1
00:00:00,240 --> 00:00:03,560
Speaker 1: Hello the Internet, and welcome to this episode of The

2
00:00:03,600 --> 00:00:08,440
Speaker 1: Weekly Zeitgeist. These are some of our favorite segments from

3
00:00:08,640 --> 00:00:19,440
Speaker 1: this week, all edited together into one NonStop infotainment laugh stravaganza. Yeah, so,

4
00:00:19,880 --> 00:00:26,640
Speaker 1: without further ado, here is the Weekly Zeitgeist. Anyways, Miles,

5
00:00:27,000 --> 00:00:29,880
Speaker 1: we are thrilled to be joined in our third seat

6
00:00:30,400 --> 00:00:33,760
Speaker 1: by a hilarious stand up comedian, actor, musician with the

7
00:00:33,840 --> 00:00:37,960
Speaker 1: seven point four rated album on Pitchfork to his name.

8
00:00:38,960 --> 00:00:41,680
Speaker 1: That's right. You can listen to his podcast called brew

9
00:00:41,720 --> 00:00:44,120
Speaker 1: Got Me like anywhere fine podcasts, or give it away

10
00:00:44,120 --> 00:00:47,960
Speaker 1: for free. His new book, The Advice King Anthology, available

11
00:00:48,000 --> 00:00:51,080
Speaker 1: anywhere fine books are sold or given away for free.

12
00:00:51,120 --> 00:00:53,680
Speaker 1: At the library, you know, the poetry window is open

13
00:00:53,760 --> 00:00:58,480
Speaker 1: because it's Chris motherfucking Craft did what's up?

14
00:00:58,760 --> 00:00:59,800
Speaker 2: Good to see you guys.

15
00:01:00,080 --> 00:01:03,120
Speaker 1: What's up? Man? I noticed something about it.

16
00:01:03,120 --> 00:01:05,480
Speaker 3: You look like you have your your fist over your

17
00:01:05,560 --> 00:01:07,959
Speaker 3: chest like you're doing half a Waconda salute.

18
00:01:07,959 --> 00:01:12,640
Speaker 4: But yeah, yeah, I broke my scapula Thursday. I got

19
00:01:12,640 --> 00:01:16,319
Speaker 4: an email from or No. I sent an email saying, hey,

20
00:01:16,360 --> 00:01:18,200
Speaker 4: I would like to go back on The Daily Zeitgeist.

21
00:01:18,240 --> 00:01:22,120
Speaker 4: Didn't and I got an email back that said hey, you.

22
00:01:22,240 --> 00:01:25,720
Speaker 4: We were thinking about it on Tuesday and I said, sure, Wednesday,

23
00:01:25,760 --> 00:01:30,200
Speaker 4: Today's Wednesday, I guess in the podcast world, but a

24
00:01:30,240 --> 00:01:34,440
Speaker 4: little podcast ruin every over, ruin everything. I've ruined everything.

25
00:01:34,840 --> 00:01:40,240
Speaker 4: So anyway, when I email to start the whole show over,

26
00:01:40,920 --> 00:01:44,800
Speaker 4: when when the when I sent that email, I did

27
00:01:44,800 --> 00:01:47,400
Speaker 4: not have a broken scapula. I had never even heard

28
00:01:47,400 --> 00:01:51,160
Speaker 4: of a scapula. And then when I when I got when,

29
00:01:51,200 --> 00:01:53,680
Speaker 4: I confirmed, yeah, sure I'll do it. Tuesday I was

30
00:01:53,880 --> 00:01:57,240
Speaker 4: on like, you know, morphine. But I figured by Tuesday

31
00:01:57,240 --> 00:02:01,320
Speaker 4: i'd be all right. But I broke my scapulaay afternoon,

32
00:02:02,120 --> 00:02:06,200
Speaker 4: and I broke my rib and uh so Nashville has

33
00:02:06,200 --> 00:02:09,040
Speaker 4: been like this ice skating rink for a week and

34
00:02:09,080 --> 00:02:11,760
Speaker 4: now it just went away because it just got finally

35
00:02:11,760 --> 00:02:13,720
Speaker 4: went up to like forty or fifty or whatever, right,

36
00:02:13,760 --> 00:02:15,520
Speaker 4: and so all this stuff melted. But for a week,

37
00:02:16,440 --> 00:02:19,840
Speaker 4: I guess Nashville has like a couple snowblouse.

38
00:02:19,240 --> 00:02:21,880
Speaker 1: I mean, yeah, you me literally like two.

39
00:02:22,480 --> 00:02:24,880
Speaker 2: They have a few, I guess. But it was not cool.

40
00:02:24,919 --> 00:02:28,600
Speaker 4: It was like everybody was falling down. My neighborhood was

41
00:02:28,639 --> 00:02:30,640
Speaker 4: like a fucking circus. Like it was like the whole

42
00:02:30,680 --> 00:02:32,680
Speaker 4: the street was an ice sheet for a week, so

43
00:02:33,200 --> 00:02:34,880
Speaker 4: people were trying to go to work and have to

44
00:02:34,919 --> 00:02:39,240
Speaker 4: turn back. My neighbors a guy, a drunk guy, came

45
00:02:39,280 --> 00:02:42,079
Speaker 4: down our street. Like, first of all, our street, it's

46
00:02:42,080 --> 00:02:43,920
Speaker 4: got no sidewalks or anything. So like if you go

47
00:02:43,960 --> 00:02:46,280
Speaker 4: off the street, you're in a lawn, right and so

48
00:02:46,720 --> 00:02:49,760
Speaker 4: and so this my neighbors, like I did. I was

49
00:02:49,800 --> 00:02:53,640
Speaker 4: asleep because it was after I broke my scapula, So

50
00:02:53,720 --> 00:02:54,480
Speaker 4: I was in bed.

51
00:02:54,880 --> 00:02:55,480
Speaker 1: But I woke up.

52
00:02:55,560 --> 00:03:00,120
Speaker 2: My roommate was like, that's right, I said, roommate, my roommate.

53
00:03:00,639 --> 00:03:07,040
Speaker 1: Your buddy, my buddy. Yeah. My wife, my mother, my

54
00:03:07,280 --> 00:03:08,880
Speaker 1: beautiful wife.

55
00:03:10,040 --> 00:03:13,760
Speaker 4: Was doing animation on the new Fast and Furious movie

56
00:03:13,800 --> 00:03:17,680
Speaker 4: in the living room of my well appointed Silver Lake.

57
00:03:20,040 --> 00:03:26,840
Speaker 1: Je Fast and Furious whatever.

58
00:03:26,880 --> 00:03:27,640
Speaker 2: She pays the bills.

59
00:03:27,639 --> 00:03:28,360
Speaker 1: So I was asleep.

60
00:03:28,520 --> 00:03:31,760
Speaker 4: I was asleep. She pays the bills. I live right

61
00:03:31,800 --> 00:03:33,760
Speaker 4: next to the Silver Lakes.

62
00:03:33,760 --> 00:03:35,640
Speaker 1: Got this note from Vin Diesel.

63
00:03:35,760 --> 00:03:38,920
Speaker 4: I'm friends with Jimmy Kimmel. It's unbelievable. So me and

64
00:03:39,000 --> 00:03:40,840
Speaker 4: Jimmy Kimmel we are taking a nap in the same bed,

65
00:03:41,360 --> 00:03:44,560
Speaker 4: and my wife was animating Fast and Furious twelve or

66
00:03:44,600 --> 00:03:46,880
Speaker 4: whatever it is. Yeah, and no, okay, I'm gonna go

67
00:03:46,880 --> 00:03:49,640
Speaker 4: back to what's really happening. My roommate said that the

68
00:03:49,680 --> 00:03:51,920
Speaker 4: next door neighbors, He's like, did you see what happened?

69
00:03:51,920 --> 00:03:53,640
Speaker 4: And I was like, no, I was in there, and

70
00:03:53,800 --> 00:03:54,800
Speaker 4: he said.

71
00:03:54,640 --> 00:03:58,440
Speaker 2: Oh my god, this car came down the street, lost

72
00:03:58,480 --> 00:04:03,320
Speaker 2: control and went into my neighbor's yard. They it slid

73
00:04:03,320 --> 00:04:05,040
Speaker 2: on the ice and landed in their yard.

74
00:04:05,400 --> 00:04:05,680
Speaker 1: Wow.

75
00:04:05,680 --> 00:04:08,880
Speaker 2: And then it was a drunk person too, even though

76
00:04:08,880 --> 00:04:09,720
Speaker 2: it was the middle of the day.

77
00:04:10,360 --> 00:04:14,040
Speaker 4: And yeah, because I always think drinking is nighttime thing, right,

78
00:04:14,080 --> 00:04:16,040
Speaker 4: but not for this guy. And so then while he

79
00:04:16,120 --> 00:04:17,800
Speaker 4: was trying to get out of the yard, which he

80
00:04:17,839 --> 00:04:19,560
Speaker 4: also couldn't get out of because it was ice too,

81
00:04:19,600 --> 00:04:21,560
Speaker 4: and it's down in a gully like our streets on

82
00:04:21,640 --> 00:04:23,200
Speaker 4: kind of a hill, and like it's it's just a

83
00:04:23,320 --> 00:04:26,000
Speaker 4: very you know, this was not an ideal piece of

84
00:04:26,120 --> 00:04:29,040
Speaker 4: land for houses when they put them in here. So

85
00:04:29,080 --> 00:04:31,960
Speaker 4: the so this car, this car like smashing into the

86
00:04:31,960 --> 00:04:34,160
Speaker 4: people who lives there. He's trying to back up and stuff,

87
00:04:34,200 --> 00:04:35,440
Speaker 4: and he smashed into their car.

88
00:04:36,040 --> 00:04:37,040
Speaker 2: And then they eventually had.

89
00:04:36,920 --> 00:04:38,880
Speaker 4: To go out there and take his keys away from him.

90
00:04:39,240 --> 00:04:41,279
Speaker 4: And then they gave him a blanket because it was

91
00:04:41,320 --> 00:04:43,520
Speaker 4: so cold, and they gave him some water and then

92
00:04:43,560 --> 00:04:45,480
Speaker 4: they then the police came and I guess I didn't

93
00:04:45,520 --> 00:04:47,200
Speaker 4: see the police, but they he said the police were

94
00:04:47,279 --> 00:04:49,680
Speaker 4: very mean to the guy who he didn't speak English.

95
00:04:49,720 --> 00:04:54,400
Speaker 4: So you know, anyway, Nashville is like needs more snowplows

96
00:04:55,160 --> 00:04:57,200
Speaker 4: on the double. So if anybody out there in Delly's

97
00:04:57,400 --> 00:05:00,400
Speaker 4: Guy's world has an extra snowplow, you might I call

98
00:05:00,480 --> 00:05:01,840
Speaker 4: up the city city hall.

99
00:05:01,880 --> 00:05:04,920
Speaker 1: Here, how's your rib? How's your scapula? That's what I'm

100
00:05:05,000 --> 00:05:05,240
Speaker 1: I mean.

101
00:05:06,520 --> 00:05:08,919
Speaker 4: The thing is, I've broken so many fucking bones that

102
00:05:08,960 --> 00:05:12,360
Speaker 4: it's really just embarrassing. Like I was more embarrassed I

103
00:05:12,800 --> 00:05:15,120
Speaker 4: hit the ground than anything else because I heard or

104
00:05:15,160 --> 00:05:16,880
Speaker 4: I hit the ground, I hit the steps, but I

105
00:05:16,920 --> 00:05:24,960
Speaker 4: heard things crack, so I knew it's something. Yeah, you know,

106
00:05:25,000 --> 00:05:27,880
Speaker 4: And it's just like I haven't broken that much stuff.

107
00:05:28,480 --> 00:05:30,080
Speaker 2: I mean, yeah I have, but I haven't. I mean,

108
00:05:30,120 --> 00:05:30,960
Speaker 2: I broke my hip.

109
00:05:30,760 --> 00:05:34,200
Speaker 4: In twenty eighteen, you know, roller skating, you know, and

110
00:05:34,200 --> 00:05:36,400
Speaker 4: everybody thinks that's funny, but you know, it's not funny.

111
00:05:37,920 --> 00:05:39,919
Speaker 1: Only left it's funny.

112
00:05:40,279 --> 00:05:43,680
Speaker 4: It's funny. That's LA's fault. That's because people in La

113
00:05:43,839 --> 00:05:46,920
Speaker 4: grown people go roller skating because they're reliving their childhood

114
00:05:46,960 --> 00:05:49,520
Speaker 4: that they didn't get to have in Wisconsin because they're

115
00:05:49,640 --> 00:05:50,760
Speaker 4: mean dentist.

116
00:05:50,360 --> 00:05:51,880
Speaker 2: Father never talked to them or whatever.

117
00:05:52,160 --> 00:05:55,240
Speaker 1: Right, so they moved out to La roller skating.

118
00:05:55,279 --> 00:05:58,159
Speaker 2: We're crazy clothes now, even I'm forty.

119
00:05:58,800 --> 00:06:02,279
Speaker 4: So then hell, these socks are yeah, exactly at least

120
00:06:02,320 --> 00:06:03,040
Speaker 4: crazy socks.

121
00:06:03,040 --> 00:06:05,160
Speaker 2: I got them on the internet, you know, and.

122
00:06:05,240 --> 00:06:09,479
Speaker 1: Uh, the film on there.

123
00:06:09,400 --> 00:06:11,200
Speaker 2: So I broke Yeah, so I wrote, I'm doing a

124
00:06:11,279 --> 00:06:12,160
Speaker 2: YETI Fuck film.

125
00:06:12,160 --> 00:06:15,760
Speaker 4: We're pitching it to Adult Swim.

126
00:06:15,760 --> 00:06:17,640
Speaker 5: It's gonna be called YETI Fuck film. But we're gonna

127
00:06:17,640 --> 00:06:19,600
Speaker 5: censor put stars instead of the fuck. You know, it's

128
00:06:19,680 --> 00:06:22,680
Speaker 5: gonna be f and then three stars. And yeah, I

129
00:06:22,720 --> 00:06:25,240
Speaker 5: know somebody an Adult Swim. He's like a lower down guy.

130
00:06:25,279 --> 00:06:32,760
Speaker 5: But I know the guy put the roof on Adult Swims. Uh,

131
00:06:32,880 --> 00:06:35,080
Speaker 5: he replaced the roof and he talked to one of

132
00:06:35,080 --> 00:06:35,720
Speaker 5: the guys there.

133
00:06:35,760 --> 00:06:39,039
Speaker 1: So didn't they switch buildings though? Yeah, but he still

134
00:06:39,080 --> 00:06:39,880
Speaker 1: got the guy's number.

135
00:06:40,000 --> 00:06:42,080
Speaker 6: Yeah, well he also Yeah, he's just like in the loop,

136
00:06:42,120 --> 00:06:44,000
Speaker 6: you know what I mean, he's got the guy's number.

137
00:06:44,040 --> 00:06:45,599
Speaker 6: I don't know how it's gonna work, but it's pretty

138
00:06:45,680 --> 00:06:50,520
Speaker 6: much all set. Yeah, yeah, it's all set. So I

139
00:06:50,600 --> 00:06:53,279
Speaker 6: fucking I don't even know I'm talking about it anymore.

140
00:06:53,279 --> 00:06:57,440
Speaker 6: But it was like the my roommates said, the day

141
00:06:57,480 --> 00:06:59,360
Speaker 6: had happened. My roommate's car got stuck in the middle

142
00:06:59,360 --> 00:07:01,400
Speaker 6: of the road and it was stuck, and he came

143
00:07:01,480 --> 00:07:03,000
Speaker 6: running in and he said, oh my god, Oh my god,

144
00:07:03,000 --> 00:07:04,920
Speaker 6: oh my god, car stuck in the middle of the road,

145
00:07:04,920 --> 00:07:06,520
Speaker 6: and somebody has come over the hills. We live over

146
00:07:06,560 --> 00:07:07,880
Speaker 6: a hill, and he's like, somebody's.

147
00:07:07,680 --> 00:07:09,440
Speaker 2: Running to my car. And I was like, oh, fuck, yeah,

148
00:07:09,440 --> 00:07:11,120
Speaker 2: I love jobs, you know what I mean? Yeah, And

149
00:07:11,360 --> 00:07:12,520
Speaker 2: I like helping this.

150
00:07:12,680 --> 00:07:14,880
Speaker 4: I was like, shit, you was standing barefoot in the

151
00:07:14,960 --> 00:07:18,400
Speaker 4: kitchen eating a cold case of DIA and I put

152
00:07:18,440 --> 00:07:21,200
Speaker 4: on my sneakers and he said, the stairs are slippery.

153
00:07:21,240 --> 00:07:23,560
Speaker 4: But that was the only thing I holed against them,

154
00:07:23,600 --> 00:07:28,000
Speaker 4: because the stairs were not slippery. They were impassable, unusable.

155
00:07:28,240 --> 00:07:31,000
Speaker 4: Each one was a solid fucking piece of ice. So

156
00:07:31,040 --> 00:07:33,320
Speaker 4: I went outside and happily put my foot on the

157
00:07:33,320 --> 00:07:36,640
Speaker 4: top step and immediately flew up in the air and

158
00:07:36,920 --> 00:07:42,280
Speaker 4: landed in the air. I realized, oh my god, I

159
00:07:42,320 --> 00:07:44,800
Speaker 4: am fifty four years old and hovering over set of

160
00:07:44,880 --> 00:07:48,240
Speaker 4: concrete fucking steps, and I am like fucked. And so

161
00:07:48,440 --> 00:07:51,680
Speaker 4: one step broke my rib and the other step broke

162
00:07:51,760 --> 00:07:53,920
Speaker 4: my scapula, which is the piece on the back of

163
00:07:54,000 --> 00:07:57,640
Speaker 4: your shoulder, the wing. And you know, I didn't even

164
00:07:57,680 --> 00:08:00,280
Speaker 4: feel the shoulder thing because the rib hurts so much.

165
00:08:00,320 --> 00:08:02,760
Speaker 4: So my only thing was I actually thought I probably

166
00:08:02,760 --> 00:08:05,720
Speaker 4: broke my back. So when I stood up, I was happy.

167
00:08:05,800 --> 00:08:08,360
Speaker 1: Yeah, you're like, it's a miracle. It was really lucky

168
00:08:08,440 --> 00:08:13,280
Speaker 1: I hadn't. Yeah, that sounds like it could have been worse. Yeah,

169
00:08:13,440 --> 00:08:14,920
Speaker 1: it's it felt like.

170
00:08:14,880 --> 00:08:15,800
Speaker 2: The mundane way.

171
00:08:15,920 --> 00:08:18,080
Speaker 4: I mean, you know, the no one plans on getting

172
00:08:18,120 --> 00:08:20,320
Speaker 4: paralyzed or I mean, it's like that's what it felt like.

173
00:08:20,360 --> 00:08:23,360
Speaker 2: It felt like, oh fuck, this is really dangerous.

174
00:08:23,400 --> 00:08:24,760
Speaker 4: Like when I was when I was in the middle

175
00:08:24,800 --> 00:08:27,040
Speaker 4: of slipping, I was like, oh my fucking god.

176
00:08:27,120 --> 00:08:29,560
Speaker 1: I mean, it's like, credit to you, man. I'm glad

177
00:08:29,600 --> 00:08:30,120
Speaker 1: you're doing well.

178
00:08:30,160 --> 00:08:31,760
Speaker 3: And also I'm glad you know you listen to our

179
00:08:31,800 --> 00:08:33,880
Speaker 3: email where you said, get the fuck over it. You

180
00:08:33,920 --> 00:08:35,680
Speaker 3: said you'd wanted to were doing this show.

181
00:08:35,960 --> 00:08:36,400
Speaker 7: You want.

182
00:08:39,360 --> 00:08:41,400
Speaker 1: To be in the big time. You're gonna slip big time.

183
00:08:41,640 --> 00:08:42,679
Speaker 2: This is Hollywood.

184
00:08:43,080 --> 00:08:47,559
Speaker 1: Yeah, I thought you were a professional with the never talked.

185
00:08:47,280 --> 00:08:50,520
Speaker 2: To you again. If you don't get on this zoom,

186
00:08:50,800 --> 00:08:51,160
Speaker 2: what do.

187
00:08:51,080 --> 00:08:53,360
Speaker 1: You mean something's broken? Like I get I believe you,

188
00:08:53,440 --> 00:08:55,360
Speaker 1: but like, why are you telling me that? What does

189
00:08:55,400 --> 00:08:56,880
Speaker 1: that have to do with me?

190
00:08:56,920 --> 00:09:05,520
Speaker 2: Anything? By tell it to your personal assistant you, oh man,

191
00:09:06,400 --> 00:09:06,760
Speaker 2: So I.

192
00:09:06,679 --> 00:09:08,720
Speaker 4: Have a go fund me anyway, if anybody I already

193
00:09:08,720 --> 00:09:11,559
Speaker 4: made the I made the goal though, and and people

194
00:09:11,559 --> 00:09:14,120
Speaker 4: from Daily Zeitgeist and you guys whoever's in charge of

195
00:09:14,160 --> 00:09:14,760
Speaker 4: your social.

196
00:09:14,600 --> 00:09:16,679
Speaker 2: Media retweeted my go fund me.

197
00:09:16,920 --> 00:09:19,800
Speaker 4: So I'm very grateful as usual to the Daily zeit

198
00:09:19,840 --> 00:09:22,479
Speaker 4: Gu's community because you know, they've just been a huge.

199
00:09:22,400 --> 00:09:24,839
Speaker 2: Part of my life in the last three four years.

200
00:09:24,840 --> 00:09:29,199
Speaker 2: So that gang, yeah amazing.

201
00:09:29,320 --> 00:09:31,600
Speaker 1: Canceled my health insurance because I feel like I can

202
00:09:31,679 --> 00:09:32,640
Speaker 1: just like reach out to them.

203
00:09:33,679 --> 00:09:35,120
Speaker 2: Good idea. That's a good idea.

204
00:09:35,360 --> 00:09:38,079
Speaker 1: Yeah, that's probably the best idea you've ever heard.

205
00:09:39,160 --> 00:09:41,559
Speaker 4: Your wife's I'm sure your wife is an animator for

206
00:09:41,640 --> 00:09:42,679
Speaker 4: one of the major movies.

207
00:09:42,760 --> 00:09:46,080
Speaker 1: Yeah, so well her work on.

208
00:09:46,920 --> 00:09:50,280
Speaker 3: Yeah, she's a stunt coordinator for the New Avengers flick.

209
00:09:50,440 --> 00:09:50,960
Speaker 1: Yeah.

210
00:09:51,040 --> 00:09:52,880
Speaker 2: Yeah, I know how Hollywood works.

211
00:09:52,960 --> 00:09:56,120
Speaker 1: Kevin what's his name fi? Yeah, yeah, he gives he

212
00:09:56,160 --> 00:09:59,800
Speaker 1: gives us good health insurance. What is something from your

213
00:10:00,120 --> 00:10:02,319
Speaker 1: search history that's revealing about who you are?

214
00:10:02,760 --> 00:10:04,760
Speaker 7: Oh gosh, I don't really know if I like what

215
00:10:04,800 --> 00:10:07,199
Speaker 7: this reveals about me. But you know, we need to

216
00:10:07,240 --> 00:10:10,559
Speaker 7: get like really obsessed with something. And it's not even recent,

217
00:10:10,640 --> 00:10:13,880
Speaker 7: it's not trending, like there's no reason why, but for

218
00:10:14,040 --> 00:10:15,679
Speaker 7: all my search history for the last two days and

219
00:10:15,800 --> 00:10:20,320
Speaker 7: just really wild deep dives on the twenty nineteen film Cats,

220
00:10:20,360 --> 00:10:22,839
Speaker 7: you know, the awful one with all the like cgi

221
00:10:22,840 --> 00:10:25,560
Speaker 7: fur and you know, all the superstars kind of crawling

222
00:10:25,600 --> 00:10:27,640
Speaker 7: around at all fours And I don't know why, but

223
00:10:27,679 --> 00:10:29,839
Speaker 7: for some reason, like a specter just came back and

224
00:10:29,960 --> 00:10:32,400
Speaker 7: wanted me. And I was like, today, I need to

225
00:10:32,520 --> 00:10:34,680
Speaker 7: like listen to an hour and a half YouTube video

226
00:10:34,800 --> 00:10:37,200
Speaker 7: on the making of Cats, the movie twenty nineteen and

227
00:10:37,200 --> 00:10:40,960
Speaker 7: why it was a disaster. And so I'm not busy enough.

228
00:10:41,000 --> 00:10:43,880
Speaker 7: I think it's I need more hobbies and I need

229
00:10:43,920 --> 00:10:46,920
Speaker 7: more productive uses of my time. But I am now

230
00:10:46,920 --> 00:10:51,600
Speaker 7: an encyclopedia on terrible cgi slash why you shouldn't try

231
00:10:51,640 --> 00:10:54,560
Speaker 7: and make very weird Broadway musicals into films.

232
00:10:54,960 --> 00:10:58,520
Speaker 1: Yeah, I was immediately like trying to connect the dots.

233
00:10:58,520 --> 00:11:01,000
Speaker 1: I was like, oh, well, I I think I remember

234
00:11:01,080 --> 00:11:04,480
Speaker 1: that they used AI technology to remove the cat assholes

235
00:11:04,760 --> 00:11:07,400
Speaker 1: from like was that one of the things where it

236
00:11:07,440 --> 00:11:09,400
Speaker 1: was about to come out and they did a test

237
00:11:09,400 --> 00:11:13,520
Speaker 1: screening and everyone's like, they're a pink cat assholes are

238
00:11:13,520 --> 00:11:16,520
Speaker 1: in our face the entire movie? Or are we going

239
00:11:16,600 --> 00:11:20,360
Speaker 1: to just do that? And I went back and digitally

240
00:11:20,400 --> 00:11:24,120
Speaker 1: removed them. But now, were you into the movie when

241
00:11:24,120 --> 00:11:27,120
Speaker 1: it came out? Were you anticipating it or you just

242
00:11:27,640 --> 00:11:28,679
Speaker 1: kind of have it?

243
00:11:29,480 --> 00:11:32,240
Speaker 7: So I love I'm a dancer, so like I love musicals,

244
00:11:32,240 --> 00:11:33,600
Speaker 7: and like you know, I was one of the only

245
00:11:33,640 --> 00:11:36,319
Speaker 7: people that was like un ironically excited and thought of

246
00:11:36,320 --> 00:11:39,360
Speaker 7: the Cat's musicals. I realized that's like four people who

247
00:11:39,480 --> 00:11:41,280
Speaker 7: like actually would have wanted to see this.

248
00:11:41,559 --> 00:11:43,679
Speaker 8: And then the trailer came out and it was just

249
00:11:43,679 --> 00:11:44,400
Speaker 8: so frightening.

250
00:11:44,440 --> 00:11:46,440
Speaker 7: What it was a really good example of it's like

251
00:11:46,720 --> 00:11:49,400
Speaker 7: in like AI and robotics, either term the uncanny valley.

252
00:11:49,440 --> 00:11:51,320
Speaker 7: This might be something you've heard of, this idea of

253
00:11:51,440 --> 00:11:54,000
Speaker 7: like the closest something looks like to a human or

254
00:11:54,040 --> 00:11:57,040
Speaker 7: to like a living creature, but like being different enough

255
00:11:57,080 --> 00:11:59,160
Speaker 7: that you can tell, like it's not quite human. Like

256
00:11:59,280 --> 00:12:03,839
Speaker 7: the creepier and so this original robotics experiment Matito Mari

257
00:12:04,120 --> 00:12:06,600
Speaker 7: or the person who like writes thisay about this us

258
00:12:06,720 --> 00:12:10,000
Speaker 7: the example of like a mechanical hand that moves, and

259
00:12:10,040 --> 00:12:12,760
Speaker 7: it's like, that's super creepy, Like no one wants to

260
00:12:12,800 --> 00:12:15,040
Speaker 7: see that. And all I could think of is this

261
00:12:15,160 --> 00:12:18,560
Speaker 7: like essay from this Jefpaness robots this. But I saw

262
00:12:18,559 --> 00:12:22,839
Speaker 7: those cat humans things moving and I was like, it's

263
00:12:22,880 --> 00:12:25,000
Speaker 7: going to be so bad, like no one wants to

264
00:12:25,000 --> 00:12:27,079
Speaker 7: see this, whether or not the pink assholes are there

265
00:12:27,200 --> 00:12:29,040
Speaker 7: or not, you know, and they just end up being

266
00:12:29,080 --> 00:12:31,440
Speaker 7: all smooth doubt, which is also awful.

267
00:12:32,240 --> 00:12:35,800
Speaker 8: So yes, it was a kind of terrifying disappointment.

268
00:12:35,520 --> 00:12:38,360
Speaker 3: Because yeah, there's kind of a strisiand effect. I feel

269
00:12:38,360 --> 00:12:40,520
Speaker 3: like when people are like, but where are the assholes?

270
00:12:41,040 --> 00:12:41,640
Speaker 1: You know what I mean?

271
00:12:41,679 --> 00:12:44,000
Speaker 3: And then people were like, well, we actually, like I

272
00:12:44,000 --> 00:12:45,960
Speaker 3: don't know, might have been better with them.

273
00:12:46,200 --> 00:12:46,560
Speaker 1: How do you?

274
00:12:46,960 --> 00:12:50,320
Speaker 3: Speaking of cats and an interesting, uh sort of obsession

275
00:12:50,360 --> 00:12:53,760
Speaker 3: with them, have you listened to your fellow compatriots podcasts,

276
00:12:53,840 --> 00:12:57,880
Speaker 3: Guy Montgomery and Tim Batt's podcast called My Week with Cats,

277
00:12:57,920 --> 00:13:00,240
Speaker 3: where they keep watching cats over and over and talking

278
00:13:00,280 --> 00:13:00,719
Speaker 3: about it.

279
00:13:01,640 --> 00:13:05,520
Speaker 8: No, but this sounds exactly up my street. Yeah, yeah, yeah, now.

280
00:13:05,400 --> 00:13:06,760
Speaker 1: Yeah, we've had them on the show.

281
00:13:07,000 --> 00:13:10,120
Speaker 3: Guy is one of our favorite guests and fellow Kiwi

282
00:13:10,160 --> 00:13:13,640
Speaker 3: And yeah, like to such an absurd podcast to just

283
00:13:13,760 --> 00:13:16,040
Speaker 3: keep revisiting Cats over and over.

284
00:13:16,440 --> 00:13:18,880
Speaker 7: Yeah, I mean, have you listened to the iconic Kiwi

285
00:13:19,000 --> 00:13:21,400
Speaker 7: podcast who shout on the floor at my wedding with

286
00:13:21,880 --> 00:13:24,280
Speaker 7: kind of viral? I still haven't listened to it yet,

287
00:13:24,280 --> 00:13:25,880
Speaker 7: but that's also on my listening list.

288
00:13:26,080 --> 00:13:28,920
Speaker 1: Yeah, that's one that I've heard many times. Be like,

289
00:13:28,960 --> 00:13:29,640
Speaker 1: have you come up?

290
00:13:29,920 --> 00:13:31,560
Speaker 3: And I saw all the write ups about it too,

291
00:13:31,559 --> 00:13:35,040
Speaker 3: are like, it's absolutely the most riveting thing that we've

292
00:13:35,080 --> 00:13:37,040
Speaker 3: listened to this year, is what I feel like most

293
00:13:37,080 --> 00:13:37,920
Speaker 3: people say about that.

294
00:13:38,840 --> 00:13:42,439
Speaker 1: That's amazing. So are you how many viewings deep are

295
00:13:42,480 --> 00:13:44,720
Speaker 1: you of twenty nineteen Cats or is it just like

296
00:13:44,880 --> 00:13:48,400
Speaker 1: you watched it once and then it's all YouTube like explainers?

297
00:13:48,679 --> 00:13:50,560
Speaker 7: Oh yeah, I watched it once. I don't know if

298
00:13:50,600 --> 00:13:52,240
Speaker 7: I could do it again, to be honest, Like I

299
00:13:52,240 --> 00:13:55,240
Speaker 7: did it once on like a trans atlantic flight and

300
00:13:55,559 --> 00:13:58,920
Speaker 7: that will was, you know, myself trapped in the middle

301
00:13:58,960 --> 00:14:01,200
Speaker 7: Tube had this moment with Pats when I was like,

302
00:14:01,720 --> 00:14:04,439
Speaker 7: oh it is as bad as everyone said. And then

303
00:14:04,559 --> 00:14:08,679
Speaker 7: now it's just extremely scathing movie reviews on YouTube.

304
00:14:08,200 --> 00:14:08,839
Speaker 8: And in print.

305
00:14:09,240 --> 00:14:12,400
Speaker 7: So I'm like single handedly keeping the movie YouTube review

306
00:14:12,440 --> 00:14:14,280
Speaker 7: economy alive at this point.

307
00:14:16,720 --> 00:14:18,240
Speaker 1: What's something you think is overrated?

308
00:14:18,920 --> 00:14:19,760
Speaker 9: Authenticity?

309
00:14:20,680 --> 00:14:23,480
Speaker 1: M M, what do you mean what you're saying here? Yeah,

310
00:14:23,520 --> 00:14:24,080
Speaker 1: I'm over it.

311
00:14:24,200 --> 00:14:26,760
Speaker 9: Just at first I was going to be specific and

312
00:14:26,840 --> 00:14:31,200
Speaker 9: talk about like gray hairs and personal grooming, but then

313
00:14:31,280 --> 00:14:35,280
Speaker 9: I realized, just in general, I'm over authenticity.

314
00:14:36,280 --> 00:14:36,520
Speaker 1: Give it.

315
00:14:36,680 --> 00:14:39,080
Speaker 3: Give us an example of how you are bucking the

316
00:14:40,120 --> 00:14:41,600
Speaker 3: scourge of authenticity.

317
00:14:41,920 --> 00:14:46,360
Speaker 9: Okay, number one, please take a note of my attire today,

318
00:14:46,600 --> 00:14:53,240
Speaker 9: folks at home. I am man flannel covered in eggs, banana, avocado.

319
00:14:53,120 --> 00:14:53,680
Speaker 2: And apple.

320
00:14:55,360 --> 00:14:58,000
Speaker 1: Yeah I did. I was gonna say, wait, don't forget

321
00:14:58,040 --> 00:14:59,160
Speaker 1: the apple, but yeah, okay.

322
00:14:59,040 --> 00:15:01,040
Speaker 9: Yeah it from last week.

323
00:15:01,680 --> 00:15:05,120
Speaker 1: Yeah oh yeah, oh yeah, wow. That's not stains on

324
00:15:05,120 --> 00:15:07,120
Speaker 1: the Swia shirt and wearing right now now.

325
00:15:07,440 --> 00:15:11,800
Speaker 9: I also have like all my grays peeking through a

326
00:15:11,800 --> 00:15:13,680
Speaker 9: little bit of sunburn on one side.

327
00:15:15,880 --> 00:15:19,600
Speaker 1: One side, you don't want to know that they're breaking

328
00:15:19,680 --> 00:15:23,920
Speaker 1: up the parenting by being a lung haul trucker. Is

329
00:15:23,960 --> 00:15:24,880
Speaker 1: that where.

330
00:15:24,720 --> 00:15:30,000
Speaker 9: The I don't want to be fucking authentic. I don't

331
00:15:30,000 --> 00:15:32,160
Speaker 9: want to be authentic. I watch you at home, just

332
00:15:32,280 --> 00:15:37,200
Speaker 9: imagining that I look fucking glamorous, that my hair is

333
00:15:37,240 --> 00:15:40,800
Speaker 9: like blown out, my makeup is contoured and perfect. My

334
00:15:40,880 --> 00:15:45,920
Speaker 9: Snapchat filter is like, really, how I look? You know

335
00:15:45,920 --> 00:15:48,600
Speaker 9: what I'm saying, that I'm like a Kim Kardashian mom

336
00:15:48,760 --> 00:15:51,000
Speaker 9: that's just like together.

337
00:15:51,360 --> 00:15:55,880
Speaker 3: All the time time, even I'm always on my banana

338
00:15:55,920 --> 00:16:03,280
Speaker 3: phone having long, like three hour long phone calls that like,

339
00:16:03,280 --> 00:16:07,280
Speaker 3: Mommy's busy, Mommy's busy. Anyway, Rothy, get back to the team.

340
00:16:08,720 --> 00:16:11,160
Speaker 9: We all know it's not true, but isn't it better?

341
00:16:11,680 --> 00:16:13,960
Speaker 9: You know, I've already been on the show talking about

342
00:16:14,000 --> 00:16:17,800
Speaker 9: how important lying is, right m hm, and this is

343
00:16:17,880 --> 00:16:21,200
Speaker 9: really just part two event. Just keep the lies going.

344
00:16:21,560 --> 00:16:23,680
Speaker 9: You don't need to know how people are doing. Really,

345
00:16:23,720 --> 00:16:25,520
Speaker 9: whenever people are like, how are you doing? Like, it

346
00:16:25,560 --> 00:16:26,160
Speaker 9: doesn't matter.

347
00:16:26,680 --> 00:16:29,200
Speaker 1: Yeah, I'll tell you. Look, I'll tell you if I'm

348
00:16:29,240 --> 00:16:33,360
Speaker 1: doing bad. How about that? Otherwise, let's just presume I'm

349
00:16:33,400 --> 00:16:34,480
Speaker 1: balling out of control.

350
00:16:34,800 --> 00:16:39,080
Speaker 9: Okay, I'm just out here just killing it, just slaying. Yeah,

351
00:16:39,200 --> 00:16:43,120
Speaker 9: you know, drinking from my hydroflask that should have been

352
00:16:43,160 --> 00:16:43,720
Speaker 9: a Stanley.

353
00:16:44,280 --> 00:16:45,320
Speaker 1: Okay, yeah, I did.

354
00:16:45,360 --> 00:16:48,960
Speaker 3: We did clock that before we started rolling, and I

355
00:16:49,000 --> 00:16:50,760
Speaker 3: was like, oh, you came with the Stanley and then

356
00:16:50,800 --> 00:16:51,760
Speaker 3: you turned it around.

357
00:16:51,800 --> 00:16:52,640
Speaker 1: Set the record straight.

358
00:16:52,680 --> 00:16:55,720
Speaker 3: It is a hydroflask, but it looks functionally exactly the

359
00:16:55,800 --> 00:16:56,680
Speaker 3: same as a quencher.

360
00:16:57,240 --> 00:16:59,360
Speaker 1: And you say you were not Stanley.

361
00:17:00,280 --> 00:17:02,600
Speaker 9: You know, I screwed up. I should have lied.

362
00:17:04,600 --> 00:17:08,280
Speaker 1: Yeah, this is the one area that we have to disagree.

363
00:17:08,320 --> 00:17:13,080
Speaker 1: Authenticity isn't good damn it. If it's a Stanley quencher

364
00:17:13,240 --> 00:17:15,760
Speaker 1: or a hydroflat, it needs to be a Stanley. Unfortunately

365
00:17:16,480 --> 00:17:19,400
Speaker 1: for us to be okay, because we're gen Z. We're

366
00:17:19,440 --> 00:17:21,960
Speaker 1: gen Z gen Z. That's why did you not know?

367
00:17:22,560 --> 00:17:25,800
Speaker 1: I forgot It's very important. So fucking cringe, like you

368
00:17:25,840 --> 00:17:27,040
Speaker 1: don't even know your gen Z.

369
00:17:27,119 --> 00:17:29,080
Speaker 9: S Like, I'm so sorry.

370
00:17:29,400 --> 00:17:31,480
Speaker 1: Oh my, did you try a Stanley?

371
00:17:31,520 --> 00:17:31,720
Speaker 3: Though?

372
00:17:32,160 --> 00:17:34,520
Speaker 1: Wait for real, though? Did you try a Stanley?

373
00:17:35,119 --> 00:17:36,640
Speaker 9: Said Stanley?

374
00:17:36,720 --> 00:17:39,720
Speaker 1: I didn't like it, okay, so break it down house

375
00:17:39,880 --> 00:17:41,520
Speaker 1: not using it really well?

376
00:17:42,080 --> 00:17:48,040
Speaker 9: Stanley is narrower, m okay, And I was extra conscious

377
00:17:48,080 --> 00:17:49,320
Speaker 9: of how it might tip over.

378
00:17:50,040 --> 00:17:52,120
Speaker 1: Oh it didn't feel stable, like.

379
00:17:53,760 --> 00:17:56,920
Speaker 9: I care about my center of gravity. I also don't

380
00:17:56,960 --> 00:18:00,960
Speaker 9: have as much collagen in any of my joints anymore.

381
00:18:02,800 --> 00:18:09,280
Speaker 1: Things are wobbly y. Yeah, just thought you were a

382
00:18:09,320 --> 00:18:12,199
Speaker 1: great dance that's true. I mean it's narrow. So this

383
00:18:12,320 --> 00:18:14,400
Speaker 1: might answer the question that I was asking yesterday because

384
00:18:14,400 --> 00:18:18,760
Speaker 1: I was like, okay, the stanley being the size of

385
00:18:18,960 --> 00:18:22,720
Speaker 1: a cup holder makes sense. Why do so many of

386
00:18:22,720 --> 00:18:25,120
Speaker 1: my cups not fit into a cup holder? What are

387
00:18:25,119 --> 00:18:30,160
Speaker 1: you thinking? And it's for stability in non cup holder situations,

388
00:18:30,200 --> 00:18:35,680
Speaker 1: it would seem girth matters right, like a good wild cup,

389
00:18:38,359 --> 00:18:40,840
Speaker 1: all the way down to the base. Right, The girth

390
00:18:40,920 --> 00:18:43,040
Speaker 1: matters all the way down exactly.

391
00:18:44,760 --> 00:18:46,240
Speaker 9: This fits. This fits in everything.

392
00:18:46,720 --> 00:18:48,560
Speaker 1: It does in my car.

393
00:18:48,600 --> 00:18:50,520
Speaker 9: If it's in my vagina, I try.

394
00:18:50,320 --> 00:18:58,960
Speaker 1: To still got it every night, just making sure.

395
00:19:00,400 --> 00:19:01,800
Speaker 9: I could put the baby back in.

396
00:19:01,920 --> 00:19:05,600
Speaker 1: I probably could.

397
00:19:06,400 --> 00:19:08,040
Speaker 9: We didn't talk about this last time.

398
00:19:08,280 --> 00:19:09,880
Speaker 1: I don't think we did know I had a.

399
00:19:10,080 --> 00:19:13,920
Speaker 9: I should say vaginal birth. Yeah that took twenty minutes.

400
00:19:14,920 --> 00:19:18,120
Speaker 10: Oh that We did talk about it off mic though,

401
00:19:18,160 --> 00:19:20,359
Speaker 10: but I think we weren't prepared to be like yeah,

402
00:19:20,480 --> 00:19:24,760
Speaker 10: twenty yo shoutout, shout out to a quick good for

403
00:19:24,800 --> 00:19:26,359
Speaker 10: you man.

404
00:19:27,240 --> 00:19:29,239
Speaker 9: My doctor asked me if I would like to do

405
00:19:29,280 --> 00:19:31,200
Speaker 9: this professionally right?

406
00:19:31,359 --> 00:19:34,639
Speaker 3: And also and uh going to the bathroom with this

407
00:19:34,880 --> 00:19:36,280
Speaker 3: Stanley mug really quick enough.

408
00:19:38,880 --> 00:19:40,920
Speaker 1: It was easy. Sure you haven't done this before? That

409
00:19:41,160 --> 00:19:42,119
Speaker 1: was wow?

410
00:19:42,680 --> 00:19:46,160
Speaker 9: I had no issh. Also, this fits in my vagina.

411
00:19:46,240 --> 00:19:46,920
Speaker 1: Now my.

412
00:19:48,359 --> 00:19:50,160
Speaker 9: Hydroflask and Stanley.

413
00:19:50,560 --> 00:19:55,359
Speaker 3: Oh man, jacki'e gonna get a Stanley though you said

414
00:19:55,400 --> 00:19:57,479
Speaker 3: you you you're hearing I'm thinking about it.

415
00:19:57,520 --> 00:20:01,280
Speaker 1: Okay, it's it's definitely U underd.

416
00:20:01,760 --> 00:20:04,440
Speaker 9: Yeah, you're just waiting for hydroflass to be more popular.

417
00:20:04,480 --> 00:20:08,479
Speaker 1: That's what our household has a Stanley in it. I

418
00:20:08,520 --> 00:20:12,000
Speaker 1: am just not permitted to use it or look at it,

419
00:20:12,280 --> 00:20:16,520
Speaker 1: but you know it's we have one. We've invested in

420
00:20:16,600 --> 00:20:20,600
Speaker 1: a Stanley and now you know, it just takes some time.

421
00:20:21,600 --> 00:20:24,600
Speaker 1: Whether we are at to Stanley household is just a

422
00:20:24,600 --> 00:20:26,679
Speaker 1: thing that you you kind of have to have a

423
00:20:26,680 --> 00:20:29,399
Speaker 1: conversation about with your loved ones. That's for every family.

424
00:20:29,440 --> 00:20:30,359
Speaker 1: That's it for every family.

425
00:20:30,840 --> 00:20:34,679
Speaker 9: Stanley also has a rim Do you know how easy

426
00:20:34,720 --> 00:20:39,160
Speaker 9: it is to wash this rimless hydroflask flat face? Oh?

427
00:20:39,240 --> 00:20:40,240
Speaker 1: Yeah? That that like that.

428
00:20:40,960 --> 00:20:43,080
Speaker 9: It's so easy to wash this. I just hold it

429
00:20:43,160 --> 00:20:48,000
Speaker 9: under the sink and just easy to wash. Yeah, I

430
00:20:48,040 --> 00:20:49,200
Speaker 9: have you have to like.

431
00:20:50,720 --> 00:20:53,080
Speaker 1: Yeah, yeah, a little yeah, we don't have time for that.

432
00:20:53,240 --> 00:20:56,200
Speaker 1: Do detailing. Yeah, I don't want to have to do detailing.

433
00:20:56,320 --> 00:20:57,760
Speaker 9: It sounds like when I washed dishes.

434
00:21:00,160 --> 00:21:01,000
Speaker 1: Same same for me.

435
00:21:01,040 --> 00:21:06,520
Speaker 3: Yeah, that's definitely my internal monologue.

436
00:21:06,680 --> 00:21:06,880
Speaker 1: Yeah.

437
00:21:06,880 --> 00:21:09,240
Speaker 3: I open like an old bottle and I'm like, oh,

438
00:21:10,320 --> 00:21:13,800
Speaker 3: I have it here, clean it quickly.

439
00:21:14,640 --> 00:21:17,280
Speaker 1: I do every household chore like Paul Rudd picking those

440
00:21:17,280 --> 00:21:18,959
Speaker 1: things up in wet, hot American summer.

441
00:21:19,240 --> 00:21:25,840
Speaker 3: Yeah, putting his utensils away, exasperated, zero energy with every movement.

442
00:21:25,640 --> 00:21:32,399
Speaker 9: Right, Yeah, that's how orgasm now, Yeah, that's.

443
00:21:35,240 --> 00:21:35,359
Speaker 2: It?

444
00:21:35,400 --> 00:21:36,120
Speaker 1: Was it good for you?

445
00:21:36,240 --> 00:21:36,480
Speaker 11: Yeah?

446
00:21:37,240 --> 00:21:43,200
Speaker 1: Whatever? Give me the Stanley, just hand me the Stanley,

447
00:21:43,720 --> 00:21:44,720
Speaker 1: Give me ten minutes.

448
00:21:44,480 --> 00:21:47,680
Speaker 9: And my phone, give me my banana phone.

449
00:21:47,680 --> 00:21:51,159
Speaker 1: My banana phone, and my and my hydroflask. Let me

450
00:21:51,200 --> 00:21:57,600
Speaker 1: get right real quick, get right with the lord France osca.

451
00:21:57,680 --> 00:21:59,560
Speaker 1: What's something you think is underrated? Though?

452
00:22:00,119 --> 00:22:02,000
Speaker 11: Okay, so I have to correct something that I said

453
00:22:02,040 --> 00:22:04,560
Speaker 11: last time, which was underrated not sleeping with your cat

454
00:22:04,680 --> 00:22:08,719
Speaker 11: or your husband in bed. And I realize I've you know,

455
00:22:09,840 --> 00:22:13,119
Speaker 11: it caused some pangs of panic. Nobody knew how to respond.

456
00:22:13,960 --> 00:22:16,679
Speaker 11: Put everyone on blasts that. Yes, sometimes you know, we

457
00:22:16,720 --> 00:22:18,440
Speaker 11: have to sleep in different beds, so everyone gets a

458
00:22:18,440 --> 00:22:20,440
Speaker 11: good night's sleep, even though it was terrible. Let nights

459
00:22:20,440 --> 00:22:24,160
Speaker 11: sleep last night. So underrated is my loving, wonderful husband,

460
00:22:24,600 --> 00:22:29,040
Speaker 11: the father of my daughter, Matt Leeb, who is great,

461
00:22:29,480 --> 00:22:32,399
Speaker 11: who even though we sleep in different beds, we have

462
00:22:32,440 --> 00:22:35,760
Speaker 11: a very robust we have we have a perfect sex life.

463
00:22:35,800 --> 00:22:40,119
Speaker 11: To quote Alan Dershowitz, and and that is who you

464
00:22:40,160 --> 00:22:43,480
Speaker 11: want to be quoted. No, no, but but but just

465
00:22:43,520 --> 00:22:46,800
Speaker 11: to say, look, it doesn't mean that you're going to

466
00:22:46,840 --> 00:22:48,840
Speaker 11: get a divorced, doesn't me you don't love each other.

467
00:22:49,080 --> 00:22:51,560
Speaker 11: Everyone should feel secure out there. Don't you hear the

468
00:22:51,640 --> 00:22:53,399
Speaker 11: security in my voice?

469
00:22:54,760 --> 00:22:59,480
Speaker 1: Yeah? Just h yeah.

470
00:22:59,560 --> 00:23:04,640
Speaker 11: Miles Uh and Jack were very they used are both

471
00:23:04,640 --> 00:23:06,320
Speaker 11: awkward when I said we were like not sleeping in

472
00:23:06,320 --> 00:23:09,880
Speaker 11: the same bed, you guy like, but I'm here to say,

473
00:23:10,800 --> 00:23:13,440
Speaker 11: I'm here to say that it's okay and that I

474
00:23:13,480 --> 00:23:16,359
Speaker 11: love him no matter what. And one day we're gonna

475
00:23:16,359 --> 00:23:19,440
Speaker 11: get one of those king beds. They're like, actually separate.

476
00:23:20,680 --> 00:23:23,800
Speaker 1: Yeah, oh Jack, this sounds like damage control to me. Man,

477
00:23:26,119 --> 00:23:33,120
Speaker 1: is all the times I just kept saying mm yeah, yeah, yeah,

478
00:23:33,160 --> 00:23:33,560
Speaker 1: this is no.

479
00:23:33,760 --> 00:23:36,360
Speaker 11: This is one hundred percent damage control, and it's directed

480
00:23:36,359 --> 00:23:37,360
Speaker 11: at only Matt Leeb.

481
00:23:37,560 --> 00:23:39,840
Speaker 1: So yeah.

482
00:23:40,480 --> 00:23:45,360
Speaker 3: Oh man, Yeah, I need I definitely need my own blanket. Yeah,

483
00:23:45,480 --> 00:23:47,720
Speaker 3: you know, I'm definitely the more I get to that,

484
00:23:47,720 --> 00:23:49,840
Speaker 3: that Scandinavian style was like, bro, you need your own

485
00:23:49,840 --> 00:23:54,639
Speaker 3: blanket because the fights that occur over the blanket tussling occur.

486
00:23:55,320 --> 00:24:00,280
Speaker 12: Yeah wow, especially because you're taller and so like Myhillary,

487
00:24:04,880 --> 00:24:06,159
Speaker 12: the fights that you.

488
00:24:08,160 --> 00:24:11,119
Speaker 1: And Bill over the sheets.

489
00:24:12,200 --> 00:24:15,960
Speaker 3: Oh my god, I'm surprised you didn't do a tweet

490
00:24:16,000 --> 00:24:19,640
Speaker 3: like that. She's like, you know, despite what is according

491
00:24:20,119 --> 00:24:25,040
Speaker 3: in power, it's like, oh no, no, no, no, no Hillary.

492
00:24:24,840 --> 00:24:28,640
Speaker 1: Hillary, Hillary put the Barbie down. Yeah. I think it's great.

493
00:24:28,840 --> 00:24:32,520
Speaker 1: I've definitely taken some time to sleep in separate room.

494
00:24:32,960 --> 00:24:38,480
Speaker 1: I think it's wonderful for your sleep. Sometimes it just depends,

495
00:24:38,640 --> 00:24:40,639
Speaker 1: you know, Yeah, it really does just depend.

496
00:24:40,680 --> 00:24:43,080
Speaker 3: I mean also, like I think for anyone who like

497
00:24:43,200 --> 00:24:44,800
Speaker 3: actually reacted to that, they being like.

498
00:24:44,760 --> 00:24:48,360
Speaker 1: Oh my fucking god, they sleep like it's the fucking death. Now,

499
00:24:49,040 --> 00:24:53,560
Speaker 1: that's a fucking death rattle relationship. It's like, come on, y'all, like, no,

500
00:24:53,760 --> 00:24:55,679
Speaker 1: it isn't. Well, no it's not.

501
00:24:55,800 --> 00:24:57,439
Speaker 11: And that's why I think it's But it's okay to

502
00:24:57,640 --> 00:25:00,560
Speaker 11: here because here's the thing. If all the audience found

503
00:25:00,600 --> 00:25:03,520
Speaker 11: out after, like or if you guys found out, anyone

504
00:25:03,520 --> 00:25:06,600
Speaker 11: found out after like I didn't say it, they'd be like, oh,

505
00:25:06,760 --> 00:25:08,719
Speaker 11: now you're like why are you keeping it a secret

506
00:25:08,800 --> 00:25:10,760
Speaker 11: kind of thing. That's why I'm like telling my friends

507
00:25:10,760 --> 00:25:13,240
Speaker 11: so that when when they you know what I mean.

508
00:25:13,280 --> 00:25:15,240
Speaker 11: So it's like so so that they don't find out

509
00:25:15,320 --> 00:25:16,720
Speaker 11: later and like I didn't realize you guys have been

510
00:25:16,920 --> 00:25:18,560
Speaker 11: being separate, but like that, it's not about that.

511
00:25:18,640 --> 00:25:19,760
Speaker 8: It's like no, no, she's like.

512
00:25:19,840 --> 00:25:22,720
Speaker 11: Here, I'm pro I know, I'm getting very warm even

513
00:25:22,720 --> 00:25:25,000
Speaker 11: just something it's like a pro sleep move, you know,

514
00:25:25,080 --> 00:25:26,320
Speaker 11: it's just about sleep.

515
00:25:26,840 --> 00:25:30,320
Speaker 3: Yeah, of course, I mean that's not there's not the

516
00:25:30,480 --> 00:25:33,080
Speaker 3: real for me. I get into bed like this second,

517
00:25:33,160 --> 00:25:34,719
Speaker 3: I'm about to sleep. Like I'm not one of those

518
00:25:34,760 --> 00:25:36,520
Speaker 3: people who hangs out in bed and does a ton

519
00:25:36,560 --> 00:25:38,760
Speaker 3: of shit. So it's not like that's really not a

520
00:25:38,840 --> 00:25:41,399
Speaker 3: venue where like my relationship with her Majesty is like

521
00:25:41,480 --> 00:25:44,560
Speaker 3: we're making like dreams and memories and shit like that.

522
00:25:44,640 --> 00:25:47,800
Speaker 3: It's more just like, yeah, here's the place I sleep exactly.

523
00:25:48,160 --> 00:25:48,960
Speaker 2: That's sleep.

524
00:25:49,200 --> 00:25:50,920
Speaker 11: The problem is that I think also it's a little

525
00:25:50,920 --> 00:25:52,359
Speaker 11: bit of a flex It's like saying you have like

526
00:25:52,400 --> 00:25:54,640
Speaker 11: seven kids or whatever, or like seeing in suburb beds

527
00:25:54,720 --> 00:25:56,639
Speaker 11: or separate rooms. It's like you guys have two rooms,

528
00:25:56,680 --> 00:25:57,960
Speaker 11: Like who has a luxury to do that?

529
00:25:58,160 --> 00:26:00,880
Speaker 1: You know, Yeah, that's called the time out bedroom.

530
00:26:01,000 --> 00:26:04,560
Speaker 11: Yeah, exactly, but you know it's it's good, Mike. My

531
00:26:04,680 --> 00:26:06,680
Speaker 11: cat's still pissed. She's been peeing all over the place.

532
00:26:06,760 --> 00:26:09,440
Speaker 11: Luckily Matt does not pee all over the place to protest.

533
00:26:10,119 --> 00:26:13,240
Speaker 1: So but and that is a flex also that you

534
00:26:13,280 --> 00:26:18,200
Speaker 1: have a husband who doesn't pee all over the place. Yeah, yeah, congratulations, wow,

535
00:26:18,280 --> 00:26:20,560
Speaker 1: because yeah, Jack and I are like looking nervously at

536
00:26:20,560 --> 00:26:23,200
Speaker 1: each other right now. Yeah. Yeah, no, my wife would

537
00:26:23,200 --> 00:26:27,280
Speaker 1: say the same thing, right I don't pee in the

538
00:26:27,320 --> 00:26:31,880
Speaker 1: linen closet. Yeah, and she's very proud of me for that. Yeah.

539
00:26:31,920 --> 00:26:34,960
Speaker 1: We're going to David Busters later to celebrate. I've been

540
00:26:35,000 --> 00:26:39,359
Speaker 1: about that. Would you like to go to Dave and Busters?

541
00:26:39,920 --> 00:26:40,240
Speaker 9: She should.

542
00:26:40,240 --> 00:26:43,240
Speaker 1: I can play whatever I want. Yeah, I can play

543
00:26:43,240 --> 00:26:45,520
Speaker 1: the Jurassic Way. Halo for like five hours in a

544
00:26:45,600 --> 00:26:51,200
Speaker 1: row is fucking awesome. In the arcade. Yeah, dude, Halo,

545
00:26:51,520 --> 00:26:55,640
Speaker 1: that's that's the big game. Yeah, you can play Halo now,

546
00:26:56,040 --> 00:26:59,199
Speaker 1: and like it's they've got like this giant like wide

547
00:26:59,280 --> 00:27:00,600
Speaker 1: screen experience.

548
00:27:01,240 --> 00:27:04,200
Speaker 3: Well, they do have that big shooter game, you're right, Yeah,

549
00:27:04,400 --> 00:27:05,399
Speaker 3: you play that with your kids.

550
00:27:06,119 --> 00:27:09,000
Speaker 1: I don't play it, but my seven year old will

551
00:27:09,040 --> 00:27:13,280
Speaker 1: play it until I like drag him away because he

552
00:27:13,359 --> 00:27:14,920
Speaker 1: is suffering from malnutrition.

553
00:27:15,359 --> 00:27:23,320
Speaker 3: Like, he will just play wizering away Like, Yeah.

554
00:27:22,720 --> 00:27:24,280
Speaker 9: I just imagine a seven year old like a beer

555
00:27:24,359 --> 00:27:25,359
Speaker 9: a very long beard.

556
00:27:25,720 --> 00:27:29,879
Speaker 1: Yeah, so wispy. It's good though, because I always know

557
00:27:29,920 --> 00:27:34,640
Speaker 1: where he is, you know, just follow the long beard. Yeah,

558
00:27:34,680 --> 00:27:36,720
Speaker 1: all right, we're going to take a quick break. We're

559
00:27:36,760 --> 00:27:38,480
Speaker 1: going to come back and we're going to check in

560
00:27:38,720 --> 00:27:42,000
Speaker 1: with our good friends at Exon who we can trust

561
00:27:42,240 --> 00:27:46,359
Speaker 1: to solve this climate thing. NBD, we'll be right back,

562
00:27:55,200 --> 00:28:00,119
Speaker 1: and we're back. We're and freedom not friendulum just for

563
00:28:00,119 --> 00:28:02,440
Speaker 1: the rescreen. We knew that, and we knew that. We

564
00:28:02,520 --> 00:28:05,040
Speaker 1: knew that, and we knew that, and we knew that

565
00:28:05,119 --> 00:28:12,480
Speaker 1: all along and well passed congratulations. As we all know,

566
00:28:12,520 --> 00:28:17,440
Speaker 1: Elon Musk has repeatedly amplified anti Semitic conspiracy theories on Twitter,

567
00:28:18,080 --> 00:28:22,159
Speaker 1: allows hate speech to proliferate all over the face of

568
00:28:22,680 --> 00:28:27,080
Speaker 1: his social media platform, with some Nazi loving accounts even

569
00:28:27,200 --> 00:28:31,320
Speaker 1: earning money through its ad revenue sharing program, So in

570
00:28:31,440 --> 00:28:36,880
Speaker 1: order to save face, he instituted some serious new policies

571
00:28:37,080 --> 00:28:41,000
Speaker 1: to kind of clamp down on No way, I'm sorry, No,

572
00:28:41,120 --> 00:28:44,800
Speaker 1: that's incorrect. No, he traveled to Auschwitz for a nakedly

573
00:28:44,880 --> 00:28:50,120
Speaker 1: self serving publicity stunt. My bet I was shocked here.

574
00:28:50,480 --> 00:28:54,240
Speaker 1: He didn't change anything of substance about how he operates.

575
00:28:54,280 --> 00:28:56,960
Speaker 9: You see, wouldn't it be nice if Elon wasn't so

576
00:28:57,200 --> 00:28:58,200
Speaker 9: on brand.

577
00:28:58,600 --> 00:29:03,520
Speaker 1: Right, right, But then he wouldn't be Elon Musk now. So,

578
00:29:03,640 --> 00:29:07,040
Speaker 1: beginning on Monday of this week, he participated in a

579
00:29:07,120 --> 00:29:11,760
Speaker 1: two day conference themed around combating anti Semitism, hosted by

580
00:29:11,760 --> 00:29:17,520
Speaker 1: the European Jewish Association, and predictably, it was a complete

581
00:29:17,520 --> 00:29:21,040
Speaker 1: shit show. He got a private tour of Auschwitz along

582
00:29:21,080 --> 00:29:24,440
Speaker 1: with Ben Shapiro, who was also at the conference, and

583
00:29:24,840 --> 00:29:27,400
Speaker 1: like later interviewed him and just lobbed him a bunch

584
00:29:27,400 --> 00:29:32,000
Speaker 1: of softballs that didn't raise any of his history. That's

585
00:29:32,040 --> 00:29:35,680
Speaker 1: even disrespect to a softball. Yeah, meatballs.

586
00:29:35,760 --> 00:29:39,200
Speaker 3: They were like hot and candy dreams in the shape

587
00:29:39,200 --> 00:29:42,240
Speaker 3: of a sphere basically, and he had a tennis racket

588
00:29:42,280 --> 00:29:46,040
Speaker 3: to just think that, I mean, beyond that quote unquote

589
00:29:46,080 --> 00:29:48,560
Speaker 3: interview where they're like, because you would have thought any

590
00:29:48,680 --> 00:29:52,480
Speaker 3: serious examination of anti Semitism dealing with the person who's

591
00:29:52,520 --> 00:29:54,920
Speaker 3: running Twitter, you'd be like. And also in our next

592
00:29:54,920 --> 00:29:58,200
Speaker 3: segment called this you here's some of the posts that

593
00:29:58,280 --> 00:30:00,280
Speaker 3: you have been putting up for the last couple ye years.

594
00:30:00,360 --> 00:30:02,000
Speaker 1: Really would like to talk about that. But it was

595
00:30:02,240 --> 00:30:08,320
Speaker 1: not the case. You've been putting up retweeting, liking, retweeting

596
00:30:08,360 --> 00:30:12,080
Speaker 1: with comment being like this is weird about like antisemitic

597
00:30:12,120 --> 00:30:15,960
Speaker 1: conspiracy theories. Yeah, he also brought his three year old,

598
00:30:18,040 --> 00:30:23,000
Speaker 1: so the you know, the website for the Auschwitz Memorial

599
00:30:23,600 --> 00:30:26,720
Speaker 1: says it is not recommended that children under fourteen visit

600
00:30:26,800 --> 00:30:29,880
Speaker 1: the memorial, which seems like one of those things that

601
00:30:30,080 --> 00:30:34,640
Speaker 1: absolutely goes without saying, but I guess needs to be said.

602
00:30:34,760 --> 00:30:36,880
Speaker 1: But you see it and you're like, yeah, no, of course,

603
00:30:36,960 --> 00:30:39,520
Speaker 1: of course he brought his three year old and was

604
00:30:39,560 --> 00:30:43,680
Speaker 1: like giving him shoulder rides like it was fucking the circus.

605
00:30:43,880 --> 00:30:46,600
Speaker 1: Oh wow, is it the daughter? How wait? Is shen

606
00:30:47,560 --> 00:30:51,600
Speaker 1: x twelve or whatever that name is. That's that's his

607
00:30:51,720 --> 00:30:57,240
Speaker 1: son or daughter. That's the sun according to sources who

608
00:30:57,280 --> 00:30:59,880
Speaker 1: I'm looking at, Okay, how do we desert it? We

609
00:31:00,160 --> 00:31:03,200
Speaker 1: have a what are we what are we calling? How

610
00:31:03,200 --> 00:31:05,760
Speaker 1: do we pronounce that jumble of no. I think you

611
00:31:05,840 --> 00:31:08,400
Speaker 1: just nailed it. She nailed it so hard that I'm

612
00:31:08,440 --> 00:31:14,120
Speaker 1: not even going to attempt to reproduce Okachi. Yeah yeah,

613
00:31:14,280 --> 00:31:17,239
Speaker 1: but yeah. So after the Tory sat down for an

614
00:31:17,280 --> 00:31:23,000
Speaker 1: interview with Ben Shapiro, and Shapiro just didn't in no

615
00:31:23,080 --> 00:31:26,720
Speaker 1: way mentioned his history of anti Semitism. The talk even

616
00:31:26,840 --> 00:31:31,800
Speaker 1: opened with a video produced by the European Jewish Association

617
00:31:32,760 --> 00:31:35,920
Speaker 1: that imagined what the Holocaust would have been like if

618
00:31:36,000 --> 00:31:39,320
Speaker 1: social media and specifically X had existed at the time,

619
00:31:42,000 --> 00:31:45,560
Speaker 1: and ended by posing the question, if we had had

620
00:31:45,800 --> 00:31:49,280
Speaker 1: X in nineteen thirty nine, could have been saved? We

621
00:31:49,360 --> 00:31:53,400
Speaker 1: had X inn, I mean if they.

622
00:31:53,360 --> 00:31:57,200
Speaker 9: Were talking about like ecstasy like molly, yeah, yeah.

623
00:31:57,520 --> 00:32:00,640
Speaker 3: Maybe yeah, rather than like math yeah there mainly on

624
00:32:00,800 --> 00:32:05,120
Speaker 3: myth yeeah, mainly on like very rudimentary myth.

625
00:32:05,360 --> 00:32:08,479
Speaker 1: And I feel like generally people are less likely to

626
00:32:08,480 --> 00:32:15,080
Speaker 1: commit mass genocide when they're on MDMA rather than rudimentary myth.

627
00:32:15,280 --> 00:32:18,760
Speaker 9: But also like Twitter is stoking Nazism.

628
00:32:18,360 --> 00:32:28,120
Speaker 1: Now, yeah, oh you notice that? Okay, yeah, shit, I

629
00:32:28,120 --> 00:32:29,360
Speaker 1: guess we didn't really think about that.

630
00:32:29,440 --> 00:32:31,720
Speaker 3: Well, but did they answer the question how many lives

631
00:32:31,760 --> 00:32:35,160
Speaker 3: would have been saved for absolutely unuseful question.

632
00:32:35,400 --> 00:32:39,800
Speaker 1: Useless question they implied. They implied an answer they had

633
00:32:40,160 --> 00:32:43,680
Speaker 1: in one of the videos, like fake tweets. They had

634
00:32:43,720 --> 00:32:48,760
Speaker 1: an official account posting about Auschwitz's thriving inhabitants, only to

635
00:32:48,840 --> 00:32:52,320
Speaker 1: have that claim debunked by excess community note. That is

636
00:32:52,360 --> 00:32:53,800
Speaker 1: so fucking grim?

637
00:32:54,360 --> 00:33:01,640
Speaker 3: Are these doctored tweets from at Auschwitz Camp official? This

638
00:33:01,720 --> 00:33:04,960
Speaker 3: is such a grim thought experiment. Then, I'm like, who

639
00:33:05,000 --> 00:33:07,320
Speaker 3: does this fucking benefit? I mean, it's just really just

640
00:33:08,120 --> 00:33:11,600
Speaker 3: like creating more cover for X, which is a legitimate

641
00:33:12,760 --> 00:33:15,240
Speaker 3: I mean at this point four chan, eight chan and

642
00:33:15,320 --> 00:33:17,240
Speaker 3: now X or whatever we want to call this thing.

643
00:33:17,600 --> 00:33:21,320
Speaker 3: It's just like a terrible it's a sesspit. But yeah,

644
00:33:21,360 --> 00:33:24,600
Speaker 3: community notes would have come through and been like, m yeah,

645
00:33:24,640 --> 00:33:27,000
Speaker 3: actually this is this is actually the site.

646
00:33:26,840 --> 00:33:29,520
Speaker 1: Of untold horror. Is that what they're saying?

647
00:33:29,520 --> 00:33:31,640
Speaker 3: Because the community notes right now, I feel like half

648
00:33:31,640 --> 00:33:33,600
Speaker 3: the community notes just being like this is from a

649
00:33:33,680 --> 00:33:36,240
Speaker 3: drop ship company and you can get this product for

650
00:33:36,400 --> 00:33:40,320
Speaker 3: much cheaper and another outlet for like this consumer good.

651
00:33:40,320 --> 00:33:42,880
Speaker 3: I feel like that's the most community notes I see recently.

652
00:33:43,360 --> 00:33:46,720
Speaker 1: He literally claimed this is a quote. If there had

653
00:33:46,760 --> 00:33:50,040
Speaker 1: been social media, it would have been impossible for the

654
00:33:50,160 --> 00:33:56,080
Speaker 1: Nazis to hide. Elon Musk said that like tried to

655
00:33:56,120 --> 00:34:02,760
Speaker 1: imagine literally any other CEO making that argument about their products,

656
00:34:03,080 --> 00:34:06,440
Speaker 1: Like if the head of Pepsi Co was just like,

657
00:34:07,040 --> 00:34:09,400
Speaker 1: you know, I think Hitler would have had a hard

658
00:34:09,440 --> 00:34:13,080
Speaker 1: time rising to power with the refreshing taste of Mountain

659
00:34:13,120 --> 00:34:14,320
Speaker 1: dews Baja blasts.

660
00:34:14,360 --> 00:34:17,759
Speaker 3: Okay, Jack, let's not let's not just saying let's that

661
00:34:17,800 --> 00:34:20,920
Speaker 3: may but let's let's let's create a space where that

662
00:34:21,000 --> 00:34:21,520
Speaker 3: is possible.

663
00:34:21,560 --> 00:34:25,600
Speaker 1: Though you know, maybe Baja blast in a stand bad

664
00:34:25,640 --> 00:34:28,480
Speaker 1: example because that's probably tr Yeah, yeah, yeah, that's a

665
00:34:28,480 --> 00:34:31,520
Speaker 1: bad example. Bad example, a bad example. Yeah, just the

666
00:34:31,719 --> 00:34:32,640
Speaker 1: wild ash shit.

667
00:34:32,719 --> 00:34:36,480
Speaker 3: If it would have been impossible to hide, Like well

668
00:34:36,560 --> 00:34:39,480
Speaker 3: right now, they're not even hiding on your website.

669
00:34:39,600 --> 00:34:41,920
Speaker 1: They're like fucking full force.

670
00:34:42,000 --> 00:34:44,360
Speaker 3: So I don't even understand that or is he trying

671
00:34:44,400 --> 00:34:47,919
Speaker 3: to say that like X Also, because of like sort

672
00:34:47,920 --> 00:34:51,640
Speaker 3: of like open source intelligence, people who like kind of

673
00:34:51,680 --> 00:34:54,600
Speaker 3: begin to like identify who Nazis are, are you siding

674
00:34:54,600 --> 00:34:57,680
Speaker 3: with those people who actually do try and drag Nazis

675
00:34:57,719 --> 00:35:01,080
Speaker 3: out into the sunlight? Like that's it's so wild. I'm like,

676
00:35:01,080 --> 00:35:03,040
Speaker 3: what version of Twitter are you celebrating?

677
00:35:03,120 --> 00:35:03,560
Speaker 1: Exactly?

678
00:35:04,760 --> 00:35:08,000
Speaker 9: This is wild to me also because like, you know,

679
00:35:08,080 --> 00:35:10,200
Speaker 9: everything that's going on right now in terms of just

680
00:35:10,239 --> 00:35:15,000
Speaker 9: like talking about genocide in Israel is anti Semitic, right,

681
00:35:15,480 --> 00:35:23,000
Speaker 9: But right Elon Musk having this conversation where you just

682
00:35:23,040 --> 00:35:27,680
Speaker 9: like wax is poetic, right while he stokes anti Semitism

683
00:35:28,040 --> 00:35:29,399
Speaker 9: on his platform.

684
00:35:29,800 --> 00:35:33,600
Speaker 3: It's such a weird thing because the revenue from it, right,

685
00:35:34,000 --> 00:35:36,160
Speaker 3: And I know that people like at the ADL were

686
00:35:36,200 --> 00:35:38,839
Speaker 3: really upset with the leadership there because on one hand

687
00:35:38,880 --> 00:35:41,760
Speaker 3: they're like we they've called out Elon Musk's anti semitism,

688
00:35:41,800 --> 00:35:43,279
Speaker 3: and then the other hand be like they're a great

689
00:35:43,320 --> 00:35:46,640
Speaker 3: partner in the fight against anti Semitism, and it's like

690
00:35:46,680 --> 00:35:51,920
Speaker 3: a very it's just like it's really really inconsistent, and yeah,

691
00:35:52,040 --> 00:35:56,799
Speaker 3: it you begin to wonder, like because you know that

692
00:35:56,840 --> 00:35:59,040
Speaker 3: there's like this whole thing right where the Israeli government

693
00:35:59,080 --> 00:36:02,240
Speaker 3: is like we've completely lost the digital battlefield in terms

694
00:36:02,239 --> 00:36:06,520
Speaker 3: of like sentiment on social media, and like there's now

695
00:36:06,600 --> 00:36:09,319
Speaker 3: like a real concerted effort to really address that because

696
00:36:09,320 --> 00:36:10,920
Speaker 3: they're like, I don't know what happened like on the

697
00:36:10,920 --> 00:36:15,160
Speaker 3: Internet like we so they may see like having the

698
00:36:15,200 --> 00:36:18,880
Speaker 3: power of Twitter, harnessing the power of Twitter, or Elon's

699
00:36:18,920 --> 00:36:21,760
Speaker 3: desire to try and like you know, whitewash his anti

700
00:36:21,760 --> 00:36:25,040
Speaker 3: Semitism away as a as like a potent tool to

701
00:36:25,080 --> 00:36:27,920
Speaker 3: begin like battling that messaging. Because you also have a

702
00:36:27,960 --> 00:36:31,440
Speaker 3: lot of like these accounts that are like the apparent

703
00:36:31,520 --> 00:36:33,719
Speaker 3: that are like that are They're like there's like one

704
00:36:33,760 --> 00:36:36,400
Speaker 3: called at defund Israel Now or something like that that

705
00:36:36,480 --> 00:36:39,600
Speaker 3: apparently Elon Musk like actually is the one who's like, Okay,

706
00:36:39,600 --> 00:36:42,120
Speaker 3: I'm going to give you that like silver gold, blue

707
00:36:42,320 --> 00:36:45,279
Speaker 3: like badge or whatever that apparently he has to have

708
00:36:45,360 --> 00:36:49,160
Speaker 3: like a say in verifying, and that account is like

709
00:36:49,600 --> 00:36:52,840
Speaker 3: basically doing all this stuff to be like Hitler's talked

710
00:36:52,880 --> 00:36:55,160
Speaker 3: about so like poorly.

711
00:36:54,920 --> 00:36:58,799
Speaker 1: But meanwhile there's a real genocide happening that Jews are

712
00:36:58,800 --> 00:37:02,080
Speaker 1: doing and you're like, what is this all content? Oh

713
00:37:02,120 --> 00:37:02,560
Speaker 1: my god.

714
00:37:02,840 --> 00:37:07,320
Speaker 3: So it's like he's like he's like playing every single angle.

715
00:37:07,320 --> 00:37:10,480
Speaker 3: And I think cynical people are saying that accounts like that.

716
00:37:10,760 --> 00:37:13,440
Speaker 3: We're not cynical, but like the cynical read on promoting

717
00:37:13,480 --> 00:37:16,600
Speaker 3: account like that is to just tie any stance that

718
00:37:16,719 --> 00:37:20,640
Speaker 3: is anti apartheid or genocide as being part and parcel

719
00:37:20,719 --> 00:37:23,600
Speaker 3: of like full blown Nazi stuff, like.

720
00:37:23,960 --> 00:37:25,279
Speaker 1: Full blown antisemity.

721
00:37:25,320 --> 00:37:28,600
Speaker 3: Yeah, to completely tie that those two ideas together, so

722
00:37:28,640 --> 00:37:31,520
Speaker 3: they're like inextricable. So then like the shorthand for people

723
00:37:31,560 --> 00:37:33,120
Speaker 3: to be like, oh, you're saying that means they're like

724
00:37:33,200 --> 00:37:33,960
Speaker 3: actually a Nazi.

725
00:37:34,040 --> 00:37:34,560
Speaker 2: Yeah.

726
00:37:34,920 --> 00:37:39,560
Speaker 1: See, yeah, just to I mean maybe maybe this doesn't

727
00:37:39,560 --> 00:37:42,640
Speaker 1: need to be said, but X, Like even if there

728
00:37:42,640 --> 00:37:45,239
Speaker 1: magically was like a social media platform at the time,

729
00:37:45,800 --> 00:37:49,160
Speaker 1: Jewish citizens wouldn't just be able to leave Germany after

730
00:37:49,200 --> 00:37:53,600
Speaker 1: seeing some tweets because the Nazis revoked their right to

731
00:37:53,880 --> 00:37:56,640
Speaker 1: free movement long before the death camps were built. And

732
00:37:56,760 --> 00:37:59,360
Speaker 1: also much of the world did know about what the

733
00:37:59,440 --> 00:38:01,480
Speaker 1: Nazis were doing but turned to blind eye, which I

734
00:38:01,520 --> 00:38:05,320
Speaker 1: think is important to keep in mind at this time

735
00:38:05,840 --> 00:38:11,320
Speaker 1: because that feels like what we're going through right now

736
00:38:11,680 --> 00:38:13,960
Speaker 1: is a lot of the world just kind of turning

737
00:38:13,960 --> 00:38:16,839
Speaker 1: a blind eye and just being like, well, that's not

738
00:38:16,920 --> 00:38:18,880
Speaker 1: really my problem, is it. Yeah.

739
00:38:18,920 --> 00:38:21,480
Speaker 3: Well it's weird too because the social media has allowed

740
00:38:21,520 --> 00:38:24,319
Speaker 3: more people to sort of engage with, like engage with

741
00:38:24,400 --> 00:38:28,560
Speaker 3: the topic. While governments for sure, it's like that's why

742
00:38:28,560 --> 00:38:30,680
Speaker 3: they feel like there's such a tension existing in many

743
00:38:30,680 --> 00:38:33,200
Speaker 3: countries where people like, I'm sorry, what are we what's

744
00:38:33,239 --> 00:38:34,600
Speaker 3: our part in this as a nation?

745
00:38:35,840 --> 00:38:37,880
Speaker 1: Can we do something about that? And like, oh, you

746
00:38:37,880 --> 00:38:38,239
Speaker 1: saw that.

747
00:38:39,520 --> 00:38:41,799
Speaker 3: We were just hoping to like let that pass until

748
00:38:41,800 --> 00:38:44,640
Speaker 3: there's some other global controversy that can kind of keep

749
00:38:44,719 --> 00:38:48,080
Speaker 3: this thing moving. But yeah, like just to say like

750
00:38:49,520 --> 00:38:52,520
Speaker 3: it it's just the tweets would have changed everything. It's

751
00:38:52,640 --> 00:38:56,560
Speaker 3: just disingenuous and just makes like an utter mockery of

752
00:38:56,600 --> 00:39:00,160
Speaker 3: like everything that's happening. Because right now I feel like

753
00:39:00,160 --> 00:39:03,080
Speaker 3: like while people are like a lot of media is

754
00:39:03,160 --> 00:39:05,760
Speaker 3: unable to really contend with what's happening, especially in Gaza

755
00:39:05,800 --> 00:39:07,799
Speaker 3: in the West Bank. Now it's like now it's more

756
00:39:07,880 --> 00:39:09,760
Speaker 3: like everyone's being like, would you see what Hillary Clinton

757
00:39:09,800 --> 00:39:13,160
Speaker 3: had to say about Greta Gerwick and Margo Robbie, you know,

758
00:39:13,280 --> 00:39:15,880
Speaker 3: not getting snubbed and like that's getting to it's just

759
00:39:16,200 --> 00:39:19,000
Speaker 3: we're we're in a bizarre, upside down world.

760
00:39:19,640 --> 00:39:23,239
Speaker 1: Yeah, but I mean he did make kind of an

761
00:39:23,239 --> 00:39:26,800
Speaker 1: air type case that he can't be doing in anything

762
00:39:26,800 --> 00:39:29,840
Speaker 1: that would be confused with anti Semitism, because he said

763
00:39:29,840 --> 00:39:34,919
Speaker 1: that he has Jewish friends. Oh no, oh yeah, then

764
00:39:34,920 --> 00:39:38,960
Speaker 1: that that Okay, Nope, Actually two thirds of my friends

765
00:39:38,960 --> 00:39:45,920
Speaker 1: are Jewish. I'm like Jewish by association, yo, problem.

766
00:39:45,640 --> 00:39:47,799
Speaker 3: He said, I'm asked, he said, I'm asked that full

767
00:39:47,880 --> 00:39:52,160
Speaker 3: quote is I'm Then he said, I'm aspirationally Jewish. Dude,

768
00:39:52,239 --> 00:39:55,920
Speaker 3: you are out here like retweeting that kind of stuff

769
00:39:55,960 --> 00:39:58,400
Speaker 3: where you're saying like it's that like fake ass, non

770
00:39:58,680 --> 00:40:01,360
Speaker 3: not real voltaor quote about being like you should be

771
00:40:01,400 --> 00:40:04,000
Speaker 3: worried of, Like it's to the effect of, like you

772
00:40:04,080 --> 00:40:05,920
Speaker 3: have to think about the people you are not allowed

773
00:40:05,920 --> 00:40:08,600
Speaker 3: to criticize in a society, and like that's where you

774
00:40:08,640 --> 00:40:10,680
Speaker 3: know where the power lies. But that's really just comes

775
00:40:10,680 --> 00:40:13,680
Speaker 3: from an anti Semitic fucking creep. But they're like those

776
00:40:13,719 --> 00:40:16,080
Speaker 3: actually a full taiar, Like you're doing that kind.

777
00:40:15,920 --> 00:40:19,360
Speaker 1: Of as Foltaire one said, and then quoting a straight

778
00:40:19,440 --> 00:40:20,880
Speaker 1: up like four chance not.

779
00:40:21,000 --> 00:40:24,799
Speaker 3: Yeah, you're like that from a Reinhardt Hydrich speech that

780
00:40:24,880 --> 00:40:25,640
Speaker 3: he gave to the book.

781
00:40:25,640 --> 00:40:30,480
Speaker 1: Okay, whatever, Sure, but despite his oddly specific fraction of

782
00:40:30,560 --> 00:40:36,200
Speaker 1: Jewish friends and calculated photo ops, X still not solving

783
00:40:36,200 --> 00:40:40,319
Speaker 1: its anti semitism problem. Several antisemitic posts on X which

784
00:40:40,360 --> 00:40:44,279
Speaker 1: have been identified as anti Semitic. Moderators have refused to delete,

785
00:40:44,320 --> 00:40:47,560
Speaker 1: claiming that they do not violate the platform's rules, so

786
00:40:47,600 --> 00:40:52,360
Speaker 1: they've been reviewed and not deleted. There's been a spike

787
00:40:52,440 --> 00:40:55,160
Speaker 1: in anti Semitic posts in the country that must just

788
00:40:55,280 --> 00:40:58,480
Speaker 1: visited in Poland because of an incident in December in

789
00:40:58,520 --> 00:41:02,080
Speaker 1: which a far right MP used a fire extinguisher to

790
00:41:02,120 --> 00:41:05,520
Speaker 1: snuff out a menora during Hanukkah, which was a major

791
00:41:05,800 --> 00:41:08,880
Speaker 1: news story and inspired a slew of what supremacist means.

792
00:41:09,440 --> 00:41:12,440
Speaker 1: And they're just like, yeah, I mean, what what are

793
00:41:12,520 --> 00:41:15,239
Speaker 1: we gonna do? It's you know, so they they're just

794
00:41:15,480 --> 00:41:18,839
Speaker 1: very selective and where they care about this. That's why

795
00:41:18,880 --> 00:41:22,400
Speaker 1: it's such a it's so this is so fucking dangerous

796
00:41:22,440 --> 00:41:25,200
Speaker 1: to play around with what is hate or what is

797
00:41:25,280 --> 00:41:28,480
Speaker 1: not hate speech? You know what I mean, Like it's

798
00:41:28,600 --> 00:41:33,600
Speaker 1: completely well, it's gonna lose meaning because.

799
00:41:33,320 --> 00:41:36,680
Speaker 3: It's if it's if it's almost yeah, like to the

800
00:41:36,719 --> 00:41:39,640
Speaker 3: point where like it's it's flying like I don't it

801
00:41:39,680 --> 00:41:41,840
Speaker 3: really It really blows my mind because I do not

802
00:41:42,080 --> 00:41:45,840
Speaker 3: see how beginning to weaponize anti semitism in like a

803
00:41:45,920 --> 00:41:48,320
Speaker 3: very cynical way makes anyone safe.

804
00:41:48,760 --> 00:41:50,320
Speaker 1: And it's just used to sort.

805
00:41:50,160 --> 00:41:52,840
Speaker 3: Of like stoppo any kind of discussion or debate or

806
00:41:52,880 --> 00:41:56,239
Speaker 3: dissent or whatever. But meanwhile, you have somebody who is

807
00:41:56,280 --> 00:42:00,560
Speaker 3: so open about like what they're like, They're philosophic view

808
00:42:00,680 --> 00:42:03,799
Speaker 3: is supposedly be like the standard bear for the fight

809
00:42:03,880 --> 00:42:04,560
Speaker 3: against it.

810
00:42:04,560 --> 00:42:08,839
Speaker 1: It's just like, wo, what on earth? Yeah, it's just

811
00:42:09,040 --> 00:42:11,080
Speaker 1: it just for me and just say like it.

812
00:42:11,280 --> 00:42:14,640
Speaker 3: I only see this getting worse, like to begin to

813
00:42:15,040 --> 00:42:17,960
Speaker 3: just to fuck around like this constantly. But again, I

814
00:42:18,000 --> 00:42:21,920
Speaker 3: think like there's clearly there's clearly been this thing of

815
00:42:21,960 --> 00:42:26,600
Speaker 3: like online the sentiment, whether there's like young people or

816
00:42:26,640 --> 00:42:30,120
Speaker 3: whatever it is to blame people's just general disgust for

817
00:42:30,160 --> 00:42:34,920
Speaker 3: what is happening or them being completely taken aback by

818
00:42:34,960 --> 00:42:37,880
Speaker 3: the violence that's happening against innocent people.

819
00:42:38,000 --> 00:42:38,759
Speaker 1: And I don't know.

820
00:42:38,920 --> 00:42:41,880
Speaker 9: I'm just like, well, this is the problem with like

821
00:42:41,920 --> 00:42:44,879
Speaker 9: the FCC not being on top of this shit right right,

822
00:42:45,320 --> 00:42:47,880
Speaker 9: And this is why I just want to bring us

823
00:42:47,920 --> 00:42:52,759
Speaker 9: back again to the fact that authenticity sucks. We don't

824
00:42:52,800 --> 00:42:56,839
Speaker 9: need to hear about these like hate filled assholes and

825
00:42:56,880 --> 00:43:01,000
Speaker 9: their antisemitic bullshit, Like they don't get to have this

826
00:43:01,280 --> 00:43:07,239
Speaker 9: much voice and presence and energy like we are supposed

827
00:43:07,280 --> 00:43:11,040
Speaker 9: to censor shit like that, you know, or is it

828
00:43:11,120 --> 00:43:15,360
Speaker 9: censure you know or both? Like it's that's not okay,

829
00:43:15,680 --> 00:43:18,719
Speaker 9: and you don't get to just like walk around. And

830
00:43:18,760 --> 00:43:22,080
Speaker 9: in fact, half the reason why we're having to deal

831
00:43:22,120 --> 00:43:25,160
Speaker 9: with this all again and again and again is because

832
00:43:25,880 --> 00:43:29,680
Speaker 9: X isn't on top of regulating it as well as

833
00:43:29,760 --> 00:43:30,960
Speaker 9: other media platforms.

834
00:43:31,200 --> 00:43:33,920
Speaker 3: Yeah, that's why I'm like, yeah, I remember in the

835
00:43:33,920 --> 00:43:36,560
Speaker 3: beginning that like it felt like the EU was really

836
00:43:37,040 --> 00:43:39,279
Speaker 3: being like, yo, you need to fucking answer for the

837
00:43:39,440 --> 00:43:42,440
Speaker 3: kinds of garbage that's on this website, because like we

838
00:43:42,480 --> 00:43:45,080
Speaker 3: see that as like a threat to our like stability here.

839
00:43:45,600 --> 00:43:48,680
Speaker 1: But I'm not sure like where that's headed. And yeah,

840
00:43:48,680 --> 00:43:49,080
Speaker 1: I don't know.

841
00:43:49,760 --> 00:43:53,240
Speaker 3: Again, I mean, I understand why the US especially doesn't

842
00:43:53,239 --> 00:43:56,880
Speaker 3: have a reckoning with hate speech, because it's it's so

843
00:43:57,080 --> 00:43:59,759
Speaker 3: such part and parcel of the culture that they there's

844
00:43:59,800 --> 00:44:01,880
Speaker 3: no way like that we could begin to do that

845
00:44:02,040 --> 00:44:04,439
Speaker 3: and have people come out of the woodwork and.

846
00:44:04,360 --> 00:44:07,040
Speaker 1: Be like, it's it's all of our free speech, not just.

847
00:44:07,040 --> 00:44:10,279
Speaker 9: Saying that people possibly we can't even get gun control.

848
00:44:09,960 --> 00:44:12,480
Speaker 3: Together, yeah right exactly, They're like, man, we can't even

849
00:44:12,480 --> 00:44:14,600
Speaker 3: control objects like that.

850
00:44:14,680 --> 00:44:18,919
Speaker 1: We could easily control. Yeah, like words, Nah, you gotta say,

851
00:44:19,040 --> 00:44:20,520
Speaker 1: we can't even fucking fit.

852
00:44:20,760 --> 00:44:21,960
Speaker 9: There's no regulation.

853
00:44:22,560 --> 00:44:24,000
Speaker 1: Yeah, and yeah, like I don't know.

854
00:44:24,040 --> 00:44:26,200
Speaker 3: I mean, the FCC obviously is dealing with stuff that's

855
00:44:26,239 --> 00:44:29,280
Speaker 3: like broadcasted, so I don't know who you know obviously,

856
00:44:29,440 --> 00:44:31,120
Speaker 3: right is the real regulator there.

857
00:44:31,160 --> 00:44:34,759
Speaker 1: But I mean, it's just we thought that maybe the.

858
00:44:35,400 --> 00:44:38,040
Speaker 3: Advertiser exodus would do something, but it just seems like

859
00:44:38,120 --> 00:44:41,319
Speaker 3: now we're just watching it fall apart. But it's like

860
00:44:41,760 --> 00:44:45,520
Speaker 3: watching like a star collapse on itself and then it's

861
00:44:45,520 --> 00:44:47,760
Speaker 3: probably gonna end in something really fucking gross.

862
00:44:48,120 --> 00:44:49,920
Speaker 9: What I'm saying, Miles is I don't know why they

863
00:44:49,920 --> 00:44:50,920
Speaker 9: don't let me do it.

864
00:44:51,440 --> 00:44:54,440
Speaker 1: Let you be the charge ass kicker on Twitter.

865
00:44:54,880 --> 00:44:58,399
Speaker 9: Yeah, I would be really good at it. You don't

866
00:44:58,400 --> 00:45:00,120
Speaker 9: get to play here anymore.

867
00:45:00,200 --> 00:45:06,520
Speaker 1: Yeah, and that is the banana phones final word. Click. Yeah.

868
00:45:06,840 --> 00:45:09,279
Speaker 1: All right, let's take a quick break and we'll be

869
00:45:09,360 --> 00:45:22,640
Speaker 1: right back. And we're back. And one of the things

870
00:45:22,680 --> 00:45:26,880
Speaker 1: that your new book, doctor mcnernie, you talk about is

871
00:45:27,120 --> 00:45:29,920
Speaker 1: what is good technology and is it possible? And I

872
00:45:30,040 --> 00:45:34,080
Speaker 1: feel like I'm so used to this hyper capitalism paradigm

873
00:45:34,560 --> 00:45:38,760
Speaker 1: that I don't think we can have technology without having

874
00:45:39,239 --> 00:45:42,680
Speaker 1: like loss of jobs and free will. But I don't know,

875
00:45:42,960 --> 00:45:46,240
Speaker 1: I've seen this like recent reappraisal of like the Luddite movement,

876
00:45:46,600 --> 00:45:49,080
Speaker 1: which is just a phrase that I grew up using

877
00:45:49,120 --> 00:45:52,759
Speaker 1: to be like anybody who didn't want to use a

878
00:45:52,800 --> 00:45:57,760
Speaker 1: computer and you know, was slightly resistant to technology technological progress.

879
00:45:58,040 --> 00:46:00,279
Speaker 1: And now people are like pointing out, you know, they

880
00:46:00,320 --> 00:46:03,080
Speaker 1: didn't just want to destroy all machines. They were focused

881
00:46:03,120 --> 00:46:06,160
Speaker 1: on the ones that took jobs and led to wage losses.

882
00:46:06,520 --> 00:46:08,919
Speaker 1: But we turned them into like old man screaming at

883
00:46:08,920 --> 00:46:13,440
Speaker 1: cloud because of our paradigm of like, yeah, but that's counterprogress,

884
00:46:13,440 --> 00:46:18,520
Speaker 1: that's unrealistic. Like so what is my closed off capitalist

885
00:46:19,000 --> 00:46:22,680
Speaker 1: mind missing out on when I think about like the

886
00:46:22,800 --> 00:46:26,480
Speaker 1: direction that technology can take, Like what are the good

887
00:46:26,560 --> 00:46:31,279
Speaker 1: things that aren't just like basically AI being McKinsey.

888
00:46:32,440 --> 00:46:34,759
Speaker 7: Yeah, no, no, I mean first, I do love this

889
00:46:34,840 --> 00:46:38,080
Speaker 7: reappraisal of the Lutist movement. I know Brian Merchant has

890
00:46:38,080 --> 00:46:39,960
Speaker 7: a book out called Blood in the Machine, which is

891
00:46:40,000 --> 00:46:43,600
Speaker 7: like specifically try to reframe the ludes this movement. It

892
00:46:43,600 --> 00:46:46,840
Speaker 7: gives what a mation in the UK as the origins

893
00:46:46,840 --> 00:46:49,319
Speaker 7: of the revolution against Big pick. And I do love

894
00:46:49,320 --> 00:46:51,320
Speaker 7: this because I do think the Luddites have been unfairly

895
00:46:51,400 --> 00:46:55,400
Speaker 7: maligned as these tech paters. But yeah, second, you know, so,

896
00:46:55,719 --> 00:46:59,839
Speaker 7: myself and my work wife, doctor Eleanol Drage co edited

897
00:46:59,880 --> 00:47:01,879
Speaker 7: this book called The Good Robot, which is the same

898
00:47:01,880 --> 00:47:05,239
Speaker 7: as our podcast on this provocative question, and we mean

899
00:47:05,320 --> 00:47:07,680
Speaker 7: it very much as like a suggestion and idea, not

900
00:47:07,840 --> 00:47:11,719
Speaker 7: like an inevitability that technology, you know, maybe can be

901
00:47:11,800 --> 00:47:14,839
Speaker 7: good in a lot of spaces. That definitely doesn't sound

902
00:47:14,880 --> 00:47:17,640
Speaker 7: like a particularly radical idea, particularly in the like tech

903
00:47:17,680 --> 00:47:20,560
Speaker 7: type spaces we've discussed, but like for people like us

904
00:47:20,560 --> 00:47:23,080
Speaker 7: who spend our day to day looking at these really

905
00:47:23,120 --> 00:47:26,320
Speaker 7: awful effects technology like either because they have been designed

906
00:47:26,360 --> 00:47:29,759
Speaker 7: to be really awful, like predictive policing tools, or because

907
00:47:29,760 --> 00:47:33,000
Speaker 7: they're being exploited and used in like really harmful ways,

908
00:47:33,080 --> 00:47:36,720
Speaker 7: like the way that technologies used to perpetrate gender based violence.

909
00:47:37,000 --> 00:47:40,120
Speaker 7: It can be really easy to be unable to see

910
00:47:40,160 --> 00:47:42,799
Speaker 7: any kind of positive possibilities for a lot of these

911
00:47:42,840 --> 00:47:46,200
Speaker 7: new technologies. But while I definitely, you know, think that

912
00:47:46,200 --> 00:47:48,640
Speaker 7: there's a real place for just the total refusal of

913
00:47:48,680 --> 00:47:51,560
Speaker 7: people like the Bloodites or the neo Luodite movement, which

914
00:47:51,600 --> 00:47:53,320
Speaker 7: is kind of trying to bring back a lot of

915
00:47:53,360 --> 00:47:56,440
Speaker 7: these ideas. We want to challenge ourselves and all the

916
00:47:56,440 --> 00:47:59,239
Speaker 7: guests we have in our podcasts to say, like, you know,

917
00:47:59,360 --> 00:48:00,000
Speaker 7: what does.

918
00:48:00,000 --> 00:48:01,560
Speaker 8: What would it mean for technology to be good?

919
00:48:01,600 --> 00:48:04,400
Speaker 7: And so for us that's feminist and pro justice and

920
00:48:04,480 --> 00:48:07,759
Speaker 7: afformed by all these different kinds of ideas about equality

921
00:48:07,800 --> 00:48:10,960
Speaker 7: and fairness and also what would that look like sort

922
00:48:10,960 --> 00:48:14,200
Speaker 7: of grounded in our everyday lives. So for me, for example,

923
00:48:14,960 --> 00:48:17,000
Speaker 7: a lot of thinking about good technology is trying to

924
00:48:17,040 --> 00:48:20,040
Speaker 7: reclaim technologies that we might not think of as being

925
00:48:20,200 --> 00:48:23,640
Speaker 7: very like high tech, often because they've been associated with women.

926
00:48:23,760 --> 00:48:25,719
Speaker 7: So like I knit, and I have a pair of

927
00:48:25,800 --> 00:48:28,520
Speaker 7: knitting needles on the table next to me, and knitting

928
00:48:28,560 --> 00:48:30,640
Speaker 7: is often not understood as being like a very high

929
00:48:30,719 --> 00:48:34,359
Speaker 7: tech practice. But in the nineteen eighties, when people are

930
00:48:34,360 --> 00:48:37,160
Speaker 7: trying to get more girls and women back into computer science,

931
00:48:37,640 --> 00:48:39,239
Speaker 7: there was this idea that if you can knit, you

932
00:48:39,239 --> 00:48:41,400
Speaker 7: can code, because if you can read a knitting pattern,

933
00:48:41,680 --> 00:48:44,080
Speaker 7: then you can use a coding program like and so

934
00:48:44,280 --> 00:48:47,640
Speaker 7: sometimes in our computer science conferences you'll see people put

935
00:48:47,680 --> 00:48:50,160
Speaker 7: a knitting pattern up on the screen and just say

936
00:48:50,160 --> 00:48:52,839
Speaker 7: what coding language is this? And then usually only one

937
00:48:52,920 --> 00:48:56,000
Speaker 7: or two people. Often one of the few female attendees

938
00:48:56,000 --> 00:48:58,960
Speaker 7: will say, oh, that's a knitting pattern, because they're the

939
00:48:59,040 --> 00:49:01,440
Speaker 7: only ones that can unders stand that kind of code.

940
00:49:01,560 --> 00:49:04,360
Speaker 7: So I think there's something really beautiful and like reclaiming

941
00:49:04,760 --> 00:49:08,520
Speaker 7: those particular kinds of technologies that have been maybe excluded

942
00:49:08,560 --> 00:49:11,359
Speaker 7: from the way we talk about tech on my again

943
00:49:11,440 --> 00:49:14,640
Speaker 7: work wife, who co edited this book, well she edited

944
00:49:14,680 --> 00:49:17,400
Speaker 7: most of it actually, so who really did the heavy

945
00:49:17,480 --> 00:49:20,359
Speaker 7: lifting on this book? Eleanor talks about the whisk as

946
00:49:20,400 --> 00:49:22,920
Speaker 7: her example of a good technology, and she says she

947
00:49:22,960 --> 00:49:24,480
Speaker 7: loves the way it looks, she likes how she can

948
00:49:24,560 --> 00:49:26,280
Speaker 7: use it in all these different ways, and she says,

949
00:49:26,560 --> 00:49:28,920
Speaker 7: you know, she's sure there's ways that you can misuse this,

950
00:49:29,040 --> 00:49:31,319
Speaker 7: but you know, she's it's something that just like makes

951
00:49:31,320 --> 00:49:33,560
Speaker 7: her life better and is designed well in a very

952
00:49:33,560 --> 00:49:37,920
Speaker 7: simple way. I unfortunately have already undermined this good technology

953
00:49:37,920 --> 00:49:39,840
Speaker 7: for her because I then told her about when I

954
00:49:39,880 --> 00:49:43,080
Speaker 7: was about fourteen, my school went to a trip at

955
00:49:43,080 --> 00:49:44,880
Speaker 7: the technology museum in Auckland.

956
00:49:45,160 --> 00:49:46,000
Speaker 8: It's quod Motet.

957
00:49:46,000 --> 00:49:48,279
Speaker 7: If you're from New Zealand, everyone's in Auckland's been to

958
00:49:48,320 --> 00:49:50,879
Speaker 7: this museum because it's not that much to do.

959
00:49:52,520 --> 00:49:53,480
Speaker 8: For a school trip.

960
00:49:53,920 --> 00:49:56,280
Speaker 7: And then one of the girls in my class wound

961
00:49:56,400 --> 00:49:59,240
Speaker 7: the hair of another girl into like an antique egg beater.

962
00:50:00,040 --> 00:50:01,680
Speaker 8: Couldn't get her out, she got stuck.

963
00:50:02,800 --> 00:50:06,080
Speaker 7: You know, children can make full technologies bad. But apart

964
00:50:06,120 --> 00:50:08,239
Speaker 7: from that, you know, I think like trying to find

965
00:50:08,239 --> 00:50:11,960
Speaker 7: these like little examples of technology that aren't about the

966
00:50:12,040 --> 00:50:14,399
Speaker 7: kind of like big hype of AI, but maybe bring

967
00:50:14,480 --> 00:50:16,920
Speaker 7: us back into the ways that we use technology to

968
00:50:17,000 --> 00:50:19,520
Speaker 7: reshape how worlds and make things a bit better. Is

969
00:50:19,520 --> 00:50:21,040
Speaker 7: what I like to do with this question.

970
00:50:22,680 --> 00:50:26,000
Speaker 3: Is when you think of like you know, I think

971
00:50:26,280 --> 00:50:28,760
Speaker 3: that the one version is like, well, this generative AI,

972
00:50:28,920 --> 00:50:31,600
Speaker 3: like it democratizes certain things, and I think while on

973
00:50:31,600 --> 00:50:34,200
Speaker 3: one hand it may allow people access to like create

974
00:50:34,239 --> 00:50:37,759
Speaker 3: things that they haven't before, it also makes other Like

975
00:50:37,800 --> 00:50:39,880
Speaker 3: you're saying, it's the use of it that makes things

976
00:50:40,960 --> 00:50:43,800
Speaker 3: that sort of ultimately determines whether or not a technology

977
00:50:43,840 --> 00:50:45,800
Speaker 3: is good or you know, used in a positive or

978
00:50:45,880 --> 00:50:49,040
Speaker 3: negative way. Is there like when you look at all

979
00:50:49,520 --> 00:50:52,080
Speaker 3: like for all the people that are preaching and proclaiming

980
00:50:52,120 --> 00:50:55,080
Speaker 3: about how AI is opening the door to something new,

981
00:50:55,880 --> 00:50:58,840
Speaker 3: what like as it relates to sort of these large

982
00:50:58,920 --> 00:51:02,520
Speaker 3: language models, what are the ways that that can't Like,

983
00:51:02,680 --> 00:51:04,520
Speaker 3: is that more about a use case or we need

984
00:51:04,560 --> 00:51:06,960
Speaker 3: to lean more into the regulations to make sure that

985
00:51:07,000 --> 00:51:10,080
Speaker 3: AI isn't wielded by nefarious powers, Like how do you

986
00:51:10,120 --> 00:51:14,319
Speaker 3: look at that specific technology and think, Okay, while there's

987
00:51:14,360 --> 00:51:17,400
Speaker 3: definitely like a lot of biased or weird uses of it, like,

988
00:51:17,840 --> 00:51:20,480
Speaker 3: there's also another way to look at this and not

989
00:51:20,640 --> 00:51:23,240
Speaker 3: just kind of like lean into the skynet version.

990
00:51:24,520 --> 00:51:26,719
Speaker 7: Yeah, I mean I would take this in a lot

991
00:51:26,760 --> 00:51:28,919
Speaker 7: of different ways. Like my first and sort of most

992
00:51:28,920 --> 00:51:31,480
Speaker 7: important route in is I would say who makes these

993
00:51:31,520 --> 00:51:33,920
Speaker 7: models and who has control over them and who can

994
00:51:34,000 --> 00:51:37,160
Speaker 7: afford to because you know, one of the big changes

995
00:51:37,200 --> 00:51:39,360
Speaker 7: I think that's happened in the last few years is

996
00:51:39,360 --> 00:51:42,560
Speaker 7: that language models have gone from much smaller models that

997
00:51:42,640 --> 00:51:45,919
Speaker 7: maybe like one researcher with a reasonable budget could train

998
00:51:46,040 --> 00:51:49,680
Speaker 7: themselves a lab, to being these absolutely huge models that

999
00:51:49,719 --> 00:51:52,760
Speaker 7: you need a massive amount of energy, a massive amount

1000
00:51:52,800 --> 00:51:55,960
Speaker 7: of data, and a massive amount of money to create.

1001
00:51:56,040 --> 00:51:58,440
Speaker 7: And so what that means is that companies with a

1002
00:51:58,480 --> 00:52:02,759
Speaker 7: first move advantage like Google, like open ai are the

1003
00:52:02,800 --> 00:52:04,640
Speaker 7: ones who can afford to make these models. And I

1004
00:52:04,640 --> 00:52:07,799
Speaker 7: think increasingly it's going to be harder and harder as

1005
00:52:07,840 --> 00:52:10,760
Speaker 7: the models get bigger for small firms to enter that market.

1006
00:52:10,920 --> 00:52:12,960
Speaker 8: So what we end up with them is a monopoly.

1007
00:52:13,080 --> 00:52:15,200
Speaker 7: And I think we're starting to see some of the

1008
00:52:15,200 --> 00:52:17,680
Speaker 7: effects of that monopoly right now when you have a

1009
00:52:17,880 --> 00:52:20,799
Speaker 7: few big tech firms kind of having a hand on

1010
00:52:20,880 --> 00:52:24,279
Speaker 7: like most of the most powerful and effective models. And

1011
00:52:24,320 --> 00:52:26,239
Speaker 7: so I think, like, even though people say, oh, this

1012
00:52:26,280 --> 00:52:29,680
Speaker 7: is going to democratize AI because everyone can generate text

1013
00:52:29,719 --> 00:52:31,879
Speaker 7: with these models, It's like, yes, but you know, very

1014
00:52:31,920 --> 00:52:34,920
Speaker 7: few people are profiting from it. And also, you know,

1015
00:52:35,360 --> 00:52:38,319
Speaker 7: I think very few people then have control as well

1016
00:52:38,360 --> 00:52:40,799
Speaker 7: over how long we're able to use those models for

1017
00:52:41,280 --> 00:52:43,120
Speaker 7: one day. Will they just all be turned off or

1018
00:52:43,120 --> 00:52:44,480
Speaker 7: will they be shifted in a way?

1019
00:52:44,719 --> 00:52:44,920
Speaker 8: You know?

1020
00:52:45,080 --> 00:52:48,520
Speaker 7: So I think there's still kind of a concentration of control. Yeah,

1021
00:52:48,520 --> 00:52:51,239
Speaker 7: And I think second, like you mentioned kind of biases

1022
00:52:51,280 --> 00:52:54,319
Speaker 7: and like weird stuff in the models super important, like

1023
00:52:54,400 --> 00:52:58,040
Speaker 7: large language models are trained on data scrape from the Internet.

1024
00:52:58,239 --> 00:53:00,719
Speaker 7: The Internet can be not the best place, as we

1025
00:53:00,760 --> 00:53:03,000
Speaker 7: all know. It can be right for like all kinds

1026
00:53:03,040 --> 00:53:06,960
Speaker 7: of information, but it's also full of a lot of exclusions.

1027
00:53:07,040 --> 00:53:10,320
Speaker 7: So like, for example, again when I was working talking

1028
00:53:10,360 --> 00:53:12,600
Speaker 7: to these data scientists and engineers at this big tech

1029
00:53:12,640 --> 00:53:15,040
Speaker 7: firm with us things like, oh, well, where do you

1030
00:53:15,040 --> 00:53:19,239
Speaker 7: pull your training data from? And they'd say Wikipedia for example,

1031
00:53:19,480 --> 00:53:22,239
Speaker 7: and you know, would say, oh, but like Wikipedia is

1032
00:53:22,280 --> 00:53:24,920
Speaker 7: not a very equitable place. Like women are really really

1033
00:53:25,000 --> 00:53:28,480
Speaker 7: vasically underrepresented on Wikipedia pages, both in terms of who

1034
00:53:28,560 --> 00:53:31,320
Speaker 7: writes them and also in terms of who gets Wikipedia

1035
00:53:31,320 --> 00:53:34,640
Speaker 7: pages written about them. So the physicist es Wighed here

1036
00:53:34,640 --> 00:53:37,160
Speaker 7: in the UK has had this long running project where

1037
00:53:37,200 --> 00:53:41,759
Speaker 7: she just adds a woman to Wikipedia like every day,

1038
00:53:42,040 --> 00:53:44,560
Speaker 7: and she's done that I think now for like years.

1039
00:53:44,840 --> 00:53:47,200
Speaker 7: But that it kind of just shows like how inequitable

1040
00:53:47,239 --> 00:53:50,080
Speaker 7: though that distribution is. If you're training a model on

1041
00:53:50,160 --> 00:53:53,200
Speaker 7: data from Wikipedia, implicitly, you might not be trying to

1042
00:53:53,239 --> 00:53:55,520
Speaker 7: do this in any way. Like you're also training that

1043
00:53:55,600 --> 00:53:58,879
Speaker 7: model to believe in a world where say, like women

1044
00:53:58,920 --> 00:54:03,359
Speaker 7: make up the population, not fifty percent plus. So there's

1045
00:54:03,360 --> 00:54:05,279
Speaker 7: a lot of like Bia Saeson harms that come just

1046
00:54:05,280 --> 00:54:08,680
Speaker 7: from exclusion. Another example of this is, you know, I

1047
00:54:08,800 --> 00:54:10,840
Speaker 7: have a good friend who is a linguist, and something

1048
00:54:10,880 --> 00:54:14,640
Speaker 7: she talks about is communities that don't have written languages

1049
00:54:15,040 --> 00:54:18,000
Speaker 7: are already automatically just not going to be able to

1050
00:54:18,040 --> 00:54:21,120
Speaker 7: partake in whatever benefits might come from large language models,

1051
00:54:21,120 --> 00:54:24,479
Speaker 7: whether that's signed languages or languages that are only oral,

1052
00:54:24,880 --> 00:54:26,279
Speaker 7: and so you know, I think there's just a lot

1053
00:54:26,320 --> 00:54:29,480
Speaker 7: of different ways that even beyond these kind of immediate

1054
00:54:29,520 --> 00:54:31,560
Speaker 7: harms of like the AI has produced something that we

1055
00:54:31,600 --> 00:54:34,800
Speaker 7: think is really offensive or gross, that we could see

1056
00:54:34,960 --> 00:54:38,320
Speaker 7: the use of large language models maybe creating further inequities.

1057
00:54:39,040 --> 00:54:44,080
Speaker 1: Now, a lot of the AI like promise, like the

1058
00:54:44,239 --> 00:54:48,160
Speaker 1: developments that are being promised. So even at Davos, like

1059
00:54:48,200 --> 00:54:53,400
Speaker 1: this past Davos, at this page I had to miss unfortunately,

1060
00:54:53,560 --> 00:54:55,480
Speaker 1: and I hate to miss Davos because I learned so

1061
00:54:55,560 --> 00:54:58,040
Speaker 1: much there. But you've been to four davi, havn't you?

1062
00:54:58,120 --> 00:55:03,000
Speaker 1: DIVI my fourth dive, I was the best man. We

1063
00:55:03,080 --> 00:55:06,160
Speaker 1: all did mally and just had a cuddle puddle. But

1064
00:55:06,400 --> 00:55:10,320
Speaker 1: Sam Altman, I don't know if he's like consciously scaling

1065
00:55:10,320 --> 00:55:13,160
Speaker 1: back people's expectations, but he was like, soon you might

1066
00:55:13,239 --> 00:55:15,280
Speaker 1: just be able to say what are my most important

1067
00:55:15,280 --> 00:55:19,440
Speaker 1: emails today and have AI summarize them. I was just like,

1068
00:55:20,800 --> 00:55:24,920
Speaker 1: all right, Like doesn't like outlook already have an offer,

1069
00:55:25,280 --> 00:55:27,960
Speaker 1: like offer a shitty version of that already. So it's

1070
00:55:28,480 --> 00:55:32,120
Speaker 1: like it just feels like the versions of AI that

1071
00:55:32,200 --> 00:55:35,960
Speaker 1: I'm hearing, Like there's this older New Yorker article that

1072
00:55:36,120 --> 00:55:39,239
Speaker 1: was like, I'm not that worried about AI. I think

1073
00:55:39,280 --> 00:55:42,320
Speaker 1: it's from like the kill us all perspective. I think

1074
00:55:42,600 --> 00:55:46,280
Speaker 1: it's going to be like a little McKenzie in everyone's pocket,

1075
00:55:46,680 --> 00:55:50,280
Speaker 1: Like it's going to be this like economic optimization tool

1076
00:55:50,400 --> 00:55:53,359
Speaker 1: that like everybody has access to, and that's going to

1077
00:55:53,640 --> 00:55:58,680
Speaker 1: just make everything shitty and boring. So I don't know,

1078
00:55:58,760 --> 00:56:00,640
Speaker 1: like I'm just curious for your thoughts on that, and

1079
00:56:00,719 --> 00:56:05,160
Speaker 1: like if there are examples of just like functionality from

1080
00:56:05,239 --> 00:56:08,560
Speaker 1: AI that actually like capture your imagination where you're like,

1081
00:56:08,640 --> 00:56:12,480
Speaker 1: oh shit, that would be like cool. That's a cool

1082
00:56:12,600 --> 00:56:17,040
Speaker 1: idea of like something that would be fun and you know,

1083
00:56:17,280 --> 00:56:19,640
Speaker 1: improve people's lives, even if it's just like make their

1084
00:56:19,719 --> 00:56:21,160
Speaker 1: video games better or whatever.

1085
00:56:21,680 --> 00:56:21,839
Speaker 8: Yeah.

1086
00:56:21,880 --> 00:56:25,239
Speaker 7: I mean maybe the email thing appeals to some people. Personally,

1087
00:56:25,680 --> 00:56:28,080
Speaker 7: I want fewer emails. I don't want a summary of

1088
00:56:28,120 --> 00:56:30,600
Speaker 7: my emails. I just want my inbox to quietly shut

1089
00:56:30,680 --> 00:56:34,759
Speaker 7: down with the hours of like five pm to like

1090
00:56:35,200 --> 00:56:37,440
Speaker 7: in the am every day and just be like I'm

1091
00:56:37,480 --> 00:56:40,480
Speaker 7: email free. And then the run at em Bogast I

1092
00:56:40,480 --> 00:56:42,960
Speaker 7: think has this idea of hyper employment, which is like

1093
00:56:43,160 --> 00:56:45,440
Speaker 7: the technologies that say they're going to make our lives

1094
00:56:45,480 --> 00:56:48,719
Speaker 7: easier and more stress free actually make our lives much

1095
00:56:48,719 --> 00:56:51,000
Speaker 7: busier and we now waste a lot more time. So

1096
00:56:51,040 --> 00:56:53,279
Speaker 7: he talks about emails as a way of, you know,

1097
00:56:53,600 --> 00:56:55,680
Speaker 7: saying like, oh, we're gonna have far fewer meetings and

1098
00:56:55,680 --> 00:56:57,960
Speaker 7: we're gonna like have to spend less time like sending

1099
00:56:57,960 --> 00:57:02,120
Speaker 7: each other letters or whatever. Sorry from the post internet generation.

1100
00:57:02,560 --> 00:57:04,680
Speaker 7: But then now we spend like so much time like

1101
00:57:04,800 --> 00:57:07,719
Speaker 7: answering emails. And I think AI feels a bit like this,

1102
00:57:07,960 --> 00:57:09,919
Speaker 7: Like when people say, like AI is going to save

1103
00:57:09,960 --> 00:57:12,520
Speaker 7: you so much time, I'm like, you are not a

1104
00:57:12,560 --> 00:57:15,040
Speaker 7: teacher an educator because the amount of time we have

1105
00:57:15,160 --> 00:57:17,600
Speaker 7: wasted this year trying to figure out what to do

1106
00:57:17,680 --> 00:57:22,560
Speaker 7: with AI generated essays like absolutely not. Yeah, And so

1107
00:57:22,680 --> 00:57:25,240
Speaker 7: I feel like things like the AI email summarize I

1108
00:57:25,760 --> 00:57:27,760
Speaker 7: since could end up in a very similar pile.

1109
00:57:27,960 --> 00:57:29,680
Speaker 8: But you know, kind of to come to the more

1110
00:57:29,720 --> 00:57:30,560
Speaker 8: positive side.

1111
00:57:30,400 --> 00:57:33,280
Speaker 7: Of your question, like what makes me excited, I think

1112
00:57:33,320 --> 00:57:36,160
Speaker 7: a couple of things like one is like anything where

1113
00:57:36,400 --> 00:57:39,560
Speaker 7: like AI can genuinely scale up in a way that

1114
00:57:39,640 --> 00:57:43,840
Speaker 7: is not too ecologically damaging or costly a process that

1115
00:57:43,920 --> 00:57:46,920
Speaker 7: is already going well, where the statistics and the procedures

1116
00:57:46,920 --> 00:57:49,720
Speaker 7: in place are working for us. Because AI is able

1117
00:57:49,720 --> 00:57:53,000
Speaker 7: to scale things, you know, it's not necessarily able to

1118
00:57:53,040 --> 00:57:55,720
Speaker 7: do new things always. So if we know we have

1119
00:57:55,760 --> 00:57:58,760
Speaker 7: a sorting or categorization process that works, that's when I

1120
00:57:58,800 --> 00:58:02,160
Speaker 7: think AI computer these kinds of systems can be really useful.

1121
00:58:02,400 --> 00:58:04,560
Speaker 7: Where it doesn't work is when you're asking AI to

1122
00:58:04,600 --> 00:58:07,680
Speaker 7: do something that like we don't actually have good processes

1123
00:58:07,680 --> 00:58:10,040
Speaker 7: in place to do so, Like when a tool says, oh,

1124
00:58:10,160 --> 00:58:14,400
Speaker 7: I can like power candidate's personality from their face, Like, no,

1125
00:58:14,640 --> 00:58:17,320
Speaker 7: you can't do that. That's just a straight up phrenology.

1126
00:58:17,360 --> 00:58:18,200
Speaker 8: Please don't do that.

1127
00:58:18,280 --> 00:58:21,480
Speaker 7: But also secondly, like you know, if we had easy

1128
00:58:21,520 --> 00:58:23,120
Speaker 7: ways of telling if someone was going to be good

1129
00:58:23,120 --> 00:58:24,600
Speaker 7: for a job, like humans will be able to do

1130
00:58:24,640 --> 00:58:27,640
Speaker 7: it already, Like this is a much much more complicated

1131
00:58:27,640 --> 00:58:30,240
Speaker 7: than you're making it out to be. A second kind

1132
00:58:30,240 --> 00:58:32,920
Speaker 7: of use I think I find really exciting or makes.

1133
00:58:32,760 --> 00:58:34,720
Speaker 8: Me like you're really happy.

1134
00:58:34,840 --> 00:58:38,360
Speaker 7: I think it's particularly tools around trying to kind of

1135
00:58:38,400 --> 00:58:42,720
Speaker 7: like support particular community there's needs in a way that

1136
00:58:42,800 --> 00:58:45,280
Speaker 7: is really driven by that community. So for example, in

1137
00:58:45,400 --> 00:58:47,600
Speaker 7: New Zealand where I'm from, there's been a lot of

1138
00:58:47,600 --> 00:58:51,040
Speaker 7: effort put into different kinds of like AI powered tools

1139
00:58:51,080 --> 00:58:56,840
Speaker 7: and data sovereignty programs around Maudi traditions, the Maldi language

1140
00:58:56,920 --> 00:58:59,200
Speaker 7: or three A Maudi and like, I think, you know,

1141
00:58:59,360 --> 00:59:01,840
Speaker 7: this is an example of where like that's been led

1142
00:59:01,880 --> 00:59:05,000
Speaker 7: by Maudi people and is in a response to kind

1143
00:59:05,000 --> 00:59:07,800
Speaker 7: of the way that in colonial New Zealand, like Threa,

1144
00:59:07,880 --> 00:59:10,640
Speaker 7: Maudi was like very deliberately stamped out, and there's been

1145
00:59:10,640 --> 00:59:13,440
Speaker 7: a huge movement to try and kind of protect and

1146
00:59:13,520 --> 00:59:14,680
Speaker 7: revive the language.

1147
00:59:15,080 --> 00:59:16,240
Speaker 8: And I think it's like when your.

1148
00:59:16,080 --> 00:59:18,200
Speaker 7: Projects like this, that makes me a bit more hopeful

1149
00:59:18,240 --> 00:59:20,680
Speaker 7: about the way that AI machine learning could be used

1150
00:59:21,000 --> 00:59:25,240
Speaker 7: to you know, promote these pro justice projects. But you know,

1151
00:59:25,320 --> 00:59:27,120
Speaker 7: I think those projects that always have to exist in

1152
00:59:27,120 --> 00:59:29,800
Speaker 7: a little bit of tension with like big tech. And

1153
00:59:29,840 --> 00:59:33,080
Speaker 7: we've seen this like with other organizations, for example, like Masacana,

1154
00:59:33,120 --> 00:59:36,800
Speaker 7: which is the amazing grassroots organization which aims to bring

1155
00:59:36,880 --> 00:59:41,360
Speaker 7: the like four thousand different African languages into our natural

1156
00:59:41,400 --> 00:59:43,920
Speaker 7: language processing models or large language models.

1157
00:59:44,200 --> 00:59:44,400
Speaker 9: You know.

1158
00:59:44,440 --> 00:59:46,240
Speaker 7: But I know that these kinds of groups often do

1159
00:59:46,320 --> 00:59:49,040
Speaker 7: struggle with this idea, like do we commercialize because then

1160
00:59:49,400 --> 00:59:52,560
Speaker 7: will we be brought into this hyper capitalist world? Do

1161
00:59:52,640 --> 00:59:54,840
Speaker 7: we keep this to ourselves? But yeah, I think it's

1162
00:59:54,840 --> 00:59:56,680
Speaker 7: it is important sometimes to step back and be like,

1163
00:59:56,720 --> 01:00:00,080
Speaker 7: there are really interesting community projects which are trying to

1164
01:00:00,160 --> 01:00:03,280
Speaker 7: use these techniques and these kinds of knowledge in ways

1165
01:00:03,280 --> 01:00:07,160
Speaker 7: that push back against like the email summarizing bought right?

1166
01:00:08,400 --> 01:00:12,600
Speaker 1: Is there a historical present Like I was reading some

1167
01:00:13,400 --> 01:00:17,920
Speaker 1: articles about the competition between the United States and China

1168
01:00:17,960 --> 01:00:21,520
Speaker 1: and how the US is like trying to freeze export

1169
01:00:21,600 --> 01:00:24,120
Speaker 1: of like certain chips to China because they think that

1170
01:00:24,160 --> 01:00:27,840
Speaker 1: will allow China to catch up with them. And it

1171
01:00:27,880 --> 01:00:32,160
Speaker 1: feels like we're there's going to be inevitably an argument

1172
01:00:32,400 --> 01:00:35,720
Speaker 1: where they're like we need to just go pedal to

1173
01:00:35,760 --> 01:00:39,720
Speaker 1: the floor on AI development because this is the new

1174
01:00:39,760 --> 01:00:44,880
Speaker 1: Manhattan project. Trust us. You talk a lot about just

1175
01:00:44,880 --> 01:00:48,680
Speaker 1: like this, this alternate possibility of like what what if

1176
01:00:48,800 --> 01:00:55,560
Speaker 1: we didn't use this technology for hypercapitalism and militarism? Are

1177
01:00:55,600 --> 01:01:00,120
Speaker 1: there examples where like from history that you're aware of,

1178
01:01:00,200 --> 01:01:04,760
Speaker 1: where like technology hasn't been has successfully been like protected

1179
01:01:04,840 --> 01:01:08,360
Speaker 1: from those sorts of things. Are there any even a

1180
01:01:08,560 --> 01:01:12,760
Speaker 1: very small examples where people have been able to keep

1181
01:01:12,840 --> 01:01:16,200
Speaker 1: technology like fenced off from that sort of thing.

1182
01:01:17,200 --> 01:01:20,160
Speaker 7: Yeah, No, I mean this is something that really preoccupies me.

1183
01:01:20,240 --> 01:01:22,520
Speaker 7: I spend a lot of time mapping and tracking with

1184
01:01:22,560 --> 01:01:25,680
Speaker 7: the AI now institute, this narrative of an AI arms

1185
01:01:25,760 --> 01:01:29,160
Speaker 7: race between the US and China and how that story

1186
01:01:29,280 --> 01:01:31,959
Speaker 7: is super damaging because it can cause us like race

1187
01:01:32,000 --> 01:01:35,360
Speaker 7: to the bottom and lead to US trying to develop

1188
01:01:35,400 --> 01:01:38,640
Speaker 7: AI faster and faster without necessarily trying to make it.

1189
01:01:38,560 --> 01:01:39,600
Speaker 8: Better or safer.

1190
01:01:40,120 --> 01:01:42,040
Speaker 7: And do you know, I think we've seen some positive

1191
01:01:42,120 --> 01:01:45,280
Speaker 7: movements when it comes to AI regulation recently, from like

1192
01:01:45,640 --> 01:01:49,960
Speaker 7: the US's commitment or declaration around AI through to the

1193
01:01:49,960 --> 01:01:54,480
Speaker 7: Bletchley Declaration in the UK and the EUAI Act. But

1194
01:01:54,720 --> 01:01:56,880
Speaker 7: at the same time, you know, I think that this

1195
01:01:57,120 --> 01:02:00,520
Speaker 7: sort of racing narrative like still looks and it's still

1196
01:02:00,520 --> 01:02:03,600
Speaker 7: sometimes used to try and push back against regulatory measures,

1197
01:02:03,760 --> 01:02:07,040
Speaker 7: particularly by people with investments in big tech. But yeah,

1198
01:02:07,040 --> 01:02:09,240
Speaker 7: at the same time, I also think this question of

1199
01:02:09,280 --> 01:02:12,040
Speaker 7: like can we look to history to find ways to

1200
01:02:12,120 --> 01:02:15,400
Speaker 7: tell different stories about AI maybe bring about different futures

1201
01:02:15,840 --> 01:02:18,400
Speaker 7: is something that really interests me. I think the comparison

1202
01:02:18,480 --> 01:02:22,240
Speaker 7: that is most often made between AI and another technology

1203
01:02:22,280 --> 01:02:25,320
Speaker 7: when it comes to like regulation and governance is nuclear

1204
01:02:25,400 --> 01:02:29,720
Speaker 7: and specifically nuclear weapons like and like you mentioned Manhattan Project,

1205
01:02:30,040 --> 01:02:32,360
Speaker 7: certainly kind of this sort of language of like an

1206
01:02:32,360 --> 01:02:35,360
Speaker 7: Oppenheimer moment when it comes to AI, and this kind

1207
01:02:35,440 --> 01:02:37,600
Speaker 7: of idea of being like the cusp of a new

1208
01:02:37,680 --> 01:02:40,640
Speaker 7: cold water will be AI driven rather than nuclear weapons

1209
01:02:40,720 --> 01:02:43,880
Speaker 7: driven is a very common media narrative. But something I

1210
01:02:44,040 --> 01:02:46,000
Speaker 7: try to do, like with my own research, is to

1211
01:02:46,040 --> 01:02:49,680
Speaker 7: try and look for and support different kinds of historical

1212
01:02:49,680 --> 01:02:54,640
Speaker 7: analogies that maybe offer maybe less kind of less hawkish

1213
01:02:55,720 --> 01:02:59,360
Speaker 7: futures when it comes to international politics. So for example,

1214
01:02:59,360 --> 01:03:02,479
Speaker 7: my Indian who's a fantastic researcher at the Lievi Human

1215
01:03:02,480 --> 01:03:06,200
Speaker 7: Center where I'm at, looks to like histories of feminists

1216
01:03:06,200 --> 01:03:09,360
Speaker 7: cyber governance in the early two thousands as a way

1217
01:03:09,360 --> 01:03:11,600
Speaker 7: of saying, like, actually, we have a lot of precedents

1218
01:03:11,640 --> 01:03:14,400
Speaker 7: for thinking about the ethics of the internet. Why aren't

1219
01:03:14,400 --> 01:03:18,479
Speaker 7: we bringing this into thinking about AI? Or Matischmas who's

1220
01:03:18,520 --> 01:03:22,280
Speaker 7: a legal researchers the histories of technological restraints, so like,

1221
01:03:22,360 --> 01:03:25,040
Speaker 7: when did we choose not to make a technology even

1222
01:03:25,040 --> 01:03:27,560
Speaker 7: though we could because we thought that it actually wouldn't

1223
01:03:27,560 --> 01:03:29,800
Speaker 7: be good for the world? And for societies, and so

1224
01:03:30,000 --> 01:03:32,520
Speaker 7: I think, you know, like making sure we have examples

1225
01:03:32,520 --> 01:03:36,160
Speaker 7: outside of nuclear because wild nuclear can be still useful

1226
01:03:36,200 --> 01:03:39,440
Speaker 7: in some ways, like it's only one metaphor, and metaphors

1227
01:03:39,480 --> 01:03:42,320
Speaker 7: are inherently limited. They tell us something about the world,

1228
01:03:42,440 --> 01:03:44,880
Speaker 7: but they can't tell us everything. And I thinks having

1229
01:03:44,880 --> 01:03:48,800
Speaker 7: these alternative historical examples can be really useful for thinking

1230
01:03:48,800 --> 01:03:49,560
Speaker 7: a bit differently.

1231
01:03:50,560 --> 01:03:50,880
Speaker 1: Yeah.

1232
01:03:51,320 --> 01:03:53,920
Speaker 3: So, yeah, it's just funny because in the end, it

1233
01:03:53,960 --> 01:03:58,080
Speaker 3: always feels like thing with potential to create like unforeseen

1234
01:03:58,240 --> 01:04:01,080
Speaker 3: levels of productivity or powers, and then we made it

1235
01:04:01,080 --> 01:04:02,000
Speaker 3: a weapon.

1236
01:04:01,760 --> 01:04:04,280
Speaker 1: Then we like put it in the bomb, you know, kind.

1237
01:04:04,080 --> 01:04:06,320
Speaker 3: Of like and yeah, like we really do have to

1238
01:04:06,360 --> 01:04:08,440
Speaker 3: sort of break out of that thinking. I mean, I

1239
01:04:08,480 --> 01:04:12,160
Speaker 3: wonder precisely because our brains are filled with sky Net

1240
01:04:12,200 --> 01:04:16,440
Speaker 3: and Oppenheimer and the Manhattan Project, that that's we're just

1241
01:04:16,520 --> 01:04:19,640
Speaker 3: we're just in this really weird pattern of always looking

1242
01:04:19,680 --> 01:04:23,120
Speaker 3: at something that has the potential to unlock new levels

1243
01:04:23,120 --> 01:04:26,160
Speaker 3: of something are inherently going to always be like, but

1244
01:04:26,200 --> 01:04:28,320
Speaker 3: how do how do our enemies kill us with it?

1245
01:04:28,840 --> 01:04:31,760
Speaker 3: And then we begin to lose the plot there. So yeah,

1246
01:04:31,800 --> 01:04:35,600
Speaker 3: I'm yeah, look to these other examples to try and

1247
01:04:35,640 --> 01:04:38,600
Speaker 3: again open my mind to looking at it less of

1248
01:04:38,680 --> 01:04:40,680
Speaker 3: like and then how they make and then how they

1249
01:04:40,760 --> 01:04:42,240
Speaker 3: make global domination with that?

1250
01:04:43,040 --> 01:04:44,880
Speaker 7: Right, Yeah, I mean I think it's either it's like

1251
01:04:44,920 --> 01:04:47,320
Speaker 7: how do we kill someone with it? Or as I

1252
01:04:47,360 --> 01:04:50,120
Speaker 7: think the history of like how tech is represented in

1253
01:04:50,120 --> 01:04:51,160
Speaker 7: Hollywood would show.

1254
01:04:51,080 --> 01:04:52,920
Speaker 8: Us like how do we have sex with it? And

1255
01:04:52,960 --> 01:04:55,040
Speaker 8: this is like a very classic trip in sci fi.

1256
01:04:55,200 --> 01:04:56,920
Speaker 7: Right, It's like you get like a dude in his

1257
01:04:56,960 --> 01:04:59,440
Speaker 7: basement who makes like a sex spot. And I remember

1258
01:04:59,760 --> 01:05:03,560
Speaker 7: it Jack Harbuston, who's like this very famous feminist and

1259
01:05:03,600 --> 01:05:07,200
Speaker 7: queer theorist with Eleanor on the podcast, and he was

1260
01:05:07,240 --> 01:05:09,240
Speaker 7: talking about the film x Markina. Have you seen this

1261
01:05:09,360 --> 01:05:12,240
Speaker 7: since from like twenty fourteen. It's like a kind of

1262
01:05:12,280 --> 01:05:14,040
Speaker 7: a kind of an indie film but had quite a

1263
01:05:14,040 --> 01:05:17,800
Speaker 7: lot of prestige I think success particularly.

1264
01:05:17,440 --> 01:05:18,520
Speaker 8: Like tech circles.

1265
01:05:18,600 --> 01:05:19,360
Speaker 1: Yeah for sure.

1266
01:05:19,680 --> 01:05:22,720
Speaker 7: Yeah, yeah, And so I remember we were talking about

1267
01:05:22,840 --> 01:05:25,680
Speaker 7: x Markina and Jack Harbison was saying like it sort

1268
01:05:25,680 --> 01:05:28,600
Speaker 7: of shows like the limits of the imagination, like particularly

1269
01:05:28,640 --> 01:05:31,520
Speaker 7: like the tech bro imagination that he has like all

1270
01:05:31,640 --> 01:05:34,960
Speaker 7: this expertise at his fingertips, all this data and he

1271
01:05:35,000 --> 01:05:38,600
Speaker 7: basically just makes like sex robots and like that's really good,

1272
01:05:38,600 --> 01:05:40,560
Speaker 7: really thing to do with it. And I think, you know,

1273
01:05:41,000 --> 01:05:43,320
Speaker 7: to some extent, like we're still a little bit trapped

1274
01:05:43,320 --> 01:05:46,280
Speaker 7: in that imagination, which is why I think like both

1275
01:05:46,360 --> 01:05:49,560
Speaker 7: like different projects to do with AI, but also different

1276
01:05:49,600 --> 01:05:52,320
Speaker 7: stories about AI are really crucial.

1277
01:05:52,520 --> 01:05:54,360
Speaker 3: Right, Yeah, yeah, we have to get out of the

1278
01:05:54,440 --> 01:05:58,520
Speaker 3: fuck or fear paradigm. Right that we have the technology,

1279
01:05:58,640 --> 01:06:01,800
Speaker 3: it's going to do one of the two, man, So yeah,

1280
01:06:01,840 --> 01:06:03,600
Speaker 3: we need a new way.

1281
01:06:04,000 --> 01:06:07,320
Speaker 1: I'm here to do two things. Fuck something or kill

1282
01:06:07,320 --> 01:06:12,520
Speaker 1: it because I'm scared of it. Yeah, all right, that's

1283
01:06:12,640 --> 01:06:16,840
Speaker 1: gonna do it. For this week's Weekly Zeitgeist, please like

1284
01:06:16,880 --> 01:06:21,360
Speaker 1: and review the show if you like, the show means

1285
01:06:21,360 --> 01:06:24,960
Speaker 1: the world de Miles he he needs your validation, folks.

1286
01:06:25,560 --> 01:06:28,640
Speaker 1: I hope you're having a great weekend and I will

1287
01:06:28,640 --> 01:07:17,560
Speaker 1: talk to you Monday. Byett Doctor Doctor