1 00:00:02,160 --> 00:00:04,600 Speaker 1: Welcome to the Truth with Lisa Booth, where we cut 2 00:00:04,600 --> 00:00:06,400 Speaker 1: through the noise to try to getst the heart of 3 00:00:06,480 --> 00:00:10,040 Speaker 1: what matters. So today I'm going to scare you. I'm 4 00:00:10,039 --> 00:00:12,119 Speaker 1: actually going to scare the crap out of you, because 5 00:00:12,119 --> 00:00:15,280 Speaker 1: they're coming for you. We're diving into a chilling expose 6 00:00:15,800 --> 00:00:19,840 Speaker 1: with former congressmen and my Fox News colleague and friend 7 00:00:19,960 --> 00:00:23,600 Speaker 1: Jason Schaffitz, who is also Oversight Chairman, about his new book, 8 00:00:24,160 --> 00:00:27,760 Speaker 1: They're Coming for You, how deep state spies, NGOs, and 9 00:00:27,840 --> 00:00:32,200 Speaker 1: woke corporations plan to punish you for your politics. Now, 10 00:00:32,240 --> 00:00:34,600 Speaker 1: a lot of us thought, because we won twenty twenty 11 00:00:34,600 --> 00:00:38,680 Speaker 1: four the presidential election, we won Congress, that we were 12 00:00:38,680 --> 00:00:41,800 Speaker 1: good to go, that we were safe. Well, Jason says, 13 00:00:42,080 --> 00:00:46,159 Speaker 1: not so fast. He reveals how a shadowy alliance of 14 00:00:46,200 --> 00:00:50,080 Speaker 1: government insiders, big tech, and activist organizations that they're working 15 00:00:50,120 --> 00:00:55,680 Speaker 1: to silence US conservatives, weaponizing our institutions against US data 16 00:00:55,720 --> 00:00:58,800 Speaker 1: gathering as well, and also using the government to try 17 00:00:58,800 --> 00:01:02,560 Speaker 1: to control future elections. So we're going to dig into 18 00:01:02,640 --> 00:01:06,920 Speaker 1: some of this AI driven surveillance and manipulative algorithms that 19 00:01:07,040 --> 00:01:10,000 Speaker 1: he's talking about in his book and pulling the curtain 20 00:01:10,080 --> 00:01:13,200 Speaker 1: back on all these threats to our freedom and what 21 00:01:13,240 --> 00:01:16,080 Speaker 1: we can do to fight back. So buckle up for 22 00:01:16,200 --> 00:01:21,400 Speaker 1: this scary but important conversation with my friend and colleague, 23 00:01:21,480 --> 00:01:29,120 Speaker 1: Jason Shaffins. Jason Schaffits, it's great to have you on 24 00:01:29,160 --> 00:01:31,720 Speaker 1: the show again, my friend. Always great to be on 25 00:01:31,800 --> 00:01:35,080 Speaker 1: with you and looking forward to digging into your new book. 26 00:01:35,319 --> 00:01:36,959 Speaker 2: Well, thank you. It's always good to be with you, 27 00:01:37,280 --> 00:01:39,240 Speaker 2: and I wish show was with you in person. But 28 00:01:39,360 --> 00:01:40,800 Speaker 2: this is the next best thing. 29 00:01:41,200 --> 00:01:42,920 Speaker 1: It is the next best thing. So I didn't really 30 00:01:43,160 --> 00:01:46,520 Speaker 1: so you were a three time New York Times bestselling author. Yeah, 31 00:01:49,160 --> 00:01:51,200 Speaker 1: I can't. I'm sorry. 32 00:01:51,800 --> 00:01:54,280 Speaker 2: I never thought i'd write a book, and this is 33 00:01:54,360 --> 00:01:58,720 Speaker 2: my fifth book. And I love this one though, because 34 00:01:58,960 --> 00:02:01,120 Speaker 2: you know, it takes a long time. It took year 35 00:02:01,120 --> 00:02:03,200 Speaker 2: and a half, almost two years to put this together. 36 00:02:03,240 --> 00:02:07,640 Speaker 2: It's got four hundred and fifty footnotes. But you know 37 00:02:07,840 --> 00:02:10,520 Speaker 2: that's why it makes it a book worth reading. 38 00:02:10,600 --> 00:02:15,320 Speaker 1: I hope, and obviously I meant that you're very, very intelligent. 39 00:02:15,440 --> 00:02:17,520 Speaker 1: You've served in Congress, we've worked together. 40 00:02:17,240 --> 00:02:23,560 Speaker 2: In numerous wait alone, three times is a lot, though, 41 00:02:23,600 --> 00:02:25,239 Speaker 2: That's like, that's really impressive. 42 00:02:25,280 --> 00:02:27,720 Speaker 1: I didn't realize that that's the big deal. You're kind 43 00:02:27,760 --> 00:02:28,720 Speaker 1: of a big deal, Jason. 44 00:02:29,160 --> 00:02:32,280 Speaker 2: Let's all understand that you shouldn't say, well, you're intelligent 45 00:02:32,360 --> 00:02:35,320 Speaker 2: and you're in Congress. That does not usually flow in 46 00:02:35,400 --> 00:02:36,880 Speaker 2: one sentence together. 47 00:02:36,919 --> 00:02:39,880 Speaker 1: And those two things don't always overlap with you. They 48 00:02:39,919 --> 00:02:40,720 Speaker 1: do so. 49 00:02:40,880 --> 00:02:43,480 Speaker 2: Well, thank you. I served there for eight and a 50 00:02:43,480 --> 00:02:45,440 Speaker 2: half years, and there are a lot of idiots there. 51 00:02:45,480 --> 00:02:47,880 Speaker 2: I can tell you that I have no doubt I 52 00:02:47,919 --> 00:02:49,880 Speaker 2: could prove my thesis on that one. 53 00:02:50,200 --> 00:02:53,200 Speaker 1: If you had to give a percentage, what percentage are idiots? 54 00:02:54,360 --> 00:02:56,760 Speaker 2: It's like a third to third? A third is the 55 00:02:56,800 --> 00:02:59,399 Speaker 2: way I know it really is. You got a third 56 00:02:59,440 --> 00:03:02,040 Speaker 2: of people who who work hard, they work smart, they're 57 00:03:02,040 --> 00:03:04,480 Speaker 2: there for all the right reasons. They're not out carousing, 58 00:03:04,720 --> 00:03:06,800 Speaker 2: you know. Just then you got another third of them 59 00:03:06,880 --> 00:03:10,200 Speaker 2: that are out there and they just they're in the power. 60 00:03:10,600 --> 00:03:12,640 Speaker 2: They just want the power, but they couldn't tell you 61 00:03:12,760 --> 00:03:16,160 Speaker 2: policy to save their lives. And then the other third 62 00:03:16,360 --> 00:03:18,720 Speaker 2: they're just kind of I have nothing else to do, 63 00:03:18,760 --> 00:03:21,160 Speaker 2: you know, I got elected and I'm still here, and 64 00:03:21,280 --> 00:03:23,280 Speaker 2: by golly, it's kind of fun to wear this pin. 65 00:03:23,600 --> 00:03:28,160 Speaker 2: But they're just like doing nothing. And it's so true, 66 00:03:28,440 --> 00:03:32,600 Speaker 2: like there's this story and I was told this before 67 00:03:32,600 --> 00:03:34,239 Speaker 2: I got to Congress, and it's so true. When you 68 00:03:34,280 --> 00:03:38,760 Speaker 2: get there, you're about six months in and every day 69 00:03:38,800 --> 00:03:40,360 Speaker 2: you walk on the floor and you think, how did 70 00:03:40,400 --> 00:03:43,760 Speaker 2: I get here? This is unbelievable, Like this amazing what's 71 00:03:43,800 --> 00:03:46,680 Speaker 2: going on here? But about six months in, Lisa, you 72 00:03:46,720 --> 00:03:49,160 Speaker 2: look around and you start to say, now, how did 73 00:03:49,360 --> 00:03:53,240 Speaker 2: they all get here? Because did anybody ever meet this person? 74 00:03:53,320 --> 00:03:54,160 Speaker 2: They're an idiot? 75 00:03:55,240 --> 00:03:57,640 Speaker 1: Well, well I think that we have, you know, like 76 00:03:57,720 --> 00:04:01,880 Speaker 1: Jasmine Crockett comes to mind, you know, a there are 77 00:04:01,920 --> 00:04:04,800 Speaker 1: some that just sort of immediately pop up in the 78 00:04:04,840 --> 00:04:09,280 Speaker 1: thut bubble. But so your book is scary, Jason. They're 79 00:04:09,360 --> 00:04:11,840 Speaker 1: coming for you. How the deep state spies NGOs and 80 00:04:11,880 --> 00:04:14,640 Speaker 1: what corporations plan to punish you for your politics. This 81 00:04:14,680 --> 00:04:18,000 Speaker 1: is a scary book. So thanks a lot for scaring us. 82 00:04:18,560 --> 00:04:21,320 Speaker 1: But so we think we win the twenty twenty four election, 83 00:04:21,400 --> 00:04:24,200 Speaker 1: which we did, thank god, and so we finally breathed 84 00:04:24,200 --> 00:04:26,359 Speaker 1: a sigh of relief that you know, the country is 85 00:04:26,960 --> 00:04:30,000 Speaker 1: turning back the common sense that you know we won, 86 00:04:30,839 --> 00:04:34,560 Speaker 1: but you say, not so fast, that Democrats are still 87 00:04:34,600 --> 00:04:39,000 Speaker 1: working behind the scenes. Walk us through that, and why 88 00:04:39,320 --> 00:04:42,600 Speaker 1: even though you know it's nice to be in power. 89 00:04:43,200 --> 00:04:45,680 Speaker 1: Why we're not necessarily fully empower. 90 00:04:45,880 --> 00:04:49,719 Speaker 2: Well, I think what happened was particularly like my frame 91 00:04:49,760 --> 00:04:51,880 Speaker 2: of reference is Barack Obama. I was elected at the 92 00:04:51,880 --> 00:04:55,240 Speaker 2: same time as he was. But I think what the Democrats, 93 00:04:55,320 --> 00:04:58,839 Speaker 2: particularly Obama, had figured out is that legislating and changing 94 00:04:58,880 --> 00:05:02,719 Speaker 2: things was really hard. It's really difficult, and it wasn't 95 00:05:02,800 --> 00:05:05,240 Speaker 2: popular with the American people. And so what they did 96 00:05:05,320 --> 00:05:08,360 Speaker 2: is they just loaded up the piggy bank and started 97 00:05:08,400 --> 00:05:14,080 Speaker 2: flushing all this money to these corporations and to these 98 00:05:14,480 --> 00:05:19,160 Speaker 2: Deep States buys and to these NGOs, non government organizations, 99 00:05:19,720 --> 00:05:22,039 Speaker 2: and then they could do what the government couldn't do. 100 00:05:22,520 --> 00:05:24,960 Speaker 2: And that's what Doge was all about. Right. Doze went 101 00:05:25,000 --> 00:05:28,560 Speaker 2: in by the hundreds of billions of dollars and identified 102 00:05:29,120 --> 00:05:32,640 Speaker 2: the waste that was going out the door and how 103 00:05:32,760 --> 00:05:37,000 Speaker 2: it was funding these groups for nefarious activities that weren't 104 00:05:37,000 --> 00:05:40,800 Speaker 2: conducive to what Congress and the President and others had 105 00:05:40,880 --> 00:05:45,039 Speaker 2: laid out, and if they were exposed, they would be 106 00:05:45,279 --> 00:05:50,919 Speaker 2: wildly unpopular, if not illegal. So and then what my 107 00:05:51,040 --> 00:05:53,320 Speaker 2: book really cut up, so think elon and think of 108 00:05:53,520 --> 00:05:57,880 Speaker 2: all the money. Okay they're coming for you really dives 109 00:05:57,920 --> 00:06:01,279 Speaker 2: deep into the data, because that's what it scares me, 110 00:06:01,520 --> 00:06:05,560 Speaker 2: is they have so much data about what you're doing 111 00:06:05,600 --> 00:06:08,760 Speaker 2: and how you're doing it. So like you'll go online 112 00:06:08,800 --> 00:06:10,960 Speaker 2: and you're happy to trade. You know, hey you can 113 00:06:11,000 --> 00:06:13,400 Speaker 2: follow me, or I'll follow your app and you know, yeah, 114 00:06:13,440 --> 00:06:16,039 Speaker 2: you'll send me ads and you can. It's much much 115 00:06:16,120 --> 00:06:20,080 Speaker 2: deeper than that. The government is buying and selling your 116 00:06:20,160 --> 00:06:24,200 Speaker 2: data and it I mean, there's so much to this. 117 00:06:24,400 --> 00:06:28,200 Speaker 2: That's why I had to write a book. Because the 118 00:06:28,360 --> 00:06:32,000 Speaker 2: data that is flowing never goes away, and they can 119 00:06:32,120 --> 00:06:35,159 Speaker 2: use that to manipulate you in such a way. Even 120 00:06:35,240 --> 00:06:39,320 Speaker 2: in this It literally in the last twenty four hours, 121 00:06:39,839 --> 00:06:42,880 Speaker 2: the Federal Reserve said that they're going to stop for 122 00:06:42,960 --> 00:06:47,960 Speaker 2: the remarkably, they're going to stop profiling people based on 123 00:06:48,000 --> 00:06:53,039 Speaker 2: their political persuasion to defend, to determine what kind of 124 00:06:53,080 --> 00:06:56,840 Speaker 2: credit score you should get. I mean, it's that detailed, 125 00:06:56,880 --> 00:06:59,320 Speaker 2: and it's it is very scary when you dive in 126 00:06:59,400 --> 00:07:01,880 Speaker 2: deep in under the hood well. 127 00:07:01,720 --> 00:07:03,839 Speaker 1: And in particularly when you look at something like the 128 00:07:04,080 --> 00:07:08,640 Speaker 1: RS Right, which has come under a scrutiny numerous times, 129 00:07:09,120 --> 00:07:11,679 Speaker 1: you know, particularly during the Obobbe years and the targeting 130 00:07:11,680 --> 00:07:13,840 Speaker 1: of the Tea Party. How was the r r S 131 00:07:14,240 --> 00:07:17,880 Speaker 1: or the I R S weaponizing like artificial intelligence and 132 00:07:18,640 --> 00:07:20,520 Speaker 1: monitoring us for political beliefs. 133 00:07:20,920 --> 00:07:23,040 Speaker 2: I mean what I was in Congress, we did hearings. 134 00:07:23,080 --> 00:07:26,880 Speaker 2: They had what were called stingrays, stingrays and simulated cell 135 00:07:26,880 --> 00:07:29,920 Speaker 2: phone tower and they had a couple of these, like 136 00:07:29,960 --> 00:07:32,560 Speaker 2: why would you need more than one? You're the I 137 00:07:32,760 --> 00:07:35,880 Speaker 2: R S. And what they're doing is they're building a 138 00:07:35,920 --> 00:07:39,360 Speaker 2: profile so they can literally either on an airplane or 139 00:07:39,360 --> 00:07:42,760 Speaker 2: in a vehicle and any they'll capture all of the 140 00:07:42,880 --> 00:07:47,000 Speaker 2: data of all the phones that are within this certain radius, 141 00:07:47,080 --> 00:07:49,320 Speaker 2: and that'll tell you a lot about what's going on. 142 00:07:49,400 --> 00:07:54,120 Speaker 2: Who's that protest, Maybe who's going to cancer clinic, Maybe 143 00:07:54,200 --> 00:08:00,800 Speaker 2: who's going to an AA meeting. Maybe you don't know, 144 00:08:01,120 --> 00:08:03,640 Speaker 2: And that's the point. But they keep filling the bucket, 145 00:08:04,200 --> 00:08:07,760 Speaker 2: and they combine this with facial recognition, and so we 146 00:08:07,840 --> 00:08:12,960 Speaker 2: detail the companies and the organizations and then we tie 147 00:08:13,000 --> 00:08:15,480 Speaker 2: it back to what, for instance, the FBI was doing. 148 00:08:15,560 --> 00:08:17,280 Speaker 2: And I actually held the hearing about this when I 149 00:08:17,320 --> 00:08:19,640 Speaker 2: was in Congress, but I didn't see the fuller picture 150 00:08:20,200 --> 00:08:24,600 Speaker 2: of how they were traversing this chasm that they would 151 00:08:24,680 --> 00:08:28,040 Speaker 2: never be able to do legally. So for instance, Lisa Booth, 152 00:08:29,040 --> 00:08:32,080 Speaker 2: I want to go dive deeper and find out something 153 00:08:32,080 --> 00:08:34,160 Speaker 2: about you, Well you'd have to go get a warrant. 154 00:08:35,080 --> 00:08:38,240 Speaker 2: But they bypass all of that by tapping into private 155 00:08:38,280 --> 00:08:42,120 Speaker 2: companies who own all the data about Lisa Booth. But 156 00:08:42,160 --> 00:08:44,920 Speaker 2: it gets worse than that because the government, for instance, 157 00:08:45,080 --> 00:08:50,040 Speaker 2: there are states out there like Florida where in the 158 00:08:50,120 --> 00:08:53,920 Speaker 2: past they have when you go to get your driver's license, 159 00:08:54,160 --> 00:08:59,360 Speaker 2: think of all your information, name data, birth, hair, color, weight, height, 160 00:08:59,520 --> 00:09:02,000 Speaker 2: all of that. Do you wear glasses, not wear glasses, 161 00:09:02,440 --> 00:09:05,600 Speaker 2: and they sell that to a data broker and they 162 00:09:05,640 --> 00:09:06,679 Speaker 2: make money from it. 163 00:09:07,160 --> 00:09:08,679 Speaker 1: So like, what what do they want to do with 164 00:09:08,720 --> 00:09:09,240 Speaker 1: that data? 165 00:09:10,280 --> 00:09:14,040 Speaker 2: So it's what they've done started to do the nefarious 166 00:09:14,120 --> 00:09:17,440 Speaker 2: left the Democrats are doing. You know, there's a there's 167 00:09:17,480 --> 00:09:19,559 Speaker 2: a part of the book that says, this is how 168 00:09:19,559 --> 00:09:23,200 Speaker 2: they deal with it in China, Okay, and that's where 169 00:09:23,200 --> 00:09:26,640 Speaker 2: we're coming to here. I think there's seven hundred million 170 00:09:26,760 --> 00:09:31,400 Speaker 2: CCTV cameras in China. Every time you move somewhere, and 171 00:09:32,160 --> 00:09:36,320 Speaker 2: they're checking your gate, they're checking your eyes, they're checking 172 00:09:36,400 --> 00:09:38,760 Speaker 2: your face, and they want to be able to see 173 00:09:38,760 --> 00:09:40,800 Speaker 2: where you are, what you're doing. And the worst part 174 00:09:40,840 --> 00:09:44,880 Speaker 2: about it is they then want to limit where you're going, 175 00:09:45,040 --> 00:09:48,120 Speaker 2: what you're doing, so what rates you pay for insurance, 176 00:09:48,520 --> 00:09:52,040 Speaker 2: whether or not you can get healthcare insurance, whether you 177 00:09:52,320 --> 00:09:56,839 Speaker 2: are more appt to do this or that. And the 178 00:09:56,960 --> 00:10:00,160 Speaker 2: D banking is very real. I mean they even went 179 00:10:00,200 --> 00:10:04,000 Speaker 2: after Milania and Baron Trump and said no, so like 180 00:10:04,200 --> 00:10:07,200 Speaker 2: if we found out that Lisa Booth was a gun owner, 181 00:10:08,000 --> 00:10:12,360 Speaker 2: I am well, remember you had Operation choke Point with 182 00:10:12,400 --> 00:10:16,840 Speaker 2: Barack Obama. And if you and there are key words 183 00:10:17,360 --> 00:10:21,600 Speaker 2: if you search the word Cabbella's or Dick's sporting goods 184 00:10:22,240 --> 00:10:27,720 Speaker 2: or Constitution, you know there were other key words like 185 00:10:27,800 --> 00:10:31,120 Speaker 2: if you were a MAGA pro American, love your country, 186 00:10:31,200 --> 00:10:35,160 Speaker 2: love the flag. You are on a highlight list where 187 00:10:35,200 --> 00:10:37,840 Speaker 2: you may not pass your credit score, you may not 188 00:10:37,880 --> 00:10:39,800 Speaker 2: be able to get alone, you may not galle to 189 00:10:39,800 --> 00:10:42,800 Speaker 2: get into college. You may not be able to and 190 00:10:42,920 --> 00:10:45,559 Speaker 2: that is happening in real time. Even the Federal Reserve 191 00:10:45,720 --> 00:10:50,400 Speaker 2: was involved and engaged in this. But the D banking 192 00:10:50,600 --> 00:10:54,240 Speaker 2: and not allowing you to participate in the public square 193 00:10:54,440 --> 00:10:58,120 Speaker 2: or pay higher rates is just so fundamentally wrong. 194 00:10:59,160 --> 00:11:02,160 Speaker 3: We've been well aft the commercial break, but first, a 195 00:11:02,280 --> 00:11:04,959 Speaker 3: run a nation that is vowed to wipe Israel in 196 00:11:05,000 --> 00:11:08,439 Speaker 3: the United States off the map made calculated strikes at Israel. 197 00:11:08,600 --> 00:11:12,320 Speaker 3: The onslaught of missiles targeting hospitals, schools, and nursing homes 198 00:11:12,360 --> 00:11:16,600 Speaker 3: caused widespread devastation day after day, night after night. Israeli's 199 00:11:16,640 --> 00:11:21,600 Speaker 3: face relentless and unprecedented attacks that killed dozens of innocent civilians. 200 00:11:22,200 --> 00:11:27,120 Speaker 3: Thousands fled bombed out homes. Many required immediate medical care. 201 00:11:27,679 --> 00:11:29,960 Speaker 3: These attacks were only the start of something that could 202 00:11:30,000 --> 00:11:33,320 Speaker 3: be so much worse. Your urgently needed gift to the 203 00:11:33,360 --> 00:11:36,920 Speaker 3: International Fellowship of Christians and Jews will help provide wide 204 00:11:36,960 --> 00:11:41,560 Speaker 3: scale food distribution along with critical first aid and emergency supplies, 205 00:11:41,880 --> 00:11:45,320 Speaker 3: especially to Israel's most vulnerable, the sick and the elderly, 206 00:11:45,800 --> 00:11:49,040 Speaker 3: children and families. Your gift today will help place new 207 00:11:49,080 --> 00:11:53,000 Speaker 3: bomb shelters across Israel, along with first responder flak jackets 208 00:11:53,040 --> 00:11:56,200 Speaker 3: and armored emergency vehicles. Now is your time to save 209 00:11:56,240 --> 00:12:00,200 Speaker 3: and heal Israel's innocent and most vulnerable. To rush your 210 00:12:00,240 --> 00:12:03,840 Speaker 3: gift called eight eight eight for eight eight IFCJ. That's 211 00:12:03,920 --> 00:12:06,440 Speaker 3: eight eight eight for A eight I f CJ or 212 00:12:06,440 --> 00:12:10,400 Speaker 3: go on mine I f CJ dot org IFCJ dot org. 213 00:12:13,480 --> 00:12:18,240 Speaker 1: How much of President Trump winning slows that down? Or 214 00:12:18,520 --> 00:12:21,680 Speaker 1: is that just or inevitable future? Like, is it possible 215 00:12:21,679 --> 00:12:23,880 Speaker 1: to put the genie back in the bottle or is 216 00:12:24,000 --> 00:12:26,200 Speaker 1: the you know, is the genie out well? 217 00:12:26,240 --> 00:12:28,880 Speaker 2: I think Donald Trump certainly slows it down. I think 218 00:12:28,920 --> 00:12:33,200 Speaker 2: people recognize that conservatives are catching up to it. But 219 00:12:33,800 --> 00:12:37,559 Speaker 2: the data that is out there already, particularly for a 220 00:12:37,640 --> 00:12:42,920 Speaker 2: young person, like we detail in large part, like you 221 00:12:43,000 --> 00:12:46,440 Speaker 2: have kids that go to these schools and you think, oh, hey, 222 00:12:46,679 --> 00:12:50,160 Speaker 2: Google will just give you a computer. How nice? Isn't 223 00:12:50,200 --> 00:12:53,559 Speaker 2: that nice that Johnny has a computer? Well, guests, who 224 00:12:53,559 --> 00:12:56,920 Speaker 2: gets to keep all that data? I mean in California, 225 00:12:57,000 --> 00:12:59,560 Speaker 2: they're spending a lot of money for what are called 226 00:12:59,600 --> 00:13:04,000 Speaker 2: commute to the schools, and they're they're collecting everything they 227 00:13:04,080 --> 00:13:06,960 Speaker 2: want you soup to nuts, start the bottom, where you 228 00:13:07,000 --> 00:13:10,920 Speaker 2: get your haircut, who cuts your hair, what style of haircut? 229 00:13:11,040 --> 00:13:15,480 Speaker 2: I mean, all of that is captured into this data profile, 230 00:13:15,720 --> 00:13:18,599 Speaker 2: and we it's hard to put it back in the 231 00:13:19,280 --> 00:13:23,000 Speaker 2: genie back in the bottle. We go into what goes 232 00:13:23,000 --> 00:13:26,239 Speaker 2: on in your automobile. You may think, you know, basically 233 00:13:26,360 --> 00:13:31,360 Speaker 2: automobiles today are rolling computers and the amount of data 234 00:13:31,440 --> 00:13:34,280 Speaker 2: that is collected about you, your habits and what you're 235 00:13:34,320 --> 00:13:37,640 Speaker 2: doing is going to be used against you, and it 236 00:13:37,679 --> 00:13:40,240 Speaker 2: shouldn't be. It should be you should have a basic 237 00:13:40,360 --> 00:13:45,760 Speaker 2: right of privacy. Trump understands that, but ooh, it's it's 238 00:13:45,800 --> 00:13:48,160 Speaker 2: getting to be a scary, scary world when you combine 239 00:13:48,200 --> 00:13:52,559 Speaker 2: in deep fakes, artificial intelligence, all of those types of things. 240 00:13:53,000 --> 00:13:55,280 Speaker 1: Finds a lot of sinus issues right now. So that 241 00:13:55,320 --> 00:13:58,959 Speaker 1: would be fun information because I just had a sinus procedure. 242 00:13:59,000 --> 00:14:01,000 Speaker 1: Doing is basically find a I've been able to breathe 243 00:14:01,000 --> 00:14:03,280 Speaker 1: for two months. So it's like it's like a lot 244 00:14:03,320 --> 00:14:11,720 Speaker 1: about snot really fun things. I hope they're enjoying collecting that. Uh, Lisa, 245 00:14:11,760 --> 00:14:17,560 Speaker 1: both exactly why can't I breathe? Hopefully? 246 00:14:18,360 --> 00:14:22,320 Speaker 2: But yeah, let me give you another example that climate 247 00:14:22,360 --> 00:14:26,800 Speaker 2: groups really went after meat and dairy. They they believed 248 00:14:27,400 --> 00:14:31,960 Speaker 2: that cows create flatulence in the flatulence is just destroying 249 00:14:31,960 --> 00:14:34,240 Speaker 2: the earth. They don't want cows to even be on 250 00:14:34,320 --> 00:14:35,160 Speaker 2: the planet, let alone. 251 00:14:35,240 --> 00:14:38,080 Speaker 1: Can't fart in piece exactly that. 252 00:14:38,080 --> 00:14:41,520 Speaker 2: You're snot reminded me of this so well. What they 253 00:14:41,560 --> 00:14:44,640 Speaker 2: did is they had one hundred and five groups signing 254 00:14:44,840 --> 00:14:50,200 Speaker 2: this letter because they were starting to be debated, and 255 00:14:50,800 --> 00:14:55,640 Speaker 2: how do you participate in today's community, electronic community with 256 00:14:55,880 --> 00:15:00,200 Speaker 2: transferring to funds, using a credit card, even right a 257 00:15:00,360 --> 00:15:03,720 Speaker 2: check if you can't have a banking relationship. I mean, 258 00:15:03,760 --> 00:15:06,640 Speaker 2: they so they go after that. So they've been going 259 00:15:06,680 --> 00:15:09,160 Speaker 2: after a meet and dairy. They go after Trump supporters, 260 00:15:09,200 --> 00:15:13,960 Speaker 2: they go after firearms, they've gone after Christians. They certainly 261 00:15:14,000 --> 00:15:17,720 Speaker 2: went after, you know, January sixth, anything that might have 262 00:15:17,920 --> 00:15:23,320 Speaker 2: a reputational risk was what the Federal reserve. Although they're 263 00:15:23,440 --> 00:15:25,720 Speaker 2: starting to back off again. I don't think that happens 264 00:15:25,760 --> 00:15:31,800 Speaker 2: without without the Trump And it's it's pretty scary how 265 00:15:31,800 --> 00:15:34,280 Speaker 2: many times they do that, Like how. 266 00:15:34,120 --> 00:15:37,440 Speaker 1: Early does this information gathering begin? Is it like with 267 00:15:37,600 --> 00:15:40,160 Speaker 1: kids as well? Or you know, what can you tell 268 00:15:40,240 --> 00:15:40,680 Speaker 1: us about that? 269 00:15:41,280 --> 00:15:43,320 Speaker 2: Yeah, as soon as you're as soon as you're born, 270 00:15:43,360 --> 00:15:46,400 Speaker 2: there's a record. But you know, in America, we don't 271 00:15:46,440 --> 00:15:49,480 Speaker 2: collect your DNA, we don't collect your fingerprints. You know, 272 00:15:49,520 --> 00:15:52,960 Speaker 2: it'll be easy enough to do, but you're supposed to, 273 00:15:53,000 --> 00:15:56,640 Speaker 2: as a suspicionless American be able to have this right 274 00:15:56,760 --> 00:16:02,840 Speaker 2: to privacy. This bypasses all of that and just negates it. 275 00:16:03,000 --> 00:16:05,680 Speaker 2: And the government could never do it by itself. That's 276 00:16:05,680 --> 00:16:08,280 Speaker 2: why they send out the money. That's why they sell 277 00:16:08,320 --> 00:16:11,120 Speaker 2: the data. They sell the data, then they buy it. 278 00:16:11,720 --> 00:16:16,000 Speaker 2: And that's what your today, your law enforcement and your 279 00:16:16,040 --> 00:16:19,520 Speaker 2: regulatory bodies are doing at the federal and state level. 280 00:16:19,800 --> 00:16:21,960 Speaker 2: And that in the moment you start as a kid 281 00:16:22,080 --> 00:16:25,160 Speaker 2: in some schools they give you a quote unquote free computer. 282 00:16:25,560 --> 00:16:28,160 Speaker 2: But the real question is who collects all that data? 283 00:16:28,640 --> 00:16:33,080 Speaker 2: Because it's not only what you're typing into your computer. 284 00:16:33,320 --> 00:16:38,000 Speaker 2: You also have video chat, and you have pictures, and 285 00:16:38,040 --> 00:16:41,440 Speaker 2: you have all of that is being scraped. So we 286 00:16:41,520 --> 00:16:44,200 Speaker 2: talk about this company. I'm not saying they did anything illegally, 287 00:16:44,240 --> 00:16:50,000 Speaker 2: but Clearview AI has tens of billions of photos. So 288 00:16:50,080 --> 00:16:52,400 Speaker 2: if you sign up for Venmo, if you sign up 289 00:16:52,400 --> 00:16:55,760 Speaker 2: for Instagram, if you're on Snapchat, if you're on name 290 00:16:55,840 --> 00:16:59,720 Speaker 2: your social media or name somewhere, you put your picture 291 00:17:00,000 --> 00:17:03,000 Speaker 2: out on the internet. They're scraping that to build your 292 00:17:03,040 --> 00:17:05,120 Speaker 2: profile and then they're matching that. 293 00:17:05,480 --> 00:17:06,000 Speaker 1: And there are. 294 00:17:05,880 --> 00:17:08,680 Speaker 2: People out there. We go out to the absurd people 295 00:17:08,720 --> 00:17:11,119 Speaker 2: think it can tell whether you're gay or straight, and 296 00:17:11,160 --> 00:17:14,840 Speaker 2: they'll be like eighty to ninety percent correct, just based 297 00:17:14,840 --> 00:17:18,439 Speaker 2: on a variety of factors. And then they'll they have 298 00:17:18,680 --> 00:17:24,280 Speaker 2: who's of certain political persuasions. And so then you look 299 00:17:24,320 --> 00:17:27,080 Speaker 2: at what the government does with that, and we talk 300 00:17:27,119 --> 00:17:29,600 Speaker 2: about Executive Order one for zero one nine. This is 301 00:17:29,600 --> 00:17:33,399 Speaker 2: what Joe Biden did where they directed the federal government 302 00:17:33,440 --> 00:17:36,000 Speaker 2: to help register to get people to vote, and they 303 00:17:36,040 --> 00:17:40,080 Speaker 2: took these profiles to get so that they're almost all Democrats. 304 00:17:40,280 --> 00:17:42,320 Speaker 2: That's how they turned out the vote using your federal 305 00:17:42,359 --> 00:17:43,320 Speaker 2: taxpayer dollars. 306 00:17:44,200 --> 00:17:48,240 Speaker 1: Yeah, so talk about that, the Executive Order fourteen zero 307 00:17:48,320 --> 00:17:52,760 Speaker 1: one to nine and voter mobilization, you know, kind of 308 00:17:52,800 --> 00:17:56,000 Speaker 1: walk us through how Democrats turned some of these federal 309 00:17:56,040 --> 00:18:00,360 Speaker 1: agencies into the voter mobilization tools for themselves. 310 00:18:01,080 --> 00:18:04,399 Speaker 2: Yeah. What they did was literally days after Joe Biden 311 00:18:05,040 --> 00:18:08,760 Speaker 2: was sworn in he put out this executive order, they 312 00:18:08,760 --> 00:18:11,880 Speaker 2: had a meeting. In fact, the first chapter, first thing 313 00:18:12,000 --> 00:18:13,960 Speaker 2: we talk about is this meeting, and they brought it 314 00:18:14,000 --> 00:18:17,359 Speaker 2: in these outside groups and they started talking about key 315 00:18:17,400 --> 00:18:20,560 Speaker 2: buzzwords that nobody would disagree with. You know, we want 316 00:18:20,560 --> 00:18:23,520 Speaker 2: everybody to vote, we wanted people to have access to voting. 317 00:18:23,920 --> 00:18:26,359 Speaker 2: But what they did was much more nefarious, and we 318 00:18:26,440 --> 00:18:29,280 Speaker 2: lay this out in detail in this They're Coming for 319 00:18:29,440 --> 00:18:35,040 Speaker 2: U book. Because they they took housing and urban development. 320 00:18:35,920 --> 00:18:40,880 Speaker 2: They took the Department of Commerce, for instance, and they 321 00:18:40,920 --> 00:18:45,879 Speaker 2: were in the Small Business Administration. Really bad examples that 322 00:18:45,920 --> 00:18:51,480 Speaker 2: we highlight in Michigan where they didn't go into neutral 323 00:18:51,640 --> 00:18:55,200 Speaker 2: you know, they went into areas with a high propensity 324 00:18:55,480 --> 00:18:58,520 Speaker 2: of voters that are most likely to vote for Democrats. 325 00:18:59,160 --> 00:19:02,960 Speaker 2: And that's where they add these these changes and these 326 00:19:03,440 --> 00:19:07,399 Speaker 2: affairs and these and they were taking government employees on 327 00:19:07,520 --> 00:19:13,480 Speaker 2: government time, and they were walking around registering people to vote. Now, 328 00:19:13,840 --> 00:19:17,080 Speaker 2: to do that and not and exclude the Republican there 329 00:19:17,240 --> 00:19:19,679 Speaker 2: is and exclude that. It's just so rough. It's not 330 00:19:19,720 --> 00:19:22,560 Speaker 2: that what the federal government was doing. So they spent 331 00:19:22,640 --> 00:19:26,080 Speaker 2: an untold millions of dollars doing it. They leveraged the 332 00:19:26,680 --> 00:19:30,000 Speaker 2: physical facilities and the people of the federal government, and 333 00:19:30,080 --> 00:19:33,080 Speaker 2: it has an effect, and it still needs to be 334 00:19:33,160 --> 00:19:36,560 Speaker 2: further explored, but that was a very real scenario. 335 00:19:37,320 --> 00:19:41,240 Speaker 1: So what is the cyber Security and Infrastructure Security Agency? 336 00:19:41,440 --> 00:19:45,360 Speaker 1: What was the original purpose and what was it repurposed 337 00:19:45,400 --> 00:19:45,720 Speaker 1: to do? 338 00:19:46,280 --> 00:19:49,960 Speaker 2: I mean, it sounds like some we need, right, everything 339 00:19:50,000 --> 00:19:55,320 Speaker 2: from water treatment plants to you know, dams, to electrical facilities. 340 00:19:56,760 --> 00:20:00,080 Speaker 2: We have to be able to protect those, right. And 341 00:20:00,240 --> 00:20:06,240 Speaker 2: but again these things morph into something that benefits one 342 00:20:06,280 --> 00:20:10,080 Speaker 2: side politically and not the other side. And so this 343 00:20:10,240 --> 00:20:13,600 Speaker 2: is one of those departments and agencies where again they 344 00:20:13,640 --> 00:20:17,600 Speaker 2: try to continue to dive in and grab information and 345 00:20:17,640 --> 00:20:18,879 Speaker 2: then share it and sell it. 346 00:20:19,200 --> 00:20:20,920 Speaker 1: We've got to take a quick commercial break, but we've 347 00:20:20,960 --> 00:20:23,000 Speaker 1: got more with Jason. If you like what you're hearing, 348 00:20:23,160 --> 00:20:25,480 Speaker 1: shared on social media or maybe send it to a friend, 349 00:20:25,720 --> 00:20:31,920 Speaker 1: stay tuned. I think one thing we learned with Doge 350 00:20:31,920 --> 00:20:35,159 Speaker 1: but you didn't learn this, you've known this is how 351 00:20:35,280 --> 00:20:37,720 Speaker 1: much of like a money laundering scheme it is for 352 00:20:37,800 --> 00:20:42,280 Speaker 1: the left. Even looking at Stacey Abrams, who did she 353 00:20:42,320 --> 00:20:48,600 Speaker 1: ever conceide losing? She probably still thinks she's you know, 354 00:20:48,720 --> 00:20:52,320 Speaker 1: got her Yeah, but I think it was like two 355 00:20:52,400 --> 00:20:57,919 Speaker 1: billion dollars right that her group received from Biden's EPA, 356 00:20:58,240 --> 00:21:00,480 Speaker 1: and like it had been around for like say months 357 00:21:00,520 --> 00:21:03,880 Speaker 1: and hadn't done anything. So like what you know tell 358 00:21:03,960 --> 00:21:07,560 Speaker 1: us about that? And how you know tell us about 359 00:21:07,600 --> 00:21:10,320 Speaker 1: these like kind of like the web of partnerships between 360 00:21:10,359 --> 00:21:13,720 Speaker 1: the government and these NGOs and the political left. 361 00:21:13,960 --> 00:21:16,400 Speaker 2: I think what was scary is that in twenty twenty 362 00:21:16,720 --> 00:21:19,560 Speaker 2: for the twenty twenty five budget, Remember this is at 363 00:21:19,560 --> 00:21:22,479 Speaker 2: the end of the Biden administration, even though it's you know, 364 00:21:22,560 --> 00:21:25,439 Speaker 2: back in twenty four they're asking for twenty five Remember 365 00:21:25,520 --> 00:21:29,199 Speaker 2: when the Joe Biden asked for an additional this is 366 00:21:29,240 --> 00:21:33,240 Speaker 2: additional one hundred and four billion dollars for the IRS. 367 00:21:34,280 --> 00:21:38,840 Speaker 2: That massive weaponization of the IRS. That was eight times 368 00:21:38,840 --> 00:21:41,720 Speaker 2: it's regular annual budget. That's what they wanted to do. 369 00:21:44,000 --> 00:21:47,640 Speaker 2: It was, you know, that was one group after another 370 00:21:48,119 --> 00:21:51,440 Speaker 2: where they went after people in a in a huge, 371 00:21:51,560 --> 00:21:56,880 Speaker 2: huge way. There that's happening at the local level. They're 372 00:21:56,920 --> 00:22:01,600 Speaker 2: giving it out from They're targeting people that are Christians. 373 00:22:02,119 --> 00:22:05,080 Speaker 2: You know, we have this example, for instance, Kamala Harris 374 00:22:05,119 --> 00:22:11,439 Speaker 2: and Senator Herono going after the Knights of Columbus judges. 375 00:22:11,520 --> 00:22:13,600 Speaker 2: These are judges who you know were part of the 376 00:22:13,640 --> 00:22:16,880 Speaker 2: Knights of Columbus were they have one point five million 377 00:22:17,320 --> 00:22:20,720 Speaker 2: members and they just did not they would put a 378 00:22:20,760 --> 00:22:25,680 Speaker 2: religious litmus test on people. And then how they hired. 379 00:22:25,960 --> 00:22:27,879 Speaker 2: The other thing is we said, it's like how they 380 00:22:27,960 --> 00:22:33,199 Speaker 2: hired It wasn't just DEI, but like we have this 381 00:22:33,280 --> 00:22:36,359 Speaker 2: example in the book of Denver Public Schools. One of 382 00:22:36,400 --> 00:22:39,560 Speaker 2: their criteria was that they have to hire somebody who 383 00:22:40,119 --> 00:22:45,080 Speaker 2: has an anti racist mindset. So when they plus up 384 00:22:45,160 --> 00:22:48,360 Speaker 2: all this money that goes to the Department of Education, 385 00:22:48,920 --> 00:22:52,000 Speaker 2: then it goes out there. There's some expectations that they're 386 00:22:52,040 --> 00:22:55,440 Speaker 2: going to be quote unquote hiring the right people, and 387 00:22:55,520 --> 00:22:59,439 Speaker 2: that's it's just unbelievable. I mean, there's a lot of 388 00:22:59,480 --> 00:23:02,239 Speaker 2: examples in the book on how they go after the 389 00:23:02,400 --> 00:23:05,280 Speaker 2: education market to try to manipulate them. 390 00:23:05,920 --> 00:23:07,560 Speaker 1: Do we still have civil liberties? 391 00:23:08,280 --> 00:23:11,720 Speaker 2: Yea. What we're losing is a right to privacy. And 392 00:23:11,800 --> 00:23:16,080 Speaker 2: if you don't have privacy, you've lost your ability to 393 00:23:16,240 --> 00:23:21,240 Speaker 2: have the full array of liberties at your disposal. Because 394 00:23:21,320 --> 00:23:25,280 Speaker 2: I think if you're a suspicionless American, when you walk 395 00:23:25,320 --> 00:23:28,120 Speaker 2: out your door, you shouldn't feel like you're being tracked 396 00:23:28,160 --> 00:23:30,520 Speaker 2: in your every move from the moment you get in 397 00:23:30,560 --> 00:23:32,720 Speaker 2: your car to the time you go into the grocery 398 00:23:32,720 --> 00:23:34,639 Speaker 2: store at to the time you look on your phone 399 00:23:35,359 --> 00:23:40,240 Speaker 2: to you are building so much data at every moment. 400 00:23:40,560 --> 00:23:42,640 Speaker 2: It really is, it really is scary. 401 00:23:43,840 --> 00:23:47,399 Speaker 1: Well, I mean you look at Tolsi Gabbard, who is 402 00:23:47,440 --> 00:23:52,840 Speaker 1: placed on a watch list, yeah, right for overseas travel, 403 00:23:53,000 --> 00:23:56,240 Speaker 1: and she is i think is still a reservist in 404 00:23:56,280 --> 00:23:57,440 Speaker 1: the military. 405 00:23:57,320 --> 00:23:59,520 Speaker 2: And she was a member of Congress, and she was 406 00:23:59,560 --> 00:24:02,880 Speaker 2: being actually was being excluded. We have examples. I think 407 00:24:02,920 --> 00:24:06,560 Speaker 2: Tom McClintock, the congressman out of California, was put into 408 00:24:06,560 --> 00:24:10,680 Speaker 2: this category. They work so hard, you know. I guess 409 00:24:10,680 --> 00:24:14,200 Speaker 2: one way Lisa to kind of described this is if 410 00:24:14,240 --> 00:24:16,360 Speaker 2: we just thought the deep state was going to roll 411 00:24:16,400 --> 00:24:19,680 Speaker 2: over and accept that Donald Trump won the election. No 412 00:24:20,280 --> 00:24:23,920 Speaker 2: they didn't. They're not just going to roll over. They're 413 00:24:23,960 --> 00:24:26,120 Speaker 2: going to use this data and they're going to continue 414 00:24:26,160 --> 00:24:31,120 Speaker 2: to manipulate, to play the long ball game, to try 415 00:24:31,160 --> 00:24:34,520 Speaker 2: to turn and manipulate people to the point they don't 416 00:24:34,520 --> 00:24:38,000 Speaker 2: even understand. Because I think a lot of people say, eh, 417 00:24:38,040 --> 00:24:39,800 Speaker 2: I got to give up a little privacy. I don't 418 00:24:39,800 --> 00:24:41,920 Speaker 2: do anything wrong. What do I care? You know, it's 419 00:24:41,920 --> 00:24:44,160 Speaker 2: helpful to me. And if I want to go find 420 00:24:44,160 --> 00:24:48,680 Speaker 2: my local you know, in my case place it sells cheeseburgers, 421 00:24:49,320 --> 00:24:53,880 Speaker 2: new your case, you know, let local fitness gym. I'm 422 00:24:53,880 --> 00:24:54,880 Speaker 2: willing i'd you both. 423 00:24:55,160 --> 00:24:56,119 Speaker 1: I'm all about that once. 424 00:24:58,320 --> 00:25:03,560 Speaker 2: Good point, but yeah, that's perhaps. Let me give you 425 00:25:03,560 --> 00:25:05,960 Speaker 2: an example of something that was one of the most 426 00:25:05,960 --> 00:25:11,119 Speaker 2: nefarious things that we found. There's a it's called vote er, 427 00:25:12,240 --> 00:25:16,399 Speaker 2: and what they decided to do is this doctor created 428 00:25:16,600 --> 00:25:21,760 Speaker 2: a non government organization and they wanted to recruit doctors 429 00:25:21,800 --> 00:25:25,480 Speaker 2: to do voter registration, and we detail us in the book. 430 00:25:25,560 --> 00:25:28,400 Speaker 2: I really like they're coming for you. I know I'm 431 00:25:28,400 --> 00:25:30,119 Speaker 2: trying to sell a book, but it's like when I 432 00:25:30,160 --> 00:25:33,560 Speaker 2: see something in all my oversight roles, I want everybody 433 00:25:33,560 --> 00:25:35,560 Speaker 2: else to see it too, because I think everybody would 434 00:25:35,560 --> 00:25:38,240 Speaker 2: be equally outright. That's why I want you to buy 435 00:25:38,280 --> 00:25:41,679 Speaker 2: the book or listen to a book. But this doctor, 436 00:25:41,800 --> 00:25:49,600 Speaker 2: Alistair Martin, he sent out an array of doctors. There 437 00:25:49,720 --> 00:25:55,320 Speaker 2: is something like sixty five thousand text messages that was 438 00:25:55,359 --> 00:25:58,640 Speaker 2: out there to not only kill legislation, but they were 439 00:26:01,119 --> 00:26:04,600 Speaker 2: What we found is this vote er was being funded 440 00:26:04,640 --> 00:26:08,479 Speaker 2: by the Tides Foundation, the Open Society Foundation, Billing Melinda 441 00:26:08,520 --> 00:26:14,119 Speaker 2: Gates Foundation, and they ended up getting fifty thousand physicians 442 00:26:14,160 --> 00:26:19,600 Speaker 2: in seven hundred hospitals, even people who cognitively could not 443 00:26:19,760 --> 00:26:23,800 Speaker 2: make decisions, who were mentally impaired, they took They even 444 00:26:23,880 --> 00:26:28,760 Speaker 2: targeted psychiatric patients who were considered a danger to themselves 445 00:26:29,440 --> 00:26:31,919 Speaker 2: and they got them to register to vote, and in 446 00:26:31,960 --> 00:26:36,399 Speaker 2: many cases, the allegation is helped them to vote. So 447 00:26:36,480 --> 00:26:39,359 Speaker 2: imagine there you're on your deathbed, your doctor comes and says, 448 00:26:39,359 --> 00:26:42,960 Speaker 2: why don't you sign this form? Or have you voted yet? 449 00:26:43,600 --> 00:26:49,640 Speaker 2: I mean, it's just unbelievable. They created software to do it, 450 00:26:49,760 --> 00:26:53,480 Speaker 2: and it literally I'm just kind of reading through my 451 00:26:53,520 --> 00:26:58,119 Speaker 2: notes this thing called epic system software. Okay, the doctors 452 00:26:58,200 --> 00:27:04,040 Speaker 2: record in your chart the status of whether or not 453 00:27:04,080 --> 00:27:08,359 Speaker 2: they voted. Nothing scary about that. 454 00:27:09,800 --> 00:27:14,280 Speaker 1: How much did Covid accelerate sort of or march to 455 00:27:14,440 --> 00:27:17,040 Speaker 1: being more like China with all of this stuff? 456 00:27:18,200 --> 00:27:23,440 Speaker 2: I think it personally, we didn't tackle that head on. 457 00:27:23,560 --> 00:27:26,479 Speaker 2: That's a great question, Lisa. I think for some people 458 00:27:29,119 --> 00:27:32,800 Speaker 2: like you and me, it probably made us even more 459 00:27:32,920 --> 00:27:36,439 Speaker 2: resolute that we've been lied to by our federal government 460 00:27:36,480 --> 00:27:39,960 Speaker 2: and I don't trust them. It's like I've lost almost 461 00:27:40,040 --> 00:27:42,800 Speaker 2: thirty pounds over last year and a half, and I 462 00:27:43,480 --> 00:27:44,960 Speaker 2: sit back and look at it. I think I have 463 00:27:45,080 --> 00:27:47,679 Speaker 2: been lied to by my federal government my whole life. 464 00:27:48,160 --> 00:27:51,280 Speaker 2: I took that food pyramid seriously. If I didn't have 465 00:27:51,480 --> 00:27:53,360 Speaker 2: and I can't remember what the right number was, eleven 466 00:27:53,400 --> 00:27:55,720 Speaker 2: bowls of grains and cereals, I felt like I was 467 00:27:55,720 --> 00:27:57,920 Speaker 2: falling short, so I'd have two balls in the cereal 468 00:27:58,000 --> 00:28:02,200 Speaker 2: before I went to bed, and you know, carbs and 469 00:28:02,400 --> 00:28:05,560 Speaker 2: bread and eat more bread. I mean that that was 470 00:28:05,600 --> 00:28:09,640 Speaker 2: the whole mission I heard my whole life. But since COVID, 471 00:28:10,160 --> 00:28:12,760 Speaker 2: since the food pyramid, since I just like, I don't 472 00:28:12,760 --> 00:28:15,360 Speaker 2: trust the federal government. I just don't touch trust them. 473 00:28:15,440 --> 00:28:18,440 Speaker 2: Now on the other side, you got people who are so, oh, 474 00:28:18,480 --> 00:28:21,920 Speaker 2: we have to do exactly what they say. They're the experts. 475 00:28:22,960 --> 00:28:25,920 Speaker 2: And then there's the people in the middle who are like, yeah, 476 00:28:26,040 --> 00:28:30,520 Speaker 2: I'm still here. Life's good. But I think the world 477 00:28:30,600 --> 00:28:35,840 Speaker 2: is much more cognizant of their government manipulating them in 478 00:28:35,880 --> 00:28:36,800 Speaker 2: an evil way. 479 00:28:37,119 --> 00:28:41,080 Speaker 1: No, my tinfoil hat has gone a lot bigger over they, 480 00:28:41,720 --> 00:28:47,040 Speaker 1: particularly since COVID. Then now it's massive. So well, Jason, 481 00:28:47,400 --> 00:28:50,240 Speaker 1: you've convinced me that they're coming for me, and you've 482 00:28:50,280 --> 00:28:53,440 Speaker 1: officially scared the crap out of me. But this is 483 00:28:53,520 --> 00:28:57,000 Speaker 1: really important information to get to light. So thank you 484 00:28:57,040 --> 00:28:59,720 Speaker 1: for scaring me, and thank you for sharing the audience, 485 00:29:00,560 --> 00:29:03,320 Speaker 1: and thank you for putting in the research to get 486 00:29:03,320 --> 00:29:04,080 Speaker 1: this book together. 487 00:29:04,600 --> 00:29:07,080 Speaker 2: Well, thank you. I appreciate it, and I look forward 488 00:29:07,120 --> 00:29:09,280 Speaker 2: to seeing you Soon's not at all. 489 00:29:10,280 --> 00:29:13,120 Speaker 1: Hopefully, hopefully that won't be a part of the equation. 490 00:29:13,440 --> 00:29:14,520 Speaker 2: Lets solve that problem. 491 00:29:14,560 --> 00:29:18,400 Speaker 1: Okay, yes, for the for the audience, for the Fox audience, 492 00:29:18,480 --> 00:29:25,640 Speaker 1: and for every legs and all right, Jason shaff it's 493 00:29:25,760 --> 00:29:27,040 Speaker 1: great to have you on, my friend. I hope to 494 00:29:27,040 --> 00:29:32,240 Speaker 1: see you soon. Those Jason Shaffits my friend and colleague. 495 00:29:32,320 --> 00:29:34,400 Speaker 1: Appreciate him for coming on the show. Appreciate you guys 496 00:29:34,440 --> 00:29:36,560 Speaker 1: at home for listening every Tuesday and Thursday. But you 497 00:29:36,560 --> 00:29:38,360 Speaker 1: can listen throughout the week until next time.