1 00:00:00,800 --> 00:00:03,720 Speaker 1: Hey, guys, ready or not, twenty twenty four is here, 2 00:00:03,880 --> 00:00:06,360 Speaker 1: and we here at breaking points, are already thinking of 3 00:00:06,400 --> 00:00:08,600 Speaker 1: ways we can up our game for this critical election. 4 00:00:08,800 --> 00:00:11,760 Speaker 2: We rely on our premium subs to expand coverage, upgrade 5 00:00:11,760 --> 00:00:15,120 Speaker 2: the studio ad staff give you, guys, the best independent 6 00:00:15,160 --> 00:00:17,440 Speaker 2: coverage that is possible. If you like what we're all about, 7 00:00:17,680 --> 00:00:20,080 Speaker 2: it just means the absolute world to have your support. 8 00:00:20,160 --> 00:00:25,400 Speaker 2: But enough with that, let's get to the show. Let's 9 00:00:25,400 --> 00:00:27,400 Speaker 2: go to polymarket. I've been wanting to cover this for 10 00:00:27,560 --> 00:00:30,800 Speaker 2: a long time, and of course, like everybody I think 11 00:00:30,840 --> 00:00:34,080 Speaker 2: who's online, they see these polymarket odds, and in particular 12 00:00:34,159 --> 00:00:38,680 Speaker 2: lately they have seen Trump just absolutely blow away Kamala 13 00:00:38,920 --> 00:00:41,199 Speaker 2: in the polymarket odds. And there's been a lot of 14 00:00:41,200 --> 00:00:44,000 Speaker 2: discussion what's going on here Because if you look at 15 00:00:44,000 --> 00:00:46,920 Speaker 2: a polling analysis, if you look at even the average 16 00:00:46,960 --> 00:00:49,080 Speaker 2: of all of the models that are out there, in 17 00:00:49,159 --> 00:00:52,560 Speaker 2: terms of the reliable ones, the average I checked yesterday 18 00:00:52,680 --> 00:00:54,920 Speaker 2: is fifty percent. So if the average is there and 19 00:00:54,960 --> 00:00:57,320 Speaker 2: the Betty market is different, well what do we know. 20 00:00:57,920 --> 00:01:01,000 Speaker 2: Usually when you see a bit different between that, somebody 21 00:01:01,000 --> 00:01:05,360 Speaker 2: would think potentially have inside information. But then one of 22 00:01:05,400 --> 00:01:07,520 Speaker 2: my friends pointed this out, there's no such thing as 23 00:01:07,560 --> 00:01:10,320 Speaker 2: inside information in an election, because it's like, what do 24 00:01:10,480 --> 00:01:13,720 Speaker 2: they know? How undecided voters in Pennsylvania are the telefuture? 25 00:01:13,800 --> 00:01:16,880 Speaker 3: Yeah, exactly, they literally forecast the future. If not, you 26 00:01:16,959 --> 00:01:19,679 Speaker 3: don't have any inside information. So what is happening? 27 00:01:19,680 --> 00:01:22,679 Speaker 2: Why is Polymarket, which is roughly a billion dollar market 28 00:01:22,840 --> 00:01:25,000 Speaker 2: from the election, just on that question of who is 29 00:01:25,040 --> 00:01:28,360 Speaker 2: going to win? How did it become so different than 30 00:01:28,440 --> 00:01:30,240 Speaker 2: all their models? Was put this on the screen? The 31 00:01:30,240 --> 00:01:34,560 Speaker 2: Wall Street Journal did an investigation. It's actually super interesting. Effectively, 32 00:01:34,680 --> 00:01:38,319 Speaker 2: what has happened is that four separate accounts, potentially all 33 00:01:38,360 --> 00:01:42,200 Speaker 2: belonging to the same person, have bet some thirty million 34 00:01:42,360 --> 00:01:47,240 Speaker 2: dollars to thirty million dollars on the Donald Trump winning 35 00:01:47,319 --> 00:01:51,080 Speaker 2: the presidential election. And those four separate, massive size bets 36 00:01:51,280 --> 00:01:55,520 Speaker 2: were enough to push the average for the Trump question 37 00:01:55,600 --> 00:01:57,880 Speaker 2: of whether he's going to win up to some sixty 38 00:01:57,920 --> 00:02:02,600 Speaker 2: percent and push Kamos down to now Again. What's interesting, 39 00:02:02,680 --> 00:02:07,720 Speaker 2: too is that this actually happened after Elon Musk tweeted 40 00:02:07,760 --> 00:02:10,840 Speaker 2: about Polymarket on October six, So we. 41 00:02:10,840 --> 00:02:12,440 Speaker 3: Have one of those tweets. Can we put that on 42 00:02:12,480 --> 00:02:13,120 Speaker 3: the screen please? 43 00:02:13,760 --> 00:02:18,239 Speaker 2: This seems to be one of the original ones where 44 00:02:18,360 --> 00:02:22,239 Speaker 2: Trump where Elon tweeted about this prediction market where he 45 00:02:22,280 --> 00:02:24,560 Speaker 2: said Trump is not leading Kama by three percent. Betting 46 00:02:24,560 --> 00:02:27,320 Speaker 2: markets more accurate than polls as actual money is on 47 00:02:27,360 --> 00:02:30,160 Speaker 2: the line. That was the demarcation point. Prior to that, 48 00:02:30,200 --> 00:02:32,680 Speaker 2: I've been tracking poly market almost every single day. It 49 00:02:32,720 --> 00:02:35,440 Speaker 2: was roughly in line within Nate Silver consensus, but it 50 00:02:35,440 --> 00:02:38,960 Speaker 2: has now completely split again. You can take two different 51 00:02:39,080 --> 00:02:41,160 Speaker 2: parts of that for what you want as to whether 52 00:02:41,200 --> 00:02:43,600 Speaker 2: it is more predictive or not. If you ask me, 53 00:02:43,800 --> 00:02:45,320 Speaker 2: and you know, we do this for a living, not 54 00:02:45,360 --> 00:02:47,320 Speaker 2: that I guess it makes me all that much of 55 00:02:47,360 --> 00:02:49,560 Speaker 2: an expert. But right now they have Comma only has 56 00:02:49,639 --> 00:02:52,680 Speaker 2: thirty six point two percent chance of winning. That, in 57 00:02:52,720 --> 00:02:56,000 Speaker 2: my opinion, is crazy considering the amount of caveats and 58 00:02:56,080 --> 00:02:58,000 Speaker 2: all the things we've had to drop on the show. 59 00:02:58,600 --> 00:03:01,919 Speaker 3: That is like, it's so bullish. They have her right. 60 00:03:01,800 --> 00:03:04,000 Speaker 2: Now sixty one percent chance for Trump in the state 61 00:03:04,040 --> 00:03:08,200 Speaker 2: of Pennsylvania. In Wisconsin, fifty eight percent Trump, fifty nine 62 00:03:08,200 --> 00:03:11,119 Speaker 2: percent Trump in Michigan, Each one of those Blue Wall 63 00:03:11,160 --> 00:03:13,920 Speaker 2: states totally miss priced if you ask me so. If 64 00:03:13,960 --> 00:03:15,760 Speaker 2: you're a line shopper out there, in the words of 65 00:03:15,919 --> 00:03:17,960 Speaker 2: sports betters, you may want to get out on the action. 66 00:03:18,200 --> 00:03:20,680 Speaker 2: And I don't even think that Comma is going to win. 67 00:03:21,000 --> 00:03:23,280 Speaker 2: But if you were like, oh, i'll give you two 68 00:03:23,280 --> 00:03:25,600 Speaker 2: to one odds that Comma would win when she's got 69 00:03:25,600 --> 00:03:28,880 Speaker 2: a fifty to fifty shot, anybody out there who bets sports. 70 00:03:28,680 --> 00:03:32,000 Speaker 3: For a living, that's usually you should take that bet. 71 00:03:32,080 --> 00:03:33,880 Speaker 2: And a lot of people made a ton of money 72 00:03:34,080 --> 00:03:37,200 Speaker 2: doing exactly this betting on Trump back in two thousand 73 00:03:37,240 --> 00:03:39,280 Speaker 2: and sixteen. When I show you, guys at twenty sixteen odds, 74 00:03:39,360 --> 00:03:41,480 Speaker 2: you're going to be blown away from what betting markets were. 75 00:03:41,640 --> 00:03:45,080 Speaker 1: So just to recap, there's four accounts that about thirty 76 00:03:45,080 --> 00:03:48,240 Speaker 1: million dollars that are basically responsible for this huge surge 77 00:03:48,360 --> 00:03:51,760 Speaker 1: for Trump, and all of those accounts are behaving in 78 00:03:51,800 --> 00:03:55,040 Speaker 1: a very similar fashion, raising the possibility that is actually 79 00:03:55,320 --> 00:03:59,160 Speaker 1: one individual who is gaming this market and pushing up 80 00:03:59,200 --> 00:04:00,560 Speaker 1: the odds in favor of Trump. 81 00:04:00,880 --> 00:04:02,000 Speaker 4: And it just so happens that. 82 00:04:01,960 --> 00:04:06,160 Speaker 1: It comes immediately after Elon Musk tweets about how accurate 83 00:04:06,160 --> 00:04:09,000 Speaker 1: polymarket is and how we should all be looking at 84 00:04:09,040 --> 00:04:12,520 Speaker 1: to understand the real odds going on in this election. 85 00:04:12,720 --> 00:04:15,640 Speaker 1: It also was a Peter teelback platform Worth noting that 86 00:04:15,720 --> 00:04:20,159 Speaker 1: as well. But yeah, the big question mark has been 87 00:04:20,200 --> 00:04:23,200 Speaker 1: whether there was you know, some organic something going on there. 88 00:04:23,200 --> 00:04:25,800 Speaker 1: Like my initial thought was once Elon Musk tweeted that 89 00:04:25,800 --> 00:04:27,599 Speaker 1: that there were a bunch of like Elon Musk bros. 90 00:04:27,720 --> 00:04:29,400 Speaker 4: Really cool, I got to get in on this, But 91 00:04:29,560 --> 00:04:32,320 Speaker 4: it looks like it was much more it was you. 92 00:04:32,279 --> 00:04:35,400 Speaker 1: Know, one whale or a number of them who decided 93 00:04:35,440 --> 00:04:38,320 Speaker 1: they wanted to push up the odds. And this is 94 00:04:38,360 --> 00:04:44,000 Speaker 1: significant also because we know that part of how uh 95 00:04:44,480 --> 00:04:48,240 Speaker 1: Trump last time justified his stop this Deal lies was 96 00:04:48,279 --> 00:04:51,640 Speaker 1: by claiming that Republicans had you know, an insturmountable lead 97 00:04:51,720 --> 00:04:54,080 Speaker 1: on election night and it was you know, preposterous to 98 00:04:54,160 --> 00:04:56,320 Speaker 1: imagine that they were going to lose. And so this 99 00:04:56,440 --> 00:04:59,080 Speaker 1: also very much seems like and Trump has leaned into 100 00:04:59,080 --> 00:05:02,200 Speaker 1: this too, setting the expectation that Trump is one hundred 101 00:05:02,200 --> 00:05:05,320 Speaker 1: percent going to win and so if he doesn't, there 102 00:05:05,440 --> 00:05:08,680 Speaker 1: must have been something nefarious. So I think that's part 103 00:05:08,680 --> 00:05:10,400 Speaker 1: of what's going on here. By the way, Elon didn't 104 00:05:10,440 --> 00:05:14,800 Speaker 1: just promote this one the polymarket one time. He's tweeted 105 00:05:14,800 --> 00:05:18,680 Speaker 1: like twenty different times about polymarket, all of a bunch 106 00:05:18,720 --> 00:05:21,839 Speaker 1: of the right wing influencer accounts on Twitter have picked 107 00:05:21,920 --> 00:05:25,800 Speaker 1: up this same approach and have been pumping the polymarket 108 00:05:25,800 --> 00:05:29,240 Speaker 1: odds and you, claiming that this shows the real truth 109 00:05:29,640 --> 00:05:32,320 Speaker 1: of how likely Donald Trump is to win. And again, 110 00:05:32,600 --> 00:05:34,479 Speaker 1: I think it's for some of these people it's just 111 00:05:34,480 --> 00:05:37,080 Speaker 1: about clout and whatever. But I think for Trump specifically 112 00:05:37,120 --> 00:05:39,760 Speaker 1: and potentially for Elon Musk, it's also about trying to 113 00:05:39,839 --> 00:05:42,000 Speaker 1: lay the groundwork to claim that the election is stolen 114 00:05:42,040 --> 00:05:42,960 Speaker 1: if he ends up not winning. 115 00:05:43,040 --> 00:05:43,960 Speaker 3: It's certainly possible. 116 00:05:44,000 --> 00:05:45,880 Speaker 2: I mean, it's just one of those where everyone always 117 00:05:45,960 --> 00:05:47,920 Speaker 2: likes to say, like, oh, when money is on the line, 118 00:05:48,080 --> 00:05:48,960 Speaker 2: it's more accurate. 119 00:05:49,040 --> 00:05:51,040 Speaker 3: But okay, I don't watch sports. 120 00:05:51,120 --> 00:05:53,320 Speaker 2: I'm friends with a lot of people who are obsessed 121 00:05:53,320 --> 00:05:56,320 Speaker 2: with sports betting and the general consensus around sports and 122 00:05:56,400 --> 00:05:58,800 Speaker 2: even the line like what Vegas and all those come 123 00:05:58,839 --> 00:06:01,080 Speaker 2: up with. They take all the information, they try and 124 00:06:01,080 --> 00:06:04,000 Speaker 2: distill it into a number. That number. It's not bad, 125 00:06:04,120 --> 00:06:07,360 Speaker 2: it can sometimes be accurate. But then how often are 126 00:06:07,400 --> 00:06:09,800 Speaker 2: you watching a game where there's a spread that we 127 00:06:09,839 --> 00:06:12,520 Speaker 2: think is quite reasonable and then something happens at the 128 00:06:12,640 --> 00:06:15,240 Speaker 2: very last minute and everybody loses. So even though the 129 00:06:15,279 --> 00:06:17,160 Speaker 2: team that you bet on might win, they may not 130 00:06:17,200 --> 00:06:20,720 Speaker 2: cover the spread, or how often does somebody get hurt 131 00:06:20,960 --> 00:06:23,400 Speaker 2: or one thing goes a different way and then the 132 00:06:23,560 --> 00:06:25,560 Speaker 2: entire thing shifts in a different direction. If you don't 133 00:06:25,560 --> 00:06:27,520 Speaker 2: believe me, you know, for the first three weeks of 134 00:06:27,560 --> 00:06:30,000 Speaker 2: the NFL season, I believe the public was on the 135 00:06:30,040 --> 00:06:32,080 Speaker 2: wrong side of the bet for some eighty percent. So 136 00:06:32,279 --> 00:06:35,440 Speaker 2: if you look at how good sports betters actually are, 137 00:06:35,600 --> 00:06:38,240 Speaker 2: separate conversation that I do want to save for later. 138 00:06:38,400 --> 00:06:41,279 Speaker 3: Similarly, it's like exuberant. 139 00:06:40,600 --> 00:06:43,400 Speaker 2: So whether it's a whale or it's dumb, or it's 140 00:06:43,480 --> 00:06:44,479 Speaker 2: just like Elon Bros. 141 00:06:44,520 --> 00:06:45,600 Speaker 3: And dumb money. 142 00:06:45,680 --> 00:06:48,480 Speaker 2: Like you know, irrational exuberance is a tale as old 143 00:06:48,480 --> 00:06:51,920 Speaker 2: as time in the stock market. And this idea that, oh, 144 00:06:52,120 --> 00:06:54,160 Speaker 2: just because people are willing to put millions of dollars 145 00:06:54,200 --> 00:06:56,560 Speaker 2: behind something that they may not be totally wrong is 146 00:06:56,640 --> 00:07:00,280 Speaker 2: also completely inaccurate. So let's, for example, put C three 147 00:07:00,600 --> 00:07:03,760 Speaker 2: up on the screen. I had no idea about this actually, 148 00:07:03,880 --> 00:07:05,919 Speaker 2: and it was raised in this Wall Treat journal piece. 149 00:07:06,240 --> 00:07:11,960 Speaker 2: A single trader lost between four and seven million dollars 150 00:07:12,480 --> 00:07:17,080 Speaker 2: exactly twelve years ago betting on Mitt Romney. 151 00:07:16,640 --> 00:07:20,040 Speaker 3: To win the election four to seven million. 152 00:07:20,160 --> 00:07:22,840 Speaker 2: All of the money from the single trader were placed 153 00:07:22,920 --> 00:07:27,320 Speaker 2: exactly two weeks before the overall presidential election in twenty twelve. 154 00:07:27,560 --> 00:07:31,360 Speaker 2: They believed wholeheartedly there was a similar thing, miss priced trade. 155 00:07:31,400 --> 00:07:32,960 Speaker 3: They thought he was totally. 156 00:07:34,160 --> 00:07:37,560 Speaker 2: They were watching way too much television, watching Fox News, 157 00:07:37,720 --> 00:07:40,240 Speaker 2: and they were out there and bet four to seven 158 00:07:40,400 --> 00:07:44,000 Speaker 2: million that they ended up losing that Mitt Romney would 159 00:07:44,000 --> 00:07:48,280 Speaker 2: win the presidential election. Similarly, in twenty sixteen, could we 160 00:07:48,360 --> 00:07:51,400 Speaker 2: put this up there please, because this is crazy. These 161 00:07:51,400 --> 00:07:55,320 Speaker 2: are the live betting odds from OURCP average that were 162 00:07:55,360 --> 00:07:58,000 Speaker 2: of the prediction markets at the time. On the day 163 00:07:58,160 --> 00:08:02,240 Speaker 2: of the presidential election November in twenty sixteen, Donald Trump 164 00:08:02,280 --> 00:08:05,280 Speaker 2: had a thirteen percent chance of winning according to the 165 00:08:05,320 --> 00:08:09,680 Speaker 2: betting markets at that time, thirteen percent, Clinton at eighty eight. 166 00:08:09,880 --> 00:08:12,280 Speaker 2: And as after the wisdom of the market, yes exactly. 167 00:08:12,280 --> 00:08:15,160 Speaker 2: This is what I'm trying to really like underscore for everybody. 168 00:08:15,320 --> 00:08:17,679 Speaker 2: And in fact, one of the things that Nate talks 169 00:08:17,680 --> 00:08:19,920 Speaker 2: about in his book is that there are a ton 170 00:08:19,960 --> 00:08:23,000 Speaker 2: of gamblers who love Nate Silver, and they were looking 171 00:08:23,000 --> 00:08:25,120 Speaker 2: at Nate Silver's odds on the day of the election. 172 00:08:25,440 --> 00:08:27,640 Speaker 2: He gave Donald Trump a twenty eight percent chance of 173 00:08:27,640 --> 00:08:29,880 Speaker 2: winning or something like that. And they're like, hey, twenty 174 00:08:29,920 --> 00:08:32,520 Speaker 2: eight percent chance twelve percent that the market's giving me 175 00:08:32,760 --> 00:08:35,040 Speaker 2: mispriced line. They didn't even think Trump was going to win. 176 00:08:35,200 --> 00:08:37,000 Speaker 2: They put one thousand dollars or something like that. 177 00:08:37,000 --> 00:08:39,000 Speaker 3: They got like nine to one odds. 178 00:08:38,720 --> 00:08:41,040 Speaker 2: In terms of the payout that they received. He said, 179 00:08:41,080 --> 00:08:42,920 Speaker 2: to this day, whatever he goes out to eat, people 180 00:08:42,920 --> 00:08:45,800 Speaker 2: still buying dinner because they've won so much money betting 181 00:08:45,840 --> 00:08:48,120 Speaker 2: on Donald Trump to win the election because of Nate 182 00:08:48,160 --> 00:08:50,840 Speaker 2: Silver's forecast. So that's my point is that if you 183 00:08:50,880 --> 00:08:53,959 Speaker 2: look at these markets and especially where they are right 184 00:08:54,000 --> 00:08:57,439 Speaker 2: now on top of with crypto, I mean, look, I'm 185 00:08:57,480 --> 00:08:59,840 Speaker 2: pro crypto and all that, but with polymarket, we have 186 00:09:00,080 --> 00:09:02,800 Speaker 2: very little insight into what this is. And too even 187 00:09:03,360 --> 00:09:05,959 Speaker 2: we have very little insight into like who these traders are, 188 00:09:06,200 --> 00:09:08,520 Speaker 2: for example, who are moving these big bets. They're not 189 00:09:08,600 --> 00:09:12,000 Speaker 2: probably as used to having to deal with like literal 190 00:09:12,240 --> 00:09:16,679 Speaker 2: massive whales. Never before seen situation with such a large 191 00:09:16,720 --> 00:09:20,319 Speaker 2: marketplace on the issue of the presidential election. So if 192 00:09:20,360 --> 00:09:23,400 Speaker 2: you just think that this is a correct and accurate reflection, 193 00:09:23,760 --> 00:09:25,960 Speaker 2: I mean, again, we don't even have that in the 194 00:09:26,000 --> 00:09:29,439 Speaker 2: most well regulated market in our stock market. 195 00:09:29,080 --> 00:09:31,640 Speaker 3: As opposed to what's going on on poly Market. 196 00:09:31,840 --> 00:09:34,440 Speaker 1: I am also highly suspicious that this is actually one 197 00:09:34,480 --> 00:09:37,320 Speaker 1: individual because not only are they behaving in a very 198 00:09:37,360 --> 00:09:41,640 Speaker 1: similar fashion, they also were all funded by deposits from Kraken, 199 00:09:41,720 --> 00:09:46,040 Speaker 1: a US based crypto exchange. So is highly suspicious that 200 00:09:46,120 --> 00:09:49,160 Speaker 1: this is all one individual. So it's trying to create 201 00:09:49,600 --> 00:09:53,080 Speaker 1: a portrait of you know, this being in the bag 202 00:09:53,120 --> 00:09:56,679 Speaker 1: for Donald Trump and you know, using that to create 203 00:09:57,240 --> 00:10:00,439 Speaker 1: a sense, a psychological sense of imminent victory on the 204 00:10:00,480 --> 00:10:04,640 Speaker 1: Trump side. And Polymarket themselves said that they are actually 205 00:10:04,679 --> 00:10:09,120 Speaker 1: investigating what is going on here too. So even to 206 00:10:09,200 --> 00:10:11,680 Speaker 1: your point, Sager, even if it wasn't for this, you know, 207 00:10:11,840 --> 00:10:14,920 Speaker 1: one or four individuals putting in thirty million dollars to 208 00:10:14,960 --> 00:10:16,839 Speaker 1: get this to look the way that they wanted to look, 209 00:10:17,120 --> 00:10:19,320 Speaker 1: even if it was just you know, a bunch of people, 210 00:10:19,320 --> 00:10:21,600 Speaker 1: wisdom of the crowd or whatever, you still should not 211 00:10:21,679 --> 00:10:25,000 Speaker 1: put a lot of stock in these betting odds because 212 00:10:25,040 --> 00:10:27,960 Speaker 1: people can very eat on markets can very very. 213 00:10:27,840 --> 00:10:29,079 Speaker 4: Easily be wrong. 214 00:10:29,160 --> 00:10:31,400 Speaker 1: But you know, part of what I was getting at 215 00:10:31,440 --> 00:10:33,320 Speaker 1: and why, you know, I do think that this is 216 00:10:34,080 --> 00:10:38,160 Speaker 1: potentially on the part of some sort of ominous and 217 00:10:38,360 --> 00:10:43,160 Speaker 1: nefarious is we also have people like Marjorie Taylor Green 218 00:10:43,880 --> 00:10:48,880 Speaker 1: now floating new dominion voter conspiracies, claiming that she saw 219 00:10:48,920 --> 00:10:52,560 Speaker 1: a Facebook post that said that some Dominion voting machine 220 00:10:53,080 --> 00:10:56,640 Speaker 1: was flipping votes. Elon Musk has also gotten in on 221 00:10:56,720 --> 00:11:01,080 Speaker 1: this dominion voting like, you know, a ledging that they 222 00:11:01,160 --> 00:11:03,400 Speaker 1: there could be some fraud afoot with where there are 223 00:11:03,440 --> 00:11:06,800 Speaker 1: dominion voting machines. We can go ahead. Let's go ahead 224 00:11:06,800 --> 00:11:09,760 Speaker 1: and play for you. Marjorie Taylor Green's comments, that's see 225 00:11:09,800 --> 00:11:10,440 Speaker 1: six guys. 226 00:11:10,800 --> 00:11:13,040 Speaker 5: So they went up to one of the election workers 227 00:11:13,040 --> 00:11:17,079 Speaker 5: and they said, here's the problem. The machine switched it 228 00:11:17,200 --> 00:11:20,160 Speaker 5: and the printed my printed ballot. I did not vote 229 00:11:20,200 --> 00:11:22,040 Speaker 5: for these people. So they had to start over, and 230 00:11:22,080 --> 00:11:25,080 Speaker 5: they went through it several times, and it kept on 231 00:11:25,160 --> 00:11:29,000 Speaker 5: making the same error, kept on switching the boat. 232 00:11:29,440 --> 00:11:33,280 Speaker 1: So you know, once again back to the dominion voting machines. 233 00:11:33,320 --> 00:11:36,439 Speaker 1: These people also never learned because Dominion sued a bunch 234 00:11:36,480 --> 00:11:38,520 Speaker 1: of the news networks for all they were worth when 235 00:11:38,520 --> 00:11:42,400 Speaker 1: they were spreading lies about the operations of their machines. 236 00:11:42,640 --> 00:11:44,640 Speaker 1: We can put see five excuse me up on the 237 00:11:44,640 --> 00:11:48,400 Speaker 1: screen with the Elon Musk details here he said at 238 00:11:48,400 --> 00:11:51,720 Speaker 1: one of these town halls he did in Pennsylvania. He says, 239 00:11:51,920 --> 00:11:54,280 Speaker 1: when you have mail in ballots and no proof of citizenship, 240 00:11:54,280 --> 00:11:57,040 Speaker 1: it's almost impossible to prove cheating. Statistically, there are some 241 00:11:57,200 --> 00:12:00,520 Speaker 1: very strange things that happened that are statistically incredibly likely. 242 00:12:00,760 --> 00:12:03,840 Speaker 1: There's also this question of, say, the dominion voting machines. 243 00:12:03,920 --> 00:12:05,480 Speaker 1: It is weird that I think they were used in 244 00:12:05,480 --> 00:12:08,200 Speaker 1: Philadelphia and Maricopa County, Arizona, but not in a lot 245 00:12:08,240 --> 00:12:10,480 Speaker 1: of other places. Doesn't that seem like a heck of 246 00:12:10,480 --> 00:12:11,240 Speaker 1: a coincidence? 247 00:12:11,280 --> 00:12:13,120 Speaker 4: He added? The last thing I would do is trust 248 00:12:13,160 --> 00:12:15,439 Speaker 4: a computer program. And apparently, sorry, he was not even. 249 00:12:15,280 --> 00:12:19,080 Speaker 1: Correct about how and where and when the dominion voting 250 00:12:19,120 --> 00:12:20,040 Speaker 1: machines were being used. 251 00:12:20,120 --> 00:12:22,920 Speaker 2: Yeah, I'm just like, Okay, here we go again. You 252 00:12:22,960 --> 00:12:25,839 Speaker 2: really want to do this, even for you know, list 253 00:12:25,920 --> 00:12:28,000 Speaker 2: Elon is rich. He can defend himself in court and 254 00:12:28,040 --> 00:12:30,480 Speaker 2: decide you can hire a lawyer, Marjorie the rest of 255 00:12:30,520 --> 00:12:32,200 Speaker 2: these other folks who want to toy with this stuff. 256 00:12:32,320 --> 00:12:34,480 Speaker 2: Look how it worked out for everybody in the state 257 00:12:34,520 --> 00:12:37,480 Speaker 2: of Georgia who's under indictment, ends up pleading guilty, giving 258 00:12:37,559 --> 00:12:40,400 Speaker 2: up their law license, spending hundreds of thousands of dollars 259 00:12:40,800 --> 00:12:42,800 Speaker 2: on lawyers. So if they want to do that again, 260 00:12:42,920 --> 00:12:44,480 Speaker 2: go for it. Yeah, and see how it works out 261 00:12:44,520 --> 00:12:44,839 Speaker 2: for you. 262 00:12:44,920 --> 00:12:47,200 Speaker 1: Well, you remember what Trump said at McDonald's when he 263 00:12:47,200 --> 00:12:49,240 Speaker 1: had asked about it. He said, you know, something very 264 00:12:49,240 --> 00:12:51,480 Speaker 1: He was like, he didn't say dominion votingstudents, but he 265 00:12:51,600 --> 00:12:53,960 Speaker 1: was like, if it's very I'll accept the results. And 266 00:12:54,040 --> 00:12:56,480 Speaker 1: by the way, you know, the predictions say that I 267 00:12:56,480 --> 00:12:58,640 Speaker 1: have a ninety three percent chance of winning, so of 268 00:12:58,679 --> 00:13:01,000 Speaker 1: course we're going to win. So that's how all of 269 00:13:01,040 --> 00:13:04,720 Speaker 1: this plays into, you know, what they have planned post 270 00:13:04,800 --> 00:13:07,439 Speaker 1: election day, And it's not just this. They've already filed 271 00:13:07,480 --> 00:13:11,680 Speaker 1: a range of lawsuits in battleground states to try to challenge, 272 00:13:11,720 --> 00:13:15,360 Speaker 1: you know, the voter's eligibility. They've stacked a lot of 273 00:13:15,400 --> 00:13:18,760 Speaker 1: the election boards with people who are like pro Trump, 274 00:13:18,840 --> 00:13:22,120 Speaker 1: MAGA loyalists. And then there's this sort of like PSI 275 00:13:22,240 --> 00:13:24,480 Speaker 1: op to create an impression that Trump is a sure thing. 276 00:13:24,559 --> 00:13:27,280 Speaker 1: And listen, he may well win on his own, in 277 00:13:27,320 --> 00:13:30,040 Speaker 1: which case all of this ends up being moot. But 278 00:13:30,520 --> 00:13:34,080 Speaker 1: that's why the polymarket piece fits in together with what 279 00:13:34,440 --> 00:13:36,960 Speaker 1: Elon is saying about dominion. Marjorie Taylor Green is saying 280 00:13:37,000 --> 00:13:39,440 Speaker 1: about dominion Trump is saying about whether or not he'll 281 00:13:39,480 --> 00:13:42,360 Speaker 1: accept the election results, all these lawsuits being filed and 282 00:13:42,400 --> 00:13:44,920 Speaker 1: all of the state boards of elections being stocked with 283 00:13:45,480 --> 00:13:46,640 Speaker 1: these MAGA loyalists. 284 00:13:46,720 --> 00:13:49,720 Speaker 3: Yeah, on that ladder part. And just because the price 285 00:13:49,840 --> 00:13:50,120 Speaker 3: was wrong. 286 00:13:50,160 --> 00:13:51,880 Speaker 2: I mean, imagine a company is saying we shouldn't have 287 00:13:51,920 --> 00:13:54,000 Speaker 2: gone bankrupt because our stock price was x, L and 288 00:13:54,120 --> 00:13:54,920 Speaker 2: z before that. 289 00:13:55,200 --> 00:13:57,000 Speaker 3: Yeah, well, the market was saying that it was going 290 00:13:57,080 --> 00:13:57,400 Speaker 3: to work. 291 00:13:57,559 --> 00:14:00,760 Speaker 2: It's like, okay, but that doesn't predict the actual results, Bob, 292 00:14:00,920 --> 00:14:03,160 Speaker 2: you know, yeah, we say it's ridiculous. 293 00:14:03,240 --> 00:14:04,880 Speaker 4: Yeah, very true, very true. 294 00:14:07,360 --> 00:14:10,199 Speaker 1: All right, let's move on to some strange campaign doings 295 00:14:10,200 --> 00:14:13,760 Speaker 1: from Kamala Harris. They clearly, you know, she's been She 296 00:14:13,840 --> 00:14:16,199 Speaker 1: went on with Reverend Sharpton. We showed you that interview, 297 00:14:16,240 --> 00:14:19,400 Speaker 1: went on the Shade Room, had Barack Obama go out 298 00:14:19,440 --> 00:14:21,440 Speaker 1: and lecture black men for some reason that was very 299 00:14:21,440 --> 00:14:25,280 Speaker 1: disturbing and uncomfortable. They clearly realized that they need to 300 00:14:25,320 --> 00:14:28,240 Speaker 1: shore up their support with what is a key voting 301 00:14:28,280 --> 00:14:32,560 Speaker 1: demographic for the Democratic Party, and it's been consistently very supportive. 302 00:14:33,120 --> 00:14:36,120 Speaker 1: So one of the efforts they put out was the 303 00:14:36,160 --> 00:14:37,720 Speaker 1: following ad take a look. 304 00:14:37,520 --> 00:14:38,520 Speaker 3: He little ladies entre. 305 00:14:38,720 --> 00:14:41,120 Speaker 2: It's good to be here. 306 00:14:39,800 --> 00:14:43,720 Speaker 3: He So, what do you do and how much do 307 00:14:43,760 --> 00:14:45,960 Speaker 3: you work in finance? Making six figures? 308 00:14:47,080 --> 00:14:47,840 Speaker 4: How tall are you? 309 00:14:48,360 --> 00:14:48,920 Speaker 3: Six wives? 310 00:14:48,960 --> 00:14:50,120 Speaker 4: Okay, do you work out? 311 00:14:50,320 --> 00:14:52,280 Speaker 3: I like to stay active for you. Do you have 312 00:14:52,360 --> 00:15:00,160 Speaker 3: a plan to vote? I didn't plan on it. 313 00:15:03,360 --> 00:15:07,120 Speaker 1: So basically the message here is guys, if you don't 314 00:15:07,200 --> 00:15:09,920 Speaker 1: vote and vote for Kamala Harris, then you're. 315 00:15:09,720 --> 00:15:11,240 Speaker 4: Not going to get any dates. 316 00:15:12,280 --> 00:15:18,240 Speaker 2: Pretty while okay, so shocking, crazy, also indicative of a 317 00:15:18,280 --> 00:15:20,000 Speaker 2: real phenomenon. I mean, if we're going to be real 318 00:15:20,680 --> 00:15:23,040 Speaker 2: and let's put this up there on the screen, this 319 00:15:23,080 --> 00:15:24,760 Speaker 2: has been I mean, you know how many monologues have 320 00:15:24,760 --> 00:15:26,560 Speaker 2: we done about this now at this point? How many 321 00:15:26,560 --> 00:15:27,920 Speaker 2: times have I talked about it here in the show. 322 00:15:28,200 --> 00:15:31,120 Speaker 2: Gender is going to be a huge factor in this election. 323 00:15:31,280 --> 00:15:33,600 Speaker 2: Here's what the data shows. What they shay is that 324 00:15:33,640 --> 00:15:35,800 Speaker 2: we're on track for one of the largest gender gaps 325 00:15:36,120 --> 00:15:38,160 Speaker 2: in modern American history. 326 00:15:38,440 --> 00:15:38,760 Speaker 3: Quote. 327 00:15:38,800 --> 00:15:41,040 Speaker 2: Younger women are registering to vote at record rates. They 328 00:15:41,040 --> 00:15:44,880 Speaker 2: tell abortion polsters abortion rights are their most crucial voting concern. 329 00:15:45,240 --> 00:15:47,440 Speaker 2: If you look at the gender gap, which has been 330 00:15:47,520 --> 00:15:50,520 Speaker 2: in every election since nineteen eighty, it is at a 331 00:15:50,640 --> 00:15:53,560 Speaker 2: record high that is the gap specifically between men and 332 00:15:53,600 --> 00:15:55,800 Speaker 2: women and what they're voting on. If we look at 333 00:15:55,840 --> 00:15:58,920 Speaker 2: the Georgia poll that I just referenced that dropped this 334 00:15:59,040 --> 00:16:02,480 Speaker 2: morning from the Atlanta Journal Constitution, you see a massive 335 00:16:02,560 --> 00:16:05,400 Speaker 2: fifty points spread between men and women on the question 336 00:16:05,440 --> 00:16:08,240 Speaker 2: of who you're going to support for president. And actually 337 00:16:08,280 --> 00:16:12,640 Speaker 2: when we dig into young men, that is even more interesting. 338 00:16:12,680 --> 00:16:15,280 Speaker 2: So let's put this up there on the screen Wall 339 00:16:15,280 --> 00:16:15,960 Speaker 2: Street Journal. 340 00:16:16,200 --> 00:16:17,680 Speaker 3: Same phenomenon. 341 00:16:17,880 --> 00:16:22,640 Speaker 2: Gender gap is defining feature of the deadlocked Trump Harris race. Now, 342 00:16:22,920 --> 00:16:26,200 Speaker 2: as we have seen racial de alignment in the US, 343 00:16:26,680 --> 00:16:30,160 Speaker 2: a lot of it is polarizing amongst gender lines. Now 344 00:16:30,240 --> 00:16:33,520 Speaker 2: still the most polarizing thing in America is education. But 345 00:16:33,760 --> 00:16:37,480 Speaker 2: education is also polarizing amongst gender lines. We've had now 346 00:16:37,520 --> 00:16:42,080 Speaker 2: some four years of massive female enrollment in colleges relative 347 00:16:42,120 --> 00:16:44,600 Speaker 2: to men, huge numbers of young men gen Z men 348 00:16:44,640 --> 00:16:47,800 Speaker 2: specifically dropping out of college and or not even attending 349 00:16:47,840 --> 00:16:50,640 Speaker 2: college in the first place, pursuing a different career path. 350 00:16:50,800 --> 00:16:53,760 Speaker 2: I think that's great, but culturally sets people up for 351 00:16:53,920 --> 00:16:56,000 Speaker 2: a lot of divisions whenever it comes to the dating 352 00:16:56,040 --> 00:17:00,160 Speaker 2: market in terms of wages, where you move. I've talked 353 00:17:00,160 --> 00:17:02,720 Speaker 2: about this a lot it really determines like who you 354 00:17:02,800 --> 00:17:05,720 Speaker 2: are and like what you do. Now, based on that, 355 00:17:05,960 --> 00:17:09,440 Speaker 2: what we see is this dating thing is a real 356 00:17:09,520 --> 00:17:12,679 Speaker 2: phenomenon which you actually pull. This is fantastic. Let's put 357 00:17:12,680 --> 00:17:15,879 Speaker 2: it up there on the screen. Some three quarters of 358 00:17:16,000 --> 00:17:19,760 Speaker 2: college educated young women are less likely to date a 359 00:17:19,840 --> 00:17:22,960 Speaker 2: Trump supporter. So here we have all young women people 360 00:17:23,000 --> 00:17:25,159 Speaker 2: who say that they are a lot less likely to 361 00:17:25,240 --> 00:17:28,200 Speaker 2: date a Trump support some fifty five percent. Somewhat less 362 00:17:28,240 --> 00:17:30,520 Speaker 2: likely is actually still more amongst Republicans. 363 00:17:30,520 --> 00:17:31,320 Speaker 3: It's a little bit different. 364 00:17:32,600 --> 00:17:33,960 Speaker 4: Republican women are like, that's. 365 00:17:33,800 --> 00:17:35,920 Speaker 2: Why I'm like, it's no true, you know, I mean, 366 00:17:35,960 --> 00:17:37,440 Speaker 2: but this is the survey of American life. 367 00:17:37,440 --> 00:17:39,040 Speaker 3: It's actually a pretty rock solid survey. 368 00:17:39,240 --> 00:17:41,360 Speaker 2: Now, it says young women with a college degree, some 369 00:17:41,400 --> 00:17:43,800 Speaker 2: seventy six percent say that they are less likely to 370 00:17:44,080 --> 00:17:46,080 Speaker 2: date someone who is a Trump support some fifty two 371 00:17:46,080 --> 00:17:49,280 Speaker 2: percent say Republican. And then young women with the high 372 00:17:49,320 --> 00:17:52,480 Speaker 2: school education it's still some thirty eight percent and thirty 373 00:17:52,520 --> 00:17:55,439 Speaker 2: percent on if they're the question of a Republican or not. 374 00:17:55,600 --> 00:17:59,840 Speaker 2: So that ad kind of hits at something. And I mean, 375 00:17:59,840 --> 00:18:01,760 Speaker 2: I think it's very unfortunate, you know, given them the 376 00:18:01,800 --> 00:18:05,560 Speaker 2: way politics are and just sociologically and like what that 377 00:18:05,600 --> 00:18:08,560 Speaker 2: means for the country. But that's what the AD, I guess, 378 00:18:08,640 --> 00:18:10,280 Speaker 2: is trying to capitalize on. 379 00:18:10,480 --> 00:18:13,080 Speaker 4: Especially interesting they're going right at it. Huh, Well, what I. 380 00:18:13,080 --> 00:18:14,719 Speaker 3: Think they're really going on is, I don't think it's 381 00:18:14,760 --> 00:18:15,280 Speaker 3: a coincidence. 382 00:18:15,320 --> 00:18:17,080 Speaker 2: There was a black guy, a young black guy who 383 00:18:17,119 --> 00:18:19,920 Speaker 2: was in there, and because it's part of the blackmail strategy. 384 00:18:20,000 --> 00:18:23,680 Speaker 2: So first we lecture, then we shame about Oh what 385 00:18:23,760 --> 00:18:26,359 Speaker 2: Obama said, I need to speak to the brothers, you know. 386 00:18:26,400 --> 00:18:27,879 Speaker 2: So now they're like, oh, well, now we need to 387 00:18:27,920 --> 00:18:30,520 Speaker 2: make sure that they know that these black women, they're 388 00:18:30,520 --> 00:18:31,879 Speaker 2: not going to want to get with you if you 389 00:18:31,880 --> 00:18:34,119 Speaker 2: don't vote for Kamala Harris. I don't think it, frankly 390 00:18:34,119 --> 00:18:36,520 Speaker 2: think is disgusting, but it is a real phenomenon. 391 00:18:36,600 --> 00:18:37,199 Speaker 4: So there you go. 392 00:18:37,400 --> 00:18:41,120 Speaker 1: Yeah, if you dig into the numbers also of what 393 00:18:41,240 --> 00:18:45,480 Speaker 1: issues men versus women say are their top priorities. And again, 394 00:18:45,840 --> 00:18:48,919 Speaker 1: as you're reporting before, like I think it's difficult for 395 00:18:48,960 --> 00:18:51,399 Speaker 1: people to actually say, like this is my top issue 396 00:18:51,440 --> 00:18:53,240 Speaker 1: and this is how I'm ranking things. But anyway, it's 397 00:18:53,280 --> 00:18:57,080 Speaker 1: interesting to look at the numbers. In August, the economy 398 00:18:57,119 --> 00:19:00,680 Speaker 1: and inflation were men's most important issues and decide their vote. 399 00:19:00,720 --> 00:19:04,720 Speaker 1: For women, abortion and the economy and inflation are all 400 00:19:04,800 --> 00:19:07,840 Speaker 1: tied as the most important issues, and for women under 401 00:19:07,920 --> 00:19:13,400 Speaker 1: age forty five, abortion is the single most important voting issue. 402 00:19:14,200 --> 00:19:16,800 Speaker 1: Similar findings. You know, in that Wall Street Journal piece, 403 00:19:16,840 --> 00:19:20,119 Speaker 1: we had up as well, twenty seven percent of women 404 00:19:20,359 --> 00:19:23,679 Speaker 1: but only eight percent of men list abortion as the 405 00:19:23,680 --> 00:19:27,560 Speaker 1: top issue motivating their vote for president. And you know, 406 00:19:27,600 --> 00:19:32,200 Speaker 1: you see, like we have long seen how the gender 407 00:19:32,240 --> 00:19:35,280 Speaker 1: gap is probably going to be the defining issue of 408 00:19:35,320 --> 00:19:37,119 Speaker 1: this election. I mean, there are a lot of different 409 00:19:37,119 --> 00:19:41,480 Speaker 1: ways that the electric demo electorate demographically is divided, but 410 00:19:41,640 --> 00:19:43,639 Speaker 1: in this one we are likely to see if the 411 00:19:43,640 --> 00:19:47,719 Speaker 1: polls bear out the largest gender gap in modern American history. 412 00:19:47,920 --> 00:19:51,040 Speaker 1: The gender gap emerged basically in nineteen eighty and women 413 00:19:51,080 --> 00:19:53,679 Speaker 1: have tended to be more democratic and men more Republican 414 00:19:53,840 --> 00:19:57,280 Speaker 1: for you know, our entire lives, certainly, but we're getting 415 00:19:57,359 --> 00:20:02,720 Speaker 1: too new and extraordinary levels of divergence between the two sexes. 416 00:20:02,880 --> 00:20:04,879 Speaker 1: And you also saw this even in the strategy of 417 00:20:04,960 --> 00:20:07,560 Speaker 1: like Republicans at the RNC. It was this very like 418 00:20:07,720 --> 00:20:10,680 Speaker 1: you know, hul Cogan and Dana White and this very 419 00:20:10,800 --> 00:20:15,240 Speaker 1: like campy kind of masculinity, and you know the women 420 00:20:15,320 --> 00:20:18,600 Speaker 1: tend to vote at slightly higher rates than men, so 421 00:20:18,880 --> 00:20:21,240 Speaker 1: you know, in some ways you would rather be on 422 00:20:21,440 --> 00:20:24,679 Speaker 1: the female side of that gender divide. But it just 423 00:20:24,720 --> 00:20:27,200 Speaker 1: all depends on how large the gap is in which. 424 00:20:27,040 --> 00:20:28,320 Speaker 3: Direction, It all depends on the gap. 425 00:20:28,440 --> 00:20:31,160 Speaker 2: There's also a lot of If you look at married women, 426 00:20:31,240 --> 00:20:33,520 Speaker 2: they actually end up voting Republican a little bit more 427 00:20:33,560 --> 00:20:36,320 Speaker 2: than unmarried women. But again, marriage rates are declining, so 428 00:20:36,359 --> 00:20:38,520 Speaker 2: it's not necessarily I bet that you want to be on. 429 00:20:38,680 --> 00:20:39,920 Speaker 3: So overall it is. 430 00:20:40,040 --> 00:20:43,960 Speaker 2: It is clearly part of a major sociological phenomenon. It 431 00:20:44,040 --> 00:20:45,800 Speaker 2: is not one that I think the campaign should be 432 00:20:45,840 --> 00:20:48,560 Speaker 2: getting into because it probably exacerbates and makes it worse, 433 00:20:48,960 --> 00:20:52,240 Speaker 2: especially once people dig into all this, but it is 434 00:20:52,320 --> 00:20:54,560 Speaker 2: one that we are likely going to have to live with. 435 00:20:57,200 --> 00:20:57,520 Speaker 4: All right. 436 00:20:57,520 --> 00:20:59,159 Speaker 1: We wanted to give you an update on the latest 437 00:20:59,200 --> 00:21:03,639 Speaker 1: allegations surrounding Ditty. We have a new flood of lawsuits 438 00:21:03,640 --> 00:21:07,840 Speaker 1: that raise additional allegations against him. We can put this 439 00:21:07,920 --> 00:21:12,199 Speaker 1: up on the screen from who Else dmz. They say 440 00:21:12,240 --> 00:21:14,639 Speaker 1: Ditty was slapped with a flood of new lawsuits sites 441 00:21:14,720 --> 00:21:19,560 Speaker 1: other celebrities involved. That's probably the most noteworthy and also 442 00:21:19,600 --> 00:21:22,320 Speaker 1: potentially salacious part of this. We read a little bit 443 00:21:22,400 --> 00:21:25,320 Speaker 1: of this article from TMZ. They say Diddy's been hit 444 00:21:25,320 --> 00:21:27,320 Speaker 1: with a flood of new lawsuits, including one brought by 445 00:21:27,359 --> 00:21:31,879 Speaker 1: a girl who referenced unnamed celebrities and was just thirteen 446 00:21:32,600 --> 00:21:36,320 Speaker 1: years old when she says the music mobil drugged and 447 00:21:36,400 --> 00:21:38,399 Speaker 1: raped her at a house party. I'll go ahead and 448 00:21:38,480 --> 00:21:42,680 Speaker 1: say Ditty's lawyers deny all of these allegations. In total, 449 00:21:43,160 --> 00:21:47,280 Speaker 1: he is now facing five additional federal suits. All of 450 00:21:47,280 --> 00:21:50,600 Speaker 1: them were filed by this one Texas attorney on behalf 451 00:21:50,640 --> 00:21:53,240 Speaker 1: of his clients. They all claim that Ditty sexually assaulted 452 00:21:53,280 --> 00:21:56,520 Speaker 1: them and separate attacks between two thousand and twenty twenty two, 453 00:21:56,840 --> 00:22:00,520 Speaker 1: and additional two lawsuits were filed Sunday night in state 454 00:22:00,560 --> 00:22:04,040 Speaker 1: court in New York, so total of seven lawsuits from 455 00:22:04,080 --> 00:22:07,840 Speaker 1: a number of accusers. As TMC details, they say, perhaps 456 00:22:07,840 --> 00:22:10,920 Speaker 1: the most disturbing allegations were levied by this woman who 457 00:22:11,000 --> 00:22:14,040 Speaker 1: was thirteen at the time when she was dropped off 458 00:22:14,080 --> 00:22:16,959 Speaker 1: by a friend at Radio City Music Hall in New 459 00:22:17,040 --> 00:22:21,159 Speaker 1: York City to attend MTV's Video Music Awards. She says 460 00:22:21,320 --> 00:22:23,919 Speaker 1: that she sort of waited outside, you know, she wanted 461 00:22:23,920 --> 00:22:25,560 Speaker 1: to get a look at Ditty. 462 00:22:25,680 --> 00:22:26,960 Speaker 4: She wanted to be able to go. 463 00:22:26,960 --> 00:22:29,600 Speaker 1: To the party and you know, have her moment of 464 00:22:29,640 --> 00:22:35,000 Speaker 1: celebrity interaction encounter. But what she alleges happens is she 465 00:22:35,320 --> 00:22:37,840 Speaker 1: got picked up by a driver said effectively, yeah, I 466 00:22:37,880 --> 00:22:41,840 Speaker 1: think you're his type, gets into the party, is immediately 467 00:22:42,160 --> 00:22:45,840 Speaker 1: drugged with some sort of you know, Cocktail has to 468 00:22:45,920 --> 00:22:50,040 Speaker 1: lie down because she's so dizzy. And then she says 469 00:22:50,480 --> 00:22:54,879 Speaker 1: that Ditty eventually entered that bedroom where she was lying 470 00:22:54,920 --> 00:22:58,639 Speaker 1: down after likely being drugged, with two other celebrities, a 471 00:22:58,680 --> 00:23:01,080 Speaker 1: male and a female, both of whom are unnamed in 472 00:23:01,119 --> 00:23:04,439 Speaker 1: the suit. The accuser says the male celebrity ripped off 473 00:23:04,480 --> 00:23:07,960 Speaker 1: her clothes and raped her while the female celebrity watched. 474 00:23:08,119 --> 00:23:12,199 Speaker 1: Didty also allegedly violently sexually assaulted her while the celebrities watched. 475 00:23:12,480 --> 00:23:14,800 Speaker 1: She says she was able to escape the bedroom stagger 476 00:23:14,840 --> 00:23:16,760 Speaker 1: out of the house to a gas station nearby, where 477 00:23:16,800 --> 00:23:19,320 Speaker 1: she received some help. And as I said before, the 478 00:23:19,400 --> 00:23:21,840 Speaker 1: legal team on Didty side says in court, the truth 479 00:23:21,880 --> 00:23:24,719 Speaker 1: will prevail. Mister Colmbs has never sexually assaulted anyone adult 480 00:23:24,800 --> 00:23:28,040 Speaker 1: or minor man or woman. But saga, of course you know, 481 00:23:28,200 --> 00:23:32,679 Speaker 1: these allegations come on the heels of federal criminal charges 482 00:23:32,680 --> 00:23:36,280 Speaker 1: filed against Ditty for things like sex trafficking. We've seen 483 00:23:36,320 --> 00:23:39,320 Speaker 1: that horrific video of him assaulting R and B singer 484 00:23:39,480 --> 00:23:44,200 Speaker 1: Cassie in a hotel hallway and then dragging her back 485 00:23:44,280 --> 00:23:47,440 Speaker 1: into a hotel room. She really is the person who 486 00:23:47,680 --> 00:23:50,639 Speaker 1: kicked off all of this investigation and really opened the 487 00:23:50,680 --> 00:23:56,679 Speaker 1: floodgates viz Avi Diddy, and you know this new allegations 488 00:23:56,760 --> 00:24:01,080 Speaker 1: involving two other celebrities really does raise the question of 489 00:24:01,240 --> 00:24:04,800 Speaker 1: who else was involved, who else knew, and who else 490 00:24:05,240 --> 00:24:09,040 Speaker 1: should also be having their day in court facing similar 491 00:24:09,200 --> 00:24:12,240 Speaker 1: criminal criminal charges as Diddy himself. 492 00:24:12,320 --> 00:24:15,320 Speaker 2: Yeah, I mean, for example, it's very like the Weinstein 493 00:24:15,400 --> 00:24:17,760 Speaker 2: case where it was an open secret. So, for example, 494 00:24:17,880 --> 00:24:20,399 Speaker 2: fifty Cent was asked about this in a new interview. 495 00:24:20,440 --> 00:24:23,000 Speaker 2: Let's put this on the screen, and he said that 496 00:24:23,280 --> 00:24:26,440 Speaker 2: He's like, look, it seems like I've been doing some 497 00:24:27,160 --> 00:24:30,040 Speaker 2: like I've been saying some extremely outrageous things, but it's 498 00:24:30,080 --> 00:24:32,760 Speaker 2: just me saying what I've been saying for ten years now. 499 00:24:32,800 --> 00:24:35,280 Speaker 2: It's becoming more full facing in the news about the 500 00:24:35,320 --> 00:24:37,240 Speaker 2: puppy stuff. But away from that, I'm like it's just 501 00:24:37,280 --> 00:24:39,480 Speaker 2: my perspective because I stayed away from that stuff the 502 00:24:39,600 --> 00:24:40,160 Speaker 2: entire time. 503 00:24:40,440 --> 00:24:42,200 Speaker 3: That is not my style. 504 00:24:42,359 --> 00:24:44,760 Speaker 2: And I think what he's trying to get at here 505 00:24:44,840 --> 00:24:47,000 Speaker 2: is obviously he's been in the news because he was 506 00:24:47,000 --> 00:24:49,040 Speaker 2: one of the people who would very often bring this 507 00:24:49,200 --> 00:24:51,760 Speaker 2: up in public, one of the few actually in the 508 00:24:51,800 --> 00:24:54,679 Speaker 2: industry that would openly talk about it. It's in the 509 00:24:54,720 --> 00:24:58,400 Speaker 2: same way that I remember looking at videos like from 510 00:24:58,400 --> 00:25:00,720 Speaker 2: the Oscars from like twenty five. I think it was 511 00:25:00,760 --> 00:25:03,879 Speaker 2: Seth MacFarland who made a joke about women having to 512 00:25:03,880 --> 00:25:06,720 Speaker 2: be with Harvey Weinstein. Everybody looks kind of uncomfortable, but 513 00:25:06,760 --> 00:25:08,960 Speaker 2: there was a light laugh in the room. But it's 514 00:25:08,960 --> 00:25:11,600 Speaker 2: like they're joking about it at the Oscars, like a 515 00:25:11,680 --> 00:25:14,480 Speaker 2: decade before the whole me too situation. 516 00:25:14,640 --> 00:25:15,600 Speaker 3: So how does that work? 517 00:25:15,680 --> 00:25:15,800 Speaker 6: You know? 518 00:25:16,000 --> 00:25:17,000 Speaker 3: Exactly same here. 519 00:25:17,160 --> 00:25:19,280 Speaker 2: It was enough of an open secret to be able 520 00:25:19,359 --> 00:25:22,600 Speaker 2: to be talked about basically everywhere, for rumors, for somebody 521 00:25:22,720 --> 00:25:25,080 Speaker 2: very powerful like fifty cent to be able to speak 522 00:25:25,080 --> 00:25:28,560 Speaker 2: about it, and then the actual lawsuits. I mean eventually, 523 00:25:28,560 --> 00:25:29,879 Speaker 2: if you even look at the lead up to this 524 00:25:29,920 --> 00:25:33,800 Speaker 2: whole thing, it's crazy that it took that initial lawsuit 525 00:25:34,080 --> 00:25:37,160 Speaker 2: to even lead to that HSI investigation and now they're 526 00:25:37,160 --> 00:25:40,320 Speaker 2: looking back some thirty years. It's like the Epstein case, 527 00:25:40,400 --> 00:25:42,480 Speaker 2: like the Weinstein case, they're both very similar. Yeah, and 528 00:25:42,880 --> 00:25:44,280 Speaker 2: how they actually broke into the public. 529 00:25:44,480 --> 00:25:48,480 Speaker 1: Yeah, that's exactly right. And how widespread, potentially the web 530 00:25:48,520 --> 00:25:51,879 Speaker 1: of abuse was here. And you know, again just based 531 00:25:51,920 --> 00:25:55,280 Speaker 1: on what the federal indictment criminal indictment says. 532 00:25:55,720 --> 00:25:57,240 Speaker 4: They alleged that his. 533 00:25:59,160 --> 00:26:03,480 Speaker 1: Business was effectively criminal enterprise and that many of the 534 00:26:03,520 --> 00:26:09,520 Speaker 1: people involved were directly involved in facilitating this criminal behavior. 535 00:26:09,960 --> 00:26:12,359 Speaker 1: So you know, I suspect some of those people already flipped. 536 00:26:12,359 --> 00:26:14,399 Speaker 1: That's probably how they had the level of insight and 537 00:26:14,440 --> 00:26:17,680 Speaker 1: knowledge that they had. There's also allegations that, in addition 538 00:26:17,760 --> 00:26:21,399 Speaker 1: to the Cassie video, which was horrific, where I mean 539 00:26:21,440 --> 00:26:23,160 Speaker 1: you can't look at it and come to any conclusion 540 00:26:23,200 --> 00:26:25,720 Speaker 1: other than this man as a monster, that there are 541 00:26:25,720 --> 00:26:29,440 Speaker 1: many other videotapes is another thing that has been alleged 542 00:26:29,480 --> 00:26:32,679 Speaker 1: because many of these encounters were apparently recorded. So in 543 00:26:32,720 --> 00:26:34,920 Speaker 1: any case, there's a lot more that's likely to come out, 544 00:26:34,920 --> 00:26:36,720 Speaker 1: but we wanted to give you the very latest about 545 00:26:36,720 --> 00:26:43,200 Speaker 1: the allegations being raised. Let's turn to what is going 546 00:26:43,280 --> 00:26:46,240 Speaker 1: on in the Middle East, and in particular a kind 547 00:26:46,240 --> 00:26:50,040 Speaker 1: of a noteworthy moment on the podcast of comedian Theo Vaughn. 548 00:26:50,520 --> 00:26:54,960 Speaker 1: He had on doctor Gibor Mate, who is an expert 549 00:26:55,119 --> 00:27:00,080 Speaker 1: on emotional distress and trauma, and he was talking to 550 00:27:00,400 --> 00:27:05,439 Speaker 1: Theo about what Palestinian children are going through and what 551 00:27:05,680 --> 00:27:09,400 Speaker 1: impact that is having on them psychologically, and it really 552 00:27:09,680 --> 00:27:10,600 Speaker 1: got to THEO. 553 00:27:10,640 --> 00:27:12,800 Speaker 4: You saw him breaking down in real time. Let's take 554 00:27:12,800 --> 00:27:13,359 Speaker 4: a listen to that. 555 00:27:13,640 --> 00:27:16,680 Speaker 7: Such terrible things as the children and Guds are experiencing 556 00:27:16,760 --> 00:27:18,600 Speaker 7: right now, with the daily bombings and all this kind 557 00:27:18,640 --> 00:27:24,440 Speaker 7: of stuff, What can they think that there's something wrong 558 00:27:24,520 --> 00:27:24,760 Speaker 7: with me? 559 00:27:25,200 --> 00:27:27,600 Speaker 6: Oh imagine some kid over there and gods are looking 560 00:27:27,680 --> 00:27:30,680 Speaker 6: up and there's a bomb and they think, Man, I'm 561 00:27:30,720 --> 00:27:32,520 Speaker 6: so horrible. I deserve to be bombed. 562 00:27:32,880 --> 00:27:35,119 Speaker 7: Well, you know what, there was a study done of 563 00:27:35,119 --> 00:27:35,959 Speaker 7: guds and children. 564 00:27:36,000 --> 00:27:38,040 Speaker 6: Man, that's crazy. I hadn't thought about that. Like, really, 565 00:27:38,040 --> 00:27:39,640 Speaker 6: look imagine that though. 566 00:27:39,760 --> 00:27:42,960 Speaker 7: There was a study done of children in Palestine, can 567 00:27:43,000 --> 00:27:46,480 Speaker 7: you imagine what's going to happen to that generation years 568 00:27:46,520 --> 00:27:49,159 Speaker 7: from now, years from now? I mean, it just breaks 569 00:27:49,200 --> 00:27:53,600 Speaker 7: my heart every day when I think about it. And 570 00:27:53,600 --> 00:27:55,720 Speaker 7: I know I know a lot of my fellow Jews 571 00:27:55,760 --> 00:27:59,160 Speaker 7: don't agree with me, but as the Jewish person, I'm 572 00:27:59,200 --> 00:28:02,000 Speaker 7: not the only one feels that way. It especially breaks 573 00:28:02,040 --> 00:28:02,400 Speaker 7: my heart. 574 00:28:02,680 --> 00:28:04,960 Speaker 6: Yeah, well when you put it in that sense and 575 00:28:05,040 --> 00:28:10,280 Speaker 6: a kid, imagine a kid like you know, yeah, because 576 00:28:10,280 --> 00:28:12,560 Speaker 6: what are they going to think? They don't know, They 577 00:28:12,720 --> 00:28:14,720 Speaker 6: just think, Man, something's so wrong with me. I deserve 578 00:28:14,800 --> 00:28:19,160 Speaker 6: to be killed, yeah, you know, or something I don't. 579 00:28:19,160 --> 00:28:21,840 Speaker 6: I don't know. It's just a terrible no, it's heartbreaking. 580 00:28:21,880 --> 00:28:23,639 Speaker 6: I mean, it feels like a genocide is going on 581 00:28:23,680 --> 00:28:25,399 Speaker 6: over there and you don't know what to do, you know. 582 00:28:25,520 --> 00:28:30,240 Speaker 6: For me, it's like you know, I mean, you can pray, 583 00:28:30,280 --> 00:28:31,800 Speaker 6: you can speak up about it, and I know that 584 00:28:31,960 --> 00:28:34,680 Speaker 6: there's like a more political aspects of it. And we've 585 00:28:34,720 --> 00:28:36,480 Speaker 6: had different people come on to talk about Israel and 586 00:28:36,520 --> 00:28:39,440 Speaker 6: Palestine on here, and it was very knowledgeable for a 587 00:28:39,520 --> 00:28:42,000 Speaker 6: lot of our listeners because you hear about it a lot, 588 00:28:42,000 --> 00:28:43,640 Speaker 6: but you don't know the history and everything. 589 00:28:43,680 --> 00:28:46,920 Speaker 7: But well, I've been there. I went there two and 590 00:28:46,920 --> 00:28:49,400 Speaker 7: a half years ago to work with the Palestine Women 591 00:28:49,920 --> 00:28:52,720 Speaker 7: Torture than Israeli jails. Wow, and they had posted my 592 00:28:52,800 --> 00:28:55,800 Speaker 7: extress disorder. I've seen it with my own eyes. 593 00:28:56,120 --> 00:28:58,880 Speaker 1: Soccer I honestly haven't really you know, watched much of 594 00:28:58,920 --> 00:28:59,840 Speaker 1: THEO Vonn's podcast. 595 00:29:00,080 --> 00:29:01,600 Speaker 4: What did you make of this moment? Because I think 596 00:29:01,640 --> 00:29:02,600 Speaker 4: you're more familiar with. 597 00:29:02,880 --> 00:29:05,400 Speaker 2: I mean, THEO is is a very open minded guy. 598 00:29:05,880 --> 00:29:09,600 Speaker 2: He's I think it actually excels at the podcast format 599 00:29:09,680 --> 00:29:12,360 Speaker 2: and in I don't think it's a surprise that Trump 600 00:29:12,400 --> 00:29:14,280 Speaker 2: was the first person. He was the first person to 601 00:29:14,280 --> 00:29:17,640 Speaker 2: have Trump on the Big Comedy podcast. And I mean 602 00:29:17,800 --> 00:29:20,840 Speaker 2: he's had Kennedy on in the past. He's had actually 603 00:29:20,880 --> 00:29:23,880 Speaker 2: a lot of intellectuals. He's not He's not a dummy 604 00:29:23,920 --> 00:29:25,800 Speaker 2: in the caricature that a lot of people like to 605 00:29:25,840 --> 00:29:28,960 Speaker 2: put him. He's very aware, I think of what everything 606 00:29:29,040 --> 00:29:32,920 Speaker 2: that is going on, and so for him, yeah, I 607 00:29:32,960 --> 00:29:35,000 Speaker 2: think they all do which which you look, you know, 608 00:29:35,000 --> 00:29:37,280 Speaker 2: and having spent some time around these folks, you cannot 609 00:29:37,360 --> 00:29:39,360 Speaker 2: get up there sell tickets to thousands of people and 610 00:29:39,400 --> 00:29:43,360 Speaker 2: be stupid. It's it's almost impossible. Some limited cases, but 611 00:29:43,520 --> 00:29:49,520 Speaker 2: they're Kevin Hart. But what we see with THEO, and 612 00:29:49,920 --> 00:29:53,320 Speaker 2: specifically he is big on addiction and he talks a 613 00:29:53,320 --> 00:29:55,480 Speaker 2: lot about that. He talked about that with Trump and 614 00:29:55,840 --> 00:29:59,680 Speaker 2: Doctor Monte also has talked quite a bit about trauma, 615 00:30:00,080 --> 00:30:02,360 Speaker 2: an addiction how to get past that. So that was 616 00:30:02,400 --> 00:30:05,200 Speaker 2: where a lot of the bonding happened with them. But 617 00:30:05,320 --> 00:30:08,000 Speaker 2: you know what, some of the something that Matte in particular, 618 00:30:08,240 --> 00:30:12,200 Speaker 2: doctor Monte excels at is this description of the horror 619 00:30:12,200 --> 00:30:15,680 Speaker 2: of like childhood trauma and his own experiences of his 620 00:30:15,800 --> 00:30:19,320 Speaker 2: mother escaping the Holocaust and how it internalized for him. 621 00:30:19,560 --> 00:30:22,480 Speaker 2: And he talks a lot about parenthood. I highly recommend 622 00:30:22,880 --> 00:30:25,360 Speaker 2: if you have not listened to any episodes or really 623 00:30:25,400 --> 00:30:27,600 Speaker 2: any of his discussions books, et cetera, I really think 624 00:30:27,600 --> 00:30:29,600 Speaker 2: you should check it out. It will help you really 625 00:30:29,680 --> 00:30:33,960 Speaker 2: change my perception actually of internalized trauma for children and 626 00:30:34,000 --> 00:30:37,480 Speaker 2: how that can be you know, how that can materialize 627 00:30:37,520 --> 00:30:40,000 Speaker 2: over years, even for someone like him who experienced these 628 00:30:40,040 --> 00:30:41,959 Speaker 2: things when he was just a little baby. So in 629 00:30:42,000 --> 00:30:44,400 Speaker 2: that context as well, I thought it was pretty powerful. 630 00:30:44,440 --> 00:30:45,479 Speaker 3: I mean for someone like the O two. 631 00:30:45,520 --> 00:30:47,360 Speaker 2: Look, let's be honest, like the vast majority of the 632 00:30:47,440 --> 00:30:51,160 Speaker 2: old audience probably codes right wing. A lot of dudes, 633 00:30:51,400 --> 00:30:53,440 Speaker 2: people who were young you don't think about this type 634 00:30:53,480 --> 00:30:56,080 Speaker 2: of stuff, especially in this context for a while. So 635 00:30:56,160 --> 00:30:57,840 Speaker 2: for someone like THEO to break down, I mean he 636 00:30:57,840 --> 00:31:00,000 Speaker 2: even used the genocide word, right I think that's pretty crazy. 637 00:31:00,400 --> 00:31:02,960 Speaker 2: Those two things together. That's got to have some impact. 638 00:31:03,080 --> 00:31:05,240 Speaker 1: I think, yeah, I mean, I think there's just a 639 00:31:05,320 --> 00:31:09,920 Speaker 1: basic human moment here where if your heart is at 640 00:31:09,960 --> 00:31:13,640 Speaker 1: all open to what is being done to these children 641 00:31:13,680 --> 00:31:18,680 Speaker 1: in particular, you can't help but be, you know, be 642 00:31:18,800 --> 00:31:24,800 Speaker 1: heartbroken about it. And I read an article yesterday about 643 00:31:24,960 --> 00:31:27,560 Speaker 1: we can put this up on the screen, this image. 644 00:31:27,520 --> 00:31:30,080 Speaker 1: It's very hard to watch this little girl, I don't know, 645 00:31:30,160 --> 00:31:35,400 Speaker 1: maybe she's seven years old, who is carrying her injured 646 00:31:36,040 --> 00:31:41,320 Speaker 1: sister through the streets, who's just you know, maybe a toddler, 647 00:31:41,320 --> 00:31:45,280 Speaker 1: maybe a little bit older, likely orphaned. I I was 648 00:31:45,320 --> 00:31:48,440 Speaker 1: just saying, I was reading this article yesterday. There's nobody 649 00:31:48,480 --> 00:31:52,080 Speaker 1: knows how many orphans now in Gaza, but it's at 650 00:31:52,120 --> 00:31:56,000 Speaker 1: least in the tens of thousands. And just imagine, like, 651 00:31:56,640 --> 00:31:59,240 Speaker 1: you know, her little sister is injured, and I guess 652 00:31:59,280 --> 00:32:01,160 Speaker 1: she's lucky that she at least has a big sister 653 00:32:01,200 --> 00:32:04,240 Speaker 1: who's there to hold her hand and try her best 654 00:32:04,600 --> 00:32:07,840 Speaker 1: to care for her. Imagine that these doctors are talking 655 00:32:07,880 --> 00:32:11,240 Speaker 1: about little kids in the hospital undergoing amputations with literally 656 00:32:11,280 --> 00:32:17,240 Speaker 1: not a soul there to hold their hand, to comfort them, 657 00:32:17,360 --> 00:32:19,960 Speaker 1: to help them get through the pain and the trauma. 658 00:32:20,080 --> 00:32:25,880 Speaker 1: And I mean, it's just unimaginable what we are doing 659 00:32:26,000 --> 00:32:30,360 Speaker 1: to these kids, in particular in Gaza, who did absolutely 660 00:32:30,560 --> 00:32:31,520 Speaker 1: nothing wrong. 661 00:32:31,760 --> 00:32:34,120 Speaker 4: And you know, I actually didn't. 662 00:32:35,320 --> 00:32:40,400 Speaker 1: I didn't really know that kids tend to blame themselves 663 00:32:40,840 --> 00:32:44,480 Speaker 1: for whatever trauma they're experiencing, and their interpretation of the 664 00:32:44,480 --> 00:32:46,520 Speaker 1: world is like, well, if this bad thing is happening 665 00:32:46,560 --> 00:32:50,560 Speaker 1: to me, I must have somehow deserved it. But I mean, 666 00:32:50,600 --> 00:32:55,360 Speaker 1: that is it's unspeakably awful to contemplate. Some of these 667 00:32:55,480 --> 00:32:59,600 Speaker 1: kids have been through so much that they've literally gone mute, 668 00:33:00,040 --> 00:33:03,880 Speaker 1: so they'll you know, arrive at the hospital, they'll be 669 00:33:03,920 --> 00:33:06,440 Speaker 1: they brought to the hospital after perhaps all of their 670 00:33:06,440 --> 00:33:10,320 Speaker 1: family has been killed, that they're alone. They are so 671 00:33:10,440 --> 00:33:14,040 Speaker 1: traumatized that they literally lose the ability to speak, so 672 00:33:14,080 --> 00:33:17,760 Speaker 1: they can't give doctors and other social workers even their 673 00:33:17,840 --> 00:33:21,280 Speaker 1: name to try to find if they have any relatives 674 00:33:21,320 --> 00:33:23,240 Speaker 1: who might be able to care for them. It's just, 675 00:33:23,480 --> 00:33:25,080 Speaker 1: I mean, I don't know what you say about it. 676 00:33:25,160 --> 00:33:27,840 Speaker 1: And this is the latest, you know, whore that's unfolding here. 677 00:33:27,920 --> 00:33:30,080 Speaker 1: We can put this up on the screen. These are 678 00:33:30,120 --> 00:33:36,440 Speaker 1: people who are once again being forcibly displaced from Northern Gaza. 679 00:33:37,800 --> 00:33:40,800 Speaker 1: The Israelis have said, effectively, if you don't leave Northern Gaza, 680 00:33:40,840 --> 00:33:43,920 Speaker 1: which as you can see, is already basically reduced to rubble, 681 00:33:44,240 --> 00:33:46,040 Speaker 1: then you will be considered a terrorist. 682 00:33:46,160 --> 00:33:47,560 Speaker 4: You will be considered fair game. 683 00:33:48,720 --> 00:33:51,960 Speaker 1: So, you know, for God knows how many times these 684 00:33:52,000 --> 00:33:55,600 Speaker 1: people have moved, They've been living in tents. There's little food, 685 00:33:55,640 --> 00:33:58,920 Speaker 1: there's a little aid, there's next to no sanitation, communicable 686 00:33:59,000 --> 00:34:01,800 Speaker 1: diseases they're spreading. Almost everybody in the godsas Strip at 687 00:34:01,840 --> 00:34:04,880 Speaker 1: this point is sick or injured. And yet here they are, 688 00:34:05,160 --> 00:34:07,720 Speaker 1: once again, you know, trying to flee to somewhere that 689 00:34:07,760 --> 00:34:12,160 Speaker 1: will be slightly safer. And as this is going on, 690 00:34:14,400 --> 00:34:20,759 Speaker 1: the journalistic angle that CNN decided to take is, Oh 691 00:34:20,800 --> 00:34:24,640 Speaker 1: my goodness, isn't it so terrible for the idea of 692 00:34:24,760 --> 00:34:29,840 Speaker 1: soldiers who are inflicting these atrocities and horrors on the 693 00:34:29,880 --> 00:34:34,759 Speaker 1: Palestinian people. I literally cannot believe this article. As I write, 694 00:34:34,840 --> 00:34:37,440 Speaker 1: we can put this up on the screen. Their headline 695 00:34:37,520 --> 00:34:43,480 Speaker 1: is Israeli soldiers returning from war struggle with trauma and suicide. 696 00:34:43,640 --> 00:34:46,360 Speaker 1: They have a trigger warning at the beginning of this article, 697 00:34:46,800 --> 00:34:50,440 Speaker 1: warning readers about you know if their sensitivity because one 698 00:34:50,480 --> 00:34:54,120 Speaker 1: of the individuals here pictured ends up taking his own 699 00:34:54,480 --> 00:34:58,839 Speaker 1: life not a trigger warning, because they describe how this 700 00:34:58,920 --> 00:35:02,279 Speaker 1: individual and some of the others that they profile talk 701 00:35:02,320 --> 00:35:07,479 Speaker 1: about running bulldozers over hundreds of Palestinians, and the take 702 00:35:07,520 --> 00:35:10,040 Speaker 1: on that is not my god, the atrocities that are 703 00:35:10,040 --> 00:35:14,560 Speaker 1: being committed. The take on this is, oh, these poor 704 00:35:14,600 --> 00:35:18,400 Speaker 1: IDF soldiers are coming back with PTSD. I mean, you 705 00:35:19,000 --> 00:35:23,320 Speaker 1: can't even you can't even make it up, they write here. 706 00:35:23,600 --> 00:35:25,759 Speaker 1: For many soldiers, the war in Gaza is a fight 707 00:35:25,880 --> 00:35:28,480 Speaker 1: for Israel's survival must be won by any means, but 708 00:35:28,560 --> 00:35:30,920 Speaker 1: the battle is also taking a mental toll that, due 709 00:35:30,960 --> 00:35:35,480 Speaker 1: to stigma, is largely hidden from view. Interviews with Israeli soldiers, 710 00:35:35,480 --> 00:35:37,920 Speaker 1: a medic, and the family of Mizrahi, the reservists who 711 00:35:37,920 --> 00:35:40,800 Speaker 1: took his own life, provide a window into the psychological 712 00:35:40,920 --> 00:35:47,879 Speaker 1: burden the war is casting on Israeli society unbelievable. They 713 00:35:47,920 --> 00:35:50,919 Speaker 1: profile one guy who, like I said, talked about how 714 00:35:50,960 --> 00:35:53,560 Speaker 1: he's a vegan now because he had to run over 715 00:35:54,160 --> 00:35:58,880 Speaker 1: so many hundreds of Palestinians and see their insides squired 716 00:35:58,880 --> 00:36:01,040 Speaker 1: out that he can no longer eat meat. And the 717 00:36:01,080 --> 00:36:03,960 Speaker 1: person we're supposed to be sympathetic for is not the 718 00:36:04,040 --> 00:36:07,919 Speaker 1: Palestinians who are being murdered, but for the soldier who's 719 00:36:07,960 --> 00:36:09,480 Speaker 1: traumatized by doing the murdering. 720 00:36:09,680 --> 00:36:11,600 Speaker 3: Like what are we well, that was the wild part 721 00:36:11,600 --> 00:36:11,799 Speaker 3: to me. 722 00:36:11,960 --> 00:36:14,120 Speaker 2: I was like, Okay, look, I could get you know, 723 00:36:14,200 --> 00:36:16,400 Speaker 2: this idea that young people sent off the war that 724 00:36:16,440 --> 00:36:19,279 Speaker 2: they didn't even necessarily choose and all of that, But 725 00:36:19,360 --> 00:36:23,160 Speaker 2: you're like, they're profiling PTSD about people having to run 726 00:36:23,200 --> 00:36:26,560 Speaker 2: over bodies, and You're like, you don't see a little 727 00:36:26,560 --> 00:36:30,000 Speaker 2: bit of the Like you don't see the difference between 728 00:36:30,440 --> 00:36:33,840 Speaker 2: this or how it would be equally applicable maybe for 729 00:36:33,920 --> 00:36:34,520 Speaker 2: the people. 730 00:36:34,320 --> 00:36:37,600 Speaker 3: Getting run over or also have to watch other people 731 00:36:37,960 --> 00:36:38,719 Speaker 3: run over them. 732 00:36:39,040 --> 00:36:41,920 Speaker 2: That's where it all just looks ridiculous, you know, especially 733 00:36:42,000 --> 00:36:44,520 Speaker 2: with that CNN angle. But I mean in general, that's 734 00:36:44,560 --> 00:36:46,560 Speaker 2: really the That's what the West like wants to look 735 00:36:46,560 --> 00:36:48,879 Speaker 2: at it, right, They would just want they It's very 736 00:36:48,920 --> 00:36:53,680 Speaker 2: simplistic for them. It's like this is you know, Nazi Germany. 737 00:36:53,880 --> 00:36:56,400 Speaker 2: So yes, it's very sad what happened to the German people, 738 00:36:56,560 --> 00:36:58,399 Speaker 2: but at the end of the day, it was worth it, 739 00:36:58,600 --> 00:37:01,360 Speaker 2: and that's what it all is. But the problem for that, 740 00:37:01,520 --> 00:37:04,840 Speaker 2: obviously is not only is it much more deeply complex 741 00:37:05,040 --> 00:37:10,680 Speaker 2: or whatever than that, but they also refuse to see 742 00:37:10,680 --> 00:37:14,759 Speaker 2: the pushback that this is leading to the US itself, 743 00:37:15,040 --> 00:37:18,440 Speaker 2: which we talked about yesterday with sinwar and with how 744 00:37:18,480 --> 00:37:20,400 Speaker 2: we basically is like a hero amongst a lot of 745 00:37:20,520 --> 00:37:24,560 Speaker 2: people inside the Middle East. Now, but now by enabling 746 00:37:24,600 --> 00:37:27,560 Speaker 2: a lot of this, we have this war in Lebanon 747 00:37:27,920 --> 00:37:30,000 Speaker 2: where when you start to look at the demands for 748 00:37:30,080 --> 00:37:32,400 Speaker 2: what the end of that war is, you're actually looking 749 00:37:32,800 --> 00:37:35,480 Speaker 2: at the same reason for why we're spending one hundred 750 00:37:35,520 --> 00:37:38,840 Speaker 2: billion dollars to support Ukraine. So how does that work? 751 00:37:39,000 --> 00:37:42,480 Speaker 2: We're talking about Israeli basically demanding territory in Lebanon. Okay, fine, 752 00:37:42,520 --> 00:37:44,640 Speaker 2: but then don't tell me that we need to support 753 00:37:44,920 --> 00:37:49,080 Speaker 2: Ukraine because of an unjustified illegal invasion, as if what 754 00:37:49,120 --> 00:37:52,480 Speaker 2: the un sanctity of borders is so important for one 755 00:37:52,520 --> 00:37:54,240 Speaker 2: client state but then not for another. 756 00:37:54,520 --> 00:37:55,600 Speaker 3: It just doesn't make any sense. 757 00:37:55,680 --> 00:37:58,319 Speaker 1: Yeah, no, that's exactly right. And just to go back 758 00:37:58,360 --> 00:38:02,719 Speaker 1: because I relate the CNN article really floored me. Other 759 00:38:02,800 --> 00:38:04,880 Speaker 1: people like I'm not the first one to say this, 760 00:38:05,040 --> 00:38:07,759 Speaker 1: but imagine during World War two, a profile, and like, 761 00:38:07,880 --> 00:38:10,520 Speaker 1: you know, I switch prison guards and the trauma that 762 00:38:10,560 --> 00:38:13,120 Speaker 1: their experience from the horror of their job. I don't 763 00:38:13,120 --> 00:38:16,360 Speaker 1: see this as any different. Sorry, I don't one of 764 00:38:16,400 --> 00:38:20,319 Speaker 1: the people they are profiling here as so traumatized. Was 765 00:38:20,440 --> 00:38:22,360 Speaker 1: you know, one of the idea of soldiers like posting 766 00:38:22,400 --> 00:38:26,200 Speaker 1: his war crimes on TikTok for the world, is proud 767 00:38:26,200 --> 00:38:27,840 Speaker 1: of proud of what he's doing. It's proud of what 768 00:38:27,880 --> 00:38:30,279 Speaker 1: he's doing there, he's posting it for the world. So 769 00:38:32,440 --> 00:38:37,399 Speaker 1: I don't know's it is a sick, sick view that 770 00:38:37,480 --> 00:38:42,600 Speaker 1: you look at these kids who are orphaned, who are amputu, 771 00:38:43,000 --> 00:38:45,320 Speaker 1: will spend the rest of their life without a parent, 772 00:38:45,360 --> 00:38:48,319 Speaker 1: without family, if they even escape with their life, who 773 00:38:48,360 --> 00:38:51,319 Speaker 1: will deal with this trauma for the entire rest of 774 00:38:51,360 --> 00:38:55,400 Speaker 1: their days? And this is the profile that you decide 775 00:38:55,400 --> 00:38:58,400 Speaker 1: to write. But you know, to get to the larger 776 00:38:59,320 --> 00:39:02,640 Speaker 1: geopolitical situation here, I can put this up on the screen. 777 00:39:03,200 --> 00:39:08,440 Speaker 1: Israel has issued to the US their demands for ending 778 00:39:08,680 --> 00:39:11,759 Speaker 1: the war in Lebanon. Was in Bill Clinton who said, 779 00:39:11,760 --> 00:39:15,319 Speaker 1: who was the fucking superpower here? Apparently Israel? Apparently we're 780 00:39:15,360 --> 00:39:17,480 Speaker 1: the client state. And I'm not even joking about that, 781 00:39:17,520 --> 00:39:21,360 Speaker 1: because how else can you read the situation at this point. 782 00:39:21,960 --> 00:39:24,680 Speaker 1: Israel gave the US document last week with its conditions 783 00:39:24,680 --> 00:39:28,240 Speaker 1: for a diplomatic solution and the war in Lebanon. Israel's 784 00:39:28,280 --> 00:39:31,920 Speaker 1: demanded its Ida forces be allowed to engage in active 785 00:39:32,080 --> 00:39:35,160 Speaker 1: enforcement to make sure Haswala doesn't rearm and rebuild its 786 00:39:35,200 --> 00:39:38,480 Speaker 1: military infrastructure close to the border. Israel also demanded its 787 00:39:38,520 --> 00:39:42,960 Speaker 1: air force have freedom of operation in Lebanese airspace. Now, 788 00:39:43,000 --> 00:39:46,240 Speaker 1: I want you to imagine from an American perspective. Let's say 789 00:39:46,640 --> 00:39:51,520 Speaker 1: that Canada was some you know, client state of global 790 00:39:51,600 --> 00:39:55,520 Speaker 1: sup of China, global superpower. Right, But let's say that 791 00:39:55,560 --> 00:39:58,359 Speaker 1: there were the client state of of China, and they 792 00:39:58,440 --> 00:40:02,680 Speaker 1: wanted the ability to have access, unlimited access with their 793 00:40:02,719 --> 00:40:06,880 Speaker 1: air force to our airspace, and they wanted to be 794 00:40:06,920 --> 00:40:11,200 Speaker 1: able to conduct military operations in some significant part of 795 00:40:11,239 --> 00:40:15,279 Speaker 1: our country whenever and however they placed. There is no 796 00:40:15,440 --> 00:40:20,080 Speaker 1: country on Earth that would accept this type of not 797 00:40:20,160 --> 00:40:22,560 Speaker 1: just breach of their sovereignty, but this is an all 798 00:40:22,600 --> 00:40:28,680 Speaker 1: assault on Lebanese sovereignty. And you know they've already talked 799 00:40:28,680 --> 00:40:33,640 Speaker 1: about effectively annexing Lebanese land, creating this quote unquote buffer 800 00:40:33,719 --> 00:40:37,240 Speaker 1: zones exactly what they were talking about with regard to Gaza. 801 00:40:37,320 --> 00:40:42,280 Speaker 1: Now they're talking about just one of the party coalition 802 00:40:42,640 --> 00:40:45,640 Speaker 1: partners was just at a conference talking about how we 803 00:40:45,680 --> 00:40:48,080 Speaker 1: need to push out all Palacinaians from Gaza and just 804 00:40:48,120 --> 00:40:51,600 Speaker 1: resettle all of this because quote, the land is ours. Okay, 805 00:40:51,920 --> 00:40:55,040 Speaker 1: that's been the trajectory in Gaza. We also see now 806 00:40:55,960 --> 00:40:59,680 Speaker 1: they're putting out these propaganda videos exactly like the ones 807 00:40:59,680 --> 00:41:03,640 Speaker 1: that they used in Gaza, claiming that there's Hezbolah money 808 00:41:03,719 --> 00:41:08,200 Speaker 1: underneath of a hospital in Lebanon to lay the groundwork 809 00:41:08,239 --> 00:41:11,960 Speaker 1: to justifying an assault on the Lebanese health system and 810 00:41:12,120 --> 00:41:15,080 Speaker 1: hospital system, just as they did in the Gazas trip. 811 00:41:15,200 --> 00:41:18,480 Speaker 1: So you needless to say, this is not a serious 812 00:41:18,520 --> 00:41:21,840 Speaker 1: proposal for any sort of peace or cessation of hostilities. 813 00:41:22,160 --> 00:41:25,320 Speaker 1: There is no way that anyone is going to accept 814 00:41:25,320 --> 00:41:27,640 Speaker 1: these kind of conditions, which is basically are like, we 815 00:41:27,719 --> 00:41:29,640 Speaker 1: want to be able to do whatever the hell we want, 816 00:41:29,680 --> 00:41:31,440 Speaker 1: whatever and however we want to. 817 00:41:31,640 --> 00:41:34,040 Speaker 2: Yeah, that's basically right. All right, Thank you so much 818 00:41:34,080 --> 00:41:36,080 Speaker 2: for watching, guys, We appreciate you. There'll be a great 819 00:41:36,120 --> 00:41:43,040 Speaker 2: counterpoints show for everyone tomorrow. We'll see you all on Thursday.