1 00:00:04,200 --> 00:00:05,920 Speaker 1: There Are No Girls on the Internet, as a production 2 00:00:05,960 --> 00:00:13,480 Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridget Tad and this 3 00:00:13,560 --> 00:00:18,000 Speaker 1: is There Are No Girls on the Internet. Hi, and 4 00:00:18,000 --> 00:00:19,960 Speaker 1: welcome to There Are No Girls on the Internet. So 5 00:00:20,000 --> 00:00:22,680 Speaker 1: we are starting a brand new thing on the podcast 6 00:00:22,840 --> 00:00:24,720 Speaker 1: where we are going to be rounding up news and 7 00:00:24,760 --> 00:00:27,320 Speaker 1: analysis about tech, the Internet and media that you might 8 00:00:27,360 --> 00:00:29,560 Speaker 1: have missed. I will do this every other week, but 9 00:00:29,720 --> 00:00:31,240 Speaker 1: if you want to get it weekly, be sure to 10 00:00:31,240 --> 00:00:34,040 Speaker 1: subscribe to our patreon at patreon dot com slash Tangody. 11 00:00:34,440 --> 00:00:37,720 Speaker 1: So let's get into it. I'm here with my producer, Mike. Mike, 12 00:00:37,760 --> 00:00:38,880 Speaker 1: thank you so much for being here. 13 00:00:39,400 --> 00:00:40,239 Speaker 2: Thanks for having me. 14 00:00:40,360 --> 00:00:43,400 Speaker 3: I'm super excited to be here for this inaugural news 15 00:00:43,400 --> 00:00:43,879 Speaker 3: around us. 16 00:00:44,240 --> 00:00:46,360 Speaker 1: So we're starting out hot with a story that I 17 00:00:46,479 --> 00:00:49,600 Speaker 1: followed obsessively. I have watched multiple documentaries about it. I 18 00:00:49,640 --> 00:00:52,240 Speaker 1: have read books about it, I have watched Hulu series 19 00:00:52,280 --> 00:00:55,600 Speaker 1: about it. And that is my girl, Elizabeth Holmes. Elizabeth 20 00:00:55,600 --> 00:00:58,440 Speaker 1: Holmes has officially girl bossed a little bit too close 21 00:00:58,440 --> 00:01:01,639 Speaker 1: to the Sun and must report to prison on May thirtieth. 22 00:01:01,960 --> 00:01:04,480 Speaker 1: Y'all probably remember Elizabeth Holmes. She used to run a 23 00:01:04,520 --> 00:01:07,560 Speaker 1: blood testing startup called Pharaohose that said that using a 24 00:01:07,640 --> 00:01:10,320 Speaker 1: drop of blood they could screen for diseases and health information, 25 00:01:10,640 --> 00:01:13,839 Speaker 1: but actually it never did any of that. Elizabeth Holmes 26 00:01:13,880 --> 00:01:16,759 Speaker 1: like she wore a lot of black turtlenecks and sort 27 00:01:16,800 --> 00:01:18,840 Speaker 1: of always had this messy hair, and she kind of 28 00:01:18,880 --> 00:01:22,440 Speaker 1: talked like this all that's part of this like tech 29 00:01:22,840 --> 00:01:26,240 Speaker 1: CEO persona that was also kind of a fraud. Holmes 30 00:01:26,280 --> 00:01:28,760 Speaker 1: was convicted on charges of defrauding her investors in her 31 00:01:28,800 --> 00:01:32,520 Speaker 1: failed blood testing startup. She's appealing those charges, but the 32 00:01:32,520 --> 00:01:35,440 Speaker 1: court rejected her request to remain out on bail while 33 00:01:35,440 --> 00:01:37,560 Speaker 1: she appeals her case. She's sort of been able to 34 00:01:37,680 --> 00:01:40,440 Speaker 1: drag her feet on reporting to prison, but it seems 35 00:01:40,520 --> 00:01:43,039 Speaker 1: like maybe her time has finally run out now. Back 36 00:01:43,080 --> 00:01:45,839 Speaker 1: in November, she was sentenced to eleven years and three 37 00:01:45,880 --> 00:01:49,000 Speaker 1: months in prison for conspiracy and fraud against investors. Now, 38 00:01:49,000 --> 00:01:51,120 Speaker 1: she was supposed to report to prison back in April, 39 00:01:51,240 --> 00:01:53,760 Speaker 1: but her prison time was delayed while the court considered 40 00:01:53,760 --> 00:01:58,120 Speaker 1: her appeal. This comes after kind of a very glowing, 41 00:01:58,360 --> 00:02:02,920 Speaker 1: fawning New York Times like Redemption Arc profile that really 42 00:02:02,960 --> 00:02:06,360 Speaker 1: painted her in a sympathetic light. But let's not forget 43 00:02:06,400 --> 00:02:10,040 Speaker 1: that Elizabeth Holmes's scam hurt real people. There was a 44 00:02:10,040 --> 00:02:12,680 Speaker 1: mother with a history of miscarriages who was wrongly told 45 00:02:12,680 --> 00:02:14,320 Speaker 1: that she would never be able to have a baby. 46 00:02:14,560 --> 00:02:17,160 Speaker 1: There was someone who was given a false HIV diagnosis 47 00:02:17,200 --> 00:02:19,120 Speaker 1: and had to wait for months until they could afford 48 00:02:19,200 --> 00:02:23,239 Speaker 1: another test. And someone was even given a false cancer diagnosis. 49 00:02:23,600 --> 00:02:26,480 Speaker 3: Yeah, that's what's so egregious about her story. You know, 50 00:02:26,639 --> 00:02:29,360 Speaker 3: as somebody who works in health tech, it was like, 51 00:02:30,680 --> 00:02:35,760 Speaker 3: really just egregious that she would just be lying about 52 00:02:35,880 --> 00:02:39,919 Speaker 3: what this technology can do. But it says a lot 53 00:02:40,080 --> 00:02:47,120 Speaker 3: about our legal frameworks for policing or regulating health tech 54 00:02:47,440 --> 00:02:51,120 Speaker 3: that none of her jail sentences related to harming those 55 00:02:51,160 --> 00:02:54,760 Speaker 3: people or the thousands of more people who you know, 56 00:02:54,800 --> 00:02:57,600 Speaker 3: received an accurate medical information. It was all just about 57 00:02:57,960 --> 00:03:02,560 Speaker 3: the investors. And you know, it's I'm like kind of 58 00:03:02,560 --> 00:03:08,079 Speaker 3: torn about this because it's good to see some justice happening, 59 00:03:08,560 --> 00:03:12,280 Speaker 3: but it's it's not happening because people were harmed. It's 60 00:03:12,320 --> 00:03:15,880 Speaker 3: just because investors were harmed, and it's a real shortcoming. 61 00:03:16,360 --> 00:03:20,440 Speaker 1: No, I firmly believe had Elizabeth Holmes not harmed and 62 00:03:20,480 --> 00:03:24,000 Speaker 1: defrauded wealthy investors, rich people, she would not be going 63 00:03:24,000 --> 00:03:25,360 Speaker 1: to jail. I think that if it was if she 64 00:03:25,360 --> 00:03:28,880 Speaker 1: had only harmed regular ordinary people, which she did harm, 65 00:03:29,120 --> 00:03:30,800 Speaker 1: I don't think that she'd be facing jail time like 66 00:03:30,840 --> 00:03:32,480 Speaker 1: she is. And I also don't think that she would 67 00:03:32,520 --> 00:03:34,960 Speaker 1: be hated like she is. It is kind of a 68 00:03:35,000 --> 00:03:38,280 Speaker 1: sad thing that the reason why she's facing consequences is 69 00:03:38,320 --> 00:03:42,080 Speaker 1: probably because she defrauded investors, because people don't care when 70 00:03:42,120 --> 00:03:44,760 Speaker 1: you harm. I think that we've just kind of been 71 00:03:44,800 --> 00:03:47,960 Speaker 1: conditioned that you can be a tech company that harms 72 00:03:47,960 --> 00:03:52,000 Speaker 1: everyday people and that's totally fine. Harm wealthy people and investors, 73 00:03:52,200 --> 00:03:53,160 Speaker 1: then you're going to jail. 74 00:03:53,920 --> 00:03:54,240 Speaker 2: Yeah. 75 00:03:54,280 --> 00:03:56,360 Speaker 3: And so you know, as we think of talk about 76 00:03:56,400 --> 00:04:00,520 Speaker 3: on the show, like what are what is the look 77 00:04:00,600 --> 00:04:04,440 Speaker 3: like that does a better job of protecting people? I 78 00:04:04,520 --> 00:04:09,080 Speaker 3: think this is a real area whereas health technology becomes 79 00:04:09,080 --> 00:04:14,640 Speaker 3: a bigger part of the health landscape overall, we're really 80 00:04:15,240 --> 00:04:19,480 Speaker 3: going to need regulatory frameworks to address it, and right 81 00:04:19,520 --> 00:04:22,159 Speaker 3: now we just like don't have them to the extent 82 00:04:22,200 --> 00:04:25,640 Speaker 3: that we do in FDA. They're just very inadequate for 83 00:04:26,240 --> 00:04:28,719 Speaker 3: what's coming. And we haven't even talked about AI. 84 00:04:29,200 --> 00:04:31,680 Speaker 1: Well, speaking of regulation, let's talk about those AI. Senate 85 00:04:31,680 --> 00:04:34,159 Speaker 1: hearings that also happened this week. There were Senate hearings 86 00:04:34,160 --> 00:04:36,839 Speaker 1: on AI this week. The hearings started with Senator Richard 87 00:04:36,839 --> 00:04:41,760 Speaker 1: Blumenthal playing testimony in air quotes from a deep fake 88 00:04:41,839 --> 00:04:44,560 Speaker 1: audio recording of his own voice that was written by 89 00:04:44,640 --> 00:04:48,120 Speaker 1: chat GPT and vocalized by an audio application trained on 90 00:04:48,160 --> 00:04:50,159 Speaker 1: his Senate floor speeches. You got to hear it. I 91 00:04:50,160 --> 00:04:52,479 Speaker 1: know that doesn't make sense, but you'll understand what I'm 92 00:04:52,520 --> 00:04:53,839 Speaker 1: saying when you hear it. You gotta hear it. 93 00:04:53,880 --> 00:04:58,120 Speaker 4: Here we go too often. We have seen what happens 94 00:04:58,120 --> 00:05:03,920 Speaker 4: when technology outpaces regularly, the unbridled exploitation of personal data, 95 00:05:04,560 --> 00:05:10,600 Speaker 4: the proliferation of disinformation, and the deepening of societal inequalities. 96 00:05:11,560 --> 00:05:18,840 Speaker 4: We have seen how algorithmic biases can perpetuate discrimination and prejudice, 97 00:05:19,560 --> 00:05:23,800 Speaker 4: and how the lack of transparency can undermine public trust. 98 00:05:24,560 --> 00:05:26,680 Speaker 4: This is not the future we want. 99 00:05:27,320 --> 00:05:30,919 Speaker 1: So that is pretty eerie to hear, right like that 100 00:05:31,040 --> 00:05:33,800 Speaker 1: sounds just like him. He makes a good point that 101 00:05:34,240 --> 00:05:36,560 Speaker 1: you know, it happened to be saying things that he 102 00:05:36,640 --> 00:05:39,200 Speaker 1: actually agreed with, but they could This is easily be 103 00:05:39,200 --> 00:05:41,640 Speaker 1: saying something that he would never say and doesn't agree with. 104 00:05:41,760 --> 00:05:43,200 Speaker 1: And you can sort of get an idea of the 105 00:05:43,240 --> 00:05:45,599 Speaker 1: implications of that kind of technology. 106 00:05:46,000 --> 00:05:49,040 Speaker 3: Yeah, it was eerie, and it's it's such a good 107 00:05:49,120 --> 00:05:51,400 Speaker 3: little stunt, Like I kind of love it when senators 108 00:05:51,440 --> 00:05:54,480 Speaker 3: get up there and do little stunts like this. This 109 00:05:54,520 --> 00:05:56,799 Speaker 3: one is particularly good because it's not just an attention stunt, 110 00:05:56,839 --> 00:06:00,600 Speaker 3: but it really highlights how it's not just these individual 111 00:06:00,640 --> 00:06:04,640 Speaker 3: technologies that are at risk, but really the combination of 112 00:06:04,680 --> 00:06:11,520 Speaker 3: them that create some really like terrifying possibilities for the 113 00:06:11,560 --> 00:06:12,480 Speaker 3: future holds. 114 00:06:13,080 --> 00:06:17,279 Speaker 1: So these hearings really seemed like very chummy. CNN reported 115 00:06:17,320 --> 00:06:20,120 Speaker 1: that before the hearings, Sam Altman, who you may know 116 00:06:20,240 --> 00:06:23,040 Speaker 1: is the CEO of Open AI who has basically kind 117 00:06:23,080 --> 00:06:25,960 Speaker 1: of become the face of AI, met with more than 118 00:06:26,000 --> 00:06:29,000 Speaker 1: sixty House lawmakers over a dinner. It was a bipartisan 119 00:06:29,040 --> 00:06:32,280 Speaker 1: gathering featuring an even split of Republicans and Democrats, and 120 00:06:32,480 --> 00:06:37,159 Speaker 1: Altman demonstrated like various uses of chat GPT to quote 121 00:06:37,360 --> 00:06:39,880 Speaker 1: much amusement, according to a person in the room who 122 00:06:39,920 --> 00:06:45,960 Speaker 1: described the lawmakers as riveted by the display. Yeah, something 123 00:06:46,000 --> 00:06:48,320 Speaker 1: about that. There's something about that. I don't like that. 124 00:06:48,400 --> 00:06:54,240 Speaker 1: This was such a chummy meeting of lawmakers and AI technologists. 125 00:06:54,560 --> 00:06:58,279 Speaker 1: I would like, I guess I'll just I'll just stop 126 00:06:58,279 --> 00:07:01,400 Speaker 1: it there. There's something about the chumminess that I find 127 00:07:01,440 --> 00:07:03,240 Speaker 1: a little sus What do you think. 128 00:07:03,560 --> 00:07:06,440 Speaker 3: Yeah, absolutely, it is a little sus right, Like, usually 129 00:07:06,680 --> 00:07:12,120 Speaker 3: regulation is an adversarial thing, and in those occasions when 130 00:07:12,360 --> 00:07:16,640 Speaker 3: the industry is like leading the charge on the regulation, 131 00:07:18,000 --> 00:07:19,240 Speaker 3: it's usually weird. 132 00:07:19,440 --> 00:07:19,600 Speaker 2: Right. 133 00:07:19,600 --> 00:07:23,000 Speaker 3: There's usually some kind of like perverse incentive or like 134 00:07:23,320 --> 00:07:27,360 Speaker 3: agency capture or something going on, and it feels weird. 135 00:07:27,440 --> 00:07:28,920 Speaker 2: Yeah, you're not the only one that feels that way. 136 00:07:29,160 --> 00:07:29,360 Speaker 5: Yeah. 137 00:07:29,360 --> 00:07:31,600 Speaker 1: And if you were just kind of casually following the 138 00:07:31,680 --> 00:07:34,840 Speaker 1: story via headlines that you didn't click into, you probably 139 00:07:34,840 --> 00:07:38,840 Speaker 1: saw that Sam Altman actually pleaded for Senate to regulate AI. 140 00:07:38,920 --> 00:07:42,200 Speaker 1: He said, we think that regulatory intervention by governments will 141 00:07:42,240 --> 00:07:45,200 Speaker 1: be critical to mitigate the risk of increasingly powerful models. 142 00:07:45,880 --> 00:07:48,240 Speaker 1: And so here's my take. This whole thing just feels 143 00:07:48,320 --> 00:07:50,680 Speaker 1: very suss to me. And Scott Galloway actually put it 144 00:07:50,720 --> 00:07:53,800 Speaker 1: really well on Twitter. He tweeted this very long list 145 00:07:53,880 --> 00:07:56,840 Speaker 1: of all the different times that tech CEOs made a 146 00:07:56,880 --> 00:08:00,600 Speaker 1: big show in public asking for their technology to be regulated. 147 00:08:00,680 --> 00:08:03,120 Speaker 1: And so I think that what so like Mark Zuckerberg 148 00:08:03,200 --> 00:08:07,320 Speaker 1: saying please regulate us, or Jack Dorsey saying please regulate Twitter. 149 00:08:07,600 --> 00:08:10,960 Speaker 1: And I think that what Allman is actually saying is, yes, 150 00:08:11,080 --> 00:08:14,520 Speaker 1: we totally agree we need regulation, and we would also 151 00:08:14,560 --> 00:08:17,240 Speaker 1: be very happy to write that regulation for you. I 152 00:08:17,240 --> 00:08:21,720 Speaker 1: think he's advocating for AI companies making the rules themselves. 153 00:08:21,960 --> 00:08:26,200 Speaker 1: I don't think that he's actually like genuinely asking for 154 00:08:26,400 --> 00:08:28,280 Speaker 1: lawmakers to regulate AI. 155 00:08:29,360 --> 00:08:29,560 Speaker 5: Yeah. 156 00:08:29,880 --> 00:08:33,439 Speaker 3: I think that's probably right. And you could forgive him 157 00:08:33,440 --> 00:08:37,400 Speaker 3: for thinking that Congress may do nothing, right, Like, they 158 00:08:37,440 --> 00:08:39,600 Speaker 3: have a long track record of doing nothing, And so 159 00:08:40,040 --> 00:08:43,080 Speaker 3: it also feels like a good way to just avoid 160 00:08:43,120 --> 00:08:47,000 Speaker 3: responsibility for having some sort of like ethical safeguards in 161 00:08:47,000 --> 00:08:50,120 Speaker 3: place on their own, which we can't expect them to 162 00:08:50,160 --> 00:08:51,040 Speaker 3: because they're companies. 163 00:08:51,280 --> 00:08:52,080 Speaker 2: They wouldn't do that. 164 00:08:53,559 --> 00:08:55,480 Speaker 3: But yeah, like calling for regulation, it feels like a 165 00:08:55,480 --> 00:08:58,800 Speaker 3: good way to pass the buck and be like, oh it, Congress, 166 00:08:58,800 --> 00:09:03,480 Speaker 3: why don't you regulate us. 167 00:09:04,760 --> 00:09:16,640 Speaker 5: Let's take a quick break at our back. 168 00:09:18,480 --> 00:09:22,000 Speaker 1: While speaking of passing the buck and posting while really 169 00:09:22,080 --> 00:09:25,559 Speaker 1: doing nothing, Let's talk about Montana. So this week, Montana 170 00:09:25,600 --> 00:09:28,560 Speaker 1: became the first date to ban the social media platform TikTok, 171 00:09:28,600 --> 00:09:31,280 Speaker 1: which is a pretty big step in the backlash against 172 00:09:31,280 --> 00:09:35,080 Speaker 1: this Chinese owned platform over concerns about things like data privacy. 173 00:09:35,559 --> 00:09:38,680 Speaker 1: Montana's governor signed the measure on Wednesday. He said that 174 00:09:38,720 --> 00:09:42,320 Speaker 1: he did so to quote protect Montana's personal and private 175 00:09:42,360 --> 00:09:45,280 Speaker 1: data from the Chinese Communist Party. The band takes effect 176 00:09:45,440 --> 00:09:48,480 Speaker 1: in January of twenty twenty four, but people can actually 177 00:09:48,600 --> 00:09:52,120 Speaker 1: still use TikTok after that, like, this law does not 178 00:09:52,240 --> 00:09:54,880 Speaker 1: punish them if they use TikTok, But what it actually 179 00:09:54,920 --> 00:09:57,520 Speaker 1: does is target the availability of the app by threatening 180 00:09:57,679 --> 00:10:00,800 Speaker 1: entities such as TikTok, Google and Apple with a ten 181 00:10:00,880 --> 00:10:03,880 Speaker 1: thousand dollars fine for each day that the platform remains 182 00:10:03,880 --> 00:10:07,080 Speaker 1: accessible in app stores for users in Montana. So from 183 00:10:07,120 --> 00:10:10,120 Speaker 1: the user end, you don't actually have to do anything 184 00:10:10,320 --> 00:10:13,440 Speaker 1: once this band, once this law goes into effect. But 185 00:10:13,520 --> 00:10:16,319 Speaker 1: eventually when you need to update TikTok for it to 186 00:10:16,400 --> 00:10:19,080 Speaker 1: run properly, that's when it will probably become an issue. 187 00:10:19,080 --> 00:10:21,880 Speaker 1: So it's kind of a way of like phasing TikTok 188 00:10:21,920 --> 00:10:24,480 Speaker 1: out because people won't be able to access the app 189 00:10:24,600 --> 00:10:27,760 Speaker 1: in the app store to like get the the updates 190 00:10:27,800 --> 00:10:29,240 Speaker 1: that you would need for it to run properly on 191 00:10:29,280 --> 00:10:32,360 Speaker 1: your device. According to the Washington Post, app stores are 192 00:10:32,400 --> 00:10:35,120 Speaker 1: divided by country or global region, and that they don't 193 00:10:35,240 --> 00:10:37,720 Speaker 1: change or discriminate based on what state a user is in. 194 00:10:38,160 --> 00:10:41,040 Speaker 1: Changing that system would require not just carving the stores 195 00:10:41,040 --> 00:10:44,440 Speaker 1: into state specific chunks, but also closer monitoring of people's 196 00:10:44,440 --> 00:10:47,000 Speaker 1: locations and a buy the minute system to define what 197 00:10:47,040 --> 00:10:50,400 Speaker 1: happens when, for instance, a user drives over state lines, 198 00:10:50,679 --> 00:10:53,040 Speaker 1: and keep it in mind that like people can always 199 00:10:53,080 --> 00:10:55,320 Speaker 1: just sort of get around this by using a VPN. 200 00:10:55,600 --> 00:10:58,600 Speaker 1: So it seems like this law will be very difficult 201 00:10:58,640 --> 00:11:00,599 Speaker 1: to enforce, to the point where you almost kind of 202 00:11:00,600 --> 00:11:03,040 Speaker 1: have to wonder what the point of this law would be. 203 00:11:03,480 --> 00:11:06,120 Speaker 1: The law would be nullified if TikTok is no longer 204 00:11:06,160 --> 00:11:10,199 Speaker 1: headquartered in quote any country designated as a foreign adversary 205 00:11:10,240 --> 00:11:12,840 Speaker 1: by the US government. The law will probably face a 206 00:11:12,880 --> 00:11:16,000 Speaker 1: significant legal challenge. Free speech groups like the acl YOU 207 00:11:16,040 --> 00:11:19,160 Speaker 1: have already been vocally criticizing this law, and five TikTok 208 00:11:19,200 --> 00:11:22,160 Speaker 1: creators filed a lawsuit saying that this ban infringes on 209 00:11:22,200 --> 00:11:24,679 Speaker 1: their First Amendment rights. In the suit, they say the 210 00:11:24,679 --> 00:11:27,880 Speaker 1: state of Montana quote can no more ban its residents 211 00:11:27,880 --> 00:11:30,240 Speaker 1: from viewing or posting to TikTok than it could ban 212 00:11:30,320 --> 00:11:32,440 Speaker 1: the wall street journal because of who owns it or 213 00:11:32,440 --> 00:11:35,320 Speaker 1: the ideas it publishes. So y'all probably know that we 214 00:11:35,360 --> 00:11:39,079 Speaker 1: did an entire episode with misinformation specialist and TikTok creator 215 00:11:39,120 --> 00:11:42,320 Speaker 1: Abby Richards about why she feels like banning TikTok just 216 00:11:42,440 --> 00:11:44,520 Speaker 1: is not a good idea. We'll link to the episode 217 00:11:44,559 --> 00:11:47,359 Speaker 1: in the show notes. Definitely worth listening to. But ultimately, 218 00:11:47,440 --> 00:11:49,600 Speaker 1: my take is that there is so much wrong with 219 00:11:49,640 --> 00:11:52,800 Speaker 1: our current data privacy laws and landscape in the US 220 00:11:53,000 --> 00:11:55,800 Speaker 1: that if lawmakers truly wanted to protect our data and 221 00:11:55,840 --> 00:11:59,360 Speaker 1: our privacy, banning TikTok would not really accomplish that. So 222 00:11:59,559 --> 00:12:01,920 Speaker 1: I think that laws like this one, especially given that 223 00:12:01,960 --> 00:12:04,760 Speaker 1: they're so difficult to actually enforce, are just sort of 224 00:12:04,760 --> 00:12:08,160 Speaker 1: meant to signal to us the American public, that lawmakers 225 00:12:08,160 --> 00:12:11,160 Speaker 1: are like cracking down on and sort of getting tough 226 00:12:11,240 --> 00:12:15,000 Speaker 1: with big tech while actually doing nothing. And I firmly 227 00:12:15,000 --> 00:12:17,839 Speaker 1: believe that what we need is meaningful policies to protect 228 00:12:17,880 --> 00:12:21,640 Speaker 1: our privacy and data, not just grandstanding and postering. 229 00:12:21,720 --> 00:12:23,679 Speaker 2: Yeah, yeah, I agree. 230 00:12:24,800 --> 00:12:28,640 Speaker 3: On the one hand, it's nice to see politicians taking 231 00:12:28,640 --> 00:12:31,520 Speaker 3: some action. On the other hand, this law seems like 232 00:12:31,720 --> 00:12:35,000 Speaker 3: bad in half of different ways, and like you said unenforceable. 233 00:12:35,320 --> 00:12:39,959 Speaker 3: I'm not very sympathetic to Google crying that they wouldn't 234 00:12:39,960 --> 00:12:42,560 Speaker 3: know how to do this, right, Like, you can geo 235 00:12:42,720 --> 00:12:46,600 Speaker 3: fence Google ads down to like a city block if 236 00:12:46,640 --> 00:12:50,080 Speaker 3: you want to. So the idea that they are going 237 00:12:50,160 --> 00:12:55,559 Speaker 3: to be completely unable to handle state boundaries is just dishonest, right, 238 00:12:55,640 --> 00:12:59,160 Speaker 3: Like they could technically figure it out if they wanted to, 239 00:12:59,200 --> 00:13:02,599 Speaker 3: but they don't want to. Not that this law is 240 00:13:03,160 --> 00:13:04,679 Speaker 3: a good idea because like you said, people can get 241 00:13:04,720 --> 00:13:07,280 Speaker 3: around it a bunch of different ways. It's probably unconstitutional, 242 00:13:08,920 --> 00:13:11,560 Speaker 3: but yeah, wouldn't it be nice if we saw more 243 00:13:12,000 --> 00:13:19,120 Speaker 3: thoughtful legislation that instead of just banning one app for 244 00:13:19,200 --> 00:13:25,520 Speaker 3: reasons that feel awfully sinophobic, what if they prohibited categories 245 00:13:25,679 --> 00:13:29,960 Speaker 3: of data use or prohibited categories of data collection so 246 00:13:30,000 --> 00:13:37,480 Speaker 3: that social media platforms could not harvest the personal data 247 00:13:37,600 --> 00:13:41,360 Speaker 3: of Montanin's without their explicit consent. You know, that feels 248 00:13:41,400 --> 00:13:44,800 Speaker 3: like it would be a much better approach to actually 249 00:13:44,800 --> 00:13:46,000 Speaker 3: protect people's privacy. 250 00:13:46,320 --> 00:13:49,080 Speaker 1: There are a million different better approaches that are out there. 251 00:13:49,280 --> 00:13:53,600 Speaker 1: I firmly think that lawmakers want the credit for appearing 252 00:13:53,640 --> 00:13:56,440 Speaker 1: to do something while actually doing nothing. That is my 253 00:13:56,520 --> 00:13:58,800 Speaker 1: big problem. With all of this talk of banning TikTok. 254 00:13:59,200 --> 00:14:00,839 Speaker 3: Yeah, and a big of what they're trying to do 255 00:14:00,880 --> 00:14:04,920 Speaker 3: with the TikTok band is feed anti China sentiment, which 256 00:14:06,320 --> 00:14:08,240 Speaker 3: I think there's a lot of good reasons to be 257 00:14:08,480 --> 00:14:12,880 Speaker 3: concerned about China's relationship with the United States and spying 258 00:14:12,920 --> 00:14:13,599 Speaker 3: on Americans. 259 00:14:14,040 --> 00:14:17,760 Speaker 2: But we could definitely do without the racist rhetoric right. 260 00:14:17,600 --> 00:14:19,800 Speaker 1: Pretty much always. You know who needs to hear that, 261 00:14:20,120 --> 00:14:22,800 Speaker 1: Elon Musk. I know that I said that I wasn't 262 00:14:22,800 --> 00:14:25,200 Speaker 1: going to talk about Elon Musk anymore like her my 263 00:14:25,280 --> 00:14:28,840 Speaker 1: own personal mental health, and I was going to stop 264 00:14:28,840 --> 00:14:30,960 Speaker 1: talking about Elon Musk, but I have to talk about 265 00:14:31,000 --> 00:14:33,880 Speaker 1: this interview that he did. So last week, Elon Musk 266 00:14:33,920 --> 00:14:36,640 Speaker 1: did this interview with CNBC. During the interview, who was 267 00:14:36,680 --> 00:14:39,240 Speaker 1: asked about his tweets about the mass shooting in Allen, Texas, 268 00:14:39,400 --> 00:14:42,240 Speaker 1: which was a total tragedy on May eighth that left 269 00:14:42,280 --> 00:14:44,920 Speaker 1: eight people dead and seven people wounded. Now, let's just 270 00:14:44,960 --> 00:14:47,960 Speaker 1: be super super clear. The Texas Department of Public Safety 271 00:14:48,000 --> 00:14:50,680 Speaker 1: has said that the shooter showed indications of holding neo 272 00:14:50,760 --> 00:14:53,880 Speaker 1: Nazi ideology, with an official saying that he had patches 273 00:14:53,920 --> 00:14:56,840 Speaker 1: he had tattoos, and multiple news outlets, including The New 274 00:14:56,920 --> 00:15:00,200 Speaker 1: York Times, confirm this too. But Elon Musk basically just 275 00:15:00,240 --> 00:15:03,040 Speaker 1: said a bunch of inaccurate stuff about the shooting. Elon 276 00:15:03,120 --> 00:15:06,600 Speaker 1: Musk said quote ascribing it to white supremacy was bullshit. 277 00:15:06,840 --> 00:15:09,080 Speaker 1: There's no proof that he is a white supremacist. We 278 00:15:09,120 --> 00:15:11,840 Speaker 1: should not be ascribing things to white supremacy if it's false. 279 00:15:12,200 --> 00:15:14,760 Speaker 1: When he doubled down on this claim on Twitter, people 280 00:15:14,800 --> 00:15:18,320 Speaker 1: actually use Twitter's community notes feature to correct him, adding 281 00:15:18,320 --> 00:15:20,760 Speaker 1: a footnote to the post that said Texas police have 282 00:15:20,800 --> 00:15:24,520 Speaker 1: confirmed that Alan the mall shooter had neo Nazi tattoos 283 00:15:24,560 --> 00:15:26,680 Speaker 1: and beliefs, and he wore a pad signifying right wing 284 00:15:26,760 --> 00:15:29,600 Speaker 1: deav squad. The community note was deleted. We do not 285 00:15:29,720 --> 00:15:31,560 Speaker 1: know if Elon Musk had editing to do with that 286 00:15:31,720 --> 00:15:35,320 Speaker 1: that community note being deleted, but does something he would do, 287 00:15:35,360 --> 00:15:38,720 Speaker 1: we have to admit. So this is my take. Obviously, 288 00:15:38,920 --> 00:15:41,920 Speaker 1: the things that Elon Musk says are not true and 289 00:15:42,000 --> 00:15:45,280 Speaker 1: also horrible, right, So like, that's not what I'm giving 290 00:15:45,280 --> 00:15:48,280 Speaker 1: a perspective on here. My take is that we absolutely 291 00:15:48,480 --> 00:15:50,400 Speaker 1: need to talk about the fact that Elon Musk has 292 00:15:50,440 --> 00:15:54,240 Speaker 1: been amplifying and engaging with extremist right wing talking points, 293 00:15:54,240 --> 00:15:56,520 Speaker 1: and he's been doing it for a really long time 294 00:15:56,600 --> 00:15:59,680 Speaker 1: out in public. I think that for a while, like 295 00:15:59,760 --> 00:16:03,280 Speaker 1: it was kind of easy or maybe tempting for tech journalists, 296 00:16:03,640 --> 00:16:07,040 Speaker 1: even tech journalists that I really personally like and respect, 297 00:16:07,120 --> 00:16:10,000 Speaker 1: like Kara Swisher. I'm obsessed with Kara Swisher. She is 298 00:16:10,000 --> 00:16:12,880 Speaker 1: one of my idols. However, I do think that some 299 00:16:12,920 --> 00:16:15,720 Speaker 1: of the tech press really kind of let him off 300 00:16:15,720 --> 00:16:18,320 Speaker 1: the hook from what is what he is obviously doing 301 00:16:18,440 --> 00:16:20,880 Speaker 1: in plain sight like it, I think that people gave 302 00:16:20,920 --> 00:16:23,080 Speaker 1: him the benefit of the doubt. They assumed that maybe 303 00:16:23,120 --> 00:16:25,520 Speaker 1: something else was going on, like maybe Elon Musk was 304 00:16:25,680 --> 00:16:28,160 Speaker 1: just trolling or kind of just trying to be edgy 305 00:16:28,360 --> 00:16:31,480 Speaker 1: use edgy humor, or he doesn't actually believe the things 306 00:16:31,480 --> 00:16:33,840 Speaker 1: that he's talking about, or that maybe he's somehow just 307 00:16:33,920 --> 00:16:37,360 Speaker 1: trying to like hear out both sides, as if someone 308 00:16:37,440 --> 00:16:40,560 Speaker 1: like Elon Musk is far too smart to fall for 309 00:16:40,680 --> 00:16:43,480 Speaker 1: like a right wing extremist echo chamber. But this has 310 00:16:43,520 --> 00:16:46,200 Speaker 1: been going on in plain sight for kind of a 311 00:16:46,240 --> 00:16:49,520 Speaker 1: long time. Like remember when Nancy Pelosi's husband Paul Pelosi 312 00:16:49,880 --> 00:16:52,440 Speaker 1: was attacked by a man with a hammer Elon Musk 313 00:16:52,480 --> 00:16:55,120 Speaker 1: tweeted a piece from the fringe website called the Santa 314 00:16:55,160 --> 00:16:58,480 Speaker 1: Monica Observer, which has previously reported things like that Hillary 315 00:16:58,520 --> 00:17:00,600 Speaker 1: Clinton died at nine to eleven and replaced by a 316 00:17:00,600 --> 00:17:04,119 Speaker 1: body double. This piece falsely claimed that Paul Pelosi knew 317 00:17:04,160 --> 00:17:06,399 Speaker 1: his attacker and that they had had some kind of 318 00:17:06,440 --> 00:17:11,400 Speaker 1: a relationship. This is obviously not true, completely baseless. Pelosi's 319 00:17:11,400 --> 00:17:14,280 Speaker 1: attacker admitted that he broke into their home specifically to 320 00:17:14,320 --> 00:17:18,439 Speaker 1: attack Speaker Pelosi, but after Musk tweeted this, other extremists 321 00:17:18,440 --> 00:17:21,720 Speaker 1: amplified this baseless claim on Twitter, including Marjorie Taylor Green, 322 00:17:21,960 --> 00:17:24,400 Speaker 1: who defended Musk with a tweet that repeated the lie 323 00:17:24,440 --> 00:17:27,399 Speaker 1: that quote Paul Pelosi's friend attacked him with the hammer. 324 00:17:27,680 --> 00:17:29,720 Speaker 1: So Elon Musk, we just have to call it what 325 00:17:29,800 --> 00:17:34,040 Speaker 1: it is. He traffics in well worn conspiracy theories that 326 00:17:34,080 --> 00:17:37,240 Speaker 1: are oftentimes anti Semitic, right, so he has trafficked in 327 00:17:37,280 --> 00:17:40,159 Speaker 1: a well worn anti Semitic trope and talking about the 328 00:17:40,240 --> 00:17:45,240 Speaker 1: influential philanthropist George Soros, he tweeted, George Soros reminds me 329 00:17:45,280 --> 00:17:49,240 Speaker 1: of Magneto, which is obviously comparing Soros to the Marvel supervillain, 330 00:17:49,480 --> 00:17:53,480 Speaker 1: presumably because they're both Jewish Holocaust survivors. Just in case 331 00:17:53,560 --> 00:17:56,600 Speaker 1: you were confused about what he was trying to say, 332 00:17:56,800 --> 00:17:59,440 Speaker 1: he followed up with another tweet saying that George Soros 333 00:17:59,480 --> 00:18:05,359 Speaker 1: hates human Listen, Soros is an incredibly influential, connected philanthropist, 334 00:18:05,480 --> 00:18:07,840 Speaker 1: Like I think he's the biggest donor to the Democratic Party. 335 00:18:08,200 --> 00:18:12,040 Speaker 1: It is absolutely fine to criticize someone like George Soros's, 336 00:18:12,359 --> 00:18:17,159 Speaker 1: you know, agenda, perspective, whatever. However, there is absolutely a 337 00:18:17,200 --> 00:18:20,160 Speaker 1: way to do that that does not traffic in well 338 00:18:20,240 --> 00:18:23,040 Speaker 1: worn anti Semitic tropes, like you don't have to compare 339 00:18:23,119 --> 00:18:25,600 Speaker 1: him to a comic book supervillain. You don't have to 340 00:18:25,640 --> 00:18:28,200 Speaker 1: say that he hates humanity. You don't have to traffic 341 00:18:28,240 --> 00:18:31,880 Speaker 1: and all of these very well worn anti Semitic tropes 342 00:18:32,000 --> 00:18:36,159 Speaker 1: and stereotypes in order to criticize George Soros. And I 343 00:18:36,200 --> 00:18:38,600 Speaker 1: think the fact that Elon Musk continues to do so 344 00:18:39,000 --> 00:18:41,760 Speaker 1: is him telling us who he is. It's him showing 345 00:18:41,840 --> 00:18:44,439 Speaker 1: us who he is, where he stands, and what he believes, 346 00:18:44,440 --> 00:18:45,760 Speaker 1: and we got to take him for his word. 347 00:18:47,040 --> 00:18:49,800 Speaker 3: Yeah, I think you're you're right, And you know, a 348 00:18:49,840 --> 00:18:54,239 Speaker 3: notable difference about his defense of the shooter is that 349 00:18:54,280 --> 00:18:58,720 Speaker 3: there is nothing like funny or jokey that in what 350 00:18:58,800 --> 00:19:01,000 Speaker 3: he was saying, right like the idea that oh, he's 351 00:19:01,040 --> 00:19:05,480 Speaker 3: just like kidding or trolling or you know, playing to 352 00:19:05,520 --> 00:19:08,760 Speaker 3: the to the trolls for funsies or whatever people might 353 00:19:08,840 --> 00:19:10,600 Speaker 3: say to let him off the hook. He was just 354 00:19:10,640 --> 00:19:13,720 Speaker 3: straight up defending a Nazi, right, Like, there's photos of 355 00:19:13,720 --> 00:19:16,400 Speaker 3: this guy's tattoos, and it's like he's got a swastika, 356 00:19:16,440 --> 00:19:19,199 Speaker 3: he's got the ss lighting bolls, Like dude was a 357 00:19:19,320 --> 00:19:22,639 Speaker 3: Nazi and Elon Musk is out there saying that he 358 00:19:23,040 --> 00:19:26,480 Speaker 3: wasn't just pure disinformation. 359 00:19:26,440 --> 00:19:29,080 Speaker 1: Yeah, it's And I also think like when the Nancy 360 00:19:29,119 --> 00:19:33,320 Speaker 1: Pelosi thing happened, I don't remember who, I want to say, 361 00:19:33,320 --> 00:19:36,240 Speaker 1: it was like a major funder for MySpace going you know, 362 00:19:36,400 --> 00:19:38,399 Speaker 1: a bit of a throwback. But you know, he pointed 363 00:19:38,440 --> 00:19:42,439 Speaker 1: out that it's almost like Elon Musk doesn't understand or 364 00:19:42,480 --> 00:19:46,280 Speaker 1: misrejecting the responsibility that comes with being the CEO of 365 00:19:46,320 --> 00:19:48,679 Speaker 1: a platform, but that he thinks he's any other user, 366 00:19:48,880 --> 00:19:50,800 Speaker 1: he should be able to disci like tweet whatever he wants. 367 00:19:50,840 --> 00:19:54,240 Speaker 1: And that's you know, a complete fiction that like owning 368 00:19:54,280 --> 00:19:56,840 Speaker 1: this platform comes with a responsibility, and if you're not 369 00:19:57,000 --> 00:20:00,399 Speaker 1: able to responsibly and ethically handle that responsibilit then you 370 00:20:00,440 --> 00:20:04,400 Speaker 1: have no business leading a platform, and full stop. Jonathan Greenblatz, 371 00:20:04,440 --> 00:20:06,680 Speaker 1: the CEO of the Anti Defamation League, the civil rights 372 00:20:06,720 --> 00:20:09,520 Speaker 1: group that combat antisemitism, said that the way that Elon 373 00:20:09,600 --> 00:20:13,240 Speaker 1: Musk talks about George sorows will embolden extremists, and I 374 00:20:13,280 --> 00:20:16,840 Speaker 1: absolutely agree with him. Greenblat says Soro's often is held 375 00:20:16,880 --> 00:20:19,199 Speaker 1: up by the far right using anti Semitic tropes as 376 00:20:19,240 --> 00:20:22,000 Speaker 1: the source of the world's problems. To see Elon Musk, 377 00:20:22,119 --> 00:20:25,439 Speaker 1: regardless of his intent, feed this segment comparing him to 378 00:20:25,440 --> 00:20:29,240 Speaker 1: a Jewish supervillain, claiming that Soros hates Humanity's not just distressing, 379 00:20:29,560 --> 00:20:33,480 Speaker 1: it's dangerous. It will embolden extremists who already contrive anti 380 00:20:33,560 --> 00:20:36,000 Speaker 1: Jewish conspiracies and have tried to attack Soros and the 381 00:20:36,040 --> 00:20:39,840 Speaker 1: Jewish community as a result. I absolutely agree, and in 382 00:20:39,840 --> 00:20:43,160 Speaker 1: my opinion, it is really time to see Elon Musk 383 00:20:43,200 --> 00:20:46,160 Speaker 1: for who he is. If in May of twenty twenty three, 384 00:20:46,200 --> 00:20:48,320 Speaker 1: you are still giving this person the benefit of the doubt, 385 00:20:48,359 --> 00:20:51,439 Speaker 1: you are still saying that maybe he's joking, Maybe this 386 00:20:51,520 --> 00:20:53,600 Speaker 1: is some sort of like genius move that we're too 387 00:20:53,640 --> 00:20:57,560 Speaker 1: stupid to understand. It's time to let that go. Elon 388 00:20:57,640 --> 00:20:59,680 Speaker 1: Musk is telling us who he is. He has told 389 00:20:59,760 --> 00:21:02,080 Speaker 1: us who is time and time again. It is time 390 00:21:02,119 --> 00:21:04,520 Speaker 1: for us to believe him. You know, I think it 391 00:21:04,560 --> 00:21:07,360 Speaker 1: was Maya Angelou who said, when someone shows you who 392 00:21:07,400 --> 00:21:10,640 Speaker 1: they are, believe them. Elon Musk is showing us who 393 00:21:10,640 --> 00:21:13,560 Speaker 1: he is. It's time for all of us to believe him. 394 00:21:13,680 --> 00:21:16,520 Speaker 1: That this is you know, he's not just engaging with 395 00:21:16,600 --> 00:21:20,480 Speaker 1: this extremist garbage by a mistake. This is what he believes. 396 00:21:20,680 --> 00:21:22,439 Speaker 1: This is who he is. And the sooner that we 397 00:21:22,520 --> 00:21:23,639 Speaker 1: accept that, the better. 398 00:21:24,480 --> 00:21:25,240 Speaker 2: Totally agree. 399 00:21:25,640 --> 00:21:42,600 Speaker 6: Yeah more, after a quick break, let's get right back 400 00:21:42,600 --> 00:21:43,000 Speaker 6: into it. 401 00:21:45,240 --> 00:21:48,320 Speaker 1: Okay, So one last kind of fun thing. Did you 402 00:21:48,480 --> 00:21:50,080 Speaker 1: see that weird? I mean, I don't know if you 403 00:21:50,160 --> 00:21:53,080 Speaker 1: follow Essence Magazine on Facebook, Mike, probably not, But did 404 00:21:53,119 --> 00:21:56,960 Speaker 1: you see this weird Facebook post that Essence Magazine put 405 00:21:57,000 --> 00:21:58,400 Speaker 1: out about Viola Davis. 406 00:21:59,000 --> 00:22:00,919 Speaker 3: You sent it to me and I saw it, and 407 00:22:00,960 --> 00:22:04,560 Speaker 3: I was like, Wow, people are just like writing in 408 00:22:04,600 --> 00:22:07,080 Speaker 3: bizarre ways that I can't even understand anymore. I guess 409 00:22:07,119 --> 00:22:09,040 Speaker 3: I am out of touch with what the kids are doing. 410 00:22:10,080 --> 00:22:10,439 Speaker 5: Okay. 411 00:22:10,520 --> 00:22:13,879 Speaker 1: So basically it's this picture of Viola Davis at con. 412 00:22:14,160 --> 00:22:18,119 Speaker 1: She looks fantastic. She's wearing this like beautiful white gown, 413 00:22:18,240 --> 00:22:20,040 Speaker 1: just like, oh, it looks amazing. Ten or ten. So 414 00:22:20,240 --> 00:22:23,040 Speaker 1: Essence On Facebook, they wrote, I'm gonna read this verbatim. 415 00:22:23,520 --> 00:22:27,240 Speaker 1: We give credit when credit is due, and Villa Davis 416 00:22:27,520 --> 00:22:30,800 Speaker 1: is looking like eight point fifty. Everyone at con can 417 00:22:30,840 --> 00:22:34,440 Speaker 1: now pack your bags and return home. This is a eat, 418 00:22:34,920 --> 00:22:38,680 Speaker 1: This is an eight. This is supper. God damn, the 419 00:22:38,800 --> 00:22:41,920 Speaker 1: damn is broken and the flood of goodness has overflowed. 420 00:22:42,040 --> 00:22:45,480 Speaker 1: Ten out of ten. Okay, so this is clearly like 421 00:22:45,600 --> 00:22:49,879 Speaker 1: Essence magazine is a magazine that has a black woman readership, right, 422 00:22:49,880 --> 00:22:53,040 Speaker 1: and so this is obviously a post that is trying 423 00:22:53,200 --> 00:22:57,080 Speaker 1: to use like black woman slang like oh she ate 424 00:22:57,200 --> 00:23:01,560 Speaker 1: blah blah blah. I get that. However, I believe and 425 00:23:01,600 --> 00:23:04,160 Speaker 1: no one I have no proof, but no one will 426 00:23:04,160 --> 00:23:07,080 Speaker 1: be able to convince me otherwise. This is written by 427 00:23:07,119 --> 00:23:12,040 Speaker 1: AI somebody. Somebody has tried to train an AI model 428 00:23:12,359 --> 00:23:15,760 Speaker 1: to speak like a black woman and do black woman slang. 429 00:23:15,960 --> 00:23:18,520 Speaker 1: And I believe this, like, no one will ever be 430 00:23:18,600 --> 00:23:20,199 Speaker 1: able to convince me that that's not what this is. 431 00:23:20,240 --> 00:23:22,760 Speaker 1: I should say that this post has since been edited 432 00:23:22,800 --> 00:23:25,359 Speaker 1: and it's it reads much clearer. They definitely like have 433 00:23:25,600 --> 00:23:29,480 Speaker 1: polished it up. They've They've spelled Viola Davis's name correctly 434 00:23:29,480 --> 00:23:31,840 Speaker 1: in the edited version. But you know, maybe we don't 435 00:23:31,840 --> 00:23:33,879 Speaker 1: need to worry about AI taking all of our jobs 436 00:23:33,920 --> 00:23:36,639 Speaker 1: because this is what it looks like when AI tries 437 00:23:36,680 --> 00:23:38,479 Speaker 1: to do aave black slang. 438 00:23:38,800 --> 00:23:40,879 Speaker 3: It trips them up every time. Right, we gotta you 439 00:23:40,880 --> 00:23:43,680 Speaker 3: should call it Shippika Hudson, your slip is showing back 440 00:23:43,680 --> 00:23:44,000 Speaker 3: in here. 441 00:23:44,320 --> 00:23:47,760 Speaker 1: Yes, obviously racist word salad is what she called it. Okay, 442 00:23:47,760 --> 00:23:49,920 Speaker 1: So I'm gonna end on a little segment that I'm 443 00:23:49,920 --> 00:23:52,240 Speaker 1: hoping to return to, which is called is this happening 444 00:23:52,240 --> 00:23:53,600 Speaker 1: for everyone or happening just to me? 445 00:23:54,040 --> 00:23:54,119 Speaker 5: So? 446 00:23:54,960 --> 00:23:59,400 Speaker 1: Has anyone else on TikTok noticed segments of movies taking 447 00:23:59,440 --> 00:24:01,959 Speaker 1: over your for you page? It'll be just like a 448 00:24:02,080 --> 00:24:05,720 Speaker 1: juicy or climactic clip from a movie. So I can't 449 00:24:05,760 --> 00:24:09,720 Speaker 1: explain why, but I find these clips so deeply satisfying 450 00:24:09,720 --> 00:24:12,200 Speaker 1: to watch, even if they're movies that I haven't seen, 451 00:24:12,480 --> 00:24:14,439 Speaker 1: or even more when they're movies that I've seen one 452 00:24:14,480 --> 00:24:16,800 Speaker 1: hundred times. The last clip that came up for me 453 00:24:17,040 --> 00:24:19,359 Speaker 1: was that clip from the movie Selena, a movie that 454 00:24:19,359 --> 00:24:22,400 Speaker 1: I've seen no less than a dozen times, and it's 455 00:24:22,440 --> 00:24:26,080 Speaker 1: I know the movie by heart, where Selena is shopping 456 00:24:26,119 --> 00:24:28,119 Speaker 1: in the mall and the salesgirl is like rude to 457 00:24:28,160 --> 00:24:30,560 Speaker 1: her and her sister when she's shopping and she has 458 00:24:30,560 --> 00:24:32,440 Speaker 1: her like pretty woman moment. You know, we we don't 459 00:24:32,480 --> 00:24:35,440 Speaker 1: need the dress. So that segment came up on TikTok 460 00:24:35,560 --> 00:24:37,040 Speaker 1: and I watched the hell out of it, and I 461 00:24:37,080 --> 00:24:38,880 Speaker 1: was like, well, and I've seen this movie a million times, 462 00:24:38,920 --> 00:24:41,280 Speaker 1: I know exactly what's gonna happen, but I don't know. 463 00:24:41,320 --> 00:24:45,879 Speaker 1: There's just something deeply satisfying about these movie clips. I like, 464 00:24:45,920 --> 00:24:47,240 Speaker 1: there are a couple of movies that I have been 465 00:24:47,240 --> 00:24:50,600 Speaker 1: watching in like two minute segments on TikTok, So I 466 00:24:50,640 --> 00:24:53,440 Speaker 1: want to know is this just me? Is this happening 467 00:24:53,480 --> 00:24:55,440 Speaker 1: on your for you page? If you are on TikTok, 468 00:24:55,720 --> 00:24:58,320 Speaker 1: and if it is happening, do you find it as 469 00:24:58,359 --> 00:25:01,080 Speaker 1: satisfying as I do? Like? Why is it so satisfying? 470 00:25:01,080 --> 00:25:03,560 Speaker 1: What's going on? What's the brain chemistry behind why this 471 00:25:03,680 --> 00:25:04,879 Speaker 1: is so satisfying to watch? 472 00:25:05,240 --> 00:25:06,200 Speaker 2: Yeah, it's a great question. 473 00:25:08,080 --> 00:25:10,400 Speaker 3: You know, I don't spend a whole lot of time 474 00:25:10,440 --> 00:25:13,400 Speaker 3: on TikTok, but I know a lot of our listeners do. So, 475 00:25:14,480 --> 00:25:18,560 Speaker 3: you know, send an email at hello at tangoty dot com. 476 00:25:18,720 --> 00:25:22,360 Speaker 2: How else can people share their their experiences? 477 00:25:22,880 --> 00:25:26,120 Speaker 1: Well, you can send us an email, you can find 478 00:25:26,119 --> 00:25:29,200 Speaker 1: me on social, or you can subscribe to our patreon 479 00:25:29,320 --> 00:25:32,080 Speaker 1: at patreon dot com, slash tangody. That's t A N 480 00:25:32,160 --> 00:25:34,359 Speaker 1: G O T I, y'all. I promise this will not 481 00:25:34,400 --> 00:25:37,320 Speaker 1: be one of those podcasts where like every time you 482 00:25:37,320 --> 00:25:40,280 Speaker 1: listen it's like, subscribe to our patreon, subscribe to our patreon. 483 00:25:41,160 --> 00:25:43,480 Speaker 1: But it's new. I'm excited about it. We just got 484 00:25:43,520 --> 00:25:45,879 Speaker 1: our very first subscriber. Thank you, Karen Jay for you 485 00:25:46,119 --> 00:25:48,960 Speaker 1: for your support. But yeah, and if you want more 486 00:25:49,680 --> 00:25:53,560 Speaker 1: news analysis media, subscribe to the patreon and let us 487 00:25:53,600 --> 00:25:55,119 Speaker 1: know what you want to hear. Like, I want it 488 00:25:55,119 --> 00:25:56,360 Speaker 1: to be a resource that is helpful. 489 00:25:56,960 --> 00:25:57,200 Speaker 5: Yeah. 490 00:25:57,240 --> 00:25:58,560 Speaker 1: So I can't wait to hang out. 491 00:25:58,400 --> 00:25:58,880 Speaker 5: With y'all there. 492 00:26:02,200 --> 00:26:04,280 Speaker 1: If you're looking for ways to support the show, check 493 00:26:04,320 --> 00:26:06,920 Speaker 1: out our March store at tegoty dot com slash store. 494 00:26:08,119 --> 00:26:10,159 Speaker 1: Got a story about an interesting thing in tech, or 495 00:26:10,240 --> 00:26:12,080 Speaker 1: just want to say hi, You can reach us at 496 00:26:12,119 --> 00:26:14,840 Speaker 1: Hello at tangody dot com. You can also find transcripts 497 00:26:14,840 --> 00:26:17,280 Speaker 1: for today's episode at tengody dot com. There Are No 498 00:26:17,359 --> 00:26:19,440 Speaker 1: Girls on the Internet was created by me Bridget Tod. 499 00:26:19,800 --> 00:26:22,959 Speaker 1: It's a production of iHeartRadio and Unbossed Creative edited by 500 00:26:23,040 --> 00:26:27,120 Speaker 1: Joey pat Jonathan Strickland is our executive producer. Tari Harrison 501 00:26:27,160 --> 00:26:29,959 Speaker 1: is our producer and sound engineer. Michael Almado is our 502 00:26:29,960 --> 00:26:33,040 Speaker 1: contributing producer. I'm your host, Bridget Todd. If you want 503 00:26:33,040 --> 00:26:35,440 Speaker 1: to help us grow, rate and review us on Apple Podcasts. 504 00:26:36,240 --> 00:26:39,040 Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app, 505 00:26:39,080 --> 00:26:41,439 Speaker 1: Apple podcast, or wherever you get your podcasts.