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 --> 00:00:18,160
Speaker 1: is there are No Girls on the Internet. I'm here

4
00:00:18,160 --> 00:00:20,319
Speaker 1: with my producer Mike and here's what you may have

5
00:00:20,400 --> 00:00:23,200
Speaker 1: missed this week on the Internet. So, in news that

6
00:00:23,239 --> 00:00:26,880
Speaker 1: will probably shock no one, it turns out Twitter under

7
00:00:26,880 --> 00:00:29,080
Speaker 1: Elon Musk is not doing a very good job of

8
00:00:29,080 --> 00:00:32,520
Speaker 1: dealing with anti LGBTQU hate speech on the platform. Who

9
00:00:32,520 --> 00:00:33,639
Speaker 1: would have guessed, Am I right?

10
00:00:34,159 --> 00:00:36,559
Speaker 2: You know, savvy listeners might have guessed.

11
00:00:36,920 --> 00:00:39,720
Speaker 1: So this is all According to glad's annual report on

12
00:00:39,760 --> 00:00:43,120
Speaker 1: the Safety of Social media Platforms for LGBTQ users. GOD

13
00:00:43,159 --> 00:00:45,639
Speaker 1: said that all five of the major social media platforms

14
00:00:45,680 --> 00:00:49,160
Speaker 1: examined had taken steps to improve safety over the past year,

15
00:00:49,520 --> 00:00:53,040
Speaker 1: with the single exception of Twitter. Twitter not only scored

16
00:00:53,080 --> 00:00:56,320
Speaker 1: the lowest but also changed its policies to become even

17
00:00:56,440 --> 00:00:59,040
Speaker 1: less safe. I gotta say that one is sort of

18
00:00:59,560 --> 00:01:03,279
Speaker 1: personal for me. Policies that I personally worked on in

19
00:01:03,320 --> 00:01:06,360
Speaker 1: my platform accountability work. It was a little bit rough

20
00:01:06,400 --> 00:01:09,240
Speaker 1: to see them rolled back, but you know that is

21
00:01:09,319 --> 00:01:11,759
Speaker 1: to be expected when you're dealing with somebody like Elon

22
00:01:11,880 --> 00:01:15,240
Speaker 1: Musk who just clearly does not take these issues seriously.

23
00:01:15,880 --> 00:01:21,640
Speaker 2: Sometimes it's easy to feel like progress is inevitable towards

24
00:01:21,840 --> 00:01:25,960
Speaker 2: a more inclusive world where we all recognize that other

25
00:01:26,040 --> 00:01:31,000
Speaker 2: people are valuable and respect them. But that's not something

26
00:01:31,080 --> 00:01:34,360
Speaker 2: that is inevitable, and it is due to the hard

27
00:01:34,440 --> 00:01:38,040
Speaker 2: work of people like you Bridget who push and fight

28
00:01:38,160 --> 00:01:43,320
Speaker 2: to make that positive change happen. And it's awesome when

29
00:01:43,319 --> 00:01:46,880
Speaker 2: it does. And then there are times like this where

30
00:01:46,920 --> 00:01:50,320
Speaker 2: there's a reminder that there are counter forces who will

31
00:01:50,360 --> 00:01:51,680
Speaker 2: push back against it.

32
00:01:52,000 --> 00:01:54,240
Speaker 1: I say this and I mean it pretty much how

33
00:01:54,280 --> 00:01:57,559
Speaker 1: it sounds that whenever a tech company does something good

34
00:01:58,040 --> 00:02:00,440
Speaker 1: nine times out of ten and is not because they

35
00:02:00,520 --> 00:02:03,600
Speaker 1: went to bed and woke up and their their hearts

36
00:02:03,720 --> 00:02:06,640
Speaker 1: grew three sizes like the Grinch's heart when it breaks

37
00:02:06,680 --> 00:02:09,920
Speaker 1: out of that thing. Usually there is someone, an organizer,

38
00:02:10,320 --> 00:02:13,440
Speaker 1: a professional pain in the ass who is pushing them

39
00:02:13,520 --> 00:02:16,000
Speaker 1: to be better. And so I means, as much as

40
00:02:16,000 --> 00:02:20,400
Speaker 1: I hate that Twitter is is not just failing to

41
00:02:20,440 --> 00:02:23,160
Speaker 1: moderate their platforms in a way that allows for LGBTQ

42
00:02:23,360 --> 00:02:26,960
Speaker 1: users to feel safe there, and you know is actually

43
00:02:27,680 --> 00:02:31,320
Speaker 1: rolling back those policies, it is good to see that

44
00:02:31,400 --> 00:02:35,160
Speaker 1: other platforms are taking some steps in the right direction.

45
00:02:35,560 --> 00:02:38,680
Speaker 2: Yeah, that's right. Twitter is an outlier here, right, even

46
00:02:38,800 --> 00:02:44,760
Speaker 2: among like Facebook and YouTube, Twitter is an outlier in

47
00:02:44,919 --> 00:02:49,040
Speaker 2: rolling back protections for LGBTQ people. So that's worth noting.

48
00:02:49,560 --> 00:02:52,400
Speaker 1: So the reports scored each platform in twelve areas of

49
00:02:52,440 --> 00:02:56,320
Speaker 1: specific relevance to LGBTQ users, like the platform should have

50
00:02:56,360 --> 00:02:59,240
Speaker 1: a policy that to protect LGBTQ users from hate, and

51
00:02:59,400 --> 00:03:02,280
Speaker 1: the platform should have a policy that prohibits targeted dead naming.

52
00:03:03,160 --> 00:03:04,880
Speaker 1: I really think this should really be a wake up

53
00:03:04,919 --> 00:03:08,040
Speaker 1: call that Twitter under Elon Musk is really going out

54
00:03:08,080 --> 00:03:11,640
Speaker 1: of its way to signal that anti trans, anti LGBTQ

55
00:03:11,800 --> 00:03:14,440
Speaker 1: hate is welcome on the platform. And I think it's

56
00:03:14,440 --> 00:03:17,080
Speaker 1: a problem because it leaves marginalized people out of the

57
00:03:17,120 --> 00:03:19,280
Speaker 1: conversation and it keeps us from being able to be

58
00:03:19,360 --> 00:03:23,359
Speaker 1: equal participants in our discourse and democracy. For a platform

59
00:03:23,400 --> 00:03:26,519
Speaker 1: that wants to be the public square, that is really

60
00:03:26,560 --> 00:03:29,880
Speaker 1: a problem. You know, Twitter is the place where news

61
00:03:30,000 --> 00:03:33,560
Speaker 1: is broken, where stories become news. It moves so much

62
00:03:33,600 --> 00:03:37,200
Speaker 1: faster than other platforms like Facebook or Instagram that I

63
00:03:37,200 --> 00:03:39,640
Speaker 1: think that's one of the reasons why you see this

64
00:03:39,840 --> 00:03:42,680
Speaker 1: battle for it, and like it's really a proxy war

65
00:03:42,840 --> 00:03:44,800
Speaker 1: for power and who gets to take up space and

66
00:03:44,800 --> 00:03:47,040
Speaker 1: who gets to be part of the conversation. The fact

67
00:03:47,120 --> 00:03:52,440
Speaker 1: that because of Elon Musk rolling back what little policies

68
00:03:52,480 --> 00:03:56,240
Speaker 1: there were to protect LGBTQ users, that anti trans bigots

69
00:03:56,280 --> 00:03:59,440
Speaker 1: would feel comfortable showing up there and feel embolden and empowered,

70
00:03:59,440 --> 00:04:01,840
Speaker 1: which sounds like what is happening is a real problem

71
00:04:01,880 --> 00:04:05,280
Speaker 1: for a platform that heretofore have been about, you know,

72
00:04:06,280 --> 00:04:10,600
Speaker 1: making news where journalists and newsmakers are congregating, and so

73
00:04:10,640 --> 00:04:13,760
Speaker 1: if people don't feel safe or good about spending time there,

74
00:04:14,080 --> 00:04:16,120
Speaker 1: that's a real problem. And I also think it's not

75
00:04:16,279 --> 00:04:20,320
Speaker 1: just an issue of you know, the user experience of

76
00:04:20,440 --> 00:04:24,279
Speaker 1: LGBTQ users. It's also about the security and the safety

77
00:04:24,360 --> 00:04:28,120
Speaker 1: of these platforms, because when extremists and hate bongers are

78
00:04:28,400 --> 00:04:31,839
Speaker 1: allowed to gather in these platforms, they become less secure,

79
00:04:32,200 --> 00:04:35,600
Speaker 1: they become more polarized, they're easier to have bad actors

80
00:04:35,640 --> 00:04:38,239
Speaker 1: disrupt them. And so it's not just it is about

81
00:04:38,279 --> 00:04:42,680
Speaker 1: the LGBTQ users and their experiences online, but also it

82
00:04:42,760 --> 00:04:45,480
Speaker 1: makes it less secure and less safe for all of us,

83
00:04:45,480 --> 00:04:47,479
Speaker 1: whether you identify its LGBTQ.

84
00:04:47,200 --> 00:04:51,520
Speaker 2: Or not absolutely like it is about the LGBTQ users,

85
00:04:51,520 --> 00:04:55,400
Speaker 2: but also it's not about them, right like the right

86
00:04:55,400 --> 00:05:00,480
Speaker 2: wing fascist types who love to hate, they're going to

87
00:05:00,520 --> 00:05:04,200
Speaker 2: find somebody. It's always going to be somebody. So yes,

88
00:05:04,240 --> 00:05:06,200
Speaker 2: it's about them, but no, it's not about them. It's

89
00:05:06,240 --> 00:05:07,320
Speaker 2: about oppression.

90
00:05:07,760 --> 00:05:10,800
Speaker 1: Folks can read the full report in our show notes.

91
00:05:11,080 --> 00:05:14,600
Speaker 1: But in addition to ranking the platforms for LGBTQ safety,

92
00:05:14,880 --> 00:05:18,520
Speaker 1: the report also highlights other dangerous trends on platforms. One

93
00:05:18,520 --> 00:05:21,960
Speaker 1: of those trends is called stochastic harassment. If you don't

94
00:05:21,960 --> 00:05:23,960
Speaker 1: know what that is, it is a form of harassment

95
00:05:24,160 --> 00:05:28,880
Speaker 1: where online accounts or media figures don't directly attack individuals

96
00:05:28,960 --> 00:05:32,680
Speaker 1: or call for violence, but instead amplify a general narrative

97
00:05:32,880 --> 00:05:37,400
Speaker 1: that indirectly dehumanizes specific groups, in doing so, increasing the

98
00:05:37,520 --> 00:05:41,039
Speaker 1: likelihood that others will engage in such violence. A great

99
00:05:41,040 --> 00:05:43,719
Speaker 1: example right now would be the groomer narrative, which we

100
00:05:43,800 --> 00:05:47,600
Speaker 1: know like baselessly claims that queer folks and their allies

101
00:05:47,640 --> 00:05:50,919
Speaker 1: are trying to recruit children into sexual acts. We talked

102
00:05:51,000 --> 00:05:54,159
Speaker 1: quite a bit about this in our opening episode this

103
00:05:54,200 --> 00:05:58,120
Speaker 1: season with Nina Jenkowitz. How you know Tucker Carlson would

104
00:05:58,120 --> 00:06:02,200
Speaker 1: spend hours talking about her on Fox News, never directly

105
00:06:02,279 --> 00:06:05,279
Speaker 1: threatening her, but people would get the message.

106
00:06:05,279 --> 00:06:05,440
Speaker 3: You know.

107
00:06:05,480 --> 00:06:08,040
Speaker 1: Trump did a lot of this where he would specifically

108
00:06:08,320 --> 00:06:11,240
Speaker 1: call out black women or women of color journalists by

109
00:06:11,360 --> 00:06:15,400
Speaker 1: name to his like fan base and followers, never saying,

110
00:06:16,080 --> 00:06:20,040
Speaker 1: you know, harassed this person, attack this person, but it

111
00:06:20,080 --> 00:06:22,240
Speaker 1: was a kind of a wink wink, nudge nudge thing

112
00:06:22,279 --> 00:06:24,599
Speaker 1: where people would get the message pretty quickly.

113
00:06:25,240 --> 00:06:25,480
Speaker 3: Yeah.

114
00:06:25,520 --> 00:06:28,520
Speaker 2: I thought it was a really interesting part of that

115
00:06:28,560 --> 00:06:31,320
Speaker 2: Glad report that they called that out explicitly, because I

116
00:06:31,360 --> 00:06:38,400
Speaker 2: had never heard that term before, stochastic harassment, and it's

117
00:06:38,440 --> 00:06:40,479
Speaker 2: it's so common now that we have a term for it.

118
00:06:40,560 --> 00:06:42,679
Speaker 2: We see it everywhere in Nina Jenkowitch was a great

119
00:06:42,720 --> 00:06:47,440
Speaker 2: example where, you know, and Trump is a master of it,

120
00:06:47,520 --> 00:06:51,760
Speaker 2: right of never directly explicitly calling for violence against a

121
00:06:51,760 --> 00:06:56,240
Speaker 2: particular person, but talking about them in a way that

122
00:06:56,640 --> 00:07:01,680
Speaker 2: just incites all theirs to violence.

123
00:07:02,160 --> 00:07:05,960
Speaker 1: Yeah, I've definitely seen right wing extremists are so good

124
00:07:06,000 --> 00:07:09,279
Speaker 1: at this. They're so good at knowing what to say

125
00:07:09,840 --> 00:07:13,800
Speaker 1: to have that sheen of plausible deniability where it's like, oh, well,

126
00:07:14,000 --> 00:07:17,440
Speaker 1: I never said to attack so and so, I never

127
00:07:17,520 --> 00:07:20,080
Speaker 1: said to storm the capitol. I never said to do this,

128
00:07:20,560 --> 00:07:23,440
Speaker 1: but it's just like, well, here are eighteen different examples

129
00:07:23,480 --> 00:07:25,680
Speaker 1: where you all but said that. So knowing how to

130
00:07:25,720 --> 00:07:28,559
Speaker 1: stay right on that line is something that these folks

131
00:07:28,600 --> 00:07:31,000
Speaker 1: are very good at. And that's something that the Glad

132
00:07:31,080 --> 00:07:35,040
Speaker 1: Report really spotlights that folks are really utilizing this as

133
00:07:35,080 --> 00:07:38,520
Speaker 1: a loophole in social media platforms policies, which is contributing

134
00:07:38,520 --> 00:07:41,240
Speaker 1: to the rising tide of anti lgbtqu hate and legislation

135
00:07:41,560 --> 00:07:46,000
Speaker 1: that we're all currently living through. And so it's this

136
00:07:46,040 --> 00:07:48,680
Speaker 1: is all very distressing. It's like hard to hear, but

137
00:07:48,960 --> 00:07:51,600
Speaker 1: it's good to have some insight into this. You know,

138
00:07:51,680 --> 00:07:55,360
Speaker 1: for a long time, we would only have social media's

139
00:07:55,680 --> 00:08:00,000
Speaker 1: companies and they're like pr leadership to be able to

140
00:08:00,240 --> 00:08:02,960
Speaker 1: like have any understanding of what's going on on these platforms.

141
00:08:02,960 --> 00:08:06,280
Speaker 1: And so having visibility into this is good because that's

142
00:08:06,280 --> 00:08:08,680
Speaker 1: really going to be the first step of creating a

143
00:08:08,680 --> 00:08:11,920
Speaker 1: better system where folks can show up and meaningfully make

144
00:08:11,920 --> 00:08:15,520
Speaker 1: their voices heard on digital platforms, you know, the same

145
00:08:15,720 --> 00:08:17,040
Speaker 1: way that all of us deserve to.

146
00:08:17,960 --> 00:08:21,080
Speaker 2: In public health, one of the things we say sometimes

147
00:08:21,160 --> 00:08:23,760
Speaker 2: is that like if you don't measure something it doesn't exist.

148
00:08:24,000 --> 00:08:27,320
Speaker 2: And so I think it's extremely valuable that GLAD is

149
00:08:27,960 --> 00:08:32,719
Speaker 2: measuring these policies with respect to how they protect LGBTQ

150
00:08:32,840 --> 00:08:36,080
Speaker 2: people to show up online and you know how that

151
00:08:36,120 --> 00:08:41,400
Speaker 2: contributes to a more to an inclusive internet where we

152
00:08:41,480 --> 00:08:44,760
Speaker 2: can all show up and enjoy the internet. You know,

153
00:08:44,800 --> 00:08:47,080
Speaker 2: who doesn't want to enjoy the internet exactly.

154
00:08:47,120 --> 00:08:49,640
Speaker 1: And I do think that something that did not get

155
00:08:49,800 --> 00:08:54,520
Speaker 1: enough attention is the very clear ways that Elon Musk's

156
00:08:54,520 --> 00:08:57,480
Speaker 1: ownership of Twitter, the way that he's running Twitter is

157
00:08:57,480 --> 00:09:02,760
Speaker 1: connected to transphobia. Documents revealed that it was a protecting

158
00:09:03,000 --> 00:09:06,760
Speaker 1: the ability of the Babylon Bee to say a transphobia

159
00:09:06,880 --> 00:09:10,040
Speaker 1: joke about Rachel Levine, who is a trans woman in

160
00:09:10,080 --> 00:09:13,360
Speaker 1: the Biden administration. Uh, that was the thing that first

161
00:09:13,400 --> 00:09:15,640
Speaker 1: prompted him like, I gotta buy Twitter this, you know,

162
00:09:15,679 --> 00:09:17,760
Speaker 1: and make it make it a free a quote free

163
00:09:17,800 --> 00:09:21,680
Speaker 1: speech haven. And so he's been pretty clear that transphobia

164
00:09:22,080 --> 00:09:24,800
Speaker 1: is a big part of his mo O. And we

165
00:09:25,080 --> 00:09:27,440
Speaker 1: you know, I don't think I would be telling tales

166
00:09:27,440 --> 00:09:30,760
Speaker 1: out of school to say that his ownership of Twitter

167
00:09:30,800 --> 00:09:34,040
Speaker 1: has been a disaster by pretty much every metric. He's

168
00:09:34,120 --> 00:09:37,120
Speaker 1: losing money, it's bleeding users people. It's not a fun

169
00:09:37,160 --> 00:09:39,800
Speaker 1: place to show up anymore. And I think that's the

170
00:09:39,880 --> 00:09:43,280
Speaker 1: thing that's the gag about transphobia is that doubling down

171
00:09:43,280 --> 00:09:47,480
Speaker 1: on transphobia is unpopular. It makes it makes everything worse.

172
00:09:47,679 --> 00:09:47,839
Speaker 3: Right.

173
00:09:48,320 --> 00:09:51,360
Speaker 1: It took a platform that was like not perfect but

174
00:09:51,520 --> 00:09:55,480
Speaker 1: like usable and made it garbage, right, And so it

175
00:09:55,559 --> 00:09:58,880
Speaker 1: really I hope that this is something that I want

176
00:09:58,920 --> 00:10:02,640
Speaker 1: to see all their business and tech leaders taking note

177
00:10:02,760 --> 00:10:06,520
Speaker 1: that trafficking in transphobia is not just cruel and hateful

178
00:10:06,520 --> 00:10:08,280
Speaker 1: and bigoted and all the things that we know it is,

179
00:10:08,440 --> 00:10:12,559
Speaker 1: but also building a trying to build a community around transphobia,

180
00:10:12,800 --> 00:10:15,720
Speaker 1: it's always going to fail, And so I wish that

181
00:10:15,760 --> 00:10:19,280
Speaker 1: we saw more reporting that tied Elon Musk's clear and

182
00:10:19,320 --> 00:10:24,360
Speaker 1: explicit transphobia with his tanking of a you know, of

183
00:10:24,400 --> 00:10:29,199
Speaker 1: businesses and tanking of community and experience and vibes. Listen,

184
00:10:29,200 --> 00:10:31,360
Speaker 1: if I was throwing a party and a transphobe showed

185
00:10:31,400 --> 00:10:33,880
Speaker 1: up and started spouting transphob shit, of course it would

186
00:10:33,960 --> 00:10:35,520
Speaker 1: kill the vibe and I would make them leave. And

187
00:10:35,559 --> 00:10:39,160
Speaker 1: so Elon Musk is throwing the party and he's the transphobe,

188
00:10:39,240 --> 00:10:40,520
Speaker 1: so of course the vibes are off.

189
00:10:41,240 --> 00:10:44,560
Speaker 2: Thank you for calling that out. We don't acknowledge it

190
00:10:44,920 --> 00:10:48,000
Speaker 2: enough to just say, like, yeah, Elon Musk is a transphobe.

191
00:10:48,080 --> 00:10:52,720
Speaker 2: His whole free speech thing. Transphobia is really at the

192
00:10:52,760 --> 00:10:57,320
Speaker 2: center of it. It has been. You know, remember when

193
00:10:57,400 --> 00:11:02,480
Speaker 2: he went out on stage in San Francisco at the

194
00:11:02,559 --> 00:11:07,800
Speaker 2: Dave Chappelle con not concert but comedy show, you know,

195
00:11:07,880 --> 00:11:10,560
Speaker 2: comedy and scare quotes whatever Dave Chappelle does now, and

196
00:11:10,600 --> 00:11:14,600
Speaker 2: people booed him like that's the common denominator of these folks,

197
00:11:14,600 --> 00:11:18,360
Speaker 2: and it's just like not palatable, it's not interesting. It

198
00:11:18,640 --> 00:11:22,320
Speaker 2: reminds me of Dennis Miller. Most of our listeners are

199
00:11:22,360 --> 00:11:26,480
Speaker 2: probably too young to even remember him, but like, his

200
00:11:26,600 --> 00:11:29,040
Speaker 2: whole gag was that he was like a right wing

201
00:11:29,520 --> 00:11:32,920
Speaker 2: comic and he would have these like right wing jokes,

202
00:11:33,080 --> 00:11:35,840
Speaker 2: but it was all just punching down, and it's just

203
00:11:36,280 --> 00:11:38,640
Speaker 2: it's not funny to punch down.

204
00:11:39,280 --> 00:11:41,880
Speaker 1: Oh my god. I think about that Elon Musk and

205
00:11:42,000 --> 00:11:45,800
Speaker 1: Dave Chappelle thing so often because I think it was

206
00:11:46,160 --> 00:11:48,679
Speaker 1: it was either in Austin or in San Francisco, one

207
00:11:48,720 --> 00:11:51,800
Speaker 1: of the other. I can't remember which one, but I

208
00:11:51,880 --> 00:11:56,760
Speaker 1: can't think of an audience more primed to be sympathetic

209
00:11:56,880 --> 00:12:01,080
Speaker 1: to Elona Musk's whole thing. People who are going to

210
00:12:01,120 --> 00:12:03,840
Speaker 1: see Dave Chappelle in twenty twenty three or twenty twenty

211
00:12:03,880 --> 00:12:07,280
Speaker 1: two or whenever that happened in Austin or San Francisco

212
00:12:07,400 --> 00:12:08,959
Speaker 1: or wherever it was. I think it was San Francisco.

213
00:12:09,000 --> 00:12:10,880
Speaker 1: And no, that I'm thinking about it, I can't think

214
00:12:10,920 --> 00:12:14,200
Speaker 1: of an audience more prime to be like, yeah, we

215
00:12:14,280 --> 00:12:17,600
Speaker 1: love Elon Musk and they boot him. That's the thing is, like,

216
00:12:18,040 --> 00:12:20,640
Speaker 1: you know, imagine hating trans people so much that you

217
00:12:21,480 --> 00:12:24,280
Speaker 1: light forty four billion dollars on fire, Like that really

218
00:12:24,280 --> 00:12:26,320
Speaker 1: says a lot about who you are as a person.

219
00:12:26,679 --> 00:12:28,440
Speaker 1: And it's not bad enough that you need to be

220
00:12:28,440 --> 00:12:31,040
Speaker 1: a trans phobe and be running a website into the ground.

221
00:12:31,200 --> 00:12:33,320
Speaker 1: You also got to be like a vibe killer. Two.

222
00:12:33,480 --> 00:12:40,079
Speaker 1: Come on, it's too much. Kick a lane.

223
00:12:41,240 --> 00:12:53,520
Speaker 3: Let's take a quick break at our back.

224
00:12:55,640 --> 00:12:59,040
Speaker 1: Let's talk about women and gaming, because it turns out

225
00:13:00,000 --> 00:13:03,800
Speaker 1: and be gaming more than half fifty two percent of

226
00:13:03,840 --> 00:13:06,280
Speaker 1: all Nintendo Switch players in the United States are women.

227
00:13:06,640 --> 00:13:09,959
Speaker 1: This is based on a new report from Sircana Player Pulse.

228
00:13:10,280 --> 00:13:14,680
Speaker 1: So women be gaming and Nintendo's Switch ownership is following

229
00:13:14,800 --> 00:13:17,560
Speaker 1: a more broad trend in the United States when compared

230
00:13:17,600 --> 00:13:20,800
Speaker 1: to other gaming consoles too, So Nintendo Switch does have

231
00:13:20,840 --> 00:13:23,400
Speaker 1: the highest amount of female users at fifty two percent,

232
00:13:23,760 --> 00:13:27,600
Speaker 1: but fifty percent of gaming PCs are also owned by women.

233
00:13:27,880 --> 00:13:30,600
Speaker 1: The next closest is the Xbox series, with forty five

234
00:13:30,640 --> 00:13:33,240
Speaker 1: percent of those being owned by women. PlayStation five is

235
00:13:33,240 --> 00:13:35,559
Speaker 1: the lowest at forty one percent. This year, in the

236
00:13:35,640 --> 00:13:38,920
Speaker 1: United States, forty seven percent of console video game players

237
00:13:38,960 --> 00:13:42,480
Speaker 1: were women, and half of all PC video game players

238
00:13:42,679 --> 00:13:45,120
Speaker 1: are women, and fifty four percent of mobile video game

239
00:13:45,120 --> 00:13:48,440
Speaker 1: players are women. So guess who is not thrilled about

240
00:13:48,440 --> 00:13:49,839
Speaker 1: all this, Mike, Is.

241
00:13:49,800 --> 00:13:52,520
Speaker 2: It the shittiest segment of the American public?

242
00:13:52,880 --> 00:13:56,760
Speaker 1: Yes, gamer guys, like not all gamer guys, but like

243
00:13:57,200 --> 00:14:01,080
Speaker 1: a very specific type of gamer guys is not thrilled

244
00:14:01,080 --> 00:14:03,520
Speaker 1: about this and is really working over time to poke

245
00:14:03,559 --> 00:14:07,640
Speaker 1: holes in these findings. So when Matt Piscatella, the executive

246
00:14:07,640 --> 00:14:10,960
Speaker 1: director of Sirkana, tweeted out these stats showing how women

247
00:14:11,040 --> 00:14:14,280
Speaker 1: were into gaming, he had these angry dudes in his

248
00:14:14,400 --> 00:14:17,680
Speaker 1: mentions whose whole worlds were being shattered by the idea

249
00:14:17,720 --> 00:14:19,520
Speaker 1: that women make up a big part of the gaming

250
00:14:19,560 --> 00:14:22,280
Speaker 1: market in some cases more than men. So I would

251
00:14:22,320 --> 00:14:25,480
Speaker 1: say that the replies really fell into a couple of buckets.

252
00:14:25,760 --> 00:14:29,360
Speaker 1: First was this idea that you know, taking issue with

253
00:14:29,440 --> 00:14:33,000
Speaker 1: the methodology, saying that since men are far too busy

254
00:14:33,040 --> 00:14:35,840
Speaker 1: being I don't know, titans of industry or whatever, they

255
00:14:35,840 --> 00:14:39,239
Speaker 1: don't have time for silly surveys, So that survey itself

256
00:14:39,520 --> 00:14:43,080
Speaker 1: and all surveys are skewed toward women because only a

257
00:14:43,120 --> 00:14:46,000
Speaker 1: woman would take a survey. As somebody who does a

258
00:14:46,000 --> 00:14:48,000
Speaker 1: lot of surveys for a living, what do you think about.

259
00:14:47,760 --> 00:14:51,200
Speaker 2: That, Mike well Bridget, as someone who does a lot

260
00:14:51,240 --> 00:14:54,120
Speaker 2: of surveys for a living, I think it's stupid.

261
00:14:57,040 --> 00:14:59,800
Speaker 1: That was very emphatic. I appreciated how emphatic that was.

262
00:15:00,400 --> 00:15:04,560
Speaker 1: Another is that the data couldn't possibly be correct, because

263
00:15:04,680 --> 00:15:07,720
Speaker 1: if there are really so many women playing video games

264
00:15:07,720 --> 00:15:11,120
Speaker 1: out there, why haven't these guys found them and, like

265
00:15:12,000 --> 00:15:14,840
Speaker 1: I don't know, made contact with them, Which, yeah, I

266
00:15:14,840 --> 00:15:16,960
Speaker 1: can kind of imagine why these women aren't going out

267
00:15:17,040 --> 00:15:18,760
Speaker 1: of their way to buddy up with these guys who

268
00:15:18,760 --> 00:15:23,000
Speaker 1: sound so charming. Probably the most infuriating bucket of the

269
00:15:23,040 --> 00:15:26,040
Speaker 1: responses from men who could not believe this idea that

270
00:15:26,080 --> 00:15:30,360
Speaker 1: women are playing games is that women don't play real games.

271
00:15:30,760 --> 00:15:34,000
Speaker 1: Like there was some conversation about the games that women

272
00:15:34,040 --> 00:15:37,880
Speaker 1: are playing, like the sims are not real games, like

273
00:15:37,960 --> 00:15:40,680
Speaker 1: the games that men play. There's something else, and that

274
00:15:40,800 --> 00:15:43,600
Speaker 1: really reminds me of this classic George Carlin routine that

275
00:15:43,640 --> 00:15:45,640
Speaker 1: I'm sure I've talked about on the show, that I've

276
00:15:45,680 --> 00:15:49,880
Speaker 1: probably gotten more wisdom and use out of than anything

277
00:15:49,920 --> 00:15:52,000
Speaker 1: I've ever read in any kind of history book or

278
00:15:52,000 --> 00:15:54,800
Speaker 1: philosophy book or anything. You ever notice how your shit

279
00:15:54,880 --> 00:15:58,120
Speaker 1: is stuff and everybody else's stuff is shit. It's like, oh, no,

280
00:15:58,600 --> 00:16:02,080
Speaker 1: I'm a man, So whatever those women are playing, they're

281
00:16:02,120 --> 00:16:04,120
Speaker 1: not real games. The only real games are the games

282
00:16:04,120 --> 00:16:07,080
Speaker 1: that I play, because only the things that I engage

283
00:16:07,080 --> 00:16:09,800
Speaker 1: in are real and worthy of any kind of like

284
00:16:09,880 --> 00:16:10,880
Speaker 1: intellectual rigor.

285
00:16:11,520 --> 00:16:16,760
Speaker 2: I hate the adjective real. I feel it is code

286
00:16:17,480 --> 00:16:23,960
Speaker 2: for others, like dehumanized others, like real gamers, real games,

287
00:16:24,360 --> 00:16:29,640
Speaker 2: real Americans, real men. It's it's a complete It's like

288
00:16:29,680 --> 00:16:34,680
Speaker 2: a vacuous term that doesn't actually have any meaning other

289
00:16:34,760 --> 00:16:37,360
Speaker 2: than them versus us.

290
00:16:38,120 --> 00:16:41,160
Speaker 1: Totally absolutely, and like some of the tweets I saw,

291
00:16:41,160 --> 00:16:43,960
Speaker 1: I saw one was like half of the women who

292
00:16:44,080 --> 00:16:48,320
Speaker 1: responded to this survey are probably wine moms playing Candy

293
00:16:48,400 --> 00:16:51,600
Speaker 1: Crush on their iPad or things like that. And it's like,

294
00:16:51,960 --> 00:16:54,120
Speaker 1: what difference does it make, you know, if you're trying

295
00:16:54,120 --> 00:16:56,240
Speaker 1: to get a landscape of who is playing games and

296
00:16:56,280 --> 00:16:58,440
Speaker 1: who is making up this industry in like who is

297
00:16:58,480 --> 00:17:00,840
Speaker 1: representative of the space, what difference does it make what

298
00:17:00,880 --> 00:17:03,080
Speaker 1: they're playing? What difference does it make? Like, I don't know,

299
00:17:03,080 --> 00:17:08,760
Speaker 1: it just seems like such a weird, arbitrary distinction. And

300
00:17:08,800 --> 00:17:11,960
Speaker 1: it's a way of like creating a hierarchy where there

301
00:17:12,080 --> 00:17:14,600
Speaker 1: is none, right, because it's not like if you're playing

302
00:17:14,600 --> 00:17:16,960
Speaker 1: Call of Duty that game is so high falutin that

303
00:17:17,040 --> 00:17:20,240
Speaker 1: oh my god, like it's the you know, most philosophical

304
00:17:20,280 --> 00:17:22,639
Speaker 1: game out there or something like. It's just another I mean,

305
00:17:22,640 --> 00:17:25,760
Speaker 1: it's honestly just misogyny. It's another way to dress up

306
00:17:25,800 --> 00:17:28,760
Speaker 1: misogyny and say that the things that women enjoy, the

307
00:17:28,800 --> 00:17:31,080
Speaker 1: things that women that are bringing women enjoy the way

308
00:17:31,080 --> 00:17:33,640
Speaker 1: that women are spending their time when they're gaming is

309
00:17:34,280 --> 00:17:38,080
Speaker 1: worse than less worthy than less worthy of scrutiny, and

310
00:17:38,760 --> 00:17:41,920
Speaker 1: you know, looking at less serious than what men are doing.

311
00:17:41,920 --> 00:17:45,359
Speaker 1: It's just misogyny. And you know, I honestly am not

312
00:17:45,560 --> 00:17:48,679
Speaker 1: surprised by this data. Most of my friends who are

313
00:17:48,720 --> 00:17:51,280
Speaker 1: women play some kind of game. I think a lot

314
00:17:51,320 --> 00:17:53,800
Speaker 1: of them do have Nintendo switches. I would love to

315
00:17:53,840 --> 00:17:56,560
Speaker 1: own a switch. Nintendo. If you are listening, you can

316
00:17:56,600 --> 00:17:59,080
Speaker 1: send me a switch. I will happily like make content

317
00:17:59,080 --> 00:18:02,360
Speaker 1: about it. So there is a historical precedence of this,

318
00:18:02,760 --> 00:18:07,120
Speaker 1: as journalist Zach Zewyn points out, writing Barbie Fashion Designer

319
00:18:07,240 --> 00:18:10,720
Speaker 1: outsold Doom and Quake in nineteen ninety six and was

320
00:18:10,760 --> 00:18:14,040
Speaker 1: the ninth best selling PC game of that year. Of course,

321
00:18:14,200 --> 00:18:16,800
Speaker 1: women were also playing Doom and Quake back then too,

322
00:18:17,240 --> 00:18:20,600
Speaker 1: So angry dudes online maybe stop acting so defensive and

323
00:18:20,640 --> 00:18:24,120
Speaker 1: shitty about games being played by women and instead realize

324
00:18:24,200 --> 00:18:25,960
Speaker 1: that they've always been here, and if you feel like

325
00:18:26,000 --> 00:18:29,479
Speaker 1: they're hiding from you, maybe ask yourself why that is.

326
00:18:30,040 --> 00:18:32,679
Speaker 1: And so I love that as a response, and I

327
00:18:32,680 --> 00:18:36,439
Speaker 1: think it's really it's really about the gaming industry catching

328
00:18:36,520 --> 00:18:39,440
Speaker 1: up with this information that we have. If nearly half

329
00:18:39,480 --> 00:18:42,239
Speaker 1: of gaming consumers are women, then it is imperative that

330
00:18:42,280 --> 00:18:45,080
Speaker 1: gaming companies act like it and catch up and make

331
00:18:45,119 --> 00:18:48,760
Speaker 1: sure that these platforms are places where everyone all of

332
00:18:48,800 --> 00:18:52,000
Speaker 1: their consumers can safely show up to and you know,

333
00:18:52,040 --> 00:18:55,520
Speaker 1: have things like better safeguards against harassment or more gender

334
00:18:55,560 --> 00:18:56,399
Speaker 1: inclusive games.

335
00:18:56,480 --> 00:18:56,640
Speaker 2: Right.

336
00:18:56,680 --> 00:18:59,320
Speaker 1: So I think for me, it's really about the people

337
00:18:59,320 --> 00:19:02,760
Speaker 1: who have power in the space recognizing the reality of

338
00:19:02,840 --> 00:19:05,440
Speaker 1: who makes up that space, who their consumers, and who

339
00:19:05,440 --> 00:19:08,640
Speaker 1: their demographic actually is. And according to this data, it's

340
00:19:08,680 --> 00:19:10,800
Speaker 1: a lot of women because women be gaming.

341
00:19:11,200 --> 00:19:14,760
Speaker 2: Yeah, women be gaming. You know. The there's so many

342
00:19:14,880 --> 00:19:19,920
Speaker 2: domains where this sort of like misogynistic gatekeeping plays out.

343
00:19:19,960 --> 00:19:23,520
Speaker 2: But one nice thing about video games is that there

344
00:19:23,560 --> 00:19:28,439
Speaker 2: are very well defined metrics of success, right, and that

345
00:19:28,480 --> 00:19:32,760
Speaker 2: metric is money, Right, how much money do you make

346
00:19:32,880 --> 00:19:37,240
Speaker 2: from the sale of your game? And games that appeal

347
00:19:37,320 --> 00:19:42,120
Speaker 2: to women, it turns out sell so many more copies

348
00:19:42,280 --> 00:19:45,080
Speaker 2: than games that do not appeal to women, because there's

349
00:19:45,119 --> 00:19:47,280
Speaker 2: a lot of women in the world, right, Like I

350
00:19:47,320 --> 00:19:49,800
Speaker 2: was just looking at up. Animal Crossing is a game

351
00:19:49,960 --> 00:19:52,040
Speaker 2: that a lot of women I know, like a lot

352
00:19:52,040 --> 00:19:54,680
Speaker 2: of men I know. Like it's not a gendered game, right,

353
00:19:54,760 --> 00:19:58,600
Speaker 2: Like you're not. Yeah, it's just like a fun game

354
00:19:58,640 --> 00:20:01,199
Speaker 2: that a lot of my friends really love. And just

355
00:20:01,200 --> 00:20:04,480
Speaker 2: looked it up. It made like a quarter billion dollars

356
00:20:04,600 --> 00:20:07,520
Speaker 2: in the past couple of years. That's so much money.

357
00:20:07,800 --> 00:20:09,679
Speaker 1: I mean, like this gets into like a whole other

358
00:20:09,760 --> 00:20:13,159
Speaker 1: there's a whole other topic. But I have seen and

359
00:20:13,200 --> 00:20:18,320
Speaker 1: I have so many examples where industries would rather leave money,

360
00:20:18,600 --> 00:20:23,560
Speaker 1: good money on the table that not be misogynistic and

361
00:20:23,600 --> 00:20:30,400
Speaker 1: not you know, not perpetuate visogyny and racism and all

362
00:20:30,440 --> 00:20:33,920
Speaker 1: of the badisms. Right Like, you know, look at Blizzard,

363
00:20:34,240 --> 00:20:37,679
Speaker 1: look at their you know, their lawsuit where it sounded

364
00:20:37,720 --> 00:20:41,440
Speaker 1: like for women working at the uber popular video game

365
00:20:41,480 --> 00:20:45,000
Speaker 1: company Blizzard, it sounded like a terrible environment. And it's like,

366
00:20:45,280 --> 00:20:48,360
Speaker 1: if you are a company that is producing a product

367
00:20:48,440 --> 00:20:52,080
Speaker 1: that half of your consumer base are women, that you

368
00:20:52,200 --> 00:20:54,399
Speaker 1: just can't you just can't run a company like that.

369
00:20:54,520 --> 00:20:59,280
Speaker 1: But I think that there are definitely other incentives than

370
00:20:59,400 --> 00:21:01,639
Speaker 1: money that the people have. And I think that people

371
00:21:01,640 --> 00:21:05,320
Speaker 1: are very entrenched in and bought into the idea that

372
00:21:05,560 --> 00:21:07,359
Speaker 1: this is a boys club. They're not women around. And

373
00:21:07,359 --> 00:21:09,119
Speaker 1: so even if that's not true, even if all the

374
00:21:09,119 --> 00:21:11,320
Speaker 1: evidence is showing them that's not true, even if they

375
00:21:11,320 --> 00:21:16,720
Speaker 1: can make more money by acknowledging that reality, the sirens

376
00:21:16,720 --> 00:21:20,639
Speaker 1: song of misogyny and perpetuating it in that industry is

377
00:21:20,640 --> 00:21:23,680
Speaker 1: just too much to resist.

378
00:21:24,400 --> 00:21:28,080
Speaker 2: This is a weird moment in the history of there

379
00:21:28,119 --> 00:21:30,359
Speaker 2: are no girls on the Internet, Bridget, because it's like,

380
00:21:31,440 --> 00:21:34,359
Speaker 2: I feel like you and me are both being like, yes, capitalism,

381
00:21:34,800 --> 00:21:38,960
Speaker 2: that is what will free us from the chains of misogyny.

382
00:21:39,320 --> 00:21:40,920
Speaker 2: I don't know, there's a lot of damn women out

383
00:21:40,920 --> 00:21:45,600
Speaker 2: there right, Like it's like it's almost incompatible with a

384
00:21:45,640 --> 00:21:49,760
Speaker 2: system where women are just like devalued and locked out.

385
00:21:50,320 --> 00:21:54,879
Speaker 1: Yeah, welcome to patriarchy. It it harms everyone. It doesn't

386
00:21:54,880 --> 00:21:57,159
Speaker 1: just harm the people who are its targets women, It

387
00:21:57,560 --> 00:21:59,920
Speaker 1: harms us all that keeps us all back from innovation.

388
00:22:00,280 --> 00:22:03,960
Speaker 1: With did a great episode about how misogyny and bias

389
00:22:04,119 --> 00:22:10,040
Speaker 1: and you know, really rigid incorrect thinking around gender stifles innovation.

390
00:22:10,480 --> 00:22:12,399
Speaker 1: And it does it harms us all.

391
00:22:12,920 --> 00:22:15,640
Speaker 2: Yeah, it really does. And like what does it say

392
00:22:15,640 --> 00:22:19,879
Speaker 2: about the people who really cling to the misogyny that

393
00:22:20,040 --> 00:22:23,600
Speaker 2: like they value it so much that they're willing to

394
00:22:23,800 --> 00:22:29,919
Speaker 2: throw away innovation, inclusivity, a larger market share, everything in

395
00:22:30,080 --> 00:22:36,320
Speaker 2: order to protect a gender hierarchy, and like what happened

396
00:22:36,359 --> 00:22:37,200
Speaker 2: to them?

397
00:22:37,560 --> 00:22:40,240
Speaker 1: I mean, misogyny is a hell of a drug. Let's

398
00:22:40,240 --> 00:22:42,679
Speaker 1: talk about this story Dad in Georgia, where a radio

399
00:22:42,760 --> 00:22:46,280
Speaker 1: host is suing open ai for defamation because of something

400
00:22:46,320 --> 00:22:49,040
Speaker 1: a chatbot said about him. So last week, a radio

401
00:22:49,119 --> 00:22:52,480
Speaker 1: host in Georgia filed the first, but probably not the last,

402
00:22:52,560 --> 00:22:55,960
Speaker 1: lawsuit against open ai, the company who created chat gpt,

403
00:22:56,560 --> 00:23:00,320
Speaker 1: over false statements that chat gpt made about him. It's

404
00:23:00,320 --> 00:23:02,639
Speaker 1: sexually a pretty notable case, partly just because it's so

405
00:23:03,000 --> 00:23:06,200
Speaker 1: novel and interesting, and partly because it connects to two

406
00:23:06,280 --> 00:23:07,879
Speaker 1: themes that we've been talking about a lot on this

407
00:23:07,920 --> 00:23:10,639
Speaker 1: season of the podcast. One the fact that AI is

408
00:23:10,680 --> 00:23:13,080
Speaker 1: making it harder to tell what's real from what's fake,

409
00:23:13,400 --> 00:23:16,159
Speaker 1: and two, the fact that we know that legal frameworks

410
00:23:16,200 --> 00:23:19,000
Speaker 1: are necessary and are going to be necessary going forward,

411
00:23:19,119 --> 00:23:21,720
Speaker 1: since it's clear that platforms and tech companies cannot be

412
00:23:21,800 --> 00:23:25,600
Speaker 1: trusted to police themselves. In this case, chat gpt just

413
00:23:25,680 --> 00:23:30,160
Speaker 1: completely made up a bunch of negative, slimy, grimy lies

414
00:23:30,200 --> 00:23:34,040
Speaker 1: about this radio host. Chat gpt confidently stated that he

415
00:23:34,080 --> 00:23:37,040
Speaker 1: had been the treasurer of an advocacy organization until he

416
00:23:37,160 --> 00:23:41,520
Speaker 1: was forced to resign following a lawsuit over misappropriation of funds.

417
00:23:41,920 --> 00:23:44,520
Speaker 1: Except none of that ever happened. It was just a

418
00:23:44,800 --> 00:23:47,680
Speaker 1: made up thing that chat gpt pulled out of its

419
00:23:48,080 --> 00:23:51,840
Speaker 1: computer butt. I guess I'll say he was never the

420
00:23:51,880 --> 00:23:55,040
Speaker 1: treasurer of that organization, he was never accused of embezzling funds,

421
00:23:55,080 --> 00:23:57,520
Speaker 1: and he was never part of that lawsuit. Chat GPT

422
00:23:57,960 --> 00:24:00,880
Speaker 1: just made it up. So you've probably seen stories about

423
00:24:01,000 --> 00:24:04,719
Speaker 1: chat GPT hallucinating. That's what it's called when chat gpt

424
00:24:05,240 --> 00:24:07,800
Speaker 1: spits out facts that are not true, like getting math

425
00:24:07,880 --> 00:24:11,040
Speaker 1: problems wrong, or saying that planets have extra moons, or

426
00:24:11,080 --> 00:24:14,760
Speaker 1: just getting basic facts wrong. Most of these hallucinations are

427
00:24:15,119 --> 00:24:17,320
Speaker 1: kind of funny. It's easy to laugh at them, and

428
00:24:17,560 --> 00:24:19,720
Speaker 1: if nothing else, they provide kind of a smug piece

429
00:24:19,760 --> 00:24:22,840
Speaker 1: of evidence that maybe AI isn't going to displace us

430
00:24:22,880 --> 00:24:26,600
Speaker 1: all after all. But this one is different because it

431
00:24:26,640 --> 00:24:30,160
Speaker 1: wasn't a hallucination about some abstract fact. It was about

432
00:24:30,200 --> 00:24:34,600
Speaker 1: a specific person who exists, and based on this person's lawsuit,

433
00:24:35,000 --> 00:24:39,199
Speaker 1: that person is pissed. So it raises a good question.

434
00:24:39,359 --> 00:24:43,200
Speaker 1: Are tech companies responsible for false statements made by their

435
00:24:43,240 --> 00:24:47,240
Speaker 1: AI chatbots? Hopefully from this lawsuit we will find out,

436
00:24:47,520 --> 00:24:50,160
Speaker 1: and personally, I'm hoping that the answer is yes. Because

437
00:24:50,160 --> 00:24:54,400
Speaker 1: our information ecosystem is already overflowing with mis and disinformation.

438
00:24:54,840 --> 00:24:57,040
Speaker 1: We can't just say it's okay for big tech companies

439
00:24:57,119 --> 00:25:00,840
Speaker 1: to add more fake information on top of that information

440
00:25:00,920 --> 00:25:04,359
Speaker 1: economy with impunity. You know, there needs to be standards,

441
00:25:04,359 --> 00:25:08,359
Speaker 1: There needs to be accountability, especially when specific living people

442
00:25:08,520 --> 00:25:10,040
Speaker 1: are harmed by this technology.

443
00:25:10,359 --> 00:25:13,280
Speaker 2: Bridget I'm so glad you brought this story up. I

444
00:25:13,359 --> 00:25:17,520
Speaker 2: love this story. I'm so interested in it. I can't

445
00:25:17,560 --> 00:25:20,240
Speaker 2: wait to see what comes of it. You know, last

446
00:25:20,400 --> 00:25:22,520
Speaker 2: week and I think maybe in the week before, we

447
00:25:22,520 --> 00:25:28,080
Speaker 2: were talking about this idea of how with all this

448
00:25:28,200 --> 00:25:33,199
Speaker 2: generative AI, it's increasingly difficult to tell fact from fiction,

449
00:25:35,480 --> 00:25:41,840
Speaker 2: and this feels like one route to try to restore

450
00:25:41,920 --> 00:25:47,280
Speaker 2: the importance of fact right and protect against the proliferation

451
00:25:47,720 --> 00:25:52,439
Speaker 2: of fake information that makes it harder to know what's real.

452
00:25:53,480 --> 00:25:57,719
Speaker 1: So, speaking of facts versus fici, can you guess a

453
00:25:57,760 --> 00:26:02,040
Speaker 1: platform that is currently caching in breaking in that sweet

454
00:26:02,040 --> 00:26:04,000
Speaker 1: sweet dough from fiction?

455
00:26:05,040 --> 00:26:06,200
Speaker 2: Ashley Medicine.

456
00:26:07,200 --> 00:26:12,520
Speaker 1: Google. It turns out that Google is cashing in from

457
00:26:12,560 --> 00:26:15,240
Speaker 1: fake clinics. This is according to a new report from

458
00:26:15,240 --> 00:26:18,199
Speaker 1: the Center for Countering Digital Had which found that Google

459
00:26:18,320 --> 00:26:20,920
Speaker 1: made more than ten million dollars over the past two

460
00:26:20,960 --> 00:26:25,240
Speaker 1: years from ads for crisis pregnancy centers. If you don't

461
00:26:25,240 --> 00:26:28,600
Speaker 1: know what crisis pregnancy centers are. They're basically anti choice

462
00:26:28,640 --> 00:26:31,480
Speaker 1: clinics that try to convince people not to have abortions,

463
00:26:31,720 --> 00:26:35,600
Speaker 1: but they often do so by masquerading as real medical

464
00:26:35,640 --> 00:26:38,320
Speaker 1: facilities or clinics, which they are not. But because they're

465
00:26:38,359 --> 00:26:43,560
Speaker 1: not clinics, they're often just basically unregulated outfits. Their primary

466
00:26:43,600 --> 00:26:46,680
Speaker 1: goal is to prevent people from getting abortions. They'll sometimes

467
00:26:46,680 --> 00:26:51,320
Speaker 1: give out really unscientific, sometimes harmful medical advice or make

468
00:26:51,400 --> 00:26:54,200
Speaker 1: lofty promises of services that they can provide for somebody

469
00:26:54,520 --> 00:26:57,119
Speaker 1: after they have a baby. Ultimately, their goal is to

470
00:26:57,240 --> 00:26:59,439
Speaker 1: waste the time of the person who is pregnant so

471
00:26:59,440 --> 00:27:01,720
Speaker 1: that they can no longer obtain an abortion within the

472
00:27:01,760 --> 00:27:05,399
Speaker 1: window of legality, so essentially tricking folks into carrying the

473
00:27:05,440 --> 00:27:08,359
Speaker 1: pregnancy to term even if they don't want to. Because

474
00:27:08,359 --> 00:27:11,560
Speaker 1: they are not real medical facilities, they're unregulated, which means

475
00:27:11,560 --> 00:27:14,560
Speaker 1: that they're not subject to data privacy and patient confidentiality

476
00:27:14,640 --> 00:27:18,159
Speaker 1: laws that govern legitimate medical facilities, So listen to this.

477
00:27:18,359 --> 00:27:20,879
Speaker 1: The Center for Countering Digital Hate found that one hundred

478
00:27:20,920 --> 00:27:24,080
Speaker 1: and eighty eight crisis of pregnancy centers placed ads on

479
00:27:24,160 --> 00:27:27,080
Speaker 1: Google worth an estimated total of more than ten million

480
00:27:27,160 --> 00:27:30,320
Speaker 1: dollars over ten years. Now, obviously, there was a major

481
00:27:30,400 --> 00:27:33,520
Speaker 1: spike in advertising about six months before the Supreme Court's

482
00:27:33,520 --> 00:27:36,760
Speaker 1: decision on Roe versus Wade. Many of these fake clinics

483
00:27:36,840 --> 00:27:40,520
Speaker 1: are registered as five OHO one C three nonprofit organizations,

484
00:27:40,600 --> 00:27:42,480
Speaker 1: and that means that they get to take advantage of

485
00:27:42,480 --> 00:27:46,520
Speaker 1: Google's ad grant program, which allows qualifying nonprofits to get

486
00:27:46,600 --> 00:27:49,679
Speaker 1: up to ten thousand dollars worth of ads for free

487
00:27:49,920 --> 00:27:53,000
Speaker 1: per month. So it's really just like a scam. They're

488
00:27:53,000 --> 00:27:57,640
Speaker 1: able to for free put ads on Google that you know,

489
00:27:58,040 --> 00:28:02,360
Speaker 1: are filled with lies and harmful, inaccurate information about abortion,

490
00:28:03,040 --> 00:28:06,199
Speaker 1: and then Google makes money from that. It's just like

491
00:28:06,280 --> 00:28:08,199
Speaker 1: a scam on top of a scam on top of

492
00:28:08,200 --> 00:28:08,800
Speaker 1: a scam.

493
00:28:09,359 --> 00:28:14,439
Speaker 2: Those yeah, pregnancy crisis centers. I feel like we've been

494
00:28:14,480 --> 00:28:19,160
Speaker 2: talking about them for years. They were a genius move

495
00:28:19,560 --> 00:28:24,480
Speaker 2: by the anti choice forces because they just like skirt

496
00:28:24,800 --> 00:28:28,880
Speaker 2: under the radar of like so many things, and they

497
00:28:28,920 --> 00:28:33,200
Speaker 2: seem so benign and not even benign but like benevolent.

498
00:28:33,640 --> 00:28:34,480
Speaker 2: But they're bad.

499
00:28:35,000 --> 00:28:39,360
Speaker 1: They're bad. And the fact that Google is essentially making

500
00:28:39,440 --> 00:28:43,360
Speaker 1: money from propping up their badness and allowing them to

501
00:28:43,360 --> 00:28:46,680
Speaker 1: do it for essentially you know, if not for free,

502
00:28:46,840 --> 00:28:49,080
Speaker 1: like probably not for that much money. If they're getting

503
00:28:49,080 --> 00:28:51,160
Speaker 1: ten thousand dollars worth of ads for free, this is

504
00:28:51,200 --> 00:28:51,720
Speaker 1: really telling.

505
00:28:51,800 --> 00:28:52,000
Speaker 3: You know.

506
00:28:52,040 --> 00:28:54,719
Speaker 1: I described these a moment ago as scams, but it

507
00:28:54,840 --> 00:28:59,760
Speaker 1: really is, like think of the scammiest SEO practices that

508
00:28:59,800 --> 00:29:03,480
Speaker 1: you have ever encountered. Like when you are googling something

509
00:29:03,560 --> 00:29:05,000
Speaker 1: and the first thing that comes up, you click on

510
00:29:05,040 --> 00:29:07,280
Speaker 1: it and you're like, oh wait, it's a scammy spammy ad,

511
00:29:07,560 --> 00:29:09,640
Speaker 1: and then they do that thing where like to click

512
00:29:09,680 --> 00:29:11,520
Speaker 1: out of it. The ex is really hard to click,

513
00:29:11,520 --> 00:29:12,560
Speaker 1: and then you try to click it and then you

514
00:29:12,560 --> 00:29:14,360
Speaker 1: actually click into it, and then it like think of

515
00:29:14,400 --> 00:29:17,840
Speaker 1: like the scammiest Internet stuff that drives you out of

516
00:29:17,880 --> 00:29:20,440
Speaker 1: your fucking mind. That's what these clinics are doing, and

517
00:29:20,480 --> 00:29:22,840
Speaker 1: Google is allowing them to do that. Center for Digital

518
00:29:22,840 --> 00:29:25,880
Speaker 1: Hate found that several marketing firms help these fake clinics

519
00:29:26,120 --> 00:29:29,600
Speaker 1: hijack search keywords to ensure that their content appears next

520
00:29:29,600 --> 00:29:32,920
Speaker 1: to legitimate reproductive health information. So if you google something

521
00:29:32,960 --> 00:29:36,400
Speaker 1: that is a obvious keyword for somebody looking for an abortion,

522
00:29:36,520 --> 00:29:39,120
Speaker 1: like I need an abortion or pregnant and scared help,

523
00:29:39,520 --> 00:29:44,200
Speaker 1: you might find results that max real health information alongside

524
00:29:44,360 --> 00:29:47,880
Speaker 1: these fake clinics which are peddling harmful lies. And the

525
00:29:47,920 --> 00:29:50,280
Speaker 1: fact that we're talking about this with Google is a

526
00:29:50,280 --> 00:29:53,920
Speaker 1: really big deal. Ninety three percent of all searches worldwide

527
00:29:54,200 --> 00:29:56,600
Speaker 1: happen on Google. And when someone doesn't know what to do,

528
00:29:56,760 --> 00:29:58,800
Speaker 1: or they find themselves in a situation where they need

529
00:29:58,880 --> 00:30:02,000
Speaker 1: answers and they're looking to the internet, odds are the

530
00:30:02,040 --> 00:30:04,240
Speaker 1: first thing they're going to do is a Google search,

531
00:30:04,480 --> 00:30:06,760
Speaker 1: And when they do that search, they should not be

532
00:30:06,800 --> 00:30:10,760
Speaker 1: served up lies, and certainly Google should not be profiting

533
00:30:11,040 --> 00:30:13,719
Speaker 1: off of those lies. Like it is an economy. It

534
00:30:13,760 --> 00:30:17,400
Speaker 1: is a scam economy of lies and grifts, and Google

535
00:30:17,480 --> 00:30:19,280
Speaker 1: is cashing in on it. And it really takes me

536
00:30:19,320 --> 00:30:22,760
Speaker 1: back to something that Ifoma Uzoma, who you know, formerly

537
00:30:22,760 --> 00:30:25,840
Speaker 1: worked at pinterest combating medical misinformation, a lot of which

538
00:30:25,920 --> 00:30:30,040
Speaker 1: was misinformation around abortion. She always says that these people,

539
00:30:30,120 --> 00:30:33,000
Speaker 1: these extremists who are pushing lives about abortion, they are

540
00:30:33,080 --> 00:30:35,800
Speaker 1: scammers and spammers, and they have built an industry of

541
00:30:35,880 --> 00:30:40,160
Speaker 1: lives for profit, and Google, as the country's number one

542
00:30:40,200 --> 00:30:43,880
Speaker 1: search platform, should not be in the business of amplifying

543
00:30:43,880 --> 00:30:45,720
Speaker 1: these lies, and they certainly should not be in the

544
00:30:45,720 --> 00:30:47,480
Speaker 1: business of profiting from it.

545
00:30:47,960 --> 00:30:52,320
Speaker 2: Yeah, this it doesn't feel so terrible, but like, these

546
00:30:52,320 --> 00:30:54,720
Speaker 2: are some of the more disgusting actors that we've talked

547
00:30:54,760 --> 00:30:58,240
Speaker 2: about on this show, because they are preying on people

548
00:30:58,480 --> 00:31:02,680
Speaker 2: who are at their most part vulnerable, often young, probably

549
00:31:03,160 --> 00:31:07,320
Speaker 2: terrified if they have suddenly discovered that they are unintendedly

550
00:31:07,480 --> 00:31:13,080
Speaker 2: pregnant and looking for some kind of like information about

551
00:31:13,080 --> 00:31:16,800
Speaker 2: what the hell to do, and to deceive people at

552
00:31:16,800 --> 00:31:23,920
Speaker 2: that moment is terrible. Yeah, it's It's infuriating, and the

553
00:31:23,960 --> 00:31:27,479
Speaker 2: fact that Google is profiting off it to such an

554
00:31:27,560 --> 00:31:33,000
Speaker 2: extent is not surprising but shameful.

555
00:31:33,400 --> 00:31:35,920
Speaker 1: It is shameful, and if you're the biggest search engine

556
00:31:35,920 --> 00:31:39,080
Speaker 1: in the world, some responsibility comes with that, and you

557
00:31:39,080 --> 00:31:43,720
Speaker 1: should be in the business of amplifying actual information and

558
00:31:43,840 --> 00:31:46,720
Speaker 1: not lies and scams. Some of the lies that these

559
00:31:46,720 --> 00:31:50,719
Speaker 1: fake clinics are pushing are actually dangerous and harmful, things

560
00:31:50,800 --> 00:31:54,600
Speaker 1: like telling people that they can have their medication abortions

561
00:31:54,640 --> 00:31:58,320
Speaker 1: reversed and you know, telling them to take certain medications

562
00:31:58,360 --> 00:32:02,120
Speaker 1: to reverse pill based abortion, which can harm them and

563
00:32:02,240 --> 00:32:05,240
Speaker 1: also is not true. Google should not be in a

564
00:32:05,320 --> 00:32:08,840
Speaker 1: place where they are amplifying these lies and certainly not

565
00:32:08,880 --> 00:32:10,880
Speaker 1: making money off of it. I mean, it's really ghulish.

566
00:32:11,160 --> 00:32:15,240
Speaker 1: Speaking of Ghulish, let's talk about Tesla. So this report, like,

567
00:32:15,280 --> 00:32:18,120
Speaker 1: people should read the whole report, because I found myself

568
00:32:18,760 --> 00:32:22,480
Speaker 1: gasping each paragraph. I was like, oh God, oh God,

569
00:32:22,680 --> 00:32:25,880
Speaker 1: oh God, it just got worse and worse. So a

570
00:32:25,920 --> 00:32:27,840
Speaker 1: new report from the Washington Post says that there have

571
00:32:27,920 --> 00:32:30,720
Speaker 1: been seven hundred and thirty six crashes in the US

572
00:32:30,760 --> 00:32:34,560
Speaker 1: since twenty nineteen involving Tesla's in autopilot mode, which is

573
00:32:34,640 --> 00:32:37,680
Speaker 1: far more than previously reported. According to the report, the

574
00:32:37,800 --> 00:32:40,240
Speaker 1: number of crashes has surged in the last four years

575
00:32:40,280 --> 00:32:42,600
Speaker 1: because of the increase of the number of Tesla's on

576
00:32:42,640 --> 00:32:46,080
Speaker 1: the road. Some of these crashes sound pretty serious. The

577
00:32:46,160 --> 00:32:49,880
Speaker 1: number of deaths and serious injuries associated with autopilot has

578
00:32:49,920 --> 00:32:53,840
Speaker 1: also grown significantly. When authorities first released a partial accounting

579
00:32:53,880 --> 00:32:56,520
Speaker 1: of accidents involving autopilot in June of twenty twenty two,

580
00:32:56,680 --> 00:33:00,200
Speaker 1: they counted only three deaths definitively linked to this technology,

581
00:33:00,400 --> 00:33:04,240
Speaker 1: But the most recent data includes seventeen fatal incidents, eleven

582
00:33:04,280 --> 00:33:06,479
Speaker 1: of them happening since May of twenty twenty two, and

583
00:33:06,640 --> 00:33:09,680
Speaker 1: five serious injuries. So you might be asking, why is

584
00:33:09,720 --> 00:33:12,680
Speaker 1: the number of crashes so much higher now than was

585
00:33:12,720 --> 00:33:15,120
Speaker 1: previously reported. What we know all of this because of

586
00:33:15,120 --> 00:33:18,320
Speaker 1: a twenty twenty one federal order that the National Highway

587
00:33:18,360 --> 00:33:22,320
Speaker 1: Traffic Safety Administration, which is the country's top auto safety regulator,

588
00:33:22,480 --> 00:33:26,920
Speaker 1: required automakers to disclose crashes involving driver assistance technology. Now,

589
00:33:26,920 --> 00:33:29,520
Speaker 1: I should say that the total number of crashes involving

590
00:33:29,520 --> 00:33:33,440
Speaker 1: this technology is really small compared to all road accidents,

591
00:33:33,560 --> 00:33:36,600
Speaker 1: but it's so pretty troubling now. Since this new reporting

592
00:33:36,640 --> 00:33:39,800
Speaker 1: requirements were introduced, we now know that the vast majority

593
00:33:39,840 --> 00:33:42,920
Speaker 1: of the eight hundred and seven automation related crashes have

594
00:33:43,080 --> 00:33:47,000
Speaker 1: involved Tesla's and it's linked to almost all deaths involving automation,

595
00:33:47,360 --> 00:33:50,320
Speaker 1: in part because Tesla has experimented much more aggressively with

596
00:33:50,360 --> 00:33:53,640
Speaker 1: automation than other carmakers have. The uptick and crashes also

597
00:33:53,680 --> 00:33:57,440
Speaker 1: coincides with Tesla's aggressive rollout of full self driving mode,

598
00:33:57,600 --> 00:34:01,000
Speaker 1: which has expanded from about twelve thousand users generally four

599
00:34:01,080 --> 00:34:03,440
Speaker 1: hundred thousand users in a little more than a year.

600
00:34:03,960 --> 00:34:07,200
Speaker 1: And this is not just Tesla either. After a mass

601
00:34:07,200 --> 00:34:10,080
Speaker 1: shooting in San Francisco, there was a video of a cruise,

602
00:34:10,239 --> 00:34:13,960
Speaker 1: which is General Motors's line of driverless cars blocking first

603
00:34:14,000 --> 00:34:16,880
Speaker 1: responders from being able to administer aid to the victims.

604
00:34:17,120 --> 00:34:19,759
Speaker 1: In the video, you can see the police screaming about this.

605
00:34:20,120 --> 00:34:25,200
Speaker 1: He's screaming, you know that this driverless car is blocking emergency, medical,

606
00:34:25,200 --> 00:34:27,040
Speaker 1: and fire. They need to get it out of there.

607
00:34:27,080 --> 00:34:29,319
Speaker 1: In another video, you can hear an officer worrying that

608
00:34:29,360 --> 00:34:31,719
Speaker 1: the car might crash into an ambulance or a firetruck

609
00:34:31,760 --> 00:34:34,439
Speaker 1: when they're trying to get out. So General Motors has

610
00:34:34,480 --> 00:34:38,200
Speaker 1: said that although the vehicle did stop at the crime scene,

611
00:34:38,239 --> 00:34:41,839
Speaker 1: the car did not actually obstruct any emergency vehicles. They said,

612
00:34:41,880 --> 00:34:44,239
Speaker 1: our car initially stopped as it was approaching an active

613
00:34:44,280 --> 00:34:46,800
Speaker 1: emergency scene, then proceeded to perform a U turn and

614
00:34:46,840 --> 00:34:50,880
Speaker 1: pull over. Throughout this time, all vehicles, including emergency response vehicles,

615
00:34:50,920 --> 00:34:54,719
Speaker 1: were able to proceed around our car. Emergency personnel in

616
00:34:54,760 --> 00:34:57,680
Speaker 1: San Francisco seemed to more or less confirm this. They

617
00:34:57,680 --> 00:35:02,000
Speaker 1: told SFGate that the car did not technically obstruct emergency

618
00:35:02,080 --> 00:35:05,440
Speaker 1: vehicles because another road happened to be available that was

619
00:35:05,560 --> 00:35:07,399
Speaker 1: in an adjoining lane that they could use to get

620
00:35:07,400 --> 00:35:10,799
Speaker 1: around this driverless car. But honestly, they didn't sound too

621
00:35:10,880 --> 00:35:13,319
Speaker 1: thrilled that this was something they had to navigate in

622
00:35:13,400 --> 00:35:15,560
Speaker 1: order to help nine people who had just been shot.

623
00:35:15,840 --> 00:35:18,880
Speaker 1: And it's pretty clear like had this been a car

624
00:35:19,040 --> 00:35:21,120
Speaker 1: with a human, a human would have been able to

625
00:35:21,160 --> 00:35:24,680
Speaker 1: be like, oh, this is an active emergency scene, let

626
00:35:24,719 --> 00:35:27,600
Speaker 1: me immediately do a quick U turn or whatever. But

627
00:35:27,640 --> 00:35:30,960
Speaker 1: because it was driverless, there wasn't a real protocol of

628
00:35:31,040 --> 00:35:32,880
Speaker 1: what to do or how to move it. They're just

629
00:35:33,040 --> 00:35:35,879
Speaker 1: very lucky that there was an available lane in order

630
00:35:35,920 --> 00:35:38,880
Speaker 1: to maneuver around this car. So Elon Musk argues that

631
00:35:38,960 --> 00:35:42,239
Speaker 1: autopilot is safer than human drivers and that the use

632
00:35:42,280 --> 00:35:46,560
Speaker 1: of autopilot will usher in a safer, virtually accident free future.

633
00:35:46,760 --> 00:35:50,200
Speaker 1: In a March presentation, Tesla claim that cars in full

634
00:35:50,280 --> 00:35:53,080
Speaker 1: self driving mode crashes at a rate of at least

635
00:35:53,200 --> 00:35:56,120
Speaker 1: one fifth that of vehicles with normal driving in a

636
00:35:56,120 --> 00:35:59,520
Speaker 1: comparison of miles driven per collision. But that claim and

637
00:35:59,640 --> 00:36:04,440
Speaker 1: musks characterization of autopilot as unequivocally safer, is impossible to

638
00:36:04,520 --> 00:36:07,880
Speaker 1: test without access to the detailed data that Tesla possesses,

639
00:36:08,160 --> 00:36:12,000
Speaker 1: So it's basically impossible to say how many accidents this

640
00:36:12,040 --> 00:36:15,239
Speaker 1: technology has diverted. But what is super clear is how

641
00:36:15,320 --> 00:36:18,400
Speaker 1: dangerous it is to be testing this out on actual

642
00:36:18,600 --> 00:36:21,759
Speaker 1: roads with the public because it is killing people. So

643
00:36:22,400 --> 00:36:24,359
Speaker 1: I didn't know this. I don't have a Tesla because

644
00:36:24,360 --> 00:36:27,360
Speaker 1: I'm a broke bitch. But I was reading this and

645
00:36:27,400 --> 00:36:30,920
Speaker 1: I was thinking, surely they're not just out here testing

646
00:36:31,000 --> 00:36:35,600
Speaker 1: out full self driving mode with just regular motorists on

647
00:36:35,640 --> 00:36:39,560
Speaker 1: our public road and making anybody who exists out in public.

648
00:36:39,760 --> 00:36:43,480
Speaker 1: They'reea Tesla's guinea pig. But I'm sorry to say that

649
00:36:43,600 --> 00:36:46,080
Speaker 1: is kind of exactly what is happening.

650
00:36:50,920 --> 00:36:51,080
Speaker 2: More.

651
00:36:51,080 --> 00:37:03,560
Speaker 1: After a quick break, let's get right back into it.

652
00:37:05,080 --> 00:37:07,200
Speaker 1: So if you don't drive a Tesla, here's what you

653
00:37:07,239 --> 00:37:11,319
Speaker 1: need to know. Teslas have three modes. One is autopilot,

654
00:37:11,320 --> 00:37:14,560
Speaker 1: which is standard on all Teslas, which has intelligent cruise

655
00:37:14,560 --> 00:37:16,680
Speaker 1: control that matches the car to the speed of the

656
00:37:16,719 --> 00:37:20,040
Speaker 1: surrounding traffic and a lane centering function that helps keep

657
00:37:20,080 --> 00:37:22,239
Speaker 1: the vehicle in the center of its lane. Then there

658
00:37:22,320 --> 00:37:26,759
Speaker 1: is Enhanced Autopilot, which adds navigating highway on and off

659
00:37:26,840 --> 00:37:30,319
Speaker 1: ramps and interchanges. On top of what autopilot does, it

660
00:37:30,360 --> 00:37:33,960
Speaker 1: also adds a self parking system and a summon feature

661
00:37:34,200 --> 00:37:36,720
Speaker 1: that lets owners call the car to them at parking

662
00:37:36,760 --> 00:37:40,879
Speaker 1: lot speed from nearby and lastly, full self driving, which

663
00:37:40,920 --> 00:37:43,600
Speaker 1: Tesla says will do all of the above, but also

664
00:37:44,040 --> 00:37:47,120
Speaker 1: read and react to traffic lights and stop signs, and

665
00:37:47,200 --> 00:37:51,440
Speaker 1: steer around some terms with the driver's quote active supervision. Now,

666
00:37:51,440 --> 00:37:55,279
Speaker 1: according to Tesla's own website, full self driving mode is

667
00:37:55,360 --> 00:37:57,680
Speaker 1: still being tested out. It's still in beta. It is

668
00:37:57,719 --> 00:38:00,680
Speaker 1: not fully ready to go. The Tesla website says a

669
00:38:00,719 --> 00:38:03,759
Speaker 1: full self driving mode this feature is in beta and

670
00:38:03,840 --> 00:38:09,840
Speaker 1: may not stop for all traffic controls. Yeah, that's pretty scary.

671
00:38:10,600 --> 00:38:13,200
Speaker 1: This is from blue Book. The term test would seem

672
00:38:13,239 --> 00:38:16,520
Speaker 1: to suggest some sort of laboratory conditions or controlled experiment

673
00:38:16,719 --> 00:38:19,320
Speaker 1: with close monitoring, but that's not how Tesla is testing

674
00:38:19,320 --> 00:38:22,600
Speaker 1: the software. Instead, it's allowing owners to use it on

675
00:38:22,640 --> 00:38:24,800
Speaker 1: public roads with the rest of us as if it

676
00:38:24,840 --> 00:38:28,880
Speaker 1: were a finished product. Now, Tesla does require owners to

677
00:38:28,960 --> 00:38:31,839
Speaker 1: sign a waiver to use this mode, but what about

678
00:38:31,840 --> 00:38:34,160
Speaker 1: the rest of us? You know, I never consented to

679
00:38:34,400 --> 00:38:37,759
Speaker 1: or opted into being whenever I was out on a

680
00:38:37,760 --> 00:38:41,600
Speaker 1: public roadway being part of Elon Musk's text kitchen on

681
00:38:41,640 --> 00:38:44,160
Speaker 1: whether or not this technology is safe.

682
00:38:44,320 --> 00:38:47,920
Speaker 2: Yeah, I'm so curious what the content of that waiver is, Like,

683
00:38:48,160 --> 00:38:50,759
Speaker 2: what is being waive? What sort of liability is the

684
00:38:51,280 --> 00:38:55,840
Speaker 2: owner assuming like, is the owner of the Tesla just

685
00:38:55,960 --> 00:39:01,200
Speaker 2: deciding or just like taking on all lives ability to themselves,

686
00:39:01,239 --> 00:39:04,239
Speaker 2: because yeah, like what about you and me who don't

687
00:39:04,320 --> 00:39:08,959
Speaker 2: own a Tesla. We're just out there walking around, driving around,

688
00:39:09,520 --> 00:39:14,279
Speaker 2: being at risk. What does this test status of full

689
00:39:14,320 --> 00:39:16,240
Speaker 2: self driving mode mean for us?

690
00:39:16,520 --> 00:39:21,200
Speaker 1: The report speaks to a young person who was really

691
00:39:21,280 --> 00:39:25,040
Speaker 1: lucky to be alive after tesla using this technology crashed

692
00:39:25,040 --> 00:39:27,279
Speaker 1: into him. I think he fractured his neck and like

693
00:39:27,640 --> 00:39:29,520
Speaker 1: you know, was on a ventilator and still has a

694
00:39:29,520 --> 00:39:31,879
Speaker 1: lot of issues. I think he's from North Carolina, and

695
00:39:32,400 --> 00:39:35,400
Speaker 1: it is really scary, and I think it really speaks

696
00:39:35,440 --> 00:39:37,120
Speaker 1: to this idea that we talk about a lot, which

697
00:39:37,160 --> 00:39:40,680
Speaker 1: is just this very messed up relationship between the people

698
00:39:40,680 --> 00:39:43,840
Speaker 1: who make this technology and the public. Like we should

699
00:39:43,840 --> 00:39:46,719
Speaker 1: not have to be unpaid lab rats in this experiment

700
00:39:47,000 --> 00:39:52,160
Speaker 1: with potentially harmful or deadly consequences just because Elon Musk

701
00:39:52,200 --> 00:39:54,960
Speaker 1: says it's cool. I don't think that's very cool. I've

702
00:39:54,960 --> 00:39:57,319
Speaker 1: never signed up for that. Before we go, I want

703
00:39:57,360 --> 00:39:59,360
Speaker 1: to talk about this interesting poll that I read in

704
00:39:59,440 --> 00:40:02,080
Speaker 1: Fast Company. So, according to a new Harris poll shared

705
00:40:02,160 --> 00:40:06,120
Speaker 1: exclusively with Fast Company. Most Americans would prefer to live

706
00:40:06,160 --> 00:40:10,560
Speaker 1: in eras before the rise of things like social media, smartphones,

707
00:40:10,600 --> 00:40:13,720
Speaker 1: and the Internet. This sentiment is particularly true for older

708
00:40:13,760 --> 00:40:17,040
Speaker 1: millennials and Gen xers. When asked if they would like

709
00:40:17,120 --> 00:40:19,920
Speaker 1: to return to a time before smartphones and social media,

710
00:40:20,360 --> 00:40:23,040
Speaker 1: seventy seven percent of Americans age thirty five to fifty

711
00:40:23,040 --> 00:40:26,400
Speaker 1: four said yes, the highest of any group. So you

712
00:40:26,480 --> 00:40:30,080
Speaker 1: might be thinking, oh, like, these people are just nostalgic

713
00:40:30,640 --> 00:40:34,040
Speaker 1: because they're old enough to remember, you know, using payphones

714
00:40:34,120 --> 00:40:38,440
Speaker 1: and you know, wired internet and all of the things

715
00:40:38,480 --> 00:40:40,160
Speaker 1: that you and I talk about because we're old, right,

716
00:40:40,400 --> 00:40:42,600
Speaker 1: But that's actually that might be some of it. But

717
00:40:42,680 --> 00:40:46,160
Speaker 1: it actually might not be totally the case, because according

718
00:40:46,160 --> 00:40:49,480
Speaker 1: to this poll, even younger people who have no memory

719
00:40:49,480 --> 00:40:52,200
Speaker 1: of a world before social media indicated that they would

720
00:40:52,239 --> 00:40:55,120
Speaker 1: like to return to the pre internet vibe. That's sixty

721
00:40:55,160 --> 00:40:57,720
Speaker 1: three percent of eighteen to thirty four year olds agreed

722
00:40:57,760 --> 00:41:00,880
Speaker 1: with that idea versus only thirty seven percent who disagreed.

723
00:41:01,280 --> 00:41:03,600
Speaker 1: So I don't actually find that surprising at all. But

724
00:41:04,280 --> 00:41:08,239
Speaker 1: can you guess the demographic who are most likely to

725
00:41:08,280 --> 00:41:11,160
Speaker 1: be like, Actually, I like how things are right now.

726
00:41:11,160 --> 00:41:12,640
Speaker 1: I don't want to go back to the pre social

727
00:41:12,680 --> 00:41:17,920
Speaker 1: media days. I can't baby boomers. So baby boomers do

728
00:41:18,000 --> 00:41:21,600
Speaker 1: not share that feeling. Only sixty percent of people over

729
00:41:21,640 --> 00:41:23,600
Speaker 1: the age of fifty five say that they would like

730
00:41:23,640 --> 00:41:26,000
Speaker 1: to return to those times. And this does not surprise

731
00:41:26,120 --> 00:41:30,920
Speaker 1: me at all. My mom loves Facebook, you know, she's

732
00:41:30,960 --> 00:41:34,879
Speaker 1: completely obsessed with it. I think that baby boomers love

733
00:41:35,000 --> 00:41:38,359
Speaker 1: the Internet. Like, who is keeping Facebook afloat in twenty

734
00:41:38,400 --> 00:41:40,520
Speaker 1: twenty three. It's baby boomers.

735
00:41:41,000 --> 00:41:45,200
Speaker 2: Yeah, I guess that's right. It's a weird way to

736
00:41:45,280 --> 00:41:46,080
Speaker 2: think about it.

737
00:41:46,640 --> 00:41:50,760
Speaker 1: So what's interesting is that overwhelmingly all people still feel

738
00:41:50,760 --> 00:41:54,160
Speaker 1: like technology is important. Ninety percent said that being open

739
00:41:54,200 --> 00:41:56,680
Speaker 1: minded about new tech is important. Of finding that is

740
00:41:56,680 --> 00:41:59,000
Speaker 1: held up across all the demographics that they looked at,

741
00:41:59,080 --> 00:42:01,319
Speaker 1: And I think that's really to like. I almost think

742
00:42:01,360 --> 00:42:07,640
Speaker 1: that what that shows is that people are just really tired.

743
00:42:07,800 --> 00:42:12,640
Speaker 1: I think as we're seeing like the cost of being

744
00:42:12,680 --> 00:42:16,080
Speaker 1: connected all the time and being like hyper aware of

745
00:42:16,120 --> 00:42:19,359
Speaker 1: what that feels like being connected via technology all the time,

746
00:42:19,520 --> 00:42:21,840
Speaker 1: and the ways that tech is becoming even more entrenched

747
00:42:21,840 --> 00:42:24,839
Speaker 1: in our daily lives, it's like we don't like it,

748
00:42:25,000 --> 00:42:26,880
Speaker 1: but we're aware of it, and it's just kind of

749
00:42:27,760 --> 00:42:30,560
Speaker 1: like resignation that we're gonna have to accept it, right,

750
00:42:30,680 --> 00:42:33,840
Speaker 1: Like we're gonna have to accept that these digital tools

751
00:42:33,840 --> 00:42:36,880
Speaker 1: and experiences are part of our lives, whether we like

752
00:42:36,920 --> 00:42:38,480
Speaker 1: it or not. And it turns out we don't really

753
00:42:38,520 --> 00:42:40,120
Speaker 1: like it that much. Like I almost see this as

754
00:42:40,120 --> 00:42:42,839
Speaker 1: a kind of like, I don't know, I find it

755
00:42:42,880 --> 00:42:47,280
Speaker 1: interesting that people don't like this. They yearn for a time,

756
00:42:48,239 --> 00:42:50,399
Speaker 1: even sometimes in the case of young people, a time

757
00:42:50,400 --> 00:42:53,560
Speaker 1: that they never even experienced before social media, but they

758
00:42:53,719 --> 00:42:56,640
Speaker 1: understand the importance of keeping up with technology.

759
00:42:57,080 --> 00:43:00,680
Speaker 2: It makes you wonder if they is a different in

760
00:43:01,320 --> 00:43:05,239
Speaker 2: appreciating the costs of what has been lost and like

761
00:43:05,400 --> 00:43:07,680
Speaker 2: what we have to deal with, Like we talk on

762
00:43:07,719 --> 00:43:11,520
Speaker 2: this show all the time about this information climate we

763
00:43:11,560 --> 00:43:15,719
Speaker 2: live in where there's like so much disinformation, And we

764
00:43:15,760 --> 00:43:20,319
Speaker 2: were talking about stochastic harassment earlier, where people are just

765
00:43:20,360 --> 00:43:26,320
Speaker 2: like repeating harmful, hateful narratives that are just sufficiently under

766
00:43:26,360 --> 00:43:29,239
Speaker 2: the radar that they don't violate terms of service of

767
00:43:29,520 --> 00:43:33,440
Speaker 2: social media platforms. And it feels like that's just like

768
00:43:33,560 --> 00:43:37,040
Speaker 2: normal and how it is. And like, you know, with AI,

769
00:43:37,200 --> 00:43:41,320
Speaker 2: we've talked a lot about the how it's increasingly difficult

770
00:43:41,360 --> 00:43:43,839
Speaker 2: to separate fact from fiction, and as we look into

771
00:43:43,840 --> 00:43:46,680
Speaker 2: the future, it's probably only going to get harder. And

772
00:43:47,480 --> 00:43:49,680
Speaker 2: that doesn't feel good. Doesn't make me feel like we're

773
00:43:49,719 --> 00:43:54,240
Speaker 2: going to be approaching a future where things are better

774
00:43:54,400 --> 00:43:58,560
Speaker 2: than they are now. And you know, no offense to

775
00:43:58,640 --> 00:44:01,839
Speaker 2: the baby boomers, but maybe they're just not like as

776
00:44:01,920 --> 00:44:04,040
Speaker 2: clued into all of these harms.

777
00:44:04,080 --> 00:44:04,560
Speaker 3: I don't know.

778
00:44:05,320 --> 00:44:07,239
Speaker 1: It really takes me back to the conversation that I

779
00:44:07,280 --> 00:44:09,839
Speaker 1: had with Paris Marks from the podcast Tech Won't Save

780
00:44:09,960 --> 00:44:14,560
Speaker 1: Us around who are the tech leaders who are building

781
00:44:14,880 --> 00:44:17,719
Speaker 1: what our future looks like for all of us do?

782
00:44:17,920 --> 00:44:20,680
Speaker 1: And when they think about what a good life looks like,

783
00:44:20,760 --> 00:44:24,200
Speaker 1: a full, meaningful life, what that looks like, what that entails,

784
00:44:24,280 --> 00:44:26,640
Speaker 1: are they thinking about the same thing that the rest

785
00:44:26,640 --> 00:44:29,280
Speaker 1: of us are thinking about? Because parents made a great point,

786
00:44:29,440 --> 00:44:31,440
Speaker 1: probably not a lot of these people who are designing

787
00:44:31,440 --> 00:44:33,640
Speaker 1: what our futures are going to look like probably have

788
00:44:33,719 --> 00:44:36,080
Speaker 1: a very different idea. But what a good future looks

789
00:44:36,120 --> 00:44:38,600
Speaker 1: like versus what I think or what people listening ninety

790
00:44:38,600 --> 00:44:40,680
Speaker 1: percent of us might think. And so when they're the

791
00:44:40,680 --> 00:44:44,480
Speaker 1: ones who are in charge of using technology and building

792
00:44:44,520 --> 00:44:47,160
Speaker 1: technology to build that future, we've got to really ask like,

793
00:44:47,239 --> 00:44:49,960
Speaker 1: who put them in charge? If the majority of us

794
00:44:50,200 --> 00:44:53,000
Speaker 1: from all these different demographics are wanting to go back

795
00:44:53,000 --> 00:44:57,319
Speaker 1: to a time before social media, before technology was where

796
00:44:57,320 --> 00:44:59,960
Speaker 1: it is, before the rise and the ubiquity of the Internet,

797
00:45:00,200 --> 00:45:01,960
Speaker 1: that really says something. And I almost feel like it's

798
00:45:02,000 --> 00:45:04,120
Speaker 1: kind of a positive thing because I hope that the

799
00:45:04,160 --> 00:45:08,760
Speaker 1: people who build technology are really listening and paying attention

800
00:45:08,880 --> 00:45:11,520
Speaker 1: to what the rest of us are saying. Because people

801
00:45:11,560 --> 00:45:14,600
Speaker 1: don't want futures or we're increasingly being asked to give

802
00:45:14,920 --> 00:45:17,840
Speaker 1: more and more of ourselves. Our experience is our time,

803
00:45:17,920 --> 00:45:21,719
Speaker 1: our energy, our data to technology companies to make some

804
00:45:22,560 --> 00:45:26,680
Speaker 1: tech billionaire a little bit richer. We are exhausted. Every

805
00:45:27,000 --> 00:45:29,960
Speaker 1: drop of us, anything that we have has already been

806
00:45:30,000 --> 00:45:32,280
Speaker 1: taken in mind from us. We are exhausted. We cannot

807
00:45:32,280 --> 00:45:34,920
Speaker 1: give anymore. I think that that's what people are responding to.

808
00:45:34,960 --> 00:45:37,640
Speaker 1: I think that people are tired, They want to slow down.

809
00:45:37,880 --> 00:45:41,480
Speaker 1: They are craving connection, they are craving real experiences, and

810
00:45:41,520 --> 00:45:44,719
Speaker 1: I think that tech leaders should be listening and not

811
00:45:44,920 --> 00:45:49,480
Speaker 1: just trying to fill that gap with more tech, with

812
00:45:49,560 --> 00:45:54,680
Speaker 1: more screens, with more extractive policies and you know, mining

813
00:45:54,760 --> 00:45:58,000
Speaker 1: from us. Because I don't think they don't think that's

814
00:45:58,000 --> 00:46:01,520
Speaker 1: making us happy, and I think that if it's I

815
00:46:01,560 --> 00:46:04,279
Speaker 1: hope that they're really listening, because I think that what

816
00:46:04,320 --> 00:46:07,640
Speaker 1: I'm hearing from this poll is that people want better

817
00:46:07,920 --> 00:46:11,279
Speaker 1: for themselves, for their futures, for all of us, and

818
00:46:11,480 --> 00:46:13,680
Speaker 1: got I hope that people who make technology are listening.

819
00:46:14,200 --> 00:46:16,080
Speaker 1: Thanks so much for being here Mike to walk through

820
00:46:16,120 --> 00:46:19,360
Speaker 1: these stories, and thanks to everyone for listening. If you

821
00:46:19,400 --> 00:46:22,719
Speaker 1: want more ad free stories like this, please check out

822
00:46:22,760 --> 00:46:25,440
Speaker 1: our patreon. If you have not already, please go to

823
00:46:25,480 --> 00:46:28,799
Speaker 1: Patreon patreon dot com slash Tangody and vote in our

824
00:46:28,840 --> 00:46:31,560
Speaker 1: poll of whether or not you think that esen Supposed

825
00:46:32,120 --> 00:46:34,239
Speaker 1: is written by AI. You do not have to be

826
00:46:34,280 --> 00:46:36,560
Speaker 1: a subscriber or pay to vote in the poll and

827
00:46:36,560 --> 00:46:38,239
Speaker 1: see the post that I'm talking about. Although while you're

828
00:46:38,239 --> 00:46:40,480
Speaker 1: there you could thank you so much for listening. I

829
00:46:40,480 --> 00:46:46,399
Speaker 1: will see you next time. If you're looking for ways

830
00:46:46,400 --> 00:46:48,640
Speaker 1: to support the show, check out our March store at

831
00:46:48,640 --> 00:46:52,360
Speaker 1: tegoty dot com slash store. Got a story about an

832
00:46:52,400 --> 00:46:54,319
Speaker 1: interesting thing in tech, or just want to say hi,

833
00:46:54,719 --> 00:46:56,880
Speaker 1: You can reach us at Hello at tegody dot com.

834
00:46:57,040 --> 00:46:59,440
Speaker 1: You can also find transcripts for today's episode at tengody

835
00:46:59,480 --> 00:47:01,560
Speaker 1: dot com. There Are No Girls on the Internet was

836
00:47:01,600 --> 00:47:04,480
Speaker 1: created by me Bridget Tod. It's a production of iHeartRadio

837
00:47:04,520 --> 00:47:08,600
Speaker 1: and Unbossed Creative edited by Joey pat Jonathan Strickland is

838
00:47:08,600 --> 00:47:12,040
Speaker 1: our executive producer. Tarry Harrison is our producer and sound engineer.

839
00:47:12,440 --> 00:47:15,600
Speaker 1: Michael Almado is our contributing producer. I'm your host, Bridget Todd.

840
00:47:16,000 --> 00:47:17,680
Speaker 1: If you want to help us grow, rate and review

841
00:47:17,760 --> 00:47:21,359
Speaker 1: us on Apple Podcasts. For more podcasts from iHeartRadio, check

842
00:47:21,400 --> 00:47:23,719
Speaker 1: out the iHeartRadio app, Apple Podcasts, or wherever you get

843
00:47:23,760 --> 00:47:25,960
Speaker 1: your podcasts.