1 00:00:11,697 --> 00:00:14,937 Speaker 1: You're listening to the Buck Sexton Show podcast, make sure 2 00:00:14,977 --> 00:00:17,977 Speaker 1: you subscribe to the podcast on the iHeartRadio app or 3 00:00:18,017 --> 00:00:21,497 Speaker 1: wherever you get your podcasts. Hey, welcome to the Buck Brief. 4 00:00:21,537 --> 00:00:25,057 Speaker 2: This episode, we got Aaron Wexler back in the house. 5 00:00:25,617 --> 00:00:30,617 Speaker 2: She is nonlib take on Instagram and TikTok, conservative commentator, 6 00:00:31,297 --> 00:00:33,097 Speaker 2: knows many things about many things. 7 00:00:33,177 --> 00:00:34,497 Speaker 1: Aaron. Great to see you again. 8 00:00:35,337 --> 00:00:36,977 Speaker 3: Beg thanks so much for having me on. Great to 9 00:00:37,017 --> 00:00:37,617 Speaker 3: see you so. 10 00:00:38,257 --> 00:00:41,297 Speaker 2: FDNY firefighters, you brought this story to my attention. I 11 00:00:41,337 --> 00:00:44,337 Speaker 2: appreciate that. I was like, wait, what's going on. One 12 00:00:44,377 --> 00:00:46,577 Speaker 2: of my favorite things that working at the NYPD was 13 00:00:46,657 --> 00:00:49,657 Speaker 2: learning all of the jokes that firefighters and cops make 14 00:00:49,777 --> 00:00:52,217 Speaker 2: about each other. Which there's this whole I don't know 15 00:00:52,257 --> 00:00:53,537 Speaker 2: if you know this. It's a little bit like the 16 00:00:53,617 --> 00:00:57,857 Speaker 2: Vampires and the Lichens from the Underworld series of movies, 17 00:00:57,937 --> 00:01:01,737 Speaker 2: like this feud of jokes that goes on for generations 18 00:01:01,737 --> 00:01:07,017 Speaker 2: between cops and firefighters. But anyway, FDNY firefighters in this case, 19 00:01:07,057 --> 00:01:10,657 Speaker 2: this is the story Fire Department brass or an East 20 00:01:10,737 --> 00:01:15,737 Speaker 2: Village ladder company to remove its redline American flag, honoring 21 00:01:15,777 --> 00:01:19,137 Speaker 2: the squad's six brothers killed on nine to eleven after 22 00:01:19,217 --> 00:01:23,417 Speaker 2: a neighborhood resident complained it was fascist and a local 23 00:01:23,537 --> 00:01:28,377 Speaker 2: left wing politician question whether it was politically charged. Okay, 24 00:01:28,817 --> 00:01:29,937 Speaker 2: what is going on here? 25 00:01:31,577 --> 00:01:33,617 Speaker 4: I mean, New York has been a liberal healthcape for 26 00:01:33,657 --> 00:01:35,617 Speaker 4: a really long time, and now I think it's transitioning 27 00:01:35,617 --> 00:01:38,457 Speaker 4: from a liberal healthcape to a just total leftist cesspool. 28 00:01:38,857 --> 00:01:40,177 Speaker 3: So I'm just not surprised. 29 00:01:40,217 --> 00:01:43,657 Speaker 4: I feel really badly for all the men in you. 30 00:01:43,657 --> 00:01:45,857 Speaker 3: Know, in service in New York City right now. I 31 00:01:45,897 --> 00:01:46,697 Speaker 3: really don't know what to say. 32 00:01:46,697 --> 00:01:48,817 Speaker 4: I have a lot of friends who are cops also 33 00:01:49,057 --> 00:01:51,977 Speaker 4: in New York City, and my recommendation for anyone who 34 00:01:52,017 --> 00:01:53,897 Speaker 4: can leave New York is to leave New York. 35 00:01:53,897 --> 00:01:55,617 Speaker 3: When you have people who are insisting. 36 00:01:55,297 --> 00:01:58,457 Speaker 4: That a red Lives Matter flag, a flag that honors 37 00:01:58,457 --> 00:02:01,177 Speaker 4: the work of the firemen and women of New York City, 38 00:02:01,417 --> 00:02:05,417 Speaker 4: that that is fascist and politically charged, then you have 39 00:02:05,457 --> 00:02:07,057 Speaker 4: no business being a member in our society. 40 00:02:07,297 --> 00:02:09,697 Speaker 3: Right Like, there's really nothing left to say to that. 41 00:02:10,457 --> 00:02:13,817 Speaker 4: Apparently the councilwoman, when she first saw the flag, thought 42 00:02:13,857 --> 00:02:16,217 Speaker 4: it was a blue Lives Matter flag, and the fact 43 00:02:16,257 --> 00:02:18,617 Speaker 4: that it was Red lives for the firefighters who died 44 00:02:18,657 --> 00:02:21,657 Speaker 4: on nine to eleven fighting for freedom and saving you know, 45 00:02:21,697 --> 00:02:25,337 Speaker 4: innocent people in New York City. The fact that that's 46 00:02:25,377 --> 00:02:28,297 Speaker 4: what changed the narrative even is to me ridiculous. 47 00:02:28,457 --> 00:02:30,657 Speaker 3: What if it had been a Blue Lives Matter flag? 48 00:02:30,817 --> 00:02:31,657 Speaker 3: Is that not okay? 49 00:02:31,937 --> 00:02:34,497 Speaker 4: Are we not proud of our New York's finess of 50 00:02:34,537 --> 00:02:36,817 Speaker 4: the men and women who defend us in the city 51 00:02:36,817 --> 00:02:40,177 Speaker 4: that is becoming increasingly dangerous? So yeah, no kidding, Buck. 52 00:02:40,217 --> 00:02:42,377 Speaker 4: You and I both moved from New York City. We're 53 00:02:42,377 --> 00:02:44,217 Speaker 4: both in the free state of Florida, and this is 54 00:02:44,257 --> 00:02:44,857 Speaker 4: exactly why. 55 00:02:45,377 --> 00:02:45,577 Speaker 1: Yeah. 56 00:02:45,777 --> 00:02:49,697 Speaker 2: I also I'm curious, like, has the left given us 57 00:02:49,737 --> 00:02:51,937 Speaker 2: an official ruling? We know they're not okay with Blue 58 00:02:51,977 --> 00:02:56,497 Speaker 2: Lives Matter because cops, racism, George Floyd, et cetera. Right, 59 00:02:56,497 --> 00:02:59,777 Speaker 2: So blue lives matter like that really sets them off, 60 00:03:00,817 --> 00:03:03,977 Speaker 2: and they stick to that even after you know, there 61 00:03:04,137 --> 00:03:07,137 Speaker 2: was that whole cop being shot last week and then 62 00:03:07,217 --> 00:03:11,177 Speaker 2: Biden going to a big fundraiser while Trump went to 63 00:03:11,337 --> 00:03:15,777 Speaker 2: the wake for the fallen officer. The left, you know, 64 00:03:15,817 --> 00:03:18,217 Speaker 2: they blue lives matter not okay for them. 65 00:03:18,617 --> 00:03:20,097 Speaker 1: Is red lives matter okay? 66 00:03:20,297 --> 00:03:23,937 Speaker 2: Or is any lives that are not just black lives 67 00:03:23,937 --> 00:03:27,377 Speaker 2: matter somehow indefensible and unacceptable? 68 00:03:29,217 --> 00:03:30,057 Speaker 3: It's a good question. 69 00:03:30,137 --> 00:03:32,137 Speaker 4: I think this one because they just got so much 70 00:03:32,137 --> 00:03:34,417 Speaker 4: flack and it's this kind of random council woman whose 71 00:03:34,457 --> 00:03:36,897 Speaker 4: name escapes me right now, and I don't even part 72 00:03:36,937 --> 00:03:38,377 Speaker 4: of me wants to find it, just so we can 73 00:03:38,377 --> 00:03:40,497 Speaker 4: make sure everyone hates on her, because I think people 74 00:03:40,537 --> 00:03:41,417 Speaker 4: like this should. 75 00:03:42,337 --> 00:03:46,177 Speaker 2: Were the of the Manhattan City Council Carlina Rever. 76 00:03:47,257 --> 00:03:51,217 Speaker 4: Yes, so everyone should make sure to vote this woman out, 77 00:03:51,337 --> 00:03:54,737 Speaker 4: get her the out of that seat. I think we 78 00:03:54,737 --> 00:03:58,297 Speaker 4: should publicly shame her anytime you see her on the street, Scream, shame, 79 00:03:58,457 --> 00:04:01,137 Speaker 4: whatever you could do. But yeah, she's kind of an 80 00:04:01,137 --> 00:04:04,137 Speaker 4: irrelevant person, so I think because of who was dealing 81 00:04:04,217 --> 00:04:05,897 Speaker 4: with it and the fact that it became so big 82 00:04:05,937 --> 00:04:07,897 Speaker 4: on Twitter. And this is also where we just have 83 00:04:07,977 --> 00:04:10,897 Speaker 4: to be so thankful that Elon took over and now X, 84 00:04:10,977 --> 00:04:13,657 Speaker 4: because without X, I don't know that the story ever 85 00:04:13,657 --> 00:04:14,937 Speaker 4: would have been circulated. 86 00:04:14,977 --> 00:04:17,457 Speaker 3: It wouldn't have gotten all this attention. It started last. 87 00:04:17,257 --> 00:04:19,657 Speaker 4: Week on Twitter and now it's made its way into 88 00:04:19,977 --> 00:04:23,577 Speaker 4: the media and different news outlets this week. So it's 89 00:04:23,617 --> 00:04:25,777 Speaker 4: great to see how how that could really change the 90 00:04:25,817 --> 00:04:28,857 Speaker 4: narrative and change outcomes for what these leftists are trying to. 91 00:04:28,857 --> 00:04:29,617 Speaker 3: Do in our cities. 92 00:04:30,097 --> 00:04:31,817 Speaker 4: But no, yeah, in terms of the lives, I think 93 00:04:31,817 --> 00:04:35,897 Speaker 4: they realized that fire doesn't see color and realized that 94 00:04:35,937 --> 00:04:39,457 Speaker 4: they don't want they need the firefighters on their side 95 00:04:39,577 --> 00:04:40,697 Speaker 4: in their district. 96 00:04:40,817 --> 00:04:43,737 Speaker 3: So in this case, the firefighters got lucky. 97 00:04:44,537 --> 00:04:47,057 Speaker 2: And you know, you mentioned how we're both New York 98 00:04:47,137 --> 00:04:51,417 Speaker 2: City refugees of sorts to Florida, the Free State of 99 00:04:52,417 --> 00:04:54,737 Speaker 2: There was a another. I mean, there's so many of 100 00:04:54,737 --> 00:04:57,177 Speaker 2: these that I think that it starts to all blend together, 101 00:04:57,217 --> 00:04:59,417 Speaker 2: which is part of the tragedy of all this. But 102 00:04:59,537 --> 00:05:03,057 Speaker 2: a Boardwalk Empire actor, I actually haven't seen Boardwalk Empire. 103 00:05:03,057 --> 00:05:05,217 Speaker 2: I know, people say it's a great show. I saw 104 00:05:05,297 --> 00:05:08,497 Speaker 2: The Sopranos, you know, which I like. But I love 105 00:05:08,537 --> 00:05:09,457 Speaker 2: it as much as some other people. 106 00:05:09,457 --> 00:05:09,857 Speaker 1: Anyway. 107 00:05:10,297 --> 00:05:16,097 Speaker 2: Boardwalk Empire actor Michael Stohlbarg was hit by a homeless 108 00:05:16,137 --> 00:05:17,897 Speaker 2: man in the back of the head with a rock 109 00:05:18,577 --> 00:05:21,977 Speaker 2: on Sunday night walking on the Upper east Side ninetieth 110 00:05:22,017 --> 00:05:26,097 Speaker 2: Street on the Upper east Side, and this guy struck 111 00:05:26,177 --> 00:05:28,217 Speaker 2: him in the back of the head with a rock. 112 00:05:29,697 --> 00:05:31,537 Speaker 2: You know, this is when I try to tell people, 113 00:05:31,577 --> 00:05:33,337 Speaker 2: like this is just what happens in New York Now right. 114 00:05:33,377 --> 00:05:35,897 Speaker 2: I mean they always say, oh, well, you know, maybe 115 00:05:35,937 --> 00:05:39,097 Speaker 2: homicides are down two or three percent this year. Yeah, 116 00:05:39,177 --> 00:05:42,977 Speaker 2: obviously homicides are the most serious crime. But people just 117 00:05:43,017 --> 00:05:44,737 Speaker 2: feel like some lunatics going to hit them in the 118 00:05:44,777 --> 00:05:46,937 Speaker 2: face with a bottle at any moment, just because. 119 00:05:47,937 --> 00:05:51,377 Speaker 4: Yes, no New Yorkers have this really bizarre Stockholm syndrome. Right, 120 00:05:51,417 --> 00:05:53,177 Speaker 4: You and I did not have this. We escaped, We 121 00:05:53,177 --> 00:05:56,337 Speaker 4: were in we were in captivity, we were hostages, and 122 00:05:56,377 --> 00:05:59,097 Speaker 4: we left and came to Florida. I know a lot 123 00:05:59,097 --> 00:06:00,577 Speaker 4: of people. I have a lot of friends who are 124 00:06:00,657 --> 00:06:02,457 Speaker 4: still up in New York. I do not get it, 125 00:06:02,857 --> 00:06:05,577 Speaker 4: and it's absolutely an accepted fact of life. And I 126 00:06:05,617 --> 00:06:07,377 Speaker 4: don't know if you saw there was a big trend 127 00:06:07,697 --> 00:06:10,697 Speaker 4: over the last week or two on maybe an Instagram 128 00:06:10,737 --> 00:06:13,697 Speaker 4: TikTok where a lot of women are talking about getting 129 00:06:13,697 --> 00:06:15,097 Speaker 4: sucker punched in the face. 130 00:06:15,737 --> 00:06:18,057 Speaker 3: And this is harrifying because it's also one thing. 131 00:06:18,177 --> 00:06:19,737 Speaker 4: I mean, not that anyone needs to get hit, but 132 00:06:19,777 --> 00:06:22,537 Speaker 4: to be hitting these like women in the face. 133 00:06:22,817 --> 00:06:24,457 Speaker 3: And you know what I have to say, I want 134 00:06:24,457 --> 00:06:25,257 Speaker 3: to know how they voted. 135 00:06:25,457 --> 00:06:27,697 Speaker 4: I want to know if those women voted for Joe 136 00:06:27,697 --> 00:06:30,457 Speaker 4: Biden last election season and if they plan on keeping 137 00:06:30,457 --> 00:06:33,817 Speaker 4: their vote for this election, because if you just can't 138 00:06:33,897 --> 00:06:36,537 Speaker 4: change stupid, you can't change crazy. And if those women 139 00:06:36,537 --> 00:06:39,177 Speaker 4: are all still planning on voting for Joe Biden, then 140 00:06:40,057 --> 00:06:41,497 Speaker 4: I just don't know what to tell them. Right. These 141 00:06:41,497 --> 00:06:43,497 Speaker 4: are the same women that have that were marching in 142 00:06:43,537 --> 00:06:45,457 Speaker 4: the BLM riots in New York City. I was there 143 00:06:45,497 --> 00:06:48,017 Speaker 4: in the city, watching the city beyond fire, yelling at 144 00:06:48,257 --> 00:06:50,417 Speaker 4: a lot of times black cops, telling them that they 145 00:06:50,417 --> 00:06:52,657 Speaker 4: were racist. And now you have liberal white women walking 146 00:06:52,697 --> 00:06:55,297 Speaker 4: around New York City afraid for their lives, not understanding 147 00:06:55,457 --> 00:06:56,257 Speaker 4: how they got here. 148 00:06:56,337 --> 00:06:59,217 Speaker 3: So, uh, yeah, that is New York City right now? 149 00:06:59,297 --> 00:07:00,377 Speaker 1: Do you remember it was? 150 00:07:00,577 --> 00:07:03,737 Speaker 2: It was I want to say, maybe a decade ago, 151 00:07:04,097 --> 00:07:06,457 Speaker 2: so it was a while ago, but just on the 152 00:07:06,457 --> 00:07:11,497 Speaker 2: way liberal white women react to things. There was a 153 00:07:11,497 --> 00:07:17,137 Speaker 2: a shapely female actress who walked all around New York 154 00:07:17,217 --> 00:07:20,337 Speaker 2: City to show all the cat calling that was going 155 00:07:20,377 --> 00:07:23,177 Speaker 2: on and with a you know, she had a video 156 00:07:23,297 --> 00:07:27,577 Speaker 2: camera following her the whole time. And initially, the left, fully, 157 00:07:27,577 --> 00:07:29,537 Speaker 2: if you haven't seen this video, you absolutely have to, 158 00:07:29,617 --> 00:07:32,697 Speaker 2: because the left fully embraced this because you know, there 159 00:07:32,697 --> 00:07:34,697 Speaker 2: were guys who were like, you know, hey, you know, 160 00:07:34,937 --> 00:07:38,177 Speaker 2: you know, saying stuff whatever, and they embraced this because 161 00:07:38,257 --> 00:07:41,817 Speaker 2: it was meant to show like the patriarchy and the 162 00:07:41,817 --> 00:07:45,497 Speaker 2: oppression of you know, misogyny and all the patriarchy and 163 00:07:45,537 --> 00:07:48,257 Speaker 2: all this kind of sexist stuff. And then there was 164 00:07:48,257 --> 00:07:53,577 Speaker 2: within twenty four hours a huge backlash against that because 165 00:07:54,217 --> 00:07:58,097 Speaker 2: every single guy in the video who cat called the 166 00:07:58,097 --> 00:08:02,377 Speaker 2: woman or said something inappropriate was black or Hispanic, every 167 00:08:02,377 --> 00:08:05,497 Speaker 2: single one without exception. And so then it was wait, no, 168 00:08:05,617 --> 00:08:09,857 Speaker 2: we have to pull this video because it's perpetuating stereotypes 169 00:08:09,937 --> 00:08:12,577 Speaker 2: or cultural stereotype of who we do. So, you know, 170 00:08:12,737 --> 00:08:14,737 Speaker 2: with the left, you can't win, right, like it all 171 00:08:14,737 --> 00:08:15,657 Speaker 2: depends on which. 172 00:08:15,617 --> 00:08:16,057 Speaker 1: Day it is. 173 00:08:17,217 --> 00:08:19,297 Speaker 4: Of course, No, it's a davit ends and why I mean, 174 00:08:19,337 --> 00:08:21,097 Speaker 4: it's the same way if you saw the other night 175 00:08:21,257 --> 00:08:25,057 Speaker 4: when on the same day that officer Dylan I think 176 00:08:25,137 --> 00:08:26,937 Speaker 4: is his last name, a rest in peace, the officer 177 00:08:26,937 --> 00:08:28,497 Speaker 4: who was killed last week in the line of duty 178 00:08:29,097 --> 00:08:32,297 Speaker 4: against a convicted criminal who'd been let out twenty one times. 179 00:08:32,737 --> 00:08:36,137 Speaker 4: Where President Trump was at his funeral, Joe Biden was 180 00:08:36,177 --> 00:08:39,217 Speaker 4: at a fundraiser, and all the leftists coming out of 181 00:08:39,217 --> 00:08:42,457 Speaker 4: this fundraiser were getting shouted at by pro hamas protesters 182 00:08:42,737 --> 00:08:44,457 Speaker 4: and I just looked at that and I think you 183 00:08:44,617 --> 00:08:47,857 Speaker 4: reap what you so right, let's say that the left creates. 184 00:08:47,457 --> 00:08:50,097 Speaker 3: Its own monsters. It's like Frankenstein with them. 185 00:08:50,337 --> 00:08:53,577 Speaker 2: Yeah, well, we'll get into I feel like we need 186 00:08:53,617 --> 00:08:55,737 Speaker 2: to get people anger at you on the internet, so 187 00:08:56,217 --> 00:08:58,457 Speaker 2: or angrier than we already have done here. So we're 188 00:08:58,457 --> 00:09:00,617 Speaker 2: going to talk about your take on and this is 189 00:09:00,657 --> 00:09:03,977 Speaker 2: from one of your videos on Instagram which get tons 190 00:09:04,017 --> 00:09:04,777 Speaker 2: and tons of views. 191 00:09:05,577 --> 00:09:08,577 Speaker 1: We're going to talk about your view of female uber drivers. 192 00:09:08,697 --> 00:09:13,817 Speaker 2: And Aaron Is is quite clearly decidedly female herself, so 193 00:09:13,857 --> 00:09:17,257 Speaker 2: she's speaking from the female perspective on this. And we 194 00:09:17,297 --> 00:09:20,617 Speaker 2: will get to this momentarily. But you know, first up 195 00:09:20,617 --> 00:09:23,017 Speaker 2: here on the podcast, I try to cut through the noise, 196 00:09:23,057 --> 00:09:25,777 Speaker 2: the nonsense, and the ulterior motives that are out there 197 00:09:26,537 --> 00:09:29,617 Speaker 2: so we can uncover those important truths no one else 198 00:09:29,657 --> 00:09:31,737 Speaker 2: is going to tell you. And that's what my colleague 199 00:09:31,817 --> 00:09:34,377 Speaker 2: Mark Chakin does. But he does this when it comes 200 00:09:34,377 --> 00:09:37,417 Speaker 2: to the stock market. Mark worked on Wall Street for 201 00:09:37,457 --> 00:09:40,897 Speaker 2: fifty years. Across those decades, he invented three new indices 202 00:09:40,897 --> 00:09:43,137 Speaker 2: for the Nasdaq, and he has predicted some of the 203 00:09:43,137 --> 00:09:46,617 Speaker 2: biggest market shifts of the past decade, including the recent 204 00:09:46,657 --> 00:09:50,057 Speaker 2: mania and AI stocks. 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Once more, you 215 00:10:22,337 --> 00:10:25,657 Speaker 2: can see Mark's favorite AI stocks right now at twenty 216 00:10:25,777 --> 00:10:32,817 Speaker 2: twenty four aistock dot com paid for by Shakin Analytics. Now, Aaron, 217 00:10:34,017 --> 00:10:35,897 Speaker 2: you it seems, and I want to let you put 218 00:10:35,897 --> 00:10:39,697 Speaker 2: this in your own words. You, it seems, have made 219 00:10:39,697 --> 00:10:43,497 Speaker 2: a clear preference or have stated your clear preference for 220 00:10:43,737 --> 00:10:48,137 Speaker 2: male over female uber drivers. Are you suggesting that men 221 00:10:48,177 --> 00:10:50,537 Speaker 2: are in fact better drivers than women on average? 222 00:10:52,057 --> 00:10:55,297 Speaker 4: I am not suggesting. I am stating a fact that 223 00:10:55,417 --> 00:10:57,817 Speaker 4: everyone knows to be true in their hearts. And if 224 00:10:57,857 --> 00:11:00,057 Speaker 4: you think it's not true, then you are a lying 225 00:11:00,137 --> 00:11:02,777 Speaker 4: to yourself or you're a lying to everyone else around you. 226 00:11:02,897 --> 00:11:08,177 Speaker 4: Men are better drivers than women, period, QWD mic drop. 227 00:11:08,377 --> 00:11:11,057 Speaker 3: That's all there is. Yes, I have been cursed. 228 00:11:10,777 --> 00:11:14,217 Speaker 4: Today with not one, but two female Uber drivers. Both 229 00:11:14,257 --> 00:11:16,257 Speaker 4: of them made wrong turns. They each got me their 230 00:11:16,297 --> 00:11:18,577 Speaker 4: way later than they expected DTA, and I was running 231 00:11:18,577 --> 00:11:20,577 Speaker 4: pretty late because I am a woman. And you know, 232 00:11:20,657 --> 00:11:23,457 Speaker 4: there's a female only driver option on these apps. The 233 00:11:23,497 --> 00:11:25,697 Speaker 4: idea is that so women could feel safer by having 234 00:11:25,737 --> 00:11:26,697 Speaker 4: only female drivers. 235 00:11:26,697 --> 00:11:27,297 Speaker 3: Absolutely not. 236 00:11:27,457 --> 00:11:29,137 Speaker 4: If I want to know that I will get to 237 00:11:29,177 --> 00:11:32,377 Speaker 4: my destination on time, then I want a male only 238 00:11:32,457 --> 00:11:35,697 Speaker 4: driver option. I needed that today. I would pay a premium. 239 00:11:35,697 --> 00:11:37,137 Speaker 3: Get at me. I don't even care. You all know 240 00:11:37,177 --> 00:11:38,897 Speaker 3: it's true, and Buck, I will let you know. 241 00:11:38,937 --> 00:11:41,297 Speaker 4: You don't actually know this, but I have been in 242 00:11:41,577 --> 00:11:45,097 Speaker 4: trouble for this before once before. I also had commentary 243 00:11:45,217 --> 00:11:48,857 Speaker 4: on female Uber drivers where every time I order an 244 00:11:48,897 --> 00:11:51,257 Speaker 4: Uber to go to the airport and it's a female driver, 245 00:11:51,697 --> 00:11:53,897 Speaker 4: they never get out to help with the luggage, and 246 00:11:53,937 --> 00:11:56,177 Speaker 4: the male drivers always get out to help with the luggage. 247 00:11:56,177 --> 00:11:58,497 Speaker 4: And I'm wondering why the men get paid the same 248 00:11:58,537 --> 00:12:00,737 Speaker 4: as the women, and the Feminazis on the left will 249 00:12:00,817 --> 00:12:03,977 Speaker 4: argue women don't make as much as men, and it's 250 00:12:04,017 --> 00:12:05,657 Speaker 4: a lot of times because women don't do the same 251 00:12:05,737 --> 00:12:08,737 Speaker 4: jobs as men, whether that's mostly in marketing careers or 252 00:12:08,857 --> 00:12:11,097 Speaker 4: you know, softer skill sets whereas men are in like 253 00:12:11,177 --> 00:12:13,977 Speaker 4: engineering and science, and you know, they choose different career 254 00:12:14,057 --> 00:12:15,177 Speaker 4: paths that earn more money. 255 00:12:15,817 --> 00:12:16,777 Speaker 3: But same thing with Uber. 256 00:12:16,817 --> 00:12:19,057 Speaker 4: And I'm just wondering, like, you know, generally women don't 257 00:12:19,057 --> 00:12:21,537 Speaker 4: help other women, so I guess maybe that's where. 258 00:12:21,337 --> 00:12:23,097 Speaker 3: This comes from. But I'm like, I'm also a woman. 259 00:12:23,137 --> 00:12:26,017 Speaker 4: I'd like some help, like putting the suitcase into the trunk. 260 00:12:26,217 --> 00:12:29,657 Speaker 4: No help, none at all. And I did a video 261 00:12:29,697 --> 00:12:32,537 Speaker 4: on this back in October end of September, early October 262 00:12:32,977 --> 00:12:35,977 Speaker 4: wound up getting put in the Daily Mail UK about. 263 00:12:35,657 --> 00:12:37,857 Speaker 3: This during controversy. 264 00:12:37,937 --> 00:12:41,217 Speaker 4: Yes, sir, this was before a lot of people kind 265 00:12:41,217 --> 00:12:43,737 Speaker 4: of saw my videos and apparently there was a Reddit 266 00:12:43,777 --> 00:12:44,697 Speaker 4: thread about this as well. 267 00:12:44,777 --> 00:12:46,457 Speaker 3: So I hope the people on Reddit. 268 00:12:46,617 --> 00:12:48,417 Speaker 4: I hope the people at the Daily Mail UK are 269 00:12:48,457 --> 00:12:51,297 Speaker 4: ready because I am back again to say I. 270 00:12:51,257 --> 00:12:54,457 Speaker 3: Need a male only driver option on Uber. 271 00:12:54,937 --> 00:12:57,337 Speaker 4: It is necessary when I need to get somewhere fast, 272 00:12:57,537 --> 00:12:58,897 Speaker 4: when I want to get somewhere safe. 273 00:12:59,017 --> 00:12:59,777 Speaker 3: I want the option. 274 00:13:00,057 --> 00:13:03,017 Speaker 4: And also there is an option on at least Lift. 275 00:13:03,017 --> 00:13:05,297 Speaker 4: I don't know if on Uber as well, to request 276 00:13:05,297 --> 00:13:09,017 Speaker 4: a female only driver. It's actually female or female identifying, 277 00:13:09,057 --> 00:13:12,017 Speaker 4: so I'm not actually sure how female that is. But 278 00:13:12,057 --> 00:13:14,057 Speaker 4: you are able to request a female only driver to 279 00:13:14,177 --> 00:13:15,297 Speaker 4: feel safe as a woman. 280 00:13:15,577 --> 00:13:17,257 Speaker 1: I did not know that that is. 281 00:13:17,457 --> 00:13:19,257 Speaker 4: That is, I don't know how there isn't a lawsuit 282 00:13:19,257 --> 00:13:21,297 Speaker 4: about it like that is discrimination. If you can, you're 283 00:13:21,337 --> 00:13:23,457 Speaker 4: allowed to request a woman And I also have another 284 00:13:23,537 --> 00:13:26,137 Speaker 4: video on this somewhere else on my Instagram page, non 285 00:13:26,137 --> 00:13:27,617 Speaker 4: lip take, so you could find that. I have a 286 00:13:27,657 --> 00:13:31,377 Speaker 4: screenshot of it from Lift showing that I was allowed 287 00:13:31,417 --> 00:13:35,577 Speaker 4: able to request a female only or female identifying driver 288 00:13:35,617 --> 00:13:36,777 Speaker 4: and actually specifies both. 289 00:13:36,817 --> 00:13:37,537 Speaker 3: It's saying both. 290 00:13:37,617 --> 00:13:40,657 Speaker 4: So again I don't know who what kind of driver 291 00:13:40,737 --> 00:13:44,737 Speaker 4: that is. But if you're offering that as as an option, 292 00:13:45,177 --> 00:13:47,457 Speaker 4: why not male only? And I think male only is 293 00:13:47,457 --> 00:13:48,897 Speaker 4: way more important at this point. 294 00:13:49,817 --> 00:13:52,857 Speaker 2: Yeah, I am learning new things here. I had no idea. 295 00:13:52,897 --> 00:13:58,097 Speaker 2: I will tell you my biggest Uber complaint that I 296 00:13:58,537 --> 00:14:02,057 Speaker 2: have about about ride share service. I've come across this before, 297 00:14:02,097 --> 00:14:04,377 Speaker 2: and you know I took cabs in New York City 298 00:14:04,417 --> 00:14:07,817 Speaker 2: for many, many years. And some of them do drive 299 00:14:08,137 --> 00:14:10,457 Speaker 2: like like the proverbial bad out of hell. 300 00:14:10,537 --> 00:14:12,897 Speaker 1: I mean, they go super fast, but I'm okay with that. 301 00:14:13,057 --> 00:14:15,977 Speaker 2: Like they usually have got pretty good wheel skills, even yeah, 302 00:14:16,017 --> 00:14:17,857 Speaker 2: even if they're if they're they're weaving in and through 303 00:14:17,897 --> 00:14:20,097 Speaker 2: traffic and they almost kill a delivery bike guy but 304 00:14:20,177 --> 00:14:22,137 Speaker 2: they don't, but they don't because they know what they're doing. 305 00:14:22,297 --> 00:14:25,057 Speaker 2: You know, I'm okay with it. In ubers, I've come 306 00:14:25,097 --> 00:14:27,177 Speaker 2: across this. I don't know if you've ever had this before. 307 00:14:27,177 --> 00:14:29,577 Speaker 2: And I'm gonna tell you this is a it is 308 00:14:29,617 --> 00:14:33,697 Speaker 2: a form of actual torture. Like I would spill the secrets, 309 00:14:33,737 --> 00:14:35,777 Speaker 2: you know what I mean, I would tell people the things, 310 00:14:36,137 --> 00:14:37,857 Speaker 2: you know, the Russians, if they got me, they would 311 00:14:37,897 --> 00:14:40,337 Speaker 2: learn all they want to know if they did this 312 00:14:40,377 --> 00:14:42,897 Speaker 2: to me long enough. And it's the people who tap 313 00:14:43,937 --> 00:14:46,137 Speaker 2: the accelerator to move, meaning they. 314 00:14:45,977 --> 00:14:48,537 Speaker 1: Go broom broom, you. 315 00:14:48,497 --> 00:14:51,057 Speaker 2: Know, as they're going. They have no understanding of first 316 00:14:51,057 --> 00:14:52,857 Speaker 2: of all, of like using cruise control if thrown a 317 00:14:52,897 --> 00:14:56,377 Speaker 2: highway or something, but beyond that of just like making 318 00:14:56,417 --> 00:14:59,337 Speaker 2: the car go at a steady state. They they are 319 00:14:59,377 --> 00:15:01,337 Speaker 2: tapping on the acceleer the whole time, and then some 320 00:15:01,417 --> 00:15:03,897 Speaker 2: of them will tap on and off the brake and 321 00:15:03,977 --> 00:15:09,177 Speaker 2: let the car lurch forward at red lights. This I 322 00:15:09,177 --> 00:15:11,497 Speaker 2: I've got. This is the I will get out of ubers. 323 00:15:11,737 --> 00:15:13,817 Speaker 2: I will stay in the uber if it reeks of 324 00:15:13,857 --> 00:15:17,297 Speaker 2: cigarette smoke, if they are blasting the worst loudest music, whatever, 325 00:15:17,577 --> 00:15:18,617 Speaker 2: I will get out of the car. 326 00:15:18,657 --> 00:15:21,857 Speaker 1: If they are a break and accelerator tapper. 327 00:15:23,577 --> 00:15:23,817 Speaker 4: Listen. 328 00:15:23,897 --> 00:15:24,297 Speaker 3: I get that. 329 00:15:24,457 --> 00:15:26,937 Speaker 4: I get very nauseous from that, but even that, I 330 00:15:27,377 --> 00:15:29,097 Speaker 4: would live with that if it means getting to my 331 00:15:29,177 --> 00:15:31,777 Speaker 4: destination and the problem I keep running into. And it 332 00:15:31,817 --> 00:15:35,137 Speaker 4: happened to me twice in one day where two female 333 00:15:35,177 --> 00:15:38,977 Speaker 4: drivers and they are timid drivers. They're like afraid of cars, 334 00:15:39,097 --> 00:15:43,177 Speaker 4: They're afraid of this job. And I understand people are 335 00:15:43,217 --> 00:15:46,017 Speaker 4: looking for opportunities to make money. We are in bide inflation. 336 00:15:46,257 --> 00:15:50,017 Speaker 4: I don't blame people for looking for opportunities to earn more. 337 00:15:50,097 --> 00:15:50,897 Speaker 3: I really get it. 338 00:15:51,097 --> 00:15:54,497 Speaker 4: There are other ways if you're not comfortable to be 339 00:15:54,657 --> 00:15:56,897 Speaker 4: in the car to earn money, you know, And I 340 00:15:57,257 --> 00:15:59,017 Speaker 4: actually I just don't get it. I don't get it. 341 00:15:59,297 --> 00:16:01,057 Speaker 4: And I wish they were a way to get out 342 00:16:01,057 --> 00:16:03,417 Speaker 4: of this healthscape and I'm certain that if this clip 343 00:16:03,457 --> 00:16:06,217 Speaker 4: goes anywhere that Uber will like freeze my account or 344 00:16:06,257 --> 00:16:08,137 Speaker 4: give me only women for the rest of time, and 345 00:16:08,177 --> 00:16:09,177 Speaker 4: I will be cursed with that. 346 00:16:10,097 --> 00:16:11,257 Speaker 3: But it needs to end. 347 00:16:12,297 --> 00:16:15,337 Speaker 2: I still I will never forgive them, although unfortunately it's 348 00:16:15,337 --> 00:16:17,537 Speaker 2: so this is what they count on. It's like Amazon, 349 00:16:17,617 --> 00:16:20,217 Speaker 2: they can abuse you and you'll just come back because 350 00:16:20,377 --> 00:16:21,977 Speaker 2: you're so used to the convenience of it. I'm not 351 00:16:22,017 --> 00:16:24,257 Speaker 2: gonna lie. Ubers got me like, That's the thing. I 352 00:16:24,297 --> 00:16:27,257 Speaker 2: will never forgive or forget them making me take photos 353 00:16:27,297 --> 00:16:29,217 Speaker 2: of myself with a mask on before I get in 354 00:16:29,257 --> 00:16:31,697 Speaker 2: the car, especially because every time they did they did 355 00:16:31,737 --> 00:16:32,857 Speaker 2: they ever do that to you? They did that to 356 00:16:32,897 --> 00:16:35,297 Speaker 2: me many times. I was being threatened with my account 357 00:16:35,337 --> 00:16:38,697 Speaker 2: being suspended for mask violations over and over and over again. 358 00:16:39,577 --> 00:16:41,257 Speaker 4: You know, I actually don't know that I was even 359 00:16:41,337 --> 00:16:44,977 Speaker 4: taking ubers back then, because it was so prohibitively expensive 360 00:16:44,977 --> 00:16:47,017 Speaker 4: when I was living in New York City. Because you 361 00:16:47,057 --> 00:16:49,097 Speaker 4: know all the liberals that love to say New York 362 00:16:49,177 --> 00:16:52,017 Speaker 4: or nowhere, It's like anywhere but New York for most 363 00:16:52,017 --> 00:16:53,937 Speaker 4: months of the year except for two weeks in the 364 00:16:53,977 --> 00:16:55,977 Speaker 4: spring and two weeks in the fall, where everyone's around, 365 00:16:55,977 --> 00:16:58,417 Speaker 4: and especially during COVID, they all voted for these policies and. 366 00:16:58,377 --> 00:16:58,897 Speaker 3: They all left. 367 00:16:59,097 --> 00:17:01,057 Speaker 4: Meanwhile, I was stuck with my lease, and so I 368 00:17:01,097 --> 00:17:03,697 Speaker 4: was actually in New York City living there because I 369 00:17:03,697 --> 00:17:05,817 Speaker 4: don't have, you know, the parents with the second home, 370 00:17:05,857 --> 00:17:07,137 Speaker 4: the way a lot of people did to get out 371 00:17:07,137 --> 00:17:07,617 Speaker 4: of the city. 372 00:17:07,937 --> 00:17:11,017 Speaker 3: And so all those people were voting for all these blue. 373 00:17:10,777 --> 00:17:13,857 Speaker 4: Policies, all these superliberal policies while leaving places like New 374 00:17:13,937 --> 00:17:14,697 Speaker 4: York for Florida. 375 00:17:15,097 --> 00:17:16,857 Speaker 3: I reversed with them. 376 00:17:16,897 --> 00:17:19,217 Speaker 4: So they all went back to New York as COVID ended, 377 00:17:19,257 --> 00:17:21,897 Speaker 4: although if you ask Tayler Lorenz, it's still going on. 378 00:17:21,977 --> 00:17:24,577 Speaker 4: But I actually left then when I was out of 379 00:17:24,617 --> 00:17:26,377 Speaker 4: my lease and came to Florida for good. 380 00:17:27,297 --> 00:17:28,697 Speaker 3: But that sounds familiar. 381 00:17:28,737 --> 00:17:31,857 Speaker 4: I do remember having to show my ID at New 382 00:17:31,937 --> 00:17:34,737 Speaker 4: York City restaurants and things like that during COVID, and 383 00:17:34,817 --> 00:17:36,977 Speaker 4: that was pretty rich, and showing my COVID tests and 384 00:17:37,017 --> 00:17:40,097 Speaker 4: all that stuff. And I mean, you could call it racist, 385 00:17:40,137 --> 00:17:42,177 Speaker 4: but it was New York City under the Liberals, so 386 00:17:42,217 --> 00:17:43,417 Speaker 4: it wasn't racist when they did it. 387 00:17:44,337 --> 00:17:47,137 Speaker 2: We're going to come in and talk Barbie movie here 388 00:17:47,377 --> 00:17:49,777 Speaker 2: in a second, because someone has weighed in and has 389 00:17:49,817 --> 00:17:55,017 Speaker 2: finally a good take, perhaps even non lib take on Barbie. 390 00:17:55,257 --> 00:17:57,537 Speaker 2: So we'll get to that. But the Tunnel Towers Foundation 391 00:17:57,697 --> 00:18:00,337 Speaker 2: is an incredible organization that I'm very pleased to have 392 00:18:00,417 --> 00:18:02,897 Speaker 2: been partnered with for years. They are committed to supporting 393 00:18:02,897 --> 00:18:06,337 Speaker 2: our nations first responders and veterans, heroes who put their 394 00:18:06,377 --> 00:18:08,697 Speaker 2: lives on the line for our communities and our country. 395 00:18:08,937 --> 00:18:13,577 Speaker 2: Heroes like Army Major Jonathan Turnbull. He sustained devastating injuries 396 00:18:13,617 --> 00:18:16,417 Speaker 2: the hands of an Isis suicide bomber, but the Tunata 397 00:18:16,497 --> 00:18:19,897 Speaker 2: Towers Foundation paid off his mortgage and gave Major Turnbull 398 00:18:19,977 --> 00:18:23,737 Speaker 2: a specially adapted smart home designed for his needs. He 399 00:18:23,777 --> 00:18:25,817 Speaker 2: moves around his home more easily now, and his home 400 00:18:25,897 --> 00:18:28,017 Speaker 2: gives him hope. With help from people like you. The 401 00:18:28,057 --> 00:18:33,577 Speaker 2: foundation supports families like Turnbulls joined Tunata Towers in supporting 402 00:18:33,617 --> 00:18:37,177 Speaker 2: America's heroes, our nation, severely injured veterans and first responders, 403 00:18:37,817 --> 00:18:40,697 Speaker 2: homeless veterans, gold Star families, and the families of fallen 404 00:18:40,737 --> 00:18:43,417 Speaker 2: first responders doing it eleven dollars a month. The Tunata 405 00:18:43,457 --> 00:18:46,217 Speaker 2: Towers at T two t dot org. That's t the 406 00:18:46,297 --> 00:18:48,977 Speaker 2: number two T dot org ninety five cents of every 407 00:18:49,017 --> 00:18:52,737 Speaker 2: dollar goes directly to their programs. All right, eron, I 408 00:18:52,817 --> 00:18:57,217 Speaker 2: know you may have seen this. Shakira says that she 409 00:18:57,457 --> 00:19:01,897 Speaker 2: and her sons hate the Barbie movie and it is trash. 410 00:19:01,937 --> 00:19:04,577 Speaker 2: I do not believe that anyone actually likes this is 411 00:19:04,577 --> 00:19:07,977 Speaker 2: my contention. It's not that the barbiemovie isn't good, which 412 00:19:07,977 --> 00:19:10,057 Speaker 2: it manifestsly. I don't care that made a billion dollar. 413 00:19:10,337 --> 00:19:12,217 Speaker 2: I don't care that, you know, oh, we'ren't supposed to 414 00:19:12,217 --> 00:19:14,417 Speaker 2: freak out or didn't get nominated for an Oscar. I 415 00:19:14,457 --> 00:19:18,697 Speaker 2: think the Barbie movie is obviously terrible, and I do 416 00:19:18,737 --> 00:19:20,977 Speaker 2: not believe that anyone actually likes it. I think they 417 00:19:20,977 --> 00:19:23,417 Speaker 2: all think they have to like it. It's like Hamilton, which 418 00:19:23,417 --> 00:19:25,697 Speaker 2: by the way, is also terrible, but everyone thought they 419 00:19:25,697 --> 00:19:26,337 Speaker 2: had to like it. 420 00:19:26,417 --> 00:19:27,097 Speaker 1: What do you think? 421 00:19:28,577 --> 00:19:31,857 Speaker 4: So, yeah, Shakiro one of the great political philosophers of 422 00:19:31,897 --> 00:19:33,737 Speaker 4: our time. I'm really glad she find mad in on that. 423 00:19:33,857 --> 00:19:36,137 Speaker 1: But do her hips lie? I don't think so. 424 00:19:37,257 --> 00:19:39,257 Speaker 4: Listen, I had to come up with a better response 425 00:19:39,337 --> 00:19:41,057 Speaker 4: than hips don't lie. I was trying to give you 426 00:19:41,097 --> 00:19:43,257 Speaker 4: something a little interesting for the audience. You know, you 427 00:19:43,337 --> 00:19:45,017 Speaker 4: had me back once we're trying to get back a 428 00:19:45,017 --> 00:19:47,737 Speaker 4: second time. So folks, if you're listening, let them know 429 00:19:47,817 --> 00:19:48,537 Speaker 4: that you like having me on. 430 00:19:48,697 --> 00:19:51,617 Speaker 2: This is because so many more people downloaded and listened 431 00:19:51,617 --> 00:19:53,057 Speaker 2: to the Oh you were on, and we're like, oh, 432 00:19:53,057 --> 00:19:53,657 Speaker 2: we had a spike. 433 00:19:53,697 --> 00:19:54,617 Speaker 1: Who'd you have on? You know? 434 00:19:54,657 --> 00:19:56,777 Speaker 2: Do you have a on like some you know, some 435 00:19:56,857 --> 00:19:59,257 Speaker 2: person who's been a conservative move for twenty years. I'm like, no, 436 00:19:59,657 --> 00:20:03,017 Speaker 2: I had Wexler, So there you go. Aaron Wexler came on. 437 00:20:03,577 --> 00:20:03,897 Speaker 1: Bamn. 438 00:20:04,057 --> 00:20:04,257 Speaker 4: Yeah. 439 00:20:04,337 --> 00:20:07,497 Speaker 3: Yeah, So everyone's confused asking who's that. 440 00:20:08,097 --> 00:20:11,577 Speaker 4: Also, for anyone listening, I don't get anything for doing this, 441 00:20:11,697 --> 00:20:13,377 Speaker 4: So I don't know why I'm peddling this so much. 442 00:20:13,417 --> 00:20:15,417 Speaker 4: But it's fun to have time with Buck and to 443 00:20:15,457 --> 00:20:19,017 Speaker 4: talk to everyone. But uh no, on Shakira, I actually 444 00:20:19,097 --> 00:20:21,577 Speaker 4: refused to see the Barbe movie for exactly the reasons 445 00:20:21,577 --> 00:20:23,977 Speaker 4: you're talking about. I did go to see Oppenheimer cause 446 00:20:23,977 --> 00:20:25,257 Speaker 4: I wanted my money to go there, and you and 447 00:20:25,257 --> 00:20:26,057 Speaker 4: I are in agreement. 448 00:20:26,137 --> 00:20:27,577 Speaker 3: You watched it very late. 449 00:20:27,657 --> 00:20:30,577 Speaker 4: Not to call you out, I actually my first I 450 00:20:30,617 --> 00:20:33,137 Speaker 4: had my first dragging on TikTok back when I saw 451 00:20:33,177 --> 00:20:35,777 Speaker 4: the movie and decided to tell the internet that I 452 00:20:35,777 --> 00:20:38,137 Speaker 4: thought it was a trash movie is, and that's what 453 00:20:38,817 --> 00:20:39,097 Speaker 4: it is. 454 00:20:39,137 --> 00:20:39,457 Speaker 3: Trash. 455 00:20:39,577 --> 00:20:41,017 Speaker 2: I didn't know that you knew that it was trash. 456 00:20:41,057 --> 00:20:44,097 Speaker 2: My opinion of you, which was already pretty solid, gone 457 00:20:44,177 --> 00:20:47,657 Speaker 2: up a bit here because it's a garbage movie and 458 00:20:47,697 --> 00:20:49,257 Speaker 2: everyone's acting like it's a masterpiece. 459 00:20:49,337 --> 00:20:49,697 Speaker 1: It's not. 460 00:20:49,817 --> 00:20:54,097 Speaker 2: It is trash, it is boring. The politics are commy nonsense. 461 00:20:54,457 --> 00:20:55,577 Speaker 2: It's garbage. 462 00:20:55,897 --> 00:20:57,857 Speaker 3: Yeah, yeah, there are a lot of problems with it. 463 00:20:57,897 --> 00:20:59,577 Speaker 4: One of the things I also thought was funny is 464 00:20:59,617 --> 00:21:02,057 Speaker 4: in one of the classes that he's teaching or in 465 00:21:02,137 --> 00:21:04,577 Speaker 4: with physics or something, it's like half women. I'm like, okay, 466 00:21:04,857 --> 00:21:06,697 Speaker 4: there were no classes like that back in the day. 467 00:21:06,737 --> 00:21:08,857 Speaker 4: That makes absolutely no sense to me. So that that 468 00:21:08,937 --> 00:21:10,577 Speaker 4: really threw me for a loop, Like there are other 469 00:21:10,617 --> 00:21:13,577 Speaker 4: things we could talk about with Oppenheimer. And yes, you 470 00:21:13,617 --> 00:21:15,737 Speaker 4: can find my video back in like the night it 471 00:21:15,777 --> 00:21:18,497 Speaker 4: came out. So I went the night it you know, 472 00:21:18,497 --> 00:21:21,337 Speaker 4: the movie was released in Miami Beach, and I like 473 00:21:21,457 --> 00:21:24,497 Speaker 4: left nauseous. Also, like the style they used to try 474 00:21:24,537 --> 00:21:26,417 Speaker 4: to make you nauseous with all the questioning at the 475 00:21:26,497 --> 00:21:28,497 Speaker 4: end was too much. It was like the uber drivers 476 00:21:28,497 --> 00:21:31,697 Speaker 4: that you keep getting but no on Shakira. Yeah, Barbie's 477 00:21:31,777 --> 00:21:33,977 Speaker 4: just this is everything with the left. It's like they 478 00:21:34,017 --> 00:21:36,697 Speaker 4: pretend to laugh at SNL, they pretend to laugh at 479 00:21:36,737 --> 00:21:40,017 Speaker 4: Jimmy Kimmel like everything's great or funny, or we pretend 480 00:21:40,017 --> 00:21:43,337 Speaker 4: like Lizzo's not like morbidly obese. Like everything with the 481 00:21:43,417 --> 00:21:45,897 Speaker 4: left we present matter women right, like everything with them 482 00:21:46,417 --> 00:21:49,697 Speaker 4: is fantasyland. And so yeah, I think Hillary Clinton also 483 00:21:49,697 --> 00:21:54,777 Speaker 4: tweeted out about how Barbie got like talking about stolen elections, right, 484 00:21:54,937 --> 00:21:56,977 Speaker 4: Hillary Clinton loves to talk about that, and she said 485 00:21:56,977 --> 00:21:59,937 Speaker 4: that Barbie was cheated at the Oscars or whatever stupid 486 00:21:59,937 --> 00:22:00,417 Speaker 4: award show. 487 00:22:00,417 --> 00:22:01,537 Speaker 3: It was. So yeah. 488 00:22:01,577 --> 00:22:04,817 Speaker 4: I mean, I'm glad that Shakira found her voice and 489 00:22:04,857 --> 00:22:07,177 Speaker 4: I hope she continues to use it in this way. 490 00:22:07,457 --> 00:22:10,457 Speaker 4: I have this feeling that we diverge from Shakira most 491 00:22:10,617 --> 00:22:14,417 Speaker 4: other topics, So maybe not, maybe this will be. 492 00:22:15,177 --> 00:22:17,417 Speaker 2: Have you heard any other celebrity come out and say, 493 00:22:17,537 --> 00:22:19,737 Speaker 2: like any other globally known celebrity come out and say 494 00:22:19,737 --> 00:22:22,257 Speaker 2: that the Barbe movie sucks because it is it is unwatchable. 495 00:22:22,337 --> 00:22:23,977 Speaker 2: First of all, how can you not have You need 496 00:22:24,017 --> 00:22:25,857 Speaker 2: to do it. You need to watch it so then 497 00:22:25,897 --> 00:22:27,777 Speaker 2: you can do a TikTok video on how awful it 498 00:22:27,817 --> 00:22:29,377 Speaker 2: is and then maybe and be like shout out Bucks 499 00:22:29,417 --> 00:22:30,457 Speaker 2: Axon because he told me it. 500 00:22:30,377 --> 00:22:33,097 Speaker 3: Was awfulally bad. Fuck. 501 00:22:33,177 --> 00:22:36,617 Speaker 4: I would literally rather be waterboarded and watch the Barbie movie. 502 00:22:36,697 --> 00:22:38,337 Speaker 3: I will not do it at this point. It is 503 00:22:38,417 --> 00:22:38,857 Speaker 3: too late. 504 00:22:39,017 --> 00:22:41,457 Speaker 4: I don't need to, and I can't like it's not 505 00:22:41,537 --> 00:22:43,057 Speaker 4: it's not even worth it because also the left. 506 00:22:43,057 --> 00:22:45,617 Speaker 3: You could tell the left it's horrible, and they just 507 00:22:45,617 --> 00:22:46,937 Speaker 3: just insist that it's good. 508 00:22:47,337 --> 00:22:49,777 Speaker 4: I heard through the grapevine through friends that saw it, 509 00:22:49,857 --> 00:22:52,737 Speaker 4: that it's meant to be a feminist movie, but they 510 00:22:52,817 --> 00:22:55,777 Speaker 4: actually it ends up being kind of showing how men 511 00:22:55,817 --> 00:22:57,377 Speaker 4: are good at the end. I don't know if that's 512 00:22:57,417 --> 00:23:01,337 Speaker 4: actually accurate, but there's something really off and imbalanced about 513 00:23:01,377 --> 00:23:03,657 Speaker 4: the movie that and it's very very long. If it 514 00:23:03,697 --> 00:23:05,777 Speaker 4: were an hour and a half, I would I would 515 00:23:05,777 --> 00:23:07,897 Speaker 4: try to stomach through it. But given the length of 516 00:23:07,897 --> 00:23:08,497 Speaker 4: the movie. 517 00:23:08,377 --> 00:23:10,217 Speaker 2: I only got an hour into it and Carer and 518 00:23:10,257 --> 00:23:11,617 Speaker 2: I bailed, and she agreed with him by the way. 519 00:23:11,657 --> 00:23:13,937 Speaker 2: She was like, this movie's trash. So anyway, there is 520 00:23:14,497 --> 00:23:16,977 Speaker 2: hope for the world. There's hope for women, maybe not 521 00:23:17,057 --> 00:23:19,257 Speaker 2: to be better drivers than men, according to Aaron. According 522 00:23:19,337 --> 00:23:21,617 Speaker 2: to Erin, as Aaron would say, just so you could 523 00:23:21,657 --> 00:23:25,417 Speaker 2: all get mad at her, but still there is hope for. 524 00:23:25,377 --> 00:23:25,977 Speaker 1: Women out there. 525 00:23:26,017 --> 00:23:31,537 Speaker 2: Follow non lib take on Instagram and on the TikTok. 526 00:23:32,097 --> 00:23:34,217 Speaker 2: You know that I'm like the only right wing guy 527 00:23:34,577 --> 00:23:36,297 Speaker 2: at least a guy I can say that I know 528 00:23:36,337 --> 00:23:38,977 Speaker 2: of who's like pro TikTok. I like TikTok, and I'm 529 00:23:39,017 --> 00:23:40,897 Speaker 2: not afraid to say it's funny. Everyonan's look, it's a 530 00:23:40,977 --> 00:23:43,977 Speaker 2: Chinese spine tool, and I'm always like, yeah, so is Google. 531 00:23:44,177 --> 00:23:46,617 Speaker 2: And they actually are destroying your freedoms in your country 532 00:23:46,697 --> 00:23:49,297 Speaker 2: under your nose, and they're paying off all your senators. 533 00:23:48,897 --> 00:23:49,457 Speaker 1: While they do it. 534 00:23:49,497 --> 00:23:52,857 Speaker 2: But let's worry about TikTok anyway, conversation for another time. 535 00:23:52,977 --> 00:23:56,017 Speaker 2: That's what I'm here for, Aaron Rexler. Everybody spread this 536 00:23:56,057 --> 00:23:58,297 Speaker 2: far and wide to Aaron gets even more famous, and 537 00:23:58,337 --> 00:23:59,977 Speaker 2: then we have to have her back again because the 538 00:24:00,017 --> 00:24:00,817 Speaker 2: numbers don't lie. 539 00:24:00,857 --> 00:24:02,217 Speaker 1: Thanks Erin, good to see you. 540 00:24:02,337 --> 00:24:02,897 Speaker 3: Thanks Buck