1
00:00:02,800 --> 00:00:03,560
Speaker 1: Zone Media.

2
00:00:05,600 --> 00:00:08,480
Speaker 2: This is water extermination, fights cell by cell, through bodies

3
00:00:08,480 --> 00:00:10,879
Speaker 2: and mindscreens of the earth. Soul's rotten from the orgasm,

4
00:00:10,960 --> 00:00:13,720
Speaker 2: drug flesh shuddering from the ovens. Prisoners of the Earth,

5
00:00:13,760 --> 00:00:16,239
Speaker 2: come out storm the studio. This is better offline and

6
00:00:16,280 --> 00:00:31,080
Speaker 2: I'm at Zetron. We have an incredible studio guest assortment.

7
00:00:31,120 --> 00:00:33,920
Speaker 2: Today we have the wonderful Victoria song from the Verge,

8
00:00:33,880 --> 00:00:37,760
Speaker 2: hating Victoria. I'm good geez great, and we of course

9
00:00:37,760 --> 00:00:42,520
Speaker 2: of Alison Morrow of CNN CNN Nightcap Newsletter as well. Yes, wonderful,

10
00:00:42,720 --> 00:00:46,480
Speaker 2: and of course Gare Davis, the wonderful Gear Davis who

11
00:00:46,479 --> 00:00:48,800
Speaker 2: didn't insult me on Bluesky for being late because I

12
00:00:48,960 --> 00:00:51,519
Speaker 2: was on time. Of Cool Zone Media, Gear, thank you

13
00:00:51,560 --> 00:00:52,279
Speaker 2: for joining us.

14
00:00:52,440 --> 00:00:55,639
Speaker 1: Thank you for having me again, despite our despite our

15
00:00:55,640 --> 00:00:57,960
Speaker 1: brief fight on Blue Sky tiff.

16
00:00:58,160 --> 00:01:00,280
Speaker 2: It was not even a tiff. It was a friendly

17
00:01:00,400 --> 00:01:02,640
Speaker 2: thing by some merchandise though, if you're listening to it.

18
00:01:02,680 --> 00:01:04,319
Speaker 2: We have new hoodies, we have new t shirts, we

19
00:01:04,319 --> 00:01:06,840
Speaker 2: have new hats. We have an upcoming challenge coin that

20
00:01:06,920 --> 00:01:08,640
Speaker 2: you can spend your money on and it will flow

21
00:01:08,720 --> 00:01:11,839
Speaker 2: somewhat to me. That's what's great. But today we're talking

22
00:01:11,840 --> 00:01:14,399
Speaker 2: about artificial intelligence. There have been a few stories in

23
00:01:14,440 --> 00:01:17,040
Speaker 2: the media. Allison is freshback from vacation, so she has

24
00:01:17,080 --> 00:01:18,760
Speaker 2: to learn about all the good things that have been happening.

25
00:01:18,880 --> 00:01:20,800
Speaker 2: Don't want to start with one of my favorite stories

26
00:01:20,800 --> 00:01:23,560
Speaker 2: at the moment, and this is the negotiation between Microsoft

27
00:01:23,640 --> 00:01:27,160
Speaker 2: and open Ai. Now, the negotiation is just to run

28
00:01:27,200 --> 00:01:29,319
Speaker 2: this down because open ai, by the end of the

29
00:01:29,360 --> 00:01:32,000
Speaker 2: year needs to become a for profit entity. It's a

30
00:01:32,000 --> 00:01:34,080
Speaker 2: little more complex than that. It's the for profit part

31
00:01:34,080 --> 00:01:37,160
Speaker 2: of a nonprofit needs to convert. That alone would be difficult,

32
00:01:37,200 --> 00:01:40,480
Speaker 2: but Microsoft owns forty nine percent of this company's future

33
00:01:40,520 --> 00:01:42,320
Speaker 2: profits and a bunch of other stuff. They get a

34
00:01:42,360 --> 00:01:46,120
Speaker 2: revenue share, they get right store of their IP through

35
00:01:46,120 --> 00:01:48,480
Speaker 2: twenty thirty and all these other things. And open ai

36
00:01:48,600 --> 00:01:51,840
Speaker 2: has said, Okay, what if we give you thirty three

37
00:01:51,840 --> 00:01:54,960
Speaker 2: percent equity less revenue share and you don't get access

38
00:01:54,960 --> 00:01:59,720
Speaker 2: to ORIP And understandably Microsoft has said no. So we

39
00:01:59,760 --> 00:02:03,480
Speaker 2: are in the funniest possible scenario here in the Microsoft

40
00:02:03,520 --> 00:02:06,120
Speaker 2: could literally just fold their arms and let open ai die.

41
00:02:06,920 --> 00:02:10,160
Speaker 2: And I feel like at the moment, this is an

42
00:02:10,320 --> 00:02:15,440
Speaker 2: underdiscussed topic because this is a gun to Sam Worman's head,

43
00:02:16,080 --> 00:02:18,040
Speaker 2: and everyone's just kind of acting like it's fine. I

44
00:02:18,040 --> 00:02:21,400
Speaker 2: guess maybe it's just two complex and it's confusing to

45
00:02:21,440 --> 00:02:25,880
Speaker 2: me why more people aren't a little bit worried. Anyone

46
00:02:26,160 --> 00:02:27,440
Speaker 2: numbers hard numbers.

47
00:02:27,480 --> 00:02:29,040
Speaker 3: That's scary, that's.

48
00:02:28,760 --> 00:02:30,800
Speaker 4: Scary, that's yeah.

49
00:02:30,840 --> 00:02:34,320
Speaker 2: But I think I think why I'm going so insane

50
00:02:34,360 --> 00:02:37,760
Speaker 2: about it is this could kill open AI one hundred percent.

51
00:02:37,840 --> 00:02:40,320
Speaker 2: Like this is they don't turn into a for profit,

52
00:02:40,480 --> 00:02:43,799
Speaker 2: they're dead dead, And I'm just wondering why everyone's just

53
00:02:43,880 --> 00:02:47,160
Speaker 2: kind of chilling walking around about it. I feel crazy.

54
00:02:47,639 --> 00:02:51,800
Speaker 5: I think it's just the sense that like that seems

55
00:02:51,840 --> 00:02:54,520
Speaker 5: implausible to most people, like if you look on it,

56
00:02:54,919 --> 00:02:57,720
Speaker 5: like maybe, but you know, chat GBT is synonymous with

57
00:02:57,760 --> 00:03:01,280
Speaker 5: AI in the sense that kleanex is synonymous with tissues

58
00:03:01,360 --> 00:03:03,560
Speaker 5: right now, So you know, we're at a point in

59
00:03:03,600 --> 00:03:07,960
Speaker 5: time where when you see the big one of of

60
00:03:08,120 --> 00:03:11,359
Speaker 5: like a tech thing, you kind of feel that they're infallible.

61
00:03:11,400 --> 00:03:14,160
Speaker 5: It's sort of like saying, well, if Apple doesn't get

62
00:03:14,160 --> 00:03:16,640
Speaker 5: its ducks in a row with these tariffs, they're fucked,

63
00:03:17,000 --> 00:03:19,880
Speaker 5: in which case you're like, are they are they? It's

64
00:03:19,960 --> 00:03:22,200
Speaker 5: like a Marvel movie at the end, Are they dead.

65
00:03:22,320 --> 00:03:24,280
Speaker 5: Are they are they really dead? Or will they come

66
00:03:24,320 --> 00:03:28,480
Speaker 5: back in some mutated form in Avengers like Part seventy two,

67
00:03:28,880 --> 00:03:30,639
Speaker 5: The avengein ing right.

68
00:03:31,000 --> 00:03:34,600
Speaker 6: And I think everyone's still under the spell of Sam Altman, right,

69
00:03:34,880 --> 00:03:38,320
Speaker 6: there's a sense that he's the visionary who's going to

70
00:03:38,440 --> 00:03:42,800
Speaker 6: lead us into this AI utopia. And you know, I

71
00:03:43,000 --> 00:03:45,240
Speaker 6: at others, you know, a bunch of us have reported

72
00:03:45,280 --> 00:03:47,840
Speaker 6: that a lot of it is smoke and mirrors. But

73
00:03:48,640 --> 00:03:53,280
Speaker 6: I think investors and shareholders and people who like frankly,

74
00:03:53,760 --> 00:03:56,640
Speaker 6: the people who work for him, desperately want to see succeed.

75
00:03:56,720 --> 00:04:00,400
Speaker 6: I think Silicon Valley wants him to succeed. So maybe

76
00:04:00,400 --> 00:04:02,320
Speaker 6: it's just a willful blindness.

77
00:04:02,400 --> 00:04:05,480
Speaker 2: Yeah, it's the most strange time in history because in

78
00:04:06,040 --> 00:04:08,480
Speaker 2: Victoria you've written quite a lot about this. When you

79
00:04:08,520 --> 00:04:11,280
Speaker 2: look at the actual things that this shit does, it

80
00:04:11,360 --> 00:04:14,040
Speaker 2: don't do that much. Right now, you've been on the

81
00:04:14,080 --> 00:04:17,000
Speaker 2: lead a on the verge for a few months now,

82
00:04:17,240 --> 00:04:19,400
Speaker 2: have you seen anything exciting at all?

83
00:04:20,640 --> 00:04:22,640
Speaker 5: Define exciting, Anything.

84
00:04:22,240 --> 00:04:24,240
Speaker 2: That you looked at and you felt delighted by in

85
00:04:24,320 --> 00:04:27,760
Speaker 2: any way, Because I'm genuinely curious by delighted.

86
00:04:27,960 --> 00:04:29,920
Speaker 3: I think delighted is a strong word.

87
00:04:30,279 --> 00:04:33,120
Speaker 5: Have I seen things that have been surprising. Yeah, with

88
00:04:33,240 --> 00:04:35,120
Speaker 5: some of the Like I wrote a story not that

89
00:04:35,160 --> 00:04:37,800
Speaker 5: long ago about the what I call the hug and

90
00:04:37,920 --> 00:04:39,120
Speaker 5: Kiss generators.

91
00:04:39,600 --> 00:04:40,960
Speaker 3: Uh yeah, exactly.

92
00:04:41,920 --> 00:04:46,840
Speaker 5: So there are these apps there are called Hugging and

93
00:04:47,000 --> 00:04:51,240
Speaker 5: Kiss AI generators. So you take a picture and like

94
00:04:51,279 --> 00:04:54,120
Speaker 5: they were advertised in a kind of skivy way where

95
00:04:54,160 --> 00:04:56,400
Speaker 5: it's just like, oh, you take a picture of you

96
00:04:56,520 --> 00:04:59,200
Speaker 5: and your crush and make them kiss, and like that's

97
00:04:59,360 --> 00:05:02,919
Speaker 5: good that you can do. AI doesn't understand what to

98
00:05:02,960 --> 00:05:06,760
Speaker 5: do with tongues yet, so you know, I was generating

99
00:05:06,920 --> 00:05:09,200
Speaker 5: very cursed content for the Verge dot com.

100
00:05:09,240 --> 00:05:11,000
Speaker 2: That was what those horrible things you're sending me?

101
00:05:11,120 --> 00:05:13,760
Speaker 3: Right, yeah, yeah, yeah, I sent you some horrible things.

102
00:05:13,520 --> 00:05:14,680
Speaker 2: Really awful.

103
00:05:15,000 --> 00:05:17,200
Speaker 6: It turns to the AI bots. A lot of humans

104
00:05:17,200 --> 00:05:18,239
Speaker 6: don't know what to do with tongue.

105
00:05:18,520 --> 00:05:20,760
Speaker 5: But no, they but they really don't know what to

106
00:05:20,800 --> 00:05:22,920
Speaker 5: do with the songs, like you know, you're supposed to

107
00:05:22,920 --> 00:05:25,320
Speaker 5: make people kissing, and so like, I generated a couple

108
00:05:25,320 --> 00:05:28,320
Speaker 5: of videos of me and Edward Collin because not because

109
00:05:28,360 --> 00:05:30,640
Speaker 5: I'm like a twyhard but because he was like a

110
00:05:30,680 --> 00:05:34,480
Speaker 5: preset in the app, right, and you know, you just

111
00:05:34,560 --> 00:05:37,800
Speaker 5: you just watch yourself kiss Edward Cullen with full on

112
00:05:37,880 --> 00:05:38,680
Speaker 5: tongue and you're just.

113
00:05:38,600 --> 00:05:40,880
Speaker 2: Like, but little wrong tongue.

114
00:05:40,960 --> 00:05:44,279
Speaker 5: It's wrong tongue because honestly, it's it's like if you

115
00:05:44,360 --> 00:05:46,960
Speaker 5: told a toddler what kissing looks like and they just

116
00:05:47,160 --> 00:05:52,120
Speaker 5: you know, imagined two faces smooshing together and like things

117
00:05:52,240 --> 00:05:54,919
Speaker 5: coming out of the mouths and odd rhythms. That's what

118
00:05:54,960 --> 00:05:55,400
Speaker 5: it looked like.

119
00:05:55,440 --> 00:05:57,200
Speaker 1: I mean, I could be wrong here, but I assume

120
00:05:57,240 --> 00:05:59,279
Speaker 1: most of these products are made by straight man who

121
00:05:59,480 --> 00:06:02,119
Speaker 1: do not know what to do with the tongue.

122
00:06:02,240 --> 00:06:05,359
Speaker 5: These apps are just very like, yeah, weird, but you

123
00:06:05,360 --> 00:06:07,839
Speaker 5: know so I tested them. I deep faked my parents

124
00:06:09,160 --> 00:06:10,880
Speaker 5: parents at my wedding, and I was like, oh, this

125
00:06:11,000 --> 00:06:16,960
Speaker 5: makes me feel weird. I have emotions that mom's teeth

126
00:06:17,000 --> 00:06:20,360
Speaker 5: are not correct in this story. But you know so

127
00:06:20,520 --> 00:06:22,560
Speaker 5: that was just like one of the I think one

128
00:06:22,560 --> 00:06:25,960
Speaker 5: of the things that I've tested most recently and I went, oh, okay.

129
00:06:25,760 --> 00:06:27,240
Speaker 4: This is something.

130
00:06:27,560 --> 00:06:29,400
Speaker 1: I mean, the thing that I've seen this with is

131
00:06:29,440 --> 00:06:32,320
Speaker 1: like this this woman who made like a video of

132
00:06:32,880 --> 00:06:35,640
Speaker 1: like her mom hugging her as a kid, based on

133
00:06:35,720 --> 00:06:37,880
Speaker 1: like an old picture, right, and it's like this is

134
00:06:38,320 --> 00:06:40,960
Speaker 1: I've never seen a video of my mom before, and

135
00:06:41,000 --> 00:06:44,599
Speaker 1: it's you start obsessing over this, this like artificially generated video.

136
00:06:44,960 --> 00:06:47,200
Speaker 1: When you're ignoring you you actually have a picture of

137
00:06:47,240 --> 00:06:49,840
Speaker 1: your mom hugging yeah, Like you can look at like

138
00:06:49,880 --> 00:06:53,280
Speaker 1: that and that actually is her, that is like what

139
00:06:53,360 --> 00:06:54,120
Speaker 1: she looks.

140
00:06:53,920 --> 00:06:58,560
Speaker 2: Like, yeah, versus watching something imagine, not imagine just generate.

141
00:06:58,400 --> 00:07:00,839
Speaker 1: Put on like a skin suit of your mom hugging you,

142
00:07:00,920 --> 00:07:03,240
Speaker 1: which it just isn't like it's weird. The video is

143
00:07:03,279 --> 00:07:04,640
Speaker 1: not real, but the picture is.

144
00:07:04,960 --> 00:07:07,760
Speaker 5: I just it's a bizarre I feel those I felt

145
00:07:07,839 --> 00:07:10,120
Speaker 5: very judgmental of it before I tried it, and then

146
00:07:10,160 --> 00:07:11,200
Speaker 5: I tried it and I sobbed.

147
00:07:11,240 --> 00:07:13,960
Speaker 3: I like genuinely sobbed because that's interesting.

148
00:07:14,240 --> 00:07:18,320
Speaker 1: That reminds me of like the the VR thing where

149
00:07:18,400 --> 00:07:23,000
Speaker 1: you're like reconnecting with like dead family members only VR headsets,

150
00:07:23,000 --> 00:07:25,040
Speaker 1: which which yeah, people were very skeptical of. And then

151
00:07:25,080 --> 00:07:28,040
Speaker 1: I saw saw some people try it in Japan and

152
00:07:28,080 --> 00:07:29,960
Speaker 1: they like like just totally broke down.

153
00:07:30,040 --> 00:07:32,960
Speaker 5: It's just like when I like, I think the phrasing

154
00:07:33,000 --> 00:07:35,080
Speaker 5: that I used was like, I know it's fake. It

155
00:07:35,120 --> 00:07:37,640
Speaker 5: didn't look anything like my dad gave him hair. My

156
00:07:37,720 --> 00:07:40,040
Speaker 5: dad had never had hair in his life, but like

157
00:07:40,240 --> 00:07:44,400
Speaker 5: the shape of it was enough to like sketch this

158
00:07:44,560 --> 00:07:46,520
Speaker 5: like part of me that was very much longing for

159
00:07:46,600 --> 00:07:48,560
Speaker 5: my father to have been able to go to my wedding.

160
00:07:49,120 --> 00:07:51,440
Speaker 5: So like doing it, I was like, this is weird.

161
00:07:51,480 --> 00:07:53,760
Speaker 5: I don't feel this is not comforting. I mean it's

162
00:07:53,760 --> 00:07:55,280
Speaker 5: not comforting, but it's.

163
00:07:55,360 --> 00:07:56,360
Speaker 2: Makes you feel bad.

164
00:07:56,600 --> 00:08:00,800
Speaker 1: That nausea is like like indicative of like the hyperreality problem. Yeah,

165
00:08:00,840 --> 00:08:03,400
Speaker 1: which which mean people like like Altman in the whole

166
00:08:03,440 --> 00:08:05,560
Speaker 1: the whole industry or can you break them rapidly pushing

167
00:08:05,640 --> 00:08:06,280
Speaker 1: us towards.

168
00:08:06,080 --> 00:08:07,760
Speaker 2: On the hyperreality problem? Can you break that down?

169
00:08:07,920 --> 00:08:09,560
Speaker 1: Well, I mean, I guess if turn gets used in

170
00:08:09,560 --> 00:08:12,160
Speaker 1: a few different ways, but it's like the more something

171
00:08:12,200 --> 00:08:14,760
Speaker 1: is so fake that it's more real than real hmm.

172
00:08:15,680 --> 00:08:17,920
Speaker 1: And we see this problem with a lot of like

173
00:08:18,480 --> 00:08:21,600
Speaker 1: the VR stuff, But now now you see this a

174
00:08:21,600 --> 00:08:25,640
Speaker 1: lot with with a AI generated images which are like

175
00:08:25,720 --> 00:08:30,200
Speaker 1: quote unquote photorealistic, but they're like two photorealistic because they're

176
00:08:30,200 --> 00:08:32,000
Speaker 1: being trained on on on a data set of like

177
00:08:32,040 --> 00:08:34,800
Speaker 1: highly photoshoped images, So it looks like reality, but it

178
00:08:34,800 --> 00:08:38,559
Speaker 1: looks more than reality. It's it's it's it's stronger than

179
00:08:38,559 --> 00:08:41,480
Speaker 1: what reality actually is. Supposed to be and that's like

180
00:08:41,679 --> 00:08:44,720
Speaker 1: completely poisoning the data set and like this can affect

181
00:08:44,760 --> 00:08:45,880
Speaker 1: you like emotionally too.

182
00:08:46,000 --> 00:08:48,679
Speaker 2: Yeah, and what But the thing is that gets me

183
00:08:48,720 --> 00:08:51,480
Speaker 2: about this is you look at everything. You look at

184
00:08:51,520 --> 00:08:54,560
Speaker 2: all the AI stuff and this is a fairly old

185
00:08:54,600 --> 00:08:57,000
Speaker 2: example at this point. And I'm not I'm not insulting

186
00:08:57,000 --> 00:08:59,400
Speaker 2: you would. This is nothing bad about your point. It's

187
00:08:59,440 --> 00:09:02,199
Speaker 2: just they've not been able to find a dude at

188
00:09:02,280 --> 00:09:05,600
Speaker 2: or a gizmo that at least brings you cheer.

189
00:09:07,800 --> 00:09:09,840
Speaker 5: I mean you can if you are twisted, then my

190
00:09:09,920 --> 00:09:13,040
Speaker 5: mind is twisted. And you'll like come up with some

191
00:09:13,120 --> 00:09:16,280
Speaker 5: prompts that are truly cursed and no editor will let

192
00:09:16,360 --> 00:09:19,120
Speaker 5: you publish in good faith, Like yeah, you could have

193
00:09:19,240 --> 00:09:20,199
Speaker 5: a little fun with it.

194
00:09:20,320 --> 00:09:22,679
Speaker 2: I'm talking about a thing that people used to do

195
00:09:22,800 --> 00:09:23,600
Speaker 2: something normal.

196
00:09:23,960 --> 00:09:27,720
Speaker 1: No, yeah, no one, it's I mean it helped that

197
00:09:27,760 --> 00:09:29,320
Speaker 1: one kid graduate from UCLA.

198
00:09:29,480 --> 00:09:30,160
Speaker 4: So there you go.

199
00:09:30,440 --> 00:09:31,240
Speaker 2: Which one was that?

200
00:09:31,640 --> 00:09:32,760
Speaker 7: Oh, this has just been.

201
00:09:32,600 --> 00:09:35,360
Speaker 1: A viral video the past like two weeks of this

202
00:09:35,400 --> 00:09:38,280
Speaker 1: guy like showing the prompts he used to graduate from college,

203
00:09:38,360 --> 00:09:40,320
Speaker 1: like during his graduation ceremony.

204
00:09:40,440 --> 00:09:41,000
Speaker 2: So cool.

205
00:09:41,440 --> 00:09:44,000
Speaker 6: Yeah, that's the thing I've pointed out a lot of that.

206
00:09:44,200 --> 00:09:46,640
Speaker 6: AI has found a lot of use cases. They're just

207
00:09:46,920 --> 00:09:48,400
Speaker 6: all kind of bad.

208
00:09:48,480 --> 00:09:50,880
Speaker 2: But they don't generate money, right.

209
00:09:50,800 --> 00:09:55,920
Speaker 6: They don't generate money, and they take away This will

210
00:09:55,960 --> 00:09:58,080
Speaker 6: be a metaphor that you relate to. I can't remember

211
00:09:58,080 --> 00:09:59,440
Speaker 6: who said it first.

212
00:10:00,320 --> 00:10:01,040
Speaker 2: It was a tech.

213
00:10:00,840 --> 00:10:05,560
Speaker 6: Columnist anyway, it was about letting kids use AI to

214
00:10:05,600 --> 00:10:08,120
Speaker 6: do their homework is like going to the gym and

215
00:10:08,160 --> 00:10:11,439
Speaker 6: having a machine lift the weights for you. Like, the

216
00:10:11,600 --> 00:10:15,360
Speaker 6: point is the process and the learning, and AI just

217
00:10:15,440 --> 00:10:18,520
Speaker 6: kind of subverts that, which can be useful if you're

218
00:10:19,040 --> 00:10:23,080
Speaker 6: coding or doing some high level technical stuff. I suppose yeah,

219
00:10:23,240 --> 00:10:23,920
Speaker 6: not my area.

220
00:10:24,080 --> 00:10:27,080
Speaker 2: But but even then, it's like with the coding stuff,

221
00:10:27,080 --> 00:10:30,040
Speaker 2: they massively overstate what coding like, coding is only one

222
00:10:30,080 --> 00:10:33,440
Speaker 2: part of the software engineering stack. And even then you

223
00:10:33,480 --> 00:10:35,280
Speaker 2: can't like help They're like, oh, I could build an

224
00:10:35,360 --> 00:10:38,680
Speaker 2: entire application? Could it has anyone? Actually, And this is

225
00:10:38,679 --> 00:10:40,440
Speaker 2: Matt Hughes Murtis that brought this up the other day.

226
00:10:40,640 --> 00:10:43,439
Speaker 2: How did it all this bullshit Kevin Rucy and bullshit

227
00:10:43,480 --> 00:10:45,960
Speaker 2: about oh, vibe coding is taking up. I've got seen

228
00:10:46,000 --> 00:10:47,760
Speaker 2: one fucking vibe coded company.

229
00:10:48,080 --> 00:10:51,360
Speaker 5: Man. The phrase vibe code, it's just I haven't heard

230
00:10:51,400 --> 00:10:51,880
Speaker 5: this before.

231
00:10:51,960 --> 00:10:53,480
Speaker 4: Vibe coding is it's horrible.

232
00:10:53,720 --> 00:10:56,840
Speaker 5: It's it's it's it's horrible. Like at Google Io they're

233
00:10:56,880 --> 00:10:59,520
Speaker 5: like vibe coating and I was like, please kill me,

234
00:11:00,400 --> 00:11:02,440
Speaker 5: because what does it even mean.

235
00:11:02,640 --> 00:11:05,840
Speaker 2: It's so, here's what it's meant to mean. It's meant

236
00:11:05,840 --> 00:11:08,440
Speaker 2: to mean that you, as a person do not understand software.

237
00:11:09,040 --> 00:11:09,240
Speaker 4: True.

238
00:11:09,440 --> 00:11:11,880
Speaker 2: True, you are able to use the coding thing to

239
00:11:11,920 --> 00:11:15,559
Speaker 2: build software. And the idea which the massive liberty they

240
00:11:15,559 --> 00:11:17,880
Speaker 2: take from there is that because someone can do a

241
00:11:18,040 --> 00:11:20,880
Speaker 2: thing like this, whether it works or not, whether it's

242
00:11:20,920 --> 00:11:24,960
Speaker 2: secure or not, who cares, it means that someone who

243
00:11:24,960 --> 00:11:27,560
Speaker 2: doesn't understand coding at all could build a huge company

244
00:11:27,559 --> 00:11:30,440
Speaker 2: that does whatever. It's kind of like if you saw

245
00:11:30,600 --> 00:11:34,280
Speaker 2: the watch The Simpsons anyone. Yeah, there's an episode of

246
00:11:34,320 --> 00:11:37,880
Speaker 2: The Simpsons where they rebuild Ned Flanders his house and

247
00:11:37,920 --> 00:11:39,679
Speaker 2: like the rooms get smaller and they're like, this room

248
00:11:39,720 --> 00:11:42,200
Speaker 2: has no electricity, this room has too much electricity, and

249
00:11:42,240 --> 00:11:44,320
Speaker 2: all the hair stands up. It's like seeing that and

250
00:11:44,360 --> 00:11:46,440
Speaker 2: being like, holy shit, these people could build a city.

251
00:11:46,800 --> 00:11:49,600
Speaker 2: It's fucking insane. And of course there is a Kevin

252
00:11:49,640 --> 00:11:51,840
Speaker 2: Ruth's article in New York Times, so he's like, that's arm.

253
00:11:51,920 --> 00:11:54,840
Speaker 2: Oh my god, I made a recipe application. It's just

254
00:11:55,840 --> 00:11:59,640
Speaker 2: real like peekaboo moments in AI. And I know I've

255
00:11:59,679 --> 00:12:02,199
Speaker 2: been thinking about AI for what feels like seven years now,

256
00:12:02,240 --> 00:12:06,360
Speaker 2: but it's only one. But it's just I don't know

257
00:12:06,400 --> 00:12:09,480
Speaker 2: why more people again, I get the Microsoft Open Air,

258
00:12:09,520 --> 00:12:11,040
Speaker 2: I think I do, but I don't get why more

259
00:12:11,080 --> 00:12:13,960
Speaker 2: people aren't more alarm. There's nothing. There's not a thing.

260
00:12:14,080 --> 00:12:15,880
Speaker 2: There's not a thing that you can point and be like, Wow,

261
00:12:15,880 --> 00:12:17,520
Speaker 2: this is actually kind of fucking cool. That costs too

262
00:12:17,600 --> 00:12:20,360
Speaker 2: much money, it requires stealing, it boils legs with all

263
00:12:20,400 --> 00:12:22,840
Speaker 2: these things. But at least we have this. Not really,

264
00:12:23,880 --> 00:12:27,840
Speaker 2: I genuinely, I'm not even asking the question sarcastically anymore.

265
00:12:27,840 --> 00:12:32,880
Speaker 2: I'm just like anything, anything, anything, one thing that I

266
00:12:32,880 --> 00:12:35,520
Speaker 2: don't mean kind of works. I mean, this is a

267
00:12:35,559 --> 00:12:38,240
Speaker 2: tool that I use every day, like a drop box

268
00:12:38,320 --> 00:12:40,120
Speaker 2: style thing. I don't even mean a filse. I just

269
00:12:40,120 --> 00:12:42,560
Speaker 2: mean a useful piece of software you can point to

270
00:12:42,600 --> 00:12:44,400
Speaker 2: and go, I use this, and it's good.

271
00:12:45,679 --> 00:12:47,440
Speaker 6: I have I have one, but I don't know how

272
00:12:47,520 --> 00:12:50,400
Speaker 6: much of it is LM based. Do you use otter

273
00:12:50,640 --> 00:12:51,200
Speaker 6: in your way?

274
00:12:51,320 --> 00:12:51,600
Speaker 5: I do?

275
00:12:51,760 --> 00:12:54,400
Speaker 6: Yeah, Like Otter is a dictation service.

276
00:12:54,480 --> 00:12:55,599
Speaker 2: Yea transcription.

277
00:12:55,720 --> 00:12:57,360
Speaker 6: I was just gonna say transcription.

278
00:12:57,480 --> 00:12:59,000
Speaker 7: I was just gonna say clips captioning.

279
00:12:59,040 --> 00:13:02,840
Speaker 1: That's like the only thing like Dropbox has integrated like

280
00:13:02,960 --> 00:13:06,400
Speaker 1: of auto transcriptions for almost all their uploads and then

281
00:13:06,440 --> 00:13:08,600
Speaker 1: mix My job really easy because I have to do

282
00:13:08,640 --> 00:13:10,400
Speaker 1: a lot of interviews and now I can just refer

283
00:13:10,440 --> 00:13:12,240
Speaker 1: to that. If I need a more complex transcription, I

284
00:13:12,280 --> 00:13:14,280
Speaker 1: could set it to one of our services. But but no,

285
00:13:14,400 --> 00:13:17,560
Speaker 1: like that's that's it, But like that's LLL Hulm's have

286
00:13:17,559 --> 00:13:18,959
Speaker 1: been doing that for a long time.

287
00:13:19,280 --> 00:13:23,080
Speaker 2: Yeah, it's just transcription. I'm not even being a misanthrope.

288
00:13:23,160 --> 00:13:26,640
Speaker 2: I'm just so much money is going into this, so

289
00:13:26,800 --> 00:13:29,560
Speaker 2: much money is not going into other things. And the

290
00:13:29,600 --> 00:13:31,880
Speaker 2: only thing we're having popped out is, hey, we've got

291
00:13:31,920 --> 00:13:35,199
Speaker 2: a Google search that kind of works but doesn't and

292
00:13:35,240 --> 00:13:37,719
Speaker 2: we can do transcriptions, which they did almost immediately. I

293
00:13:37,760 --> 00:13:40,200
Speaker 2: feel I feel like that like rev had their AI

294
00:13:40,200 --> 00:13:42,600
Speaker 2: transcriptions almost immediately. Yeah, they've been around for it at

295
00:13:43,080 --> 00:13:46,400
Speaker 2: There were various companies I worked with an AI transcription

296
00:13:46,440 --> 00:13:49,320
Speaker 2: coming dead now A couple of years back with the

297
00:13:49,600 --> 00:13:51,280
Speaker 2: like twenty twenty three, I think it was. It was

298
00:13:51,320 --> 00:13:53,320
Speaker 2: like meeting transcription zooms had it.

299
00:13:53,600 --> 00:13:55,400
Speaker 1: I was I was using where I was AI transcriptions

300
00:13:55,400 --> 00:13:56,160
Speaker 1: back in twenty twenty.

301
00:13:56,240 --> 00:14:00,280
Speaker 2: Yeah, it's just like it feels it feels like I'm

302
00:14:00,280 --> 00:14:02,440
Speaker 2: going insane sometimes. It feels like when I read these

303
00:14:02,440 --> 00:14:05,080
Speaker 2: stories and they're like in the revolutionary power of Ai,

304
00:14:05,600 --> 00:14:07,640
Speaker 2: but you look at it, it's like you don't even

305
00:14:07,679 --> 00:14:09,800
Speaker 2: have a funny You have a funny thing. I guess

306
00:14:10,120 --> 00:14:11,880
Speaker 2: a joker level thing. Yeah.

307
00:14:12,040 --> 00:14:15,280
Speaker 5: I think the problem is is just like we are

308
00:14:15,360 --> 00:14:20,360
Speaker 5: promised one thing. Yeah, you're promised this personalization, this automat,

309
00:14:20,680 --> 00:14:23,320
Speaker 5: this automation that it's gonna know you. And like we've

310
00:14:23,360 --> 00:14:27,280
Speaker 5: been fed through so many generations of like science fiction

311
00:14:27,440 --> 00:14:30,000
Speaker 5: what we think uh ai is going to be. This

312
00:14:30,120 --> 00:14:33,440
Speaker 5: is not that this requires so much work from you

313
00:14:33,520 --> 00:14:36,360
Speaker 5: to train it. Like you have to understand the language

314
00:14:36,360 --> 00:14:39,320
Speaker 5: which with to prompt, chat cept or any of these

315
00:14:39,360 --> 00:14:42,440
Speaker 5: other ais in order to get something remotely useful. So

316
00:14:42,480 --> 00:14:45,400
Speaker 5: you're actually having to learn a new language. And like

317
00:14:45,480 --> 00:14:48,600
Speaker 5: if you look at the most successful chat ChiPT prompts,

318
00:14:48,960 --> 00:14:52,400
Speaker 5: they're like four four paragraphs along, they're insane. You have

319
00:14:52,480 --> 00:14:56,880
Speaker 5: to like you have to be preempting what like this

320
00:14:56,960 --> 00:14:59,240
Speaker 5: thing could be like I had. I was just like,

321
00:14:59,320 --> 00:15:02,200
Speaker 5: you know, to your point about vibe coding, I'm not

322
00:15:02,280 --> 00:15:04,320
Speaker 5: a I'm like a spreadsheet girly, but I'm not like

323
00:15:04,360 --> 00:15:06,760
Speaker 5: an advanced spreadsheet early and I was trying to pull

324
00:15:06,800 --> 00:15:10,120
Speaker 5: some data insights from this set I was looking at,

325
00:15:10,120 --> 00:15:13,360
Speaker 5: and I was like, Okay, I don't know fuck all

326
00:15:13,520 --> 00:15:17,720
Speaker 5: about spreadsheet formulas. Besides like the really basic ones. How

327
00:15:17,720 --> 00:15:20,040
Speaker 5: am I going to do this conditional logic program? Let

328
00:15:20,120 --> 00:15:24,440
Speaker 5: me ask chat CHETPT And it took me so long

329
00:15:25,000 --> 00:15:27,680
Speaker 5: just to figure out the stuff, and it was always wrong.

330
00:15:28,120 --> 00:15:30,960
Speaker 5: And because I could, like, because I understand math, I

331
00:15:31,000 --> 00:15:34,960
Speaker 5: could parse out how to fix the completely wrong formulas

332
00:15:34,960 --> 00:15:35,680
Speaker 5: it was giving me.

333
00:15:36,320 --> 00:15:39,960
Speaker 3: But that was such a painful process, and.

334
00:15:39,920 --> 00:15:41,960
Speaker 2: So was the output even that good?

335
00:15:42,360 --> 00:15:44,480
Speaker 5: Oh no, it was an excellent output. I got a

336
00:15:44,480 --> 00:15:45,440
Speaker 5: beautiful spreadsheet.

337
00:15:45,440 --> 00:15:47,480
Speaker 2: That's cool. I had much time. Would you say you

338
00:15:47,560 --> 00:15:50,400
Speaker 2: invested two and a half hours? Nice?

339
00:15:51,440 --> 00:15:54,640
Speaker 6: You're you're a journalist who understands math, like you're a

340
00:15:54,760 --> 00:15:56,200
Speaker 6: rare and special Yeah, you know.

341
00:15:56,320 --> 00:15:56,520
Speaker 1: It was.

342
00:15:56,880 --> 00:15:59,400
Speaker 5: I was just basically like this, the fact that this

343
00:15:59,440 --> 00:16:02,440
Speaker 5: conditional if and statement is not working, or it's working

344
00:16:02,440 --> 00:16:04,800
Speaker 5: opposite to what I want for to find this one

345
00:16:04,840 --> 00:16:09,640
Speaker 5: particular data set is driving me cuckoo for cocoa puffs.

346
00:16:10,360 --> 00:16:13,680
Speaker 5: It's very simple. I know how to do the math manually.

347
00:16:13,840 --> 00:16:16,040
Speaker 5: Why can't the computer tell me how to write it

348
00:16:16,080 --> 00:16:19,840
Speaker 5: to the other fucking computers two and a half hours.

349
00:16:19,880 --> 00:16:22,360
Speaker 2: I love innovation, and I think I think that that

350
00:16:22,400 --> 00:16:25,560
Speaker 2: speaks to the larger problem, which is generative. AI isn't

351
00:16:25,600 --> 00:16:28,160
Speaker 2: completely useless. If it had been sold as it is,

352
00:16:28,200 --> 00:16:33,280
Speaker 2: which is kind of niche cloud software, like cloud compute stuff,

353
00:16:33,640 --> 00:16:35,080
Speaker 2: they wouldn't have been able to fund any of the

354
00:16:35,160 --> 00:16:36,880
Speaker 2: data centers. If they would have been like, all right,

355
00:16:36,920 --> 00:16:38,120
Speaker 2: we're going to be able to in two and a

356
00:16:38,120 --> 00:16:40,840
Speaker 2: half hours give you the world's best spreadsheet, they would

357
00:16:41,200 --> 00:16:41,440
Speaker 2: it was.

358
00:16:41,600 --> 00:16:44,680
Speaker 5: It was a good spread okay, lot the best, Okay,

359
00:16:45,520 --> 00:16:46,520
Speaker 5: a decent spreadsheet.

360
00:16:46,600 --> 00:16:50,440
Speaker 2: The thing even the asterisks have asterisks, and it's it's

361
00:16:50,520 --> 00:16:53,800
Speaker 2: just I feel like in the feedback I get from

362
00:16:53,880 --> 00:16:56,720
Speaker 2: listeners is very much that everyone is like a lot.

363
00:16:57,280 --> 00:16:59,680
Speaker 2: I don't get any emails from people being like, hey,

364
00:16:59,680 --> 00:17:01,960
Speaker 2: atu man, I have the most useful thing I get

365
00:17:02,000 --> 00:17:04,480
Speaker 2: the occasional bright spark is like I have, over the

366
00:17:04,480 --> 00:17:06,840
Speaker 2: course of hours, created a very useful thing that I

367
00:17:06,960 --> 00:17:15,119
Speaker 2: use Sometimes it's like cool, Okay, it's fine. Bidet sounds

368
00:17:15,119 --> 00:17:17,359
Speaker 2: more useful than that, Like I'm trying to think of

369
00:17:17,440 --> 00:17:20,520
Speaker 2: like other innovations that exist that could be Yeah, everything

370
00:17:20,560 --> 00:17:23,040
Speaker 2: is more useful, like Apple pay is more useful than

371
00:17:23,080 --> 00:17:25,720
Speaker 2: any of the shit that they've built. But it's this

372
00:17:25,800 --> 00:17:28,080
Speaker 2: thing where we are being told again and again and

373
00:17:28,119 --> 00:17:31,240
Speaker 2: again that it's the future, and we're being told that

374
00:17:31,280 --> 00:17:34,080
Speaker 2: it's this ultracomplex thing that will never understand, which leads

375
00:17:34,119 --> 00:17:36,639
Speaker 2: really neatly into my favorite story of the week, which

376
00:17:36,720 --> 00:17:39,439
Speaker 2: is all of you, I assume have heard about this

377
00:17:40,200 --> 00:17:43,280
Speaker 2: Meta offering one hundred million dollars to Open AI staff

378
00:17:43,680 --> 00:17:46,399
Speaker 2: and how there's this big talent more and four people

379
00:17:46,480 --> 00:17:49,720
Speaker 2: just left Open AI to go to Meta. Alison, you're

380
00:17:49,760 --> 00:17:52,000
Speaker 2: on vacation, so you missed some of this, which is

381
00:17:52,000 --> 00:17:53,399
Speaker 2: probably best for your mental health.

382
00:17:53,680 --> 00:17:57,959
Speaker 6: Oh I saw a bit. The seven figure bonus story

383
00:17:58,080 --> 00:18:00,359
Speaker 6: came out right before I went on vacations, so I

384
00:18:00,440 --> 00:18:01,199
Speaker 6: was getting that.

385
00:18:02,000 --> 00:18:05,400
Speaker 5: I can't blame Oh no, get way to a four

386
00:18:05,480 --> 00:18:09,520
Speaker 5: oh one k that's fat and sizable, Like yeah, no, no.

387
00:18:09,520 --> 00:18:12,800
Speaker 2: I think it rocks. I think it's great. I think

388
00:18:12,840 --> 00:18:15,040
Speaker 2: they should demand There was a part of the erin

389
00:18:15,080 --> 00:18:16,879
Speaker 2: Woo from the information of a great story about this,

390
00:18:17,480 --> 00:18:20,199
Speaker 2: where she she was talking about how some people are like,

391
00:18:20,240 --> 00:18:22,960
Speaker 2: oh yeah, I just threatened to quit and they gave

392
00:18:23,000 --> 00:18:25,160
Speaker 2: me more money. I would join one of these companies

393
00:18:25,160 --> 00:18:28,240
Speaker 2: and day two slug fucking quit. Mark, What are you

394
00:18:28,320 --> 00:18:30,160
Speaker 2: gonna do about it? Mark, I'm gonna go right back

395
00:18:30,160 --> 00:18:32,480
Speaker 2: to Open Ai. They're gonna give me this money and

396
00:18:32,520 --> 00:18:34,760
Speaker 2: then Mark Zuckerbo will give you whatever you He's been

397
00:18:34,760 --> 00:18:37,160
Speaker 2: flying them to his house in Tahoe and they're still

398
00:18:37,160 --> 00:18:40,400
Speaker 2: saying no. That's the best part that people just like, nah,

399
00:18:40,400 --> 00:18:42,720
Speaker 2: I don't want to, don't want to mate, But it

400
00:18:42,840 --> 00:18:45,280
Speaker 2: might be because they're all in a weird I'm gonna

401
00:18:45,280 --> 00:18:48,600
Speaker 2: say click, but I want to I want to believe

402
00:18:48,720 --> 00:18:53,600
Speaker 2: Polly situation. So all okay, I have no no knowledge

403
00:18:53,600 --> 00:18:56,920
Speaker 2: that they're fucking. But the recruits on the list, which

404
00:18:56,920 --> 00:19:00,399
Speaker 2: refers to the meta list for potential people that they

405
00:19:00,440 --> 00:19:04,159
Speaker 2: could hire, typically have PhDs from elite schools like Berkeley

406
00:19:04,200 --> 00:19:07,000
Speaker 2: and Carnegie. Melon They have experienced places like open Ai

407
00:19:07,119 --> 00:19:09,439
Speaker 2: in San Francisco and Google Deep Mind in London. They

408
00:19:09,440 --> 00:19:11,560
Speaker 2: are usually in their twenties and thirties, and they all

409
00:19:11,600 --> 00:19:13,880
Speaker 2: know each other. They spend their days staring at screens

410
00:19:13,880 --> 00:19:16,560
Speaker 2: to solve the kinds of inscrutable problems that require spectacular

411
00:19:16,600 --> 00:19:19,399
Speaker 2: amounts of computing power, and their previously obscure talents have

412
00:19:19,480 --> 00:19:23,280
Speaker 2: never been so highly valued. So these stories have a

413
00:19:23,440 --> 00:19:26,440
Speaker 2: thread through them I'm really enjoying, which is that the

414
00:19:26,480 --> 00:19:29,840
Speaker 2: writer and the companies don't know what these people are doing,

415
00:19:30,600 --> 00:19:33,600
Speaker 2: and I just feel like they are scamming I think

416
00:19:33,640 --> 00:19:36,000
Speaker 2: they're scamming them. Another quote from The Wall Street Journal,

417
00:19:36,040 --> 00:19:38,679
Speaker 2: Megan Borodowski, I believe it, wrote this. The handful of

418
00:19:38,680 --> 00:19:40,960
Speaker 2: researchers who are smartest about AI have built up what

419
00:19:41,000 --> 00:19:44,400
Speaker 2: one described as tribal knowledge that is almost impossible to replicate.

420
00:19:44,600 --> 00:19:46,720
Speaker 2: Rival researchers have lived in the same group houses in

421
00:19:46,760 --> 00:19:49,800
Speaker 2: San Francisco, where they discuss papers that might provide clues

422
00:19:49,840 --> 00:19:53,399
Speaker 2: for achieving the next great breakthrough. We are very aligned

423
00:19:53,400 --> 00:19:55,560
Speaker 2: on research directions and interests. One of them wrote, I

424
00:19:55,640 --> 00:19:57,320
Speaker 2: hope we get to work on more stuff together in

425
00:19:57,320 --> 00:20:00,200
Speaker 2: the future. They are fucking lying to them. I'm sorry, sorry,

426
00:20:00,440 --> 00:20:03,119
Speaker 2: they are just making shit up. This has to be

427
00:20:03,160 --> 00:20:05,320
Speaker 2: I think that this is the funniest thing ever. I

428
00:20:05,359 --> 00:20:07,880
Speaker 2: think that this is a true revenge of the nerds situation.

429
00:20:07,960 --> 00:20:10,520
Speaker 1: Have they reinvented collective bargaining?

430
00:20:10,720 --> 00:20:15,960
Speaker 2: They have, They've basically done unionization. It's amazing. It's good

431
00:20:16,000 --> 00:20:16,800
Speaker 2: for them.

432
00:20:17,400 --> 00:20:20,240
Speaker 6: Higher education is really expensive. They have a lot of debt.

433
00:20:20,400 --> 00:20:23,280
Speaker 2: Yeah, I love this. I think that they should ask

434
00:20:23,320 --> 00:20:25,639
Speaker 2: for more money. I think they should all get together

435
00:20:25,720 --> 00:20:28,399
Speaker 2: and just refuse to take less than fifty million a day,

436
00:20:28,800 --> 00:20:32,879
Speaker 2: eight figure, site, nine figure, like Sky is the limit?

437
00:20:33,040 --> 00:20:36,199
Speaker 2: Sam Samon want to burn anything. But back to erin Wouhi.

438
00:20:37,240 --> 00:20:38,959
Speaker 2: This is another quote about this which this is from

439
00:20:39,000 --> 00:20:41,000
Speaker 2: inside the Great AI Talent auction that deals with the

440
00:20:41,000 --> 00:20:44,879
Speaker 2: free agents and the egosham But a senior leader another

441
00:20:44,920 --> 00:20:46,720
Speaker 2: of the major AI labs said it was hard to

442
00:20:46,720 --> 00:20:49,959
Speaker 2: know what research specialties actually mattered for improving AI models.

443
00:20:50,000 --> 00:20:53,080
Speaker 2: AI is field where researchers are designing such complicated systems

444
00:20:53,200 --> 00:20:55,480
Speaker 2: that is difficult to break up one aspect of the

445
00:20:55,480 --> 00:20:58,439
Speaker 2: work from another, the leader said. Ultimately, they said recruiting

446
00:20:58,520 --> 00:21:00,320
Speaker 2: often comes down to word of mouth, the game knowing

447
00:21:00,359 --> 00:21:03,000
Speaker 2: a person or having worked with them before. It's a scam.

448
00:21:03,280 --> 00:21:07,399
Speaker 5: Isn't that just like normal job stuff like word of mouth,

449
00:21:07,520 --> 00:21:09,320
Speaker 5: knowing having worked with someone before?

450
00:21:09,359 --> 00:21:14,199
Speaker 6: And yeah, yeah, it feels like hyper focused Silicon Valley.

451
00:21:14,680 --> 00:21:17,400
Speaker 2: But stuff generally you know what they did at the job.

452
00:21:18,080 --> 00:21:22,000
Speaker 2: Generally you do. And Mark Zuckerberger, guy, we did AI.

453
00:21:34,800 --> 00:21:38,119
Speaker 2: There is a WhatsApp group I think it's called recruitment

454
00:21:38,240 --> 00:21:41,960
Speaker 2: and it's the party Emoji. And that's where Mark Zuckerberg

455
00:21:42,000 --> 00:21:46,000
Speaker 2: invites people. He like has this little weird little WhatsApp

456
00:21:46,080 --> 00:21:49,200
Speaker 2: hot like whole he invites people. He's like, hey, I'm

457
00:21:49,200 --> 00:21:51,440
Speaker 2: Mark Zuckerberg. Do you want all the money in the world?

458
00:21:51,640 --> 00:21:54,040
Speaker 2: What do you do? I don't care how many? And

459
00:21:54,080 --> 00:21:56,239
Speaker 2: apparently one of the metrics they measure them on is

460
00:21:56,280 --> 00:21:59,760
Speaker 2: like citations in papers. I genuinely think this is a scam.

461
00:22:00,119 --> 00:22:02,320
Speaker 2: It's genuinely a scam. I think it's the coolest scam

462
00:22:02,359 --> 00:22:06,480
Speaker 2: of all time. We find it's nerds versus management consultants.

463
00:22:06,119 --> 00:22:08,520
Speaker 7: So nerds versus different types of nerds.

464
00:22:08,560 --> 00:22:10,760
Speaker 2: Management consultants are not nerds. Actually, I want to say

465
00:22:10,760 --> 00:22:12,760
Speaker 2: management consultants are not nerds. They're jocks.

466
00:22:13,600 --> 00:22:15,840
Speaker 5: I would agree with that. I would agree with that.

467
00:22:16,359 --> 00:22:16,879
Speaker 4: They see it.

468
00:22:17,080 --> 00:22:21,720
Speaker 5: They are professional deckmakers about and they go in slide one,

469
00:22:21,760 --> 00:22:24,560
Speaker 5: we can see that number go up. Yeah, and slide too,

470
00:22:24,600 --> 00:22:31,399
Speaker 5: we can see number flat. Here's a pie chart.

471
00:22:29,680 --> 00:22:32,440
Speaker 2: Someone else made. Yes, that's it.

472
00:22:32,640 --> 00:22:33,159
Speaker 7: I can see it.

473
00:22:33,160 --> 00:22:33,800
Speaker 2: It's jock shit.

474
00:22:34,040 --> 00:22:34,480
Speaker 7: Yeah.

475
00:22:34,520 --> 00:22:37,880
Speaker 2: I went to a drama private school. I know everyone,

476
00:22:38,080 --> 00:22:41,359
Speaker 2: this is not shocking. Yeah, I thank you all boys

477
00:22:41,359 --> 00:22:44,359
Speaker 2: as well. It's yeah yeah, but I was like the

478
00:22:44,400 --> 00:22:46,600
Speaker 2: dumbest kid in that school, which is really and I

479
00:22:46,640 --> 00:22:49,040
Speaker 2: was the fastest as well. So school was great for me.

480
00:22:49,800 --> 00:22:51,560
Speaker 2: But you run into a lot of people who can

481
00:22:51,560 --> 00:22:54,199
Speaker 2: memorize a lot of things but don't know how to

482
00:22:54,280 --> 00:22:57,520
Speaker 2: put them together. There's no real like synthetic thought. It's

483
00:22:57,520 --> 00:22:59,840
Speaker 2: all just like, I don't know, like having a big

484
00:23:00,040 --> 00:23:02,440
Speaker 2: isle of information that they portunly draw from and that

485
00:23:02,720 --> 00:23:04,320
Speaker 2: they don't really know what any of it means.

486
00:23:04,520 --> 00:23:07,680
Speaker 5: Sounds familiar to all the AI fitness summaries that I've

487
00:23:07,720 --> 00:23:09,960
Speaker 5: been suffering through. I just wrote a thing about.

488
00:23:09,760 --> 00:23:13,040
Speaker 2: It that's tell us about that. What's what's the AI

489
00:23:13,119 --> 00:23:14,679
Speaker 2: fitness ship been doing? This is?

490
00:23:14,720 --> 00:23:15,040
Speaker 1: Ah?

491
00:23:15,119 --> 00:23:18,320
Speaker 5: Yeah, So I ate it during a run last week.

492
00:23:18,640 --> 00:23:21,119
Speaker 5: Yeah no, it was too hot outside. Uh and I

493
00:23:21,240 --> 00:23:24,560
Speaker 5: was you know, I was on the Box union bargaining committee.

494
00:23:24,560 --> 00:23:26,520
Speaker 5: So I've been like sleep deprived for two months.

495
00:23:26,600 --> 00:23:30,200
Speaker 2: Well, so congratulations, yeah, congratulations.

496
00:23:29,880 --> 00:23:32,119
Speaker 5: It was it was. It came down to the wire,

497
00:23:32,280 --> 00:23:35,040
Speaker 5: but we averted a strike. It was real great stuff, uh,

498
00:23:35,080 --> 00:23:37,400
Speaker 5: and I was I basically was like, oh now time

499
00:23:37,440 --> 00:23:39,520
Speaker 5: to look into my fitness and wear a wile data

500
00:23:39,920 --> 00:23:42,879
Speaker 5: from this time period and and kind of gain insights.

501
00:23:42,880 --> 00:23:45,920
Speaker 5: And it was just like not that it was beyond

502
00:23:46,320 --> 00:23:49,520
Speaker 5: I called my article the Unbearable Obviousness of AI Fitness

503
00:23:49,840 --> 00:23:53,200
Speaker 5: nice Summaries because it was just not great. But to

504
00:23:53,240 --> 00:23:56,399
Speaker 5: my point, I ate it on on this run, like

505
00:23:56,480 --> 00:23:59,840
Speaker 5: you can kind of see my hands fuck up playing bust,

506
00:24:00,440 --> 00:24:04,119
Speaker 5: my knees are fucked up. So like I was basically like,

507
00:24:04,119 --> 00:24:06,000
Speaker 5: all right, let me see what all of these things

508
00:24:06,080 --> 00:24:09,359
Speaker 5: said about my data and could I find Could I

509
00:24:09,400 --> 00:24:12,159
Speaker 5: get these ais to say like, hey, you've been like

510
00:24:12,240 --> 00:24:15,080
Speaker 5: really strung out over the last two months, your sleep

511
00:24:15,080 --> 00:24:19,560
Speaker 5: schedule has been supremely disrupted, your metrics are completely off.

512
00:24:19,600 --> 00:24:21,359
Speaker 5: These are all things I know from looking at my

513
00:24:21,400 --> 00:24:25,320
Speaker 5: baselines and knowing what they are. But could I get

514
00:24:25,320 --> 00:24:28,720
Speaker 5: it to say you showed haw like you've shown signs

515
00:24:28,800 --> 00:24:31,640
Speaker 5: of elevated risk of injury.

516
00:24:32,200 --> 00:24:33,760
Speaker 3: Not a single one of them could do it.

517
00:24:33,920 --> 00:24:36,919
Speaker 5: And Strava's was like the most egregious because it's like

518
00:24:36,960 --> 00:24:40,560
Speaker 5: you had an intense run and I had uploaded pictures

519
00:24:40,600 --> 00:24:43,200
Speaker 5: of my injury. I had like uploaded a note saying

520
00:24:43,200 --> 00:24:46,199
Speaker 5: that I had like injured myself pretty badly. There was

521
00:24:46,240 --> 00:24:49,080
Speaker 5: no context of like what I should do with that.

522
00:24:49,240 --> 00:24:52,880
Speaker 5: It was just it's literally stuff like you ran three

523
00:24:52,880 --> 00:24:56,399
Speaker 5: point one miles, it was eighty eight degrees fahrenheit. This

524
00:24:56,640 --> 00:24:59,680
Speaker 5: was slightly higher effort than other efforts that you've made

525
00:25:00,000 --> 00:25:01,040
Speaker 5: the last thirty days.

526
00:25:02,280 --> 00:25:03,040
Speaker 3: Have a nice day.

527
00:25:03,320 --> 00:25:06,520
Speaker 5: And I was like, that's not useful when you put

528
00:25:06,520 --> 00:25:09,520
Speaker 5: it right next to a chart that says the same thing.

529
00:25:09,920 --> 00:25:12,960
Speaker 5: Your elevation was eighty eight You had an eighty eight

530
00:25:12,960 --> 00:25:14,600
Speaker 5: feet of elevation gain and it was.

531
00:25:14,600 --> 00:25:16,240
Speaker 3: Up and down during your run.

532
00:25:16,320 --> 00:25:18,560
Speaker 5: And literally it's next to a thing that says elevation

533
00:25:18,680 --> 00:25:20,800
Speaker 5: gain eighty eight feet and a graph that shows up

534
00:25:20,840 --> 00:25:21,200
Speaker 5: and down.

535
00:25:21,640 --> 00:25:23,359
Speaker 3: Like it's that there's no intelligence.

536
00:25:23,440 --> 00:25:24,440
Speaker 4: Yeah, there is intelligence.

537
00:25:24,640 --> 00:25:26,040
Speaker 7: AI, there's no intelligence.

538
00:25:26,280 --> 00:25:28,040
Speaker 2: It feels like the thing it should be able to

539
00:25:28,040 --> 00:25:28,520
Speaker 2: do already.

540
00:25:28,680 --> 00:25:30,640
Speaker 5: This is what people wanted to do because at least

541
00:25:30,640 --> 00:25:34,200
Speaker 5: in my field, where you generate a massive mountain of

542
00:25:34,320 --> 00:25:37,159
Speaker 5: like quantified self data that you're looking at and you

543
00:25:37,160 --> 00:25:40,159
Speaker 5: want insights from. I wanted Aura to tell me, like

544
00:25:40,680 --> 00:25:45,040
Speaker 5: what my average weekly number, like how many hours per

545
00:25:45,119 --> 00:25:49,200
Speaker 5: week do I sleep on average on a twelve month basis,

546
00:25:49,359 --> 00:25:51,919
Speaker 5: and then how much of a sleep debt did I

547
00:25:52,000 --> 00:25:53,800
Speaker 5: incur in this specific week?

548
00:25:53,800 --> 00:25:56,280
Speaker 3: And it's like, ooh, we can't do that.

549
00:25:56,600 --> 00:25:58,600
Speaker 5: We can only do it the most recent week in

550
00:25:58,680 --> 00:26:00,520
Speaker 5: the most recent month for Trent, and I was like,

551
00:26:00,560 --> 00:26:03,240
Speaker 5: that's fucking useless. I have six years worth of AURA

552
00:26:03,359 --> 00:26:05,119
Speaker 5: data that I should be able to mine for that

553
00:26:05,200 --> 00:26:08,720
Speaker 5: kind of insight, and I can't do that. So that's

554
00:26:09,040 --> 00:26:11,760
Speaker 5: not at all useful for what I for like the

555
00:26:11,800 --> 00:26:13,400
Speaker 5: purposes of what I was trying to prove.

556
00:26:13,480 --> 00:26:15,440
Speaker 3: And so I ended up arguing with this thing for

557
00:26:15,520 --> 00:26:16,080
Speaker 3: like an hour.

558
00:26:16,200 --> 00:26:18,400
Speaker 2: But I feel like you did prove something though, yeah

559
00:26:18,440 --> 00:26:18,679
Speaker 2: I did.

560
00:26:18,840 --> 00:26:20,679
Speaker 5: I did, but it was at the same time, just

561
00:26:20,720 --> 00:26:24,520
Speaker 5: like it's sort of like a wikiped It's like a

562
00:26:24,560 --> 00:26:26,960
Speaker 5: book report written by a fourth grader who decided to

563
00:26:27,040 --> 00:26:30,000
Speaker 5: read the entry of the book on Wikipedia instead of

564
00:26:30,040 --> 00:26:31,920
Speaker 5: actually reading the book for insights.

565
00:26:32,400 --> 00:26:36,119
Speaker 3: So it's like a here you go, here you go.

566
00:26:36,640 --> 00:26:38,440
Speaker 2: It feels like the most elementary thing it should be

567
00:26:38,480 --> 00:26:41,280
Speaker 2: able to do. I have over a decade of fitness

568
00:26:41,359 --> 00:26:43,840
Speaker 2: day up. Yeah, and I still don't have shit. I

569
00:26:43,840 --> 00:26:46,240
Speaker 2: don't it told my Aura ring the other day, I

570
00:26:46,280 --> 00:26:48,600
Speaker 2: got Aura, I got somny, I got the thing that

571
00:26:48,640 --> 00:26:51,359
Speaker 2: electrocutes your head. Yes, why I'm so smart. It's like

572
00:26:51,400 --> 00:26:53,320
Speaker 2: I just watched the X Files episode with the computer

573
00:26:53,440 --> 00:26:55,359
Speaker 2: bit and that's what's happening to me. I'm getting electrocuted

574
00:26:55,400 --> 00:26:58,359
Speaker 2: every day. I got Aura, I got the eight sleep

575
00:26:58,400 --> 00:27:01,720
Speaker 2: in Vegas. I got also some gumph and I don't

576
00:27:01,720 --> 00:27:04,760
Speaker 2: know a single goddamn thing. It told me two days ago.

577
00:27:05,040 --> 00:27:07,480
Speaker 2: I am like something is wrong. Yeah, I'd slept three

578
00:27:07,520 --> 00:27:10,040
Speaker 2: hours two days straight. It was just a bad combination

579
00:27:10,080 --> 00:27:11,840
Speaker 2: of red ice. And it's like, yeah, you should do

580
00:27:11,880 --> 00:27:16,560
Speaker 2: something about that. Thanks, I'm glad I pay you ten

581
00:27:16,600 --> 00:27:20,040
Speaker 2: dollars a month for But this just feels like the

582
00:27:20,040 --> 00:27:22,880
Speaker 2: obvious and how does it not know? How? And maybe

583
00:27:22,880 --> 00:27:26,040
Speaker 2: it is just the ultimate limitation that we've been complaining about.

584
00:27:26,200 --> 00:27:28,320
Speaker 2: It's just it's kind of insulting. I don't know.

585
00:27:28,640 --> 00:27:30,159
Speaker 6: Yeah, not to feel like I don't want to be

586
00:27:30,200 --> 00:27:32,840
Speaker 6: the hippie here, because I use Strava and I like

587
00:27:32,920 --> 00:27:37,000
Speaker 6: track my workouts and things. But it's like I know

588
00:27:37,080 --> 00:27:40,360
Speaker 6: when I'm tired, yeah, and I know when I'm hungry

589
00:27:40,520 --> 00:27:42,679
Speaker 6: and when I've eaten too much or eaten too little,

590
00:27:42,800 --> 00:27:45,639
Speaker 6: Like you know, why do we need the computer to

591
00:27:45,640 --> 00:27:48,520
Speaker 6: do that for us? Is a real question, and it's

592
00:27:48,560 --> 00:27:50,760
Speaker 6: part of this like consumer trend of just trying to

593
00:27:50,800 --> 00:27:54,439
Speaker 6: get AI into every single app on my phone. And

594
00:27:54,480 --> 00:27:57,560
Speaker 6: it's like, I don't need Strava, Like Strava's doing great

595
00:27:57,600 --> 00:27:59,199
Speaker 6: for me for what I need it for. I like

596
00:27:59,240 --> 00:28:01,359
Speaker 6: it to show me how many miles I ran that week,

597
00:28:01,400 --> 00:28:03,600
Speaker 6: and like my bike ride to work.

598
00:28:03,640 --> 00:28:04,440
Speaker 2: Look, but I.

599
00:28:04,359 --> 00:28:06,320
Speaker 5: Wanted it, I like, I would have loved it to

600
00:28:06,320 --> 00:28:09,280
Speaker 5: be like, Okay, so when you go run after a

601
00:28:09,320 --> 00:28:13,320
Speaker 5: prolonged break, particularly in hot weather, you have self reported

602
00:28:13,560 --> 00:28:15,160
Speaker 5: and increased number of injuries.

603
00:28:15,440 --> 00:28:17,200
Speaker 3: That's the type of shit that I want.

604
00:28:17,440 --> 00:28:19,280
Speaker 5: And so it's like, so seeing that it was really

605
00:28:19,320 --> 00:28:21,880
Speaker 5: hot after a prolonged injury, you have a real bad

606
00:28:21,880 --> 00:28:24,720
Speaker 5: habit of getting injured after that. So like you dumb, dumb.

607
00:28:24,640 --> 00:28:25,679
Speaker 6: Yeah, maybe the pack.

608
00:28:27,400 --> 00:28:27,880
Speaker 5: Do the thing.

609
00:28:28,400 --> 00:28:29,560
Speaker 3: So like that is the.

610
00:28:29,560 --> 00:28:31,600
Speaker 5: Type of insight I would have liked after this most

611
00:28:31,640 --> 00:28:33,360
Speaker 5: recent run where I ate it.

612
00:28:33,600 --> 00:28:35,920
Speaker 2: Well, the last three months, I've increased my cardio. Shit,

613
00:28:36,040 --> 00:28:39,000
Speaker 2: I'm playing basketball. I would love to see and it

614
00:28:39,040 --> 00:28:41,600
Speaker 2: has all the stuff And this doesn't feel that difficult

615
00:28:41,680 --> 00:28:44,800
Speaker 2: if it could say yeah, your cardio vascula has improved

616
00:28:45,160 --> 00:28:47,320
Speaker 2: or likes the occasionally be like yeah, you're four years

617
00:28:47,400 --> 00:28:50,320
Speaker 2: or two years younger than your age cardio. Why isn't

618
00:28:50,320 --> 00:28:51,560
Speaker 2: this like, oh, don't get started.

619
00:28:51,640 --> 00:28:54,600
Speaker 5: That mean, don't get me started on those like longevity metrics.

620
00:28:54,640 --> 00:28:58,680
Speaker 2: I but just there's nothing useful to it other than

621
00:28:58,720 --> 00:29:01,040
Speaker 2: I'm a data perva and like looking at the numbers,

622
00:29:01,080 --> 00:29:05,400
Speaker 2: going hmmm, number up, number down? Why? And but even

623
00:29:05,440 --> 00:29:07,280
Speaker 2: then the simplest things are kind of hard to get.

624
00:29:07,280 --> 00:29:09,200
Speaker 2: With Strava. You have to go through like three menus

625
00:29:09,240 --> 00:29:11,440
Speaker 2: to just see how much you've worked out. There's like

626
00:29:11,520 --> 00:29:14,040
Speaker 2: eight different options. None of them you can't turn any

627
00:29:14,040 --> 00:29:16,239
Speaker 2: of them off. There's one about biking because I used

628
00:29:16,280 --> 00:29:18,760
Speaker 2: to bike. I don't bike anymore. I don't need that

629
00:29:19,160 --> 00:29:21,360
Speaker 2: now you need to share the biking, mate, gotta make

630
00:29:21,360 --> 00:29:26,120
Speaker 2: sure zero miles, you fucking idiot loser. It's just it.

631
00:29:26,680 --> 00:29:28,760
Speaker 2: It is the wider thing of tech just not being

632
00:29:28,800 --> 00:29:31,800
Speaker 2: for us anymore. Almost, it's like, hey, got some data,

633
00:29:31,920 --> 00:29:35,880
Speaker 2: I guess pay me, now, pay me. And even then

634
00:29:35,920 --> 00:29:39,440
Speaker 2: with Aura and A, I've been using them for five years,

635
00:29:39,480 --> 00:29:41,960
Speaker 2: six years. No, I've never got a single bit of

636
00:29:42,000 --> 00:29:45,720
Speaker 2: advice about my sleep. I've never had it. Say hey,

637
00:29:46,400 --> 00:29:48,360
Speaker 2: what if you did this? Nope, it'll be.

638
00:29:48,480 --> 00:29:50,400
Speaker 5: You tried using the chat pot. That's an aura.

639
00:29:51,120 --> 00:29:51,280
Speaker 4: No.

640
00:29:51,640 --> 00:29:54,600
Speaker 2: I thought about it yesterday and I was like, I'm

641
00:29:54,600 --> 00:29:55,720
Speaker 2: gonna get angry at this.

642
00:29:56,040 --> 00:29:57,480
Speaker 5: It's actually one of the better implements.

643
00:29:57,480 --> 00:29:58,120
Speaker 2: Does it work?

644
00:29:58,400 --> 00:30:02,480
Speaker 5: It's one of the better implementation If you like, I

645
00:30:02,920 --> 00:30:03,400
Speaker 5: know how to.

646
00:30:03,360 --> 00:30:05,480
Speaker 2: Talk to it, Okay, how do I talk to it?

647
00:30:05,960 --> 00:30:08,160
Speaker 5: Well, you have to be very specific about the information

648
00:30:08,240 --> 00:30:10,200
Speaker 5: that you're wanting. So you're like, tell me about my

649
00:30:10,240 --> 00:30:13,720
Speaker 5: sleep trend and I've noticed that I have this problem.

650
00:30:13,800 --> 00:30:17,360
Speaker 3: What are some ways that I could uh get around that?

651
00:30:17,760 --> 00:30:20,400
Speaker 5: Or just like, do I show signs in the past

652
00:30:20,440 --> 00:30:22,520
Speaker 5: month of sleep irregularities?

653
00:30:22,560 --> 00:30:24,800
Speaker 3: If so, like, what are some the.

654
00:30:24,760 --> 00:30:26,400
Speaker 5: Thing is like, the things that's going to tell you

655
00:30:26,400 --> 00:30:28,240
Speaker 5: to do that are actionable are going to be.

656
00:30:28,200 --> 00:30:30,239
Speaker 2: Like, well duh right?

657
00:30:30,320 --> 00:30:34,000
Speaker 5: Really only helpful if this is your first foray into

658
00:30:34,040 --> 00:30:36,800
Speaker 5: fixing your health, into fixing your health, if you've literally

659
00:30:36,840 --> 00:30:40,160
Speaker 5: googled anything before, have any base knowledge of like you

660
00:30:40,200 --> 00:30:44,560
Speaker 5: should have consistent sleep schedules. You maybe just don't eat

661
00:30:44,640 --> 00:30:47,480
Speaker 5: ice cream before bed, like common sense things like that.

662
00:30:47,600 --> 00:30:51,600
Speaker 5: It's just not gonna necessarily help you, but you know

663
00:30:51,800 --> 00:30:53,680
Speaker 5: other things. I were because I'm also I've got a

664
00:30:53,680 --> 00:30:55,400
Speaker 5: CGM at the moment, and so it's.

665
00:30:55,360 --> 00:30:57,560
Speaker 2: Just like, does that constant glucose Yeah.

666
00:30:57,440 --> 00:31:00,440
Speaker 5: Yeah, continuous glucose monitor. And I was like, I've had.

667
00:31:00,360 --> 00:31:01,200
Speaker 3: A lot of stress.

668
00:31:01,320 --> 00:31:04,280
Speaker 5: Does stress impact glucose levels? And I was like, yes,

669
00:31:04,320 --> 00:31:06,120
Speaker 5: it does. And I was like, okay, cool, that's nice.

670
00:31:06,280 --> 00:31:08,120
Speaker 5: Nice to know that it's high because of that, and

671
00:31:08,160 --> 00:31:10,920
Speaker 5: then it'll remember that when you ask it questions in

672
00:31:10,960 --> 00:31:11,360
Speaker 5: the future.

673
00:31:11,400 --> 00:31:13,760
Speaker 3: So you just have to like you just have.

674
00:31:13,800 --> 00:31:17,640
Speaker 5: To talk to it so much and like kind of

675
00:31:18,080 --> 00:31:21,120
Speaker 5: train it. It's like literally like training a toddler. So

676
00:31:21,240 --> 00:31:24,040
Speaker 5: the amount of effort that you're putting in versus what

677
00:31:24,080 --> 00:31:26,560
Speaker 5: they're telling you, like all the insights they'll be so personal,

678
00:31:27,120 --> 00:31:28,720
Speaker 5: personalized and so automated.

679
00:31:28,760 --> 00:31:30,640
Speaker 2: It's like, just have to do all the work to

680
00:31:30,640 --> 00:31:32,920
Speaker 2: make it useful, which it appears to be the AI theme.

681
00:31:33,240 --> 00:31:34,200
Speaker 3: Yeah, you have to.

682
00:31:34,160 --> 00:31:37,080
Speaker 5: Do an immense amount of legwork and like training and

683
00:31:37,240 --> 00:31:40,800
Speaker 5: thinking like an AI in order for it to spit

684
00:31:40,840 --> 00:31:43,400
Speaker 5: out something that might make you go huh.

685
00:31:43,640 --> 00:31:49,520
Speaker 2: Wow, yeah cool. I love living in this wonderful period.

686
00:31:51,560 --> 00:31:54,880
Speaker 2: I saw Wired mentioned this thing called limitless earlier if you.

687
00:31:54,840 --> 00:31:57,920
Speaker 3: Have yes, yes, it's the it's the class. It's like

688
00:31:58,000 --> 00:31:59,080
Speaker 3: b the thing.

689
00:31:59,360 --> 00:32:02,600
Speaker 2: Yeah, I say time, the AI device that constantly listens

690
00:32:02,640 --> 00:32:04,080
Speaker 2: to you. What is limitless?

691
00:32:04,120 --> 00:32:08,440
Speaker 5: Though similar? It constantly listens to you and generates insights

692
00:32:08,440 --> 00:32:10,320
Speaker 5: based not listening to you constantly.

693
00:32:10,680 --> 00:32:15,800
Speaker 2: Stephen Stephen Levy, the Larry Bird of Big Wet Kisses

694
00:32:15,840 --> 00:32:18,080
Speaker 2: in Tech Journalism wrote about it like it is the future,

695
00:32:18,080 --> 00:32:22,680
Speaker 2: and it's just like, I just wish some writers would

696
00:32:22,680 --> 00:32:26,760
Speaker 2: experience humanity just once, because the idea of someone constantly

697
00:32:26,800 --> 00:32:32,520
Speaker 2: listening is not fun good, nor has it ever really worked.

698
00:32:32,760 --> 00:32:35,080
Speaker 6: I don't want to age anyone here, but I feel

699
00:32:35,080 --> 00:32:39,360
Speaker 6: like we're roughly the same age. And we experienced kind

700
00:32:39,360 --> 00:32:43,760
Speaker 6: of the revolution of social media as this kind of like, oh,

701
00:32:43,840 --> 00:32:46,520
Speaker 6: look look at how cool it can be if we're

702
00:32:46,560 --> 00:32:51,240
Speaker 6: able to connect right in mass, all the time anywhere,

703
00:32:51,800 --> 00:32:55,720
Speaker 6: And that was cool, that was revolutionary in its time,

704
00:32:56,080 --> 00:32:59,320
Speaker 6: and now we're at kind of like the denoma of that,

705
00:32:59,760 --> 00:33:03,360
Speaker 6: and it's turned us all inward. We're all like tracking

706
00:33:03,360 --> 00:33:08,360
Speaker 6: our personal data and like, you know, we're more isolated

707
00:33:08,400 --> 00:33:12,000
Speaker 6: than we've ever been. Not entirely social media's fault, but

708
00:33:12,320 --> 00:33:15,640
Speaker 6: doesn't help. We're like talking to our AI therapists and

709
00:33:15,640 --> 00:33:17,720
Speaker 6: our AI boyfriends and girlfriends.

710
00:33:17,880 --> 00:33:19,880
Speaker 2: I don't know how many people are actually doing that,

711
00:33:19,920 --> 00:33:21,920
Speaker 2: though I know a lot of them are.

712
00:33:22,040 --> 00:33:24,720
Speaker 6: But I think that like the narrative, like the step

713
00:33:24,760 --> 00:33:27,479
Speaker 6: back narrative right now, is just like, well, there should

714
00:33:27,520 --> 00:33:30,480
Speaker 6: be a next thing. It shouldn't just be social media

715
00:33:30,600 --> 00:33:33,040
Speaker 6: was cool and the Internet was cool for a while,

716
00:33:33,560 --> 00:33:35,720
Speaker 6: and things are getting kind of stale, and it's like, well,

717
00:33:35,720 --> 00:33:37,720
Speaker 6: what's the next thing. And I think I think tech

718
00:33:37,840 --> 00:33:42,280
Speaker 6: journalists can be guilty of this sometimes, of feeling like, well,

719
00:33:42,280 --> 00:33:45,440
Speaker 6: twenty twelve was really exciting, so twenty twenty two has

720
00:33:45,480 --> 00:33:47,600
Speaker 6: got to be just as exciting, and twenty twenty.

721
00:33:47,360 --> 00:33:48,760
Speaker 4: Five, boy can't wait.

722
00:33:50,280 --> 00:33:52,520
Speaker 6: Maybe the technology is just not there yet and it's

723
00:33:52,520 --> 00:33:55,720
Speaker 6: going to take a lot longer than anyone expects.

724
00:33:55,440 --> 00:33:57,560
Speaker 5: Or maybe this is just not the right approach, because

725
00:33:57,640 --> 00:34:00,840
Speaker 5: the way to lllm's work is that it's a and

726
00:34:00,920 --> 00:34:03,480
Speaker 5: like we have tech journalists left and right failing the

727
00:34:03,480 --> 00:34:06,160
Speaker 5: mirror test and like not understanding that when you talk

728
00:34:06,240 --> 00:34:09,120
Speaker 5: to chat GPT, or you talk to these AI girlfriends

729
00:34:09,160 --> 00:34:11,440
Speaker 5: and boyfriends and a different avatar, you are.

730
00:34:11,320 --> 00:34:12,800
Speaker 3: Just talking to yourself in the mirror.

731
00:34:13,000 --> 00:34:15,799
Speaker 5: It is the digital version of talking to yourself in

732
00:34:15,800 --> 00:34:18,040
Speaker 5: the mirror, which can be useful. It can be useful

733
00:34:18,080 --> 00:34:19,680
Speaker 5: to talk to yourself in the mirror. There's a reason

734
00:34:19,680 --> 00:34:24,360
Speaker 5: why people go like you're great, yes, awesome. Sometimes you

735
00:34:24,400 --> 00:34:26,799
Speaker 5: need to like hear yourself think out loud, and that

736
00:34:26,840 --> 00:34:31,440
Speaker 5: can be useful and helpful. But that's what you're doing. Like,

737
00:34:31,680 --> 00:34:34,800
Speaker 5: you have to understand that you are talking to yourself.

738
00:34:34,920 --> 00:34:38,560
Speaker 5: You are just having a conversation with yourself. And I

739
00:34:38,600 --> 00:34:41,919
Speaker 5: think a lot of people don't get that. They don't

740
00:34:41,960 --> 00:34:44,560
Speaker 5: they think they're talking to a higher intelligence. Literally, know,

741
00:34:44,680 --> 00:34:47,920
Speaker 5: you were just talking to yourself. If yourself could Google

742
00:34:47,960 --> 00:34:49,480
Speaker 5: a little faster than you currently.

743
00:34:49,560 --> 00:34:53,560
Speaker 6: Yeah, that was That was the gist of Kashmir Hill's

744
00:34:55,120 --> 00:34:58,160
Speaker 6: amazing New York Times piece about the people who really

745
00:34:58,200 --> 00:35:02,360
Speaker 6: fell into a rabbit hole around these chat bots like

746
00:35:02,520 --> 00:35:07,320
Speaker 6: chat GPT convinced them that they were in a matrix,

747
00:35:07,480 --> 00:35:12,520
Speaker 6: like you know, alternate universe. And like one guy committed suicide.

748
00:35:12,560 --> 00:35:14,680
Speaker 2: Didn't he pull a knife on a cop or something?

749
00:35:14,800 --> 00:35:17,279
Speaker 6: Yeah, he like told the bot that he was going

750
00:35:17,320 --> 00:35:20,440
Speaker 6: to kill himself to suicide by cop. And then his

751
00:35:20,600 --> 00:35:24,040
Speaker 6: dad was like worried about his mental health, did call

752
00:35:24,080 --> 00:35:25,600
Speaker 6: the cops and he was like, listen, I think my

753
00:35:25,800 --> 00:35:28,520
Speaker 6: son's going to try to kill himself by attacking you.

754
00:35:29,040 --> 00:35:32,120
Speaker 6: And guess what he did, because the chatbot was like.

755
00:35:32,400 --> 00:35:34,759
Speaker 2: And guess what. The cop just came right over with.

756
00:35:35,120 --> 00:35:35,319
Speaker 1: Yeah.

757
00:35:35,600 --> 00:35:39,160
Speaker 2: It's like, yeah, you know, someone mental health, what do

758
00:35:39,239 --> 00:35:41,600
Speaker 2: they need? Oh, they definitely want to die by a cop.

759
00:35:41,960 --> 00:35:42,719
Speaker 2: Let's pull our guns.

760
00:35:42,719 --> 00:35:44,359
Speaker 6: I'm saying it's our society is not in a good

761
00:35:44,400 --> 00:35:44,839
Speaker 6: spot right.

762
00:35:45,480 --> 00:35:49,920
Speaker 2: Our society is not prepared policing is prepared to arrest

763
00:35:50,040 --> 00:35:54,000
Speaker 2: rather than help or service. So yeah, it's the natural.

764
00:35:54,040 --> 00:35:56,839
Speaker 2: I think it's all of this is the natural comeopance

765
00:35:56,880 --> 00:36:00,600
Speaker 2: of a society built with very little intention and suck

766
00:36:00,719 --> 00:36:04,120
Speaker 2: because yes, because that's the thing is vibes and large

767
00:36:04,160 --> 00:36:07,800
Speaker 2: language models are the ultimate like vibe slop. It's something

768
00:36:07,880 --> 00:36:10,160
Speaker 2: built with no real and people love to say, well,

769
00:36:10,200 --> 00:36:16,120
Speaker 2: Samulton's plan. No one has a single goddamn plan at all.

770
00:36:16,400 --> 00:36:19,040
Speaker 2: That's why there are no use cases because if anyone

771
00:36:19,080 --> 00:36:20,600
Speaker 2: sat there and went what can we do with this?

772
00:36:20,880 --> 00:36:24,880
Speaker 2: They go, fuck, I don't, I don't know, I don't.

773
00:36:24,920 --> 00:36:26,600
Speaker 2: Is there anyone that we can pay one hundred million

774
00:36:26,640 --> 00:36:27,120
Speaker 2: dollars too.

775
00:36:27,200 --> 00:36:29,919
Speaker 5: Well, you can do anything that you can imagine. That's

776
00:36:29,920 --> 00:36:33,200
Speaker 5: why Auntie Jesse is saying the future is up to you.

777
00:36:33,200 --> 00:36:35,280
Speaker 3: Guys imagine solo.

778
00:36:35,400 --> 00:36:39,080
Speaker 2: I can imagine a lot. And it's but that's the thing,

779
00:36:39,120 --> 00:36:41,680
Speaker 2: Like Alison, you had an excellent piece on this. It's

780
00:36:41,680 --> 00:36:44,520
Speaker 2: like the whole job loss story is just them being like,

781
00:36:44,640 --> 00:36:47,920
Speaker 2: please please up the shares up, shares, gun number go up. Please.

782
00:36:48,080 --> 00:36:48,279
Speaker 4: Yeah.

783
00:36:48,320 --> 00:36:50,600
Speaker 6: It's like I have to say this because everyone's paying

784
00:36:50,600 --> 00:36:53,920
Speaker 6: attention to me. And it's like, I'm sure shareholders and

785
00:36:54,400 --> 00:36:56,600
Speaker 6: vesters on Wall Street are saying, what is your AI

786
00:36:56,680 --> 00:37:00,879
Speaker 6: strategy and how are you planning to mass lay off

787
00:37:01,360 --> 00:37:04,080
Speaker 6: your staff so that you can cut corners and replace

788
00:37:04,120 --> 00:37:07,440
Speaker 6: people with AI? And so you have someone like Andy

789
00:37:07,520 --> 00:37:09,959
Speaker 6: Jazzy come out and say, you guys are all doing

790
00:37:10,000 --> 00:37:16,200
Speaker 6: great work. We've built incredible products. Alexa everyone loves it. Spoilerler,

791
00:37:16,320 --> 00:37:17,680
Speaker 6: No one loves Alexa. That's stupid.

792
00:37:17,880 --> 00:37:19,440
Speaker 2: Yeah.

793
00:37:19,560 --> 00:37:22,840
Speaker 6: And as a result of that, sometime in the near future,

794
00:37:22,880 --> 00:37:27,040
Speaker 6: I won't say when, but sometime soon, a lot of

795
00:37:27,080 --> 00:37:28,640
Speaker 6: you are going to be laid off or have your

796
00:37:28,719 --> 00:37:35,399
Speaker 6: jobs changed by AI. And I'm shocked every time one

797
00:37:35,400 --> 00:37:39,000
Speaker 6: of these CEOs does this, we report it out as

798
00:37:39,080 --> 00:37:43,640
Speaker 6: if it's like, oh.

799
00:37:41,920 --> 00:37:42,440
Speaker 2: It's true.

800
00:37:42,520 --> 00:37:45,640
Speaker 6: God in Heaven just just said like this is what's

801
00:37:45,680 --> 00:37:48,400
Speaker 6: going to happen, and that is what's going to happen.

802
00:37:48,480 --> 00:37:53,200
Speaker 6: Like we had, you know, banner headlines saying like Amazon

803
00:37:53,280 --> 00:37:55,400
Speaker 6: CEO says Ai is going to take your jobs, and

804
00:37:55,440 --> 00:37:58,440
Speaker 6: it's like, well, he didn't really say how or when

805
00:37:58,760 --> 00:38:02,279
Speaker 6: or by what mechanism, and there's no evidence of it

806
00:38:02,400 --> 00:38:04,560
Speaker 6: happening yet, so what are we talking about.

807
00:38:04,719 --> 00:38:06,719
Speaker 2: It's I think that there is an alarming amount of

808
00:38:06,760 --> 00:38:09,919
Speaker 2: journalism that's excited for it, or there's just a doomerism

809
00:38:09,960 --> 00:38:10,560
Speaker 2: behind it.

810
00:38:10,719 --> 00:38:13,279
Speaker 1: I think you can replace the word Ai in a

811
00:38:13,280 --> 00:38:15,799
Speaker 1: lot of these articles was just God and like this

812
00:38:15,840 --> 00:38:17,520
Speaker 1: is something that like we talked about the first time

813
00:38:17,520 --> 00:38:19,480
Speaker 1: we met in Vegas, right, is how all of these

814
00:38:19,520 --> 00:38:21,719
Speaker 1: like the people who are int Ai talk about it

815
00:38:21,760 --> 00:38:23,960
Speaker 1: as if it's just a cult, and we could like

816
00:38:23,960 --> 00:38:26,680
Speaker 1: like like there's this divine aspect that has like ordain,

817
00:38:26,760 --> 00:38:28,600
Speaker 1: like you have to lose your job because Ai is

818
00:38:29,040 --> 00:38:33,839
Speaker 1: taking God. God has mandated this. And like even even

819
00:38:33,920 --> 00:38:35,880
Speaker 1: like with the you know, talking to an Ai like

820
00:38:35,880 --> 00:38:39,800
Speaker 1: it's a person, they often gain this like divine aspect

821
00:38:39,800 --> 00:38:42,040
Speaker 1: when when people are treating these chatbots like they are

822
00:38:42,040 --> 00:38:43,840
Speaker 1: like scenting in beings, it's like the same way we

823
00:38:43,960 --> 00:38:48,520
Speaker 1: like project divinity onto aspects of nature. And I think

824
00:38:48,520 --> 00:38:50,600
Speaker 1: that is like a big, a big part of it,

825
00:38:50,640 --> 00:38:52,480
Speaker 1: because now you have God telling you that you're actually

826
00:38:52,480 --> 00:38:54,400
Speaker 1: in the matrix and you and you need to do

827
00:38:54,440 --> 00:38:57,360
Speaker 1: this thing, and for some reason, there's not safeguards put

828
00:38:57,440 --> 00:39:01,160
Speaker 1: on this God program to protect you God's creation.

829
00:39:01,360 --> 00:39:04,200
Speaker 5: They're all programmed to be super friendly and to tell

830
00:39:04,239 --> 00:39:06,279
Speaker 5: you that you're great. And it's like if you if

831
00:39:06,320 --> 00:39:08,080
Speaker 5: you're actually like trying to use this thing and you're

832
00:39:08,160 --> 00:39:10,719
Speaker 5: viewing it as you talking to you, like I have

833
00:39:10,760 --> 00:39:12,920
Speaker 5: to tell chat JPT all the time you're putting too

834
00:39:13,000 --> 00:39:14,640
Speaker 5: much flattery in. You have to cap all of your

835
00:39:14,640 --> 00:39:17,719
Speaker 5: flattery at one percent because I can't, I can't handle you,

836
00:39:17,800 --> 00:39:20,080
Speaker 5: and I need you to like explain why everything you

837
00:39:20,120 --> 00:39:22,960
Speaker 5: said has no bias or as little bias as possible,

838
00:39:23,040 --> 00:39:24,560
Speaker 5: or to explain all of your bias in it so

839
00:39:24,600 --> 00:39:26,960
Speaker 5: that I can read and evaluate and just go like, no,

840
00:39:27,200 --> 00:39:29,440
Speaker 5: that's not it. And I've been told I'm insane for

841
00:39:29,880 --> 00:39:32,600
Speaker 5: like programming all the AI I talk to you that way.

842
00:39:32,719 --> 00:39:35,799
Speaker 2: Sample output shit, it's making it's adjusting a program to

843
00:39:35,840 --> 00:39:36,440
Speaker 2: do a thing for you.

844
00:39:36,680 --> 00:39:38,719
Speaker 3: It is because like if you just leave it at

845
00:39:38,719 --> 00:39:39,520
Speaker 3: the default.

846
00:39:39,200 --> 00:39:40,279
Speaker 7: It's trying to whop your brain.

847
00:39:40,480 --> 00:39:41,520
Speaker 3: Like if you leave it at the.

848
00:39:41,440 --> 00:39:44,239
Speaker 5: Default, it's just like you've done nothing wrong, you are

849
00:39:44,400 --> 00:39:47,960
Speaker 5: absolutely correct and everything you said, what a brilliant thing

850
00:39:48,000 --> 00:39:48,560
Speaker 5: that you said.

851
00:39:48,600 --> 00:39:51,480
Speaker 3: And if you like listen to that enough times, it

852
00:39:51,560 --> 00:39:52,560
Speaker 3: did well.

853
00:39:52,640 --> 00:39:55,600
Speaker 2: I mean, I think it's whooping your brain because there

854
00:39:55,640 --> 00:39:58,440
Speaker 2: is no intention behind this. They train it whatever, but

855
00:39:58,800 --> 00:40:01,400
Speaker 2: they could relatively ease put a thing saying just to

856
00:40:01,400 --> 00:40:03,879
Speaker 2: be clear you are talking to you. This is They

857
00:40:03,880 --> 00:40:05,480
Speaker 2: don't want to do that, and I think it might

858
00:40:05,560 --> 00:40:08,359
Speaker 2: be want to be another of the goombas from open AI.

859
00:40:08,560 --> 00:40:10,759
Speaker 1: It breaks the illusion, right, like it's they need to.

860
00:40:10,760 --> 00:40:12,799
Speaker 1: It's like the Wizard of Oz thing. Yes, but they

861
00:40:12,840 --> 00:40:15,759
Speaker 1: should break the illusions. They have to, but they don't want.

862
00:40:15,800 --> 00:40:17,600
Speaker 7: They don't want to because it means they will get.

863
00:40:17,480 --> 00:40:21,000
Speaker 5: Less tension, economy the less. But it breaks the engagement.

864
00:40:21,040 --> 00:40:22,640
Speaker 5: You're not going to engage with it if you're like,

865
00:40:22,680 --> 00:40:24,080
Speaker 5: this is obviously a robot.

866
00:40:24,120 --> 00:40:26,960
Speaker 6: That's that's the part where I get actually emotional and

867
00:40:27,040 --> 00:40:31,440
Speaker 6: angry about AI is when the CEOs of these companies

868
00:40:31,560 --> 00:40:33,920
Speaker 6: talk about it. They talk about it as if it's

869
00:40:33,920 --> 00:40:36,640
Speaker 6: inevitable and we have no agency. That's where the god

870
00:40:36,719 --> 00:40:39,920
Speaker 6: thing comes in, where it's like, yes, this is happening,

871
00:40:39,960 --> 00:40:42,279
Speaker 6: whether we keep doing it or not, and.

872
00:40:42,440 --> 00:40:43,879
Speaker 2: We are the ones who will fix it of course.

873
00:40:43,920 --> 00:40:46,359
Speaker 6: Yeah, it's like, oh yeah, I mean Sam Altman, I

874
00:40:46,440 --> 00:40:48,200
Speaker 6: was just rereading, like.

875
00:40:48,320 --> 00:40:53,040
Speaker 2: Uh the Gentle Singularity that fucking blog.

876
00:40:53,280 --> 00:40:55,080
Speaker 6: Oh I hadn't read that one. No, I was talking

877
00:40:55,080 --> 00:40:59,520
Speaker 6: about he created world coin. Oh God, because he's so

878
00:40:59,640 --> 00:41:00,560
Speaker 6: convinced that.

879
00:41:03,520 --> 00:41:07,200
Speaker 2: Let me stop you there. Okay, No, I just want

880
00:41:07,200 --> 00:41:09,200
Speaker 2: to stop you because you said he's so convinced. Your

881
00:41:09,200 --> 00:41:11,360
Speaker 2: only source of information for that claim is Sam Wiltman.

882
00:41:12,280 --> 00:41:14,640
Speaker 2: Sam Oltman did world coin so we could sell a cryptocurrency.

883
00:41:14,680 --> 00:41:16,400
Speaker 2: That's the only reason. Yes, so we could collect a

884
00:41:16,440 --> 00:41:18,640
Speaker 2: bunch of No, he's so convinced. He's convinced of nothing,

885
00:41:18,640 --> 00:41:21,960
Speaker 2: I believe. Okay, this is literally and the pitch, the.

886
00:41:21,960 --> 00:41:28,480
Speaker 6: Pitch of world Coin, I like, I interviewed their ceo.

887
00:41:28,719 --> 00:41:33,400
Speaker 6: I had a lovely conversation with him. Sorry, but you

888
00:41:33,400 --> 00:41:36,759
Speaker 6: know the pitch is AI is going to become so

889
00:41:37,680 --> 00:41:41,080
Speaker 6: ubiquitous and it's going to destroy all the jobs and

890
00:41:41,760 --> 00:41:43,799
Speaker 6: flatten the economy, and we're going to have to have

891
00:41:43,840 --> 00:41:49,840
Speaker 6: this ubi that's distributed by by this blockchain mechanism. And

892
00:41:49,880 --> 00:41:53,120
Speaker 6: I'm sitting here and I'm going, yes, but we don't

893
00:41:53,160 --> 00:41:56,840
Speaker 6: have we are actually full agents in this world. Like

894
00:41:57,120 --> 00:41:59,799
Speaker 6: we all need to step back and realize that we

895
00:41:59,880 --> 00:42:03,560
Speaker 6: have of sovereignty over what we do in the technology

896
00:42:03,600 --> 00:42:05,920
Speaker 6: we make and how we regulate it. And it's a

897
00:42:05,960 --> 00:42:12,680
Speaker 6: real just like kind of uh abdication of responsibility for

898
00:42:13,160 --> 00:42:16,279
Speaker 6: society where I'm just like, what what do we want

899
00:42:16,360 --> 00:42:20,239
Speaker 6: to make for future generations? And that's all the like

900
00:42:20,320 --> 00:42:22,880
Speaker 6: problem that comes out of Silicon Valley is like building

901
00:42:22,880 --> 00:42:26,560
Speaker 6: a better future, and it's like you can't have both things.

902
00:42:26,920 --> 00:42:27,640
Speaker 2: Where are you building?

903
00:42:28,160 --> 00:42:31,080
Speaker 5: It's because like what they really are building is money

904
00:42:31,120 --> 00:42:34,120
Speaker 5: machine go up, and like that's the main thing. It's

905
00:42:34,160 --> 00:42:36,880
Speaker 5: like they say all this lip service about making everything better,

906
00:42:36,960 --> 00:42:38,320
Speaker 5: Well you could gear cancer.

907
00:42:38,360 --> 00:42:40,400
Speaker 3: That would like legitimately make everything.

908
00:42:41,040 --> 00:42:45,439
Speaker 5: They will, Sure, so you could do that, but it's

909
00:42:45,560 --> 00:42:47,960
Speaker 5: like their first and foremost.

910
00:42:47,480 --> 00:42:50,600
Speaker 2: I believe Joe Biden will cure cancer before Samulman. Both

911
00:42:50,600 --> 00:42:54,120
Speaker 2: have claimed they will. I believe Joe Biden would. No,

912
00:42:54,239 --> 00:42:55,759
Speaker 2: I mean neither of them will do it, which is

913
00:42:56,120 --> 00:42:59,320
Speaker 2: my larger point. But I mean, you're both wrong because

914
00:42:59,520 --> 00:43:02,479
Speaker 2: it's a click to quote Sam Altman. As data center

915
00:43:02,520 --> 00:43:05,200
Speaker 2: production gets automated, the cost of intelligence should eventually converge

916
00:43:05,200 --> 00:43:07,200
Speaker 2: to near the cost of electricity. People are often curious

917
00:43:07,200 --> 00:43:10,200
Speaker 2: about how much energy chat GPT queries use. The average

918
00:43:10,239 --> 00:43:13,200
Speaker 2: query is zero point three four what hours? About what

919
00:43:13,280 --> 00:43:15,879
Speaker 2: an oven would use a little over a second. Now,

920
00:43:16,280 --> 00:43:21,000
Speaker 2: someone who's almost immediately thrown water on this entire thing.

921
00:43:21,080 --> 00:43:24,520
Speaker 2: But this blog A Gentle Singularity by Sam Mortman was

922
00:43:24,600 --> 00:43:29,880
Speaker 2: quoted like scripture by everyone, and that I think it.

923
00:43:30,000 --> 00:43:32,239
Speaker 2: I'd love that you brought up divinity GEG, because it

924
00:43:32,280 --> 00:43:35,520
Speaker 2: really is just like this pseudo religious it's religion, capitalism,

925
00:43:35,560 --> 00:43:38,000
Speaker 2: it's all. It's just we found a way to love

926
00:43:38,040 --> 00:43:42,200
Speaker 2: a company like a god and also kind of abdicate

927
00:43:42,239 --> 00:43:46,160
Speaker 2: any responsibility or thinking about it, because when you look

928
00:43:46,200 --> 00:43:48,400
Speaker 2: at the people who love AI and the way they

929
00:43:48,440 --> 00:43:50,600
Speaker 2: talk about it, it's not about what's happening today. It's

930
00:43:50,600 --> 00:43:52,400
Speaker 2: no in it. They're like in the future when this happens.

931
00:43:52,440 --> 00:43:54,640
Speaker 1: No, it's always it's always this future privacy. And if

932
00:43:54,680 --> 00:43:56,400
Speaker 1: if we believe in this god enough and if we

933
00:43:56,400 --> 00:43:59,200
Speaker 1: build up this religion enough it can deliver us a

934
00:43:59,280 --> 00:44:01,560
Speaker 1: infinite automated money machine.

935
00:44:01,640 --> 00:44:04,160
Speaker 2: But what's crazy is the money machine is bad. It

936
00:44:04,239 --> 00:44:07,120
Speaker 2: doesn't make any money. It loses some. They spent three

937
00:44:07,200 --> 00:44:10,799
Speaker 2: hundred and seventy twenty seven billion dollars in cattle expenditures

938
00:44:10,840 --> 00:44:13,080
Speaker 2: this year are projected to and the revenue of this

939
00:44:13,160 --> 00:44:14,920
Speaker 2: industry is like forty billion dollars.

940
00:44:15,000 --> 00:44:17,000
Speaker 5: That's because money is fake until you have none.

941
00:44:17,040 --> 00:44:18,520
Speaker 3: That's the only time real the thing.

942
00:44:18,680 --> 00:44:23,080
Speaker 2: Money will run out. Money go down, money go down.

943
00:44:23,160 --> 00:44:25,840
Speaker 2: That work good. And the story that's really been twisting

944
00:44:25,840 --> 00:44:27,319
Speaker 2: me up this week was telling you about Early and

945
00:44:27,320 --> 00:44:30,759
Speaker 2: I swear it won't be this boring is open Ai

946
00:44:31,000 --> 00:44:35,200
Speaker 2: is Microsoft's biggest customer ten billion dollars in projected revenue

947
00:44:35,239 --> 00:44:38,160
Speaker 2: this year for Azure, and a zero revenue has been

948
00:44:38,800 --> 00:44:41,600
Speaker 2: kind of like not growing so good. So we just

949
00:44:41,680 --> 00:44:43,760
Speaker 2: have one of the largest tech companies in the world

950
00:44:44,640 --> 00:44:47,400
Speaker 2: that is just handing itself cash. And as part of

951
00:44:47,480 --> 00:44:49,439
Speaker 2: the deal when they funded them in twenty twenty three,

952
00:44:49,480 --> 00:44:53,680
Speaker 2: they funded them principally in cloud compute credits. So ten

953
00:44:53,680 --> 00:44:57,319
Speaker 2: billion dollars of microsoft reddit of revenue is going to

954
00:44:57,320 --> 00:45:00,640
Speaker 2: be partially in air miles and everyone just sitting around

955
00:45:00,640 --> 00:45:02,319
Speaker 2: being like this is great. On top of that, ten

956
00:45:02,360 --> 00:45:05,120
Speaker 2: billion dollars a revenue from open ai, so thirteen billion

957
00:45:05,160 --> 00:45:08,439
Speaker 2: total from Microsoft. Then open ai projected to make twelve

958
00:45:08,440 --> 00:45:10,680
Speaker 2: point seven billion dollars. Half of the revenue in this

959
00:45:10,800 --> 00:45:14,240
Speaker 2: fucking industry is open ai or the slop, the cost

960
00:45:14,360 --> 00:45:15,480
Speaker 2: slop of open Ai.

961
00:45:16,080 --> 00:45:18,640
Speaker 1: It's okay, yed, we can we can just keep blowing

962
00:45:18,680 --> 00:45:21,359
Speaker 1: up the balloon. It's never gonna pop. You can keep

963
00:45:21,400 --> 00:45:22,040
Speaker 1: blowing it up.

964
00:45:22,200 --> 00:45:23,080
Speaker 2: It's just crazy.

965
00:45:23,160 --> 00:45:28,640
Speaker 1: We found the infinite balloon. It's this crazy material that's indestructible.

966
00:45:28,680 --> 00:45:31,680
Speaker 1: You can keep blowing more in and it's gonna be fine.

967
00:45:32,000 --> 00:45:34,400
Speaker 2: It's just it's so funny as it kind of is

968
00:45:34,440 --> 00:45:35,320
Speaker 2: gonna be fine.

969
00:45:35,640 --> 00:45:36,279
Speaker 4: It's so good.

970
00:45:36,320 --> 00:45:39,440
Speaker 2: I just feel like AI is just like the imbecile magnet.

971
00:45:39,680 --> 00:45:42,719
Speaker 2: It's just this idea that people who don't really know

972
00:45:42,840 --> 00:45:45,120
Speaker 2: stuff but have got to positions of power can go Yes,

973
00:45:45,280 --> 00:45:47,680
Speaker 2: finally a thing that will make up the reason I

974
00:45:47,719 --> 00:45:48,719
Speaker 2: have to buy it for me.

975
00:45:49,560 --> 00:45:52,240
Speaker 6: I see this in journalism all the time. I'm getting

976
00:45:52,280 --> 00:45:58,960
Speaker 6: like conversations about well AI could replace entry level journalism jobs.

977
00:45:59,000 --> 00:46:01,360
Speaker 6: I think that's like a real concern if you're like

978
00:46:01,760 --> 00:46:06,200
Speaker 6: just a I started as a copy editor with zero responsibilities,

979
00:46:06,320 --> 00:46:09,920
Speaker 6: is other than like finding typos and misspellings and occasionally

980
00:46:09,960 --> 00:46:11,640
Speaker 6: getting to write a headline and being like, oh, thank

981
00:46:11,680 --> 00:46:15,080
Speaker 6: you for letting me write a headline. And I could

982
00:46:15,160 --> 00:46:18,839
Speaker 6: see a large language model filling that in, in which

983
00:46:18,880 --> 00:46:21,560
Speaker 6: case I don't get a jumping off point to do

984
00:46:21,800 --> 00:46:22,520
Speaker 6: what I want to do.

985
00:46:22,600 --> 00:46:24,319
Speaker 2: I mean, they've been off showing those jobs as well.

986
00:46:24,320 --> 00:46:25,560
Speaker 2: It's just another off showing.

987
00:46:25,719 --> 00:46:27,680
Speaker 6: My point is I've been in this for like twenty

988
00:46:27,760 --> 00:46:31,480
Speaker 6: years and it was happening. Then it might happen at

989
00:46:31,480 --> 00:46:38,000
Speaker 6: a more accelerated timeframe with AI. But I'm skeptical you're

990
00:46:38,040 --> 00:46:40,239
Speaker 6: still going to need as we saw with that Chicago

991
00:46:40,719 --> 00:46:44,720
Speaker 6: Uhhago summer book list, you know, like in case anyone

992
00:46:44,760 --> 00:46:46,640
Speaker 6: missed it, you know, you got the authors right and

993
00:46:46,680 --> 00:46:49,799
Speaker 6: then just made up complete horseshit for the books that

994
00:46:49,840 --> 00:46:53,919
Speaker 6: they didn't write and were about whatever AI made hallucinated

995
00:46:53,960 --> 00:46:57,399
Speaker 6: they were about. You know, an entry level copy editor

996
00:46:57,440 --> 00:47:01,000
Speaker 6: would have caught that. But we've all already fired those people.

997
00:47:01,040 --> 00:47:04,920
Speaker 6: We've already laid off the entry copy editors. So you know,

998
00:47:05,440 --> 00:47:09,000
Speaker 6: all of these things, these ways that like technology is

999
00:47:09,080 --> 00:47:13,759
Speaker 6: going to create job losses. That's that's standard in our age.

1000
00:47:14,080 --> 00:47:17,520
Speaker 6: AI is going to create some job losses. Yes, is

1001
00:47:17,520 --> 00:47:20,200
Speaker 6: it going to be the what was it white color

1002
00:47:20,239 --> 00:47:22,640
Speaker 6: blood bath? I don't think so.

1003
00:47:22,960 --> 00:47:25,759
Speaker 2: On to twenty percent unemployment according to Warrio Ami Day

1004
00:47:26,280 --> 00:47:29,560
Speaker 2: and it's that's a CEO of Anthropic and his name

1005
00:47:29,600 --> 00:47:32,879
Speaker 2: is Wario. Everyone makes the type on type Staria. I'm

1006
00:47:32,880 --> 00:47:35,680
Speaker 2: not sure why it's in books and literature, so let's

1007
00:47:35,680 --> 00:47:53,359
Speaker 2: start correcting the record. It fucking pisses me off as well,

1008
00:47:53,400 --> 00:47:56,480
Speaker 2: because there I mentioned it earlier. There's almost like a

1009
00:47:56,560 --> 00:47:59,759
Speaker 2: excitement in journalism for job loss in AI. Maybe it's

1010
00:47:59,800 --> 00:48:02,240
Speaker 2: due rism, maybe they're just like, oh, I'll get ahead

1011
00:48:02,280 --> 00:48:06,120
Speaker 2: of this, but it feels almost like they want it

1012
00:48:06,160 --> 00:48:08,560
Speaker 2: to happen and they want it so that it proves

1013
00:48:08,640 --> 00:48:11,480
Speaker 2: AI is the big point is religious to get fucking Chrystal.

1014
00:48:12,360 --> 00:48:13,839
Speaker 7: It always it always comes back to that.

1015
00:48:14,040 --> 00:48:16,040
Speaker 1: Yeah, is that we've been talking about this for two

1016
00:48:16,080 --> 00:48:20,719
Speaker 1: years like like this this like cultish nexus around around AI.

1017
00:48:20,840 --> 00:48:23,759
Speaker 1: I mean this is this goes like the like the

1018
00:48:23,840 --> 00:48:26,800
Speaker 1: early super intelligence hype of like the twenty teens.

1019
00:48:26,920 --> 00:48:28,600
Speaker 7: Right, It's it's it's all God of this idea.

1020
00:48:28,680 --> 00:48:30,440
Speaker 2: Wait, there was a hype cycle on that. How'd I

1021
00:48:30,440 --> 00:48:31,680
Speaker 2: fucking forget that one?

1022
00:48:32,080 --> 00:48:36,680
Speaker 1: There was well, like like the rockospasiless thing, right, Oh god,

1023
00:48:36,880 --> 00:48:39,200
Speaker 1: it's it's the very like the earliestages of this.

1024
00:48:39,280 --> 00:48:42,160
Speaker 7: Yeah, like viewing AI like this inevitable God.

1025
00:48:42,120 --> 00:48:45,640
Speaker 2: I have you heard of Eliza Yudowski? Sounds familiar, but

1026
00:48:45,840 --> 00:48:48,000
Speaker 2: is the I keep having this person brought up to

1027
00:48:48,040 --> 00:48:51,200
Speaker 2: me by serious people. He is a person that writes

1028
00:48:51,200 --> 00:48:53,080
Speaker 2: about a g I and writes scary. He wrote like

1029
00:48:53,080 --> 00:48:56,279
Speaker 2: a fan fic, Harry Potter fan fiction, with ag yeah, yeah, yeah,

1030
00:48:56,320 --> 00:48:58,160
Speaker 2: I just want to be clear. If anyone else brings

1031
00:48:58,239 --> 00:49:02,439
Speaker 2: him up to me, I'm gonna email back just some

1032
00:49:02,480 --> 00:49:05,680
Speaker 2: sort of obscenity, because you should not take this man seriously,

1033
00:49:05,719 --> 00:49:09,040
Speaker 2: he writes Harry Potter fan fiction. He has just ingratiated

1034
00:49:09,120 --> 00:49:13,000
Speaker 2: himself with other cultists from Less Wrong, and I see

1035
00:49:13,000 --> 00:49:16,400
Speaker 2: real journalists mentioning them, and it's almost like people just

1036
00:49:16,480 --> 00:49:19,080
Speaker 2: want to find any possible proof they're right so that

1037
00:49:19,120 --> 00:49:22,600
Speaker 2: they can ignore the signs that everyone's wrong. And I

1038
00:49:22,640 --> 00:49:25,040
Speaker 2: also think that regular people see the problems of AI

1039
00:49:25,160 --> 00:49:28,760
Speaker 2: way more than the journalists though, And it's strange, it's bizarre.

1040
00:49:28,800 --> 00:49:31,799
Speaker 2: It's like another thing I forget who said this. If

1041
00:49:31,840 --> 00:49:33,200
Speaker 2: you're the person that said this to me, I'm very

1042
00:49:33,239 --> 00:49:37,239
Speaker 2: sorry for forgetting. But it's like everyone feels bad that

1043
00:49:37,239 --> 00:49:39,600
Speaker 2: they missed out on social media and calling social media

1044
00:49:39,719 --> 00:49:42,160
Speaker 2: is the best biggest movement, or they feel bad on

1045
00:49:42,280 --> 00:49:44,640
Speaker 2: missing out in GPUs and not saying GPUs have pushed

1046
00:49:44,640 --> 00:49:46,399
Speaker 2: the next thing. And by the way, there are people

1047
00:49:46,400 --> 00:49:48,560
Speaker 2: who tried in like twenty seventeen to say GPUs would

1048
00:49:48,560 --> 00:49:51,680
Speaker 2: be the next computing thing. They were ignored. It was

1049
00:49:52,320 --> 00:49:53,640
Speaker 2: crazy how early they were.

1050
00:49:54,360 --> 00:49:58,879
Speaker 6: But it's and journalists in particular were late to the Internet. Yes,

1051
00:49:59,320 --> 00:50:04,839
Speaker 6: so we're there's probably an institutional bias toward taking tech

1052
00:50:05,000 --> 00:50:09,080
Speaker 6: seriously because we are paying for brushing off the Internet

1053
00:50:09,160 --> 00:50:10,840
Speaker 6: in two thousand.

1054
00:50:11,360 --> 00:50:12,759
Speaker 2: I think I'm going to start in a war show

1055
00:50:12,760 --> 00:50:15,520
Speaker 2: called the fel for It Awards. That's a good idea,

1056
00:50:15,719 --> 00:50:18,520
Speaker 2: and I'm gonna when because it's like you say that

1057
00:50:18,640 --> 00:50:20,880
Speaker 2: and they were late. They weren't late to the metaverse.

1058
00:50:20,960 --> 00:50:26,040
Speaker 2: How'd that go? They were late to crypto but NFTs, that's.

1059
00:50:25,880 --> 00:50:28,000
Speaker 6: Says crypto sucks And it's hard to talk, like it's

1060
00:50:28,080 --> 00:50:29,280
Speaker 6: hard to explain.

1061
00:50:30,239 --> 00:50:33,799
Speaker 3: Yeah, having to explain the blockchain.

1062
00:50:33,960 --> 00:50:37,040
Speaker 2: No, it's it's the problem with crypto is it's complex.

1063
00:50:37,080 --> 00:50:40,160
Speaker 2: But you can explain it quite simply as a decentralized database.

1064
00:50:40,200 --> 00:50:41,960
Speaker 2: But when you explain it like that, it sounds fucking

1065
00:50:42,000 --> 00:50:45,400
Speaker 2: boring because it is. Yes, it's connected to money. Sometimes.

1066
00:50:45,680 --> 00:50:47,960
Speaker 2: I wrote about crypto for years, and every time I

1067
00:50:48,040 --> 00:50:51,480
Speaker 2: think about writing it again, I feel sad. But it

1068
00:50:51,520 --> 00:50:56,719
Speaker 2: come do my job, sure, sure, absolutely, give me a

1069
00:50:56,719 --> 00:51:01,120
Speaker 2: CNN column. Michael Balabano kicked my ass up, but it's there.

1070
00:51:01,760 --> 00:51:05,480
Speaker 2: He's a wonderful merite. It's just so frustrating as well,

1071
00:51:05,520 --> 00:51:08,200
Speaker 2: because as ever, and I'm kind of paraphrasing The Big Show,

1072
00:51:08,200 --> 00:51:10,560
Speaker 2: it's like the people who get fucked here are regular

1073
00:51:10,600 --> 00:51:13,800
Speaker 2: people AI bubble bursts. It's not like Sa sam Oltman

1074
00:51:13,840 --> 00:51:16,560
Speaker 2: will be humiliated. I will make sure of it. But

1075
00:51:17,320 --> 00:51:20,800
Speaker 2: it's not like Warrio or Sammy Clammy. Sammy Clammy. Sammy

1076
00:51:21,239 --> 00:51:23,759
Speaker 2: is going to get done in. At the end of this,

1077
00:51:23,960 --> 00:51:26,439
Speaker 2: it's gonna be the stocks are going to crash to fuck.

1078
00:51:27,040 --> 00:51:29,400
Speaker 2: People's pensions will be fucked. I mean, thirty five percent

1079
00:51:29,440 --> 00:51:31,920
Speaker 2: of the American stock market is magnificent seven nineteen percent

1080
00:51:31,920 --> 00:51:33,920
Speaker 2: of that it's in video. I think forty two percent

1081
00:51:33,960 --> 00:51:37,200
Speaker 2: of Nvidia's revenue is magnificent seven stocks. I will have

1082
00:51:37,239 --> 00:51:40,360
Speaker 2: a citation in this, notes Laura Bratton at Yahoo Finance

1083
00:51:40,520 --> 00:51:44,400
Speaker 2: the goat. But it's going to hit everyone, but it's

1084
00:51:44,400 --> 00:51:45,840
Speaker 2: not going to I don't think it'd be great financial

1085
00:51:45,840 --> 00:51:47,560
Speaker 2: crisis level, but it's going to be really bad. And

1086
00:51:49,000 --> 00:51:51,520
Speaker 2: I don't think these AI companies are going to be

1087
00:51:51,560 --> 00:51:55,160
Speaker 2: offering the free spigot anymore. It costs them so much money.

1088
00:51:55,320 --> 00:51:56,839
Speaker 2: So we're going to see all of this get like

1089
00:51:56,960 --> 00:51:59,360
Speaker 2: it will still be there, but like this festering hole

1090
00:52:00,120 --> 00:52:02,360
Speaker 2: the side of in the side of the tech industry

1091
00:52:02,360 --> 00:52:03,799
Speaker 2: as everyone goes, what the fuck's next?

1092
00:52:03,840 --> 00:52:05,480
Speaker 1: Do you have such a beautiful way with where it's in?

1093
00:52:05,640 --> 00:52:07,439
Speaker 2: Yes, kind of the way.

1094
00:52:07,680 --> 00:52:12,359
Speaker 6: The metaverse still exists within ye book, but like it's

1095
00:52:12,440 --> 00:52:15,320
Speaker 6: not the star Child okay anymore.

1096
00:52:15,440 --> 00:52:17,640
Speaker 2: I was talking talking to me lad later last night

1097
00:52:17,640 --> 00:52:19,920
Speaker 2: about this, and it was driving me insane. Isn't it

1098
00:52:20,000 --> 00:52:24,520
Speaker 2: fucking insane? Meta A multi trillion market cap company just went,

1099
00:52:24,680 --> 00:52:27,839
Speaker 2: don't worry, legs are coming in the metaverse and then

1100
00:52:27,920 --> 00:52:29,160
Speaker 2: just went, actually they're not.

1101
00:52:29,880 --> 00:52:30,680
Speaker 5: Actually we're pitty.

1102
00:52:32,320 --> 00:52:36,520
Speaker 2: A huge company just lied. They lied constantly for like

1103
00:52:36,760 --> 00:52:39,800
Speaker 2: over a year you had people being like, absolutely believe

1104
00:52:39,840 --> 00:52:42,000
Speaker 2: you one hundred percent, and then everyone just went, Oh,

1105
00:52:42,080 --> 00:52:45,360
Speaker 2: isn't that fucking straight? What the fuck is going It's

1106
00:52:45,400 --> 00:52:48,239
Speaker 2: it's weirder than the AI thing, though the AI thing

1107
00:52:48,320 --> 00:52:52,720
Speaker 2: is pretty weird. It's like we live tech journalism sometimes

1108
00:52:52,719 --> 00:52:55,920
Speaker 2: lives in an alternate reality. Oh yeah, it's so. I

1109
00:52:55,960 --> 00:52:59,319
Speaker 2: don't know. I don't know what's going on with a

1110
00:52:59,320 --> 00:53:02,239
Speaker 2: lot of things, but in particular, it just feels like

1111
00:53:02,320 --> 00:53:05,560
Speaker 2: it would be more fun if we were honest. It

1112
00:53:05,560 --> 00:53:07,839
Speaker 2: would be so much more fun. I wrote a thing

1113
00:53:07,880 --> 00:53:10,440
Speaker 2: today since coming out in a few days. So the

1114
00:53:10,480 --> 00:53:14,239
Speaker 2: thing they'reright on Monday is just make fun of them

1115
00:53:14,239 --> 00:53:16,919
Speaker 2: because they're not charming. They're not interesting, they're not hot,

1116
00:53:18,480 --> 00:53:19,640
Speaker 2: none of them are tasty looking.

1117
00:53:20,239 --> 00:53:22,760
Speaker 1: I think sen Altman has had some work done recently.

1118
00:53:22,800 --> 00:53:25,759
Speaker 1: Do you think he as his lips are looking a

1119
00:53:25,760 --> 00:53:26,600
Speaker 1: little fuller.

1120
00:53:26,320 --> 00:53:33,279
Speaker 2: Than Juicy Sam? Juicy Samultman. All right, that's a new

1121
00:53:33,400 --> 00:53:35,200
Speaker 2: that's a new phrase to me to text eight people.

1122
00:53:36,120 --> 00:53:39,080
Speaker 2: But it's it's like, they're not charming, they're not interesting.

1123
00:53:39,400 --> 00:53:43,160
Speaker 2: Seve Job's complete monster, but at least interesting to listen to.

1124
00:53:43,400 --> 00:53:46,440
Speaker 2: They're boring, they're all management consultants. They don't. I went

1125
00:53:46,480 --> 00:53:49,600
Speaker 2: and reread the iPhone announcement today, and the beginning is

1126
00:53:49,680 --> 00:53:51,759
Speaker 2: him just being like, yeah, we did this, we did this,

1127
00:53:52,120 --> 00:53:53,480
Speaker 2: and then we came up with the thing that did

1128
00:53:53,520 --> 00:53:55,799
Speaker 2: all of this, and I'm about to show you. Yeah,

1129
00:53:56,000 --> 00:53:58,600
Speaker 2: I get why people cheered that. But now it's like.

1130
00:53:58,640 --> 00:54:01,760
Speaker 1: I tried watching the Liquid Glas presentation. I fell asleep

1131
00:54:01,800 --> 00:54:06,920
Speaker 1: in like five to seven minutes there, damn. And like,

1132
00:54:07,320 --> 00:54:09,680
Speaker 1: I think there's parts of liquid Glass that seemed compelling.

1133
00:54:09,760 --> 00:54:12,719
Speaker 1: I hopefully it'll get worked out before it has the

1134
00:54:12,800 --> 00:54:16,399
Speaker 1: full launch, but like it's presented in the most non

1135
00:54:16,520 --> 00:54:17,880
Speaker 1: compelling way possible.

1136
00:54:18,200 --> 00:54:22,200
Speaker 5: It's glass based on vision os from the most successful

1137
00:54:22,200 --> 00:54:22,640
Speaker 5: the most.

1138
00:54:22,520 --> 00:54:23,680
Speaker 4: Successful Apple product.

1139
00:54:24,880 --> 00:54:32,399
Speaker 2: You remember the vision pro No, me neither. But that's

1140
00:54:32,440 --> 00:54:34,560
Speaker 2: the thing as well. I'm trying to get us excited

1141
00:54:34,560 --> 00:54:39,080
Speaker 2: for liquid ass. And it's very confusing as well, because

1142
00:54:39,760 --> 00:54:41,960
Speaker 2: you could just describe it in a boring mind said, yeah,

1143
00:54:42,400 --> 00:54:45,400
Speaker 2: just be very matter of fact about it. But I

1144
00:54:45,440 --> 00:54:48,600
Speaker 2: guess it's for shareholders, but is it even No, even

1145
00:54:48,680 --> 00:54:51,279
Speaker 2: the most slimy Apple people were kind.

1146
00:54:51,200 --> 00:54:55,439
Speaker 6: Of like they needed a literal shiny thing to distract

1147
00:54:55,600 --> 00:54:57,359
Speaker 6: from the how.

1148
00:54:57,280 --> 00:55:01,520
Speaker 2: About they fixed tapping in screenshots. My screenshots do not

1149
00:55:01,560 --> 00:55:04,160
Speaker 2: crop properly. You work for Apple and you know why,

1150
00:55:04,160 --> 00:55:07,480
Speaker 2: email me, but it's oh, I don't know. Make up

1151
00:55:07,520 --> 00:55:11,640
Speaker 2: devices work good. They're all a mess everything.

1152
00:55:12,000 --> 00:55:14,600
Speaker 5: Like if you think about the iPhone, it's seventeen years old.

1153
00:55:15,440 --> 00:55:19,000
Speaker 5: It's ready to go to college. Carry people who are

1154
00:55:19,080 --> 00:55:21,719
Speaker 5: twenty one, who can vote and go to war and

1155
00:55:21,760 --> 00:55:24,600
Speaker 5: do all those things, most likely have no memory of

1156
00:55:24,600 --> 00:55:27,480
Speaker 5: a life before the smartphone. Right, Like, we're at a

1157
00:55:27,520 --> 00:55:30,600
Speaker 5: point where people want what's next, and so I think

1158
00:55:30,760 --> 00:55:33,680
Speaker 5: you just have like tech journalism, we have to chase

1159
00:55:33,719 --> 00:55:36,400
Speaker 5: clicks and seo farm bait, we have to chase all

1160
00:55:36,400 --> 00:55:39,560
Speaker 5: of the to stay alive. And so it's very much like.

1161
00:55:39,760 --> 00:55:40,640
Speaker 3: This is the next thing.

1162
00:55:41,040 --> 00:55:43,600
Speaker 5: Get height because if you all care, it's like we're

1163
00:55:43,600 --> 00:55:45,680
Speaker 5: looking for the next Game of Thrones, but for tech.

1164
00:55:46,000 --> 00:55:48,640
Speaker 5: Because Game of Thrones was such a huge traffic magnet

1165
00:55:48,680 --> 00:55:52,720
Speaker 5: that literally anything that happened like wrah, we're all drinking

1166
00:55:52,719 --> 00:55:56,120
Speaker 5: at the tit of like seo traffic and ad moneys

1167
00:55:56,120 --> 00:55:58,319
Speaker 5: are coming in. So I just think there is like

1168
00:55:58,360 --> 00:56:02,480
Speaker 5: an incentive and like you know, journalists always get pushed

1169
00:56:02,640 --> 00:56:05,080
Speaker 5: in any doesn't matter who you write for, you always

1170
00:56:05,080 --> 00:56:07,719
Speaker 5: get pushed to like do the next big thing, find

1171
00:56:07,719 --> 00:56:08,799
Speaker 5: the next big trend, and.

1172
00:56:10,480 --> 00:56:12,200
Speaker 6: If you're a bit way journalist, you have to think

1173
00:56:12,200 --> 00:56:14,080
Speaker 6: about what's the next election, Who's going to be the

1174
00:56:14,080 --> 00:56:14,520
Speaker 6: next thing?

1175
00:56:14,600 --> 00:56:17,160
Speaker 3: You know, It's like what's the what's the take?

1176
00:56:17,239 --> 00:56:19,120
Speaker 2: And I think that the reason no one wants to

1177
00:56:19,120 --> 00:56:22,600
Speaker 2: write that is the there's nothing left.

1178
00:56:23,480 --> 00:56:25,520
Speaker 1: I don't mean that's a scary thought, right, It's like

1179
00:56:25,600 --> 00:56:26,239
Speaker 1: what if this is?

1180
00:56:26,280 --> 00:56:26,360
Speaker 5: It?

1181
00:56:26,480 --> 00:56:29,240
Speaker 1: Like like we're trying to come up with what's the

1182
00:56:29,280 --> 00:56:31,320
Speaker 1: thing that will be the new thing after the smartphone?

1183
00:56:31,360 --> 00:56:35,880
Speaker 1: And like it's like a like ar contacts, like come

1184
00:56:35,920 --> 00:56:38,520
Speaker 1: on every red in a minute, Like it's it's until

1185
00:56:38,520 --> 00:56:42,000
Speaker 1: we start getting the chips implanted, Like and I.

1186
00:56:41,920 --> 00:56:44,680
Speaker 6: Thought Joni I was hired to fix this, to figure

1187
00:56:44,719 --> 00:56:46,240
Speaker 6: out what the what that's the phone?

1188
00:56:46,280 --> 00:56:48,000
Speaker 2: That's not a phone. We've come up with a new

1189
00:56:48,080 --> 00:56:48,800
Speaker 2: kind of phone.

1190
00:56:49,080 --> 00:56:52,040
Speaker 5: It's I mean, I just think we're at a point

1191
00:56:52,040 --> 00:56:54,040
Speaker 5: where like the tech is stalled out, right, because if

1192
00:56:54,080 --> 00:56:56,399
Speaker 5: you have like the law of diminishing returns and just.

1193
00:56:56,400 --> 00:57:00,759
Speaker 2: Wrote com bubble our episode lost you is though it's

1194
00:57:00,800 --> 00:57:02,919
Speaker 2: we're at the are we at the I'm not gonna

1195
00:57:02,920 --> 00:57:05,120
Speaker 2: say end of history because people misquote for Kuyama. But

1196
00:57:05,520 --> 00:57:08,960
Speaker 2: it's we're at the end actually, anyway, I'll get back

1197
00:57:08,960 --> 00:57:13,640
Speaker 2: to Laa. It's we're at the end of innovation for

1198
00:57:13,680 --> 00:57:15,879
Speaker 2: a minute, because if you look at how they're trying

1199
00:57:15,880 --> 00:57:18,080
Speaker 2: to innovate right now. First of all, I know I've

1200
00:57:18,120 --> 00:57:21,080
Speaker 2: been making fun of the overpaying people, but the overpaying

1201
00:57:21,120 --> 00:57:22,920
Speaker 2: thing they're doing is only something you do if you

1202
00:57:22,960 --> 00:57:24,000
Speaker 2: have no fucking clues.

1203
00:57:24,080 --> 00:57:24,640
Speaker 7: They don't know what to do.

1204
00:57:24,880 --> 00:57:28,120
Speaker 2: Yea. Yeah, they're just throwing around money. They're investing three

1205
00:57:28,240 --> 00:57:31,440
Speaker 2: hundred and twenty seven billion dollars this year into something

1206
00:57:31,560 --> 00:57:34,959
Speaker 2: they don't They built massive data centers. I don't think

1207
00:57:35,000 --> 00:57:36,800
Speaker 2: that any of I think they all kind of have

1208
00:57:36,880 --> 00:57:41,040
Speaker 2: realized there might not be you next thing, and I

1209
00:57:41,080 --> 00:57:43,920
Speaker 2: think that their obsession has become something different, which is

1210
00:57:44,000 --> 00:57:45,800
Speaker 2: line go up, money, go up. But how can I

1211
00:57:45,880 --> 00:57:48,080
Speaker 2: charge you ten dollars a month? How can I charge

1212
00:57:48,080 --> 00:57:50,800
Speaker 2: you and a thousand people at your organization thirty dollars

1213
00:57:50,880 --> 00:57:52,840
Speaker 2: a month? To the point that they don't know how

1214
00:57:52,880 --> 00:57:55,000
Speaker 2: to do math anymore, And they're like, well, I got

1215
00:57:55,040 --> 00:57:57,360
Speaker 2: you to pay thirty dollars. It cost me seventy five

1216
00:57:57,880 --> 00:58:01,800
Speaker 2: to get that money from you. But I don't know

1217
00:58:01,840 --> 00:58:04,680
Speaker 2: how to fix that, But what if I spent more

1218
00:58:04,760 --> 00:58:08,480
Speaker 2: money to find out? Maybe I don't know and they

1219
00:58:08,520 --> 00:58:10,920
Speaker 2: don't know how. And they will say this if you

1220
00:58:11,000 --> 00:58:13,960
Speaker 2: ask questions, which people tend not to with these people.

1221
00:58:14,560 --> 00:58:16,520
Speaker 2: And it's just even you've got analysts doing And so

1222
00:58:16,520 --> 00:58:18,360
Speaker 2: there was an analyst earlier who's like, oh, yeah, Google's

1223
00:58:18,360 --> 00:58:20,960
Speaker 2: are they're making three point one billion dollars in Google

1224
00:58:20,960 --> 00:58:23,080
Speaker 2: one subscriptions because of AI and it's not they just

1225
00:58:23,120 --> 00:58:26,360
Speaker 2: like Google one subscriptions have gone up, and they're like, fuck,

1226
00:58:26,400 --> 00:58:27,160
Speaker 2: I think it's AI.

1227
00:58:27,720 --> 00:58:29,400
Speaker 1: I mean this is this is why everyone's focused on

1228
00:58:29,440 --> 00:58:31,640
Speaker 1: it on AIS because it's like it's like the final

1229
00:58:31,960 --> 00:58:35,360
Speaker 1: boss of our like collective tech like unconsciousness. Right, It's

1230
00:58:35,360 --> 00:58:38,080
Speaker 1: the thing Victoria you were talking about earlier. It's like

1231
00:58:38,080 --> 00:58:39,920
Speaker 1: like in like you know, like eighties sci fi. This

1232
00:58:40,000 --> 00:58:42,320
Speaker 1: is always like the final thing. Yeah, after we've gotten

1233
00:58:42,400 --> 00:58:44,760
Speaker 1: like you know, augmented reality, you got the contacts, got

1234
00:58:44,760 --> 00:58:47,640
Speaker 1: the glasses, like AIS, that's like the last thing that's

1235
00:58:47,640 --> 00:58:49,880
Speaker 1: the last like ghost of our of our past that

1236
00:58:49,920 --> 00:58:51,959
Speaker 1: we're still trying to chase. That's this thing that's still

1237
00:58:51,960 --> 00:58:55,400
Speaker 1: trying to like control us, like time traveling, like backwards

1238
00:58:55,440 --> 00:58:58,120
Speaker 1: in time, this idea that eventually we will we will

1239
00:58:58,160 --> 00:59:01,120
Speaker 1: have this AI thing and like it is, Yeah, it is.

1240
00:59:01,200 --> 00:59:04,200
Speaker 1: It is like the final boss and we're always trying

1241
00:59:04,240 --> 00:59:07,040
Speaker 1: to chase it and we're coming up with like the

1242
00:59:08,040 --> 00:59:11,080
Speaker 1: barrier of like maybe this thing isn't actually ever gonna

1243
00:59:11,080 --> 00:59:11,640
Speaker 1: be real.

1244
00:59:11,880 --> 00:59:14,200
Speaker 2: And I think they think it's the magical thing because

1245
00:59:14,200 --> 00:59:15,960
Speaker 2: it will tell them how to run their business.

1246
00:59:15,720 --> 00:59:18,360
Speaker 1: Because it'll it'll tell them what's actually the only this

1247
00:59:18,440 --> 00:59:20,600
Speaker 1: is as far as we can get, and then we

1248
00:59:20,640 --> 00:59:22,600
Speaker 1: have to create something smarter than us so it can

1249
00:59:22,600 --> 00:59:24,720
Speaker 1: tell us what the next thing will be, because this

1250
00:59:24,760 --> 00:59:26,840
Speaker 1: is the final thing we can imagine and it's like

1251
00:59:26,880 --> 00:59:27,600
Speaker 1: this is like it.

1252
00:59:27,800 --> 00:59:30,560
Speaker 2: But even when you listen to Clammy Sammy talking about it,

1253
00:59:30,600 --> 00:59:34,040
Speaker 2: and he'll say he will be like you see Sami,

1254
00:59:34,200 --> 00:59:40,680
Speaker 2: you Sie sam Oldman. Clammy Sammy's thing he always says

1255
00:59:40,800 --> 00:59:42,480
Speaker 2: is I can't wait to see what you'll build with this,

1256
00:59:42,600 --> 00:59:46,080
Speaker 2: and it's like, motherfucker, that is your job. And that's

1257
00:59:46,080 --> 00:59:48,640
Speaker 2: actually the thing with AI as well. I'm always being

1258
00:59:48,680 --> 00:59:51,280
Speaker 2: to But the few haters who dare ender my dojo

1259
00:59:52,040 --> 00:59:54,320
Speaker 2: is they say, oh, well you don't know how to

1260
00:59:54,400 --> 00:59:56,360
Speaker 2: use it, right, You're not doing it correctly. I think

1261
00:59:56,400 --> 00:59:58,760
Speaker 2: even Alison you might have mentioned this like holding it

1262
00:59:58,800 --> 01:00:01,800
Speaker 2: wrong thing. But it's like, no, I am the customer.

1263
01:00:02,400 --> 01:00:05,160
Speaker 2: I am I am paying you for work. I shouldn't

1264
01:00:05,200 --> 01:00:08,280
Speaker 2: have to write a nine hundred and fifty word prompt

1265
01:00:08,280 --> 01:00:11,400
Speaker 2: that tells you to imagine you're a spaceman or whatever,

1266
01:00:11,480 --> 01:00:14,600
Speaker 2: like whatever makes it work. Fuck you, fuck you man,

1267
01:00:14,720 --> 01:00:16,600
Speaker 2: I'm me pay you money.

1268
01:00:16,600 --> 01:00:19,480
Speaker 1: You give me thing, nine hundred word prompt to generate

1269
01:00:19,480 --> 01:00:21,120
Speaker 1: a nine hundred word story.

1270
01:00:21,200 --> 01:00:23,120
Speaker 4: Yeah, that sucks.

1271
01:00:23,360 --> 01:00:27,320
Speaker 2: They're like straight upset. There's just like bereft of. And

1272
01:00:26,960 --> 01:00:30,479
Speaker 2: it's also we've handed over society to people that don't

1273
01:00:30,480 --> 01:00:33,920
Speaker 2: create things. We've had people who are just like showing

1274
01:00:34,000 --> 01:00:36,800
Speaker 2: different faces to enough people that they get where they

1275
01:00:36,840 --> 01:00:39,880
Speaker 2: need to go. The business idiot writ large and it's

1276
01:00:39,960 --> 01:00:44,200
Speaker 2: just it's so funny as well because it's going to

1277
01:00:44,280 --> 01:00:47,600
Speaker 2: fall apart, and when it does, everyone I genuinely, I

1278
01:00:47,600 --> 01:00:49,640
Speaker 2: mean I look forward to it because of the obvious

1279
01:00:49,920 --> 01:00:52,280
Speaker 2: yucks and chuckles. I will get out of it, but

1280
01:00:52,480 --> 01:00:55,720
Speaker 2: there's going to be a really interesting period of journalism

1281
01:00:56,280 --> 01:00:59,640
Speaker 2: having to see it and go why did we fall

1282
01:00:59,680 --> 01:01:02,400
Speaker 2: for this? And if there isn't, I will make this

1283
01:01:02,480 --> 01:01:05,240
Speaker 2: happen with all of my energy. I will hold everyone

1284
01:01:05,400 --> 01:01:12,720
Speaker 2: because it's I think, and I'm not insulting. You're out there. No, no, no, no,

1285
01:01:12,920 --> 01:01:15,080
Speaker 2: this is not an insult. I promise. Read the comments

1286
01:01:15,120 --> 01:01:16,640
Speaker 2: on AI stories on the Verge.

1287
01:01:16,760 --> 01:01:17,320
Speaker 3: I do read that.

1288
01:01:17,440 --> 01:01:20,680
Speaker 2: I love reading because you see, regular people are so

1289
01:01:20,720 --> 01:01:22,080
Speaker 2: skeptical of this shit.

1290
01:01:22,240 --> 01:01:24,440
Speaker 5: Oh no, I read. I read love the comments on

1291
01:01:24,480 --> 01:01:26,760
Speaker 5: all the stories that I write, which are just like,

1292
01:01:27,360 --> 01:01:29,320
Speaker 5: thank you for calling out how stupid it is.

1293
01:01:29,360 --> 01:01:31,600
Speaker 2: Well, that's the thing, though the regular people seem to

1294
01:01:31,600 --> 01:01:31,880
Speaker 2: get it.

1295
01:01:31,880 --> 01:01:34,280
Speaker 1: There's gonna be well, it depends. Like there's I think

1296
01:01:34,320 --> 01:01:38,520
Speaker 1: there's a sector of counter culture that's developing like a

1297
01:01:38,600 --> 01:01:42,080
Speaker 1: neo Loodite perspective like beyond just like like you know,

1298
01:01:42,120 --> 01:01:45,040
Speaker 1: like like anti tech, like Green anarchists, which have carried

1299
01:01:45,080 --> 01:01:47,439
Speaker 1: the bloodite torch for the past like thirty years. We're

1300
01:01:47,480 --> 01:01:50,080
Speaker 1: starting to see like like the quote unquote the cool

1301
01:01:50,200 --> 01:01:55,200
Speaker 1: kids adopting this like neo Loodite tendency mostly in response

1302
01:01:55,240 --> 01:01:57,840
Speaker 1: to like you know, like like alienation and like automation. Right,

1303
01:01:58,640 --> 01:02:00,640
Speaker 1: And I think that's trend is going to like continue,

1304
01:02:00,680 --> 01:02:02,720
Speaker 1: Like that's gonna it's gonna be like almost like a

1305
01:02:02,720 --> 01:02:06,720
Speaker 1: social status, like status like a signifier, is the fact

1306
01:02:06,720 --> 01:02:10,000
Speaker 1: that you're not reliant on on these things, because there

1307
01:02:10,000 --> 01:02:12,160
Speaker 1: will be a version of the I that does keep

1308
01:02:12,200 --> 01:02:16,920
Speaker 1: getting like Normy Afied, I think it's gonna it's there's

1309
01:02:16,960 --> 01:02:18,520
Speaker 1: gonna be very like even if you look at the

1310
01:02:18,520 --> 01:02:21,959
Speaker 1: way higher education is working right now, like the level

1311
01:02:22,000 --> 01:02:25,360
Speaker 1: of people who are graduating just on the basis of

1312
01:02:25,400 --> 01:02:27,840
Speaker 1: like AI helping them with large parts of their assignments,

1313
01:02:27,880 --> 01:02:29,640
Speaker 1: like I have, I have a few friends who are professors,

1314
01:02:29,960 --> 01:02:32,280
Speaker 1: and the majority of assignments that are turned in are

1315
01:02:33,040 --> 01:02:35,720
Speaker 1: majority written by AI. And we get to this point,

1316
01:02:35,760 --> 01:02:36,960
Speaker 1: we're gonna have like a we're gonna have a new

1317
01:02:37,000 --> 01:02:38,720
Speaker 1: generation of the workforce that doesn't really know how to

1318
01:02:38,760 --> 01:02:41,840
Speaker 1: do anything because AI has been doing everything for them.

1319
01:02:41,920 --> 01:02:44,000
Speaker 1: But they got their degree, you know, like I am

1320
01:02:44,040 --> 01:02:46,880
Speaker 1: employable certificate, but now that now they don't really know

1321
01:02:46,880 --> 01:02:47,160
Speaker 1: what to do.

1322
01:02:47,200 --> 01:02:48,360
Speaker 2: So'sification.

1323
01:02:48,440 --> 01:02:51,400
Speaker 1: There's gonna be but there's gonna be a counterculture that

1324
01:02:51,560 --> 01:02:54,320
Speaker 1: is like that like refuses to use that. Right, that's

1325
01:02:54,480 --> 01:02:56,280
Speaker 1: like I will not be using AI. That's gonna be

1326
01:02:56,280 --> 01:02:57,439
Speaker 1: a thing you have to you have to like prove

1327
01:02:57,440 --> 01:02:59,880
Speaker 1: and like talk about and it's it's it's gonna be

1328
01:02:59,880 --> 01:03:01,080
Speaker 1: a version of a social like.

1329
01:03:01,080 --> 01:03:02,760
Speaker 7: Wild like status card.

1330
01:03:02,840 --> 01:03:03,720
Speaker 3: Have you heard of clearly?

1331
01:03:09,200 --> 01:03:11,920
Speaker 5: Yeah, it's the cheat on everything app. And so it

1332
01:03:12,000 --> 01:03:16,520
Speaker 5: was invented by this uh Columbia kid drop It, who

1333
01:03:16,560 --> 01:03:17,320
Speaker 5: I interviewed him for.

1334
01:03:17,640 --> 01:03:19,120
Speaker 3: Oh I heard about this guy.

1335
01:03:19,240 --> 01:03:21,840
Speaker 5: Okay, so it's the cheat on everything app. I tested it.

1336
01:03:21,840 --> 01:03:23,360
Speaker 5: It helped me cheat on nothing.

1337
01:03:23,520 --> 01:03:24,960
Speaker 3: It was terrible.

1338
01:03:25,160 --> 01:03:30,000
Speaker 1: I cannot wait all dates go flawless. Now that I

1339
01:03:30,040 --> 01:03:31,800
Speaker 1: read from a teleprompter.

1340
01:03:31,400 --> 01:03:33,720
Speaker 5: I can't even do that. Like in its current state,

1341
01:03:33,880 --> 01:03:37,520
Speaker 5: it's like you use it, it's like a prompt machine

1342
01:03:37,520 --> 01:03:39,800
Speaker 5: for when you are on a video call, and it

1343
01:03:39,920 --> 01:03:43,040
Speaker 5: very slowly can answer prompts based on a transcript of

1344
01:03:43,080 --> 01:03:46,720
Speaker 5: your video. It's it's not that usable. It messed up

1345
01:03:46,760 --> 01:03:49,360
Speaker 5: the mics on my computer from testing it to this

1346
01:03:49,440 --> 01:03:52,040
Speaker 5: point where like I deleted it from my computer and

1347
01:03:52,200 --> 01:03:57,120
Speaker 5: video uh software programs like Google Meet and Zoom will

1348
01:03:57,240 --> 01:03:59,800
Speaker 5: pick a non existent cluly mic And I'm just like,

1349
01:04:00,840 --> 01:04:01,960
Speaker 5: I don't love.

1350
01:04:01,800 --> 01:04:03,120
Speaker 7: That's concerning it.

1351
01:04:03,280 --> 01:04:05,640
Speaker 5: But you know, cool, yeah, but you know, like as

1352
01:04:05,680 --> 01:04:08,720
Speaker 5: soon as I wrote that story up, someone another company

1353
01:04:08,760 --> 01:04:10,600
Speaker 5: messaged me is like, we're using AI to catch the

1354
01:04:10,640 --> 01:04:11,760
Speaker 5: AI cheaters, and I was.

1355
01:04:11,760 --> 01:04:17,200
Speaker 2: Like, Okay, finally we have bare Force from the Simpsons

1356
01:04:17,240 --> 01:04:19,080
Speaker 2: solving this problem we created.

1357
01:04:19,320 --> 01:04:20,960
Speaker 4: That's the perfect use case for AI.

1358
01:04:21,080 --> 01:04:24,240
Speaker 1: Though, using AI to fix all the problems cast by AI,

1359
01:04:24,800 --> 01:04:25,720
Speaker 1: that's the real.

1360
01:04:25,800 --> 01:04:26,840
Speaker 2: I think it's beautiful.

1361
01:04:26,880 --> 01:04:28,720
Speaker 4: I think that's the actual singularity.

1362
01:04:29,000 --> 01:04:33,440
Speaker 2: We've created problems to create solutions for neither of them work.

1363
01:04:33,680 --> 01:04:36,440
Speaker 2: But clearly the one of the people from Andresen went

1364
01:04:36,480 --> 01:04:39,959
Speaker 2: on a podcast is like, yeah, you know, it's when

1365
01:04:40,760 --> 01:04:46,080
Speaker 2: marketing and virality overtake making a perfect Artesian product. It's like,

1366
01:04:46,400 --> 01:04:49,200
Speaker 2: what you mean is when something goes viral for fucking

1367
01:04:49,360 --> 01:04:53,160
Speaker 2: lying lying, like it was a lie. Like when you

1368
01:04:53,200 --> 01:04:56,560
Speaker 2: say something that is not true intentionally, that's called lying.

1369
01:04:57,480 --> 01:05:00,400
Speaker 1: It's called an i RL hallution.

1370
01:05:00,640 --> 01:05:03,240
Speaker 5: Yes, it's weird because the way that app was born,

1371
01:05:03,400 --> 01:05:05,720
Speaker 5: and initially it was like some other app that was

1372
01:05:05,800 --> 01:05:08,000
Speaker 5: used to kind of make a point about how stupid

1373
01:05:08,000 --> 01:05:11,320
Speaker 5: technical interviews are for devs, right, and I was like,

1374
01:05:11,320 --> 01:05:13,960
Speaker 5: that's actually kind of radical, and like it is proving

1375
01:05:13,960 --> 01:05:16,160
Speaker 5: a point and you have lost the point and now

1376
01:05:16,200 --> 01:05:20,200
Speaker 5: you have five million dollars in some somebody is going

1377
01:05:21,160 --> 01:05:24,680
Speaker 5: into one hundred thousand dollars commercials and nice apartment.

1378
01:05:24,440 --> 01:05:26,840
Speaker 2: Did you hear about Mira Marathi though, the former CTO

1379
01:05:26,840 --> 01:05:30,120
Speaker 2: of Open Ai and her new startup, Intelligent Machines. No

1380
01:05:30,240 --> 01:05:32,840
Speaker 2: one is so they raised I think like a billion dollars,

1381
01:05:32,880 --> 01:05:36,080
Speaker 2: I want to say, and they did not share anything

1382
01:05:36,080 --> 01:05:39,560
Speaker 2: about the financials. They also did not share anything about

1383
01:05:39,560 --> 01:05:42,840
Speaker 2: the product, so anyone investing just went into room mirror.

1384
01:05:42,880 --> 01:05:46,919
Speaker 2: Maarati went yes, so I need money, mean money now,

1385
01:05:47,640 --> 01:05:51,040
Speaker 2: and they went fuck yeah. Absolutely. There were reports where

1386
01:05:51,040 --> 01:05:53,000
Speaker 2: people would do the vcs were just like, can you

1387
01:05:53,040 --> 01:05:55,560
Speaker 2: share anything? You said no. On top of this, and

1388
01:05:55,720 --> 01:05:57,480
Speaker 2: by the way, I admire the scam at this point,

1389
01:05:57,600 --> 01:06:02,280
Speaker 2: I just get it. She has her voting rights supersede everyone.

1390
01:06:03,720 --> 01:06:06,640
Speaker 2: Get it now. I fuck these pigs who cannot even

1391
01:06:06,720 --> 01:06:10,040
Speaker 2: bother to even think about what they're building or what

1392
01:06:10,080 --> 01:06:12,720
Speaker 2: it is they do. They just like, I will take

1393
01:06:12,760 --> 01:06:14,840
Speaker 2: an I will give you an unlimited amount of money

1394
01:06:14,920 --> 01:06:19,400
Speaker 2: because of the vibes I have, because of the I

1395
01:06:19,400 --> 01:06:22,160
Speaker 2: would love to know. I definitely had a moment where

1396
01:06:22,200 --> 01:06:22,560
Speaker 2: I'm like.

1397
01:06:23,200 --> 01:06:27,880
Speaker 5: Could I vibes give me money, give me one hundred

1398
01:06:27,920 --> 01:06:30,919
Speaker 5: million money, and I will vague vaguely promise to mark

1399
01:06:31,120 --> 01:06:33,320
Speaker 5: something in the future at a time I will.

1400
01:06:34,280 --> 01:06:37,680
Speaker 2: I will never do anything, I will guarantee you that.

1401
01:06:37,760 --> 01:06:39,800
Speaker 2: I mean, Ilia Suits gave it the other open aye

1402
01:06:39,840 --> 01:06:43,480
Speaker 2: guy misreported by Pivot to AI that generally does good

1403
01:06:43,480 --> 01:06:46,440
Speaker 2: work but needs to work on their fucking headlines, suggesting

1404
01:06:46,440 --> 01:06:49,560
Speaker 2: that they guaranteed they would not do anything into a superintelligence,

1405
01:06:49,560 --> 01:06:52,240
Speaker 2: when the actual headline was that they said they have

1406
01:06:52,360 --> 01:06:55,480
Speaker 2: nothing and they want to build superintelligence, which is way funnier.

1407
01:06:55,840 --> 01:06:58,919
Speaker 2: I honestly at this point like it's evil to lie

1408
01:06:58,960 --> 01:07:02,080
Speaker 2: and extract capital, but the people you're extracting it from

1409
01:07:02,160 --> 01:07:07,080
Speaker 2: your wallet inspecting them. But also you could fix almost

1410
01:07:07,440 --> 01:07:10,200
Speaker 2: you could fix like world hunger, I think for six

1411
01:07:10,280 --> 01:07:11,520
Speaker 2: billion dollars. They worked at it.

1412
01:07:11,880 --> 01:07:14,919
Speaker 6: Yeah, yeah, you could and challenge Gilan Musk and he said,

1413
01:07:14,920 --> 01:07:16,600
Speaker 6: if you figure out the number, I'll give it to you.

1414
01:07:16,680 --> 01:07:18,320
Speaker 6: And then they figured out the number.

1415
01:07:18,680 --> 01:07:23,400
Speaker 2: The numbers not based. It's very upsetting. I saw an

1416
01:07:23,440 --> 01:07:25,880
Speaker 2: out there as well say today that Grock only makes

1417
01:07:25,880 --> 01:07:28,800
Speaker 2: a hundred million dollars in revenue. It's so fucking cool

1418
01:07:28,800 --> 01:07:30,080
Speaker 2: as well, they'll burn money.

1419
01:07:30,840 --> 01:07:34,400
Speaker 5: I I mean, just give it to me, give it

1420
01:07:34,440 --> 01:07:37,560
Speaker 5: to me, or so to me. Give sponsor a journalist.

1421
01:07:38,320 --> 01:07:41,240
Speaker 5: We too can sponsor a journalist to live a life.

1422
01:07:41,520 --> 01:07:43,640
Speaker 2: And the funny thing is is, like, I actually think

1423
01:07:43,640 --> 01:07:46,080
Speaker 2: you could make a shit ton of profitable journalism just

1424
01:07:46,200 --> 01:07:49,000
Speaker 2: by like talking about this bluntly. I'm doing it all

1425
01:07:49,040 --> 01:07:52,760
Speaker 2: the time. But it's it's weird, and I actually it's

1426
01:07:52,800 --> 01:07:55,040
Speaker 2: kind of wrap us up as well. I have to

1427
01:07:55,120 --> 01:07:58,160
Speaker 2: wonder if at the end of this farce whether there

1428
01:07:58,200 --> 01:08:00,800
Speaker 2: will be a rise of critical tech journalists. I'm not

1429
01:08:00,840 --> 01:08:03,280
Speaker 2: holding my hopes out, but I think that there is

1430
01:08:03,320 --> 01:08:06,880
Speaker 2: a slog Like you two, actually, Victoria Allison, You've both

1431
01:08:06,880 --> 01:08:08,560
Speaker 2: inspired me a bit that it's possible.

1432
01:08:10,080 --> 01:08:11,440
Speaker 4: It's just right, that's true.

1433
01:08:11,480 --> 01:08:13,600
Speaker 3: I try it, and I tell you what happened.

1434
01:08:13,280 --> 01:08:14,120
Speaker 4: When I tried it.

1435
01:08:14,200 --> 01:08:14,560
Speaker 5: That's it.

1436
01:08:14,800 --> 01:08:16,120
Speaker 3: Imagine my job.

1437
01:08:16,479 --> 01:08:21,760
Speaker 2: Imagine that. Imagine if that happened on Hardfalk. Sorry but

1438
01:08:23,000 --> 01:08:24,920
Speaker 2: I'm saying nothing. No, I know, I know, I know.

1439
01:08:25,000 --> 01:08:27,800
Speaker 2: I'm not going to let these are my opinions just mine.

1440
01:08:28,400 --> 01:08:31,559
Speaker 2: I think about them all the time. It's I'm hoping

1441
01:08:31,600 --> 01:08:33,559
Speaker 2: this happens because I think you've got Brian Merchant, You've

1442
01:08:33,560 --> 01:08:35,599
Speaker 2: got Edwinald grayce So Junior, and Molly White, of course,

1443
01:08:35,640 --> 01:08:37,880
Speaker 2: one of the fucking best in the business. Molly's great

1444
01:08:38,000 --> 01:08:40,880
Speaker 2: Molly is You've got really good criticism, and I think

1445
01:08:40,920 --> 01:08:42,439
Speaker 2: people are hungry for it. The reason I brought up

1446
01:08:42,439 --> 01:08:45,439
Speaker 2: the Virgin's comments is this isn't like any kind of

1447
01:08:45,479 --> 01:08:48,320
Speaker 2: fun making. It's you see people saying it now, just

1448
01:08:48,360 --> 01:08:50,400
Speaker 2: being like, hey, why the fuck am I having to

1449
01:08:50,400 --> 01:08:54,439
Speaker 2: pretend here? And I just I think it's the business

1450
01:08:54,439 --> 01:08:58,200
Speaker 2: idiot idea I had a few months ago where it's like, hey,

1451
01:08:58,439 --> 01:09:00,960
Speaker 2: maybe we've handed over our eccon and me are finances,

1452
01:09:01,000 --> 01:09:03,760
Speaker 2: the editorial structure of some publications and the people with

1453
01:09:03,760 --> 01:09:06,160
Speaker 2: the biggest microphones to people that don't understand a single

1454
01:09:06,200 --> 01:09:10,160
Speaker 2: goddamn thing. And it's kind of that scares me more

1455
01:09:10,200 --> 01:09:13,160
Speaker 2: than anything, because we talk about the harms of generative

1456
01:09:13,200 --> 01:09:17,719
Speaker 2: AI and it's yeah, there is no intention. No intention

1457
01:09:17,880 --> 01:09:20,720
Speaker 2: is far scarier than people being like they want all

1458
01:09:20,760 --> 01:09:23,000
Speaker 2: of your data and all this when they have no idea.

1459
01:09:23,080 --> 01:09:26,120
Speaker 2: I was actually kind of disappointed. Meredith's Meredith Wird toa

1460
01:09:26,160 --> 01:09:29,400
Speaker 2: care of signal. She's really good Admira her daily. She

1461
01:09:30,280 --> 01:09:32,759
Speaker 2: said a thing on stage about how like, yeah, AI agents,

1462
01:09:32,760 --> 01:09:34,920
Speaker 2: which is the marketing term, but they're going to do this,

1463
01:09:34,960 --> 01:09:37,800
Speaker 2: and they're going to do this. Whenever anyone speaks about

1464
01:09:37,840 --> 01:09:43,680
Speaker 2: AI agents just imagine like clown noises, circus music if

1465
01:09:43,680 --> 01:09:46,200
Speaker 2: it helps. Because AI agents do not exist, they do

1466
01:09:46,240 --> 01:09:48,960
Speaker 2: not work. There are people who are getting close to

1467
01:09:49,000 --> 01:09:51,360
Speaker 2: a thing that might work. One time, they had a

1468
01:09:51,400 --> 01:09:54,360
Speaker 2: big study out of salesforce. So like after with multi

1469
01:09:54,360 --> 01:09:56,439
Speaker 2: step processes, you know, things that require you to do

1470
01:09:56,479 --> 01:10:00,000
Speaker 2: more than one thing. Yeah, it failed like less more

1471
01:10:00,080 --> 01:10:02,000
Speaker 2: than I think it's like more than thirty two percent

1472
01:10:02,040 --> 01:10:04,760
Speaker 2: of the time. It's like, sorry, I only succeeded thirty

1473
01:10:04,760 --> 01:10:07,080
Speaker 2: two percent of the time, which is very bad and

1474
01:10:07,240 --> 01:10:10,799
Speaker 2: not getting better. And it's like, this is what everyone's

1475
01:10:10,840 --> 01:10:14,280
Speaker 2: talking about AI agents despite they're not working. I realized

1476
01:10:14,280 --> 01:10:16,120
Speaker 2: the sentence started at one point and it's going to

1477
01:10:16,240 --> 01:10:18,360
Speaker 2: end it that I feel like I'm going fucking insane

1478
01:10:18,400 --> 01:10:19,920
Speaker 2: every time I read the news and they see a

1479
01:10:19,960 --> 01:10:22,639
Speaker 2: new thing that does not exist and everyone says it exists.

1480
01:10:23,200 --> 01:10:25,719
Speaker 2: Am I crazy? Am I having a problem? Like it's

1481
01:10:26,000 --> 01:10:28,840
Speaker 2: I mean, yes, but is that why I'm reading the

1482
01:10:28,840 --> 01:10:32,120
Speaker 2: stuff that doesn't exist? It's just driving me insane. But

1483
01:10:32,240 --> 01:10:34,559
Speaker 2: now you've said the divinity thing, I'm just like, this

1484
01:10:34,600 --> 01:10:38,680
Speaker 2: isn't actually about building stuff. This is a belief system

1485
01:10:38,800 --> 01:10:42,200
Speaker 2: and plate spinning. This is just an intention to build

1486
01:10:44,000 --> 01:10:49,160
Speaker 2: a vibeespased economy, except it can't last long term, oh God.

1487
01:10:49,360 --> 01:10:53,200
Speaker 6: Or until the next the next revolution that replaces the

1488
01:10:53,240 --> 01:10:56,600
Speaker 6: AI god. You know, if like industrial revolution and modernity

1489
01:10:56,720 --> 01:11:03,240
Speaker 6: killed God, quak God, then maybe AI is the is

1490
01:11:03,360 --> 01:11:07,960
Speaker 6: the demigod that we that we replace God with, and

1491
01:11:08,000 --> 01:11:09,639
Speaker 6: then something else will happen.

1492
01:11:09,760 --> 01:11:13,120
Speaker 2: Sam Altman is the Antichrist.

1493
01:11:12,479 --> 01:11:14,960
Speaker 4: Many many people are saying that.

1494
01:11:15,080 --> 01:11:19,360
Speaker 2: Many people, many.

1495
01:11:17,640 --> 01:11:21,000
Speaker 6: People in a New York City podcasting studio are saying, did.

1496
01:11:20,840 --> 01:11:21,439
Speaker 4: I do that?

1497
01:11:22,240 --> 01:11:25,760
Speaker 2: It's sorry, it's but even then, the reason I say

1498
01:11:25,800 --> 01:11:27,560
Speaker 2: is the Antichrist is not any recent interviews on the

1499
01:11:27,600 --> 01:11:31,200
Speaker 2: New York Times podcast. It's specifically about the fact that

1500
01:11:31,360 --> 01:11:33,559
Speaker 2: he is able to charm every business idiot. He's so

1501
01:11:33,640 --> 01:11:35,479
Speaker 2: good at it. He's good at saying nothing in a

1502
01:11:35,520 --> 01:11:38,160
Speaker 2: way that makes people give him a billion dollars. And

1503
01:11:38,240 --> 01:11:40,640
Speaker 2: he is wrapped. I think he's wrapped everyone up in

1504
01:11:40,640 --> 01:11:43,840
Speaker 2: this insanity, and no one really knows why they're doing it.

1505
01:11:43,880 --> 01:11:46,160
Speaker 2: You've got journalists, you've got investors, you've got consumers even

1506
01:11:46,200 --> 01:11:49,320
Speaker 2: who are like chasing this dream. And I genuinely think

1507
01:11:49,320 --> 01:11:51,840
Speaker 2: he may lead the tech industry to a kind of ruin.

1508
01:11:51,920 --> 01:11:53,720
Speaker 2: I don't think all the companies are going to shut down.

1509
01:11:53,760 --> 01:11:56,640
Speaker 2: But this is the ultimate hubris of basing everything in

1510
01:11:56,680 --> 01:12:01,120
Speaker 2: tech on venture capital invested by people don't really understand,

1511
01:12:01,520 --> 01:12:04,960
Speaker 2: and public companies run by people with nbas. Every single

1512
01:12:05,400 --> 01:12:08,519
Speaker 2: Mark Zuckerberg's rarity. He doesn't have an MBA, but all

1513
01:12:08,520 --> 01:12:10,679
Speaker 2: the rest of them do. Even the guy who replaced

1514
01:12:10,720 --> 01:12:13,920
Speaker 2: Andy Jesse Aws the cloud part of Amazon has an NBA.

1515
01:12:14,320 --> 01:12:17,000
Speaker 2: Nbas are everywhere. I think we should bar them from

1516
01:12:17,040 --> 01:12:23,680
Speaker 2: running companies and also being allowed to just don't give

1517
01:12:24,080 --> 01:12:27,280
Speaker 2: houses or healthcare. No, sorry, I'll stop that one there,

1518
01:12:27,640 --> 01:12:30,800
Speaker 2: But it's just I don't know. We're all going to

1519
01:12:30,840 --> 01:12:33,519
Speaker 2: suffer for this. I will I'll blog about it, I guess,

1520
01:12:33,600 --> 01:12:36,439
Speaker 2: and we'll all blog, we'll do podcasts, gere, but you

1521
01:12:36,439 --> 01:12:38,200
Speaker 2: know what, I'm gonna wrap it on that happy note there,

1522
01:12:38,439 --> 01:12:39,680
Speaker 2: get Where can people find you?

1523
01:12:40,800 --> 01:12:45,720
Speaker 1: Well? I help run a daily Yike's news show, four calls,

1524
01:12:45,760 --> 01:12:48,160
Speaker 1: and media called it could happen here. That's who you

1525
01:12:48,200 --> 01:12:50,400
Speaker 1: can find most of my work. Right now, I'm finishing

1526
01:12:50,439 --> 01:12:54,320
Speaker 1: my final piece on the stop cup City movement in Atlanta,

1527
01:12:54,479 --> 01:12:57,720
Speaker 1: as well as an upcoming piece on liberal accelerationism.

1528
01:12:58,000 --> 01:12:58,800
Speaker 2: So what is that?

1529
01:12:59,360 --> 01:12:59,559
Speaker 7: You know?

1530
01:12:59,720 --> 01:13:01,080
Speaker 4: That's the pieces about.

1531
01:13:01,920 --> 01:13:04,000
Speaker 2: Well, we'll have to find out and listen to it. Alison,

1532
01:13:04,040 --> 01:13:04,920
Speaker 2: Where can people find you?

1533
01:13:05,520 --> 01:13:08,439
Speaker 6: I read a business newsletter for CNN called Nightcap. You

1534
01:13:08,439 --> 01:13:12,000
Speaker 6: can just google CNN Business Nightcap and I'm on Blue

1535
01:13:12,000 --> 01:13:13,639
Speaker 6: Sky and Victoria.

1536
01:13:14,200 --> 01:13:16,519
Speaker 5: You can find me at the Verge and all my

1537
01:13:16,560 --> 01:13:19,880
Speaker 5: handles on everything Blue Sky, Twitter, Instagram, all the things

1538
01:13:19,920 --> 01:13:20,920
Speaker 5: is at vicmsong.

1539
01:13:21,720 --> 01:13:24,599
Speaker 2: You can find me inside your computer. That's where I live.

1540
01:13:24,640 --> 01:13:27,000
Speaker 2: I'm d Zetron. You've been listening to Better Offline. Thank you,

1541
01:13:27,040 --> 01:13:29,880
Speaker 2: of course to our wonderful producer Daniel Goodman. You've been

1542
01:13:30,040 --> 01:13:32,880
Speaker 2: hearing us recorded out of the beautiful New York City, Nevada.

1543
01:13:33,400 --> 01:13:35,640
Speaker 2: And yeah, you keep listening to my goddamn show. We'll

1544
01:13:35,680 --> 01:13:46,360
Speaker 2: have a monologue this week as well. Be sou thank

1545
01:13:46,360 --> 01:13:49,080
Speaker 2: you for listening to Better Offline. The editor and composer

1546
01:13:49,120 --> 01:13:51,960
Speaker 2: of the Better Offline theme song is Metasowski. You can

1547
01:13:52,040 --> 01:13:54,320
Speaker 2: check out more of his music and audio projects at

1548
01:13:54,400 --> 01:13:56,080
Speaker 2: Matasowski dot com.

1549
01:13:56,240 --> 01:13:56,640
Speaker 4: M A. T.

1550
01:13:56,800 --> 01:14:02,360
Speaker 2: T OsO w Ski. You can email me at easy

1551
01:14:02,400 --> 01:14:05,040
Speaker 2: at Better Offline dot com or visit Better Offline dot

1552
01:14:05,080 --> 01:14:07,720
Speaker 2: com to find more podcast links and of course my newsletter.

1553
01:14:08,080 --> 01:14:10,599
Speaker 2: I also really recommend you go to chat dot Where's

1554
01:14:10,600 --> 01:14:13,080
Speaker 2: youread dot at to visit the discord, and go to

1555
01:14:13,160 --> 01:14:16,519
Speaker 2: our slash Better Offline to check out I'll Reddit. Thank

1556
01:14:16,560 --> 01:14:17,679
Speaker 2: you so much for listening.

1557
01:14:18,520 --> 01:14:21,000
Speaker 4: Better Offline is a production of cool Zone Media.

1558
01:14:21,120 --> 01:14:23,960
Speaker 2: For more from cool Zone Media, visit our website cool

1559
01:14:24,040 --> 01:14:25,960
Speaker 2: Zonemedia dot com, or check us

1560
01:14:25,960 --> 01:14:28,960
Speaker 6: Out on the iHeartRadio app, Apple Podcasts, or wherever you

1561
01:14:29,000 --> 01:14:30,120
Speaker 6: get your podcasts.