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 --> 00:00:05,120
Speaker 1: Weekly Zeitgeist.

3
00:00:05,519 --> 00:00:05,760
Speaker 2: Uh.

4
00:00:05,840 --> 00:00:09,280
Speaker 1: These are some of our favorite segments from this week,

5
00:00:09,760 --> 00:00:17,800
Speaker 1: all edited together into one NonStop infotainment laugh stravaganza.

6
00:00:18,320 --> 00:00:19,279
Speaker 2: Uh yeah.

7
00:00:19,320 --> 00:00:25,840
Speaker 1: So, without further ado, here is the Weekly Zeitgeist. Miles,

8
00:00:26,360 --> 00:00:28,760
Speaker 1: it's appropriate that we have a little special AKA song

9
00:00:29,200 --> 00:00:32,760
Speaker 1: today because we are joined in our third and fourth

10
00:00:33,400 --> 00:00:37,800
Speaker 1: seats by two of the hosts of the podcast George Center.

11
00:00:37,920 --> 00:00:42,040
Speaker 1: It's the them Jorge Center Boys. But first we've got

12
00:00:42,120 --> 00:00:46,720
Speaker 1: America Stepdad, Christy Amagucci man aka will Pool, and we

13
00:00:46,800 --> 00:00:50,560
Speaker 1: got Josh Robbins that they are them Jeorge Center Boys.

14
00:00:50,760 --> 00:00:52,400
Speaker 1: Welcome fellas to.

15
00:00:53,440 --> 00:00:55,920
Speaker 3: D and I'm thrown out because I think there's an aka.

16
00:00:56,000 --> 00:01:00,320
Speaker 3: But let you an upper willow Josh.

17
00:00:59,560 --> 00:01:05,320
Speaker 4: Josh to take this opening Workkay, thanks for having us

18
00:01:05,319 --> 00:01:07,959
Speaker 4: on again. We're gonna go back to nineteen seventy seven.

19
00:01:11,240 --> 00:01:15,160
Speaker 5: On a podcast with Miles Grade Wo Big Gas with

20
00:01:15,400 --> 00:01:16,240
Speaker 5: no hair.

21
00:01:17,560 --> 00:01:18,480
Speaker 1: Worm passing.

22
00:01:18,560 --> 00:01:24,200
Speaker 5: Gord eat Us rights up from Jack O'Brien's chair, numb

23
00:01:24,200 --> 00:01:27,679
Speaker 5: ahead for the listeners, Wow, futures not.

24
00:01:27,880 --> 00:01:32,399
Speaker 1: Looking, brian us Us heavy and the topics grin.

25
00:01:33,200 --> 00:01:37,560
Speaker 5: It's time for Daily's that guys will we find out

26
00:01:37,640 --> 00:01:43,959
Speaker 5: some more ways rock going to hell. I was thinking

27
00:01:44,040 --> 00:01:49,200
Speaker 5: to myself, I could really use some taco bear, So

28
00:01:49,400 --> 00:01:53,760
Speaker 5: a little fat one said, first let's get place.

29
00:01:55,560 --> 00:01:58,080
Speaker 1: Then I heard the voices from my phone.

30
00:01:58,720 --> 00:02:03,320
Speaker 5: It was Jack and Miles. Great, Hello the Internet, we

31
00:02:03,440 --> 00:02:08,720
Speaker 5: got bad news for you, United States.

32
00:02:08,960 --> 00:02:12,800
Speaker 1: The United States sent a shy please who.

33
00:02:14,120 --> 00:02:15,240
Speaker 2: Forced too late and.

34
00:02:15,400 --> 00:02:17,560
Speaker 1: We die We're hair to born you.

35
00:02:20,120 --> 00:02:21,320
Speaker 2: It's election year.

36
00:02:21,680 --> 00:02:24,680
Speaker 1: It's election. You should hate it here.

37
00:02:27,000 --> 00:02:31,799
Speaker 4: So smartest tip on it's will I'm just I'm just kidding.

38
00:02:34,200 --> 00:02:40,040
Speaker 1: Through the entire I was like, when it starts coming

39
00:02:40,080 --> 00:02:47,480
Speaker 1: into play, wow hours, Hello the Internet, We've got bad news. Yeah,

40
00:02:47,760 --> 00:02:50,800
Speaker 1: it's great. Might as well be the subtitle of the show.

41
00:02:51,160 --> 00:02:54,239
Speaker 4: Yeah, and not to not to outshine, uh, the the

42
00:02:54,400 --> 00:02:56,400
Speaker 4: other guests that I'm on here with. But I know

43
00:02:56,480 --> 00:02:59,720
Speaker 4: he just had PTSD from high school of me breaking

44
00:02:59,800 --> 00:03:01,640
Speaker 4: up the guitar.

45
00:03:01,480 --> 00:03:04,799
Speaker 6: Once again, plans on somebody he will outshine me.

46
00:03:05,400 --> 00:03:09,840
Speaker 1: Whatever, Josh, let me take this real quick, Josh, this

47
00:03:10,000 --> 00:03:10,679
Speaker 1: is all you three.

48
00:03:10,919 --> 00:03:14,280
Speaker 6: Okay, let's see all right, A K Josh Robbins and

49
00:03:14,880 --> 00:03:16,480
Speaker 6: we got every.

50
00:03:16,400 --> 00:03:20,560
Speaker 1: Kiss begins okay, jewelries on the way.

51
00:03:21,120 --> 00:03:26,799
Speaker 7: My wa please down lay may Now about a lay may,

52
00:03:27,520 --> 00:03:32,200
Speaker 7: I'll get your chick fl if you promise, Mester.

53
00:03:33,720 --> 00:03:38,080
Speaker 6: Promise rings not fank gold, one phone fries, not a

54
00:03:38,200 --> 00:03:38,920
Speaker 6: dang owl.

55
00:03:41,600 --> 00:03:42,280
Speaker 8: That's what I got.

56
00:03:42,360 --> 00:03:44,680
Speaker 6: That's from the before and went to see fantastic.

57
00:03:44,840 --> 00:03:50,200
Speaker 1: Wow. I love Yeah, beautiful work there's in there.

58
00:03:50,680 --> 00:03:53,400
Speaker 6: Yeah, I'm actually impressed. I don't I didn't need a guitar.

59
00:03:53,960 --> 00:03:58,640
Speaker 3: Yeah, you just went a straight voice.

60
00:03:58,720 --> 00:04:02,040
Speaker 1: Yeah, I'm not like, I'm not a prop guy, so

61
00:04:02,560 --> 00:04:05,000
Speaker 1: you know, but I know I respect like, you know,

62
00:04:05,480 --> 00:04:07,040
Speaker 1: meetings like Carrot Top and stuff.

63
00:04:07,120 --> 00:04:09,360
Speaker 3: But yeah, yeah, for sure, man Gallagher, I think you

64
00:04:09,440 --> 00:04:09,800
Speaker 3: know really.

65
00:04:10,040 --> 00:04:13,360
Speaker 4: Also, apologies to whoever has to edit this episode and

66
00:04:13,560 --> 00:04:16,240
Speaker 4: level all of that bullshit that I just did, because

67
00:04:16,279 --> 00:04:18,960
Speaker 4: I have no idea the wave file like the it

68
00:04:19,120 --> 00:04:22,240
Speaker 4: looked okay, but I have no fucking idea whether any

69
00:04:22,279 --> 00:04:23,360
Speaker 4: of that's going to be usable.

70
00:04:24,160 --> 00:04:24,360
Speaker 8: Yeah.

71
00:04:24,640 --> 00:04:27,960
Speaker 6: One one eye on the on the lyrics and one

72
00:04:28,040 --> 00:04:29,040
Speaker 6: eye on the wave files.

73
00:04:29,240 --> 00:04:29,840
Speaker 1: Yeah exactly.

74
00:04:29,960 --> 00:04:31,680
Speaker 6: Yeah, that's what's very considered.

75
00:04:31,680 --> 00:04:31,880
Speaker 2: Okay.

76
00:04:32,240 --> 00:04:35,000
Speaker 3: I thought you're just confused anyway, how are what so?

77
00:04:35,200 --> 00:04:37,800
Speaker 3: What are you both coming to us from North cac.

78
00:04:38,640 --> 00:04:41,880
Speaker 4: Yeah that's right, I'm down at the beach, Josh's former

79
00:04:41,960 --> 00:04:47,479
Speaker 4: hometown of Wilmington, Okay, as always, And then Josh, I.

80
00:04:48,040 --> 00:04:51,400
Speaker 6: Live in Charlotte, North Carolina. I've lived here for probably

81
00:04:51,440 --> 00:04:53,640
Speaker 6: so long. I'll just say I'm from here, but you know,

82
00:04:53,760 --> 00:04:54,400
Speaker 6: my heart.

83
00:04:54,240 --> 00:04:57,760
Speaker 1: Is in Wilmington. But Monkey Junction, Monkey Junction.

84
00:04:57,680 --> 00:05:00,560
Speaker 6: Yeah, yeah, that's where we grew up, near Totem and Zoo,

85
00:05:00,800 --> 00:05:04,719
Speaker 6: which probably called something else that's not as inappropriate now. Yeah,

86
00:05:05,120 --> 00:05:07,160
Speaker 6: but yeah, I don't know, I moved away.

87
00:05:07,600 --> 00:05:10,000
Speaker 3: Yeah, yeah, here we are. It's good to have you both.

88
00:05:10,160 --> 00:05:13,559
Speaker 3: You know, we've had you know, Will over here, Christy

89
00:05:13,600 --> 00:05:20,000
Speaker 3: Yamagucci Gucci Maine many times and obviously been a lifelong

90
00:05:20,120 --> 00:05:22,560
Speaker 3: contributor to the show. So it's dope to have you yeah,

91
00:05:22,640 --> 00:05:26,040
Speaker 3: both on and you're both podcasting. It's just dope to

92
00:05:26,120 --> 00:05:28,480
Speaker 3: kind of see how you know just and then like

93
00:05:28,480 --> 00:05:30,559
Speaker 3: you guys have been best friends since we said sixth grade.

94
00:05:31,080 --> 00:05:34,680
Speaker 4: Yeah, sixth grade. Josh was one of my groom's men

95
00:05:34,880 --> 00:05:36,880
Speaker 4: in my wedding and I was in his wedding as well.

96
00:05:37,040 --> 00:05:40,520
Speaker 4: So yeah, we've been so like the first time I

97
00:05:40,720 --> 00:05:45,599
Speaker 4: remember Josh was like I knew Josh since sixth grade,

98
00:05:46,040 --> 00:05:48,000
Speaker 4: but the first time, you know, you have those moments

99
00:05:48,040 --> 00:05:51,080
Speaker 4: where you're like brain comes online. Yeah, and you're like

100
00:05:51,240 --> 00:05:54,840
Speaker 4: that that's your first like truly true memory of something

101
00:05:55,040 --> 00:05:57,880
Speaker 4: or someone is when Josh got in a fight in

102
00:05:57,920 --> 00:06:03,480
Speaker 4: the cafeteria and one of the counselors like Superman, tackled

103
00:06:03,600 --> 00:06:06,600
Speaker 4: him and the other kid like mister mister Taylor was

104
00:06:06,680 --> 00:06:07,040
Speaker 4: his name.

105
00:06:07,200 --> 00:06:07,400
Speaker 9: He was.

106
00:06:07,640 --> 00:06:09,560
Speaker 4: He was like a good like six three six four,

107
00:06:09,920 --> 00:06:12,760
Speaker 4: and he just comes out of like my periphery and

108
00:06:13,000 --> 00:06:15,400
Speaker 4: tackles both of them. I think it was over like

109
00:06:15,520 --> 00:06:19,719
Speaker 4: throwing French fries at each other and uh you know, yeah,

110
00:06:19,760 --> 00:06:23,800
Speaker 4: French cafeteria And I was like, holy ship, that dude

111
00:06:23,880 --> 00:06:26,040
Speaker 4: is awesome. He just got tackled by mister Taylo.

112
00:06:27,160 --> 00:06:30,040
Speaker 6: So yeah, which is funny because it's funny that it

113
00:06:30,120 --> 00:06:32,720
Speaker 6: took that long for Will to remember me because we

114
00:06:32,800 --> 00:06:34,200
Speaker 6: sat at the same table for like.

115
00:06:34,279 --> 00:06:34,880
Speaker 8: The whole year.

116
00:06:35,440 --> 00:06:37,320
Speaker 1: Is that what you were doing, You were like getting

117
00:06:37,360 --> 00:06:39,600
Speaker 1: into a fight to get him to notice you.

118
00:06:39,880 --> 00:06:40,040
Speaker 9: Yes.

119
00:06:42,279 --> 00:06:44,680
Speaker 1: Yeah, it was a long time to get from the

120
00:06:44,760 --> 00:06:48,120
Speaker 1: corner singing softly with an acoustic guitar to like just

121
00:06:48,200 --> 00:06:49,480
Speaker 1: a cloud of girls.

122
00:06:49,839 --> 00:06:52,800
Speaker 3: Yeah, we'll just yeah, we'll go with that.

123
00:06:53,000 --> 00:06:56,159
Speaker 4: We'll totally say that that's my middle school and high school. Yeah.

124
00:06:56,279 --> 00:06:59,160
Speaker 3: Sure, geometry teacher who's like, yeah, let me go get

125
00:06:59,200 --> 00:07:05,280
Speaker 3: my guitar man, or the institute, well remember the youth

126
00:07:05,360 --> 00:07:08,280
Speaker 3: pastor comes right. Actually, I got a song about a

127
00:07:09,080 --> 00:07:10,280
Speaker 3: guy named j C.

128
00:07:10,800 --> 00:07:12,400
Speaker 1: Who is pretty all right.

129
00:07:13,320 --> 00:07:16,200
Speaker 6: We had a substitute. His name was mister Beveridge, and

130
00:07:16,280 --> 00:07:18,520
Speaker 6: every time you started a class, he will go, all right,

131
00:07:18,600 --> 00:07:21,320
Speaker 6: my initial spell tab it's mister Beverage. Get the jokes out,

132
00:07:21,560 --> 00:07:23,560
Speaker 6: and like we were like okay.

133
00:07:23,560 --> 00:07:26,320
Speaker 10: Okay, all right, and then he's like, now let's crack

134
00:07:26,480 --> 00:07:29,840
Speaker 10: this one open and get into he juggled.

135
00:07:32,320 --> 00:07:34,200
Speaker 1: Oh yeah, you got Yeah.

136
00:07:34,720 --> 00:07:38,840
Speaker 3: I think everyone knows everyone knows a juggling unicyclist teacher

137
00:07:38,960 --> 00:07:39,960
Speaker 3: at some point in their life.

138
00:07:39,960 --> 00:07:41,040
Speaker 4: I think so, I think yeah.

139
00:07:41,480 --> 00:07:43,520
Speaker 1: But yeah, it will author of many of the best

140
00:07:43,560 --> 00:07:46,080
Speaker 1: aks of all time, the voice of an Angel, as

141
00:07:46,160 --> 00:07:49,560
Speaker 1: you heard, I mean, as you may have just heard.

142
00:07:49,760 --> 00:07:52,320
Speaker 1: There is a chance that he actually was so close

143
00:07:52,480 --> 00:07:56,160
Speaker 1: to the real song that it will get a takedown

144
00:07:56,240 --> 00:07:58,000
Speaker 1: notice for the first time from an AK.

145
00:07:58,360 --> 00:08:02,160
Speaker 4: But that'd be amazing. And apparently the Eagles are very litigious.

146
00:08:02,240 --> 00:08:04,600
Speaker 4: I just found out that they're in a lawsuit right

147
00:08:04,640 --> 00:08:09,440
Speaker 4: now over some handwritten notes to Hotel California. I just

148
00:08:09,520 --> 00:08:10,640
Speaker 4: learned about that yesterday.

149
00:08:10,800 --> 00:08:12,960
Speaker 1: They got some they got some skeletons, they got some

150
00:08:13,080 --> 00:08:17,240
Speaker 1: things to be over. Let's just yeah, I can imagine, Yeah,

151
00:08:17,520 --> 00:08:20,680
Speaker 1: you might, you might not want to look into their history.

152
00:08:21,480 --> 00:08:24,000
Speaker 1: And then Josh is in a band called Late Bloomer.

153
00:08:24,080 --> 00:08:28,360
Speaker 1: Let's dropping an album on Friday. The musicality of this

154
00:08:28,560 --> 00:08:34,280
Speaker 1: episode is out of every pore that right, What is

155
00:08:34,360 --> 00:08:38,960
Speaker 1: something from your search history that is revealing about who

156
00:08:39,040 --> 00:08:39,480
Speaker 1: you guys are?

157
00:08:39,840 --> 00:08:42,280
Speaker 8: I have an exciting one. I can start, Hey, this

158
00:08:42,440 --> 00:08:42,920
Speaker 8: is Alex.

159
00:08:43,200 --> 00:08:43,440
Speaker 10: Yeah.

160
00:08:44,480 --> 00:08:48,400
Speaker 8: So I'm building a chicken coop because my second job,

161
00:08:48,600 --> 00:08:50,480
Speaker 8: or rather than were one of my third jobs, is

162
00:08:51,160 --> 00:08:55,079
Speaker 8: kind of being a little suburban farmer. And so I'm

163
00:08:55,120 --> 00:08:59,440
Speaker 8: getting some chicks delivered from a hatchery and they said,

164
00:09:00,040 --> 00:09:02,760
Speaker 8: you need to put this directly into the brooder. And

165
00:09:02,840 --> 00:09:06,080
Speaker 8: I'm like, what's a brooder? And I had looked up

166
00:09:06,240 --> 00:09:08,920
Speaker 8: what a brooder is and then came up and found

167
00:09:08,960 --> 00:09:11,920
Speaker 8: that it can be anything from a rubber made tub

168
00:09:12,040 --> 00:09:15,520
Speaker 8: where you put the chickens well they're very small, until

169
00:09:15,840 --> 00:09:19,520
Speaker 8: like a repurposed rabbit hut. So then I started thinking

170
00:09:19,600 --> 00:09:21,600
Speaker 8: all morning about how to build a chicken brewder.

171
00:09:23,320 --> 00:09:25,439
Speaker 1: And wait, just so it's just like a like it's

172
00:09:25,480 --> 00:09:27,400
Speaker 1: like a pen, like a mini pen for.

173
00:09:27,640 --> 00:09:30,679
Speaker 8: The chien to just kind of yeah, it's like a

174
00:09:30,720 --> 00:09:32,520
Speaker 8: little pen for the chicks, and you put in a

175
00:09:32,600 --> 00:09:35,720
Speaker 8: heat lamp, you know, because they need to be warm. Small,

176
00:09:35,840 --> 00:09:38,120
Speaker 8: and they don't. They have only got that chicken fuzz.

177
00:09:38,160 --> 00:09:39,480
Speaker 8: They don't have the chicken feathers.

178
00:09:39,800 --> 00:09:43,559
Speaker 1: Yeah, yeah, yeah, they need to me this sounds like

179
00:09:43,600 --> 00:09:47,600
Speaker 1: a job for a shoe box. But that's probably too small,

180
00:09:47,760 --> 00:09:51,400
Speaker 1: too small, small, Unfortunately, that's what they're getting. If I'm

181
00:09:51,440 --> 00:09:55,600
Speaker 1: building a chicken, you can if you're in if you're

182
00:09:55,640 --> 00:09:57,640
Speaker 1: in a if you're in a rush, you can use

183
00:09:57,679 --> 00:09:59,640
Speaker 1: your bathtub if you have one of those, and put

184
00:09:59,679 --> 00:10:01,600
Speaker 1: the some puppy pads.

185
00:10:02,320 --> 00:10:02,480
Speaker 2: Yeah.

186
00:10:02,520 --> 00:10:05,360
Speaker 8: I learned a lot about this today as I was

187
00:10:05,480 --> 00:10:07,880
Speaker 8: just waiting for another meeting to start and just read

188
00:10:07,920 --> 00:10:08,600
Speaker 8: everything I could.

189
00:10:09,120 --> 00:10:12,000
Speaker 1: Nice And of course that chicken coop is gonna come

190
00:10:12,040 --> 00:10:15,600
Speaker 1: in handy free eggs during the robot apocalypse, right, which

191
00:10:15,840 --> 00:10:18,640
Speaker 1: Sam Altman has told me is coming. So I have

192
00:10:18,800 --> 00:10:22,079
Speaker 1: to assume that's also what you're thinking, right, right, She's

193
00:10:22,600 --> 00:10:24,160
Speaker 1: she's prepping for the eventual DEMID.

194
00:10:24,400 --> 00:10:24,559
Speaker 2: Yeah.

195
00:10:24,720 --> 00:10:25,520
Speaker 1: I think during.

196
00:10:25,440 --> 00:10:27,840
Speaker 8: COVID, I think there was a run on chicken eggs.

197
00:10:27,960 --> 00:10:30,120
Speaker 8: And then so I'd go to Costco and you couldn't

198
00:10:30,160 --> 00:10:33,720
Speaker 8: get the gross of chicken eggs. And then I'd go, hey,

199
00:10:33,800 --> 00:10:35,560
Speaker 8: and I went to my backyard.

200
00:10:38,920 --> 00:10:39,160
Speaker 1: Market.

201
00:10:39,960 --> 00:10:41,559
Speaker 3: Got it for eight bucks an egg if you want to.

202
00:10:41,679 --> 00:10:44,760
Speaker 1: Yeah, yeah, they are still really expensive, Emily, how about you?

203
00:10:44,960 --> 00:10:46,360
Speaker 1: What's something from your search history?

204
00:10:46,960 --> 00:10:48,199
Speaker 11: So I took a look, and it's a bunch of

205
00:10:48,240 --> 00:10:50,480
Speaker 11: boring stuff like I can't remember it can't be bothered

206
00:10:50,480 --> 00:10:52,199
Speaker 11: to remember the website of a certain journal that I

207
00:10:52,280 --> 00:10:53,800
Speaker 11: was interested in. So I'm like searching the name of

208
00:10:53,840 --> 00:10:56,760
Speaker 11: the journal. But then like down below that, and we've

209
00:10:56,880 --> 00:11:01,800
Speaker 11: been watching for All Mankind, which is this like alternate

210
00:11:01,920 --> 00:11:06,120
Speaker 11: timeline thing that that's a what happens if the Soviets.

211
00:11:05,800 --> 00:11:06,760
Speaker 1: Landed on the moon first?

212
00:11:06,800 --> 00:11:11,000
Speaker 11: Right, right, So it diverges in the nineteen sixties, but

213
00:11:11,080 --> 00:11:14,120
Speaker 11: it keeps referencing actual history. So my search history is

214
00:11:14,160 --> 00:11:16,240
Speaker 11: full of like, okay, so when did the Vietnam War

215
00:11:16,320 --> 00:11:16,880
Speaker 11: actually end?

216
00:11:17,120 --> 00:11:17,360
Speaker 2: Right?

217
00:11:17,440 --> 00:11:20,040
Speaker 11: And like you know which Apollo mission did what. So

218
00:11:20,080 --> 00:11:21,800
Speaker 11: it's a bunch of queries like that sort of comparing

219
00:11:21,840 --> 00:11:23,559
Speaker 11: what's in the show to actual history.

220
00:11:23,679 --> 00:11:25,880
Speaker 3: How does that line up? Based on your sort of

221
00:11:26,080 --> 00:11:29,719
Speaker 3: cursory research as you watch the show so interestingly.

222
00:11:29,800 --> 00:11:33,120
Speaker 11: So there's there's a point where Ted Kennedy is like

223
00:11:33,280 --> 00:11:36,679
Speaker 11: in the background being talked about not going to chap Equittic. Oh,

224
00:11:37,120 --> 00:11:40,360
Speaker 11: and then an episode later he's president.

225
00:11:41,040 --> 00:11:41,319
Speaker 2: Wow.

226
00:11:42,559 --> 00:11:44,719
Speaker 11: So there's and I suspect there's way more of that

227
00:11:44,800 --> 00:11:46,960
Speaker 11: kind of stuff that I'm not catching right, right, right, a.

228
00:11:47,000 --> 00:11:48,280
Speaker 3: Subtle things right right.

229
00:11:48,480 --> 00:11:51,640
Speaker 1: The media is like, and Ted Kennedy missed a barbecue

230
00:11:51,720 --> 00:11:58,040
Speaker 1: this weekend in chap Equittic. That's that's super interesting. Yeah,

231
00:11:58,320 --> 00:12:01,800
Speaker 1: this is ye know, third or fourth person who's mentioned

232
00:12:01,840 --> 00:12:05,080
Speaker 1: for all mankind to us. I think this is pushing

233
00:12:05,120 --> 00:12:08,160
Speaker 1: it over the threshold to where I have to have

234
00:12:08,280 --> 00:12:09,640
Speaker 1: to watch this damn thing.

235
00:12:10,040 --> 00:12:12,720
Speaker 11: It's enjoyable, but it's tense. I don't know anything worth

236
00:12:12,800 --> 00:12:16,439
Speaker 11: Like people in outer space really creeps me out because

237
00:12:16,480 --> 00:12:19,199
Speaker 11: like that that degree of like loneliness and sort of

238
00:12:19,440 --> 00:12:23,280
Speaker 11: lack of failsafes, you know, like when you when all

239
00:12:23,320 --> 00:12:24,920
Speaker 11: the fail SATs are are the ones that you built

240
00:12:25,080 --> 00:12:27,120
Speaker 11: and beyond that you know your s o L.

241
00:12:27,280 --> 00:12:33,559
Speaker 1: That's that's creepy, seems uncomfortable outer space. Yeah, Caitlin, what

242
00:12:33,720 --> 00:12:35,240
Speaker 1: is something you think is overrated?

243
00:12:36,240 --> 00:12:39,480
Speaker 12: I might have said this before, and I am running

244
00:12:39,520 --> 00:12:45,079
Speaker 12: out of thoughts and opinions also because my brain along.

245
00:12:44,840 --> 00:12:45,320
Speaker 6: With the rest of.

246
00:12:48,520 --> 00:12:50,200
Speaker 1: Have you done the massage gone on your brain?

247
00:12:50,960 --> 00:12:55,680
Speaker 3: On your temple, just like.

248
00:12:56,760 --> 00:13:02,120
Speaker 12: Like chatter all my teeth and just break them, bite

249
00:13:02,200 --> 00:13:05,880
Speaker 12: my tongue off. You know, something I think is overrated

250
00:13:06,200 --> 00:13:09,640
Speaker 12: is soup, just in general soup.

251
00:13:10,040 --> 00:13:12,920
Speaker 3: Someone who's I feel like two weeks ago someone came

252
00:13:12,960 --> 00:13:16,839
Speaker 3: in hot soup was overrated? Yeah, yeah, yeah, and we're

253
00:13:16,840 --> 00:13:18,920
Speaker 3: talking this is pretty Is it filling or not?

254
00:13:19,320 --> 00:13:19,520
Speaker 13: Yeah?

255
00:13:19,960 --> 00:13:22,880
Speaker 3: So the one person came in I forget who's so that?

256
00:13:23,000 --> 00:13:26,040
Speaker 3: Forgive me they're overrated was soup because it wasn't filling

257
00:13:26,160 --> 00:13:28,480
Speaker 3: that you would have to drink or eat a lot

258
00:13:28,559 --> 00:13:30,640
Speaker 3: of the soup for it to feel like a meal.

259
00:13:30,800 --> 00:13:32,559
Speaker 3: Now what do you say to that?

260
00:13:33,360 --> 00:13:35,840
Speaker 12: I know I agree because this is soup? Is my overrate?

261
00:13:36,240 --> 00:13:40,640
Speaker 12: I think it's I think it's hot salty water.

262
00:13:40,679 --> 00:13:42,079
Speaker 1: With some shit in it.

263
00:13:42,280 --> 00:13:47,480
Speaker 12: Like I don't like, Okay, a little.

264
00:13:47,480 --> 00:13:49,319
Speaker 1: Hot salty water with some shit in it?

265
00:13:50,280 --> 00:13:50,599
Speaker 3: Thank you?

266
00:13:51,000 --> 00:13:52,719
Speaker 1: Yeah, you mean I'm gonna eat up this whatever I

267
00:13:52,800 --> 00:13:53,880
Speaker 1: just scoop out of the toilet.

268
00:13:55,559 --> 00:13:56,559
Speaker 12: I just think it's a racket.

269
00:13:56,600 --> 00:13:57,360
Speaker 13: I don't like brath.

270
00:13:57,760 --> 00:14:04,000
Speaker 12: Yeah, there's just I find it to be a frustrating

271
00:14:04,400 --> 00:14:07,840
Speaker 12: meal to eat. I don't think it's filling. I think

272
00:14:08,200 --> 00:14:11,160
Speaker 12: broth is a racket. I do like a tomato bisk.

273
00:14:11,920 --> 00:14:17,160
Speaker 1: Yeah, you're saying it's underrated. Over on the over the

274
00:14:17,240 --> 00:14:21,960
Speaker 1: massage gun was underrated, dude, I'm my brain is racket, dude.

275
00:14:22,000 --> 00:14:24,880
Speaker 1: I'm telling you the Massage Gun to the Temple aka

276
00:14:25,080 --> 00:14:26,120
Speaker 1: the hard Reset.

277
00:14:27,480 --> 00:14:31,240
Speaker 10: I learned about it on the Cuban lad Factory. Reset

278
00:14:32,600 --> 00:14:36,920
Speaker 10: your brain, yeah, man, just go, oh my damn, what

279
00:14:37,200 --> 00:14:37,920
Speaker 10: is wrong with me?

280
00:14:38,200 --> 00:14:42,200
Speaker 1: Sorry? Yeah, we had someone who said it was overrated.

281
00:14:42,280 --> 00:14:43,280
Speaker 1: You should fight them.

282
00:14:44,200 --> 00:14:47,600
Speaker 3: Yeah, my god, sorry, my brain.

283
00:14:48,400 --> 00:14:52,720
Speaker 1: It's whoever that was? Maybe I think it might have

284
00:14:52,760 --> 00:14:54,600
Speaker 1: been More Was it More or Joe?

285
00:14:55,360 --> 00:14:57,480
Speaker 3: I think it was last week? It was one of them.

286
00:15:00,000 --> 00:15:03,480
Speaker 12: Best friend because yeah, anytime my friends are like, let's

287
00:15:03,520 --> 00:15:04,520
Speaker 12: go get some soup.

288
00:15:04,280 --> 00:15:08,160
Speaker 1: I'm like, to what end you're you have specific group

289
00:15:08,240 --> 00:15:10,720
Speaker 1: of friends though I've never had a friend come. Or

290
00:15:10,760 --> 00:15:12,520
Speaker 1: maybe you just have more friends than me. But I've

291
00:15:12,560 --> 00:15:14,640
Speaker 1: never had a friend suggest we go get soup.

292
00:15:15,440 --> 00:15:19,400
Speaker 3: I've done that. Well, here's the thing I liked. The

293
00:15:19,840 --> 00:15:23,720
Speaker 3: lobster bisk a hamburger Hamlet in the Valley. I would

294
00:15:23,760 --> 00:15:25,840
Speaker 3: always go there like that was like my fancy meal.

295
00:15:25,880 --> 00:15:27,560
Speaker 3: I remember like when I was something, do you say

296
00:15:27,680 --> 00:15:28,000
Speaker 3: let's go.

297
00:15:28,040 --> 00:15:30,800
Speaker 1: Get Hamburger Hamlet or do you say let's.

298
00:15:30,680 --> 00:15:35,040
Speaker 3: Go get bisk And they're like what And I remember

299
00:15:35,360 --> 00:15:37,960
Speaker 3: I it gave me the worst gas. It's like to

300
00:15:38,440 --> 00:15:41,240
Speaker 3: this day, my friends remember we call a Hamburger Hamlet

301
00:15:41,320 --> 00:15:45,240
Speaker 3: when I fart really loud because it was so it

302
00:15:45,440 --> 00:15:47,960
Speaker 3: was so intense. But I haven't had it since to be.

303
00:15:48,080 --> 00:15:50,840
Speaker 1: Let's take a little trip down to a Biscayne Bay

304
00:15:51,280 --> 00:15:56,560
Speaker 1: exact exactly. You like do a like thing you gesture

305
00:15:56,640 --> 00:15:58,760
Speaker 1: with your hands like you're doing a coke reference.

306
00:15:59,120 --> 00:16:01,680
Speaker 3: Yeah, I really means just like, what are you doing

307
00:16:01,720 --> 00:16:02,240
Speaker 3: a line now?

308
00:16:02,280 --> 00:16:02,560
Speaker 2: I et?

309
00:16:03,920 --> 00:16:05,800
Speaker 3: Yeah? Yeah, how do you fart a bunch?

310
00:16:06,080 --> 00:16:09,600
Speaker 1: Yeah? Saturday night, get with it. I like soup that

311
00:16:09,720 --> 00:16:13,320
Speaker 1: eats like a meal, you know, like a stew. Yeah,

312
00:16:13,320 --> 00:16:13,600
Speaker 1: I love.

313
00:16:14,440 --> 00:16:19,880
Speaker 12: Oh fuck, I guess I just don't like anything like

314
00:16:20,200 --> 00:16:23,520
Speaker 12: just like a thin chicken or beef bra Yeah.

315
00:16:23,560 --> 00:16:24,000
Speaker 3: I feel that.

316
00:16:25,000 --> 00:16:30,280
Speaker 1: So yeah, when when I'm having soup for dinner, there's

317
00:16:30,320 --> 00:16:32,680
Speaker 1: a part of me that's saying this isn't this isn't

318
00:16:32,720 --> 00:16:35,080
Speaker 1: what I signed up for this isn't this don't count

319
00:16:35,120 --> 00:16:37,600
Speaker 1: as a meal. This is a drink. You gave me

320
00:16:37,720 --> 00:16:38,120
Speaker 1: drink for.

321
00:16:38,200 --> 00:16:41,160
Speaker 12: Meal, hot salty drink and who wants that?

322
00:16:41,760 --> 00:16:48,400
Speaker 1: Now? How do you feel about smoothie for meal? It's

323
00:16:48,440 --> 00:16:52,000
Speaker 1: a rare similar object for me. Yeah, a meal that

324
00:16:52,080 --> 00:16:55,120
Speaker 1: does not make I would say, yeah, I need.

325
00:16:55,160 --> 00:17:01,120
Speaker 12: I need solid. I'm not a baby, a baby, apple

326
00:17:01,280 --> 00:17:07,800
Speaker 12: headed baby, and I want solid, grown up.

327
00:17:08,520 --> 00:17:12,000
Speaker 1: Tell me, thank you. I will just yeah, just on principle,

328
00:17:12,080 --> 00:17:16,000
Speaker 1: I will eat like something solid to go with a smoothie.

329
00:17:16,119 --> 00:17:18,960
Speaker 1: Like I just said, this can't my body will not

330
00:17:19,080 --> 00:17:21,400
Speaker 1: accept it. That's a full meal.

331
00:17:22,680 --> 00:17:22,960
Speaker 2: What is?

332
00:17:23,320 --> 00:17:25,800
Speaker 1: What's something that you think is underrated?

333
00:17:26,359 --> 00:17:30,000
Speaker 13: I think being a hater is underrated. And we talked

334
00:17:30,000 --> 00:17:33,080
Speaker 13: about this in one of our episodes. Haters really are

335
00:17:33,160 --> 00:17:36,159
Speaker 13: the impetus to so much like if you had somebody

336
00:17:36,240 --> 00:17:37,800
Speaker 13: hating on you, you just feel like you have to

337
00:17:37,920 --> 00:17:40,280
Speaker 13: prove them wrong. You have to go so hard and

338
00:17:40,359 --> 00:17:42,119
Speaker 13: a lot of things that we have in society, you

339
00:17:42,160 --> 00:17:44,880
Speaker 13: wouldn't be there without haters. But they get a really

340
00:17:44,880 --> 00:17:46,760
Speaker 13: bad rap. And I was like, y'all need to like

341
00:17:47,160 --> 00:17:49,159
Speaker 13: genuinely thank your haters and not like I like to

342
00:17:49,200 --> 00:17:53,480
Speaker 13: thank my haters, but like, for real, right you yeah you.

343
00:17:53,520 --> 00:17:57,320
Speaker 1: Gave Yeah, your outside opinion of me sort of spurred

344
00:17:57,359 --> 00:17:57,600
Speaker 1: me on.

345
00:17:57,720 --> 00:17:59,960
Speaker 3: To do something different, something great for sure.

346
00:18:00,720 --> 00:18:01,920
Speaker 13: Yeah, if you were just being nice to me, I

347
00:18:01,960 --> 00:18:03,560
Speaker 13: would have just been sitting around doing nothing.

348
00:18:04,640 --> 00:18:08,000
Speaker 1: I just like in my search history is somebody so

349
00:18:08,280 --> 00:18:12,720
Speaker 1: Sony had this like outspoken critic who just hated all

350
00:18:12,800 --> 00:18:18,240
Speaker 1: things Sony and like criticized everything they did and his

351
00:18:18,640 --> 00:18:21,600
Speaker 1: he made so many good points when he was criticizing

352
00:18:21,680 --> 00:18:26,080
Speaker 1: them that they hired him and he became the president

353
00:18:26,200 --> 00:18:27,400
Speaker 1: of Sony eventually.

354
00:18:27,640 --> 00:18:29,840
Speaker 14: WHOA, That's not how I thought.

355
00:18:30,560 --> 00:18:35,000
Speaker 1: I was, like, what so, I mean, truly you can

356
00:18:35,080 --> 00:18:38,000
Speaker 1: find a well informed hater like where all their criticism

357
00:18:38,119 --> 00:18:42,200
Speaker 1: is like actually, like the reason I'm so mad is

358
00:18:42,320 --> 00:18:46,000
Speaker 1: because they make some really good points. Then maybe listen

359
00:18:46,040 --> 00:18:48,719
Speaker 1: to them, maybe keep them close for reasons other than

360
00:18:49,359 --> 00:18:53,080
Speaker 1: you know that you're just watching them, make them your boss. Yeah.

361
00:18:53,200 --> 00:18:53,760
Speaker 14: I feel like the.

362
00:18:53,880 --> 00:18:57,159
Speaker 13: United States should do that for me, for I'm the

363
00:18:57,240 --> 00:18:58,800
Speaker 13: biggest hater of the United States.

364
00:18:58,880 --> 00:19:01,960
Speaker 14: Like president, Yeah, but you don't want to be president though.

365
00:19:02,160 --> 00:19:03,719
Speaker 13: I would do some stuff that we would not come

366
00:19:03,760 --> 00:19:04,080
Speaker 13: back from.

367
00:19:06,560 --> 00:19:08,359
Speaker 15: But I was thinking it could be this could be

368
00:19:08,520 --> 00:19:10,680
Speaker 15: everybody could just be a hater, like in the aftermath

369
00:19:10,720 --> 00:19:14,720
Speaker 15: of the quote unquote great resignation, like everybody could just

370
00:19:14,840 --> 00:19:18,280
Speaker 15: focus on hating and that that'll be our pathway.

371
00:19:18,280 --> 00:19:19,000
Speaker 14: It's employment.

372
00:19:19,880 --> 00:19:23,080
Speaker 3: Yeah, here first we heard of your first.

373
00:19:23,480 --> 00:19:25,840
Speaker 1: Plus, it's such a thin line, Like whenever somebody gets

374
00:19:25,920 --> 00:19:29,480
Speaker 1: caught doing something really bad, their first response is always like,

375
00:19:29,560 --> 00:19:33,200
Speaker 1: I'm not listening to the haters on this. So it's

376
00:19:33,200 --> 00:19:35,719
Speaker 1: a real thin line between hater and like person who

377
00:19:35,880 --> 00:19:39,600
Speaker 1: is just calling out you know, horrible behavior.

378
00:19:40,080 --> 00:19:42,720
Speaker 13: For sure, especially if like you see your fath getting

379
00:19:43,600 --> 00:19:46,840
Speaker 13: hated on mm hmm, sometimes you're like, yeah, they're just hating,

380
00:19:46,880 --> 00:19:48,320
Speaker 13: but it's someone you don't like. There's like, it's a

381
00:19:48,400 --> 00:19:50,600
Speaker 13: very principal critique that person, right.

382
00:19:52,040 --> 00:19:52,639
Speaker 14: Start coming out.

383
00:19:53,160 --> 00:19:57,520
Speaker 1: Yeah, it's the principal critique, and I will stand by

384
00:19:58,040 --> 00:20:01,040
Speaker 1: as it's made. Eves, what how about you? What's something

385
00:20:01,080 --> 00:20:02,320
Speaker 1: you think is underrated?

386
00:20:02,880 --> 00:20:08,720
Speaker 14: So I think rekindling old friendships is underrated and this is.

387
00:20:10,280 --> 00:20:10,760
Speaker 1: Underrated.

388
00:20:10,880 --> 00:20:17,119
Speaker 14: Yes, she's thinking for you, because that would have been

389
00:20:17,160 --> 00:20:18,440
Speaker 14: how I kind of felt in the past.

390
00:20:18,480 --> 00:20:21,080
Speaker 15: I was like, you know, it's a new day, it's

391
00:20:21,119 --> 00:20:24,240
Speaker 15: a new dawn, and I don't need to go back

392
00:20:24,320 --> 00:20:27,119
Speaker 15: to these old friendships. But you know, I'm rethinking it

393
00:20:27,240 --> 00:20:30,840
Speaker 15: now after having communications with you know, somebody who I

394
00:20:30,960 --> 00:20:32,919
Speaker 15: used to know is mother today and she was.

395
00:20:32,960 --> 00:20:36,520
Speaker 14: Like, oh, you y'all should talk and I'm like, I'll

396
00:20:36,600 --> 00:20:37,159
Speaker 14: think about it.

397
00:20:37,880 --> 00:20:40,680
Speaker 15: You know, I'll consider it, because before I'm like, you know,

398
00:20:40,720 --> 00:20:42,879
Speaker 15: if they're not in my life anymore, then it's for

399
00:20:42,960 --> 00:20:43,320
Speaker 15: a reason.

400
00:20:43,440 --> 00:20:45,680
Speaker 14: You know how all the kids say they're in your.

401
00:20:45,600 --> 00:20:48,080
Speaker 1: Life for a reason or a season or something like that. Yeah.

402
00:20:48,160 --> 00:20:51,040
Speaker 15: Right, So I don't know if I subscribe to that anymore.

403
00:20:51,119 --> 00:20:53,840
Speaker 15: So that's that's what my underrated is going to be, Okay,

404
00:20:54,040 --> 00:20:54,400
Speaker 15: all right?

405
00:20:54,640 --> 00:20:56,919
Speaker 1: And I actually needed to hear that. There there's somebody

406
00:20:57,000 --> 00:20:59,560
Speaker 1: that I've just been like toying with the idea of, like, man,

407
00:21:00,119 --> 00:21:01,720
Speaker 1: really get back in touch with them. I haven't talked

408
00:21:01,720 --> 00:21:05,440
Speaker 1: to them in years. And yeah, that needed to hear that.

409
00:21:05,560 --> 00:21:08,040
Speaker 1: Thank you very much. What's something you think is overrated?

410
00:21:08,760 --> 00:21:13,120
Speaker 15: I think biopics are overrated? And this is coming from

411
00:21:13,160 --> 00:21:17,119
Speaker 15: someone who is I love biography and written form. I

412
00:21:17,240 --> 00:21:21,359
Speaker 15: love it in podcast form. I love it in those forms.

413
00:21:21,480 --> 00:21:24,200
Speaker 15: But for some reason I have something against biopics. Like

414
00:21:24,760 --> 00:21:27,800
Speaker 15: my husband coerced me to go to the Bob Parley

415
00:21:28,080 --> 00:21:32,080
Speaker 15: Bob Marley Biopics the other day, and I'm just for

416
00:21:32,240 --> 00:21:35,000
Speaker 15: some reason, it's just something about seeing somebody who looks

417
00:21:35,080 --> 00:21:38,440
Speaker 15: nothing like the person and sounds nothing like the person.

418
00:21:38,480 --> 00:21:41,240
Speaker 15: And it's really hard to get somebody's accent down, Like

419
00:21:41,320 --> 00:21:43,160
Speaker 15: that's not an easy thing to do for the best

420
00:21:43,200 --> 00:21:46,600
Speaker 15: of outs. There's something that misses the translation. And I

421
00:21:46,680 --> 00:21:49,240
Speaker 15: think also it's like very hard to cover a person's

422
00:21:49,240 --> 00:21:51,480
Speaker 15: story in an hour and forty five minutes in a

423
00:21:51,520 --> 00:21:54,399
Speaker 15: way that feels really meaningful now that I think it

424
00:21:54,520 --> 00:21:57,080
Speaker 15: can't be done in that Like it's just to me,

425
00:21:57,160 --> 00:22:00,240
Speaker 15: they always come off as medium.

426
00:22:00,600 --> 00:22:03,400
Speaker 3: Or right or like someone who could do no wrong

427
00:22:03,600 --> 00:22:05,200
Speaker 3: or something like that, like and he was actually the

428
00:22:05,240 --> 00:22:05,760
Speaker 3: hero of.

429
00:22:05,800 --> 00:22:08,400
Speaker 14: Everything, right, yeah, because they had structure.

430
00:22:08,840 --> 00:22:11,600
Speaker 3: There's some complexities there with his life for sure. And

431
00:22:11,760 --> 00:22:14,960
Speaker 3: also we were hating on the wig. First of all.

432
00:22:16,200 --> 00:22:17,679
Speaker 3: The first thing I said, are like, no.

433
00:22:18,440 --> 00:22:22,119
Speaker 14: No, no, no, nobody wis right?

434
00:22:22,880 --> 00:22:25,920
Speaker 3: Nobody does That's like you could have find it a Jamaican,

435
00:22:26,960 --> 00:22:30,399
Speaker 3: a Jamaican actor who already had dreads or something like that.

436
00:22:30,520 --> 00:22:32,320
Speaker 1: You had to go find this isn't he from the UK?

437
00:22:33,160 --> 00:22:35,879
Speaker 3: Like they do that. Yeah, I just remember being like

438
00:22:36,000 --> 00:22:38,040
Speaker 3: this is trash man, Like I can't even I can't.

439
00:22:38,160 --> 00:22:40,280
Speaker 3: I can't look at this because the wigs were the

440
00:22:40,359 --> 00:22:41,240
Speaker 3: wig was so bad.

441
00:22:41,400 --> 00:22:44,280
Speaker 15: The wig was wiggy. Okay, it's always the scalp with locks.

442
00:22:44,320 --> 00:22:44,959
Speaker 15: It's the scalp.

443
00:22:45,359 --> 00:22:47,200
Speaker 3: Yeah, they get wrong. It's really hard to do, and

444
00:22:47,240 --> 00:22:49,639
Speaker 3: it's always like a helmet with like it feels like

445
00:22:49,680 --> 00:22:51,560
Speaker 3: a helmet was popping off.

446
00:22:51,800 --> 00:22:58,640
Speaker 15: Yes, it was giving Tyler Perry wigs or from Walking Dead. Yeah, yeah,

447
00:22:58,800 --> 00:23:03,080
Speaker 15: it's that's exactly what it was. And they try it too,

448
00:23:03,280 --> 00:23:04,920
Speaker 15: Like shout out to the wigmakers. I know it's a

449
00:23:04,960 --> 00:23:06,480
Speaker 15: hard job. I'm not saying that.

450
00:23:06,560 --> 00:23:09,240
Speaker 14: They are fully talented and skilled and have been working

451
00:23:09,320 --> 00:23:10,000
Speaker 14: on their crafts.

452
00:23:10,400 --> 00:23:11,119
Speaker 13: They like tried to.

453
00:23:11,240 --> 00:23:14,280
Speaker 15: They lengthened the wig throughout the movie. Yeah, and all

454
00:23:14,359 --> 00:23:18,800
Speaker 15: of that, and I'm like, okay, okay, all right, wow you.

455
00:23:19,600 --> 00:23:23,760
Speaker 3: Did the guess the church. Oh okay, all right, well

456
00:23:23,840 --> 00:23:28,040
Speaker 3: take you yeah, okay, bless your heart. At least you tried.

457
00:23:29,480 --> 00:23:32,119
Speaker 3: There is there a thing too, like with because we

458
00:23:32,200 --> 00:23:34,280
Speaker 3: were talking about this, I think maybe off Mike Jack,

459
00:23:34,359 --> 00:23:37,119
Speaker 3: but like some people are just kind of so cool,

460
00:23:37,520 --> 00:23:39,960
Speaker 3: like don't even bother trying to get somebody else to

461
00:23:40,080 --> 00:23:42,200
Speaker 3: capture that cool on screen, like there was like a

462
00:23:42,280 --> 00:23:44,560
Speaker 3: Miles Davis biopic.

463
00:23:44,680 --> 00:23:46,560
Speaker 1: I'm like, you can't that. This dude is also all

464
00:23:46,640 --> 00:23:48,719
Speaker 1: over the place. You can't just be like, yeah, now

465
00:23:48,760 --> 00:23:50,920
Speaker 1: you're Miles Day, You're Bob Marley or whatever. They're so

466
00:23:51,200 --> 00:23:55,920
Speaker 1: iconic that you either have to like cast the person

467
00:23:56,040 --> 00:23:58,480
Speaker 1: who looks the most like them, like they did with

468
00:23:58,560 --> 00:24:00,879
Speaker 1: that Tupac one, but then person he could not be

469
00:24:00,960 --> 00:24:04,800
Speaker 1: able to act, So yeah, then yeah, it's you're it's

470
00:24:04,880 --> 00:24:09,080
Speaker 1: a real like you are trying to thread the thinnest needle.

471
00:24:09,400 --> 00:24:12,960
Speaker 1: And also yeah, like maybe like a Tupac biography in

472
00:24:13,040 --> 00:24:16,480
Speaker 1: one hundred years, maybe you know, like but when no

473
00:24:16,520 --> 00:24:19,680
Speaker 1: one remembers who, yeah remembers and they're just like they've

474
00:24:19,720 --> 00:24:24,080
Speaker 1: seen the pictures. But like right now, Bob Marley Tupac

475
00:24:24,320 --> 00:24:25,440
Speaker 1: like way way too soon.

476
00:24:25,640 --> 00:24:28,280
Speaker 15: I feel, yeah, it needs some distance, and it didn't

477
00:24:28,320 --> 00:24:29,920
Speaker 15: make it any better that at the end of the

478
00:24:29,960 --> 00:24:32,359
Speaker 15: Bob Marley film. I don't know if I'm going too

479
00:24:32,400 --> 00:24:35,200
Speaker 15: hard on this right now that much, but at the

480
00:24:35,320 --> 00:24:37,840
Speaker 15: end of the Bob Marley film, they show like actual

481
00:24:37,920 --> 00:24:41,840
Speaker 15: footage of Bob Marley and comparing his energy on stage

482
00:24:41,880 --> 00:24:43,879
Speaker 15: to the energy that the actor was giving was just

483
00:24:44,040 --> 00:24:48,200
Speaker 15: miles away. It was like it was so spirited and

484
00:24:48,520 --> 00:24:51,679
Speaker 15: you could really see that coming out in the clips

485
00:24:51,840 --> 00:24:54,280
Speaker 15: and having just seen the actor do it in the film,

486
00:24:54,320 --> 00:24:57,280
Speaker 15: I was like something was missing that that Genna sa

487
00:24:57,400 --> 00:24:58,160
Speaker 15: Quaw wasn't there.

488
00:24:58,440 --> 00:25:05,200
Speaker 13: Yeah, Chatwick, If Chatwick was alive, I feel like you

489
00:25:05,200 --> 00:25:07,399
Speaker 13: could play anybody great actor.

490
00:25:07,520 --> 00:25:10,760
Speaker 1: Yeah, that's what you need. Like sometimes it works because yeah,

491
00:25:11,160 --> 00:25:11,359
Speaker 1: just like.

492
00:25:13,119 --> 00:25:16,200
Speaker 13: Marshall, I don't remember you played someone super super you

493
00:25:16,359 --> 00:25:18,879
Speaker 13: better quit it in that.

494
00:25:19,359 --> 00:25:21,520
Speaker 14: Yeah, I think he did. He did pay their good Marshall.

495
00:25:21,560 --> 00:25:23,440
Speaker 13: I believe that wasn't matter at it actually Yeah.

496
00:25:24,160 --> 00:25:28,959
Speaker 3: Yeah, also with a Kingsley ben Adeer, that's the name

497
00:25:29,000 --> 00:25:31,440
Speaker 3: of the actor who played him. But yeah, like I'm

498
00:25:31,480 --> 00:25:34,560
Speaker 3: sure when you see that juxtaposition of actual Bob Marley footage,

499
00:25:34,600 --> 00:25:36,159
Speaker 3: it really feels like your parents being like, yeah, we

500
00:25:36,200 --> 00:25:37,800
Speaker 3: got Bob Marley at home, don't worry.

501
00:25:40,440 --> 00:25:44,280
Speaker 1: Yeah, Katie, what's something that you think is overrated?

502
00:25:45,080 --> 00:25:49,040
Speaker 13: I think Chick fil A is overrated, And I think

503
00:25:50,280 --> 00:25:52,399
Speaker 13: once we all band together and stop eating Chick fil A,

504
00:25:52,440 --> 00:25:53,600
Speaker 13: that's when the revolution will happen.

505
00:25:53,920 --> 00:25:54,240
Speaker 8: Wow.

506
00:25:54,520 --> 00:25:57,240
Speaker 13: Because they don't like the gays. They don't like the

507
00:25:57,320 --> 00:25:59,600
Speaker 13: blacks for real, because the Blacks and the Latinos being

508
00:25:59,640 --> 00:26:02,120
Speaker 13: a bad and not in the front interacting with customers.

509
00:26:02,640 --> 00:26:04,560
Speaker 1: Wow, the chicken.

510
00:26:04,840 --> 00:26:07,280
Speaker 13: I haven't had chicken in maybe ten years. It was

511
00:26:07,400 --> 00:26:13,320
Speaker 13: it algohol. But other people make good chickens. They're not

512
00:26:13,440 --> 00:26:15,200
Speaker 13: the only ones, and people be acting like they're the

513
00:26:15,240 --> 00:26:17,159
Speaker 13: only ones just because they say please and thank you

514
00:26:17,240 --> 00:26:20,840
Speaker 13: in my pleasure. We how to high higher standards for ourselves.

515
00:26:21,560 --> 00:26:23,960
Speaker 14: The hard thing about this is that they're expanding.

516
00:26:24,400 --> 00:26:26,520
Speaker 13: They're expanding like crazy, especially in Georgia.

517
00:26:26,960 --> 00:26:29,920
Speaker 15: Oh really, country across the country. Yeah, there's like four

518
00:26:29,960 --> 00:26:32,040
Speaker 15: of them on my exit. Yeah, they're a franchise model,

519
00:26:32,080 --> 00:26:34,240
Speaker 15: and it's like super hard to get into. Like their franchise,

520
00:26:34,400 --> 00:26:38,920
Speaker 15: they have a specific way and they're never gonna po

521
00:26:39,000 --> 00:26:40,280
Speaker 15: They're never gonna go public.

522
00:26:41,160 --> 00:26:42,240
Speaker 3: Right, I don't know.

523
00:26:42,280 --> 00:26:44,520
Speaker 14: How, but I believe.

524
00:26:44,640 --> 00:26:45,119
Speaker 1: I believe it.

525
00:26:45,320 --> 00:26:46,440
Speaker 14: If you believe it, I believe it.

526
00:26:46,800 --> 00:26:49,200
Speaker 13: Yeah, the revolution is gonna start right when we give.

527
00:26:49,160 --> 00:26:51,400
Speaker 3: Up to a when people can like, yeah, the first

528
00:26:51,520 --> 00:26:54,760
Speaker 3: principled consumer decision like the entire country can collectively make,

529
00:26:54,840 --> 00:26:57,880
Speaker 3: they'd be like, aside from the morality, the chicken, also

530
00:26:58,240 --> 00:27:00,480
Speaker 3: like Popeyes is better man? When I in the Atlanta

531
00:27:00,520 --> 00:27:04,720
Speaker 3: studio that's by Delilah's. I had a chicken sandwich from

532
00:27:04,720 --> 00:27:08,040
Speaker 3: there that blew my socks right off my feet. Like

533
00:27:08,119 --> 00:27:10,040
Speaker 3: I saw that. I was like a Willy Wonka exhibit.

534
00:27:11,880 --> 00:27:14,520
Speaker 3: But yeah, that was definitely like, Yeah, I think it's

535
00:27:14,520 --> 00:27:15,960
Speaker 3: just one of those things. And I said this before

536
00:27:16,000 --> 00:27:18,920
Speaker 3: when I think was it Dulce Sloan who maybe or

537
00:27:18,960 --> 00:27:21,160
Speaker 3: somebody was talking about it, or maybe it was doctor

538
00:27:21,200 --> 00:27:23,560
Speaker 3: John anyway, somebody was also saying was coming with that

539
00:27:23,680 --> 00:27:24,840
Speaker 3: overrated and.

540
00:27:25,320 --> 00:27:29,080
Speaker 1: It's probably one of our most common overrateds And yeah, yeah,

541
00:27:29,240 --> 00:27:30,000
Speaker 1: I think it's time.

542
00:27:30,200 --> 00:27:32,000
Speaker 3: I think it's like one of those things. It's like

543
00:27:32,040 --> 00:27:33,640
Speaker 3: one of those things too, because in the West Coast

544
00:27:33,720 --> 00:27:36,360
Speaker 3: we never had it, Like so when it came out

545
00:27:36,359 --> 00:27:37,760
Speaker 3: on the West Coast, it was like what in and

546
00:27:37,840 --> 00:27:41,240
Speaker 3: out is to other places, the thing I heard about

547
00:27:41,280 --> 00:27:43,000
Speaker 3: from our relatives that live in this out through the

548
00:27:43,040 --> 00:27:46,360
Speaker 3: East Coast or whatever. And yeah, and I think once

549
00:27:46,480 --> 00:27:49,159
Speaker 3: that subsides, maybe people can, you know, we can all

550
00:27:49,240 --> 00:27:51,240
Speaker 3: band together break just.

551
00:27:51,280 --> 00:27:52,840
Speaker 13: The novel a little bit.

552
00:27:53,040 --> 00:27:55,600
Speaker 15: Yeah, yeah, yeah, And I think if anybody Katie and

553
00:27:55,640 --> 00:27:58,240
Speaker 15: I are actually from the land of Truett, Kathy spent

554
00:27:58,320 --> 00:28:01,359
Speaker 15: a lot of time going up there where he lived, and.

555
00:28:01,560 --> 00:28:03,240
Speaker 14: I think if anybody can do it, we can do it.

556
00:28:03,240 --> 00:28:03,679
Speaker 14: Because we can.

557
00:28:04,200 --> 00:28:07,159
Speaker 15: Yeah, we have original quaim the land of the Chick

558
00:28:07,200 --> 00:28:09,399
Speaker 15: fil A. And if we can do it, anybody in

559
00:28:09,520 --> 00:28:10,399
Speaker 15: this country can do it.

560
00:28:10,640 --> 00:28:10,800
Speaker 1: You know.

561
00:28:10,880 --> 00:28:12,600
Speaker 13: I went on a field trip to Chick fil A

562
00:28:12,800 --> 00:28:14,639
Speaker 13: ranch in elementary school.

563
00:28:16,080 --> 00:28:16,359
Speaker 1: Ranch.

564
00:28:17,040 --> 00:28:19,480
Speaker 13: I don't know if it still exists. It was so fun.

565
00:28:19,600 --> 00:28:21,879
Speaker 13: They had not picking them up now.

566
00:28:23,800 --> 00:28:27,040
Speaker 1: Yeah, they're like we actually you know thought I love that.

567
00:28:29,359 --> 00:28:31,359
Speaker 13: It was so fun that it was like they had

568
00:28:31,440 --> 00:28:33,520
Speaker 13: I guess it was like a cow sanctuary something because

569
00:28:33,520 --> 00:28:36,920
Speaker 13: you know they don't, right. Yeah, and they had like

570
00:28:37,000 --> 00:28:44,840
Speaker 13: those like what are they call connas Conna stago wagons? Yeah,

571
00:28:45,840 --> 00:28:49,040
Speaker 13: the people going west was that we're in uh so

572
00:28:49,120 --> 00:28:51,320
Speaker 13: we slept in those. It was an overnight trip, mind you.

573
00:28:51,840 --> 00:28:52,080
Speaker 1: Wow.

574
00:28:52,360 --> 00:28:54,720
Speaker 13: We did somemores, We like cooked on the fire. It

575
00:28:54,840 --> 00:28:55,400
Speaker 13: was real fun.

576
00:28:55,680 --> 00:28:58,960
Speaker 14: That does sound fun today. We should look you.

577
00:29:00,800 --> 00:29:02,040
Speaker 3: Let me change my overrates.

578
00:29:04,040 --> 00:29:05,640
Speaker 13: Actually they're underrated.

579
00:29:08,320 --> 00:29:12,320
Speaker 1: Under it over the restaurant. Yeah. I think I've talked

580
00:29:12,320 --> 00:29:14,720
Speaker 1: about this before, but I got as an award in

581
00:29:14,800 --> 00:29:17,520
Speaker 1: middle school. I got to go on this field trip

582
00:29:18,000 --> 00:29:21,480
Speaker 1: and it was just the long John Silver's headquarters like

583
00:29:21,640 --> 00:29:27,640
Speaker 1: corporate headquarters, the worst version of your field trip, right,

584
00:29:28,120 --> 00:29:30,800
Speaker 1: not even like they didn't have any fun themed thing.

585
00:29:31,080 --> 00:29:34,440
Speaker 1: You just like sat and watched the fucking them go

586
00:29:34,560 --> 00:29:36,880
Speaker 1: through some spreadsheets and then got to eat one.

587
00:29:37,920 --> 00:29:40,360
Speaker 3: Yeah, they need to work on their propaganda game, you know.

588
00:29:40,600 --> 00:29:42,560
Speaker 15: Yeah, I think it would be pretty cool if they

589
00:29:42,600 --> 00:29:44,240
Speaker 15: would have showed you all the different kinds of fish

590
00:29:44,320 --> 00:29:45,040
Speaker 15: that go into.

591
00:29:44,920 --> 00:29:52,280
Speaker 1: The Actually yeah right, we usually at a pet store

592
00:29:52,560 --> 00:29:54,080
Speaker 1: to get this stuff put together.

593
00:29:55,120 --> 00:29:56,320
Speaker 14: That would be wild if the.

594
00:30:00,560 --> 00:30:04,719
Speaker 1: Goldfish mash is what you're actually getting. Oh man, all right,

595
00:30:05,720 --> 00:30:07,840
Speaker 1: well that's disgusting. Sorry to leave it on that note,

596
00:30:08,480 --> 00:30:10,600
Speaker 1: but we are going to take a quick break and

597
00:30:10,720 --> 00:30:13,640
Speaker 1: we're gonna come back and we're gonna talk some news.

598
00:30:13,880 --> 00:30:26,760
Speaker 1: We'll be right back and we're back, and Josh, you

599
00:30:26,760 --> 00:30:27,960
Speaker 1: want to kick us off with a little bit of

600
00:30:28,280 --> 00:30:29,640
Speaker 1: what you think something underrated?

601
00:30:30,040 --> 00:30:34,800
Speaker 6: Underrated as it actually ties into my overrated but plane

602
00:30:34,960 --> 00:30:40,640
Speaker 6: Zelda Tiers of the Kingdom. Because you're unemployed, it's just because, yeah,

603
00:30:40,760 --> 00:30:45,280
Speaker 6: because it hits different. Yeah, when you're unemployed. It has

604
00:30:45,400 --> 00:30:48,920
Speaker 6: helped and probably hurt my job search. Playing Tiers of

605
00:30:48,960 --> 00:30:51,800
Speaker 6: the Gadom not like a game player guy. But I

606
00:30:51,880 --> 00:30:55,840
Speaker 6: got laid off around the holidays and my in laws

607
00:30:55,960 --> 00:30:58,120
Speaker 6: gave me Tears of the Kingdom and I was, I

608
00:30:58,160 --> 00:31:01,400
Speaker 6: don't have the time, right then, I wait, wait, I

609
00:31:01,600 --> 00:31:04,880
Speaker 6: do have the time. Yeah, and so yeah, I've I've

610
00:31:05,000 --> 00:31:08,440
Speaker 6: logged many many hours on it. So I'd say that

611
00:31:08,640 --> 00:31:11,200
Speaker 6: is my underrated You should be playing Tiers of the

612
00:31:11,280 --> 00:31:12,840
Speaker 6: Kingdom if you aren't.

613
00:31:13,280 --> 00:31:16,760
Speaker 1: You mentioned that it has both hurt and helped your

614
00:31:16,880 --> 00:31:21,120
Speaker 1: job search. How have you met prospective employers while playing

615
00:31:21,760 --> 00:31:23,640
Speaker 1: Cheers of the Kingdom? How has it helped your job?

616
00:31:23,920 --> 00:31:25,480
Speaker 3: I brought it to a job interview and I was

617
00:31:25,520 --> 00:31:26,200
Speaker 3: playing during the.

618
00:31:26,240 --> 00:31:29,560
Speaker 4: Interview, and to go to interview to figure out why

619
00:31:29,600 --> 00:31:31,520
Speaker 4: I can't find a job, have no clue.

620
00:31:32,080 --> 00:31:35,720
Speaker 6: I think it hurt because it's probably hours I could

621
00:31:35,760 --> 00:31:36,080
Speaker 6: have spent.

622
00:31:36,360 --> 00:31:39,320
Speaker 1: The hurt is clear. The hurt is clear. How's it helping?

623
00:31:39,800 --> 00:31:43,680
Speaker 6: Oh uh, it's helped because it's helped my mental health.

624
00:31:43,800 --> 00:31:46,720
Speaker 6: Like there you go, like I have this, Yeah, I

625
00:31:46,800 --> 00:31:47,960
Speaker 6: have this to look forward.

626
00:31:47,720 --> 00:31:48,280
Speaker 1: To you today.

627
00:31:48,280 --> 00:31:50,840
Speaker 6: I have to finish this temple, you know. I tell

628
00:31:50,920 --> 00:31:53,120
Speaker 6: my wife like, hey, I know you're in a meeting,

629
00:31:53,720 --> 00:31:57,600
Speaker 6: but I really have to beat this boss down. Yeah,

630
00:31:59,800 --> 00:32:03,280
Speaker 6: I'm focusing Daddy's cacusing, can't you use headphones?

631
00:32:03,360 --> 00:32:10,600
Speaker 3: Josh, No, I can't. Really didn't have headphones back then

632
00:32:11,840 --> 00:32:14,200
Speaker 3: in high role. So then what's you're overrated?

633
00:32:14,760 --> 00:32:19,360
Speaker 6: My overrated is being unemployed due to tech layoffs. It

634
00:32:19,520 --> 00:32:22,960
Speaker 6: is is uh fun enough when you start out, you're

635
00:32:23,000 --> 00:32:24,959
Speaker 6: like you're getting some money in when you get unemployment

636
00:32:25,000 --> 00:32:27,640
Speaker 6: and things, but then that runs out. Because we live

637
00:32:27,680 --> 00:32:30,400
Speaker 6: in North Carolina. I'm not sure what the how it

638
00:32:30,480 --> 00:32:34,840
Speaker 6: works in California, but here in the South we get

639
00:32:34,960 --> 00:32:37,400
Speaker 6: like a certain period of times and then you're like done,

640
00:32:37,440 --> 00:32:43,480
Speaker 6: You're cut off. You can't like reapply, and you're just actually.

641
00:32:43,360 --> 00:32:45,040
Speaker 4: Come to your house and kick you in the nuts.

642
00:32:47,280 --> 00:32:49,480
Speaker 3: Yeah, you could get I think like somewhere around twenty

643
00:32:49,880 --> 00:32:52,880
Speaker 3: something weeks in California. I think it's like six weeks

644
00:32:53,000 --> 00:32:53,320
Speaker 3: or something.

645
00:32:55,080 --> 00:32:58,280
Speaker 1: Yeah, well the swift kick in the nuts. Yeah.

646
00:32:58,320 --> 00:33:02,040
Speaker 6: So with that, I mean, with that, it's led me into, oh,

647
00:33:02,240 --> 00:33:04,560
Speaker 6: I need to figure out where I'm going to go

648
00:33:04,680 --> 00:33:07,520
Speaker 6: and tex So that's where there's ui UX classes come

649
00:33:07,600 --> 00:33:09,360
Speaker 6: in there, because you got you got to get things

650
00:33:09,400 --> 00:33:12,600
Speaker 6: on your resume. I think you gotta, you know, you

651
00:33:12,640 --> 00:33:14,880
Speaker 6: get it's like catching Pokemon. You got to have these

652
00:33:14,920 --> 00:33:16,640
Speaker 6: little things so that people will look at you and

653
00:33:16,720 --> 00:33:19,520
Speaker 6: go ooh something. Besides Tiers of the Kingdom, you can

654
00:33:19,600 --> 00:33:22,120
Speaker 6: also add Pokemon to yours. Put that on a bunch

655
00:33:22,160 --> 00:33:24,479
Speaker 6: and they said please take that off. They're like, yeah, well,

656
00:33:24,760 --> 00:33:26,120
Speaker 6: looking for people to play pow World.

657
00:33:26,400 --> 00:33:28,520
Speaker 3: Sorry, oh that's my problem.

658
00:33:28,840 --> 00:33:29,200
Speaker 1: There you go.

659
00:33:30,080 --> 00:33:30,240
Speaker 10: Will.

660
00:33:30,320 --> 00:33:32,480
Speaker 1: How about you? What's something you think is underrated? What's

661
00:33:32,480 --> 00:33:33,600
Speaker 1: something you think is overrated?

662
00:33:34,000 --> 00:33:38,360
Speaker 4: Underrated? I would say, uh, normal human beings attempting to

663
00:33:38,480 --> 00:33:43,520
Speaker 4: fight professional football players. Did you guys see the video

664
00:33:43,600 --> 00:33:47,440
Speaker 4: of Cam Newton, Yeah, handling those three dudes, four dudes,

665
00:33:47,480 --> 00:33:51,880
Speaker 4: however many of there were. It was incredible and disclaimer disclaimer,

666
00:33:52,080 --> 00:33:55,120
Speaker 4: don't actually do this, but you should absolutely attempt to

667
00:33:55,200 --> 00:33:59,080
Speaker 4: fight uh, former football players who are used to having

668
00:33:59,240 --> 00:34:02,120
Speaker 4: eleven p people trying to kill them, you know, and

669
00:34:02,520 --> 00:34:05,200
Speaker 4: being paid millions of dollars because they're good at preventing

670
00:34:05,240 --> 00:34:05,840
Speaker 4: that from happening.

671
00:34:06,320 --> 00:34:06,520
Speaker 1: Yeah.

672
00:34:06,600 --> 00:34:09,360
Speaker 4: I And and it's underrated because I then get to

673
00:34:09,440 --> 00:34:12,160
Speaker 4: watch the video of you getting your ass kicked by

674
00:34:12,280 --> 00:34:15,359
Speaker 4: a football player. People do not understand how big these

675
00:34:15,440 --> 00:34:18,000
Speaker 4: human beings are and how strong they are, and how

676
00:34:18,120 --> 00:34:22,080
Speaker 4: they're used to just like again, like Cam Newton's one

677
00:34:22,120 --> 00:34:25,040
Speaker 4: of the greatest runners of all time, not for a

678
00:34:25,200 --> 00:34:26,760
Speaker 4: quarterback but in general.

679
00:34:27,120 --> 00:34:28,560
Speaker 1: Just period, just period.

680
00:34:28,840 --> 00:34:31,839
Speaker 4: He's six four six four or sixty five and two

681
00:34:31,920 --> 00:34:34,720
Speaker 4: hundred and forty five pounds and he just he handled

682
00:34:34,760 --> 00:34:37,680
Speaker 4: those guys. His hat didn't come off, like his hat

683
00:34:37,680 --> 00:34:39,319
Speaker 4: hat literally stayed.

684
00:34:39,040 --> 00:34:41,040
Speaker 3: On his head. Yeah, what kind of hat.

685
00:34:41,080 --> 00:34:43,399
Speaker 1: It looks like he's wearing like a witch's hat or something. Yeah.

686
00:34:43,600 --> 00:34:46,319
Speaker 1: He he absolutely looks like like he's in wather. He's

687
00:34:46,320 --> 00:34:49,680
Speaker 1: got that hipster flat brim kilgrim hat with like his

688
00:34:49,840 --> 00:34:50,919
Speaker 1: dreads coming out of the top.

689
00:34:51,040 --> 00:34:53,680
Speaker 4: Yeah, he looks he looks like he's Dick Tracy almost,

690
00:34:53,800 --> 00:34:56,480
Speaker 4: or like a Dick Tracy villain. But it's this specific

691
00:34:56,560 --> 00:34:59,480
Speaker 4: brand of hat that he's been having made custom for

692
00:34:59,640 --> 00:35:00,520
Speaker 4: him for a while now.

693
00:35:00,600 --> 00:35:02,560
Speaker 6: It looks like the old guy from Pultrygeist too.

694
00:35:03,520 --> 00:35:08,560
Speaker 1: Yeah, he does have like Quaker oats guys. Yeah, a

695
00:35:08,560 --> 00:35:11,239
Speaker 1: lot of people don't realize that. I think kids.

696
00:35:11,320 --> 00:35:13,839
Speaker 6: I feel like kids forgot. Do you remember the Mike

697
00:35:13,880 --> 00:35:17,720
Speaker 6: Balley fighting videos he was a professional skateboard.

698
00:35:17,320 --> 00:35:20,040
Speaker 3: Oh yeah, they were like inky and stuff.

699
00:35:19,840 --> 00:35:22,880
Speaker 6: Like kids need to watch those, And that's like the

700
00:35:23,239 --> 00:35:25,880
Speaker 6: probably like Cam Newton is going to suck you up,

701
00:35:26,000 --> 00:35:29,360
Speaker 6: like it's like Mike Valley can take on fight five people.

702
00:35:29,600 --> 00:35:30,080
Speaker 3: Yeah he was.

703
00:35:30,120 --> 00:35:32,960
Speaker 6: He was like a big guy. Yeah, but like that's

704
00:35:33,320 --> 00:35:35,160
Speaker 6: that's the lower and that's the lower.

705
00:35:35,000 --> 00:35:40,400
Speaker 3: And especially like, yeah, like a quarterback who then starts

706
00:35:40,440 --> 00:35:43,960
Speaker 3: growing dreads, like that's because they've had some kind of evolution,

707
00:35:44,520 --> 00:35:47,000
Speaker 3: you know exactly, So like they were probably on top

708
00:35:47,040 --> 00:35:49,480
Speaker 3: of the physical prowess. They're like mentally now on another

709
00:35:49,560 --> 00:35:51,840
Speaker 3: plane too, where they're like, oh, the three of you

710
00:35:52,239 --> 00:35:52,759
Speaker 3: against me.

711
00:35:54,480 --> 00:35:58,040
Speaker 4: And the amazing thing is that Cam didn't even like

712
00:35:58,239 --> 00:36:02,200
Speaker 4: he didn't really get violent back with them. He just

713
00:36:02,360 --> 00:36:04,680
Speaker 4: used their momentum and did the quarterback thing of like

714
00:36:05,000 --> 00:36:07,520
Speaker 4: I'm like, it's it's like he went it's like he

715
00:36:07,640 --> 00:36:10,439
Speaker 4: had a a like a Vietnam flashback and went into

716
00:36:10,560 --> 00:36:13,480
Speaker 4: soldier mode and just didn't want to get sacked. So

717
00:36:13,680 --> 00:36:16,520
Speaker 4: he's like immediately it's like it's like the pocket closed

718
00:36:16,560 --> 00:36:19,280
Speaker 4: in on him by three guys who are also swinging

719
00:36:19,400 --> 00:36:21,920
Speaker 4: on him, and he just he just moved them around

720
00:36:22,040 --> 00:36:22,600
Speaker 4: like they were.

721
00:36:22,520 --> 00:36:24,880
Speaker 6: Absolute racked unnecessary roughness.

722
00:36:25,320 --> 00:36:28,680
Speaker 1: Yeah, yeah, penalty. Yeah, it was.

723
00:36:28,880 --> 00:36:31,239
Speaker 4: It was super funny to watch, and I obviously don't

724
00:36:31,280 --> 00:36:34,920
Speaker 4: want people going and trying professional athletes, but also I

725
00:36:35,000 --> 00:36:36,800
Speaker 4: kind of do because then I get to watch the videos,

726
00:36:36,920 --> 00:36:37,840
Speaker 4: so you.

727
00:36:37,840 --> 00:36:40,040
Speaker 6: Get bonuses as a professional athlete.

728
00:36:40,320 --> 00:36:43,040
Speaker 1: Yes, absolutely, Yeah, well something you think is overrated, will

729
00:36:43,480 --> 00:36:44,560
Speaker 1: I'll keep this one short.

730
00:36:45,080 --> 00:36:49,319
Speaker 4: Not wishing things on your worst enemy, not they're your

731
00:36:49,360 --> 00:36:51,959
Speaker 4: worst enemy. You should try it sometime and it feels great.

732
00:36:52,040 --> 00:36:55,040
Speaker 4: You should wish all the worst things on your worst enemy.

733
00:36:55,239 --> 00:36:57,200
Speaker 4: So yeah, it's pretty straightforward.

734
00:36:57,960 --> 00:36:59,040
Speaker 3: Do you think we're right now?

735
00:36:59,160 --> 00:37:01,320
Speaker 1: Like America is just it's just chicken shit now, we

736
00:37:01,360 --> 00:37:03,719
Speaker 1: don't even worst wish the worst on our work. Yeah,

737
00:37:03,800 --> 00:37:05,720
Speaker 1: it's like it's nice to our worst enemies.

738
00:37:06,160 --> 00:37:08,640
Speaker 4: So what happens, I'll say, you know, because so much

739
00:37:08,719 --> 00:37:13,480
Speaker 4: of my h I guess, like persona revolves around Twitter

740
00:37:14,000 --> 00:37:16,000
Speaker 4: and shit, so I get scolded on there a lot.

741
00:37:16,200 --> 00:37:19,760
Speaker 4: Like there's been a few recent tweets where I've started

742
00:37:19,840 --> 00:37:24,279
Speaker 4: fake rumors about awful people dying and like like Mitch

743
00:37:24,560 --> 00:37:29,560
Speaker 4: McConnell and Ian Miles Chong recently I started a whole

744
00:37:29,680 --> 00:37:33,560
Speaker 4: like Time quoted my tweet on their website and stuff.

745
00:37:34,160 --> 00:37:36,680
Speaker 4: I said that the president of the Premiere of Malaysia

746
00:37:37,200 --> 00:37:40,040
Speaker 4: executed him, and I just like popped. I just tweeted

747
00:37:40,080 --> 00:37:41,840
Speaker 4: it and then went to sleep and then woke up

748
00:37:41,880 --> 00:37:45,000
Speaker 4: and it had like like fifty thousand likes and had

749
00:37:45,120 --> 00:37:46,600
Speaker 4: like millions and millions.

750
00:37:46,280 --> 00:37:48,560
Speaker 1: Of views, and people were like, what the fuck did

751
00:37:48,600 --> 00:37:49,279
Speaker 1: this really happen?

752
00:37:49,600 --> 00:37:51,680
Speaker 4: And people, and of course I get scolded by people

753
00:37:51,719 --> 00:37:53,839
Speaker 4: who are like this is it's wrong when the right

754
00:37:54,080 --> 00:37:55,920
Speaker 4: does it, and it's wrong when the left does it,

755
00:37:56,040 --> 00:37:59,120
Speaker 4: and I'm like, yeah, but I'm correct and I'm not a.

756
00:37:59,160 --> 00:37:59,719
Speaker 1: Piece of shit.

757
00:38:00,800 --> 00:38:03,400
Speaker 3: You know this, This this quote came out of a

758
00:38:03,520 --> 00:38:06,520
Speaker 3: Dale Earnhart avatar account exactly exactly.

759
00:38:06,880 --> 00:38:09,279
Speaker 4: The only reason it got traction is because I used

760
00:38:09,320 --> 00:38:13,760
Speaker 4: the little red light alert emojis and put like breaking

761
00:38:14,120 --> 00:38:17,480
Speaker 4: I and my song executor signed by the premiere of

762
00:38:17,520 --> 00:38:20,000
Speaker 4: Malaysia he was thirty four years old or something like that,

763
00:38:20,120 --> 00:38:21,000
Speaker 4: and pusted his picture.

764
00:38:21,040 --> 00:38:23,800
Speaker 3: Hey man, Dale would know because he's up there. Exactly,

765
00:38:24,040 --> 00:38:25,239
Speaker 3: thank you, thank you very much.

766
00:38:25,560 --> 00:38:29,319
Speaker 4: But yeah, people, so I have fun online and people

767
00:38:29,400 --> 00:38:32,160
Speaker 4: get mad about it, and I'm just saying, get off

768
00:38:32,200 --> 00:38:34,520
Speaker 4: your high horse, stop being high and mighty and like,

769
00:38:34,800 --> 00:38:36,959
Speaker 4: wish you know, they're your worst enemy.

770
00:38:36,960 --> 00:38:40,680
Speaker 1: You should it's a wish. Your wish doesn't matter exactly exactly,

771
00:38:40,840 --> 00:38:45,840
Speaker 1: there's no matter was kind of annoying to me because

772
00:38:45,960 --> 00:38:49,959
Speaker 1: I started my painting of Dale Earnhardt and Ruth Bader

773
00:38:50,000 --> 00:38:55,560
Speaker 1: Ginsburg welcoming him into heaven, and a week later it

774
00:38:55,680 --> 00:38:57,200
Speaker 1: was debu Yeah, fake news.

775
00:38:57,880 --> 00:38:59,719
Speaker 4: Yeah, it was fake news. Well, I'm glad to know

776
00:39:00,040 --> 00:39:03,120
Speaker 4: that you're gonna have the same post like your second

777
00:39:03,200 --> 00:39:05,279
Speaker 4: career as uh, you know, George W.

778
00:39:05,440 --> 00:39:05,720
Speaker 3: Bush.

779
00:39:06,560 --> 00:39:09,360
Speaker 4: You're gonna yeah, you're just gonna get into jack.

780
00:39:09,360 --> 00:39:10,640
Speaker 6: Both war criminals.

781
00:39:10,840 --> 00:39:13,239
Speaker 1: Yeah, a lot of people don't know that about me. Yeah,

782
00:39:13,800 --> 00:39:16,319
Speaker 1: little fact, little un fact. I'm glad. Do you guys

783
00:39:16,440 --> 00:39:18,920
Speaker 1: like my flight suit? My mission, mission accomplished?

784
00:39:19,680 --> 00:39:21,120
Speaker 4: I love the banner behind you too.

785
00:39:22,719 --> 00:39:25,640
Speaker 3: Don't ask them, let them compliment you organically if they

786
00:39:25,680 --> 00:39:26,920
Speaker 3: have to, they'll do that if they like it.

787
00:39:26,960 --> 00:39:30,280
Speaker 1: Don't I'm taking I'm taking this pick up course for Miles,

788
00:39:30,360 --> 00:39:31,880
Speaker 1: and he's got to teach me a little.

789
00:39:34,080 --> 00:39:36,000
Speaker 3: Man. Just let the outfit do the talking.

790
00:39:36,640 --> 00:39:43,720
Speaker 1: Do you guys like my shirt man? All right? Should

791
00:39:43,719 --> 00:39:45,600
Speaker 1: we talk about libs of TikTok?

792
00:39:45,880 --> 00:39:49,600
Speaker 3: Yeah, here's a daily zeitgeist, a typical hard turn into

793
00:39:49,760 --> 00:39:56,440
Speaker 3: dark ship. Yeah, so libs of TikTok shaia chaya rachick, right, chick.

794
00:39:56,560 --> 00:39:59,359
Speaker 3: You know this, This account has seen massive growth over

795
00:39:59,400 --> 00:40:01,960
Speaker 3: the years and like you know, what started out as

796
00:40:02,000 --> 00:40:05,680
Speaker 3: like a place for election denihilism and COVID misinformation and

797
00:40:06,719 --> 00:40:09,200
Speaker 3: tales of child trafficking has turned down into like a

798
00:40:09,239 --> 00:40:13,239
Speaker 3: full blown lgbtqu plus hate machine and sending like you know,

799
00:40:13,440 --> 00:40:19,880
Speaker 3: the accounts followers on harassment campaigns against innocent people, and recently, Richik,

800
00:40:20,000 --> 00:40:24,520
Speaker 3: the accounts operated, was interviewed by Taylor Lorenz. And while

801
00:40:24,640 --> 00:40:27,279
Speaker 3: at times there are moments that make you laugh at

802
00:40:27,320 --> 00:40:31,239
Speaker 3: how stupid and ignorant and uninformed she is, her views

803
00:40:31,280 --> 00:40:34,360
Speaker 3: are inspiring, you know, like real world violence, like bomb threats,

804
00:40:34,680 --> 00:40:37,120
Speaker 3: death threats, docsing, you name it. And she has this

805
00:40:37,280 --> 00:40:41,640
Speaker 3: pattern of directing followers towards LGBTQ plus teachers or school

806
00:40:41,640 --> 00:40:44,960
Speaker 3: administrators other figures like that, and when things get too wild,

807
00:40:45,200 --> 00:40:47,759
Speaker 3: she'll delete the posts and act like nothing happened, even

808
00:40:47,800 --> 00:40:50,000
Speaker 3: though she recently said that she wears the label of

809
00:40:50,160 --> 00:40:55,160
Speaker 3: stochastic terrorists with pride and like in the interview, it's

810
00:40:55,280 --> 00:40:59,160
Speaker 3: clear that, like Rerichik doesn't really give much thought to

811
00:40:59,239 --> 00:41:02,160
Speaker 3: her beliefs in like a way that she can actually

812
00:41:02,320 --> 00:41:06,359
Speaker 3: articulate herself, probably a symptom of just being in your

813
00:41:06,719 --> 00:41:09,879
Speaker 3: little bubble of hate speech and people like raw rahing

814
00:41:09,920 --> 00:41:12,160
Speaker 3: the shit on. So, so here's an excerpt where Taylor

815
00:41:12,200 --> 00:41:15,000
Speaker 3: Lorenz is asking her just sort of like, what exactly

816
00:41:15,160 --> 00:41:18,440
Speaker 3: is your issue with trans adults? What harm are they causing?

817
00:41:18,560 --> 00:41:22,239
Speaker 1: And again really unable to articulate anything resembling a thought

818
00:41:22,800 --> 00:41:23,880
Speaker 1: it's a lie and.

819
00:41:23,960 --> 00:41:24,960
Speaker 9: What harm is it causing?

820
00:41:25,200 --> 00:41:26,000
Speaker 14: Do you believe.

821
00:41:28,360 --> 00:41:29,120
Speaker 13: I like the truth?

822
00:41:29,560 --> 00:41:33,919
Speaker 9: I like truth, right, but I'm saying, what's the harm

823
00:41:34,160 --> 00:41:37,799
Speaker 9: of people expressing their gender identity differently than you believe.

824
00:41:37,800 --> 00:41:38,120
Speaker 1: It to be?

825
00:41:38,239 --> 00:41:39,360
Speaker 9: What harm are they causing?

826
00:41:40,760 --> 00:41:45,920
Speaker 1: Like I said, we are a nation of truth and

827
00:41:46,120 --> 00:41:47,440
Speaker 1: I seek the truth.

828
00:41:48,200 --> 00:41:51,439
Speaker 9: But that's but I'm asking about the harm. What's the harm?

829
00:41:51,520 --> 00:41:52,680
Speaker 9: You might believe it to be poss but.

830
00:41:52,840 --> 00:41:54,719
Speaker 5: The harm is that there's a lie that is very

831
00:41:54,760 --> 00:41:56,880
Speaker 5: mainstream and as being abouted into every institution.

832
00:41:57,640 --> 00:42:01,360
Speaker 9: I guess I'm wondering what the material harm is, aside

833
00:42:01,440 --> 00:42:03,560
Speaker 9: from it's maybe something that you disagree with, is in

834
00:42:03,640 --> 00:42:05,560
Speaker 9: your version of the truth is different than their version

835
00:42:05,640 --> 00:42:08,920
Speaker 9: of the truth. What is the material harm of them

836
00:42:09,000 --> 00:42:10,840
Speaker 9: living in their life as a woman or man or

837
00:42:10,920 --> 00:42:11,680
Speaker 9: gender that you don't have.

838
00:42:11,840 --> 00:42:16,120
Speaker 1: Not anything that's wrong? Is there a material harm necessarily.

839
00:42:15,760 --> 00:42:16,560
Speaker 13: So there's no harm.

840
00:42:16,680 --> 00:42:19,800
Speaker 1: Not everything that's wrong as a material harm. Eight not

841
00:42:19,920 --> 00:42:21,839
Speaker 1: anything that's wrong as a material harm.

842
00:42:22,040 --> 00:42:24,320
Speaker 3: And then there's this other clip too that kind of

843
00:42:24,400 --> 00:42:26,800
Speaker 3: summed up just how like all over the place this

844
00:42:26,960 --> 00:42:29,200
Speaker 3: interview was. This is this is like another section of

845
00:42:29,280 --> 00:42:31,880
Speaker 3: the interview where Taylor Loren's asking a question and then

846
00:42:31,880 --> 00:42:33,759
Speaker 3: gets interrupted. Oh, this is my favorite part.

847
00:42:33,840 --> 00:42:34,640
Speaker 1: I think that's good.

848
00:42:35,360 --> 00:42:37,480
Speaker 9: I'm curious kind of how you're thinking, you know, when

849
00:42:37,520 --> 00:42:39,719
Speaker 9: you think about your the way that you put out

850
00:42:39,800 --> 00:42:42,600
Speaker 9: content and the way that you think about growing your

851
00:42:42,920 --> 00:42:43,600
Speaker 9: media empire.

852
00:42:43,800 --> 00:42:45,640
Speaker 1: Kara, this is a blowdob.

853
00:42:49,640 --> 00:42:49,680
Speaker 2: What?

854
00:42:49,840 --> 00:42:50,239
Speaker 1: I don't know what?

855
00:42:51,440 --> 00:42:51,800
Speaker 2: You know? What?

856
00:42:52,640 --> 00:42:53,200
Speaker 1: I don't know what?

857
00:42:53,400 --> 00:42:53,440
Speaker 2: What?

858
00:42:53,680 --> 00:42:56,000
Speaker 3: What are you showing me this for? And yeah, like

859
00:42:56,080 --> 00:42:58,799
Speaker 3: I said, like the interview, it's it's not anything where

860
00:42:58,800 --> 00:42:59,360
Speaker 3: you're like, wow, this.

861
00:42:59,480 --> 00:43:02,920
Speaker 1: Is one of these like this person is here, this

862
00:43:03,080 --> 00:43:06,080
Speaker 1: is a blowjobhow.

863
00:43:04,920 --> 00:43:07,480
Speaker 4: Sorry, it's like two middle it's like two middle schoolers

864
00:43:08,080 --> 00:43:10,440
Speaker 4: on the internet exactly on the internet.

865
00:43:10,560 --> 00:43:12,320
Speaker 1: What do you want me not to show it to you?

866
00:43:12,840 --> 00:43:12,880
Speaker 2: So?

867
00:43:13,080 --> 00:43:17,080
Speaker 1: Now what but it's a blowjob? Do you do you deny? Sir?

868
00:43:18,280 --> 00:43:18,640
Speaker 3: I don't.

869
00:43:19,760 --> 00:43:20,160
Speaker 1: I'm sorry.

870
00:43:20,200 --> 00:43:22,160
Speaker 3: How is this relevant to what I was asking about

871
00:43:22,160 --> 00:43:24,680
Speaker 3: about the real world harm that your account is causing

872
00:43:24,719 --> 00:43:26,839
Speaker 3: in your actions, and you know this thing has taking

873
00:43:26,920 --> 00:43:29,799
Speaker 3: evolution like rather now it's just beyond like just sort

874
00:43:29,800 --> 00:43:32,480
Speaker 3: of this right wing you know account where people are

875
00:43:32,760 --> 00:43:35,080
Speaker 3: just you know, get to all the the people can

876
00:43:35,120 --> 00:43:38,320
Speaker 3: fill their tanks filled with hate by ingesting her content.

877
00:43:38,719 --> 00:43:40,320
Speaker 3: She now has. You know, she found a fan in

878
00:43:40,400 --> 00:43:43,440
Speaker 3: this guy, Michael Walters, who's the Republican school Attendant school

879
00:43:43,560 --> 00:43:46,680
Speaker 3: superintendent of Oklahoma, and gave her a spot on the

880
00:43:46,760 --> 00:43:51,160
Speaker 3: Oklahoma Library Media Advisory Committee where she can sort of

881
00:43:51,239 --> 00:43:55,040
Speaker 3: continue her campaign to get wokeness out of schools. It

882
00:43:55,080 --> 00:43:58,360
Speaker 3: should be also noted Reijaik has only been to Oklahoma

883
00:43:58,560 --> 00:44:00,759
Speaker 3: once in her life. She doesn't live there. She like

884
00:44:00,960 --> 00:44:04,319
Speaker 3: lives between California and Florida. But her posts on her

885
00:44:04,440 --> 00:44:07,240
Speaker 3: libs of TikTok account did lead to a school receiving

886
00:44:07,320 --> 00:44:09,680
Speaker 3: a bomb threat in Tulsa, and so so.

887
00:44:09,800 --> 00:44:12,880
Speaker 1: She's basically a resident. Yeah you've almost gotten the school

888
00:44:12,960 --> 00:44:15,359
Speaker 1: blown up in a state you basically live. You can.

889
00:44:15,600 --> 00:44:18,560
Speaker 3: That's how you register to vote, Yeah, that's that's how

890
00:44:18,640 --> 00:44:21,719
Speaker 3: that works. And so now a lot of people are

891
00:44:21,840 --> 00:44:24,840
Speaker 3: noticing her role in Oklahoma, especially after this death of

892
00:44:25,000 --> 00:44:28,360
Speaker 3: a sixteen year old non binary student, Next Benedict. So this,

893
00:44:28,680 --> 00:44:31,600
Speaker 3: this student was violently attacked in a school bathroom in

894
00:44:31,640 --> 00:44:35,759
Speaker 3: suburban Tulsa and passed away the following day. Police say

895
00:44:36,200 --> 00:44:38,600
Speaker 3: they don't think that they died as a result of

896
00:44:38,680 --> 00:44:42,120
Speaker 3: physical trauma, but NeXT's friend, who was also attacked that day,

897
00:44:42,239 --> 00:44:45,480
Speaker 3: said that Next had indeed suffered head trauma during the incident.

898
00:44:45,520 --> 00:44:48,480
Speaker 3: So it's like a very murky but fucked up incident.

899
00:44:48,840 --> 00:44:51,720
Speaker 3: And like the timing of when the when NeXT's parents

900
00:44:51,760 --> 00:44:54,720
Speaker 3: are informed is just like it's just like a total

901
00:44:54,800 --> 00:44:57,200
Speaker 3: fuck up at every level, and a lot of Ray

902
00:44:57,320 --> 00:44:59,680
Speaker 3: Chick Rightschik's allies or second well that she has nothing

903
00:44:59,719 --> 00:45:02,040
Speaker 3: to do with this, like Odwick, why are you saying

904
00:45:02,080 --> 00:45:04,440
Speaker 3: that she has blood on her hand? These kinds of

905
00:45:04,560 --> 00:45:08,640
Speaker 3: extreme political views absolutely have real world effects on kids.

906
00:45:08,880 --> 00:45:12,200
Speaker 3: So like Next Benedict's mother, just as an example, said

907
00:45:12,200 --> 00:45:15,719
Speaker 3: that the bullying began at in high school right after

908
00:45:15,840 --> 00:45:19,040
Speaker 3: Oklahoma Governor Kevin Stitt signed a bill into law that

909
00:45:19,160 --> 00:45:24,000
Speaker 3: forbids trans and gender expansive kids from accessing restrooms consistent

910
00:45:24,080 --> 00:45:26,680
Speaker 3: with their gender identity. So it's not hard to imagine

911
00:45:26,719 --> 00:45:30,080
Speaker 3: how the hate that was inspired by Rachex's past posts

912
00:45:30,120 --> 00:45:33,400
Speaker 3: would translate to danger for these marginalized kids, and like,

913
00:45:33,560 --> 00:45:35,840
Speaker 3: especially when you look at the fact that two years

914
00:45:35,880 --> 00:45:38,160
Speaker 3: ago the a lot of people are polinting the fact

915
00:45:38,200 --> 00:45:41,320
Speaker 3: that the libs of TikTok account went after a teacher

916
00:45:41,480 --> 00:45:44,719
Speaker 3: in the exact same in the very school district that

917
00:45:44,880 --> 00:45:47,400
Speaker 3: Next Benedict was part of, for saying that, oh, this

918
00:45:47,520 --> 00:45:50,240
Speaker 3: person is like a groomer or whatever because they support

919
00:45:50,400 --> 00:45:54,200
Speaker 3: LGBTQ plus students. And according to Next Benedict's mother, this

920
00:45:54,360 --> 00:45:56,080
Speaker 3: was like a teacher that they looked up to a lot,

921
00:45:56,440 --> 00:46:00,840
Speaker 3: and then two years later this tragedy before their family.

922
00:46:01,239 --> 00:46:03,200
Speaker 3: So a lot of people like this is just like

923
00:46:03,360 --> 00:46:05,520
Speaker 3: we're like, what the fuck is going on? And you're

924
00:46:05,560 --> 00:46:09,200
Speaker 3: inviting this person to be part of the school administrative body,

925
00:46:09,280 --> 00:46:12,360
Speaker 3: or at least the overarching or overseeing body of the

926
00:46:12,400 --> 00:46:15,719
Speaker 3: school district. And again you look at like there's a

927
00:46:15,800 --> 00:46:18,960
Speaker 3: recent report that found that like only a quarter of

928
00:46:19,120 --> 00:46:21,840
Speaker 3: trans youth who are victimized at school were able to

929
00:46:22,000 --> 00:46:24,840
Speaker 3: report this to a teacher or staff member, and of

930
00:46:24,960 --> 00:46:28,719
Speaker 3: those who did, half reported that staff helped only a

931
00:46:28,800 --> 00:46:32,640
Speaker 3: little or not at all. So like the current backdrop

932
00:46:32,800 --> 00:46:36,520
Speaker 3: of these hostile bills that like target these students only

933
00:46:36,600 --> 00:46:39,320
Speaker 3: makes this kind of behavior acceptable to their peers, like

934
00:46:39,440 --> 00:46:43,319
Speaker 3: in this very like indirect or direct way. And yeah,

935
00:46:43,320 --> 00:46:45,160
Speaker 3: I mean, like we're also just looking at a whole

936
00:46:45,600 --> 00:46:48,279
Speaker 3: just a total failure on the part of schools and

937
00:46:48,360 --> 00:46:52,320
Speaker 3: administrators to actually protect kids. So yeah, I mean this

938
00:46:52,480 --> 00:46:55,120
Speaker 3: is like, I don't right now, there's a lot of people,

939
00:46:55,360 --> 00:46:58,360
Speaker 3: there's a lot more pressure for Racheck to be like

940
00:46:58,520 --> 00:47:01,239
Speaker 3: ousted from this school body, but as I mean, it

941
00:47:01,280 --> 00:47:03,480
Speaker 3: seems like there is enough support within the people that

942
00:47:03,600 --> 00:47:05,359
Speaker 3: make those decisions to keep her there.

943
00:47:05,280 --> 00:47:05,760
Speaker 1: At the moment.

944
00:47:06,480 --> 00:47:08,560
Speaker 3: But yeah, it's like, but it's just also alarming when

945
00:47:08,560 --> 00:47:11,400
Speaker 3: you have these kinds of people, like like you know,

946
00:47:11,600 --> 00:47:15,239
Speaker 3: running this libs of TikTok account and they truly have

947
00:47:15,600 --> 00:47:20,560
Speaker 3: no idea of like what they're like, what their actions

948
00:47:20,600 --> 00:47:22,880
Speaker 3: are doing, how they reverberate in space. They're just kind

949
00:47:22,920 --> 00:47:25,319
Speaker 3: of like, yeah, I get clicks like this and it's fun.

950
00:47:25,440 --> 00:47:27,800
Speaker 3: And I don't know, they call me stochastic terrorists. I

951
00:47:27,840 --> 00:47:29,200
Speaker 3: think it makes me feel important.

952
00:47:29,200 --> 00:47:32,719
Speaker 1: I think that's fucking tough as hell dog, right, But yeah,

953
00:47:32,840 --> 00:47:36,719
Speaker 1: and then meanwhile, just attacking the most vulnerable people in

954
00:47:36,840 --> 00:47:41,880
Speaker 1: a society, like children struggling with gender identity and just

955
00:47:42,640 --> 00:47:45,480
Speaker 1: and then attacking the people who might support them and

956
00:47:45,600 --> 00:47:48,719
Speaker 1: make them like slightly less vulnerable. It's just like going

957
00:47:48,800 --> 00:47:54,759
Speaker 1: down the list of like the easiest like people to

958
00:47:54,960 --> 00:47:58,640
Speaker 1: fucking bully and harm in a society and making that

959
00:47:58,880 --> 00:47:59,400
Speaker 1: your mission.

960
00:47:59,680 --> 00:48:01,279
Speaker 3: Yeah, like we've said this in the past, like a

961
00:48:01,360 --> 00:48:03,840
Speaker 3: lot of this is to do with, you know, trying

962
00:48:03,920 --> 00:48:07,520
Speaker 3: to make mainstream the shame of not being like a

963
00:48:07,640 --> 00:48:11,879
Speaker 3: cishead person because from their perspective, the world has become

964
00:48:11,960 --> 00:48:14,640
Speaker 3: too inclusive. So the way to push back against that

965
00:48:14,920 --> 00:48:18,239
Speaker 3: is to try and revive, like this the culture of

966
00:48:18,360 --> 00:48:21,080
Speaker 3: shaming people to do that. And yeah, so unfortunately it's

967
00:48:21,080 --> 00:48:23,839
Speaker 3: like going after people who support these very like vulnerable it's.

968
00:48:23,880 --> 00:48:25,800
Speaker 1: Still there, Like it's not it doesn't need to be

969
00:48:25,920 --> 00:48:29,080
Speaker 1: revived that much. Like there's still a fucking hell of

970
00:48:29,160 --> 00:48:32,560
Speaker 1: a shame culture for these people to deal with. They're

971
00:48:32,719 --> 00:48:35,800
Speaker 1: already you know, the most vulnerable to this sort of

972
00:48:35,920 --> 00:48:40,600
Speaker 1: shaming culture, and then they're just piling on it.

973
00:48:40,680 --> 00:48:44,320
Speaker 4: Didn't it didn't go anywhere, that's for sure. They're just

974
00:48:44,440 --> 00:48:46,560
Speaker 4: like adding fuel to the fire. They're dumping you know,

975
00:48:46,719 --> 00:48:52,080
Speaker 4: gasoline on it. Yeah, yeah, this this rache is is

976
00:48:52,280 --> 00:48:56,239
Speaker 4: uh watching that interview. I didn't watch the entire thing.

977
00:48:56,360 --> 00:48:58,560
Speaker 1: I could not take it. Yet, I could not. I

978
00:48:58,640 --> 00:48:58,960
Speaker 1: could not.

979
00:49:00,440 --> 00:49:03,960
Speaker 4: How just for lack of a better term, stupid she

980
00:49:04,080 --> 00:49:07,080
Speaker 4: comes across because she you know, not that I expected

981
00:49:07,080 --> 00:49:10,800
Speaker 4: her to have some like well thought out reasoning behind

982
00:49:11,120 --> 00:49:15,680
Speaker 4: her her hate campaign. She doesn't know what she believes exactly.

983
00:49:16,000 --> 00:49:19,360
Speaker 4: She's never given it thought because she's never been pressed

984
00:49:19,400 --> 00:49:22,680
Speaker 4: to explain it. She's only ever, you know, she only

985
00:49:22,880 --> 00:49:27,279
Speaker 4: sees the things that she wants to see online. She

986
00:49:27,719 --> 00:49:33,880
Speaker 4: is an online person, like definitively, that is exclusively and

987
00:49:34,000 --> 00:49:36,959
Speaker 4: the moment she has to defend herself in real life

988
00:49:37,080 --> 00:49:40,720
Speaker 4: to any kind of scrutiny. Not the thing about the interview,

989
00:49:41,040 --> 00:49:42,560
Speaker 4: There was not a single.

990
00:49:42,360 --> 00:49:46,160
Speaker 1: Tough question in it. No, nothing, nothing tough was asked

991
00:49:46,200 --> 00:49:51,879
Speaker 1: of her whatsoever, Like, Hey, you grew up in this community, right, yep,

992
00:49:52,200 --> 00:49:54,719
Speaker 1: so you've never like met you don't know that you've

993
00:49:54,800 --> 00:49:58,680
Speaker 1: met an LGBTQ plus person. Nope, just online? And like

994
00:49:58,880 --> 00:50:02,040
Speaker 1: have you met them online? Have you interacted with them online? Nope?

995
00:50:02,480 --> 00:50:04,759
Speaker 1: Just just seen videos about them.

996
00:50:05,000 --> 00:50:07,920
Speaker 6: But she had the nickelback portrait, things like this is

997
00:50:08,160 --> 00:50:09,080
Speaker 6: this is a bow job.

998
00:50:09,560 --> 00:50:13,040
Speaker 1: Yeah, it was.

999
00:50:13,280 --> 00:50:15,600
Speaker 4: It was absolutely incredible. And also for the listeners if

1000
00:50:15,640 --> 00:50:18,880
Speaker 4: you haven't seen it, her fit that she is wearing

1001
00:50:19,040 --> 00:50:21,040
Speaker 4: during this interview. I put a picture of it in

1002
00:50:21,120 --> 00:50:23,160
Speaker 4: the chat. I don't I don't know if y'all can

1003
00:50:23,200 --> 00:50:26,879
Speaker 4: see it. She's wearing like the most Christian homeschool mom

1004
00:50:27,200 --> 00:50:32,080
Speaker 4: denim jean skirt and a picture of Taylor Lorenz on

1005
00:50:32,320 --> 00:50:33,840
Speaker 4: her on her T shirt.

1006
00:50:33,760 --> 00:50:35,560
Speaker 1: Like that is the yeah.

1007
00:50:35,800 --> 00:50:39,359
Speaker 4: Like, and then she sounds as like if you wear

1008
00:50:39,719 --> 00:50:43,680
Speaker 4: that outfit to an interview and then sound as stupid

1009
00:50:43,840 --> 00:50:47,960
Speaker 4: as you do as she does, yeah, you you've You've

1010
00:50:48,120 --> 00:50:51,359
Speaker 4: lost on all fronts, on all there's no coming back

1011
00:50:51,440 --> 00:50:53,880
Speaker 4: from that. Now, Having said that, I know her fans

1012
00:50:53,880 --> 00:50:56,279
Speaker 4: are gonna still support her no matter what she says,

1013
00:50:56,320 --> 00:50:59,120
Speaker 4: no matter how stupid she is from that outfit.

1014
00:50:59,160 --> 00:51:01,200
Speaker 3: They're like, oh, two shirt, it's perfect.

1015
00:51:01,560 --> 00:51:05,000
Speaker 4: Yeah, yeah, you have to you have to come stronger.

1016
00:51:05,160 --> 00:51:08,919
Speaker 4: I still I still feel like, like ideally in my mind,

1017
00:51:09,360 --> 00:51:12,400
Speaker 4: her Ardent supporters were just like typing that out. But

1018
00:51:12,560 --> 00:51:15,480
Speaker 4: then like you know, with the with the crying behind

1019
00:51:15,560 --> 00:51:19,160
Speaker 4: the mask, yeah, faces, like there is something undeniable about

1020
00:51:19,200 --> 00:51:21,759
Speaker 4: that interview of like, oh, this is this is the

1021
00:51:21,880 --> 00:51:23,960
Speaker 4: leader of our little stochastic terrorism.

1022
00:51:24,760 --> 00:51:26,560
Speaker 3: She sounds this stupid.

1023
00:51:27,000 --> 00:51:29,480
Speaker 6: It is weird when you see people that can't support

1024
00:51:29,520 --> 00:51:32,040
Speaker 6: their own argument, because it feels like if I don't

1025
00:51:32,120 --> 00:51:35,000
Speaker 6: agree with someone, and it's like the horrible thing. I'm like, well,

1026
00:51:35,040 --> 00:51:37,120
Speaker 6: if you have a thought out, so you know, like

1027
00:51:37,200 --> 00:51:39,799
Speaker 6: some libertarians, you're like, oh, I guess you believe if

1028
00:51:39,840 --> 00:51:43,120
Speaker 6: you believe what you think. But it's like she truly doesn't.

1029
00:51:43,000 --> 00:51:46,279
Speaker 1: Twenty minutes of conversation to get to the bullshit with them,

1030
00:51:46,680 --> 00:51:50,320
Speaker 1: like you're like, oh I so you're like picking up momentum,

1031
00:51:50,480 --> 00:51:54,160
Speaker 1: I see how you made this mistake and then it's

1032
00:51:54,239 --> 00:51:55,960
Speaker 1: like but yeah, But.

1033
00:51:56,080 --> 00:51:58,320
Speaker 6: With her, it was just straight up it's hard to

1034
00:51:58,360 --> 00:52:00,840
Speaker 6: look at that fit because I was raised Kinecostal and

1035
00:52:01,040 --> 00:52:05,440
Speaker 6: that's like how all all of us dressed, you know,

1036
00:52:05,680 --> 00:52:08,359
Speaker 6: And but it's weird. It's like, but it's like if

1037
00:52:08,440 --> 00:52:12,360
Speaker 6: my church was in Brooklyn kind of it's got this

1038
00:52:12,520 --> 00:52:16,200
Speaker 6: kind of thing where it's like like, you shouldn't be

1039
00:52:16,520 --> 00:52:18,960
Speaker 6: if you have those views on top of anything, you

1040
00:52:19,000 --> 00:52:23,240
Speaker 6: shouldn't be allowed to dress halfway hip like that should

1041
00:52:23,280 --> 00:52:23,680
Speaker 6: you should?

1042
00:52:24,120 --> 00:52:24,520
Speaker 1: You should?

1043
00:52:24,880 --> 00:52:26,680
Speaker 6: I don't know what you should be dressed in, but

1044
00:52:27,239 --> 00:52:30,279
Speaker 6: it shouldn't be something that's sort of it's like it's

1045
00:52:30,320 --> 00:52:33,480
Speaker 6: like the hip version of like the movie Mimic, you know,

1046
00:52:33,760 --> 00:52:36,160
Speaker 6: like I think that's a human being over there wearing

1047
00:52:36,280 --> 00:52:39,520
Speaker 6: that that fit, but it's really just like a cockroach

1048
00:52:40,040 --> 00:52:40,839
Speaker 6: hands on its face.

1049
00:52:40,920 --> 00:52:44,200
Speaker 3: Yeah. I think she did a good job of basically

1050
00:52:44,480 --> 00:52:48,600
Speaker 3: distilling the like this this mentality of hatred down to

1051
00:52:48,719 --> 00:52:51,360
Speaker 3: its essence, which in the end is stupid, you know

1052
00:52:51,400 --> 00:52:53,920
Speaker 3: what I mean, you can't actually say what are the

1053
00:52:54,040 --> 00:52:59,759
Speaker 3: damages that like a more articulate sort of homophobic, transphobic

1054
00:52:59,760 --> 00:53:01,400
Speaker 3: person might be able to be like, well, then this

1055
00:53:01,520 --> 00:53:03,279
Speaker 3: could happen to our kids, But really, at the end

1056
00:53:03,280 --> 00:53:07,479
Speaker 3: of the day is not There's not really none, none really,

1057
00:53:07,840 --> 00:53:08,760
Speaker 3: and it gives.

1058
00:53:08,840 --> 00:53:11,400
Speaker 4: It gives the game away of like their their ultimate

1059
00:53:11,480 --> 00:53:15,320
Speaker 4: goal is to get rid of trans people altogether. You know,

1060
00:53:15,480 --> 00:53:19,120
Speaker 4: it's like, well, what harm are they causing? Well, this, ultimately,

1061
00:53:19,320 --> 00:53:21,720
Speaker 4: I'm gonna sit here and sounds stupid, but that's because

1062
00:53:21,719 --> 00:53:24,040
Speaker 4: I don't want to say out loud they're not causing

1063
00:53:24,120 --> 00:53:25,799
Speaker 4: any harm. We just want to get rid of them,

1064
00:53:25,840 --> 00:53:29,920
Speaker 4: because exactly that's that's ultimately, if she had just skipped

1065
00:53:29,960 --> 00:53:31,840
Speaker 4: all of the hemming and haueing and just said that,

1066
00:53:32,640 --> 00:53:36,120
Speaker 4: like it's almost more of a noble response than than like,

1067
00:53:36,320 --> 00:53:38,760
Speaker 4: you know, sounding like a complete idiot.

1068
00:53:39,040 --> 00:53:41,759
Speaker 6: You think that owning the libs thing would have a

1069
00:53:41,840 --> 00:53:45,160
Speaker 6: platform by now it doesn't, but it's like it feels

1070
00:53:45,239 --> 00:53:47,960
Speaker 6: like it eventually you would put fill in the bullet points.

1071
00:53:48,480 --> 00:53:52,440
Speaker 6: But the only purpose is to essentially like reak havoc.

1072
00:53:52,760 --> 00:53:55,600
Speaker 6: But they don't have any actual platform that they stand on.

1073
00:53:56,000 --> 00:53:59,279
Speaker 4: It's liberal tears. That's their platform. That's that's the reactionaries

1074
00:53:59,360 --> 00:54:01,640
Speaker 4: thing is just like I don't know why I'm so angry,

1075
00:54:01,719 --> 00:54:03,640
Speaker 4: but I'm angry. And as long as it upsets my

1076
00:54:04,200 --> 00:54:07,279
Speaker 4: you know, left leaning ideological opponents, that's all I give

1077
00:54:07,280 --> 00:54:07,840
Speaker 4: a shit about.

1078
00:54:08,200 --> 00:54:11,080
Speaker 1: Yeah, I mean, one thing we can learn right wing

1079
00:54:11,200 --> 00:54:15,040
Speaker 1: hate mongering fascists are getting better at wearing ironic T

1080
00:54:15,200 --> 00:54:17,640
Speaker 1: shirts and the left is terrified.

1081
00:54:18,000 --> 00:54:18,680
Speaker 4: I am scared.

1082
00:54:18,800 --> 00:54:23,279
Speaker 1: Yes, Yeah, all right, let's take a quick break and

1083
00:54:23,680 --> 00:54:26,840
Speaker 1: we're going to come back and talk about the coolest

1084
00:54:27,000 --> 00:54:31,880
Speaker 1: museum coming to an empty, closed down Kroger near you.

1085
00:54:32,360 --> 00:54:44,239
Speaker 1: We'll be right back and we're back. So yeah, just

1086
00:54:44,440 --> 00:54:48,239
Speaker 1: in terms of juxtaposing like what these companies are saying

1087
00:54:48,320 --> 00:54:53,560
Speaker 1: and the mainstream media is buying versus what is actually happening.

1088
00:54:54,080 --> 00:54:57,440
Speaker 1: So I was reminded of Sam Altman telling The New

1089
00:54:57,560 --> 00:55:01,520
Speaker 1: Yorker that he keeps like a a bag with cyanide

1090
00:55:01,600 --> 00:55:05,600
Speaker 1: capsules ready to go in case of the AI apocalypse.

1091
00:55:06,280 --> 00:55:10,480
Speaker 1: So you know, he is an expert who gets billions

1092
00:55:10,520 --> 00:55:14,239
Speaker 1: of dollars richer if people think his technology is so

1093
00:55:14,520 --> 00:55:18,040
Speaker 1: powerful that he's like freaked out by it. So, but

1094
00:55:18,200 --> 00:55:22,640
Speaker 1: that that's something that I don't know I've seen reprinted

1095
00:55:22,760 --> 00:55:25,960
Speaker 1: in like long articles about the danger of AI. And

1096
00:55:26,080 --> 00:55:31,040
Speaker 1: then there's this other like more real world trend where

1097
00:55:31,960 --> 00:55:35,560
Speaker 1: like you you talk about a Google engineer who admitted

1098
00:55:36,239 --> 00:55:39,839
Speaker 1: they're not going to use any large language model as

1099
00:55:39,920 --> 00:55:42,560
Speaker 1: part of their families healthcare journey.

1100
00:55:43,040 --> 00:55:45,080
Speaker 8: Oh that was a Google That was a Google senior

1101
00:55:45,440 --> 00:55:52,719
Speaker 8: vice president, not not a senior of Google VP. Greg

1102
00:55:52,800 --> 00:55:55,719
Speaker 8: Corrado is one of the heads of Google Health.

1103
00:55:56,480 --> 00:56:01,279
Speaker 1: Yea, there is yeah, And there's also this story from

1104
00:56:01,719 --> 00:56:04,239
Speaker 1: your show has a fresh health segment at the end

1105
00:56:04,520 --> 00:56:09,480
Speaker 1: where you talk about just saying examples headlines that are

1106
00:56:09,719 --> 00:56:13,560
Speaker 1: just fresh. Yeah, it's just fresh out. It's new versions

1107
00:56:13,560 --> 00:56:17,760
Speaker 1: of Yeah. The one about duo lingo from a recent

1108
00:56:17,840 --> 00:56:23,520
Speaker 1: episode where they're getting rid of human translators, firing them,

1109
00:56:23,600 --> 00:56:28,200
Speaker 1: cutting the workforce, replacing them with translation AI, even though

1110
00:56:28,239 --> 00:56:32,960
Speaker 1: the technology isn't there yet. But the point that you

1111
00:56:33,160 --> 00:56:35,800
Speaker 1: were making on the show is that they're willing to

1112
00:56:35,880 --> 00:56:39,160
Speaker 1: go forward with that because the user base won't notice

1113
00:56:39,200 --> 00:56:43,359
Speaker 1: the difference until they're in Spain and need to get

1114
00:56:43,560 --> 00:56:47,160
Speaker 1: to a hospital and asking for the biblioteca, you know,

1115
00:56:47,640 --> 00:56:51,600
Speaker 1: like it's they are specifically an audience who is not

1116
00:56:51,800 --> 00:56:55,760
Speaker 1: going to know how bad the product that they're getting

1117
00:56:56,640 --> 00:56:59,280
Speaker 1: is because they're just like not a in a position

1118
00:56:59,520 --> 00:57:02,600
Speaker 1: to know that by the nature of the product. And

1119
00:57:02,719 --> 00:57:08,080
Speaker 1: so just this distinction between being hyped to the mainstream

1120
00:57:08,560 --> 00:57:11,320
Speaker 1: media and like these long read like New York or

1121
00:57:11,400 --> 00:57:15,560
Speaker 1: Atlantic articles as this is a future that we should

1122
00:57:15,560 --> 00:57:18,200
Speaker 1: be scared of because like it's going to become self

1123
00:57:18,240 --> 00:57:21,320
Speaker 1: aware and Sam Altman is freaked out, and then what

1124
00:57:21,480 --> 00:57:27,080
Speaker 1: it's actually doing, which is just making everything shittier around us,

1125
00:57:27,560 --> 00:57:31,840
Speaker 1: is I think a big kind of chunk that I

1126
00:57:31,920 --> 00:57:34,600
Speaker 1: took away from your show that was just like, oh, yeah,

1127
00:57:34,680 --> 00:57:37,520
Speaker 1: that makes way more sense. That feels much more likely

1128
00:57:37,720 --> 00:57:40,640
Speaker 1: to be how this thing progresses.

1129
00:57:41,480 --> 00:57:44,200
Speaker 11: Yeah, So the AI doumerism, which is when Altman says

1130
00:57:44,400 --> 00:57:46,400
Speaker 11: I've got my bug out bag and my sinanite capsules

1131
00:57:46,440 --> 00:57:49,480
Speaker 11: in case the robot apocalypse comes. Or when Jeff Hinton,

1132
00:57:50,120 --> 00:57:54,320
Speaker 11: who's credited, has a Turing Award for his work on

1133
00:57:54,960 --> 00:57:58,400
Speaker 11: the specific kind of computer program that's us see Gabbo

1134
00:57:58,480 --> 00:58:02,040
Speaker 11: statistics that's behind these Lars line models. He's now concerned

1135
00:58:02,120 --> 00:58:04,200
Speaker 11: that it's on track to becoming smarter than us, and

1136
00:58:04,280 --> 00:58:07,120
Speaker 11: it's gonna like these piles of linear algebra are not

1137
00:58:07,280 --> 00:58:10,880
Speaker 11: going to combust into consciousness. And anytime someone is like,

1138
00:58:11,200 --> 00:58:14,160
Speaker 11: you know, pointing to that boogeyman, what they're doing is

1139
00:58:14,200 --> 00:58:16,800
Speaker 11: they're basically hiding the actions of the people in the

1140
00:58:16,840 --> 00:58:19,760
Speaker 11: picture who are using it to do things like make

1141
00:58:19,800 --> 00:58:23,480
Speaker 11: a shitty language learning product because it's cheaper to do

1142
00:58:23,560 --> 00:58:25,000
Speaker 11: it that way than to pay the people with the

1143
00:58:25,040 --> 00:58:26,720
Speaker 11: actual expertise to do the translations.

1144
00:58:27,080 --> 00:58:30,240
Speaker 8: Right, yeah, Yeah, And it's just leading just to I mean,

1145
00:58:30,520 --> 00:58:32,360
Speaker 8: I like this kind of thought on this, I mean

1146
00:58:32,440 --> 00:58:35,240
Speaker 8: this kind of process. And Corey Doctor has got this

1147
00:58:35,880 --> 00:58:40,640
Speaker 8: kind of sister concept of AI hype, which is in shitification,

1148
00:58:41,640 --> 00:58:44,480
Speaker 8: which I think that the luistic society America, it was

1149
00:58:44,520 --> 00:58:45,880
Speaker 8: their their overall.

1150
00:58:46,440 --> 00:58:49,920
Speaker 11: Yeah, American dialect Society Dialects. Yeah, picked it as the

1151
00:58:49,960 --> 00:58:52,280
Speaker 11: overall word of theyear for twenty twenty three. But in

1152
00:58:52,320 --> 00:58:55,080
Speaker 11: shientification is something very specific, right, It's not just like

1153
00:58:55,440 --> 00:58:59,000
Speaker 11: we've now we're now swimming in ai extruded junk, right

1154
00:58:59,280 --> 00:59:04,320
Speaker 11: ais always it's something more. The companies create a platform

1155
00:59:04,880 --> 00:59:07,040
Speaker 11: that you sort of get lured in because initially it's

1156
00:59:07,080 --> 00:59:09,840
Speaker 11: really useful. So this is like you think about how

1157
00:59:10,000 --> 00:59:12,960
Speaker 11: Amazon was great for finding products. Sucked for your local

1158
00:59:12,960 --> 00:59:15,000
Speaker 11: brick and mortar businesses, but as a consumer it was

1159
00:59:15,000 --> 00:59:18,360
Speaker 11: super convenient because you could find things. But then the

1160
00:59:18,520 --> 00:59:21,400
Speaker 11: companies basically turn around and they extract all of the

1161
00:59:21,560 --> 00:59:24,280
Speaker 11: value that the customer is getting out of it, and

1162
00:59:24,400 --> 00:59:27,600
Speaker 11: then they turn around to the other parties there the

1163
00:59:27,600 --> 00:59:29,680
Speaker 11: people trying to sell things, and they extract value out

1164
00:59:29,720 --> 00:59:31,320
Speaker 11: of them. So you start off with this thing that's

1165
00:59:31,360 --> 00:59:34,720
Speaker 11: initially quite useful and usable, and then it getsified in

1166
00:59:34,760 --> 00:59:37,080
Speaker 11: the name of making profits for the platform. And that's

1167
00:59:37,160 --> 00:59:39,040
Speaker 11: like the specific thing about in certification.

1168
00:59:39,920 --> 00:59:43,160
Speaker 8: And I would say there's some kinds of processes you

1169
00:59:43,200 --> 00:59:45,360
Speaker 8: can think about in sentification. The kind of idea that

1170
00:59:45,440 --> 00:59:47,919
Speaker 8: you have to rush to a certain kind of market

1171
00:59:48,040 --> 00:59:50,400
Speaker 8: that you have a monopoly on this kind of thing,

1172
00:59:51,120 --> 00:59:53,160
Speaker 8: and I guess yeah. I mean, the thing about large

1173
00:59:53,240 --> 00:59:56,000
Speaker 8: language models I think that we get on and talk

1174
00:59:56,040 --> 00:59:59,040
Speaker 8: a lot about is that large language models are kind

1175
00:59:59,080 --> 01:00:03,160
Speaker 8: of born shitty with content. So it's not like the

1176
01:00:03,240 --> 01:00:06,680
Speaker 8: platform started and that platform monopoly led to this kind

1177
01:00:06,680 --> 01:00:10,520
Speaker 8: of process of incertification. It's more like you decided to

1178
01:00:10,640 --> 01:00:14,840
Speaker 8: make a tool that is a really good word prediction machine,

1179
01:00:15,280 --> 01:00:17,960
Speaker 8: and you use it for a substitute for places in

1180
01:00:18,000 --> 01:00:20,400
Speaker 8: which people are meant to be speaking kind of with

1181
01:00:20,480 --> 01:00:22,720
Speaker 8: their own tone, with their own voice, with their own

1182
01:00:22,840 --> 01:00:26,440
Speaker 8: forms and their own putting, so they're on expertise and

1183
01:00:26,520 --> 01:00:29,080
Speaker 8: then and so it's kind of yeah, it's it's bored

1184
01:00:29,120 --> 01:00:32,520
Speaker 8: and shitty, and so you know this this kind of

1185
01:00:32,560 --> 01:00:34,280
Speaker 8: thing I think is really helpful. It makes me think

1186
01:00:34,320 --> 01:00:37,240
Speaker 8: of kind of a thing I think I saw a

1187
01:00:37,280 --> 01:00:39,440
Speaker 8: few times on Twitter where people are like, well, if

1188
01:00:39,480 --> 01:00:41,520
Speaker 8: you're an expert in any of these fields and you

1189
01:00:41,600 --> 01:00:45,080
Speaker 8: read content by large language models, you actually know anything

1190
01:00:45,120 --> 01:00:48,920
Speaker 8: about this, You're going to know that it's absolute bullshit, right,

1191
01:00:49,520 --> 01:00:52,880
Speaker 8: you know, if you ask it to write you a short,

1192
01:00:53,160 --> 01:00:58,360
Speaker 8: you know, treatise on I don't know, sociological methodology, something

1193
01:00:58,680 --> 01:01:01,400
Speaker 8: specific that I know a little bit about. It's going

1194
01:01:01,480 --> 01:01:05,200
Speaker 8: to be absolute bullshit, right, but good enough to computer

1195
01:01:05,280 --> 01:01:09,160
Speaker 8: engineers and you know, higher ups at these companies.

1196
01:01:09,680 --> 01:01:09,919
Speaker 1: Yeah.

1197
01:01:10,160 --> 01:01:11,040
Speaker 14: So here's an example.

1198
01:01:11,320 --> 01:01:13,720
Speaker 11: The other day, I came across an article that supposedly

1199
01:01:13,800 --> 01:01:16,640
Speaker 11: quoted me out of this publication from India called Bihar Praba,

1200
01:01:17,120 --> 01:01:19,200
Speaker 11: and I'm like, I never said those words. I could

1201
01:01:19,200 --> 01:01:21,360
Speaker 11: see how someone might think I would, And then I

1202
01:01:21,400 --> 01:01:23,880
Speaker 11: searched my emails, like, no, I never corresponded with any

1203
01:01:23,960 --> 01:01:25,880
Speaker 11: journalist at this outfit. So I wrote them, I said,

1204
01:01:26,520 --> 01:01:30,360
Speaker 11: fabricated quote, Please take it down in printer retraction, which

1205
01:01:30,440 --> 01:01:32,120
Speaker 11: they did, and they wrote back and said, oh, yeah,

1206
01:01:32,160 --> 01:01:34,680
Speaker 11: that actually we prompted some large language models to create

1207
01:01:34,720 --> 01:01:36,240
Speaker 11: that before us and posted.

1208
01:01:36,200 --> 01:01:39,440
Speaker 1: Yes, because it seems like you might have, and the

1209
01:01:39,840 --> 01:01:43,280
Speaker 1: large language models they don't like that. That's isn't that?

1210
01:01:43,320 --> 01:01:45,600
Speaker 1: What hallucinations are a lot of the time is just

1211
01:01:45,840 --> 01:01:48,960
Speaker 1: the large language model making up stuff it seems like

1212
01:01:50,080 --> 01:01:53,240
Speaker 1: is what the person wants them to say exactly.

1213
01:01:53,320 --> 01:01:54,080
Speaker 14: But here's the whole thing.

1214
01:01:54,560 --> 01:01:57,120
Speaker 11: Every single thing output from a large language model has

1215
01:01:57,160 --> 01:01:59,400
Speaker 11: that property issues. Sometimes it seems to make sense.

1216
01:01:59,480 --> 01:02:01,720
Speaker 1: The whole thing is trying to do a trick of

1217
01:02:01,960 --> 01:02:04,160
Speaker 1: like predict, I figured out what you wanted me to say,

1218
01:02:04,400 --> 01:02:07,880
Speaker 1: ha ha, But it's like, well, but what I wanted

1219
01:02:08,000 --> 01:02:10,600
Speaker 1: you to say is not always That's not how I

1220
01:02:10,680 --> 01:02:15,560
Speaker 1: want my questions answered that that's actually a wildly flawed

1221
01:02:15,640 --> 01:02:20,320
Speaker 1: way of coming up like answering people. It's definitely something

1222
01:02:20,400 --> 01:02:23,200
Speaker 1: that I do in my day to day life because

1223
01:02:23,320 --> 01:02:26,320
Speaker 1: I'm scared of conflict and a people pleaser. But that's

1224
01:02:26,440 --> 01:02:30,120
Speaker 1: not I'm not a good scientific instrument for that reason,

1225
01:02:30,440 --> 01:02:31,280
Speaker 1: you know, Like, but.

1226
01:02:31,600 --> 01:02:35,760
Speaker 8: You just got you just an avoidant attachment style, which

1227
01:02:35,960 --> 01:02:37,680
Speaker 8: as a as another avoidance.

1228
01:02:39,480 --> 01:02:44,400
Speaker 1: They've just made me a scientific model that's terrible, Like,

1229
01:02:44,520 --> 01:02:45,680
Speaker 1: you can't believe this.

1230
01:02:47,960 --> 01:02:49,520
Speaker 3: I was like, I don't know if you saw that

1231
01:02:50,040 --> 01:02:52,600
Speaker 3: headline where Tyler Perry was like I was going to

1232
01:02:52,760 --> 01:02:56,320
Speaker 3: open an eight hundred million dollar studio, but I stopped

1233
01:02:56,440 --> 01:03:00,360
Speaker 3: the second I saw what Sora, this video generator AI

1234
01:03:00,520 --> 01:03:03,760
Speaker 3: could do, and I realized we're in trouble. He said, quote,

1235
01:03:03,800 --> 01:03:07,240
Speaker 3: I had no idea until I saw recently the demonstrations

1236
01:03:07,280 --> 01:03:09,880
Speaker 3: of what it's able to do. It's shocking to me.

1237
01:03:10,400 --> 01:03:13,000
Speaker 3: And he's basically saying he's like, you could make up

1238
01:03:13,240 --> 01:03:16,120
Speaker 3: a pilot and save millions of dollars. This is going

1239
01:03:16,200 --> 01:03:18,880
Speaker 3: to have all kinds of ramifications. That feels like quite,

1240
01:03:19,000 --> 01:03:22,360
Speaker 3: that feels like half like just ignorance because this person's like,

1241
01:03:22,440 --> 01:03:25,240
Speaker 3: oh my god, look like total wow factor but also

1242
01:03:25,360 --> 01:03:28,280
Speaker 3: maybe hype. But I'm also curious from your perspective, what

1243
01:03:28,920 --> 01:03:32,520
Speaker 3: what are the actual dangers that we're facing that you know,

1244
01:03:32,560 --> 01:03:34,840
Speaker 3: because right now I think everything is just all about

1245
01:03:35,200 --> 01:03:36,800
Speaker 3: these are the jobs it's going to take. I think

1246
01:03:36,880 --> 01:03:40,040
Speaker 3: in the l ll M episode where the LM predicted

1247
01:03:40,520 --> 01:03:43,480
Speaker 3: what that what the potential of llms were and the

1248
01:03:43,640 --> 01:03:45,000
Speaker 3: jobs that it could take in.

1249
01:03:45,080 --> 01:03:46,960
Speaker 12: A very o GPT's paper was.

1250
01:03:47,080 --> 01:03:50,400
Speaker 3: So yeah, where it's like, huh, you know, like just

1251
01:03:50,480 --> 01:03:53,360
Speaker 3: sort of the unethical nature of how even these companies

1252
01:03:53,440 --> 01:03:56,760
Speaker 3: are doing research and creating data to support this.

1253
01:03:57,200 --> 01:03:59,600
Speaker 1: Can we just stoples, Can you just stop and explain

1254
01:03:59,680 --> 01:04:02,800
Speaker 1: exactly with the methodology of that paper one nose? I will.

1255
01:04:02,800 --> 01:04:04,480
Speaker 3: I will allow the experts to do it, because it's

1256
01:04:04,560 --> 01:04:08,720
Speaker 3: it's it's absolutely bonkers to hear because any person who's

1257
01:04:08,760 --> 01:04:11,120
Speaker 3: like been at any like tried to look at a

1258
01:04:11,160 --> 01:04:13,760
Speaker 3: study or something, and you look at methodology like om.

1259
01:04:14,840 --> 01:04:17,240
Speaker 11: So, methodology is a very kind term for what was

1260
01:04:20,000 --> 01:04:23,200
Speaker 11: truly truly, So Alex, did you want to summarize real quick?

1261
01:04:23,240 --> 01:04:24,600
Speaker 11: What was in There was two different things we were

1262
01:04:24,600 --> 01:04:26,160
Speaker 11: looking at. It was something that came out of Open

1263
01:04:26,200 --> 01:04:28,760
Speaker 11: AI and something from Goldman Sacks. And the Golden Sacks

1264
01:04:28,800 --> 01:04:32,560
Speaker 11: one was silly, but not as silly as the Open

1265
01:04:32,600 --> 01:04:33,040
Speaker 11: Eye one.

1266
01:04:33,560 --> 01:04:36,400
Speaker 8: Yeah, I mean getting in the detail. And I went

1267
01:04:36,520 --> 01:04:39,080
Speaker 8: through this and I puzzle this paper, I like poked

1268
01:04:39,120 --> 01:04:41,760
Speaker 8: my friend. As a labor sociologist, I'm like, what the

1269
01:04:41,840 --> 01:04:45,320
Speaker 8: hell is going on here? And you know, okay, so

1270
01:04:45,440 --> 01:04:47,440
Speaker 8: there's this kind of metric that you can use to

1271
01:04:47,520 --> 01:04:50,560
Speaker 8: judge how hard a task is that the government collects,

1272
01:04:51,400 --> 01:04:53,640
Speaker 8: and there's this kind of job classification. They rd him

1273
01:04:53,640 --> 01:04:57,280
Speaker 8: from you know, one to seven effectively. And so what

1274
01:04:57,440 --> 01:05:00,600
Speaker 8: Goldman Sachs said was well, basically, any from one to

1275
01:05:00,680 --> 01:05:03,240
Speaker 8: four probably a machine can do. And you're like, okay,

1276
01:05:03,320 --> 01:05:07,560
Speaker 8: that's kind of silly. That's huge assumptions there. I understand though,

1277
01:05:07,600 --> 01:05:09,800
Speaker 8: as a researcher you have to make some assumptions when

1278
01:05:09,840 --> 01:05:12,800
Speaker 8: you don't have great data. But what Opening Eye did

1279
01:05:13,000 --> 01:05:19,120
Speaker 8: is that they asked two entities what like how well

1280
01:05:19,680 --> 01:05:22,919
Speaker 8: like what could be automated? They asked one other Open

1281
01:05:23,040 --> 01:05:25,960
Speaker 8: AI employees, Hey, what jobs do you think could be automated?

1282
01:05:26,320 --> 01:05:30,120
Speaker 8: Already hilarious because you know they're they're you know, pretty

1283
01:05:30,600 --> 01:05:32,960
Speaker 8: doing those jobs. Yeah, they're not doing those oubs. They're

1284
01:05:33,000 --> 01:05:35,880
Speaker 8: pretty primed to think that their technology is great. And

1285
01:05:35,960 --> 01:05:39,720
Speaker 8: then they asked GPT for itself. They prompted and say, hey,

1286
01:05:39,760 --> 01:05:41,360
Speaker 8: can we automate these jobs?

1287
01:05:42,360 --> 01:05:44,680
Speaker 1: So you know, and so you'll never guess what the

1288
01:05:44,720 --> 01:05:46,640
Speaker 1: answer was. You'll never guess.

1289
01:05:47,560 --> 01:05:48,320
Speaker 8: You'll never guess.

1290
01:05:48,640 --> 01:05:51,640
Speaker 11: They took the output of that as if it were data. Yeah,

1291
01:05:51,680 --> 01:05:53,960
Speaker 11: and then like you know, these ridiculous graphs and blah

1292
01:05:53,960 --> 01:05:54,320
Speaker 11: blah blah.

1293
01:05:54,360 --> 01:05:57,480
Speaker 3: It's just like the whole thing is is is fantasy, right,

1294
01:05:58,240 --> 01:06:01,640
Speaker 3: so is the danger You're they're just sort of this reliance,

1295
01:06:01,920 --> 01:06:04,400
Speaker 3: Like I guess, so if we're classifying the sort of

1296
01:06:04,840 --> 01:06:08,240
Speaker 3: threats to our sense of like how information is distributed

1297
01:06:08,360 --> 01:06:10,720
Speaker 3: or what is real or what is hype or whatever,

1298
01:06:11,200 --> 01:06:14,080
Speaker 3: or if they're actually taking jobs, what is something that

1299
01:06:14,120 --> 01:06:16,360
Speaker 3: I think people that people actually need to be aware

1300
01:06:16,360 --> 01:06:19,480
Speaker 3: of or to sort of prepare themselves for how this

1301
01:06:19,680 --> 01:06:21,840
Speaker 3: is going to disrupt things in a way that you know,

1302
01:06:21,880 --> 01:06:24,280
Speaker 3: isn't necessarily the end of the world, but definitely changing

1303
01:06:24,360 --> 01:06:25,480
Speaker 3: things for the worst.

1304
01:06:25,880 --> 01:06:27,760
Speaker 11: There's a whole bunch of things, and the one that

1305
01:06:27,840 --> 01:06:30,160
Speaker 11: I'm sort of most going on about these days is

1306
01:06:30,160 --> 01:06:32,600
Speaker 11: the threats to the information ecosystem, so I want to

1307
01:06:32,640 --> 01:06:35,880
Speaker 11: talk about that. But there's also things like automated decision

1308
01:06:35,960 --> 01:06:38,680
Speaker 11: systems being used by governments to decide who gets social

1309
01:06:38,760 --> 01:06:42,520
Speaker 11: benefits and who gets them yanked right, things like doctor

1310
01:06:42,600 --> 01:06:44,640
Speaker 11: joybul and WHINNI worked with a group of people in

1311
01:06:45,080 --> 01:06:46,560
Speaker 11: I want to say, in New York City who were

1312
01:06:46,600 --> 01:06:51,600
Speaker 11: pushing back against having a face recognition system installed as

1313
01:06:51,680 --> 01:06:54,160
Speaker 11: their like entry system. So they were going to be

1314
01:06:54,240 --> 01:06:58,560
Speaker 11: continuously surveilled because the company who owned the house that

1315
01:06:58,640 --> 01:07:00,320
Speaker 11: they lived in or maybe it was a sure if.

1316
01:07:00,240 --> 01:07:03,240
Speaker 8: It was apartment complex, yeah.

1317
01:07:03,680 --> 01:07:08,000
Speaker 11: Wanted to basically use biometrics as a way to have

1318
01:07:08,160 --> 01:07:10,880
Speaker 11: them gain entry to their own homes where they lived.

1319
01:07:11,160 --> 01:07:14,240
Speaker 11: So there's there's dangerous of lots of lots of different kinds.

1320
01:07:15,000 --> 01:07:17,080
Speaker 11: The one that's maybe closest to we've been talking about, though,

1321
01:07:17,120 --> 01:07:19,880
Speaker 11: is these dangerous to the information ecosystem where you now

1322
01:07:20,040 --> 01:07:22,800
Speaker 11: have the synthetic media, like that news article I was

1323
01:07:22,800 --> 01:07:24,919
Speaker 11: talking about before being posted as if it were real,

1324
01:07:25,720 --> 01:07:28,320
Speaker 11: without any watermarks, without anything that either a person can

1325
01:07:28,360 --> 01:07:30,120
Speaker 11: look at and say, I know that's fake, Like I

1326
01:07:30,200 --> 01:07:31,800
Speaker 11: knew because it was my name and I knew I

1327
01:07:31,840 --> 01:07:34,720
Speaker 11: didn't say that thing right, but somebody else wouldn't have.

1328
01:07:35,120 --> 01:07:37,840
Speaker 11: And there's also not a machine readable watermarking in there,

1329
01:07:37,880 --> 01:07:39,680
Speaker 11: so you can just filter it out. And this stuff

1330
01:07:39,760 --> 01:07:42,280
Speaker 11: goes and goes. So there was, oh a few months ago,

1331
01:07:42,400 --> 01:07:47,400
Speaker 11: someone posted a fake mushroom foraging guide that they had

1332
01:07:47,440 --> 01:07:49,720
Speaker 11: created using a large language model to Amazon as a

1333
01:07:49,760 --> 01:07:52,280
Speaker 11: self published book, so then it's just up there as

1334
01:07:52,400 --> 01:07:56,560
Speaker 11: a book. And coming back around to Sora, those videos

1335
01:07:56,760 --> 01:07:59,480
Speaker 11: like they look impressive at first glance, but then just

1336
01:07:59,600 --> 01:08:01,760
Speaker 11: like e was saying about the art having this sort

1337
01:08:01,760 --> 01:08:04,680
Speaker 11: of facile style to it, there's similar things going on

1338
01:08:04,720 --> 01:08:08,440
Speaker 11: in the videos, but still like it should be watermarked,

1339
01:08:08,440 --> 01:08:10,640
Speaker 11: it should be obvious that you're looking at something fake.

1340
01:08:11,000 --> 01:08:12,959
Speaker 11: And what open ai has done is they've put this tiny,

1341
01:08:13,000 --> 01:08:15,240
Speaker 11: little faint thing down the lower right hand corner that

1342
01:08:15,360 --> 01:08:18,880
Speaker 11: looks like some sort of readout from a sensor going

1343
01:08:19,000 --> 01:08:20,960
Speaker 11: up and down, and then it swirls into the open

1344
01:08:21,040 --> 01:08:24,519
Speaker 11: ai logo and it's faint and it's in the same

1345
01:08:24,640 --> 01:08:26,920
Speaker 11: spot that Twitter puts the button for turning the sound

1346
01:08:27,000 --> 01:08:27,400
Speaker 11: on and off.

1347
01:08:27,560 --> 01:08:28,400
Speaker 12: So it's hidden by that.

1348
01:08:28,439 --> 01:08:31,960
Speaker 11: If you're seeing those things on Twitter, and it's completely opaque, right,

1349
01:08:32,000 --> 01:08:33,639
Speaker 11: if you are not in the note, if you don't

1350
01:08:33,640 --> 01:08:35,160
Speaker 11: know what open ai is, if you don't know that

1351
01:08:35,280 --> 01:08:38,120
Speaker 11: fake videos might exist, that doesn't tell you anything, right.

1352
01:08:38,439 --> 01:08:40,680
Speaker 14: So these are the things I'm worried about, And the.

1353
01:08:40,760 --> 01:08:43,639
Speaker 8: Things I'm really worried about is, you know, these things

1354
01:08:44,320 --> 01:08:48,800
Speaker 8: doing a pretty terrible job at producing written content and

1355
01:08:48,960 --> 01:08:55,000
Speaker 8: videos and images. And so it's not that they could

1356
01:08:55,320 --> 01:08:59,160
Speaker 8: replace a human person, but it just takes a boss

1357
01:08:59,439 --> 01:09:02,640
Speaker 8: or a vik he to think that it's good enough, right,

1358
01:09:02,680 --> 01:09:08,160
Speaker 8: and then this replaces whole classes of occupation. So again,

1359
01:09:08,880 --> 01:09:11,720
Speaker 8: talking to Carla Ortiz, who's a concept artist. She's done

1360
01:09:11,760 --> 01:09:15,519
Speaker 8: work for Marvel and and DC, had huge studios and

1361
01:09:15,800 --> 01:09:21,000
Speaker 8: magic togathering and you know, and she's basically saying, after

1362
01:09:21,520 --> 01:09:26,800
Speaker 8: mid journey stability, AI produces this incredibly crappy content. Jobs

1363
01:09:26,840 --> 01:09:30,920
Speaker 8: for concept artists have really dried up. They've gone and

1364
01:09:31,320 --> 01:09:33,519
Speaker 8: it's really hard. And I mean, especially for folks who

1365
01:09:33,520 --> 01:09:36,000
Speaker 8: are just breaking into the industry. You might just be

1366
01:09:36,120 --> 01:09:38,840
Speaker 8: trying to get their their you know, their work out

1367
01:09:38,920 --> 01:09:42,439
Speaker 8: there for entry level jobs. They can't find anything right now,

1368
01:09:43,080 --> 01:09:46,559
Speaker 8: and so imagine what that's going to replace, what that's

1369
01:09:46,600 --> 01:09:48,840
Speaker 8: going to encroach on, right, I mean that's kind of

1370
01:09:48,920 --> 01:09:52,080
Speaker 8: unique thing about kind of creative fields and coding fields

1371
01:09:52,120 --> 01:09:55,479
Speaker 8: and and and whatnot. And then I would say this, yeah,

1372
01:09:55,560 --> 01:09:59,519
Speaker 8: this this automated decision making kind of in government and hiring.

1373
01:09:59,560 --> 01:10:04,000
Speaker 8: I mean, they'll those are you know, definitely you know, terrifying, right,

1374
01:10:04,080 --> 01:10:06,519
Speaker 8: And I mean this is already being deployed. I mean

1375
01:10:06,560 --> 01:10:09,600
Speaker 8: at the US Mexico border, there's kind of massive. The

1376
01:10:09,720 --> 01:10:12,880
Speaker 8: Markup actually just put out this interview with David Moss

1377
01:10:12,920 --> 01:10:16,960
Speaker 8: who's at the Electronic Freedom Foundation, no either the Electronic

1378
01:10:17,000 --> 01:10:19,240
Speaker 8: Freedom Refression. I think he's at the ACLU. I have

1379
01:10:19,320 --> 01:10:22,040
Speaker 8: to look this up, but it's about basically a survey

1380
01:10:22,320 --> 01:10:26,360
Speaker 8: of like surveillance technology that's not the southern border, and

1381
01:10:26,800 --> 01:10:29,519
Speaker 8: Dave Moss is at EFF. The Markdown put just put

1382
01:10:29,560 --> 01:10:31,560
Speaker 8: a published something about with him. It was like a

1383
01:10:31,680 --> 01:10:35,640
Speaker 8: virtual reality tour of certains technology or something wild like that.

1384
01:10:36,080 --> 01:10:36,639
Speaker 9: I was gonna say.

1385
01:10:36,640 --> 01:10:39,320
Speaker 11: There's also things like shot Spotter, which reports to be

1386
01:10:39,360 --> 01:10:41,200
Speaker 11: able to detect what a gun has been fired. And

1387
01:10:41,280 --> 01:10:44,000
Speaker 11: this has been deployed by police departments all over the country,

1388
01:10:44,560 --> 01:10:46,760
Speaker 11: and there's you know, there's no transparency into how it

1389
01:10:46,840 --> 01:10:48,599
Speaker 11: was evaluated, how it even works, or why you should

1390
01:10:48,640 --> 01:10:51,160
Speaker 11: believe it works. And so what we have is a

1391
01:10:51,320 --> 01:10:54,120
Speaker 11: system that tells the cops that they're running into an

1392
01:10:54,120 --> 01:10:57,799
Speaker 11: active shooter situation, which is definitely a recipe for reducing

1393
01:10:57,880 --> 01:10:58,920
Speaker 11: police violence.

1394
01:10:58,680 --> 01:11:02,680
Speaker 3: Right right, yeah, right, our time, Microsoft.

1395
01:11:03,000 --> 01:11:05,720
Speaker 1: Yeah, and there is a reason an investigation that that

1396
01:11:05,840 --> 01:11:10,920
Speaker 1: showed that it's always almost always used in neighborhoods.

1397
01:11:10,520 --> 01:11:13,840
Speaker 8: Yeah, communities, the communities of color. Yeah, the Wired, the

1398
01:11:13,920 --> 01:11:17,160
Speaker 8: Wired piece on that basically, Yeah, I think they said

1399
01:11:17,200 --> 01:11:20,479
Speaker 8: about what seventy percent of the census tracts something ridiculous

1400
01:11:20,560 --> 01:11:20,720
Speaker 8: like that.

1401
01:11:20,880 --> 01:11:22,360
Speaker 14: Yeah, Yeah, it's absurd.

1402
01:11:22,560 --> 01:11:25,839
Speaker 11: And then there's things like EPIC, which is a healthcare

1403
01:11:25,840 --> 01:11:29,719
Speaker 11: electronic health records company, is partnering with Microsoft to push

1404
01:11:29,800 --> 01:11:33,800
Speaker 11: the synthetic text into so many different systems, so that

1405
01:11:33,880 --> 01:11:36,839
Speaker 11: you're going to get like reports in your patient records

1406
01:11:37,040 --> 01:11:39,720
Speaker 11: that were automatically generated and then supposedly checked by a

1407
01:11:39,800 --> 01:11:42,200
Speaker 11: doctor who doesn't have time to check it thoroughly, right,

1408
01:11:42,640 --> 01:11:45,040
Speaker 11: And they're going to be doing things like, you know,

1409
01:11:45,560 --> 01:11:47,760
Speaker 11: randomly putting in information about BMI and when it's not

1410
01:11:47,840 --> 01:11:51,320
Speaker 11: even relevant or misgendering people over the place or you know,

1411
01:11:51,840 --> 01:11:53,920
Speaker 11: this kind of stuff is going to hurt. Yeah, And

1412
01:11:54,000 --> 01:11:57,320
Speaker 11: I want to add to the issue of like entry

1413
01:11:57,400 --> 01:12:00,479
Speaker 11: level jobs drying up for people who do, for example, illustration,

1414
01:12:01,120 --> 01:12:03,280
Speaker 11: doctor Joyble and Winni points out that we're getting what's

1415
01:12:03,320 --> 01:12:07,960
Speaker 11: called an apprentice gap, where those positions where the easy

1416
01:12:07,960 --> 01:12:10,120
Speaker 11: stuff gets automated. And I don't think this is just

1417
01:12:10,200 --> 01:12:12,200
Speaker 11: in creative fields, but the positions where you're doing the

1418
01:12:12,240 --> 01:12:14,000
Speaker 11: easy stuff and you're learning how to master it and

1419
01:12:14,080 --> 01:12:16,599
Speaker 11: you are working alongside someone who's doing the harder stuff,

1420
01:12:17,080 --> 01:12:20,080
Speaker 11: if that gets replaced by automation, then it becomes harder

1421
01:12:20,160 --> 01:12:23,120
Speaker 11: to train the people to do the stuff that requires expertise.

1422
01:12:23,040 --> 01:12:26,080
Speaker 1: Right, Yeah, And it's easier to do in creative fields

1423
01:12:26,120 --> 01:12:31,240
Speaker 1: because there's such a just inability for executives to you know,

1424
01:12:31,680 --> 01:12:34,400
Speaker 1: like they don't know it. They've never known anything about

1425
01:12:34,479 --> 01:12:37,200
Speaker 1: like what what is quality creative work? So I feel

1426
01:12:37,240 --> 01:12:39,120
Speaker 1: like it's much easier for them to just be like, yeah,

1427
01:12:39,160 --> 01:12:41,800
Speaker 1: get rid of that and we'll, yeah, like the way

1428
01:12:41,920 --> 01:12:45,080
Speaker 1: that we'll find out about that that isn't working is

1429
01:12:45,240 --> 01:12:48,840
Speaker 1: the quality of the creative output will be far worse.

1430
01:12:49,439 --> 01:12:51,920
Speaker 8: Right, I mean, what I mean, all those folks really

1431
01:12:52,160 --> 01:12:55,759
Speaker 8: tend to care about our you know, content and engagement metrics.

1432
01:12:55,960 --> 01:12:58,880
Speaker 8: You know, you can't actually have something that's kind of

1433
01:12:58,960 --> 01:13:00,920
Speaker 8: known for quality or or creativity.

1434
01:13:01,040 --> 01:13:01,160
Speaker 2: Right.

1435
01:13:01,840 --> 01:13:03,600
Speaker 8: It does remind me a little bit about kind of

1436
01:13:04,040 --> 01:13:08,840
Speaker 8: you know, the first rebels against automation, the Luodites, and

1437
01:13:09,320 --> 01:13:10,840
Speaker 8: you know, the kind of the kind of way in

1438
01:13:10,880 --> 01:13:14,200
Speaker 8: which they did have this apprentice guilt system in which

1439
01:13:14,240 --> 01:13:17,240
Speaker 8: they trained for you know, a decade or so before

1440
01:13:17,360 --> 01:13:19,880
Speaker 8: they could you know, perhaps go ahead and open their

1441
01:13:19,920 --> 01:13:23,000
Speaker 8: own shop in a way that you know, those folks

1442
01:13:23,040 --> 01:13:26,880
Speaker 8: were replaced by these these water frames that were they

1443
01:13:26,960 --> 01:13:29,719
Speaker 8: called water frames because they were produced by hydraulic power.

1444
01:13:30,200 --> 01:13:34,519
Speaker 8: But then uh, you know, effectively powered by unskilled people

1445
01:13:34,560 --> 01:13:39,559
Speaker 8: and by unskilled usually children doing incredibly dangerous work. But yeah,

1446
01:13:39,720 --> 01:13:42,040
Speaker 8: folks that have been training for this, the apprentices were

1447
01:13:43,000 --> 01:13:44,400
Speaker 8: incredibly steeming mad.

1448
01:13:45,040 --> 01:13:48,560
Speaker 1: Yeah, and then we made their name a synonym for

1449
01:13:49,280 --> 01:13:53,719
Speaker 1: like hater, Yeah, decater.

1450
01:13:55,479 --> 01:13:58,840
Speaker 8: There's been some nice efforts to reclaim them by Brian

1451
01:13:58,960 --> 01:13:59,600
Speaker 8: Merchant and.

1452
01:14:00,160 --> 01:14:04,519
Speaker 1: Yeah, totally so. Just in the comparison to crypto, it

1453
01:14:04,640 --> 01:14:10,080
Speaker 1: feels like the adoption here, the hype cycle here is

1454
01:14:10,920 --> 01:14:15,280
Speaker 1: more widespread than crypto like with Crypto, there was that

1455
01:14:15,560 --> 01:14:20,960
Speaker 1: moment where we saw the person behind the curtain who

1456
01:14:21,920 --> 01:14:25,160
Speaker 1: was you know, the people that there was the fall

1457
01:14:25,200 --> 01:14:29,240
Speaker 1: of Crypto with With AI, I don't feel like there

1458
01:14:29,560 --> 01:14:34,440
Speaker 1: is any incentive for anyone to any of the stakeholders.

1459
01:14:34,720 --> 01:14:38,479
Speaker 1: I guess I got it involved to yeah, to just

1460
01:14:38,560 --> 01:14:40,920
Speaker 1: come out and be like, yeah, it was bullshit. You know,

1461
01:14:41,479 --> 01:14:44,679
Speaker 1: there's just so much buy in across the board where

1462
01:14:45,680 --> 01:14:48,240
Speaker 1: I guess we've already talked about where you see this going.

1463
01:14:48,320 --> 01:14:50,200
Speaker 1: But is there Do you think there's any hope for

1464
01:14:51,160 --> 01:14:56,840
Speaker 1: this getting kind of found out, the truth catching on,

1465
01:14:57,360 --> 01:14:58,840
Speaker 1: or do you think it's just going to have to

1466
01:14:58,880 --> 01:15:04,200
Speaker 1: be a hundred years from now when somebody changes their

1467
01:15:04,280 --> 01:15:07,040
Speaker 1: mind about AI haters and it's like, actually they were

1468
01:15:07,120 --> 01:15:10,040
Speaker 1: onto something the way we are about ledites.

1469
01:15:10,560 --> 01:15:12,240
Speaker 11: We're still trying. That's what we're up to with the

1470
01:15:12,280 --> 01:15:15,320
Speaker 11: podcast right a lot of our other public scholarship. There

1471
01:15:15,400 --> 01:15:19,240
Speaker 11: was an interesting moment last week when chat gpt sort

1472
01:15:19,240 --> 01:15:22,800
Speaker 11: of glitched and was outputting sense. Yes, I mean it's

1473
01:15:22,880 --> 01:15:25,280
Speaker 11: actually Spanglish or people just calling it because it has

1474
01:15:25,280 --> 01:15:25,920
Speaker 11: some Spanish in it.

1475
01:15:26,080 --> 01:15:28,040
Speaker 8: It had some it wasn't all I mean, it was

1476
01:15:28,120 --> 01:15:30,479
Speaker 8: doing some spangless stuff with this stuff is just even

1477
01:15:31,200 --> 01:15:35,240
Speaker 8: more inscrutable than usual. It was just completely nonsense.

1478
01:15:35,400 --> 01:15:39,559
Speaker 11: Yeah yeah, And that the sort of open Aye statement

1479
01:15:39,560 --> 01:15:42,800
Speaker 11: about what went wrong basically described it for what it is.

1480
01:15:43,360 --> 01:15:45,160
Speaker 11: They had to like say something other than what they

1481
01:15:45,240 --> 01:15:46,960
Speaker 11: usually say. And then there was a whole thing with

1482
01:15:47,800 --> 01:15:51,880
Speaker 11: Google's image generator, which you know, so the baseline thing

1483
01:15:52,160 --> 01:15:55,600
Speaker 11: is super duper biased and like makes everybody look like

1484
01:15:56,040 --> 01:16:00,680
Speaker 11: white people. And there's this wonderful thread on media and

1485
01:16:00,720 --> 01:16:01,800
Speaker 11: here I'm doing the thing where I can't think of

1486
01:16:01,800 --> 01:16:03,599
Speaker 11: a person's name, but it's a wonderful post on medium

1487
01:16:03,600 --> 01:16:06,320
Speaker 11: where someone goes through this thread of pictures that I

1488
01:16:06,360 --> 01:16:08,200
Speaker 11: think we're initially on Reddit, where someone asked one of

1489
01:16:08,240 --> 01:16:12,519
Speaker 11: these generators to create warriors from different cultures taking a

1490
01:16:12,600 --> 01:16:16,360
Speaker 11: selfie and they're all doing the American selfie smile. And

1491
01:16:16,479 --> 01:16:20,040
Speaker 11: so does this really uncanny valley thing going on there

1492
01:16:20,080 --> 01:16:22,759
Speaker 11: where these people from all these different cultures are posing

1493
01:16:22,800 --> 01:16:26,320
Speaker 11: the way Americans pose so huge bias in these things. Underlyingly,

1494
01:16:26,600 --> 01:16:28,679
Speaker 11: Google has some kind of a patch that basically whatever

1495
01:16:28,720 --> 01:16:31,200
Speaker 11: prompt you put in, they add some additional prompting to

1496
01:16:31,280 --> 01:16:34,160
Speaker 11: make the people look diverse. And then last week or so,

1497
01:16:34,320 --> 01:16:36,679
Speaker 11: someone figured out that if you asked it to show

1498
01:16:36,680 --> 01:16:39,479
Speaker 11: you a painting of Nazi soldiers, they're not all white

1499
01:16:40,000 --> 01:16:45,840
Speaker 11: because of this patch, right right. So yeah, So Google's

1500
01:16:45,960 --> 01:16:49,680
Speaker 11: backpedaling of that I think was ridiculous. There was some

1501
01:16:49,760 --> 01:16:53,519
Speaker 11: statement in there about how they certainly don't intend for

1502
01:16:53,600 --> 01:16:57,000
Speaker 11: their system to put out historically inaccurate images. I'm like,

1503
01:16:57,520 --> 01:17:00,600
Speaker 11: what the hell, it's a fake image, Like there's no

1504
01:17:00,760 --> 01:17:05,160
Speaker 11: accuracy there, no matter what, right, Right, These mishaps maybe

1505
01:17:05,479 --> 01:17:08,320
Speaker 11: sort of pulled back the curtain for a broader group

1506
01:17:08,400 --> 01:17:08,719
Speaker 11: of people.

1507
01:17:08,920 --> 01:17:09,599
Speaker 6: There's some true.

1508
01:17:09,479 --> 01:17:11,519
Speaker 11: Believers out there who are not going to be reached, sure,

1509
01:17:11,600 --> 01:17:13,679
Speaker 11: but I think it may have helped for some slice

1510
01:17:13,680 --> 01:17:14,240
Speaker 11: of society.

1511
01:17:14,479 --> 01:17:16,439
Speaker 3: Yeah, I know some people who are like, how do

1512
01:17:16,560 --> 01:17:19,720
Speaker 3: we know that Reinhard Heydrich didn't have dreadlocks?

1513
01:17:21,920 --> 01:17:22,160
Speaker 2: Clear?

1514
01:17:22,560 --> 01:17:22,760
Speaker 1: Wow?

1515
01:17:22,960 --> 01:17:24,160
Speaker 3: But okay, sure, go off.

1516
01:17:24,240 --> 01:17:24,439
Speaker 2: Yeah.

1517
01:17:25,240 --> 01:17:26,800
Speaker 8: And I mean I think it's this is such an

1518
01:17:26,880 --> 01:17:30,000
Speaker 8: interesting question, right is like where does when is the

1519
01:17:30,040 --> 01:17:31,439
Speaker 8: AI bubble going to pop?

1520
01:17:31,600 --> 01:17:31,760
Speaker 1: Right?

1521
01:17:31,920 --> 01:17:35,560
Speaker 8: And I mean in some ways it feels like, you know,

1522
01:17:35,640 --> 01:17:37,720
Speaker 8: we're where I mean, as much as we can do,

1523
01:17:37,800 --> 01:17:40,439
Speaker 8: we're kind of prodding it, right, you know and seeing

1524
01:17:40,960 --> 01:17:44,080
Speaker 8: you know, And one thing that I handedly said one

1525
01:17:44,200 --> 01:17:47,520
Speaker 8: one time, and Emily loves is really cule as practice.

1526
01:17:47,960 --> 01:17:50,280
Speaker 8: So you know, one thing, you know, and I will

1527
01:17:50,680 --> 01:17:53,720
Speaker 8: in a turn of one of Emily's great quotes is

1528
01:17:53,880 --> 01:17:56,120
Speaker 8: you know, oh gosh, I'm want to mess it up

1529
01:17:56,439 --> 01:17:59,320
Speaker 8: right now. It's I and I it's it's the refuse

1530
01:17:59,400 --> 01:18:03,040
Speaker 8: to be impressed. Oh yeah, resist the urge to resist,

1531
01:18:03,200 --> 01:18:05,280
Speaker 8: resist the year to be impressed. It's much it's much

1532
01:18:05,320 --> 01:18:07,800
Speaker 8: better when when when she says it, and it's and

1533
01:18:07,960 --> 01:18:10,240
Speaker 8: it's and it's kind of the idea of like some

1534
01:18:10,400 --> 01:18:13,040
Speaker 8: of it is a bit of the sheene right, But

1535
01:18:13,160 --> 01:18:15,840
Speaker 8: it feels like at some point, you know, if if

1536
01:18:16,120 --> 01:18:19,360
Speaker 8: in the kind of operations of these things, you know enough,

1537
01:18:19,479 --> 01:18:23,200
Speaker 8: there'll be enough buy in, especially with automation, that's hard

1538
01:18:23,240 --> 01:18:27,360
Speaker 8: to reverse a lot of the automation without just a

1539
01:18:27,560 --> 01:18:30,080
Speaker 8: huge fight. And so I mean, I think something that

1540
01:18:30,280 --> 01:18:34,920
Speaker 8: helps our you know, worker led efforts, you know. And

1541
01:18:35,200 --> 01:18:37,519
Speaker 8: one of the most awesome things that we've seen this

1542
01:18:37,680 --> 01:18:43,679
Speaker 8: is something a scholar Blair A tear Frost calls AI

1543
01:18:43,840 --> 01:18:48,439
Speaker 8: counter governance. She calls uh in one example she is

1544
01:18:48,800 --> 01:18:53,120
Speaker 8: is the w GA strike and how folks struck for

1545
01:18:53,200 --> 01:18:56,720
Speaker 8: one hundred and forty eight days and after strike, they

1546
01:18:56,960 --> 01:19:01,480
Speaker 8: not only got a bunch of new kinds of guarantees

1547
01:19:01,600 --> 01:19:04,240
Speaker 8: for minimum pay when it came to streaming and the

1548
01:19:04,320 --> 01:19:07,720
Speaker 8: residuals they get for it, they also, you know, have

1549
01:19:07,960 --> 01:19:12,120
Speaker 8: to basically be informed if any AI is being used

1550
01:19:12,160 --> 01:19:15,120
Speaker 8: in the writer's room. They can't they can't be forced

1551
01:19:15,200 --> 01:19:19,639
Speaker 8: to edit or re edit or rewrite AI generated content.

1552
01:19:20,200 --> 01:19:22,920
Speaker 8: They have to be Everything has to be basically above board.

1553
01:19:23,080 --> 01:19:25,519
Speaker 8: I mean, isn't as far as you could have gone

1554
01:19:25,560 --> 01:19:27,800
Speaker 8: and banned it out right right, but if there's any

1555
01:19:27,920 --> 01:19:30,080
Speaker 8: use of it, it has to be disclosed and you

1556
01:19:30,200 --> 01:19:34,040
Speaker 8: can't bring in these tools to hold you know, whole

1557
01:19:34,120 --> 01:19:35,000
Speaker 8: plot to place.

1558
01:19:34,880 --> 01:19:39,120
Speaker 1: The writer's room. All right, that's gonna do it for

1559
01:19:39,479 --> 01:19:43,519
Speaker 1: this week's weekly Zeitgeist. Please like and review the show

1560
01:19:44,160 --> 01:19:48,120
Speaker 1: if you like, the show means the world demiles he

1561
01:19:48,720 --> 01:19:52,479
Speaker 1: he needs your validation, folks. I hope you're having a

1562
01:19:52,560 --> 01:19:55,120
Speaker 1: great weekend and I will talk to you Monday.

1563
01:19:55,479 --> 01:20:21,479
Speaker 2: By sting nothing