1
00:00:04,400 --> 00:00:06,120
Speaker 1: There Are No Girls on the Internet, as a production

2
00:00:06,160 --> 00:00:13,680
Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridget Todd and this

3
00:00:13,760 --> 00:00:14,760
Speaker 1: is There Are No Girls.

4
00:00:14,520 --> 00:00:15,120
Speaker 2: On the Internet.

5
00:00:18,480 --> 00:00:20,400
Speaker 1: Welcome to There No Girls on the Internet, where we

6
00:00:20,440 --> 00:00:24,200
Speaker 1: explore the intersection of identity, technology and social media. And

7
00:00:24,239 --> 00:00:27,000
Speaker 1: this is another installment of our weekly news roundup, where

8
00:00:27,040 --> 00:00:30,240
Speaker 1: we break down and explain all the different news stories

9
00:00:30,280 --> 00:00:32,519
Speaker 1: from the Internet that you might have missed. Yes, it

10
00:00:32,560 --> 00:00:34,320
Speaker 1: has been a while since we've done one of these.

11
00:00:34,479 --> 00:00:36,160
Speaker 1: I took a little bit of a break from the news,

12
00:00:36,360 --> 00:00:39,200
Speaker 1: but now we are back and better than ever. Mike,

13
00:00:39,440 --> 00:00:41,479
Speaker 1: you are also back. Thanks for being here.

14
00:00:41,640 --> 00:00:43,680
Speaker 3: Thanks for having me, Bridget. It's great to be back.

15
00:00:44,200 --> 00:00:46,680
Speaker 3: It has indeed been a minute. There's been a lot

16
00:00:46,680 --> 00:00:48,960
Speaker 3: of tech news in that time, and it's great to

17
00:00:49,000 --> 00:00:50,200
Speaker 3: be here talking with you again.

18
00:00:50,720 --> 00:00:54,000
Speaker 1: I'm a little bit distracted because I'm scrolling through these

19
00:00:54,560 --> 00:00:59,280
Speaker 1: like polished Mark Zuckerberg pictures. Have you seen these?

20
00:01:00,040 --> 00:01:02,760
Speaker 4: I have. Yeah, it's like a whole new zuck.

21
00:01:02,960 --> 00:01:08,319
Speaker 1: Someone from Mark Zuckerberg or Meta pr is working overtime

22
00:01:08,440 --> 00:01:11,880
Speaker 1: to try to convince us the public that Mark Zuckerberg

23
00:01:12,120 --> 00:01:16,319
Speaker 1: is a more polished, more stylished, more refined kind of guy,

24
00:01:16,400 --> 00:01:18,360
Speaker 1: and farbe it of me to spend a lot of

25
00:01:18,400 --> 00:01:21,959
Speaker 1: time talking about the sartorial choices of a tech leader. However,

26
00:01:22,480 --> 00:01:24,760
Speaker 1: we know there is a ton of precedent for tech

27
00:01:24,840 --> 00:01:28,440
Speaker 1: leaders using their fashion and style and hair to try

28
00:01:28,440 --> 00:01:30,759
Speaker 1: to project things at us, the public. So it does

29
00:01:30,880 --> 00:01:32,880
Speaker 1: kind of seem like their game to be talking about

30
00:01:33,360 --> 00:01:37,559
Speaker 1: Mark Zuckerberg rocking a chain at his fortieth birthday party,

31
00:01:37,680 --> 00:01:40,080
Speaker 1: et cetera, et cetera, clearly trying to signal to us

32
00:01:40,120 --> 00:01:42,720
Speaker 1: that he's a little more polished, a little more refined,

33
00:01:42,760 --> 00:01:44,120
Speaker 1: a little more stylish.

34
00:01:44,560 --> 00:01:47,720
Speaker 3: Yeah, right, you know you've talked about this before. Didn't

35
00:01:47,760 --> 00:01:51,160
Speaker 3: you write a whole op ed about Elizabeth Holmes's hair

36
00:01:51,400 --> 00:01:58,880
Speaker 3: and her active choices to have questionably nourished looking hair.

37
00:01:58,880 --> 00:02:01,760
Speaker 1: Good memory, I definitely did. I wrote a whole piece

38
00:02:01,800 --> 00:02:06,040
Speaker 1: about Elizabeth Holmes his hair choices. If you remember her,

39
00:02:06,320 --> 00:02:09,680
Speaker 1: she definitely was known for a certain type of hairstyle

40
00:02:09,720 --> 00:02:12,800
Speaker 1: that I think was with intention. It did not look

41
00:02:13,240 --> 00:02:16,079
Speaker 1: nourished or conditioned, and I think that was my choice.

42
00:02:16,520 --> 00:02:18,200
Speaker 1: We'll throw the article in the show notes if you

43
00:02:18,200 --> 00:02:20,639
Speaker 1: want more information, but she was definitely somebody who knew

44
00:02:20,639 --> 00:02:24,360
Speaker 1: how to use her fashion and hair to signal a

45
00:02:24,360 --> 00:02:26,680
Speaker 1: certain kind of thing to us.

46
00:02:27,200 --> 00:02:29,800
Speaker 3: Yeah, I think you're right that. You know, all of

47
00:02:29,840 --> 00:02:32,040
Speaker 3: these tech leaders do that. You know, they get up

48
00:02:32,040 --> 00:02:34,120
Speaker 3: on a stage. There's a lot of photos of them.

49
00:02:34,720 --> 00:02:37,400
Speaker 3: It's all, I think, pretty deliberate. It's and so like

50
00:02:37,440 --> 00:02:39,640
Speaker 3: for this new Zuckerberg rebrand, do you think they brought

51
00:02:39,639 --> 00:02:41,600
Speaker 3: in the Rick Perry guy, the guy who brought us

52
00:02:41,680 --> 00:02:45,080
Speaker 3: the smart Rick Perry, the one who wears glasses.

53
00:02:45,320 --> 00:02:48,120
Speaker 1: Oh that was my favorite like rebrand when it was

54
00:02:48,160 --> 00:02:50,920
Speaker 1: like they put them in glasses and it was like,

55
00:02:51,280 --> 00:02:55,600
Speaker 1: guess what, y'all, Rick Perry is smart. Now from the

56
00:02:55,600 --> 00:02:58,720
Speaker 1: team that brought you smart, Rick Perry comes Zuckerberg.

57
00:02:58,800 --> 00:03:01,680
Speaker 4: Glow Up, Zucker glow up, the Zuck blow up.

58
00:03:01,560 --> 00:03:04,320
Speaker 1: The Zuck glow. So basically in these pictures, there's one

59
00:03:05,040 --> 00:03:07,600
Speaker 1: he made an announcement about Meta AI and he was

60
00:03:07,639 --> 00:03:10,880
Speaker 1: wearing like a silver like a silver Cuban link chain

61
00:03:11,240 --> 00:03:14,320
Speaker 1: and everyone was like, oh, nice chain. And then somebody

62
00:03:14,880 --> 00:03:17,680
Speaker 1: like used AI deep fakes to put a little like

63
00:03:18,000 --> 00:03:19,840
Speaker 1: one of those kind of thin beards on him. So

64
00:03:19,880 --> 00:03:23,400
Speaker 1: if you saw that picture, the chain that was legit,

65
00:03:23,760 --> 00:03:27,200
Speaker 1: the beard that was not legit. But even though that

66
00:03:27,240 --> 00:03:31,880
Speaker 1: picture wasn't real. It really highlights my belief that what

67
00:03:32,760 --> 00:03:36,839
Speaker 1: contouring makeup is for some women, beards are for some men.

68
00:03:36,960 --> 00:03:40,400
Speaker 1: Like a beard can completely change the way a man's

69
00:03:40,400 --> 00:03:43,040
Speaker 1: face looks. It can like it is like magic.

70
00:03:43,600 --> 00:03:47,080
Speaker 3: Yeah, absolutely, beard really changes a man's face.

71
00:03:47,320 --> 00:03:49,880
Speaker 1: So it sounds like Over the weekend, Zuckerberg had a

72
00:03:49,880 --> 00:03:55,839
Speaker 1: birthday party where for the party, his wife recreated all

73
00:03:55,880 --> 00:03:59,200
Speaker 1: of the places that he lived when he was growing up,

74
00:03:59,280 --> 00:04:03,280
Speaker 1: so like his Harvard dorm room where he created Facebook,

75
00:04:03,480 --> 00:04:05,720
Speaker 1: and there are all these pictures of him wearing like

76
00:04:05,720 --> 00:04:09,480
Speaker 1: a fitted black tea and a chain, like in a

77
00:04:09,520 --> 00:04:14,000
Speaker 1: fake dorm room kind of diorama, like adult sized diorama.

78
00:04:14,120 --> 00:04:16,800
Speaker 1: The whole thing is like, I don't know, it's clear

79
00:04:16,839 --> 00:04:19,159
Speaker 1: to me that somebody wants us to think of Mark

80
00:04:19,240 --> 00:04:22,800
Speaker 1: Zuckerberg as this affable guy with friends and family who

81
00:04:22,839 --> 00:04:24,880
Speaker 1: love him, you know what I mean. That's that clear

82
00:04:24,920 --> 00:04:26,520
Speaker 1: to me that that's what we're all supposed to be

83
00:04:26,560 --> 00:04:28,040
Speaker 1: taken away from this god.

84
00:04:28,120 --> 00:04:30,120
Speaker 3: I have to look for those photos of him in

85
00:04:30,200 --> 00:04:32,200
Speaker 3: the dorm rooms with a chain, because that was definitely

86
00:04:32,240 --> 00:04:35,479
Speaker 3: a type of guy in Boston in like the early

87
00:04:35,520 --> 00:04:37,840
Speaker 3: two thousands when he was there. That's when I was

88
00:04:38,279 --> 00:04:40,800
Speaker 3: in dorms in Boston, and like that was definitely a

89
00:04:40,800 --> 00:04:45,040
Speaker 3: type of guy, like fitted black shirt chain usually not like,

90
00:04:45,400 --> 00:04:46,640
Speaker 3: you know, a forty something.

91
00:04:46,920 --> 00:04:54,400
Speaker 4: Year old Zuckerberg. What a nice and strange gift from

92
00:04:54,440 --> 00:04:55,040
Speaker 4: his wife.

93
00:04:55,279 --> 00:05:00,960
Speaker 1: The pictures are unreal. I did see if someone comparing

94
00:05:01,000 --> 00:05:04,560
Speaker 1: it to Marie Antoinette that apparently Marie Ane Toinette had

95
00:05:04,600 --> 00:05:10,200
Speaker 1: built an entire fake village in Versailles that was equipped

96
00:05:10,240 --> 00:05:13,680
Speaker 1: with like a working farm and fake people to work it,

97
00:05:13,680 --> 00:05:17,480
Speaker 1: so that whenever she wanted to pretend to be a

98
00:05:17,520 --> 00:05:19,800
Speaker 1: peasant girl on the farm, she could do that. And

99
00:05:19,839 --> 00:05:25,080
Speaker 1: somebody compared Mark Zuckerberg's kind of fake set pieces of

100
00:05:25,120 --> 00:05:28,080
Speaker 1: his college dorm and like crappy first apartment to that,

101
00:05:28,200 --> 00:05:31,000
Speaker 1: which I thought was kind of funny. That's actually a

102
00:05:31,000 --> 00:05:33,440
Speaker 1: pretty good segue into what I want to talk about first,

103
00:05:33,600 --> 00:05:38,080
Speaker 1: which is the digital guillotine movement aka digiting if you

104
00:05:38,080 --> 00:05:40,679
Speaker 1: don't know what that is. Basically, people on social media

105
00:05:40,720 --> 00:05:44,160
Speaker 1: are using hashtags like hashtag let the meat cake, hashtag

106
00:05:44,200 --> 00:05:48,039
Speaker 1: blockout twenty twenty four, and hashtag celebrity blocklist to rally

107
00:05:48,080 --> 00:05:52,320
Speaker 1: people to boycott celebrities like Zindea, Taylor Swift, and the Kardashians.

108
00:05:52,640 --> 00:05:56,520
Speaker 1: Here's how Mashable describes it, labeled quote Celebrity Blackout twenty

109
00:05:56,560 --> 00:06:00,840
Speaker 1: twenty four and digitine aka the Digital Guillotine. The movement

110
00:06:00,920 --> 00:06:04,640
Speaker 1: is a protest against celebrity culture, specifically blocking people of

111
00:06:04,680 --> 00:06:07,560
Speaker 1: influence who have not yet used their power or privilege

112
00:06:07,720 --> 00:06:10,280
Speaker 1: to take a stance on the humanitarian crisis that has

113
00:06:10,320 --> 00:06:14,200
Speaker 1: devastated millions. So this all seems to have really been

114
00:06:14,320 --> 00:06:16,880
Speaker 1: sparked by the MET Gala last week. The night at

115
00:06:16,880 --> 00:06:19,560
Speaker 1: the Met Gala was also the night that airstrikes targeted

116
00:06:19,640 --> 00:06:21,960
Speaker 1: Ratha and Gaza, which is an enclave on the border

117
00:06:21,960 --> 00:06:24,599
Speaker 1: of the Gaza Strip, which is really key for transporting

118
00:06:24,680 --> 00:06:28,000
Speaker 1: aid and supplies to Palestinians. So there has been a

119
00:06:28,000 --> 00:06:31,120
Speaker 1: lot of chatter about the Mech Gala as this symbol

120
00:06:31,320 --> 00:06:35,719
Speaker 1: for out of touch, wealthy celebrities really reveling in their

121
00:06:35,760 --> 00:06:39,800
Speaker 1: privilege and opulence, while just a few blocks away protesters

122
00:06:39,920 --> 00:06:43,159
Speaker 1: are on the streets calling for a ceasefire in Palestine.

123
00:06:43,240 --> 00:06:46,719
Speaker 1: People were even comparing this to the Hunger Games, and honestly,

124
00:06:46,839 --> 00:06:49,679
Speaker 1: from like an optics perspective, it is kind of hard

125
00:06:49,680 --> 00:06:52,960
Speaker 1: to disagree. I will say that for what it's worth.

126
00:06:53,360 --> 00:06:56,640
Speaker 1: I don't really feel like I have a clear take here.

127
00:06:57,040 --> 00:06:59,400
Speaker 1: On the one hand, I totally get how it looks

128
00:06:59,480 --> 00:07:01,359
Speaker 1: very out of time much to have these celebrities like

129
00:07:01,680 --> 00:07:04,479
Speaker 1: flaunting their wealth while so much is going on in

130
00:07:04,480 --> 00:07:06,560
Speaker 1: the world. I don't say that to say that I'm

131
00:07:06,600 --> 00:07:09,560
Speaker 1: like above paying attention to the Mechala. In fact, I

132
00:07:09,640 --> 00:07:12,040
Speaker 1: own a T shirt that says Rihanna is my Pope,

133
00:07:12,040 --> 00:07:14,080
Speaker 1: which is a reference to the time that Rihanna addressed

134
00:07:14,080 --> 00:07:16,360
Speaker 1: as the Pope at the Mechala. But I didn't even

135
00:07:16,400 --> 00:07:18,120
Speaker 1: watch it this year, and I didn't even really pay

136
00:07:18,120 --> 00:07:19,880
Speaker 1: attention to any of the coverage or any of the

137
00:07:19,960 --> 00:07:24,680
Speaker 1: like online posts about it, in part because Conde Nas

138
00:07:24,680 --> 00:07:26,640
Speaker 1: staff were boycotting the Mechala, so I was like, I'm

139
00:07:26,640 --> 00:07:29,040
Speaker 1: not going to pay attention. And also it just seemed

140
00:07:29,120 --> 00:07:33,400
Speaker 1: like kind of boring, like not in a like I'm

141
00:07:33,440 --> 00:07:35,560
Speaker 1: above this kind of way, more anda like this just

142
00:07:35,560 --> 00:07:37,480
Speaker 1: doesn't seem like a good use of my time to

143
00:07:37,520 --> 00:07:40,360
Speaker 1: be paying attention to kind of way. I also really

144
00:07:40,360 --> 00:07:44,640
Speaker 1: get the argument around how much money so is spent

145
00:07:44,760 --> 00:07:47,760
Speaker 1: at the Mechala. I've seen people say things like, oh, well,

146
00:07:47,800 --> 00:07:51,400
Speaker 1: the mechala is really expensive, but celebrities aren't actually paying

147
00:07:51,440 --> 00:07:54,840
Speaker 1: to attend, And of course all the proceeds from the

148
00:07:54,880 --> 00:07:58,400
Speaker 1: Mechala goes to the Metropolitan Museum of Arts Costume Institute,

149
00:07:58,400 --> 00:08:00,560
Speaker 1: which is like a worthy cause, and that's true, But

150
00:08:00,960 --> 00:08:04,600
Speaker 1: I have also feel that like bringing that up feels

151
00:08:04,640 --> 00:08:06,680
Speaker 1: like a bit of a dodge to what people are

152
00:08:06,680 --> 00:08:09,280
Speaker 1: pointing out when they are talking about the Metcala, which

153
00:08:09,320 --> 00:08:12,680
Speaker 1: is like this dissonance between the mechala and what is

154
00:08:12,720 --> 00:08:14,920
Speaker 1: going on in the world, which I think is like

155
00:08:14,960 --> 00:08:16,680
Speaker 1: a valid point, and I don't think people should just

156
00:08:16,760 --> 00:08:19,440
Speaker 1: be dismissing that. However, I also don't want this to

157
00:08:19,440 --> 00:08:22,480
Speaker 1: be a conversation where women are just being scolded for

158
00:08:22,680 --> 00:08:25,960
Speaker 1: enjoying things like deemed trivial, right, and so easy to

159
00:08:26,000 --> 00:08:29,120
Speaker 1: fall into that this could sort of be a bit

160
00:08:29,160 --> 00:08:32,880
Speaker 1: of an unpopular opinion, but I do feel a little

161
00:08:32,880 --> 00:08:36,760
Speaker 1: bit weird about the entire thing. Like, truthfully, there's been

162
00:08:36,760 --> 00:08:39,719
Speaker 1: a humanitarian crisis in Palestine for many, many years, right,

163
00:08:40,040 --> 00:08:42,760
Speaker 1: and in all of those years, there were met galas

164
00:08:42,800 --> 00:08:46,600
Speaker 1: and rich celebrities doing rich celebrity things, you know. I

165
00:08:46,600 --> 00:08:50,800
Speaker 1: think there's an element of people scolding celebrities for partying

166
00:08:50,840 --> 00:08:53,280
Speaker 1: while there's a big human rights crisis going on, but

167
00:08:53,640 --> 00:08:55,640
Speaker 1: it seems to me that has been the dynamic for

168
00:08:55,840 --> 00:08:58,120
Speaker 1: a very long time, and like, I don't know, it

169
00:08:58,120 --> 00:09:00,480
Speaker 1: almost sort of reads like, how dare you go to

170
00:09:00,520 --> 00:09:02,880
Speaker 1: a party when I have just started being more tuned

171
00:09:02,920 --> 00:09:05,240
Speaker 1: into this situation in Palestine that has been ongoing for

172
00:09:05,280 --> 00:09:08,400
Speaker 1: many years, which I'm not sure that is a useful stance.

173
00:09:09,240 --> 00:09:14,760
Speaker 1: But that said, counterpoint, some of these people seem freaking insufferable,

174
00:09:14,920 --> 00:09:16,760
Speaker 1: and it kind of almost sounds like they were asking

175
00:09:16,760 --> 00:09:20,520
Speaker 1: for it. So I'm talking about people like influencer Haley

176
00:09:20,640 --> 00:09:24,719
Speaker 1: Bailey Khalil, who goes by Hailey Bailey on TikTok, who

177
00:09:24,760 --> 00:09:27,080
Speaker 1: posted a video of herself at the Mecala pre event

178
00:09:27,600 --> 00:09:31,199
Speaker 1: lip syncing to the alleged Marie Antoinette quote let Them

179
00:09:31,240 --> 00:09:34,000
Speaker 1: Eat Cake from the Sofia Coppola two thousand and six

180
00:09:34,080 --> 00:09:37,840
Speaker 1: movie Marie Antoinette, which is a masterpiece, go see it. However,

181
00:09:38,400 --> 00:09:40,960
Speaker 1: I just learned this. Did you know that Marie Antoinette

182
00:09:41,040 --> 00:09:43,280
Speaker 1: might not have actually even ever said let them eat cake?

183
00:09:44,040 --> 00:09:45,600
Speaker 4: I did not know that. I thought it was an

184
00:09:45,640 --> 00:09:46,560
Speaker 4: actual quote.

185
00:09:46,679 --> 00:09:49,120
Speaker 1: No, historians don't even think she ever actually said that.

186
00:09:49,720 --> 00:09:50,960
Speaker 4: Huh.

187
00:09:51,360 --> 00:09:57,880
Speaker 1: Fascinating so basically, this influencer really not something I would

188
00:09:57,920 --> 00:10:00,280
Speaker 1: have recommended she'd do. She posted a video from the

189
00:10:00,320 --> 00:10:04,400
Speaker 1: Mechala lip syncing Marie antoinettes Let Them Eat Cake, and

190
00:10:04,720 --> 00:10:08,640
Speaker 1: people unsurprisingly did not like this. And so this whole

191
00:10:09,120 --> 00:10:13,520
Speaker 1: celebrity blockout thing started with TikTok user Ray, who goes

192
00:10:13,559 --> 00:10:16,760
Speaker 1: by Lady from the Outside, who really kicked off the movement,

193
00:10:17,080 --> 00:10:22,000
Speaker 1: specifically asking for TikTok users to unfollow Bailey, the influencer

194
00:10:22,000 --> 00:10:24,320
Speaker 1: who made that let Them Eat Cake video from the Mechala,

195
00:10:24,840 --> 00:10:28,960
Speaker 1: specifically asking for users to block her in particular. But

196
00:10:29,120 --> 00:10:31,319
Speaker 1: now it has led the people making lists of celebrities

197
00:10:31,320 --> 00:10:34,640
Speaker 1: to block just generally who have not used their platform

198
00:10:34,720 --> 00:10:37,360
Speaker 1: to speak up. Here's what Ray said about the celebrity

199
00:10:37,400 --> 00:10:41,240
Speaker 1: blockout movement on TikTok. She said, we gave them their platforms.

200
00:10:41,280 --> 00:10:43,520
Speaker 1: It's time to take it back. Take our views away,

201
00:10:43,520 --> 00:10:46,760
Speaker 1: our likes, our comments, our money now. There has been

202
00:10:46,840 --> 00:10:49,520
Speaker 1: a suggestion that this movement could be making a real

203
00:10:49,600 --> 00:10:53,160
Speaker 1: dent in some celebrities social media followings. Middle East Monitor

204
00:10:53,200 --> 00:10:55,880
Speaker 1: reported that according to Social Blade, a US based social

205
00:10:55,920 --> 00:10:58,800
Speaker 1: media and a lids website, it has caused some celebrities

206
00:10:58,840 --> 00:11:02,240
Speaker 1: to lose followers. They reported that Selena Gomez lost one

207
00:11:02,280 --> 00:11:04,960
Speaker 1: million followers on Instagram and one hundred thousand on x

208
00:11:05,160 --> 00:11:07,760
Speaker 1: Zindeya lost one hundred and fifty three thousand followers on

209
00:11:07,800 --> 00:11:11,400
Speaker 1: Instagram and forty thousand on x Kim Kardashian lost seven

210
00:11:11,520 --> 00:11:13,960
Speaker 1: hundred and eighty thousand followers on Instagram, and her sister

211
00:11:14,040 --> 00:11:17,320
Speaker 1: Kaylie Jenner lost five hundred and forty thousand followers on

212
00:11:17,360 --> 00:11:21,280
Speaker 1: Instagram and fifty three thousand followers on x SO. I

213
00:11:21,320 --> 00:11:24,440
Speaker 1: would say those are like decent chunks of followings that

214
00:11:24,480 --> 00:11:27,360
Speaker 1: they have lost. However, I don't know if you have

215
00:11:27,400 --> 00:11:29,760
Speaker 1: millions and millions and millions of followers, I don't know

216
00:11:29,800 --> 00:11:31,600
Speaker 1: that that it sounds like it seems like a drop

217
00:11:31,640 --> 00:11:32,839
Speaker 1: in the bucket. I don't know. I don't know that

218
00:11:32,840 --> 00:11:34,720
Speaker 1: they're really going to feel this in their wallets, I guess,

219
00:11:34,760 --> 00:11:36,959
Speaker 1: is what I'm saying. If that is the intent of this.

220
00:11:37,000 --> 00:11:38,800
Speaker 4: Movement, Yeah, I mean.

221
00:11:40,480 --> 00:11:44,960
Speaker 3: I and you said the hashtag was digitein like a

222
00:11:45,000 --> 00:11:47,079
Speaker 3: mashup of digital and guillotine.

223
00:11:47,120 --> 00:11:47,600
Speaker 1: That's right.

224
00:11:48,040 --> 00:11:51,240
Speaker 3: Yeah, that's like pretty intense, you know, like you could

225
00:11:51,240 --> 00:11:54,360
Speaker 3: be mad at celebrities. But I don't know, I feel

226
00:11:54,400 --> 00:12:00,280
Speaker 3: like maybe invoking the French Revolution and the right of

227
00:12:00,400 --> 00:12:03,640
Speaker 3: terror specifically is like not what we need in twenty

228
00:12:03,679 --> 00:12:04,200
Speaker 3: twenty four.

229
00:12:05,080 --> 00:12:06,400
Speaker 4: Maybe these people feel differently.

230
00:12:06,640 --> 00:12:08,720
Speaker 1: Mike doesn't make it's time to roll in the gallows?

231
00:12:09,800 --> 00:12:12,960
Speaker 4: Yeah, like maybe not.

232
00:12:13,840 --> 00:12:16,040
Speaker 3: Yeah, I mean, I'm not necessarily saying that, you know,

233
00:12:16,120 --> 00:12:19,160
Speaker 3: who knows who's going to be in power when you know,

234
00:12:19,280 --> 00:12:22,680
Speaker 3: I'm not trying to be some sort of counter revolutionary here.

235
00:12:26,000 --> 00:12:28,160
Speaker 1: I mean, I kind of I'm I guess I'm a

236
00:12:28,200 --> 00:12:31,640
Speaker 1: little bit conflicted. I do think this whole thing represents

237
00:12:31,679 --> 00:12:34,120
Speaker 1: where we are when it comes to celebrity right now.

238
00:12:34,480 --> 00:12:37,319
Speaker 1: I think that unless you are somebody who is a

239
00:12:37,320 --> 00:12:41,079
Speaker 1: hardcore stan of somebody, I don't think that in general

240
00:12:41,160 --> 00:12:44,960
Speaker 1: people are really looking to celebrities the same way that

241
00:12:45,000 --> 00:12:48,280
Speaker 1: maybe they once were. Ultimately, I personally just do not

242
00:12:48,520 --> 00:12:52,000
Speaker 1: have any kind of faith and celebrity whatsoever. Like obviously,

243
00:12:52,080 --> 00:12:54,000
Speaker 1: I think it's like nice and good or whatever when

244
00:12:54,000 --> 00:12:55,920
Speaker 1: they use their platforms for good, and they should do

245
00:12:55,960 --> 00:12:58,080
Speaker 1: that and all of that. But I just think that

246
00:12:58,080 --> 00:13:01,560
Speaker 1: we should be looking the celebrities less in general, and truly,

247
00:13:01,600 --> 00:13:04,440
Speaker 1: if there was one moment in the in the recent

248
00:13:05,040 --> 00:13:09,320
Speaker 1: past that I think solidified how useless celebrity is and

249
00:13:09,360 --> 00:13:12,400
Speaker 1: how we also be looking to celebrity less for all things.

250
00:13:12,800 --> 00:13:16,959
Speaker 1: It was the twenty twenty Imagine video when it was

251
00:13:17,000 --> 00:13:21,240
Speaker 1: the early days of COVID, and celebrities were like, you

252
00:13:21,280 --> 00:13:23,959
Speaker 1: know what everyone is in their houses. People are scared.

253
00:13:24,679 --> 00:13:27,520
Speaker 1: You know, we can offer the world us singing John

254
00:13:27,600 --> 00:13:30,720
Speaker 1: Lennon's Imagine. Do you remember this?

255
00:13:31,440 --> 00:13:31,959
Speaker 4: I do?

256
00:13:32,240 --> 00:13:35,120
Speaker 3: Yeah, maybe you're right, maybe that was like I don't

257
00:13:35,120 --> 00:13:37,840
Speaker 3: know that that caused the end of celebrity, but it

258
00:13:37,880 --> 00:13:39,080
Speaker 3: definitely does seem like a.

259
00:13:38,960 --> 00:13:40,760
Speaker 4: Good indicator that.

260
00:13:42,280 --> 00:13:46,200
Speaker 3: Things are not like they were, right, like, uh, yeah,

261
00:13:46,240 --> 00:13:51,520
Speaker 3: we celebrities do not have that place in culture that

262
00:13:51,559 --> 00:13:54,560
Speaker 3: they used to. They're just like they're just like us, right,

263
00:13:54,600 --> 00:13:56,080
Speaker 3: it's like page five everywhere.

264
00:13:56,320 --> 00:14:00,360
Speaker 1: Yeah, I really think of that video. That Imagine video

265
00:14:00,480 --> 00:14:04,880
Speaker 1: is my Roman empire. I always wonder do our celebrities like, well,

266
00:14:04,920 --> 00:14:07,920
Speaker 1: I only got roped into this because Gal Gadot asked me,

267
00:14:08,080 --> 00:14:10,240
Speaker 1: or Will Ferrell asked me. He wrote me into it,

268
00:14:10,280 --> 00:14:14,280
Speaker 1: like like celerity celebrities look at who convinced them to

269
00:14:14,280 --> 00:14:15,800
Speaker 1: do that, and they're like, why did I get mixed

270
00:14:15,840 --> 00:14:17,920
Speaker 1: up in that? And I really see it in conjunction

271
00:14:18,160 --> 00:14:19,920
Speaker 1: with I know you're not gonna know what this is,

272
00:14:19,960 --> 00:14:24,080
Speaker 1: so bear with me. But the twenty fourteen selfie that

273
00:14:24,160 --> 00:14:29,000
Speaker 1: Ellen took at the Oscars where it's like Ellen and

274
00:14:29,080 --> 00:14:33,480
Speaker 1: Bradley Cooper and Jennifer Lawrence and Kevin Spacey, I think

275
00:14:33,520 --> 00:14:36,960
Speaker 1: like it's it's like a bunch of celebrities all crowded

276
00:14:37,040 --> 00:14:41,400
Speaker 1: into a selfie that Ellen is taking. And I remember

277
00:14:41,400 --> 00:14:43,200
Speaker 1: when it came out, everybody was like losing their mind, like,

278
00:14:43,200 --> 00:14:45,000
Speaker 1: oh my god, all these celebrities together, Oh, oh my god,

279
00:14:45,040 --> 00:14:47,360
Speaker 1: Oh my god, oh my god. And it's just funny.

280
00:14:47,400 --> 00:14:51,120
Speaker 1: Now how like five years later, six years later, in

281
00:14:51,200 --> 00:14:54,120
Speaker 1: twenty twenty, how collectively we were like, we don't need

282
00:14:54,160 --> 00:14:56,160
Speaker 1: celebrities right now. We don't want to see this.

283
00:14:57,040 --> 00:14:59,800
Speaker 4: Yeah, not like that. Yeah, I guess is it? Is it?

284
00:15:00,080 --> 00:15:01,400
Speaker 4: Lusers now? Is it? Nobody?

285
00:15:01,640 --> 00:15:01,800
Speaker 1: Is it?

286
00:15:01,840 --> 00:15:02,560
Speaker 4: Mister beast?

287
00:15:03,240 --> 00:15:07,280
Speaker 1: You keep trying to sprinkle mister Beast into conversation and

288
00:15:07,280 --> 00:15:08,800
Speaker 1: then he will be like, I don't want I don't

289
00:15:08,840 --> 00:15:11,080
Speaker 1: want to talk about him, I don't want any follow up.

290
00:15:11,120 --> 00:15:12,680
Speaker 1: I want to reference him, and I want to move on.

291
00:15:12,840 --> 00:15:16,120
Speaker 4: I gets I'm curious. Yeah, I don't get it.

292
00:15:17,360 --> 00:15:19,880
Speaker 1: You've not done this on the mic so listeners have

293
00:15:19,960 --> 00:15:23,520
Speaker 1: no idea what I'm talking about. But in non recorded

294
00:15:23,560 --> 00:15:27,400
Speaker 1: conversation you will reference mister Beast, like, who is this

295
00:15:27,480 --> 00:15:29,360
Speaker 1: mister Beasts? Why is he everywhere? Why am I reading

296
00:15:29,360 --> 00:15:31,240
Speaker 1: about him in the New York Times? And I'll start

297
00:15:31,280 --> 00:15:32,760
Speaker 1: to go like tell you, and You're like, I don't

298
00:15:32,760 --> 00:15:34,920
Speaker 1: actually want to know, I just want to like raise

299
00:15:34,920 --> 00:15:35,360
Speaker 1: the question.

300
00:15:36,760 --> 00:15:38,880
Speaker 4: I'm just asking questions. Bridget, Yeah, you're.

301
00:15:38,760 --> 00:15:41,920
Speaker 1: Just asking questions that need answers about mister Beasts.

302
00:15:47,200 --> 00:15:59,760
Speaker 2: Let's take quick break at our back.

303
00:16:03,280 --> 00:16:05,720
Speaker 1: So you know who else is just asking questions. The

304
00:16:05,840 --> 00:16:09,400
Speaker 1: FTC to Better Help, and if you get your therapy

305
00:16:09,560 --> 00:16:12,200
Speaker 1: and mental health services from better Help, you might have

306
00:16:12,240 --> 00:16:16,240
Speaker 1: gotten notice that will be getting an insultingly low refund. Soon,

307
00:16:16,960 --> 00:16:20,160
Speaker 1: around eight hundred thousand customers of the online therapy platform

308
00:16:20,160 --> 00:16:23,000
Speaker 1: Better Help will start getting refund notices related to a

309
00:16:23,000 --> 00:16:26,360
Speaker 1: settlement with the Federal Trade Commission because last year Better

310
00:16:26,440 --> 00:16:29,360
Speaker 1: Help agreed to pay seven point eight million dollars to

311
00:16:29,400 --> 00:16:32,440
Speaker 1: settle the FTC charges that it co opted user data,

312
00:16:32,800 --> 00:16:37,920
Speaker 1: including personal health questions, for advertising purposes, sharing sensitive information

313
00:16:38,000 --> 00:16:41,720
Speaker 1: with social media platforms like Facebook and Snapchat. So the

314
00:16:41,800 --> 00:16:44,600
Speaker 1: FTC allegs that better help not only failed to get

315
00:16:44,600 --> 00:16:48,120
Speaker 1: consent before sharing this data, they also misled users by

316
00:16:48,160 --> 00:16:51,560
Speaker 1: promising they would keep their data private but then sharing

317
00:16:51,600 --> 00:16:56,600
Speaker 1: it with advertising companies like Meta and Snapchat. Now Betterhelp

318
00:16:56,880 --> 00:16:58,720
Speaker 1: did not admit to any of this, although they are

319
00:16:58,720 --> 00:17:00,960
Speaker 1: paying the settlement. I had in a statement this week

320
00:17:00,960 --> 00:17:03,560
Speaker 1: that they were quote deeply committed to the privacy of

321
00:17:03,560 --> 00:17:05,720
Speaker 1: our members and we value the trust people put in

322
00:17:05,840 --> 00:17:08,080
Speaker 1: us by using our services. So I don't know how

323
00:17:08,080 --> 00:17:09,879
Speaker 1: deeply committed to the privacy of their members they can

324
00:17:09,920 --> 00:17:13,200
Speaker 1: be if they're just given their sensitive information to whoever

325
00:17:13,240 --> 00:17:14,600
Speaker 1: wants it for money.

326
00:17:15,040 --> 00:17:18,720
Speaker 3: Yeah, and after telling people that they wouldn't do that,

327
00:17:18,720 --> 00:17:21,119
Speaker 3: that they would keep their information private. Yeah, with some

328
00:17:21,119 --> 00:17:22,680
Speaker 3: pretty damning allegations.

329
00:17:22,920 --> 00:17:26,840
Speaker 1: And this is something that you have particular expertise in, right, Mike.

330
00:17:28,040 --> 00:17:28,600
Speaker 4: Yeah. Some.

331
00:17:28,760 --> 00:17:31,800
Speaker 3: I mean it's not like, you know, rocket science, the

332
00:17:31,840 --> 00:17:35,840
Speaker 3: idea that when you are dealing with people's sensitive health

333
00:17:35,880 --> 00:17:39,040
Speaker 3: information and you tell them that it will be kept private,

334
00:17:40,359 --> 00:17:43,200
Speaker 3: they could reasonably expect that it would be kept private

335
00:17:43,240 --> 00:17:47,879
Speaker 3: and not shared with advertising companies to market goods to them.

336
00:17:49,040 --> 00:17:49,240
Speaker 1: You know.

337
00:17:49,320 --> 00:17:53,320
Speaker 3: It was I think I was reading the FTC complaint

338
00:17:53,320 --> 00:18:00,080
Speaker 3: here and it seemed like that misleading aspect was a

339
00:18:00,080 --> 00:18:04,239
Speaker 3: big core part of their complaint.

340
00:18:04,320 --> 00:18:07,800
Speaker 1: So it is not just Better Help. Eligible refund customers

341
00:18:07,840 --> 00:18:10,800
Speaker 1: include anybody who paid for services on Better Help or

342
00:18:10,960 --> 00:18:14,399
Speaker 1: any of its affiliated websites that cater to certain communities

343
00:18:14,480 --> 00:18:19,280
Speaker 1: like youth or queer people, places like Team Counseling, Faithful Counseling,

344
00:18:19,640 --> 00:18:23,080
Speaker 1: or Pride Counseling. If you use those services from August first,

345
00:18:23,119 --> 00:18:26,560
Speaker 1: twenty seventeen to December thirty first, twenty twenty, you are

346
00:18:26,600 --> 00:18:30,320
Speaker 1: eligible for a refund. Okay, so you are a member

347
00:18:30,480 --> 00:18:34,159
Speaker 1: of a vulnerable community. Right, Your therapy app has just

348
00:18:34,200 --> 00:18:38,960
Speaker 1: gotten pinched for selling your deepest, most intimate details that

349
00:18:39,040 --> 00:18:42,119
Speaker 1: you thought you were sharing with your mental health professional

350
00:18:42,160 --> 00:18:45,159
Speaker 1: and them alone. Right, So now Betterhelp is like, okay,

351
00:18:45,680 --> 00:18:48,199
Speaker 1: maybe we did this. We need to compensate you for

352
00:18:48,280 --> 00:18:53,720
Speaker 1: this wrongdoing. What do you think is a number amount

353
00:18:53,840 --> 00:18:57,080
Speaker 1: that you think is commiserate with that with what happened there?

354
00:18:57,720 --> 00:19:01,919
Speaker 3: For like, what is the the value of my privacy?

355
00:19:02,040 --> 00:19:03,520
Speaker 3: Like how much would it be worth to me to

356
00:19:04,320 --> 00:19:10,840
Speaker 3: not unwittingly share personal information about me and my health?

357
00:19:10,840 --> 00:19:14,639
Speaker 3: How much would that be worth? Yes, I don't know.

358
00:19:14,680 --> 00:19:17,040
Speaker 3: I mean that seems like some pretty pretty big stuff.

359
00:19:17,080 --> 00:19:18,840
Speaker 3: It's hard to put a dollar amount on that kind

360
00:19:18,880 --> 00:19:20,200
Speaker 3: of personal information, you.

361
00:19:20,080 --> 00:19:22,560
Speaker 1: Know, how about less than ten of them.

362
00:19:24,400 --> 00:19:25,240
Speaker 4: Dollars?

363
00:19:25,680 --> 00:19:29,560
Speaker 1: How about less than ten dollars? People got less than

364
00:19:29,720 --> 00:19:34,560
Speaker 1: ten dollars. When I saw that, it's just it's I'm

365
00:19:34,600 --> 00:19:37,600
Speaker 1: a big proponent of like take your little money, like

366
00:19:37,720 --> 00:19:41,920
Speaker 1: even if it's a dollar, get your coffee whatever. Ten

367
00:19:42,040 --> 00:19:45,840
Speaker 1: dollars is like insultingly low for what they have been

368
00:19:45,880 --> 00:19:50,359
Speaker 1: alleged to have done wrong, selling the most intimate data

369
00:19:50,920 --> 00:19:53,119
Speaker 1: and then telling people like don't worry, it's going to

370
00:19:53,160 --> 00:19:56,840
Speaker 1: be between you and a doctor and Snapchat and Mark

371
00:19:56,920 --> 00:20:01,400
Speaker 1: Zuckerberg and his stylist team, and like it's like I'm like, whoa, what,

372
00:20:02,000 --> 00:20:05,360
Speaker 1: here's how ella unchained? Put it on threads better help

373
00:20:05,440 --> 00:20:07,399
Speaker 1: sold my data to Facebook? And all I got was

374
00:20:07,440 --> 00:20:11,160
Speaker 1: this nine dollars and seventy two cent refund. Corporate accountability

375
00:20:11,240 --> 00:20:14,440
Speaker 1: is a shoke, And I completely agree. When I saw

376
00:20:14,440 --> 00:20:17,199
Speaker 1: that amount of money, I was like shocked that that

377
00:20:17,359 --> 00:20:19,480
Speaker 1: is how low that they were giving out. And I

378
00:20:19,520 --> 00:20:22,199
Speaker 1: think that threads is that what we're calling threads. I

379
00:20:22,200 --> 00:20:25,080
Speaker 1: think that thread or whatever post on threads really nails

380
00:20:25,080 --> 00:20:28,159
Speaker 1: how I feel about it. That like tech platforms and

381
00:20:28,240 --> 00:20:31,680
Speaker 1: lacks policy and lack of accountability have created this dynamic

382
00:20:31,720 --> 00:20:36,560
Speaker 1: where nothing private or intimate about us is not for sale.

383
00:20:37,160 --> 00:20:38,920
Speaker 1: Even if they tell you that it's not for sale,

384
00:20:38,960 --> 00:20:41,080
Speaker 1: it is still for sale, even the stuff that you

385
00:20:41,119 --> 00:20:44,320
Speaker 1: talk about with your mental health professional. A dynamic that

386
00:20:44,359 --> 00:20:48,400
Speaker 1: we once really understood as private and sacred and intimate,

387
00:20:48,840 --> 00:20:51,159
Speaker 1: even that it's for sale. And when they sell it

388
00:20:51,200 --> 00:20:53,160
Speaker 1: in a way that the government says breaks the law,

389
00:20:53,600 --> 00:20:57,000
Speaker 1: they will barely face any consequences for it, And the

390
00:20:57,080 --> 00:21:00,200
Speaker 1: compensation that you, as the person who was exploited, will

391
00:21:00,240 --> 00:21:04,160
Speaker 1: get from that will be not just low, insultingly low,

392
00:21:04,280 --> 00:21:05,800
Speaker 1: like an embarrassingly low.

393
00:21:06,160 --> 00:21:06,920
Speaker 4: Yeah, you're right.

394
00:21:07,040 --> 00:21:11,399
Speaker 3: It really speaks to the expectation we have that people

395
00:21:11,440 --> 00:21:15,640
Speaker 3: don't have privacy, that all of our private information is

396
00:21:15,960 --> 00:21:19,240
Speaker 3: available to be sold and traded and used to market

397
00:21:19,320 --> 00:21:25,600
Speaker 3: us goods. And I think that's a really destructive norm

398
00:21:25,720 --> 00:21:30,960
Speaker 3: to have. And you know, it's not like in some ways,

399
00:21:32,359 --> 00:21:37,399
Speaker 3: it's just endemic to everything being online, right, Like it's

400
00:21:37,520 --> 00:21:42,040
Speaker 3: just a fact of the digital world that every product,

401
00:21:42,080 --> 00:21:47,160
Speaker 3: every service is just like one business entity away from

402
00:21:47,160 --> 00:21:50,040
Speaker 3: an advertising company who's trying to use your eyeballs and

403
00:21:50,040 --> 00:21:52,159
Speaker 3: your attention to sell you stuff, and so there's just

404
00:21:52,760 --> 00:21:59,560
Speaker 3: like so much pressure there and incentive to violate people's

405
00:21:59,560 --> 00:22:04,080
Speaker 3: privacy and use personal information to surface them advertising. That

406
00:22:04,240 --> 00:22:09,040
Speaker 3: just wasn't there in previous eras of like health in

407
00:22:09,280 --> 00:22:12,359
Speaker 3: you know, personal health information, when it was all kept

408
00:22:12,440 --> 00:22:15,520
Speaker 3: in paper files at a hospital. They just didn't have

409
00:22:16,000 --> 00:22:18,720
Speaker 3: the opportunity or the incentive to try to use that

410
00:22:18,800 --> 00:22:22,800
Speaker 3: for advertising. Whereas now that's you know, for companies like

411
00:22:23,520 --> 00:22:27,000
Speaker 3: Better Help. I think probably a big part of their

412
00:22:27,119 --> 00:22:28,880
Speaker 3: business model is advertising.

413
00:22:29,200 --> 00:22:34,159
Speaker 1: Yeah, And I guess, just philosophically, I think that we

414
00:22:34,200 --> 00:22:37,920
Speaker 1: should all be stopping to wonder, like, do we want

415
00:22:37,960 --> 00:22:40,520
Speaker 1: the most intimate parts of who we are, the parts

416
00:22:40,560 --> 00:22:42,679
Speaker 1: that we share when we're vulnerable, the parts that we

417
00:22:42,720 --> 00:22:45,080
Speaker 1: share when we're told no one else will hear them

418
00:22:45,160 --> 00:22:48,560
Speaker 1: except for this one health professional. Do we want that

419
00:22:48,640 --> 00:22:50,800
Speaker 1: to be fair game to be taken from us and

420
00:22:50,960 --> 00:22:53,800
Speaker 1: use to exploit us? Like I just you know, you know,

421
00:22:53,880 --> 00:22:56,040
Speaker 1: I went to Catholic school. It would be like if

422
00:22:56,359 --> 00:22:59,960
Speaker 1: you did confession and you confess something in the confess

423
00:23:00,280 --> 00:23:02,600
Speaker 1: what you were told was between you and your religious leader,

424
00:23:02,800 --> 00:23:04,880
Speaker 1: and then the next thing, you know, it's like, oh,

425
00:23:04,960 --> 00:23:07,879
Speaker 1: did you cheat on your spouse. Here's an advertisement for

426
00:23:08,040 --> 00:23:11,760
Speaker 1: a spouse cheating. You're like, what, wait a minute, like

427
00:23:11,840 --> 00:23:15,280
Speaker 1: that would be we would not accept that. So, like,

428
00:23:15,640 --> 00:23:18,000
Speaker 1: I think it's really like a philosophical thing of like,

429
00:23:18,480 --> 00:23:21,639
Speaker 1: do we want these very intimate things that we share

430
00:23:21,760 --> 00:23:24,679
Speaker 1: to just be fodder to exploit us further and for

431
00:23:24,800 --> 00:23:26,840
Speaker 1: some tech company to make money from us.

432
00:23:26,960 --> 00:23:29,919
Speaker 4: I argue, now this indulgence has been brought to you

433
00:23:30,000 --> 00:23:31,080
Speaker 4: by Ashley Madison.

434
00:23:33,440 --> 00:23:36,240
Speaker 1: I'm sure somebody somewhere is working on if it doesn't

435
00:23:36,240 --> 00:23:40,720
Speaker 1: exist already, somebody somewhere is working on like a confessional

436
00:23:41,720 --> 00:23:45,000
Speaker 1: app where when you make the confession, how they make

437
00:23:45,080 --> 00:23:48,080
Speaker 1: money is selling the data about the confession about you.

438
00:23:48,720 --> 00:23:51,120
Speaker 1: Someone has that app. You can't convince me that does

439
00:23:51,119 --> 00:23:51,600
Speaker 1: not exist.

440
00:23:52,240 --> 00:23:54,840
Speaker 3: Yeah, and I think I totally agree there needs to

441
00:23:54,880 --> 00:23:59,359
Speaker 3: be more I don't know safeguards against this right, like

442
00:23:59,400 --> 00:24:04,800
Speaker 3: we need a federal law protecting privacy. We don't have

443
00:24:04,880 --> 00:24:09,360
Speaker 3: that right. So we're left with companies like Better Help.

444
00:24:09,400 --> 00:24:11,959
Speaker 3: They can can do things like this, and you know,

445
00:24:12,720 --> 00:24:17,040
Speaker 3: may point to the fact that it's kind of a

446
00:24:17,080 --> 00:24:20,320
Speaker 3: new space and there'ren't really rules, so maybe they can

447
00:24:20,640 --> 00:24:22,320
Speaker 3: you know, write a check and not have to admit

448
00:24:22,359 --> 00:24:27,800
Speaker 3: to any wrongdoing. What we need are some like clear

449
00:24:27,920 --> 00:24:33,520
Speaker 3: standards around privacy where you know, as a society and

450
00:24:35,600 --> 00:24:39,200
Speaker 3: you know, backed up by laws, we've clearly stated that

451
00:24:39,520 --> 00:24:44,280
Speaker 3: this sort of thing is not okay. Privacy is worth something,

452
00:24:44,480 --> 00:24:48,520
Speaker 3: and hopefully that is more than nine dollars and seventy

453
00:24:48,560 --> 00:24:49,119
Speaker 3: two cents.

454
00:24:49,240 --> 00:24:51,480
Speaker 1: Yes. So the reason that I wanted to talk about

455
00:24:51,520 --> 00:24:54,320
Speaker 1: this is because we actually did an episode about Betterhelp

456
00:24:54,359 --> 00:24:57,080
Speaker 1: in the wake of the Astro World crowd crush incident

457
00:24:57,080 --> 00:24:59,560
Speaker 1: that left a bunch of people, including children dead. I

458
00:24:59,600 --> 00:25:02,000
Speaker 1: was really take it up by that story, especially when

459
00:25:02,040 --> 00:25:05,439
Speaker 1: in the wake of that the organizers announced that they

460
00:25:05,440 --> 00:25:09,600
Speaker 1: were going to be offering free, like a free trial

461
00:25:09,680 --> 00:25:13,280
Speaker 1: of Better Help mental health services. That whole thing that's

462
00:25:13,320 --> 00:25:15,800
Speaker 1: left a terrible taste in my mouth, from the fact

463
00:25:15,880 --> 00:25:17,720
Speaker 1: that they were that it was better Help to begin

464
00:25:17,800 --> 00:25:21,560
Speaker 1: with and not like go talk to a licensed mental

465
00:25:21,600 --> 00:25:23,760
Speaker 1: health professional in your community or whatever, and the fact

466
00:25:23,800 --> 00:25:25,040
Speaker 1: that it was like I think they were offering like

467
00:25:25,119 --> 00:25:26,919
Speaker 1: three months or something. I'm like, oh, yeah, if you

468
00:25:26,920 --> 00:25:29,720
Speaker 1: almost died in a crowd crush incident, you might need

469
00:25:29,760 --> 00:25:32,159
Speaker 1: more than a free Better Help trial to help you

470
00:25:32,280 --> 00:25:34,840
Speaker 1: navigate that. And it just felt to me like, Oh,

471
00:25:34,840 --> 00:25:39,840
Speaker 1: these people were harmed, and now to compensate for that harm,

472
00:25:39,880 --> 00:25:42,480
Speaker 1: they are going to be served up for another platform

473
00:25:42,680 --> 00:25:45,760
Speaker 1: that will only further exploit and take from them. I

474
00:25:45,800 --> 00:25:48,640
Speaker 1: found it really despicable. And so one of the things

475
00:25:48,640 --> 00:25:50,120
Speaker 1: that came up in that episode that we did about

476
00:25:50,160 --> 00:25:53,320
Speaker 1: Better Help was the relationship that Better Help has with

477
00:25:53,440 --> 00:25:57,080
Speaker 1: the podcasting industry. If you listen to podcasts, which I

478
00:25:57,080 --> 00:25:59,359
Speaker 1: guess you obviously do because you're hearing me say this

479
00:25:59,480 --> 00:26:01,919
Speaker 1: right now, I or I guess maybe you're my upstairs

480
00:26:01,960 --> 00:26:04,880
Speaker 1: neighbor who is like listening to me for court this,

481
00:26:04,960 --> 00:26:07,840
Speaker 1: I bet you know. If you listen to podcasts, you've

482
00:26:07,840 --> 00:26:10,920
Speaker 1: probably heard an ad for Better Help or two. According

483
00:26:10,960 --> 00:26:14,439
Speaker 1: to NBC in March, Better Help spend eight point three

484
00:26:14,440 --> 00:26:19,040
Speaker 1: million dollars on podcast opportunities, nearly double the next biggest sponsor,

485
00:26:19,160 --> 00:26:22,280
Speaker 1: which is Amazon. And also, just to put that in context,

486
00:26:22,320 --> 00:26:25,960
Speaker 1: they spend eight point three million dollars on podcasting opportunity

487
00:26:25,960 --> 00:26:29,840
Speaker 1: advertising in March alone. What they had to pay for

488
00:26:29,960 --> 00:26:33,320
Speaker 1: misusing the data of almost a million people was seven

489
00:26:33,359 --> 00:26:36,800
Speaker 1: point eight million dollars. So there's really it's like, when

490
00:26:36,800 --> 00:26:38,320
Speaker 1: I say that, it feels like a slap on the

491
00:26:38,320 --> 00:26:40,159
Speaker 1: wrist or a drop in the bucket to them. That

492
00:26:40,320 --> 00:26:43,080
Speaker 1: is what I mean. They could just pull their advertising

493
00:26:43,119 --> 00:26:45,640
Speaker 1: from podcasts for a month and it would still be

494
00:26:45,920 --> 00:26:48,280
Speaker 1: more than they paid out to these people whose data

495
00:26:48,320 --> 00:26:51,320
Speaker 1: they misused. And despite the fact that we have made

496
00:26:51,400 --> 00:26:55,239
Speaker 1: episodes talking about better Help and they're sometimes not so

497
00:26:55,359 --> 00:26:58,840
Speaker 1: great policies, they have reached out to us to advertise

498
00:26:58,920 --> 00:27:01,600
Speaker 1: on this podcast. And it kind of goes back to

499
00:27:01,680 --> 00:27:04,239
Speaker 1: something that came up in our Andrew Huberman episodes that

500
00:27:04,480 --> 00:27:08,760
Speaker 1: you know, podcasting is this weird space where it's a business,

501
00:27:08,920 --> 00:27:10,920
Speaker 1: and that in a lot of cases you just have

502
00:27:10,960 --> 00:27:12,640
Speaker 1: to take ads to make sure that the people who

503
00:27:12,640 --> 00:27:15,479
Speaker 1: make the show can continue to be paid. But you

504
00:27:15,560 --> 00:27:19,600
Speaker 1: walk this line of not wanting to take money from

505
00:27:19,640 --> 00:27:23,320
Speaker 1: companies whose practices are not on the level with wanting

506
00:27:23,359 --> 00:27:24,720
Speaker 1: to keep the lights on. And so I don't want

507
00:27:24,720 --> 00:27:27,480
Speaker 1: to make it seem like I'm gloating that, like, oh,

508
00:27:27,840 --> 00:27:30,679
Speaker 1: podcasters who made ads for better Help are bad and

509
00:27:30,720 --> 00:27:33,960
Speaker 1: they should be doing XYZ, because I recognize, like, as

510
00:27:34,000 --> 00:27:37,280
Speaker 1: a podcaster, I completely get that it is complicated, but

511
00:27:38,320 --> 00:27:42,199
Speaker 1: because Better Help took up so much space in the

512
00:27:42,280 --> 00:27:45,320
Speaker 1: podcasting landscape, I just think it's important to talk about

513
00:27:45,359 --> 00:27:48,000
Speaker 1: this on a podcast that if you've heard better Help

514
00:27:48,000 --> 00:27:50,359
Speaker 1: ads and you've thought about using better Help, this is

515
00:27:50,400 --> 00:27:52,920
Speaker 1: just information and context that you should know about how

516
00:27:52,920 --> 00:27:58,040
Speaker 1: a government says they have been exploiting people who pay

517
00:27:58,040 --> 00:28:01,399
Speaker 1: for their services. Okay, so quick heads up that this

518
00:28:01,480 --> 00:28:06,080
Speaker 1: story does involve sex crimes against children. A New York

519
00:28:06,119 --> 00:28:09,040
Speaker 1: Times piece just added a lot more context to a

520
00:28:09,080 --> 00:28:11,560
Speaker 1: story that we've been talking about a ton on the podcast,

521
00:28:11,800 --> 00:28:14,440
Speaker 1: and that is the way that Instagram has been knowingly

522
00:28:14,520 --> 00:28:19,160
Speaker 1: connecting children to adult men who are sexually interested in kids.

523
00:28:19,400 --> 00:28:22,200
Speaker 1: So people who run brands related to children, for products

524
00:28:22,200 --> 00:28:26,080
Speaker 1: like children's jewelry or children's clothing, will run paid ads

525
00:28:26,160 --> 00:28:29,040
Speaker 1: on Instagram, and they'll tell Instagram like, I want to

526
00:28:29,080 --> 00:28:31,200
Speaker 1: show these ads to people who are interested in things

527
00:28:31,240 --> 00:28:33,679
Speaker 1: related to kids, like obviously trying to reach moms who

528
00:28:33,760 --> 00:28:36,439
Speaker 1: might buy kids clothing or kids jewelry, which makes a

529
00:28:36,440 --> 00:28:38,960
Speaker 1: ton of sense. But then they will look at their

530
00:28:39,000 --> 00:28:41,440
Speaker 1: back end data, and that data will show them that

531
00:28:41,600 --> 00:28:46,280
Speaker 1: Instagram is actually serving those ads, which are pictures of

532
00:28:46,360 --> 00:28:51,280
Speaker 1: kids using these products to adult men. This whole thing

533
00:28:51,400 --> 00:28:54,360
Speaker 1: started when a mom happened to reach out to the

534
00:28:54,360 --> 00:28:57,240
Speaker 1: New York Times after seeing their previous reporting. It's The

535
00:28:57,240 --> 00:29:00,360
Speaker 1: New York Times writes. When a children's jewelry maker began

536
00:29:00,440 --> 00:29:03,440
Speaker 1: advertising on Instagram, she promoted photos of a five year

537
00:29:03,440 --> 00:29:06,280
Speaker 1: old girl wearing a sparkly charm to users interested in

538
00:29:06,320 --> 00:29:09,640
Speaker 1: parenting children, ballet, and other topics identified by Meta as

539
00:29:09,640 --> 00:29:12,240
Speaker 1: appealing mostly to women. But when the merchant got the

540
00:29:12,280 --> 00:29:15,560
Speaker 1: automated results of her ad campaign from Instagram, the opposite

541
00:29:15,560 --> 00:29:19,000
Speaker 1: had happened. The ads had gone almost entirely to adult men.

542
00:29:19,320 --> 00:29:22,480
Speaker 1: Perplexed and concerned, the merchant contact with The New York Times,

543
00:29:22,480 --> 00:29:25,200
Speaker 1: which had recently published multiple articles about the abuse of

544
00:29:25,280 --> 00:29:29,000
Speaker 1: children on social media platforms. In February, the Times investigated

545
00:29:29,000 --> 00:29:32,160
Speaker 1: Instagram accounts run by parents for their young daughters and

546
00:29:32,240 --> 00:29:35,480
Speaker 1: the dark underworld of men who have sexualized interactions with

547
00:29:35,520 --> 00:29:39,360
Speaker 1: those accounts. With the photos from the jewelry ads in hand,

548
00:29:39,680 --> 00:29:42,040
Speaker 1: the Time set out to understand why they attracted an

549
00:29:42,120 --> 00:29:45,320
Speaker 1: unwonted audience. Test Ads run by The Times using the

550
00:29:45,320 --> 00:29:49,240
Speaker 1: same photos with no text not only replicated the merchant's experience,

551
00:29:49,560 --> 00:29:52,560
Speaker 1: they drew the attention of convicted sex offenders and other

552
00:29:52,640 --> 00:29:56,120
Speaker 1: men whose accounts indicated a sexual interest in children or

553
00:29:56,200 --> 00:29:59,440
Speaker 1: who wrote sexual messages. So The Times was like, we're

554
00:29:59,440 --> 00:30:01,760
Speaker 1: going to get to the this. They did an investigation

555
00:30:01,800 --> 00:30:05,120
Speaker 1: where their reporters opened two Instagram accounts and promoted posts

556
00:30:05,120 --> 00:30:07,760
Speaker 1: showing a five year old girl her face turned away

557
00:30:07,760 --> 00:30:09,960
Speaker 1: from the camera, wearing a tank top and a charm

558
00:30:10,280 --> 00:30:13,080
Speaker 1: Then separate posts showed the clothing and jewelry without the

559
00:30:13,160 --> 00:30:15,479
Speaker 1: child model or with a black box kind of covering

560
00:30:15,480 --> 00:30:17,840
Speaker 1: her up. All of the paid ads were promoted to

561
00:30:17,880 --> 00:30:21,240
Speaker 1: people interested in topics like childhood, dance and cheerleading, which

562
00:30:21,280 --> 00:30:25,120
Speaker 1: metas audience tools estimated as predominantly women. What they found

563
00:30:25,240 --> 00:30:30,200
Speaker 1: from this experiment is horrifying. Aside from reaching a surprisingly

564
00:30:30,320 --> 00:30:34,120
Speaker 1: large proportion of men, the ads got direct responses from

565
00:30:34,200 --> 00:30:38,400
Speaker 1: dozens of Instagram users, including phone calls from two accused

566
00:30:38,480 --> 00:30:42,080
Speaker 1: sex offenders, offers to pay the child for sexual acts,

567
00:30:42,080 --> 00:30:43,960
Speaker 1: and professions of love.

568
00:30:45,200 --> 00:30:49,840
Speaker 3: H That is gross. That seems like the sort of

569
00:30:49,880 --> 00:30:52,800
Speaker 3: thing that we should be involved in.

570
00:30:53,440 --> 00:30:55,600
Speaker 1: Well yeah, and I guess what makes it even more

571
00:30:55,680 --> 00:30:58,680
Speaker 1: horrifying is that all of this really suggests that the

572
00:30:58,720 --> 00:31:02,520
Speaker 1: platform's algorithm are playing a role in directing these men

573
00:31:02,560 --> 00:31:06,160
Speaker 1: to photos of children to sexualize and DM and reach

574
00:31:06,240 --> 00:31:09,080
Speaker 1: out to and so previously we had talked about this

575
00:31:09,160 --> 00:31:13,320
Speaker 1: bombshell New York Times investigation about how Instagram was using

576
00:31:13,360 --> 00:31:17,880
Speaker 1: a loophole to allow grown adult men to connect directly

577
00:31:17,920 --> 00:31:20,320
Speaker 1: with children like as young as like five six seven

578
00:31:20,800 --> 00:31:24,600
Speaker 1: on the platform by allowing men to subscribe to these

579
00:31:24,680 --> 00:31:27,200
Speaker 1: like Mommy Run accounts that were set up on behalf

580
00:31:27,240 --> 00:31:30,320
Speaker 1: of the children, because adults are not meant to be

581
00:31:30,360 --> 00:31:34,320
Speaker 1: able to subscribe to the accounts of kids. But if

582
00:31:34,360 --> 00:31:37,480
Speaker 1: the account is technically like a mom run account, that

583
00:31:37,680 --> 00:31:41,120
Speaker 1: is a loophole where adults can subscribe to the content

584
00:31:41,160 --> 00:31:44,719
Speaker 1: of kids. And like this New York Times investigation showed

585
00:31:44,760 --> 00:31:47,640
Speaker 1: that people who worked at Facebook were like, oh, this

586
00:31:47,720 --> 00:31:51,520
Speaker 1: seems to be a loophole that is allowing grown men

587
00:31:51,600 --> 00:31:54,160
Speaker 1: to connect with children. So Facebook was certainly aware this

588
00:31:54,280 --> 00:31:56,440
Speaker 1: was happening, like their own engineers were telling them this

589
00:31:56,520 --> 00:31:59,960
Speaker 1: was happening. And so this new report says there is

590
00:32:00,240 --> 00:32:03,120
Speaker 1: a lot of overlap between the men who are connecting

591
00:32:03,280 --> 00:32:06,800
Speaker 1: to children via these mom run accounts and the men

592
00:32:06,920 --> 00:32:10,680
Speaker 1: for whom the algorithm is surfacing this paid ad content

593
00:32:10,840 --> 00:32:13,920
Speaker 1: involving kids. The New York Times rights an analysis of

594
00:32:13,920 --> 00:32:16,640
Speaker 1: the users who interacted with these ads posted by the

595
00:32:16,640 --> 00:32:19,600
Speaker 1: Times found an overlap between these two worlds. About three

596
00:32:19,720 --> 00:32:22,400
Speaker 1: dozen of the men followed child influencer accounts that were

597
00:32:22,480 --> 00:32:25,080
Speaker 1: run by parents and were previously studied by The Times.

598
00:32:25,480 --> 00:32:28,240
Speaker 1: One followed one hundred and forty of them. In addition,

599
00:32:28,360 --> 00:32:30,800
Speaker 1: nearly one hundred of the men followed accounts featured or

600
00:32:30,840 --> 00:32:35,640
Speaker 1: advertising adult pornography, which is barred under Instagram's rules. So

601
00:32:36,080 --> 00:32:40,719
Speaker 1: all of this is like, pretty horrifying, pretty terrible. Do

602
00:32:40,800 --> 00:32:43,040
Speaker 1: you want to take a guest at Instagram's response to

603
00:32:43,040 --> 00:32:44,320
Speaker 1: all of this was.

604
00:32:44,280 --> 00:32:47,000
Speaker 4: It like a snappy sweater and a silver chain.

605
00:32:48,400 --> 00:32:51,920
Speaker 1: It was Have you seen Mark Zuckerberg's new look? People

606
00:32:51,920 --> 00:32:53,719
Speaker 1: are saying he has a hashtag glow up.

607
00:32:55,600 --> 00:32:55,680
Speaker 4: No.

608
00:32:55,960 --> 00:32:58,520
Speaker 1: Their response was it's really not that big of a deal.

609
00:32:58,800 --> 00:33:02,200
Speaker 1: Danny Lever, a spokesperson for Meta, dismissed the Times ad

610
00:33:02,240 --> 00:33:06,200
Speaker 1: tests as quote a manufactured experience that failed to account

611
00:33:06,240 --> 00:33:09,280
Speaker 1: for the many factors that contribute to who ultimately sees

612
00:33:09,360 --> 00:33:12,000
Speaker 1: an ad, and suggested that it was flawed and unsound

613
00:33:12,040 --> 00:33:15,160
Speaker 1: to draw conclusions from limited data. This really reminds me

614
00:33:15,200 --> 00:33:18,680
Speaker 1: of how Twitter responded when Media Matters and other watchdog

615
00:33:18,760 --> 00:33:24,120
Speaker 1: organizations were pointing out these dart pockets where nefarious people

616
00:33:24,160 --> 00:33:26,600
Speaker 1: were using the platform together, and they were like, oh, well,

617
00:33:27,120 --> 00:33:30,400
Speaker 1: you would only see that if you met xyz circumstances.

618
00:33:30,440 --> 00:33:34,680
Speaker 1: That's manufactured data. But that is exactly the point, Like

619
00:33:35,160 --> 00:33:38,800
Speaker 1: pedophiles are taking advantage of these unexamined corners of the

620
00:33:38,840 --> 00:33:41,880
Speaker 1: platform to connect with kids. So even if they're saying, well,

621
00:33:41,880 --> 00:33:45,360
Speaker 1: most people wouldn't get these results and most people wouldn't

622
00:33:45,400 --> 00:33:47,960
Speaker 1: have this experience, that is exactly what these people are saying,

623
00:33:48,040 --> 00:33:51,160
Speaker 1: is that pedophiles are taking advantage of the fact that

624
00:33:51,200 --> 00:33:53,680
Speaker 1: this is a little bit under the radar to operate

625
00:33:53,800 --> 00:33:56,480
Speaker 1: in plain sight. So the way that Facebook is responding

626
00:33:56,520 --> 00:33:58,520
Speaker 1: it makes me feel that they don't really understand what's

627
00:33:58,560 --> 00:33:59,920
Speaker 1: actually the threat.

628
00:34:00,960 --> 00:34:05,280
Speaker 3: Yeah, and I think I totally agree that they It

629
00:34:05,400 --> 00:34:08,640
Speaker 3: sounds a lot like that way the way Twitter responded

630
00:34:08,640 --> 00:34:14,000
Speaker 3: to that Media Matters investigation of like how easy it

631
00:34:14,200 --> 00:34:20,719
Speaker 3: was to get to hateful comment through specific searches. But

632
00:34:20,840 --> 00:34:23,480
Speaker 3: this actually feels substantially different from that. When you were

633
00:34:23,480 --> 00:34:26,920
Speaker 3: describing what The Times did, it sounded like a quite

634
00:34:27,120 --> 00:34:30,960
Speaker 3: good experiment that was set up using paid ads to

635
00:34:31,000 --> 00:34:35,200
Speaker 3: see what the algorithm would do and to you know,

636
00:34:35,239 --> 00:34:37,200
Speaker 3: who would surface this ad.

637
00:34:36,960 --> 00:34:42,399
Speaker 4: To so you know, they can this spokesperson can say

638
00:34:42,440 --> 00:34:45,440
Speaker 4: that the methodology was flawed and unsound, but when you

639
00:34:45,480 --> 00:34:49,040
Speaker 4: described it to me, it sounded pretty sound.

640
00:34:49,440 --> 00:34:51,760
Speaker 1: Well, what's interesting is that the New York Times talked

641
00:34:51,760 --> 00:34:55,680
Speaker 1: to like former Facebook engineers, and then they talked to

642
00:34:55,840 --> 00:34:59,880
Speaker 1: Peter Sapienski, a research scientist at Northeastern University who specializes

643
00:35:00,120 --> 00:35:04,400
Speaker 1: testing online algorithms, and he basically said that it sounds

644
00:35:04,480 --> 00:35:08,960
Speaker 1: like these algorithms are working as intended and as designed

645
00:35:09,120 --> 00:35:11,960
Speaker 1: by Facebook. So what's really interesting to me is that

646
00:35:12,000 --> 00:35:15,560
Speaker 1: the potential algorithmic reason that this is happening, which is

647
00:35:15,600 --> 00:35:19,880
Speaker 1: that most ads on Instagram are designed to reach women

648
00:35:19,920 --> 00:35:21,839
Speaker 1: because women are more likely to be buying stuff from

649
00:35:21,840 --> 00:35:25,600
Speaker 1: Instagram as and so we're like a more competitive audience. However,

650
00:35:25,880 --> 00:35:29,200
Speaker 1: reaching men is a lot easier. Peter Sapienski that research

651
00:35:29,280 --> 00:35:32,200
Speaker 1: scientists at Northeastern put it this way. He said that

652
00:35:32,239 --> 00:35:35,120
Speaker 1: advertisers compete with one another to reach women because they

653
00:35:35,160 --> 00:35:38,400
Speaker 1: dominate US consumer spending. He said, as a result, the

654
00:35:38,440 --> 00:35:42,640
Speaker 1: algorithm probably focused on highly interested easier to reach men

655
00:35:42,760 --> 00:35:46,160
Speaker 1: who had interacted with similar content. The men do engage,

656
00:35:46,200 --> 00:35:49,000
Speaker 1: he said, the machine is doing exactly what you want

657
00:35:49,040 --> 00:35:49,560
Speaker 1: it to do.

658
00:35:50,480 --> 00:35:53,680
Speaker 3: Yeah, Like, on one level, that is what the machine

659
00:35:53,760 --> 00:35:57,560
Speaker 3: is supposed to do. On another level, it seems pretty

660
00:35:57,640 --> 00:36:01,839
Speaker 3: bad that that is what the machine is supposed to do, Like,

661
00:36:03,440 --> 00:36:07,200
Speaker 3: there should probably be something to prevent that from happening.

662
00:36:07,600 --> 00:36:10,520
Speaker 1: So it's horrifying as all of this is. The Times

663
00:36:10,520 --> 00:36:13,400
Speaker 1: also looked into the kind of men who were dming

664
00:36:13,440 --> 00:36:16,200
Speaker 1: and engaging with these accounts, some of whom use their

665
00:36:16,239 --> 00:36:18,480
Speaker 1: real names or link to their real identities from their

666
00:36:18,520 --> 00:36:21,960
Speaker 1: Facebook pages, and some of them have convictions for sex

667
00:36:22,000 --> 00:36:26,120
Speaker 1: crimes against children. Now, per Meta's rules, people who are

668
00:36:26,120 --> 00:36:28,760
Speaker 1: sex offenders or have been convicted of sex crimes against

669
00:36:28,840 --> 00:36:33,120
Speaker 1: kids are not allowed to have Instagram accounts. However, these

670
00:36:33,160 --> 00:36:36,040
Speaker 1: people did have Instagram accounts, and it's not clear how

671
00:36:36,080 --> 00:36:37,800
Speaker 1: they did have Instagram accounts that they're meant to be

672
00:36:37,880 --> 00:36:40,920
Speaker 1: registering their email addresses, and Meta has a program that

673
00:36:41,040 --> 00:36:44,840
Speaker 1: is meant to keep them from having these accounts. I

674
00:36:44,840 --> 00:36:47,200
Speaker 1: don't know why or how, but that did not work.

675
00:36:47,600 --> 00:36:50,520
Speaker 1: So when The New York Times used Meta's internal tool

676
00:36:50,680 --> 00:36:53,600
Speaker 1: to flag these accounts and tell Facebook like, hey, these

677
00:36:53,640 --> 00:36:56,480
Speaker 1: people have been convicted of sex crimes against kids, or hey,

678
00:36:56,520 --> 00:36:59,920
Speaker 1: these people are on sex offender registries. Per your rule,

679
00:37:00,320 --> 00:37:04,040
Speaker 1: they should not have Instagram accounts. They used Meta's own

680
00:37:04,080 --> 00:37:07,080
Speaker 1: internal tools to flag these accounts, and Meta did not

681
00:37:07,320 --> 00:37:09,920
Speaker 1: remove them. According to The New York Times, the accounts

682
00:37:09,960 --> 00:37:12,600
Speaker 1: remained online for about a week until the Times flag

683
00:37:12,680 --> 00:37:15,520
Speaker 1: them to accompany spokesperson. And so I guess this is

684
00:37:15,560 --> 00:37:19,640
Speaker 1: my point. If you have these rules in place but

685
00:37:19,680 --> 00:37:22,360
Speaker 1: then do nothing to make sure that they are followed,

686
00:37:22,400 --> 00:37:24,719
Speaker 1: in what way is it a meaningful rule? The fact

687
00:37:24,760 --> 00:37:28,160
Speaker 1: that your own tool that you internally used to flag

688
00:37:28,160 --> 00:37:30,319
Speaker 1: accounts that should not be on your platform, that The

689
00:37:30,360 --> 00:37:33,200
Speaker 1: New York Times could be flagging people who have been

690
00:37:33,320 --> 00:37:36,560
Speaker 1: convicted of sex crimes against kids that you know are

691
00:37:36,560 --> 00:37:41,000
Speaker 1: then using your platform to try to connect with children sexually,

692
00:37:41,400 --> 00:37:43,640
Speaker 1: and you just leave them up until the New York

693
00:37:43,680 --> 00:37:47,000
Speaker 1: Times has to directly reach out to a spokesperson. I

694
00:37:47,040 --> 00:37:50,560
Speaker 1: would say that something is very broken, but per the

695
00:37:50,719 --> 00:37:54,800
Speaker 1: algorithm expert at Northeastern, things are working as intended. And so,

696
00:37:55,320 --> 00:37:59,480
Speaker 1: you know, it's really hard to say that something is

697
00:37:59,480 --> 00:38:02,560
Speaker 1: broken when it actually kind of seems like things are

698
00:38:02,600 --> 00:38:04,000
Speaker 1: working on with what they were designed to work.

699
00:38:04,760 --> 00:38:05,920
Speaker 4: Yeah, Wilson.

700
00:38:10,239 --> 00:38:11,080
Speaker 1: More, after a quick.

701
00:38:10,960 --> 00:38:23,440
Speaker 2: Break, let's get right back into it.

702
00:38:27,400 --> 00:38:29,839
Speaker 1: Okay, so we have to talk about what is being

703
00:38:29,920 --> 00:38:33,520
Speaker 1: called on the internet bumble fumble. So there's a ton

704
00:38:33,640 --> 00:38:36,160
Speaker 1: going on in the dating app scene right now, namely

705
00:38:36,280 --> 00:38:39,080
Speaker 1: that a lot of women are just fed up with

706
00:38:39,239 --> 00:38:42,640
Speaker 1: dating apps. As y'all might recall bumble the dating app

707
00:38:42,680 --> 00:38:45,160
Speaker 1: down than by Whitney wolf Heard used to have this

708
00:38:45,239 --> 00:38:48,160
Speaker 1: thing where their whole sort of gimmick was that the

709
00:38:48,239 --> 00:38:50,920
Speaker 1: women on the dating app would have to message first,

710
00:38:51,040 --> 00:38:53,359
Speaker 1: and you couldn't talk to a woman without her being

711
00:38:53,400 --> 00:38:56,719
Speaker 1: the first person to initiate conversation. But last week they

712
00:38:56,760 --> 00:39:01,040
Speaker 1: announced they are not doing that anymore. According to women

713
00:39:01,120 --> 00:39:03,799
Speaker 1: can now add prompts to their profiles for men to

714
00:39:03,880 --> 00:39:07,640
Speaker 1: respond to. For same sex and non binariy users, either

715
00:39:07,719 --> 00:39:11,400
Speaker 1: person can set and respond to these prompts called opening moves.

716
00:39:11,960 --> 00:39:15,680
Speaker 1: This is all happening against a backdrop of I guess

717
00:39:15,719 --> 00:39:19,040
Speaker 1: what you could call dating app fatigue. People are sort

718
00:39:19,080 --> 00:39:21,920
Speaker 1: of sick of dating apps. I think that the idea

719
00:39:22,000 --> 00:39:24,279
Speaker 1: that you would have to put so much work in

720
00:39:24,440 --> 00:39:27,520
Speaker 1: to actually find a suitable mate. I also think there's

721
00:39:27,520 --> 00:39:30,160
Speaker 1: some screen time fatigue happening, where the idea that you

722
00:39:30,200 --> 00:39:33,560
Speaker 1: would be on your phone on your screen all the

723
00:39:33,640 --> 00:39:36,320
Speaker 1: time and that that is the key to finding your mate.

724
00:39:36,400 --> 00:39:38,080
Speaker 1: I think a lot of people are just fed up

725
00:39:38,080 --> 00:39:41,240
Speaker 1: with that, So listen, I have to say, like, I

726
00:39:41,280 --> 00:39:45,440
Speaker 1: am fascinated by the dating app Bumble as a company.

727
00:39:45,800 --> 00:39:49,200
Speaker 1: When it first started, the creator, Whitney wolf Heard, was

728
00:39:49,239 --> 00:39:52,279
Speaker 1: working at Tinder, and in twenty fourteen, she was part

729
00:39:52,320 --> 00:39:55,320
Speaker 1: of a team that launched Bumble after this like pretty

730
00:39:55,360 --> 00:39:59,440
Speaker 1: tumultuous departure from Tinder, which she also helped launch. According

731
00:39:59,440 --> 00:40:02,560
Speaker 1: to NPR, she had this like string of bad relationships,

732
00:40:02,840 --> 00:40:06,240
Speaker 1: one of which even involved a sexual harassment lawsuit against

733
00:40:06,280 --> 00:40:09,520
Speaker 1: one of Tender's co founders, which was later settled. So

734
00:40:09,560 --> 00:40:13,080
Speaker 1: she used all of this experience to build a platform

735
00:40:13,400 --> 00:40:16,360
Speaker 1: that put more power in the hands of women. So

736
00:40:16,680 --> 00:40:19,839
Speaker 1: all of that sounds great, right, Like more power and

737
00:40:19,920 --> 00:40:23,600
Speaker 1: the women messaging first, like that gives women a lot

738
00:40:23,640 --> 00:40:28,640
Speaker 1: more autonomy, right, sounds great. However, NPR spoke to Demona Hoffman,

739
00:40:28,680 --> 00:40:30,960
Speaker 1: an online dating coach and the author of the book

740
00:40:31,200 --> 00:40:34,560
Speaker 1: f the fairy Tale, who basically said this whole women

741
00:40:34,760 --> 00:40:38,880
Speaker 1: message first thing just leads a lot of women feeling

742
00:40:39,000 --> 00:40:41,200
Speaker 1: like they have to do the bulk of the work

743
00:40:41,239 --> 00:40:43,680
Speaker 1: on dating sites, and like the bulk of the work

744
00:40:43,719 --> 00:40:47,759
Speaker 1: to keep the conversation going, keep things moving, is on them,

745
00:40:47,840 --> 00:40:50,640
Speaker 1: which if you are already working a full time job,

746
00:40:50,920 --> 00:40:54,320
Speaker 1: who wants dating to feel like another job? Like dating

747
00:40:54,360 --> 00:40:56,920
Speaker 1: is supposed to be fun, it's supposed to be enjoyable.

748
00:40:57,160 --> 00:41:00,400
Speaker 1: Who wants something that's like, oh yeah, more work piled

749
00:41:00,440 --> 00:41:02,840
Speaker 1: on it. And because it's dating apps and they're like

750
00:41:02,920 --> 00:41:06,400
Speaker 1: algorithmically generated, at the end of the day, we're only

751
00:41:06,480 --> 00:41:08,640
Speaker 1: surfacing you the duves.

752
00:41:08,680 --> 00:41:13,440
Speaker 4: Not the duds. Oh man, it's a rough group to

753
00:41:13,480 --> 00:41:14,760
Speaker 4: be in the dud group.

754
00:41:15,080 --> 00:41:17,880
Speaker 1: Yeah, I mean you're Miilache may vary for what a

755
00:41:17,960 --> 00:41:20,000
Speaker 1: dud is to you, but you know a dud when

756
00:41:20,000 --> 00:41:23,640
Speaker 1: you see one. And algorithms know who the duds are,

757
00:41:23,840 --> 00:41:27,160
Speaker 1: and they those are the only profiles they surface to

758
00:41:27,239 --> 00:41:30,399
Speaker 1: you unless you are willing to pay for premium. Then

759
00:41:30,440 --> 00:41:32,680
Speaker 1: they might show you somebody that you might actually want

760
00:41:32,680 --> 00:41:35,400
Speaker 1: to have sex with, But until you pay up, it's.

761
00:41:35,320 --> 00:41:39,200
Speaker 4: Duds, poor broke duds.

762
00:41:39,640 --> 00:41:42,880
Speaker 1: So I think all of this is kind of adding

763
00:41:42,960 --> 00:41:46,920
Speaker 1: to this backdrop of dating app fatigue that people, particularly women,

764
00:41:47,000 --> 00:41:51,080
Speaker 1: are feeling with these these apps. However, Whitney, we've heard,

765
00:41:51,320 --> 00:41:55,800
Speaker 1: the co founder of Bumble actually has suggested an answer.

766
00:41:56,520 --> 00:41:59,239
Speaker 1: Your AI could just go on a date for you.

767
00:41:59,640 --> 00:42:01,880
Speaker 1: Here's what she said. If you want to get really

768
00:42:01,920 --> 00:42:04,840
Speaker 1: out there, there's a world where your AI dating concierge

769
00:42:04,880 --> 00:42:08,920
Speaker 1: to go on a date for you with other dating concierges, truly,

770
00:42:08,960 --> 00:42:10,920
Speaker 1: and then you don't have to talk to six hundred people.

771
00:42:11,120 --> 00:42:13,600
Speaker 1: It will scan all of San Francisco for you and

772
00:42:13,640 --> 00:42:16,400
Speaker 1: say these are the three people you really ought to meet. So,

773
00:42:16,520 --> 00:42:19,160
Speaker 1: for example, you could in the near future be talking

774
00:42:19,200 --> 00:42:22,919
Speaker 1: to your AI dating concierge and share your insecurities. I've

775
00:42:22,920 --> 00:42:25,000
Speaker 1: just come out of a breakup, I've got commitment issues,

776
00:42:25,280 --> 00:42:27,479
Speaker 1: and it could help you train yourself into a better

777
00:42:27,520 --> 00:42:29,120
Speaker 1: way of thinking about yourself.

778
00:42:29,520 --> 00:42:34,279
Speaker 3: Sounds nice but also creepy and also like, come on,

779
00:42:34,520 --> 00:42:35,759
Speaker 3: that's that's not gonna work.

780
00:42:35,920 --> 00:42:37,520
Speaker 1: I would be remiss if I did not mention there

781
00:42:37,560 --> 00:42:42,239
Speaker 1: was literally a Black Mirror episode about two people's ais

782
00:42:42,440 --> 00:42:45,839
Speaker 1: dating each other within the framework of a dating app,

783
00:42:45,880 --> 00:42:47,759
Speaker 1: and like that's how they find each other and know

784
00:42:47,840 --> 00:42:50,879
Speaker 1: they're compatible. So, given the story that we talked about

785
00:42:50,920 --> 00:42:53,440
Speaker 1: earlier about better help about how they just give the

786
00:42:53,480 --> 00:42:57,880
Speaker 1: most intimate data and information about us to Facebook and

787
00:42:57,960 --> 00:43:01,800
Speaker 1: Snapchat and whoever, else for money. I think we really

788
00:43:01,800 --> 00:43:04,759
Speaker 1: should be wary of this kind of AI integration into

789
00:43:04,760 --> 00:43:07,920
Speaker 1: our romantic and sexual lives. So I know that the

790
00:43:07,920 --> 00:43:10,959
Speaker 1: idea of an AI dating concierge is very different from

791
00:43:11,000 --> 00:43:15,239
Speaker 1: like an AI enabled chat bot or love bot or

792
00:43:15,360 --> 00:43:19,600
Speaker 1: sex bot or romantic partner. But Mozilla Foundation looked into

793
00:43:19,600 --> 00:43:22,800
Speaker 1: it and found in an analysis of eleven romantic chatbot

794
00:43:22,840 --> 00:43:27,120
Speaker 1: apps released in February that nearly every app tested sells

795
00:43:27,200 --> 00:43:31,440
Speaker 1: user data, shares it for advertising, or doesn't provide adequate

796
00:43:31,480 --> 00:43:35,520
Speaker 1: information about any of these points and its privacy policies.

797
00:43:35,920 --> 00:43:40,080
Speaker 1: So basically it's just the wild wild West already when

798
00:43:40,120 --> 00:43:43,120
Speaker 1: it comes to AI and romance, and I think these

799
00:43:43,200 --> 00:43:46,880
Speaker 1: kinds of things become even more concerning for like queer

800
00:43:47,040 --> 00:43:50,879
Speaker 1: or gender expansive people, like are you someplace where it's

801
00:43:50,880 --> 00:43:54,560
Speaker 1: safe to have AI offering up intimate information about who

802
00:43:54,600 --> 00:43:57,560
Speaker 1: you are and your identity to a potential date that

803
00:43:57,640 --> 00:44:01,240
Speaker 1: you yourself have not personally vetted, Like are you safe

804
00:44:01,280 --> 00:44:06,000
Speaker 1: if that information has pretty much no policies around who

805
00:44:06,040 --> 00:44:09,080
Speaker 1: that platform is going to share it with, how they

806
00:44:09,080 --> 00:44:11,200
Speaker 1: will share it with them, what third parties will have

807
00:44:11,280 --> 00:44:14,400
Speaker 1: access to it? You know, I have said this before that,

808
00:44:14,480 --> 00:44:17,680
Speaker 1: when you're talking about connecting with people in ways that

809
00:44:17,719 --> 00:44:23,480
Speaker 1: are intimate and happening. Irl, safety and privacy isn't just

810
00:44:23,520 --> 00:44:26,480
Speaker 1: a nice to have, Like it genuinely does matter. And

811
00:44:26,520 --> 00:44:29,080
Speaker 1: I'm just not sure that I would trust AI designed

812
00:44:29,120 --> 00:44:33,120
Speaker 1: by like mostly white straight CIS men that we know

813
00:44:33,320 --> 00:44:36,279
Speaker 1: is like buggy and full of problems to do that

814
00:44:36,400 --> 00:44:38,320
Speaker 1: for me if the stakes really were high.

815
00:44:38,960 --> 00:44:41,840
Speaker 4: Yeah, the stakes definitely do seem pretty high.

816
00:44:41,960 --> 00:44:44,880
Speaker 1: Yeah, especially if you're dating someplace where it's not safe

817
00:44:44,920 --> 00:44:47,840
Speaker 1: to be your identity, if you're a queer or trands

818
00:44:47,840 --> 00:44:52,160
Speaker 1: are gay. Like, this is real. So we have to

819
00:44:52,239 --> 00:44:55,000
Speaker 1: talk about those Bumble ads, which are sort of being

820
00:44:55,040 --> 00:44:58,319
Speaker 1: referred to as the bumble Fumble. When I first saw

821
00:44:58,360 --> 00:45:00,360
Speaker 1: this reported, I was like, well, they're really seeing an

822
00:45:00,360 --> 00:45:02,880
Speaker 1: opportunity to like make bumble fumble a thing. But we

823
00:45:02,960 --> 00:45:07,960
Speaker 1: got there eventually, So bumble Fumble basically Bumble released these

824
00:45:07,960 --> 00:45:12,240
Speaker 1: billboards saying things like you know full well a valve

825
00:45:12,320 --> 00:45:15,640
Speaker 1: celibacy is not the answer, and thou shalt not give

826
00:45:15,719 --> 00:45:18,359
Speaker 1: up on dating and become a nun. The ads were

827
00:45:18,360 --> 00:45:22,399
Speaker 1: criticized and Bumble apologized, saying we made a mistake. Our

828
00:45:22,440 --> 00:45:25,120
Speaker 1: ads referencing celibacy were an attempt to lean into a

829
00:45:25,120 --> 00:45:28,719
Speaker 1: community frustrated by modern dating, and instead of bringing joy

830
00:45:28,760 --> 00:45:34,080
Speaker 1: and humor, we unintentionally did the opposite. So I actually

831
00:45:35,000 --> 00:45:38,160
Speaker 1: saw a lot of the criticism of these billboards and

832
00:45:38,239 --> 00:45:41,000
Speaker 1: people were saying like, oh, they're knocking celibacy as a

833
00:45:41,040 --> 00:45:44,440
Speaker 1: lifestyle all of that. Totally get that, but I actually

834
00:45:44,520 --> 00:45:48,200
Speaker 1: have a different take. So I think that if anything,

835
00:45:48,520 --> 00:45:52,719
Speaker 1: the problem that Bumble was actually like making light of

836
00:45:53,200 --> 00:45:55,560
Speaker 1: in a way that was in poor taste is that

837
00:45:55,680 --> 00:45:59,480
Speaker 1: Bumble is just no longer serving women, who they say

838
00:45:59,600 --> 00:46:03,200
Speaker 1: is their life like main intended user base. So more

839
00:46:03,239 --> 00:46:05,960
Speaker 1: and more women are just getting off of these platforms,

840
00:46:06,320 --> 00:46:09,759
Speaker 1: and on top of that, more and more bots are

841
00:46:09,800 --> 00:46:13,240
Speaker 1: on these platforms too. Like I recently watched the Ashley

842
00:46:13,280 --> 00:46:16,719
Speaker 1: Madison documentary on Netflix, and it is so wild how

843
00:46:16,719 --> 00:46:19,799
Speaker 1: they're basically like, oh yeah, on top of the like

844
00:46:19,960 --> 00:46:23,280
Speaker 1: cheating most of the time, we were just like charging

845
00:46:23,920 --> 00:46:27,640
Speaker 1: stupid men twenty dollars a month to chat with bots

846
00:46:27,680 --> 00:46:31,440
Speaker 1: and other men and themselves. Right, So, like, dating apps

847
00:46:31,440 --> 00:46:34,880
Speaker 1: are kind of a like house of cards where women

848
00:46:35,200 --> 00:46:38,560
Speaker 1: have been like this sucks, We're leaving, and these platforms

849
00:46:38,600 --> 00:46:41,000
Speaker 1: are like, oh god, we have to charge men who

850
00:46:41,040 --> 00:46:44,040
Speaker 1: want to be in conversation with women for something, and

851
00:46:44,080 --> 00:46:46,800
Speaker 1: a lot of that is bots. And so I actually

852
00:46:46,840 --> 00:46:51,760
Speaker 1: see these ads as almost this like weird tone death

853
00:46:51,960 --> 00:46:56,440
Speaker 1: plea to women to come back to these platforms, even

854
00:46:56,440 --> 00:46:59,280
Speaker 1: though these platforms are kind of acknowledging that they failed

855
00:46:59,280 --> 00:47:03,040
Speaker 1: them because the platforms need women on them to make money.

856
00:47:03,080 --> 00:47:06,520
Speaker 1: And so I totally see what people who are saying like, oh,

857
00:47:06,560 --> 00:47:08,640
Speaker 1: this was a dig on celibacy are saying, but I

858
00:47:08,680 --> 00:47:11,200
Speaker 1: think it's actually more insulting than that. I think what

859
00:47:11,200 --> 00:47:15,360
Speaker 1: these platforms are saying is like, listen, women, we need

860
00:47:15,400 --> 00:47:18,160
Speaker 1: you on this platform to make money. So like, what

861
00:47:18,200 --> 00:47:20,640
Speaker 1: are you gonna do? Be celibate? No way, come back

862
00:47:20,680 --> 00:47:21,720
Speaker 1: to our platform.

863
00:47:22,000 --> 00:47:27,000
Speaker 3: Yeah, it does feel a little like desperate and like

864
00:47:27,640 --> 00:47:30,200
Speaker 3: trying to be edgy, but just coming off as desperate,

865
00:47:30,480 --> 00:47:32,920
Speaker 3: which is definitely not a good look if you've ever

866
00:47:32,960 --> 00:47:33,800
Speaker 3: been on a dating.

867
00:47:33,600 --> 00:47:38,560
Speaker 1: Platform, especially if you are the dating platform. And I

868
00:47:38,600 --> 00:47:41,680
Speaker 1: think desperate is the right word for it. And I

869
00:47:41,760 --> 00:47:43,720
Speaker 1: just feel like I wish we lived in a world

870
00:47:43,719 --> 00:47:50,200
Speaker 1: where what these platforms were offering was functionally and meaningfully

871
00:47:50,400 --> 00:47:54,240
Speaker 1: just something better, something that feels like respect and fun

872
00:47:54,400 --> 00:47:59,600
Speaker 1: and exploration and excitement, not just fatigue, Like I don't know,

873
00:47:59,680 --> 00:48:02,120
Speaker 1: I get mirrors a lot of the different ways that

874
00:48:02,120 --> 00:48:04,560
Speaker 1: people are feeling about digital experience these days, that it's

875
00:48:04,640 --> 00:48:10,360
Speaker 1: just fatigue. So Bumble really fumbled on this one. I'm

876
00:48:10,400 --> 00:48:14,120
Speaker 1: glad they apologized, But I think the problem is deeper

877
00:48:14,160 --> 00:48:16,840
Speaker 1: than just these billboards. I think that they are not

878
00:48:17,000 --> 00:48:20,720
Speaker 1: serving women. Women are smart enough to not stick around

879
00:48:20,760 --> 00:48:23,600
Speaker 1: where they are not having good experiences and where it's

880
00:48:23,600 --> 00:48:27,160
Speaker 1: clear that they, you know, aren't wanted or aren't value

881
00:48:27,320 --> 00:48:29,120
Speaker 1: And I think that's a real it's a it's a

882
00:48:29,160 --> 00:48:31,560
Speaker 1: real problem, not just for the women that they're failing

883
00:48:31,560 --> 00:48:34,080
Speaker 1: to serve, but also for their business model, Like you

884
00:48:34,120 --> 00:48:37,759
Speaker 1: can't just have a dynamic that women feel like they're

885
00:48:37,760 --> 00:48:41,560
Speaker 1: being mistreated and not served and expect them to continue

886
00:48:41,600 --> 00:48:43,240
Speaker 1: showing up for more of the same.

887
00:48:44,160 --> 00:48:48,840
Speaker 3: Yeah, And maybe the problem is even broader and deeper

888
00:48:50,160 --> 00:48:53,839
Speaker 3: that you know. The problem they're trying to solve is

889
00:48:54,480 --> 00:49:01,239
Speaker 3: people lacking human connection in their life. And and there's

890
00:49:01,280 --> 00:49:05,320
Speaker 3: just the fundamental tension of offering people a digital tool

891
00:49:05,520 --> 00:49:09,520
Speaker 3: to solve that problem, where the financial success of that

892
00:49:09,560 --> 00:49:13,520
Speaker 3: digital tool depends on people continuing to use that tool

893
00:49:13,560 --> 00:49:16,320
Speaker 3: and continuing to engage with it and pay subscription fees

894
00:49:16,400 --> 00:49:17,240
Speaker 3: month after month.

895
00:49:19,760 --> 00:49:21,520
Speaker 4: Yes, you know you can. You can have one, but

896
00:49:21,560 --> 00:49:22,480
Speaker 4: you can't have them both.

897
00:49:22,960 --> 00:49:27,359
Speaker 1: And I fundamentally believe you know. In later on, in

898
00:49:27,400 --> 00:49:31,880
Speaker 1: this conversation with Bloomberg that that Wendy wolf Heard had

899
00:49:32,760 --> 00:49:35,920
Speaker 1: about the future of online dating, she was talking about

900
00:49:35,960 --> 00:49:39,279
Speaker 1: this loneliness epidemic that we're all in, and that's very real.

901
00:49:39,400 --> 00:49:44,040
Speaker 1: But I'm not sure that the people who have given

902
00:49:44,120 --> 00:49:48,000
Speaker 1: us our current tech and digital landscape are the same

903
00:49:48,160 --> 00:49:51,560
Speaker 1: ones that I would trust or want to be using

904
00:49:51,640 --> 00:49:56,680
Speaker 1: technology to solve our current loneliness crisis. Like the people

905
00:49:56,719 --> 00:49:59,360
Speaker 1: that got us into our current situation are not the

906
00:49:59,360 --> 00:50:00,960
Speaker 1: people that I would trust to get us out of it.

907
00:50:01,040 --> 00:50:03,920
Speaker 1: Like if you drove my car into a ditch, I

908
00:50:03,960 --> 00:50:05,680
Speaker 1: don't think I would trust you to drive it out

909
00:50:05,719 --> 00:50:07,799
Speaker 1: of the ditch. I might say it's time for you

910
00:50:07,840 --> 00:50:09,560
Speaker 1: to sit in the back seat let somebody else take

911
00:50:09,560 --> 00:50:12,400
Speaker 1: a turn at the wheel. Yeah, well, Mike, thanks for

912
00:50:12,440 --> 00:50:16,239
Speaker 1: being in the passenger seat on this journey through this

913
00:50:16,320 --> 00:50:17,840
Speaker 1: week's tech stories.

914
00:50:17,920 --> 00:50:21,719
Speaker 3: I appreciate it, Bridgid. It's always a pleasant ride, and

915
00:50:23,239 --> 00:50:24,640
Speaker 3: let's do it again sometime soon.

916
00:50:24,960 --> 00:50:26,919
Speaker 1: And thanks to all of you for listening. I will

917
00:50:26,920 --> 00:50:34,359
Speaker 1: see you on the Internet. If you're looking for ways

918
00:50:34,400 --> 00:50:36,600
Speaker 1: to support the show, check out our merch store at

919
00:50:36,640 --> 00:50:40,360
Speaker 1: tegodi dot com slash store. Got a story about an

920
00:50:40,400 --> 00:50:42,319
Speaker 1: interesting thing in tech, or just want to say hi,

921
00:50:42,719 --> 00:50:45,000
Speaker 1: You can reach us at Hello at tegodi dot com.

922
00:50:45,040 --> 00:50:47,440
Speaker 1: You can also find transcripts for today's episode at tengody

923
00:50:47,480 --> 00:50:49,560
Speaker 1: dot com. There Are No Girls on the Internet was

924
00:50:49,560 --> 00:50:52,480
Speaker 1: created by me Bridget Todd. It's a production of iHeartRadio

925
00:50:52,520 --> 00:50:56,560
Speaker 1: and Unboss Creative edited by Joey Patt. Jonathan Strickland is

926
00:50:56,560 --> 00:51:00,000
Speaker 1: our executive producer. Tarry Harrison is our producer and sound engineer.

927
00:51:00,400 --> 00:51:03,600
Speaker 1: Michael Amado is our contributing producer. I'm your host, Bridget Todd.

928
00:51:04,000 --> 00:51:06,000
Speaker 1: If you want to help us grow, rate and review.

929
00:51:05,760 --> 00:51:06,799
Speaker 2: Us on Apple Podcasts.

930
00:51:07,560 --> 00:51:10,399
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

931
00:51:10,440 --> 00:51:16,720
Speaker 1: Apple Podcasts, or wherever you get your podcasts.