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

2
00:00:06,160 --> 00:00:13,840
Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridgett and this is

3
00:00:13,840 --> 00:00:18,240
Speaker 1: There Are No Girls on the Internet. I'm here with

4
00:00:18,280 --> 00:00:21,000
Speaker 1: my producer, Mike. Mike, I am so excited to run

5
00:00:21,040 --> 00:00:22,880
Speaker 1: down the news with you because we have so many

6
00:00:22,960 --> 00:00:24,360
Speaker 1: good updates in this episode.

7
00:00:24,880 --> 00:00:27,080
Speaker 2: Yeah, it's been a busy week, so let's get into

8
00:00:27,120 --> 00:00:28,000
Speaker 2: a bridget.

9
00:00:28,280 --> 00:00:29,880
Speaker 1: And here's what you might have missed this week on

10
00:00:29,920 --> 00:00:30,480
Speaker 1: the Internet.

11
00:00:30,600 --> 00:00:32,519
Speaker 3: But first, if.

12
00:00:32,320 --> 00:00:34,680
Speaker 1: You feel like our audio is a little bit off,

13
00:00:34,760 --> 00:00:36,680
Speaker 1: it is not because one of us has made an

14
00:00:36,800 --> 00:00:39,839
Speaker 1: error that we will bring up until the day we die.

15
00:00:40,520 --> 00:00:42,800
Speaker 1: It's because I'm traveling and I am not recording in

16
00:00:42,840 --> 00:00:46,120
Speaker 1: my normal studio, So hopefully it's not too bad. So

17
00:00:46,280 --> 00:00:48,880
Speaker 1: back in June, when the Supreme Court ruled on affirmative action,

18
00:00:49,360 --> 00:00:52,280
Speaker 1: we told you about the conservative activist named Edward Blum

19
00:00:52,440 --> 00:00:55,920
Speaker 1: and the group group might be a little bit of

20
00:00:55,960 --> 00:00:58,520
Speaker 1: a misnomer because it's literally just him. It is a

21
00:00:58,520 --> 00:01:01,640
Speaker 1: group of one called the Erican Alliance for Equal Rights,

22
00:01:01,760 --> 00:01:06,640
Speaker 1: which really curtails progress in the United States by filing lawsuits.

23
00:01:07,040 --> 00:01:09,080
Speaker 1: Dude is not even a lawyer. I think I described

24
00:01:09,120 --> 00:01:12,080
Speaker 1: him as a professional hater in the last episode that

25
00:01:12,120 --> 00:01:15,000
Speaker 1: we did about affirmative action. So Blum was behind the

26
00:01:15,080 --> 00:01:18,080
Speaker 1: legal challenge to affirmative action, both the one led by

27
00:01:18,120 --> 00:01:20,120
Speaker 1: that white woman who did not get into the University

28
00:01:20,120 --> 00:01:23,120
Speaker 1: of Texas and the most recent challenge on behalf of

29
00:01:23,160 --> 00:01:25,720
Speaker 1: both white and Asian students that led to the consideration

30
00:01:25,800 --> 00:01:29,400
Speaker 1: of race in college admissions being struck down as unconstitutional.

31
00:01:29,640 --> 00:01:32,040
Speaker 1: His legal challenges also led to the gutting of the

32
00:01:32,120 --> 00:01:36,800
Speaker 1: Voting Rights Act. Well high on those big wins, my

33
00:01:36,920 --> 00:01:41,520
Speaker 1: man Edward Blum is back after gutting affirmative action. His

34
00:01:41,680 --> 00:01:45,120
Speaker 1: next move is suing a black venture capital firm called

35
00:01:45,160 --> 00:01:49,320
Speaker 1: Fearless Fund, because he alleges the Fearless Fund is practicing

36
00:01:49,920 --> 00:01:53,800
Speaker 1: unlawful racial discrimination. His claim states at the firm is

37
00:01:53,920 --> 00:01:57,040
Speaker 1: violating Section nineteen eighty one of the Civil Rights Acts

38
00:01:57,040 --> 00:02:00,320
Speaker 1: of eighteen sixty six, a US law barring racial bias

39
00:02:00,440 --> 00:02:04,400
Speaker 1: in private contracts, by making only black women eligible in

40
00:02:04,440 --> 00:02:08,480
Speaker 1: a grant competition. So basically, Fearless Fund was launched in

41
00:02:08,480 --> 00:02:13,280
Speaker 1: twenty nineteen by three prominent black women, actress Keisha Knight Pullman. Yes,

42
00:02:13,480 --> 00:02:15,920
Speaker 1: that Keisha Knight Pullman, who you might recall as Rudy

43
00:02:16,000 --> 00:02:21,080
Speaker 1: Huxtable from The Cosby Show, entrepreneur arian Simone, and corporate

44
00:02:21,080 --> 00:02:25,240
Speaker 1: executive Aana Parsons. Now Fearless Fund has a pretty impressive

45
00:02:25,280 --> 00:02:30,120
Speaker 1: list of investors like Bank of America, Costco, General Mills, MasterCard, JP,

46
00:02:30,240 --> 00:02:33,760
Speaker 1: Morgan Chase, and more. The lawsuit centers on Fearless Funds

47
00:02:33,919 --> 00:02:37,600
Speaker 1: Fearless Strivers Grant Contest, which awards Black women who own

48
00:02:37,639 --> 00:02:40,920
Speaker 1: small businesses twenty thousand dollars in grants, digital tools to

49
00:02:40,919 --> 00:02:44,600
Speaker 1: help them grow their businesses and mentorship opportunities provided in

50
00:02:44,639 --> 00:02:48,880
Speaker 1: conjunction with MasterCard. Now Blum hater that he is says

51
00:02:48,880 --> 00:02:51,440
Speaker 1: that because this is a program that is specifically designed

52
00:02:51,480 --> 00:02:54,760
Speaker 1: to uplift black women business owners, that that means that

53
00:02:54,800 --> 00:02:58,520
Speaker 1: it is discriminatory against white people and Asian Americans because

54
00:02:58,520 --> 00:03:01,639
Speaker 1: they're not eligible to be part of this grant program. Now,

55
00:03:02,000 --> 00:03:04,840
Speaker 1: I have to say, it is so clear to me

56
00:03:05,120 --> 00:03:07,200
Speaker 1: what Blum is doing. I think that he is high

57
00:03:07,200 --> 00:03:09,840
Speaker 1: off of the success that he had in gutting our

58
00:03:09,919 --> 00:03:14,720
Speaker 1: Voting Rights Act and also gutting affirmative action and really

59
00:03:14,919 --> 00:03:18,640
Speaker 1: shaping the considerations around race that colleges are giving to

60
00:03:19,120 --> 00:03:23,480
Speaker 1: people trying to apply. It's not as if black women

61
00:03:23,639 --> 00:03:27,519
Speaker 1: business owners are out here being overrepresented. As a black

62
00:03:27,560 --> 00:03:30,360
Speaker 1: women business owner myself, I can certainly tell you that

63
00:03:30,919 --> 00:03:34,000
Speaker 1: black entrepreneurs typically receive less than two percent of all

64
00:03:34,120 --> 00:03:37,600
Speaker 1: VC dollars each year, while companies led by black women

65
00:03:37,960 --> 00:03:40,440
Speaker 1: receive even less than that. They received even less than

66
00:03:40,600 --> 00:03:43,720
Speaker 1: one percent, according to data from Crunchbase. So that means

67
00:03:43,760 --> 00:03:48,280
Speaker 1: that Edward Blum basically saw that ninety nine percent of

68
00:03:48,640 --> 00:03:52,880
Speaker 1: VC funding and support is going to white people, white men. Really,

69
00:03:53,040 --> 00:03:54,880
Speaker 1: I was like, you know what, I'm coming for that

70
00:03:54,960 --> 00:03:56,960
Speaker 1: last one percent too, that should be ours two.

71
00:03:57,800 --> 00:04:03,280
Speaker 2: What a jerk, you know, Like, why why? Why is

72
00:04:03,320 --> 00:04:04,080
Speaker 2: he doing this?

73
00:04:04,560 --> 00:04:08,720
Speaker 1: I just think there are people out there who really

74
00:04:08,840 --> 00:04:11,160
Speaker 1: have it in their head that they that we need

75
00:04:11,200 --> 00:04:14,040
Speaker 1: to go back to the way quote the way things were,

76
00:04:14,360 --> 00:04:16,680
Speaker 1: make America great again. It's a great example of what

77
00:04:16,720 --> 00:04:21,039
Speaker 1: I mean. And I think it's not just in sort

78
00:04:21,080 --> 00:04:25,080
Speaker 1: of like these bigger ideas around political issues, but also

79
00:04:25,200 --> 00:04:28,760
Speaker 1: in just in like who gets support around owning a business,

80
00:04:28,839 --> 00:04:32,000
Speaker 1: who gets support in terms of venture capital. I think

81
00:04:32,000 --> 00:04:36,279
Speaker 1: that that people like Blum have been watching the progress

82
00:04:36,320 --> 00:04:38,880
Speaker 1: that we have made, progress that I would actually argue

83
00:04:38,880 --> 00:04:41,479
Speaker 1: has not been particularly fast, Like I would say like

84
00:04:41,839 --> 00:04:45,279
Speaker 1: incremental slow progress that marginalized people have made in this country,

85
00:04:45,480 --> 00:04:47,760
Speaker 1: and they're like, no, we need to go back to

86
00:04:47,800 --> 00:04:50,480
Speaker 1: a time when this was not happening. And the way

87
00:04:50,520 --> 00:04:52,719
Speaker 1: that they are doing it is through the courts. It

88
00:04:52,800 --> 00:04:55,200
Speaker 1: is horrifying to me to hear people talk about this

89
00:04:55,320 --> 00:04:58,040
Speaker 1: and legitimize this as if it is an illegal challenge

90
00:04:58,279 --> 00:05:02,400
Speaker 1: genuinely based in you know, just leveling the racial playing field,

91
00:05:02,440 --> 00:05:03,960
Speaker 1: because it's not. It's obviously not.

92
00:05:04,839 --> 00:05:09,560
Speaker 2: Yeah, it's like an argument happening in a vacuum that

93
00:05:09,720 --> 00:05:13,360
Speaker 2: is separate from the America that we all live in,

94
00:05:13,520 --> 00:05:20,120
Speaker 2: where racism and sexism and a history of both of

95
00:05:20,160 --> 00:05:25,960
Speaker 2: those things very much influence what's happening today, and like

96
00:05:26,400 --> 00:05:31,599
Speaker 2: who has what resources? The idea that any attempt to

97
00:05:32,520 --> 00:05:39,600
Speaker 2: balance the playing field is itself racist is get It

98
00:05:39,680 --> 00:05:43,360
Speaker 2: is just maddening, you know, like the like words fail,

99
00:05:43,400 --> 00:05:49,520
Speaker 2: it's just maddening and like incorrect and and it'sself racist, right,

100
00:05:49,520 --> 00:05:52,440
Speaker 2: Like these are these are racist things that he is doing.

101
00:05:53,200 --> 00:05:57,240
Speaker 1: Absolutely, And I think using the Affirmative Action and the

102
00:05:57,320 --> 00:06:01,159
Speaker 1: Voting Rights Act as templates, I think I really see

103
00:06:01,279 --> 00:06:04,040
Speaker 1: the kind of path that he's trying to argue against,

104
00:06:04,080 --> 00:06:08,240
Speaker 1: which is that you know, any kind of process or

105
00:06:08,360 --> 00:06:12,680
Speaker 1: program that is not race neutral is inherently racist. But

106
00:06:12,839 --> 00:06:15,479
Speaker 1: what that does, like you, if you look at affirmative action,

107
00:06:16,120 --> 00:06:20,359
Speaker 1: affirmative action, that case did not strike down legacy admissions

108
00:06:20,360 --> 00:06:22,560
Speaker 1: to colleges, although there are colleges who are now looking

109
00:06:22,600 --> 00:06:25,719
Speaker 1: at that thankfully and asking whether or not that is

110
00:06:25,760 --> 00:06:28,080
Speaker 1: something they want to keep on the books. But you know,

111
00:06:28,640 --> 00:06:33,680
Speaker 1: programs that have either explicitly or implicitly given a leg

112
00:06:33,800 --> 00:06:35,880
Speaker 1: up to non black people and people who are not

113
00:06:35,920 --> 00:06:39,360
Speaker 1: people of color are marginalized. Those have existed, those continue

114
00:06:39,400 --> 00:06:42,760
Speaker 1: to exist. And I think that the very clear through

115
00:06:42,800 --> 00:06:44,880
Speaker 1: line that I see him making is this idea that

116
00:06:45,440 --> 00:06:51,160
Speaker 1: if anything is for black women specifically, inherently it is racist.

117
00:06:51,200 --> 00:06:57,280
Speaker 1: And it really argued, it really uses this incorrect attitude

118
00:06:57,320 --> 00:07:00,880
Speaker 1: that we are all on an equal play in field now,

119
00:07:01,000 --> 00:07:02,360
Speaker 1: which all you have to do is look at the

120
00:07:02,440 --> 00:07:06,200
Speaker 1: numbers around who is getting funding in the BC space

121
00:07:06,360 --> 00:07:07,720
Speaker 1: to see that that's not true.

122
00:07:07,760 --> 00:07:11,800
Speaker 2: Right exactly. It's a complete fiction. It's based the whole

123
00:07:11,920 --> 00:07:17,600
Speaker 2: enterprises based on the fiction that race doesn't matter, and

124
00:07:17,880 --> 00:07:22,120
Speaker 2: the only way that race matters in America today is

125
00:07:22,680 --> 00:07:27,880
Speaker 2: affirmative action. Programs that somehow bias things towards black people,

126
00:07:28,120 --> 00:07:32,920
Speaker 2: which is just so separate from reality. Like you said,

127
00:07:33,280 --> 00:07:36,800
Speaker 2: you just look at those numbers, look at any any

128
00:07:37,160 --> 00:07:43,360
Speaker 2: numbers related to median income or mortality, or who starts

129
00:07:43,400 --> 00:07:47,880
Speaker 2: businesses or who owns stock like anything. You can see

130
00:07:48,000 --> 00:07:50,720
Speaker 2: this is not a level playing field.

131
00:07:51,360 --> 00:07:53,680
Speaker 1: And I think that we're in this place right now

132
00:07:53,760 --> 00:07:58,280
Speaker 1: where what you just said just basic reality. We are

133
00:07:58,320 --> 00:08:00,840
Speaker 1: all supposed to go back to a time where we

134
00:08:00,920 --> 00:08:04,480
Speaker 1: don't talk about it, where the basic inequities that are

135
00:08:04,480 --> 00:08:07,440
Speaker 1: built into the experience of being a person of color

136
00:08:07,560 --> 00:08:09,760
Speaker 1: or a marginalized person in the United States, we are

137
00:08:09,800 --> 00:08:12,360
Speaker 1: supposed to act like we're all on an equal playing field,

138
00:08:12,440 --> 00:08:16,000
Speaker 1: that things like racism, sexism, transphobia, all of that as

139
00:08:16,040 --> 00:08:18,920
Speaker 1: the thing of the past. We're all equal today, even

140
00:08:18,960 --> 00:08:22,880
Speaker 1: though it's clearly not true. And so I'm I'm just

141
00:08:22,920 --> 00:08:25,280
Speaker 1: so tired of this guy. And honestly, if this challenge

142
00:08:25,320 --> 00:08:28,040
Speaker 1: had come a couple of years earlier, I would be like, oh,

143
00:08:28,160 --> 00:08:30,560
Speaker 1: that's gonna get lapped out at the court. But truly,

144
00:08:30,640 --> 00:08:34,400
Speaker 1: I don't know, right like, he has had some wins

145
00:08:34,520 --> 00:08:39,600
Speaker 1: at successfully single handedly rolling back progress in our country,

146
00:08:39,640 --> 00:08:43,360
Speaker 1: and so I do worry because, like, I don't see

147
00:08:43,400 --> 00:08:45,840
Speaker 1: this specific story getting the kind of attention that I

148
00:08:45,960 --> 00:08:50,040
Speaker 1: might expect coming after his big win with gutting affirmative Action.

149
00:08:51,200 --> 00:08:54,280
Speaker 1: And I think that's because even though affirmative action was

150
00:08:54,400 --> 00:08:57,720
Speaker 1: kind of branded as only helping black people, it actually

151
00:08:57,800 --> 00:09:01,040
Speaker 1: statistically helped white people as well white women. And I'm

152
00:09:01,080 --> 00:09:05,760
Speaker 1: worried that because this move has Blum actually going after

153
00:09:05,840 --> 00:09:09,440
Speaker 1: something that specifically helps black women, this mentorship and financial

154
00:09:09,480 --> 00:09:12,199
Speaker 1: grant for black women entrepreneurs, it's not going to get

155
00:09:12,240 --> 00:09:15,720
Speaker 1: as much attention. And you know, this little thing that

156
00:09:15,800 --> 00:09:18,920
Speaker 1: we have to support us as black women entrepreneurs is

157
00:09:18,960 --> 00:09:23,360
Speaker 1: going to be quietly swept away without much fanfare. As

158
00:09:23,400 --> 00:09:26,560
Speaker 1: a black woman business owner, a black woman entrepreneur, I

159
00:09:26,600 --> 00:09:29,079
Speaker 1: can tell you there is not much out there for us.

160
00:09:30,000 --> 00:09:32,439
Speaker 1: We'll have our little programs. I'm not going to say

161
00:09:32,440 --> 00:09:34,160
Speaker 1: that I'm not like thankful for what it's out there,

162
00:09:34,160 --> 00:09:37,760
Speaker 1: because I've definitely been part of entrepreneurship programs and I'm

163
00:09:37,760 --> 00:09:39,880
Speaker 1: thankful for those opportunities. I don't want to make it

164
00:09:39,880 --> 00:09:43,040
Speaker 1: seem like I'm not thankful for them, but there's just

165
00:09:43,120 --> 00:09:45,280
Speaker 1: not a lot out there for us, and that's why

166
00:09:45,320 --> 00:09:48,240
Speaker 1: you have to have these kinds of programs, and so yeah,

167
00:09:48,600 --> 00:09:51,200
Speaker 1: I wish I could say that, oh, this is going

168
00:09:51,240 --> 00:09:53,600
Speaker 1: to be laughed out of court, but we'll keep an

169
00:09:53,640 --> 00:09:56,199
Speaker 1: eye on that. We'll keep updating you with how this goes.

170
00:09:56,240 --> 00:10:01,000
Speaker 1: And yeah, Edward Blum not a good guy. Not not what's

171
00:10:01,000 --> 00:10:03,320
Speaker 1: what's the opposite of a friend of the show? Enemy

172
00:10:03,320 --> 00:10:03,800
Speaker 1: of the show?

173
00:10:04,480 --> 00:10:07,480
Speaker 2: Yeah, he is not a good guy. And you know, Bridge,

174
00:10:07,600 --> 00:10:10,760
Speaker 2: I feel like you're selling it a little short, even

175
00:10:10,760 --> 00:10:14,800
Speaker 2: in that like damning condemnation. But like, you know, having

176
00:10:14,800 --> 00:10:18,600
Speaker 2: worked with you for a few years now, I've seen

177
00:10:19,000 --> 00:10:24,400
Speaker 2: you talking with people about deals, you know, getting and

178
00:10:24,520 --> 00:10:29,160
Speaker 2: just getting what often seems like a more raw deal

179
00:10:29,679 --> 00:10:33,920
Speaker 2: then someone else might get in that position. You know,

180
00:10:34,120 --> 00:10:39,800
Speaker 2: like there's there is real discrimination out there in the

181
00:10:39,840 --> 00:10:43,240
Speaker 2: world of business, and anybody who says otherwise is like

182
00:10:44,760 --> 00:10:50,360
Speaker 2: has their head in the sand at best. And yeah,

183
00:10:50,400 --> 00:10:56,160
Speaker 2: this guy two to try to pretend like, you know,

184
00:10:56,800 --> 00:11:00,000
Speaker 2: he doesn't see color. It doesn't exist, race doesn't exist.

185
00:11:01,320 --> 00:11:05,360
Speaker 2: It's it is so tiresome and it is also scary,

186
00:11:05,600 --> 00:11:08,439
Speaker 2: especially because, like you said, it seems to be working

187
00:11:09,840 --> 00:11:10,800
Speaker 2: enemy of the show.

188
00:11:11,320 --> 00:11:13,480
Speaker 1: Yeah, I think that you're so. First of all, thank

189
00:11:13,520 --> 00:11:16,040
Speaker 1: you for that it's I could talk all day, it

190
00:11:16,040 --> 00:11:19,440
Speaker 1: would not be a very interesting podcast. But any business

191
00:11:19,480 --> 00:11:23,840
Speaker 1: owner entrepreneur out there knows how hard it is. And

192
00:11:23,960 --> 00:11:26,880
Speaker 1: you go to these meetings and like you you sometimes

193
00:11:26,920 --> 00:11:29,240
Speaker 1: you're very aware that you're being treated differently and you

194
00:11:29,320 --> 00:11:30,920
Speaker 1: just have to smile through it. It's like there's no

195
00:11:31,280 --> 00:11:35,480
Speaker 1: there's no alternative other than just smile through it, keep going.

196
00:11:36,000 --> 00:11:37,600
Speaker 1: It's not my favorite thing.

197
00:11:38,080 --> 00:11:40,600
Speaker 2: Yeah, And one more thing I want to say for

198
00:11:40,800 --> 00:11:43,640
Speaker 2: our listeners that you won't say, Bridget, but I want

199
00:11:43,679 --> 00:11:46,360
Speaker 2: to say it. You and I have talked about how

200
00:11:46,880 --> 00:11:52,040
Speaker 2: one of your superpowers is smiling through it and like

201
00:11:52,160 --> 00:11:56,559
Speaker 2: being willing to just like take it and keep going.

202
00:11:57,800 --> 00:12:00,920
Speaker 2: Which yeah, I it is maddening.

203
00:12:01,559 --> 00:12:04,720
Speaker 1: I mean, this is like the kind of stuff that

204
00:12:05,679 --> 00:12:08,120
Speaker 1: nobody likes to listen to on a podcast. But it's

205
00:12:08,280 --> 00:12:12,559
Speaker 1: it's you're not wrong, and it's just one of those

206
00:12:12,600 --> 00:12:17,440
Speaker 1: things that like, if you want to build something, like

207
00:12:17,480 --> 00:12:20,280
Speaker 1: if you want to like really build a legacy and

208
00:12:20,360 --> 00:12:25,120
Speaker 1: really start a thing, ask anybody who has done it.

209
00:12:26,080 --> 00:12:29,080
Speaker 1: You have to really eat shit, like I like, you

210
00:12:29,240 --> 00:12:33,520
Speaker 1: really have to like learn to eat shit, like learn

211
00:12:33,640 --> 00:12:37,839
Speaker 1: to take stuff on the chin that doesn't feel very good.

212
00:12:38,240 --> 00:12:40,760
Speaker 1: I have a whole support system, Mike. You are a

213
00:12:40,800 --> 00:12:43,760
Speaker 1: part of that support system where when things when I

214
00:12:43,760 --> 00:12:48,439
Speaker 1: feel like I'm experiencing these kinds of setbacks, where I

215
00:12:48,520 --> 00:12:51,200
Speaker 1: know how to pump myself back up and like get

216
00:12:51,240 --> 00:12:54,680
Speaker 1: myself emotionally and mentally where I need to be to

217
00:12:54,760 --> 00:12:57,040
Speaker 1: keep going. But I'm not going to sit here and

218
00:12:57,080 --> 00:12:59,760
Speaker 1: tell you that it's easy and to have people like

219
00:13:00,160 --> 00:13:04,800
Speaker 1: Blum that the small amount of things that we do

220
00:13:04,880 --> 00:13:07,240
Speaker 1: have that are meant to support us. And I could

221
00:13:07,240 --> 00:13:09,720
Speaker 1: talk all day about entrepreneurship programs, half of them are

222
00:13:09,760 --> 00:13:11,960
Speaker 1: like like the fact that this one has real money,

223
00:13:12,480 --> 00:13:16,640
Speaker 1: a twenty thousand dollars financial grant involved. Oftentimes it is like, oh,

224
00:13:16,679 --> 00:13:19,360
Speaker 1: what do you get for this? Just mentorship? You don't

225
00:13:19,360 --> 00:13:22,440
Speaker 1: get any money, And I'm like, oh, well, that's great.

226
00:13:22,520 --> 00:13:28,679
Speaker 1: But sometimes what marginalized entrepreneurs need is like a check.

227
00:13:28,760 --> 00:13:32,160
Speaker 1: Sometimes sometimes like we're good on mentorship. We have our circle,

228
00:13:32,200 --> 00:13:33,760
Speaker 1: we have our people, like I have the people that

229
00:13:33,800 --> 00:13:36,920
Speaker 1: I consider my mentors. I have my circle. Sometimes what

230
00:13:36,920 --> 00:13:38,520
Speaker 1: we need is money. So the fact that he's going

231
00:13:38,559 --> 00:13:41,160
Speaker 1: after a program that gives actual money when there are

232
00:13:41,280 --> 00:13:45,280
Speaker 1: so many more that don't give money just mentorship is

233
00:13:45,320 --> 00:13:49,480
Speaker 1: really upsetting to me. And I think that, like what

234
00:13:49,520 --> 00:13:52,600
Speaker 1: he is actually saying is these things should be race neutral.

235
00:13:52,800 --> 00:13:56,079
Speaker 1: Things that we know aren't race neutral. Like it's really

236
00:13:56,160 --> 00:13:59,000
Speaker 1: like playing in our face about something that we all know.

237
00:13:59,480 --> 00:14:03,320
Speaker 1: We all know that race and gender and identity all

238
00:14:03,400 --> 00:14:06,520
Speaker 1: of that plays so much into who gets opportunities and

239
00:14:06,559 --> 00:14:09,040
Speaker 1: who gets to succeed and who gets ahead. And he's

240
00:14:09,160 --> 00:14:12,240
Speaker 1: playing in our face and saying, no, let's not consider

241
00:14:12,280 --> 00:14:14,360
Speaker 1: that at all. And what that is actually saying is,

242
00:14:14,679 --> 00:14:17,199
Speaker 1: let's ensure that it is only white men who get

243
00:14:17,200 --> 00:14:19,600
Speaker 1: to get ahead. Let's make sure that when colleges are

244
00:14:19,600 --> 00:14:22,520
Speaker 1: doing admissions, it is not race they're getting consideration to.

245
00:14:22,880 --> 00:14:26,040
Speaker 1: It is gender, it is legacy, It is whether or

246
00:14:26,120 --> 00:14:29,800
Speaker 1: not your father donated a gymnasium, right, And so it

247
00:14:29,840 --> 00:14:31,560
Speaker 1: is just so clear to me what he is saying.

248
00:14:31,640 --> 00:14:33,960
Speaker 1: And yeah, it's just it's it's not like we're it's

249
00:14:33,960 --> 00:14:37,240
Speaker 1: not like we have so much Like I feel like

250
00:14:37,280 --> 00:14:41,720
Speaker 1: people like Blum would probably see the progress that movements

251
00:14:41,760 --> 00:14:43,920
Speaker 1: like Black Lives Matter and Me Too have made in

252
00:14:43,960 --> 00:14:47,880
Speaker 1: the last five or ten years and said, oh, when,

253
00:14:48,200 --> 00:14:50,880
Speaker 1: like I mean, I do I have to say, like

254
00:14:50,960 --> 00:14:54,080
Speaker 1: I do think there is a certain popular segment of

255
00:14:54,160 --> 00:15:01,200
Speaker 1: the population who believes that like, marginalize people get everything

256
00:15:01,320 --> 00:15:04,160
Speaker 1: in this country, and that like we get into college

257
00:15:04,200 --> 00:15:08,600
Speaker 1: for free, we don't we just getting lots of government programs,

258
00:15:08,640 --> 00:15:11,600
Speaker 1: we get free houses. That is a common that is

259
00:15:11,640 --> 00:15:14,480
Speaker 1: a common misconception, and I think that what he is

260
00:15:14,520 --> 00:15:18,040
Speaker 1: doing is playing to that idea that for too long

261
00:15:18,120 --> 00:15:20,920
Speaker 1: it has been marginalized people who are the powerholders in

262
00:15:20,960 --> 00:15:24,120
Speaker 1: this country because of the very small gains that we

263
00:15:24,200 --> 00:15:27,360
Speaker 1: have made in this country. And so yeah, it just

264
00:15:27,480 --> 00:15:30,760
Speaker 1: it is upsetting. If I sound like I'm taking this

265
00:15:30,800 --> 00:15:33,600
Speaker 1: a little bit personally, it's because I am. This feels

266
00:15:33,640 --> 00:15:37,080
Speaker 1: very personal to me. And yeah, Blum, you are not,

267
00:15:37,840 --> 00:15:41,560
Speaker 1: I think, first ever explicitly named enemy of the show.

268
00:15:41,920 --> 00:15:45,160
Speaker 2: Yeah, let's start keeping a list. You know, he can

269
00:15:45,200 --> 00:15:45,800
Speaker 2: be at the top.

270
00:15:46,120 --> 00:15:48,800
Speaker 1: Yeah, Crooked Media has friends of the Pod. We're gonna

271
00:15:48,840 --> 00:15:50,800
Speaker 1: be the first, the first podcast out there that has

272
00:15:51,000 --> 00:15:54,080
Speaker 1: enemies of the Pod a whole written out list. Number one,

273
00:15:54,240 --> 00:15:54,760
Speaker 1: Edward Blum.

274
00:15:55,360 --> 00:16:01,520
Speaker 2: Yeah, I can keep going. But like, maybe we just

275
00:16:01,600 --> 00:16:05,120
Speaker 2: jumped into this episode. Maybe there's something a little lighter

276
00:16:05,160 --> 00:16:07,120
Speaker 2: we could talk about, maybe like pornography.

277
00:16:07,880 --> 00:16:09,480
Speaker 1: I mean, I guess it depends on the kind of

278
00:16:09,560 --> 00:16:11,520
Speaker 1: pornography that you watch.

279
00:16:11,760 --> 00:16:29,880
Speaker 3: But sure, let's take a quick break at our back.

280
00:16:31,880 --> 00:16:34,360
Speaker 1: So we do have an update for all you online

281
00:16:34,400 --> 00:16:36,960
Speaker 1: porn enthusiasts out there. We told you about the age

282
00:16:37,040 --> 00:16:40,360
Speaker 1: verification laws rolling out in states like Virginia, Louisiana, Utah,

283
00:16:40,400 --> 00:16:43,880
Speaker 1: and Arkansas, and here's an update. In Utah, the new

284
00:16:44,000 --> 00:16:46,880
Speaker 1: law requiring adult websites to verify the age of their

285
00:16:47,000 --> 00:16:50,600
Speaker 1: users will remain in effect after a federal judge dismissed

286
00:16:50,600 --> 00:16:54,240
Speaker 1: a lawsuit from digital privacy advocates and the Free Speech Coalition,

287
00:16:54,480 --> 00:16:58,840
Speaker 1: who were trying to challenge the law's constitutionality. Importantly, the judge,

288
00:16:58,960 --> 00:17:02,160
Speaker 1: Judge Ted Stewart, did not address the group's argument that

289
00:17:02,200 --> 00:17:06,160
Speaker 1: the law unfairly discriminates against certain kinds of speech, violates

290
00:17:06,160 --> 00:17:08,840
Speaker 1: the First Amendment rights of porn providers, and intrudes on

291
00:17:08,880 --> 00:17:12,520
Speaker 1: the privacy of individuals who want to view sexually explicit materials.

292
00:17:12,600 --> 00:17:15,040
Speaker 1: But he did say that these digital rights groups could

293
00:17:15,040 --> 00:17:17,840
Speaker 1: not sue Utah officials because of how the law is written.

294
00:17:18,040 --> 00:17:21,080
Speaker 1: The law specifically does not direct the state to pursue

295
00:17:21,160 --> 00:17:25,520
Speaker 1: or prosecute adult websites. Instead, the law gives Utah residents

296
00:17:25,520 --> 00:17:28,479
Speaker 1: the power to sue adult websites and collect damages if

297
00:17:28,560 --> 00:17:32,240
Speaker 1: those websites do not take precautions to verify users ages.

298
00:17:32,560 --> 00:17:37,520
Speaker 1: So basically it's the difference between oh, porn websites get

299
00:17:37,560 --> 00:17:40,159
Speaker 1: this kind of punishment. What the law actually says is

300
00:17:40,160 --> 00:17:43,840
Speaker 1: that if porn websites do not actually take steps to

301
00:17:43,920 --> 00:17:47,679
Speaker 1: verify users' ages, people can sue them and that's how

302
00:17:47,680 --> 00:17:50,000
Speaker 1: they'll get damages. So the last time that we talked

303
00:17:50,040 --> 00:17:52,639
Speaker 1: about this, we also told you about sites like mind geek,

304
00:17:53,320 --> 00:17:57,000
Speaker 1: but is the parent company of very popular pornography tube

305
00:17:57,040 --> 00:17:59,639
Speaker 1: sites like red tube and porn Hub. I don't have

306
00:17:59,640 --> 00:18:03,440
Speaker 1: any day on where most Americans are getting their pornography,

307
00:18:03,480 --> 00:18:06,320
Speaker 1: but I would be willing to bet that mine geek

308
00:18:06,480 --> 00:18:11,680
Speaker 1: represents a very big lie in share of where people

309
00:18:11,720 --> 00:18:15,119
Speaker 1: who are viewing online pornography are doing the viewing. So

310
00:18:15,280 --> 00:18:18,760
Speaker 1: mine geek is pulling their sites from states enforcing age

311
00:18:18,800 --> 00:18:22,400
Speaker 1: verification laws altogether rather than actually trying to go through

312
00:18:22,440 --> 00:18:26,240
Speaker 1: the process of enforcing these age verifications. They actually did

313
00:18:26,280 --> 00:18:29,760
Speaker 1: try to start enforcing age verification in Louisiana earlier this year,

314
00:18:30,000 --> 00:18:32,719
Speaker 1: but the company said that traffic dropped by eighty percent,

315
00:18:33,080 --> 00:18:36,560
Speaker 1: So instead of rolling out age verification systems. Mine Geek

316
00:18:36,600 --> 00:18:39,280
Speaker 1: says they're going to be blocking access entirely to their

317
00:18:39,320 --> 00:18:42,800
Speaker 1: websites like Pornhub and red Tube, calling on users to

318
00:18:42,840 --> 00:18:46,960
Speaker 1: contact their state representatives to oppose these laws, and that

319
00:18:47,040 --> 00:18:51,000
Speaker 1: actually happened in Arkansas this week. Pornhubs operator mind geek

320
00:18:51,040 --> 00:18:54,639
Speaker 1: has blocked all Arkansas users from the site and also

321
00:18:54,760 --> 00:18:57,840
Speaker 1: red Tube after the state's new age verification law went

322
00:18:57,880 --> 00:19:01,480
Speaker 1: into effect this Tuesday. All of that is to say,

323
00:19:02,200 --> 00:19:05,040
Speaker 1: if you're planning on looking at pornography online in Arkansas,

324
00:19:05,280 --> 00:19:07,000
Speaker 1: you might need to make other plans.

325
00:19:07,359 --> 00:19:09,880
Speaker 2: Yeah, I mean, it seems like if that's what you're

326
00:19:09,880 --> 00:19:11,880
Speaker 2: planning to do, it would be smart to make other

327
00:19:11,960 --> 00:19:17,040
Speaker 2: plans because the site is blocked. But also if your

328
00:19:17,200 --> 00:19:20,280
Speaker 2: big plan for the weekend is to go to Arkansas

329
00:19:20,320 --> 00:19:23,920
Speaker 2: and look at pornography, maybe you should think about making

330
00:19:23,960 --> 00:19:26,400
Speaker 2: some other plans, just like anyway, I don't.

331
00:19:26,200 --> 00:19:28,400
Speaker 1: Know, that doesn't sound so bad, Like somebody out there

332
00:19:28,400 --> 00:19:30,919
Speaker 1: has probably had a big weekend of like watching online

333
00:19:30,920 --> 00:19:34,400
Speaker 1: pornography in Arkansas. I don't I don't know. There are

334
00:19:34,400 --> 00:19:35,920
Speaker 1: worse plans, I.

335
00:19:35,840 --> 00:19:39,760
Speaker 2: Guess, so, yeah, yeah, it's fair, you know, if that's

336
00:19:39,760 --> 00:19:41,960
Speaker 2: what people want to do, they should have the right

337
00:19:42,000 --> 00:19:44,160
Speaker 2: to do that. And I guess that's really the core

338
00:19:44,440 --> 00:19:48,720
Speaker 2: of this issue, right, Like maybe I have my opinions

339
00:19:48,760 --> 00:19:52,760
Speaker 2: about the merits of traveling to Arkansas to want to

340
00:19:52,800 --> 00:19:56,000
Speaker 2: look at porn, but like people should have the right

341
00:19:56,040 --> 00:19:57,719
Speaker 2: to do it if that's what they want, and some

342
00:19:57,760 --> 00:19:58,520
Speaker 2: people live there.

343
00:19:58,680 --> 00:20:02,360
Speaker 1: Oh, if you saw a lineup for my weekend plans,

344
00:20:03,000 --> 00:20:05,639
Speaker 1: you would not be judging it is. It is nothing

345
00:20:05,720 --> 00:20:08,520
Speaker 1: quite as glamorous as watching online porn in Arkansas, I

346
00:20:08,520 --> 00:20:12,080
Speaker 1: can tell you that. Okay, So, speaking of things being

347
00:20:12,119 --> 00:20:14,840
Speaker 1: blocked online, we have another update for you, and that

348
00:20:14,960 --> 00:20:18,520
Speaker 1: is that Meta has officially began blocking news content from

349
00:20:18,560 --> 00:20:21,520
Speaker 1: its platforms in Canada. Y'all might remember that we talked

350
00:20:21,560 --> 00:20:24,680
Speaker 1: about this a few newscasts Togo also major shout outs

351
00:20:24,680 --> 00:20:28,600
Speaker 1: to listener Sarah Jay from Canada, who I've kind of

352
00:20:28,600 --> 00:20:31,920
Speaker 1: been calling my Canadian correspondent because she's been really helping

353
00:20:31,920 --> 00:20:35,760
Speaker 1: me understand this story and all stories happening in Canada. Basically,

354
00:20:36,040 --> 00:20:38,520
Speaker 1: this block is in response to a new Canadian law

355
00:20:38,600 --> 00:20:41,720
Speaker 1: called the Online News Act that requires tech platforms like

356
00:20:41,760 --> 00:20:45,840
Speaker 1: Facebook and Google to negotiate with and pay news organizations

357
00:20:45,880 --> 00:20:48,840
Speaker 1: to distribute their content. A few months ago, Facebook was

358
00:20:48,880 --> 00:20:51,080
Speaker 1: going on and on and back and forth kind of

359
00:20:51,119 --> 00:20:54,320
Speaker 1: threatening to pull news content in Canada if the Online

360
00:20:54,320 --> 00:20:56,679
Speaker 1: News Act was passed. Y'all might recall they did the

361
00:20:56,680 --> 00:21:00,159
Speaker 1: same thing in Australia and news content was actually temporary

362
00:21:00,160 --> 00:21:02,959
Speaker 1: blocked in Australia. And Facebook is actually threatening to do

363
00:21:03,000 --> 00:21:05,440
Speaker 1: the very same thing here in the United States because

364
00:21:05,440 --> 00:21:07,800
Speaker 1: of a California bill that would require tech companies to

365
00:21:07,840 --> 00:21:11,280
Speaker 1: pay media outlets for posting news content. But that California

366
00:21:11,320 --> 00:21:13,600
Speaker 1: bill has been put on hold until next year. But

367
00:21:13,680 --> 00:21:17,080
Speaker 1: back in Canada, Facebook really did make good on their

368
00:21:17,080 --> 00:21:20,240
Speaker 1: threats and start blocking news content. They wrote in a

369
00:21:20,240 --> 00:21:22,879
Speaker 1: blog post this week, in order to comply with the

370
00:21:22,920 --> 00:21:25,720
Speaker 1: Online News Act, we have begun the process of ending

371
00:21:25,760 --> 00:21:28,919
Speaker 1: news availability in Canada. These changes start today and will

372
00:21:28,960 --> 00:21:31,920
Speaker 1: be implemented for all people accessing Facebook and Instagram and

373
00:21:32,000 --> 00:21:35,040
Speaker 1: Canada over the next few weeks. So what does this

374
00:21:35,080 --> 00:21:38,440
Speaker 1: actually mean. Well, Facebook said that it means that news

375
00:21:38,480 --> 00:21:41,840
Speaker 1: links and content posted by news publishers and broadcasters in

376
00:21:41,880 --> 00:21:45,280
Speaker 1: Canada will no longer be viewable by users in Canada,

377
00:21:45,720 --> 00:21:48,040
Speaker 1: and this also might be the case for news content

378
00:21:48,080 --> 00:21:50,480
Speaker 1: on Google in Canada as well. Because Google is also

379
00:21:50,560 --> 00:21:53,560
Speaker 1: threatening to pull out up news content altogether in the

380
00:21:53,560 --> 00:21:55,840
Speaker 1: country if this was past and Mike, what I thought

381
00:21:55,920 --> 00:21:58,440
Speaker 1: was really wild was this was happening at a time

382
00:21:58,440 --> 00:22:01,440
Speaker 1: where there was actually a ton of big news in Canada.

383
00:22:01,600 --> 00:22:04,440
Speaker 1: Right as is all was happening, Canadian Prime Minister Justin

384
00:22:04,440 --> 00:22:07,760
Speaker 1: Trudeau made a major overhaul to his cabinet and replaced

385
00:22:07,760 --> 00:22:10,840
Speaker 1: seven ministers ahead of the country's next election, and also

386
00:22:10,920 --> 00:22:14,520
Speaker 1: announced a separation of his longtime wife, Sophie after eighteen

387
00:22:14,600 --> 00:22:17,000
Speaker 1: years of marriage. So that means that as all this

388
00:22:17,119 --> 00:22:20,520
Speaker 1: news was happening, you had to be like finding other

389
00:22:20,640 --> 00:22:23,840
Speaker 1: ways other than Facebook or Instagram to get this news.

390
00:22:24,119 --> 00:22:26,480
Speaker 1: It actually sounds a little bit silly, but it was

391
00:22:26,520 --> 00:22:29,600
Speaker 1: a very accurate tweet that I saw from user elmin

392
00:22:29,760 --> 00:22:32,520
Speaker 1: eighty eight, who really summed it up. When Popcrave, a

393
00:22:32,560 --> 00:22:36,159
Speaker 1: Twitter account is really known more for tweeting about like

394
00:22:36,760 --> 00:22:40,680
Speaker 1: Taylor Swift and Doja cat Drama, tweeted about Trudeau's separation,

395
00:22:41,200 --> 00:22:44,000
Speaker 1: l Main eighty eight tweeted, imagine hearing about this from

396
00:22:44,040 --> 00:22:48,199
Speaker 1: popcrave first, because Canadian news sites can't be displayed on Instagram,

397
00:22:48,440 --> 00:22:50,679
Speaker 1: and that user is one hundred percent right. That is

398
00:22:50,720 --> 00:22:53,680
Speaker 1: exactly what's going on. I saw people in Canada replying

399
00:22:53,680 --> 00:22:56,640
Speaker 1: to that tweet being like, yeah, wow, this pop crave

400
00:22:56,680 --> 00:22:59,280
Speaker 1: tweet is the first I've heard of Trudeau's separation.

401
00:22:59,680 --> 00:23:03,440
Speaker 2: Yes, this is a weird sitch.

402
00:23:03,840 --> 00:23:05,879
Speaker 1: Yeah, we talked quite a bit about this in the

403
00:23:05,960 --> 00:23:08,520
Speaker 1: last episode that we did about when when Facebook was

404
00:23:08,600 --> 00:23:13,320
Speaker 1: first threatening to pull this news content. I don't know, Like,

405
00:23:13,680 --> 00:23:16,639
Speaker 1: I this is like a little like above my pay grade.

406
00:23:17,240 --> 00:23:22,800
Speaker 1: I don't like the vibe where Facebook and big tech companies.

407
00:23:22,840 --> 00:23:26,000
Speaker 1: But really, Facebook get to have this level of control

408
00:23:26,119 --> 00:23:30,880
Speaker 1: over entire industries. I worked in news media and we

409
00:23:31,000 --> 00:23:35,000
Speaker 1: really did like when Facebook said it's video, we pivoted

410
00:23:35,000 --> 00:23:38,840
Speaker 1: to video. When Facebook said, oh, it's links that keep

411
00:23:38,880 --> 00:23:41,960
Speaker 1: you on Facebook, we pivoted to that. And so I

412
00:23:42,000 --> 00:23:44,920
Speaker 1: don't like this idea that Facebook or any big tech

413
00:23:44,960 --> 00:23:48,159
Speaker 1: company has such a hold over an entire industry and

414
00:23:48,320 --> 00:23:52,280
Speaker 1: can play such a big part over, you know, shaping

415
00:23:52,320 --> 00:23:53,040
Speaker 1: that industry.

416
00:23:53,480 --> 00:23:57,760
Speaker 2: Yeah, I remember news aggregator sites were like a big

417
00:23:57,800 --> 00:24:02,160
Speaker 2: concern a while ago, right, Like, what.

418
00:24:02,200 --> 00:24:02,719
Speaker 1: Happened with that?

419
00:24:02,720 --> 00:24:03,719
Speaker 2: Do they just go away?

420
00:24:04,440 --> 00:24:04,760
Speaker 1: Did they?

421
00:24:05,040 --> 00:24:10,240
Speaker 2: Did Facebook just become like the news aggregator site?

422
00:24:10,760 --> 00:24:13,399
Speaker 1: Yeah, that's a good question. But I think it speaks

423
00:24:13,440 --> 00:24:19,080
Speaker 1: to how the our news landscape just changes so quickly,

424
00:24:19,119 --> 00:24:21,320
Speaker 1: and it's like it's like, wait, whatever happened to this

425
00:24:21,440 --> 00:24:26,359
Speaker 1: or that? And something that my Canadian correspondent in My

426
00:24:26,440 --> 00:24:28,960
Speaker 1: Head points out is that one of her personal fears

427
00:24:29,040 --> 00:24:32,480
Speaker 1: is that the Canadian media landscape is right now so

428
00:24:32,760 --> 00:24:36,320
Speaker 1: overrun with American media that it can be perhaps confusing

429
00:24:36,560 --> 00:24:41,680
Speaker 1: for Canadians who who do need access to like thoughtful, accurate,

430
00:24:42,000 --> 00:24:45,040
Speaker 1: nuanced news sources based in Canada. And I can only

431
00:24:45,080 --> 00:24:47,240
Speaker 1: imagine that this move from Facebook it's going to make

432
00:24:47,280 --> 00:24:50,480
Speaker 1: the Canadian media landscape that much more complicated.

433
00:24:50,800 --> 00:24:54,480
Speaker 2: It both feels like a pretty huge thing and also

434
00:24:54,520 --> 00:24:58,240
Speaker 2: it's like hard to know what to say on it,

435
00:24:58,320 --> 00:25:03,160
Speaker 2: because you know, on one hand, people not being able

436
00:25:03,200 --> 00:25:05,840
Speaker 2: to get their news from Facebook, part of that actually

437
00:25:05,840 --> 00:25:09,159
Speaker 2: sounds kind of appealing, like people looking elsewhere for their news,

438
00:25:10,720 --> 00:25:15,600
Speaker 2: But also it does seem dangerous because people are gonna

439
00:25:15,640 --> 00:25:17,760
Speaker 2: be that much less informed. Like a lot of people

440
00:25:17,800 --> 00:25:20,480
Speaker 2: do get their news from Facebook, and if they stop

441
00:25:20,520 --> 00:25:23,639
Speaker 2: getting it there there, I guess there's gonna be that

442
00:25:23,880 --> 00:25:28,960
Speaker 2: much less informed or that space in their cognitive attention

443
00:25:29,240 --> 00:25:32,520
Speaker 2: that would have been taken up by reading news about

444
00:25:32,520 --> 00:25:38,360
Speaker 2: what's going on with government or climate or whatever. News

445
00:25:39,080 --> 00:25:42,320
Speaker 2: is instead going to be taken up by reading things

446
00:25:42,359 --> 00:25:47,359
Speaker 2: on Facebook that are not news, right, And I guess

447
00:25:47,359 --> 00:25:50,080
Speaker 2: that kind of makes me hear like, what what isn't

448
00:25:50,200 --> 00:25:56,000
Speaker 2: news on Facebook? Is it just Aunt Mary's posts about

449
00:25:57,000 --> 00:25:59,239
Speaker 2: what's going on in her garden, which, like I am

450
00:25:59,320 --> 00:26:03,160
Speaker 2: interested in what's going on in her gardens, but like, uh,

451
00:26:03,320 --> 00:26:05,280
Speaker 2: like what isn't news? You know, how do they how

452
00:26:05,280 --> 00:26:07,880
Speaker 2: do how do they decide what is news and what isn't.

453
00:26:08,280 --> 00:26:10,120
Speaker 1: Well, we do have a little bit of a template

454
00:26:10,280 --> 00:26:12,560
Speaker 1: for how this might look and how this might go

455
00:26:12,640 --> 00:26:15,120
Speaker 1: down with what happened in Australia. We did a whole

456
00:26:15,119 --> 00:26:18,159
Speaker 1: episode with my friend Tabitha who works with human and

457
00:26:18,200 --> 00:26:23,199
Speaker 1: digital rights activists in Australia, and when Facebook temporarily blocked

458
00:26:23,240 --> 00:26:26,360
Speaker 1: news content they're one of the biggest concerns was how

459
00:26:26,560 --> 00:26:30,440
Speaker 1: marginalized people, how it was going to impact their organizing efforts,

460
00:26:30,440 --> 00:26:34,560
Speaker 1: and so she saw right away a pretty big impact

461
00:26:34,640 --> 00:26:40,040
Speaker 1: in how campaigns like human rights campaigners and organizers were

462
00:26:40,160 --> 00:26:42,760
Speaker 1: or were not able to get their messages out to

463
00:26:42,920 --> 00:26:46,119
Speaker 1: spread their causes when this was blocked. We'll throw the

464
00:26:46,160 --> 00:26:49,199
Speaker 1: episode in the show notes, but it could provide a

465
00:26:49,240 --> 00:26:51,880
Speaker 1: template for what it might look like now. But as

466
00:26:51,880 --> 00:26:55,400
Speaker 1: always with these big shifts, my my thinking always goes

467
00:26:55,440 --> 00:26:59,640
Speaker 1: to marginalize people, like, how will this impact people who

468
00:26:59,760 --> 00:27:04,720
Speaker 1: have been using online platforms like Instagram and Facebook to

469
00:27:05,040 --> 00:27:06,680
Speaker 1: create change for their communities.

470
00:27:07,359 --> 00:27:10,200
Speaker 2: Yes, that's a great point. Well's yeah, let's stay checked

471
00:27:10,200 --> 00:27:12,960
Speaker 2: in with that. What else have you got for us?

472
00:27:13,040 --> 00:27:14,159
Speaker 2: Bridget So let's move.

473
00:27:14,000 --> 00:27:16,480
Speaker 1: On to a story that I am really excited about.

474
00:27:16,480 --> 00:27:18,760
Speaker 1: I have been like reading about it all week. Basically,

475
00:27:18,840 --> 00:27:21,840
Speaker 1: if you've read the book The Immortal Life of Henrietta Lax,

476
00:27:21,880 --> 00:27:24,080
Speaker 1: who probably know where I'm going because more than seventy

477
00:27:24,160 --> 00:27:28,400
Speaker 1: years ago, doctors at John Hopkins Hospital took Henrietta Lex's

478
00:27:28,400 --> 00:27:31,960
Speaker 1: cervical cells without her knowledge or consent before she died

479
00:27:31,960 --> 00:27:36,119
Speaker 1: of cervical cancer. Those cells became the first human cells

480
00:27:36,160 --> 00:27:40,679
Speaker 1: to continuously grow and reproduce in lab dishes. Hala cells,

481
00:27:40,720 --> 00:27:43,320
Speaker 1: as they were called, went on to be a cornerstone

482
00:27:43,320 --> 00:27:48,120
Speaker 1: of modern medicine, enabling countless scientific and medical innovations, including

483
00:27:48,119 --> 00:27:51,560
Speaker 1: the development of the polio vaccine, genetic mapping, and even

484
00:27:51,720 --> 00:27:54,680
Speaker 1: the COVID nineteen vaccines that many of us got just recently,

485
00:27:55,240 --> 00:27:58,159
Speaker 1: but because Henrietta A. Lax is a black woman, she

486
00:27:58,720 --> 00:28:02,200
Speaker 1: nor her family or her her descendants were ever compensated.

487
00:28:02,720 --> 00:28:05,600
Speaker 1: That is until now. That is because a lawyer for

488
00:28:05,640 --> 00:28:08,040
Speaker 1: her family said that they have finally reached a settlement

489
00:28:08,040 --> 00:28:11,919
Speaker 1: with the biotechnology company that they say reaped billions of

490
00:28:12,000 --> 00:28:15,640
Speaker 1: dollars in profit from the racist medical system that essentially

491
00:28:15,800 --> 00:28:20,239
Speaker 1: stole Henrietta Laxi's cells without her consent. Henrietta cells were

492
00:28:20,240 --> 00:28:23,879
Speaker 1: harvested back in nineteen fifty one, and sadly, it was

493
00:28:23,960 --> 00:28:27,679
Speaker 1: not illegal to take someone's sells without their permission or consent,

494
00:28:28,200 --> 00:28:30,960
Speaker 1: but lawyers for her family say that the biotechnology company

495
00:28:31,359 --> 00:28:35,920
Speaker 1: Thermo Fisher Scientific Ink of Waltham, Massachusetts, still continued to

496
00:28:35,960 --> 00:28:39,720
Speaker 1: commercialize these results long after the origins of her cell

497
00:28:39,760 --> 00:28:43,200
Speaker 1: line became well known. So basically, they discontinued to profit

498
00:28:43,240 --> 00:28:47,440
Speaker 1: off of Henrietta LAX's cells while giving her family nothing. Now,

499
00:28:47,520 --> 00:28:50,600
Speaker 1: John Hopkins says that they have never sold or profited

500
00:28:50,680 --> 00:28:54,560
Speaker 1: from the cell lines, but many companies have patented ways

501
00:28:54,560 --> 00:28:56,760
Speaker 1: of using them and made lots and lots of money

502
00:28:56,800 --> 00:28:59,600
Speaker 1: from it. Now, while her family was not financially compensated

503
00:28:59,680 --> 00:29:02,760
Speaker 1: until week, they did reach an agreement with the National

504
00:29:02,760 --> 00:29:05,600
Speaker 1: Institutes of Health back in twenty thirteen that gave them

505
00:29:05,600 --> 00:29:08,200
Speaker 1: some control of how the DNA code from those cells

506
00:29:08,200 --> 00:29:13,600
Speaker 1: were used. Something that really just feels palpably emotional, I

507
00:29:13,640 --> 00:29:16,400
Speaker 1: guess I'll say, is that all of this happened on

508
00:29:16,520 --> 00:29:20,120
Speaker 1: what would have been Henrietta Laxe's one hundred and third birthday,

509
00:29:20,240 --> 00:29:24,240
Speaker 1: and her only surviving child, Lawrence Lack Senior, at age

510
00:29:24,440 --> 00:29:28,920
Speaker 1: eighty six, finally got to see justice done and finally

511
00:29:28,960 --> 00:29:32,520
Speaker 1: got to see his family get some compensation for the

512
00:29:32,760 --> 00:29:38,080
Speaker 1: immeasurable debt that the medical industry owes him and his family.

513
00:29:38,640 --> 00:29:40,480
Speaker 1: There couldn't have been a more fitting day for her

514
00:29:40,480 --> 00:29:42,800
Speaker 1: to have justice, for her family to have relief.

515
00:29:42,840 --> 00:29:43,240
Speaker 3: He said.

516
00:29:43,720 --> 00:29:47,000
Speaker 1: It was a long fight over seventy years, and Henrietta

517
00:29:47,080 --> 00:29:51,120
Speaker 1: Lax gets her day. This is sort of a bitter

518
00:29:51,240 --> 00:29:57,800
Speaker 1: sweet thing for me because I obviously this family deserves

519
00:29:58,120 --> 00:30:01,600
Speaker 1: some kind of compensation of justice. It doesn't say how

520
00:30:01,680 --> 00:30:05,600
Speaker 1: much money they got, but I hope it was a lot,

521
00:30:05,720 --> 00:30:08,080
Speaker 1: because they deserve it. But it's one of those things

522
00:30:08,120 --> 00:30:11,800
Speaker 1: that the impact of the of the of the money

523
00:30:11,960 --> 00:30:17,200
Speaker 1: made off of this unethical harvesting of their ancestors cells

524
00:30:17,240 --> 00:30:20,240
Speaker 1: without her consent or knowledge. It's there's no amount of

525
00:30:20,280 --> 00:30:23,520
Speaker 1: money that could that could make that right. There's no

526
00:30:23,560 --> 00:30:26,560
Speaker 1: amount of money. There's no dollar amount that could write

527
00:30:26,800 --> 00:30:31,160
Speaker 1: that deep and immeasurable wound that I think this family suffered.

528
00:30:31,200 --> 00:30:33,400
Speaker 1: You know, if you read the book, you know that

529
00:30:33,480 --> 00:30:37,760
Speaker 1: while biotechnology companies were making billions of dollars, the Lacks

530
00:30:37,800 --> 00:30:41,440
Speaker 1: family sometimes went without health care or health insurance because

531
00:30:41,440 --> 00:30:44,480
Speaker 1: they didn't have money. And all of this time, people

532
00:30:44,480 --> 00:30:47,440
Speaker 1: were making so much money off of these cells. I

533
00:30:47,480 --> 00:30:51,560
Speaker 1: would argue unethically and inappropriately while their family just got nothing.

534
00:30:51,600 --> 00:30:54,520
Speaker 1: I think this case really highlights the reality of medical

535
00:30:54,640 --> 00:30:56,600
Speaker 1: racism in the United States.

536
00:30:57,000 --> 00:31:00,560
Speaker 2: Yeah, you know, I would like to hear Edward Blooms

537
00:31:00,640 --> 00:31:03,680
Speaker 2: take on this, you know, like.

538
00:31:04,360 --> 00:31:08,320
Speaker 1: Oh, I'm sure, I'm sure it's very nuanced. I'm sure

539
00:31:08,360 --> 00:31:14,000
Speaker 1: it really deals with grapples with our country's complex legacy

540
00:31:14,080 --> 00:31:14,640
Speaker 1: with race.

541
00:31:14,920 --> 00:31:18,160
Speaker 2: Yeah, the story like the stories like this where you know,

542
00:31:18,200 --> 00:31:23,000
Speaker 2: this woman's cells were harvested without her consent. They were

543
00:31:23,040 --> 00:31:29,959
Speaker 2: just like taken and used by the medical industry for decades. Uh,

544
00:31:31,680 --> 00:31:37,280
Speaker 2: that doesn't happen to white people, right, Like racism is real,

545
00:31:37,640 --> 00:31:43,680
Speaker 2: It's like a real thing. This story is a great

546
00:31:43,960 --> 00:31:46,840
Speaker 2: reminder to the people who apparently need to be reminded.

547
00:31:47,120 --> 00:31:49,040
Speaker 1: I could probably think of somebody who might need a

548
00:31:49,040 --> 00:31:51,320
Speaker 1: little reminder, and that's Elon Musk.

549
00:31:51,680 --> 00:31:53,720
Speaker 2: Really, what's Elon done now?

550
00:31:54,360 --> 00:31:58,280
Speaker 1: Well, that's a little a little what's Elon done now? Quickie? Twitter?

551
00:31:58,920 --> 00:32:02,200
Speaker 1: By the way, I'm refusing to call it X, you'll

552
00:32:02,200 --> 00:32:06,240
Speaker 1: probably I'm not doing it. I'm not doing it. Stop

553
00:32:06,280 --> 00:32:08,680
Speaker 1: trying to make that happen. It's not gonna take with me.

554
00:32:09,000 --> 00:32:11,200
Speaker 1: I don't think it's taking with people. I saw an

555
00:32:11,280 --> 00:32:13,560
Speaker 1: Elon Musk tweet that he was like, one day you'll

556
00:32:13,600 --> 00:32:15,760
Speaker 1: be saying to your friends like, oh, I already paid

557
00:32:15,760 --> 00:32:17,680
Speaker 1: you the money I loaned you on X or oh

558
00:32:17,720 --> 00:32:19,960
Speaker 1: I talked to my girlfriend on X and I was like, like, hell,

559
00:32:20,040 --> 00:32:22,479
Speaker 1: we will. That's not gonna happen. I'm not doing it.

560
00:32:22,480 --> 00:32:24,080
Speaker 1: I don't think anybody should do it anyway. I'm not

561
00:32:24,080 --> 00:32:26,920
Speaker 1: calling it X. But here's what's happening over at Twitter.

562
00:32:27,240 --> 00:32:30,479
Speaker 1: Twitter just rolled out a pretty telling new feature, and

563
00:32:30,520 --> 00:32:34,640
Speaker 1: that is allowing Twitter Blue subscribers to manually hide their

564
00:32:34,680 --> 00:32:39,120
Speaker 1: blue check marks. Gee, I can't imagine why I thought

565
00:32:39,160 --> 00:32:41,880
Speaker 1: that Twitter Blue. Is this like really cool thing? That

566
00:32:41,960 --> 00:32:44,880
Speaker 1: everybody was gonna be really excited to have. Why would

567
00:32:44,920 --> 00:32:47,400
Speaker 1: you need to have the option to hide the blue

568
00:32:47,480 --> 00:32:50,000
Speaker 1: check mark that at one point was this like I

569
00:32:50,120 --> 00:32:53,800
Speaker 1: was told, was this like big online status symbol? Why

570
00:32:53,800 --> 00:32:56,000
Speaker 1: would you want to be able to hide the check mark?

571
00:32:56,240 --> 00:32:58,920
Speaker 1: Could it be that maybe paying eight dollars to Elon

572
00:32:59,040 --> 00:33:03,040
Speaker 1: Musk is so uncool that Twitter has decided that the

573
00:33:03,080 --> 00:33:06,200
Speaker 1: only way to make it possibly something that somebody would

574
00:33:06,200 --> 00:33:09,320
Speaker 1: want to opt into is the option to hide it.

575
00:33:09,360 --> 00:33:11,520
Speaker 2: They've come so far, so fast.

576
00:33:13,680 --> 00:33:15,160
Speaker 1: So that's what Elon Musk is doing now.

577
00:33:20,160 --> 00:33:20,320
Speaker 2: More.

578
00:33:20,320 --> 00:33:21,040
Speaker 3: After a quick.

579
00:33:20,840 --> 00:33:35,400
Speaker 1: Break, let's get right back into it. Okay, Mike, I

580
00:33:35,520 --> 00:33:39,160
Speaker 1: have a question for you. Okay, sometimes when we're talking,

581
00:33:40,320 --> 00:33:43,080
Speaker 1: I will make a reference or like use that phrase

582
00:33:43,360 --> 00:33:46,040
Speaker 1: and you will say, I don't know, I don't know

583
00:33:46,040 --> 00:33:48,320
Speaker 1: what you're saying, Like what are you saying? This is

584
00:33:48,320 --> 00:33:51,120
Speaker 1: the this is the it happens frequently. It is it

585
00:33:51,200 --> 00:33:53,440
Speaker 1: is the It's what happens when you make a tech

586
00:33:53,520 --> 00:33:58,240
Speaker 1: podcast where one person is chronically online and the other

587
00:33:58,360 --> 00:34:01,680
Speaker 1: is chronically offline, but like a deep tech person where

588
00:34:01,720 --> 00:34:04,360
Speaker 1: I'm like, oh, you know, blah blah blah, and You're like,

589
00:34:04,440 --> 00:34:06,720
Speaker 1: I don't know what these words are. What are you saying?

590
00:34:07,280 --> 00:34:10,440
Speaker 1: Have you heard the phrase milkshake duck? Does this mean

591
00:34:10,520 --> 00:34:12,040
Speaker 1: anything to you? Milkshake duck.

592
00:34:12,640 --> 00:34:15,120
Speaker 2: I wish I could say I have, but I have

593
00:34:15,200 --> 00:34:18,920
Speaker 2: to admit that the word, the phrase milkshake duck, means

594
00:34:19,000 --> 00:34:21,680
Speaker 2: nothing to me. But I am intrigued. I want to know.

595
00:34:21,880 --> 00:34:24,720
Speaker 1: So a milkshake duck, I think it comes from a comic.

596
00:34:25,200 --> 00:34:29,239
Speaker 1: Milkshake duck is when somebody, somebody or something is like

597
00:34:29,640 --> 00:34:33,200
Speaker 1: a positive phenomenon in the news and then everybody's like, oh,

598
00:34:33,239 --> 00:34:36,759
Speaker 1: we love this. So the comic is like a duck

599
00:34:36,800 --> 00:34:39,520
Speaker 1: that drinks milkshakes and everybody's like, Oh, that's so cute.

600
00:34:39,560 --> 00:34:42,840
Speaker 1: This duck drinks milkshakes, and then screen flips and it's

601
00:34:43,400 --> 00:34:47,000
Speaker 1: I regret to report milkshake duck is racist. It's basically

602
00:34:47,040 --> 00:34:50,000
Speaker 1: this idea that when people get popular in culture and

603
00:34:50,000 --> 00:34:53,879
Speaker 1: everybody's like, oh, that's delightful, usually it's not too long

604
00:34:53,960 --> 00:34:57,480
Speaker 1: until like something unseemly comes out and you're like, oh,

605
00:34:57,480 --> 00:35:00,319
Speaker 1: that's less delightful. I wish I didn't know that, so

606
00:35:00,640 --> 00:35:03,960
Speaker 1: new milkshake duck just dropped. I take no pleasure in

607
00:35:04,040 --> 00:35:06,440
Speaker 1: reporting this, and really it's just kind of a PSA

608
00:35:06,560 --> 00:35:08,640
Speaker 1: for all my TikTok earlies out there. So if you

609
00:35:08,680 --> 00:35:12,320
Speaker 1: spend any time scrolling TikTok, you've probably seen the attention

610
00:35:13,080 --> 00:35:17,360
Speaker 1: Pickpocket Lady. She's this woman in Italy who goes to

611
00:35:17,520 --> 00:35:22,799
Speaker 1: like crowded tourists outdoor locations and vocally warns people about pickpocket.

612
00:35:22,840 --> 00:35:27,080
Speaker 1: She says at big pocket, thief, thief. She kind of

613
00:35:27,120 --> 00:35:32,000
Speaker 1: became an icon. I like definitely saw those videos and

614
00:35:32,120 --> 00:35:34,640
Speaker 1: was like, oh, look at this haha. The New York

615
00:35:34,640 --> 00:35:37,520
Speaker 1: Times today they're glowing profile of this woman. Her name

616
00:35:37,560 --> 00:35:40,520
Speaker 1: is Monica Poli. She's fifty seven. I read the New

617
00:35:40,600 --> 00:35:42,960
Speaker 1: York Times profile because I did, like, I saw the

618
00:35:43,880 --> 00:35:47,480
Speaker 1: pickpocket chant on TikTok at that time. Do you remember

619
00:35:47,480 --> 00:35:50,400
Speaker 1: that time where the where my cat was trying to

620
00:35:50,440 --> 00:35:52,880
Speaker 1: eat food off of my plate when we were eating dinner,

621
00:35:53,080 --> 00:35:56,560
Speaker 1: and I was like a thief, thief, pickpocket, Like it

622
00:35:56,760 --> 00:35:59,520
Speaker 1: became a thing in my life.

623
00:35:59,640 --> 00:36:02,120
Speaker 2: Yeah, you were really into this lady. I remember that,

624
00:36:02,280 --> 00:36:06,399
Speaker 2: and I watched the videos she Yeah, I was into her.

625
00:36:06,440 --> 00:36:09,280
Speaker 2: You know, everybody hates a thief. I hate a thief,

626
00:36:09,320 --> 00:36:13,400
Speaker 2: You hate a thief, and yeah, she was really taking

627
00:36:13,440 --> 00:36:15,040
Speaker 2: a stand against them.

628
00:36:15,560 --> 00:36:17,440
Speaker 1: Well, I am so sorry to report that the New

629
00:36:17,520 --> 00:36:20,040
Speaker 1: York Times profile of this woman that was very glowing

630
00:36:20,440 --> 00:36:23,720
Speaker 1: failed to point out her affiliation with the far right group,

631
00:36:24,000 --> 00:36:27,680
Speaker 1: the Lega Party of Venice. This is from Eurews quote.

632
00:36:27,719 --> 00:36:31,080
Speaker 1: The Lega Party, formerly known as the Lega Nord Northern League,

633
00:36:31,160 --> 00:36:34,040
Speaker 1: is one of Italy's most far right political parties. The

634
00:36:34,080 --> 00:36:36,759
Speaker 1: foundation of the party is largely based on policies that

635
00:36:36,800 --> 00:36:41,040
Speaker 1: are anti immigrant, anti Southern Italian, and anti LGBTQ. One

636
00:36:41,080 --> 00:36:44,799
Speaker 1: of the most outspoken Lega politicians was Jean Carlo Gentalini,

637
00:36:44,840 --> 00:36:48,760
Speaker 1: the two time mayor of Trevisio in the northern Vinto region.

638
00:36:49,360 --> 00:36:52,120
Speaker 1: Gentlini was famous for saying that he wanted to perpetrate

639
00:36:52,239 --> 00:36:55,600
Speaker 1: a quote ethnic cleansing of butt lovers referring to gay people,

640
00:36:55,880 --> 00:36:58,880
Speaker 1: as well as advocating to dress up migrants as rabbits

641
00:36:58,880 --> 00:37:03,279
Speaker 1: in order to hunt them down and shoot them. And honestly,

642
00:37:03,960 --> 00:37:07,040
Speaker 1: this is like a weird situation where like with this

643
00:37:07,160 --> 00:37:10,200
Speaker 1: new clarity, I look back on those videos and I'm like, oh,

644
00:37:10,280 --> 00:37:13,160
Speaker 1: of course they were racists, Like you know what I mean, Like,

645
00:37:13,719 --> 00:37:18,080
Speaker 1: you know, her loud accusations of people being pickpockets just

646
00:37:18,080 --> 00:37:21,320
Speaker 1: comes into such Sharper Focus. In the New York Times profile,

647
00:37:21,400 --> 00:37:23,440
Speaker 1: she says that she like they ask her like, how

648
00:37:23,440 --> 00:37:25,239
Speaker 1: do you tell who's a pickpocket? And she's like, oh,

649
00:37:25,239 --> 00:37:26,840
Speaker 1: I can just tell. I have a sixth sense with

650
00:37:26,880 --> 00:37:29,759
Speaker 1: these people. And as Twitter user Civo Mengro put it,

651
00:37:30,120 --> 00:37:32,640
Speaker 1: just so we're all clear, this has always always been

652
00:37:32,680 --> 00:37:36,640
Speaker 1: a campaign of racist harassment against Roma. Her I just know,

653
00:37:36,960 --> 00:37:40,040
Speaker 1: is just racism. You have to understand that pickpocket is

654
00:37:40,120 --> 00:37:43,160
Speaker 1: the number one stereotype of Roma in Europe, and this

655
00:37:43,200 --> 00:37:47,319
Speaker 1: is about removing Roma from public spaces. And honestly, like,

656
00:37:47,760 --> 00:37:49,239
Speaker 1: looking back at this, I'm like, oh my god, how

657
00:37:49,280 --> 00:37:51,319
Speaker 1: did I not see this? And it really makes you

658
00:37:51,400 --> 00:37:55,880
Speaker 1: see how social media makes this kind of thing like

659
00:37:56,000 --> 00:37:58,840
Speaker 1: digestible or even charming, because when you go back and

660
00:37:58,840 --> 00:38:02,200
Speaker 1: look at her videos, you see actual pickpockets, right, Like

661
00:38:02,200 --> 00:38:04,120
Speaker 1: you don't see actual evidence of people you're like, oh

662
00:38:04,120 --> 00:38:06,879
Speaker 1: that's a pickpocket. You just see her. You just hear

663
00:38:06,920 --> 00:38:09,799
Speaker 1: her screaming and see people running away, right, And so

664
00:38:10,239 --> 00:38:14,680
Speaker 1: it is really wild how this behavior when you look

665
00:38:14,719 --> 00:38:18,000
Speaker 1: at it with fresh eyes, you're like, oh wait, obviously

666
00:38:18,040 --> 00:38:23,440
Speaker 1: this was always about you know, harassing and targeting minoritize people.

667
00:38:24,520 --> 00:38:28,839
Speaker 2: Is such an interesting story because exactly like you said,

668
00:38:29,120 --> 00:38:35,080
Speaker 2: in hindsight, obviously it's racist, but we all wanted to

669
00:38:35,160 --> 00:38:37,720
Speaker 2: believe it was true, and so we did.

670
00:38:38,000 --> 00:38:40,919
Speaker 1: Yeah. I when I saw this, I was like, damn,

671
00:38:40,920 --> 00:38:44,080
Speaker 1: we can't have anything. We can't have nothing like this

672
00:38:44,160 --> 00:38:46,919
Speaker 1: gave me a tiny bit of joy. And now I'm like, oh,

673
00:38:47,280 --> 00:38:50,879
Speaker 1: now I feel complicit in this woman. This like far right.

674
00:38:51,760 --> 00:38:54,480
Speaker 1: This far right woman's active racism. Cool.

675
00:38:55,000 --> 00:38:59,640
Speaker 2: Yeah, that's like the new the new thing for spotting disinformation.

676
00:38:59,760 --> 00:39:02,400
Speaker 2: Right used to be like if it makes you feel

677
00:39:02,440 --> 00:39:08,800
Speaker 2: like a really strong, visceral emotional reaction, uh, just stop

678
00:39:08,880 --> 00:39:13,360
Speaker 2: before you share it because maybe it's disinformation. But like

679
00:39:14,120 --> 00:39:15,560
Speaker 2: now it's just like if you feel anything.

680
00:39:18,239 --> 00:39:22,680
Speaker 1: I've been waiting to feel something for years, Mike. No,

681
00:39:22,840 --> 00:39:26,080
Speaker 1: I know. And something that you said I think really

682
00:39:26,880 --> 00:39:31,240
Speaker 1: is important to highlight is that everybody hates a thief.

683
00:39:31,560 --> 00:39:34,760
Speaker 1: I have been pickpocket I have been near pickpocketed in Europe.

684
00:39:34,800 --> 00:39:37,920
Speaker 1: I was able to stop the pickpocketing in progress, and

685
00:39:38,040 --> 00:39:41,400
Speaker 1: like that's a whole other story, but nobody likes a thief,

686
00:39:41,680 --> 00:39:42,200
Speaker 1: and I think.

687
00:39:42,080 --> 00:39:47,600
Speaker 2: No, no, no, we need to tell that story. We need Okay, Bridget,

688
00:39:47,719 --> 00:39:52,120
Speaker 2: how did you stop the pickpocket who attempted to pickpocket

689
00:39:52,120 --> 00:39:53,960
Speaker 2: you in Europe, the real pickpocket.

690
00:39:54,040 --> 00:39:58,000
Speaker 1: So first of all, I was like pretty drunk, so

691
00:39:58,040 --> 00:39:59,799
Speaker 1: I was already made. I was already making some like

692
00:40:00,800 --> 00:40:03,759
Speaker 1: Paris mistakes. I was drunk. I had my phone just

693
00:40:03,880 --> 00:40:06,880
Speaker 1: in my back pocket as opposed to like someplace where

694
00:40:06,960 --> 00:40:10,359
Speaker 1: someone can't easily take it, and a group of very

695
00:40:10,400 --> 00:40:13,840
Speaker 1: young people surrounded me, and one of them just like

696
00:40:13,920 --> 00:40:15,840
Speaker 1: I obviously just like felt I'm taking out of my

697
00:40:15,880 --> 00:40:18,600
Speaker 1: pocket and what I did. I don't recommend this, like

698
00:40:18,640 --> 00:40:20,160
Speaker 1: this just happened to work out for me. I am

699
00:40:20,200 --> 00:40:22,400
Speaker 1: not suggesting this is what somebody should do, but like,

700
00:40:22,920 --> 00:40:25,000
Speaker 1: I'm not gonna let somebody take my fucking phone. So

701
00:40:25,120 --> 00:40:28,640
Speaker 1: I just grabbed the smallest one, who was like a

702
00:40:28,760 --> 00:40:31,880
Speaker 1: child right like he was like very small. I just

703
00:40:31,920 --> 00:40:35,719
Speaker 1: grabbed the smallest one and just started screaming at him.

704
00:40:35,760 --> 00:40:37,279
Speaker 1: I was like, if I can just like make it

705
00:40:37,320 --> 00:40:41,680
Speaker 1: clear that I am a huge nuisance, I'm not going away.

706
00:40:42,000 --> 00:40:45,560
Speaker 1: I'm not going to like cry or like like I'm

707
00:40:45,680 --> 00:40:49,040
Speaker 1: I'm going to like make this a big annoying pain,

708
00:40:49,320 --> 00:40:51,879
Speaker 1: and I'm not gonna let go of your tiniest member here.

709
00:40:52,400 --> 00:40:56,040
Speaker 1: I just grabbed him just started screaming and eventually they

710
00:40:56,080 --> 00:40:59,080
Speaker 1: literally just like were like here, fine, okay, and like

711
00:40:59,200 --> 00:41:01,799
Speaker 1: gave me the phone and could not get away from

712
00:41:01,800 --> 00:41:05,279
Speaker 1: me fast enough. Like they were like sorry that They

713
00:41:05,280 --> 00:41:09,080
Speaker 1: were like we we we regret this transaction. Let's leave.

714
00:41:09,719 --> 00:41:12,319
Speaker 2: They tried to steal the phone from the wrong drunk girl.

715
00:41:13,880 --> 00:41:16,960
Speaker 1: Honestly, this is something I mean, again, I'm not saying that,

716
00:41:17,080 --> 00:41:21,000
Speaker 1: like I'm not endorsing this as a as a strategy,

717
00:41:21,440 --> 00:41:23,840
Speaker 1: but something that my mom always told me about saying

718
00:41:23,920 --> 00:41:29,239
Speaker 1: say is like make yourself seem not worth the trouble, Like, oh,

719
00:41:29,280 --> 00:41:33,120
Speaker 1: this person is really loud and behaving really crazy, and

720
00:41:33,160 --> 00:41:34,960
Speaker 1: you know what, I would actually like to get away

721
00:41:34,960 --> 00:41:38,279
Speaker 1: from her. Now that was in my drunk mind. That

722
00:41:38,400 --> 00:41:40,200
Speaker 1: was what I did, and it actually worked out, worked

723
00:41:40,200 --> 00:41:43,759
Speaker 1: out effectively. But that's the thing though, It's like we've

724
00:41:43,800 --> 00:41:48,520
Speaker 1: all probably had instances where we've been scammed or pickpocketed

725
00:41:48,640 --> 00:41:51,920
Speaker 1: or stolen from and nobody likes that, and so I

726
00:41:51,920 --> 00:41:55,640
Speaker 1: think it's such an It really highlights what an effective

727
00:41:55,680 --> 00:42:00,920
Speaker 1: strategy this is. Tapping into that collective disdain being stolen

728
00:42:01,000 --> 00:42:07,440
Speaker 1: from and using that totally understandable emotional like trigger to

729
00:42:07,880 --> 00:42:13,360
Speaker 1: connect to demonizing and harassing a minoritize people in Europe

730
00:42:13,560 --> 00:42:17,359
Speaker 1: right like that. It's it's so it's so insidious but

731
00:42:17,440 --> 00:42:20,560
Speaker 1: also so effective. Because I'm somebody who works on this

732
00:42:20,640 --> 00:42:23,279
Speaker 1: kind of stuff for a living, I like make a

733
00:42:23,440 --> 00:42:27,680
Speaker 1: habit of trying to train myself to, you know, watching

734
00:42:27,719 --> 00:42:30,239
Speaker 1: out for these tensions and these triggers. But this one

735
00:42:30,320 --> 00:42:34,240
Speaker 1: got me, right. It's it's just such a relatable sentiment

736
00:42:34,280 --> 00:42:37,200
Speaker 1: of like, yeah, I don't like thieves. But what she's

737
00:42:37,520 --> 00:42:39,400
Speaker 1: what it actually sounds like she's saying, is like I

738
00:42:39,440 --> 00:42:42,560
Speaker 1: don't like roma people. I'm going to scream at them

739
00:42:42,600 --> 00:42:45,439
Speaker 1: and harass them. And also I'm going to be part

740
00:42:45,480 --> 00:42:48,759
Speaker 1: of this far right party that makes the lives of

741
00:42:48,800 --> 00:42:50,040
Speaker 1: marginalized people in Europe.

742
00:42:50,080 --> 00:42:54,160
Speaker 2: Hell, yeah, that's the story here. The story isn't her.

743
00:42:54,320 --> 00:42:59,000
Speaker 2: The story is us that we were all so ready

744
00:42:59,080 --> 00:43:03,080
Speaker 2: and willing to you just take her at face value,

745
00:43:03,080 --> 00:43:05,759
Speaker 2: that she was calling out pickpockets, that she had some

746
00:43:05,760 --> 00:43:08,840
Speaker 2: sort of like magical ability to identify them and was

747
00:43:08,880 --> 00:43:10,839
Speaker 2: just gonna like yell about them and blow up their

748
00:43:10,880 --> 00:43:15,040
Speaker 2: spot so that they couldn't pick the pockets. Yeah, it's

749
00:43:15,080 --> 00:43:15,600
Speaker 2: on us.

750
00:43:16,040 --> 00:43:18,440
Speaker 1: I mean even the New York Times, like the New

751
00:43:18,520 --> 00:43:24,200
Speaker 1: York Times profile notably did not mention her affiliation with

752
00:43:24,239 --> 00:43:27,920
Speaker 1: this group, right, and so like pretty glaring omission to

753
00:43:27,960 --> 00:43:30,160
Speaker 1: be like, oh, what a what a charming profile of

754
00:43:30,200 --> 00:43:32,759
Speaker 1: this like folk hero. Like I think I used the

755
00:43:32,760 --> 00:43:34,960
Speaker 1: phrase when I was telling you about her. I think

756
00:43:35,000 --> 00:43:38,040
Speaker 1: I used the phrase Italian folk hero. And it's like, yeah,

757
00:43:38,080 --> 00:43:40,839
Speaker 1: this is actually like relevant information to this folk hero,

758
00:43:41,320 --> 00:43:44,279
Speaker 1: just like you know person we're all elevating is that

759
00:43:44,320 --> 00:43:47,560
Speaker 1: she's associated with this like far right hate campaign.

760
00:43:48,040 --> 00:43:49,920
Speaker 2: You know, I think she had things going in her favor,

761
00:43:50,200 --> 00:43:55,360
Speaker 2: that she's she's like an older woman kind of seems

762
00:43:55,400 --> 00:43:59,160
Speaker 2: like a grandma, but still like full of energy.

763
00:44:00,080 --> 00:44:00,200
Speaker 3: Uh.

764
00:44:00,640 --> 00:44:05,400
Speaker 2: And being Italian for at least for us here in America,

765
00:44:05,840 --> 00:44:08,120
Speaker 2: you know, it's a little bit exotic, and so there's

766
00:44:08,160 --> 00:44:11,239
Speaker 2: like some otherness there, and so maybe we were just

767
00:44:11,280 --> 00:44:13,600
Speaker 2: like not as on guard as we might be for

768
00:44:13,760 --> 00:44:18,319
Speaker 2: like a white lady who was screaming at like the

769
00:44:18,480 --> 00:44:26,000
Speaker 2: Giant downtown here in BC. Uh and Giant being a

770
00:44:26,040 --> 00:44:29,000
Speaker 2: mid Atlantic grocery store, not like a literal giant.

771
00:44:29,040 --> 00:44:31,239
Speaker 1: Fer you might know it as if depending on where

772
00:44:31,239 --> 00:44:33,760
Speaker 1: you're listening, you might know it as Martin they're listening

773
00:44:33,800 --> 00:44:34,560
Speaker 1: down south.

774
00:44:34,880 --> 00:44:40,160
Speaker 2: Yeah, yeah, so, yeah, another sad story and this news

775
00:44:40,280 --> 00:44:40,759
Speaker 2: round up.

776
00:44:41,360 --> 00:44:44,640
Speaker 1: I know, I mean I I I wanted to include this.

777
00:44:44,680 --> 00:44:46,080
Speaker 1: I mean, it's a little bit of a different story

778
00:44:46,120 --> 00:44:48,080
Speaker 1: for us. I wanted to include it because, Yeah, I

779
00:44:48,120 --> 00:44:51,120
Speaker 1: just think like it was a reminder to me to

780
00:44:51,120 --> 00:44:56,680
Speaker 1: stay vigilant that we often talk about intense negative emotions

781
00:44:56,800 --> 00:44:59,759
Speaker 1: being that the trigger point or the emotional flashpoint that

782
00:44:59,800 --> 00:45:06,680
Speaker 1: gets us to share racialized disinformation or content that is

783
00:45:06,760 --> 00:45:09,880
Speaker 1: that is really rooted in ugly stereotypes about marginalized people.

784
00:45:10,280 --> 00:45:13,680
Speaker 1: But I think like this is really tapping into something else,

785
00:45:13,719 --> 00:45:18,480
Speaker 1: like it's it's it's it's like comedy and Catharsis, you know,

786
00:45:18,840 --> 00:45:23,400
Speaker 1: mixed it's. It's really like a really interesting case study

787
00:45:23,480 --> 00:45:26,520
Speaker 1: in what moves people. Because these videos went super viral

788
00:45:26,560 --> 00:45:29,200
Speaker 1: and got millions of eyeballs, And I wonder if all

789
00:45:29,200 --> 00:45:31,360
Speaker 1: those millions of eyeballs, how many of them knew they

790
00:45:31,360 --> 00:45:35,840
Speaker 1: were engaging with content from a far right, anti immigrant,

791
00:45:35,880 --> 00:45:39,799
Speaker 1: anti lgbtqu person. I didn't, so it's got to be

792
00:45:40,200 --> 00:45:41,920
Speaker 1: got to be more people out there that didn't. So

793
00:45:42,040 --> 00:45:44,279
Speaker 1: that's why I wanted to include it. Okay, I have

794
00:45:44,719 --> 00:45:47,800
Speaker 1: one more story, just really quickly, so I just found

795
00:45:47,800 --> 00:45:51,360
Speaker 1: this really funny, not like in a haha way so

796
00:45:51,440 --> 00:45:54,759
Speaker 1: much as in like a like wow, wealthy CEOs just

797
00:45:54,800 --> 00:45:58,439
Speaker 1: have no idea way. So we all know that ride

798
00:45:58,440 --> 00:46:02,560
Speaker 1: shares like Uber and Live have gotten ridiculously expensive, Like

799
00:46:02,640 --> 00:46:04,160
Speaker 1: gone are the days where you can get from point

800
00:46:04,239 --> 00:46:07,080
Speaker 1: A to point B for like five dollars. Those were

801
00:46:07,360 --> 00:46:10,200
Speaker 1: the heydays. Those days are over. So if you are

802
00:46:10,280 --> 00:46:14,240
Speaker 1: somebody who takes ride shares, you probably know this. Wired's

803
00:46:14,400 --> 00:46:16,960
Speaker 1: editor at large, Stephen Levy, was doing an in person

804
00:46:17,040 --> 00:46:20,480
Speaker 1: interview with the CEO of Uber, Dara Kushrashi, and Stephen

805
00:46:20,560 --> 00:46:23,439
Speaker 1: Levy took a two point nine to five mile Uber

806
00:46:23,520 --> 00:46:25,760
Speaker 1: ride from downtown New York City to the West Side

807
00:46:25,760 --> 00:46:29,440
Speaker 1: to meet him. So honestly, it sounds like Steven Levy

808
00:46:29,680 --> 00:46:32,439
Speaker 1: must have been salty about the price of that ride,

809
00:46:32,480 --> 00:46:34,600
Speaker 1: like it must have chopped his ass, because in the

810
00:46:34,640 --> 00:46:38,200
Speaker 1: interview he asked Uber CEO kush Rashi to guess how

811
00:46:38,320 --> 00:46:41,279
Speaker 1: much it costs, and the Uber CEO was like, oh,

812
00:46:41,360 --> 00:46:45,360
Speaker 1: I don't know, like twenty dollars, classic like arrested development,

813
00:46:45,440 --> 00:46:48,800
Speaker 1: Like it's one Uber ride, Michael, how much could it cost?

814
00:46:48,960 --> 00:46:51,920
Speaker 1: Twenty dollars? So Stephen Levy was like, no, it was

815
00:46:52,000 --> 00:46:55,400
Speaker 1: actually fifty one dollars and sixty nine cents including a

816
00:46:55,400 --> 00:46:59,440
Speaker 1: tip for the driver. So uber ceo his response is

817
00:46:59,480 --> 00:47:02,920
Speaker 1: like says, oh my god, wow, but it gets worse.

818
00:47:02,960 --> 00:47:05,279
Speaker 1: It's that's not the worst of it. Levy says that

819
00:47:05,320 --> 00:47:07,600
Speaker 1: at first he tried to book an Uber to get

820
00:47:07,600 --> 00:47:11,240
Speaker 1: to the interview and the price was twenty dollars higher.

821
00:47:11,680 --> 00:47:15,759
Speaker 1: Kosh Rashi tried to say that it was like surge pricing,

822
00:47:15,840 --> 00:47:18,480
Speaker 1: like maybe something else was going on, but Levy just

823
00:47:18,520 --> 00:47:20,880
Speaker 1: like straight up was not having it. He was not

824
00:47:20,960 --> 00:47:23,000
Speaker 1: letting him get away with this, which I love because

825
00:47:23,040 --> 00:47:25,759
Speaker 1: he said it's ten am on a sunny weekday and

826
00:47:25,800 --> 00:47:28,480
Speaker 1: it's not like the presidents in town, which I love

827
00:47:28,560 --> 00:47:31,799
Speaker 1: that response, like this cost this must have chopped his

828
00:47:31,960 --> 00:47:34,279
Speaker 1: ass how much this ride costs? So if you use

829
00:47:34,400 --> 00:47:37,600
Speaker 1: ride share, you know the prices have been outrageous lately.

830
00:47:38,400 --> 00:47:42,040
Speaker 1: Uber has promised that by September their prices are going

831
00:47:42,120 --> 00:47:44,880
Speaker 1: to go back down. I don't know. Uber says a

832
00:47:44,920 --> 00:47:46,879
Speaker 1: lot of things. Tech companies say a lot of things.

833
00:47:46,920 --> 00:47:50,240
Speaker 1: We'll let you know. Recouton Intelligence, which is part of Nielsen,

834
00:47:50,520 --> 00:47:53,200
Speaker 1: found that the cost of ride shares actually increased by

835
00:47:53,320 --> 00:47:56,520
Speaker 1: ninety two percent from twenty eighteen to twenty twenty one.

836
00:47:57,120 --> 00:48:00,200
Speaker 1: Uber CEO then tried to say, like, oh, well, it's

837
00:48:00,239 --> 00:48:03,080
Speaker 1: just inflation and that most of the money that you're

838
00:48:03,160 --> 00:48:06,200
Speaker 1: charged actually goes to the driver, which would be great

839
00:48:07,000 --> 00:48:10,600
Speaker 1: if that were actually true, because the UCLA Labor Center

840
00:48:10,640 --> 00:48:13,600
Speaker 1: found that from February twenty nineteen to April twenty twenty two,

841
00:48:13,960 --> 00:48:16,279
Speaker 1: the median passenger fare for Uber and Lyft in New

842
00:48:16,360 --> 00:48:20,480
Speaker 1: York City increased by fifty percent, while median driver pay

843
00:48:20,719 --> 00:48:25,880
Speaker 1: increased by only thirty one percent. So likely story Uber,

844
00:48:26,320 --> 00:48:28,960
Speaker 1: I don't know. I love this story because one I

845
00:48:28,960 --> 00:48:31,920
Speaker 1: think that Stephen Levy was pissed about how expensive that

846
00:48:32,040 --> 00:48:34,920
Speaker 1: ride was, and I can definitely relate. I don't take

847
00:48:35,000 --> 00:48:36,680
Speaker 1: Uber and Lyft. I try not to take Uber and

848
00:48:36,760 --> 00:48:40,839
Speaker 1: Lyft too much unless I have to. But we've all

849
00:48:40,880 --> 00:48:43,279
Speaker 1: had that experience where you're like, oh, I'll just take

850
00:48:43,280 --> 00:48:45,440
Speaker 1: an Uber and then you look and you're like, well,

851
00:48:45,440 --> 00:48:47,520
Speaker 1: I'm already late and I need to get in this.

852
00:48:47,719 --> 00:48:49,640
Speaker 1: I need to get where I'm going, so like, I

853
00:48:49,680 --> 00:48:52,400
Speaker 1: guess I'll spend fifty dollars. I have had that experience.

854
00:48:52,560 --> 00:48:55,000
Speaker 1: It sounds like Steven was not happy about that experience,

855
00:48:55,160 --> 00:48:57,680
Speaker 1: And despite being like a pretty small story, I think

856
00:48:57,680 --> 00:49:00,680
Speaker 1: it's worth paying attention to because I I just think

857
00:49:00,920 --> 00:49:07,000
Speaker 1: that CEOs just have no idea what life is like

858
00:49:07,120 --> 00:49:09,080
Speaker 1: for the rest of us. I think that like people

859
00:49:09,200 --> 00:49:12,400
Speaker 1: who are wealthy billionaires, people who make a ton of money,

860
00:49:12,560 --> 00:49:14,719
Speaker 1: people who own these companies that we all use that

861
00:49:14,880 --> 00:49:17,759
Speaker 1: impact our lives so much, I just think they have

862
00:49:17,960 --> 00:49:20,200
Speaker 1: no idea. I don't think that he had any sense

863
00:49:20,239 --> 00:49:22,879
Speaker 1: of how much it would cost to just go less

864
00:49:22,880 --> 00:49:26,280
Speaker 1: than three miles. I was watching this really interesting episode

865
00:49:26,320 --> 00:49:29,040
Speaker 1: of the Betches podcast with Alana Glazer from broad City,

866
00:49:29,360 --> 00:49:32,160
Speaker 1: and they were talking about the SAG and WGA strikes.

867
00:49:32,600 --> 00:49:36,839
Speaker 4: These billionaires do not move their bodies among people. They

868
00:49:36,840 --> 00:49:40,440
Speaker 4: do not travel next to people, they do not sweat

869
00:49:40,640 --> 00:49:45,759
Speaker 4: near other people. They literally don't understand what's happening. And

870
00:49:45,800 --> 00:49:48,319
Speaker 4: I'm pissed, I'm angry, but I'm like, actually, they're they're

871
00:49:48,320 --> 00:49:51,920
Speaker 4: weak minded. They truly thought that they could write off actors.

872
00:49:51,920 --> 00:49:55,200
Speaker 4: You thought you could write off Meryl Street, bitch. You

873
00:49:55,280 --> 00:49:58,640
Speaker 4: thought you could write off Jason Bourne. He left his

874
00:49:58,880 --> 00:50:01,560
Speaker 4: press dog. And these billionaires want to go to space.

875
00:50:01,680 --> 00:50:05,680
Speaker 1: Go Yeah, And I think that the CEO of Uber

876
00:50:06,160 --> 00:50:09,839
Speaker 1: really having no idea how much it costs to drive

877
00:50:09,920 --> 00:50:12,320
Speaker 1: three miles on the platform that he is the CEO

878
00:50:12,440 --> 00:50:14,879
Speaker 1: of is a really good example of exactly what she's

879
00:50:14,880 --> 00:50:15,399
Speaker 1: talking about.

880
00:50:15,760 --> 00:50:21,319
Speaker 2: Yeah, I just googled his salary. Why don't you take

881
00:50:21,360 --> 00:50:22,359
Speaker 2: a guess what it is?

882
00:50:23,160 --> 00:50:32,439
Speaker 1: Oh my god, his yearly salary. Yeah, I couldn't even guess. Wait,

883
00:50:32,520 --> 00:50:34,040
Speaker 1: let me let me let me think about this.

884
00:50:34,120 --> 00:50:35,920
Speaker 2: Okay, you can think about it. We'll just keep the

885
00:50:35,960 --> 00:50:37,839
Speaker 2: listeners on the line while you think about it.

886
00:50:38,920 --> 00:50:41,960
Speaker 1: I feel like I'm going to say something that is

887
00:50:42,040 --> 00:50:44,160
Speaker 1: so like. That's the thing. The reason why it's hard

888
00:50:44,200 --> 00:50:46,600
Speaker 1: is because like I can't even conceptualize it. Like I

889
00:50:46,600 --> 00:50:48,200
Speaker 1: feel like I'm going to say something that is like

890
00:50:49,160 --> 00:50:50,240
Speaker 1: so wrong.

891
00:50:51,760 --> 00:50:51,960
Speaker 3: Yeah.

892
00:50:51,960 --> 00:50:54,760
Speaker 1: Five, Well, it's just five billion dollars a year.

893
00:50:54,719 --> 00:50:59,200
Speaker 2: To empathize a little bit. That's exactly the process that

894
00:50:59,239 --> 00:51:02,239
Speaker 2: he's going through. And he tries to think what the

895
00:51:02,400 --> 00:51:06,000
Speaker 2: price of like a three mile ride across Manhattan is

896
00:51:06,040 --> 00:51:06,400
Speaker 2: going to be.

897
00:51:07,160 --> 00:51:10,439
Speaker 1: It's one ceo salary, Michael, what can it cost ten

898
00:51:10,480 --> 00:51:12,680
Speaker 1: million dollars? Is it ten million dollars a year? That's

899
00:51:12,680 --> 00:51:13,080
Speaker 1: my guess.

900
00:51:13,360 --> 00:51:15,680
Speaker 2: Twenty four million dollars are you kidding.

901
00:51:19,840 --> 00:51:23,000
Speaker 1: I mean, doesn't that really? Doesn't that really say at all?

902
00:51:24,000 --> 00:51:26,480
Speaker 1: Me trying to guess his salary is the same as

903
00:51:26,560 --> 00:51:29,000
Speaker 1: him trying to guess how much it costs to drive

904
00:51:29,239 --> 00:51:32,319
Speaker 1: less than three miles in New York City. What else

905
00:51:32,360 --> 00:51:32,959
Speaker 1: is there to say?

906
00:51:33,239 --> 00:51:34,600
Speaker 2: Neither do you have any idea?

907
00:51:35,200 --> 00:51:35,799
Speaker 3: That's what I guess.

908
00:51:35,840 --> 00:51:39,120
Speaker 1: That's what I'm saying. Is that like him trying to

909
00:51:39,560 --> 00:51:44,400
Speaker 1: hit at the head of Uber or Netflix or Disney,

910
00:51:44,840 --> 00:51:48,759
Speaker 1: trying to get into the head and really imagine what

911
00:51:48,800 --> 00:51:50,480
Speaker 1: it will be like for the rest of us to live,

912
00:51:50,560 --> 00:51:52,440
Speaker 1: and what the rest of US's priorities are, and what

913
00:51:52,480 --> 00:51:55,000
Speaker 1: the rest of our lives look like. It's it's it's

914
00:51:55,000 --> 00:51:56,600
Speaker 1: just impossible. And I guess I guess it goes the

915
00:51:56,680 --> 00:51:58,680
Speaker 1: other way too. I couldn't even imagine what it would

916
00:51:58,680 --> 00:52:00,719
Speaker 1: be like to have that salary, but a fraction of

917
00:52:00,760 --> 00:52:02,239
Speaker 1: that salary I couldn't imagine.

918
00:52:02,520 --> 00:52:05,240
Speaker 2: Yeah, I guess it's uh, you know, it's pretty equal.

919
00:52:05,719 --> 00:52:06,840
Speaker 2: You're both confused.

920
00:52:08,960 --> 00:52:10,480
Speaker 1: I feel like this episode has been a lot of

921
00:52:10,480 --> 00:52:13,680
Speaker 1: me complaining about money, which I hope is not I mean,

922
00:52:13,760 --> 00:52:15,960
Speaker 1: it is what it is. I don't really have a lot.

923
00:52:15,800 --> 00:52:19,680
Speaker 2: Of you know, it's not like Edward Bloom was suing

924
00:52:20,320 --> 00:52:24,640
Speaker 2: uh because he felt like he wasn't receiving the mentorship

925
00:52:24,680 --> 00:52:27,560
Speaker 2: that he was owed yet, right, Like money's the thing.

926
00:52:27,960 --> 00:52:31,760
Speaker 1: Yeah, I like he should sue the programs that only

927
00:52:31,800 --> 00:52:35,799
Speaker 1: offer mentorship with no financial compensation. I would actually be

928
00:52:35,840 --> 00:52:37,680
Speaker 1: fine with that. Go ahead and sue them.

929
00:52:38,239 --> 00:52:43,000
Speaker 2: Yeah, Like he demands equal access to show up to

930
00:52:43,200 --> 00:52:47,440
Speaker 2: a couple of Zoom meetings and like write some essays

931
00:52:47,600 --> 00:52:49,680
Speaker 2: and receive mentorship.

932
00:52:49,080 --> 00:52:51,680
Speaker 1: If he can, go ahead and have it. Mike, thank

933
00:52:51,719 --> 00:52:53,719
Speaker 1: you so much for running down these stories with me.

934
00:52:53,760 --> 00:52:56,400
Speaker 2: As always, thanks for having me here. It was a

935
00:52:56,440 --> 00:52:58,480
Speaker 2: fun one, even though kind of a downer. I hope

936
00:52:58,480 --> 00:53:00,320
Speaker 2: people stuck with us because it got kind of funny

937
00:53:00,320 --> 00:53:00,920
Speaker 2: towards the end.

938
00:53:01,200 --> 00:53:03,719
Speaker 1: Well, if for folks that did stick it out, thank

939
00:53:03,760 --> 00:53:06,040
Speaker 1: you so much for listening. To support the show and

940
00:53:06,080 --> 00:53:09,920
Speaker 1: for more ad free bonus content, check out our Patreon

941
00:53:09,960 --> 00:53:13,479
Speaker 1: at patreon dot com slash Tangody. There you can also

942
00:53:13,560 --> 00:53:16,279
Speaker 1: leave me questions for my live ask me anything that

943
00:53:16,360 --> 00:53:19,440
Speaker 1: I am planning. We are checking the emails. Thank you

944
00:53:19,480 --> 00:53:28,520
Speaker 1: so much for listening. We will see you soon. If

945
00:53:28,520 --> 00:53:30,640
Speaker 1: you're looking for ways to support the show, check out

946
00:53:30,640 --> 00:53:34,520
Speaker 1: our merch store at tangoti dot com, slash Store. Got

947
00:53:34,520 --> 00:53:36,520
Speaker 1: a story about an interesting thing in tech, or just

948
00:53:36,520 --> 00:53:38,520
Speaker 1: want to say hi, You can reach us at Hello

949
00:53:38,560 --> 00:53:41,080
Speaker 1: at tenggody dot com. You can also find transcripts for

950
00:53:41,080 --> 00:53:43,799
Speaker 1: today's episode at tengody dot com. There Are No Girls

951
00:53:43,800 --> 00:53:46,160
Speaker 1: on the Internet was created by me Bridget Tod. It's

952
00:53:46,160 --> 00:53:49,520
Speaker 1: a production of iHeartRadio and Unboss Creative edited by Joey

953
00:53:49,560 --> 00:53:53,400
Speaker 1: pat Jonathan Strickland is our executive producer. Tari Harrison is

954
00:53:53,400 --> 00:53:57,040
Speaker 1: our producer and sound engineer. Michael Amado is our contributing producer.

955
00:53:57,280 --> 00:53:59,440
Speaker 1: I'm your host, Bridget Todd. If you want to help

956
00:53:59,520 --> 00:54:02,480
Speaker 1: us grow, rate and review us on Apple Podcasts. For

957
00:54:02,560 --> 00:54:05,960
Speaker 1: more podcasts from iHeartRadio, check out the iHeartRadio app, Apple Podcasts,

958
00:54:06,000 --> 00:54:11,040
Speaker 1: or wherever you get your podcasts.