1 00:00:00,160 --> 00:00:03,440 Speaker 1: Hello the Internet, and welcome to this episode of the 2 00:00:03,560 --> 00:00:07,840 Speaker 1: Weekly Zeitgeist. Uh. These are some of our favorite segments 3 00:00:08,119 --> 00:00:14,880 Speaker 1: from this week, all edited together into one NonStop infotainment 4 00:00:16,040 --> 00:00:22,440 Speaker 1: last stravaganza. Uh yeah, So, without further ado, here is 5 00:00:22,680 --> 00:00:27,720 Speaker 1: the Weekly Zeitgeist. What is something from your search history 6 00:00:27,760 --> 00:00:32,839 Speaker 1: that is revealing about who you are? Okay? Um, I 7 00:00:34,159 --> 00:00:39,120 Speaker 1: did a deep dive on John Fetterman. Um, just uh 8 00:00:39,560 --> 00:00:41,800 Speaker 1: John Fetterman, if you don't know, it's a lieutenant governor 9 00:00:41,840 --> 00:00:49,159 Speaker 1: of Pennsylvania and his wife was right step a racial 10 00:00:49,720 --> 00:00:55,680 Speaker 1: verbal assault in the supermarket. She is Brazilian, so naturally 11 00:00:56,200 --> 00:00:59,800 Speaker 1: somebody cornerator in the supermarket and uh yell the end 12 00:00:59,800 --> 00:01:04,040 Speaker 1: word that repeatedly and followed her into the parking lot. 13 00:01:04,920 --> 00:01:11,400 Speaker 1: And um, so then I looked up Fetterman, and I 14 00:01:11,400 --> 00:01:14,440 Speaker 1: I ask everyone with a phone right now to do 15 00:01:14,520 --> 00:01:20,880 Speaker 1: so because this man, Um, he looks Yes, he definitely 16 00:01:20,920 --> 00:01:25,280 Speaker 1: looks like he stole a correction officer's uniform and used 17 00:01:25,319 --> 00:01:33,679 Speaker 1: it to escape prison. Um. Them as a couple, I'm like, 18 00:01:33,959 --> 00:01:37,000 Speaker 1: hold on, there are things where like he's got a 19 00:01:37,120 --> 00:01:42,560 Speaker 1: really really intense be goatee um, and he's also got 20 00:01:42,600 --> 00:01:46,720 Speaker 1: the same vibe of Jaws from James Bond. Yeah, like 21 00:01:46,800 --> 00:01:52,880 Speaker 1: kind around, like very big. Yeah, look at that. Wow, 22 00:01:53,120 --> 00:01:58,280 Speaker 1: Like that guy. If that person assailant had known what 23 00:01:58,400 --> 00:02:03,960 Speaker 1: this woman's husband looked like, I'm assuming I think she did. 24 00:02:04,080 --> 00:02:07,240 Speaker 1: I think if she knows what that guy's wife looks like, right, 25 00:02:07,920 --> 00:02:11,600 Speaker 1: she probably knows. Um. But no, he went to Harvard. 26 00:02:11,840 --> 00:02:14,720 Speaker 1: He didn't. He didn't escape from prison. No, he's like, 27 00:02:14,800 --> 00:02:17,560 Speaker 1: on paper, like a really chill guy. You're like he 28 00:02:17,680 --> 00:02:20,280 Speaker 1: worked with the Big Brothers and Big Sisters and like 29 00:02:20,400 --> 00:02:22,799 Speaker 1: got obsessed with the idea of like your birth being 30 00:02:22,800 --> 00:02:25,520 Speaker 1: a random lottery for your outcomes and things like that 31 00:02:25,600 --> 00:02:28,920 Speaker 1: and how that affects his idea of fairness. But he 32 00:02:28,960 --> 00:02:36,239 Speaker 1: looks as yeah, he just never changed his look, you know. Yeah, yeah, 33 00:02:36,240 --> 00:02:38,720 Speaker 1: but he doesn't need to. There's just something even like 34 00:02:38,760 --> 00:02:42,400 Speaker 1: when you look at his Wikipedia page, he's like looks 35 00:02:42,440 --> 00:02:45,880 Speaker 1: just so underdressed for whatever official photo this is, like 36 00:02:45,919 --> 00:02:48,720 Speaker 1: it's clearly his bureaucrat, like your book photo where he 37 00:02:48,720 --> 00:02:50,400 Speaker 1: goes to the capital and like this is like a 38 00:02:50,400 --> 00:02:54,160 Speaker 1: lieutenant governor and he's wearing like a ray On Safari shirt, 39 00:02:54,360 --> 00:03:00,880 Speaker 1: not happy to be there, like button ups. Oh for sure. Yeah, 40 00:03:01,320 --> 00:03:04,080 Speaker 1: that's that's definitely a top level bill of bong. But 41 00:03:04,200 --> 00:03:07,640 Speaker 1: his smirk definitely says that he wasn't smiling, and they can't. 42 00:03:07,680 --> 00:03:10,799 Speaker 1: The photographers like, come on, Lieutenant governor, give us a 43 00:03:10,840 --> 00:03:14,920 Speaker 1: little something more, and he just went, I will raise 44 00:03:15,000 --> 00:03:18,760 Speaker 1: the corners of my mouth flatly out of time. All right, 45 00:03:18,840 --> 00:03:21,919 Speaker 1: thanks John, And I did not bother to look at yeah, 46 00:03:21,960 --> 00:03:25,120 Speaker 1: because I read that headline and usually in my morning 47 00:03:25,160 --> 00:03:27,640 Speaker 1: I'm like, Okay, where's my daily dose of racism? And 48 00:03:27,639 --> 00:03:31,079 Speaker 1: I'm like, god, the fucking lieutenant governor's wife, But yeah, 49 00:03:31,080 --> 00:03:34,160 Speaker 1: I did not connect. Thank you for doing that little 50 00:03:34,200 --> 00:03:38,560 Speaker 1: bit of work to get us to see the whole picture. Wow. Yeah. 51 00:03:38,560 --> 00:03:41,160 Speaker 1: The closest he comes to smiling is looking a little 52 00:03:41,160 --> 00:03:45,120 Speaker 1: bit piste off. That's as far as he goes, just 53 00:03:45,160 --> 00:03:52,520 Speaker 1: a little. Yeah. What is something you think is overrated? Okay, 54 00:03:52,600 --> 00:03:55,360 Speaker 1: I'm annoyed with this ship. I hate it. Um Like 55 00:03:55,480 --> 00:03:58,920 Speaker 1: stealing content online bugs the ship out of me. Because 56 00:03:59,680 --> 00:04:05,400 Speaker 1: why comedians? Because I mean, my favorite comedian is the 57 00:04:05,440 --> 00:04:11,880 Speaker 1: Fat Jew and he's underrated. I agree, He's the Scorsesey 58 00:04:11,920 --> 00:04:17,000 Speaker 1: of tweet stealers. He's a taste maker. It's just I 59 00:04:17,000 --> 00:04:19,200 Speaker 1: don't know, it's so annoying because like I've gotten to 60 00:04:19,279 --> 00:04:23,320 Speaker 1: know more like online personalities, and it's like, I don't know, 61 00:04:23,440 --> 00:04:27,320 Speaker 1: you just like fun like this affects their income like directly, 62 00:04:27,480 --> 00:04:30,520 Speaker 1: it affects like their opportunities directly. And so I've started 63 00:04:30,520 --> 00:04:33,200 Speaker 1: to tell my like non comic friends, I'm like, unfollowed 64 00:04:33,240 --> 00:04:36,240 Speaker 1: this fake gas account. They stole my friends tweets, and 65 00:04:36,240 --> 00:04:39,480 Speaker 1: my non comic friends like don't care, feel like don't 66 00:04:39,600 --> 00:04:41,440 Speaker 1: They're just like I don't care. I like the follow 67 00:04:41,480 --> 00:04:43,520 Speaker 1: it's a good follow, And I'm like, these are people 68 00:04:43,600 --> 00:04:46,560 Speaker 1: behind these accounts that are just benefiting off of like 69 00:04:46,640 --> 00:04:49,760 Speaker 1: my friends work, Like what are you doing? So it's 70 00:04:49,760 --> 00:04:52,479 Speaker 1: been bugging this ship out of me, Like a lot 71 00:04:52,520 --> 00:04:55,960 Speaker 1: of them, A lot of those like content curators, which 72 00:04:56,160 --> 00:04:58,200 Speaker 1: what the funk are you talking? Like? It sounds like 73 00:04:58,560 --> 00:05:01,080 Speaker 1: like an art gallery but for dick jokes, like what 74 00:05:01,120 --> 00:05:05,800 Speaker 1: you're doing? Um, these content curators, some of them will 75 00:05:06,000 --> 00:05:08,400 Speaker 1: pay or like ask and tag your thing, but like 76 00:05:08,440 --> 00:05:10,599 Speaker 1: a lot of them just like take ship yeah and 77 00:05:10,600 --> 00:05:13,960 Speaker 1: even then the tag like sure, But at the end 78 00:05:13,960 --> 00:05:17,040 Speaker 1: of the day, they're guaranteeing like impressions or a certain 79 00:05:17,080 --> 00:05:20,520 Speaker 1: amount of interaction, which makes them more You know, attractive 80 00:05:20,560 --> 00:05:23,280 Speaker 1: to people who want to pay for sponsored posts and 81 00:05:23,320 --> 00:05:25,599 Speaker 1: things like that, and that whole echos it's all tied together. 82 00:05:25,640 --> 00:05:27,839 Speaker 1: But I can only imagine like trying to get someone 83 00:05:27,880 --> 00:05:32,320 Speaker 1: to care about like how this kind of joke theft 84 00:05:32,440 --> 00:05:35,200 Speaker 1: or content theft like actually affects humans in real life 85 00:05:35,200 --> 00:05:38,159 Speaker 1: because it's like another layer someone like too many people 86 00:05:38,200 --> 00:05:41,200 Speaker 1: already don't even understand how they're like labors being exploited. 87 00:05:41,480 --> 00:05:43,120 Speaker 1: So then on top of you, like, hey, you know 88 00:05:43,200 --> 00:05:45,599 Speaker 1: that fucking thing you stare at with your mouth open 89 00:05:45,600 --> 00:05:48,360 Speaker 1: and giggle at, that's also fucking someone ever, like, what 90 00:05:48,400 --> 00:05:52,560 Speaker 1: do you tell It's just memes, It's just memess What 91 00:05:52,600 --> 00:05:54,560 Speaker 1: do you mean I'm just giving sixty hours of my 92 00:05:54,640 --> 00:05:57,680 Speaker 1: life for fucking nothing. What are you talking about? It's 93 00:05:57,720 --> 00:06:01,880 Speaker 1: just work, It's all good it. It's also like I 94 00:06:01,880 --> 00:06:04,400 Speaker 1: don't know, it's like how like we all are trying 95 00:06:04,400 --> 00:06:07,400 Speaker 1: to like raise our social media whatever, like we try 96 00:06:07,440 --> 00:06:09,719 Speaker 1: to like tweet and like put up content or whatever. 97 00:06:10,120 --> 00:06:13,039 Speaker 1: I can't imagine, Like I hate that part. I hate 98 00:06:13,040 --> 00:06:15,640 Speaker 1: the part of like like I love tweeting, I love 99 00:06:15,720 --> 00:06:18,040 Speaker 1: coming up with jokes, I love whatever, But I hate 100 00:06:18,040 --> 00:06:20,159 Speaker 1: the part where you're like you have to screenshot and 101 00:06:20,160 --> 00:06:22,960 Speaker 1: then posted on Instagram and then post that to your store, 102 00:06:23,040 --> 00:06:25,880 Speaker 1: like the business part of it, where like you're trying 103 00:06:25,920 --> 00:06:28,560 Speaker 1: to get more followers. I hate that part, so imagine 104 00:06:28,600 --> 00:06:31,400 Speaker 1: that's like that's all your job is is to like 105 00:06:31,920 --> 00:06:36,520 Speaker 1: but like what kind of rotted brainwormy like thinking, if 106 00:06:36,560 --> 00:06:38,240 Speaker 1: that's all you do, you don't even come up with 107 00:06:38,240 --> 00:06:42,599 Speaker 1: a creative part. I can't imagine what a dinner conversation 108 00:06:42,640 --> 00:06:45,640 Speaker 1: would be like with you, But I feel like that's 109 00:06:45,680 --> 00:06:48,680 Speaker 1: the direction. So much of our culture is going like 110 00:06:48,760 --> 00:06:53,800 Speaker 1: sort of the curating, like valuing people who are good 111 00:06:53,800 --> 00:06:59,120 Speaker 1: at like curating things and like finding cool things out 112 00:06:59,120 --> 00:07:02,680 Speaker 1: of like a just massive amount of information. Like I 113 00:07:02,680 --> 00:07:06,719 Speaker 1: don't think it's great, but I do. I do feel 114 00:07:06,720 --> 00:07:10,160 Speaker 1: like that is just generally like the values that have 115 00:07:10,520 --> 00:07:14,480 Speaker 1: taken hold in general, like on social media, Like like 116 00:07:14,600 --> 00:07:19,560 Speaker 1: memes come from a book that was literally it's the 117 00:07:19,600 --> 00:07:23,520 Speaker 1: book that introduced the idea of genes also introduced the 118 00:07:23,560 --> 00:07:26,600 Speaker 1: idea of memes, and it's just like a meme is 119 00:07:26,680 --> 00:07:30,760 Speaker 1: like this disembodied idea where the whole goal of the 120 00:07:30,840 --> 00:07:34,800 Speaker 1: idea is to reproduce itself in it like that book, 121 00:07:35,000 --> 00:07:37,880 Speaker 1: like Richard Dawkins didn't write about it as like you know, 122 00:07:37,920 --> 00:07:41,120 Speaker 1: and that's intellectual property. It was just like this disembodied 123 00:07:41,160 --> 00:07:45,560 Speaker 1: thing where like if the idea that is created is replicable, 124 00:07:45,720 --> 00:07:50,920 Speaker 1: then it is a successful meme. But it's just weird there. 125 00:07:50,960 --> 00:07:55,280 Speaker 1: There is like an inherent contradiction there, especially when you're 126 00:07:56,040 --> 00:08:00,240 Speaker 1: a really talented writer who is putting your talents into 127 00:08:00,320 --> 00:08:04,160 Speaker 1: creating something and then it just reproduces out of control 128 00:08:04,320 --> 00:08:09,000 Speaker 1: and people remove the author's name from it, which is 129 00:08:09,000 --> 00:08:13,000 Speaker 1: annoying and nobody should ever do. Dana Donley, who's been 130 00:08:13,040 --> 00:08:15,160 Speaker 1: on this podcast before, had like a really great tweet 131 00:08:15,160 --> 00:08:17,040 Speaker 1: that I like have like I've always like thought that, 132 00:08:17,040 --> 00:08:19,080 Speaker 1: but she put into words and it was about how 133 00:08:19,120 --> 00:08:21,920 Speaker 1: like if not enough people I forget exactly what she said, 134 00:08:21,920 --> 00:08:23,880 Speaker 1: but if like if not enough people replicate it, then 135 00:08:23,880 --> 00:08:25,960 Speaker 1: it's stealing. But if enough people do it, then it's 136 00:08:25,960 --> 00:08:28,480 Speaker 1: a meme, you know what I mean. So it's like 137 00:08:28,560 --> 00:08:32,240 Speaker 1: it's but I think that's like slight. Yeah, yeah, I 138 00:08:32,240 --> 00:08:34,200 Speaker 1: don't know. I don't know where that threshold is. But 139 00:08:34,400 --> 00:08:36,839 Speaker 1: I think even beyond that, like with memes, like people 140 00:08:36,840 --> 00:08:39,160 Speaker 1: like change the punchline, the change they try to come 141 00:08:39,240 --> 00:08:42,120 Speaker 1: up with, like club boys. But this like screenshotting and 142 00:08:42,200 --> 00:08:44,559 Speaker 1: just like posting it onto your own thing with that, 143 00:08:44,760 --> 00:08:47,720 Speaker 1: like that's like you get money for that. That's like 144 00:08:47,720 --> 00:08:50,080 Speaker 1: like that's more obvious of like a fucked up thing 145 00:08:50,720 --> 00:08:54,200 Speaker 1: at least start there. But that's almost like bringing like 146 00:08:54,240 --> 00:08:56,320 Speaker 1: a cell phone camera to a movie and taping it 147 00:08:56,360 --> 00:08:58,680 Speaker 1: on your cell phone and picking your phone up to 148 00:08:58,679 --> 00:09:03,880 Speaker 1: a projector at your own Yeah, and you're like, this 149 00:09:03,960 --> 00:09:07,760 Speaker 1: is the shitty version of the real one. What are 150 00:09:07,800 --> 00:09:09,600 Speaker 1: you doing? Like, I don't know, I just it's less 151 00:09:09,679 --> 00:09:12,800 Speaker 1: less to think about. I follow this one thing. Yeah. 152 00:09:12,960 --> 00:09:15,960 Speaker 1: But then there's also the thing where everybody has the 153 00:09:16,000 --> 00:09:18,200 Speaker 1: same idea at the same time, and it seems like 154 00:09:18,240 --> 00:09:22,600 Speaker 1: they're copying off each other. I forget which news story 155 00:09:22,720 --> 00:09:25,440 Speaker 1: involving Trump. Uh oh, I think it was when he 156 00:09:25,520 --> 00:09:30,160 Speaker 1: tested positive for COVID and like I saw thirty people 157 00:09:30,280 --> 00:09:33,959 Speaker 1: tweet I did not see that coming, like not see, 158 00:09:34,120 --> 00:09:37,880 Speaker 1: but not see, And uh. At first, I was like, 159 00:09:37,920 --> 00:09:40,640 Speaker 1: oh man, they're like ripping each other off. But then 160 00:09:40,840 --> 00:09:43,760 Speaker 1: as more and more people made that exact same joke, 161 00:09:43,800 --> 00:09:46,400 Speaker 1: I was like, Wow, it's just our brands are all 162 00:09:47,440 --> 00:09:50,840 Speaker 1: like the same. I've tried to recently and there's like 163 00:09:50,920 --> 00:09:53,800 Speaker 1: some people I've made friends with some Twitter people, and 164 00:09:54,040 --> 00:09:56,640 Speaker 1: some of us have this attitude of like, well, like 165 00:09:56,720 --> 00:09:59,839 Speaker 1: I I've deleted viral tweets where it was parallel thought, 166 00:10:00,200 --> 00:10:04,000 Speaker 1: because you want your jokes to be like unique, right, 167 00:10:04,360 --> 00:10:06,920 Speaker 1: So I've started doing that, and before I with Sanda, 168 00:10:06,920 --> 00:10:08,079 Speaker 1: but I used to not do that as much. I 169 00:10:08,160 --> 00:10:09,360 Speaker 1: used to be like, well, we both thought of it, 170 00:10:09,360 --> 00:10:11,400 Speaker 1: We're going to deliver in a different way. It's gonna 171 00:10:11,400 --> 00:10:13,600 Speaker 1: be a different thing, um. But now with like tweets, 172 00:10:13,600 --> 00:10:15,560 Speaker 1: I'm like, oh, it's like the exact same line. Like 173 00:10:15,600 --> 00:10:17,199 Speaker 1: I don't want someone to look up this joke and 174 00:10:17,240 --> 00:10:20,320 Speaker 1: then see like thirty other people paying this joke. So 175 00:10:20,400 --> 00:10:22,360 Speaker 1: like there have been ones that have been like like 176 00:10:22,559 --> 00:10:23,920 Speaker 1: it was on the way up and it looked like 177 00:10:23,920 --> 00:10:25,600 Speaker 1: it would hit like maybe a hundred k and the 178 00:10:25,640 --> 00:10:27,680 Speaker 1: next day and I just like deleted it because somebody 179 00:10:27,679 --> 00:10:30,400 Speaker 1: with like nine followers also did it or whatever. But 180 00:10:30,440 --> 00:10:32,720 Speaker 1: it's like, so I I have that attitude. I know 181 00:10:32,760 --> 00:10:34,720 Speaker 1: like a lot of other people don't. But I'm like, 182 00:10:34,760 --> 00:10:37,000 Speaker 1: why wouldn't you want your feed to be Like why 183 00:10:37,000 --> 00:10:39,640 Speaker 1: wouldn't you if you're creative, if you're like making the 184 00:10:39,720 --> 00:10:42,480 Speaker 1: content like you don't you don't want to have hack jokes, 185 00:10:42,480 --> 00:10:44,880 Speaker 1: you know, so like I will just tell my friends 186 00:10:44,920 --> 00:10:46,800 Speaker 1: I'm like, hey, by the way, somebody else did this joke, 187 00:10:46,880 --> 00:10:48,840 Speaker 1: and then like leave it up to them to delete. 188 00:10:48,880 --> 00:10:50,560 Speaker 1: But like you know, there are a few of us 189 00:10:50,600 --> 00:10:52,600 Speaker 1: who were like, oh yeah, I'm deleting that immediately because 190 00:10:52,600 --> 00:10:56,400 Speaker 1: it's not unique. I don't know, Um, it's hard. But 191 00:10:56,440 --> 00:10:58,920 Speaker 1: I think in terms of like stand up, because that's 192 00:10:58,920 --> 00:11:05,080 Speaker 1: the thing that also like again being policed incredibly strictly. Uh, 193 00:11:05,240 --> 00:11:08,920 Speaker 1: like if people had like similar observations, and it's just 194 00:11:08,960 --> 00:11:11,040 Speaker 1: like but I mean at a certain point, like a 195 00:11:11,080 --> 00:11:13,920 Speaker 1: lot of it is who is saying it and how 196 00:11:13,960 --> 00:11:17,559 Speaker 1: they're saying it, yeah, for sure, and like the context 197 00:11:17,600 --> 00:11:20,080 Speaker 1: and like yeah, a lot of us are coming up 198 00:11:20,120 --> 00:11:22,800 Speaker 1: with like very similar ideas. It's like I think stand 199 00:11:22,880 --> 00:11:25,120 Speaker 1: up has more flexibility, and like how in all of 200 00:11:25,120 --> 00:11:29,960 Speaker 1: those things? Yea, what is something you think is overrated? 201 00:11:30,440 --> 00:11:32,560 Speaker 1: This is something I've been banging the drum about for 202 00:11:32,679 --> 00:11:36,559 Speaker 1: like four straight years. The concept of gaffs, as in 203 00:11:36,679 --> 00:11:39,360 Speaker 1: like the win a politician or a candidate says something 204 00:11:39,440 --> 00:11:43,040 Speaker 1: dumb in public, those like the concept that that's something 205 00:11:43,080 --> 00:11:48,120 Speaker 1: that matters and we'll actually sink a candidate. Trump has 206 00:11:48,200 --> 00:11:52,040 Speaker 1: killed that forever, Okay, And I'm I'm glad because Twitter 207 00:11:52,280 --> 00:11:55,120 Speaker 1: loves this. If Trump misspells a word into tweet, that 208 00:11:55,200 --> 00:11:59,640 Speaker 1: misspelled word will trend all day. And I'm telling you 209 00:12:00,440 --> 00:12:03,439 Speaker 1: I watched for three and a half years when everyone 210 00:12:03,520 --> 00:12:06,360 Speaker 1: was like, oh, fe that's going to be the thing 211 00:12:06,400 --> 00:12:10,280 Speaker 1: that alerts America that this man is a moron. It 212 00:12:10,280 --> 00:12:13,720 Speaker 1: doesn't work like that anymore. The average American actually find 213 00:12:13,800 --> 00:12:16,840 Speaker 1: it kind of endearing when you're not polished as a politician. 214 00:12:16,880 --> 00:12:19,600 Speaker 1: For whatever reason. In the modern era, we we we 215 00:12:19,760 --> 00:12:21,320 Speaker 1: kind of like it when they screw up. And it 216 00:12:21,360 --> 00:12:22,960 Speaker 1: was the same thing of the primaries, and they're like 217 00:12:23,040 --> 00:12:25,880 Speaker 1: Biden as a gaff machine. He he can't string together 218 00:12:25,920 --> 00:12:28,400 Speaker 1: two sentences without getting the name of the city he's in, 219 00:12:28,440 --> 00:12:33,040 Speaker 1: wrong or something like that. Nobody cares. Let that be 220 00:12:33,080 --> 00:12:36,360 Speaker 1: dead forever. Because what happened with Trump where his numbers 221 00:12:36,360 --> 00:12:42,199 Speaker 1: didn't It took a worldwide apocalypse to make his approval 222 00:12:42,280 --> 00:12:45,160 Speaker 1: moved down by like the four points it required to 223 00:12:45,200 --> 00:12:47,520 Speaker 1: give the other candidate a lead and some kind of 224 00:12:47,720 --> 00:12:52,240 Speaker 1: substantial lead in the polling. But the twenty thousand gaffs 225 00:12:52,240 --> 00:12:57,080 Speaker 1: he committed did not do that at all. Yeah. In general, 226 00:12:57,120 --> 00:12:59,920 Speaker 1: it's like seems like nothing even attacks from the right 227 00:13:00,080 --> 00:13:03,320 Speaker 1: about like the behavior of politicians who are Democrats, just 228 00:13:03,360 --> 00:13:06,000 Speaker 1: like they ring so hollow, and it's like, I think 229 00:13:06,240 --> 00:13:08,679 Speaker 1: this is where past all of this now because you're 230 00:13:08,679 --> 00:13:11,440 Speaker 1: so craving over there, like the fact that Europe set 231 00:13:11,760 --> 00:13:14,680 Speaker 1: I'm having a failure to connect the outrage that the 232 00:13:14,720 --> 00:13:17,080 Speaker 1: people on the right have, you know, like for even 233 00:13:17,080 --> 00:13:19,640 Speaker 1: like with the Hunter Biden things where they're like, oh, 234 00:13:19,640 --> 00:13:22,280 Speaker 1: this will get them, and it's like, no, everyone's completely 235 00:13:22,360 --> 00:13:24,600 Speaker 1: numb to everything. Actually, I feel like it it might 236 00:13:24,800 --> 00:13:26,640 Speaker 1: motivate your base, but I don't know if it's gonna 237 00:13:26,640 --> 00:13:28,960 Speaker 1: have that effect of like, oh God, did you guys 238 00:13:28,960 --> 00:13:32,240 Speaker 1: see this thing? It's lights out for them. Huh. Right, 239 00:13:33,080 --> 00:13:35,880 Speaker 1: there was an underage a woman who had claimed that 240 00:13:35,880 --> 00:13:39,120 Speaker 1: Trump had sexually assaulted her when she was underage. That 241 00:13:39,120 --> 00:13:41,960 Speaker 1: that story just came and went in a blip, like 242 00:13:42,000 --> 00:13:44,000 Speaker 1: no one listening, he even knows what I'm talking about, 243 00:13:44,160 --> 00:13:46,960 Speaker 1: Like it just like something that would have been a 244 00:13:46,960 --> 00:13:49,720 Speaker 1: show stopper twenty years ago. And then the New York 245 00:13:49,760 --> 00:13:52,960 Speaker 1: Times did this huge breakdown about how Trump was basically 246 00:13:53,000 --> 00:13:56,480 Speaker 1: just using the White House to divert money to his businesses, 247 00:13:56,520 --> 00:14:00,000 Speaker 1: and they probably devoted a hundred and fifty thousand dollars 248 00:14:00,040 --> 00:14:02,680 Speaker 1: and resources and reporting in man hours putting it together. 249 00:14:03,080 --> 00:14:05,360 Speaker 1: And no, it did trended for like an hour. I 250 00:14:05,360 --> 00:14:08,199 Speaker 1: saw a few like political types on Twitter tweeting It's like, oh, 251 00:14:08,240 --> 00:14:10,640 Speaker 1: this is great reporting. It's a great breakdown at the 252 00:14:11,000 --> 00:14:16,240 Speaker 1: level of corruption here. The fly on Mike Pencil's head 253 00:14:16,280 --> 00:14:18,960 Speaker 1: trended longer than than that. I mean, but that thing 254 00:14:19,040 --> 00:14:21,160 Speaker 1: was hilarious, you see. I think it was it was 255 00:14:21,200 --> 00:14:28,000 Speaker 1: sitting there and it didn't even leave boarding to dreams. Um. Yeah, 256 00:14:28,080 --> 00:14:32,200 Speaker 1: I I do think that it's some sometimes, like because 257 00:14:32,320 --> 00:14:37,680 Speaker 1: people do do say that Hillary Clinton's email, like the 258 00:14:38,040 --> 00:14:41,360 Speaker 1: fact that Comey opened up reopened that investigation a week 259 00:14:41,480 --> 00:14:47,960 Speaker 1: before the election had an impact on on voters. And 260 00:14:48,640 --> 00:14:52,800 Speaker 1: I think it's something to do with like how the 261 00:14:52,840 --> 00:14:57,720 Speaker 1: negative story interacts with like what we suspect about that 262 00:14:57,800 --> 00:15:01,320 Speaker 1: person and or like what both suspect and like what 263 00:15:01,400 --> 00:15:06,640 Speaker 1: the overall negative feelings are. Like with the Rick Perry 264 00:15:06,680 --> 00:15:11,040 Speaker 1: thing where he couldn't name anything, like he's just couldn't 265 00:15:11,120 --> 00:15:14,320 Speaker 1: remember anything like that interacted with the fact that he's 266 00:15:14,360 --> 00:15:17,520 Speaker 1: clearly like not not smart and like propped up by 267 00:15:17,960 --> 00:15:21,240 Speaker 1: speech writers and uh, you know, and then they can 268 00:15:21,240 --> 00:15:28,240 Speaker 1: start wearing glasses after that, Like, um, but what Hillary. 269 00:15:28,400 --> 00:15:30,360 Speaker 1: That's the thing is that that was a culmination of 270 00:15:30,560 --> 00:15:36,440 Speaker 1: literally twenty however many years since, since twenty some years 271 00:15:36,440 --> 00:15:39,760 Speaker 1: of anti Hillary anti Clinton. The Clintons are corrupt hatred. 272 00:15:39,800 --> 00:15:42,000 Speaker 1: Like if you were in conservative circles in the nineties, 273 00:15:42,040 --> 00:15:44,640 Speaker 1: you had like the Clinton murder list, like the list 274 00:15:44,680 --> 00:15:47,000 Speaker 1: of forty nine people that Hillary and Bill Clinton had 275 00:15:47,080 --> 00:15:50,800 Speaker 1: murdered together arranged a plane crash was one of them, 276 00:15:50,800 --> 00:15:53,800 Speaker 1: and like car accidents like it's made them and in 277 00:15:53,840 --> 00:15:57,240 Speaker 1: conservative circles like that, they were these deeply corrupt people, 278 00:15:57,840 --> 00:16:01,360 Speaker 1: so that even though the email else didn't say anything, 279 00:16:02,000 --> 00:16:05,360 Speaker 1: it was just a signal to people who were already 280 00:16:05,400 --> 00:16:07,480 Speaker 1: ready to hate her. And I think that's one lesson 281 00:16:07,520 --> 00:16:12,000 Speaker 1: we have about now. Is it probably any other candidate, 282 00:16:12,040 --> 00:16:17,000 Speaker 1: even one chosen at random, probably squeaks out that election. Um, 283 00:16:17,000 --> 00:16:21,360 Speaker 1: But they just uniquely hated Hillary so much for a 284 00:16:21,360 --> 00:16:23,520 Speaker 1: bunch of reasons, some of its sexism, some of it 285 00:16:23,600 --> 00:16:26,680 Speaker 1: the Clinton's but she was like the like in terms 286 00:16:26,680 --> 00:16:29,840 Speaker 1: of her negatives and all that she was. It was 287 00:16:29,880 --> 00:16:31,640 Speaker 1: the case. They're kind of like asking people to hold 288 00:16:31,680 --> 00:16:34,520 Speaker 1: their nose and vote for And that's one reason why 289 00:16:34,520 --> 00:16:38,000 Speaker 1: this year is different. People genuinely do like Biden like that, 290 00:16:38,200 --> 00:16:40,480 Speaker 1: but you know, again, it's the contrast with him and 291 00:16:40,520 --> 00:16:44,040 Speaker 1: Trump is just so strong that maybe anybody would have 292 00:16:44,080 --> 00:16:48,000 Speaker 1: the same effect. All right, let's take a quick break. 293 00:16:48,000 --> 00:17:00,720 Speaker 1: We'll be right back, and we're back then. I also 294 00:17:00,720 --> 00:17:04,120 Speaker 1: wanted to mention that Cops, you know, earlier in the year, 295 00:17:04,200 --> 00:17:07,359 Speaker 1: because of the Black Lives Matter movement and you know, 296 00:17:07,480 --> 00:17:11,000 Speaker 1: movements across the country that we're pointing out just the 297 00:17:11,080 --> 00:17:16,960 Speaker 1: injustices around policing in America, Cops was canceled by the 298 00:17:17,000 --> 00:17:22,680 Speaker 1: Paramount Network and that it has quietly been uncanceled with 299 00:17:22,720 --> 00:17:30,520 Speaker 1: regards to foreign distribution, so distribution any non American market, 300 00:17:30,640 --> 00:17:33,160 Speaker 1: they're still going to be making it, and then who knows, 301 00:17:33,280 --> 00:17:35,680 Speaker 1: like how it's going to be distributed in the US. 302 00:17:35,760 --> 00:17:38,280 Speaker 1: I'm sure they'll find us channel. They're like, well, if 303 00:17:38,320 --> 00:17:42,480 Speaker 1: you get BBC America, then you get Cops, right, Wait, what, 304 00:17:43,840 --> 00:17:45,960 Speaker 1: I don't know, I mean, yeah, now, it's just like, oh, 305 00:17:46,040 --> 00:17:49,720 Speaker 1: but we're still in the market of exporting copaganda. If 306 00:17:49,720 --> 00:17:55,200 Speaker 1: you're interested in that for your burgeoning authoritarian state where 307 00:17:55,240 --> 00:17:57,960 Speaker 1: you want the police to walk around like militarized gons. 308 00:17:58,160 --> 00:18:01,440 Speaker 1: So cool, but yeah, I like how it just sort 309 00:18:01,440 --> 00:18:03,760 Speaker 1: of like we care so but it's just for internationals, 310 00:18:03,920 --> 00:18:07,240 Speaker 1: just for international we know it's illegal here, Okay, we 311 00:18:07,320 --> 00:18:09,760 Speaker 1: know this is totally illegal and not cool to do here, 312 00:18:09,800 --> 00:18:13,920 Speaker 1: but just across the borders for Canada. Yeah, and it 313 00:18:14,040 --> 00:18:17,080 Speaker 1: also think of the same company, So it's still like, 314 00:18:17,359 --> 00:18:20,480 Speaker 1: you know that that gesture was just kind of performative, 315 00:18:21,480 --> 00:18:26,359 Speaker 1: no performative gestures in such a critical moment regarding the 316 00:18:26,440 --> 00:18:33,480 Speaker 1: civil rights of black people and other minorities. God who 317 00:18:34,000 --> 00:18:38,159 Speaker 1: made a big deal about retiring Anjemima, and of course 318 00:18:38,280 --> 00:18:42,040 Speaker 1: the right made a huge deal about it because they 319 00:18:42,119 --> 00:18:46,160 Speaker 1: love to have have stories like that where they can 320 00:18:46,160 --> 00:18:52,159 Speaker 1: be like, come on, what's next. According to one executive, 321 00:18:52,320 --> 00:18:56,480 Speaker 1: they still haven't actually retired Anjemima. According to one executive, 322 00:18:56,480 --> 00:19:00,959 Speaker 1: it's because get ready to feel queazy. During these trying 323 00:19:01,080 --> 00:19:04,880 Speaker 1: pandemic times, customers are still looking for the ant jemima 324 00:19:04,920 --> 00:19:10,520 Speaker 1: products they know and love. Yeah, so we've yea Rather 325 00:19:10,560 --> 00:19:13,960 Speaker 1: than using the terrible trope of ant Jemima, We've rebranded 326 00:19:13,960 --> 00:19:19,159 Speaker 1: to Mammy's pancakes. It's like, oh no, they're doubling down. 327 00:19:19,960 --> 00:19:23,000 Speaker 1: It's uh yeah, of course, of course it's in the end. 328 00:19:23,000 --> 00:19:27,520 Speaker 1: It's like any companies like, look, we would sever ties 329 00:19:27,600 --> 00:19:31,040 Speaker 1: with the racist tumor of our business, but it's part 330 00:19:31,080 --> 00:19:34,320 Speaker 1: of our business and it generates money, and money is 331 00:19:34,320 --> 00:19:38,000 Speaker 1: our god, not fairness. So yeah, I think that's that's 332 00:19:38,000 --> 00:19:39,720 Speaker 1: that's kind of the reality that people just have to 333 00:19:39,800 --> 00:19:45,440 Speaker 1: kind of you know, money, money pretty much guides. I mean, 334 00:19:45,760 --> 00:19:49,280 Speaker 1: if it wasn't profitable for them, you know, yeah, they 335 00:19:49,280 --> 00:19:51,280 Speaker 1: wouldn't be doing it. And and the fact that I 336 00:19:51,280 --> 00:19:53,280 Speaker 1: think the scary part to me is that it's still 337 00:19:53,320 --> 00:19:55,639 Speaker 1: continuously even even as bad as it is, it's still 338 00:19:55,680 --> 00:19:57,600 Speaker 1: continues to be profitable, meaning that there are people out 339 00:19:57,640 --> 00:19:59,480 Speaker 1: there who are buying it and they're finding it viable 340 00:19:59,600 --> 00:20:04,040 Speaker 1: enough to continue. I wonder if they just said they 341 00:20:04,119 --> 00:20:08,159 Speaker 1: called it fucking they did a Lady ante Bellum type 342 00:20:08,200 --> 00:20:12,679 Speaker 1: rebrand and they called the A J's Pancakes or Auntie Jay's, 343 00:20:13,280 --> 00:20:16,320 Speaker 1: and it's just text, there's no face. Are people gonna 344 00:20:16,320 --> 00:20:19,520 Speaker 1: be like, where the fund did it go? Where's the fun? 345 00:20:20,760 --> 00:20:23,399 Speaker 1: I guess I'm gonna that's a really good idea like 346 00:20:23,760 --> 00:20:26,240 Speaker 1: that's a really good where the like, what's the psychology 347 00:20:26,280 --> 00:20:29,080 Speaker 1: of the consumer that they're concerned about, like in this 348 00:20:29,119 --> 00:20:33,600 Speaker 1: thing where like in these times people need their racist pancakes. 349 00:20:34,240 --> 00:20:36,560 Speaker 1: It's I think it's just I think it's people's like 350 00:20:37,040 --> 00:20:41,760 Speaker 1: um uh romantic connection with memory, you know, And it 351 00:20:41,760 --> 00:20:43,159 Speaker 1: comes down to this thing where it's just like, oh, 352 00:20:43,200 --> 00:20:44,800 Speaker 1: you're gonna take this and what else you gonna take? 353 00:20:44,800 --> 00:20:47,280 Speaker 1: You take my my mom or my childhood, my mom? 354 00:20:47,440 --> 00:20:49,320 Speaker 1: Like I can, I can take away G I Joe 355 00:20:49,359 --> 00:20:50,960 Speaker 1: for a lot of those guys right now, you know 356 00:20:51,000 --> 00:20:52,359 Speaker 1: what I mean. And they would hate me for it. 357 00:20:52,400 --> 00:20:55,679 Speaker 1: But that's like one of the most racist cartoons on 358 00:20:55,760 --> 00:20:58,600 Speaker 1: the on the fucking planet. Like and I know this 359 00:20:58,680 --> 00:21:01,320 Speaker 1: as a brown person because I remember watching it as 360 00:21:01,359 --> 00:21:05,399 Speaker 1: a kid, you know. And then there was like uh um, 361 00:21:05,440 --> 00:21:08,080 Speaker 1: like I remember every time we would play G I 362 00:21:08,160 --> 00:21:10,480 Speaker 1: Joe like with with my friends, I would I could 363 00:21:10,480 --> 00:21:15,040 Speaker 1: only be Cobra only because Cobra had this thing where 364 00:21:15,359 --> 00:21:20,680 Speaker 1: they remember that why go co And and I thought 365 00:21:20,720 --> 00:21:22,879 Speaker 1: about it and I was like, oh, that's supposed to 366 00:21:22,960 --> 00:21:27,080 Speaker 1: be us. I mean like there and Cobra space is 367 00:21:27,080 --> 00:21:29,720 Speaker 1: always in the desert. And it was like it was 368 00:21:29,800 --> 00:21:36,800 Speaker 1: clearly like this programming famously American Snake. Yeah, yeah, right right. 369 00:21:36,960 --> 00:21:40,760 Speaker 1: It was just it was like this, but it bothered me, 370 00:21:40,800 --> 00:21:42,960 Speaker 1: and I remember like feeling so sad as a kid 371 00:21:43,000 --> 00:21:45,440 Speaker 1: because I did like g I jo, you know what 372 00:21:45,520 --> 00:21:47,520 Speaker 1: I mean, But I didn't want to feel like I 373 00:21:47,560 --> 00:21:50,080 Speaker 1: was always going to be painted as the enemy. So 374 00:21:50,600 --> 00:21:52,200 Speaker 1: but that I mean, there's just there's is a ship 375 00:21:52,280 --> 00:21:54,920 Speaker 1: ton of that stuff, ship tons, I mean from back 376 00:21:54,920 --> 00:21:57,359 Speaker 1: in the day. I think what their research is probably 377 00:21:57,400 --> 00:22:01,000 Speaker 1: telling them, you know, and their search being listening to 378 00:22:01,000 --> 00:22:04,760 Speaker 1: the beginning of this episode of this podcast, that people 379 00:22:05,240 --> 00:22:10,880 Speaker 1: are you know, drawn and especially especially during these trying times, 380 00:22:11,600 --> 00:22:15,840 Speaker 1: people are drawn to, you know, nostalgia. There there's a 381 00:22:15,880 --> 00:22:19,040 Speaker 1: general Mills thing that's happening that we talked about in 382 00:22:19,000 --> 00:22:23,080 Speaker 1: a recent episode where they are going back to original 383 00:22:23,200 --> 00:22:28,280 Speaker 1: recipe breakfast cereal on like all there on golden grams 384 00:22:28,320 --> 00:22:33,080 Speaker 1: and Coco puffs and all these things that we didn't 385 00:22:33,080 --> 00:22:36,760 Speaker 1: know they had changed the recipe on. But just being 386 00:22:36,840 --> 00:22:40,600 Speaker 1: like it's gonna taste like childhood was enough. It's gonna 387 00:22:40,600 --> 00:22:43,040 Speaker 1: taste like your parents are together. But that part where 388 00:22:43,040 --> 00:22:45,679 Speaker 1: they were fighting a bunch and about to get divorced. 389 00:22:47,000 --> 00:22:48,960 Speaker 1: I'm not. I'm not gonna lie. That sounds good to me, Like, 390 00:22:49,000 --> 00:22:51,000 Speaker 1: I think i'd be right. I haven't touched breakfast cereally 391 00:22:51,040 --> 00:22:53,040 Speaker 1: in a long time, but that made me feel like, 392 00:22:53,080 --> 00:22:56,520 Speaker 1: oh man, I think Donald Trump was smarty would campaign 393 00:22:56,520 --> 00:22:58,400 Speaker 1: on that. He's like, we're gonna make it feel like back. 394 00:22:58,400 --> 00:23:03,920 Speaker 1: Remember your parents are together, Remember that ship. Yeah, it's 395 00:23:03,920 --> 00:23:06,240 Speaker 1: gonna be like that. You just didn't even worry about ship, 396 00:23:06,320 --> 00:23:10,280 Speaker 1: did you? Yeah? Okay, but yeah, I mean in these 397 00:23:10,320 --> 00:23:14,879 Speaker 1: trying times, you know, as Black Americans struggle for equality, 398 00:23:15,040 --> 00:23:17,800 Speaker 1: we want our customers to retreat to the safe harbors 399 00:23:17,840 --> 00:23:22,600 Speaker 1: of the Antebellum South and chattel slavery. Right. Yeah, they're 400 00:23:22,840 --> 00:23:28,360 Speaker 1: demanding that the market basically regulate itself, which has never 401 00:23:29,119 --> 00:23:34,480 Speaker 1: ever worked. Um, I mean memories. I guess Retro Jordan's too. 402 00:23:34,480 --> 00:23:37,640 Speaker 1: It's kind of profiting off memories. Yeah, exactly. Yeah. I've 403 00:23:37,640 --> 00:23:40,040 Speaker 1: been regressing though, so I was ready for it all. 404 00:23:40,280 --> 00:23:45,960 Speaker 1: I've been in my mind for many years. Hell yeah, 405 00:23:46,000 --> 00:23:48,240 Speaker 1: I'm gonna go put the Fuji's the Score on right now. 406 00:23:50,000 --> 00:23:53,000 Speaker 1: I didn't want to talk about this story that I 407 00:23:53,040 --> 00:23:57,879 Speaker 1: think doom scrollers everywhere noticed, which is gentleman by the 408 00:23:57,960 --> 00:24:01,560 Speaker 1: name of Robert Kayley, who was one of the only 409 00:24:01,640 --> 00:24:07,280 Speaker 1: posters that predicted Trump's win in ten. And you know, 410 00:24:07,359 --> 00:24:10,320 Speaker 1: they're a handful most of them are predicting that this 411 00:24:10,359 --> 00:24:14,520 Speaker 1: time is different. Robert Kayley is saying, Nah, we've got 412 00:24:14,520 --> 00:24:19,040 Speaker 1: another We've got another one coming to real he's gonna win. Yeah. 413 00:24:19,040 --> 00:24:21,199 Speaker 1: I mean, his whole thing in Seen was that he 414 00:24:21,200 --> 00:24:23,280 Speaker 1: said a lot of posters weren't. He has like his 415 00:24:23,359 --> 00:24:26,040 Speaker 1: own method where he's trying to he asked other questions 416 00:24:26,040 --> 00:24:29,040 Speaker 1: to try and nail down who this person might be. 417 00:24:29,720 --> 00:24:31,600 Speaker 1: And you know, his the big thing that he always 418 00:24:31,600 --> 00:24:36,480 Speaker 1: talks about is this idea of um like social acceptance 419 00:24:36,560 --> 00:24:40,240 Speaker 1: or social desirability bias and how that affects people's response 420 00:24:40,240 --> 00:24:43,159 Speaker 1: and a lot of things. And he said that in fact, 421 00:24:43,320 --> 00:24:46,520 Speaker 1: in he thinks it's like a worst take to have 422 00:24:46,560 --> 00:24:49,159 Speaker 1: now to say you're supporting the president. That he's trying 423 00:24:49,200 --> 00:24:52,040 Speaker 1: to adjust for that as well. Um. But one of 424 00:24:52,080 --> 00:24:53,919 Speaker 1: the big things he was saying is like, you know, 425 00:24:54,240 --> 00:24:56,560 Speaker 1: a lot of the posts say Biden's gonna win Georgia 426 00:24:56,640 --> 00:24:58,879 Speaker 1: or Florida or North Carolina are like, you know, leading 427 00:24:58,920 --> 00:25:00,960 Speaker 1: and he's like, that's all fun. There's no way. He's 428 00:25:00,960 --> 00:25:04,879 Speaker 1: just saying Georgia, Florida, North Carolina, Ohio, Texas, Arizona, They're 429 00:25:05,160 --> 00:25:07,800 Speaker 1: they're not nailed on for Biden, like by any stretch 430 00:25:07,800 --> 00:25:10,199 Speaker 1: of the imagination, despite what the polls are saying, and 431 00:25:10,240 --> 00:25:13,359 Speaker 1: he predicts Trump will win in all of those states. Um. 432 00:25:13,400 --> 00:25:15,840 Speaker 1: And he was also saying that the you know, while 433 00:25:15,880 --> 00:25:17,800 Speaker 1: he has lost a lot of support, you know, he 434 00:25:17,840 --> 00:25:19,399 Speaker 1: was saying, like, yeah, he's lost a lot of support 435 00:25:19,440 --> 00:25:22,360 Speaker 1: with suburban women, He's lost some support with the elderly, 436 00:25:22,440 --> 00:25:27,320 Speaker 1: but he has picked up support with black and Latino voters. Surprisingly, 437 00:25:27,440 --> 00:25:31,160 Speaker 1: he said, he was really like, it's it's gone up slightly. Um, 438 00:25:31,320 --> 00:25:33,959 Speaker 1: so there is some movement in that area. But you know, 439 00:25:34,160 --> 00:25:35,879 Speaker 1: with all that to say, I was just there and 440 00:25:35,960 --> 00:25:37,399 Speaker 1: like we got Jason on so you know what that 441 00:25:37,520 --> 00:25:41,040 Speaker 1: just I'll put that down and let us discuss that, 442 00:25:41,119 --> 00:25:44,960 Speaker 1: because yes, he he feels very strongly that this there's 443 00:25:45,080 --> 00:25:49,120 Speaker 1: there's not much, not much difference between the two. This 444 00:25:49,280 --> 00:25:51,399 Speaker 1: is for one thing, no one who gives an answer 445 00:25:51,520 --> 00:25:55,040 Speaker 1: answer to this is not somewhat motivated in the reasoning, 446 00:25:55,119 --> 00:25:57,639 Speaker 1: like I don't want I don't want to give anyone 447 00:25:57,640 --> 00:26:00,320 Speaker 1: a reason to not vote. And obviously people who are 448 00:26:00,359 --> 00:26:03,399 Speaker 1: afraid of a second Trump to term are also very 449 00:26:03,440 --> 00:26:06,159 Speaker 1: afraid of saying anything that causes people to maybe be 450 00:26:06,560 --> 00:26:09,879 Speaker 1: to relax. And so there's a motivation to kind of 451 00:26:10,720 --> 00:26:13,440 Speaker 1: in those channels to kind of boost anyone who's saying, hey, 452 00:26:13,480 --> 00:26:16,680 Speaker 1: Trump's chances are better than you think, because you want 453 00:26:16,680 --> 00:26:18,359 Speaker 1: people to go out and vote. It does from the 454 00:26:18,400 --> 00:26:22,520 Speaker 1: early voting it appears people we're gonna have like record turnout, 455 00:26:22,560 --> 00:26:27,960 Speaker 1: like and anything can happen. It's, you know, the whole 456 00:26:28,000 --> 00:26:30,200 Speaker 1: issue that I like Nate Silver. I know a lot 457 00:26:30,200 --> 00:26:32,040 Speaker 1: of people have grown to hate him. What I like 458 00:26:32,119 --> 00:26:33,680 Speaker 1: about him is he's one of the few people that 459 00:26:33,760 --> 00:26:36,760 Speaker 1: points out that, look, we have not had that many 460 00:26:36,840 --> 00:26:41,520 Speaker 1: presidential elections period in the modern era, in the polling era, 461 00:26:41,600 --> 00:26:43,800 Speaker 1: and in the mass media era that you're only talking 462 00:26:43,800 --> 00:26:45,920 Speaker 1: about the ones going back to what nineteen sixty and 463 00:26:46,000 --> 00:26:48,520 Speaker 1: the air in which TV has been a thing like 464 00:26:48,560 --> 00:26:51,600 Speaker 1: that's just that's a small sample size. And this is why. 465 00:26:51,720 --> 00:26:54,919 Speaker 1: And so anybody who says, well, you know, because you 466 00:26:55,000 --> 00:26:57,440 Speaker 1: hear this these headlines like well, no president has ever 467 00:26:57,800 --> 00:27:01,600 Speaker 1: lost when the unemployment has been blank, or or this 468 00:27:01,640 --> 00:27:05,040 Speaker 1: guy who's predicted four straight elections, you know, says blanket 469 00:27:05,040 --> 00:27:07,439 Speaker 1: is gonna happen, and in every case it's a really 470 00:27:07,440 --> 00:27:10,920 Speaker 1: small sample size. And every election, you know, they used 471 00:27:10,920 --> 00:27:15,120 Speaker 1: to consider it almost an extreme scenario that you would 472 00:27:15,160 --> 00:27:18,199 Speaker 1: have a popular vote in electoral college split like we 473 00:27:18,320 --> 00:27:21,919 Speaker 1: just had. And I don't remember anybody predicting that Hillary 474 00:27:21,960 --> 00:27:24,800 Speaker 1: would win by three million votes and lose the electoral college. 475 00:27:25,720 --> 00:27:29,200 Speaker 1: But now that it's happened, it makes perfect sense. It's like, oh, yeah, 476 00:27:29,280 --> 00:27:31,040 Speaker 1: this is kind of baked in that, you know, like 477 00:27:31,080 --> 00:27:34,320 Speaker 1: the electoral college has like a conservative slant because it 478 00:27:34,400 --> 00:27:40,960 Speaker 1: gives more preference to rural voters. Right, So, knowing that, yeah, 479 00:27:41,000 --> 00:27:44,240 Speaker 1: it's not they've in the simulations, they run eight they've 480 00:27:44,280 --> 00:27:47,159 Speaker 1: got somewhere. I think Biden wins by like six points 481 00:27:47,240 --> 00:27:50,760 Speaker 1: and still loses the electoral college because of so many 482 00:27:50,800 --> 00:27:54,399 Speaker 1: of the votes are concentrated in California or whatever states 483 00:27:54,400 --> 00:27:57,520 Speaker 1: he flips, and then Trump narrowly wins. All these others 484 00:27:57,520 --> 00:28:01,119 Speaker 1: in that can do it. It's mathematically possible. It's just 485 00:28:01,240 --> 00:28:05,640 Speaker 1: that virtually every other polling agency other than this guy 486 00:28:05,920 --> 00:28:08,960 Speaker 1: disagrees because the whole the whole concept of people being 487 00:28:09,000 --> 00:28:12,760 Speaker 1: re electant to give like an unpopular answer. That's been 488 00:28:12,880 --> 00:28:15,359 Speaker 1: that's been a part of polling methodology, going back to 489 00:28:15,520 --> 00:28:19,520 Speaker 1: the dawn of polls, Like this is an ongoing problem. 490 00:28:19,600 --> 00:28:22,600 Speaker 1: They actually have algorithms where they try to account for it. 491 00:28:23,359 --> 00:28:25,199 Speaker 1: I've got a link here we can include in the 492 00:28:25,200 --> 00:28:29,280 Speaker 1: footnotes water when they talk about that the issue is 493 00:28:29,320 --> 00:28:34,040 Speaker 1: not one it's I don't know how many Trump supporters, 494 00:28:34,080 --> 00:28:38,040 Speaker 1: you know, they tend to not be shy, and and 495 00:28:38,160 --> 00:28:39,960 Speaker 1: this guy is saying, well, yeah, but in a blue state, 496 00:28:40,000 --> 00:28:42,520 Speaker 1: they'd be afraid to admit that they're But these polls 497 00:28:42,520 --> 00:28:44,560 Speaker 1: are not conducted on the street in front of their 498 00:28:44,680 --> 00:28:49,280 Speaker 1: their liberal friends. They're they're often anonymous online surveys and 499 00:28:49,520 --> 00:28:53,960 Speaker 1: a blue a Biden supporter in deep red Alabama, you 500 00:28:53,960 --> 00:28:56,040 Speaker 1: could say the same thing, like they're they're afraid to 501 00:28:56,080 --> 00:28:57,920 Speaker 1: come out. It's like that's a place where you put 502 00:28:57,920 --> 00:29:00,360 Speaker 1: a Biden sign in your yard and somebody's gonna shoot 503 00:29:00,360 --> 00:29:05,480 Speaker 1: it with a shotgun. So again, the reason people like 504 00:29:05,560 --> 00:29:08,760 Speaker 1: this narrative is because of this this kind of thing 505 00:29:08,840 --> 00:29:13,320 Speaker 1: that liberals are so mean to Trump supporters, um and 506 00:29:13,440 --> 00:29:17,160 Speaker 1: we're and the cancel culture and whatever the whatever they 507 00:29:17,160 --> 00:29:19,800 Speaker 1: throw around, like these people are so vicious that everyone's 508 00:29:19,840 --> 00:29:24,920 Speaker 1: afraid they're that they to admit they support Trump. That 509 00:29:25,280 --> 00:29:27,000 Speaker 1: I mean, Trump is running ahead of a lot of 510 00:29:27,000 --> 00:29:30,680 Speaker 1: Republican Senate candidates, he's running ahead of of Congress. So 511 00:29:31,560 --> 00:29:33,480 Speaker 1: it's that's if that's the case, you would think you 512 00:29:33,480 --> 00:29:35,880 Speaker 1: would have those same Republicans saying, oh, yeah, I support 513 00:29:35,920 --> 00:29:38,400 Speaker 1: Blank for Senate, but I won't vote for that filled 514 00:29:38,480 --> 00:29:40,760 Speaker 1: the criminal Trump even though they secreted are you're not 515 00:29:40,800 --> 00:29:44,880 Speaker 1: getting that. You're getting the opposite answer. So anything can 516 00:29:44,920 --> 00:29:48,320 Speaker 1: happen again. Every time you say something like this on Twitter, 517 00:29:48,360 --> 00:29:51,200 Speaker 1: you immediately get a wave of answers like, don't get complacent. 518 00:29:51,920 --> 00:29:55,040 Speaker 1: At least don't get complacent. I'm telling you there are many, 519 00:29:55,080 --> 00:29:58,680 Speaker 1: many motivated Trump voters. It's just that it would be 520 00:29:58,760 --> 00:30:02,240 Speaker 1: much weirder if he one now. Then if he had 521 00:30:02,320 --> 00:30:05,720 Speaker 1: done one, sixteen was unlikely. I think if you redo 522 00:30:05,800 --> 00:30:08,360 Speaker 1: that election he wins at three times and loses seven 523 00:30:09,120 --> 00:30:13,320 Speaker 1: this time, it's it would be much stranger. So just 524 00:30:13,400 --> 00:30:16,200 Speaker 1: going off five thirty eight, because I agree their methodology 525 00:30:16,680 --> 00:30:20,000 Speaker 1: is more valid than a lot of the other ones, 526 00:30:20,160 --> 00:30:23,800 Speaker 1: they're still and I think rightly, so they still have 527 00:30:23,880 --> 00:30:27,360 Speaker 1: a sizeable chance that he wins. It's not like it's 528 00:30:27,680 --> 00:30:31,640 Speaker 1: thirteen in a hundred is what they currently have it at. 529 00:30:32,120 --> 00:30:36,360 Speaker 1: They currently give him a eight and ten chance that 530 00:30:36,400 --> 00:30:39,640 Speaker 1: Biden wins the popular vote but loses the electoral college, 531 00:30:40,080 --> 00:30:48,200 Speaker 1: so sorry sorry, and then five percent chance that Trump 532 00:30:48,240 --> 00:30:52,560 Speaker 1: wins both the electoral college and the popular vote. So 533 00:30:52,600 --> 00:30:55,280 Speaker 1: that's what happened the first time. I think we look 534 00:30:55,360 --> 00:30:59,000 Speaker 1: for polls to do something that they can't do. Like 535 00:30:59,160 --> 00:31:02,000 Speaker 1: last time, I think heading into the election, it was 536 00:31:02,680 --> 00:31:05,840 Speaker 1: high thirty percent chance that Trump will win, So he's 537 00:31:05,880 --> 00:31:10,520 Speaker 1: still an underdog, but that's not that much of an underdog. 538 00:31:10,600 --> 00:31:14,440 Speaker 1: Like if a baseball player going up to bat bats 539 00:31:14,480 --> 00:31:18,840 Speaker 1: over three hundred, that is a good hitter. You not surprised, Yeah, 540 00:31:18,880 --> 00:31:20,640 Speaker 1: you're not surprised at all. If he gets a hit 541 00:31:21,120 --> 00:31:25,280 Speaker 1: and this is a one in ten or a thirteen 542 00:31:25,360 --> 00:31:30,520 Speaker 1: and a hundred, chants is not likely, But it's also 543 00:31:31,000 --> 00:31:33,760 Speaker 1: you know, it's a thing that sometimes happens, like if 544 00:31:33,800 --> 00:31:36,880 Speaker 1: a pitcher gets up in baseball and gets a hit, 545 00:31:37,040 --> 00:31:39,520 Speaker 1: Like you see that all the time. That happens all 546 00:31:39,560 --> 00:31:41,760 Speaker 1: the time if you watch a lot of baseball. So 547 00:31:42,640 --> 00:31:45,880 Speaker 1: I don't know, I just think that, like anything can happen, 548 00:31:46,480 --> 00:31:50,400 Speaker 1: those states that he talks about Trump winning like that 549 00:31:50,400 --> 00:31:55,040 Speaker 1: that are being granted to Biden are all very close 550 00:31:55,080 --> 00:32:03,720 Speaker 1: in the polling averages. And even if Trump win Texas, Ohio, Georgia, Iowa, 551 00:32:04,080 --> 00:32:08,920 Speaker 1: North Carolina, Arizona, and Florida, he would still lose if 552 00:32:08,960 --> 00:32:12,160 Speaker 1: Biden won Pennsylvania. Yes, to be, it has to be 553 00:32:12,440 --> 00:32:15,200 Speaker 1: basically sweep everything where he's down by a few points, 554 00:32:15,600 --> 00:32:18,480 Speaker 1: So you need a polling error that is somehow wrong 555 00:32:18,600 --> 00:32:22,840 Speaker 1: across demographic groups across states, which again, which happens. Yeah, 556 00:32:22,880 --> 00:32:26,520 Speaker 1: I mean they've made the point that when it's not 557 00:32:26,640 --> 00:32:30,400 Speaker 1: like these are all independent variables, right, It's not like 558 00:32:30,520 --> 00:32:34,760 Speaker 1: if suddenly he overperforms by six points in Ohio, it 559 00:32:34,800 --> 00:32:40,320 Speaker 1: would be it would require him to equally unlikely overperformed 560 00:32:40,320 --> 00:32:43,680 Speaker 1: by six points in Pennsylvania. It's like, no, they're responding 561 00:32:43,760 --> 00:32:48,960 Speaker 1: to the same condition that was true across Ohio and 562 00:32:49,000 --> 00:32:52,200 Speaker 1: Pennsylvania and Florida, which is what happened in the last election. 563 00:32:52,600 --> 00:32:55,640 Speaker 1: But those are all things they take into account. Uh, 564 00:32:55,680 --> 00:32:59,480 Speaker 1: They're all things that can happen, which is why Biden. 565 00:32:59,800 --> 00:33:04,360 Speaker 1: They were not the ones who in the election. We're like, yeah, 566 00:33:04,360 --> 00:33:07,360 Speaker 1: Trump has less than a one in hundred chants of 567 00:33:07,360 --> 00:33:10,520 Speaker 1: when they were like, no, it's like he's an underdog, 568 00:33:10,560 --> 00:33:13,600 Speaker 1: but it could easily happen, and it did. There we 569 00:33:13,680 --> 00:33:15,680 Speaker 1: had some of the pundits the way they were talking. 570 00:33:15,720 --> 00:33:18,120 Speaker 1: He felt like the halftime of Game six of the 571 00:33:18,200 --> 00:33:21,000 Speaker 1: NBA Finals were like the Lakers were had such a lead, 572 00:33:21,040 --> 00:33:23,360 Speaker 1: where like at halftime they're like, yeah, credit to the 573 00:33:23,400 --> 00:33:27,640 Speaker 1: Miami Heat. They really hard and I was like, yo, yo, 574 00:33:27,680 --> 00:33:30,479 Speaker 1: don't do this ship right, like, let the game fucking end. 575 00:33:30,560 --> 00:33:33,640 Speaker 1: And it has like this similar feeling. But yeah, I 576 00:33:33,640 --> 00:33:36,960 Speaker 1: think I think most people still have that trauma of 577 00:33:37,240 --> 00:33:39,800 Speaker 1: feeling like the polls are accurate, and I think at 578 00:33:39,800 --> 00:33:41,080 Speaker 1: the end of the day, all you can do is 579 00:33:41,120 --> 00:33:43,720 Speaker 1: just try and get as much turned out as possible. 580 00:33:43,800 --> 00:33:46,239 Speaker 1: But the one thing that anything happened, the one thing 581 00:33:46,280 --> 00:33:50,480 Speaker 1: I think they're vastly underrating that they say a four 582 00:33:50,600 --> 00:33:55,440 Speaker 1: in one hundred chance that the election hinges on a recount. Um, 583 00:33:55,520 --> 00:34:01,080 Speaker 1: I think that we're very likely to see Trump and 584 00:34:01,160 --> 00:34:05,360 Speaker 1: the Republicans and Fox News find a way to invalidate 585 00:34:06,120 --> 00:34:10,760 Speaker 1: UH states where it's close or even not that close, 586 00:34:10,960 --> 00:34:16,480 Speaker 1: but they will find ways to throw question on a 587 00:34:16,520 --> 00:34:19,840 Speaker 1: bunch of votes and claim voter fraud, and you know, 588 00:34:20,040 --> 00:34:23,799 Speaker 1: it's just something Republicans have done before. And this is 589 00:34:24,000 --> 00:34:28,000 Speaker 1: an administration and a Republican party that no longer is 590 00:34:28,120 --> 00:34:31,120 Speaker 1: required to hide it when when they're wrong or when 591 00:34:31,120 --> 00:34:35,759 Speaker 1: they're doing something illegal, like they have lost all uh 592 00:34:35,840 --> 00:34:40,120 Speaker 1: you know, any tether too accountability. So why wouldn't they 593 00:34:40,480 --> 00:34:44,480 Speaker 1: do that. Yeah, it's like a case where if well, 594 00:34:44,480 --> 00:34:46,560 Speaker 1: I like, let's say you're in the NBA Finals game 595 00:34:46,600 --> 00:34:49,080 Speaker 1: where for some reason, according to some rule, we have 596 00:34:49,239 --> 00:34:52,959 Speaker 1: to win by fifteen points. Yeah, it's not it can't 597 00:34:52,960 --> 00:34:55,479 Speaker 1: be close. We can't win by one by one vote. 598 00:34:55,480 --> 00:34:57,720 Speaker 1: And I don't know if the Republicans in your audience 599 00:34:57,760 --> 00:35:00,960 Speaker 1: hate if I keep saying we like we're on the 600 00:35:01,320 --> 00:35:05,279 Speaker 1: by part of the Biden administration. But it's it's a 601 00:35:05,320 --> 00:35:08,440 Speaker 1: case where it has to be a big it doesn't 602 00:35:08,440 --> 00:35:10,279 Speaker 1: have to be a landslide, but it's got to be 603 00:35:10,440 --> 00:35:14,400 Speaker 1: enough that on election night we pretty much know and 604 00:35:14,480 --> 00:35:17,120 Speaker 1: that there's not enough of where where there's not it's 605 00:35:17,120 --> 00:35:19,279 Speaker 1: not close enough to trigger any recounts or anything like that. 606 00:35:19,320 --> 00:35:22,680 Speaker 1: It's got to be you know, seven points in Pennsylvania, 607 00:35:22,880 --> 00:35:25,120 Speaker 1: you know, maybe three points in Florida, like where even 608 00:35:25,160 --> 00:35:27,399 Speaker 1: if all those that where they won by two, one 609 00:35:27,440 --> 00:35:29,719 Speaker 1: to three points, where it still wouldn't get him to 610 00:35:30,200 --> 00:35:32,120 Speaker 1: to to seventy or whatever. It's got to be an 611 00:35:32,120 --> 00:35:36,280 Speaker 1: overwhelming victory. So it's it. Yeah, that's what I'm nervous 612 00:35:36,320 --> 00:35:38,719 Speaker 1: about it. It's not it's it would be stunning to 613 00:35:38,800 --> 00:35:41,600 Speaker 1: me if Trump got more votes, that would mean something 614 00:35:41,640 --> 00:35:44,520 Speaker 1: truly weird had happened. And especially in fact that ten 615 00:35:44,520 --> 00:35:47,320 Speaker 1: percent of people have already voted. There's something like seventeen 616 00:35:47,360 --> 00:35:51,080 Speaker 1: million votes in already. Um, what I'm more alarmed by 617 00:35:51,160 --> 00:35:53,280 Speaker 1: is that it's it has to be by a comfortable 618 00:35:53,280 --> 00:35:55,200 Speaker 1: margin if you want this to do what it's going 619 00:35:55,280 --> 00:35:59,560 Speaker 1: to do, which is truly be a rebuke to Trump 620 00:35:59,600 --> 00:36:02,239 Speaker 1: and true will send the message that doing it the 621 00:36:02,239 --> 00:36:06,240 Speaker 1: way Trump did it is not a model for future Republicans, 622 00:36:06,440 --> 00:36:08,719 Speaker 1: like having you know, an attorney general that's just your 623 00:36:08,760 --> 00:36:11,080 Speaker 1: attack offer, right, this is not a model that works. 624 00:36:11,120 --> 00:36:13,640 Speaker 1: I don't want I don't want Tucker Carlson to be 625 00:36:13,680 --> 00:36:17,360 Speaker 1: out there dreaming of running in as like Trump, but 626 00:36:17,560 --> 00:36:20,280 Speaker 1: a little bit more media savvy, a little bit smarter 627 00:36:20,400 --> 00:36:22,640 Speaker 1: and younger. I don't I don't want that. I want 628 00:36:22,640 --> 00:36:25,440 Speaker 1: this too. I wanted to obliterate the idea that this 629 00:36:25,520 --> 00:36:30,200 Speaker 1: is a good way to function as a president. Yeah, 630 00:36:30,400 --> 00:36:32,919 Speaker 1: that is the thing that's being talked about it a lot. 631 00:36:32,960 --> 00:36:37,680 Speaker 1: On the right, is Tucker Carlson, the future Republican presidential candidate. 632 00:36:38,080 --> 00:36:42,960 Speaker 1: I mean he'll do great. Yeah, he's uh, I mean 633 00:36:43,000 --> 00:36:45,239 Speaker 1: he probably will. I mean he will because there is 634 00:36:45,280 --> 00:36:49,120 Speaker 1: some actual like you know, racist asshole with his ship 635 00:36:49,160 --> 00:36:52,480 Speaker 1: together that's looking at Trump's like, you're fucking this all up, man, 636 00:36:53,040 --> 00:36:54,960 Speaker 1: Like it could have done so much, it could have 637 00:36:54,960 --> 00:36:56,840 Speaker 1: been so much worse of Trump actually what he was 638 00:36:56,880 --> 00:36:59,360 Speaker 1: doing exactly. And I know those people are like rubbing 639 00:36:59,400 --> 00:37:02,480 Speaker 1: their myth for another bite of that apple. If Tucker 640 00:37:02,520 --> 00:37:06,960 Speaker 1: Carlson's already done the uh you know populists economic thing 641 00:37:07,280 --> 00:37:12,440 Speaker 1: that Trump like made head fakes towards populist economic policies 642 00:37:12,480 --> 00:37:17,080 Speaker 1: but didn't embrace them at all. And still it's funny 643 00:37:17,120 --> 00:37:19,560 Speaker 1: because then Tucker carlsonization is like, why is it a 644 00:37:19,560 --> 00:37:23,319 Speaker 1: surprise now that millennials prefer socialism? Well, if you look 645 00:37:23,320 --> 00:37:25,839 Speaker 1: at the economics of it, and like he's it's like 646 00:37:25,880 --> 00:37:28,520 Speaker 1: he's smart enough to know like where what the motivations 647 00:37:28,520 --> 00:37:32,080 Speaker 1: are generationally, and I think that's where that's how you 648 00:37:32,320 --> 00:37:35,040 Speaker 1: that's where it gets dangerous because if some people are like, well, 649 00:37:35,040 --> 00:37:37,000 Speaker 1: you know, Tucker Carlson got a point about that. I mean, 650 00:37:37,040 --> 00:37:39,000 Speaker 1: I'm not a person of color or you know, in 651 00:37:39,000 --> 00:37:41,560 Speaker 1: an in a marginalized group. But what he's saying about 652 00:37:41,600 --> 00:37:45,480 Speaker 1: like my class thing, that's facts. So and he knows 653 00:37:45,480 --> 00:37:47,480 Speaker 1: how to be a messy bitch who loves drama, like 654 00:37:48,600 --> 00:37:52,040 Speaker 1: accused the New York Times of giving away like doxing him. 655 00:37:52,160 --> 00:37:57,480 Speaker 1: Oh yeah, arrest. Do you guys know what you're gonna 656 00:37:57,520 --> 00:38:00,239 Speaker 1: do for Halloween? Are you going to celebrate? Have been? 657 00:38:01,320 --> 00:38:03,359 Speaker 1: Have not thought about it once? In fact, the first 658 00:38:03,360 --> 00:38:05,400 Speaker 1: time I thought about Halloween was about thirty seconds ago 659 00:38:05,440 --> 00:38:09,080 Speaker 1: when you started talking about the Beverly Jack. I even 660 00:38:09,200 --> 00:38:11,880 Speaker 1: forgot that we were even in October. When I was 661 00:38:11,920 --> 00:38:13,839 Speaker 1: like down like in the summer, I was like, oh, ship, 662 00:38:13,920 --> 00:38:16,280 Speaker 1: we're about to hit three years because they had seen 663 00:38:16,440 --> 00:38:19,920 Speaker 1: I just realized we were in October, right, We just penned, 664 00:38:20,239 --> 00:38:22,400 Speaker 1: isn't Halloween on like a Friday or Saturday this year? 665 00:38:22,440 --> 00:38:24,319 Speaker 1: It would have been it's a full moon and it's 666 00:38:24,360 --> 00:38:28,279 Speaker 1: a blue moon. It's the second full moon of October, 667 00:38:28,440 --> 00:38:34,120 Speaker 1: which is it's gonna be and it's Halloween during a pandemic. Uh, 668 00:38:34,160 --> 00:38:36,680 Speaker 1: my neighborhood gets a lot of tricker treaters, so we 669 00:38:36,719 --> 00:38:39,520 Speaker 1: have to like figure out what we're gonna do. Yeah, 670 00:38:39,560 --> 00:38:41,160 Speaker 1: what are what are you going to do? I think 671 00:38:41,239 --> 00:38:43,920 Speaker 1: just put like a bench out that just says like 672 00:38:44,000 --> 00:38:46,399 Speaker 1: see you next year. But then and then just hope 673 00:38:46,400 --> 00:38:49,880 Speaker 1: people aren't too mad. But I also think like nobody, 674 00:38:50,080 --> 00:38:52,479 Speaker 1: like none of our neighbors are planning on like doing 675 00:38:52,480 --> 00:38:56,040 Speaker 1: tricker treating. So I was so excited on New Year's 676 00:38:56,080 --> 00:38:59,080 Speaker 1: for I was one of those guys who was like twenties, 677 00:38:59,160 --> 00:39:04,160 Speaker 1: the year want change, the country will change, We're gonna 678 00:39:04,160 --> 00:39:07,000 Speaker 1: get a new leader. Everything's gonna be great, blah blah blah. 679 00:39:07,000 --> 00:39:11,080 Speaker 1: And then like wow, just like life comes at you quick, 680 00:39:11,160 --> 00:39:17,400 Speaker 1: I've gotten rocked. Yeah yeah, it's uh, it's rough, and 681 00:39:17,440 --> 00:39:20,359 Speaker 1: it's rough on those trigger treaters. The naive hope of 682 00:39:21,080 --> 00:39:26,239 Speaker 1: New Year's December where the funk I was on New 683 00:39:26,320 --> 00:39:29,120 Speaker 1: Year's part of me wants to get the PVC pipe 684 00:39:29,160 --> 00:39:32,520 Speaker 1: and drop the candy down from the second floor, like 685 00:39:32,560 --> 00:39:35,840 Speaker 1: the slide. Not about dope. But then it's like some 686 00:39:35,920 --> 00:39:42,880 Speaker 1: kids head open, just like firing them. There comes some 687 00:39:43,000 --> 00:39:45,960 Speaker 1: job breakers kids. But you're also like putting pressure on 688 00:39:46,000 --> 00:39:49,040 Speaker 1: your neighbors to then like come up with some strategy. 689 00:39:49,280 --> 00:39:52,600 Speaker 1: And also it's like for the people receiving the candy, 690 00:39:52,719 --> 00:39:56,400 Speaker 1: like do they want to receive candy from like some house? 691 00:39:56,840 --> 00:39:59,960 Speaker 1: You know? That's like you knock on the PVC pipe 692 00:40:00,120 --> 00:40:03,520 Speaker 1: boom boom. Oh that in my neighborhood. Like you just 693 00:40:03,560 --> 00:40:07,960 Speaker 1: sit out front and there's a line like people. So 694 00:40:08,000 --> 00:40:10,320 Speaker 1: you're like one of those mobile games, like those mobile 695 00:40:10,400 --> 00:40:13,200 Speaker 1: zombie games where you have the machine gun mowing down zombies. 696 00:40:13,840 --> 00:40:17,640 Speaker 1: It's like walls of enemies, like walls of trick or treaters, 697 00:40:18,080 --> 00:40:23,120 Speaker 1: turret of candy, Yeah, candy turret. Everybody knows Jack doesn't 698 00:40:23,160 --> 00:40:29,319 Speaker 1: live in Beverly Hills, now, yeah, that's yeah, I don't. 699 00:40:29,360 --> 00:40:31,279 Speaker 1: I have never lived in an l a neighborhood that 700 00:40:31,800 --> 00:40:33,319 Speaker 1: felt like that, at least not in the street that 701 00:40:33,360 --> 00:40:35,840 Speaker 1: felt like that. I've always felt like it was dead. 702 00:40:36,400 --> 00:40:40,359 Speaker 1: I've always lived in or like, yeah, I've always lived 703 00:40:40,360 --> 00:40:42,360 Speaker 1: in a neighborhood where I had to go to another 704 00:40:42,400 --> 00:40:46,080 Speaker 1: neighborhood trick or treat. I never lived in like the 705 00:40:46,160 --> 00:40:48,759 Speaker 1: lit trick or treating neighborhood. Yeah, we did the we 706 00:40:48,800 --> 00:40:50,279 Speaker 1: did the when I was a kid, We did the 707 00:40:50,280 --> 00:40:55,880 Speaker 1: pillowcases and we got out, We got we got on bikes. Yeah, 708 00:40:56,040 --> 00:40:58,040 Speaker 1: we would go down to a street, run up and 709 00:40:58,080 --> 00:41:00,200 Speaker 1: down the street. It was not about fun. It was 710 00:41:00,200 --> 00:41:05,160 Speaker 1: about who gets the most numbers. Numbers, that's always funny. 711 00:41:06,480 --> 00:41:09,239 Speaker 1: There's always that group of kids that is like sprinting 712 00:41:09,360 --> 00:41:12,840 Speaker 1: full speed. You know. Parents were drinking beers and like 713 00:41:12,880 --> 00:41:15,960 Speaker 1: what the funk was that? A bunch of fucking minions 714 00:41:16,040 --> 00:41:18,799 Speaker 1: ran by me and tex For us, it was like 715 00:41:18,920 --> 00:41:22,680 Speaker 1: how many pillowcases can I get? Right to the point? 716 00:41:23,120 --> 00:41:25,200 Speaker 1: Do you ever bring him to school? Did ever? Somebody 717 00:41:25,320 --> 00:41:28,080 Speaker 1: like yo, chuck do so and just laid down the 718 00:41:28,200 --> 00:41:31,040 Speaker 1: heavy bag on you and like get into trouble that 719 00:41:31,239 --> 00:41:33,640 Speaker 1: like the teachers like you can't bring this ship here, 720 00:41:33,440 --> 00:41:35,440 Speaker 1: what are you doing. I also grew up in upstate 721 00:41:35,480 --> 00:41:40,200 Speaker 1: New York where it was freezing by Halloween, so cold, 722 00:41:40,760 --> 00:41:44,120 Speaker 1: so people had snow entire Oh my god, I would 723 00:41:44,320 --> 00:41:46,560 Speaker 1: we would have like parkas on, you know, like a 724 00:41:46,680 --> 00:41:49,879 Speaker 1: parka over your ninja costume or whatever. It was like, 725 00:41:50,560 --> 00:41:52,880 Speaker 1: you know, it's just like a ninja warming up on 726 00:41:52,920 --> 00:41:56,680 Speaker 1: the sidelines. And I'm forgetting the brand. But there was 727 00:41:56,719 --> 00:42:00,160 Speaker 1: like a really popular brand of puffy coat that I 728 00:42:00,160 --> 00:42:03,640 Speaker 1: think it was like First Ascent or something like that. Uh, 729 00:42:03,760 --> 00:42:05,839 Speaker 1: it wasn't north Face because we couldn't afford north Face. 730 00:42:05,880 --> 00:42:09,640 Speaker 1: It was like whatever Mountain was under the bootleg version 731 00:42:09,640 --> 00:42:13,359 Speaker 1: of that and first Gear or something. Oh god, I'm 732 00:42:13,360 --> 00:42:15,200 Speaker 1: gonna remember it. But anyway, I remember like all the 733 00:42:15,239 --> 00:42:17,319 Speaker 1: old people like, well, what are you supposed to be 734 00:42:17,400 --> 00:42:21,359 Speaker 1: this year? And I was like, I'm cold, and rush 735 00:42:21,520 --> 00:42:25,000 Speaker 1: motherfucker this butter finger. God damnit, when you're gonna get 736 00:42:25,080 --> 00:42:28,680 Speaker 1: king size neighbors got him up the street man and 737 00:42:28,719 --> 00:42:31,400 Speaker 1: back in the day they would give you a plastic 738 00:42:31,440 --> 00:42:35,239 Speaker 1: baggy with homemade cookies sometimes and I was like, nowadays, 739 00:42:35,520 --> 00:42:38,520 Speaker 1: no way, now, no one would eat that nowadays. Oh yeah, 740 00:42:38,520 --> 00:42:41,440 Speaker 1: because everyone I remember like when edibles like weed edible 741 00:42:41,480 --> 00:42:44,719 Speaker 1: starting like that's always like the trick d jr. Click 742 00:42:44,800 --> 00:42:49,520 Speaker 1: bait for concerning it is handing out everyone's like too expensive, 743 00:42:49,600 --> 00:42:52,560 Speaker 1: too expensive and wasting on the fucking kid, You dumb. 744 00:42:54,160 --> 00:42:57,399 Speaker 1: Look at the labels that I'm handing kids, like what 745 00:42:57,480 --> 00:43:00,799 Speaker 1: five milligram chocolate covered blueberries or something like that. Those 746 00:43:00,800 --> 00:43:02,440 Speaker 1: are expensive and I love them. Yeah, Like you know 747 00:43:02,480 --> 00:43:05,440 Speaker 1: how much a tin of chocolate covered Kiva's cost I'm 748 00:43:05,480 --> 00:43:07,880 Speaker 1: not bawling out of control, and I wouldn't waste it 749 00:43:07,880 --> 00:43:10,719 Speaker 1: on a twelve year old dressed as Spider. Whoever, when 750 00:43:10,719 --> 00:43:12,800 Speaker 1: I go, I'm always like I always go and throwing 751 00:43:12,840 --> 00:43:15,560 Speaker 1: a couple of chocolate covered Kiva tints to the blueberries 752 00:43:15,560 --> 00:43:19,480 Speaker 1: and they're like, okay, four, okay, let's give it those. 753 00:43:19,560 --> 00:43:23,600 Speaker 1: I'll just take the rolling papers. No, no, not how much? 754 00:43:23,719 --> 00:43:29,880 Speaker 1: That's four hundred three. You know that story probably was 755 00:43:29,920 --> 00:43:33,080 Speaker 1: born out of some fucking parent whose kid found it 756 00:43:33,120 --> 00:43:34,880 Speaker 1: and had to be quick on their feet, like, oh 757 00:43:34,920 --> 00:43:38,839 Speaker 1: my god, someone must have given this. Holy sh it. 758 00:43:38,960 --> 00:43:41,520 Speaker 1: You really don't know who our neighbors are. You really 759 00:43:41,520 --> 00:43:45,840 Speaker 1: don't fairs bueller ass little kid who's gullible as parents are, Like, 760 00:43:45,880 --> 00:43:50,319 Speaker 1: I'm calling the local news. This is enough. They gave 761 00:43:50,400 --> 00:43:55,319 Speaker 1: him a whole bong um yeah, a little blunt. I 762 00:43:55,360 --> 00:44:01,080 Speaker 1: can't believe it. Uh Halloween cookies though, the the ones 763 00:44:01,120 --> 00:44:05,040 Speaker 1: that are like basically butter that go around that it 764 00:44:05,200 --> 00:44:09,520 Speaker 1: go there, like little uh cupcakes that go around the 765 00:44:11,640 --> 00:44:15,719 Speaker 1: around the Reese's cup, the mini Reese's cup. You know 766 00:44:15,760 --> 00:44:19,319 Speaker 1: that one? Oh yeah, yeah yes when you put it in, 767 00:44:19,640 --> 00:44:25,640 Speaker 1: yeah yeah, shout out to those baked goods. Yeah, yeah, Halloween. 768 00:44:26,840 --> 00:44:30,680 Speaker 1: Halloween has some good bake goods associated with it. Yeah, 769 00:44:30,960 --> 00:44:32,640 Speaker 1: you know what, I think that's what I'll do. I'm 770 00:44:32,640 --> 00:44:34,799 Speaker 1: just gonna buy one of those Pillsbury tubes where you 771 00:44:34,840 --> 00:44:38,160 Speaker 1: slice off the cookies, like the pumpkin ones. Yeah, like 772 00:44:38,280 --> 00:44:42,520 Speaker 1: three of those. Smoke a blunt, uh and watch watch 773 00:44:42,600 --> 00:44:47,200 Speaker 1: some like fucking Arsenal highlights and we're good to go, baby. Yeah, 774 00:44:47,520 --> 00:44:50,880 Speaker 1: play a little. Halloween music is pretty fun too. There's 775 00:44:50,920 --> 00:44:57,280 Speaker 1: like some good spooky jams. Somebody's watching me. Thriller, monster 776 00:44:57,360 --> 00:45:00,480 Speaker 1: mash Monster mashes a jam. Yeah, we a planned that 777 00:45:00,560 --> 00:45:04,200 Speaker 1: this morning in my house. Is there a Dave Matthews 778 00:45:04,440 --> 00:45:07,719 Speaker 1: Halloween album. There is a Dave Matthews creepy song it's 779 00:45:07,760 --> 00:45:11,319 Speaker 1: called and Another Thing. There's also a Dave Matthews song 780 00:45:11,400 --> 00:45:14,759 Speaker 1: called Halloween. I think there is. And there's a great 781 00:45:14,800 --> 00:45:17,960 Speaker 1: wear of song called Warehouse is kind of creepy too. Um. 782 00:45:18,000 --> 00:45:20,359 Speaker 1: But the and Another Thing is weird. It's the song 783 00:45:20,400 --> 00:45:27,399 Speaker 1: where like he's just like and it's just it's really weird. 784 00:45:27,440 --> 00:45:30,160 Speaker 1: It's on the crash. Like I just looked at the 785 00:45:30,239 --> 00:45:35,200 Speaker 1: lyrics to Halloween and like like this could be just 786 00:45:35,440 --> 00:45:39,040 Speaker 1: a random word generator's version of Dave Matthew's lyrics the 787 00:45:39,080 --> 00:45:43,000 Speaker 1: first verse, Hey little Dreamer's eyes open and staring up 788 00:45:43,040 --> 00:45:47,000 Speaker 1: at me. It's like, that's just such a Dave Matthews 789 00:45:47,120 --> 00:45:52,480 Speaker 1: as lyric. He's like, hey little dreamers a little lonely 790 00:45:52,640 --> 00:45:57,799 Speaker 1: as open and radiant. Hell yeah, Dave, stay with what 791 00:45:57,840 --> 00:46:00,600 Speaker 1: you do. Dave knows what he does, knows what he's 792 00:46:00,640 --> 00:46:03,800 Speaker 1: good at, and he does it. It's Halloween about being 793 00:46:03,960 --> 00:46:08,640 Speaker 1: rejected three times after proposing marriage, is it? I don't know. 794 00:46:09,000 --> 00:46:12,040 Speaker 1: When you look at like people's like, uh like genius 795 00:46:12,080 --> 00:46:15,400 Speaker 1: analysis of the lyrics, I'm like, oh wow, okay, cool. 796 00:46:16,280 --> 00:46:22,120 Speaker 1: Why that's lonely? Why that's lonely love? That's yeah, that's 797 00:46:22,120 --> 00:46:25,560 Speaker 1: that's not very Halloween. Take a turn there, Dave. I 798 00:46:25,560 --> 00:46:29,440 Speaker 1: wonder if artists look at their genius though interpretations are like, really, 799 00:46:30,200 --> 00:46:34,239 Speaker 1: huh okay, interesting. All right, let's take a quick break 800 00:46:34,280 --> 00:46:48,879 Speaker 1: and we'll be right back, and we're back Beverly Hills. 801 00:46:49,000 --> 00:46:53,800 Speaker 1: Let's talk about let's talk about Halloween and Beverly Hills. 802 00:46:53,480 --> 00:47:04,040 Speaker 1: That's all right, dude, Weser, bring us back, Weezer. Uh yeah, 803 00:47:04,080 --> 00:47:07,239 Speaker 1: they have. They have officially ruined Halloween. Now this is 804 00:47:07,239 --> 00:47:12,000 Speaker 1: more of a local story for US Angelinos because of 805 00:47:12,280 --> 00:47:14,719 Speaker 1: l A. He first started off with the City of 806 00:47:14,719 --> 00:47:18,879 Speaker 1: Los Angeles being like, Oh, we're trick or treating you've been. Oh, 807 00:47:18,960 --> 00:47:22,120 Speaker 1: hell no, it's a fucking pandemic. We're not trick or 808 00:47:22,160 --> 00:47:26,000 Speaker 1: tree but fun what you said. Cut to three hours later, Okay, 809 00:47:26,000 --> 00:47:28,840 Speaker 1: I am sorry, Uh do your own thing, l A, 810 00:47:29,280 --> 00:47:31,200 Speaker 1: my bad. Didn't mean to like try and look out 811 00:47:31,280 --> 00:47:33,640 Speaker 1: for y'all. As as the fucking mayor. Really, all they 812 00:47:33,640 --> 00:47:36,960 Speaker 1: did just said is like they would actually would advise 813 00:47:37,280 --> 00:47:40,080 Speaker 1: against it. That's that's what they went from being like 814 00:47:40,200 --> 00:47:42,680 Speaker 1: it ain't happening to like, we're gonna say, like, don't 815 00:47:42,680 --> 00:47:45,600 Speaker 1: do that. But that's I don't. We'll see what happens 816 00:47:46,040 --> 00:47:48,960 Speaker 1: in Beverly Hills though, you know, and that little enclave. 817 00:47:49,560 --> 00:47:51,719 Speaker 1: The mayor is not fucking playing around. And it is 818 00:47:51,760 --> 00:47:55,279 Speaker 1: a very specific the band on trick or treating in 819 00:47:55,480 --> 00:48:00,000 Speaker 1: Beverly Hills. It says specifically, giving candy, toys or treats 820 00:48:00,000 --> 00:48:02,960 Speaker 1: to anyone outside one's household is prohibited, no matter if 821 00:48:03,000 --> 00:48:05,319 Speaker 1: the handouts passed through a front door or the trunk 822 00:48:05,400 --> 00:48:08,759 Speaker 1: of a car, the latter being known as trunk or treating. Um, 823 00:48:08,960 --> 00:48:11,280 Speaker 1: and they also said the city is banned, has banned 824 00:48:11,360 --> 00:48:15,400 Speaker 1: quote spring shaving cream on others end quote, though it 825 00:48:15,520 --> 00:48:19,760 Speaker 1: gave licensed barbers an exemption. Thank god. Beverly Hills residents 826 00:48:19,800 --> 00:48:23,200 Speaker 1: can also feel free to spray cream spray cream members 827 00:48:23,239 --> 00:48:29,000 Speaker 1: of their own households. Within those households, what is the 828 00:48:29,000 --> 00:48:34,440 Speaker 1: mayor the most concerned parents on earth? What happened to 829 00:48:34,600 --> 00:48:40,000 Speaker 1: the mayor with the shaving cream? Yeah? Someone they got 830 00:48:40,040 --> 00:48:43,560 Speaker 1: that shaving cream creak and just have never recovered. Their 831 00:48:43,560 --> 00:48:47,600 Speaker 1: whole career has been an excuse to legislate people's ability 832 00:48:47,719 --> 00:48:51,919 Speaker 1: is to spray cream each other. Um, I don't it's 833 00:48:52,719 --> 00:48:55,920 Speaker 1: it doesn't even it's so specific, like in the actual 834 00:48:56,000 --> 00:48:59,520 Speaker 1: wording of this. You know, from the city bamboom house, 835 00:48:59,560 --> 00:49:01,359 Speaker 1: the house you're you're treating, or car to car, shrink 836 00:49:01,400 --> 00:49:03,400 Speaker 1: or treating the second thing that's for him it is 837 00:49:03,600 --> 00:49:10,600 Speaker 1: spring shaping cream. I mean, I get it. You're getting 838 00:49:10,600 --> 00:49:14,399 Speaker 1: like a bunch of possibly contaminated like liquids on your 839 00:49:14,440 --> 00:49:17,279 Speaker 1: face or some ship and it's around, but like it 840 00:49:17,960 --> 00:49:21,799 Speaker 1: seems like such an overly overly I don't understand. I 841 00:49:21,840 --> 00:49:25,279 Speaker 1: never even did that ship. Yeah, No, the only time 842 00:49:25,320 --> 00:49:29,440 Speaker 1: I used shaving cream was the with the prank where 843 00:49:29,480 --> 00:49:31,120 Speaker 1: you put it on someone's hand and then tickle their 844 00:49:31,160 --> 00:49:35,680 Speaker 1: nose and a sleepover. Uh And I think I that's 845 00:49:35,680 --> 00:49:38,880 Speaker 1: punishable by death in Beverly Hills. Yeah yeah, yeah yeah, 846 00:49:38,920 --> 00:49:40,600 Speaker 1: that will put you will get put away for all 847 00:49:40,640 --> 00:49:43,680 Speaker 1: to put your nose off for that ship. All right, 848 00:49:44,000 --> 00:49:48,400 Speaker 1: that's gonna do it. For this week's Weekly Zeitgeist, please 849 00:49:48,520 --> 00:49:51,840 Speaker 1: like and review the show. If you like the show, 850 00:49:52,400 --> 00:49:56,800 Speaker 1: UH means the world two miles. He needs your validation, folks. 851 00:49:57,400 --> 00:50:00,480 Speaker 1: I hope you're having a great weekend and I will 852 00:50:00,520 --> 00:50:47,319 Speaker 1: talk to you Monday. By