1 00:00:05,000 --> 00:00:07,560 Speaker 1: On this episode of the News World, Who needs a 2 00:00:07,600 --> 00:00:12,600 Speaker 1: deep state when academia, big banks, corporations, and the healthcare 3 00:00:12,640 --> 00:00:15,280 Speaker 1: system are willing to do what the deep state camp. 4 00:00:16,000 --> 00:00:17,720 Speaker 1: Now that the Democrats are out of power in the 5 00:00:17,720 --> 00:00:20,880 Speaker 1: White House in Congress, they are mobilizing in the shadows, 6 00:00:21,400 --> 00:00:24,200 Speaker 1: working to make sure an election like twenty twenty four 7 00:00:24,760 --> 00:00:28,960 Speaker 1: never happens again. In his new book, They're Coming for You, 8 00:00:29,560 --> 00:00:33,520 Speaker 1: Jason Schaffitz reveals how Democrats have been planning the seeds 9 00:00:33,560 --> 00:00:39,080 Speaker 1: of political control using unelected institutions that collect and weaponize 10 00:00:39,120 --> 00:00:43,800 Speaker 1: private information. They're Coming for You reveals the frightening truth 11 00:00:44,159 --> 00:00:48,040 Speaker 1: about these institutions and how they collect, buy, sell, and 12 00:00:48,120 --> 00:00:52,479 Speaker 1: share our data without regard for our privacy, our civil liberties, 13 00:00:52,600 --> 00:00:58,080 Speaker 1: or national security. Republican political victories will ring hollow until 14 00:00:58,080 --> 00:01:02,320 Speaker 1: this bureaucratic partnership with the nonprofit and private sectors is 15 00:01:02,360 --> 00:01:06,759 Speaker 1: exposed and the power of the administrative state checked. Here 16 00:01:06,800 --> 00:01:09,280 Speaker 1: to talk about his new book, I'm really pleased to 17 00:01:09,319 --> 00:01:12,960 Speaker 1: welcome my guest and good friend, Jason Shafts. He served 18 00:01:13,000 --> 00:01:17,120 Speaker 1: as congressman for Utah's third Congressional District from two thousand 19 00:01:17,160 --> 00:01:20,640 Speaker 1: and nine to twenty seventeen. And he is currently a 20 00:01:20,680 --> 00:01:24,319 Speaker 1: Fox News contributor. He also hosts the Jason in the 21 00:01:24,360 --> 00:01:28,679 Speaker 1: House podcast. He previously served as chief of staff in 22 00:01:28,720 --> 00:01:44,640 Speaker 1: the Utah Governor's Office. Jason, welcome and thank you for 23 00:01:44,720 --> 00:01:45,800 Speaker 1: joining me on News World. 24 00:01:46,360 --> 00:01:49,040 Speaker 2: Oh, mister speaker, honored is always to be with you. 25 00:01:49,120 --> 00:01:49,520 Speaker 2: Thank you. 26 00:01:49,920 --> 00:01:52,800 Speaker 1: It's interesting you are the only member of your two 27 00:01:52,840 --> 00:01:56,200 Speaker 1: thousand and eight freshman class to put the House Oversight 28 00:01:56,240 --> 00:01:59,240 Speaker 1: Committee as your first choice. Why did you pick House 29 00:01:59,240 --> 00:02:01,680 Speaker 1: Oversight as your most important assignment? 30 00:02:02,520 --> 00:02:05,400 Speaker 2: You know, back then it wasn't quite as popular, but 31 00:02:05,600 --> 00:02:08,280 Speaker 2: in understanding the history and reading about it, what I 32 00:02:08,400 --> 00:02:11,960 Speaker 2: figured out is that there had no bounds to their jurisdiction. 33 00:02:12,080 --> 00:02:15,560 Speaker 2: You could dive into anything and everything. And it was 34 00:02:15,600 --> 00:02:18,840 Speaker 2: also the committee when Abraham Lincoln came to Congress, who 35 00:02:18,880 --> 00:02:20,800 Speaker 2: was known under a different name, but this is the 36 00:02:20,840 --> 00:02:23,880 Speaker 2: committee that he was on, and it's a fascinating history 37 00:02:23,880 --> 00:02:26,200 Speaker 2: of what he did. And so I just figured, you 38 00:02:26,240 --> 00:02:30,160 Speaker 2: know what, until there's accountability, until you expose what's actually 39 00:02:30,200 --> 00:02:32,480 Speaker 2: going on there, you're never going to move the meter. 40 00:02:32,639 --> 00:02:35,079 Speaker 2: And that's what attracted it to me. And now it's 41 00:02:35,120 --> 00:02:38,640 Speaker 2: one of the most, if not the most popular committee 42 00:02:38,639 --> 00:02:39,960 Speaker 2: for new members to try to get on. 43 00:02:41,000 --> 00:02:43,000 Speaker 1: How did you see the job of that committee while 44 00:02:43,000 --> 00:02:43,480 Speaker 1: you were there? 45 00:02:44,680 --> 00:02:47,680 Speaker 2: John Bahner was our leader and then speaker. I remember 46 00:02:47,760 --> 00:02:50,000 Speaker 2: him telling us something. It was very profound. He said, 47 00:02:50,000 --> 00:02:51,720 Speaker 2: you know, you've got a great business card. You can 48 00:02:51,760 --> 00:02:54,640 Speaker 2: talk to anybody, but don't assume somebody else is working 49 00:02:54,720 --> 00:02:56,440 Speaker 2: on it. If you want to dive into it, dive 50 00:02:56,480 --> 00:02:59,520 Speaker 2: into it. And that really rang true to me. And 51 00:02:59,560 --> 00:03:02,080 Speaker 2: so I was just a kid in the candy store. 52 00:03:02,160 --> 00:03:04,600 Speaker 2: I could see these things going wrong. The very first 53 00:03:04,600 --> 00:03:07,760 Speaker 2: thing I jumped into was Barack Obama and I got 54 00:03:07,800 --> 00:03:09,799 Speaker 2: elected at the same time. One of the very first 55 00:03:09,840 --> 00:03:13,040 Speaker 2: things Ina did in his first week is the spent 56 00:03:13,320 --> 00:03:16,480 Speaker 2: brass from our military when they do training. He got 57 00:03:16,600 --> 00:03:20,160 Speaker 2: rid of that recycling program. I'm like, what democrat gets 58 00:03:20,200 --> 00:03:23,200 Speaker 2: rid of a recycling program? And so I started to 59 00:03:23,200 --> 00:03:25,160 Speaker 2: dive into it. And it was because he wanted to 60 00:03:25,200 --> 00:03:28,320 Speaker 2: drive up the price of ammunition. And he wasn't wrong 61 00:03:28,360 --> 00:03:31,040 Speaker 2: because he did have that effect. But that was the 62 00:03:31,040 --> 00:03:34,760 Speaker 2: first piece of legislation I drafted, the first letters I sent. 63 00:03:35,480 --> 00:03:38,120 Speaker 2: He changed it a few weeks later once it was exposed, 64 00:03:38,160 --> 00:03:40,240 Speaker 2: and I thought, wow, Okay, this is fun. Let's just 65 00:03:40,320 --> 00:03:43,360 Speaker 2: keep going you know, I dove into everything from Fast 66 00:03:43,440 --> 00:03:45,920 Speaker 2: and Furious to Benghazi. I was the first member of 67 00:03:45,920 --> 00:03:48,560 Speaker 2: Congress who go to Libya to try to sort that 68 00:03:48,800 --> 00:03:52,720 Speaker 2: that mess the IRS targeting, and which you realize is 69 00:03:52,760 --> 00:03:55,360 Speaker 2: that the deep state is very real, It's very pervasive, 70 00:03:55,600 --> 00:03:57,880 Speaker 2: and unless you expose it, you'll never fix it. 71 00:03:58,280 --> 00:04:00,560 Speaker 1: Given that you want to Libya, what is your take 72 00:04:00,600 --> 00:04:03,520 Speaker 1: on that whole experience and what happened there. 73 00:04:04,800 --> 00:04:06,480 Speaker 2: I was there on the ground. They wouldn't let me 74 00:04:06,520 --> 00:04:09,720 Speaker 2: into Benghazi, but they did let me into Tripoli. Very 75 00:04:09,760 --> 00:04:12,200 Speaker 2: difficult to get in there, very scary to get in there. 76 00:04:13,280 --> 00:04:16,760 Speaker 2: What I found was people that were there on the 77 00:04:16,760 --> 00:04:19,560 Speaker 2: ground were in part the people in Benghazi in part 78 00:04:19,600 --> 00:04:22,320 Speaker 2: the people that were there in TRIPLEI. And they were frightened, 79 00:04:22,320 --> 00:04:24,520 Speaker 2: they were scared, they had tears in their eyes. And 80 00:04:24,560 --> 00:04:27,760 Speaker 2: what I realized is that Susan Rice and Hillary Clinton 81 00:04:27,800 --> 00:04:31,279 Speaker 2: and Barack Obama and Joe Biden and like I said, 82 00:04:31,320 --> 00:04:33,560 Speaker 2: Hillary Clinton, they were all lying to the American people. 83 00:04:33,720 --> 00:04:37,240 Speaker 2: It was a total fabrication. And people that were there 84 00:04:37,240 --> 00:04:40,600 Speaker 2: on the ground who were believe me not conservative Republicans, 85 00:04:40,760 --> 00:04:43,240 Speaker 2: were so highly offended. And I thought, Who's going to 86 00:04:43,320 --> 00:04:46,039 Speaker 2: expose the truth here, and I fought that for years 87 00:04:46,040 --> 00:04:49,479 Speaker 2: and I still fighting to get all the truth out there. 88 00:04:50,200 --> 00:04:52,919 Speaker 2: But what we were doing, how we were doing it, 89 00:04:53,040 --> 00:04:56,080 Speaker 2: how we were part of the problem. I thought everybody 90 00:04:56,120 --> 00:04:58,560 Speaker 2: you could possibly imagine to try to get the truth 91 00:04:58,560 --> 00:05:00,400 Speaker 2: out and I think we made it, had a good 92 00:05:00,400 --> 00:05:03,040 Speaker 2: deal of progress, but they still think there's more to expose. 93 00:05:03,440 --> 00:05:05,720 Speaker 1: I think it's important for people to understand that one 94 00:05:05,720 --> 00:05:08,760 Speaker 1: of the great things about House oversight is it really 95 00:05:08,839 --> 00:05:13,000 Speaker 1: lets you dig into how government actually works and a 96 00:05:13,040 --> 00:05:15,919 Speaker 1: lot of things that on the surface may not be obvious, 97 00:05:15,960 --> 00:05:18,320 Speaker 1: but as you dig into them, you learn things. And 98 00:05:18,800 --> 00:05:20,240 Speaker 1: one of the things that you did it I think 99 00:05:20,320 --> 00:05:25,040 Speaker 1: is extraordinarily important. You argue that President Biden's Executive Order 100 00:05:25,160 --> 00:05:30,279 Speaker 1: fourteen oh one nine allows third party organizations to register voters, 101 00:05:30,760 --> 00:05:34,520 Speaker 1: and I think you correctly identify this as potentially very dangerous. 102 00:05:34,520 --> 00:05:38,120 Speaker 1: But could you explain why you think that particular executive 103 00:05:38,160 --> 00:05:39,039 Speaker 1: order is such a threat. 104 00:05:39,680 --> 00:05:43,000 Speaker 2: So in March, after President Biden is put into office, 105 00:05:43,040 --> 00:05:45,279 Speaker 2: he comes out with this executive order, and I kick 106 00:05:45,320 --> 00:05:48,000 Speaker 2: off the book about how he brought in all these 107 00:05:48,000 --> 00:05:50,400 Speaker 2: outside groups. And on the surface, you think, oh, it's 108 00:05:50,480 --> 00:05:52,919 Speaker 2: register people to vote. That sounds great, except it's not 109 00:05:52,960 --> 00:05:55,680 Speaker 2: the role of the federal government. But what Biden in 110 00:05:55,800 --> 00:05:59,799 Speaker 2: his scronies did is he weaponized it. He took, for instance, 111 00:06:00,040 --> 00:06:03,440 Speaker 2: small business administration, he took the US Marshals, he took 112 00:06:03,880 --> 00:06:06,840 Speaker 2: the Housing and Urban Development and basically took all those 113 00:06:06,880 --> 00:06:10,520 Speaker 2: physical facilities the federal employees. 114 00:06:10,839 --> 00:06:11,520 Speaker 1: And then they. 115 00:06:11,480 --> 00:06:17,080 Speaker 2: Targeted Democratic strongholds to try to maximize voter turnout and 116 00:06:17,200 --> 00:06:21,080 Speaker 2: voter registration. And they spent millions of dollars. It was 117 00:06:21,160 --> 00:06:24,560 Speaker 2: not a concerted effort to get everybody to register. They 118 00:06:24,560 --> 00:06:29,440 Speaker 2: for instance, went after people who were incarcerated, and that's 119 00:06:29,480 --> 00:06:33,120 Speaker 2: where the US Marshals changed over six hundred rules, six 120 00:06:33,200 --> 00:06:36,440 Speaker 2: hundred rule changes to make sure that everybody could get 121 00:06:36,480 --> 00:06:40,200 Speaker 2: their information. There's another thing that happened. It was started 122 00:06:40,600 --> 00:06:44,280 Speaker 2: to target doctors. It's called vote er. This is one 123 00:06:44,320 --> 00:06:47,839 Speaker 2: of the most offensive things I found out is this doctor, 124 00:06:47,960 --> 00:06:53,880 Speaker 2: Alistair Martin, decided to get this network of doctors. And 125 00:06:53,880 --> 00:06:56,320 Speaker 2: what they ended up doing is they expanded to fifty 126 00:06:56,440 --> 00:07:01,080 Speaker 2: thousand physicians in more than seven hundred hospitals. It was 127 00:07:01,160 --> 00:07:05,080 Speaker 2: funded by the Tides Foundation, Open Society Foundation, Bill and 128 00:07:05,120 --> 00:07:09,559 Speaker 2: Melinda Gates Foundation, and they just bypassed all the hippo laws, 129 00:07:09,920 --> 00:07:12,880 Speaker 2: and these doctors were going around to their patients, including 130 00:07:12,960 --> 00:07:18,280 Speaker 2: psychiatric clinics, and going to psychiatric patients and their doctor 131 00:07:18,320 --> 00:07:21,080 Speaker 2: would know whether or not they were registered to vote. Well, 132 00:07:21,920 --> 00:07:25,520 Speaker 2: mister speaker, once you figure out that somebody is registered, 133 00:07:25,680 --> 00:07:28,000 Speaker 2: is it registered? Imagine you're in the hospital room and 134 00:07:28,040 --> 00:07:30,800 Speaker 2: the doctor has this information and gets you to register. 135 00:07:31,360 --> 00:07:34,160 Speaker 2: Then you have all that person's data. And once you 136 00:07:34,200 --> 00:07:36,840 Speaker 2: have all that data, then you can target them. And 137 00:07:36,880 --> 00:07:39,520 Speaker 2: what they ended up doing, like for instance, in Kansas 138 00:07:39,560 --> 00:07:44,080 Speaker 2: with the abortion referendum, they said sixty five thousand texts 139 00:07:44,840 --> 00:07:48,960 Speaker 2: to patients that were in the medical system, all one 140 00:07:49,080 --> 00:07:52,240 Speaker 2: sided because Biden said, hey, we're not going to afforce 141 00:07:52,320 --> 00:07:57,320 Speaker 2: that law. It was just pervasive and there's nothing more 142 00:07:57,360 --> 00:08:00,440 Speaker 2: disgusting than that violating somebody's personal private See, what are 143 00:08:00,440 --> 00:08:02,080 Speaker 2: you going to do. You're in the hospital, you're probably 144 00:08:02,120 --> 00:08:05,880 Speaker 2: under medication, you're not allowed to drive, but you are 145 00:08:05,920 --> 00:08:08,760 Speaker 2: allowed to register to vote and to vote, and they 146 00:08:08,800 --> 00:08:09,440 Speaker 2: know who you are. 147 00:08:09,600 --> 00:08:12,720 Speaker 1: It's scary stuff, you know, and it's not just in medicine, 148 00:08:12,760 --> 00:08:14,800 Speaker 1: but also, as you point out, and you really did 149 00:08:14,880 --> 00:08:19,120 Speaker 1: a lot of research for this. The Education Secretary five 150 00:08:19,120 --> 00:08:22,120 Speaker 1: months before the election, sent a mass email to student 151 00:08:22,240 --> 00:08:25,880 Speaker 1: loan borrowers just sort of pumping up the importance of 152 00:08:25,920 --> 00:08:30,480 Speaker 1: the Biden Harris administration. I mean, are these things basically 153 00:08:30,520 --> 00:08:32,280 Speaker 1: this campaigning with tax paid money. 154 00:08:33,160 --> 00:08:35,240 Speaker 2: Yeah, that's what it is. We don't even know the 155 00:08:35,320 --> 00:08:39,280 Speaker 2: total number of millions of dollars. But Executive Order one 156 00:08:39,360 --> 00:08:43,200 Speaker 2: four zero one nine was an executive order to leverage 157 00:08:43,480 --> 00:08:47,080 Speaker 2: everybody in federal government, all the departments, all the agencies. 158 00:08:47,720 --> 00:08:51,240 Speaker 2: They would take, for instance, three thousand housing in urban 159 00:08:51,280 --> 00:08:56,920 Speaker 2: development locations and use those as voter registration facilities. Do 160 00:08:56,960 --> 00:09:00,520 Speaker 2: you think they were out there registering Republicans. No, Small 161 00:09:00,559 --> 00:09:03,720 Speaker 2: Business administration was going into twenty five of the twenty 162 00:09:03,760 --> 00:09:08,960 Speaker 2: eight Democratic counties that they had targeted to register people 163 00:09:09,040 --> 00:09:13,120 Speaker 2: to vote. They went after Native Americans and did this. 164 00:09:13,840 --> 00:09:16,760 Speaker 2: And as far as kids, we spent a lot about 165 00:09:16,840 --> 00:09:22,640 Speaker 2: how they target kids and manipulate kids. For instance, you 166 00:09:22,720 --> 00:09:26,720 Speaker 2: about computers to some people in for instance in California, 167 00:09:26,880 --> 00:09:28,680 Speaker 2: and if you're a parent, you could save one thousand 168 00:09:28,720 --> 00:09:31,600 Speaker 2: dollars because your school hands you a chromebook from Google. 169 00:09:31,960 --> 00:09:35,760 Speaker 2: Sounds good, right, except all that data, all that information 170 00:09:36,080 --> 00:09:39,240 Speaker 2: they get to keep. And one of the things that 171 00:09:39,360 --> 00:09:43,440 Speaker 2: is stunning to me is how much the government is 172 00:09:43,600 --> 00:09:48,120 Speaker 2: selling their data, buying data. So we have rules, we 173 00:09:48,200 --> 00:09:52,320 Speaker 2: have laws against law enforcement. You have to have probable cause, 174 00:09:52,400 --> 00:09:53,920 Speaker 2: you have to go get a warrant, you have to 175 00:09:53,960 --> 00:09:57,840 Speaker 2: have articulable suspicion. But in order to get around that, 176 00:09:58,640 --> 00:10:01,319 Speaker 2: what these departments and agents, he's not just the FBI 177 00:10:01,520 --> 00:10:04,600 Speaker 2: trying to find a bad guy. They will buy the 178 00:10:04,720 --> 00:10:07,319 Speaker 2: data and then once they buy the data, they feel 179 00:10:07,360 --> 00:10:09,320 Speaker 2: like it's free gate. And let me give you one 180 00:10:09,320 --> 00:10:13,520 Speaker 2: other quick example. You're in Florida, for instance, and you 181 00:10:13,720 --> 00:10:16,120 Speaker 2: got a driver's license. Let's say your sixteen year old 182 00:10:16,160 --> 00:10:23,120 Speaker 2: daughter got a driver's license. It'll show name, address, telephone number, height, weight, 183 00:10:23,559 --> 00:10:27,680 Speaker 2: eye color, whether or not she wears glasses. Of Florida 184 00:10:27,840 --> 00:10:30,640 Speaker 2: was selling that information and making money of it. 185 00:10:31,200 --> 00:10:33,839 Speaker 1: You point out from a Republican standpoint, one of the 186 00:10:33,920 --> 00:10:40,120 Speaker 1: reasons to be very worried about federal employees getting involved 187 00:10:40,720 --> 00:10:45,040 Speaker 1: is that in twenty sixteen, for example, ninety five percent 188 00:10:45,600 --> 00:10:49,120 Speaker 1: of federal employee donations went to Hillary Clinton. Ninety five percent. 189 00:10:49,760 --> 00:10:50,840 Speaker 2: That's exactly right. 190 00:10:51,000 --> 00:10:53,720 Speaker 1: So it strikes me that to think that the bureaucracy 191 00:10:53,880 --> 00:10:56,880 Speaker 1: is even handed is a fantasy. 192 00:10:57,040 --> 00:11:00,800 Speaker 2: Well they're not even handed, but they're taking the information. 193 00:11:00,920 --> 00:11:04,040 Speaker 2: And it started with Operation Choke Point with Barack Obama, 194 00:11:04,440 --> 00:11:08,080 Speaker 2: but it continued on with the Federal Reserve. Federal Reserve 195 00:11:08,160 --> 00:11:10,760 Speaker 2: literally in the last couple of days said they were 196 00:11:10,840 --> 00:11:14,680 Speaker 2: not going to look at reputational risk when evaluating a 197 00:11:14,720 --> 00:11:18,440 Speaker 2: bank or credit union. So imagine a bank or credit union. 198 00:11:18,800 --> 00:11:21,600 Speaker 2: You're not in business unless the Federal Reserve gives you 199 00:11:21,640 --> 00:11:24,680 Speaker 2: the stamp of approval. And what they looked at is 200 00:11:24,800 --> 00:11:29,600 Speaker 2: under reputational risk is if you shopped at Cabella's, you 201 00:11:29,640 --> 00:11:32,600 Speaker 2: were probably a conservative because you may have purchased a gun, 202 00:11:33,240 --> 00:11:37,640 Speaker 2: you maybe bought a Maga hat. Then you are increasing 203 00:11:37,720 --> 00:11:43,280 Speaker 2: your reputational risk. They debanked Milania Trump, Baron Trump. They 204 00:11:43,280 --> 00:11:46,600 Speaker 2: went after the ag sector. The meat producers were having 205 00:11:46,640 --> 00:11:50,600 Speaker 2: a hard time participating in the banking system and in 206 00:11:50,640 --> 00:11:55,160 Speaker 2: some cases being charged higher rates. All of these things 207 00:11:55,200 --> 00:11:59,280 Speaker 2: came into play. Even automobiles. You know, your automobile of 208 00:11:59,360 --> 00:12:03,439 Speaker 2: today is a rolling computer, and they're capturing all this information. 209 00:12:03,520 --> 00:12:06,640 Speaker 2: What radio station you're listening to, how often you look 210 00:12:06,640 --> 00:12:09,360 Speaker 2: in the rear view mirror, what speed are you traveling, 211 00:12:09,520 --> 00:12:12,720 Speaker 2: how many people in the car, where you're going where 212 00:12:12,720 --> 00:12:16,400 Speaker 2: you're stopping. All of this data is then collected and 213 00:12:16,440 --> 00:12:17,240 Speaker 2: it is sold. 214 00:12:35,040 --> 00:12:38,520 Speaker 1: You also make the point that you have private sector 215 00:12:38,559 --> 00:12:42,600 Speaker 1: allies of the government bureaucracy. And one of the cases 216 00:12:42,600 --> 00:12:45,520 Speaker 1: you cite, which is I think amazing, was that the 217 00:12:45,559 --> 00:12:49,280 Speaker 1: Bank of America closed the account of a Christian nonprofit 218 00:12:49,679 --> 00:12:53,599 Speaker 1: without any warning that. The Timothy Too Project is a 219 00:12:53,679 --> 00:12:58,000 Speaker 1: Christian nonprofit ministry established a trained pastors around the world 220 00:12:58,360 --> 00:13:01,480 Speaker 1: and has workshops on like sixty five countries. But all 221 00:13:01,520 --> 00:13:04,280 Speaker 1: of a sudden in twenty twenty, the founder gets a 222 00:13:04,360 --> 00:13:06,760 Speaker 1: letter from Bank of America, even though he's been doing 223 00:13:06,760 --> 00:13:10,160 Speaker 1: business with him for nine years, and they said, you know, 224 00:13:10,240 --> 00:13:11,920 Speaker 1: your bank accounts now being closed. 225 00:13:12,880 --> 00:13:14,959 Speaker 2: That's why I said they're coming for you. Don't think 226 00:13:15,000 --> 00:13:18,080 Speaker 2: they're not looking at it. And what they're looking at 227 00:13:18,160 --> 00:13:22,040 Speaker 2: is not only who the depositors are, but any checks 228 00:13:22,040 --> 00:13:27,000 Speaker 2: that are being written and making these arbitrary and always 229 00:13:27,080 --> 00:13:33,319 Speaker 2: going in favor of these woke ideologies and against conservatives, 230 00:13:33,320 --> 00:13:35,920 Speaker 2: saying we just don't want to do business. You're too risky, 231 00:13:36,600 --> 00:13:39,720 Speaker 2: too risky, I mean, in this case, they had been 232 00:13:39,800 --> 00:13:44,240 Speaker 2: a responsible citizen in their communities, you know. But remember 233 00:13:44,320 --> 00:13:46,280 Speaker 2: and I know you remember this well, but it was 234 00:13:46,400 --> 00:13:50,040 Speaker 2: Kamala Harris herself as Senator going after the Knights of 235 00:13:50,080 --> 00:13:55,280 Speaker 2: Columbus affiliation of potential judges, saying no, if you're a 236 00:13:55,400 --> 00:14:00,360 Speaker 2: religious Catholic or religious zealot, then you shouldn't be on 237 00:14:00,400 --> 00:14:02,520 Speaker 2: the bench. You know that was out there in the 238 00:14:02,559 --> 00:14:05,440 Speaker 2: public to see. But what you're not seeing as much 239 00:14:05,559 --> 00:14:08,440 Speaker 2: is behind the scenes, you're being charged different rates. So, 240 00:14:08,520 --> 00:14:11,840 Speaker 2: for instance, in California, if you get a speeding ticket, 241 00:14:12,679 --> 00:14:17,160 Speaker 2: depending on a variety of factors, including your wealth, you 242 00:14:17,240 --> 00:14:20,160 Speaker 2: pay a different rate than somebody else who sped through 243 00:14:20,200 --> 00:14:25,280 Speaker 2: the same neighborhood, who has more favorable connections to more 244 00:14:25,520 --> 00:14:30,480 Speaker 2: liberal organizations and bank accounts. That's how crazy this has gotten. 245 00:14:30,920 --> 00:14:34,640 Speaker 1: The numbers are amazing that in our fifty biggest cities 246 00:14:35,280 --> 00:14:39,480 Speaker 1: there are five hundred and thirty seven thousand cameras that 247 00:14:39,720 --> 00:14:43,840 Speaker 1: monitor about eleven cameras per thousand people. I didn't realize 248 00:14:43,920 --> 00:14:46,640 Speaker 1: Atlanta has the most, has one hundred and twenty four 249 00:14:46,720 --> 00:14:51,360 Speaker 1: cameras per thousand, Washington has fifty five thousand, Philadelphia's thirty 250 00:14:51,360 --> 00:14:54,400 Speaker 1: per thousand. I mean, you know, you make the argument 251 00:14:54,960 --> 00:14:58,280 Speaker 1: that when you're tracking a criminal, you're tracking a terrorist, 252 00:14:58,760 --> 00:15:00,840 Speaker 1: These are great assets. On the the other side, that 253 00:15:00,920 --> 00:15:05,440 Speaker 1: means virtually every American is somewhere on record as they 254 00:15:05,480 --> 00:15:07,920 Speaker 1: walk around, or as they drive or whatever. I mean. 255 00:15:08,640 --> 00:15:11,200 Speaker 1: Isn't that an astounding shift from the world of twenty 256 00:15:11,240 --> 00:15:12,040 Speaker 1: or thirty years ago. 257 00:15:12,880 --> 00:15:15,760 Speaker 2: Yes, you know in the movies it looks great, right, 258 00:15:15,800 --> 00:15:19,120 Speaker 2: and if you're chasing a would be kidnapper or bankropper 259 00:15:19,160 --> 00:15:21,720 Speaker 2: or it sounds great. But you know, we don't collect 260 00:15:21,720 --> 00:15:24,840 Speaker 2: somebody's DNA or fingerprints when they're born because you have 261 00:15:24,920 --> 00:15:28,400 Speaker 2: it right, I would argue a right to privacy. And 262 00:15:28,880 --> 00:15:31,440 Speaker 2: the scary thing is there's a company out there, for instance, 263 00:15:31,480 --> 00:15:34,560 Speaker 2: called the Clearview AI. But I'm not saying they did 264 00:15:34,600 --> 00:15:40,359 Speaker 2: anything illegal, but they have hundreds of billions of photos 265 00:15:40,480 --> 00:15:43,840 Speaker 2: at this point, and what they're able to do is 266 00:15:44,000 --> 00:15:47,560 Speaker 2: match those photos. What they do is they scrape the Internet. 267 00:15:47,600 --> 00:15:50,560 Speaker 2: And what I mean by scraping is anytime you post 268 00:15:50,640 --> 00:15:54,120 Speaker 2: something up, if it's on Venmo, if you have a 269 00:15:54,200 --> 00:15:56,320 Speaker 2: Venmo account, has your picture on it. If you have 270 00:15:56,360 --> 00:15:58,360 Speaker 2: a Costco card, it's got a picture on it. If 271 00:15:58,400 --> 00:16:01,400 Speaker 2: you have social media, if you're on Facebook, if you're 272 00:16:01,440 --> 00:16:04,760 Speaker 2: on Instagram, your driver's license, all of this stuff all 273 00:16:04,800 --> 00:16:08,760 Speaker 2: of those photos get pushed into a file on you 274 00:16:09,200 --> 00:16:11,280 Speaker 2: so that when you walk down the street of Atlanta 275 00:16:12,320 --> 00:16:15,120 Speaker 2: can tell in real time that's you. And we tell 276 00:16:15,160 --> 00:16:18,560 Speaker 2: the story of some MIT kids who spent less than 277 00:16:18,560 --> 00:16:24,240 Speaker 2: two hundred dollars and through artificial intelligence and the processing, 278 00:16:24,600 --> 00:16:27,560 Speaker 2: they can wear a pair of glasses metas starting to 279 00:16:27,560 --> 00:16:30,680 Speaker 2: get in this business ray bands. In this business, they 280 00:16:30,720 --> 00:16:33,160 Speaker 2: can walk down the street and see a young woman 281 00:16:33,640 --> 00:16:36,640 Speaker 2: and say it was just a second for that computer 282 00:16:36,720 --> 00:16:40,440 Speaker 2: to kick in and say, oh that's Caroline. Caroline, Hi, 283 00:16:40,560 --> 00:16:43,840 Speaker 2: how are you? And then her bios you're scrolling through 284 00:16:43,880 --> 00:16:47,720 Speaker 2: your glasses and say, hey, you take that chemistry class 285 00:16:47,760 --> 00:16:51,400 Speaker 2: at Emerson don't you? And you went to that Alabama 286 00:16:51,440 --> 00:16:55,120 Speaker 2: football game, didn't you? I saw you there. They tell 287 00:16:55,200 --> 00:16:58,840 Speaker 2: this real life example and posted YouTube videos on how 288 00:16:58,840 --> 00:17:01,080 Speaker 2: they could do this in real time for less than 289 00:17:01,120 --> 00:17:03,600 Speaker 2: two hundred dollars. That's where the world is going and 290 00:17:03,640 --> 00:17:05,000 Speaker 2: it's not a world I want to live in. 291 00:17:05,520 --> 00:17:07,960 Speaker 1: And to make it worse, when the federal government gathers 292 00:17:08,040 --> 00:17:10,680 Speaker 1: up all this data it originally has proven it can't 293 00:17:10,760 --> 00:17:13,639 Speaker 1: keep it. I mean, those are twenty fifteen data breach 294 00:17:13,680 --> 00:17:17,760 Speaker 1: at the Office of Personnel Management Hacker's got background information 295 00:17:18,280 --> 00:17:23,480 Speaker 1: on twenty one million, five hundred thousand people, including fingerprints 296 00:17:23,520 --> 00:17:26,840 Speaker 1: on five point six million. Apparently I didn't know this. 297 00:17:26,840 --> 00:17:29,240 Speaker 1: This is where your book is so valuable. In the 298 00:17:29,280 --> 00:17:33,280 Speaker 1: decade between twenty ten and twenty twenty, the Government Accountability 299 00:17:33,280 --> 00:17:39,080 Speaker 1: Office made thirty three hundred recommendations to government agencies to 300 00:17:39,160 --> 00:17:44,080 Speaker 1: remedy cybersecurity gap, and hundreds of them were never implemented. 301 00:17:45,200 --> 00:17:48,280 Speaker 2: Yeah, federal government blester heart. I just don't trust them 302 00:17:48,560 --> 00:17:52,240 Speaker 2: because they gather all this information. It's just a reality. 303 00:17:52,320 --> 00:17:54,560 Speaker 2: They're not going to hire the best talent, they don't 304 00:17:54,560 --> 00:17:57,720 Speaker 2: have the revenue to do it or the expertise, and 305 00:17:57,800 --> 00:18:01,439 Speaker 2: so multiple times are data has been breached. There was 306 00:18:01,520 --> 00:18:04,639 Speaker 2: one of the most nefarious was everybody who had a 307 00:18:04,680 --> 00:18:07,639 Speaker 2: security clearance. Can you imagine how valuable that is to 308 00:18:07,680 --> 00:18:11,840 Speaker 2: the Chinese and others on understanding every security clearance is 309 00:18:11,880 --> 00:18:13,600 Speaker 2: out there and who they are, and then they can 310 00:18:13,680 --> 00:18:18,680 Speaker 2: track you. We talk in the book about cell phone simulators. 311 00:18:19,200 --> 00:18:21,439 Speaker 2: One of the brand names of one of theirs is 312 00:18:21,480 --> 00:18:25,840 Speaker 2: called a sting Ray. So I even did hearings about 313 00:18:25,840 --> 00:18:27,800 Speaker 2: this when I was in Congress, and that was, you know, 314 00:18:27,840 --> 00:18:31,480 Speaker 2: like ten years ago, and what these cell phone simulators 315 00:18:31,480 --> 00:18:35,520 Speaker 2: can do is they can tell every scoop up every 316 00:18:35,560 --> 00:18:40,080 Speaker 2: telephone number within a radius of wherever this machine is. So, 317 00:18:40,119 --> 00:18:43,359 Speaker 2: for instance, the IRS had four of them. But now 318 00:18:43,800 --> 00:18:45,760 Speaker 2: you can go to a data broker, which is a 319 00:18:45,840 --> 00:18:49,520 Speaker 2: nearly four hundred billion dollar a year industry, and I 320 00:18:49,600 --> 00:18:54,680 Speaker 2: can take Newt Gingrich's home address and I can buy 321 00:18:54,720 --> 00:18:58,679 Speaker 2: a report that says, tell me every phone number that 322 00:18:58,760 --> 00:19:02,840 Speaker 2: came within one hundred yards of that address over the 323 00:19:02,920 --> 00:19:05,280 Speaker 2: last I don't know, two months, and I can buy 324 00:19:05,280 --> 00:19:08,240 Speaker 2: that right now here today. Where is that in my 325 00:19:08,440 --> 00:19:11,800 Speaker 2: contract with Verizon? I never saw that. But you can 326 00:19:11,920 --> 00:19:14,679 Speaker 2: do it, and it's happening. And imagine how bad that 327 00:19:14,800 --> 00:19:16,960 Speaker 2: is if you're an ICE officer putting your life on 328 00:19:17,000 --> 00:19:19,439 Speaker 2: the line and what these yahoos are trying to do 329 00:19:19,520 --> 00:19:22,920 Speaker 2: to those people, or maybe you're the Cineloa drug cartell 330 00:19:22,920 --> 00:19:25,760 Speaker 2: and you're trying to figure out who the DEA officers are. 331 00:19:26,080 --> 00:19:27,640 Speaker 2: People need to know this is happening. 332 00:19:28,040 --> 00:19:29,800 Speaker 1: Isn't that really one of the great dangers that if 333 00:19:29,800 --> 00:19:32,040 Speaker 1: you're an ICE officer and you're in the middle of 334 00:19:32,119 --> 00:19:35,520 Speaker 1: deporting criminals, that you and your family are at real risk. 335 00:19:36,320 --> 00:19:39,280 Speaker 1: And that's why they normally wear a mask because when 336 00:19:39,320 --> 00:19:42,159 Speaker 1: there's a genuine risk that the cartels will not just 337 00:19:42,200 --> 00:19:44,120 Speaker 1: go after them, but they'll go after their families. 338 00:19:44,720 --> 00:19:46,800 Speaker 2: And that's why they have to use phones that they 339 00:19:46,880 --> 00:19:49,480 Speaker 2: leave at different locations. They got to not take them 340 00:19:49,520 --> 00:19:52,680 Speaker 2: to their home. They come up with their fully masked 341 00:19:52,760 --> 00:19:56,760 Speaker 2: up because again, facial recognition, once they get that face, 342 00:19:57,119 --> 00:19:59,720 Speaker 2: then they can do a whole lot with it. It again. 343 00:19:59,800 --> 00:20:03,679 Speaker 2: It Yes, the reason the book's called They're Coming for 344 00:20:03,880 --> 00:20:07,520 Speaker 2: You is it's everybody that they're trying to just do. 345 00:20:08,359 --> 00:20:11,639 Speaker 2: They have the ability. There are people that are studying 346 00:20:11,640 --> 00:20:16,080 Speaker 2: your gait, how you walk, doing facial recognition. They think 347 00:20:16,119 --> 00:20:19,440 Speaker 2: they can with an eighty percent confidence tell whether or 348 00:20:19,480 --> 00:20:23,040 Speaker 2: not you're straight or gay, whether you're conservative or liberal, 349 00:20:23,359 --> 00:20:26,840 Speaker 2: and they're using that to manipulate elections. So when you 350 00:20:26,920 --> 00:20:30,480 Speaker 2: combine that with deep bakes, which is going to be 351 00:20:30,520 --> 00:20:33,440 Speaker 2: a huge problem not just for politics, but for young 352 00:20:33,520 --> 00:20:36,840 Speaker 2: people and how they get manipulated and bullied at school. 353 00:20:37,440 --> 00:20:41,240 Speaker 2: It's a world that is right here on our doorstep, 354 00:20:41,280 --> 00:20:42,440 Speaker 2: and we better deal with it. 355 00:21:01,240 --> 00:21:06,320 Speaker 1: What should Congress be doing to ensure First Amendment rights 356 00:21:06,320 --> 00:21:09,800 Speaker 1: and privacy rights in this world of overwhelming data? 357 00:21:10,760 --> 00:21:13,120 Speaker 2: I think Congress needs to do a couple things. One 358 00:21:13,200 --> 00:21:16,560 Speaker 2: is I think you, as an American, particularly as a child, 359 00:21:17,160 --> 00:21:20,879 Speaker 2: have the right to be forgotten. For instance, we allow 360 00:21:20,960 --> 00:21:24,240 Speaker 2: in this nation, based on federal legislation, a thirteen year 361 00:21:24,240 --> 00:21:27,560 Speaker 2: old can enter into a contract with a social media company. 362 00:21:28,040 --> 00:21:30,720 Speaker 2: But what if you wanted to get out of that contract. 363 00:21:31,000 --> 00:21:33,360 Speaker 2: I don't know of any other contract than the world 364 00:21:33,800 --> 00:21:37,000 Speaker 2: where once you enter it, you can never leave because 365 00:21:37,040 --> 00:21:40,560 Speaker 2: the Googles of the world will continue to buy that information, 366 00:21:40,840 --> 00:21:44,120 Speaker 2: or I should say, sell that information and be able 367 00:21:44,160 --> 00:21:46,919 Speaker 2: to track you the rest of your life. And I 368 00:21:46,960 --> 00:21:50,119 Speaker 2: think at some point, where is the consideration for the 369 00:21:50,160 --> 00:21:52,520 Speaker 2: person who says, I don't want to be tracked anymore. 370 00:21:52,600 --> 00:21:54,760 Speaker 2: I don't want my photo out there. I gave it 371 00:21:54,800 --> 00:21:56,800 Speaker 2: to you so I get to have some convenience in 372 00:21:56,880 --> 00:21:59,639 Speaker 2: my life. But now I've gone past that point, and 373 00:21:59,680 --> 00:22:02,480 Speaker 2: I don't don't want you to continue to sell my data. 374 00:22:03,280 --> 00:22:05,320 Speaker 2: You know, we all have those stories about gosh. I 375 00:22:05,400 --> 00:22:07,400 Speaker 2: was talking to my neighbor about a couch, and now 376 00:22:07,800 --> 00:22:10,480 Speaker 2: everything on my social media feed is about couches. 377 00:22:10,960 --> 00:22:12,760 Speaker 1: Even in your car. They're listening to. 378 00:22:12,760 --> 00:22:16,280 Speaker 2: You when we talk about vehicles. It is exactly that 379 00:22:16,400 --> 00:22:18,880 Speaker 2: what are you listening to, what are you talking about? 380 00:22:19,200 --> 00:22:22,480 Speaker 2: What direction are you heading? Do you fully stop for 381 00:22:22,640 --> 00:22:25,680 Speaker 2: right hand turns or left hand turns? It's all in there. 382 00:22:26,320 --> 00:22:29,159 Speaker 1: And I think Tashla, for example, which is really an 383 00:22:29,240 --> 00:22:32,360 Speaker 1: information company that happens to have cars, I think they 384 00:22:32,400 --> 00:22:36,560 Speaker 1: have you know, now, trillions of bits of data about 385 00:22:37,040 --> 00:22:40,440 Speaker 1: how you drive, how you turn, how fast you go, 386 00:22:41,000 --> 00:22:44,639 Speaker 1: where have you been. You can literally reconstruct almost anybody 387 00:22:45,040 --> 00:22:46,520 Speaker 1: off of the track of their car. 388 00:22:47,080 --> 00:22:51,439 Speaker 2: Congress. I think there can also be compulsion so that 389 00:22:53,240 --> 00:22:56,879 Speaker 2: companies who are dealing and traversing in data have to 390 00:22:57,000 --> 00:23:01,600 Speaker 2: fully disclose in a simple ease it or easy to understand. 391 00:23:02,240 --> 00:23:04,320 Speaker 2: This is what we do and do not do with 392 00:23:04,400 --> 00:23:07,240 Speaker 2: your data, and there need to be severe penalties for 393 00:23:07,280 --> 00:23:09,960 Speaker 2: those who bypassed that. But much like what they did 394 00:23:10,040 --> 00:23:13,480 Speaker 2: food labeling laws, so I could tell how much sugar 395 00:23:13,600 --> 00:23:16,000 Speaker 2: is in a soda or so much sugar is in 396 00:23:16,119 --> 00:23:19,440 Speaker 2: my food, they could do almost like a labeling law 397 00:23:19,600 --> 00:23:22,800 Speaker 2: for data. But then I do think that you should 398 00:23:22,880 --> 00:23:24,399 Speaker 2: be able to get out of it, and maybe when 399 00:23:24,440 --> 00:23:27,720 Speaker 2: you turn eighteen your data should just be wiped clean. 400 00:23:28,000 --> 00:23:30,560 Speaker 2: And if you want to enter into that as an adult, 401 00:23:30,720 --> 00:23:33,879 Speaker 2: go ahead. But there are things like that, and I 402 00:23:33,920 --> 00:23:38,720 Speaker 2: think the privacy aspect on the spine really has to 403 00:23:38,760 --> 00:23:41,360 Speaker 2: be put into block and they need to look at 404 00:23:41,400 --> 00:23:44,800 Speaker 2: how our law enforcement is doing this, because we had 405 00:23:44,840 --> 00:23:48,360 Speaker 2: hearings with the FBI, and with all due respect, they 406 00:23:48,400 --> 00:23:52,720 Speaker 2: were doing things against the law. Once they have the data, 407 00:23:52,960 --> 00:23:55,879 Speaker 2: they're buying it and they're selling it. That's what's killing 408 00:23:55,920 --> 00:23:57,800 Speaker 2: me is that they're selling my data. 409 00:23:58,320 --> 00:24:02,600 Speaker 1: Should the government be blocked from selling data? 410 00:24:02,680 --> 00:24:06,040 Speaker 2: I think without full disclosure or compensation, Like why should 411 00:24:06,080 --> 00:24:10,560 Speaker 2: Florida be able to take my driver's license information and 412 00:24:10,640 --> 00:24:13,040 Speaker 2: be able to sell that for a profit to the state. 413 00:24:13,560 --> 00:24:15,840 Speaker 2: Why do they get that money? I mean, I paid 414 00:24:15,840 --> 00:24:18,600 Speaker 2: to get my driver's license there. I don't live in Florida, 415 00:24:18,600 --> 00:24:21,440 Speaker 2: but if I did, why should the government be able 416 00:24:21,440 --> 00:24:24,840 Speaker 2: to have that as a revenue source. That's happening much 417 00:24:24,880 --> 00:24:27,560 Speaker 2: more pervasively that I think Florida has now dealt with 418 00:24:27,640 --> 00:24:30,280 Speaker 2: it because it was exposed through a lawsuit. That's why 419 00:24:30,320 --> 00:24:34,880 Speaker 2: it's public record. But there are lots of other bits 420 00:24:34,920 --> 00:24:38,280 Speaker 2: of information about you that you don't know, and I 421 00:24:38,280 --> 00:24:41,400 Speaker 2: don't have the full universe, But I think Congress could 422 00:24:41,400 --> 00:24:44,359 Speaker 2: compel that. I think state legislators would be smart to 423 00:24:44,440 --> 00:24:49,360 Speaker 2: run legislation to understand that. Here's the thing, mister speaker, 424 00:24:50,680 --> 00:24:55,239 Speaker 2: the private sector when it's public, the public government is 425 00:24:55,359 --> 00:24:58,360 Speaker 2: very opaque. We don't know anything about them. Right, it's 426 00:24:58,400 --> 00:25:01,359 Speaker 2: the exact opposite. The private sector should be the ones private. 427 00:25:01,480 --> 00:25:04,439 Speaker 2: The public sector should be totally open. But it's not 428 00:25:04,560 --> 00:25:05,960 Speaker 2: that way in our world today. 429 00:25:06,320 --> 00:25:11,640 Speaker 1: One small building block has been this proposal that Milania 430 00:25:11,680 --> 00:25:15,480 Speaker 1: Trump supported so strongly, to Take It Down Act, which 431 00:25:15,520 --> 00:25:19,080 Speaker 1: requires that if any image of you has gone up 432 00:25:19,119 --> 00:25:23,040 Speaker 1: without your permission, that you can compel the Googles of 433 00:25:23,080 --> 00:25:25,760 Speaker 1: the world, meta and others to take it down within 434 00:25:25,800 --> 00:25:30,080 Speaker 1: a very quick time, because apparently it's a very significant 435 00:25:30,119 --> 00:25:34,000 Speaker 1: problem now with artificial intelligence and with thirteen year old 436 00:25:34,000 --> 00:25:37,240 Speaker 1: girl suddenly having pictures of them nude although it's not them, 437 00:25:37,520 --> 00:25:39,879 Speaker 1: but their face has been put on top of somebody 438 00:25:39,920 --> 00:25:43,080 Speaker 1: else's body, and it becomes a real problem in bullying, 439 00:25:43,400 --> 00:25:46,240 Speaker 1: psychological and social stress. And I thought this was a 440 00:25:46,400 --> 00:25:51,120 Speaker 1: small but real building block towards creating a more civilized Internet. 441 00:25:52,200 --> 00:25:55,119 Speaker 2: The first Lady's been amazing on this. The last Joint 442 00:25:55,160 --> 00:25:58,720 Speaker 2: Session of Congress, they had that young woman in the audience. 443 00:25:58,880 --> 00:26:01,840 Speaker 2: I guessed that the first lady that had this deep 444 00:26:01,880 --> 00:26:05,119 Speaker 2: fake and her face put on the body and passed 445 00:26:05,119 --> 00:26:08,160 Speaker 2: around everybody at school, and she was mocked and cajoled 446 00:26:08,200 --> 00:26:13,040 Speaker 2: and contemplated doing something serious to herself as a consequence, 447 00:26:13,040 --> 00:26:15,760 Speaker 2: because imagine how it's tough enough to be a teenager, 448 00:26:15,840 --> 00:26:19,080 Speaker 2: let alone dealing with the Internet all the time and 449 00:26:19,359 --> 00:26:22,720 Speaker 2: social media. There's got to be I think, a look 450 00:26:22,760 --> 00:26:26,920 Speaker 2: at this that's different for minors. And then the First 451 00:26:26,960 --> 00:26:30,960 Speaker 2: Amendment issues and the Fourth Amendment issues I think are 452 00:26:31,000 --> 00:26:34,040 Speaker 2: pervasive and Congress needs to tackle this. It's not an 453 00:26:34,040 --> 00:26:38,159 Speaker 2: easy answer, but the world's changing and so many of 454 00:26:38,160 --> 00:26:41,240 Speaker 2: these genies are already out of the bottle. Look, that's 455 00:26:41,280 --> 00:26:44,840 Speaker 2: part of the problem. Quite frankly, I supported. I'd be 456 00:26:44,880 --> 00:26:47,040 Speaker 2: fascinated to where you are, mister Speaker on this, But 457 00:26:47,560 --> 00:26:52,600 Speaker 2: I wanted a technology assessment that was nonpartisan, so that 458 00:26:52,720 --> 00:26:55,560 Speaker 2: members of Congress could go to that group to ask 459 00:26:55,640 --> 00:26:59,440 Speaker 2: them questions about how do things function. Because there's only 460 00:26:59,520 --> 00:27:01,840 Speaker 2: three or four members of Congress, House and Senate they 461 00:27:01,840 --> 00:27:04,840 Speaker 2: even know anything about the Internet. To have a body 462 00:27:04,880 --> 00:27:08,120 Speaker 2: of expertise so they could actually ask the right questions 463 00:27:08,240 --> 00:27:11,399 Speaker 2: or know what to ask would be really helpful in 464 00:27:11,440 --> 00:27:12,560 Speaker 2: a bipartisan way. 465 00:27:13,080 --> 00:27:15,600 Speaker 1: Let me ask you one of the proposals you've had. 466 00:27:15,800 --> 00:27:20,400 Speaker 1: You proposed in twenty seventeen to move federal agencies outside 467 00:27:20,440 --> 00:27:23,680 Speaker 1: of Washington, which you said would be a real opportunity 468 00:27:24,080 --> 00:27:27,320 Speaker 1: to get a more representative subsection of Americans working for 469 00:27:27,359 --> 00:27:29,760 Speaker 1: the government. Do you think that kind of proposal can 470 00:27:29,760 --> 00:27:30,320 Speaker 1: get traction? 471 00:27:31,240 --> 00:27:34,000 Speaker 2: Oh? It should, it should, it should. Look, I think 472 00:27:34,040 --> 00:27:37,280 Speaker 2: there's key military and law enforcement that needs to have 473 00:27:37,400 --> 00:27:40,680 Speaker 2: great proximity to the President. Do we need the Department 474 00:27:40,760 --> 00:27:44,439 Speaker 2: of Transportation in DC or could that be better served 475 00:27:44,480 --> 00:27:47,919 Speaker 2: in Atlanta or Los Angeles? Or maybe the Department of 476 00:27:47,960 --> 00:27:52,440 Speaker 2: Interior in Salt Lake City or Denver or even Wyoming 477 00:27:52,560 --> 00:27:55,439 Speaker 2: or something the Department of Agriculture. Why isn't that in 478 00:27:55,520 --> 00:27:59,080 Speaker 2: Omaha or Des Moines for goodness sake? So I think 479 00:27:59,200 --> 00:28:03,359 Speaker 2: setting it out the employee base, what we find particular 480 00:28:03,400 --> 00:28:04,960 Speaker 2: out west. You know, I live in the Inner mount 481 00:28:05,040 --> 00:28:09,080 Speaker 2: West in Utah. We're dealing with government bureaucrats who've never 482 00:28:09,119 --> 00:28:11,440 Speaker 2: been out here, They've never been to the Rocky Mountains. 483 00:28:11,720 --> 00:28:14,159 Speaker 2: They have no idea what our issues are. So the 484 00:28:14,200 --> 00:28:16,919 Speaker 2: employee base and why should they get all the employment 485 00:28:17,000 --> 00:28:20,720 Speaker 2: benefit there in the greater Washington, DC area. It just 486 00:28:20,760 --> 00:28:24,200 Speaker 2: doesn't make much sense to me. I think a diverse government, 487 00:28:24,320 --> 00:28:26,840 Speaker 2: and again, Department of Education. First of all, there shouldn't 488 00:28:26,800 --> 00:28:29,560 Speaker 2: even be a Department of Education. But if they had 489 00:28:29,560 --> 00:28:32,720 Speaker 2: to have it, have it in Florida or somewhere other 490 00:28:32,880 --> 00:28:35,679 Speaker 2: than based in DC all the time. Each of the 491 00:28:35,720 --> 00:28:38,600 Speaker 2: cabinet secretaries could have a small office to the approximity 492 00:28:38,640 --> 00:28:41,640 Speaker 2: to the president. I get that, But the rank and file, 493 00:28:42,000 --> 00:28:44,160 Speaker 2: those millions of employees spread them out. 494 00:28:44,680 --> 00:28:46,120 Speaker 1: I mean, there is a degree to which this has 495 00:28:46,160 --> 00:28:49,840 Speaker 1: become an imperial capital. It has one basic industry. I 496 00:28:50,000 --> 00:28:52,960 Speaker 1: really want to thank you Jason for joining me. Your 497 00:28:53,040 --> 00:28:57,040 Speaker 1: new book, They're Coming for You, How Deep stage spies, 498 00:28:57,360 --> 00:29:00,520 Speaker 1: NGOs and woke Corporations Plan to push You out of 499 00:29:00,560 --> 00:29:04,480 Speaker 1: the Economy is available now on Amazon and in bookstores everywhere. 500 00:29:04,560 --> 00:29:07,000 Speaker 1: And I really appreciate you joining me to talk about it. 501 00:29:07,600 --> 00:29:09,960 Speaker 2: Honor to be with you as always, mister speaker. Thank 502 00:29:10,000 --> 00:29:11,440 Speaker 2: you so much. I do appreciate it. 503 00:29:15,000 --> 00:29:17,760 Speaker 1: Thank you to my guest, Jason Schabits. You can get 504 00:29:17,760 --> 00:29:19,840 Speaker 1: a link to buy his new book, They're Coming for You, 505 00:29:20,280 --> 00:29:24,200 Speaker 1: How Deep state spies, NGOs, and woke corporations plan to 506 00:29:24,240 --> 00:29:26,920 Speaker 1: push you out of the Economy on our show page 507 00:29:27,120 --> 00:29:30,320 Speaker 1: at newtsworld dot com. News World is produced by Ganglishtree 508 00:29:30,400 --> 00:29:35,200 Speaker 1: sixty and iHeartMedia. Our executive producer is Guernsey Sloan. Our 509 00:29:35,240 --> 00:29:39,000 Speaker 1: researcher is Rachel Peterson. The artwork for the show was 510 00:29:39,040 --> 00:29:42,000 Speaker 1: created by Steve Penley. Special thanks to the team at 511 00:29:42,000 --> 00:29:45,360 Speaker 1: Gaist three sixty. If you've been enjoying Newtsworld, I hope 512 00:29:45,360 --> 00:29:47,840 Speaker 1: you'll go to Apple Podcasts and both rate us with 513 00:29:47,920 --> 00:29:51,000 Speaker 1: five stars and give us a review so others can 514 00:29:51,080 --> 00:29:54,080 Speaker 1: learn what it's all about. Right now, listeners of newts 515 00:29:54,080 --> 00:29:57,440 Speaker 1: World can sign up for my three free weekly columns 516 00:29:57,560 --> 00:30:01,840 Speaker 1: at Gangishtree sixty dot com slash newsletter. I'm new Gandwich. 517 00:30:02,160 --> 00:30:03,000 Speaker 1: This is news World.