1
00:00:00,160 --> 00:00:03,480
Speaker 1: Hello the Internet, and welcome to this episode of The

2
00:00:03,560 --> 00:00:08,360
Speaker 1: Weekly Zeitgeist. These are some of our favorite segments from

3
00:00:08,600 --> 00:00:19,360
Speaker 1: this week, all edited together into one NonStop infotainment laugh stravaganza. Yeah, So,

4
00:00:19,800 --> 00:00:26,120
Speaker 1: without further ado, here is the Weekly Zeitgeist. Miles. We're

5
00:00:26,160 --> 00:00:28,920
Speaker 1: thrilled to be joined once again in our third seat

6
00:00:29,440 --> 00:00:33,080
Speaker 1: by a research associate at the Leverholmes Center for the

7
00:00:33,120 --> 00:00:37,000
Speaker 1: Future of Intelligence, where she researches AI from the perspective

8
00:00:37,040 --> 00:00:39,240
Speaker 1: of gender studies. She's the co host of the great

9
00:00:39,280 --> 00:00:43,440
Speaker 1: podcast A Good Robot. Please welcome back the brilliant doctor

10
00:00:43,520 --> 00:00:44,480
Speaker 1: Kerrie McNary.

11
00:00:44,720 --> 00:00:48,640
Speaker 2: Doctor kay, Hi, thanks so much for having me back.

12
00:00:48,960 --> 00:00:51,680
Speaker 3: Oh awful, thank you, thank you for accepting our offer

13
00:00:51,800 --> 00:00:54,080
Speaker 3: to return, you know, to class up the joint as

14
00:00:54,120 --> 00:00:54,640
Speaker 3: I say.

15
00:00:55,400 --> 00:01:00,680
Speaker 1: International you know, diplomacy going on. We had yeah, you know,

16
00:01:00,840 --> 00:01:03,160
Speaker 1: work it out, but we're thrilled to have you back.

17
00:01:04,200 --> 00:01:07,200
Speaker 2: How have you been, I mean, how have we all been?

18
00:01:07,640 --> 00:01:10,560
Speaker 2: I don't answer that question anymore so I'm just like,

19
00:01:11,640 --> 00:01:13,800
Speaker 2: I guess in the big scheme of things, I'm personally

20
00:01:13,800 --> 00:01:14,640
Speaker 2: doing really well.

21
00:01:14,680 --> 00:01:20,800
Speaker 1: But yeah, yeah, thriving, Yeah yeah. What's the energy like? Answer?

22
00:01:20,920 --> 00:01:22,200
Speaker 1: What's the energy like? In Europe?

23
00:01:22,240 --> 00:01:26,360
Speaker 3: Looking at the cess pit that is a flame as

24
00:01:26,400 --> 00:01:29,360
Speaker 3: known as the United States. Is it like, is it

25
00:01:29,400 --> 00:01:32,679
Speaker 3: like a tooth like Iraq war? Anger like I experienced

26
00:01:32,720 --> 00:01:34,440
Speaker 3: going to Europe, or like what the fuck are y'all

27
00:01:34,440 --> 00:01:36,360
Speaker 3: doing over there? Or is it like how we felt

28
00:01:36,400 --> 00:01:40,320
Speaker 3: after Bregg's there people are like, oh, sorry, is fucking

29
00:01:40,400 --> 00:01:41,559
Speaker 3: themselves bad?

30
00:01:42,560 --> 00:01:45,000
Speaker 2: I feel like, yeah, a lot of confusion and fear.

31
00:01:45,280 --> 00:01:47,319
Speaker 2: I don't know if it's necessarily even just anger as

32
00:01:47,360 --> 00:01:49,560
Speaker 2: much as it's just people being like what is happening?

33
00:01:49,640 --> 00:01:51,080
Speaker 2: Like what are you doing? But I think there is

34
00:01:51,120 --> 00:01:53,720
Speaker 2: a degree of like shell shock to it, where just

35
00:01:53,760 --> 00:01:55,600
Speaker 2: so much keeps happening that I think you start to

36
00:01:55,640 --> 00:01:58,520
Speaker 2: like become slightly numb, which is very bad but also

37
00:01:58,600 --> 00:02:01,120
Speaker 2: very understandable. I got a bit of a refresh from

38
00:02:01,160 --> 00:02:02,760
Speaker 2: that because I was back home in New Zealand in

39
00:02:02,840 --> 00:02:04,760
Speaker 2: March and like, there's nothing quite like being in a

40
00:02:04,800 --> 00:02:06,760
Speaker 2: country when you watch the news every day and like

41
00:02:07,440 --> 00:02:10,600
Speaker 2: nothing happens. It's like a tree fell near the motorway,

42
00:02:10,680 --> 00:02:12,400
Speaker 2: not even on the motorway, or you know that kind

43
00:02:12,400 --> 00:02:14,000
Speaker 2: of level of me right right right, And then it

44
00:02:14,080 --> 00:02:16,120
Speaker 2: came back here and I was like, oh my goodness,

45
00:02:18,520 --> 00:02:20,600
Speaker 2: this is a very different level of news.

46
00:02:20,880 --> 00:02:26,120
Speaker 3: Yeah yeah, yeah, yeah, well we're not getting them to it. Unfortunately,

47
00:02:26,600 --> 00:02:29,800
Speaker 3: every day it hurts every day. Damn you got anything

48
00:02:29,840 --> 00:02:31,880
Speaker 3: for that hurts every day?

49
00:02:32,919 --> 00:02:33,160
Speaker 1: Yeah?

50
00:02:33,240 --> 00:02:34,720
Speaker 3: Yeah, I mean, but that is the point. I think

51
00:02:34,760 --> 00:02:37,399
Speaker 3: they want as many people to turn off and sort

52
00:02:37,440 --> 00:02:39,600
Speaker 3: of ignore what's happening. But I think I think as

53
00:02:39,639 --> 00:02:42,359
Speaker 3: things ramp up here, people more and more realize how

54
00:02:43,240 --> 00:02:45,839
Speaker 3: how much the norms, as flawed as they were, were

55
00:02:45,880 --> 00:02:49,240
Speaker 3: things that were worth keeping and trying to improve rather

56
00:02:49,240 --> 00:02:51,320
Speaker 3: than be like FuG everything, get rid of it all

57
00:02:52,360 --> 00:02:52,840
Speaker 3: and whatever.

58
00:02:52,880 --> 00:02:57,440
Speaker 1: This is, Yeah, what is something from your search history

59
00:02:57,480 --> 00:02:58,760
Speaker 1: that's revealing about who you are?

60
00:02:59,360 --> 00:03:01,800
Speaker 4: So I am working on a show called How to

61
00:03:01,800 --> 00:03:06,120
Speaker 4: Live Forever right now, which is you know, I'm just

62
00:03:06,200 --> 00:03:10,800
Speaker 4: so enamored with like how how these billionaires are spending

63
00:03:10,800 --> 00:03:12,920
Speaker 4: all their lives like trying believing that they're gonna live

64
00:03:12,919 --> 00:03:15,040
Speaker 4: to one hundred and sixty and and but they're also

65
00:03:15,080 --> 00:03:18,120
Speaker 4: like crazy things like cats that like you know, I

66
00:03:18,120 --> 00:03:20,799
Speaker 4: think Rick Perry's cousin is helping them.

67
00:03:20,639 --> 00:03:25,200
Speaker 1: Live to their thirties. They're like cats live to their thirties.

68
00:03:25,320 --> 00:03:28,000
Speaker 4: Yeah, with like tiny thimbles of wine and like there's

69
00:03:28,000 --> 00:03:29,919
Speaker 4: a whole exercise course for them.

70
00:03:30,080 --> 00:03:34,040
Speaker 1: Wait, is the wine thing real, because like I always

71
00:03:34,080 --> 00:03:38,080
Speaker 1: assumed that was just wishful thinking. Is it real? Well,

72
00:03:38,120 --> 00:03:41,240
Speaker 1: for cats it is. I don't know about us with

73
00:03:41,280 --> 00:03:44,760
Speaker 1: a little bit of wine. Well wow, okay, I mean

74
00:03:44,760 --> 00:03:46,000
Speaker 1: it takes the edge off, I guess.

75
00:03:46,080 --> 00:03:50,440
Speaker 4: But you know, they're all these like things and and

76
00:03:50,480 --> 00:03:53,640
Speaker 4: they're you know, miracles of science right now. Like they're

77
00:03:53,680 --> 00:03:57,480
Speaker 4: these dancing molecules that in a lab in Northwestern they

78
00:03:57,680 --> 00:04:01,760
Speaker 4: have severed mices spines to make them paraplegic and then

79
00:04:02,040 --> 00:04:04,520
Speaker 4: give them this injection and within a month they're like

80
00:04:04,600 --> 00:04:08,120
Speaker 4: walking normally again. And like, like the advances and science

81
00:04:08,160 --> 00:04:11,360
Speaker 4: are unbelievable. But also there's all this like snake oil,

82
00:04:11,520 --> 00:04:14,800
Speaker 4: and like what happens when like billionaires stop like giving

83
00:04:14,800 --> 00:04:17,120
Speaker 4: their money to society and taking like billionaire's pledge and

84
00:04:17,200 --> 00:04:19,800
Speaker 4: just like conserving it for you know, when they're living

85
00:04:19,800 --> 00:04:22,440
Speaker 4: to two hundred or whatever. But in all this research

86
00:04:22,920 --> 00:04:26,840
Speaker 4: I stumbled into like this rabbit hole on the enhanced games.

87
00:04:26,920 --> 00:04:30,000
Speaker 4: Have you been paying attention to this? Like it's basically

88
00:04:30,040 --> 00:04:31,920
Speaker 4: an all drug Olympics.

89
00:04:31,680 --> 00:04:32,960
Speaker 1: And they're doing it.

90
00:04:33,120 --> 00:04:36,120
Speaker 4: I know that it was a one in it, but

91
00:04:36,279 --> 00:04:40,680
Speaker 4: it is that it's supposed to happen next year, and

92
00:04:40,680 --> 00:04:43,320
Speaker 4: and there's all this craziness of like trying to figure

93
00:04:43,320 --> 00:04:46,080
Speaker 4: out where is it going to be because they're like,

94
00:04:46,440 --> 00:04:49,440
Speaker 4: you know, you can, I think, dope up as much

95
00:04:49,440 --> 00:04:49,840
Speaker 4: as you want.

96
00:04:49,839 --> 00:04:50,480
Speaker 1: For these Olympics.

97
00:04:50,520 --> 00:04:54,440
Speaker 4: They're getting people who are like retired Olympians and like

98
00:04:54,480 --> 00:04:56,320
Speaker 4: people who have sort of like failed out of the

99
00:04:56,680 --> 00:04:59,039
Speaker 4: you know, they were like a fifth place, like swimmer

100
00:04:59,120 --> 00:05:02,560
Speaker 4: or whatever, and they're getting roted up and and other

101
00:05:02,600 --> 00:05:06,120
Speaker 4: drugs obviously, and and uh and then they're getting these

102
00:05:06,200 --> 00:05:09,960
Speaker 4: huge cash prizes to like beat the like Michael Phelps

103
00:05:10,000 --> 00:05:11,160
Speaker 4: record or like you.

104
00:05:11,040 --> 00:05:14,359
Speaker 1: Know, and and uh.

105
00:05:13,720 --> 00:05:16,880
Speaker 4: It's just the whole world is fascinating to me, like

106
00:05:17,000 --> 00:05:19,760
Speaker 4: the fact that like, you know, the Olympics themselves are

107
00:05:19,839 --> 00:05:22,200
Speaker 4: are pretty corrupt. It's not like you know, they're there

108
00:05:22,200 --> 00:05:24,720
Speaker 4: are things where like Ben Johnson and and.

109
00:05:24,600 --> 00:05:27,960
Speaker 1: Uh car Johnson Johnson.

110
00:05:28,200 --> 00:05:30,560
Speaker 4: Yeah, I mean they took the same drugs right, like

111
00:05:30,960 --> 00:05:33,039
Speaker 4: and and and one of them is like a hero

112
00:05:33,200 --> 00:05:35,800
Speaker 4: and the others like you know this pariah and and like.

113
00:05:36,240 --> 00:05:37,640
Speaker 1: Carl Lewis took those drugs too.

114
00:05:38,160 --> 00:05:45,160
Speaker 3: Yeah, yeah, come on, Barcelona Olympics.

115
00:05:46,360 --> 00:05:48,479
Speaker 1: So that's been like documented since.

116
00:05:50,720 --> 00:05:53,400
Speaker 4: No, but but but but there's a documentary about it

117
00:05:54,080 --> 00:05:56,000
Speaker 4: Steroids that that talks about it.

118
00:05:56,120 --> 00:05:58,560
Speaker 1: Ben Johnson just like didn't hide it as well, because

119
00:05:58,600 --> 00:06:02,200
Speaker 1: he definitely looked more on steroids than I think anyone

120
00:06:02,240 --> 00:06:02,919
Speaker 1: I've ever seen.

121
00:06:03,320 --> 00:06:06,880
Speaker 4: Well I think also, I think the countries are better

122
00:06:06,920 --> 00:06:09,280
Speaker 4: at hiding it than other countries, right, Like certain doctors

123
00:06:09,320 --> 00:06:12,200
Speaker 4: are better at hiding it. So like you know, whether

124
00:06:12,240 --> 00:06:15,880
Speaker 4: it's like Russia or China or various places, Eastern Germany,

125
00:06:15,960 --> 00:06:17,919
Speaker 4: right was very very good at hiding it and not

126
00:06:18,000 --> 00:06:23,440
Speaker 4: hiding it. But anyway, like the fact that like you one,

127
00:06:23,640 --> 00:06:26,040
Speaker 4: like the countries are trying to bid on whether they

128
00:06:26,080 --> 00:06:29,440
Speaker 4: can like sports wash through like an all drug Olympics

129
00:06:29,560 --> 00:06:30,240
Speaker 4: is like kind of.

130
00:06:30,680 --> 00:06:31,440
Speaker 1: Stunning to me.

131
00:06:31,760 --> 00:06:35,720
Speaker 4: And and and also just the people are invested in

132
00:06:35,800 --> 00:06:38,800
Speaker 4: this because they feel like people like Peter Thiel are

133
00:06:38,839 --> 00:06:42,440
Speaker 4: invested in because they hope that all these longevity drugs

134
00:06:42,480 --> 00:06:44,839
Speaker 4: will come out of an Olympics. And it's hard to tell,

135
00:06:44,880 --> 00:06:45,960
Speaker 4: you know, is it going to be like a fire

136
00:06:46,040 --> 00:06:48,400
Speaker 4: festival of sport or is it going to be like

137
00:06:49,279 --> 00:06:52,200
Speaker 4: something that makes money and becomes like you know.

138
00:06:52,600 --> 00:06:56,960
Speaker 1: Regularly washed on TV. Or where's it located? Haven't announced

139
00:06:57,040 --> 00:06:59,680
Speaker 1: yet oh International waters.

140
00:07:00,760 --> 00:07:05,000
Speaker 3: Right, Yeah, we're going to see it like the first

141
00:07:05,000 --> 00:07:08,120
Speaker 3: televised like race where people are having like a heart

142
00:07:08,120 --> 00:07:11,120
Speaker 3: attack during the butterfly stroke of a swim competition and

143
00:07:11,160 --> 00:07:11,600
Speaker 3: you're like.

144
00:07:11,560 --> 00:07:18,640
Speaker 1: Oh, boy using air while you can literally see his

145
00:07:18,760 --> 00:07:22,840
Speaker 1: heart pumping out of his chest. I feel like it

146
00:07:22,880 --> 00:07:26,640
Speaker 1: either needs to be Vegas or Detroit in honor of RoboCop.

147
00:07:26,960 --> 00:07:31,360
Speaker 1: I'm not sure, yeah, right, like or like or to

148
00:07:31,480 --> 00:07:34,840
Speaker 1: just be like Saudi Arabia. Yeah, that makes a lot

149
00:07:34,920 --> 00:07:38,440
Speaker 1: of sense. Yeah, and and it just like plays it

150
00:07:38,480 --> 00:07:39,640
Speaker 1: to this whole like weird.

151
00:07:39,840 --> 00:07:42,440
Speaker 4: There's a GQ article about like everyone you know is

152
00:07:42,480 --> 00:07:45,160
Speaker 4: on steroids, you know, and it feels.

153
00:07:44,840 --> 00:07:49,360
Speaker 1: Like this, no, not Jack, I told you I'm not.

154
00:07:52,360 --> 00:07:52,600
Speaker 3: Yeah.

155
00:07:52,720 --> 00:07:57,920
Speaker 1: I had to ask my mom twice. She's everyone I

156
00:07:58,000 --> 00:08:00,480
Speaker 1: know that she lifted the couch with one man. It

157
00:08:00,560 --> 00:08:04,280
Speaker 1: was crazy, but got terrible back acne too, I noticed.

158
00:08:04,360 --> 00:08:07,760
Speaker 4: Yeah, but it is I mean this whole world of

159
00:08:07,800 --> 00:08:11,840
Speaker 4: like biohacking and human augmentation where like people showed up

160
00:08:11,840 --> 00:08:14,440
Speaker 4: two feet taller or like six inches taller after the

161
00:08:14,480 --> 00:08:18,840
Speaker 4: pandemic because they'd had those like you know, like like

162
00:08:19,200 --> 00:08:22,320
Speaker 4: this whole world is so bizarre and it's fascinating to

163
00:08:22,320 --> 00:08:23,119
Speaker 4: see what'll stick.

164
00:08:23,560 --> 00:08:26,160
Speaker 1: I have some suspicions that there was one time where,

165
00:08:26,240 --> 00:08:28,600
Speaker 1: like some I ran into someone I hadn't seen it

166
00:08:28,600 --> 00:08:32,040
Speaker 1: in a while, and they were definitely taller than they

167
00:08:32,120 --> 00:08:36,760
Speaker 1: used to be, and I just like wouldn't because I

168
00:08:36,760 --> 00:08:39,200
Speaker 1: hadn't really thought of that as being a real thing.

169
00:08:39,320 --> 00:08:41,560
Speaker 1: I wouldn't let it rest. I'd be like, are you

170
00:08:41,640 --> 00:08:43,480
Speaker 1: so damn tall? It's crazy.

171
00:08:43,720 --> 00:08:46,120
Speaker 5: It's like you had a goddamn Rosebury in your thirties

172
00:08:46,160 --> 00:08:49,319
Speaker 5: and they were like, yeah, man, just like stop, just like, no,

173
00:08:49,480 --> 00:08:50,480
Speaker 5: I've always been this tall.

174
00:08:50,480 --> 00:08:51,680
Speaker 1: I don't know what you're talking about.

175
00:08:51,920 --> 00:08:56,520
Speaker 3: Oh man, bro, yeah I had my bones extended.

176
00:08:58,480 --> 00:09:03,679
Speaker 1: You're like, oh yeah, exactly. And you were saying before

177
00:09:03,679 --> 00:09:06,440
Speaker 1: we recorded, Brian Johnson is hosting that show with you.

178
00:09:06,559 --> 00:09:11,280
Speaker 1: That's your co host, the guy, his son's bonus. So

179
00:09:11,400 --> 00:09:16,120
Speaker 1: that's cool. No, that's not true. That's uh, that's that

180
00:09:16,240 --> 00:09:19,560
Speaker 1: is super interesting. I can't wait to have them have

181
00:09:19,720 --> 00:09:23,360
Speaker 1: these games and they can't even get close to the

182
00:09:23,400 --> 00:09:27,360
Speaker 1: real Olympian records because they were all on steroids too.

183
00:09:29,040 --> 00:09:34,800
Speaker 1: You know, we'll see what is something you think is underrated?

184
00:09:35,559 --> 00:09:42,080
Speaker 1: Mashed potatoes underrated? Okay, that's just you think they're more versatile,

185
00:09:42,480 --> 00:09:44,760
Speaker 1: just more more delicious than people give them credit for.

186
00:09:45,440 --> 00:09:49,520
Speaker 6: Yeah, I think they're top potato.

187
00:09:50,520 --> 00:09:54,319
Speaker 1: So you're saying underrated because we're emphasizing what the fry

188
00:09:54,480 --> 00:09:56,040
Speaker 1: too much, I think so.

189
00:09:56,400 --> 00:09:59,040
Speaker 6: I think we're in too fancy with potatoes. Being an

190
00:09:59,080 --> 00:10:04,839
Speaker 6: Idaho brought me closer to the potato and you can

191
00:10:04,920 --> 00:10:07,160
Speaker 6: just throw butter and salt and that's good shit. It

192
00:10:07,200 --> 00:10:10,480
Speaker 6: doesn't need anything else. People gussy it up too much.

193
00:10:10,760 --> 00:10:12,920
Speaker 6: So mashed potatoes underrated a.

194
00:10:12,880 --> 00:10:14,719
Speaker 3: Lot of butter, you know, if you really want to

195
00:10:14,760 --> 00:10:17,680
Speaker 3: real good, a fucking a lot of butter, like enough

196
00:10:17,720 --> 00:10:18,600
Speaker 3: that their people.

197
00:10:18,360 --> 00:10:20,960
Speaker 1: Are like are you Are you okay? And I'm like, yeah,

198
00:10:21,120 --> 00:10:26,599
Speaker 1: you okay, because this she's delicious when you worry about yourself. Yeah.

199
00:10:26,800 --> 00:10:29,400
Speaker 6: No, one just makes a bowl of mashed potatoes as

200
00:10:29,440 --> 00:10:30,320
Speaker 6: they should.

201
00:10:30,600 --> 00:10:31,120
Speaker 1: Right, Yeah.

202
00:10:31,440 --> 00:10:33,560
Speaker 7: Yeah, I did it down with the movie.

203
00:10:33,240 --> 00:10:33,880
Speaker 1: Three Nights ago.

204
00:10:33,920 --> 00:10:36,400
Speaker 3: I had a bag of potatoes that like one started

205
00:10:36,400 --> 00:10:38,120
Speaker 3: to sprout a little, you know, a little eye out

206
00:10:38,120 --> 00:10:38,559
Speaker 3: of it, and I.

207
00:10:38,520 --> 00:10:41,080
Speaker 1: Was like, all right, I got to cook these straight, yeah,

208
00:10:41,400 --> 00:10:43,520
Speaker 1: just right away. And I was like, maybe I can

209
00:10:43,640 --> 00:10:44,079
Speaker 1: roast them.

210
00:10:44,080 --> 00:10:45,440
Speaker 3: I'm like, no, dude, I want to eat a big

211
00:10:45,480 --> 00:10:47,679
Speaker 3: ass bowl of mashed potatoes, and I did it, and

212
00:10:47,760 --> 00:10:50,360
Speaker 3: my life is better exactly.

213
00:10:50,400 --> 00:10:55,000
Speaker 6: You don't even need gravy. It's just comfort filling easy. Yeah,

214
00:10:55,040 --> 00:10:56,240
Speaker 6: mashed potatoes are underrated.

215
00:10:56,480 --> 00:10:58,439
Speaker 1: Where are we on the you can't see big bowl

216
00:10:59,120 --> 00:11:04,360
Speaker 1: where like where we sort of base the base that

217
00:11:04,400 --> 00:11:06,199
Speaker 1: you're working off of. I loved it.

218
00:11:07,360 --> 00:11:09,800
Speaker 6: I've been suicidal at one point in my life, but

219
00:11:09,920 --> 00:11:11,600
Speaker 6: never that sad r.

220
00:11:15,559 --> 00:11:19,400
Speaker 1: K big bull A dark experience that is also like

221
00:11:19,800 --> 00:11:21,120
Speaker 1: kind of worth it.

222
00:11:21,720 --> 00:11:25,520
Speaker 6: That's the thing. I know, it's great. It's just I

223
00:11:25,520 --> 00:11:27,640
Speaker 6: can't look at myself in the eye, you know.

224
00:11:27,720 --> 00:11:30,720
Speaker 3: Right, It almost feels it's like, yeah, like probably the

225
00:11:30,720 --> 00:11:32,000
Speaker 3: reason I never did Heroin.

226
00:11:32,240 --> 00:11:33,360
Speaker 1: I was like, I've gotten.

227
00:11:33,040 --> 00:11:35,520
Speaker 3: Close, but part of it is just like it ain't.

228
00:11:35,600 --> 00:11:37,640
Speaker 3: It's just ain't calling me like that. And I feel

229
00:11:37,679 --> 00:11:40,040
Speaker 3: like if it did, it would be all bad. It

230
00:11:40,080 --> 00:11:40,960
Speaker 3: would be all bad.

231
00:11:41,240 --> 00:11:45,600
Speaker 1: I did feel like, like William Burrows talking about Heroin,

232
00:11:45,760 --> 00:11:48,160
Speaker 1: I was like, it's dark, it's a dark experience, but

233
00:11:48,200 --> 00:11:52,840
Speaker 1: it's actually worth crying down the road that like you

234
00:11:53,040 --> 00:11:56,000
Speaker 1: had to go down at least one in your life. Yeah,

235
00:11:56,160 --> 00:11:58,640
Speaker 1: I give lunch indeed, Yeah, I.

236
00:11:58,559 --> 00:12:03,040
Speaker 6: Haven't tried that monstrosity where it's the fried chicken, cheese, bacon,

237
00:12:03,120 --> 00:12:03,760
Speaker 6: fried chicken.

238
00:12:04,120 --> 00:12:04,760
Speaker 7: What was it?

239
00:12:06,000 --> 00:12:06,320
Speaker 6: Yeah?

240
00:12:06,320 --> 00:12:06,720
Speaker 7: That thing?

241
00:12:08,520 --> 00:12:09,400
Speaker 2: Have you guys tried it?

242
00:12:09,600 --> 00:12:10,880
Speaker 6: I can't bring myself.

243
00:12:10,960 --> 00:12:14,960
Speaker 1: That one just seems gammicky to me. But the mashed

244
00:12:14,960 --> 00:12:18,760
Speaker 1: potato bowl like always made sense to me. I was like, yeah, no,

245
00:12:18,880 --> 00:12:22,200
Speaker 1: this is something I would make at home. You know,

246
00:12:22,440 --> 00:12:26,600
Speaker 1: It's basically what I did with Thanksgiving leftovers for.

247
00:12:29,320 --> 00:12:30,200
Speaker 2: It makes total sense.

248
00:12:30,200 --> 00:12:33,280
Speaker 6: And I think because my family Southern KFC is just

249
00:12:33,400 --> 00:12:38,319
Speaker 6: like an abomination of fried chicken, so that I do

250
00:12:38,400 --> 00:12:41,280
Speaker 6: have a bias, so that probably goes into it.

251
00:12:41,520 --> 00:12:43,520
Speaker 1: Yeah, wow's your favorite fried chicken?

252
00:12:44,000 --> 00:12:47,280
Speaker 6: Honestly it is Ralph's.

253
00:12:47,840 --> 00:12:49,760
Speaker 1: Ralph's Real.

254
00:12:52,280 --> 00:12:57,280
Speaker 6: Yeah, So California grocery chain has probably some of the

255
00:12:57,280 --> 00:13:02,160
Speaker 6: best fried chicken I've had. That's not my mema us right, Yeah,

256
00:13:02,360 --> 00:13:06,199
Speaker 6: bad day fried chicken, mashed potatoes, couch.

257
00:13:06,600 --> 00:13:10,920
Speaker 1: Ralph Kroger. I'm wondering if Kroger elsewhere has good fried

258
00:13:11,000 --> 00:13:13,680
Speaker 1: chicken or if it's just something about Ralph. Oh, yeah,

259
00:13:13,679 --> 00:13:16,080
Speaker 1: how's it up by you? Anywhere else?

260
00:13:17,280 --> 00:13:19,800
Speaker 6: We don't have Ralph's. Like I'm on the border of

261
00:13:20,400 --> 00:13:26,400
Speaker 6: California and Nevada, so we have a rallies and it

262
00:13:26,480 --> 00:13:30,920
Speaker 6: looks just like Ralph's logo, but it's different. I think

263
00:13:30,920 --> 00:13:32,080
Speaker 6: it's privately owned.

264
00:13:32,480 --> 00:13:34,480
Speaker 1: Yeah that's not even a name. They just made that

265
00:13:34,520 --> 00:13:39,040
Speaker 1: ship up. Yeah, Rales.

266
00:13:39,760 --> 00:13:44,680
Speaker 6: It's just as expensive as Ralph's, but different. They don't

267
00:13:44,679 --> 00:13:47,920
Speaker 6: have Kroger brands. I don't know what their brand name is,

268
00:13:48,160 --> 00:13:50,000
Speaker 6: their store brand that gang.

269
00:13:50,080 --> 00:13:52,600
Speaker 3: Let us know what is the best grocery store chicken?

270
00:13:52,720 --> 00:13:55,440
Speaker 3: Because I do agree? I mean that and Pavilions. Pavilions

271
00:13:56,120 --> 00:13:59,560
Speaker 3: get a fresh ship, fresh batch of Pavilions fried chicken too.

272
00:13:59,720 --> 00:14:03,800
Speaker 6: Yeah that in their donuts. Holy fuck. When I lived

273
00:14:03,800 --> 00:14:08,200
Speaker 6: in Orange County, our Pavilions made donuts every morning. They

274
00:14:08,240 --> 00:14:11,160
Speaker 6: were like still hot in a little greasy from the friar.

275
00:14:11,880 --> 00:14:12,720
Speaker 7: Oh my god.

276
00:14:12,880 --> 00:14:15,760
Speaker 6: If maybe they fry the chicken in the donuts.

277
00:14:17,400 --> 00:14:19,200
Speaker 1: Maybe they fry the donuts in the chicken.

278
00:14:22,320 --> 00:14:24,120
Speaker 6: KFC are you listening.

279
00:14:25,120 --> 00:14:26,680
Speaker 1: KFC Krispy Cream collab.

280
00:14:26,760 --> 00:14:28,920
Speaker 3: It's like, yeah, we brought over the friar oil from

281
00:14:28,920 --> 00:14:31,600
Speaker 3: a CAFC to fuck up these donuts over here.

282
00:14:32,200 --> 00:14:35,520
Speaker 6: Bring in a weed brand and I never get anything

283
00:14:35,560 --> 00:14:36,080
Speaker 6: done again.

284
00:14:36,320 --> 00:14:41,000
Speaker 1: Wow, Brian, what is something you think is overrated? Oh?

285
00:14:41,080 --> 00:14:44,880
Speaker 8: Yeah, so that that my pit. Thing was anti heroes.

286
00:14:45,240 --> 00:14:48,760
Speaker 8: I think they're overrated. We're done with it. Leave that

287
00:14:48,800 --> 00:14:52,920
Speaker 8: ship in when we weren't in the Age of Aquarius,

288
00:14:53,040 --> 00:14:54,640
Speaker 8: because we are in the Age of Aquarius.

289
00:14:54,680 --> 00:14:57,600
Speaker 7: It's currently as of a few months ago.

290
00:14:57,720 --> 00:15:00,760
Speaker 1: That's great news for me. Is that good? That's from hair?

291
00:15:00,920 --> 00:15:01,400
Speaker 7: It's great.

292
00:15:01,560 --> 00:15:04,320
Speaker 8: It's when all the it's like historically, I don't know

293
00:15:04,360 --> 00:15:06,400
Speaker 8: anything about what I'm talking about that, but.

294
00:15:08,320 --> 00:15:12,560
Speaker 7: Historically it's when like huge like revolutions and shit happened.

295
00:15:13,560 --> 00:15:15,480
Speaker 1: All right, Oh, hell yeah, I think we kind of

296
00:15:15,520 --> 00:15:18,120
Speaker 1: need that right now, guys low key, I think we

297
00:15:18,240 --> 00:15:20,680
Speaker 1: need that right now. All right, So that was your

298
00:15:20,760 --> 00:15:27,440
Speaker 1: under overrated? Underrated? Is the pit? Overrated? Is he anti heroes? No?

299
00:15:27,760 --> 00:15:29,960
Speaker 7: Underrated? Was uh?

300
00:15:30,960 --> 00:15:31,240
Speaker 1: Fuck?

301
00:15:31,800 --> 00:15:33,480
Speaker 7: I actually forget what I just said.

302
00:15:33,760 --> 00:15:34,960
Speaker 1: No, did you say the pit?

303
00:15:36,000 --> 00:15:38,440
Speaker 7: No? The pit was me leading into anti heroes? I

304
00:15:38,480 --> 00:15:43,360
Speaker 7: think anti and underrated is something else?

305
00:15:43,960 --> 00:15:45,600
Speaker 1: Is the pit? Wait?

306
00:15:45,960 --> 00:15:48,760
Speaker 7: Oh oh wait, I fuck this up? Processed food?

307
00:15:49,960 --> 00:15:55,480
Speaker 1: Oh that's okay, sod under let's do that, Okay, Okay,

308
00:15:55,560 --> 00:15:58,360
Speaker 1: So I think we flipped your overrated and underrated? What

309
00:15:58,360 --> 00:16:03,760
Speaker 1: what's something you think under rated? Okay? Process? Which one?

310
00:16:03,840 --> 00:16:06,520
Speaker 1: What kind? What's the most underrated? Process? Food. I think

311
00:16:06,560 --> 00:16:10,040
Speaker 1: like cheese, processed cheese, American cheese.

312
00:16:10,680 --> 00:16:13,040
Speaker 7: Yeah, I'm just kind of like over healthy.

313
00:16:13,360 --> 00:16:18,920
Speaker 1: It's like, yeah, American just oil and water in the

314
00:16:18,960 --> 00:16:21,840
Speaker 1: shape of it. I fucking I am fully in agreement

315
00:16:21,880 --> 00:16:24,680
Speaker 1: that I love American cheese. Like the melt the melting

316
00:16:24,720 --> 00:16:28,840
Speaker 1: point on that ship is so crucial, Like, yes, I guess,

317
00:16:28,840 --> 00:16:29,960
Speaker 1: and I was made in a lab.

318
00:16:30,400 --> 00:16:32,520
Speaker 3: I used to rake my teeth over the edges of

319
00:16:32,520 --> 00:16:34,240
Speaker 3: the plastic to make sure I was getting all that

320
00:16:34,320 --> 00:16:36,200
Speaker 3: cheese off when I was a kid, you know what

321
00:16:36,200 --> 00:16:38,560
Speaker 3: I mean, Like.

322
00:16:37,760 --> 00:16:43,760
Speaker 1: Like room temps, like melts at room temp is a

323
00:16:43,840 --> 00:16:47,280
Speaker 1: wild It is a wild quality for a substance to have.

324
00:16:48,200 --> 00:16:51,120
Speaker 7: Watching the craft singles get squirted into the plastic and

325
00:16:51,120 --> 00:16:53,880
Speaker 7: then the plastic kind of adhering it around it is

326
00:16:54,120 --> 00:16:55,000
Speaker 7: pretty fascinating.

327
00:16:55,000 --> 00:16:56,240
Speaker 1: Poetry poetry.

328
00:16:56,360 --> 00:16:59,880
Speaker 7: Yeah, that's the Hopecore edit that I should be watching.

329
00:17:00,600 --> 00:17:04,159
Speaker 1: Yeah, I guess. I mean we shouldn't be surprised that

330
00:17:04,560 --> 00:17:08,040
Speaker 1: processed food as you're underrated because you said that your

331
00:17:08,040 --> 00:17:14,480
Speaker 1: favorite cookie is white chocolate macadamia nut cookies from somewhere. Yeah, yeah,

332
00:17:14,520 --> 00:17:14,960
Speaker 1: that's fine.

333
00:17:15,720 --> 00:17:16,760
Speaker 7: They're always moist.

334
00:17:17,359 --> 00:17:20,560
Speaker 1: They are they are, and you know why, because they

335
00:17:20,560 --> 00:17:25,600
Speaker 1: are sprayed down with chemicals that aren't available to anybody

336
00:17:25,680 --> 00:17:30,160
Speaker 1: except No Fortune, five hundred corporations and the US military.

337
00:17:30,480 --> 00:17:32,960
Speaker 1: Brian just he trusts the process. That's right.

338
00:17:33,000 --> 00:17:34,840
Speaker 8: You gotta trust the process. I think we need to

339
00:17:34,880 --> 00:17:37,520
Speaker 8: stop having trying to have so much control over everything.

340
00:17:37,800 --> 00:17:42,040
Speaker 1: That's trust the process. Control works in the short term.

341
00:17:42,240 --> 00:17:44,640
Speaker 1: Long term, it's a bad strategy. It's gonna it's gonna

342
00:17:44,640 --> 00:17:47,280
Speaker 1: fuck you up trying to control everything. Just to let

343
00:17:47,320 --> 00:17:51,480
Speaker 1: it go. Eat the fucking processed food. Trust the process.

344
00:17:51,680 --> 00:17:55,920
Speaker 1: Trust the process food. And we we know how well

345
00:17:56,000 --> 00:17:58,440
Speaker 1: that worked out for the seventy six ers, so it'll

346
00:17:58,480 --> 00:18:01,200
Speaker 1: probably work out for all of us as well. That's

347
00:18:01,240 --> 00:18:03,800
Speaker 1: my basketball team, bro. You need a hope Core Sixers

348
00:18:03,800 --> 00:18:09,240
Speaker 1: at it? Man, really, I do not. I'm going over here.

349
00:18:09,760 --> 00:18:12,600
Speaker 1: I'm dead man. I think about that ship for a

350
00:18:12,600 --> 00:18:16,400
Speaker 1: little while. Yeah, all right, great to have you back, Brian.

351
00:18:16,480 --> 00:18:18,440
Speaker 1: We're gonna take a quick break. We're gonna come back.

352
00:18:18,440 --> 00:18:20,560
Speaker 1: We're gonna talk about some of this news. You heard

353
00:18:20,560 --> 00:18:31,880
Speaker 1: about this stuff the news. We'll be right back and

354
00:18:32,080 --> 00:18:36,719
Speaker 1: we're back. We're back and we on this podcast, we

355
00:18:36,760 --> 00:18:40,119
Speaker 1: asked the hard hitting questions such as where are we

356
00:18:40,200 --> 00:18:44,479
Speaker 1: at with AI generally just like generally, what's the vibes,

357
00:18:44,960 --> 00:18:48,879
Speaker 1: doctor McNerney, So every because I remember when we first

358
00:18:48,880 --> 00:18:50,280
Speaker 1: had you on, we were.

359
00:18:50,160 --> 00:18:54,080
Speaker 3: Like in the midst of like e AI EI, mommy

360
00:18:54,080 --> 00:18:59,520
Speaker 3: E I yeah, AI, researchers giving us like the warning

361
00:19:00,200 --> 00:19:03,120
Speaker 3: could be the next just's gonna end the world. I'm

362
00:19:03,240 --> 00:19:09,280
Speaker 3: I'm taking cyanide now to preemptively excuse myself from this apocalypse.

363
00:19:09,720 --> 00:19:12,200
Speaker 3: And after a while, I think that's as we started

364
00:19:12,200 --> 00:19:14,159
Speaker 3: to speak to more and more experts, we all became

365
00:19:14,240 --> 00:19:17,120
Speaker 3: rightly unconvinced by its capabilities and they're like, oh, it's

366
00:19:17,119 --> 00:19:20,439
Speaker 3: a fancy computer program that's like a toy basically, but

367
00:19:20,480 --> 00:19:22,520
Speaker 3: they're saying you can do so much more. Then we

368
00:19:22,560 --> 00:19:25,240
Speaker 3: see more and more articles about how people using AI

369
00:19:25,320 --> 00:19:27,960
Speaker 3: are like fucking up in like legal proceedings like the

370
00:19:28,040 --> 00:19:31,680
Speaker 3: MyPillow guy his lawyers used chat GPT recently and the

371
00:19:32,119 --> 00:19:35,879
Speaker 3: judges like they're like thirty critical errors in your like

372
00:19:36,000 --> 00:19:38,320
Speaker 3: your response here, Like I don't even know what this is.

373
00:19:38,760 --> 00:19:42,399
Speaker 3: But now I'm like, are we now? Sort of I

374
00:19:42,400 --> 00:19:44,159
Speaker 3: think because we're sort of past the thing of like

375
00:19:44,200 --> 00:19:47,760
Speaker 3: this is that world ender game changer. But still these

376
00:19:47,760 --> 00:19:50,240
Speaker 3: companies have to make the line go up. Are we

377
00:19:50,359 --> 00:19:52,520
Speaker 3: like now seeing more of a strategy to just kind

378
00:19:52,520 --> 00:19:56,200
Speaker 3: of normalize AI, use to get people to slowly warm

379
00:19:56,280 --> 00:19:59,040
Speaker 3: up to it than sort of like the big explosive

380
00:19:59,119 --> 00:20:01,960
Speaker 3: sort of debut with like chat GPT and things that

381
00:20:02,000 --> 00:20:04,440
Speaker 3: we saw, because I feel like now I hear more

382
00:20:04,520 --> 00:20:08,400
Speaker 3: people talk about AI when they are like miyazakifying themselves

383
00:20:08,840 --> 00:20:11,680
Speaker 3: or getting a recipe recommendation, rather than like this is

384
00:20:11,720 --> 00:20:13,560
Speaker 3: going to replace the entire medical field.

385
00:20:14,080 --> 00:20:14,280
Speaker 1: Yeah.

386
00:20:14,320 --> 00:20:17,280
Speaker 2: I mean, I think normalization is the exact right word.

387
00:20:17,359 --> 00:20:19,680
Speaker 2: And I was just thinking this today as I went

388
00:20:19,720 --> 00:20:21,560
Speaker 2: on to Google Search, and of course they now have

389
00:20:21,680 --> 00:20:25,640
Speaker 2: that AI overview summary and even some of my fee Yeah.

390
00:20:26,040 --> 00:20:30,240
Speaker 1: You're talking about my friend Gemini. Yeah, we're pretty intited actually, so.

391
00:20:31,320 --> 00:20:35,439
Speaker 2: They're yeah, some answers, you know, even things like that.

392
00:20:35,480 --> 00:20:37,800
Speaker 2: I think people like me who say probably wouldn't have

393
00:20:37,800 --> 00:20:41,000
Speaker 2: gone to chat GPT just to do like basic information searches,

394
00:20:41,240 --> 00:20:43,200
Speaker 2: I have to admit I do read the AI summaries

395
00:20:43,280 --> 00:20:44,840
Speaker 2: quite a lot, and you know, and I think that

396
00:20:45,080 --> 00:20:46,760
Speaker 2: it's a way of just kind of like making it

397
00:20:46,920 --> 00:20:50,520
Speaker 2: really frictionless and really really easy to immediately use an

398
00:20:50,560 --> 00:20:52,320
Speaker 2: AI tool. And I think we see that with a

399
00:20:52,320 --> 00:20:55,040
Speaker 2: lot of like AI roll out and look, and on

400
00:20:55,080 --> 00:20:57,520
Speaker 2: the one hand, like, I'm not completely against the use

401
00:20:57,560 --> 00:20:59,480
Speaker 2: of these AI tools if you're really finding it useful

402
00:20:59,480 --> 00:21:01,679
Speaker 2: and if it's from liable, you're kind of getting the

403
00:21:01,680 --> 00:21:04,000
Speaker 2: full picture behind the tool and what it's for. But

404
00:21:04,040 --> 00:21:05,800
Speaker 2: on the other hand, I think something I do worry

405
00:21:05,840 --> 00:21:08,480
Speaker 2: about is something that I think has to be integrated

406
00:21:08,480 --> 00:21:11,280
Speaker 2: into all our ethical tech use is like a meaningful

407
00:21:11,320 --> 00:21:13,320
Speaker 2: sense of the opt out or a meaningful sense of

408
00:21:13,320 --> 00:21:15,560
Speaker 2: the opt in. So like, do I have the right

409
00:21:15,600 --> 00:21:17,360
Speaker 2: in the ability to say, like, nah, I don't want

410
00:21:17,400 --> 00:21:20,000
Speaker 2: to use this, and you know, am I being like

411
00:21:20,080 --> 00:21:23,600
Speaker 2: actively consulted and being able to consent to using particular

412
00:21:23,640 --> 00:21:27,040
Speaker 2: tools or programs and so on and so forth. And yeah,

413
00:21:27,080 --> 00:21:29,679
Speaker 2: I think the current rollout of AI tools is not

414
00:21:29,840 --> 00:21:31,639
Speaker 2: really complying with that particularly.

415
00:21:31,640 --> 00:21:35,040
Speaker 1: Well mm hmm right, Yeah, they're as excited they have

416
00:21:35,080 --> 00:21:37,040
Speaker 1: a new toy that they want to that they want

417
00:21:37,080 --> 00:21:39,320
Speaker 1: to use that they're going to make a you know,

418
00:21:39,960 --> 00:21:45,520
Speaker 1: Super Bowl commercial about Gouda cheese the most did you

419
00:21:45,520 --> 00:21:50,400
Speaker 1: see that, doctor mckenry that Google advertised their AI product

420
00:21:50,800 --> 00:21:54,600
Speaker 1: with a Super Bowl ad that had incorrect information in it.

421
00:21:54,840 --> 00:21:57,679
Speaker 1: The premise was, this is a cheese farmer, and he

422
00:21:58,280 --> 00:22:00,919
Speaker 1: is a whiz when it comes to me making good cheese,

423
00:22:01,200 --> 00:22:03,960
Speaker 1: but he's all thumbs when it comes to writing marketing material.

424
00:22:04,359 --> 00:22:08,000
Speaker 1: And so he it showed Google AI like telling him

425
00:22:08,320 --> 00:22:10,720
Speaker 1: facts that he could put on his cheese. And one

426
00:22:10,720 --> 00:22:13,920
Speaker 1: of the facts was that gouda cheese is the most

427
00:22:13,960 --> 00:22:18,880
Speaker 1: popular type of cheese and sixty percent of the world's

428
00:22:19,480 --> 00:22:22,720
Speaker 1: cheese sales. What which is just like on its surface,

429
00:22:22,840 --> 00:22:25,960
Speaker 1: like obviously wrong, and they still like put it in

430
00:22:26,119 --> 00:22:29,400
Speaker 1: a in a super Bowl commercial, like somebody caught it

431
00:22:29,760 --> 00:22:32,240
Speaker 1: once the ad had like been up and so it

432
00:22:32,320 --> 00:22:34,760
Speaker 1: didn't make it to the Super Bowl, but it made

433
00:22:34,800 --> 00:22:37,520
Speaker 1: it like online and was viewed millions of times. And

434
00:22:37,560 --> 00:22:41,520
Speaker 1: it's just that seems to be like it's if if

435
00:22:41,560 --> 00:22:44,720
Speaker 1: it's not going to be one hundred percent, if it's

436
00:22:44,760 --> 00:22:46,600
Speaker 1: not going to be right one hundred percent of the time,

437
00:22:46,680 --> 00:22:51,800
Speaker 1: it's kind of useless because it's just like that. I mean, yeah,

438
00:22:51,280 --> 00:22:55,399
Speaker 1: I feel like everybody should be like would be opting

439
00:22:55,440 --> 00:22:57,159
Speaker 1: out of it if they knew, like and just so

440
00:22:57,240 --> 00:22:59,800
Speaker 1: you know, like one out of like every I don't know,

441
00:23:00,440 --> 00:23:02,439
Speaker 1: twenty of the things that you search is going to

442
00:23:02,480 --> 00:23:05,280
Speaker 1: be like blatantly wrong in a way that is going

443
00:23:05,320 --> 00:23:08,919
Speaker 1: to be humiliating to you. Yeah. Yeah, we're not going

444
00:23:09,000 --> 00:23:11,480
Speaker 1: to tell you which one enjoy the product.

445
00:23:12,000 --> 00:23:12,160
Speaker 5: Yeah.

446
00:23:12,200 --> 00:23:13,840
Speaker 2: I mean, first, I love the idea that it's not

447
00:23:13,880 --> 00:23:16,040
Speaker 2: even an era, but it's just big Goudas out there

448
00:23:16,080 --> 00:23:19,080
Speaker 2: trying to spread some misinformation about the popularity of Good

449
00:23:19,160 --> 00:23:21,600
Speaker 2: as Cheese. But second, like, yeah, I feel like one

450
00:23:21,640 --> 00:23:23,760
Speaker 2: way I've heard people describe it as this idea of like, yeah,

451
00:23:23,840 --> 00:23:26,119
Speaker 2: something like chat GPT or the AI overview can be

452
00:23:26,160 --> 00:23:29,000
Speaker 2: useful for getting like a sense of a topic. But yeah,

453
00:23:29,000 --> 00:23:30,640
Speaker 2: I guess the issue for me is like that often

454
00:23:30,680 --> 00:23:34,080
Speaker 2: requires quite a lot of expertise to be able to

455
00:23:34,119 --> 00:23:36,800
Speaker 2: know whether something is right or not. And so for example,

456
00:23:36,800 --> 00:23:39,680
Speaker 2: like my husband as from North Carolina, as a basketball fan,

457
00:23:39,800 --> 00:23:43,399
Speaker 2: like I am sort of forcibly inducted into the NBA

458
00:23:43,600 --> 00:23:45,760
Speaker 2: enough that yeah, I could probably tell the eighty percent.

459
00:23:45,920 --> 00:23:48,960
Speaker 2: Maybe actually that might be overestimating my knowledge, but yeah,

460
00:23:48,960 --> 00:23:52,240
Speaker 2: that twenty percent would be totally out of my knowledge.

461
00:23:52,280 --> 00:23:54,800
Speaker 2: And so I think that's my fears. If you're relying

462
00:23:54,800 --> 00:23:57,439
Speaker 2: on these tools, it's not that people can't tell that

463
00:23:57,560 --> 00:24:01,119
Speaker 2: something's wrong, but you know, when we're using it for

464
00:24:01,160 --> 00:24:03,439
Speaker 2: like a really wide range of applications. It does actually

465
00:24:03,440 --> 00:24:05,680
Speaker 2: require a lot of expertise in all those different things.

466
00:24:05,720 --> 00:24:08,560
Speaker 2: And I think, certainly speaking for myself, like that's not

467
00:24:08,640 --> 00:24:10,800
Speaker 2: something I would be able to do or to discern.

468
00:24:11,400 --> 00:24:14,040
Speaker 3: Yeah, because I mean sure, like you think of it like, well,

469
00:24:14,040 --> 00:24:15,679
Speaker 3: if I get to be on my test, but if

470
00:24:15,680 --> 00:24:18,320
Speaker 3: you're asking something to like explain the Civil War and

471
00:24:18,359 --> 00:24:21,120
Speaker 3: you get all the generals and the battles and dates right,

472
00:24:21,440 --> 00:24:25,480
Speaker 3: but the reason for the American Civil War wrong, where

473
00:24:25,480 --> 00:24:29,080
Speaker 3: it's like and they all fought over you know, economic rights,

474
00:24:29,200 --> 00:24:32,399
Speaker 3: and then that's now it doesn't matter that eighty percent

475
00:24:32,480 --> 00:24:35,440
Speaker 3: is completely negated by this other piece of information that

476
00:24:35,520 --> 00:24:38,960
Speaker 3: has completely colored the you know, the description of something.

477
00:24:39,000 --> 00:24:39,160
Speaker 1: Now.

478
00:24:39,359 --> 00:24:42,199
Speaker 3: That's why I'm like, even every time I see those summaries,

479
00:24:42,520 --> 00:24:45,159
Speaker 3: I'm like, no, Like I'll click on the links that

480
00:24:45,240 --> 00:24:47,679
Speaker 3: are saying like we're using these to tell us, and

481
00:24:47,720 --> 00:24:49,520
Speaker 3: I'll look at them like, this is not really even

482
00:24:49,560 --> 00:24:51,960
Speaker 3: exactly what's happening here, to the point that I feel

483
00:24:51,960 --> 00:24:54,280
Speaker 3: like it's causing more harm than good because I've at

484
00:24:54,359 --> 00:24:58,560
Speaker 3: least learned to try and just research myself. You know,

485
00:24:58,640 --> 00:25:02,159
Speaker 3: That's how I got my theories on the Earth shape

486
00:25:02,160 --> 00:25:03,679
Speaker 3: and things like that, you know, research.

487
00:25:03,680 --> 00:25:05,560
Speaker 1: I have some interesting ideas on that. I don't know

488
00:25:05,560 --> 00:25:08,680
Speaker 1: if you have a couple hours, Doctor mcinnerey.

489
00:25:08,240 --> 00:25:11,520
Speaker 3: Well, Doctor, every time I've emailed Doctor Carry, she's respectfully

490
00:25:11,520 --> 00:25:13,919
Speaker 3: declined to entertain the conversation, which I say she has a.

491
00:25:13,920 --> 00:25:15,239
Speaker 1: Luck going on. She has a luck going on, but

492
00:25:15,320 --> 00:25:16,880
Speaker 1: I mean it is she could be a useful source

493
00:25:16,920 --> 00:25:18,480
Speaker 1: to you though you're looking for somebody who's been in

494
00:25:18,520 --> 00:25:24,520
Speaker 1: an airplane. You know, I looked out. Are there cool

495
00:25:24,840 --> 00:25:28,280
Speaker 1: uses of AI that aren't getting attention or I guess

496
00:25:28,320 --> 00:25:31,199
Speaker 1: even like tech breakthroughs that you know, we asked this

497
00:25:31,240 --> 00:25:34,200
Speaker 1: the last time you were on but like we're still

498
00:25:34,240 --> 00:25:38,360
Speaker 1: in the early stages of AI. Are there directions that

499
00:25:38,480 --> 00:25:41,199
Speaker 1: have like popped out to you that you know the

500
00:25:41,240 --> 00:25:44,680
Speaker 1: future of technology and this technology in particular could take

501
00:25:44,720 --> 00:25:48,760
Speaker 1: that are promising for like the bettering of the world

502
00:25:49,160 --> 00:25:51,280
Speaker 1: for more than like twelve rich guys, which I feel

503
00:25:51,280 --> 00:25:53,680
Speaker 1: like is the current the current model. It's like these

504
00:25:53,720 --> 00:25:56,479
Speaker 1: twelve guys are like, yeah, this would be amazing if

505
00:25:56,520 --> 00:25:59,879
Speaker 1: we could replace all the people to see me with

506
00:26:00,080 --> 00:26:03,080
Speaker 1: total role. I was with Total Rope from the Miyazaki

507
00:26:03,119 --> 00:26:08,560
Speaker 1: movie but where are you seeing hope? Where are you

508
00:26:08,640 --> 00:26:09,160
Speaker 1: seeing hope?

509
00:26:09,200 --> 00:26:11,920
Speaker 2: I guess I'm genuinely terrified and like say something it

510
00:26:11,960 --> 00:26:13,800
Speaker 2: would be like that's the exact same amount so you

511
00:26:13,800 --> 00:26:19,520
Speaker 2: said two years ago, and like destroy any hope. Yeah,

512
00:26:19,640 --> 00:26:21,960
Speaker 2: I mean I think like I'm always like quite excited

513
00:26:22,000 --> 00:26:25,240
Speaker 2: by more creative uses of AI or people who are

514
00:26:25,280 --> 00:26:27,760
Speaker 2: like really trying to think about like instead of saying,

515
00:26:27,760 --> 00:26:29,359
Speaker 2: like how can we make like one product that works

516
00:26:29,359 --> 00:26:31,240
Speaker 2: for the whole world, like which tends to be the

517
00:26:31,280 --> 00:26:33,480
Speaker 2: approach of things like to GPT and then like sprow.

518
00:26:33,840 --> 00:26:35,879
Speaker 2: They obviously don't work for the whole world in all

519
00:26:35,920 --> 00:26:38,879
Speaker 2: these different cultural contexts and all these different linguistic contexts,

520
00:26:38,880 --> 00:26:42,560
Speaker 2: because I don't think a single product can. But you know,

521
00:26:42,600 --> 00:26:45,439
Speaker 2: I do think like there are really interesting examples of

522
00:26:45,440 --> 00:26:48,280
Speaker 2: groups like Tahukumdia in New Zealand where I'm from, which

523
00:26:48,320 --> 00:26:50,920
Speaker 2: have been like using AI machine learning techiks to try

524
00:26:51,000 --> 00:26:54,240
Speaker 2: and focus on tremodi or the indigenous language of New

525
00:26:54,320 --> 00:26:58,439
Speaker 2: Zealand's language preservation. And so because of like long histories

526
00:26:58,480 --> 00:27:01,200
Speaker 2: of kind of the state suppression of TERI, there was

527
00:27:01,240 --> 00:27:03,600
Speaker 2: like a period where like there weren't that many native

528
00:27:03,600 --> 00:27:06,720
Speaker 2: trail Moldi speakers, it was like really aggressively suppressed, and

529
00:27:06,840 --> 00:27:08,520
Speaker 2: now as a result, people are kind of trying to

530
00:27:08,560 --> 00:27:12,240
Speaker 2: really reinvest and support the kind of revitalization of trail

531
00:27:12,320 --> 00:27:16,320
Speaker 2: Modi and Peter Lucas Jones, who this CEO, I believe

532
00:27:16,440 --> 00:27:19,439
Speaker 2: Teaku Media and like their whole team have been really

533
00:27:19,680 --> 00:27:22,200
Speaker 2: intentional and sort of really world leading, I think, and

534
00:27:22,280 --> 00:27:24,760
Speaker 2: how they've been trying to use AI machine learning for this.

535
00:27:25,240 --> 00:27:27,080
Speaker 2: I think a big part of their project though, is

536
00:27:27,080 --> 00:27:30,800
Speaker 2: that they're very insistent on like indigenous data sovereignty or

537
00:27:30,840 --> 00:27:33,680
Speaker 2: making sure that like their platforms aren't sold to big

538
00:27:33,720 --> 00:27:35,719
Speaker 2: tech or reliant on big tech. And I think I

539
00:27:35,720 --> 00:27:39,040
Speaker 2: imagine that's like a really challenging project because like this

540
00:27:39,160 --> 00:27:41,879
Speaker 2: sort of small handful of like big tech firms like

541
00:27:42,119 --> 00:27:45,320
Speaker 2: are incredibly dominant in this space, but a lot of

542
00:27:45,320 --> 00:27:47,240
Speaker 2: that has been around like no, you know, we really

543
00:27:47,280 --> 00:27:49,280
Speaker 2: want to make sure that this remains like technology by

544
00:27:49,400 --> 00:27:52,560
Speaker 2: and for MALDI people and for our organization, And so

545
00:27:52,560 --> 00:27:55,639
Speaker 2: I think projects like that I find like really really

546
00:27:55,680 --> 00:27:58,119
Speaker 2: inspiring and really important. But I think they're also just

547
00:27:58,160 --> 00:28:01,080
Speaker 2: like a great example of like AI development being done

548
00:28:01,080 --> 00:28:03,120
Speaker 2: super well, which is like you have a clear problem,

549
00:28:03,160 --> 00:28:05,440
Speaker 2: and you have clear ideas and ways that you think

550
00:28:05,520 --> 00:28:08,200
Speaker 2: that AI machine learning can help us address and part

551
00:28:08,320 --> 00:28:11,560
Speaker 2: some of this problem without like positioning it as the solution.

552
00:28:11,960 --> 00:28:13,640
Speaker 2: Because I think if anyone comes out of the gate

553
00:28:13,640 --> 00:28:15,600
Speaker 2: and it's like AI is going to solve this problem,

554
00:28:15,680 --> 00:28:18,360
Speaker 2: that's when I think you should always be a bit like, oh,

555
00:28:18,480 --> 00:28:20,439
Speaker 2: I don't think it is, especially if the problem is

556
00:28:20,480 --> 00:28:25,480
Speaker 2: something like really really massive, right discrimination, it's kind of like, well, look,

557
00:28:25,520 --> 00:28:27,600
Speaker 2: we just have to reject that one sort of straight

558
00:28:27,640 --> 00:28:29,320
Speaker 2: out the gate and kind of think a bit more

559
00:28:29,359 --> 00:28:30,600
Speaker 2: specifically about this.

560
00:28:30,840 --> 00:28:32,359
Speaker 3: And I'm sure those companies are like and when we

561
00:28:32,400 --> 00:28:34,480
Speaker 3: made that claim, that wasn't actually meant for you to

562
00:28:34,520 --> 00:28:36,600
Speaker 3: be the receiver of that message. It was for Wall

563
00:28:36,640 --> 00:28:40,160
Speaker 3: Street and investors when we said this thing will solve everything,

564
00:28:40,960 --> 00:28:43,400
Speaker 3: because like, yeah, I mean like that application feels like

565
00:28:43,440 --> 00:28:45,680
Speaker 3: the kind of thing that like, you know, in the

566
00:28:45,800 --> 00:28:50,280
Speaker 3: US right now, Trump is very focused on eliminating any

567
00:28:50,320 --> 00:28:53,280
Speaker 3: semblance of equity or diversity inclusion.

568
00:28:53,400 --> 00:28:55,560
Speaker 1: Obviously as we've seen like the woke.

569
00:28:55,400 --> 00:29:01,000
Speaker 3: DEI initiative crusade against all those things, and that sounds

570
00:29:01,040 --> 00:29:03,160
Speaker 3: like exactly the kind of thing that Trump would be. Like,

571
00:29:03,200 --> 00:29:05,680
Speaker 3: that's not useful. It just has to be this other

572
00:29:05,720 --> 00:29:08,960
Speaker 3: thing that's a money making endeavor. Because right now his people,

573
00:29:09,000 --> 00:29:11,480
Speaker 3: like the people within the administration, like the head of

574
00:29:11,520 --> 00:29:15,440
Speaker 3: Science and Technology, have said things like Biden's AI policies

575
00:29:15,440 --> 00:29:18,400
Speaker 3: were like divisive and it's all been about all been

576
00:29:18,440 --> 00:29:21,920
Speaker 3: about the redistribution, like redistribution in the name of equity.

577
00:29:22,400 --> 00:29:25,320
Speaker 3: And naturally Trump has fired many of the AI experts

578
00:29:25,400 --> 00:29:29,600
Speaker 3: Biden hired because it was clear like obviously Biden hired them.

579
00:29:29,640 --> 00:29:32,080
Speaker 3: He's like, there's a huge bias problem with any of

580
00:29:32,120 --> 00:29:33,520
Speaker 3: this stuff, and if it's even going to be a

581
00:29:33,520 --> 00:29:37,240
Speaker 3: product people use, like that's probably a thing worth tackling.

582
00:29:37,680 --> 00:29:40,880
Speaker 3: But now like it's become sort of like normalized within

583
00:29:41,000 --> 00:29:44,600
Speaker 3: this administration to say this is all harmful now because

584
00:29:44,600 --> 00:29:48,280
Speaker 3: it's trying to advance equity, when like when we've spoken

585
00:29:48,320 --> 00:29:50,200
Speaker 3: to people like you and other experts, it's like, no,

586
00:29:50,320 --> 00:29:52,760
Speaker 3: you have to get rid of those inequities or else

587
00:29:52,840 --> 00:29:56,480
Speaker 3: it doesn't even fucking work, Like like when you're talking

588
00:29:56,520 --> 00:29:59,000
Speaker 3: about things like being able to someone who has like

589
00:29:59,040 --> 00:30:01,920
Speaker 3: a darker complexion, and how does a like a self

590
00:30:01,960 --> 00:30:05,520
Speaker 3: automated car even identify that pedestrian as an object to

591
00:30:05,600 --> 00:30:09,440
Speaker 3: avoid because again, these biases affect all these different systems.

592
00:30:09,720 --> 00:30:12,520
Speaker 3: But it feels like now, like at least from the

593
00:30:12,560 --> 00:30:15,960
Speaker 3: American conservative side of things, or just generally the tech

594
00:30:16,000 --> 00:30:18,040
Speaker 3: con con conservative movement.

595
00:30:17,800 --> 00:30:20,880
Speaker 1: Is like, yeah, maybe it's just like fine if it you.

596
00:30:20,840 --> 00:30:24,760
Speaker 3: Know, misidentifies like black people or doing these other things

597
00:30:24,760 --> 00:30:27,280
Speaker 3: that just kind of show that at the end of

598
00:30:27,320 --> 00:30:29,280
Speaker 3: the day, we only it only needs to work in

599
00:30:29,320 --> 00:30:30,840
Speaker 3: the way that we want it to work, and all

600
00:30:30,840 --> 00:30:32,880
Speaker 3: the other applications whatever be damned.

601
00:30:32,680 --> 00:30:36,400
Speaker 1: Keeps identifying black people as traffic cones. Do we think

602
00:30:36,440 --> 00:30:39,160
Speaker 1: that we need can we go to market with us still?

603
00:30:39,360 --> 00:30:40,840
Speaker 1: Or is that? Are we good here?

604
00:30:40,960 --> 00:30:44,440
Speaker 3: Well that's yeah, And I'm just I'm amazed at how

605
00:30:45,800 --> 00:30:49,640
Speaker 3: you know, I think, how much like objectively, this is

606
00:30:49,680 --> 00:30:51,920
Speaker 3: the thing. If you want a product to work, it

607
00:30:52,000 --> 00:30:54,640
Speaker 3: has to be able to be used around the world.

608
00:30:54,960 --> 00:30:56,880
Speaker 3: So how useful is that in a place that doesn't

609
00:30:56,880 --> 00:31:00,440
Speaker 3: have like an ethnic majority that's all white people in

610
00:31:00,520 --> 00:31:03,320
Speaker 3: the same way like that? You know, again, these all

611
00:31:03,360 --> 00:31:05,560
Speaker 3: just feel very counterintuitive, But that seems to be the

612
00:31:05,640 --> 00:31:08,880
Speaker 3: name of this year, this year's theme generally.

613
00:31:08,960 --> 00:31:10,720
Speaker 2: Yeah, yeah, I mean the idea of this is the

614
00:31:10,800 --> 00:31:14,840
Speaker 2: year of counterintuitive things. Really resonates. Yeah, and I think

615
00:31:14,880 --> 00:31:17,040
Speaker 2: it's like not only disappointing, because it's like I do

616
00:31:17,080 --> 00:31:18,960
Speaker 2: think that it shouldn't be super hard to buy into

617
00:31:18,960 --> 00:31:21,320
Speaker 2: the idea that like, yeah, AI that is like more

618
00:31:21,360 --> 00:31:24,120
Speaker 2: equitable or less biased and more fear like genuinely is

619
00:31:24,160 --> 00:31:26,480
Speaker 2: actually in the long run good for everyone. Although I

620
00:31:26,520 --> 00:31:29,360
Speaker 2: know there's like a reactionary group that feels like any

621
00:31:29,440 --> 00:31:31,800
Speaker 2: kind of equity or equality is you know, kind of

622
00:31:31,840 --> 00:31:35,000
Speaker 2: impinging on their own kind of share of the pie.

623
00:31:35,360 --> 00:31:37,160
Speaker 2: But you know, I really think like AI ethics and

624
00:31:37,200 --> 00:31:39,840
Speaker 2: safety is for everyone. But yeah, I also think it's

625
00:31:39,920 --> 00:31:42,040
Speaker 2: very sad because like this has huge knock on effects

626
00:31:42,080 --> 00:31:44,160
Speaker 2: for the rest of the world, because like the US

627
00:31:44,280 --> 00:31:46,960
Speaker 2: is a wild leader in AI and tech production, and

628
00:31:47,040 --> 00:31:49,200
Speaker 2: so yeah, if you have an environment that is kind

629
00:31:49,240 --> 00:31:52,160
Speaker 2: of saying, let's throw ethics out the window, then that

630
00:31:52,200 --> 00:31:53,800
Speaker 2: does have knock on effects for the rest of the

631
00:31:53,840 --> 00:31:56,520
Speaker 2: world their bias and uses still a lot of this technology.

632
00:31:57,120 --> 00:31:59,840
Speaker 2: So yeah, I think like these rollbacks obviously massively affect

633
00:31:59,880 --> 00:32:03,400
Speaker 2: the as domestic context, but they certainly don't stop.

634
00:32:03,240 --> 00:32:06,160
Speaker 3: There, right, Is there any do you see any put

635
00:32:06,400 --> 00:32:09,240
Speaker 3: like this is as stupid on its face as it

636
00:32:09,280 --> 00:32:11,840
Speaker 3: sounds right to be like, we have to we have

637
00:32:11,920 --> 00:32:15,120
Speaker 3: to stop with these inclusive efforts to address biases within

638
00:32:15,200 --> 00:32:16,600
Speaker 3: AI like models.

639
00:32:16,800 --> 00:32:17,320
Speaker 1: There's no.

640
00:32:18,840 --> 00:32:21,000
Speaker 3: Like it's as dumb as that sounds, right, because in

641
00:32:21,040 --> 00:32:23,360
Speaker 3: my mind and everything I've read, people are like, no, no, no,

642
00:32:23,440 --> 00:32:27,680
Speaker 3: like it it works worse when it has all these

643
00:32:27,760 --> 00:32:30,920
Speaker 3: like inbuilt biases, like it will not work as good

644
00:32:30,960 --> 00:32:34,800
Speaker 3: therefore is not viable. So it is that is bad, right,

645
00:32:34,880 --> 00:32:36,560
Speaker 3: There's no like secret things like well, you.

646
00:32:36,520 --> 00:32:39,360
Speaker 1: Know some bias is good for these things.

647
00:32:39,960 --> 00:32:42,720
Speaker 2: I mean, if someone if that is that is the

648
00:32:42,760 --> 00:32:45,240
Speaker 2: secret source and please tell me, definitely not what I

649
00:32:45,240 --> 00:32:47,320
Speaker 2: would think. I mean, yeah, I mean I think it

650
00:32:47,400 --> 00:32:48,960
Speaker 2: just comes down to again, like I think there's a

651
00:32:49,000 --> 00:32:51,640
Speaker 2: fundamental irony of saying, like, you know, the power of

652
00:32:51,880 --> 00:32:54,120
Speaker 2: these AI enabled tools and products is that you know,

653
00:32:54,160 --> 00:32:57,840
Speaker 2: we can perform all these like massive tasks at scale

654
00:32:57,880 --> 00:32:59,480
Speaker 2: in this idea, and again like a product that can

655
00:32:59,520 --> 00:33:01,440
Speaker 2: be sold the world, a product that can be used

656
00:33:01,480 --> 00:33:04,160
Speaker 2: at scale with the kind of knowledge that this only

657
00:33:04,200 --> 00:33:06,720
Speaker 2: really works for like a very very narrow base. I

658
00:33:06,720 --> 00:33:08,400
Speaker 2: mean my assumption to be fair though, is like I

659
00:33:08,400 --> 00:33:10,640
Speaker 2: think that a lot of people who make products that

660
00:33:10,760 --> 00:33:13,600
Speaker 2: have you know, these kinds of exclusions or biases aren't

661
00:33:13,640 --> 00:33:16,120
Speaker 2: necessarily going in being like, I know my product is

662
00:33:16,200 --> 00:33:18,520
Speaker 2: really biased, and I actually just like don't care, Like

663
00:33:18,880 --> 00:33:21,160
Speaker 2: I don't think that usually is it? I think often

664
00:33:21,200 --> 00:33:23,680
Speaker 2: to me, it's just this kind of mindset of either

665
00:33:23,920 --> 00:33:25,800
Speaker 2: we just like haven't really thought about it. I think

666
00:33:25,800 --> 00:33:29,920
Speaker 2: this particularly common with accessibility, which is that of accessibility

667
00:33:29,960 --> 00:33:32,120
Speaker 2: has to be integrated and from the very beginning of

668
00:33:32,120 --> 00:33:34,480
Speaker 2: the design process. It can't be slapped on at the end.

669
00:33:34,520 --> 00:33:37,080
Speaker 2: And too often I think that's how it gets approached,

670
00:33:37,080 --> 00:33:39,320
Speaker 2: and so people just haven't even begun to think about,

671
00:33:39,320 --> 00:33:41,920
Speaker 2: you know, oh, like will my language, I mean, sorry,

672
00:33:41,960 --> 00:33:44,840
Speaker 2: will my model work for disabled people? My product work

673
00:33:44,880 --> 00:33:48,320
Speaker 2: for people with these different kinds of like physical disabilities, Like,

674
00:33:48,400 --> 00:33:50,440
Speaker 2: you know, I think it's just that the whole groups

675
00:33:50,440 --> 00:33:54,080
Speaker 2: of populations just get ignored, or you know, maybe they've

676
00:33:54,120 --> 00:33:56,080
Speaker 2: realized and they think, oh, it's actually a really bad

677
00:33:56,120 --> 00:33:58,720
Speaker 2: thing that this product doesn't work for this particular group.

678
00:33:58,760 --> 00:34:00,520
Speaker 2: But I think I'm just going to make the trade

679
00:34:00,520 --> 00:34:02,080
Speaker 2: off and decide, like, I think that's a small enough

680
00:34:02,120 --> 00:34:04,600
Speaker 2: consumer base that I can still sell my product, and like,

681
00:34:04,800 --> 00:34:06,680
Speaker 2: I don't think I'll get too much into pushback. I

682
00:34:06,680 --> 00:34:08,960
Speaker 2: think it'll be fine. And I think this, you know,

683
00:34:09,080 --> 00:34:12,040
Speaker 2: might often be the case for populations that are perceived

684
00:34:12,040 --> 00:34:14,440
Speaker 2: as being like very very small. So I'm thinking of

685
00:34:14,480 --> 00:34:17,719
Speaker 2: say like trans or gender diverse people who often get

686
00:34:17,760 --> 00:34:20,799
Speaker 2: erased from certain data sets because they're like, oh, well,

687
00:34:20,800 --> 00:34:23,399
Speaker 2: this is like statistically a very small percentage. It's like, yeah,

688
00:34:23,400 --> 00:34:25,960
Speaker 2: but those people's exclusion like really really matters. It has

689
00:34:26,000 --> 00:34:28,359
Speaker 2: a huge impact on their life. If they go through

690
00:34:28,400 --> 00:34:31,359
Speaker 2: a scanner and their body is not recognized or seen

691
00:34:31,400 --> 00:34:34,879
Speaker 2: as non normative, or if they're excluded from different databases

692
00:34:34,960 --> 00:34:37,640
Speaker 2: like these do have huge knock on effects for people.

693
00:34:38,400 --> 00:34:40,319
Speaker 2: So yeah, so I guess I would say that, you know,

694
00:34:40,360 --> 00:34:42,439
Speaker 2: the kinds of people maybe who are not seeing AI

695
00:34:42,520 --> 00:34:45,400
Speaker 2: ethics as a priority, aren't doing it because they're just like,

696
00:34:45,480 --> 00:34:49,120
Speaker 2: you know, oh, you know, debiasing whatever. That's fine. I

697
00:34:49,160 --> 00:34:51,680
Speaker 2: think it's Yeah, it's probably more just to do with

698
00:34:51,719 --> 00:34:54,000
Speaker 2: a lot of different blinkers or like a certain kind

699
00:34:54,040 --> 00:34:56,520
Speaker 2: of narrow mindset about who your consumer is and who's

700
00:34:56,520 --> 00:34:58,200
Speaker 2: I actually using these products?

701
00:34:58,640 --> 00:35:01,920
Speaker 3: Right, Yeah, it does feel probably, But for the Trump administration,

702
00:35:01,960 --> 00:35:04,040
Speaker 3: I'd say they're probably very much focused on the fact

703
00:35:04,040 --> 00:35:06,360
Speaker 3: that because there it doesn't matter it's like, I don't know,

704
00:35:06,400 --> 00:35:08,440
Speaker 3: even if it makes everything unsafe, I just have to

705
00:35:08,440 --> 00:35:11,680
Speaker 3: say the words I don't like equity, and that's without

706
00:35:11,719 --> 00:35:13,239
Speaker 3: any consideration for what that means.

707
00:35:13,239 --> 00:35:16,040
Speaker 1: And if the whole world breaks, like the better for

708
00:35:16,120 --> 00:35:21,160
Speaker 1: him to consolidate power, you know, like that feels yeah,

709
00:35:21,200 --> 00:35:24,640
Speaker 1: I mean, did you see this Semaphore article about like

710
00:35:24,680 --> 00:35:29,319
Speaker 1: the group the Mark Andresen group chats that like it.

711
00:35:29,400 --> 00:35:32,160
Speaker 1: So he's been having like these signal chats since the

712
00:35:32,239 --> 00:35:35,360
Speaker 1: days of the pandemic, and it's like he has like

713
00:35:35,400 --> 00:35:38,160
Speaker 1: gone out of his way to bring in all of

714
00:35:38,200 --> 00:35:44,160
Speaker 1: these tech leaders and then like fucking super far right

715
00:35:44,200 --> 00:35:49,600
Speaker 1: wing like people like Richard Hannania, like the guy who's

716
00:35:49,600 --> 00:35:52,440
Speaker 1: like an outright white supremacist, and like put these people

717
00:35:52,440 --> 00:35:55,960
Speaker 1: in group chats together. Like at one point he like

718
00:35:56,040 --> 00:36:00,640
Speaker 1: brought in some progressive people and then they wrote a

719
00:36:00,960 --> 00:36:05,360
Speaker 1: New York Times op ed criticizing laws that were banning

720
00:36:05,360 --> 00:36:08,960
Speaker 1: critical race theory, and he just like had a meltdown

721
00:36:08,960 --> 00:36:12,239
Speaker 1: and was like you betrayed me by writing this thing,

722
00:36:12,440 --> 00:36:15,040
Speaker 1: and like kicked them out of the group chat, and

723
00:36:15,080 --> 00:36:18,439
Speaker 1: like since then, it's just all this like extreme right

724
00:36:18,520 --> 00:36:22,440
Speaker 1: wing propaganda that's being like kind of vomited back and

725
00:36:22,480 --> 00:36:26,880
Speaker 1: forth between these people who are like the biggest, most

726
00:36:26,920 --> 00:36:30,520
Speaker 1: powerful oligarchs and like the people who are in charge

727
00:36:30,719 --> 00:36:34,799
Speaker 1: of the direction that tech takes. And so I yeah,

728
00:36:34,840 --> 00:36:37,760
Speaker 1: I mean it feels like this whole thing is developing

729
00:36:37,760 --> 00:36:42,000
Speaker 1: in a way that feels particularly like non optimal and

730
00:36:42,120 --> 00:36:46,360
Speaker 1: like stupid and narrow minded and racist and white supremacist

731
00:36:46,400 --> 00:36:50,160
Speaker 1: and all those things, and like that this was very

732
00:36:50,200 --> 00:36:54,400
Speaker 1: helpful for a context. I just wonder, like all of

733
00:36:54,800 --> 00:36:59,239
Speaker 1: like we're seeing a model that's being developed, not in

734
00:36:59,280 --> 00:37:02,799
Speaker 1: the best way possible. It seems like like to say

735
00:37:02,840 --> 00:37:08,120
Speaker 1: the least, and we're probably going to like discount a

736
00:37:08,160 --> 00:37:11,600
Speaker 1: lot of these possibilities because they're so shitty at what

737
00:37:11,640 --> 00:37:14,840
Speaker 1: they're doing. But do you do you see that sort

738
00:37:14,840 --> 00:37:20,920
Speaker 1: of white supremacist mindset kind of pervading in the tech mainstream?

739
00:37:22,040 --> 00:37:25,120
Speaker 1: What a question? I know, it's a big one. Yeah.

740
00:37:25,320 --> 00:37:27,799
Speaker 2: No, I mean I feel like, I guess, like what

741
00:37:27,880 --> 00:37:30,160
Speaker 2: I do think this gifts just towards is this like

742
00:37:30,400 --> 00:37:33,880
Speaker 2: very public repositioning of Silicon Valley, which I think always

743
00:37:33,920 --> 00:37:36,640
Speaker 2: had this like relatively liberal venea, even if it's not

744
00:37:36,680 --> 00:37:39,640
Speaker 2: clear how deep those roots actually went. Kind of very

745
00:37:39,640 --> 00:37:42,480
Speaker 2: explicitly aligning themselves with the right and with Trump. And

746
00:37:42,520 --> 00:37:44,239
Speaker 2: it's a little bit hard to tell, like how much

747
00:37:44,280 --> 00:37:47,560
Speaker 2: of this is like political expediency people saying, well, clearly,

748
00:37:47,760 --> 00:37:50,800
Speaker 2: you know, I'm going to do anything to avoid heavier regulation,

749
00:37:50,960 --> 00:37:53,520
Speaker 2: We do anything to avoid this kind of like sort

750
00:37:53,560 --> 00:37:58,160
Speaker 2: of punitive regime that Trump's exacting on many different institutions.

751
00:37:58,160 --> 00:38:00,480
Speaker 2: So we're going to you know, put ourselves in a camp.

752
00:38:00,760 --> 00:38:02,719
Speaker 2: And how much of this is like an expression of

753
00:38:02,760 --> 00:38:06,160
Speaker 2: like deeper ideologies and deeper kind of political beliefs that

754
00:38:06,200 --> 00:38:08,719
Speaker 2: have maybe sat door mentor sort of have kind of

755
00:38:08,719 --> 00:38:12,480
Speaker 2: been cultivated within Silicon Valley and tech firms and now

756
00:38:12,640 --> 00:38:14,719
Speaker 2: kind of finally bursting onto the scene now that they

757
00:38:14,719 --> 00:38:16,799
Speaker 2: feel a bit more empowered as there's kind of been

758
00:38:16,800 --> 00:38:19,960
Speaker 2: this like global shift towards the right. So yeah, I

759
00:38:20,000 --> 00:38:23,279
Speaker 2: don't know, honestly, like how much you know, we can

760
00:38:23,320 --> 00:38:26,880
Speaker 2: discern between sort of like the real deep feeling and

761
00:38:26,920 --> 00:38:29,439
Speaker 2: the kind of political expediency argument. But I think it's

762
00:38:29,440 --> 00:38:33,280
Speaker 2: just undeniable that you have people like particularly Mark Zuckerberg,

763
00:38:33,320 --> 00:38:36,080
Speaker 2: who would have been like normally more kind of center

764
00:38:36,520 --> 00:38:39,279
Speaker 2: possibly center left a lot of issues even though he

765
00:38:39,360 --> 00:38:43,440
Speaker 2: was obviously running like a massively exploitative, gigantic, quite dangerous

766
00:38:43,440 --> 00:38:47,680
Speaker 2: tech company now sort of really aggressively rebranding as a

767
00:38:47,800 --> 00:38:51,080
Speaker 2: kind of wooing Trump, but also sort of big into

768
00:38:51,120 --> 00:38:53,879
Speaker 2: a lot of these like hyper masculiness, tech bro sort

769
00:38:53,920 --> 00:38:57,359
Speaker 2: of languages and ideologies that I don't think certainly five

770
00:38:57,440 --> 00:39:00,400
Speaker 2: years ago we would have seen him sort of pointing

771
00:39:00,400 --> 00:39:02,160
Speaker 2: in the same way. So yeah, it feels like there's

772
00:39:02,160 --> 00:39:05,840
Speaker 2: been this sort of very visible shift in Silicon Valley.

773
00:39:05,880 --> 00:39:08,040
Speaker 2: But again, as someone who's not based in Silicon Valley,

774
00:39:08,080 --> 00:39:10,120
Speaker 2: like I probably couldn't tell you like how much of

775
00:39:10,200 --> 00:39:12,560
Speaker 2: that feels like this this is actually the real face

776
00:39:12,560 --> 00:39:15,480
Speaker 2: of the beast versus this is actually just like what

777
00:39:15,520 --> 00:39:16,720
Speaker 2: people are doing for the moment.

778
00:39:16,880 --> 00:39:19,680
Speaker 3: Yeah, because I mean you see how much like Andresen

779
00:39:19,840 --> 00:39:22,800
Speaker 3: got Silicon Valley money together to get Trump into office,

780
00:39:22,840 --> 00:39:25,120
Speaker 3: along with a bunch of other crypto people's like seventy

781
00:39:25,160 --> 00:39:28,040
Speaker 3: eight million dollars from like the and recent side of things,

782
00:39:28,560 --> 00:39:32,120
Speaker 3: and you know, like these group chats they do, it's

783
00:39:32,160 --> 00:39:36,239
Speaker 3: like our modern day smoke filled lounges where you get

784
00:39:36,280 --> 00:39:39,239
Speaker 3: to see these very powerful people sort of debate these

785
00:39:39,280 --> 00:39:42,000
Speaker 3: topics and get their takes out there that are like

786
00:39:42,880 --> 00:39:45,760
Speaker 3: wacky as hell, and that's why I think, like reading

787
00:39:45,800 --> 00:39:48,600
Speaker 3: this article, it was a little bit more like damn.

788
00:39:48,600 --> 00:39:50,840
Speaker 3: I mean, I don't know if any everybody in this

789
00:39:50,880 --> 00:39:53,320
Speaker 3: group chet thinks that, but there are definitely some vocal

790
00:39:53,360 --> 00:39:56,319
Speaker 3: people in this group chat that definitely think they are

791
00:39:56,360 --> 00:39:59,440
Speaker 3: the ones who are going to solve these very complex issues,

792
00:40:00,000 --> 00:40:03,480
Speaker 3: but in the most like inelegant, one size fits all

793
00:40:03,600 --> 00:40:04,200
Speaker 3: kind of way.

794
00:40:04,280 --> 00:40:07,640
Speaker 1: That's just all about power. And that's when I'm like, oh,

795
00:40:07,719 --> 00:40:10,560
Speaker 1: I think it's really these are.

796
00:40:10,440 --> 00:40:13,080
Speaker 3: Starting to blend together, especially as we see how much

797
00:40:13,600 --> 00:40:16,880
Speaker 3: how people's like media diets and the information they receive

798
00:40:17,040 --> 00:40:19,600
Speaker 3: are informed by what these people who are running these

799
00:40:19,680 --> 00:40:23,040
Speaker 3: companies how they believe and like how information should move

800
00:40:23,080 --> 00:40:26,319
Speaker 3: and how people get siloed into information bubbles and things

801
00:40:26,360 --> 00:40:28,919
Speaker 3: like that. That's when I started to begin to be like, hmmm,

802
00:40:29,640 --> 00:40:31,560
Speaker 3: this feels a little bit more like a smoking gun

803
00:40:31,560 --> 00:40:33,520
Speaker 3: when you hear like, you know, these ideas are being

804
00:40:33,760 --> 00:40:36,680
Speaker 3: like exchanged and knowing that, like there's this guy Curtis

805
00:40:36,719 --> 00:40:38,719
Speaker 3: Yarvin that we talked about a few weeks ago who's

806
00:40:38,760 --> 00:40:42,239
Speaker 3: like basically like a tech monarchist who has a lot

807
00:40:42,320 --> 00:40:44,800
Speaker 3: of ideas that people like Elon Musk and Peter Teel

808
00:40:44,880 --> 00:40:48,239
Speaker 3: like are really subscribed to and yeah, and like in

809
00:40:48,360 --> 00:40:50,719
Speaker 3: these group chats, it's is where these a lot of

810
00:40:50,719 --> 00:40:54,240
Speaker 3: these very influential people start getting introduced to these.

811
00:40:54,160 --> 00:40:55,000
Speaker 1: Kinds of ideas.

812
00:40:55,560 --> 00:40:58,800
Speaker 3: So that's when I'm like, hmm, this feels very sinister

813
00:41:00,239 --> 00:41:03,960
Speaker 3: at best. I mean, like when you see this and

814
00:41:04,000 --> 00:41:06,200
Speaker 3: then thinking that these are the people that control the

815
00:41:06,280 --> 00:41:08,960
Speaker 3: levers that you know, just normal people who are using

816
00:41:09,000 --> 00:41:11,279
Speaker 3: the Internet and stuff get affected by their policies, it

817
00:41:11,320 --> 00:41:13,920
Speaker 3: feels like a little bit like they figured out this

818
00:41:14,080 --> 00:41:17,640
Speaker 3: is how they exercise like their immense control over people

819
00:41:18,160 --> 00:41:21,000
Speaker 3: in these sort of you know, less sort of not

820
00:41:21,040 --> 00:41:22,560
Speaker 3: like in the ways that we think in terms of

821
00:41:22,600 --> 00:41:26,239
Speaker 3: governance or whatever, but through the spread of information and ideas.

822
00:41:26,719 --> 00:41:28,440
Speaker 2: Yeah, and I guess I feel like the signal chats

823
00:41:28,480 --> 00:41:30,480
Speaker 2: like raise two things about this kind of like pivot

824
00:41:30,520 --> 00:41:33,200
Speaker 2: to the right, which I think are both really important,

825
00:41:33,239 --> 00:41:35,480
Speaker 2: and like, I think the one is what you've pointed out,

826
00:41:35,600 --> 00:41:37,879
Speaker 2: is this like small scale influencing. Like I think often

827
00:41:37,920 --> 00:41:41,320
Speaker 2: when we talk about disinformation or misinformation, we're thinking about

828
00:41:41,320 --> 00:41:43,000
Speaker 2: that in terms of like, oh, someone makes like an

829
00:41:43,000 --> 00:41:45,400
Speaker 2: ai deep fink and they like release it onto the

830
00:41:45,440 --> 00:41:48,560
Speaker 2: Internet and somehow that deep fake like convinces like many,

831
00:41:48,600 --> 00:41:51,160
Speaker 2: many many people that this event occurred or didn't occur,

832
00:41:51,200 --> 00:41:53,560
Speaker 2: and it has like seismic effects, and like, you know, again,

833
00:41:53,640 --> 00:41:57,560
Speaker 2: I don't think that's necessarily how disinformation works. I think that, yeah,

834
00:41:57,560 --> 00:42:01,040
Speaker 2: these kinds of like small cultivated circles of true maybe

835
00:42:01,040 --> 00:42:03,000
Speaker 2: the things that we should be really really concerned about,

836
00:42:03,000 --> 00:42:05,080
Speaker 2: which is like what actually causes people to change their

837
00:42:05,120 --> 00:42:07,920
Speaker 2: mind or change behaviors, Like maybe these are the levers

838
00:42:07,960 --> 00:42:11,200
Speaker 2: that can like be significantly more effective. But yet the

839
00:42:11,239 --> 00:42:13,120
Speaker 2: second is kind of a point you were raising, and

840
00:42:13,160 --> 00:42:16,200
Speaker 2: I think does again like tie into what happens when

841
00:42:16,239 --> 00:42:17,799
Speaker 2: you build these bonds of trust, which is just like

842
00:42:17,800 --> 00:42:20,040
Speaker 2: following the money. And like you mentioned sort of the

843
00:42:20,200 --> 00:42:22,720
Speaker 2: massive amount of tech funding that got Trump at the office.

844
00:42:22,760 --> 00:42:25,480
Speaker 2: And I had a colleague who now wisited Guardian and

845
00:42:25,520 --> 00:42:27,920
Speaker 2: he and the team kind of just like tracked all

846
00:42:27,960 --> 00:42:31,520
Speaker 2: the funding behind the Trump and Harris campaigns. And what

847
00:42:31,640 --> 00:42:34,720
Speaker 2: was just really astounding was just like the extraordinary amount

848
00:42:34,719 --> 00:42:37,120
Speaker 2: of money that specifically just came from tech, and like

849
00:42:37,200 --> 00:42:39,279
Speaker 2: that's been a really big change in the last five

850
00:42:39,360 --> 00:42:41,920
Speaker 2: years or so. It's just like how much money sort

851
00:42:41,960 --> 00:42:45,480
Speaker 2: of Silicon Valley has putting it into political lobbying, and

852
00:42:45,520 --> 00:42:48,520
Speaker 2: so I think, you know, yeah, it's even regardless of

853
00:42:48,640 --> 00:42:52,400
Speaker 2: like your political orientation, but like particularly right now with

854
00:42:52,440 --> 00:42:55,000
Speaker 2: the Trump administration, like I think that's something we should

855
00:42:55,000 --> 00:42:56,360
Speaker 2: all be really concerned about.

856
00:42:56,880 --> 00:42:57,120
Speaker 1: M h.

857
00:42:57,520 --> 00:42:59,600
Speaker 3: Yeah, I mean, because it feels like this is the

858
00:42:59,600 --> 00:43:03,640
Speaker 3: best a in some level, the tech industry is sort

859
00:43:03,680 --> 00:43:05,799
Speaker 3: of like, well, we're actually now the ones that are

860
00:43:05,800 --> 00:43:09,360
Speaker 3: really able to manufacture consent through social media and misinformation,

861
00:43:09,680 --> 00:43:12,239
Speaker 3: and you marry that with like an a you know,

862
00:43:12,600 --> 00:43:16,200
Speaker 3: presidential administration that would really benefit from that kind of

863
00:43:16,239 --> 00:43:20,319
Speaker 3: like full court press propaganda kind of making, and it

864
00:43:20,360 --> 00:43:23,399
Speaker 3: feels like, oh, everyone kind of wins in their own way,

865
00:43:23,480 --> 00:43:26,200
Speaker 3: although there are clearly some people in tech have their

866
00:43:26,200 --> 00:43:28,520
Speaker 3: regrets after all the tariff chaos and they're like no no, no,

867
00:43:28,520 --> 00:43:31,839
Speaker 3: no no no no not actually also, don't just fuck

868
00:43:32,840 --> 00:43:36,640
Speaker 3: fuck that one's actually really valuable. I just wanted less

869
00:43:36,680 --> 00:43:38,920
Speaker 3: regulations and for people to say the N word on

870
00:43:38,960 --> 00:43:41,480
Speaker 3: Twitter more. That was my whole thing, and now I

871
00:43:41,600 --> 00:43:45,880
Speaker 3: have no money or less money. But yeah, it's it

872
00:43:45,920 --> 00:43:49,480
Speaker 3: feels like it's just like the most clown show version

873
00:43:49,520 --> 00:43:51,480
Speaker 3: of all this. I think, which is also very frustrating

874
00:43:51,520 --> 00:43:54,279
Speaker 3: to watch or just have to sit idly buyas it's

875
00:43:54,320 --> 00:43:55,000
Speaker 3: all happening to.

876
00:43:55,000 --> 00:44:00,960
Speaker 1: Us very intentional. Very Also, it's just like really pathetic,

877
00:44:01,480 --> 00:44:05,120
Speaker 1: just like seeing how easy, easily influenced these people are.

878
00:44:05,160 --> 00:44:07,440
Speaker 1: I mean, it makes sense because it feels like they're

879
00:44:08,239 --> 00:44:12,439
Speaker 1: from a church that believes that whoever, wherever, the most

880
00:44:12,480 --> 00:44:16,160
Speaker 1: money is equals the right thing. And so you just

881
00:44:16,160 --> 00:44:18,160
Speaker 1: like get them on a group chat with a billionaire

882
00:44:18,560 --> 00:44:20,600
Speaker 1: and they're like, well, I mean that guy must be

883
00:44:20,960 --> 00:44:24,200
Speaker 1: the smartest guy in the world, and so right his

884
00:44:24,320 --> 00:44:25,480
Speaker 1: ideas I have to listen to.

885
00:44:25,960 --> 00:44:28,120
Speaker 2: Yeah, and I do think as well, Like, you know,

886
00:44:28,200 --> 00:44:30,600
Speaker 2: to some extent, I'm like, yeah, to that point, much

887
00:44:30,719 --> 00:44:33,439
Speaker 2: less interested in what people like quote unquote really think

888
00:44:33,520 --> 00:44:35,640
Speaker 2: and much more interesting than just what they do. And

889
00:44:35,680 --> 00:44:38,120
Speaker 2: so to some extent, I'm like, yeah, you know, does

890
00:44:38,160 --> 00:44:41,160
Speaker 2: it really matter with them? Mark Zuckerberg is personally invested

891
00:44:41,239 --> 00:44:45,000
Speaker 2: in these like you know, quite misogynist ideologies about like

892
00:44:45,040 --> 00:44:47,080
Speaker 2: what it means to be a man or doesn't matter

893
00:44:47,120 --> 00:44:49,160
Speaker 2: that he like has gone on Joe Rogan and now

894
00:44:49,200 --> 00:44:51,279
Speaker 2: like propagates a lot of these ideas and has you know,

895
00:44:51,400 --> 00:44:54,520
Speaker 2: publicly shift admittter to kind of align with or support

896
00:44:54,520 --> 00:44:56,959
Speaker 2: Trump's administration. Like that, to me is like what really

897
00:44:57,000 --> 00:44:59,239
Speaker 2: matters right now, which is that we have kind of

898
00:44:59,239 --> 00:45:01,799
Speaker 2: these active, kind of shifts of power in the tech

899
00:45:01,800 --> 00:45:05,200
Speaker 2: industry that go beyond kind of like politically signaling in

900
00:45:05,239 --> 00:45:07,759
Speaker 2: certain ways, like they're really really tangible. And I think

901
00:45:07,880 --> 00:45:10,080
Speaker 2: Musk is like the flagship of that in him to

902
00:45:10,120 --> 00:45:12,839
Speaker 2: someone who's just been like very very visible in the administration.

903
00:45:13,000 --> 00:45:14,799
Speaker 2: But you know, he's certainly not the only one.

904
00:45:15,760 --> 00:45:17,799
Speaker 1: Yeah, it feels like there's no shortage of these tech

905
00:45:17,840 --> 00:45:22,839
Speaker 1: billionaires who are dead set on a horrifying ideology. All right,

906
00:45:22,880 --> 00:45:25,799
Speaker 1: let's take a quick break and we'll come back and

907
00:45:26,280 --> 00:45:39,960
Speaker 1: keep talking about AI will be right back, and we're back.

908
00:45:40,239 --> 00:45:44,880
Speaker 1: We're back, and I do just add the floam reference smiles.

909
00:45:45,239 --> 00:45:49,120
Speaker 1: I don't want that to go by without being remarked on.

910
00:45:49,200 --> 00:45:52,320
Speaker 3: It's okay, Like I said, check my bio encyclopedic knowledge

911
00:45:52,360 --> 00:45:53,360
Speaker 3: of the nineties.

912
00:45:53,520 --> 00:45:55,480
Speaker 7: Is that the one that would like that would almost

913
00:45:55,480 --> 00:45:56,920
Speaker 7: like crack when you molded it.

914
00:45:56,960 --> 00:45:59,640
Speaker 1: Yeah, it was like a bunch of little beads almost, yeah,

915
00:45:59,680 --> 00:46:02,800
Speaker 1: like held together. It was really what if Rice Krispy

916
00:46:02,880 --> 00:46:05,920
Speaker 1: Treats was toy. You know what, if Rice Krispy treats

917
00:46:06,000 --> 00:46:09,560
Speaker 1: gave you bowel problems, if you even touched your mouth

918
00:46:09,600 --> 00:46:13,279
Speaker 1: after handling it, if you ate them, they were in

919
00:46:13,360 --> 00:46:15,800
Speaker 1: your body for the rest of your life and also

920
00:46:15,880 --> 00:46:18,759
Speaker 1: your children's lives, your progeny.

921
00:46:18,800 --> 00:46:22,400
Speaker 8: Also, now every third person is like gooning to asmr

922
00:46:22,480 --> 00:46:24,680
Speaker 8: shit and we have we have Floam to thank for that.

923
00:46:24,840 --> 00:46:27,160
Speaker 3: I know right exactly. We were just out here just

924
00:46:27,200 --> 00:46:29,880
Speaker 3: trying to figure it out on our own. With Nickelodeon Floam.

925
00:46:30,320 --> 00:46:32,279
Speaker 3: I gave it the full brand name because I felt

926
00:46:32,360 --> 00:46:36,040
Speaker 3: like people would forget if I didn't properly say Nickelodeon Floam.

927
00:46:36,280 --> 00:46:39,760
Speaker 1: Nickelodeon Floam, a larger and larger part of their body

928
00:46:39,840 --> 00:46:42,080
Speaker 1: is just made out of floam. It's like instead of

929
00:46:42,120 --> 00:46:46,680
Speaker 1: a left knee, I have floam down there. I can

930
00:46:46,760 --> 00:46:50,440
Speaker 1: just mold it and remold it as I walk. Anyways,

931
00:46:51,360 --> 00:46:53,600
Speaker 1: old debate and this is not this is not the

932
00:46:53,600 --> 00:46:55,040
Speaker 1: story that I thought I was going to be talking

933
00:46:55,080 --> 00:46:58,560
Speaker 1: about today. And yet but like I said, we report

934
00:46:58,600 --> 00:47:01,600
Speaker 1: on the zeitgeist, we don't make it. Yesterday, I had

935
00:47:01,600 --> 00:47:04,360
Speaker 1: the experience of checking social media and it just being

936
00:47:04,920 --> 00:47:09,040
Speaker 1: wall to wall one hundred men versus one gorilla, you know,

937
00:47:09,280 --> 00:47:15,400
Speaker 1: commentary videos, and so this is an old debate. I

938
00:47:15,440 --> 00:47:18,399
Speaker 1: mean this sort of debate. You know. There was one

939
00:47:18,400 --> 00:47:20,880
Speaker 1: of my favorite articles from the early days of Cracked,

940
00:47:20,920 --> 00:47:23,200
Speaker 1: like two thousand and eight Cracked, I think, was by

941
00:47:23,200 --> 00:47:26,400
Speaker 1: this writer Chris Buckles that was just like how to

942
00:47:26,400 --> 00:47:30,200
Speaker 1: fight twenty children like as one person, And it was

943
00:47:31,080 --> 00:47:34,520
Speaker 1: just like really well thought out and good advice that

944
00:47:34,640 --> 00:47:37,160
Speaker 1: I've that has shaped me as a person.

945
00:47:37,440 --> 00:47:38,479
Speaker 7: That you've used many times.

946
00:47:38,600 --> 00:47:40,879
Speaker 1: Yeah, yeah, I'll link off to that as my work

947
00:47:40,880 --> 00:47:44,560
Speaker 1: in media. But it is kind of one of those

948
00:47:44,640 --> 00:47:48,000
Speaker 1: questions like once you start thinking about it, it's kind

949
00:47:48,000 --> 00:47:52,800
Speaker 1: of hard to stop. I will say the original question,

950
00:47:52,920 --> 00:47:55,879
Speaker 1: as originally posed on Reddit five years ago, I think

951
00:47:55,960 --> 00:48:00,880
Speaker 1: has a fairly simple answer because they really stacked the

952
00:48:01,000 --> 00:48:05,480
Speaker 1: deck against the humans. Oh yeah, yeah. The original question

953
00:48:05,600 --> 00:48:08,160
Speaker 1: was like, all right, first round, you can only go

954
00:48:08,280 --> 00:48:11,360
Speaker 1: two at a two men at a time, Yeah, in

955
00:48:11,400 --> 00:48:14,759
Speaker 1: a gorilla and they're in a grilla exhibit. The second

956
00:48:14,800 --> 00:48:17,839
Speaker 1: one is in a construction as at a construction site,

957
00:48:17,880 --> 00:48:20,280
Speaker 1: and you can go ten at a time. They also

958
00:48:21,280 --> 00:48:23,640
Speaker 1: say that it just has to be like one hundred

959
00:48:23,880 --> 00:48:26,360
Speaker 1: average people. And then the third one, I guess you

960
00:48:26,400 --> 00:48:28,680
Speaker 1: can go all at once, but you're not gonna have

961
00:48:28,719 --> 00:48:31,640
Speaker 1: many people left after those first two rounds to go

962
00:48:31,719 --> 00:48:34,600
Speaker 1: all at once, which is like, really your only hope.

963
00:48:35,000 --> 00:48:38,960
Speaker 3: I think all of the sort of modifiers are meaningless

964
00:48:39,000 --> 00:48:43,440
Speaker 3: because really the question is, how can what just a

965
00:48:43,719 --> 00:48:44,640
Speaker 3: in a fair one?

966
00:48:45,239 --> 00:48:45,600
Speaker 1: Okay?

967
00:48:45,880 --> 00:48:49,360
Speaker 3: Yeah, no tools, no weapons, not because this guy's basically

968
00:48:49,360 --> 00:48:51,520
Speaker 3: bringing up like Rainbow Road type shit where like the

969
00:48:52,320 --> 00:48:54,200
Speaker 3: headgrill fall off the edge or some shit.

970
00:48:54,320 --> 00:48:55,959
Speaker 1: No, what is it going.

971
00:48:55,880 --> 00:48:59,560
Speaker 3: To take for one hundred human beings to beat the

972
00:48:59,600 --> 00:49:00,920
Speaker 3: fuck out of a gorilla?

973
00:49:01,280 --> 00:49:03,480
Speaker 9: Yeah, like a cage match or something like that. Yeah,

974
00:49:03,520 --> 00:49:08,200
Speaker 9: you know, Yeah, And my first reaction is it's not happening.

975
00:49:08,440 --> 00:49:12,040
Speaker 9: It's not happening. Ever, my big thing with all of

976
00:49:12,040 --> 00:49:15,360
Speaker 9: this is human beings don't have like fangs. They cannot

977
00:49:15,440 --> 00:49:19,880
Speaker 9: draw blood, So you're you're you're counting on purely overpowering

978
00:49:19,920 --> 00:49:24,439
Speaker 9: a gorilla. I mean, we do have thangs, Moms, can

979
00:49:24,480 --> 00:49:26,520
Speaker 9: you okay, can you can you draw blood from a

980
00:49:26,680 --> 00:49:27,640
Speaker 9: through that gorilla's fur?

981
00:49:28,960 --> 00:49:30,840
Speaker 1: Probably if I had to, if he.

982
00:49:32,800 --> 00:49:35,239
Speaker 3: If he stayed still, if I hit the finger real

983
00:49:35,320 --> 00:49:39,399
Speaker 3: hard like Charlie, yeah, I probably could. I can bite

984
00:49:39,440 --> 00:49:41,919
Speaker 3: through a lot I got, but I'm just saying that's

985
00:49:41,920 --> 00:49:44,279
Speaker 3: how I see a gorilla somehow, like if it fought

986
00:49:44,280 --> 00:49:46,960
Speaker 3: a tiger. It's because the tiger slashes the gorilla and

987
00:49:47,000 --> 00:49:49,520
Speaker 3: it's it's like it's leaking in a.

988
00:49:49,560 --> 00:49:52,720
Speaker 1: Straight up hold the gorilla. Not a fucking hundred people

989
00:49:52,719 --> 00:49:53,399
Speaker 1: aren't doing that.

990
00:49:54,160 --> 00:49:58,000
Speaker 3: The first guy that gets ripped in fucking half by

991
00:49:58,040 --> 00:49:59,280
Speaker 3: this thing, all ninety.

992
00:49:59,120 --> 00:50:03,000
Speaker 1: Nine other people are gonna go, no, right, you need

993
00:50:03,920 --> 00:50:05,920
Speaker 1: These are the things that I think you need. I

994
00:50:05,920 --> 00:50:07,840
Speaker 1: think first of all, we need to we need a

995
00:50:07,960 --> 00:50:11,680
Speaker 1: draft like and not not a like military breach where

996
00:50:11,719 --> 00:50:16,000
Speaker 1: we're going by birthday. I think we need to mcgroover's style,

997
00:50:16,080 --> 00:50:18,720
Speaker 1: get to put together a team mcgoover of the film,

998
00:50:18,760 --> 00:50:24,200
Speaker 1: not the SNL sketch, Oh thank you, which the film

999
00:50:24,440 --> 00:50:28,080
Speaker 1: the film sketch. That the sketch is a completely different thing.

1000
00:50:28,120 --> 00:50:30,920
Speaker 1: The film is a work of art. The you know,

1001
00:50:30,960 --> 00:50:33,680
Speaker 1: I think we need to be able to intentionally assemble

1002
00:50:33,719 --> 00:50:36,760
Speaker 1: a team. I think there needs to be some sort

1003
00:50:36,800 --> 00:50:41,680
Speaker 1: of a squid game style motivation that is going to

1004
00:50:42,640 --> 00:50:45,480
Speaker 1: prevent what you're talking about. Whether they see a person

1005
00:50:45,600 --> 00:50:47,960
Speaker 1: get ripped in half and everybody's like, this is not

1006
00:50:48,080 --> 00:50:50,279
Speaker 1: worth it. It needs to be worth like it needs

1007
00:50:50,280 --> 00:50:54,680
Speaker 1: to be one hundred men and the griller in the

1008
00:50:54,719 --> 00:50:57,360
Speaker 1: cage and like it's not over until.

1009
00:50:57,120 --> 00:50:59,800
Speaker 3: It would be sick in squid like in the actual

1010
00:51:00,040 --> 00:51:01,919
Speaker 3: get a new season of Squid game, Like when there's

1011
00:51:01,960 --> 00:51:04,880
Speaker 3: like four hundred people left, it's like all four hundred

1012
00:51:04,960 --> 00:51:06,520
Speaker 3: y'all versus gorilla right now.

1013
00:51:06,480 --> 00:51:10,440
Speaker 1: Right, Yeah, y'all are going to die. A lot of

1014
00:51:10,480 --> 00:51:11,120
Speaker 1: y'all going to die.

1015
00:51:11,120 --> 00:51:15,680
Speaker 3: But four hundred, four hundred feels like maybe four hundred,

1016
00:51:15,800 --> 00:51:16,920
Speaker 3: So how are you?

1017
00:51:17,040 --> 00:51:19,440
Speaker 1: I don't even know hundred is different than a hundred

1018
00:51:19,480 --> 00:51:23,200
Speaker 1: other than like I feel like a gorilla killing three

1019
00:51:23,280 --> 00:51:26,200
Speaker 1: hundred people with its bare hands may tire the gorilla

1020
00:51:26,200 --> 00:51:28,520
Speaker 1: out get tired, and then the other ones are just

1021
00:51:28,560 --> 00:51:32,000
Speaker 1: like coming up and like kind of yeah, I feel

1022
00:51:32,040 --> 00:51:34,839
Speaker 1: like that would probably be true after like fifty right,

1023
00:51:35,200 --> 00:51:37,000
Speaker 1: Like that's that's a lot of people to kill.

1024
00:51:37,200 --> 00:51:41,399
Speaker 8: I could see that. I almost think now, I think

1025
00:51:41,440 --> 00:51:44,000
Speaker 8: you could almost it can be. It can almost be

1026
00:51:44,080 --> 00:51:46,520
Speaker 8: a random assortment of one hundred people. It doesn't they don't.

1027
00:51:46,520 --> 00:51:48,360
Speaker 8: You don't even need to do a draft. You just

1028
00:51:48,440 --> 00:51:54,160
Speaker 8: need a coach. That's what, like like a maestro, a conductor, Yes.

1029
00:51:54,160 --> 00:51:58,000
Speaker 7: A maestro who's running plays and basically the goal of

1030
00:51:58,040 --> 00:52:01,600
Speaker 7: every play is some he goes for the eyes and

1031
00:52:01,840 --> 00:52:03,360
Speaker 7: once you get the eyes.

1032
00:52:03,560 --> 00:52:10,800
Speaker 1: Brian, Okay, that is right, It's got to be the eyes.

1033
00:52:11,160 --> 00:52:13,560
Speaker 1: Although all right, so this is what I'm saying, you

1034
00:52:13,680 --> 00:52:16,319
Speaker 1: gotta be the eyes. Like that convinced me in a

1035
00:52:16,360 --> 00:52:19,040
Speaker 1: split second. At first, I was like, we need a

1036
00:52:19,120 --> 00:52:24,319
Speaker 1: like legendary football coach. I think the coach. No, I know, well,

1037
00:52:24,520 --> 00:52:28,240
Speaker 1: now I'm thinking we need still still I want Mike Tomlin.

1038
00:52:28,800 --> 00:52:31,760
Speaker 1: Mike Tomlin from the Steelers, and was like, just every

1039
00:52:31,960 --> 00:52:34,319
Speaker 1: year they have a terrible team. Everyone's like, these guys

1040
00:52:34,360 --> 00:52:36,680
Speaker 1: are gonna win two games this year, and every year

1041
00:52:36,880 --> 00:52:41,000
Speaker 1: they are like a two seed, right, and he's just

1042
00:52:41,280 --> 00:52:46,080
Speaker 1: has energy. He like yells at everybody, cut your eyelids off,

1043
00:52:46,360 --> 00:52:49,040
Speaker 1: cut your eyelids off when they go out to play,

1044
00:52:49,080 --> 00:52:52,040
Speaker 1: which is just like so I don't know. And and

1045
00:52:52,200 --> 00:52:55,200
Speaker 1: also a great tactician, although we might not need one

1046
00:52:55,200 --> 00:52:59,399
Speaker 1: with Brian. No, no, Brian, I just thinking primatologist would

1047
00:52:59,400 --> 00:53:03,399
Speaker 1: be helpful, like somebody who sure can really speak to it.

1048
00:53:03,920 --> 00:53:06,880
Speaker 1: But like a primatologists where you've like, you know, kidnapped

1049
00:53:06,880 --> 00:53:08,680
Speaker 1: their kid, because other way they're going to be like

1050
00:53:08,800 --> 00:53:10,759
Speaker 1: we should let the gorilla kill us.

1051
00:53:11,120 --> 00:53:13,759
Speaker 3: Yeah, because I don't want to introduce like, you know,

1052
00:53:13,800 --> 00:53:17,080
Speaker 3: there could be something like gorillas hate like reflective material

1053
00:53:17,200 --> 00:53:20,600
Speaker 3: or something like maybe like a non weapon object that

1054
00:53:20,640 --> 00:53:22,680
Speaker 3: opens it up. But I don't even like that idea

1055
00:53:22,760 --> 00:53:25,480
Speaker 3: because now we're not talking. We're talking about one hundred people. Yes,

1056
00:53:25,520 --> 00:53:28,600
Speaker 3: one hundred people with tools can beat in a gorilla

1057
00:53:29,200 --> 00:53:32,880
Speaker 3: with just if we're going to brass tacks, bare hands

1058
00:53:32,880 --> 00:53:35,759
Speaker 3: and feet. I think, Brian, yes, you you take out

1059
00:53:36,000 --> 00:53:38,319
Speaker 3: the vision. Now you're dealing with an animal has no

1060
00:53:38,400 --> 00:53:40,920
Speaker 3: situational awareness, and now you can kind of get a

1061
00:53:40,920 --> 00:53:44,480
Speaker 3: little like but even then you're.

1062
00:53:44,320 --> 00:53:54,480
Speaker 1: Going to be teasing it. Yeah. Yeah, but either way

1063
00:53:54,680 --> 00:53:55,840
Speaker 1: terrified all of a sudden.

1064
00:53:56,239 --> 00:53:58,560
Speaker 3: But you still have to solve the part that you

1065
00:53:58,600 --> 00:54:03,320
Speaker 3: have negated the absolute dude exponentially higher strength of the gorilla,

1066
00:54:03,440 --> 00:54:05,640
Speaker 3: because the second you grab it, the gorilla will just

1067
00:54:05,680 --> 00:54:06,520
Speaker 3: put hands on you.

1068
00:54:06,480 --> 00:54:08,000
Speaker 1: Rip you a half, even if it can't see.

1069
00:54:08,000 --> 00:54:09,800
Speaker 3: And I feel you're gonna you're just gonna take the

1070
00:54:09,840 --> 00:54:12,480
Speaker 3: gorilla off even more with his eyes being all pick

1071
00:54:12,760 --> 00:54:13,760
Speaker 3: gorilla is pod.

1072
00:54:14,400 --> 00:54:17,759
Speaker 1: Yeah, I so I was. I was fully on the

1073
00:54:17,840 --> 00:54:20,480
Speaker 1: you just overwhelm it with one hundred at once. And

1074
00:54:20,520 --> 00:54:24,799
Speaker 1: then this Guardian article about this debate pointed out that

1075
00:54:24,920 --> 00:54:29,720
Speaker 1: somebody did a three professional soccer players, three professional football

1076
00:54:29,719 --> 00:54:33,080
Speaker 1: players verse one hundred children, Oh, I say, in Japan,

1077
00:54:33,280 --> 00:54:37,640
Speaker 1: in Japan, Yeah, and the professional soccer players won. Oh yeah,

1078
00:54:37,800 --> 00:54:39,719
Speaker 1: they beat the shit up them. They won. I mean,

1079
00:54:39,760 --> 00:54:42,480
Speaker 1: they won one nothing, But like, I don't know that

1080
00:54:42,560 --> 00:54:43,520
Speaker 1: maybe well they had if.

1081
00:54:43,440 --> 00:54:45,880
Speaker 3: You see the video, they had like sixty kids in goal,

1082
00:54:46,160 --> 00:54:49,160
Speaker 3: like literally sixty, Like there were like sixty kids dressing

1083
00:54:49,440 --> 00:54:51,799
Speaker 3: goalkeepers in the goal and they just had to kind

1084
00:54:51,840 --> 00:54:54,520
Speaker 3: of headed in over them. But like the way they

1085
00:54:54,640 --> 00:54:56,879
Speaker 3: just the three of them just spread the field out

1086
00:54:56,960 --> 00:55:00,080
Speaker 3: and were just hitting long balls that they can like

1087
00:55:00,080 --> 00:55:02,799
Speaker 3: like just had the kids chasing right uh. And it

1088
00:55:02,920 --> 00:55:05,239
Speaker 3: was pretty it was pretty impressive if it's the one

1089
00:55:05,280 --> 00:55:07,120
Speaker 3: I'm thinking of, because I've definitely seen that one.

1090
00:55:07,160 --> 00:55:09,080
Speaker 1: I was like, yeah, their skills are just way too

1091
00:55:09,160 --> 00:55:12,480
Speaker 1: high to get for the kids to handle that. But

1092
00:55:12,480 --> 00:55:15,600
Speaker 1: I think if it's one hundred random people, it depends.

1093
00:55:16,400 --> 00:55:19,080
Speaker 1: I do think you're probably gonna get one person who

1094
00:55:19,440 --> 00:55:22,200
Speaker 1: screams go for the eyes in that group, you know

1095
00:55:22,200 --> 00:55:25,120
Speaker 1: what I mean, And then and then you might have

1096
00:55:25,160 --> 00:55:28,400
Speaker 1: a chance if we're allowed to build a team. I

1097
00:55:28,440 --> 00:55:31,120
Speaker 1: do like the humans chance, like if we're allowed to

1098
00:55:31,200 --> 00:55:33,439
Speaker 1: draft on even if we're allowed to draft ten, there's

1099
00:55:33,440 --> 00:55:36,680
Speaker 1: no answer. There's no answer, Like there's no conceivable answer

1100
00:55:36,719 --> 00:55:38,640
Speaker 1: for how you gate? What are you gonna do? Like

1101
00:55:38,719 --> 00:55:42,560
Speaker 1: have ten guys try and hold the arms right like

1102
00:55:42,600 --> 00:55:46,320
Speaker 1: a gorilla will I'm talking Mike Tomlin and the foremost

1103
00:55:46,400 --> 00:55:52,800
Speaker 1: primatologist and then like Laurrence Taylor or whoever the closest

1104
00:55:52,880 --> 00:55:54,640
Speaker 1: person to lost Taylor current.

1105
00:55:54,880 --> 00:55:57,359
Speaker 3: Actually I like the eye thing, but I keep coming

1106
00:55:57,360 --> 00:55:59,280
Speaker 3: back to the thing, like you can't negate the strength

1107
00:56:00,160 --> 00:56:00,800
Speaker 3: in the sting.

1108
00:56:01,000 --> 00:56:02,840
Speaker 7: Even in the I thing. I do think ten people

1109
00:56:02,880 --> 00:56:07,520
Speaker 7: will die for sure, Oh yeah, yeah, oh yeah, Ultimately

1110
00:56:08,239 --> 00:56:11,640
Speaker 7: the people will win, I do.

1111
00:56:11,880 --> 00:56:15,200
Speaker 1: I do think that's right. Like everyone everyone's pushing vibes

1112
00:56:15,280 --> 00:56:15,560
Speaker 1: right now.

1113
00:56:15,600 --> 00:56:19,399
Speaker 7: No one's giving me plays, and then you you show

1114
00:56:19,440 --> 00:56:20,440
Speaker 7: them and give me.

1115
00:56:20,440 --> 00:56:23,480
Speaker 1: An example of the play. What happens? How are you

1116
00:56:23,480 --> 00:56:26,440
Speaker 1: gonna like, you can't can't damage your gorilla's achilles, ten

1117
00:56:26,520 --> 00:56:27,520
Speaker 1: do with your teeth, you know what I mean.

1118
00:56:27,520 --> 00:56:30,960
Speaker 7: I'm trying to think the first ten are absolutely going

1119
00:56:31,040 --> 00:56:31,680
Speaker 7: to get fucked up.

1120
00:56:31,680 --> 00:56:35,239
Speaker 8: That eleventh person goes through the eyes and then the

1121
00:56:35,400 --> 00:56:37,680
Speaker 8: ninety rush.

1122
00:56:37,320 --> 00:56:38,480
Speaker 7: The fuck out of the gorilla.

1123
00:56:38,600 --> 00:56:41,680
Speaker 1: I think a big difference is going to be if

1124
00:56:41,719 --> 00:56:44,640
Speaker 1: it's in a ring where no, you can't pick up

1125
00:56:44,680 --> 00:56:47,680
Speaker 1: something that's close by, because like our access to tools

1126
00:56:47,760 --> 00:56:50,240
Speaker 1: or like our ability to use whatever is at hand

1127
00:56:50,320 --> 00:56:54,240
Speaker 1: as a tool was like the crucial thing that allowed

1128
00:56:54,280 --> 00:56:57,000
Speaker 1: us to like get out of the food chain. So

1129
00:56:57,120 --> 00:57:01,920
Speaker 1: like if if it's a gorilla enclosure to zoo even

1130
00:57:02,040 --> 00:57:04,360
Speaker 1: or like you know, I feel like that is almost

1131
00:57:04,400 --> 00:57:07,319
Speaker 1: an advantage because then I do feel like you can

1132
00:57:07,400 --> 00:57:09,040
Speaker 1: like you have rocks, you.

1133
00:57:09,000 --> 00:57:12,319
Speaker 3: Know, Okay, how about this not see but that's the thing, Yeah,

1134
00:57:12,360 --> 00:57:15,560
Speaker 3: I get that, but I'm I think that the thing

1135
00:57:15,600 --> 00:57:18,840
Speaker 3: that is in the appealing about this problem is how

1136
00:57:19,160 --> 00:57:20,880
Speaker 3: could the pure just strength.

1137
00:57:20,520 --> 00:57:22,360
Speaker 1: Of the human body overcome a gorilla?

1138
00:57:22,480 --> 00:57:24,400
Speaker 7: Obviously powerful.

1139
00:57:27,320 --> 00:57:34,720
Speaker 5: If you've been watching these sports cars, these ope Core

1140
00:57:34,920 --> 00:57:38,960
Speaker 5: videos have run uh in a place where he believes

1141
00:57:39,680 --> 00:57:40,440
Speaker 5: I'm with him.

1142
00:57:40,800 --> 00:57:43,880
Speaker 3: Yeah, he's just like over that, like like track, you

1143
00:57:43,920 --> 00:57:46,200
Speaker 3: just hear this song playing that's like thee and all

1144
00:57:46,200 --> 00:57:50,040
Speaker 3: the Hope Core You're like, oh man, and then the

1145
00:57:50,160 --> 00:57:52,200
Speaker 3: hundred guys ran after the gorilla.

1146
00:57:52,360 --> 00:57:54,800
Speaker 7: It is very graphic and poking.

1147
00:57:54,920 --> 00:58:00,240
Speaker 1: Guy, I'm saying one like access to one rock. I

1148
00:58:00,280 --> 00:58:03,080
Speaker 1: don't know. So there's this story I like that guy

1149
00:58:03,760 --> 00:58:06,120
Speaker 1: Brice versus a guy named the Lion.

1150
00:58:08,560 --> 00:58:09,760
Speaker 7: This is what I'm talking about.

1151
00:58:09,920 --> 00:58:17,280
Speaker 1: Thank you ahead Biblically, the original sports Hope Core. Way

1152
00:58:17,320 --> 00:58:20,520
Speaker 1: to bring it back, Way to bring it back. All right,

1153
00:58:20,760 --> 00:58:25,160
Speaker 1: that's gonna do it for this week's weekly Zeitgeist. Please

1154
00:58:25,360 --> 00:58:28,600
Speaker 1: like and review the show if you like. The show

1155
00:58:29,720 --> 00:58:33,560
Speaker 1: means the world to Miles. He he needs your validation. Folks.

1156
00:58:34,160 --> 00:58:37,240
Speaker 1: I hope you're having a great weekend and I will

1157
00:58:37,240 --> 00:59:23,120
Speaker 1: talk to you Monday. Bye.