 150 00:08:02,160 --> 00:08:05,760 Speaker 2: she just beamed the smile on her face. 151 00:08:06,280 --> 00:08:08,080 Speaker 1: Wow, it was. 152 00:08:08,480 --> 00:08:11,480 Speaker 2: It was beautiful and and and it was a celebration 153 00:08:11,640 --> 00:08:14,920 Speaker 2: of life. Look, I'm sad, we're all sad that she's gone. 154 00:08:16,160 --> 00:08:20,560 Speaker 2: But she led a full life. Every minute she loved 155 00:08:20,640 --> 00:08:23,080 Speaker 2: living and and and she brought joy. 156 00:08:24,120 --> 00:08:24,960 Speaker 1: She brought. 157 00:08:26,560 --> 00:08:33,839 Speaker 2: Unconditional support. She was ferociously loyal. So yeah, I I 158 00:08:34,080 --> 00:08:38,120 Speaker 2: was thinking much more about that than than than some guy. 159 00:08:39,160 --> 00:08:41,440 Speaker 2: And and by the way, I was glad at least, 160 00:08:41,720 --> 00:08:44,000 Speaker 2: you know, the early promo said bad Bummy Buddy was 161 00:08:44,040 --> 00:08:46,960 Speaker 2: gonna wear a dress. He didn't wear a dress at 162 00:08:47,040 --> 00:08:48,840 Speaker 2: least not that I saw, so, so I was glad 163 00:08:48,880 --> 00:08:51,240 Speaker 2: of that at least that, you know, small victory. 164 00:08:51,360 --> 00:08:54,640 Speaker 1: Yeah, and like you know, the burning of flags other places, right, Yeah, 165 00:08:54,800 --> 00:08:56,040 Speaker 1: you're not doing the Super Bowl. 166 00:08:57,200 --> 00:08:57,360 Speaker 3: You know. 167 00:08:57,600 --> 00:09:02,080 Speaker 2: Just I'm just saying, I am curious though, how much 168 00:09:02,200 --> 00:09:05,160 Speaker 2: Bad Bunny is going to be taxed for having gone 169 00:09:05,200 --> 00:09:05,920 Speaker 2: to the Super Bowl. 170 00:09:06,480 --> 00:09:09,480 Speaker 1: So, by the way, this is the story that I 171 00:09:09,640 --> 00:09:14,320 Speaker 1: think everyone should be outraged over, right, if if Bad 172 00:09:14,360 --> 00:09:17,520 Speaker 1: Bunny didn't get you going, here is the story, and 173 00:09:17,640 --> 00:09:19,880 Speaker 1: we've got the tax hours. If you want to know 174 00:09:20,120 --> 00:09:24,439 Speaker 1: why California is fading fast and why so many people 175 00:09:24,480 --> 00:09:26,079 Speaker 1: that when they start making money are out of there, 176 00:09:26,120 --> 00:09:29,360 Speaker 1: and so many people in the crypto world and the 177 00:09:29,440 --> 00:09:31,880 Speaker 1: IT world and all the other worlds are like they're 178 00:09:32,080 --> 00:09:35,480 Speaker 1: getting out of there, like they're leaving fast as they can. 179 00:09:36,120 --> 00:09:38,720 Speaker 1: Let's just break down. We've got some data for people 180 00:09:38,880 --> 00:09:43,000 Speaker 1: of how much money California made off the Super Bowl, 181 00:09:43,440 --> 00:09:46,000 Speaker 1: of those that were actually making money at the Super Bowl. 182 00:09:46,080 --> 00:09:49,760 Speaker 1: It really is incredible data. Well it is. 183 00:09:51,559 --> 00:09:53,880 Speaker 2: If you look at Sam Darnold going into the game, 184 00:09:54,800 --> 00:10:00,160 Speaker 2: here's what his incentives were. If he wins. Yeah, he's 185 00:10:00,200 --> 00:10:02,600 Speaker 2: going to receive one hundred and seventy eight thousand dollars 186 00:10:02,679 --> 00:10:05,840 Speaker 2: as a bonus, yep, and he will pay two hundred 187 00:10:05,840 --> 00:10:09,000 Speaker 2: and forty nine thousand in taxes to California for his 188 00:10:09,160 --> 00:10:13,560 Speaker 2: time here, meaning he will lose seventy one thousand dollars. Yeah, 189 00:10:14,400 --> 00:10:18,120 Speaker 2: that's what ended up happening. If if he had lost, 190 00:10:19,559 --> 00:10:21,560 Speaker 2: then he would have gotten one hundred and three thousand 191 00:10:21,600 --> 00:10:25,880 Speaker 2: dollars in bonus, but he would still owe two hundred 192 00:10:25,920 --> 00:10:28,439 Speaker 2: and thirty five thousand in taxes, meaning he would have 193 00:10:28,600 --> 00:10:31,160 Speaker 2: lost one hundred and thirty five thousand dollars. So I 194 00:10:31,240 --> 00:10:35,600 Speaker 2: guess Sam, congratulations, Yeah, you only lost seventy one thousand 195 00:10:35,679 --> 00:10:41,160 Speaker 2: dollars for stepping foot in the Commonwealth of California instead 196 00:10:41,200 --> 00:10:43,319 Speaker 2: of losing one hundred and thirty five thousand dollars. And 197 00:10:43,480 --> 00:10:47,199 Speaker 2: what kind of insanity is it that that's how the 198 00:10:47,280 --> 00:10:48,599 Speaker 2: California tax system is. 199 00:10:49,120 --> 00:10:50,800 Speaker 1: Yeah, it is and it's and I mean this is 200 00:10:50,920 --> 00:10:52,640 Speaker 1: you you fly in there and you just do a 201 00:10:52,720 --> 00:10:55,280 Speaker 1: day's work and you're a performer. They are going to 202 00:10:55,440 --> 00:10:57,559 Speaker 1: take as much money as they can and they don't care. 203 00:10:57,720 --> 00:11:00,360 Speaker 1: Like there now there's there's there was inch article I 204 00:11:00,480 --> 00:11:01,319 Speaker 1: was reading online about it. 205 00:11:01,400 --> 00:11:03,520 Speaker 2: Look, look, I think but I think bad Bunny changed 206 00:11:03,559 --> 00:11:06,439 Speaker 2: his name. He's now Sad Bunny because Sad California took 207 00:11:06,440 --> 00:11:07,000 Speaker 2: all his money. 208 00:11:07,440 --> 00:11:10,000 Speaker 1: See that. You're not wrong on that one. But they 209 00:11:10,040 --> 00:11:11,600 Speaker 1: were talking about bands and. 210 00:11:11,960 --> 00:11:14,480 Speaker 2: And you know, you know, if you if you let 211 00:11:14,600 --> 00:11:18,120 Speaker 2: California keep going, he may soon be like fatal attraction bunny. 212 00:11:18,120 --> 00:11:20,400 Speaker 2: I mean, and there's a whole like like there's. 213 00:11:20,240 --> 00:11:22,400 Speaker 1: A rabbit there's a whole rabbit hole you can go 214 00:11:22,480 --> 00:11:23,000 Speaker 1: down on this one? 215 00:11:23,080 --> 00:11:23,199 Speaker 3: Is that? 216 00:11:24,480 --> 00:11:24,680 Speaker 1: Yeah? 217 00:11:25,760 --> 00:11:27,760 Speaker 2: All right, I went there than you at worst. 218 00:11:27,760 --> 00:11:30,400 Speaker 1: Okay, you're welcome. You're welcome. I'm here all night, folks. 219 00:11:30,520 --> 00:11:34,360 Speaker 1: No comedians, though, are avoiding California for the tax reasons 220 00:11:34,440 --> 00:11:37,480 Speaker 1: you just said that. I mean, it's it's real. Why 221 00:11:37,520 --> 00:11:40,240 Speaker 1: would you spend the night there if you know you're 222 00:11:40,280 --> 00:11:41,800 Speaker 1: not gonna make near as much money and you can 223 00:11:41,880 --> 00:11:44,719 Speaker 1: choose to go somewhere else, like go to Nevada, go 224 00:11:44,880 --> 00:11:46,640 Speaker 1: to New Mexico. I mean, there's there's a lot of 225 00:11:46,640 --> 00:11:49,160 Speaker 1: other place you can go to perform, and there's a 226 00:11:49,160 --> 00:11:51,240 Speaker 1: lot of people there now sitting. They're actually avoiding it 227 00:11:51,320 --> 00:11:54,120 Speaker 1: for the reasons you just described with those bonuses and taxes. 228 00:11:54,640 --> 00:11:57,840 Speaker 2: So, California has the highest marginal income tax of any state. 229 00:11:58,200 --> 00:12:01,000 Speaker 2: Its top marginal rate is thirteen point three percent on 230 00:12:01,120 --> 00:12:04,760 Speaker 2: wage income and on capital gains that exceed one million 231 00:12:04,800 --> 00:12:09,439 Speaker 2: dollars a year. The top rate for waging come climbs 232 00:12:09,559 --> 00:12:13,360 Speaker 2: to fourteen point six percent when you include the state 233 00:12:13,480 --> 00:12:17,480 Speaker 2: disability insurance rate, which has no wage cap. And that's 234 00:12:17,559 --> 00:12:20,719 Speaker 2: on top of federal taxes. So federal taxes at the 235 00:12:20,760 --> 00:12:23,400 Speaker 2: top of the tax bracket, you're paying thirty seven percent 236 00:12:24,120 --> 00:12:29,160 Speaker 2: plus fourteen point six percent. So the reason Darnald owes 237 00:12:29,200 --> 00:12:32,800 Speaker 2: that money is because he is tallied with with eight 238 00:12:33,920 --> 00:12:36,560 Speaker 2: duty days in California. While they were they get there 239 00:12:36,600 --> 00:12:38,800 Speaker 2: getting ready for the super Bowl, he got popped with it. 240 00:12:39,360 --> 00:12:42,480 Speaker 2: Welcome to California, Congratulations on the super Bowl. And by 241 00:12:42,520 --> 00:12:45,120 Speaker 2: the way, when you play the super Bowl in Texas, 242 00:12:45,160 --> 00:12:46,840 Speaker 2: we don't tax you a penny and you get great 243 00:12:46,880 --> 00:12:47,400 Speaker 2: text becks. 244 00:12:48,640 --> 00:12:50,520 Speaker 1: All right, let's move on to some other big stuff 245 00:12:50,520 --> 00:12:52,440 Speaker 1: here center that I want to deal with. And there's 246 00:12:52,480 --> 00:12:54,599 Speaker 1: something you want to talk about which I really like 247 00:12:54,679 --> 00:12:57,480 Speaker 1: this subject, and it deals with left wing funders that 248 00:12:57,559 --> 00:12:59,760 Speaker 1: are being exposed. I actually think this is going to 249 00:12:59,800 --> 00:13:03,040 Speaker 1: be a trend with what we're witnessing with all the 250 00:13:03,080 --> 00:13:05,960 Speaker 1: attacks on ice around the country. I think we're going 251 00:13:06,000 --> 00:13:08,400 Speaker 1: to more of this. Follow the money. You've been saying 252 00:13:08,440 --> 00:13:10,320 Speaker 1: that on this show for a while. Follow the money, 253 00:13:10,440 --> 00:13:12,719 Speaker 1: follow the money, follow the money. And we have a 254 00:13:12,840 --> 00:13:14,960 Speaker 1: new example of that that we're going to expose here. 255 00:13:15,040 --> 00:13:18,080 Speaker 1: So I hope everyone listening or watching will make sure 256 00:13:18,120 --> 00:13:20,640 Speaker 1: you hit that chare button share this episode on social media, 257 00:13:20,640 --> 00:13:22,400 Speaker 1: because we're going to be a part of that in 258 00:13:22,480 --> 00:13:25,240 Speaker 1: a very big way and explain what we've now found. 259 00:13:26,040 --> 00:13:28,320 Speaker 2: Well, there was a very interesting column in the Wall 260 00:13:28,400 --> 00:13:32,720 Speaker 2: Street Journal about the Mellon Foundation, and the Mellon Foundation 261 00:13:33,120 --> 00:13:36,520 Speaker 2: is America's biggest funder of the humanities, and it is 262 00:13:36,760 --> 00:13:41,200 Speaker 2: a radical leftist foundation. And let me read from this column. 263 00:13:41,760 --> 00:13:44,600 Speaker 2: The University of Virginia launched a hiring spree in twenty 264 00:13:44,679 --> 00:13:49,160 Speaker 2: twenty as it pledged to become a racial equity focused university. 265 00:13:50,160 --> 00:13:54,760 Speaker 2: A special initiative promised to recruit thirty doctoral fellows and 266 00:13:55,000 --> 00:13:58,480 Speaker 2: quote open the gateway for them to fill tenure tech jobs. 267 00:13:59,160 --> 00:14:04,760 Speaker 2: One current fellows specialties include transfeminisms and gender queer life, 268 00:14:04,920 --> 00:14:11,520 Speaker 2: writing on other researches how Filipino nurses resist racial capitalism. 269 00:14:12,559 --> 00:14:17,000 Speaker 2: The program owes its existence to the Andrew W. Mellon Foundation, 270 00:14:17,760 --> 00:14:21,520 Speaker 2: which funded it to the tune of five million dollars. 271 00:14:22,400 --> 00:14:25,800 Speaker 2: With a seven point seven billion dollar endowment, the Mellon 272 00:14:25,880 --> 00:14:29,800 Speaker 2: Foundation is the nation's largest supporter of the arts and humanities. 273 00:14:30,600 --> 00:14:34,320 Speaker 2: Its annual giving has long dwarfed that of the National 274 00:14:34,480 --> 00:14:38,360 Speaker 2: Endowment for the Humanities. In recent years, it has been 275 00:14:38,440 --> 00:14:42,920 Speaker 2: refashioned as a tool for advancing an identitarian vision of 276 00:14:43,040 --> 00:14:51,040 Speaker 2: social justice for academia. The consequences are far reaching. Through 277 00:14:51,120 --> 00:14:56,000 Speaker 2: public record requests, I've acquired dozens of proposals and progress 278 00:14:56,080 --> 00:15:01,520 Speaker 2: reports for MELON funded projects show how the foundation has 279 00:15:01,640 --> 00:15:07,080 Speaker 2: lavishly promoted progressive scholar activism. At the University of Utah, 280 00:15:08,080 --> 00:15:15,160 Speaker 2: the Transformative Intersectional Collective created workshops on transgender and queer 281 00:15:15,400 --> 00:15:21,880 Speaker 2: of color critique and quote environmental anti racism, supported by 282 00:15:21,920 --> 00:15:26,200 Speaker 2: a half million dollar MELON grant. At the University of California, 283 00:15:26,280 --> 00:15:32,760 Speaker 2: Santa Cruz. The Visualizing Abolition Project promotes research and art 284 00:15:33,920 --> 00:15:38,560 Speaker 2: that calls for the elimination of prisons, with the support 285 00:15:39,120 --> 00:15:43,600 Speaker 2: of eight million dollars from MELON. For much of its existence, 286 00:15:43,640 --> 00:15:47,720 Speaker 2: the Mellon Foundation narrowly supported the arts and humanities. One 287 00:15:47,800 --> 00:15:52,480 Speaker 2: signature product project was the creation of jstor, the widely 288 00:15:52,680 --> 00:15:57,960 Speaker 2: used online database of academic research. The foundation avoided controversy 289 00:15:58,920 --> 00:16:01,800 Speaker 2: that changed with the dawn of the social justice era. 290 00:16:02,640 --> 00:16:05,560 Speaker 2: As early as twenty fourteen, then president Earl Lewis told 291 00:16:05,600 --> 00:16:08,760 Speaker 2: The Chronicle of Higher Education that the Foundation would take 292 00:16:08,840 --> 00:16:14,000 Speaker 2: a more activist approach, especially around issues of race. On 293 00:16:14,200 --> 00:16:19,280 Speaker 2: taking over in twenty eighteen, the current president, Nutjob Nutjob 294 00:16:19,360 --> 00:16:26,320 Speaker 2: is me editorializing Nutjob, Elizabeth Alexander, announced that Melon would 295 00:16:26,440 --> 00:16:31,360 Speaker 2: view all of its giving through the lens of social justice. 296 00:16:32,240 --> 00:16:35,480 Speaker 2: This new ideological mission is notable because of Mellon's great 297 00:16:35,560 --> 00:16:40,440 Speaker 2: influence within the academy, especially in hiring and promoting faculty. 298 00:16:41,320 --> 00:16:43,960 Speaker 2: As jobs in the humanities have become ever more scarce, 299 00:16:44,640 --> 00:16:49,600 Speaker 2: Melon has poured millions of dollars into career building, fellowships, 300 00:16:49,680 --> 00:16:54,680 Speaker 2: and faculty hiring schemes, putting its stamp on professors around 301 00:16:54,720 --> 00:16:58,640 Speaker 2: the country, Melon money appears up and down the academic 302 00:16:58,720 --> 00:17:02,920 Speaker 2: career ladder. The Mellon Mays Undergraduate Fellowship, which has received 303 00:17:03,080 --> 00:17:07,920 Speaker 2: thirty eight point three million dollars since twenty twenty, steers 304 00:17:08,000 --> 00:17:12,800 Speaker 2: college students towards an academic career. The Mellon Funded Dissertation 305 00:17:13,080 --> 00:17:16,760 Speaker 2: Innovation Fellowship, supported by eight point nine million dollars since 306 00:17:16,800 --> 00:17:20,840 Speaker 2: twenty twenty two, gives a living stipend to forty five 307 00:17:20,960 --> 00:17:25,800 Speaker 2: graduate students annually. Mellon has given six million dollars to 308 00:17:25,920 --> 00:17:30,800 Speaker 2: Wayne State University, four point six million to Texas A 309 00:17:30,920 --> 00:17:35,000 Speaker 2: and M University, and fifteen million dollars to the University 310 00:17:35,040 --> 00:17:39,840 Speaker 2: of California System, all to hire faculty with the Mellon 311 00:17:39,960 --> 00:17:44,880 Speaker 2: Emerging Faculty leaders in other programs. Mellon's support continues well 312 00:17:44,960 --> 00:17:51,080 Speaker 2: into a professor's career. Mellon grantees often signal their discriminatory intent. 313 00:17:52,080 --> 00:17:55,240 Speaker 2: A University of Virginia grant proposal notes that Mellon's five 314 00:17:55,280 --> 00:17:59,680 Speaker 2: million dollar gift would go towards quote advancing our goal 315 00:17:59,800 --> 00:18:03,399 Speaker 2: of doubling the number of faculty of color at UVA 316 00:18:04,080 --> 00:18:08,399 Speaker 2: by twenty thirty. A University of Oregon proposal promises a 317 00:18:08,560 --> 00:18:14,359 Speaker 2: special post doctoral fellowship to establish a pipeline for quote black, 318 00:18:14,640 --> 00:18:20,840 Speaker 2: Indigenous and people of color. Bipock in the professoriate its 319 00:18:20,920 --> 00:18:27,960 Speaker 2: progress report notes that quote all interviewees identified as black, Indigenous, 320 00:18:28,000 --> 00:18:30,240 Speaker 2: and people of color. In other words, we are only 321 00:18:30,880 --> 00:18:35,400 Speaker 2: looking at people if we can racially discriminate. Perhaps most significantly, 322 00:18:36,320 --> 00:18:41,560 Speaker 2: Melon funded personnel building programs almost uniformly espouse a distinctly 323 00:18:41,720 --> 00:18:46,280 Speaker 2: identitarian brand of progressivism. At Emery University, You're going to 324 00:18:46,400 --> 00:18:53,080 Speaker 2: like this. Melon May's fellows study eco womanism, whatever the 325 00:18:53,119 --> 00:18:58,320 Speaker 2: hell that is. They study black trans studies and racial 326 00:18:58,800 --> 00:19:08,240 Speaker 2: colonial capitalism. Students who specialize in intersexual neologisms will be 327 00:19:08,520 --> 00:19:14,200 Speaker 2: well prepared for Melon funded faculty jobs. In twenty twenty three, 328 00:19:14,320 --> 00:19:16,399 Speaker 2: Ohio State put out a job ad. So this is 329 00:19:16,640 --> 00:19:21,440 Speaker 2: Ohio State. What are they looking for a quote assistant 330 00:19:21,520 --> 00:19:28,560 Speaker 2: professor of Black sexualities, noting a recent two million dollar 331 00:19:28,680 --> 00:19:33,359 Speaker 2: Mellon Foundation gift was that funded ten new faculty positions. 332 00:19:33,840 --> 00:19:35,680 Speaker 1: Right by the way, I'm just thinking about that. They're like, 333 00:19:35,720 --> 00:19:37,639 Speaker 1: if you get that job, and you're like, do you 334 00:19:37,760 --> 00:19:40,200 Speaker 1: call mom and are you like proud of that? Or 335 00:19:40,280 --> 00:19:42,080 Speaker 1: are you more like, yeah, I got the new job. 336 00:19:42,600 --> 00:19:48,320 Speaker 2: Well, the person who got it Zalika Ibba or Remi. 337 00:19:48,880 --> 00:19:54,000 Speaker 2: The professor hired for the job, lists black porn and 338 00:19:54,280 --> 00:19:59,320 Speaker 2: black sexual logics among her areas of expertise. 339 00:20:00,000 --> 00:20:01,119 Speaker 1: Oh there you're okay, there you go. 340 00:20:01,840 --> 00:20:06,000 Speaker 2: Melon has bankrolled many professors notorious for their activism. At 341 00:20:06,080 --> 00:20:12,240 Speaker 2: the Socialism twenty twenty five conference, Assistant Professor Emon abdel 342 00:20:13,320 --> 00:20:17,320 Speaker 2: Hadi referred to her employer, the University of Chicago, as 343 00:20:17,560 --> 00:20:20,480 Speaker 2: evil and a colonial landlord. By the way, if you 344 00:20:20,560 --> 00:20:22,960 Speaker 2: call your employer evil, you should be fired. That's just 345 00:20:23,080 --> 00:20:29,240 Speaker 2: very simple, but not hard. Conceded that working there was 346 00:20:29,480 --> 00:20:34,560 Speaker 2: useful for political organizing. Ms abdel Hadi came up through 347 00:20:34,600 --> 00:20:40,000 Speaker 2: the University of Chicago's Provost post Doctoral Fellowship program, a 348 00:20:40,160 --> 00:20:45,040 Speaker 2: longstanding Melon funded hiring program. A few months after the conference, 349 00:20:45,119 --> 00:20:50,800 Speaker 2: she was arrested at wait for it, an anti ice protest, 350 00:20:51,560 --> 00:20:57,440 Speaker 2: and charged and charged in two counts of aggravated battery 351 00:20:57,680 --> 00:20:58,720 Speaker 2: of a police officer. 352 00:20:59,040 --> 00:21:01,160 Speaker 1: Yeah you know, that's money well spent, right. 353 00:21:01,760 --> 00:21:06,800 Speaker 2: She has pleaded not guilty. Mellon's funding has amplified a 354 00:21:07,040 --> 00:21:11,160 Speaker 2: bleak trajectory for the academy today. A young person drawn 355 00:21:11,200 --> 00:21:14,880 Speaker 2: to traditional fields like military history or classics should think 356 00:21:15,040 --> 00:21:19,359 Speaker 2: twice before entering the academy, a young scholar who quote 357 00:21:19,960 --> 00:21:26,200 Speaker 2: advances an anti capitalist, prison abolitionist agenda, as one Ohio 358 00:21:26,280 --> 00:21:31,360 Speaker 2: state professor put it, can find abundance support, especially from 359 00:21:31,400 --> 00:21:36,680 Speaker 2: the Mellon Foundation. Higher education reform will only succeed when 360 00:21:36,880 --> 00:21:41,119 Speaker 2: this unfortunate trend is reversed. And this was written by 361 00:21:41,160 --> 00:21:44,280 Speaker 2: mister Saylor, who's the director of the Higher education policy 362 00:21:44,320 --> 00:21:47,479 Speaker 2: at the Manhattan Institute. This is the kind of poison. 363 00:21:47,520 --> 00:21:50,359 Speaker 2: If you look at our universities and say, why have 364 00:21:50,520 --> 00:21:53,360 Speaker 2: they gotten so bad? Why have they gone so far 365 00:21:53,960 --> 00:21:57,960 Speaker 2: off track? You have radicals who are controlling, as you 366 00:21:58,040 --> 00:22:00,000 Speaker 2: mentioned over. 367 00:22:00,080 --> 00:22:02,280 Speaker 1: And we'll give you faculty. We get to pick them 368 00:22:02,320 --> 00:22:04,200 Speaker 1: out and this is what you've got to hire. But 369 00:22:04,320 --> 00:22:06,239 Speaker 1: we'll give you the money to hire them. And then 370 00:22:06,280 --> 00:22:10,119 Speaker 1: you can radicalize university just by paying and in ploying 371 00:22:10,200 --> 00:22:12,960 Speaker 1: the people and putting them in placing them at the universities. 372 00:22:13,480 --> 00:22:16,040 Speaker 1: This is paid a play and it's in its simplest form. 373 00:22:16,359 --> 00:22:19,920 Speaker 2: Look, it's it's follow the money, and you have radicals 374 00:22:19,960 --> 00:22:21,919 Speaker 2: who are taking over. And by the way, Andrew Mellon 375 00:22:22,080 --> 00:22:26,960 Speaker 2: was not a socialist, was not a transgender like porn 376 00:22:27,359 --> 00:22:29,600 Speaker 2: officiodato that we know of that we know of, to 377 00:22:29,680 --> 00:22:33,280 Speaker 2: be fair, but you look at and this is one 378 00:22:33,359 --> 00:22:37,159 Speaker 2: of the real challenges. They're the great capitalists of a 379 00:22:37,240 --> 00:22:41,760 Speaker 2: prior era. They gave much of their money to foundations, 380 00:22:41,840 --> 00:22:44,720 Speaker 2: and they initially appointed people to run the foundations, and 381 00:22:44,760 --> 00:22:48,399 Speaker 2: then time passes and those foundations over and over and 382 00:22:48,520 --> 00:22:51,960 Speaker 2: over again have been captured by radical leftists. The Ford Foundation. 383 00:22:52,600 --> 00:22:56,399 Speaker 2: Henry Ford would would roll over in his grave if 384 00:22:56,440 --> 00:22:59,720 Speaker 2: he knew the garbage the Ford Foundation funds, and and 385 00:22:59,840 --> 00:23:01,679 Speaker 2: and it's one of the things people on the right 386 00:23:01,800 --> 00:23:05,080 Speaker 2: need to think much more carefully about if they are 387 00:23:05,760 --> 00:23:11,080 Speaker 2: engaged in philanthropy, making sure that it's not captured, because 388 00:23:11,160 --> 00:23:14,240 Speaker 2: that's been one of the real sources of a propaganda 389 00:23:14,359 --> 00:23:18,280 Speaker 2: and a perversion of these institutions is capturing the money 390 00:23:18,359 --> 00:23:21,080 Speaker 2: flow that goes into colleges and universities. 391 00:23:21,640 --> 00:23:24,480 Speaker 1: Yeah, it's incredible, and you go back to find the 392 00:23:24,520 --> 00:23:26,560 Speaker 1: money and let's go back to ice protests real quick. 393 00:23:27,040 --> 00:23:29,000 Speaker 1: This is very much going to be the same playbook. 394 00:23:29,080 --> 00:23:30,520 Speaker 1: It's part of the reason why you say follow the 395 00:23:30,560 --> 00:23:33,480 Speaker 1: money and see who's bringing it in, because they're paying 396 00:23:33,560 --> 00:23:36,080 Speaker 1: these activists. There was a video that went viral of 397 00:23:36,200 --> 00:23:40,200 Speaker 1: them practicing their chances and chance anti ice chants before 398 00:23:40,320 --> 00:23:42,679 Speaker 1: one of their protests. It looked like in a church 399 00:23:43,680 --> 00:23:46,960 Speaker 1: and getting all of the protesters there to then teach 400 00:23:47,040 --> 00:23:49,560 Speaker 1: them their chance, and the video went viral over the weekend. 401 00:23:50,040 --> 00:23:53,120 Speaker 1: That's all being funded by somebody. This is not organic. 402 00:23:54,160 --> 00:23:56,720 Speaker 2: Yeah. Look, Laura Ingram was talking to some of the 403 00:23:56,760 --> 00:23:58,760 Speaker 2: protesters on the street and you said you should be 404 00:23:58,800 --> 00:24:01,479 Speaker 2: working at a job. The protest said, I am, I'm 405 00:24:01,520 --> 00:24:06,040 Speaker 2: getting paid like it's there is real money that is 406 00:24:06,160 --> 00:24:13,280 Speaker 2: fomenting violence, and that is quite literally fomenting revolution. That's 407 00:24:13,400 --> 00:24:14,320 Speaker 2: what they're trying to do. 408 00:24:15,480 --> 00:24:17,320 Speaker 1: I want to make it Virginia real quick as well. 409 00:24:17,480 --> 00:24:20,760 Speaker 1: And this is another big story that is happening very 410 00:24:20,880 --> 00:24:25,480 Speaker 1: quickly post election day. And it's an important story because 411 00:24:25,520 --> 00:24:28,480 Speaker 1: I do think this could be a real moment of 412 00:24:28,600 --> 00:24:32,200 Speaker 1: clarity of Hey, the midterms are coming out. Pay attention. 413 00:24:32,480 --> 00:24:35,840 Speaker 1: Democrats are ready and inspired, but they are working at 414 00:24:35,960 --> 00:24:39,800 Speaker 1: light speed to enact their radical agenda. You could also 415 00:24:39,920 --> 00:24:42,119 Speaker 1: argue that they've learned this from Donald Trump. Donald Trump 416 00:24:42,240 --> 00:24:45,360 Speaker 1: came in rocking and rolling day one into this second term. 417 00:24:45,720 --> 00:24:47,840 Speaker 1: He learned a lot from his first term. He learned 418 00:24:47,840 --> 00:24:50,680 Speaker 1: about how fashion get things done. If you're prepared. And 419 00:24:50,880 --> 00:24:53,159 Speaker 1: now Democrats seem to be doing that with their radical 420 00:24:53,200 --> 00:24:56,760 Speaker 1: agenda and Virginia what they're already doing there. It is 421 00:24:56,840 --> 00:24:59,600 Speaker 1: a warning for the midterms. What can happen if Democrats 422 00:24:59,640 --> 00:25:00,160 Speaker 1: get paid. 423 00:25:01,080 --> 00:25:04,360 Speaker 2: Well and look understand that there is no such thing 424 00:25:04,400 --> 00:25:05,959 Speaker 2: as a moderate Democrat anymore. 425 00:25:06,800 --> 00:25:07,160 Speaker 1: You're dead. 426 00:25:07,560 --> 00:25:11,399 Speaker 2: Abigail Spanburger like Ran, claiming to be a moderate, and 427 00:25:11,920 --> 00:25:15,119 Speaker 2: only the most gullible believed her. All of the Democrats, 428 00:25:15,160 --> 00:25:18,359 Speaker 2: they've been radicalized, that this is a party that is 429 00:25:18,480 --> 00:25:25,679 Speaker 2: driven by the open border, uh, abolish police, transgenders, zealos. 430 00:25:25,720 --> 00:25:27,960 Speaker 2: So what has she done in her first few days. Well, 431 00:25:28,359 --> 00:25:29,960 Speaker 2: one of the very first things she did is she 432 00:25:30,080 --> 00:25:33,879 Speaker 2: cut ties with ICE in federal Immigration Enforcement, which means 433 00:25:34,520 --> 00:25:38,239 Speaker 2: if Virginia has a criminal illegal alien, if Virginia has 434 00:25:38,240 --> 00:25:41,320 Speaker 2: an illegal alien who's a murderer or a rapist or 435 00:25:41,800 --> 00:25:44,280 Speaker 2: or a child bolester, they will let him go and 436 00:25:44,320 --> 00:25:47,520 Speaker 2: even though they're illegal alien. Virginia is now going to 437 00:25:47,600 --> 00:25:51,880 Speaker 2: refuse to cooperate with ICE, refuse to handle handle hand 438 00:25:52,040 --> 00:25:56,680 Speaker 2: murderers over to ICE, and instead, what Abigail Spanburger is 439 00:25:56,720 --> 00:25:59,359 Speaker 2: saying is the people of Virginia would rather that the 440 00:25:59,440 --> 00:26:02,560 Speaker 2: murderer has been released into their communities and prey on 441 00:26:02,680 --> 00:26:10,520 Speaker 2: their children. She's also, quite dramatically this week signed a 442 00:26:10,760 --> 00:26:17,959 Speaker 2: redistricting bill that is nothing short of radical and unfortunately. 443 00:26:20,000 --> 00:26:23,919 Speaker 2: So let's do the math. Virginia has eleven congressional seats 444 00:26:24,000 --> 00:26:28,160 Speaker 2: right now. The current delegation is six Democrats five Republicans. 445 00:26:29,000 --> 00:26:31,920 Speaker 2: Virginia just had a takeover of Democrats of the state 446 00:26:32,040 --> 00:26:35,480 Speaker 2: legislature and the governor's mansion. They redrew the map and 447 00:26:35,640 --> 00:26:38,359 Speaker 2: they went from six Democrats and five Republicans to a 448 00:26:38,400 --> 00:26:44,080 Speaker 2: map that is designed to produce ten Democrats and one Republican. Now, 449 00:26:44,160 --> 00:26:46,760 Speaker 2: now what does the math mean? So Virginia is is 450 00:26:46,880 --> 00:26:49,560 Speaker 2: a pretty it's a blue to purple state. In twenty 451 00:26:49,640 --> 00:26:54,280 Speaker 2: twenty four, forty seven percent of the state voted for 452 00:26:54,400 --> 00:26:58,280 Speaker 2: Donald Trump. So Kamala won Virginia. But but fifty three 453 00:26:58,320 --> 00:27:00,480 Speaker 2: to forty seven. It was a very close cl tight. 454 00:27:01,560 --> 00:27:04,160 Speaker 2: The forty seven percent of Virginians who voted for Trump, 455 00:27:04,240 --> 00:27:07,200 Speaker 2: they are now going to get nine percent of the 456 00:27:07,280 --> 00:27:11,280 Speaker 2: congressional representation in the state. Wow, how about the fifty 457 00:27:11,280 --> 00:27:14,800 Speaker 2: two percent of Virginia who voted for Harris You know 458 00:27:14,840 --> 00:27:20,280 Speaker 2: how much they get? How much ninety one percent. There 459 00:27:20,320 --> 00:27:22,879 Speaker 2: you go, fifty two percent of the state. The Democrats 460 00:27:22,920 --> 00:27:30,000 Speaker 2: get ninety one percent of the congressional representation. Now on Twitter, 461 00:27:30,160 --> 00:27:32,840 Speaker 2: you could hear the left he saying, ah ah, but 462 00:27:32,960 --> 00:27:35,720 Speaker 2: Texas did it. This is all about Texas. Okay, let's 463 00:27:35,720 --> 00:27:42,280 Speaker 2: look at the math. Texas, fifty six percent of Texans 464 00:27:42,840 --> 00:27:47,280 Speaker 2: voted for Donald Trump. In the new map that Texas drew, 465 00:27:48,320 --> 00:27:51,640 Speaker 2: that fifty six will get seventy nine percent of the seat. 466 00:27:51,760 --> 00:27:54,359 Speaker 2: So look, it is drawn to favor the party in power, 467 00:27:55,160 --> 00:27:58,520 Speaker 2: but it is not remotely as brazen. By the way, California, 468 00:27:58,600 --> 00:28:01,720 Speaker 2: their new map all so is designed to give the 469 00:28:01,840 --> 00:28:06,080 Speaker 2: Democrats more than ninety percent of the congressional delegation. And 470 00:28:06,200 --> 00:28:10,280 Speaker 2: you know what, the Northeast is even worse. You look 471 00:28:10,359 --> 00:28:14,280 Speaker 2: at at at states like Maine, states like New Hampshire, 472 00:28:14,320 --> 00:28:17,719 Speaker 2: states like Vermont, states like Rhode Island, states like Connecticut, 473 00:28:18,760 --> 00:28:22,160 Speaker 2: where states like Massachusetts, Massachusetts. Do you know how many 474 00:28:22,200 --> 00:28:24,720 Speaker 2: Republican members of Congress elected Massachusetts. 475 00:28:24,760 --> 00:28:28,080 Speaker 1: I want to say it's one or zero zero, all of. 476 00:28:28,119 --> 00:28:31,880 Speaker 2: New England, all of New England. They're just like Republicans 477 00:28:31,920 --> 00:28:34,000 Speaker 2: don't exist. We will erase you. 478 00:28:35,520 --> 00:28:38,720 Speaker 1: And by the way, by what happens with this? Is 479 00:28:38,760 --> 00:28:40,200 Speaker 1: this going to be a back and forth tit for 480 00:28:40,280 --> 00:28:42,480 Speaker 1: tat when you see this type of egregious so then 481 00:28:42,520 --> 00:28:44,000 Speaker 1: other states are going to have to counter it. Is 482 00:28:44,080 --> 00:28:45,000 Speaker 1: that just how we're going to go? 483 00:28:45,760 --> 00:28:48,880 Speaker 2: So there was a left wing democrat. I responded to this, 484 00:28:49,000 --> 00:28:51,520 Speaker 2: and I tweeted out a brazen abuse of power and 485 00:28:51,600 --> 00:28:54,600 Speaker 2: an insult to democracy. And I went through the numbers 486 00:28:54,680 --> 00:28:58,400 Speaker 2: you just talked about. And this left winging Democrat state 487 00:28:58,480 --> 00:29:01,720 Speaker 2: senator came back, who never heard of and actually didn't 488 00:29:01,720 --> 00:29:04,080 Speaker 2: even know about this until someone of my staff told 489 00:29:04,160 --> 00:29:06,480 Speaker 2: me told me about it. But she responded by saying, 490 00:29:07,280 --> 00:29:10,160 Speaker 2: you all started it and we efing finished it. And 491 00:29:10,240 --> 00:29:11,920 Speaker 2: by the way, she did not abbreviate efing. 492 00:29:12,240 --> 00:29:12,280 Speaker 3: No. 493 00:29:13,080 --> 00:29:14,920 Speaker 2: So she's a charming and lovely person. 494 00:29:15,000 --> 00:29:15,640 Speaker 1: Clearly, yes. 495 00:29:17,160 --> 00:29:21,000 Speaker 2: And you know, there's something richly ironic about it, because 496 00:29:21,040 --> 00:29:25,920 Speaker 2: every Democrat loves to preen about how much they love democracy, 497 00:29:26,960 --> 00:29:31,080 Speaker 2: and yet it's quite evident that they don't care at 498 00:29:31,080 --> 00:29:33,960 Speaker 2: all about democracy. What they care about is keeping democrats 499 00:29:34,000 --> 00:29:37,600 Speaker 2: in power. That is practically the only thing they care about. 500 00:29:37,760 --> 00:29:41,760 Speaker 2: I want you to listen to Abigail Spanberger talking about 501 00:29:42,480 --> 00:29:44,480 Speaker 2: jerry mandering. What she thinks about it, give a. 502 00:29:44,480 --> 00:29:48,680 Speaker 3: Listen, because Republicans have to depend on redistricting and stealing 503 00:29:48,760 --> 00:29:51,280 Speaker 3: votes and taking seats like they did in North Carolina 504 00:29:51,760 --> 00:29:54,960 Speaker 3: in order to actually be able to win elections. 505 00:29:55,480 --> 00:29:57,440 Speaker 1: So well, we're just going to steal them right back, 506 00:29:57,520 --> 00:29:59,840 Speaker 1: by golly right, there's your liberal logic one on one. 507 00:30:00,280 --> 00:30:04,520 Speaker 2: But it is also the degree it is far more brazen. 508 00:30:04,600 --> 00:30:07,800 Speaker 2: As I mentioned Texas, fifty six percent of the state 509 00:30:07,920 --> 00:30:11,800 Speaker 2: voted for Donald Trump. The new map, if Republicans win 510 00:30:11,880 --> 00:30:14,320 Speaker 2: in the seats that were drawn, the new map will 511 00:30:14,360 --> 00:30:17,800 Speaker 2: produce seventy nine percent of the congressional delegation to be Republican. 512 00:30:18,600 --> 00:30:23,000 Speaker 2: That is a difference of twenty three percent. So that's 513 00:30:23,000 --> 00:30:26,440 Speaker 2: an additional twenty three percent. What did Virginia do. They 514 00:30:26,560 --> 00:30:30,320 Speaker 2: went from fifty two percent voting for Kamala Harris to 515 00:30:30,840 --> 00:30:36,000 Speaker 2: getting ninety one percent. That is thirty nine points higher. 516 00:30:36,160 --> 00:30:38,880 Speaker 2: That is a massive and it's a grotesque jery matter. 517 00:30:38,960 --> 00:30:41,520 Speaker 2: And by the way, I'm quite confident if they could 518 00:30:41,560 --> 00:30:44,840 Speaker 2: have drawn at eleven zero, they would have. You know 519 00:30:44,960 --> 00:30:50,360 Speaker 2: that there are five districts that slice through northern Virginia 520 00:30:50,440 --> 00:30:52,640 Speaker 2: to get all the liberals that work in the federal government, 521 00:30:52,960 --> 00:30:55,680 Speaker 2: and they basically like take a slice of liberals from 522 00:30:55,720 --> 00:30:59,440 Speaker 2: northern Virginia and then stick it with rural Virginia's to 523 00:30:59,600 --> 00:31:01,880 Speaker 2: rob them votes of the rest of the state. That 524 00:31:02,080 --> 00:31:03,440 Speaker 2: that is ridder. 525 00:31:04,360 --> 00:31:06,440 Speaker 1: Don't forget to share this podcast, by the way, with 526 00:31:06,520 --> 00:31:08,880 Speaker 1: your family and your friends on social media wherever you 527 00:31:08,960 --> 00:31:12,040 Speaker 1: can hit that subscribe or auto download button and I'll 528 00:31:12,080 --> 00:31:13,240 Speaker 1: talk to you again tomorrow