1 00:00:03,480 --> 00:00:07,160 Speaker 1: On this episode of news World. My guest today is 2 00:00:07,240 --> 00:00:11,479 Speaker 1: Zach Organs. He was a senior software engineer YouTube and 3 00:00:11,640 --> 00:00:15,320 Speaker 1: Google for eight and a half years. While working at YouTube, 4 00:00:15,560 --> 00:00:19,520 Speaker 1: he learned that Google was censoring fake news and investigated 5 00:00:19,560 --> 00:00:22,480 Speaker 1: further into the company, only to find that not only 6 00:00:22,520 --> 00:00:26,599 Speaker 1: had Google defined fake news to mean actual events that 7 00:00:26,680 --> 00:00:30,960 Speaker 1: had happened, but also created an artificial intelligence system to 8 00:00:30,960 --> 00:00:36,040 Speaker 1: classify all available data to Google Search. The reason Google 9 00:00:36,080 --> 00:00:39,240 Speaker 1: wanted to classify data was that this could be used 10 00:00:39,240 --> 00:00:43,600 Speaker 1: by their artificial intelligence system to re rank the entire 11 00:00:43,760 --> 00:00:49,640 Speaker 1: Internet according to Google's corporate cultural goods. In June twenty nineteen, 12 00:00:50,080 --> 00:00:54,440 Speaker 1: he resigned from Google. He took with him nine hundred 13 00:00:54,480 --> 00:00:58,880 Speaker 1: and fifty plus pages of Google internal documents and delivered 14 00:00:58,920 --> 00:01:02,920 Speaker 1: them to the Part of Justice and through Project Bartas 15 00:01:02,960 --> 00:01:08,240 Speaker 1: to inform the public about Google's extensive censorship system. Here 16 00:01:08,280 --> 00:01:13,360 Speaker 1: to tell this incredible firsthand account of censorship and bias 17 00:01:13,480 --> 00:01:16,920 Speaker 1: and big tech is Zach Gordy's, author of the new 18 00:01:16,959 --> 00:01:22,840 Speaker 1: book Google Leaks, a Whistleblower's expos a of big Tech censorship. 19 00:01:30,880 --> 00:01:34,039 Speaker 1: Za thank you for joining me, you know, as we 20 00:01:34,080 --> 00:01:37,800 Speaker 1: spent the last five years talking about big tech censorship. 21 00:01:38,319 --> 00:01:42,840 Speaker 1: Your book, Google Leaks couldn't be out at a better time. Frankly, 22 00:01:43,160 --> 00:01:46,760 Speaker 1: so many of us don't understand how these Silicon Valley 23 00:01:46,800 --> 00:01:49,920 Speaker 1: companies work to create a censored environment on the Internet, 24 00:01:50,160 --> 00:01:52,760 Speaker 1: and I'm hoping you can help us understand that today. 25 00:01:52,920 --> 00:01:55,080 Speaker 1: So let me ask you though, before we get into 26 00:01:55,640 --> 00:01:58,200 Speaker 1: your book, which are very excited about it, tell us 27 00:01:58,240 --> 00:02:01,560 Speaker 1: a little bit about your background and what brought you 28 00:02:01,720 --> 00:02:06,240 Speaker 1: to become employed by Google in twenty ten. First off, 29 00:02:06,280 --> 00:02:08,520 Speaker 1: thank you for having me on your program. It's an 30 00:02:08,520 --> 00:02:11,359 Speaker 1: absolute honor to be here. So how did I get 31 00:02:11,360 --> 00:02:14,480 Speaker 1: my start at Google. It started off with my education. 32 00:02:14,680 --> 00:02:16,959 Speaker 1: I've spent a lot of years in the university, getting 33 00:02:16,960 --> 00:02:21,680 Speaker 1: three majors in the sciences, so psychology, mathematics, and computer science. 34 00:02:22,240 --> 00:02:25,080 Speaker 1: I tripled majored out of the University of Oregon. I 35 00:02:25,160 --> 00:02:28,720 Speaker 1: went into game development, which brought me to San Francisco 36 00:02:28,880 --> 00:02:32,320 Speaker 1: to work for the Lucas Arts Studio doing the Indiana 37 00:02:32,400 --> 00:02:35,720 Speaker 1: Jones game. From there, I went to Google, which was 38 00:02:36,040 --> 00:02:38,919 Speaker 1: my dream job, and I was working for the Google 39 00:02:39,000 --> 00:02:43,920 Speaker 1: Earth team, and that Google Earth took their product and 40 00:02:43,960 --> 00:02:47,600 Speaker 1: put it onto the Audi navigation system, which I argue 41 00:02:47,680 --> 00:02:50,200 Speaker 1: is the best navigation system in the world at the time, 42 00:02:50,520 --> 00:02:55,400 Speaker 1: and then from there I actually left the company in 43 00:02:55,440 --> 00:02:59,440 Speaker 1: August of twenty thirteen before coming back. I left because 44 00:02:59,440 --> 00:03:03,600 Speaker 1: I wanted to create a product for cycling, which was 45 00:03:03,760 --> 00:03:05,679 Speaker 1: a bunch of LEDs on the back of the hand 46 00:03:05,760 --> 00:03:09,600 Speaker 1: and when you take the glove and click the fingers together, 47 00:03:09,639 --> 00:03:12,280 Speaker 1: then the lights come on. And that still exists at 48 00:03:12,360 --> 00:03:15,080 Speaker 1: zakis dot com you can check it out. But that 49 00:03:15,200 --> 00:03:18,040 Speaker 1: set me to China to manufacture this product after I 50 00:03:18,080 --> 00:03:22,760 Speaker 1: had gotten funding and done a successful kickstarter, And what 51 00:03:22,800 --> 00:03:25,800 Speaker 1: I noticed really quickly was one I couldn't manufacture the 52 00:03:25,840 --> 00:03:29,120 Speaker 1: product in the United States, and two China had rigged 53 00:03:29,120 --> 00:03:32,720 Speaker 1: its entire economic system to export to the United States. 54 00:03:32,720 --> 00:03:34,600 Speaker 1: And so I started to get this idea that something 55 00:03:34,680 --> 00:03:38,120 Speaker 1: was wrong, and when I would come back to Google 56 00:03:38,160 --> 00:03:42,080 Speaker 1: in twenty sixteen, I carried with me this knowledge that 57 00:03:42,200 --> 00:03:47,240 Speaker 1: the entire economic system had somehow been gamed in China's favor. 58 00:03:47,640 --> 00:03:52,400 Speaker 1: And so when Trump was elected, it really wasn't a 59 00:03:52,400 --> 00:03:54,400 Speaker 1: surprise for me, because I knew that there was this 60 00:03:54,560 --> 00:03:57,480 Speaker 1: existential threat of what China was doing to the United States, 61 00:03:57,560 --> 00:04:00,760 Speaker 1: and I thought that, hey, you know, here's Trump, Here's 62 00:04:00,760 --> 00:04:04,000 Speaker 1: a man that's gonna address this problem and has the 63 00:04:04,040 --> 00:04:08,680 Speaker 1: courage to speak up about it. But it turns out 64 00:04:08,720 --> 00:04:11,880 Speaker 1: that the rest of the company didn't share my perspective 65 00:04:12,240 --> 00:04:17,160 Speaker 1: on mister Donald Trump. And when he got into the presidency, 66 00:04:17,279 --> 00:04:20,200 Speaker 1: I thought to myself, well, this is pretty funny. You know, 67 00:04:20,360 --> 00:04:24,200 Speaker 1: there's the first time we've won a cultural movement in 68 00:04:24,279 --> 00:04:28,080 Speaker 1: like the last thirty years, and the left has been defeated. 69 00:04:28,720 --> 00:04:33,240 Speaker 1: But as it turned out, Google would start to form 70 00:04:33,240 --> 00:04:38,160 Speaker 1: a resistance against Trump, and it happened within one week 71 00:04:38,360 --> 00:04:41,640 Speaker 1: of Trump winning the election. You may remember that Google 72 00:04:41,720 --> 00:04:44,680 Speaker 1: had a meeting and all hands meeting with the sea 73 00:04:44,800 --> 00:04:48,880 Speaker 1: level executives came together and talked about how they didn't 74 00:04:48,960 --> 00:04:52,400 Speaker 1: like populism, how they didn't like Trump. And one of 75 00:04:52,440 --> 00:04:55,120 Speaker 1: the things that went unnoticed by most of the other 76 00:04:55,200 --> 00:04:59,560 Speaker 1: press was a question asked by one of the employees 77 00:04:59,600 --> 00:05:02,720 Speaker 1: saying what was the most effective thing that Google had 78 00:05:02,760 --> 00:05:07,720 Speaker 1: done during the twenty sixteen election, And the CEO Senter 79 00:05:07,720 --> 00:05:12,080 Speaker 1: of Achai responded to him and said that it was 80 00:05:12,120 --> 00:05:17,440 Speaker 1: their use of machine learning in order to censor fake news. 81 00:05:18,400 --> 00:05:22,599 Speaker 1: And I remember sitting there thinking to myself, Manute, we're 82 00:05:22,600 --> 00:05:27,599 Speaker 1: censoring fake news. Who decides what fake news is? And 83 00:05:27,720 --> 00:05:30,760 Speaker 1: so I started to dig in to the company to 84 00:05:30,800 --> 00:05:33,840 Speaker 1: figure out, you know, well, because obviously if there's fake 85 00:05:33,920 --> 00:05:36,000 Speaker 1: news and a definition of it, then there's a fake 86 00:05:36,080 --> 00:05:39,080 Speaker 1: news team, a committee that decided on that. And so 87 00:05:39,480 --> 00:05:42,840 Speaker 1: because the entire company is transparent to all full time employees, 88 00:05:42,880 --> 00:05:44,320 Speaker 1: I can just go and see what the fake news 89 00:05:44,360 --> 00:05:47,880 Speaker 1: team is doing. And lo and behold, there was a 90 00:05:47,920 --> 00:05:53,080 Speaker 1: whole bunch of documents that they had defining what fake 91 00:05:53,160 --> 00:05:57,479 Speaker 1: news was. And four out of the five examples that 92 00:05:57,520 --> 00:06:02,240 Speaker 1: they used all had to do with Hillary Clinton and 93 00:06:02,560 --> 00:06:06,279 Speaker 1: her involvement with arming ISIS. And they were saying that, oh, 94 00:06:06,320 --> 00:06:08,440 Speaker 1: this is fake news, and this is fake news. And 95 00:06:09,040 --> 00:06:11,880 Speaker 1: even if the sources of that news were actually fake, 96 00:06:12,400 --> 00:06:14,800 Speaker 1: the thing is is that they were so close to 97 00:06:14,920 --> 00:06:18,800 Speaker 1: events that I remembered happening that I decided, well, I'm 98 00:06:18,839 --> 00:06:21,719 Speaker 1: just going to become an expert on what happened with 99 00:06:21,800 --> 00:06:24,840 Speaker 1: Clinton and the whole Benghazi thing. And lo and behold, 100 00:06:24,880 --> 00:06:28,600 Speaker 1: it looks like Clinton was running a weapons trafficking operation 101 00:06:28,839 --> 00:06:33,320 Speaker 1: into Syria the arm ISIS. And so I said, well, okay, 102 00:06:33,320 --> 00:06:36,720 Speaker 1: well there's some legs on the story. And it's really interesting. 103 00:06:36,839 --> 00:06:39,680 Speaker 1: Is Google really trying to censor the fake news or 104 00:06:39,720 --> 00:06:42,359 Speaker 1: are they trying to put their thumb on the scale 105 00:06:42,360 --> 00:06:45,480 Speaker 1: of the election? And I started to get this idea 106 00:06:45,560 --> 00:06:48,080 Speaker 1: that it was the latter, that they were trying to 107 00:06:48,160 --> 00:06:53,640 Speaker 1: suppress some of the bad news about their preferred candidate. 108 00:06:55,040 --> 00:06:57,320 Speaker 1: And so that's really kind of like what got me 109 00:06:57,400 --> 00:07:00,800 Speaker 1: started was just this curiosity, sort of the cat trying 110 00:07:00,800 --> 00:07:03,080 Speaker 1: to figure out what exactly this company was doing with 111 00:07:03,120 --> 00:07:05,640 Speaker 1: this whole fake news mess. So that's how I got 112 00:07:05,680 --> 00:07:10,120 Speaker 1: started on this journey. Well, what is there about the 113 00:07:10,160 --> 00:07:16,080 Speaker 1: culture of Silicon Valley that leads three of its biggest 114 00:07:16,120 --> 00:07:19,920 Speaker 1: companies to believe that they have the right to define 115 00:07:20,000 --> 00:07:23,480 Speaker 1: for the rest of us what we're allowed to see. Man, 116 00:07:23,520 --> 00:07:28,600 Speaker 1: it's really an extraordinary lack of self awareness, it seems 117 00:07:28,600 --> 00:07:31,440 Speaker 1: to be. When you have these guys decide that they 118 00:07:31,440 --> 00:07:34,480 Speaker 1: will be the censors for three d and twenty five 119 00:07:34,520 --> 00:07:39,360 Speaker 1: million people? What drives that? You know? I searched for 120 00:07:39,480 --> 00:07:45,080 Speaker 1: some sort of cultural perspective that allows them to do 121 00:07:45,160 --> 00:07:49,080 Speaker 1: this sort of thing, and it's hard for me to 122 00:07:49,120 --> 00:07:52,920 Speaker 1: even find any rational explanation on why they went to 123 00:07:53,040 --> 00:07:58,600 Speaker 1: such extreme opposite end of the view of privacy. Google 124 00:07:59,440 --> 00:08:02,200 Speaker 1: said that they're going to do organic search results and 125 00:08:02,320 --> 00:08:04,600 Speaker 1: then they just switched the whole thing around so that 126 00:08:04,640 --> 00:08:08,640 Speaker 1: it was highly refined, highly processed, fake search results that 127 00:08:08,840 --> 00:08:13,120 Speaker 1: pushed a political point of view. And when I look 128 00:08:13,160 --> 00:08:16,040 Speaker 1: back at this, it it's like, well, what was Google's 129 00:08:16,080 --> 00:08:18,520 Speaker 1: signs that they were kind of a libertarian organization? Well, 130 00:08:18,560 --> 00:08:22,360 Speaker 1: it was their IPO. And their IPO they promised the 131 00:08:22,440 --> 00:08:26,560 Speaker 1: world that they would, first off, don't be evil, and 132 00:08:26,600 --> 00:08:30,680 Speaker 1: then second was organized the world's information and make it 133 00:08:30,800 --> 00:08:35,319 Speaker 1: universally accessible and useful. So they promised their stockholders that 134 00:08:35,320 --> 00:08:38,360 Speaker 1: that's what they were going to do. They promised Congress 135 00:08:38,760 --> 00:08:41,480 Speaker 1: that they were going to be politically neutral. You know, 136 00:08:41,720 --> 00:08:47,480 Speaker 1: they testified under Oh, they were politically neutral. And then 137 00:08:48,360 --> 00:08:54,120 Speaker 1: at the same time they were designing this wide scale 138 00:08:55,080 --> 00:09:02,280 Speaker 1: total information censorship engine called machine learning Fairness. And the 139 00:09:02,360 --> 00:09:07,319 Speaker 1: only sort of explanation is are they an agent because 140 00:09:07,600 --> 00:09:12,800 Speaker 1: they're acting like an intelligence organization controlled by a foreign 141 00:09:12,840 --> 00:09:18,080 Speaker 1: adversary than a natural, like organic organization that you know, 142 00:09:18,200 --> 00:09:21,800 Speaker 1: got big because of their stock value, and you know 143 00:09:23,000 --> 00:09:26,360 Speaker 1: exactly what their motivations are or how they came to 144 00:09:26,400 --> 00:09:28,440 Speaker 1: do what they do. I'm not sure. I just know 145 00:09:28,520 --> 00:09:31,840 Speaker 1: that who's ever controlling them also seems to be controlling 146 00:09:31,880 --> 00:09:35,680 Speaker 1: the mainstream media because they all decide on the exact 147 00:09:35,760 --> 00:09:42,280 Speaker 1: same thing together, like a church that is intolerant towards heterodoxy. 148 00:09:42,320 --> 00:09:45,360 Speaker 1: And that's my conclusion right now, is that there's some 149 00:09:46,000 --> 00:09:50,079 Speaker 1: form of church and they've got a specific ideology and 150 00:09:50,160 --> 00:09:52,040 Speaker 1: if you agree with it, you're on the inside, and 151 00:09:52,080 --> 00:09:55,880 Speaker 1: if you don't, you're canceled. Well, well, these guys sit 152 00:09:55,960 --> 00:10:00,840 Speaker 1: around the lunch or whatever. There's a your sense that 153 00:10:00,960 --> 00:10:05,080 Speaker 1: they are more likely to assume that the United States 154 00:10:05,240 --> 00:10:09,079 Speaker 1: is bad and other countries are by definition better. I 155 00:10:09,200 --> 00:10:13,439 Speaker 1: sometimes get this feeling that there's an inherent bias against 156 00:10:14,280 --> 00:10:19,560 Speaker 1: American values, in American patriotism, and that they don't want 157 00:10:19,559 --> 00:10:25,280 Speaker 1: to carry or cover those kind of thematics. Before twenty sixteen, 158 00:10:25,840 --> 00:10:30,040 Speaker 1: I would say that Google was very pro American. It 159 00:10:30,160 --> 00:10:33,520 Speaker 1: was literally the election of Donald Trump that they became 160 00:10:33,600 --> 00:10:37,959 Speaker 1: anti American, and it happened within less than a year. 161 00:10:38,240 --> 00:10:42,640 Speaker 1: When you began to prepare to leave, you took nine 162 00:10:42,760 --> 00:10:46,640 Speaker 1: hundred and fifty pages of internal documents with you. Why 163 00:10:46,720 --> 00:10:49,520 Speaker 1: did you decide to take the documents and how did 164 00:10:49,520 --> 00:10:53,640 Speaker 1: you choose the documents you took? So that's a great question. 165 00:10:54,880 --> 00:10:59,360 Speaker 1: As I started to dig down into the documents. They 166 00:10:59,400 --> 00:11:02,920 Speaker 1: were so outrageous about what Google was doing and so 167 00:11:03,000 --> 00:11:06,560 Speaker 1: opposite from what they were saying out in public that 168 00:11:07,040 --> 00:11:11,120 Speaker 1: I started to wonder if I was descending into some 169 00:11:11,200 --> 00:11:13,640 Speaker 1: sort of weird state. And so I just said, you 170 00:11:13,679 --> 00:11:16,360 Speaker 1: know what, this is so out there, I just have 171 00:11:16,640 --> 00:11:20,720 Speaker 1: to copy these as pds just so that, you know, 172 00:11:20,880 --> 00:11:23,240 Speaker 1: when I'm being gas lit by the media, I can 173 00:11:23,360 --> 00:11:26,960 Speaker 1: have this thing that I can look at and will 174 00:11:27,000 --> 00:11:30,560 Speaker 1: prove that what I know is real. And so I 175 00:11:30,600 --> 00:11:33,720 Speaker 1: just started downloading as many of these documents as I could, 176 00:11:33,800 --> 00:11:38,200 Speaker 1: because one, I needed to assure myself that this was happening, 177 00:11:38,640 --> 00:11:42,200 Speaker 1: and then two, I knew that once Google realized what 178 00:11:42,240 --> 00:11:44,800 Speaker 1: they were doing was going to be a pr disaster, 179 00:11:45,280 --> 00:11:48,920 Speaker 1: they would start pulling these documents off from the internal servers. 180 00:11:49,520 --> 00:11:51,720 Speaker 1: And so at that point I just said, well, I 181 00:11:51,800 --> 00:11:53,720 Speaker 1: see it now, I know that I've got it. Now, 182 00:11:53,760 --> 00:11:56,080 Speaker 1: I might as well save it so that I've got 183 00:11:56,080 --> 00:12:00,280 Speaker 1: it for later use. And to my surprise, like every 184 00:12:00,280 --> 00:12:02,920 Speaker 1: time I put a red line in the sand and said, well, 185 00:12:02,920 --> 00:12:06,599 Speaker 1: Google's not going to cross this, that would be you know, ungoogly, 186 00:12:07,200 --> 00:12:10,240 Speaker 1: and every single line they crossed and then like crossed 187 00:12:10,240 --> 00:12:13,160 Speaker 1: it more, and it was just this progressive, you know, 188 00:12:13,679 --> 00:12:20,000 Speaker 1: more authoritarian, more extremist organization that was emerging. And as 189 00:12:20,080 --> 00:12:24,400 Speaker 1: this was happening, the internal rhetoric, the internal brainwashing that 190 00:12:24,480 --> 00:12:27,080 Speaker 1: they were using on their employees also ramped up. And 191 00:12:27,080 --> 00:12:29,440 Speaker 1: it seemed to sort of like ramp up where they 192 00:12:29,440 --> 00:12:33,000 Speaker 1: would message something to the all hands meetings and then 193 00:12:33,240 --> 00:12:36,240 Speaker 1: three months later it would become policy. And I just 194 00:12:36,240 --> 00:12:39,200 Speaker 1: saw this over and over and over again in the 195 00:12:39,280 --> 00:12:44,240 Speaker 1: company and over the course of essentially four years in 196 00:12:44,360 --> 00:12:47,640 Speaker 1: Google's completely opposite of what it was in twenty sixteen. 197 00:12:47,920 --> 00:12:51,680 Speaker 1: In two sixteen, Susan Wichovsky, CEO of YouTube, had this 198 00:12:51,760 --> 00:12:56,800 Speaker 1: PowerPoint presentation expounding on libertarian values of self expression and 199 00:12:56,920 --> 00:12:59,199 Speaker 1: we're not again the way of you being able to 200 00:12:59,200 --> 00:13:01,880 Speaker 1: give you your best self to the world. And by 201 00:13:01,920 --> 00:13:04,920 Speaker 1: twenty seventeen, she's like, we gotta like kill the fake 202 00:13:05,000 --> 00:13:09,040 Speaker 1: news and boost the authoritative content, and that meant like 203 00:13:09,640 --> 00:13:12,840 Speaker 1: CNN and MSNBC, in the Wall Street Journal and all 204 00:13:12,880 --> 00:13:15,000 Speaker 1: those players up there. And so it was just this 205 00:13:15,120 --> 00:13:20,640 Speaker 1: complete one eighty degree swap in the course of one year, 206 00:13:20,679 --> 00:13:23,560 Speaker 1: and then an extreme version of that three years later 207 00:13:23,960 --> 00:13:45,040 Speaker 1: by twenty twenty. So what was there do you think 208 00:13:45,080 --> 00:13:49,880 Speaker 1: about Trump that led is such a toxic reaction among 209 00:13:49,960 --> 00:13:53,840 Speaker 1: the senior executives of Google. I'm not entirely sure why 210 00:13:53,920 --> 00:13:58,440 Speaker 1: people have trumped arrangement syndrome. I'm just not affected by 211 00:13:58,440 --> 00:14:03,560 Speaker 1: whatever it is. Often, bellicos and alpha male type personalities 212 00:14:03,640 --> 00:14:06,240 Speaker 1: are very effective at what they do, so I have 213 00:14:06,320 --> 00:14:10,480 Speaker 1: an appreciation for it. But for some reason, there's just 214 00:14:10,600 --> 00:14:14,000 Speaker 1: something in a lot of people that just have a 215 00:14:14,120 --> 00:14:20,320 Speaker 1: deep revolt for the president. And they also don't see 216 00:14:20,360 --> 00:14:24,920 Speaker 1: that the mainstream media isn't there to inform them. It's 217 00:14:24,960 --> 00:14:30,280 Speaker 1: there to push a political agenda, and everything that it 218 00:14:30,400 --> 00:14:35,920 Speaker 1: says is a statement and a projection of power. That's 219 00:14:35,960 --> 00:14:38,840 Speaker 1: what I've come to realize over the last four years. 220 00:14:38,920 --> 00:14:42,160 Speaker 1: And for a lot of people, they just don't see that. 221 00:14:42,240 --> 00:14:45,080 Speaker 1: It seems invisible to them. They don't see kind of 222 00:14:45,080 --> 00:14:49,000 Speaker 1: like the walls of the information matrix, and they can't 223 00:14:49,080 --> 00:14:52,600 Speaker 1: believe that an entire system of people that they think 224 00:14:52,680 --> 00:14:55,800 Speaker 1: is organic and free in the sense making community is 225 00:14:55,880 --> 00:15:01,240 Speaker 1: essentially captured by this very slim minority of the population. 226 00:15:01,560 --> 00:15:04,760 Speaker 1: But that's sort of the way that I can use 227 00:15:04,840 --> 00:15:07,880 Speaker 1: to explain what was going on, and not just at Google, 228 00:15:07,920 --> 00:15:10,440 Speaker 1: but also at Facebook and also the whole mainstream media 229 00:15:10,480 --> 00:15:14,240 Speaker 1: complex take it out on the intersection and you don't 230 00:15:14,280 --> 00:15:17,440 Speaker 1: have to go with me. But as they watch what's 231 00:15:17,440 --> 00:15:21,800 Speaker 1: happening in Afghanistan right now, well, there are tendency you 232 00:15:21,880 --> 00:15:23,880 Speaker 1: need to just shrug it all off because it doesn't 233 00:15:23,880 --> 00:15:29,480 Speaker 1: really matter, or to be concerned about the scale of 234 00:15:29,480 --> 00:15:33,640 Speaker 1: the American defeat, or to think that actually that things 235 00:15:33,680 --> 00:15:37,720 Speaker 1: will be better. But having discs, I honestly think that 236 00:15:37,720 --> 00:15:41,920 Speaker 1: that social media titans are trying to weaken America. And 237 00:15:41,960 --> 00:15:46,200 Speaker 1: so this humiliating defeat in Afghanistan, I maybe we couldn't 238 00:15:46,200 --> 00:15:48,960 Speaker 1: even pull out correctly. It was a complete and total 239 00:15:49,040 --> 00:15:54,760 Speaker 1: disaster and radiated an incredible amount of weakness. And what 240 00:15:54,800 --> 00:15:58,880 Speaker 1: you see is the leader of the Taliban being allowed 241 00:15:58,920 --> 00:16:03,200 Speaker 1: to be on Twitter, but our own President Trump is 242 00:16:03,240 --> 00:16:05,800 Speaker 1: not allowed to be on Twitter. I mean, they care 243 00:16:05,920 --> 00:16:10,760 Speaker 1: more about the freedom of speech for America's quote unquote 244 00:16:10,840 --> 00:16:15,520 Speaker 1: enemies than they do for former presidents, you know. And 245 00:16:15,560 --> 00:16:18,360 Speaker 1: so when people are like, well, how come nothing's being censored, 246 00:16:18,440 --> 00:16:21,520 Speaker 1: it's my belief that Google and Facebook and Twitter are 247 00:16:21,560 --> 00:16:24,200 Speaker 1: obviously trying to destroy the United States, are trying to 248 00:16:24,200 --> 00:16:28,960 Speaker 1: destabilize it, possibly at the behest of someone that's trying 249 00:16:29,000 --> 00:16:31,360 Speaker 1: to destroy the United States, whether that be China or 250 00:16:31,360 --> 00:16:34,320 Speaker 1: the Party of Davos or a combination of the two. 251 00:16:35,760 --> 00:16:38,640 Speaker 1: You know, Google seems to be acting in their favor, 252 00:16:39,640 --> 00:16:41,800 Speaker 1: and that's the reason why I think that you see 253 00:16:41,840 --> 00:16:45,560 Speaker 1: things like the Taliban having a voice, but President Trump 254 00:16:45,640 --> 00:16:50,160 Speaker 1: not having a voice. So when you took the documents 255 00:16:50,280 --> 00:16:53,440 Speaker 1: and you made in public, you only received a letter 256 00:16:53,560 --> 00:16:58,240 Speaker 1: from Google with demands to quote, cease and desist from 257 00:16:58,280 --> 00:17:01,680 Speaker 1: many use to disclosure of any internal Google file as 258 00:17:01,680 --> 00:17:06,320 Speaker 1: you possessed close quote. Well, you were all concerned about 259 00:17:06,400 --> 00:17:11,040 Speaker 1: running legal liabilities. You're sort of the biggest whistle blow 260 00:17:11,040 --> 00:17:15,240 Speaker 1: world we've seen so far in Silicon Valley, and certainly 261 00:17:15,240 --> 00:17:18,560 Speaker 1: when you have institutions as big and rich and closed 262 00:17:18,560 --> 00:17:22,240 Speaker 1: as these are, we need more people willing to stand 263 00:17:22,320 --> 00:17:24,840 Speaker 1: up for the truth and to blow the whistle. Who 264 00:17:24,840 --> 00:17:27,920 Speaker 1: were you at all concerned that this could get out 265 00:17:27,920 --> 00:17:30,520 Speaker 1: of hand and you could end up going to jail? 266 00:17:31,160 --> 00:17:33,760 Speaker 1: Oh yeah, I mean that thought crossed my mind. But 267 00:17:34,760 --> 00:17:38,320 Speaker 1: I'll say that it's not nearly as scary as the 268 00:17:38,359 --> 00:17:41,680 Speaker 1: prospect of proving myself to be a coward before the Lord. 269 00:17:42,440 --> 00:17:46,439 Speaker 1: And that's really like what gave me the power to 270 00:17:46,480 --> 00:17:49,280 Speaker 1: be able to fight through all this, because at the 271 00:17:49,320 --> 00:17:50,960 Speaker 1: end of the day, if I was a prisoner of 272 00:17:51,000 --> 00:17:55,160 Speaker 1: conscious that would be a much better outcome than proving 273 00:17:55,200 --> 00:17:57,160 Speaker 1: that these thoughts that I would do the right thing 274 00:17:57,320 --> 00:18:00,320 Speaker 1: under times of adversary would be proven wrong and that 275 00:18:00,359 --> 00:18:02,520 Speaker 1: I would just continue to eat at the troth a 276 00:18:02,640 --> 00:18:06,560 Speaker 1: big tech as a high paid employee while my country 277 00:18:06,680 --> 00:18:12,439 Speaker 1: is destroyed. That's something that is intolerable to myself, which 278 00:18:13,240 --> 00:18:15,520 Speaker 1: is the reason why I went through with my defection. 279 00:18:15,720 --> 00:18:19,359 Speaker 1: And you know, maybe one day I may have an 280 00:18:19,400 --> 00:18:23,359 Speaker 1: accident and not be here anymore, and I'm actually okay 281 00:18:23,400 --> 00:18:27,359 Speaker 1: with that because you know, through this short period and 282 00:18:27,480 --> 00:18:30,280 Speaker 1: limited time of my life span, I've been able to 283 00:18:30,840 --> 00:18:34,520 Speaker 1: have a positive impact on the planet and to let 284 00:18:34,600 --> 00:18:39,879 Speaker 1: people know that there's this plan for totalitarianism, this panopticon, 285 00:18:40,720 --> 00:18:43,720 Speaker 1: and if we want to change it, we still have 286 00:18:43,720 --> 00:18:49,040 Speaker 1: a chance. You know, it's not implemented yet, and you know, 287 00:18:49,520 --> 00:18:53,520 Speaker 1: the first step to defeating this enemy is to understand 288 00:18:54,320 --> 00:18:57,199 Speaker 1: what they're doing. And this is the reason why I 289 00:18:57,240 --> 00:19:01,640 Speaker 1: took this enormous risk to bring this information to light. Yeah, 290 00:19:01,720 --> 00:19:06,080 Speaker 1: most of our listeners well be familiar with Jeremy Bentham's 291 00:19:06,119 --> 00:19:13,000 Speaker 1: concept of the panopticontinue sort of briefly explain Bentham's idea. Yeah, 292 00:19:13,040 --> 00:19:18,240 Speaker 1: so it was this panopticon, which literally translates into the 293 00:19:18,320 --> 00:19:21,720 Speaker 1: all seeing eye. That's a rough translation, but pan means all, 294 00:19:21,800 --> 00:19:25,920 Speaker 1: opticon means I. So it was an idea for an 295 00:19:25,920 --> 00:19:29,439 Speaker 1: efficient prison complex that was easy to manage. And it 296 00:19:29,520 --> 00:19:34,920 Speaker 1: was a circular prison with an observatory one way mirrored 297 00:19:35,440 --> 00:19:39,920 Speaker 1: enclosure in the middle that was able to see into 298 00:19:40,080 --> 00:19:43,760 Speaker 1: all of the different prisons at the same time, and 299 00:19:43,840 --> 00:19:47,560 Speaker 1: so they could look in, but the prisoner could not 300 00:19:47,760 --> 00:19:52,600 Speaker 1: tell if someone was looking at them from the administration room, 301 00:19:53,119 --> 00:19:55,800 Speaker 1: and so they always lived with this fear that they 302 00:19:55,800 --> 00:19:58,280 Speaker 1: were being watched, not know if they were being watched 303 00:19:58,359 --> 00:20:02,159 Speaker 1: at that particular time. And you know, the book to 304 00:20:02,240 --> 00:20:08,440 Speaker 1: kind of popularize this point was nineteen eighty four and basically, 305 00:20:08,480 --> 00:20:12,320 Speaker 1: the television looks back at you. And now this concept 306 00:20:12,359 --> 00:20:15,000 Speaker 1: of the panopticon has come to life. Now. It's in 307 00:20:15,040 --> 00:20:18,320 Speaker 1: our phones. You know, we hear these rumors that our 308 00:20:18,359 --> 00:20:21,720 Speaker 1: phones and our TVs are turning on their microphones. I 309 00:20:21,840 --> 00:20:24,880 Speaker 1: read this other paper that says that your speaker can 310 00:20:24,920 --> 00:20:27,919 Speaker 1: also turn into a microphone because it's kind of the 311 00:20:27,960 --> 00:20:31,359 Speaker 1: same thing mechanically and that it can be read, and 312 00:20:31,400 --> 00:20:34,399 Speaker 1: so now every single speaker that's out there, you know, 313 00:20:34,480 --> 00:20:37,040 Speaker 1: we could look at that with suspicion that maybe it's 314 00:20:37,080 --> 00:20:40,040 Speaker 1: actually a dual use microphone that's listening in on our 315 00:20:40,040 --> 00:20:44,720 Speaker 1: conversations and sending it back to some sort of centralized 316 00:20:44,800 --> 00:20:49,320 Speaker 1: data repository which is keeping a shadow profile on us 317 00:20:49,920 --> 00:20:54,800 Speaker 1: in some database. And you know, people are searching for 318 00:20:54,840 --> 00:20:57,440 Speaker 1: the word, like, what's the system that's being built in. 319 00:20:58,640 --> 00:21:02,080 Speaker 1: A good name for it is the Panopticon. I mean, 320 00:21:02,119 --> 00:21:06,800 Speaker 1: in some ways it's parallels Wherwell's nineteen eighty four and 321 00:21:07,040 --> 00:21:11,399 Speaker 1: other visions of the tutilitarian system. There's a point in 322 00:21:11,440 --> 00:21:14,479 Speaker 1: one of Kamo's books where he writes that there are 323 00:21:14,520 --> 00:21:17,080 Speaker 1: times when a man can be killed for saying two 324 00:21:17,119 --> 00:21:21,760 Speaker 1: plugs two equals four because the system can't tolerate the truth. 325 00:21:22,400 --> 00:21:24,000 Speaker 1: I mean, did you have some of that sense that 326 00:21:24,119 --> 00:21:28,800 Speaker 1: you were trapped into a gigantic system worldwide that was 327 00:21:28,920 --> 00:21:33,280 Speaker 1: evolving in ways that we're going to tell us what 328 00:21:33,480 --> 00:21:38,040 Speaker 1: reality was rather than learn about reality. That our job was, 329 00:21:38,040 --> 00:21:41,879 Speaker 1: in a sense to be brainwashed into their version of 330 00:21:42,040 --> 00:21:45,000 Speaker 1: fact and their version of what they would have called 331 00:21:45,119 --> 00:21:48,040 Speaker 1: real news. Yeah, I saw it sort of the death 332 00:21:48,040 --> 00:21:50,680 Speaker 1: start being built all around me, you know, and I 333 00:21:50,760 --> 00:21:56,040 Speaker 1: started to see the manipulation of the population through this 334 00:21:56,200 --> 00:21:59,960 Speaker 1: civic brainwashing program that was being unleashed. And what's even 335 00:22:00,200 --> 00:22:04,280 Speaker 1: scarier is I would see slides which talked about the 336 00:22:04,320 --> 00:22:08,199 Speaker 1: fourth step process for programming people like us, you know, 337 00:22:08,359 --> 00:22:12,119 Speaker 1: basically civic brainwashing. And the first step is that training 338 00:22:12,200 --> 00:22:14,760 Speaker 1: data are collected and classified. And I'm reading this from 339 00:22:14,800 --> 00:22:20,200 Speaker 1: my book, algorithms are programmed, media are filtered, ranked, aggregated, 340 00:22:20,280 --> 00:22:22,760 Speaker 1: or generated. That step three, and then the final step 341 00:22:22,840 --> 00:22:26,840 Speaker 1: is people like us are programmed, and then that starts 342 00:22:26,840 --> 00:22:30,760 Speaker 1: the cycle over again with the data that new group 343 00:22:30,840 --> 00:22:33,919 Speaker 1: of people generates can then be used as a cycle 344 00:22:34,000 --> 00:22:39,440 Speaker 1: to shift the population, you know, left or right. And 345 00:22:39,680 --> 00:22:43,880 Speaker 1: to see that Google was literally thinking of its users 346 00:22:43,960 --> 00:22:49,760 Speaker 1: as programmable units was something that you know, it's like 347 00:22:49,840 --> 00:22:52,239 Speaker 1: this is a prison. Like it's not a prison with 348 00:22:52,480 --> 00:22:57,800 Speaker 1: prison bars. It's a form of mental prison where the 349 00:22:57,880 --> 00:23:01,480 Speaker 1: bars are the fake news and the fact that you're 350 00:23:01,560 --> 00:23:07,280 Speaker 1: being influenced by your access to information and access to language, 351 00:23:07,600 --> 00:23:10,840 Speaker 1: because information and language are very well mixed. If you 352 00:23:10,920 --> 00:23:14,920 Speaker 1: banned certain hate speech ideas. Then you're literally banning certain ideas, 353 00:23:15,400 --> 00:23:18,480 Speaker 1: and then you know, people just sort of expanded to 354 00:23:18,600 --> 00:23:22,679 Speaker 1: the information that they're allowed to access. And so if 355 00:23:22,720 --> 00:23:25,760 Speaker 1: you can shift that window left or right, then people 356 00:23:25,800 --> 00:23:29,119 Speaker 1: are just going to follow that window wherever it goes. 357 00:23:29,280 --> 00:23:33,240 Speaker 1: And no one elected these people. No one said let's 358 00:23:33,240 --> 00:23:36,120 Speaker 1: have a vote and put them in power. They elected themselves, 359 00:23:36,200 --> 00:23:38,880 Speaker 1: and they sat on the throne and they said this 360 00:23:38,960 --> 00:23:40,960 Speaker 1: is the way that it's going to be, and they 361 00:23:41,040 --> 00:23:44,560 Speaker 1: lied about it all the while they were laying down 362 00:23:44,600 --> 00:23:49,040 Speaker 1: the foundation to unroll this and release it to the 363 00:23:49,160 --> 00:23:52,600 Speaker 1: United States in the world. So Google talks a lot 364 00:23:52,640 --> 00:23:58,160 Speaker 1: of machine learning fairness, what does that actually being. So 365 00:23:58,359 --> 00:24:05,640 Speaker 1: machine learning fairness is the merging of artificial intelligence with 366 00:24:05,760 --> 00:24:11,040 Speaker 1: critical race theory. And what this machine learning fairness does 367 00:24:11,920 --> 00:24:16,639 Speaker 1: is that it generates classifiers, and you know, you can 368 00:24:16,640 --> 00:24:19,959 Speaker 1: almost think of them as social justice classifiers, and they 369 00:24:20,000 --> 00:24:24,040 Speaker 1: classify for things such as hate speech or right wing 370 00:24:24,160 --> 00:24:27,520 Speaker 1: network talk. So let me give you like an example 371 00:24:27,520 --> 00:24:32,760 Speaker 1: of how this works. So Google will train a classifier, 372 00:24:32,800 --> 00:24:37,080 Speaker 1: let's say, identifies right wing news, and then that gets 373 00:24:37,160 --> 00:24:41,800 Speaker 1: run on every single video that gets released on YouTube, 374 00:24:41,960 --> 00:24:45,480 Speaker 1: that gets uploaded and published and so you know, I 375 00:24:45,520 --> 00:24:48,560 Speaker 1: would go and I would look on Dave Rubens internal 376 00:24:48,640 --> 00:24:52,560 Speaker 1: dashboard of the company. I would see that these machine 377 00:24:53,160 --> 00:24:56,440 Speaker 1: learning fairness classifiers had classified his video in all these 378 00:24:56,480 --> 00:24:58,879 Speaker 1: different ways, like you know, in terms of like talk 379 00:24:58,960 --> 00:25:02,240 Speaker 1: and news and right wing, etc. And then when he 380 00:25:02,359 --> 00:25:04,840 Speaker 1: had something that they didn't like, it would get flagged 381 00:25:05,200 --> 00:25:08,560 Speaker 1: with some sort of hate speech classification and then the 382 00:25:08,640 --> 00:25:12,359 Speaker 1: video would go down. And so this was happening throughout 383 00:25:12,440 --> 00:25:16,800 Speaker 1: the content creators of YouTube. And so this machine learning 384 00:25:16,840 --> 00:25:21,480 Speaker 1: fairness is a system that generates classifiers, and those classifiers 385 00:25:21,600 --> 00:25:25,520 Speaker 1: classified the data as hate speech. And it can be news, 386 00:25:25,520 --> 00:25:29,960 Speaker 1: it can be video content, it can be websites. And 387 00:25:30,000 --> 00:25:33,840 Speaker 1: what this allows them to do is this allows Google 388 00:25:34,040 --> 00:25:38,280 Speaker 1: to essentially create blacklists, but instead of having it in 389 00:25:38,320 --> 00:25:42,320 Speaker 1: a human readable format, it's in this like rudo neuronet 390 00:25:42,400 --> 00:25:45,320 Speaker 1: language that nobody understands. You can't audit it, Like even 391 00:25:45,320 --> 00:25:48,600 Speaker 1: if you were to get this information exposed in a 392 00:25:48,640 --> 00:25:51,160 Speaker 1: court of law, no one would be able to make 393 00:25:51,200 --> 00:25:55,160 Speaker 1: heads or tails of what it means. And so this 394 00:25:55,240 --> 00:25:57,960 Speaker 1: is really the dangerous part, is that they're creating a 395 00:25:58,000 --> 00:26:02,440 Speaker 1: black box or sensor yip which runs on the entire 396 00:26:02,520 --> 00:26:08,560 Speaker 1: information landscape it's just being done people or by artificial intelligence. 397 00:26:09,440 --> 00:26:13,840 Speaker 1: So the artificial intelligence is a product of the training data, 398 00:26:13,920 --> 00:26:19,119 Speaker 1: and this training data is being generated by employees of 399 00:26:19,160 --> 00:26:22,360 Speaker 1: the company. And what they do is they go through 400 00:26:22,400 --> 00:26:27,720 Speaker 1: and they put tags, they sign labels to data, and 401 00:26:28,200 --> 00:26:32,680 Speaker 1: those assigned labels travel with the data to the AI 402 00:26:32,880 --> 00:26:37,720 Speaker 1: machine learning, and it uses that and finds the patterns 403 00:26:37,760 --> 00:26:40,800 Speaker 1: that allow those labels to be assigned to all the data. 404 00:26:40,840 --> 00:26:44,760 Speaker 1: So it finds the meeting the patterns, and then once 405 00:26:44,840 --> 00:26:48,440 Speaker 1: you give it new data, new news articles, new videos, 406 00:26:48,520 --> 00:26:53,800 Speaker 1: it's able to classify those with great performance similar to 407 00:26:53,840 --> 00:26:57,600 Speaker 1: what a human may score it. So the total volume 408 00:26:58,840 --> 00:27:03,080 Speaker 1: of information Google must have about the normal person must 409 00:27:03,119 --> 00:27:05,960 Speaker 1: be staggering. I'm thinking about what we've gone through a 410 00:27:06,040 --> 00:27:10,480 Speaker 1: long process of trying to evolve hip hop as a 411 00:27:10,600 --> 00:27:13,919 Speaker 1: law that protects you of medical records and gives you 412 00:27:14,000 --> 00:27:17,399 Speaker 1: some sense of security. But when you think about the 413 00:27:17,480 --> 00:27:21,720 Speaker 1: scale of what Google's doing, the fact is that they 414 00:27:21,720 --> 00:27:25,640 Speaker 1: know virtually everything about you that has ever touched an 415 00:27:25,640 --> 00:27:30,040 Speaker 1: electronic system. There's a mobile penetration we can't even imagine. 416 00:27:30,600 --> 00:27:32,639 Speaker 1: I mean, they've got everything about you. I mean they 417 00:27:32,640 --> 00:27:34,960 Speaker 1: could create a shadow profile. I mean they do create 418 00:27:34,960 --> 00:27:38,480 Speaker 1: shadow profiles about you. That's the whole debate that's going 419 00:27:38,520 --> 00:27:42,560 Speaker 1: on with their ad engine, because in order to target 420 00:27:42,640 --> 00:27:46,400 Speaker 1: you more effectively so that you will more likely buy 421 00:27:47,080 --> 00:27:49,560 Speaker 1: an AD that's presented to you, Google needs to know 422 00:27:50,359 --> 00:27:53,280 Speaker 1: everything they can about you, whether you're a cyclist, or 423 00:27:53,280 --> 00:27:54,959 Speaker 1: whether you live in the city, or whether you live 424 00:27:55,000 --> 00:27:57,960 Speaker 1: in the rural areas, or whether you're married, whether you're 425 00:27:57,960 --> 00:28:00,600 Speaker 1: not married, whether you're a boy or a girl, what's 426 00:28:00,600 --> 00:28:04,200 Speaker 1: your age, what's your income. All these things will predict 427 00:28:05,080 --> 00:28:09,399 Speaker 1: your likelihood for being shown a particular ad A, B, 428 00:28:09,560 --> 00:28:14,400 Speaker 1: or C. And so Google has amassed this hidden shadow 429 00:28:14,840 --> 00:28:20,480 Speaker 1: social networking profile for each individual on the planet, and 430 00:28:20,840 --> 00:28:24,520 Speaker 1: that information is largely unregulated. I'm pretty sure that it's 431 00:28:24,520 --> 00:28:26,920 Speaker 1: going to have to change. And the reason why it's 432 00:28:26,920 --> 00:28:29,879 Speaker 1: going to have to change is because China's got access 433 00:28:29,920 --> 00:28:33,520 Speaker 1: to that data too, and the United States does not 434 00:28:33,680 --> 00:28:38,440 Speaker 1: have a symmetrical penetration into China. And that's a real 435 00:28:38,480 --> 00:28:43,400 Speaker 1: big problem because if you know users location through their phones, 436 00:28:43,600 --> 00:28:47,840 Speaker 1: then you know if assass syndrome wants to take someone out, well, 437 00:28:47,840 --> 00:28:50,320 Speaker 1: you've got their live updates. And then if the United 438 00:28:50,360 --> 00:28:52,240 Speaker 1: States is like, hey, we want to do the same 439 00:28:52,280 --> 00:28:54,840 Speaker 1: thing in retaliation to China. Well, you can't do that 440 00:28:54,880 --> 00:28:57,560 Speaker 1: because China doesn't allow their citizens to be subverted by 441 00:28:57,600 --> 00:29:00,640 Speaker 1: Western ideology pretty much at all. They don't allow our 442 00:29:00,720 --> 00:29:02,520 Speaker 1: businesses to come in there, or they don't allow us 443 00:29:02,520 --> 00:29:06,040 Speaker 1: to get access to their customers to any large degree. 444 00:29:06,080 --> 00:29:09,800 Speaker 1: And so right now, what's happening is that China, which 445 00:29:09,920 --> 00:29:11,800 Speaker 1: I'm not a fan of, I think that they are 446 00:29:11,840 --> 00:29:13,760 Speaker 1: a rival to the United States. I think it's been 447 00:29:13,800 --> 00:29:17,600 Speaker 1: a mistake that we've been giving them this preferential trade practices. 448 00:29:18,120 --> 00:29:22,440 Speaker 1: They have an asymmetric advantage that if they're allowed to 449 00:29:22,520 --> 00:29:26,360 Speaker 1: continue to collect this information on the United States citizens, 450 00:29:26,400 --> 00:29:29,120 Speaker 1: then you know, the United States is going to be 451 00:29:29,280 --> 00:29:34,000 Speaker 1: at a very strong disadvantage in comparison to China. And 452 00:29:34,000 --> 00:29:35,840 Speaker 1: that's going to be really bad. If China wants to 453 00:29:35,840 --> 00:29:37,920 Speaker 1: do a sneak attack, or if China wants to get 454 00:29:38,640 --> 00:29:42,400 Speaker 1: at a politician that's not friendly to the CCP, I mean, 455 00:29:42,440 --> 00:29:46,080 Speaker 1: how do you even defend against that. It's impossible. Something 456 00:29:46,160 --> 00:29:49,040 Speaker 1: has to happen. We have to have some lockdown of 457 00:29:49,080 --> 00:29:53,800 Speaker 1: our data. We cannot allow Google the data pipe information 458 00:29:53,840 --> 00:29:56,920 Speaker 1: on the entire population of the United States to China. 459 00:29:57,160 --> 00:29:59,240 Speaker 1: Something's going to happen, something's going to change. You don't 460 00:29:59,280 --> 00:30:01,800 Speaker 1: need to be republic or a democrat to know that 461 00:30:01,920 --> 00:30:04,920 Speaker 1: right now, the system that we have is impossible to 462 00:30:04,960 --> 00:30:06,800 Speaker 1: maintain in the future, and we need to have some 463 00:30:06,840 --> 00:30:11,400 Speaker 1: sort of radical regulation on the data privacy that we have, 464 00:30:12,000 --> 00:30:30,760 Speaker 1: not just as individuals but as a country. So in 465 00:30:30,880 --> 00:30:39,400 Speaker 1: addition to their effort to proactively understand us and reshape us, YouTube, 466 00:30:39,440 --> 00:30:42,840 Speaker 1: for example, also is just creating a whole series of 467 00:30:43,680 --> 00:30:48,280 Speaker 1: blacklists where they block you from getting the new information 468 00:30:48,800 --> 00:30:52,320 Speaker 1: and they shape your sort of information environment in which 469 00:30:52,360 --> 00:30:56,920 Speaker 1: you're allowed to learn. I find myself location being very 470 00:30:56,960 --> 00:31:00,240 Speaker 1: cautious about certain tweets and certain what have you, because 471 00:31:00,240 --> 00:31:03,320 Speaker 1: I realize you can get me banned for thirty days 472 00:31:03,400 --> 00:31:07,280 Speaker 1: or ninety days or whatever. It's a totally weird system. Well, 473 00:31:07,320 --> 00:31:11,640 Speaker 1: what's your sense of the YouTube kind of approach to 474 00:31:11,920 --> 00:31:17,000 Speaker 1: just literally black lusting and blocking people from communicating, you know, 475 00:31:18,040 --> 00:31:22,360 Speaker 1: I think that Google has a produciary responsibility to maximize 476 00:31:22,400 --> 00:31:26,680 Speaker 1: their shareholder value. I personally saw a Google destroy its 477 00:31:26,680 --> 00:31:32,520 Speaker 1: own shareholder value of its parent company through the substantial 478 00:31:33,240 --> 00:31:39,000 Speaker 1: censorship that they were applying to their users. And at 479 00:31:39,040 --> 00:31:42,840 Speaker 1: this point they will ban you from the platform if 480 00:31:42,880 --> 00:31:46,719 Speaker 1: you say that vitamin C and vitamin D can be 481 00:31:46,840 --> 00:31:49,600 Speaker 1: used to lessen the effects of COVID nineteen, which has 482 00:31:49,640 --> 00:31:52,520 Speaker 1: been proved in paper after paper, but hasn't gone through 483 00:31:52,680 --> 00:31:58,280 Speaker 1: the citadel the FDA. And so Susan literally went on 484 00:31:58,360 --> 00:32:04,400 Speaker 1: to CNN said that vitamin C would be banned from 485 00:32:04,520 --> 00:32:06,800 Speaker 1: their YouTube platform if it said that it would help 486 00:32:06,880 --> 00:32:11,280 Speaker 1: treat COVID nineteen. And you know, there should be a 487 00:32:11,280 --> 00:32:13,920 Speaker 1: shareholder revolt. I really believe that there should be a 488 00:32:13,920 --> 00:32:17,200 Speaker 1: class action lawsuit where the shareholders get together and said, look, 489 00:32:17,200 --> 00:32:21,640 Speaker 1: this is false advertising. At the very least, it's false advertising. 490 00:32:21,840 --> 00:32:26,200 Speaker 1: When Google goes IPO and puts out a statement saying 491 00:32:26,200 --> 00:32:29,280 Speaker 1: that they have essentially a constitution and that their mission 492 00:32:29,320 --> 00:32:32,000 Speaker 1: statement is to organize the world's information and make it 493 00:32:32,080 --> 00:32:36,320 Speaker 1: universally accessible. How can they both say that and then 494 00:32:36,360 --> 00:32:39,000 Speaker 1: also at the same time say that they're going to 495 00:32:39,080 --> 00:32:42,320 Speaker 1: ban videos talking about vitamin C and vitamin D. It's 496 00:32:42,360 --> 00:32:46,040 Speaker 1: just amazing. It's mind blowing that they would do this, 497 00:32:46,800 --> 00:32:49,480 Speaker 1: And we've got to get together because if they're going 498 00:32:49,520 --> 00:32:52,280 Speaker 1: to do this with vitamin C, like, what chance does 499 00:32:52,360 --> 00:32:57,600 Speaker 1: anyone have of stability for their enterprise if their enterprise 500 00:32:58,120 --> 00:33:02,640 Speaker 1: relies on the Google ch index, Google tomorrow could wake 501 00:33:02,720 --> 00:33:04,920 Speaker 1: up and say, you know what, We're only going to 502 00:33:05,360 --> 00:33:11,120 Speaker 1: have links for Amazon and WebMD and Wikipedia. And if 503 00:33:11,160 --> 00:33:13,760 Speaker 1: you're not part of this inclusion lest then you're on 504 00:33:13,800 --> 00:33:16,840 Speaker 1: the outside and you're not part of our authoritarian cabal. 505 00:33:17,560 --> 00:33:20,240 Speaker 1: And that's a really dangerous thing. And that's where we're 506 00:33:20,240 --> 00:33:24,160 Speaker 1: going right now. And if you look at the parameters 507 00:33:24,200 --> 00:33:28,240 Speaker 1: that they use to rank individual websites, because that's what 508 00:33:28,320 --> 00:33:31,800 Speaker 1: Google does now, it's called a page rank score, what 509 00:33:31,800 --> 00:33:35,040 Speaker 1: you're going to find is that your page rank score 510 00:33:35,320 --> 00:33:38,960 Speaker 1: is based upon what Wikipedia has to say about you. 511 00:33:40,120 --> 00:33:45,000 Speaker 1: And Wikipedia has gone from first primary sources to secondary 512 00:33:45,080 --> 00:33:47,640 Speaker 1: echo chamber sources like oh well, this person had this 513 00:33:47,720 --> 00:33:49,440 Speaker 1: to say about it, and then it turns out that 514 00:33:49,480 --> 00:33:52,600 Speaker 1: they're usually circularly referencing someone else and so on and 515 00:33:52,600 --> 00:33:57,400 Speaker 1: so forth. So basically, right now, Wikipedia, for anything political 516 00:33:57,640 --> 00:34:01,200 Speaker 1: is relying on a group of insiders and what they 517 00:34:01,240 --> 00:34:04,480 Speaker 1: have to say in the mainstream media and then laundering 518 00:34:04,600 --> 00:34:09,160 Speaker 1: the defamation that they have onto an authoritarian source that 519 00:34:09,200 --> 00:34:13,360 Speaker 1: then Google uses to rank your website, your topic, or 520 00:34:13,440 --> 00:34:17,160 Speaker 1: rank the individual. And by the way, Google does not 521 00:34:17,680 --> 00:34:21,759 Speaker 1: show anyone what their page Rinks score. I saw a 522 00:34:21,880 --> 00:34:26,840 Speaker 1: congressional request to get the page Rinks score and Google 523 00:34:26,960 --> 00:34:31,400 Speaker 1: told them no. And this is something that I really 524 00:34:31,400 --> 00:34:35,440 Speaker 1: hope that as we get sort of regulation going for Google, 525 00:34:35,560 --> 00:34:37,359 Speaker 1: we can say, look, if you're going to have like 526 00:34:37,400 --> 00:34:41,200 Speaker 1: a secret score on every single individual person, you need 527 00:34:41,239 --> 00:34:44,200 Speaker 1: to publish that because we need to know whether it's fair, 528 00:34:44,560 --> 00:34:47,799 Speaker 1: and we need to have some sort of remedy if 529 00:34:47,800 --> 00:34:51,840 Speaker 1: it's not. No, I think that's right. So from your standpoint, 530 00:34:52,480 --> 00:34:56,200 Speaker 1: I gather we're going to have to have legislative action, 531 00:34:56,680 --> 00:35:00,919 Speaker 1: which probably means the presidential administry and after this one. 532 00:35:00,960 --> 00:35:04,520 Speaker 1: But I think this could easily become a very widespread 533 00:35:04,600 --> 00:35:08,359 Speaker 1: issue and maybe even at the twenty twenty two elections. 534 00:35:08,520 --> 00:35:10,799 Speaker 1: The you to shake things because people just have a 535 00:35:10,840 --> 00:35:15,440 Speaker 1: deep sense of how biased and how unfair Silicon Valley 536 00:35:15,440 --> 00:35:18,719 Speaker 1: Holligaroks has become. What is your sense as you want 537 00:35:18,800 --> 00:35:20,839 Speaker 1: you talk to people when you describe your new book, 538 00:35:21,280 --> 00:35:24,440 Speaker 1: what kind of reaction do you get? You know, it's funny. 539 00:35:24,600 --> 00:35:27,320 Speaker 1: We thought that the liberal press was going to destroy 540 00:35:27,800 --> 00:35:30,440 Speaker 1: this book the review, but it turns out that the 541 00:35:30,480 --> 00:35:34,799 Speaker 1: liberal press loves my book so far. Kirkus Reviews gave 542 00:35:34,800 --> 00:35:36,960 Speaker 1: a recommendation. They liked the book, they said it was 543 00:35:36,960 --> 00:35:39,440 Speaker 1: really good. This is not a left or right issue. 544 00:35:39,760 --> 00:35:42,480 Speaker 1: This is a censorship issue. Both the left and the 545 00:35:42,560 --> 00:35:45,560 Speaker 1: right hate censorship, and I mean the legitimate laught like 546 00:35:45,600 --> 00:35:50,200 Speaker 1: the people themselves, the populace left, not the authoritative citadel 547 00:35:50,520 --> 00:35:55,080 Speaker 1: msm left. In populism, censorship is at horrant and we 548 00:35:55,160 --> 00:36:00,560 Speaker 1: don't want these apex oligarchs to control the discourse of 549 00:36:00,560 --> 00:36:04,560 Speaker 1: America because even if they're rooting for your team, newt like, 550 00:36:04,920 --> 00:36:08,759 Speaker 1: a different administration can come seize control of that organization 551 00:36:09,239 --> 00:36:11,919 Speaker 1: and then swap it the other way, and now you're 552 00:36:11,920 --> 00:36:14,359 Speaker 1: in big trouble. And so the only hedge against that 553 00:36:14,520 --> 00:36:17,800 Speaker 1: is to decentralize their power, return it to the people, 554 00:36:18,360 --> 00:36:23,040 Speaker 1: and allow the people themselves to be able to come 555 00:36:23,080 --> 00:36:26,120 Speaker 1: to sort of a consensus and the best way that 556 00:36:26,160 --> 00:36:29,160 Speaker 1: they can for how they want to run their society. 557 00:36:29,320 --> 00:36:33,400 Speaker 1: The United States is biased towards entrepreneurship. You know, we 558 00:36:33,400 --> 00:36:36,360 Speaker 1: were oriented towards that way and making something out of yourself, 559 00:36:36,360 --> 00:36:38,799 Speaker 1: Like I followed that dream. Many other people come to 560 00:36:38,880 --> 00:36:42,040 Speaker 1: this country to follow that dream. One of my business 561 00:36:42,080 --> 00:36:46,759 Speaker 1: associates came from Czechoslovakia, and he left because he was 562 00:36:46,800 --> 00:36:49,640 Speaker 1: an entrepreneur at heart, and he realized that there was 563 00:36:49,760 --> 00:36:53,680 Speaker 1: nothing that he could ever do in that country to 564 00:36:53,880 --> 00:36:57,560 Speaker 1: rise up, and so he came here and he moved 565 00:36:57,560 --> 00:37:01,520 Speaker 1: to San Francisco. He got married, and he's done a 566 00:37:01,520 --> 00:37:04,080 Speaker 1: bunch of startups and his latest startup, which I've helped 567 00:37:04,160 --> 00:37:06,959 Speaker 1: him with, has made a lot of money and he's 568 00:37:07,000 --> 00:37:10,480 Speaker 1: going to be fantastically wealthy, and I'm so happy for him. 569 00:37:10,560 --> 00:37:14,400 Speaker 1: And it's such an American story and that's something that 570 00:37:14,719 --> 00:37:19,239 Speaker 1: we need to preserve. That's like the fire of Prometheus 571 00:37:19,280 --> 00:37:23,680 Speaker 1: that gives the ignition that lights the economy of the 572 00:37:23,719 --> 00:37:29,359 Speaker 1: American system. And so, you know, the reaction to going 573 00:37:29,400 --> 00:37:31,560 Speaker 1: out there and saying, yeah, censorship's bad, and this is 574 00:37:31,600 --> 00:37:33,839 Speaker 1: how they do it, it's more a fascination. And then 575 00:37:33,880 --> 00:37:36,520 Speaker 1: once people see the engine, you know how Google does 576 00:37:36,560 --> 00:37:39,600 Speaker 1: the censorship, they get revolted. They get discussed, even if 577 00:37:39,600 --> 00:37:43,960 Speaker 1: they're radical leftists. When I tell them that Google decided, 578 00:37:44,080 --> 00:37:47,040 Speaker 1: with their machine learning fairness, that they are going to 579 00:37:47,080 --> 00:37:50,359 Speaker 1: autocomplete a Google search so that when you type in 580 00:37:50,440 --> 00:37:54,319 Speaker 1: man can and hit space, Google's going to autocomplete and 581 00:37:54,360 --> 00:37:58,239 Speaker 1: say men can get pregnant, man can have periods, men 582 00:37:58,320 --> 00:38:02,960 Speaker 1: can lactate. Think that that's still available right now if 583 00:38:03,000 --> 00:38:05,680 Speaker 1: you go and search for it, and look, I don't 584 00:38:05,680 --> 00:38:08,600 Speaker 1: care if you're a radical communist like that's some messed 585 00:38:08,680 --> 00:38:12,480 Speaker 1: up stuff. And even if you personally agree that that's 586 00:38:12,840 --> 00:38:15,839 Speaker 1: what it should be for yourself, very few people are 587 00:38:15,840 --> 00:38:19,960 Speaker 1: willing to impose that onto other people. And that's the 588 00:38:20,000 --> 00:38:24,040 Speaker 1: problem that we have, is we have coercion of an 589 00:38:24,080 --> 00:38:28,760 Speaker 1: ideology and to try to normalize that on everyone else. 590 00:38:29,400 --> 00:38:32,719 Speaker 1: And if you react to it, then you find that 591 00:38:32,760 --> 00:38:35,640 Speaker 1: you're getting cut off from the network, and in extreme 592 00:38:35,640 --> 00:38:39,480 Speaker 1: cases that goes all the way to having your financial 593 00:38:39,520 --> 00:38:42,799 Speaker 1: system cut off by the banks, by PayPal. And right 594 00:38:42,840 --> 00:38:46,200 Speaker 1: now we need a new movement where we come through 595 00:38:46,239 --> 00:38:49,520 Speaker 1: and we say no, you can't cut someone off from 596 00:38:49,560 --> 00:38:52,040 Speaker 1: Twitter just because you don't like what they had to 597 00:38:52,080 --> 00:38:55,000 Speaker 1: say when it's not been hateful. You can't cut off 598 00:38:55,080 --> 00:39:00,880 Speaker 1: someone from their bank account because they associated with my centrists. 599 00:39:00,960 --> 00:39:04,239 Speaker 1: Individuals you know that aren't preaching violence, that just have 600 00:39:04,520 --> 00:39:08,640 Speaker 1: an idea that the mainstream doesn't necessarily agree with. Like 601 00:39:08,719 --> 00:39:12,560 Speaker 1: we want a tolerant society that's diverse and has different ideas. 602 00:39:13,040 --> 00:39:17,480 Speaker 1: That's what made America great and what Google is doing 603 00:39:17,560 --> 00:39:22,800 Speaker 1: right now is making America not great. It's making America worse, 604 00:39:22,920 --> 00:39:27,920 Speaker 1: and it's turning it into a rigid ideological church, similar 605 00:39:27,920 --> 00:39:32,759 Speaker 1: to what's happening cultural revolutions in places like China and elsewhere. 606 00:39:33,440 --> 00:39:37,240 Speaker 1: You know, the head of digital at our company, Dinguish Researching, 607 00:39:37,640 --> 00:39:43,000 Speaker 1: has experienced Google's suppression that you've described, and he wanted 608 00:39:43,040 --> 00:39:45,600 Speaker 1: me to ask you a couple quick questions. It's a 609 00:39:45,600 --> 00:39:50,920 Speaker 1: content producer. We follow Google's recommendation for publishing content for 610 00:39:51,040 --> 00:39:55,279 Speaker 1: nancural search. A great person. Yeah, Since early twenty twenty one, 611 00:39:55,640 --> 00:40:01,560 Speaker 1: we've seen our content listening fluctuate on a nearly daily basis. Well, 612 00:40:01,560 --> 00:40:03,840 Speaker 1: why do you think that is? In fact, for about 613 00:40:03,880 --> 00:40:07,000 Speaker 1: a week before the Georgia Senate special election, the first 614 00:40:07,000 --> 00:40:10,200 Speaker 1: one of January, we shifted on results from my name 615 00:40:10,719 --> 00:40:13,680 Speaker 1: all the way to the third page of results, then 616 00:40:13,840 --> 00:40:17,160 Speaker 1: right after the election back to page one. Literally the 617 00:40:17,239 --> 00:40:20,440 Speaker 1: day after the election. I mean, does that make sense 618 00:40:20,480 --> 00:40:26,120 Speaker 1: given what you know about how Google operates. Yes, And 619 00:40:26,239 --> 00:40:30,960 Speaker 1: what's happening is that there are changing narratives that are 620 00:40:31,000 --> 00:40:34,480 Speaker 1: being presented in the media. These narratives pop up and 621 00:40:34,760 --> 00:40:39,880 Speaker 1: those narratives in the mainstream press is what's determining ranking. 622 00:40:40,400 --> 00:40:44,600 Speaker 1: And as that oscillates back and forth, like new articles 623 00:40:44,600 --> 00:40:46,840 Speaker 1: come out and then it gets sort of like forgotten 624 00:40:46,920 --> 00:40:49,600 Speaker 1: and it goes away, then what happens is that your 625 00:40:49,600 --> 00:40:52,719 Speaker 1: search rankings can go up again because right now it's 626 00:40:52,719 --> 00:40:59,000 Speaker 1: like Google can't even archive the historical articles anymore because 627 00:40:59,120 --> 00:41:02,680 Speaker 1: the narrative is now changing so fast, Like the Wuhan leak. 628 00:41:03,000 --> 00:41:06,319 Speaker 1: Right before that was something that was so fringe that 629 00:41:06,480 --> 00:41:10,000 Speaker 1: you could be kicked off a Twitter for talking about it, 630 00:41:10,440 --> 00:41:12,840 Speaker 1: but now it's recognized is that's actually the thing that 631 00:41:12,880 --> 00:41:17,680 Speaker 1: probably happens. And so what happens is that Google is 632 00:41:17,760 --> 00:41:22,360 Speaker 1: prioritizing not just the authoritative content, but what the authoritative 633 00:41:22,400 --> 00:41:25,040 Speaker 1: content has said in the last week, in the last month, 634 00:41:25,040 --> 00:41:28,040 Speaker 1: in the last season. And so because of that, what 635 00:41:28,200 --> 00:41:30,840 Speaker 1: happens is that people are seeing a lot of volatility. 636 00:41:30,880 --> 00:41:32,480 Speaker 1: And by the way, this is all my expert opinion, 637 00:41:32,840 --> 00:41:35,760 Speaker 1: is that a lot of people are seeing volatility because 638 00:41:35,760 --> 00:41:39,120 Speaker 1: what they're experiencing is that they're experiencing an MSM attack, 639 00:41:39,440 --> 00:41:42,759 Speaker 1: and then Google is laundering that attack into their internal 640 00:41:42,800 --> 00:41:46,520 Speaker 1: search rankings, and those search rankings are then pushing you 641 00:41:46,600 --> 00:41:50,440 Speaker 1: down towards the back pages of Google. And let's be honest, 642 00:41:50,880 --> 00:41:53,279 Speaker 1: if you're not in the first three pages of the 643 00:41:53,320 --> 00:41:56,200 Speaker 1: search result, like if you're on page fifteen, you might 644 00:41:56,239 --> 00:41:59,040 Speaker 1: as well be censored off the internet. You know, it's 645 00:41:59,080 --> 00:42:03,080 Speaker 1: that serious. And so you know, whenever you drop in rankings, 646 00:42:03,080 --> 00:42:06,879 Speaker 1: what you should see is what have other people been 647 00:42:06,920 --> 00:42:10,000 Speaker 1: saying about me? And the MSM press and to what 648 00:42:10,080 --> 00:42:13,800 Speaker 1: does Wikipedia have to say? And if you see negative articles, 649 00:42:14,200 --> 00:42:16,879 Speaker 1: then you know you're gonna have to be on top 650 00:42:16,920 --> 00:42:19,799 Speaker 1: of it and send them a strongly worded letter from 651 00:42:19,880 --> 00:42:22,840 Speaker 1: your attorney on some letterhead, you know, and saying you 652 00:42:22,840 --> 00:42:25,960 Speaker 1: need to take this off because when you see negative 653 00:42:26,120 --> 00:42:29,200 Speaker 1: articles and the MSM press, you've got about three months 654 00:42:29,239 --> 00:42:32,160 Speaker 1: before that's going to start hitting the Google search index 655 00:42:32,280 --> 00:42:34,880 Speaker 1: as it gets laundered in through the Wikipedia and all 656 00:42:34,920 --> 00:42:37,920 Speaker 1: that other stuff, and it may have even gotten faster 657 00:42:38,040 --> 00:42:41,040 Speaker 1: and faster and closer together on the intervals. And you 658 00:42:41,160 --> 00:42:43,799 Speaker 1: really got to be, you know, as an influencer on 659 00:42:43,840 --> 00:42:47,200 Speaker 1: top of what the negative things that the mainstream media 660 00:42:47,239 --> 00:42:49,840 Speaker 1: press is talking about you and try to head it 661 00:42:49,840 --> 00:42:52,320 Speaker 1: off as it happens. The last thing I want to 662 00:42:52,320 --> 00:42:55,719 Speaker 1: talk about in this is also the algorithmic changes. For 663 00:42:55,760 --> 00:43:00,680 Speaker 1: some reason, Google decides that certain things are getting too big. 664 00:43:01,239 --> 00:43:06,560 Speaker 1: In two seventeen, twenty eighteen, it was alternative health organizations, 665 00:43:07,239 --> 00:43:10,759 Speaker 1: not web md, you know, something else like doctor mccola 666 00:43:10,920 --> 00:43:14,959 Speaker 1: or life Hacker, And basically these were non mainstream health 667 00:43:14,960 --> 00:43:18,320 Speaker 1: sites and they would use an evidence based approach and 668 00:43:18,440 --> 00:43:20,520 Speaker 1: sometimes they got it wrong, but more often than not 669 00:43:20,640 --> 00:43:24,960 Speaker 1: they were better than the establishment in terms of saying 670 00:43:25,000 --> 00:43:28,560 Speaker 1: what was healthier or not. And as a result of 671 00:43:28,560 --> 00:43:33,120 Speaker 1: the medica that I believe happened in twenty eighteen, places 672 00:43:33,160 --> 00:43:37,880 Speaker 1: like doctor mccola, which was the largest alternative health news site, 673 00:43:38,440 --> 00:43:45,200 Speaker 1: it saught its organic traffic drop by ninety nine percent 674 00:43:46,239 --> 00:43:49,560 Speaker 1: in one day. And so the thing is is that, yes, 675 00:43:49,600 --> 00:43:52,200 Speaker 1: it's a combination of what the MSM says, and then 676 00:43:52,239 --> 00:43:56,040 Speaker 1: also sometimes Google will decide to just destroy an entire 677 00:43:56,200 --> 00:43:59,440 Speaker 1: market sector of the economy and then you just wake 678 00:43:59,520 --> 00:44:02,280 Speaker 1: up one day and you don't have any more organic 679 00:44:02,320 --> 00:44:04,400 Speaker 1: traffic going to your page anymore. So it's one of 680 00:44:04,400 --> 00:44:08,920 Speaker 1: those two things I think is happening for your site. Well, 681 00:44:09,000 --> 00:44:13,520 Speaker 1: it's really fascinating. I think what you've done is really 682 00:44:13,560 --> 00:44:18,120 Speaker 1: begun to open the door for people to understand better 683 00:44:18,160 --> 00:44:21,920 Speaker 1: what's going on now or thank you for joining me today, 684 00:44:22,200 --> 00:44:24,600 Speaker 1: and we're gonna have a link to your new book, 685 00:44:24,760 --> 00:44:29,359 Speaker 1: Google Leaks a whistleblowers expos A Big Tech Censorship on 686 00:44:29,360 --> 00:44:32,160 Speaker 1: our show page at newsworld dot com. And I want 687 00:44:32,160 --> 00:44:35,120 Speaker 1: to encourage you to keep speaking out and writing more 688 00:44:35,160 --> 00:44:39,400 Speaker 1: books and helping educate the country because this whole issue. 689 00:44:39,640 --> 00:44:42,520 Speaker 1: In an age of all oigarchs and in an age 690 00:44:42,560 --> 00:44:46,680 Speaker 1: of gigantic IT systems, what you're doing really matters. And 691 00:44:46,680 --> 00:44:50,279 Speaker 1: I appreciate very much you're taking the time to join 692 00:44:50,400 --> 00:44:53,239 Speaker 1: us and to help educate us. To thank you, newte 693 00:44:53,280 --> 00:44:55,600 Speaker 1: It's been a pleasure to come on your show and 694 00:44:55,680 --> 00:44:58,000 Speaker 1: to be able to meet you like this is quite 695 00:44:58,000 --> 00:44:59,799 Speaker 1: an honor for me, So thank you for making my 696 00:45:05,040 --> 00:45:07,719 Speaker 1: thank you to my guests Zak four heeks. You can 697 00:45:07,719 --> 00:45:11,200 Speaker 1: get a link to Google Lease and whistle Blower's Exposit 698 00:45:11,239 --> 00:45:15,920 Speaker 1: a Big Tech Censorship on our showpage at newsworld dot com. 699 00:45:16,040 --> 00:45:20,240 Speaker 1: News World is produced by Gngwish, Sweet sixty and iHeartMedia. 700 00:45:20,760 --> 00:45:26,240 Speaker 1: Our executive producer is Debbie Myers, our producer is Guardsey Sloan, 701 00:45:26,719 --> 00:45:30,839 Speaker 1: and our researcher is Rachel Peterson. The artwork for the 702 00:45:30,880 --> 00:45:35,840 Speaker 1: show was created by Steve Penney. Special thanks to the 703 00:45:35,880 --> 00:45:39,360 Speaker 1: team at Gingwish three sixty. If you've been enjoying news World, 704 00:45:39,719 --> 00:45:42,719 Speaker 1: I hope you'll go to Apple Podcast and both rate 705 00:45:42,800 --> 00:45:46,120 Speaker 1: us with five stars and give us a review so 706 00:45:46,280 --> 00:45:49,920 Speaker 1: others can learn what it's all about. Right now listen 707 00:45:49,960 --> 00:45:52,919 Speaker 1: as a news World can sign up for my three 708 00:45:53,040 --> 00:45:57,399 Speaker 1: free weekly columns at Gingwish sweet sixty dot com slash 709 00:45:57,520 --> 00:46:01,640 Speaker 1: newsletter I'm Big Gangwish. This is news World.