1
00:00:04,120 --> 00:00:05,840
Speaker 1: There Are No Girls on the Internet. As a production

2
00:00:05,880 --> 00:00:13,400
Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridget Todd, and this

3
00:00:13,480 --> 00:00:18,360
Speaker 1: is there are no Girls on the Internet. According to

4
00:00:18,400 --> 00:00:21,360
Speaker 1: a growing body of research, women are using AI in

5
00:00:21,400 --> 00:00:24,720
Speaker 1: the workplace less than men. We're less likely to experiment

6
00:00:24,760 --> 00:00:28,000
Speaker 1: with it and more likely to be skeptical of AI too.

7
00:00:28,720 --> 00:00:31,760
Speaker 1: But does this mean that women are falling behind economically,

8
00:00:31,840 --> 00:00:35,839
Speaker 1: financially and professionally or are we actually just asking the

9
00:00:35,840 --> 00:00:39,280
Speaker 1: wrong questions to dig into all of this. I'm joined

10
00:00:39,280 --> 00:00:42,199
Speaker 1: by my friends Samantha and Nanny over at the podcast

11
00:00:42,200 --> 00:00:44,600
Speaker 1: stuff Mom never told you to look at what the

12
00:00:44,600 --> 00:00:47,720
Speaker 1: research says and what it leaves out about women, AI

13
00:00:47,960 --> 00:00:48,440
Speaker 1: and work.

14
00:00:53,960 --> 00:00:56,320
Speaker 2: Hey, this is Sanny and Samantha and welcome to Stefan

15
00:00:56,320 --> 00:00:57,680
Speaker 2: Never told You, a production of I Heart at You

16
00:01:07,440 --> 00:01:10,000
Speaker 2: and today we were once again so happy to be

17
00:01:10,080 --> 00:01:14,240
Speaker 2: joined by the incredible, the irreplaceable Fridget Todd. Welcome Bridget,

18
00:01:14,520 --> 00:01:18,360
Speaker 2: Thanks for having me so lovely to be back. Yes,

19
00:01:18,760 --> 00:01:20,800
Speaker 2: thank you so much for being back. You and I

20
00:01:20,840 --> 00:01:24,399
Speaker 2: were chatting about how stressful these times can be, so

21
00:01:25,319 --> 00:01:29,440
Speaker 2: the holidays those times being so thank you for making

22
00:01:29,480 --> 00:01:31,720
Speaker 2: the time. We always love talking to you.

23
00:01:32,600 --> 00:01:34,240
Speaker 3: How are you rigid?

24
00:01:34,280 --> 00:01:34,840
Speaker 2: How have you been?

25
00:01:35,520 --> 00:01:36,319
Speaker 4: I'm not bad.

26
00:01:36,400 --> 00:01:38,800
Speaker 1: We were talking off Mike about how it is a

27
00:01:38,840 --> 00:01:39,760
Speaker 1: known issue with me.

28
00:01:39,920 --> 00:01:42,640
Speaker 4: I'm a grinch. I don't like the holidays.

29
00:01:43,160 --> 00:01:45,679
Speaker 1: When people ask, you know this time of year, people

30
00:01:45,720 --> 00:01:48,000
Speaker 1: are like, what are you doing for the holiday? I

31
00:01:48,120 --> 00:01:50,920
Speaker 1: like my fur. I can feel my fur go up,

32
00:01:50,960 --> 00:01:53,120
Speaker 1: you know, I just don't like it. My heart beats

33
00:01:53,120 --> 00:01:55,840
Speaker 1: a little bit faster, and I just I just find

34
00:01:56,520 --> 00:02:00,440
Speaker 1: the holiday season extremely stressful. And it's my Actually, when

35
00:02:00,480 --> 00:02:03,120
Speaker 1: you're someone who is self employed, it's like, hey, you

36
00:02:03,120 --> 00:02:05,160
Speaker 1: know the work that you usually do. What if you

37
00:02:05,240 --> 00:02:08,720
Speaker 1: did that work plus a week's worth of work on

38
00:02:08,880 --> 00:02:11,880
Speaker 1: top of it, and then that'll buy you a week off.

39
00:02:12,360 --> 00:02:14,120
Speaker 1: What will you be doing with that time? Going to

40
00:02:14,120 --> 00:02:17,840
Speaker 1: see your family? Notably not stressful activity. It's it's like,

41
00:02:18,400 --> 00:02:20,919
Speaker 1: I just find it like a stress sandwich. Essentially, I

42
00:02:21,160 --> 00:02:23,120
Speaker 1: can't handle it. I cannot hang with the holidays.

43
00:02:24,720 --> 00:02:30,600
Speaker 2: Well, as I said, Samantha, you and I would agree

44
00:02:30,720 --> 00:02:33,200
Speaker 2: on this. I mean I also think they're very stressful.

45
00:02:33,280 --> 00:02:37,440
Speaker 2: I yesterday we had a we went to the office

46
00:02:37,560 --> 00:02:40,519
Speaker 2: for a Sminty team meeting. We go, like Samantha and

47
00:02:40,560 --> 00:02:44,320
Speaker 2: I go once a year and our our producer Christina

48
00:02:44,440 --> 00:02:45,720
Speaker 2: was like, how are you and I was like.

49
00:02:48,840 --> 00:02:52,840
Speaker 4: I'm so attired just a deep sigh.

50
00:02:52,960 --> 00:02:53,160
Speaker 5: Yeah.

51
00:02:53,200 --> 00:02:54,600
Speaker 4: And then it's the end of the year.

52
00:02:54,720 --> 00:02:58,680
Speaker 1: It just I just always feel like it's like everything

53
00:03:00,160 --> 00:03:02,280
Speaker 1: catching up with you all of the year.

54
00:03:03,440 --> 00:03:06,239
Speaker 4: It's been a heavy year. It's something about.

55
00:03:06,080 --> 00:03:07,960
Speaker 1: Like by the time it hits December, I'm just so

56
00:03:08,360 --> 00:03:10,640
Speaker 1: freaking done, just done spent.

57
00:03:11,960 --> 00:03:15,080
Speaker 2: Yeah, and then you have the new year barreling in

58
00:03:15,120 --> 00:03:18,640
Speaker 2: on you and you're like, well, it's still gonna be

59
00:03:19,680 --> 00:03:23,359
Speaker 2: but as all these expectations of maybe something will change

60
00:03:23,400 --> 00:03:25,440
Speaker 2: and not really.

61
00:03:26,440 --> 00:03:29,200
Speaker 1: Yeah, the new year I'm actually okay with because you

62
00:03:29,240 --> 00:03:31,399
Speaker 1: get to have those couple of days where you buy

63
00:03:31,400 --> 00:03:34,160
Speaker 1: a new journal and some new pens and for a

64
00:03:34,200 --> 00:03:36,960
Speaker 1: brief moment you're like, this is gonna be my year,

65
00:03:37,040 --> 00:03:37,800
Speaker 1: this is the year.

66
00:03:37,800 --> 00:03:40,920
Speaker 4: And then at all I get it all together. It

67
00:03:40,920 --> 00:03:42,240
Speaker 4: doesn't usually last for me.

68
00:03:42,320 --> 00:03:44,080
Speaker 1: I usually get about a week out of that, but

69
00:03:44,200 --> 00:03:45,920
Speaker 1: you get that week, which is not bad.

70
00:03:46,800 --> 00:03:47,400
Speaker 4: And you and.

71
00:03:47,440 --> 00:03:49,960
Speaker 6: Samantha did bond over Markers last time.

72
00:03:50,040 --> 00:03:52,920
Speaker 7: Oh yeah, I got a new set. I was gifted

73
00:03:52,920 --> 00:03:54,480
Speaker 7: a new set budget just to give.

74
00:03:54,400 --> 00:03:56,520
Speaker 4: You an update, how are they writing?

75
00:03:56,920 --> 00:03:58,640
Speaker 7: I had to get used to it because it was

76
00:03:58,680 --> 00:04:01,560
Speaker 7: a lot free flowing, lot more free flowing than my

77
00:04:01,640 --> 00:04:03,880
Speaker 7: previous one. So I had to practice with them so

78
00:04:03,920 --> 00:04:08,240
Speaker 7: I didn't mess up my tiny coloring books.

79
00:04:08,280 --> 00:04:10,440
Speaker 1: A good set of pans, a good set of markers.

80
00:04:10,520 --> 00:04:12,480
Speaker 1: It really will have you believing this is going to

81
00:04:12,560 --> 00:04:13,600
Speaker 1: turn everything around.

82
00:04:13,840 --> 00:04:14,920
Speaker 5: For we have split second.

83
00:04:15,080 --> 00:04:17,800
Speaker 7: I was like, I can color within the lines, and

84
00:04:17,800 --> 00:04:19,120
Speaker 7: then I was like, no, I cannot.

85
00:04:22,560 --> 00:04:26,720
Speaker 2: Well, the topic you've bought today for us, Bridget is

86
00:04:26,839 --> 00:04:30,200
Speaker 2: so so timely. I've been thinking about this because I mean,

87
00:04:30,240 --> 00:04:32,840
Speaker 2: it's hard to escape, but also you get to the

88
00:04:32,960 --> 00:04:35,080
Speaker 2: end of the year and you have like Miriam Webster

89
00:04:35,640 --> 00:04:37,880
Speaker 2: chooses their word of the year and it's slop as

90
00:04:37,920 --> 00:04:41,160
Speaker 2: an AI slop, and then you have Time chooses their

91
00:04:41,200 --> 00:04:44,840
Speaker 2: persons a year and it's like the architects of AI

92
00:04:45,000 --> 00:04:49,839
Speaker 2: and it's like all dudes. And yesterday when we were

93
00:04:50,240 --> 00:04:53,000
Speaker 2: at the office, we were having kind of a team

94
00:04:53,080 --> 00:04:56,080
Speaker 2: meeting and it came up. We were talking about AI

95
00:04:56,320 --> 00:04:59,600
Speaker 2: and our jobs and what does that look like? So

96
00:04:59,680 --> 00:05:02,239
Speaker 2: what are we talking about specifically today, Bridget.

97
00:05:02,760 --> 00:05:06,320
Speaker 1: Today you're talking about the reality that women are using

98
00:05:06,400 --> 00:05:09,840
Speaker 1: AI tools a lot less than men in the workplace.

99
00:05:10,240 --> 00:05:14,280
Speaker 1: There is a definite, well studied, well documented gender gap

100
00:05:14,279 --> 00:05:18,000
Speaker 1: when it comes to the usage of AI, and I

101
00:05:18,080 --> 00:05:21,000
Speaker 1: feel you because I think as a podcaster especially, but

102
00:05:21,640 --> 00:05:24,599
Speaker 1: any kind of creative the first conversation that comes up

103
00:05:24,640 --> 00:05:27,160
Speaker 1: when it's like a bunch of podcasters together is are

104
00:05:27,200 --> 00:05:28,799
Speaker 1: you using AI in your work? How are you seeing

105
00:05:28,800 --> 00:05:32,719
Speaker 1: AI show up? Or people asking are you afraid of AI?

106
00:05:32,920 --> 00:05:35,039
Speaker 1: Do you see AI taking your job?

107
00:05:35,080 --> 00:05:35,440
Speaker 4: One day?

108
00:05:35,520 --> 00:05:37,239
Speaker 1: Like you won't have a job, people will be listening

109
00:05:37,279 --> 00:05:41,560
Speaker 1: to AI podcasts all of that. Right, Those conversations are everywhere,

110
00:05:42,360 --> 00:05:45,719
Speaker 1: and I think often time those conversations can be grounded

111
00:05:45,760 --> 00:05:48,680
Speaker 1: in a kind of hype. And so I think the

112
00:05:48,760 --> 00:05:53,119
Speaker 1: fact that AI is not being adopted equally by people

113
00:05:53,120 --> 00:05:55,919
Speaker 1: of all genders at the same rates is pretty interesting.

114
00:05:55,960 --> 00:05:58,719
Speaker 1: So I wanted to talk through what we know about

115
00:05:58,720 --> 00:06:00,920
Speaker 1: the data when it comes to the gender gap in AI.

116
00:06:01,680 --> 00:06:03,960
Speaker 1: But I think I might be coming at this from

117
00:06:04,000 --> 00:06:05,520
Speaker 1: a little bit of a different place, right.

118
00:06:06,000 --> 00:06:07,120
Speaker 4: You know, in a lot.

119
00:06:07,000 --> 00:06:10,760
Speaker 1: Of the research, they automatically assume two points that I

120
00:06:10,800 --> 00:06:14,200
Speaker 1: want to call out early that I'll return to one,

121
00:06:14,880 --> 00:06:17,919
Speaker 1: women adopting AI less, it's going to be bad for women.

122
00:06:17,960 --> 00:06:20,680
Speaker 1: I'll talk about this later, but I don't know about

123
00:06:20,760 --> 00:06:21,400
Speaker 1: treating that.

124
00:06:21,400 --> 00:06:22,599
Speaker 4: Like a foregone conclusion.

125
00:06:23,400 --> 00:06:25,360
Speaker 1: And then this is kind of an offshoot of something

126
00:06:25,360 --> 00:06:28,400
Speaker 1: that I hear a ton when talking about AI. AI

127
00:06:28,760 --> 00:06:31,440
Speaker 1: is inevitable. If you don't get with the program, you're

128
00:06:31,480 --> 00:06:34,240
Speaker 1: going to be left behind. I have people who tell

129
00:06:34,279 --> 00:06:38,039
Speaker 1: me that not being into AI right now is like

130
00:06:38,240 --> 00:06:41,120
Speaker 1: not using email. You know, it will be the same

131
00:06:41,200 --> 00:06:43,560
Speaker 1: kind of thing in a few years, and you know,

132
00:06:43,600 --> 00:06:46,400
Speaker 1: if women are adopting it less, the idea that we're

133
00:06:46,440 --> 00:06:50,080
Speaker 1: going to be less employable, less competitive, making less money overall.

134
00:06:50,600 --> 00:06:52,760
Speaker 1: And so I want to be clear that I don't

135
00:06:52,800 --> 00:06:55,480
Speaker 1: really have the answer here, but and I want to

136
00:06:55,480 --> 00:06:57,760
Speaker 1: look at what the research says. But I don't like

137
00:06:57,839 --> 00:07:02,240
Speaker 1: this idea that frames why I'd spread AI adoption as

138
00:07:02,279 --> 00:07:04,760
Speaker 1: an inevitable thing that everyone is going to be doing,

139
00:07:04,839 --> 00:07:06,600
Speaker 1: because it really doesn't leave a lot of room for

140
00:07:07,000 --> 00:07:10,040
Speaker 1: criticism away around the way that AI is being adopted.

141
00:07:10,040 --> 00:07:12,800
Speaker 1: It's just repeating it's inevitable, it's inevitable, it's inevitable, get

142
00:07:12,840 --> 00:07:14,880
Speaker 1: on board. I do think that when it comes to

143
00:07:14,920 --> 00:07:18,720
Speaker 1: adopting any new technology in this way, we shouldn't just

144
00:07:18,760 --> 00:07:21,960
Speaker 1: be being told to get on board. It's inevitable. Just

145
00:07:22,120 --> 00:07:25,440
Speaker 1: cram it down people's throats, especially when people are saying, hey,

146
00:07:25,520 --> 00:07:27,680
Speaker 1: maybe we don't actually like this technology.

147
00:07:27,720 --> 00:07:29,840
Speaker 4: Hey maybe this technology isn't good.

148
00:07:29,960 --> 00:07:30,120
Speaker 8: Right.

149
00:07:30,520 --> 00:07:31,480
Speaker 4: I love technology.

150
00:07:31,480 --> 00:07:33,440
Speaker 1: I'm an advocate for knowing the basics of all kinds

151
00:07:33,440 --> 00:07:35,560
Speaker 1: of technology. But if a technology is showing itself to

152
00:07:35,600 --> 00:07:39,840
Speaker 1: be ineffective, biased, problematic, full of lies, actually adds work

153
00:07:39,840 --> 00:07:41,760
Speaker 1: to your plate, I don't know that we should be

154
00:07:41,760 --> 00:07:44,000
Speaker 1: just to be telling people get on board with that.

155
00:07:44,080 --> 00:07:46,600
Speaker 1: It's inevitable if that's actually what they're experiencing.

156
00:07:46,640 --> 00:07:50,360
Speaker 2: You know, yes, yes, And this has come up a

157
00:07:50,360 --> 00:07:55,040
Speaker 2: lot at our office of the at least right now,

158
00:07:55,040 --> 00:07:57,080
Speaker 2: it seems to be adding more work. It's not like

159
00:07:57,400 --> 00:08:02,000
Speaker 2: actually helping, but that you're told, like, no, it's gonna

160
00:08:02,040 --> 00:08:05,240
Speaker 2: really help you out and streamline things. I have to say,

161
00:08:05,280 --> 00:08:10,280
Speaker 2: I had not considered this gender gap in AI usage.

162
00:08:10,320 --> 00:08:13,960
Speaker 2: So I'm really interested about the research that you found.

163
00:08:14,600 --> 00:08:16,080
Speaker 4: Yeah, let's get into it.

164
00:08:16,840 --> 00:08:18,760
Speaker 1: So I took a look at some of the studies

165
00:08:18,800 --> 00:08:21,760
Speaker 1: and they all basically confirm that women are using AI

166
00:08:21,840 --> 00:08:24,400
Speaker 1: in the workplace a lot less than our mail counterparts.

167
00:08:24,880 --> 00:08:27,560
Speaker 1: Let's look at a meta analysis called Global Evidence on

168
00:08:27,640 --> 00:08:30,679
Speaker 1: Gender gaps in Generative AI published in the Harvard Business Review.

169
00:08:31,000 --> 00:08:33,079
Speaker 1: So they took a look at eighteen surveys and studies

170
00:08:33,080 --> 00:08:36,120
Speaker 1: covering one hundred and forty three thousand plus individuals across

171
00:08:36,240 --> 00:08:40,240
Speaker 1: many countries, sectors, and occupations. They also combined this survey

172
00:08:40,320 --> 00:08:43,560
Speaker 1: data with Internet traffic and mobile app download data for

173
00:08:43,960 --> 00:08:47,760
Speaker 1: major generative AI platforms like claud and SHACKQBT platforms that

174
00:08:47,800 --> 00:08:51,200
Speaker 1: have hundreds of millions of users. They found a large,

175
00:08:51,760 --> 00:08:55,960
Speaker 1: nearly universal gender gap. Here's what they found. They found

176
00:08:56,000 --> 00:08:59,439
Speaker 1: that the gender gap in generative AI usage is global, persistent,

177
00:08:59,760 --> 00:09:03,680
Speaker 1: and not fully explained by access or occupation, pointing to

178
00:09:03,720 --> 00:09:08,280
Speaker 1: some deeper social, cultural, and institutional frictions. So overall, it

179
00:09:08,360 --> 00:09:11,679
Speaker 1: just seems like women are not the ones when people

180
00:09:11,679 --> 00:09:14,160
Speaker 1: are saying it's inevitable, get on board, you gotta use it.

181
00:09:14,440 --> 00:09:16,920
Speaker 1: And so I was like, by and large, that is

182
00:09:16,960 --> 00:09:23,200
Speaker 1: not coming from women. I'm curious, do these findings surprise you, like,

183
00:09:23,320 --> 00:09:26,600
Speaker 1: as women in media, as women in TEC and podcasting, Like,

184
00:09:27,160 --> 00:09:30,360
Speaker 1: is this surprising information for me?

185
00:09:30,559 --> 00:09:33,240
Speaker 2: It's not. I don't think i'd ever clocked it until

186
00:09:33,280 --> 00:09:36,760
Speaker 2: I was looking at your outline. But once I looked

187
00:09:36,760 --> 00:09:40,640
Speaker 2: at the outline, I was like, yeah, yeah, Because Most

188
00:09:40,679 --> 00:09:44,240
Speaker 2: people I know who are into AI are men, and

189
00:09:44,280 --> 00:09:46,440
Speaker 2: they usually have a very like but it's this cool

190
00:09:46,520 --> 00:09:51,319
Speaker 2: technology thing about it. And I can't say I have

191
00:09:52,280 --> 00:09:54,600
Speaker 2: really anyone I can think of, but as a woman

192
00:09:54,679 --> 00:09:58,600
Speaker 2: that's like that. And I mean, we're going to go

193
00:09:58,600 --> 00:10:00,280
Speaker 2: over some of the reasons why this is, and I

194
00:10:00,320 --> 00:10:04,400
Speaker 2: thought a lot of them were interesting, but they did

195
00:10:04,440 --> 00:10:06,920
Speaker 2: resonate with me, and I thought like, yeah, if that

196
00:10:07,000 --> 00:10:12,880
Speaker 2: makes sense, Like I get I can't win. There's a hesitance,

197
00:10:13,880 --> 00:10:17,240
Speaker 2: but I guess I hadn't until I read this. I

198
00:10:17,280 --> 00:10:19,960
Speaker 2: hadn't picked up on it, but I do. It resonates

199
00:10:20,080 --> 00:10:22,440
Speaker 2: with me now, and I'm like, yeah, that seems right.

200
00:10:22,520 --> 00:10:23,199
Speaker 2: That seems right.

201
00:10:23,520 --> 00:10:28,480
Speaker 7: I think being on like the excellineal community, that's kind

202
00:10:28,480 --> 00:10:29,880
Speaker 7: of one of those things that I don't think about.

203
00:10:29,920 --> 00:10:33,000
Speaker 7: But my partner, who was heavily, like even employed, into

204
00:10:33,120 --> 00:10:36,360
Speaker 7: the industry of AI, the difference between him and I

205
00:10:36,440 --> 00:10:40,960
Speaker 7: are so vast that it's not surprising the way that

206
00:10:41,040 --> 00:10:43,760
Speaker 7: he has integrated that into his systems, into his life,

207
00:10:43,920 --> 00:10:46,760
Speaker 7: but also understands how he has to be cautious and

208
00:10:46,760 --> 00:10:49,360
Speaker 7: how it is one of those things that has to.

209
00:10:49,320 --> 00:10:50,960
Speaker 5: Be operated with responsibility.

210
00:10:51,679 --> 00:10:53,840
Speaker 7: I think that there's that level of understanding, but he

211
00:10:53,960 --> 00:10:58,480
Speaker 7: is also very very excited by the possibilities of what

212
00:10:58,559 --> 00:11:02,200
Speaker 7: AI could do. So there's this level like in our

213
00:11:02,559 --> 00:11:06,320
Speaker 7: in my life specifically now our responses to AI as

214
00:11:06,440 --> 00:11:09,400
Speaker 7: me being a little more like dismissive and very much

215
00:11:09,440 --> 00:11:13,600
Speaker 7: more like negative about the whole thing as where he

216
00:11:13,800 --> 00:11:15,960
Speaker 7: is trying to play it to his cards.

217
00:11:16,360 --> 00:11:17,160
Speaker 5: That makes sense.

218
00:11:17,440 --> 00:11:20,320
Speaker 1: That makes so much sense, And it's funny because you

219
00:11:20,360 --> 00:11:23,679
Speaker 1: are basically living a microchasm of what the data suggests.

220
00:11:23,760 --> 00:11:25,319
Speaker 7: And that's kind of what it feels like when I'm

221
00:11:25,360 --> 00:11:27,120
Speaker 7: like thinking about it. I'm like, honestly, I feel like I'm

222
00:11:27,160 --> 00:11:30,880
Speaker 7: living as the stereotype that is this or like in

223
00:11:30,880 --> 00:11:31,679
Speaker 7: this conversation.

224
00:11:32,400 --> 00:11:34,120
Speaker 4: Absolutely so.

225
00:11:34,320 --> 00:11:37,440
Speaker 1: In this meta analysis, they found that across almost all

226
00:11:37,480 --> 00:11:39,920
Speaker 1: the data sets they looked at, women are about twenty

227
00:11:40,080 --> 00:11:42,480
Speaker 1: to twenty five percent less likely than men to be

228
00:11:42,600 --> 00:11:46,040
Speaker 1: using generative AI. The meta analysis shows women have twenty

229
00:11:46,040 --> 00:11:49,200
Speaker 1: two percent lower odds of using AI than men. The

230
00:11:49,240 --> 00:11:53,240
Speaker 1: gap appears across regions, industries, education levels, and occupations. So

231
00:11:53,400 --> 00:11:56,680
Speaker 1: you might hear that and think, okay, but maybe the

232
00:11:56,679 --> 00:11:59,719
Speaker 1: people who did this these surveys got it wrong, look

233
00:11:59,760 --> 00:12:04,240
Speaker 1: at the wrong information that gap, however, cannot be explained

234
00:12:04,280 --> 00:12:07,079
Speaker 1: by things like survey bias, because they also, as they said,

235
00:12:07,440 --> 00:12:10,240
Speaker 1: looked at who is downloading tools like claud and chat sheebut,

236
00:12:10,720 --> 00:12:14,320
Speaker 1: and that data showed that women compromise about forty two

237
00:12:14,360 --> 00:12:18,240
Speaker 1: percent of Chat sjetpt website users globally, twenty seven percent

238
00:12:18,280 --> 00:12:21,800
Speaker 1: of chat sepet mobile app users, and even lower shares

239
00:12:21,840 --> 00:12:25,320
Speaker 1: of women are using the tool claud formanthropic. So this

240
00:12:25,440 --> 00:12:28,320
Speaker 1: really suggests that the gender gap is at a pretty

241
00:12:28,559 --> 00:12:33,959
Speaker 1: massive global scale. A couple of notable findings. If women

242
00:12:34,000 --> 00:12:37,120
Speaker 1: are more senior or have more experience, it does narrow

243
00:12:37,240 --> 00:12:39,200
Speaker 1: that gender gap a little bit, but that it does

244
00:12:39,280 --> 00:12:44,040
Speaker 1: not completely eliminate that gap. Senior women in technical roles

245
00:12:44,040 --> 00:12:48,559
Speaker 1: sometimes match or exceed men's usage, but also junior women

246
00:12:48,640 --> 00:12:52,360
Speaker 1: and women in non technical roles show much larger gaps.

247
00:12:52,400 --> 00:12:54,480
Speaker 4: So there are a couple of interesting.

248
00:12:54,679 --> 00:12:59,040
Speaker 1: Notable outliers there, But in general, just like what's happening

249
00:12:59,040 --> 00:13:04,040
Speaker 1: in Samantha's health, men are even cautiously sort of gung

250
00:13:04,080 --> 00:13:08,240
Speaker 1: ho experimenting with it, thinking pretty optimistically about what AI

251
00:13:08,360 --> 00:13:09,200
Speaker 1: might be able to do.

252
00:13:09,559 --> 00:13:12,120
Speaker 4: Women are a little more skeptical.

253
00:13:12,400 --> 00:13:25,600
Speaker 6: We'll say, I wonder.

254
00:13:26,800 --> 00:13:29,559
Speaker 2: Just because We've been talking a lot on the show

255
00:13:30,920 --> 00:13:34,559
Speaker 2: about like the quote male loneliness epidemic, which women are

256
00:13:34,960 --> 00:13:37,880
Speaker 2: also very lonely. But that's what we're talking about. But

257
00:13:37,960 --> 00:13:39,960
Speaker 2: I see a lot of stories of men like forming

258
00:13:40,040 --> 00:13:46,040
Speaker 2: these bonds with AI and these relationships with AI, And

259
00:13:46,080 --> 00:13:48,600
Speaker 2: I have no idea, but I wonder if that's also

260
00:13:48,760 --> 00:13:51,240
Speaker 2: a part of it, because women usually have more of

261
00:13:51,240 --> 00:13:52,520
Speaker 2: a support group than men.

262
00:13:52,600 --> 00:13:57,040
Speaker 4: Yeah, yeah, I mean, I absolutely think that.

263
00:13:58,480 --> 00:14:01,720
Speaker 1: Some of the way that is talked about by people's

264
00:14:01,840 --> 00:14:05,559
Speaker 1: make it, some of the marketing language and decisions around it,

265
00:14:06,040 --> 00:14:10,839
Speaker 1: I think reinforce some attitudes that I think are are

266
00:14:11,160 --> 00:14:12,079
Speaker 1: kind of sexist.

267
00:14:12,240 --> 00:14:12,480
Speaker 4: Right.

268
00:14:12,520 --> 00:14:16,560
Speaker 1: I remember very clearly it was like a grand opening

269
00:14:16,600 --> 00:14:20,040
Speaker 1: grand closing when Sam Altman, the CEO of open Ai,

270
00:14:20,960 --> 00:14:26,600
Speaker 1: was introducing this new vocal chatbot for open AI's chatbot

271
00:14:26,720 --> 00:14:30,720
Speaker 1: named Sky, and he basically said that he wanted.

272
00:14:30,360 --> 00:14:32,800
Speaker 4: The experience to be like the experience.

273
00:14:32,280 --> 00:14:35,280
Speaker 1: That people had in the movie Her, which if folks seventeen,

274
00:14:35,320 --> 00:14:38,560
Speaker 1: that movie Looking Phoenix is basically having a romantic and

275
00:14:38,600 --> 00:14:42,360
Speaker 1: sexual relationship with an AI chatbot voice by Scarlett Johansson.

276
00:14:42,760 --> 00:14:45,520
Speaker 4: And I love that movie. It's one of my favorite movies.

277
00:14:45,560 --> 00:14:48,440
Speaker 1: But obviously, within the framing of the movie, it is

278
00:14:48,480 --> 00:14:52,080
Speaker 1: about a man using technology to satisfy his emotional and

279
00:14:52,120 --> 00:14:55,520
Speaker 1: sexual needs on top of his you know, admin that

280
00:14:56,320 --> 00:14:58,840
Speaker 1: needs that you might be using AI for already.

281
00:14:59,120 --> 00:15:01,040
Speaker 4: And so when the head of open AI gets on.

282
00:15:01,000 --> 00:15:03,640
Speaker 1: A stage and promises people that they will be able

283
00:15:03,640 --> 00:15:05,680
Speaker 1: to use technology the same way it is used in

284
00:15:05,680 --> 00:15:09,440
Speaker 1: this movie, well, I can understand why that might hit

285
00:15:09,480 --> 00:15:11,720
Speaker 1: women in a kind of a weird way, right, I

286
00:15:11,760 --> 00:15:14,040
Speaker 1: can understand why it's like, well, what are you really

287
00:15:14,080 --> 00:15:16,920
Speaker 1: saying about this technology that that is your frame of

288
00:15:17,040 --> 00:15:19,200
Speaker 1: reference for how you envision people using it?

289
00:15:19,240 --> 00:15:20,120
Speaker 4: Do you know what I mean?

290
00:15:21,080 --> 00:15:25,200
Speaker 2: Yes, yes, absolutely, And going back to one of the

291
00:15:25,240 --> 00:15:28,560
Speaker 2: other points he made at the beginning that comes up

292
00:15:28,560 --> 00:15:31,800
Speaker 2: in these arguments of why women aren't using AI as much,

293
00:15:32,800 --> 00:15:35,880
Speaker 2: it did kind of remind me of the argument around STEM,

294
00:15:36,200 --> 00:15:39,560
Speaker 2: Like a lot of STEM things where it's like, well

295
00:15:40,200 --> 00:15:43,160
Speaker 2: they just that's not how their brain works, or that

296
00:15:43,160 --> 00:15:46,560
Speaker 2: they don't pursue those fils because of whatever X y Z.

297
00:15:47,760 --> 00:15:50,160
Speaker 2: So that feels very similar to me.

298
00:15:50,800 --> 00:15:53,120
Speaker 1: Oh, yes, I'm so glad that you brought that up,

299
00:15:53,160 --> 00:15:56,520
Speaker 1: because when you look at how the other conversations around

300
00:15:56,560 --> 00:15:59,920
Speaker 1: gender gaps that we know exist in workplaces more broadly,

301
00:15:59,760 --> 00:16:03,080
Speaker 1: and redership roles more broadly, stem rolls more broadly. Often,

302
00:16:03,400 --> 00:16:06,040
Speaker 1: just as you said, the pushback is that women maybe

303
00:16:06,120 --> 00:16:09,920
Speaker 1: are picking less lucrative work or fields. If you're Larry Summers,

304
00:16:10,040 --> 00:16:13,040
Speaker 1: maybe it's the idea that women are just innately less

305
00:16:13,040 --> 00:16:15,640
Speaker 1: good at these fields or something. Now, never mind that

306
00:16:15,640 --> 00:16:18,200
Speaker 1: that really discounts the question of why fields that are

307
00:16:18,240 --> 00:16:23,040
Speaker 1: associated with women are historically devalued and underpaid. So even

308
00:16:23,080 --> 00:16:26,600
Speaker 1: if you know there was a time where computing was

309
00:16:26,640 --> 00:16:30,760
Speaker 1: seen as women's administrative or secretarial work. When women were

310
00:16:30,800 --> 00:16:33,520
Speaker 1: associated with it, it was lower paid admin work. When

311
00:16:33,600 --> 00:16:36,600
Speaker 1: men became associated with it, very clearly it became a

312
00:16:36,600 --> 00:16:38,960
Speaker 1: different kind of work. So that's clearly not the women's

313
00:16:39,000 --> 00:16:41,760
Speaker 1: fault for you know, for what roles they happen to

314
00:16:41,760 --> 00:16:45,240
Speaker 1: be choosing. But in any event, not only is that

315
00:16:45,360 --> 00:16:47,560
Speaker 1: not what's really going on and not telling the full

316
00:16:47,600 --> 00:16:50,360
Speaker 1: story of what's happening in these workplaces, nor is it

317
00:16:50,400 --> 00:16:52,880
Speaker 1: really what's going on or telling the full story of

318
00:16:53,400 --> 00:16:56,640
Speaker 1: AI adoption. That's not me saying this, that's according to

319
00:16:56,680 --> 00:17:00,880
Speaker 1: this meta analysis, as social scientists katiej puts it in

320
00:17:00,920 --> 00:17:03,960
Speaker 1: a piece called there's a reason why women aren't swooning

321
00:17:03,960 --> 00:17:06,320
Speaker 1: over AI like men are, which I love that title,

322
00:17:07,040 --> 00:17:10,920
Speaker 1: she writes. The off proposed explanation is that women understand

323
00:17:10,920 --> 00:17:14,119
Speaker 1: this new technology less, largely because they work in roles

324
00:17:14,160 --> 00:17:17,440
Speaker 1: with lower exposure to it. Women are, after all, still

325
00:17:17,440 --> 00:17:21,280
Speaker 1: outnumbered in STEM degrees and careers, including AI specific roles.

326
00:17:21,600 --> 00:17:24,400
Speaker 1: The same is true in AI leadership. Women hold fewer

327
00:17:24,440 --> 00:17:27,280
Speaker 1: than fourteen percent of senior executive positions in the industry.

328
00:17:28,080 --> 00:17:31,360
Speaker 1: But Harvard's study also found that the usage gap remains

329
00:17:31,480 --> 00:17:34,560
Speaker 1: even when women are explicitly given opportunities to learn and

330
00:17:34,760 --> 00:17:38,359
Speaker 1: use AI roles. So it's not just that women have

331
00:17:38,560 --> 00:17:41,879
Speaker 1: less training or less access to AI. Even when the

332
00:17:41,880 --> 00:17:44,880
Speaker 1: people who did this meta analysis equalized for that, they

333
00:17:44,920 --> 00:17:46,000
Speaker 1: still found that this.

334
00:17:46,040 --> 00:17:48,080
Speaker 4: AI gender gap persist.

335
00:17:48,680 --> 00:17:50,680
Speaker 1: They looked at a study out of Kenya where women

336
00:17:50,720 --> 00:17:54,440
Speaker 1: were exuplicitly offered training and access to AI, yet those

337
00:17:54,760 --> 00:17:59,080
Speaker 1: women still used chat GPT thirteen percent less than men.

338
00:17:59,200 --> 00:18:01,040
Speaker 4: So this seems to suggest.

339
00:18:00,680 --> 00:18:05,760
Speaker 1: These barriers aren't really about access or awareness about AI.

340
00:18:06,000 --> 00:18:07,240
Speaker 4: It's something else going on.

341
00:18:10,640 --> 00:18:12,160
Speaker 7: I feel like there's so many things that I'm trying

342
00:18:12,200 --> 00:18:15,040
Speaker 7: to work out when it comes to this conversation about

343
00:18:15,080 --> 00:18:18,679
Speaker 7: whys that seem like, well, obviously A equals B or

344
00:18:18,720 --> 00:18:21,200
Speaker 7: A is because of B. And one of the things

345
00:18:21,200 --> 00:18:24,120
Speaker 7: that I'm thinking about is like, maybe just what I'm

346
00:18:24,160 --> 00:18:27,240
Speaker 7: seeing when it comes to things like chat gpt, which

347
00:18:27,320 --> 00:18:30,240
Speaker 7: feels like just an advanced version of Google where we

348
00:18:30,880 --> 00:18:33,720
Speaker 7: made that that the thing in researching. But it gives

349
00:18:33,920 --> 00:18:37,760
Speaker 7: the mediocre man who has a lot more confidence than

350
00:18:37,800 --> 00:18:42,080
Speaker 7: women in general, even more confidence, I guess in a

351
00:18:42,119 --> 00:18:45,160
Speaker 7: way because they think they're getting that answer without having

352
00:18:45,240 --> 00:18:47,400
Speaker 7: to go to the people who actually found those answers.

353
00:18:47,960 --> 00:18:50,760
Speaker 7: In some weird ways, like there's so many thoughts like

354
00:18:50,800 --> 00:18:54,879
Speaker 7: maybe what are some of these reasons that we are

355
00:18:54,920 --> 00:18:56,879
Speaker 7: finding that men are more likely to use it?

356
00:18:56,880 --> 00:18:58,520
Speaker 5: And not for me? Like that just seems like a

357
00:18:58,640 --> 00:19:01,640
Speaker 5: rational I think that's true.

358
00:19:02,200 --> 00:19:04,600
Speaker 1: I think twice now, I've gotten into a back and

359
00:19:04,600 --> 00:19:07,080
Speaker 1: forth with somebody on Reddit, and I guess I don't

360
00:19:07,080 --> 00:19:09,359
Speaker 1: know their gender, but I can tell that they were

361
00:19:09,480 --> 00:19:11,760
Speaker 1: responding to me using chat GPT, and I'm like.

362
00:19:11,680 --> 00:19:12,240
Speaker 4: Oh, you didn't.

363
00:19:12,280 --> 00:19:12,800
Speaker 3: You couldn't.

364
00:19:13,080 --> 00:19:13,919
Speaker 4: It wasn't worth it.

365
00:19:14,040 --> 00:19:16,159
Speaker 1: You just wanted to keep the like argument going, but

366
00:19:16,240 --> 00:19:17,560
Speaker 1: it wasn't worth it for you to come up with

367
00:19:17,560 --> 00:19:18,080
Speaker 1: your own answer.

368
00:19:18,080 --> 00:19:20,320
Speaker 4: I'm gonna take the chat sepete really right.

369
00:19:20,480 --> 00:19:25,199
Speaker 1: So the meta analysis gave some answers as to what

370
00:19:25,280 --> 00:19:27,159
Speaker 1: might be going on here, what might explain some of

371
00:19:27,200 --> 00:19:27,760
Speaker 1: these gaps.

372
00:19:28,119 --> 00:19:29,720
Speaker 4: I'll tell y'all what they said, and.

373
00:19:29,640 --> 00:19:30,840
Speaker 1: Then I'll kind of give you my thoughts on it,

374
00:19:30,840 --> 00:19:32,520
Speaker 1: because I'm not some of these I'm not so sure about.

375
00:19:33,080 --> 00:19:36,440
Speaker 1: So the first, it's a lower familiarity and knowledge with AI.

376
00:19:36,600 --> 00:19:39,520
Speaker 1: So they said women are more likely to report not

377
00:19:39,720 --> 00:19:42,280
Speaker 1: knowing how generative AI works or how to use it.

378
00:19:43,240 --> 00:19:46,480
Speaker 1: I'm not totally sold on this. You know, it is

379
00:19:46,520 --> 00:19:48,560
Speaker 1: in their meta analysis, so I wanted to include it.

380
00:19:48,560 --> 00:19:52,280
Speaker 1: But this is just my take. AI is not difficult

381
00:19:52,280 --> 00:19:55,359
Speaker 1: to understand. It is not difficult to use, nor is

382
00:19:55,400 --> 00:19:58,680
Speaker 1: it terribly complex. There is nobody listening to the sound

383
00:19:58,720 --> 00:20:02,000
Speaker 1: of my voice who could not figure it out who

384
00:20:02,440 --> 00:20:05,000
Speaker 1: it is outside of the realm of their ability to comprehend.

385
00:20:05,040 --> 00:20:08,000
Speaker 1: It is not terribly complex. I think that what this

386
00:20:08,119 --> 00:20:11,919
Speaker 1: study is actually highlighting here is I think this tendency

387
00:20:12,040 --> 00:20:15,200
Speaker 1: for the people who make technology and talk about technology

388
00:20:15,480 --> 00:20:18,240
Speaker 1: to do so in a way where it seems like

389
00:20:18,280 --> 00:20:22,520
Speaker 1: the technology they're talking about is so complex and your

390
00:20:22,600 --> 00:20:26,280
Speaker 1: puny little lady brains could never figure out what we're

391
00:20:26,320 --> 00:20:28,879
Speaker 1: talking about here. Think about the way that people like

392
00:20:28,920 --> 00:20:32,639
Speaker 1: Elon Musk, Mark Zuckerberg, Sam Altman talk about technology. They

393
00:20:32,640 --> 00:20:34,199
Speaker 1: talk about it in this way where it gives it

394
00:20:34,240 --> 00:20:38,639
Speaker 1: this gravity toss, and I think is oftentimes unearned, because

395
00:20:39,080 --> 00:20:42,520
Speaker 1: you don't need any kind of special training to understand

396
00:20:42,560 --> 00:20:44,520
Speaker 1: the basics of AI. And so I think the fact

397
00:20:44,560 --> 00:20:48,720
Speaker 1: that tech leaders who, let's be real, often happen to

398
00:20:48,720 --> 00:20:51,240
Speaker 1: be not just men, but a specific kind of man,

399
00:20:51,320 --> 00:20:53,800
Speaker 1: not just white men, but a specific kind of white guy.

400
00:20:54,480 --> 00:20:57,200
Speaker 1: I think the fact that they are consistently talking about

401
00:20:57,200 --> 00:21:00,359
Speaker 1: this technology in a way that makes it like your

402
00:21:00,440 --> 00:21:02,600
Speaker 1: ordinary person could never understand it. I think it's sort

403
00:21:02,640 --> 00:21:04,920
Speaker 1: of at play here. And I think that is with intention.

404
00:21:05,000 --> 00:21:07,560
Speaker 1: I think they do this because they want people to

405
00:21:07,600 --> 00:21:10,600
Speaker 1: feel I couldn't possibly understand what they're talking about. How

406
00:21:10,600 --> 00:21:13,119
Speaker 1: could I hold these people accountable? How could I expect

407
00:21:13,119 --> 00:21:14,639
Speaker 1: things to be better for me? How could I even

408
00:21:14,800 --> 00:21:19,600
Speaker 1: you know, know, you know, oftentimes they're telling people, oh, no, no,

409
00:21:19,960 --> 00:21:20,840
Speaker 1: you want AI.

410
00:21:21,000 --> 00:21:21,800
Speaker 4: AI is good.

411
00:21:22,040 --> 00:21:25,280
Speaker 1: People will say no, I know what I like, and

412
00:21:25,320 --> 00:21:27,119
Speaker 1: I know what my experiences are, and I know that

413
00:21:27,160 --> 00:21:29,720
Speaker 1: I'm not enjoying, you know, opening my email and not

414
00:21:29,760 --> 00:21:32,080
Speaker 1: even be able to use it because AI tools are

415
00:21:32,119 --> 00:21:35,040
Speaker 1: being foisted on me non consensually, and they're like, no, no, no,

416
00:21:35,080 --> 00:21:39,320
Speaker 1: it's actually great for you. Right that if people truly

417
00:21:39,400 --> 00:21:42,720
Speaker 1: innately felt like they understood this technology, they would tech

418
00:21:42,800 --> 00:21:44,400
Speaker 1: leaders wouldn't be able to do that anymore.

419
00:21:44,760 --> 00:21:47,399
Speaker 7: Right, It really does feel like they're just inflating the

420
00:21:47,440 --> 00:21:50,000
Speaker 7: importance of it so that we'll believe it, and then

421
00:21:50,040 --> 00:21:51,879
Speaker 7: that when we are forced to use it because we

422
00:21:52,080 --> 00:21:55,119
Speaker 7: have no way of pushing back and there's actually no

423
00:21:55,200 --> 00:21:58,480
Speaker 7: regulation anymore on how it's being used, that will just

424
00:21:58,560 --> 00:22:01,440
Speaker 7: accept how great it is without actually knowing how great

425
00:22:01,480 --> 00:22:03,640
Speaker 7: it is in the midst of the conversation of knowing

426
00:22:03,680 --> 00:22:07,440
Speaker 7: how inaccurate it has been in the past. Also, how

427
00:22:07,440 --> 00:22:09,720
Speaker 7: hard is it when you literally do not have a

428
00:22:09,840 --> 00:22:12,840
Speaker 7: choice if you use what used to be Google and

429
00:22:12,880 --> 00:22:14,879
Speaker 7: what you thought would be something that would give you

430
00:22:14,920 --> 00:22:17,600
Speaker 7: trusted sources, and the top of it is literally AI

431
00:22:18,119 --> 00:22:20,920
Speaker 7: results that you did not ask for. So how hard

432
00:22:20,960 --> 00:22:23,200
Speaker 7: is it to use when you're not given a choice

433
00:22:23,440 --> 00:22:24,920
Speaker 7: to whether or not you have to use it?

434
00:22:25,480 --> 00:22:25,720
Speaker 3: Great?

435
00:22:25,760 --> 00:22:26,919
Speaker 4: And this is what I'm saying.

436
00:22:27,240 --> 00:22:29,040
Speaker 1: It goes back to what I was when I started

437
00:22:29,040 --> 00:22:30,959
Speaker 1: the conversation with of how we talk about it as

438
00:22:31,000 --> 00:22:33,560
Speaker 1: this inevitable technology and we all have to get on board.

439
00:22:33,880 --> 00:22:35,920
Speaker 1: Things that are good, You don't have to talk about

440
00:22:35,920 --> 00:22:38,240
Speaker 1: them that way. Things that people want, you don't. That's

441
00:22:38,280 --> 00:22:40,000
Speaker 1: not how that's not how you have to frame them.

442
00:22:40,240 --> 00:22:43,000
Speaker 1: So Sam Wiles said on threads, If AI is a

443
00:22:43,000 --> 00:22:45,800
Speaker 1: good thing, why are we constantly told it's inevitable.

444
00:22:45,880 --> 00:22:47,320
Speaker 4: You don't have to do that with good things.

445
00:22:47,480 --> 00:22:49,919
Speaker 1: No one's ever like iced coffee is inevitable, so you

446
00:22:49,920 --> 00:22:52,199
Speaker 1: may as well get used to it whole. This is

447
00:22:52,240 --> 00:22:55,000
Speaker 1: so correct, right, This is things that are good. You

448
00:22:55,040 --> 00:22:57,199
Speaker 1: do not have to continue over and over and over

449
00:22:57,240 --> 00:22:59,480
Speaker 1: again to tell people that they better get on board

450
00:22:59,480 --> 00:23:02,359
Speaker 1: with this good thing, like, No, that's not that's not

451
00:23:02,400 --> 00:23:04,960
Speaker 1: how we talk about things that people like and are good.

452
00:23:05,320 --> 00:23:05,560
Speaker 5: Right.

453
00:23:06,040 --> 00:23:08,840
Speaker 7: Also, it's not forced upon us by the government, like

454
00:23:08,880 --> 00:23:12,040
Speaker 7: we are not the ice coffee being told you have

455
00:23:12,119 --> 00:23:14,520
Speaker 7: to buy ice coffee when it gets to this temperature,

456
00:23:14,800 --> 00:23:17,159
Speaker 7: and no state can regulate whether or not you can

457
00:23:17,240 --> 00:23:18,920
Speaker 7: use it, even though it can be harmful for you.

458
00:23:19,080 --> 00:23:20,960
Speaker 7: And we have seen harm done by this.

459
00:23:21,520 --> 00:23:37,399
Speaker 1: Sorry exactly that. Another thing this meta analysis found was

460
00:23:37,640 --> 00:23:40,960
Speaker 1: lower confidence with it and sort of persistence or lower

461
00:23:41,040 --> 00:23:43,760
Speaker 1: levels of persistence, and so according to the meta analysis,

462
00:23:43,760 --> 00:23:47,280
Speaker 1: women report less confidence in prompting and are less likely

463
00:23:47,320 --> 00:23:51,240
Speaker 1: to persist after poor AI outputs. Men, however, are more

464
00:23:51,400 --> 00:23:55,560
Speaker 1: likely to experiment repeatedly. So I think this is one

465
00:23:55,560 --> 00:23:59,240
Speaker 1: of those things where essentially what is being said there

466
00:23:59,320 --> 00:24:02,359
Speaker 1: is women have tried this technology and I've identified that

467
00:24:02,400 --> 00:24:06,000
Speaker 1: it can be janky and have reasonably concluded that maybe

468
00:24:06,000 --> 00:24:07,600
Speaker 1: it's not worked the pain in the butt to keep

469
00:24:07,640 --> 00:24:09,879
Speaker 1: trying with it, and men are like, I'm going to.

470
00:24:09,960 --> 00:24:14,719
Speaker 4: Keep on trying. I think that it's like framing women

471
00:24:16,240 --> 00:24:17,960
Speaker 4: taking like gleaning.

472
00:24:17,520 --> 00:24:21,040
Speaker 1: From their experiences of AI being full of vies hallucinations

473
00:24:21,080 --> 00:24:23,119
Speaker 1: actually being more work for them, which has been my

474
00:24:23,200 --> 00:24:25,200
Speaker 1: experience when I when I've tried to use AI a

475
00:24:25,200 --> 00:24:28,000
Speaker 1: lot of times, I'm like, Okay, well, I could have

476
00:24:28,040 --> 00:24:29,640
Speaker 1: done this in the time, in the time it took

477
00:24:29,680 --> 00:24:31,639
Speaker 1: me to get to finally get AI to do, I

478
00:24:31,680 --> 00:24:32,800
Speaker 1: could have done this myself and done.

479
00:24:32,720 --> 00:24:33,280
Speaker 4: A better job at it.

480
00:24:33,280 --> 00:24:34,560
Speaker 1: So I should it is on that to begin with

481
00:24:34,680 --> 00:24:37,560
Speaker 1: wasted time, Like, I don't like how this is framed

482
00:24:37,600 --> 00:24:41,480
Speaker 1: as women being risk averse, when in reality, I think

483
00:24:41,480 --> 00:24:45,359
Speaker 1: it's more like women making reasonable deductions about the time

484
00:24:45,440 --> 00:24:47,680
Speaker 1: they want to spend on something that has proved itself

485
00:24:47,680 --> 00:24:50,639
Speaker 1: to be ineffective, and like, that's that's not risk averse.

486
00:24:50,680 --> 00:24:51,440
Speaker 4: That's something else.

487
00:24:51,920 --> 00:24:55,280
Speaker 7: That's being tired of arguing with an inanimate object, which

488
00:24:55,320 --> 00:24:57,560
Speaker 7: is what I've had to do when I'm yelling back

489
00:24:57,840 --> 00:25:00,160
Speaker 7: at Google. I don't want you SIRIU but it any

490
00:25:00,200 --> 00:25:01,960
Speaker 7: of those days because they did not understand what I

491
00:25:02,000 --> 00:25:03,879
Speaker 7: was saying and I just stare at it.

492
00:25:03,920 --> 00:25:05,399
Speaker 5: And like, why am I doing this with you?

493
00:25:05,400 --> 00:25:09,520
Speaker 7: You're and then being told also they remember when you're

494
00:25:09,880 --> 00:25:10,840
Speaker 7: them Listen.

495
00:25:10,960 --> 00:25:13,199
Speaker 1: I told this story on my own podcast. There are

496
00:25:13,200 --> 00:25:16,160
Speaker 1: our goals on the Internet. After I did the episode

497
00:25:16,200 --> 00:25:19,240
Speaker 1: with you all about Larry Summers, who was formerly on

498
00:25:19,320 --> 00:25:21,680
Speaker 1: the board of Open Ai, stepping down because of those

499
00:25:21,720 --> 00:25:25,520
Speaker 1: emails that were revealed between him and convicted child sex

500
00:25:25,560 --> 00:25:29,000
Speaker 1: criminal Jeffrey Epstein, I was trying to get. I was like, oh,

501
00:25:29,040 --> 00:25:31,280
Speaker 1: maybe chatcheepet can help me come up with like metadata

502
00:25:31,320 --> 00:25:33,199
Speaker 1: and all of that, so I put in what I

503
00:25:33,240 --> 00:25:36,760
Speaker 1: had to. Chattebaut and chattypet says this is not correct.

504
00:25:37,440 --> 00:25:41,280
Speaker 1: There is no link between Larry Summers and Jeffrey Epstein

505
00:25:41,320 --> 00:25:44,960
Speaker 1: and I was like, oh, really, we don't have emails

506
00:25:45,480 --> 00:25:47,920
Speaker 1: linking the two and they're like and then chattybut is like, Okay,

507
00:25:47,960 --> 00:25:53,439
Speaker 1: well yeah, maybe there's emails, but you're suggesting that something

508
00:25:53,520 --> 00:25:55,679
Speaker 1: illegal happened if you don't know anything about that. And

509
00:25:55,680 --> 00:25:58,560
Speaker 1: I was like, I said, the emails were creepy. How

510
00:25:58,560 --> 00:26:01,560
Speaker 1: would you define emails where a grown married man is

511
00:26:01,600 --> 00:26:04,399
Speaker 1: going to a convicted pedophile for advice on how we

512
00:26:04,400 --> 00:26:05,800
Speaker 1: can have text with his mentee.

513
00:26:06,480 --> 00:26:09,880
Speaker 4: You wouldn't define those that's creepy. We got into a huge.

514
00:26:09,680 --> 00:26:13,639
Speaker 1: Argument to the point where I asked it. I was like,

515
00:26:14,119 --> 00:26:18,919
Speaker 1: cut the crap. Are you being so cagey because Larry

516
00:26:18,920 --> 00:26:20,680
Speaker 1: Summers used to be on the board of open Ai

517
00:26:20,800 --> 00:26:26,040
Speaker 1: that makes you chatchypt. Chatchapt says, let's be clear, Larry

518
00:26:26,080 --> 00:26:28,680
Speaker 1: Summers was never on the board of open Ai. Larry

519
00:26:28,720 --> 00:26:30,359
Speaker 1: Summers has been on the board of open Ai since

520
00:26:30,359 --> 00:26:32,720
Speaker 1: like twenty twenty three, so it's been kind of a while.

521
00:26:32,840 --> 00:26:36,960
Speaker 1: So it's like, not only was that infuriating, think the

522
00:26:37,040 --> 00:26:39,960
Speaker 1: work it took for me to to like take stop

523
00:26:40,040 --> 00:26:43,119
Speaker 1: my work day and have an argument, a real no

524
00:26:43,760 --> 00:26:47,240
Speaker 1: argument with chatchpt. This is the technology they're saying, is

525
00:26:47,240 --> 00:26:49,040
Speaker 1: the lgitin of our king economy right now?

526
00:26:49,080 --> 00:26:49,679
Speaker 4: I don't think so.

527
00:26:50,440 --> 00:26:53,400
Speaker 7: You literally spent emotions with the computer who is just

528
00:26:53,400 --> 00:26:55,720
Speaker 7: trying to protect their reputation, which.

529
00:26:55,560 --> 00:26:57,240
Speaker 5: Is weird because they're not a human.

530
00:26:57,600 --> 00:27:00,960
Speaker 1: This is what I says, Like, I'll say you something.

531
00:27:01,040 --> 00:27:03,680
Speaker 1: I have not asked chat GPT a single thing since then.

532
00:27:03,880 --> 00:27:05,840
Speaker 1: That was a little experiment to see if it was

533
00:27:05,880 --> 00:27:09,000
Speaker 1: gonna work. It went so astronomically left and I'm like, okay,

534
00:27:09,000 --> 00:27:10,880
Speaker 1: well that's a waste of my time and I got

535
00:27:10,880 --> 00:27:11,760
Speaker 1: my blood pressure up.

536
00:27:11,720 --> 00:27:13,200
Speaker 4: So it's a double bad.

537
00:27:13,400 --> 00:27:15,000
Speaker 5: I don't I don't need this from you too.

538
00:27:15,680 --> 00:27:17,439
Speaker 1: If I'm gonna argue with someone, it's gonna be a

539
00:27:17,440 --> 00:27:18,720
Speaker 1: flesh and blood human in my life.

540
00:27:18,760 --> 00:27:20,040
Speaker 4: That's sucking chat GPT.

541
00:27:21,200 --> 00:27:23,000
Speaker 2: Well, I think that's also a good point of like

542
00:27:23,200 --> 00:27:26,200
Speaker 2: we've we've talked about this before several times. Who makes

543
00:27:26,200 --> 00:27:28,480
Speaker 2: this technology and the fear of like the biases and

544
00:27:28,520 --> 00:27:31,119
Speaker 2: all that stuff in there, And I know, like Elon

545
00:27:31,240 --> 00:27:34,760
Speaker 2: Musk and Gronkipedia has had a lot of things come

546
00:27:34,840 --> 00:27:37,960
Speaker 2: up with what it is saying that are just flat

547
00:27:37,960 --> 00:27:40,840
Speaker 2: out ridiculous, and like these flight out lies, and so

548
00:27:40,920 --> 00:27:46,280
Speaker 2: I do think that that is a concern, and I

549
00:27:46,680 --> 00:27:53,119
Speaker 2: would I would pose it that that influences like I

550
00:27:53,560 --> 00:27:56,040
Speaker 2: would guess that women are not getting the answers they

551
00:27:56,040 --> 00:27:58,880
Speaker 2: want more often than men are not getting the answers

552
00:27:58,920 --> 00:27:59,880
Speaker 2: that they do want.

553
00:28:00,440 --> 00:28:04,639
Speaker 1: Yes, And something to remember about AI, if there's one thing,

554
00:28:05,040 --> 00:28:06,920
Speaker 1: even if you're thinking, I'm not a teche, I'm not

555
00:28:06,960 --> 00:28:10,000
Speaker 1: a computer person, this is not my forte. If there's

556
00:28:10,080 --> 00:28:12,919
Speaker 1: one thing to remember is that AI. It's easy to

557
00:28:12,920 --> 00:28:16,880
Speaker 1: think of it as robot computer brains that know everything's

558
00:28:17,160 --> 00:28:17,680
Speaker 1: all knowing.

559
00:28:17,760 --> 00:28:18,119
Speaker 4: It's not.

560
00:28:18,240 --> 00:28:20,840
Speaker 1: It's trained and built by all of us humans, and

561
00:28:20,880 --> 00:28:24,160
Speaker 1: so the same kind of biases that we know humans have.

562
00:28:24,680 --> 00:28:27,119
Speaker 4: AI is just trained on all of that.

563
00:28:27,400 --> 00:28:30,040
Speaker 1: To replicate it, right, And so keep that in mind

564
00:28:30,080 --> 00:28:32,640
Speaker 1: when you're thinking about AI. And we have so much

565
00:28:32,680 --> 00:28:35,480
Speaker 1: documentation about the ways that AI is biased when it

566
00:28:35,520 --> 00:28:38,240
Speaker 1: comes to reflecting women in workplaces. It's more likely to

567
00:28:38,280 --> 00:28:41,360
Speaker 1: reflect women as younger than they actually are. And so

568
00:28:41,440 --> 00:28:45,240
Speaker 1: if if the majority of let's say, for instance, if

569
00:28:45,240 --> 00:28:49,160
Speaker 1: the majority of women who are engineers are over forty,

570
00:28:49,400 --> 00:28:52,280
Speaker 1: if that's just like a documented fact, AI will will

571
00:28:52,320 --> 00:28:55,000
Speaker 1: tell people, oh, they're actually younger than they are, because

572
00:28:55,360 --> 00:28:59,080
Speaker 1: it's reflecting a bias that against more mature women. You know,

573
00:29:00,200 --> 00:29:04,479
Speaker 1: been studies that when women ask chat gpt for negotiation advice,

574
00:29:04,520 --> 00:29:07,240
Speaker 1: it regularly tells them to ask for less money. Right,

575
00:29:07,320 --> 00:29:10,760
Speaker 1: so all of the biases that exist in society, AI

576
00:29:10,800 --> 00:29:13,080
Speaker 1: is simply replicating those because it was trained on all

577
00:29:13,080 --> 00:29:15,680
Speaker 1: of us. And so really keep that in mind, Like

578
00:29:15,960 --> 00:29:19,760
Speaker 1: I could understand why women are not keen about going

579
00:29:19,800 --> 00:29:23,600
Speaker 1: to this technology to help them understand the world around

580
00:29:23,640 --> 00:29:28,720
Speaker 1: them in their workplace when we know it has these biases, right, I.

581
00:29:28,760 --> 00:29:30,880
Speaker 7: Mean it comes to the other point and that we

582
00:29:31,000 --> 00:29:35,160
Speaker 7: already understand and know that the data research are based

583
00:29:35,200 --> 00:29:38,400
Speaker 7: on men and there's not enough based on women or

584
00:29:38,520 --> 00:29:41,240
Speaker 7: those marginalized communities at all. Like we just got to

585
00:29:41,280 --> 00:29:43,760
Speaker 7: the point of realizing, hey, we need more, So more

586
00:29:43,800 --> 00:29:46,560
Speaker 7: people have been doing it, but all of that information

587
00:29:46,680 --> 00:29:50,280
Speaker 7: is not there to provide a background for chat GPT

588
00:29:50,440 --> 00:29:52,880
Speaker 7: to pick up or whatever AI company. And that's really

589
00:29:52,920 --> 00:29:56,200
Speaker 7: concerning that we're acting like the information we know now

590
00:29:56,240 --> 00:29:57,760
Speaker 7: is all we need to know when we know that's

591
00:29:57,760 --> 00:29:59,000
Speaker 7: not true exactly.

592
00:29:59,360 --> 00:30:00,760
Speaker 4: That is such a good way to put it.

593
00:30:02,080 --> 00:30:06,440
Speaker 1: And another concern that women report about using AI, why

594
00:30:06,480 --> 00:30:08,120
Speaker 1: they're kind of skeptical about it is that women have

595
00:30:08,160 --> 00:30:11,560
Speaker 1: stronger what the meta analysis called kind of ethical concern,

596
00:30:11,680 --> 00:30:15,320
Speaker 1: So women are more likely to view using AI, especially

597
00:30:15,360 --> 00:30:19,120
Speaker 1: in professional settings or education settings, as unethical or cheating.

598
00:30:19,560 --> 00:30:23,120
Speaker 1: So I don't disagree with this. This totally makes sense

599
00:30:23,160 --> 00:30:27,280
Speaker 1: to me anecdotally. However, I think it's more complex than that,

600
00:30:27,480 --> 00:30:30,480
Speaker 1: because I don't think it's just that women see AI

601
00:30:30,600 --> 00:30:31,120
Speaker 1: is cheating.

602
00:30:31,880 --> 00:30:33,640
Speaker 4: It is that, But I also think that.

603
00:30:33,600 --> 00:30:36,560
Speaker 1: In workplaces and in educational settings, women are more likely

604
00:30:36,600 --> 00:30:39,720
Speaker 1: to be scrutinized that our male counterparts right, and that's

605
00:30:39,840 --> 00:30:41,960
Speaker 1: just the way. Then it is like women aren't wrong

606
00:30:42,040 --> 00:30:44,200
Speaker 1: for being like, oh, I feel like everybody's checking my

607
00:30:44,360 --> 00:30:47,080
Speaker 1: work extra hard and harder than they're checking Joe's work

608
00:30:47,160 --> 00:30:50,640
Speaker 1: or whatever. The BBC spoke to psychologist Lee Chambers, who

609
00:30:50,680 --> 00:30:53,200
Speaker 1: said women are more likely to be accused of not

610
00:30:53,280 --> 00:30:56,040
Speaker 1: being competent, so they have to emphasize their credentials more

611
00:30:56,160 --> 00:30:59,040
Speaker 1: demonstrate their subject matter expertise in a particular field. There

612
00:30:59,080 --> 00:31:01,400
Speaker 1: could be this feeling that people know that you, as

613
00:31:01,400 --> 00:31:03,960
Speaker 1: a woman, use AI. It is suggesting that you might

614
00:31:04,000 --> 00:31:06,440
Speaker 1: not be as qualified as you actually are. Women are

615
00:31:06,480 --> 00:31:08,880
Speaker 1: already discredited and have their ideas taken by men and

616
00:31:08,920 --> 00:31:11,720
Speaker 1: passed off as their own. So having people knowing that

617
00:31:11,760 --> 00:31:14,280
Speaker 1: you use AI might also play into this narrative that

618
00:31:14,320 --> 00:31:17,080
Speaker 1: you're not qualified enough. It's just another thing de basing

619
00:31:17,120 --> 00:31:19,320
Speaker 1: your skills, your competence, and your value.

620
00:31:19,480 --> 00:31:21,560
Speaker 4: Now that makes total sense to me.

621
00:31:21,640 --> 00:31:27,440
Speaker 1: And I am in professional spaces of all kinds, and

622
00:31:27,560 --> 00:31:30,800
Speaker 1: in some of these professional spaces, I've been around very

623
00:31:30,840 --> 00:31:35,400
Speaker 1: accomplished men in tech who will not blink an eye

624
00:31:35,600 --> 00:31:38,040
Speaker 1: at submitting like an op ed or something, or a

625
00:31:38,040 --> 00:31:40,120
Speaker 1: piece of writing that was clearly written by AI.

626
00:31:40,440 --> 00:31:44,560
Speaker 4: And it's just like and I would never like that, Like.

627
00:31:44,960 --> 00:31:49,400
Speaker 1: The way I would be so embarrassed to have anybody

628
00:31:49,680 --> 00:31:52,280
Speaker 1: be like, oh did you submit this? And it was generated?

629
00:31:52,320 --> 00:31:55,000
Speaker 1: I can tell by whatever whatever, I would crawl into

630
00:31:55,000 --> 00:31:57,040
Speaker 1: a hole and die. I would be so embarrassed. And

631
00:31:57,120 --> 00:31:59,200
Speaker 1: the way that I can confirm that there are a

632
00:31:59,240 --> 00:32:04,040
Speaker 1: lot of men in high up positions who do not

633
00:32:04,200 --> 00:32:06,480
Speaker 1: feel that level of scrutiny. And it just goes back

634
00:32:06,520 --> 00:32:09,680
Speaker 1: to like marginalized people in these settings are We're so

635
00:32:10,040 --> 00:32:14,080
Speaker 1: used to carrying that the extra eyes, you know, extra

636
00:32:14,120 --> 00:32:15,520
Speaker 1: scrutiny of why.

637
00:32:15,320 --> 00:32:18,480
Speaker 4: We're here at all, our work, our value, our competence.

638
00:32:18,760 --> 00:32:21,920
Speaker 1: Why would you want to inject another reason to give

639
00:32:21,960 --> 00:32:23,240
Speaker 1: somebody to continue to.

640
00:32:23,280 --> 00:32:26,040
Speaker 4: Do that when you're already facing that in multiple levels.

641
00:32:27,120 --> 00:32:30,200
Speaker 2: I have to bring this back to fan fiction, you know,

642
00:32:30,360 --> 00:32:35,720
Speaker 2: I do. So there's there's a big, like concern in

643
00:32:35,760 --> 00:32:40,720
Speaker 2: the fan fiction space of like using AI, and it's

644
00:32:40,720 --> 00:32:45,240
Speaker 2: become such a thing. And as discussed, a lot of

645
00:32:45,360 --> 00:32:48,560
Speaker 2: fan fiction is mostly written by women or non binary people.

646
00:32:49,000 --> 00:32:51,000
Speaker 2: People will go out of their way to say, like,

647
00:32:51,320 --> 00:32:54,040
Speaker 2: in the tags, I use M dashes, but it's not AI.

648
00:32:54,360 --> 00:32:56,160
Speaker 2: They go way out of their way to be like

649
00:32:56,360 --> 00:33:00,760
Speaker 2: I sawyar, it's not AI. And it's just it's so

650
00:33:00,920 --> 00:33:03,480
Speaker 2: clear to me that people don't want you to think like, oh,

651
00:33:03,520 --> 00:33:05,320
Speaker 2: this is not worth like I didn't even put in

652
00:33:05,360 --> 00:33:09,440
Speaker 2: the time to do this, Whereas yeah, I do think

653
00:33:10,840 --> 00:33:14,400
Speaker 2: I don't think men would see it necessarily in that way.

654
00:33:14,440 --> 00:33:16,040
Speaker 2: They would see it as like, oh, yeah, it just

655
00:33:16,080 --> 00:33:18,320
Speaker 2: saved me some time. But meanwhile, and now I know

656
00:33:18,360 --> 00:33:20,800
Speaker 2: that AI uses a lot of M dashes because you

657
00:33:20,880 --> 00:33:21,600
Speaker 2: can't fiction.

658
00:33:22,640 --> 00:33:26,840
Speaker 1: Yeah, that doesn't surprise me at all, because I think

659
00:33:27,200 --> 00:33:31,040
Speaker 1: the fan fiction community is definitely a community that values

660
00:33:31,520 --> 00:33:33,880
Speaker 1: human creativity, Like you wouldn't be in the fan fiction

661
00:33:33,920 --> 00:33:36,120
Speaker 1: community if you didn't value human creativity, and it's not,

662
00:33:36,160 --> 00:33:37,880
Speaker 1: it doesn't surprise me that that's a community that it's

663
00:33:37,920 --> 00:33:39,240
Speaker 1: also full of women.

664
00:33:40,640 --> 00:33:43,880
Speaker 2: Yes, yes, And the people always put this thing to

665
00:33:43,960 --> 00:33:45,840
Speaker 2: at the top where they're like, please don't feed my

666
00:33:45,880 --> 00:33:47,600
Speaker 2: work to AI, and I'm like, that's just not going

667
00:33:47,680 --> 00:33:49,680
Speaker 2: to help you, but I appreciate the effort.

668
00:33:50,880 --> 00:33:51,000
Speaker 8: Well.

669
00:33:51,040 --> 00:33:53,720
Speaker 7: Last, the other part to that is when you start

670
00:33:53,720 --> 00:33:57,080
Speaker 7: thinking about using CHAGBT or any of the situations, you

671
00:33:57,120 --> 00:33:59,360
Speaker 7: don't know who you're taking from, and there is this

672
00:33:59,480 --> 00:34:01,800
Speaker 7: level of built for me on the end of like

673
00:34:01,880 --> 00:34:04,800
Speaker 7: not being the creative. But if I'm researching things and

674
00:34:04,960 --> 00:34:08,040
Speaker 7: I'm just basing it on one small bit of information

675
00:34:08,120 --> 00:34:11,480
Speaker 7: that's you know, from CHATGBT or Gemini or whatever whatnot,

676
00:34:11,719 --> 00:34:14,320
Speaker 7: which is constant, and I can't stand it. It feels

677
00:34:14,440 --> 00:34:18,480
Speaker 7: unethical in that manner of like we already know artists

678
00:34:18,600 --> 00:34:22,080
Speaker 7: are really really on edge because their work is feeling

679
00:34:22,080 --> 00:34:25,840
Speaker 7: constantly solen. Creators who are writers who are part of

680
00:34:25,840 --> 00:34:28,359
Speaker 7: this researcher who are doing lots of work to get

681
00:34:28,360 --> 00:34:31,080
Speaker 7: their educations and to be in this field, like using

682
00:34:31,080 --> 00:34:34,799
Speaker 7: that information and like oftentimes it's not cited, it just

683
00:34:34,840 --> 00:34:36,560
Speaker 7: becomes one big blob of information.

684
00:34:36,640 --> 00:34:39,799
Speaker 5: It feels so gross to see that play out.

685
00:34:39,960 --> 00:34:41,840
Speaker 4: Yeah, said such a good point.

686
00:34:41,880 --> 00:34:45,400
Speaker 1: There was this like micro controversy online a couple of

687
00:34:45,440 --> 00:34:49,080
Speaker 1: weeks ago about this literary festival, the Black Romance Literary Festival,

688
00:34:49,120 --> 00:34:52,360
Speaker 1: where the question was, do the people who run this festival,

689
00:34:52,400 --> 00:34:56,400
Speaker 1: the organizers, will they accept people who use AI in

690
00:34:56,440 --> 00:34:59,040
Speaker 1: their work at this organization? Will those people be allowed

691
00:34:59,040 --> 00:35:00,920
Speaker 1: to be included in panels? And so it sounded that

692
00:35:00,920 --> 00:35:03,920
Speaker 1: they were really talking about illustrations like cover art, and

693
00:35:03,960 --> 00:35:06,640
Speaker 1: so the organization gave what, to a lot of people

694
00:35:06,680 --> 00:35:10,560
Speaker 1: in the community felt like not a great answer, just

695
00:35:10,640 --> 00:35:12,799
Speaker 1: sort of what they wrote the people. The organizer said,

696
00:35:12,800 --> 00:35:14,920
Speaker 1: we don't use AI in our work, but we're not

697
00:35:14,960 --> 00:35:18,200
Speaker 1: turning away people who do use AI. We want to

698
00:35:18,600 --> 00:35:21,600
Speaker 1: provide resources and education to those folks, but we don't

699
00:35:21,600 --> 00:35:22,600
Speaker 1: want to disclude them.

700
00:35:22,760 --> 00:35:24,080
Speaker 4: One of my listeners made.

701
00:35:23,960 --> 00:35:27,000
Speaker 1: A very good point that it seemed like that was

702
00:35:27,040 --> 00:35:30,360
Speaker 1: a kind of attempt, a harm reduction kind of position.

703
00:35:30,480 --> 00:35:31,960
Speaker 1: But maybe they didn't say it like that, and they

704
00:35:32,040 --> 00:35:33,440
Speaker 1: perhaps they shouldn't have clarified that.

705
00:35:33,560 --> 00:35:36,160
Speaker 4: But you know, I think.

706
00:35:36,000 --> 00:35:42,560
Speaker 1: At a time when artists, particularly marginalized artists, are really

707
00:35:42,600 --> 00:35:45,080
Speaker 1: having a hard Like anybody who tries to make make

708
00:35:45,160 --> 00:35:47,319
Speaker 1: money from making a thing is having a hard time

709
00:35:47,360 --> 00:35:51,920
Speaker 1: of it right now. And I think when, as you said, Sam,

710
00:35:52,000 --> 00:35:55,560
Speaker 1: we know when you ask AI to generate an image,

711
00:35:55,960 --> 00:35:58,520
Speaker 1: it's not generating a new piece.

712
00:35:58,560 --> 00:36:00,879
Speaker 4: It's just it's just taking from what out there, right.

713
00:36:01,160 --> 00:36:04,120
Speaker 1: I remember there was a time where people were using

714
00:36:04,840 --> 00:36:08,000
Speaker 1: an AI tool to make those futuristic selfies. Half of

715
00:36:08,040 --> 00:36:12,279
Speaker 1: them had watermarks or logos in them somewhere. That's how

716
00:36:12,320 --> 00:36:15,319
Speaker 1: much they were just taking from other artists copyrighted work.

717
00:36:15,560 --> 00:36:19,640
Speaker 1: So I don't think that the creatives who are saying, hey,

718
00:36:19,680 --> 00:36:21,440
Speaker 1: we need this to be clarified, Hey, we need to

719
00:36:21,440 --> 00:36:23,239
Speaker 1: put a line on the stand of how what we're

720
00:36:23,239 --> 00:36:26,760
Speaker 1: going to do when it comes to taking other artists

721
00:36:26,880 --> 00:36:29,000
Speaker 1: work in this way, I don't think they're wrong for

722
00:36:29,120 --> 00:36:32,200
Speaker 1: being anxious about that and expecting and deserving some answers

723
00:36:32,200 --> 00:36:32,560
Speaker 1: around it.

724
00:36:32,600 --> 00:36:46,240
Speaker 3: Frankly so, when it comes to kind.

725
00:36:46,040 --> 00:36:49,080
Speaker 1: Of the reasons why women might not be adopting AI

726
00:36:49,239 --> 00:36:52,560
Speaker 1: like their male counterparts, I did find this meta analysis

727
00:36:52,560 --> 00:36:55,239
Speaker 1: pretty useful and under like giving us the scope of

728
00:36:55,280 --> 00:36:57,520
Speaker 1: the issue, giving us the scope of the gender gap,

729
00:36:57,600 --> 00:37:00,680
Speaker 1: and some reasons as to why that might be. However,

730
00:37:00,800 --> 00:37:03,000
Speaker 1: I just don't think that some of the answers really

731
00:37:03,040 --> 00:37:05,560
Speaker 1: spoke to the nuance that I see in that experience

732
00:37:05,560 --> 00:37:07,280
Speaker 1: as a woman that's a little bit skeptical of AI.

733
00:37:07,640 --> 00:37:10,160
Speaker 1: So I found this piece in the Stanford Social Innovation

734
00:37:10,280 --> 00:37:12,960
Speaker 1: Review by Mara Bolis, who is the founder of First

735
00:37:12,960 --> 00:37:15,440
Speaker 1: Prompt and a fellow at the Harvard Kennedy School, called

736
00:37:15,719 --> 00:37:18,920
Speaker 1: the AI gender Gap Paradox, And I think that this

737
00:37:19,080 --> 00:37:21,920
Speaker 1: piece does a really good job of getting at some

738
00:37:21,920 --> 00:37:25,040
Speaker 1: of the nuance that's not just women aren't smart enough

739
00:37:25,040 --> 00:37:26,480
Speaker 1: to get AI or whatever right.

740
00:37:26,520 --> 00:37:28,960
Speaker 4: And so one bit that she pointed out is that

741
00:37:29,320 --> 00:37:32,000
Speaker 4: women are already doing a lot of work.

742
00:37:32,239 --> 00:37:34,960
Speaker 1: She writes, more than half of professionals say that learning

743
00:37:35,000 --> 00:37:37,800
Speaker 1: AI feels like a second job, which for most women

744
00:37:37,960 --> 00:37:41,120
Speaker 1: is actually a third job when you consider the continued

745
00:37:41,120 --> 00:37:44,600
Speaker 1: disparities in time spent on childcare and housework.

746
00:37:45,280 --> 00:37:46,239
Speaker 4: And so that I.

747
00:37:46,200 --> 00:37:49,879
Speaker 1: Thought was such a good point, right, that doing your

748
00:37:49,920 --> 00:37:53,279
Speaker 1: current job, or your current educational workload or whatever is

749
00:37:53,320 --> 00:37:55,200
Speaker 1: already a lot of work. If you're a woman, you're

750
00:37:55,280 --> 00:37:59,160
Speaker 1: probably taking on more of the labor at home, or

751
00:37:59,200 --> 00:38:01,719
Speaker 1: of the emotionally more of the care work all of that,

752
00:38:02,200 --> 00:38:04,360
Speaker 1: and on top of it, now people are telling you

753
00:38:04,360 --> 00:38:05,880
Speaker 1: that if you don't take the time to learn how

754
00:38:05,880 --> 00:38:08,879
Speaker 1: to integrate AI into that work, you don't really care

755
00:38:08,880 --> 00:38:12,120
Speaker 1: about your career and you're holding yourself back. I just, yeah,

756
00:38:12,160 --> 00:38:13,840
Speaker 1: it's like that, Like that has to be part of

757
00:38:13,840 --> 00:38:16,160
Speaker 1: the conversation if we're going to actually address the gender

758
00:38:16,200 --> 00:38:17,360
Speaker 1: gap in AI usage.

759
00:38:17,600 --> 00:38:17,799
Speaker 2: Yes.

760
00:38:19,440 --> 00:38:21,880
Speaker 1: Yeah, And this piece, I think also just does a

761
00:38:21,880 --> 00:38:27,319
Speaker 1: great job of reframing fear or knowledge gaps into what

762
00:38:27,360 --> 00:38:30,200
Speaker 1: I suspect they actually are, which is the ability to

763
00:38:30,480 --> 00:38:34,360
Speaker 1: competently analyze risk. Women aren't looking at AI and saying

764
00:38:34,440 --> 00:38:36,520
Speaker 1: I'm afraid of it or like I can never learn it.

765
00:38:36,760 --> 00:38:38,279
Speaker 4: I think what they're actually saying.

766
00:38:38,120 --> 00:38:42,120
Speaker 1: Is I have analyzed the risk that using it might

767
00:38:42,200 --> 00:38:45,280
Speaker 1: bring into my work, and I've made a decision based

768
00:38:45,320 --> 00:38:48,239
Speaker 1: on that that'na, I'm not gonna be using.

769
00:38:47,960 --> 00:38:48,960
Speaker 4: It like that.

770
00:38:48,960 --> 00:38:51,960
Speaker 1: That piece in the Stanford Innovation Review looked at studies

771
00:38:52,000 --> 00:38:55,480
Speaker 1: from Deloitte and Pew. Both of them showed that women

772
00:38:55,640 --> 00:38:59,800
Speaker 1: predict AI will bring less benefit and do more harm

773
00:39:00,000 --> 00:39:03,799
Speaker 1: cross personal, professional and public life. Men, however, tend to

774
00:39:03,800 --> 00:39:07,759
Speaker 1: be more optimistic, confident, and self assured in their competence.

775
00:39:08,480 --> 00:39:09,600
Speaker 4: Doesn't that really say at all?

776
00:39:10,600 --> 00:39:12,600
Speaker 7: Well, there's so much like when I'm thinking about the

777
00:39:12,640 --> 00:39:15,920
Speaker 7: amount that we research and the amount that we try

778
00:39:15,960 --> 00:39:19,080
Speaker 7: to make sure that we are noting who is you know,

779
00:39:19,120 --> 00:39:22,120
Speaker 7: referencing and giving credit to whoever, so we're not doing

780
00:39:22,200 --> 00:39:26,800
Speaker 7: any violations copyright law, any of those things.

781
00:39:27,080 --> 00:39:30,160
Speaker 5: And again we know that chat GPT or any of

782
00:39:30,160 --> 00:39:32,600
Speaker 5: the A I really care. They don't care where the

783
00:39:32,640 --> 00:39:33,880
Speaker 5: sources are coming from.

784
00:39:34,239 --> 00:39:37,880
Speaker 7: And so as people who know A that that it

785
00:39:37,960 --> 00:39:40,720
Speaker 7: is not one hundred percent proof, and we are trying

786
00:39:40,719 --> 00:39:45,760
Speaker 7: to be doing our due diligence and give correct information. Yeah,

787
00:39:45,880 --> 00:39:48,319
Speaker 7: that is more work to go back to whatever we

788
00:39:48,440 --> 00:39:53,040
Speaker 7: thought we could use with AI to actually verify that

789
00:39:53,120 --> 00:39:55,520
Speaker 7: where this information is coming from. Like it's kind of

790
00:39:55,520 --> 00:39:58,239
Speaker 7: funny when we talk about having research help and having

791
00:39:58,320 --> 00:40:01,000
Speaker 7: someone else do the research, it feels like more work

792
00:40:01,000 --> 00:40:02,160
Speaker 7: because I'm like, I don't know.

793
00:40:02,160 --> 00:40:03,480
Speaker 5: If this is true, i gotta go read the whole

794
00:40:03,560 --> 00:40:04,400
Speaker 5: article again, you know what.

795
00:40:04,400 --> 00:40:07,360
Speaker 7: I'm doing more work And it feels the same way

796
00:40:08,040 --> 00:40:11,120
Speaker 7: in this type of conversation with AI, because I don't

797
00:40:11,120 --> 00:40:14,800
Speaker 7: completely trust once again and like analyzing the risk that

798
00:40:14,920 --> 00:40:16,520
Speaker 7: this is actually true.

799
00:40:16,560 --> 00:40:18,440
Speaker 4: This might sound way off dates.

800
00:40:18,480 --> 00:40:21,200
Speaker 1: But when I was reading some of the research about

801
00:40:21,200 --> 00:40:24,680
Speaker 1: why women don't use AI as much, I couldn't help

802
00:40:24,760 --> 00:40:28,399
Speaker 1: thinking about what a lot of heterosexual women who are

803
00:40:28,440 --> 00:40:31,000
Speaker 1: married to men are in relationships with men report where

804
00:40:31,840 --> 00:40:33,800
Speaker 1: if you ask a man in your life to do something,

805
00:40:34,440 --> 00:40:37,640
Speaker 1: what you're actually creating is like work for you later

806
00:40:37,680 --> 00:40:41,160
Speaker 1: when you have to answer the follow up or correct

807
00:40:41,160 --> 00:40:44,479
Speaker 1: what he's done wrong. I hate that for us. Find

808
00:40:44,480 --> 00:40:47,279
Speaker 1: the women who find themselves in close relation with men,

809
00:40:47,360 --> 00:40:51,200
Speaker 1: I hate I hate that for us, But like that

810
00:40:51,680 --> 00:40:54,600
Speaker 1: that is what we got, that we're served that all

811
00:40:54,640 --> 00:40:56,919
Speaker 1: the time we would be lying, Like I think every

812
00:40:56,920 --> 00:40:59,759
Speaker 1: woman listening has had that experience, and it's so frustrating.

813
00:41:00,280 --> 00:41:03,920
Speaker 1: But I think that's it really mirrors to me what

814
00:41:03,960 --> 00:41:06,280
Speaker 1: women are talking about when they talk about how AI

815
00:41:06,440 --> 00:41:08,080
Speaker 1: is used in their work. When you have to go

816
00:41:08,160 --> 00:41:12,000
Speaker 1: and correct the mistake, ask again, give the ball whatever

817
00:41:12,040 --> 00:41:14,560
Speaker 1: it's gonna be, that's just adding more and more to

818
00:41:14,640 --> 00:41:15,560
Speaker 1: your emotional load.

819
00:41:16,200 --> 00:41:17,680
Speaker 4: It's been easier for you to do it yourself in

820
00:41:17,680 --> 00:41:18,280
Speaker 4: the first place.

821
00:41:18,640 --> 00:41:18,960
Speaker 8: Right.

822
00:41:19,400 --> 00:41:21,720
Speaker 7: Also, that does feel like if we get something wrong,

823
00:41:21,840 --> 00:41:24,279
Speaker 7: as women are marginalized people, we're going to be penalized

824
00:41:24,360 --> 00:41:27,319
Speaker 7: a lot more than when men do. Men are given

825
00:41:27,400 --> 00:41:30,319
Speaker 7: excuses and are like, eh, you're fine as a one

826
00:41:30,320 --> 00:41:32,399
Speaker 7: time thing, or they feel like they can brush it off.

827
00:41:32,400 --> 00:41:34,640
Speaker 7: Whether it's just maybe they feel like they can do

828
00:41:34,719 --> 00:41:36,719
Speaker 7: that way as where women have the anxiety of like, no,

829
00:41:36,760 --> 00:41:39,719
Speaker 7: we're going to get the worst punishment everlash martinalized people.

830
00:41:39,760 --> 00:41:42,520
Speaker 7: So maybe that's also that level of confidence that we

831
00:41:42,600 --> 00:41:44,440
Speaker 7: don't have, which is delusional.

832
00:41:45,600 --> 00:41:49,279
Speaker 1: Yes, And I think this speaks to something else that

833
00:41:49,320 --> 00:41:51,000
Speaker 1: I saw in the Stanford piece and I just thought

834
00:41:51,080 --> 00:41:53,879
Speaker 1: was so interesting, which is that women felt purport not

835
00:41:53,920 --> 00:41:57,640
Speaker 1: feeling confident with how AI works. We might be offered

836
00:41:57,760 --> 00:42:01,239
Speaker 1: AI skills trading, but what we're not really offered is

837
00:42:01,239 --> 00:42:04,480
Speaker 1: a chance, a like actual knowledge into this technology all

838
00:42:04,520 --> 00:42:07,160
Speaker 1: the issues that go into it, like an actual fundamental

839
00:42:07,280 --> 00:42:09,759
Speaker 1: understanding and crash course on how it works. But Peace

840
00:42:09,800 --> 00:42:11,880
Speaker 1: sites a Federal Reserve Bank of New York survey that

841
00:42:11,920 --> 00:42:14,560
Speaker 1: shows that when women say they want generative AI training,

842
00:42:14,840 --> 00:42:18,040
Speaker 1: they are not just looking for skills. They're signaling awareness

843
00:42:18,040 --> 00:42:20,799
Speaker 1: of this technology's opacity and their unwillingness to trust a

844
00:42:20,800 --> 00:42:23,799
Speaker 1: system that they don't fully understand. A systemic review of

845
00:42:23,840 --> 00:42:26,560
Speaker 1: gender and AI adoption and found that women consistently cite

846
00:42:26,640 --> 00:42:29,640
Speaker 1: things like lack of transparency and the opaque nature of

847
00:42:29,680 --> 00:42:33,520
Speaker 1: AI tools as barriers to trust, and so it's exactly

848
00:42:33,560 --> 00:42:36,719
Speaker 1: what you dislaid out, Sam, And again that's not the

849
00:42:36,760 --> 00:42:40,640
Speaker 1: same thing as not feeling smart enough or competent enough

850
00:42:40,640 --> 00:42:43,840
Speaker 1: to understand something. That it's reasonable to say that people

851
00:42:43,840 --> 00:42:46,839
Speaker 1: that make AI tools have intentionally designed them and they

852
00:42:46,880 --> 00:42:50,040
Speaker 1: intentionally talk about them with such a lack of transparency

853
00:42:50,440 --> 00:42:55,040
Speaker 1: that I don't trust it. That is a really reasonable position,

854
00:42:55,120 --> 00:42:57,719
Speaker 1: that's not unreasonable. I don't like the framing that this

855
00:42:57,880 --> 00:43:03,080
Speaker 1: like totally reasonable way to be is you know, bad

856
00:43:03,160 --> 00:43:05,120
Speaker 1: in some way, or that women are shooting themselves in

857
00:43:05,160 --> 00:43:10,000
Speaker 1: the foot economically by showing this like reasonable lack of trust.

858
00:43:10,120 --> 00:43:12,719
Speaker 7: Right, And you know, another thing to that is again

859
00:43:12,760 --> 00:43:15,200
Speaker 7: I keep coming back to how we've seen the mistakes,

860
00:43:15,280 --> 00:43:17,640
Speaker 7: and maybe that's what it is. This has been so rushed,

861
00:43:17,920 --> 00:43:20,440
Speaker 7: but the constant times that we see things that are

862
00:43:20,480 --> 00:43:22,920
Speaker 7: not good are usually really bad for women. Like I

863
00:43:23,040 --> 00:43:26,480
Speaker 7: keep thinking about the AI videos of the deep fakes.

864
00:43:26,200 --> 00:43:28,719
Speaker 5: That happened with that twitch twitch streamer that we talked about.

865
00:43:28,800 --> 00:43:29,600
Speaker 5: Was it two years ago?

866
00:43:29,719 --> 00:43:31,319
Speaker 7: And I don't even remember we had, like cause we

867
00:43:31,320 --> 00:43:34,000
Speaker 7: were really talking about the fact things like this is

868
00:43:34,239 --> 00:43:38,200
Speaker 7: super concerning because it does go after women for like

869
00:43:38,560 --> 00:43:39,600
Speaker 7: just being there.

870
00:43:40,320 --> 00:43:42,640
Speaker 1: Yeah, I mean that speaks to what I was talking

871
00:43:42,640 --> 00:43:44,319
Speaker 1: about earlier when we were talking about the way that

872
00:43:44,320 --> 00:43:47,840
Speaker 1: Sam Altman, the way that Sam Altman was presenting talking

873
00:43:47,840 --> 00:43:50,359
Speaker 1: about the chatbot they were creating.

874
00:43:50,080 --> 00:43:52,960
Speaker 4: Sky that there are so many.

875
00:43:54,200 --> 00:43:58,799
Speaker 1: Examples of the vibes just being sus for women in

876
00:43:58,840 --> 00:44:02,680
Speaker 1: these spaces, right. You know, I make a podcast about

877
00:44:02,719 --> 00:44:07,239
Speaker 1: the intersection of gender and technology, and oftentimes when we

878
00:44:07,280 --> 00:44:11,000
Speaker 1: talk about AI, we're talking about things like non consensual

879
00:44:11,080 --> 00:44:14,480
Speaker 1: sexualized deep fakes. We're talking about things like Sam Altman,

880
00:44:14,920 --> 00:44:17,320
Speaker 1: you know, saying people should be able to have the

881
00:44:17,400 --> 00:44:21,320
Speaker 1: kind of relationship that Joaquin Phoenix has with the chatbot

882
00:44:21,480 --> 00:44:22,160
Speaker 1: in the movie Her.

883
00:44:22,360 --> 00:44:24,920
Speaker 4: We're talking about things like Elon.

884
00:44:24,760 --> 00:44:31,200
Speaker 1: Musk creating a anime teenager kind of sex fantasy chatbot.

885
00:44:31,400 --> 00:44:36,719
Speaker 1: We're talking about men who build technology, who fill their

886
00:44:36,760 --> 00:44:40,680
Speaker 1: teams with other men. They lack gender diversity and racial

887
00:44:40,680 --> 00:44:43,799
Speaker 1: diversity or any kind of real inclusion, and then they

888
00:44:43,840 --> 00:44:47,399
Speaker 1: talk about women in these like misogynistic ways. It's not

889
00:44:47,480 --> 00:44:50,480
Speaker 1: surprising to me that women are being slower to adopt

890
00:44:50,480 --> 00:44:55,160
Speaker 1: this technology that is made in such a misogynistic soup, And,

891
00:44:55,200 --> 00:44:58,040
Speaker 1: as Mara Bolis put it in that Stanford Review piece,

892
00:44:59,120 --> 00:45:02,160
Speaker 1: as in financial systems, women are attuned to the weaknesses

893
00:45:02,200 --> 00:45:06,120
Speaker 1: in generative AI systems that designers didn't notice or prioritize

894
00:45:06,160 --> 00:45:10,000
Speaker 1: things like bias, privacy risks, unreliable outputs before putting their

895
00:45:10,040 --> 00:45:12,520
Speaker 1: products out into the world, which speaks to that that

896
00:45:12,680 --> 00:45:15,520
Speaker 1: rush quality that you were just talking earlier. Some of

897
00:45:15,520 --> 00:45:20,080
Speaker 1: the industry's more misogynistic offerings see rox Annie fantasy chatbot,

898
00:45:20,400 --> 00:45:23,759
Speaker 1: or disturbing policies see Facebook's leach policies on children and

899
00:45:23,800 --> 00:45:26,400
Speaker 1: a list of content are enough to send most users

900
00:45:26,400 --> 00:45:30,920
Speaker 1: into a kinatonic, depressive spiral. But for women, beyond being offensive,

901
00:45:31,239 --> 00:45:34,560
Speaker 1: such outputs are evidence of what gets built when developmental

902
00:45:34,560 --> 00:45:38,040
Speaker 1: teams lack gender diversity. When women engage with systems that

903
00:45:38,040 --> 00:45:40,759
Speaker 1: they've been largely left out of creating, the products can

904
00:45:40,800 --> 00:45:44,359
Speaker 1: feel foreign, awkward, or even hostile. And so I think

905
00:45:44,400 --> 00:45:48,080
Speaker 1: that is absolutely what's going on here. The vibes around

906
00:45:48,120 --> 00:45:50,840
Speaker 1: how this technology is made, who makes it, who is

907
00:45:50,880 --> 00:45:53,120
Speaker 1: in the room, who has the power over it, and

908
00:45:53,160 --> 00:45:55,560
Speaker 1: how that power is being youth and wielded to shape

909
00:45:55,560 --> 00:45:59,680
Speaker 1: the world all of that sucks for marginalized people. So yes,

910
00:46:00,040 --> 00:46:03,160
Speaker 1: I surprised those same marginalized people are saying no, thank you,

911
00:46:03,239 --> 00:46:05,160
Speaker 1: y'all can keep it yep.

912
00:46:05,560 --> 00:46:09,880
Speaker 2: And it's not like we don't have historical societal systemic

913
00:46:09,960 --> 00:46:11,879
Speaker 2: evidence of this going wrong for us.

914
00:46:11,920 --> 00:46:12,160
Speaker 4: I can.

915
00:46:12,480 --> 00:46:15,879
Speaker 2: I love how men are like confident, optimistic. Oh, it's

916
00:46:15,920 --> 00:46:18,920
Speaker 2: gonna be great. I'm like, because its historically has for you.

917
00:46:19,239 --> 00:46:21,239
Speaker 6: Yeah, guess what.

918
00:46:21,800 --> 00:46:25,920
Speaker 4: Not so much over here, not so much at all.

919
00:46:25,960 --> 00:46:28,040
Speaker 4: And I wanted to end on this.

920
00:46:28,719 --> 00:46:30,440
Speaker 1: When I was researching this, I found this post on

921
00:46:30,480 --> 00:46:32,480
Speaker 1: Reddit that I thought really did a great job of

922
00:46:32,480 --> 00:46:34,480
Speaker 1: summarizing my thoughts. So there was a post in the

923
00:46:34,560 --> 00:46:38,200
Speaker 1: ask Feminists subreddit and the post asks why are women

924
00:46:38,440 --> 00:46:42,640
Speaker 1: using generative AI lesson men? And the redditor Trooper SJP answers,

925
00:46:43,320 --> 00:46:46,480
Speaker 1: I'm a university professor. If female students are using chat

926
00:46:46,520 --> 00:46:49,480
Speaker 1: ept less than male students, it is the male students

927
00:46:49,520 --> 00:46:51,959
Speaker 1: we should be worried about. There have been studies showing

928
00:46:51,960 --> 00:46:54,600
Speaker 1: that there is a negative cognitive impact of using generative

929
00:46:54,600 --> 00:46:58,160
Speaker 1: AI on learners. This includes loss and ability to retain information,

930
00:46:58,320 --> 00:47:01,760
Speaker 1: loss of critical thinking skills, loss of empathy, degradation of writing,

931
00:47:01,800 --> 00:47:05,520
Speaker 1: and math skills, degradation of problem solving skills, and on

932
00:47:05,640 --> 00:47:09,400
Speaker 1: and on. Furthermore, generative AIS bad for the environment, contributes

933
00:47:09,400 --> 00:47:12,680
Speaker 1: to environmental racism, is built on stolen data by corporations

934
00:47:12,719 --> 00:47:15,920
Speaker 1: who will zealously guard their own intellectual property while willingly

935
00:47:16,000 --> 00:47:20,240
Speaker 1: violating yours and capitalist excitement in replacing entry level workers

936
00:47:20,239 --> 00:47:23,439
Speaker 1: with AI is having an immediate negative impact on young

937
00:47:23,520 --> 00:47:26,200
Speaker 1: job seekers age twenty two to twenty seven, but will

938
00:47:26,200 --> 00:47:29,080
Speaker 1: have a much more profound impact on all of us

939
00:47:29,280 --> 00:47:31,839
Speaker 1: when we lose a generation of entry level workers who

940
00:47:31,840 --> 00:47:35,200
Speaker 1: won't get the experience needed to become experienced workers when

941
00:47:35,200 --> 00:47:39,200
Speaker 1: the experienced workers retire. Chat shept is also inaccurate, bland,

942
00:47:39,280 --> 00:47:43,960
Speaker 1: and produces terrible work full of false information. Furthermore, in classrooms,

943
00:47:44,000 --> 00:47:46,839
Speaker 1: including mine, the use of generative AI is considered an

944
00:47:46,880 --> 00:47:50,000
Speaker 1: academic integrity violation and will result in as zero for

945
00:47:50,040 --> 00:47:52,080
Speaker 1: that assignment and a forwarding the case to the student

946
00:47:52,120 --> 00:47:55,040
Speaker 1: conduct Board. My students would not appreciate it if I

947
00:47:55,120 --> 00:47:58,040
Speaker 1: use chat shept to create lesson plans, to grade their work,

948
00:47:58,120 --> 00:48:00,319
Speaker 1: and to write their letters of recommendation. I do not

949
00:48:00,360 --> 00:48:03,200
Speaker 1: appreciate my students passing off work they didn't right. That

950
00:48:03,239 --> 00:48:05,719
Speaker 1: has a direct negative impact on themselves and the world

951
00:48:05,760 --> 00:48:07,680
Speaker 1: as their own. So I don't think it is bad

952
00:48:07,719 --> 00:48:09,839
Speaker 1: that women are less likely to be cheating by using

953
00:48:09,880 --> 00:48:11,960
Speaker 1: chat cheapt in their work than men. I think it

954
00:48:12,040 --> 00:48:14,960
Speaker 1: is bad that so many men are wasting their educations

955
00:48:15,000 --> 00:48:19,399
Speaker 1: by cheating. And I felt like that really summed up

956
00:48:19,440 --> 00:48:22,080
Speaker 1: what I was trying to say about this reframing. I

957
00:48:22,200 --> 00:48:25,720
Speaker 1: don't like that this conversation is simply framed as women

958
00:48:25,760 --> 00:48:30,200
Speaker 1: aren't using AI and thus sort of almost blaming women

959
00:48:30,520 --> 00:48:33,680
Speaker 1: for all of these systemic reasons and reasonable reasons as

960
00:48:33,680 --> 00:48:34,480
Speaker 1: to why that.

961
00:48:34,400 --> 00:48:37,280
Speaker 4: Might be, and also sort of saying, well, if women.

962
00:48:37,160 --> 00:48:40,759
Speaker 1: Weren't so afraid and also stupid about AI, maybe they'd

963
00:48:40,760 --> 00:48:43,319
Speaker 1: be making more women. It's not society, it's a stupid women.

964
00:48:43,640 --> 00:48:46,239
Speaker 1: I really think that we need to reframe that and say, well,

965
00:48:46,280 --> 00:48:49,400
Speaker 1: what are women telling us by not using this technology? Like,

966
00:48:49,560 --> 00:48:52,320
Speaker 1: should we really just be saying these women are making.

967
00:48:52,080 --> 00:48:52,640
Speaker 4: A bad choice.

968
00:48:52,640 --> 00:48:55,000
Speaker 1: Shouldn't we be really be zeroing in on what women

969
00:48:55,040 --> 00:48:57,239
Speaker 1: are saying as that pertains to why we are not

970
00:48:58,080 --> 00:49:01,040
Speaker 1: so gung ho to be adopting technology the way that

971
00:49:01,080 --> 00:49:01,440
Speaker 1: men are.

972
00:49:01,760 --> 00:49:03,360
Speaker 4: That's that's sort of my that's sort of my my

973
00:49:03,520 --> 00:49:04,760
Speaker 4: so what in all this.

974
00:49:05,480 --> 00:49:07,160
Speaker 7: You know? And I think in the future we were

975
00:49:07,160 --> 00:49:10,000
Speaker 7: going to come back and have a conversation about the

976
00:49:10,680 --> 00:49:13,719
Speaker 7: red the lining of this type of usage in this

977
00:49:13,760 --> 00:49:18,400
Speaker 7: conversation and the growing population of the red pill community. Yes,

978
00:49:18,800 --> 00:49:20,800
Speaker 7: because we're talking more and more like it has become

979
00:49:20,840 --> 00:49:22,479
Speaker 7: even more so worthy. Have has come to the point

980
00:49:22,520 --> 00:49:26,080
Speaker 7: of saying of inceels saying being straight and having a

981
00:49:26,080 --> 00:49:29,239
Speaker 7: relationship with a woman is gay, like literally that's their

982
00:49:29,480 --> 00:49:31,080
Speaker 7: like and soul.

983
00:49:31,120 --> 00:49:31,640
Speaker 5: Have you seen this?

984
00:49:31,880 --> 00:49:34,520
Speaker 4: So really thing a man can do is sleep with

985
00:49:34,560 --> 00:49:34,879
Speaker 4: a woman.

986
00:49:35,080 --> 00:49:37,680
Speaker 7: Having with a woman, right, Submitting and pleasing a woman

987
00:49:37,800 --> 00:49:40,000
Speaker 7: essentially was kind of like that kind of intake. But

988
00:49:40,160 --> 00:49:44,279
Speaker 7: I really do wonder if this conversation of using chat

989
00:49:44,320 --> 00:49:47,760
Speaker 7: gpt to replace that interaction again with a mal loneliness epidemic,

990
00:49:47,840 --> 00:49:51,279
Speaker 7: and how it becomes violent. If this conversation and if

991
00:49:51,280 --> 00:49:54,200
Speaker 7: this push and things like what Sam Altman was trying

992
00:49:54,239 --> 00:49:56,400
Speaker 7: to do when he first created that chato as well

993
00:49:56,440 --> 00:49:59,800
Speaker 7: as what uh Elon Musk is trying to do creating

994
00:49:59,800 --> 00:50:04,080
Speaker 7: his chatbot, is just opening up a bigger community, a

995
00:50:04,080 --> 00:50:08,120
Speaker 7: bigger doorway for more intell activity and red pill activity.

996
00:50:08,120 --> 00:50:10,120
Speaker 7: And we're gonna have to come back and have to

997
00:50:10,120 --> 00:50:13,400
Speaker 7: deal with the consequence of things like AI being a

998
00:50:13,440 --> 00:50:14,640
Speaker 7: part of this narrative.

999
00:50:15,480 --> 00:50:20,480
Speaker 1: Absolutely, I completely agree. I think those things are totally linked.

1000
00:50:20,880 --> 00:50:22,160
Speaker 1: And I'm in the middle of doing a lot of

1001
00:50:22,160 --> 00:50:25,800
Speaker 1: research about the kinds of sexual and romantic attachments that

1002
00:50:25,840 --> 00:50:30,520
Speaker 1: people create with AI. And I think it's complicated because

1003
00:50:30,640 --> 00:50:33,200
Speaker 1: I want people to feel like they have nourishing relationships

1004
00:50:33,239 --> 00:50:37,800
Speaker 1: in their lives. But healthy nourishing relationships come with friction,

1005
00:50:38,000 --> 00:50:40,160
Speaker 1: like that's what you get from being in relationship with

1006
00:50:40,200 --> 00:50:40,880
Speaker 1: other humans.

1007
00:50:41,200 --> 00:50:45,239
Speaker 8: And I think that the availability of folks to kind

1008
00:50:45,239 --> 00:50:50,080
Speaker 8: of engage in frictionless relationships where they can just take

1009
00:50:50,080 --> 00:50:52,200
Speaker 8: and take and take it, extract and extracted, extract and

1010
00:50:52,239 --> 00:50:54,520
Speaker 8: always get what they want out of them in some ways,

1011
00:50:54,560 --> 00:50:56,880
Speaker 8: I can see why that is therapeutic, but that is

1012
00:50:56,920 --> 00:50:57,319
Speaker 8: not what.

1013
00:50:57,320 --> 00:50:58,960
Speaker 4: Builds healthy people.

1014
00:50:59,120 --> 00:51:03,080
Speaker 1: Like health like healthy people have to experience relationships with friction.

1015
00:51:03,200 --> 00:51:04,360
Speaker 4: And that's just the bottom line.

1016
00:51:05,880 --> 00:51:09,799
Speaker 2: Yes, yes, well topic for a future day.

1017
00:51:10,120 --> 00:51:11,560
Speaker 4: Yeah, I love it.

1018
00:51:12,719 --> 00:51:13,440
Speaker 5: I also have to.

1019
00:51:13,360 --> 00:51:17,520
Speaker 2: Say a dude told me the other day he was like, well,

1020
00:51:18,280 --> 00:51:20,640
Speaker 2: I love AI because it gives me more leisure time.

1021
00:51:20,680 --> 00:51:22,839
Speaker 2: And I was like, well, wait till you don't have

1022
00:51:22,880 --> 00:51:25,400
Speaker 2: a job, and then you're not gonna have a way.

1023
00:51:25,280 --> 00:51:26,960
Speaker 5: Of that time.

1024
00:51:27,200 --> 00:51:30,680
Speaker 1: It'll be all all the time, won't be able to

1025
00:51:30,719 --> 00:51:31,840
Speaker 1: do all those things.

1026
00:51:32,080 --> 00:51:37,000
Speaker 5: Dressed because you have no monies more leisure time.

1027
00:51:37,680 --> 00:51:41,360
Speaker 2: Oh all right, well, thank you so much Bridget for

1028
00:51:41,520 --> 00:51:45,040
Speaker 2: coming during the stressful time. We always love talking to you.

1029
00:51:45,520 --> 00:51:47,280
Speaker 2: Can't wait to see you in the new year, twenty

1030
00:51:47,320 --> 00:51:47,920
Speaker 2: twenty six.

1031
00:51:48,280 --> 00:51:50,919
Speaker 4: Yes, we're almost there.

1032
00:51:50,080 --> 00:51:53,200
Speaker 2: Almost there. In the meantime, where can the good listeners

1033
00:51:53,239 --> 00:51:53,840
Speaker 2: find you?

1034
00:51:53,840 --> 00:51:55,160
Speaker 4: You can find me at my podcast.

1035
00:51:55,160 --> 00:51:57,120
Speaker 1: There are nor Goals on the Internet where we explore

1036
00:51:57,160 --> 00:51:59,840
Speaker 1: all kinds of issues of the intersection of gender and

1037
00:52:00,160 --> 00:52:02,480
Speaker 1: identity and technology and social media. You can check me

1038
00:52:02,520 --> 00:52:05,440
Speaker 1: out on Instagram at Bridget Marie in DC or on YouTube.

1039
00:52:05,480 --> 00:52:06,800
Speaker 4: But there are no girls on the internet.

1040
00:52:07,480 --> 00:52:11,040
Speaker 2: Yes, and go do that if you have not already listeners.

1041
00:52:11,520 --> 00:52:13,360
Speaker 2: If you would like to contact us, you can You

1042
00:52:13,360 --> 00:52:15,160
Speaker 2: can email us at Hello at stuff Onenever Told You

1043
00:52:15,200 --> 00:52:16,680
Speaker 2: dot com. You can find us on Blue skyte Mom

1044
00:52:16,719 --> 00:52:18,799
Speaker 2: Stuff podcast, or on Instagram and TikTok at stuff one

1045
00:52:18,840 --> 00:52:20,840
Speaker 2: Never Told You. We're also on YouTube. We have some

1046
00:52:20,840 --> 00:52:22,480
Speaker 2: new merchandise at comp Bureau, and we have a book

1047
00:52:22,480 --> 00:52:24,200
Speaker 2: you can get wherever you get your books. Thanks as

1048
00:52:24,200 --> 00:52:27,160
Speaker 2: always too our SUPERRODUCD pres Senior Executive dus My Andercntrump

1049
00:52:27,200 --> 00:52:29,280
Speaker 2: for Joey, thank you, and thanks to you for listening.

1050
00:52:29,440 --> 00:52:31,239
Speaker 2: Stuff Never Told You is protection by Heart Radio. For

1051
00:52:31,239 --> 00:52:32,839
Speaker 2: more podcasts from my Heart Radio, you can check out

1052
00:52:32,840 --> 00:52:34,719
Speaker 2: the heart Radio app, Apple podcast wherever you listen to

1053
00:52:34,760 --> 00:52:35,759
Speaker 2: your favorite shows,