1 00:00:00,680 --> 00:00:04,160 Speaker 1: Hi, I'm Molly John Fast and this is Fast Politics, 2 00:00:04,400 --> 00:00:07,160 Speaker 1: where we discussed the top political headlines with some of 3 00:00:07,200 --> 00:00:10,319 Speaker 1: today's best minds. We're off for the holidays, but that 4 00:00:10,360 --> 00:00:13,560 Speaker 1: doesn't mean we don't have an amazing show for you Today. 5 00:00:14,080 --> 00:00:19,000 Speaker 1: NBC's Ryan Riley joins us to talk about his new book, 6 00:00:19,079 --> 00:00:23,479 Speaker 1: Sedition Hunters, How January sixth broke the justice system. But 7 00:00:23,560 --> 00:00:27,680 Speaker 1: first we talked to Peter Salad of the Hometown Project 8 00:00:27,880 --> 00:00:32,880 Speaker 1: about how they use celebrities to elect local officials. Welcome 9 00:00:33,440 --> 00:00:36,040 Speaker 1: to Fast Politics, Peter select. 10 00:00:35,840 --> 00:00:37,600 Speaker 2: Thank you so much. It's great to be here. 11 00:00:38,040 --> 00:00:41,760 Speaker 1: This is such a cool project. So we are coming 12 00:00:41,760 --> 00:00:47,800 Speaker 1: off this extremely good off year election for Democrats. Before 13 00:00:47,880 --> 00:00:50,840 Speaker 1: we start talking about what you are doing, I want 14 00:00:50,880 --> 00:00:53,840 Speaker 1: to just take a moment because all of us who 15 00:00:54,440 --> 00:00:57,680 Speaker 1: ingest the mainstream media, of which I am very much 16 00:00:57,720 --> 00:00:59,880 Speaker 1: a part, a lot of us feel like we can't 17 00:01:00,080 --> 00:01:03,000 Speaker 1: have a two second victory lab here. But I was 18 00:01:03,080 --> 00:01:06,839 Speaker 1: told that Glenn Youngkin was going to take over Virginia 19 00:01:07,120 --> 00:01:10,400 Speaker 1: and make it Florida, and instead he lost both the 20 00:01:10,400 --> 00:01:13,200 Speaker 1: House of Delegates and Democrats were able to keep the 21 00:01:13,240 --> 00:01:18,600 Speaker 1: state Senate, and in numerous races, Democrats over performed including 22 00:01:18,640 --> 00:01:21,240 Speaker 1: in Kentucky. So I just want when we go into 23 00:01:21,280 --> 00:01:24,840 Speaker 1: this interview, because this is one of these things that 24 00:01:25,120 --> 00:01:29,520 Speaker 1: is a thing that really does actually win elections. Like 25 00:01:29,640 --> 00:01:32,959 Speaker 1: these kind of projects actually do. They flip seats. They 26 00:01:32,959 --> 00:01:36,679 Speaker 1: flip Senate seats, they flip House seats, they flip governorships. 27 00:01:36,800 --> 00:01:39,800 Speaker 1: They are the sort of nuts and bolts of activism. 28 00:01:40,160 --> 00:01:44,520 Speaker 1: So with that very long winded introduction, tell us what 29 00:01:44,760 --> 00:01:47,640 Speaker 1: exactly is the Hometown Project. 30 00:01:47,920 --> 00:01:50,320 Speaker 3: So great to be here, and thanks so much. I 31 00:01:50,360 --> 00:01:52,760 Speaker 3: do want to say really quickly, I think your point 32 00:01:52,800 --> 00:01:58,400 Speaker 3: about celebrating victories is very well taken. One of the 33 00:01:58,560 --> 00:02:02,400 Speaker 3: Hometown board members is Preparabo, and she's a great actor 34 00:02:02,560 --> 00:02:05,920 Speaker 3: and political activist, and she's had us at one point 35 00:02:06,000 --> 00:02:09,080 Speaker 3: a few years ago. You know, look, we never celebrate 36 00:02:09,120 --> 00:02:12,400 Speaker 3: our victories. So we're like already into twenty twenty four. 37 00:02:12,480 --> 00:02:16,440 Speaker 3: We've just had this you know, great election, and you know, 38 00:02:16,639 --> 00:02:18,440 Speaker 3: I know for our org as well, we're kind of 39 00:02:18,480 --> 00:02:20,960 Speaker 3: trying to already look into twenty twenty four and we've 40 00:02:20,960 --> 00:02:23,280 Speaker 3: been doing so for a while. So it is good 41 00:02:23,280 --> 00:02:26,640 Speaker 3: to celebrate victories because they allow us to ingest some 42 00:02:26,720 --> 00:02:29,720 Speaker 3: of that positive energy that keeps us going and if 43 00:02:29,760 --> 00:02:32,959 Speaker 3: we're always in panic mode, we don't, you know, take 44 00:02:33,000 --> 00:02:36,080 Speaker 3: the time to have that kind of that positivity which 45 00:02:36,200 --> 00:02:38,760 Speaker 3: leads to better outcomes in the end. Okay, So that's 46 00:02:39,240 --> 00:02:44,560 Speaker 3: just about celebration. The Hometown project started after the Trump election. 47 00:02:44,880 --> 00:02:48,359 Speaker 3: I'm a musician by trade, played for many years in 48 00:02:48,400 --> 00:02:51,000 Speaker 3: New York City, still do sometimes did a lot of 49 00:02:51,040 --> 00:02:54,520 Speaker 3: movie work, and for whatever reason, a lot of well 50 00:02:54,560 --> 00:02:56,679 Speaker 3: known people are people who ended up being well known, 51 00:02:56,680 --> 00:02:58,960 Speaker 3: needs to come and see me play. After the twenty 52 00:02:58,960 --> 00:03:02,239 Speaker 3: sixteen election, I was kind of thinking about what could 53 00:03:02,320 --> 00:03:05,160 Speaker 3: I do? Like so many people, I was think, gosh, 54 00:03:05,160 --> 00:03:06,080 Speaker 3: I didn't do enough. 55 00:03:06,200 --> 00:03:08,080 Speaker 2: What could I have done? What could I have done? 56 00:03:08,480 --> 00:03:12,040 Speaker 3: And I started talking to a bunch of different activists 57 00:03:12,320 --> 00:03:14,720 Speaker 3: and I had a conversation with an environmental activist and 58 00:03:14,720 --> 00:03:17,440 Speaker 3: he said, you know, when Mark Ruffalo went back to Kenosha, 59 00:03:17,880 --> 00:03:20,960 Speaker 3: Wisconsin to talk about environmental issues, that was really powerful 60 00:03:21,200 --> 00:03:23,480 Speaker 3: because he's there. And a little light went off of 61 00:03:23,520 --> 00:03:26,320 Speaker 3: my head. I thought, I know, Mard Ruffalo, what if 62 00:03:26,360 --> 00:03:29,600 Speaker 3: we could get Mark and people like Mark to support 63 00:03:29,800 --> 00:03:33,560 Speaker 3: local candidates in their hometowns. So these races that are 64 00:03:33,600 --> 00:03:37,080 Speaker 3: so important that are decided often by five hundred votes, 65 00:03:37,120 --> 00:03:40,720 Speaker 3: a thousand votes, could we get someone well known to 66 00:03:40,920 --> 00:03:43,880 Speaker 3: support them. And in the beginning my thought process was 67 00:03:44,520 --> 00:03:46,640 Speaker 3: we'll get them to go back and lead a rally, 68 00:03:46,720 --> 00:03:49,800 Speaker 3: that kind of thing. But you know, after going about 69 00:03:49,800 --> 00:03:52,000 Speaker 3: it for a year or two, came very clear these 70 00:03:52,040 --> 00:03:55,320 Speaker 3: are busy people. They don't have time necessarily lining up 71 00:03:55,400 --> 00:03:58,560 Speaker 3: when they might be returning home or getting arranging. All 72 00:03:58,560 --> 00:04:02,280 Speaker 3: the travel is way beyond our capacity. However, if they 73 00:04:02,320 --> 00:04:05,200 Speaker 3: could take twenty minutes and just make a video on 74 00:04:05,280 --> 00:04:08,240 Speaker 3: their phone, we send them a script. We then take 75 00:04:08,280 --> 00:04:10,560 Speaker 3: that video, we slap some graphics on it with the 76 00:04:10,600 --> 00:04:14,200 Speaker 3: candidates name, their picture, and then we digitally distribute that 77 00:04:14,240 --> 00:04:17,880 Speaker 3: content within the district. Then we are reaching a lot 78 00:04:17,960 --> 00:04:21,440 Speaker 3: of the right voters within that district. That's kind of 79 00:04:21,720 --> 00:04:23,800 Speaker 3: just a little overview of what we do. 80 00:04:24,279 --> 00:04:27,000 Speaker 1: So give us an example of a place where this 81 00:04:27,200 --> 00:04:29,920 Speaker 1: happens and how this goes. 82 00:04:30,400 --> 00:04:34,040 Speaker 3: So we were in both Virginia and New Jersey this year. 83 00:04:34,120 --> 00:04:38,560 Speaker 3: It's our fourth cycle. In Virginia. Nadarius Clark, for example, 84 00:04:38,720 --> 00:04:42,360 Speaker 3: is running in the Hampton Roads area running for a 85 00:04:42,480 --> 00:04:47,159 Speaker 3: House of Delegates in a new district. And Wanda Sykes 86 00:04:47,279 --> 00:04:51,080 Speaker 3: is also from Portsmouth Or She grew up there. She's 87 00:04:51,160 --> 00:04:53,800 Speaker 3: from Portsmouth, New Hampshire, NOH Portsmouth, Virginia. 88 00:04:54,040 --> 00:04:55,640 Speaker 1: Yeah, that makes sense, Like. 89 00:04:56,720 --> 00:05:00,920 Speaker 3: We define hometown in a bunch of different ways. Essentially, 90 00:05:01,240 --> 00:05:03,919 Speaker 3: it's where people feel like they have roots. So Connie 91 00:05:03,960 --> 00:05:06,320 Speaker 3: Britten did something for us. She's from Virginia. She works 92 00:05:06,320 --> 00:05:08,119 Speaker 3: with us in Virginia all the time. But she's also 93 00:05:08,640 --> 00:05:10,799 Speaker 3: well known for spending a lot of time in Texas 94 00:05:10,800 --> 00:05:13,400 Speaker 3: for Friday night lights. So you know, something for us 95 00:05:13,400 --> 00:05:15,880 Speaker 3: in Texas there too, because she feels an affinity with 96 00:05:15,920 --> 00:05:20,520 Speaker 3: that area. So Wanda Sykes from Portsmouth. We work with 97 00:05:20,839 --> 00:05:25,479 Speaker 3: state based grassroots partners, which can be an organization like 98 00:05:25,560 --> 00:05:29,039 Speaker 3: Clean Virginia we worked with this past year in Virginia. 99 00:05:29,160 --> 00:05:32,440 Speaker 3: They're an advocacy organization. Is primary goal is to disrupt 100 00:05:32,440 --> 00:05:34,599 Speaker 3: the power monopoly, and they poured a lot of money 101 00:05:34,640 --> 00:05:37,359 Speaker 3: into the Virginia elections this year and they won and 102 00:05:37,440 --> 00:05:39,880 Speaker 3: it worked, or it could be For example, in New Jersey, 103 00:05:39,880 --> 00:05:43,159 Speaker 3: we worked with the New Jersey Democratic Caucus, so they 104 00:05:43,279 --> 00:05:47,560 Speaker 3: had three priority districts New Jersey, they're state Senate and 105 00:05:47,680 --> 00:05:50,279 Speaker 3: a State Assembly. You run as a slate because it's 106 00:05:50,320 --> 00:05:53,320 Speaker 3: the same district. It's just one senator and two Assembly 107 00:05:53,320 --> 00:05:56,120 Speaker 3: people per district. So they had three slates that they 108 00:05:56,120 --> 00:06:00,320 Speaker 3: were their highest priority slates. The Grassroots partner says, hey, 109 00:06:00,480 --> 00:06:05,040 Speaker 3: here's our top nine, top twelve, top fifteen candidates. These 110 00:06:05,080 --> 00:06:06,599 Speaker 3: are the people that we think are going to be 111 00:06:06,680 --> 00:06:10,080 Speaker 3: in the tightest races. And then we come in and 112 00:06:10,120 --> 00:06:12,320 Speaker 3: we say, you know, we do the research. I want 113 00:06:12,320 --> 00:06:14,839 Speaker 3: to give a shout out to ourn incredible executive director 114 00:06:14,880 --> 00:06:17,640 Speaker 3: of the Hometown Project, Aaron Frederick, without whom none of 115 00:06:17,680 --> 00:06:19,640 Speaker 3: this would be possible. What we do is then we 116 00:06:19,680 --> 00:06:22,560 Speaker 3: go we take those candidates. We say, okay, that they're 117 00:06:22,600 --> 00:06:25,920 Speaker 3: from a specific area, Let's do the research. See who 118 00:06:26,200 --> 00:06:29,440 Speaker 3: is well known that is from that area. And then, 119 00:06:30,040 --> 00:06:32,960 Speaker 3: because I've been in the entertainment business for a while 120 00:06:33,480 --> 00:06:35,479 Speaker 3: and now we've done this for a while, people have 121 00:06:35,520 --> 00:06:39,520 Speaker 3: had a positive experience. So at its core, the kind 122 00:06:39,560 --> 00:06:43,800 Speaker 3: of front of camera entertainment industry is pretty small, so 123 00:06:44,240 --> 00:06:46,680 Speaker 3: it's not that hard for me to reach a lot 124 00:06:46,800 --> 00:06:49,800 Speaker 3: of different people. So then I say, okay, well, look, 125 00:06:49,839 --> 00:06:53,280 Speaker 3: Wanda Sykes is from Portsmouth, Clean, Virginia. Needs we call 126 00:06:53,279 --> 00:06:56,560 Speaker 3: them hometown energizers. That's our term for the well known person. 127 00:06:56,880 --> 00:06:59,640 Speaker 3: They need somebody from that area. Oh look, Wanda Sykes 128 00:06:59,640 --> 00:07:02,599 Speaker 3: is from who knows Wanda? Well, look it turns out 129 00:07:02,800 --> 00:07:05,880 Speaker 3: Mark Ruffalo knows Wanda. He puts us in touch and 130 00:07:05,920 --> 00:07:08,599 Speaker 3: she makes a video for us. And so that's the 131 00:07:08,640 --> 00:07:09,200 Speaker 3: way we work. 132 00:07:09,520 --> 00:07:14,440 Speaker 1: Your dream is to get Taylor Swift, right, let's be honest, Well, 133 00:07:15,160 --> 00:07:17,960 Speaker 1: to run for senate's percentate? 134 00:07:18,080 --> 00:07:19,640 Speaker 2: Yeah, how would be amazing? 135 00:07:19,960 --> 00:07:21,880 Speaker 1: Marshall Blackburn, bye bye? 136 00:07:22,320 --> 00:07:23,400 Speaker 2: How great would that be? 137 00:07:23,720 --> 00:07:24,800 Speaker 1: That would be amazing? 138 00:07:24,920 --> 00:07:28,600 Speaker 3: I mean, she honestly does not have time to be no, 139 00:07:28,800 --> 00:07:35,920 Speaker 3: she is. The Taylor Swift question is fascinating because I 140 00:07:35,960 --> 00:07:37,800 Speaker 3: think we get it all the time, and I think 141 00:07:37,840 --> 00:07:42,960 Speaker 3: it speaks to how people have thought about celebrity in 142 00:07:43,000 --> 00:07:46,280 Speaker 3: the political space and how what we feel like we're 143 00:07:46,280 --> 00:07:49,360 Speaker 3: doing at the Hometown Project is different from that. Taylor 144 00:07:49,680 --> 00:07:54,200 Speaker 3: is a she's a megastar. She's a supernova. But if 145 00:07:54,280 --> 00:07:58,920 Speaker 3: we are counting on Taylor Swift to save democracy, fucked. 146 00:07:59,160 --> 00:08:02,040 Speaker 3: I so appreciate, shate all the things that she's done 147 00:08:02,080 --> 00:08:05,200 Speaker 3: and her document the documentary about her is great for us. 148 00:08:05,240 --> 00:08:08,560 Speaker 3: We're like, hey, look, somebody could be from a local area. 149 00:08:08,720 --> 00:08:11,560 Speaker 3: They may not be nationally hugely known, but within their 150 00:08:11,680 --> 00:08:15,000 Speaker 3: local area they're actually really well known. The local papers 151 00:08:15,040 --> 00:08:17,360 Speaker 3: written about them since they got their first part on 152 00:08:17,360 --> 00:08:21,200 Speaker 3: that TV show ten years ago. And you know, we've 153 00:08:21,240 --> 00:08:24,920 Speaker 3: got that little lower third, the biographical information, which says 154 00:08:25,000 --> 00:08:28,640 Speaker 3: exactly where they're from, Bergen Counties, Melissa Fromiro and so 155 00:08:28,680 --> 00:08:31,840 Speaker 3: the thing is is those connections are real and they're 156 00:08:32,600 --> 00:08:35,520 Speaker 3: you know, we get it all the time. This I 157 00:08:35,559 --> 00:08:39,080 Speaker 3: won't name names, but this most recent time in Clean Virginia, 158 00:08:39,240 --> 00:08:41,200 Speaker 3: they said to us, Oh, just so you know, this 159 00:08:41,280 --> 00:08:43,760 Speaker 3: person that you're trying to reach, his sister used to 160 00:08:43,840 --> 00:08:45,839 Speaker 3: date the candidate, you know what I mean. So it's 161 00:08:45,880 --> 00:08:50,079 Speaker 3: like these are local races where the so and so's 162 00:08:50,120 --> 00:08:53,640 Speaker 3: babysitter used to you know, babysit, this a cousin or 163 00:08:53,679 --> 00:08:55,280 Speaker 3: what you know, It's like that this used to they 164 00:08:55,360 --> 00:08:57,600 Speaker 3: used to be their second grade teacher or whatever. Those 165 00:08:57,600 --> 00:09:02,560 Speaker 3: things are. Those local connections are real, and we don't have, 166 00:09:03,120 --> 00:09:03,600 Speaker 3: you know. 167 00:09:03,760 --> 00:09:06,439 Speaker 1: What, we don't need. I mean, I think the point 168 00:09:06,559 --> 00:09:11,320 Speaker 1: is that the people want their local people to come back, right, 169 00:09:11,520 --> 00:09:14,360 Speaker 1: I mean, that's a very meaningful thing, even if they're 170 00:09:14,360 --> 00:09:15,319 Speaker 1: not superstars. 171 00:09:15,559 --> 00:09:16,160 Speaker 2: Exactly. 172 00:09:16,240 --> 00:09:18,880 Speaker 3: To me, part of this work that was so key 173 00:09:19,000 --> 00:09:21,160 Speaker 3: as we were thinking about it from the beginning is 174 00:09:21,480 --> 00:09:25,160 Speaker 3: our danger zone is the kind of like, who are 175 00:09:25,160 --> 00:09:26,640 Speaker 3: these Hollywood elites coming in? 176 00:09:26,720 --> 00:09:27,559 Speaker 2: Tell me what to do? 177 00:09:27,640 --> 00:09:27,840 Speaker 1: You know? 178 00:09:27,920 --> 00:09:28,719 Speaker 2: That kind of right? 179 00:09:28,840 --> 00:09:29,280 Speaker 1: Right, right? 180 00:09:29,440 --> 00:09:32,800 Speaker 3: And so if we sidestep that and we say, no, 181 00:09:32,920 --> 00:09:36,240 Speaker 3: this is not a Hollywood elite person. This is somebody 182 00:09:36,280 --> 00:09:40,400 Speaker 3: who is from your community. They understand your community, they 183 00:09:40,600 --> 00:09:43,680 Speaker 3: grew up there, their siblings might live there, their parents 184 00:09:43,760 --> 00:09:46,160 Speaker 3: or whatever it is, we really try to avoid that 185 00:09:46,360 --> 00:09:50,400 Speaker 3: sense of it's not a generalized person. And then from 186 00:09:50,440 --> 00:09:54,560 Speaker 3: our perspective, if I'm going out to Patton Oswald and 187 00:09:54,600 --> 00:09:58,160 Speaker 3: I'm saying, hey, Paton, would you support a candidate like 188 00:09:58,280 --> 00:10:01,040 Speaker 3: Russet Perry who's running in the district that includes a 189 00:10:01,080 --> 00:10:03,640 Speaker 3: lot of loud in county where you grew up, He's 190 00:10:03,720 --> 00:10:06,480 Speaker 3: ten times more likely to say, yeah, that sounds good. 191 00:10:06,520 --> 00:10:08,760 Speaker 2: I want to do that, versus, hey. 192 00:10:08,679 --> 00:10:12,320 Speaker 3: Could you support this generalized political cause that doesn't really 193 00:10:12,360 --> 00:10:15,120 Speaker 3: have anything to do with what his personal life story. 194 00:10:15,320 --> 00:10:19,840 Speaker 3: So it's much easier for me to recruit people because 195 00:10:19,880 --> 00:10:21,880 Speaker 3: they want to help out their hometown community. 196 00:10:22,320 --> 00:10:26,440 Speaker 1: Yes, exactly. And I think that is a really good point, 197 00:10:26,760 --> 00:10:29,600 Speaker 1: and I agree, like this is the whole thing about democracy, 198 00:10:29,679 --> 00:10:32,400 Speaker 1: right is we love these places we come from. I 199 00:10:32,440 --> 00:10:34,520 Speaker 1: know how I feel about New York City where I 200 00:10:34,559 --> 00:10:36,679 Speaker 1: grew up and my parents grew up and when the 201 00:10:36,679 --> 00:10:39,880 Speaker 1: pandemic came, like, I did not go. I just stayed 202 00:10:39,880 --> 00:10:42,200 Speaker 1: here because this is my place, this is where I'm from, 203 00:10:42,520 --> 00:10:44,719 Speaker 1: and there was no other place to go. I mean, 204 00:10:44,720 --> 00:10:47,120 Speaker 1: this is it. And so the idea of right, this 205 00:10:47,200 --> 00:10:50,319 Speaker 1: is not some celebrity parachuting in. This is someone who 206 00:10:50,600 --> 00:10:52,280 Speaker 1: is a person of the place. 207 00:10:52,720 --> 00:10:53,280 Speaker 2: That's right. 208 00:10:53,320 --> 00:10:56,959 Speaker 3: And because we're connected to the grassroots organization as well, 209 00:10:57,080 --> 00:10:59,520 Speaker 3: if there's things that the candidate wants the stress or 210 00:10:59,520 --> 00:11:04,400 Speaker 3: things are organization feels like is resonating with voters, then 211 00:11:04,600 --> 00:11:07,000 Speaker 3: you know, we can integrate that into the content as 212 00:11:07,000 --> 00:11:09,880 Speaker 3: opposed to being some national organization that kind of says, oh, 213 00:11:10,040 --> 00:11:11,520 Speaker 3: these are the national issues. 214 00:11:11,240 --> 00:11:13,120 Speaker 2: That everybody's running on wherever it is. 215 00:11:13,520 --> 00:11:16,120 Speaker 3: You know, it is it about fixing Route twenty nine, 216 00:11:16,400 --> 00:11:19,720 Speaker 3: you know, more than it's about some sort of other issue. 217 00:11:19,800 --> 00:11:22,840 Speaker 1: And it's also just about like bringing the people who 218 00:11:22,920 --> 00:11:26,280 Speaker 1: care about a place bringing them back, right. I Mean, 219 00:11:26,320 --> 00:11:30,319 Speaker 1: that's kind of the larger issue because I mean, one 220 00:11:30,320 --> 00:11:32,559 Speaker 1: of the things I think a lot about is Peter 221 00:11:32,840 --> 00:11:37,440 Speaker 1: Teal And I'm sorry. I apologized to Jesse for this tangent, 222 00:11:37,480 --> 00:11:41,559 Speaker 1: which will likely get us sued. But Peter tele got 223 00:11:41,840 --> 00:11:46,160 Speaker 1: so excited about Blake Masters and worked so hard on 224 00:11:46,160 --> 00:11:50,080 Speaker 1: this Blake Master's campaign. And Carrie Lake too ran for governor. 225 00:11:50,160 --> 00:11:52,960 Speaker 1: I mean, Kerry Lake is from Arizona. But it was 226 00:11:53,000 --> 00:11:56,760 Speaker 1: a sense in which you had a vision for the 227 00:11:56,840 --> 00:12:01,000 Speaker 1: state that may not have actually been what the state was, right, 228 00:12:01,679 --> 00:12:04,040 Speaker 1: This was a person who lived in Miami or wherever 229 00:12:04,040 --> 00:12:06,959 Speaker 1: it is, Peter Tieli's where he had a vision of 230 00:12:07,000 --> 00:12:09,360 Speaker 1: what he wanted the government to look like, of what 231 00:12:09,480 --> 00:12:11,679 Speaker 1: he wanted you know, he had candidates that he had 232 00:12:11,679 --> 00:12:14,160 Speaker 1: sort of decided where people who could win in these 233 00:12:14,240 --> 00:12:16,240 Speaker 1: And that's not what this is about, right. This is 234 00:12:16,280 --> 00:12:20,120 Speaker 1: about people who come from place endorsing candidates who come 235 00:12:20,120 --> 00:12:24,640 Speaker 1: from that place because they think it will serve their community. 236 00:12:24,840 --> 00:12:25,360 Speaker 2: That's right. 237 00:12:25,480 --> 00:12:28,880 Speaker 3: And the candidates we're supporting, we always feel like are 238 00:12:28,880 --> 00:12:31,800 Speaker 3: the ones that are better connected to their community. These 239 00:12:31,880 --> 00:12:34,640 Speaker 3: are local races, and I think that this is again 240 00:12:34,679 --> 00:12:38,520 Speaker 3: we've seen celebrity used in a national sense. Three days 241 00:12:38,520 --> 00:12:41,280 Speaker 3: before the election, there's a big rally in Springsteen plays 242 00:12:41,320 --> 00:12:44,040 Speaker 3: and all that, and again not to I'm a huge 243 00:12:44,080 --> 00:12:46,800 Speaker 3: Springsteen fan. It's not about that, it's just can we 244 00:12:46,920 --> 00:12:51,400 Speaker 3: think about this in a different way where to actually prioritize. 245 00:12:51,720 --> 00:12:55,040 Speaker 3: We see now how important state legislatures are, How many 246 00:12:55,080 --> 00:12:58,600 Speaker 3: decisions are made at that level. We talk about abortion, 247 00:12:58,720 --> 00:13:03,040 Speaker 3: we talk about medicaid, expand gun safety, the minimum wage, 248 00:13:03,320 --> 00:13:06,080 Speaker 3: so many decisions are made to the state legislation. Not 249 00:13:06,240 --> 00:13:10,160 Speaker 3: to mention jerrymandering, which impacts Congress, so many decisions are 250 00:13:10,160 --> 00:13:13,520 Speaker 3: made at that level. Yet for so long the funding 251 00:13:13,600 --> 00:13:19,560 Speaker 3: model has been so kind of devoted to Congress, Senate 252 00:13:19,880 --> 00:13:23,120 Speaker 3: and presidential races, and we've missed the boat in so 253 00:13:23,280 --> 00:13:25,319 Speaker 3: many ways. And first of all, I want to say 254 00:13:25,320 --> 00:13:28,760 Speaker 3: to you, it's so great that you've had local candidates 255 00:13:28,800 --> 00:13:30,520 Speaker 3: from Virginia on your show, and. 256 00:13:30,679 --> 00:13:33,760 Speaker 1: Jesse and I have had all. I was like love 257 00:13:33,800 --> 00:13:35,960 Speaker 1: home watching the results and I was like, wait, we 258 00:13:36,120 --> 00:13:38,760 Speaker 1: had that person on our show. We had that person. 259 00:13:39,000 --> 00:13:42,080 Speaker 3: Nobody does this on the progressive and the Democratic side. 260 00:13:42,080 --> 00:13:46,080 Speaker 3: There's the funding is screwed. It's such a blind spot 261 00:13:46,160 --> 00:13:49,520 Speaker 3: and it's very hard as a small organization who does 262 00:13:49,559 --> 00:13:50,440 Speaker 3: effective work. 263 00:13:50,480 --> 00:13:51,040 Speaker 2: Who you know? 264 00:13:51,240 --> 00:13:53,959 Speaker 3: Last year in Michigan, we worked with the Michigan Democratic Party. 265 00:13:54,000 --> 00:13:56,720 Speaker 3: We had I got sixteen famous people from Michigan to 266 00:13:56,720 --> 00:13:59,599 Speaker 3: support over twenty state ledged candidates. And we're not some 267 00:13:59,720 --> 00:14:03,720 Speaker 3: magic bullet, but we're a part of something that if 268 00:14:03,760 --> 00:14:07,080 Speaker 3: you focus on these areas, you can in theory, change 269 00:14:07,080 --> 00:14:10,000 Speaker 3: an entire state legislature for as much as one single 270 00:14:10,040 --> 00:14:12,439 Speaker 3: Senate seat. You know, it's like, do we need to 271 00:14:12,559 --> 00:14:15,440 Speaker 3: raise one hundred million dollars to run against Mitch McConnell 272 00:14:15,440 --> 00:14:18,160 Speaker 3: in Kentucky or can that money be better. 273 00:14:18,000 --> 00:14:22,840 Speaker 1: You flipping legislatures which will ultimately I mean, the reason 274 00:14:22,840 --> 00:14:27,200 Speaker 1: why Virginia didn't have an abortion ban in twenty twenty 275 00:14:27,240 --> 00:14:31,360 Speaker 1: two was because they miraculously kept the state center. 276 00:14:31,880 --> 00:14:34,320 Speaker 3: That's right, exactly, and that was part of a lot 277 00:14:34,320 --> 00:14:36,160 Speaker 3: of hard work that was done by a lot of people. 278 00:14:36,280 --> 00:14:38,800 Speaker 3: Even though at the top of that ticket, we don't 279 00:14:38,800 --> 00:14:39,720 Speaker 3: need to go into. 280 00:14:39,520 --> 00:14:43,200 Speaker 1: The red vest wearing fake moderate. 281 00:14:43,400 --> 00:14:45,600 Speaker 3: So and they were able to, you know, utilize some 282 00:14:45,720 --> 00:14:48,280 Speaker 3: of the fear of mongering tactics for us. The other 283 00:14:48,320 --> 00:14:50,760 Speaker 3: thing I want to say about our content is we 284 00:14:51,000 --> 00:14:54,480 Speaker 3: sidestep again all of that negativity. All of our content 285 00:14:54,600 --> 00:14:58,200 Speaker 3: is positive. We never mentioned an opponent, We barely ever 286 00:14:58,280 --> 00:15:01,560 Speaker 3: mentioned party. The things that we're trying to do with 287 00:15:01,640 --> 00:15:05,560 Speaker 3: these short thirty second spots and banner ads is it's 288 00:15:05,920 --> 00:15:10,040 Speaker 3: a sense of connection and community and shared values. So 289 00:15:10,400 --> 00:15:12,320 Speaker 3: in the beginning, we had people and that we're always 290 00:15:12,320 --> 00:15:15,280 Speaker 3: talking about issues, issues, issues, and we're like, okay, what 291 00:15:15,400 --> 00:15:18,680 Speaker 3: we realize is issues resonate with people like us. Who 292 00:15:18,680 --> 00:15:21,120 Speaker 3: are you know, in the kind of people power of demographic. 293 00:15:21,240 --> 00:15:23,840 Speaker 3: Essentially we're into politics all the time. But for the 294 00:15:24,280 --> 00:15:27,760 Speaker 3: much more casual voter that are the actual voters that 295 00:15:27,800 --> 00:15:31,280 Speaker 3: we need to reach to win elections. We can't be 296 00:15:31,440 --> 00:15:34,480 Speaker 3: relying on just revving people up with a sense of 297 00:15:34,480 --> 00:15:37,400 Speaker 3: fear and anger. We have to talk about a shared 298 00:15:37,480 --> 00:15:40,200 Speaker 3: set of values and they may not be well versed 299 00:15:40,240 --> 00:15:42,880 Speaker 3: on what the actual issues are. But if we can 300 00:15:42,920 --> 00:15:45,280 Speaker 3: talk about a shared set of values, and if we 301 00:15:45,320 --> 00:15:48,280 Speaker 3: can do it from our perspective with the well known person, 302 00:15:48,600 --> 00:15:52,360 Speaker 3: that is pleasing and draws people in and to the 303 00:15:52,360 --> 00:15:55,680 Speaker 3: point where we then we see right away that candidate's name, 304 00:15:55,840 --> 00:15:59,320 Speaker 3: the candidate's image comes down and then people are shown 305 00:15:59,360 --> 00:16:02,040 Speaker 3: that ad. We try to get saturations, so we're trying 306 00:16:02,080 --> 00:16:05,160 Speaker 3: to get people to watch it between ten and fourteen times. 307 00:16:05,400 --> 00:16:07,800 Speaker 3: Some part of our content, whether it's the video or 308 00:16:07,800 --> 00:16:09,960 Speaker 3: the banner ad. In the two weeks leading up to 309 00:16:10,000 --> 00:16:13,400 Speaker 3: the election. We found in Virginia, we ran an RCT 310 00:16:13,680 --> 00:16:16,360 Speaker 3: and a study in twenty twenty one, voters that had 311 00:16:16,400 --> 00:16:19,240 Speaker 3: received you know, a randomized control trial and voters that 312 00:16:19,280 --> 00:16:24,040 Speaker 3: had received our content were eight point six percent more 313 00:16:24,280 --> 00:16:28,480 Speaker 3: likely to know the name of their local candidate. And 314 00:16:28,560 --> 00:16:31,360 Speaker 3: so we're talking about races that are decided by really 315 00:16:31,400 --> 00:16:34,760 Speaker 3: small margins. And that's the most important thing that we 316 00:16:34,760 --> 00:16:37,920 Speaker 3: can do for these local candidates is raise their name recognition. 317 00:16:38,000 --> 00:16:40,800 Speaker 3: So people actually when they're going into that voting booth, 318 00:16:41,040 --> 00:16:44,400 Speaker 3: they remember, they're like, oh, that's right, Wanda Seikes. I 319 00:16:44,440 --> 00:16:47,040 Speaker 3: saw that. I saw that actually had Nindarius Clark on there. 320 00:16:47,080 --> 00:16:47,600 Speaker 3: He's awesome. 321 00:16:47,680 --> 00:16:48,520 Speaker 2: Let me vote for him. 322 00:16:48,680 --> 00:16:53,240 Speaker 1: Yeah, exactly. Thank you so much, Peter. This is super interesting, 323 00:16:53,440 --> 00:16:55,280 Speaker 1: Grain and you're doing the work man. 324 00:16:55,440 --> 00:16:57,920 Speaker 2: Great to be with you and admire all that you do. 325 00:17:02,520 --> 00:17:06,879 Speaker 1: Ryan Riley is an NBC News justice reporter and author 326 00:17:06,920 --> 00:17:11,120 Speaker 1: of Sedition Hunters, How January sixth Broke the Justice System. 327 00:17:11,840 --> 00:17:16,800 Speaker 1: Welcome to Fast Politics, Ryan Riley. 328 00:17:16,600 --> 00:17:17,520 Speaker 4: Thanks so much for having me. 329 00:17:17,880 --> 00:17:23,359 Speaker 1: So the book is called Sedition Hunters, How January sixth 330 00:17:23,560 --> 00:17:27,439 Speaker 1: Broke the Justice System? Tell me how you got to 331 00:17:27,680 --> 00:17:28,720 Speaker 1: writing this book. 332 00:17:28,840 --> 00:17:31,639 Speaker 4: You know, when January sixth I took this approach that, oh, 333 00:17:31,680 --> 00:17:33,440 Speaker 4: my gosh, this is going to take over my beat, 334 00:17:33,480 --> 00:17:35,240 Speaker 4: and I was sort of bummed about it, and it 335 00:17:35,320 --> 00:17:36,920 Speaker 4: was just figuring it was going to be case after 336 00:17:36,960 --> 00:17:39,080 Speaker 4: case after case and sort of the same thing over 337 00:17:39,119 --> 00:17:40,960 Speaker 4: and over and over again. I thought the scope of 338 00:17:40,960 --> 00:17:42,400 Speaker 4: it would be big at that point, but I didn't 339 00:17:42,400 --> 00:17:44,520 Speaker 4: realize the full scope of it, because you know, initially 340 00:17:44,560 --> 00:17:46,760 Speaker 4: after the capital was reached, the number that was being 341 00:17:46,800 --> 00:17:48,879 Speaker 4: thrown out there, it was like eight hundred cases, and 342 00:17:48,920 --> 00:17:51,440 Speaker 4: in reality the number of people who could be charged 343 00:17:51,800 --> 00:17:53,959 Speaker 4: is a norse of three thousand. Right now we're at 344 00:17:54,000 --> 00:17:56,720 Speaker 4: about one thousand, two hundred, but there's a lot more 345 00:17:56,960 --> 00:17:59,000 Speaker 4: left to go in the two plus years that they 346 00:17:59,040 --> 00:18:01,600 Speaker 4: have left the stature limitations. But then I really sort 347 00:18:01,600 --> 00:18:03,639 Speaker 4: of got to working with these slews and it just 348 00:18:03,760 --> 00:18:06,959 Speaker 4: became this really fascinating story. I had never been ahead 349 00:18:07,000 --> 00:18:10,080 Speaker 4: of so many FBI investigations. It was not something I 350 00:18:10,160 --> 00:18:12,120 Speaker 4: was used to it. I was used to covering what 351 00:18:12,240 --> 00:18:15,160 Speaker 4: the FBI sort of did and afterwards and oh there's 352 00:18:15,200 --> 00:18:18,120 Speaker 4: a case for being brought forward, but having this sort 353 00:18:18,160 --> 00:18:21,399 Speaker 4: of prior no floage of all of these criminals was 354 00:18:21,520 --> 00:18:24,280 Speaker 4: quite something, and it became this this thing that's just 355 00:18:24,320 --> 00:18:27,400 Speaker 4: really strange, where now it's like, oh, they finally brought 356 00:18:27,440 --> 00:18:29,920 Speaker 4: that case. Right, it's not this thing that I'm surprised 357 00:18:29,960 --> 00:18:31,840 Speaker 4: by something it comes up. It's like, oh, this is 358 00:18:31,880 --> 00:18:34,080 Speaker 4: coming down the pipeline, the waiting this one. Oh, finally 359 00:18:34,119 --> 00:18:36,600 Speaker 4: they got to that. So it's been this really interesting 360 00:18:36,640 --> 00:18:40,200 Speaker 4: thing and I think has just really sort of revolutionized 361 00:18:40,200 --> 00:18:43,280 Speaker 4: and brought up a lot of deeper issues within the 362 00:18:43,359 --> 00:18:47,160 Speaker 4: FBI about their sort of falling behind on the technology front. 363 00:18:47,440 --> 00:18:51,399 Speaker 1: But they were sort of rescued by the American people 364 00:18:51,440 --> 00:18:54,679 Speaker 1: on the technology front. Right explained to us that story. 365 00:18:54,880 --> 00:18:57,320 Speaker 4: Indeed they really were. This all sort of happened on 366 00:18:57,600 --> 00:19:00,760 Speaker 4: the platform formally known as Twitter. Initially, there would be 367 00:19:00,920 --> 00:19:03,960 Speaker 4: these photos that were posted. There was this really horrific 368 00:19:04,000 --> 00:19:06,440 Speaker 4: photo posted a few days after January sixth of an 369 00:19:06,440 --> 00:19:09,920 Speaker 4: officer bed dragged down the steps of the capital face first, 370 00:19:10,000 --> 00:19:12,560 Speaker 4: and that's sort of what set all of this off. 371 00:19:12,640 --> 00:19:14,879 Speaker 4: And there is someone who sort of sent out that 372 00:19:14,880 --> 00:19:17,520 Speaker 4: photo and said, let's identify everyone in its photos. So 373 00:19:17,560 --> 00:19:20,920 Speaker 4: they started giving everyone names and hashtags, and then nicknames 374 00:19:20,960 --> 00:19:23,119 Speaker 4: became a huge part of this because it helped people 375 00:19:23,240 --> 00:19:26,000 Speaker 4: track various people throughout the day because you're dealing with 376 00:19:26,119 --> 00:19:28,879 Speaker 4: just such an overwhelming amount of evidence that it's really 377 00:19:28,960 --> 00:19:31,600 Speaker 4: tough to organize. And that's one of the things that 378 00:19:31,640 --> 00:19:34,360 Speaker 4: the FBI has really really struggled with, and that's where 379 00:19:34,400 --> 00:19:37,680 Speaker 4: sort of this crowdsourcing really came in handy. And initially, 380 00:19:37,760 --> 00:19:40,560 Speaker 4: you know, this was done out in public via hashtags, 381 00:19:40,600 --> 00:19:43,720 Speaker 4: but in the years since, it's really more behind the scenes, 382 00:19:43,920 --> 00:19:46,879 Speaker 4: and a number of these sleuths are feeding information to 383 00:19:46,960 --> 00:19:49,439 Speaker 4: the FBI that's really assisted them. I mean, there are 384 00:19:49,480 --> 00:19:52,240 Speaker 4: cases that are shoot to nuts completely built by these 385 00:19:52,240 --> 00:19:55,240 Speaker 4: online sloughs and the FBI basically just has to press 386 00:19:55,280 --> 00:19:57,800 Speaker 4: the button, sign the form. And then there are other 387 00:19:57,840 --> 00:20:00,480 Speaker 4: cases that would have been brought forward anyway, but then 388 00:20:00,760 --> 00:20:03,720 Speaker 4: miss lous are really aiding in those investigations and bringing 389 00:20:03,760 --> 00:20:06,399 Speaker 4: up new information that the government was unaware of. 390 00:20:06,680 --> 00:20:14,879 Speaker 1: So this really is an interesting situation where Internet, you know, 391 00:20:14,960 --> 00:20:18,720 Speaker 1: the people who are so met with such disdain by 392 00:20:18,800 --> 00:20:21,760 Speaker 1: a lot of people, I mean a lot of people 393 00:20:22,320 --> 00:20:25,080 Speaker 1: who I think of, you know, in this sort of 394 00:20:25,680 --> 00:20:29,320 Speaker 1: journalism world, and not just journalism, but a lot of 395 00:20:29,359 --> 00:20:34,840 Speaker 1: these other worlds tend to have disdain towards these people, 396 00:20:35,000 --> 00:20:38,919 Speaker 1: and in fact they kind of saved the day. So 397 00:20:39,240 --> 00:20:41,320 Speaker 1: talk to us a little bit about that, because that's 398 00:20:41,560 --> 00:20:42,400 Speaker 1: really interesting. 399 00:20:42,840 --> 00:20:45,400 Speaker 4: You know, I've covered the FBI for a long time now, 400 00:20:45,640 --> 00:20:47,760 Speaker 4: and I always sort of knew that they were not 401 00:20:47,880 --> 00:20:50,199 Speaker 4: as you know, they were not the Hollywood image that 402 00:20:50,680 --> 00:20:53,879 Speaker 4: we see trade on Tolida. But this really sort of 403 00:20:53,960 --> 00:20:57,040 Speaker 4: just shattered it for me, getting involved with the loves 404 00:20:57,080 --> 00:20:58,720 Speaker 4: and how far behind they were in a lot of 405 00:20:58,720 --> 00:21:01,639 Speaker 4: this stuff. I remember, early on I've started covering the FBI, 406 00:21:01,680 --> 00:21:04,240 Speaker 4: there is a round table. This was probably a decade 407 00:21:04,280 --> 00:21:06,840 Speaker 4: ago now with James Comy, you know, a bunch of 408 00:21:06,840 --> 00:21:09,000 Speaker 4: reporters were brought in and I remember it being like 409 00:21:09,040 --> 00:21:11,880 Speaker 4: a stating that I was the only digital reporter there 410 00:21:11,960 --> 00:21:13,800 Speaker 4: right because they weren't sort of used to that. And 411 00:21:13,840 --> 00:21:17,080 Speaker 4: the top of the FBI spokesters at the time was like, 412 00:21:17,119 --> 00:21:18,600 Speaker 4: so do you get like this? This is when I 413 00:21:18,640 --> 00:21:19,960 Speaker 4: was at huff Post and they were like, so do 414 00:21:19,960 --> 00:21:22,280 Speaker 4: you guys? You know, do you guys print a newspaper? 415 00:21:22,359 --> 00:21:25,080 Speaker 4: And like say to no, no. So that was mebing 416 00:21:25,080 --> 00:21:26,680 Speaker 4: a little bit of a sign of things to come. 417 00:21:26,760 --> 00:21:29,280 Speaker 4: And the FBI is a use of technology but if 418 00:21:29,320 --> 00:21:31,400 Speaker 4: you did, just think about the feeders who goes into 419 00:21:31,400 --> 00:21:33,320 Speaker 4: the FBI, It doesn't make a lot of sense if 420 00:21:33,359 --> 00:21:36,280 Speaker 4: you're really technologically skilled that you would join an organization 421 00:21:36,400 --> 00:21:38,879 Speaker 4: like the FBI, because gosh, they could send you anywhere 422 00:21:38,880 --> 00:21:40,960 Speaker 4: across the country. They continue you somewhere you don't want 423 00:21:40,960 --> 00:21:43,000 Speaker 4: to live. And just also that the pay scale is 424 00:21:43,000 --> 00:21:45,399 Speaker 4: a huge thing. Also, you can't smoke pot, right, and 425 00:21:45,520 --> 00:21:49,959 Speaker 4: like that's a thing, you know, Like everyone laughs when 426 00:21:50,000 --> 00:21:52,280 Speaker 4: I say that, but like James Comy himself has pointed 427 00:21:52,280 --> 00:21:54,960 Speaker 4: this out, like they actually relaxing the rule a little 428 00:21:55,000 --> 00:21:57,159 Speaker 4: bit bit more. But like what tech person do you 429 00:21:57,240 --> 00:22:01,000 Speaker 4: know who hasn't smoked pot, like at least of their life. 430 00:22:01,040 --> 00:22:03,199 Speaker 4: That's not a thing, right, And now it's like the 431 00:22:03,280 --> 00:22:06,440 Speaker 4: FBI will about you, okay, smoke except to stop using 432 00:22:06,520 --> 00:22:09,119 Speaker 4: you know, a marijuana, great year, I guess before you apply. 433 00:22:09,359 --> 00:22:10,720 Speaker 4: But you know, it's just there are a lot of 434 00:22:10,760 --> 00:22:14,120 Speaker 4: these things, and most I think, you know, predominantly being 435 00:22:14,160 --> 00:22:16,600 Speaker 4: the pay scale issue, that really are sort of holding 436 00:22:16,640 --> 00:22:19,080 Speaker 4: them back. And it's a real issue because this is 437 00:22:19,119 --> 00:22:21,280 Speaker 4: the most important thing of our time, like open source 438 00:22:21,320 --> 00:22:26,960 Speaker 4: investigations and technological skills are really what the FBI desperately needs, 439 00:22:27,040 --> 00:22:29,159 Speaker 4: and I think it's something that they're struggling to keep 440 00:22:29,240 --> 00:22:29,920 Speaker 4: up with right now. 441 00:22:30,000 --> 00:22:32,600 Speaker 1: Yeah, So tell me like a sort of example of 442 00:22:32,640 --> 00:22:33,600 Speaker 1: one of these cases. 443 00:22:34,119 --> 00:22:38,399 Speaker 4: So let's take a guy named Logan Barnhart. So Logan 444 00:22:38,480 --> 00:22:41,520 Speaker 4: Barnhart was a construction worker. He used to work with 445 00:22:41,560 --> 00:22:43,800 Speaker 4: his dad. They had a falling out and he was 446 00:22:43,840 --> 00:22:45,680 Speaker 4: sort of on his own. You know, he's a lot 447 00:22:45,680 --> 00:22:49,160 Speaker 4: a lot of photos of him operating heavy machinery. He's 448 00:22:49,200 --> 00:22:51,400 Speaker 4: also a very fit guy, like to work out a lot. 449 00:22:51,480 --> 00:22:53,959 Speaker 4: Turned out he was a bodybuilder back in the day. 450 00:22:54,160 --> 00:22:56,480 Speaker 4: I mean. He was also on the cover of some 451 00:22:56,600 --> 00:22:59,440 Speaker 4: romance novels and so he was one of these individuals 452 00:22:59,520 --> 00:23:02,080 Speaker 4: spoiled or alert who dried to cop down the Capitol 453 00:23:02,119 --> 00:23:04,760 Speaker 4: stairs And it was a little difficult for the sluice 454 00:23:04,760 --> 00:23:07,560 Speaker 4: to idem at first because he was kind of covered up, right, 455 00:23:07,600 --> 00:23:10,679 Speaker 4: So they nicknamed him cat Sweat because he was wearing 456 00:23:10,680 --> 00:23:13,480 Speaker 4: a Caterpillar brand sweatshirt and that's how they sort of 457 00:23:13,520 --> 00:23:15,600 Speaker 4: followed him the whole day. But you know he had 458 00:23:15,640 --> 00:23:18,480 Speaker 4: sunglasses on, is that his face was covered part of 459 00:23:18,480 --> 00:23:21,360 Speaker 4: the time he had had on. It wasn't the best 460 00:23:21,400 --> 00:23:24,760 Speaker 4: shot you could get of him from a facial recognition standpoint, 461 00:23:24,880 --> 00:23:27,560 Speaker 4: but using some of the technology that the sluice developed. 462 00:23:27,560 --> 00:23:29,520 Speaker 4: In fact, the app one of these apps that I 463 00:23:29,600 --> 00:23:31,960 Speaker 4: refer to in the book, they were able to sort 464 00:23:32,000 --> 00:23:34,440 Speaker 4: of bring all this information together. And the best way 465 00:23:34,440 --> 00:23:36,280 Speaker 4: to think of this as sort of like your iPhoto 466 00:23:36,440 --> 00:23:39,560 Speaker 4: library in that if you told me, like, hey, I'm 467 00:23:39,600 --> 00:23:42,840 Speaker 4: trying to find this photo of X person, but I 468 00:23:42,880 --> 00:23:45,320 Speaker 4: don't know when it was taken, right, what you can 469 00:23:45,320 --> 00:23:46,919 Speaker 4: do on your phone is just sort of swipe up 470 00:23:46,920 --> 00:23:49,520 Speaker 4: and it'll show you as internal facial recognition, so it'll 471 00:23:49,520 --> 00:23:52,200 Speaker 4: show you every one of the photos of your friends basically, 472 00:23:52,440 --> 00:23:54,360 Speaker 4: you know, and even for my kids, it takes them 473 00:23:54,400 --> 00:23:57,440 Speaker 4: from when they were babies to Karen Day. It follows 474 00:23:57,480 --> 00:23:59,360 Speaker 4: them all the way through the facial recognitions pretty good. 475 00:23:59,600 --> 00:24:00,879 Speaker 4: And that's the best way to think of what the 476 00:24:00,920 --> 00:24:02,760 Speaker 4: Slues are able to do here. So they found a 477 00:24:02,800 --> 00:24:05,399 Speaker 4: really good shot of him where he had had his 478 00:24:05,440 --> 00:24:07,760 Speaker 4: sunglasses off, and it was just this YouTube video that 479 00:24:07,800 --> 00:24:10,760 Speaker 4: they had archived a while back. Somebody had very quickly 480 00:24:10,840 --> 00:24:13,919 Speaker 4: tanned over the crowd at the ellipse during Trump's speech, 481 00:24:14,160 --> 00:24:17,359 Speaker 4: and at that point Logan Barnhardt did not have his 482 00:24:17,480 --> 00:24:21,960 Speaker 4: sunglasses on, and that internal facial recognition matched him with 483 00:24:22,160 --> 00:24:24,840 Speaker 4: the person who was at the Capitol steps dragging down 484 00:24:25,280 --> 00:24:27,400 Speaker 4: driving a cop down the stairs. So then you had 485 00:24:27,400 --> 00:24:30,199 Speaker 4: a full face shot without the sunglasses when the sunglasses 486 00:24:30,200 --> 00:24:32,680 Speaker 4: are on, you know, strapped to his chest or something. 487 00:24:32,840 --> 00:24:36,080 Speaker 4: And then he run that through his facial recognition and search, 488 00:24:36,480 --> 00:24:39,440 Speaker 4: and then what pops up is all of us bodybuilding 489 00:24:39,480 --> 00:24:42,240 Speaker 4: photos and all of the photos of him on the 490 00:24:42,280 --> 00:24:44,520 Speaker 4: cover of romance novels. So as the results of all 491 00:24:44,600 --> 00:24:47,200 Speaker 4: of this, a few authors of romance novels have had 492 00:24:47,200 --> 00:24:50,560 Speaker 4: to change the covers of their books. Oh wow, were 493 00:24:50,760 --> 00:24:52,880 Speaker 4: done because he's on a bunch of them. I think 494 00:24:52,920 --> 00:24:55,280 Speaker 4: one of them was something to do with stepbrothers and 495 00:24:55,520 --> 00:24:57,840 Speaker 4: you know, our stepbrother or some sort of thing. That's 496 00:24:57,960 --> 00:24:59,840 Speaker 4: basically how they were able to find this in the 497 00:25:00,000 --> 00:25:02,320 Speaker 4: and was using this facial recognition. And then the way 498 00:25:02,359 --> 00:25:04,800 Speaker 4: that they confirmed us this facial recognition isn't enough for them. 499 00:25:04,840 --> 00:25:06,800 Speaker 4: Before they sent this into the VFPI, they went to 500 00:25:06,840 --> 00:25:09,560 Speaker 4: his Instagram and then there they found an image of 501 00:25:09,600 --> 00:25:11,760 Speaker 4: him wearing a hat of the same happ that he 502 00:25:11,880 --> 00:25:14,280 Speaker 4: wore to the Capitol on January sixth. They found him 503 00:25:14,320 --> 00:25:16,800 Speaker 4: punching a punching bag, wearing that same sweatshirt that he 504 00:25:16,880 --> 00:25:18,120 Speaker 4: wore January. 505 00:25:18,480 --> 00:25:21,840 Speaker 1: These people are kind of dumb, right, I mean, these 506 00:25:21,840 --> 00:25:22,960 Speaker 1: people are kind of dumb. 507 00:25:23,160 --> 00:25:25,520 Speaker 4: Some of them are tougher than you would think. That 508 00:25:25,640 --> 00:25:27,520 Speaker 4: was still a lot of their investigative work. But like 509 00:25:27,560 --> 00:25:29,760 Speaker 4: some of the ones that are really the huge breakthroughs 510 00:25:29,760 --> 00:25:31,840 Speaker 4: are when you have somebody who was like completely covered 511 00:25:31,880 --> 00:25:33,800 Speaker 4: head to toe, did a really good job of covering 512 00:25:33,800 --> 00:25:35,720 Speaker 4: their face, and then they'll still find it. And one 513 00:25:35,760 --> 00:25:38,199 Speaker 4: of the crazy stories there was somebody who was just 514 00:25:38,240 --> 00:25:40,480 Speaker 4: completely covered and they couldn't get a good face shot 515 00:25:40,520 --> 00:25:42,359 Speaker 4: out of him. As I tured out, somebody else was 516 00:25:42,400 --> 00:25:45,120 Speaker 4: filming behind them at one point and they opened up 517 00:25:45,160 --> 00:25:47,720 Speaker 4: their phone and what was their backscreen but the name 518 00:25:47,760 --> 00:25:50,199 Speaker 4: of their LLC, their company that they found it and 519 00:25:50,200 --> 00:25:52,280 Speaker 4: there it was. That was it. So someone took all 520 00:25:52,320 --> 00:25:55,040 Speaker 4: of these precautions, but because their rock screen was the 521 00:25:55,160 --> 00:25:57,520 Speaker 4: name of their LLC, they were id. So it's you 522 00:25:57,640 --> 00:26:00,400 Speaker 4: never know what detail is really going to break this through. 523 00:26:00,520 --> 00:26:03,840 Speaker 4: And the FBI just really isn't structured to investigate things 524 00:26:03,840 --> 00:26:07,040 Speaker 4: in this really collaborative way. They're sort of very siloed 525 00:26:07,040 --> 00:26:10,800 Speaker 4: off into these individual investigations, and that's the real thing. 526 00:26:10,880 --> 00:26:12,840 Speaker 4: I think the secret that the Slows have been able 527 00:26:12,880 --> 00:26:16,960 Speaker 4: to bring to this is that collaboration and those technological skills. 528 00:26:17,040 --> 00:26:20,960 Speaker 1: So interesting, did you kind of get to know these people? 529 00:26:21,320 --> 00:26:24,960 Speaker 1: Can you sort of explain to our listeners a little 530 00:26:25,000 --> 00:26:28,120 Speaker 1: bit about what they were, what they're like in whatever 531 00:26:28,240 --> 00:26:29,400 Speaker 1: vague terms you want. 532 00:26:29,800 --> 00:26:32,120 Speaker 4: Yeah, so, I mean, therefore all over the country, sort 533 00:26:32,119 --> 00:26:33,960 Speaker 4: of all the different walks of life, the things that 534 00:26:34,040 --> 00:26:35,720 Speaker 4: al This is spoiling a little bit of the book, 535 00:26:35,720 --> 00:26:38,159 Speaker 4: but one of the things that really shocks people is 536 00:26:38,160 --> 00:26:41,440 Speaker 4: that one of the key figures here, who I name Alex, 537 00:26:41,680 --> 00:26:43,600 Speaker 4: actually voted for Donald Trump twice. 538 00:26:43,920 --> 00:26:47,840 Speaker 1: Wow. So strange, not what you'd expect. 539 00:26:47,600 --> 00:26:49,159 Speaker 4: Not at all. And it just sort of came up 540 00:26:49,200 --> 00:26:51,480 Speaker 4: in a conversation where they were all talking about, you know, 541 00:26:51,480 --> 00:26:54,200 Speaker 4: oh who sort of you know there was their primary 542 00:26:54,240 --> 00:26:56,679 Speaker 4: figure or who is there you know they voted for 543 00:26:56,720 --> 00:26:59,640 Speaker 4: in the Democratic primary, and he sort of needed this joke, 544 00:27:00,119 --> 00:27:01,359 Speaker 4: just thought it was a joke at first, and then 545 00:27:01,359 --> 00:27:03,840 Speaker 4: he's like, no, no, like I did. He's sort of 546 00:27:03,920 --> 00:27:05,760 Speaker 4: by thinking now he describes himself as more of a 547 00:27:05,760 --> 00:27:08,440 Speaker 4: Liz Chady Republican. But even when he's I think he's 548 00:27:08,480 --> 00:27:10,240 Speaker 4: more in the Democratic camp. 549 00:27:10,280 --> 00:27:10,480 Speaker 2: Now. 550 00:27:10,560 --> 00:27:12,520 Speaker 4: He was a successful guy, so I think that a 551 00:27:12,520 --> 00:27:15,360 Speaker 4: lot of this was just you know, economics for him. Right. 552 00:27:15,560 --> 00:27:17,280 Speaker 4: That is something that jocks a lot of people in 553 00:27:17,640 --> 00:27:19,040 Speaker 4: here that this isn't just you know. 554 00:27:19,160 --> 00:27:20,919 Speaker 1: He didn't like crime. Right. There are a lot of 555 00:27:20,960 --> 00:27:24,080 Speaker 1: Republicans who voted for Trump but didn't like crime. 556 00:27:24,359 --> 00:27:26,760 Speaker 4: Yeah. And I think one of the reasons that a 557 00:27:26,760 --> 00:27:28,560 Speaker 4: lot of this lose. In addition to sort of just 558 00:27:28,600 --> 00:27:31,760 Speaker 4: building up this trust over more than two years, two 559 00:27:31,800 --> 00:27:34,280 Speaker 4: and a half years, almost three years, now this lose. 560 00:27:34,480 --> 00:27:35,960 Speaker 4: One of the reasons I think they spoke with me 561 00:27:36,119 --> 00:27:38,480 Speaker 4: was they were really interested in shattering some of these 562 00:27:38,560 --> 00:27:41,520 Speaker 4: myths that people think of. Like the pushback you get is, oh, 563 00:27:41,600 --> 00:27:43,879 Speaker 4: look at all these losers in their mom's basement, you know, 564 00:27:44,240 --> 00:27:47,879 Speaker 4: going through this material, and like, you know, one of 565 00:27:47,920 --> 00:27:51,080 Speaker 4: them was sort of I think antigonized by somebody on 566 00:27:51,119 --> 00:27:53,240 Speaker 4: Twitter one time and responded back like, no, dude, I 567 00:27:53,280 --> 00:27:55,960 Speaker 4: make more money than you, and I work in cancer research. 568 00:27:56,320 --> 00:27:58,440 Speaker 4: They have a very wide array of people and like, 569 00:27:58,480 --> 00:28:01,879 Speaker 4: these people are really successful. I've really rich family lines. 570 00:28:01,880 --> 00:28:04,119 Speaker 4: But this is something also, it sort of became this 571 00:28:04,760 --> 00:28:07,119 Speaker 4: passion and this obby of theirs, which you know, hooked 572 00:28:07,160 --> 00:28:09,560 Speaker 4: up with sort of their interests. But it really is 573 00:28:09,800 --> 00:28:13,160 Speaker 4: intriguing and can feel like this puzzle and you really 574 00:28:13,240 --> 00:28:16,080 Speaker 4: do get these little adrenaline first when you know you 575 00:28:16,320 --> 00:28:18,439 Speaker 4: have these ideas and you figure out a piece of 576 00:28:18,440 --> 00:28:20,960 Speaker 4: this puzzle that is not known more broadly. 577 00:28:21,320 --> 00:28:25,239 Speaker 1: Yeah, I mean such an interesting and I mean just 578 00:28:25,440 --> 00:28:33,040 Speaker 1: such a crazy kind story. So explain to me what happened. 579 00:28:33,080 --> 00:28:37,080 Speaker 1: I mean, did people feel that they had been taken 580 00:28:37,160 --> 00:28:39,760 Speaker 1: advantage of by Trump? I mean, all these people end 581 00:28:39,840 --> 00:28:43,720 Speaker 1: up in jail or you know, facing thousands and thousands 582 00:28:43,760 --> 00:28:46,239 Speaker 1: of dollars of legal A lot of them do have 583 00:28:46,320 --> 00:28:48,440 Speaker 1: sort of come to Jesus moments, right. 584 00:28:48,600 --> 00:28:50,840 Speaker 4: Some of them, And it's sometimes tough to figure out 585 00:28:50,880 --> 00:28:53,320 Speaker 4: whether it's really come to Jesus moment or whether it's 586 00:28:53,360 --> 00:28:55,520 Speaker 4: sort of an act for the judge. I think there 587 00:28:55,520 --> 00:28:58,840 Speaker 4: are a lot of people who were legitimately remorseful and 588 00:28:58,880 --> 00:29:01,080 Speaker 4: sort of realized they got up. But what's sort of 589 00:29:01,120 --> 00:29:04,000 Speaker 4: fascinating about this, and it's not something that judges can 590 00:29:04,480 --> 00:29:07,920 Speaker 4: or should take into consideration, is that there's such a 591 00:29:08,000 --> 00:29:13,239 Speaker 4: deep link between still believing the conspiracies and being apologetic, right, 592 00:29:13,320 --> 00:29:16,240 Speaker 4: like if you still believe that the twenty twenty election 593 00:29:16,440 --> 00:29:20,480 Speaker 4: was stolen and that Joe Biden is unlawful president usurper 594 00:29:20,520 --> 00:29:22,920 Speaker 4: in the White House, and you didn't do anything too serious, 595 00:29:23,000 --> 00:29:26,200 Speaker 4: It's like, what would you be apologetic for? So that's 596 00:29:26,280 --> 00:29:30,320 Speaker 4: kind of the interesting dynamic for me when it's so intertwined. Right, 597 00:29:30,320 --> 00:29:32,719 Speaker 4: there shouldn't be a situation where judges are like forcing 598 00:29:32,760 --> 00:29:35,719 Speaker 4: people to admit, hey, no, the election was legitimate. Right, 599 00:29:35,760 --> 00:29:38,680 Speaker 4: that's not America. Right. People are free to believe whatever 600 00:29:38,800 --> 00:29:41,520 Speaker 4: crazy conspiracy they want to believe. But it just is 601 00:29:41,560 --> 00:29:45,120 Speaker 4: so remarkable because it is so intertwined in your mind, 602 00:29:45,120 --> 00:29:46,760 Speaker 4: and I try to put myself in their shoes, and 603 00:29:46,800 --> 00:29:48,800 Speaker 4: it's like, you know, if I deeply believe that the 604 00:29:48,840 --> 00:29:51,480 Speaker 4: election was stolen and I'm getting in trouble because I, 605 00:29:51,680 --> 00:29:53,800 Speaker 4: you know, walked in a building, you can see why 606 00:29:53,840 --> 00:29:56,080 Speaker 4: some of these people would be really angry. But on 607 00:29:56,120 --> 00:29:57,920 Speaker 4: the other side of the coin, there are people who 608 00:29:57,960 --> 00:29:59,840 Speaker 4: just realized they were completely. 609 00:29:59,520 --> 00:30:00,520 Speaker 2: Tricked and cool. 610 00:30:00,600 --> 00:30:03,200 Speaker 4: Then you know, personally, as an American, those are the 611 00:30:03,200 --> 00:30:05,600 Speaker 4: most rewarding moments when I'm sitting in court and like, 612 00:30:05,680 --> 00:30:09,000 Speaker 4: you have someone you like just weigh themselves out there 613 00:30:09,000 --> 00:30:12,040 Speaker 4: and say I can't believe I fell for this crap, right, 614 00:30:12,160 --> 00:30:15,400 Speaker 4: and like right right, I'm machined and I'm embarrassed, and like, 615 00:30:15,560 --> 00:30:17,320 Speaker 4: you know, I don't know if if I were on 616 00:30:17,360 --> 00:30:18,920 Speaker 4: the bench, I think that would have a lot of 617 00:30:19,040 --> 00:30:22,200 Speaker 4: sway over me for someone to actually sort of state 618 00:30:22,240 --> 00:30:25,080 Speaker 4: that the truth and realize how dumb all what's in 619 00:30:25,120 --> 00:30:25,760 Speaker 4: the first place? 620 00:30:25,920 --> 00:30:31,160 Speaker 1: God, it's such an interesting and strange kind of problem. So, 621 00:30:31,560 --> 00:30:35,440 Speaker 1: I mean, what happens to these people who are the sluice? 622 00:30:35,480 --> 00:30:38,320 Speaker 1: Do they keep going? There are a lot more people, right, 623 00:30:38,360 --> 00:30:40,240 Speaker 1: I mean, not everyone's been charged. 624 00:30:40,320 --> 00:30:42,800 Speaker 4: So yeah, so, like I said, the scope is like 625 00:30:42,880 --> 00:30:46,680 Speaker 4: above three thousand. Right now, the FBI knows about the 626 00:30:46,720 --> 00:30:49,720 Speaker 4: identities of about a thousand people who haven't been arrested. 627 00:30:49,760 --> 00:30:52,960 Speaker 4: So that's almost double the number of people who in 628 00:30:53,200 --> 00:30:56,200 Speaker 4: nearly three years have been charged. And so really, you know, 629 00:30:56,200 --> 00:30:58,960 Speaker 4: you're kind of getting to this situation. One law enforcement 630 00:30:59,000 --> 00:31:01,680 Speaker 4: official sort of compared it to the Titanic, right where 631 00:31:01,720 --> 00:31:03,760 Speaker 4: it's like, okay, you know, not everyone's getting off of 632 00:31:03,800 --> 00:31:06,320 Speaker 4: this boat, So what cases it's already kind of do 633 00:31:06,520 --> 00:31:10,240 Speaker 4: before that statute of limitations expires, because really, now you know, 634 00:31:10,360 --> 00:31:13,320 Speaker 4: it's really just a little over two years. There's a 635 00:31:13,360 --> 00:31:16,320 Speaker 4: five year statute of limitations, and and so you know, 636 00:31:16,360 --> 00:31:18,720 Speaker 4: you would think that the priority would be more towards 637 00:31:18,800 --> 00:31:21,080 Speaker 4: some of these more violinist enders, and we've seen a 638 00:31:21,080 --> 00:31:23,880 Speaker 4: little bit of that shift in in recent months. But 639 00:31:24,080 --> 00:31:27,080 Speaker 4: I think there hasn't been this sort of collective from 640 00:31:27,080 --> 00:31:28,600 Speaker 4: what I've heard and the sources I talked to, there 641 00:31:28,600 --> 00:31:32,720 Speaker 4: hasn't been this collective consideration within DOJ of just like 642 00:31:32,880 --> 00:31:36,560 Speaker 4: what how the clock is ticking and how many cases 643 00:31:36,560 --> 00:31:38,880 Speaker 4: there are yet to go? And what I've heard from 644 00:31:39,000 --> 00:31:41,160 Speaker 4: you know, the book is for people who read the book, 645 00:31:41,200 --> 00:31:42,800 Speaker 4: is that that's sort of you know, been sort of 646 00:31:42,840 --> 00:31:46,120 Speaker 4: clarifying for them and has pushed forward a little bit 647 00:31:46,120 --> 00:31:49,000 Speaker 4: more mentum within DRJ to make sure that they're sort 648 00:31:49,040 --> 00:31:52,280 Speaker 4: of getting getting things going, because in order to get 649 00:31:52,320 --> 00:31:55,720 Speaker 4: to anywhere near those thousand cases that they have identified 650 00:31:55,760 --> 00:31:58,080 Speaker 4: right now, they really need to start pushing, you know, 651 00:31:58,120 --> 00:31:58,960 Speaker 4: the pedal to the metal. 652 00:31:59,440 --> 00:32:02,920 Speaker 1: So, I mean, it's just such an incredible story. So 653 00:32:03,000 --> 00:32:05,360 Speaker 1: do people continue to do this? I mean, is there 654 00:32:05,440 --> 00:32:07,040 Speaker 1: sort of or did they kind of go back to 655 00:32:07,080 --> 00:32:09,800 Speaker 1: their normal lives or is there I mean. 656 00:32:10,000 --> 00:32:12,200 Speaker 4: You know, this is less time intensive than it was 657 00:32:12,240 --> 00:32:14,720 Speaker 4: before because there are so many ideas sitting out there, 658 00:32:14,800 --> 00:32:16,560 Speaker 4: but now you know some of the slus are getting 659 00:32:16,600 --> 00:32:19,680 Speaker 4: requests from the Bureau. One of the most shocking moments 660 00:32:19,720 --> 00:32:22,160 Speaker 4: to me was actually not that long ago, was in 661 00:32:22,240 --> 00:32:25,960 Speaker 4: the Proud Boys trial earlier this year, and the FBI 662 00:32:26,040 --> 00:32:29,560 Speaker 4: had worked on this investigation involving five Proud Boys, so 663 00:32:29,560 --> 00:32:32,240 Speaker 4: it's just conspiracy trial, one of their biggest cases for 664 00:32:32,320 --> 00:32:34,880 Speaker 4: two years. Right, they had had this, they had really 665 00:32:34,880 --> 00:32:37,560 Speaker 4: put all the resources into this, but they had missed 666 00:32:37,560 --> 00:32:40,840 Speaker 4: a really major moment in which Zachary Reel, one of 667 00:32:40,840 --> 00:32:44,000 Speaker 4: the defendants, a Proud Boy from Philly, pepper sprayed a cup. 668 00:32:44,160 --> 00:32:46,560 Speaker 4: And the only reason that this came up was because 669 00:32:46,640 --> 00:32:49,840 Speaker 4: the government had released other footage and connection with another 670 00:32:49,920 --> 00:32:52,480 Speaker 4: case that was then published online that the sleus are 671 00:32:52,600 --> 00:32:56,240 Speaker 4: sucked up and over this long weekend, when Zachary Reel 672 00:32:56,320 --> 00:32:58,520 Speaker 4: was on the stand, after denying over and over again 673 00:32:58,520 --> 00:33:00,920 Speaker 4: that he had assaulted any officers that day, and in fact, 674 00:33:01,000 --> 00:33:04,680 Speaker 4: the government never accused him specifically of assaulting any officers, 675 00:33:04,920 --> 00:33:07,600 Speaker 4: the sleuth used a long weekend and found this footage 676 00:33:07,640 --> 00:33:10,560 Speaker 4: and found an image of him video of him pointing 677 00:33:10,760 --> 00:33:13,120 Speaker 4: pepper spray at a cop and found another angle on 678 00:33:13,160 --> 00:33:14,560 Speaker 4: it as well, And it was just a moment that 679 00:33:14,640 --> 00:33:17,520 Speaker 4: Gathbi completely missed. And that was really just one of 680 00:33:17,560 --> 00:33:19,959 Speaker 4: these moments because I saw how this operated and then 681 00:33:20,000 --> 00:33:21,520 Speaker 4: I saw them sort of. You know, it was just 682 00:33:21,560 --> 00:33:23,840 Speaker 4: really dramatic court room moment because the guy then is 683 00:33:23,880 --> 00:33:26,760 Speaker 4: on the stand after lying about what he gad and 684 00:33:26,760 --> 00:33:29,560 Speaker 4: then has his pastor resort two he said he did 685 00:33:29,640 --> 00:33:34,760 Speaker 4: not recall assaulting any long work. Yeah, so it was 686 00:33:34,800 --> 00:33:37,440 Speaker 4: like this really crazy moment. So this louser are definitely 687 00:33:37,440 --> 00:33:40,240 Speaker 4: still doing a lot of these cases today, but now 688 00:33:40,400 --> 00:33:42,920 Speaker 4: a lot of this is because the government's going to 689 00:33:42,960 --> 00:33:44,800 Speaker 4: them and being like, hey, you know, what do you 690 00:33:44,920 --> 00:33:48,600 Speaker 4: have on It's defended sometimes before they charge cases. Sometimes 691 00:33:48,600 --> 00:33:50,760 Speaker 4: it's when a case is about to be brought to trial. 692 00:33:50,920 --> 00:33:54,280 Speaker 4: Sometimes it's before sentencing. So you know, they really are 693 00:33:54,320 --> 00:33:58,360 Speaker 4: relying upon this outside technology and this outside group to 694 00:33:58,400 --> 00:34:00,680 Speaker 4: really bring these cases to fruition, just because I think 695 00:34:00,680 --> 00:34:05,760 Speaker 4: of how outdated the technology is within the SBI and 696 00:34:05,880 --> 00:34:07,000 Speaker 4: within the federal government. 697 00:34:07,240 --> 00:34:13,040 Speaker 1: The idea that really the federal government is so behind 698 00:34:13,200 --> 00:34:17,640 Speaker 1: with technology. It's just because these things are not getting 699 00:34:17,680 --> 00:34:20,080 Speaker 1: funded or is there something I'm missing here. 700 00:34:20,280 --> 00:34:21,880 Speaker 4: What's surprising about this is, you know, there are so 701 00:34:21,880 --> 00:34:26,120 Speaker 4: many conspiracy theories now on the right January sixth, and like, 702 00:34:26,280 --> 00:34:30,480 Speaker 4: really the explanation is like really in line with what 703 00:34:30,640 --> 00:34:34,399 Speaker 4: used to be conservative ideology that government is ineffective, big 704 00:34:34,440 --> 00:34:37,480 Speaker 4: government is bad, and the private sector is better. And 705 00:34:37,680 --> 00:34:40,440 Speaker 4: I really think it is instant. This sort of illustrates 706 00:34:40,560 --> 00:34:43,080 Speaker 4: that because you know, innovation is just not going to 707 00:34:43,120 --> 00:34:45,600 Speaker 4: be a huge thing within government, and you know, the 708 00:34:45,680 --> 00:34:49,640 Speaker 4: joke within the FBI is we have yesterday's technology tomorrow 709 00:34:52,719 --> 00:34:55,319 Speaker 4: they're just really behind. And you know that's the sort 710 00:34:55,360 --> 00:34:57,919 Speaker 4: of maddening thing is seeing all these conspiracy theories about 711 00:34:57,920 --> 00:35:01,040 Speaker 4: the FBI pulling all these strings in orgerzing all these pieces, 712 00:35:01,120 --> 00:35:03,440 Speaker 4: and then you know, I'm sorting through thousands of pages 713 00:35:03,480 --> 00:35:06,719 Speaker 4: of FBI email and it's like set through my Samsung 714 00:35:06,800 --> 00:35:09,799 Speaker 4: device and like how do you send a contact card? 715 00:35:09,880 --> 00:35:14,440 Speaker 4: And just the most bureaucratic things that you could possibly imagine, 716 00:35:14,520 --> 00:35:16,719 Speaker 4: and it's just like no, like even if you set 717 00:35:16,719 --> 00:35:18,640 Speaker 4: aside the issue of whether or not the FBI is 718 00:35:18,680 --> 00:35:22,680 Speaker 4: just that like evil, Like it's the competency and is 719 00:35:22,680 --> 00:35:24,400 Speaker 4: a real issue there in terms of just you know, 720 00:35:24,440 --> 00:35:27,480 Speaker 4: then being able to push forward and complete a lot 721 00:35:27,480 --> 00:35:29,959 Speaker 4: of these sort of schemes that a lot of people 722 00:35:30,000 --> 00:35:32,279 Speaker 4: seem to believe that they're behind. So I mean, I 723 00:35:32,320 --> 00:35:34,120 Speaker 4: would just encourage people to like look at some of 724 00:35:34,160 --> 00:35:37,120 Speaker 4: these emails. I realized that, like, this is about bureaucracy. 725 00:35:37,160 --> 00:35:39,400 Speaker 4: And I think that that's one thing that the January 726 00:35:39,400 --> 00:35:41,840 Speaker 4: sixth Committee sort of did be sitting on the floor. 727 00:35:41,840 --> 00:35:43,960 Speaker 4: And one thing that I ended up focusing a lot 728 00:35:44,000 --> 00:35:45,759 Speaker 4: on in the book is just sort of lead up 729 00:35:45,760 --> 00:35:49,040 Speaker 4: to January six and a lot of the technological failures. 730 00:35:49,320 --> 00:35:51,960 Speaker 4: Just one of the most like head slapping moments was 731 00:35:52,000 --> 00:35:55,520 Speaker 4: that the FBI had their entire system set up on 732 00:35:55,719 --> 00:35:59,280 Speaker 4: this one social media monitoring tool and they were using 733 00:35:59,320 --> 00:36:03,680 Speaker 4: that to basically look for any really threatening communications and 734 00:36:03,719 --> 00:36:06,480 Speaker 4: the lead up to January sixth, and as it turned out, 735 00:36:06,560 --> 00:36:11,080 Speaker 4: the FBI Procurement of Office had renegotiated a contract a 736 00:36:11,200 --> 00:36:14,480 Speaker 4: year earlier, and oh lo and behold, it was expiring 737 00:36:14,800 --> 00:36:18,960 Speaker 4: on New Year's Eve as twenty twenty transition to twenty 738 00:36:19,040 --> 00:36:22,160 Speaker 4: twenty one, and so you had this whole new system 739 00:36:22,200 --> 00:36:24,239 Speaker 4: and they didn't even have logins for it yet on 740 00:36:24,280 --> 00:36:26,919 Speaker 4: December thirty first, and they were trying to go from 741 00:36:27,000 --> 00:36:28,759 Speaker 4: you know, one system to the other, and it just 742 00:36:29,080 --> 00:36:32,440 Speaker 4: it was just a catastrophe. Right. They were not prepared 743 00:36:32,520 --> 00:36:35,000 Speaker 4: and all the systems were set up on one platform 744 00:36:35,080 --> 00:36:37,239 Speaker 4: and they could not possibly have set that up in time. 745 00:36:37,280 --> 00:36:39,680 Speaker 4: And you see these sort of panicked emails being sent 746 00:36:40,040 --> 00:36:42,680 Speaker 4: on December thirty first, in the middle of like you know, 747 00:36:42,840 --> 00:36:46,399 Speaker 4: basically two weeks of vacation for the FIDS, talking about, 748 00:36:46,440 --> 00:36:48,279 Speaker 4: oh my my gosh, you know, we need to get something. 749 00:36:48,280 --> 00:36:49,520 Speaker 4: Why don't we have these logins yet? 750 00:36:49,600 --> 00:36:50,640 Speaker 2: What's going on here? 751 00:36:50,719 --> 00:36:53,480 Speaker 4: So those sort of bureaucratic hurdles and just sort of 752 00:36:53,600 --> 00:36:56,040 Speaker 4: being buried in bureaucracy was a really big thing here. 753 00:36:56,280 --> 00:37:00,640 Speaker 1: Thank you, Ryan, you are amazing. Appreciate youallmech fand me. 754 00:37:01,680 --> 00:37:02,440 Speaker 4: That's it for. 755 00:37:02,480 --> 00:37:06,240 Speaker 1: This episode of Fast Politics. Tune in every Monday, Wednesday, 756 00:37:06,280 --> 00:37:09,239 Speaker 1: and Friday to hear the best minds in politics makes 757 00:37:09,239 --> 00:37:12,560 Speaker 1: sense of all this chaos. If you enjoyed what you've heard, 758 00:37:12,920 --> 00:37:15,840 Speaker 1: please send it to a friend and keep the conversation going. 759 00:37:16,239 --> 00:37:17,920 Speaker 1: And again, thanks for listening.