1
00:00:01,760 --> 00:00:02,880
Speaker 1: All the media.

2
00:00:04,080 --> 00:00:07,080
Speaker 2: Hi everyone, it's James coming at you with pretty nasty

3
00:00:07,120 --> 00:00:10,520
Speaker 2: cold here. I wanted to share with you that Worldfast

4
00:00:10,560 --> 00:00:13,399
Speaker 2: has swept through Los Angeles in the last couple of days.

5
00:00:13,640 --> 00:00:17,480
Speaker 2: While I'm recording this. Thousands of people have been displaced.

6
00:00:17,960 --> 00:00:20,320
Speaker 2: Five people have died that we know of so far,

7
00:00:21,600 --> 00:00:24,640
Speaker 2: thousands of structures have been burned, and many many people

8
00:00:24,640 --> 00:00:27,240
Speaker 2: in a will be finding themselves out of their homes

9
00:00:27,240 --> 00:00:30,440
Speaker 2: with nowhere to go, with very few resources. If you'd

10
00:00:30,480 --> 00:00:32,640
Speaker 2: like to help, We've come up with some mutual aid

11
00:00:32,640 --> 00:00:35,479
Speaker 2: groups who you can donate to, and we'll be interviewing

12
00:00:35,520 --> 00:00:38,360
Speaker 2: one of them on this show next week. So if

13
00:00:38,360 --> 00:00:41,199
Speaker 2: you'd like to help, the three places where we suggest

14
00:00:41,200 --> 00:00:45,080
Speaker 2: you would donate some cash are the Sidewalk Project. That's

15
00:00:45,400 --> 00:00:52,559
Speaker 2: the Sidewalkproject, dot org, Ktown for All that's letter k

16
00:00:53,080 --> 00:00:56,520
Speaker 2: t own, f O R A l L dot RG,

17
00:00:57,360 --> 00:01:01,560
Speaker 2: and ETNA Street Solidarity you can find on Venmo or

18
00:01:01,880 --> 00:01:05,120
Speaker 2: I think on Instagram as well. That's A E T

19
00:01:05,360 --> 00:01:09,280
Speaker 2: N A S T R E E T S O.

20
00:01:09,400 --> 00:01:12,280
Speaker 1: L I D A I T Y.

21
00:01:12,800 --> 00:01:22,000
Speaker 2: All right, I'm gonna go rest my voice.

22
00:01:19,000 --> 00:01:22,559
Speaker 1: Oh man, welcome to it could happen here a podcast.

23
00:01:22,640 --> 00:01:27,120
Speaker 1: That's happening here If here is your ears, if you're

24
00:01:27,400 --> 00:01:30,560
Speaker 1: deaf and reading this, then it's happening to your eyes.

25
00:01:30,920 --> 00:01:33,240
Speaker 1: Either way, it's happening here.

26
00:01:33,800 --> 00:01:35,319
Speaker 3: Here also being Las Vegas.

27
00:01:35,520 --> 00:01:39,240
Speaker 1: Well, yes, also Las Vegas. That's Nevada, not the other one. Nevada.

28
00:01:39,240 --> 00:01:45,800
Speaker 1: I A yeah, uh huh uh. Podcast number three. How

29
00:01:45,880 --> 00:01:49,360
Speaker 1: the time does fly? Sure does. By the time you

30
00:01:49,400 --> 00:01:51,720
Speaker 1: listen to this, Garrison and I will have just had

31
00:01:51,960 --> 00:01:54,720
Speaker 1: the best meal that we're going to have. Oh god, yeah,

32
00:01:54,760 --> 00:01:57,560
Speaker 1: it's tomorrow for us still, but we're still we're very

33
00:01:57,600 --> 00:02:01,080
Speaker 1: excited about Morimoto, which is a fantastic Every year we

34
00:02:01,240 --> 00:02:05,080
Speaker 1: have a very special dinner just them and me and

35
00:02:05,200 --> 00:02:08,360
Speaker 1: a couple of friends who will remain anonymous because people

36
00:02:08,400 --> 00:02:09,400
Speaker 1: get weird on the internet.

37
00:02:09,440 --> 00:02:12,959
Speaker 3: Sometimes it is literally the highlight of my year. Sometimes

38
00:02:13,520 --> 00:02:16,000
Speaker 3: it does keep me going. Actually really gives me a

39
00:02:16,000 --> 00:02:17,720
Speaker 3: lot of power. Some of the best tacos I've ever

40
00:02:17,800 --> 00:02:19,280
Speaker 3: had in my life, so good.

41
00:02:19,440 --> 00:02:20,760
Speaker 4: Uh huh.

42
00:02:20,800 --> 00:02:23,720
Speaker 1: Anyway, ah, we're just thinking about delicious food. Let's talk

43
00:02:23,720 --> 00:02:25,560
Speaker 1: about the dead eyed ghoul we met. Oh wait, no,

44
00:02:25,560 --> 00:02:28,440
Speaker 1: we're doing yet. We met a dead eyed ghul that

45
00:02:28,480 --> 00:02:32,520
Speaker 1: I'm gonna spoil now, real monster like real, real, real

46
00:02:32,760 --> 00:02:36,400
Speaker 1: evil vibes, like if this guy as soon as I

47
00:02:36,440 --> 00:02:38,359
Speaker 1: met him, shook his hand, like, oh if you get

48
00:02:38,360 --> 00:02:41,119
Speaker 1: if this guy gets power, you're going to be responsible

49
00:02:41,160 --> 00:02:42,639
Speaker 1: for a lot of death and suffering.

50
00:02:42,800 --> 00:02:44,520
Speaker 3: I mean speaking of he will.

51
00:02:44,560 --> 00:02:48,560
Speaker 1: He's just not that talented. He wishes, but you never

52
00:02:48,560 --> 00:02:49,760
Speaker 1: know where these guys are gonna end up.

53
00:02:49,760 --> 00:02:56,239
Speaker 3: Speaking of sad evil Uh, Twitter X everything appy, that's

54
00:02:56,280 --> 00:02:58,600
Speaker 3: what people are calling it. They gave a keynote which

55
00:02:58,680 --> 00:02:59,519
Speaker 3: was very sad.

56
00:03:00,120 --> 00:03:04,080
Speaker 1: The CEO Lindah Linda really jakerino about Twitter for a

57
00:03:04,120 --> 00:03:05,520
Speaker 1: while oh so bad.

58
00:03:06,200 --> 00:03:11,639
Speaker 3: So they started by talking about how Facebook meta has

59
00:03:11,680 --> 00:03:15,520
Speaker 3: has copied Twitter's like fact checking policy of actually not

60
00:03:15,560 --> 00:03:19,120
Speaker 3: having real fact checks. Yes, now maybe has actually kind

61
00:03:19,120 --> 00:03:22,080
Speaker 3: of failed as an industry, but for you know, our

62
00:03:22,120 --> 00:03:25,919
Speaker 3: problems perhaps with fact checking very different from these people's problems,

63
00:03:26,360 --> 00:03:29,520
Speaker 3: and the fact now that that Facebook is walking away

64
00:03:29,880 --> 00:03:32,640
Speaker 3: from actual, like genuine like fact checks against like just

65
00:03:32,680 --> 00:03:36,880
Speaker 3: disinformation misinformation and parting ways with like using like legacy

66
00:03:36,920 --> 00:03:39,320
Speaker 3: media a lets to verify information because those media aulets

67
00:03:39,360 --> 00:03:42,840
Speaker 3: are too political quote unquote, and instead is copying the

68
00:03:43,040 --> 00:03:46,720
Speaker 3: the current X model of free speech and specifically saying

69
00:03:46,880 --> 00:03:50,440
Speaker 3: like there's been way too much censorship on gender issues.

70
00:03:50,800 --> 00:03:53,520
Speaker 1: Now you can comment that women are a piece of property.

71
00:03:53,960 --> 00:03:55,640
Speaker 3: Well, I mean I think specifically this is this is

72
00:03:55,680 --> 00:03:57,440
Speaker 3: like trans like no, no, no stuff too.

73
00:03:57,680 --> 00:04:00,160
Speaker 1: One of the things that is specific exemption now is

74
00:04:00,160 --> 00:04:02,120
Speaker 1: that you can now refer to women as if they

75
00:04:02,160 --> 00:04:04,560
Speaker 1: are property on Facebook.

76
00:04:05,080 --> 00:04:07,360
Speaker 3: This is the future of communication, right.

77
00:04:07,320 --> 00:04:10,440
Speaker 1: Yeah, thank god, Linda is really blazing a trail for

78
00:04:10,520 --> 00:04:11,320
Speaker 1: women everywhere.

79
00:04:11,480 --> 00:04:15,680
Speaker 3: Linda was very excited about that, and they u Yakarino

80
00:04:15,880 --> 00:04:18,719
Speaker 3: about that for like a good a good ten minutes

81
00:04:19,360 --> 00:04:21,160
Speaker 3: about how you know this is this is where we're

82
00:04:21,200 --> 00:04:24,120
Speaker 3: really entering a new era of free speech and social media.

83
00:04:24,720 --> 00:04:28,159
Speaker 3: And then she got asked a question about how much

84
00:04:28,440 --> 00:04:31,960
Speaker 3: ex Twitter, the everything app will will take a part

85
00:04:32,120 --> 00:04:37,120
Speaker 3: in Elon Musk's plans for the Department of Government Efficiency DOGE,

86
00:04:37,480 --> 00:04:40,960
Speaker 3: And this got the the first applause of the panel.

87
00:04:41,040 --> 00:04:43,880
Speaker 3: It applause only happens two times during the DOGE section.

88
00:04:44,120 --> 00:04:47,480
Speaker 3: Was the first like you know, room, room starts clapping moment,

89
00:04:47,600 --> 00:04:48,920
Speaker 3: everyone goes crazy.

90
00:04:49,360 --> 00:04:51,040
Speaker 1: How many minutes in was that?

91
00:04:51,440 --> 00:04:53,600
Speaker 3: Oh maybe it was like maybe like maybe like twelve

92
00:04:53,640 --> 00:04:54,240
Speaker 3: thirteen minutes?

93
00:04:54,320 --> 00:04:54,599
Speaker 5: People?

94
00:04:54,640 --> 00:04:56,839
Speaker 1: Really, yeah, I had to had to be intentional here.

95
00:04:56,880 --> 00:04:59,120
Speaker 1: This is not like they were just overdue for class.

96
00:04:59,200 --> 00:05:02,039
Speaker 3: No no no talked about Vivek talked about, you know,

97
00:05:02,080 --> 00:05:05,120
Speaker 3: elon turning to Twitter, X the everything app for like

98
00:05:05,200 --> 00:05:09,000
Speaker 3: suggestions on which government agencies to get rid of.

99
00:05:11,080 --> 00:05:12,720
Speaker 1: I hope we get rid of the A t F.

100
00:05:13,440 --> 00:05:15,000
Speaker 3: So so that that was.

101
00:05:15,040 --> 00:05:18,039
Speaker 1: Machine guns mandatory? Why not at this point, right, it

102
00:05:18,080 --> 00:05:21,359
Speaker 1: can only help, It can only help. Look, if we

103
00:05:21,440 --> 00:05:24,320
Speaker 1: learned anything from a thing, I'm not going to specify

104
00:05:24,320 --> 00:05:28,760
Speaker 1: that happened late last year. More suppressors is always handy.

105
00:05:33,160 --> 00:05:35,039
Speaker 3: The second thing that got applause was what they talked

106
00:05:35,080 --> 00:05:39,360
Speaker 3: about next, was about you know, everyone's everyone's turning to

107
00:05:39,760 --> 00:05:43,640
Speaker 3: x Twitter everything everything app for information now and and

108
00:05:43,640 --> 00:05:46,240
Speaker 3: and Twitter x everything app played a crucial part in

109
00:05:46,279 --> 00:05:50,440
Speaker 3: bringing to light the Muslim rape gang story in the

110
00:05:50,560 --> 00:05:53,960
Speaker 3: UK and how that was so important for saving children

111
00:05:54,279 --> 00:05:56,920
Speaker 3: and we have to we have to post more, not less.

112
00:05:57,520 --> 00:05:59,600
Speaker 3: And like this was the other thing that got massive

113
00:05:59,600 --> 00:06:03,240
Speaker 3: applau was talking about the rape gangs.

114
00:06:03,400 --> 00:06:06,000
Speaker 1: People love rape gangs, people love rape gangs. That that

115
00:06:06,080 --> 00:06:08,280
Speaker 1: was a pretty good Star Trek episode.

116
00:06:08,360 --> 00:06:10,520
Speaker 3: That was Tasrir's Planet with the rape gangs, one of

117
00:06:10,720 --> 00:06:12,400
Speaker 3: one of the more black pilling things.

118
00:06:13,120 --> 00:06:14,800
Speaker 1: It wasn't a very good Star Trek episode.

119
00:06:14,920 --> 00:06:17,440
Speaker 3: It's also not a good Track episode. I was referring

120
00:06:17,440 --> 00:06:20,640
Speaker 3: to the panel, not the Trek episode. But that was

121
00:06:20,640 --> 00:06:22,440
Speaker 3: the other thing that got massive applause is it's like

122
00:06:22,520 --> 00:06:25,839
Speaker 3: save the children type rhetoric and you know, saying, you know,

123
00:06:25,880 --> 00:06:28,400
Speaker 3: like as a mother, it's it's so important that the

124
00:06:28,520 --> 00:06:32,360
Speaker 3: more people post about this problem. That was the two

125
00:06:32,400 --> 00:06:35,359
Speaker 3: big applause moments. But I think in general, this this

126
00:06:35,400 --> 00:06:38,760
Speaker 3: whole panel was trying to like, you know, demonstrate how

127
00:06:38,800 --> 00:06:42,320
Speaker 3: symbiotic a new Trump presidency and Elon Musk's.

128
00:06:42,040 --> 00:06:45,800
Speaker 1: Twitter this is a line, this is a tap from

129
00:06:45,800 --> 00:06:46,919
Speaker 1: the Trump presidency.

130
00:06:47,320 --> 00:06:49,159
Speaker 3: This is how you talk to the new government. Like,

131
00:06:49,320 --> 00:06:51,680
Speaker 3: this is how you talk to all of these new people,

132
00:06:51,760 --> 00:06:54,080
Speaker 3: all these new cabinet members. They're all on Twitter. They're

133
00:06:54,120 --> 00:06:56,080
Speaker 3: all talking on Twitter. This is this is how you

134
00:06:56,120 --> 00:06:58,440
Speaker 3: stay connected to the new government.

135
00:06:58,600 --> 00:07:01,200
Speaker 1: It's interesting. I want thing I'm curious about, so that

136
00:07:01,240 --> 00:07:03,320
Speaker 1: this is the thing that happened the last set of

137
00:07:03,440 --> 00:07:06,120
Speaker 1: Nazis that gained power in a country in a big way,

138
00:07:06,600 --> 00:07:10,160
Speaker 1: the German ones. There was this common attitude of like

139
00:07:10,200 --> 00:07:13,320
Speaker 1: if only Hitler knew because Nazi policies didn't help the

140
00:07:13,320 --> 00:07:15,120
Speaker 1: people that were supposed to help. They hurt a lot

141
00:07:15,120 --> 00:07:17,560
Speaker 1: of people, like they were just bad at everything, like

142
00:07:17,640 --> 00:07:20,160
Speaker 1: fascists tend to be. And there was this attitude that like, well,

143
00:07:20,240 --> 00:07:24,040
Speaker 1: Hitler can't know, like the fact that, like we the

144
00:07:24,040 --> 00:07:26,320
Speaker 1: country's been handed over the gangsters who were continuing to

145
00:07:26,400 --> 00:07:28,440
Speaker 1: hurt the people Hitler promised to help. He must not

146
00:07:28,520 --> 00:07:30,680
Speaker 1: be aware, like if he knew, he would fix it,

147
00:07:30,800 --> 00:07:33,360
Speaker 1: only he knew. So I'm wondering how that's going to

148
00:07:33,480 --> 00:07:36,760
Speaker 1: play in here. As Trump's policies continue to hurt the

149
00:07:36,800 --> 00:07:39,160
Speaker 1: people who a lot of the people who voted from,

150
00:07:39,160 --> 00:07:41,040
Speaker 1: not the rich people who voted for him, but the

151
00:07:41,080 --> 00:07:44,080
Speaker 1: people who like flipped between him and Biden or whatever, Like,

152
00:07:44,680 --> 00:07:46,840
Speaker 1: those folks are going to get fucked like the rest

153
00:07:46,920 --> 00:07:50,440
Speaker 1: of us. And I kind of wonder if they're going

154
00:07:50,520 --> 00:07:53,600
Speaker 1: to if there's going to be what win the blowback

155
00:07:54,040 --> 00:07:57,120
Speaker 1: against X the everything app will happen, right, Like yeah,

156
00:07:57,240 --> 00:08:01,160
Speaker 1: as people are like either I'm being or him being

157
00:08:01,320 --> 00:08:05,040
Speaker 1: called like a retard by Elon Musk for complaining that,

158
00:08:05,240 --> 00:08:09,680
Speaker 1: like Elon Musk tweets it and randomly to people when

159
00:08:09,720 --> 00:08:13,200
Speaker 1: they make very valid critiques of the ship that he's doing.

160
00:08:13,800 --> 00:08:16,560
Speaker 1: Like that's literally what he's calling. He's saying it like

161
00:08:16,600 --> 00:08:18,840
Speaker 1: every day, like constantly. I'm not I'm not using it

162
00:08:18,880 --> 00:08:21,280
Speaker 1: as a slur. That's just the term he's using. If

163
00:08:21,280 --> 00:08:24,680
Speaker 1: they comment that like their fucking medicaid got cut because

164
00:08:24,680 --> 00:08:27,760
Speaker 1: Trump put doctor Oz in charge of it, and Elon

165
00:08:27,840 --> 00:08:30,840
Speaker 1: Musk calls them like, you know, a slur, what does

166
00:08:30,880 --> 00:08:32,000
Speaker 1: that do to you?

167
00:08:32,160 --> 00:08:33,120
Speaker 5: Like they like, I don't even know.

168
00:08:33,160 --> 00:08:35,160
Speaker 1: I don't even have any more intelligent than like, yeah,

169
00:08:35,200 --> 00:08:37,120
Speaker 1: I wonder what that does to Twitter's bottom line?

170
00:08:37,360 --> 00:08:39,960
Speaker 3: Ye, I mean yeah, I'm not sure if they care anymore.

171
00:08:39,960 --> 00:08:42,840
Speaker 3: I mean. Something else Linda talked about is how Twitter's

172
00:08:42,840 --> 00:08:46,520
Speaker 3: the only place for independent news to spread. And as

173
00:08:47,000 --> 00:08:48,880
Speaker 3: both of us have, you know, worked in the independent

174
00:08:48,960 --> 00:08:52,760
Speaker 3: journalism minds nothing nothing spreads on Twitter anymore.

175
00:08:52,800 --> 00:08:54,839
Speaker 1: No, as long if it's news, it doesn't. The only

176
00:08:54,840 --> 00:08:57,440
Speaker 1: thing that spreads is Yeah, like the shit that makes

177
00:08:57,440 --> 00:09:01,080
Speaker 1: people very angry but keeps them on the site, like articles, videos,

178
00:09:01,080 --> 00:09:02,520
Speaker 1: If it takes you off site, it doesn't.

179
00:09:02,520 --> 00:09:04,600
Speaker 3: See. Yeah, the things that go viral and get spread

180
00:09:04,679 --> 00:09:08,520
Speaker 3: is like encouraging racial bias, yes, s pogrooms essentially.

181
00:09:08,240 --> 00:09:10,520
Speaker 1: Yeah, which is what happened last year in the UK,

182
00:09:10,600 --> 00:09:12,000
Speaker 1: and they're sure trying to do it again.

183
00:09:12,080 --> 00:09:13,920
Speaker 3: I mean, I I think some of someone's some of

184
00:09:13,960 --> 00:09:15,400
Speaker 3: what she's referencing is, you know, there's a lot of

185
00:09:15,440 --> 00:09:19,160
Speaker 3: like throttling attentionally of you know people on maybe our proclivities,

186
00:09:19,400 --> 00:09:21,840
Speaker 3: and there is a degree of boosting for more you know,

187
00:09:21,920 --> 00:09:25,199
Speaker 3: centrist or right wing journalists. And maybe that's that's some

188
00:09:25,280 --> 00:09:27,439
Speaker 3: of some of what they could be kind of more

189
00:09:27,559 --> 00:09:30,320
Speaker 3: more referring to there, But you know it was it

190
00:09:30,360 --> 00:09:33,560
Speaker 3: was a short keynote, only thirty minutes. Just the two

191
00:09:33,600 --> 00:09:36,000
Speaker 3: things that got applause are doge.

192
00:09:35,800 --> 00:09:37,839
Speaker 1: Well Linda doesn't know that many words, so they really

193
00:09:37,880 --> 00:09:39,240
Speaker 1: need to keep it under thirty minutes.

194
00:09:39,840 --> 00:09:42,800
Speaker 3: And literally Muslim rape gangs. Is you know this this

195
00:09:42,880 --> 00:09:46,200
Speaker 3: type of like like very very gross racial fear mongering,

196
00:09:46,559 --> 00:09:48,520
Speaker 3: and those are the things that like lit up the room.

197
00:09:49,000 --> 00:09:51,000
Speaker 1: You know, we all want there to be an after

198
00:09:51,320 --> 00:09:55,680
Speaker 1: where there's even the minimal degree of accountability that happened

199
00:09:55,720 --> 00:09:58,920
Speaker 1: after the Nazis. But like what I try to in

200
00:09:58,920 --> 00:10:02,160
Speaker 1: my darker moments is like, well that's another person who

201
00:10:02,200 --> 00:10:04,880
Speaker 1: like really made the argument of like what needs to

202
00:10:04,960 --> 00:10:10,440
Speaker 1: happen when this ends? Because it's just I want to

203
00:10:10,600 --> 00:10:14,160
Speaker 1: hurt people. My business is enabling harm. I want to

204
00:10:14,200 --> 00:10:19,480
Speaker 1: get mobs in the street, beating migrants. Like that's Linda's business.

205
00:10:19,760 --> 00:10:23,560
Speaker 1: That's the business she has willfully attached herself to. And

206
00:10:23,600 --> 00:10:27,400
Speaker 1: we should all see that. It's very important to not

207
00:10:27,640 --> 00:10:30,600
Speaker 1: stop talking about it like what it is. These people

208
00:10:30,679 --> 00:10:34,480
Speaker 1: are trying to cause racial violence, and they are trying

209
00:10:34,520 --> 00:10:38,040
Speaker 1: to cause gendered violence, and they are trying to cause

210
00:10:38,720 --> 00:10:43,040
Speaker 1: harm at scale to communities of people that they see

211
00:10:43,280 --> 00:10:49,200
Speaker 1: financial profit in damaging, well in other uplifting c es ues,

212
00:10:49,280 --> 00:10:52,319
Speaker 1: cool stuff. I love the Consumer Electronics Show.

213
00:10:54,320 --> 00:10:57,040
Speaker 3: Actually, I think it might be time for an ad break.

214
00:10:56,960 --> 00:11:02,320
Speaker 1: Speaking of damaging communities of people, right, there's a chance, yeah, ads,

215
00:11:02,360 --> 00:11:16,320
Speaker 1: Oh well we're back. Boy, I'm so glad that those

216
00:11:16,360 --> 00:11:19,400
Speaker 1: ads told me that Fragaccio Blow is touring with Bono.

217
00:11:19,880 --> 00:11:23,040
Speaker 1: I never thought they'd do it, but boy howdy, and

218
00:11:23,080 --> 00:11:26,800
Speaker 1: they're singing each other's song, so you know, that's really exciting.

219
00:11:26,840 --> 00:11:28,680
Speaker 1: It's like when Barbara did Selene.

220
00:11:29,080 --> 00:11:31,079
Speaker 3: I don't know who Barbara or Selene is, but that's,

221
00:11:31,080 --> 00:11:34,240
Speaker 3: oh my god, that's cool. Robert Luckily, I do know

222
00:11:34,280 --> 00:11:37,240
Speaker 3: what SKA is. I consider myself a day of culture,

223
00:11:37,720 --> 00:11:42,040
Speaker 3: and for tonight, me and Robert attended this kind of

224
00:11:42,120 --> 00:11:46,800
Speaker 3: like side event at CEES called Showstoppers. And as you

225
00:11:46,840 --> 00:11:50,160
Speaker 3: walk around the CEES floor, there's a lot of frankly garbage.

226
00:11:50,160 --> 00:11:53,360
Speaker 3: There's a lot of just like mostly garbage stuff that

227
00:11:53,360 --> 00:11:54,520
Speaker 3: you or stuff.

228
00:11:54,280 --> 00:11:56,520
Speaker 1: That like you're just not interested in because you're literally

229
00:11:56,559 --> 00:11:59,719
Speaker 1: buying like screens from a manufacturer in China. Like it's

230
00:11:59,720 --> 00:12:01,480
Speaker 1: like that which is not the business you're into, because

231
00:12:01,640 --> 00:12:03,360
Speaker 1: some of this stuff has meant for companies so.

232
00:12:03,400 --> 00:12:06,600
Speaker 3: Much floor space, Like there's like I we walked.

233
00:12:06,600 --> 00:12:09,000
Speaker 1: Twenty Town that I spent the first seven years of

234
00:12:09,000 --> 00:12:11,640
Speaker 1: my life in is smaller than one of the room CEU.

235
00:12:12,840 --> 00:12:15,839
Speaker 3: It's across like three hotels and a massive convention center.

236
00:12:16,280 --> 00:12:18,520
Speaker 1: Ninety thousand people come into town for this thing.

237
00:12:18,559 --> 00:12:21,160
Speaker 3: It could be hard to like see everything you want to. Now,

238
00:12:21,200 --> 00:12:23,880
Speaker 3: what's colo about Showstoppers, this is the side event at

239
00:12:23,920 --> 00:12:27,560
Speaker 3: the Bellaggio is that basically it's a room full of

240
00:12:27,679 --> 00:12:29,599
Speaker 3: kind of all the coolest stuff, a whole bunch of

241
00:12:29,600 --> 00:12:33,120
Speaker 3: stuff that has one SEES innovation awards all packed into

242
00:12:33,160 --> 00:12:37,319
Speaker 3: one room with food and alcohol. So oh boy, did I.

243
00:12:37,400 --> 00:12:38,960
Speaker 1: Order free food and free alcohol?

244
00:12:39,080 --> 00:12:41,160
Speaker 3: So many drinks that I then just left on tables,

245
00:12:43,040 --> 00:12:43,280
Speaker 3: and it.

246
00:12:43,400 --> 00:12:45,400
Speaker 1: Always pretty good food, pretty good food.

247
00:12:46,000 --> 00:12:48,679
Speaker 3: So we walked around show stoppers and there was a

248
00:12:48,760 --> 00:12:52,000
Speaker 3: number of pretty pretty cool stuff that we saw. Yeah,

249
00:12:52,040 --> 00:12:54,720
Speaker 3: but I think I think it's maybe time to talk

250
00:12:54,760 --> 00:12:57,240
Speaker 3: about the saddest, the saddest man.

251
00:12:57,280 --> 00:13:00,560
Speaker 1: The villain, the villain, the villain of the episode and

252
00:13:00,600 --> 00:13:03,800
Speaker 1: of our this year's cees. I have trouble. Can you

253
00:13:03,800 --> 00:13:05,400
Speaker 1: bring up their name? Because I'm gonna want to get

254
00:13:05,440 --> 00:13:09,160
Speaker 1: this right, so we could be dangerous. But we had

255
00:13:09,360 --> 00:13:11,000
Speaker 1: neither of us had eaten and I had had like

256
00:13:11,000 --> 00:13:14,240
Speaker 1: a hot dog eight hours ago and walked literally nineteen

257
00:13:14,320 --> 00:13:17,160
Speaker 1: thousand steps and also done forty minutes of push ups

258
00:13:17,200 --> 00:13:19,640
Speaker 1: in between, so I was starving. So we we like

259
00:13:19,960 --> 00:13:22,240
Speaker 1: shovel food into our faces and we turn the first

260
00:13:22,240 --> 00:13:26,599
Speaker 1: booth we see is called open Droid, Open Droid or

261
00:13:26,640 --> 00:13:29,440
Speaker 1: open droids droids. Yes, it did. There is an s

262
00:13:29,720 --> 00:13:32,959
Speaker 1: Open Droids and it's like kind of Star Wars. He

263
00:13:33,040 --> 00:13:36,160
Speaker 1: font it is and I did ask them if you

264
00:13:36,200 --> 00:13:39,320
Speaker 1: know they had any issues with Lucasfilm. Apparently not yet

265
00:13:39,800 --> 00:13:42,280
Speaker 1: sue them. Lucasfilm, by the way, sue these kids.

266
00:13:42,360 --> 00:13:44,600
Speaker 3: I know there's people who work for Lucasfilm who listened to.

267
00:13:44,640 --> 00:13:49,600
Speaker 1: This crush them, burn them like Los Angeles is burning

268
00:13:49,640 --> 00:13:50,600
Speaker 1: down as we see.

269
00:13:50,720 --> 00:13:52,280
Speaker 3: They had a giant sign that said.

270
00:13:52,200 --> 00:13:53,160
Speaker 5: R two D three.

271
00:13:53,440 --> 00:13:55,240
Speaker 1: Yeah, that's the name of the robot that they're selling.

272
00:13:55,280 --> 00:13:57,280
Speaker 1: And the robot, the robot that they're selling is like

273
00:13:57,360 --> 00:14:03,560
Speaker 1: a an AI enabled hold helping slash like retail, you know,

274
00:14:04,000 --> 00:14:07,199
Speaker 1: like you know robot where it basically is like a

275
00:14:07,280 --> 00:14:12,480
Speaker 1: human torso with articulated arms and pincher hands on. And

276
00:14:12,520 --> 00:14:15,959
Speaker 1: then the base is like a little tank. Basically it's

277
00:14:16,000 --> 00:14:18,680
Speaker 1: got like treads or wheels and it rolls its wheels. Yeah,

278
00:14:18,679 --> 00:14:21,880
Speaker 1: and then the torso there's like a tall maybe six

279
00:14:21,920 --> 00:14:25,520
Speaker 1: foot tall like pillar built into this like rolling base

280
00:14:26,280 --> 00:14:28,880
Speaker 1: that the torso slides up and down on. And this

281
00:14:29,080 --> 00:14:32,240
Speaker 1: was their way of not making like what Musk is

282
00:14:32,240 --> 00:14:34,360
Speaker 1: trying to do, right, a humanoid robot where you have

283
00:14:34,440 --> 00:14:36,880
Speaker 1: to figure out like knees and balance and stuff. It's

284
00:14:36,880 --> 00:14:39,840
Speaker 1: like no or like Boston Dynamics wheels, right, wheels are cheap.

285
00:14:39,880 --> 00:14:42,480
Speaker 1: It a roll It works in most situations, you know.

286
00:14:42,560 --> 00:14:45,080
Speaker 1: And then but you still have the ability for it

287
00:14:45,120 --> 00:14:48,240
Speaker 1: to articulate and go up higher or go down lower.

288
00:14:48,600 --> 00:14:51,240
Speaker 1: Like something that can crouch, but it's much simpler, you

289
00:14:51,240 --> 00:14:53,760
Speaker 1: don't have to deal with nearly as much. And so

290
00:14:53,800 --> 00:14:55,920
Speaker 1: I saw that, I'm like, oh, well, that's at least

291
00:14:55,920 --> 00:14:57,880
Speaker 1: somebody who's thinking about, like how do we make something

292
00:14:57,920 --> 00:15:00,280
Speaker 1: like this like more affordable and less complicated, les to

293
00:15:00,360 --> 00:15:03,360
Speaker 1: fuck up. And so I start talking with one of

294
00:15:03,400 --> 00:15:05,840
Speaker 1: the co founders of the company, who is an Indian

295
00:15:05,880 --> 00:15:09,600
Speaker 1: guy in his forties something around that he had like

296
00:15:09,760 --> 00:15:12,120
Speaker 1: gray hair, he'd clearly he said he'd spent twenty years

297
00:15:12,160 --> 00:15:15,760
Speaker 1: in robotics. Very nice guy, you know. I brought up

298
00:15:15,760 --> 00:15:17,880
Speaker 1: that I thought the design was interesting, and he was

299
00:15:18,000 --> 00:15:20,120
Speaker 1: very much specifying, like, here's the things we didn't do

300
00:15:20,160 --> 00:15:24,840
Speaker 1: because they were too difficult, too inefficient. You know, this

301
00:15:24,880 --> 00:15:26,680
Speaker 1: is what we're thinking of. This is a machine that

302
00:15:26,720 --> 00:15:29,160
Speaker 1: can fold laundry. This is a machine that can do dishes.

303
00:15:29,200 --> 00:15:31,440
Speaker 1: This is a machine. And he was very much specifying

304
00:15:32,160 --> 00:15:34,720
Speaker 1: and the way he phrases like, these are undesirable tasks

305
00:15:34,760 --> 00:15:37,360
Speaker 1: people don't want to do, and this is a robot

306
00:15:37,400 --> 00:15:40,760
Speaker 1: that can handle those for like small businesses or for households.

307
00:15:40,760 --> 00:15:43,240
Speaker 1: And we do see this as eventually like a you know,

308
00:15:43,800 --> 00:15:46,200
Speaker 1: something like this we want to have in households, but

309
00:15:46,240 --> 00:15:49,920
Speaker 1: he was more focused on small businesses, and he was

310
00:15:49,920 --> 00:15:51,960
Speaker 1: again very focused on this is a thing that will

311
00:15:52,000 --> 00:15:58,240
Speaker 1: do undesirable tasks for people, right, And as I started

312
00:15:58,240 --> 00:16:01,040
Speaker 1: asking more questions, at a certain point, I got foisted

313
00:16:01,080 --> 00:16:04,320
Speaker 1: off to the co founder of the company.

314
00:16:04,480 --> 00:16:05,720
Speaker 3: Is it the co founder or is it just like

315
00:16:06,000 --> 00:16:06,960
Speaker 3: another one of their rests.

316
00:16:07,000 --> 00:16:09,280
Speaker 1: You know, I'm assuming co founder because I think it's

317
00:16:09,360 --> 00:16:11,440
Speaker 1: just a couple of guys, but maybe I'm not about Sorry,

318
00:16:11,520 --> 00:16:13,800
Speaker 1: I got foisted over to the other of the two guys.

319
00:16:13,840 --> 00:16:16,960
Speaker 1: There were two guys there, right, I'm not sure because

320
00:16:17,000 --> 00:16:19,440
Speaker 1: they don't have listed any where what their role in

321
00:16:19,480 --> 00:16:22,560
Speaker 1: the company is. I got a co founder's vibe from them.

322
00:16:22,800 --> 00:16:25,360
Speaker 1: That's how it seemed to be to me, at least

323
00:16:25,360 --> 00:16:27,160
Speaker 1: in terms of like the way these two were talking.

324
00:16:27,600 --> 00:16:31,320
Speaker 1: But I don't know the scope of the open Droid's company.

325
00:16:31,360 --> 00:16:34,160
Speaker 1: Maybe there's a lot more there, but these were the

326
00:16:34,200 --> 00:16:35,920
Speaker 1: two guys who were they're talking to us. So one

327
00:16:35,960 --> 00:16:39,120
Speaker 1: of them is this very wonky engineer who's been at

328
00:16:39,120 --> 00:16:41,640
Speaker 1: this a long time and was really focused on the

329
00:16:41,720 --> 00:16:43,920
Speaker 1: nuts and bolts details and wanted to build a robot

330
00:16:43,920 --> 00:16:46,720
Speaker 1: that could handle unpleasant tasks for human beings, right, the

331
00:16:46,720 --> 00:16:48,560
Speaker 1: same thing we've all been wanting to see. So at

332
00:16:48,560 --> 00:16:51,040
Speaker 1: this point I'm like, this could work. Maybe this is

333
00:16:51,080 --> 00:16:57,440
Speaker 1: a viable product. Right. The second guy, Jack j Jessenowski,

334
00:16:58,000 --> 00:17:02,080
Speaker 1: So he is wearing what Gryson described as a Jordan

335
00:17:02,200 --> 00:17:08,640
Speaker 1: Peterson suit because it is half purple. Is a face

336
00:17:08,720 --> 00:17:12,120
Speaker 1: suit split down the motherfucking middle.

337
00:17:12,200 --> 00:17:16,920
Speaker 3: With like like new age hippie like necklaces, five necklacesla

338
00:17:17,680 --> 00:17:21,800
Speaker 3: five necklaces. He had pants with like like embroidered flowers

339
00:17:21,840 --> 00:17:22,480
Speaker 3: on those.

340
00:17:22,320 --> 00:17:24,560
Speaker 1: And like a nose bridge, like it looked like one

341
00:17:24,560 --> 00:17:25,600
Speaker 1: of those things you put in your nose.

342
00:17:25,840 --> 00:17:29,040
Speaker 3: That was one of the other things at showstoppers. There

343
00:17:29,040 --> 00:17:30,159
Speaker 3: was a company that was doing that.

344
00:17:30,280 --> 00:17:33,280
Speaker 1: So yeah, he had want to be Steve Jobs vibes

345
00:17:33,320 --> 00:17:37,280
Speaker 1: from his half unbuttoned shirt and like many many spiritual

346
00:17:37,320 --> 00:17:42,800
Speaker 1: medallions to his like Jordan Peterson's suit, and very much

347
00:17:42,960 --> 00:17:46,679
Speaker 1: just that. Like I am the charismatic founder and what

348
00:17:46,800 --> 00:17:48,679
Speaker 1: I bring to the table. My partner knows how to

349
00:17:48,680 --> 00:17:55,080
Speaker 1: build robots. I'm charismatic. I'm Jack j Jessanowski, and Jack

350
00:17:55,119 --> 00:17:58,879
Speaker 1: and I started talking, and boy howdy, we had us

351
00:17:58,920 --> 00:18:01,840
Speaker 1: a conversation and I think we're just gonna play that.

352
00:18:02,200 --> 00:18:03,639
Speaker 1: What do I need to do to set this up?

353
00:18:03,760 --> 00:18:06,280
Speaker 3: No, I think you've set it up. We walk up

354
00:18:06,320 --> 00:18:09,560
Speaker 3: to Jack, I start, I start recording, and we start

355
00:18:09,600 --> 00:18:12,239
Speaker 3: talking about the robot, and then things spin in some

356
00:18:12,560 --> 00:18:13,919
Speaker 3: pretty interesting directions.

357
00:18:14,000 --> 00:18:20,119
Speaker 1: Yeah, all right, So what is this thing useful for?

358
00:18:21,800 --> 00:18:25,800
Speaker 6: Well, generally capable, just like a human can reach to

359
00:18:25,840 --> 00:18:28,560
Speaker 6: the floor and reach up high to a cupboard, go

360
00:18:28,680 --> 00:18:29,160
Speaker 6: up and down.

361
00:18:29,200 --> 00:18:30,199
Speaker 5: That's what we made this for.

362
00:18:30,320 --> 00:18:33,280
Speaker 6: Obviously in a little bit of a different fashion because

363
00:18:33,359 --> 00:18:36,000
Speaker 6: most surfaces are level.

364
00:18:36,240 --> 00:18:37,600
Speaker 5: We don't need to reinvent the wheel.

365
00:18:38,520 --> 00:18:43,280
Speaker 6: And the biggest market that we're going after is households.

366
00:18:43,320 --> 00:18:46,879
Speaker 6: Domestic dishes, laundry, make the bed, clean up around the house,

367
00:18:47,320 --> 00:18:50,639
Speaker 6: eventually cooking. That's more fine tuned, you know, dishes and

368
00:18:50,720 --> 00:18:53,960
Speaker 6: laundry is really that first task that is.

369
00:18:53,920 --> 00:18:55,240
Speaker 5: Gonna be fully autonomous.

370
00:18:55,920 --> 00:18:59,879
Speaker 6: Obviously from a folding standpoint and cooking standpoint, you can

371
00:19:00,040 --> 00:19:04,919
Speaker 6: do tele operation today, so can use cheaper labor internationally

372
00:19:05,440 --> 00:19:09,240
Speaker 6: through a robot, but full autonomous is coming very quickly,

373
00:19:09,320 --> 00:19:10,960
Speaker 6: like Jensen talked about recently.

374
00:19:11,920 --> 00:19:15,119
Speaker 7: So I see there's a lot of folks in the

375
00:19:15,200 --> 00:19:18,720
Speaker 7: robot space that are trying robots based on the human form, right,

376
00:19:18,960 --> 00:19:20,720
Speaker 7: you guys have not gone that route.

377
00:19:20,920 --> 00:19:23,520
Speaker 1: Talk to me about that droid form.

378
00:19:23,640 --> 00:19:28,360
Speaker 6: Yes, Well, as we know, robots didn't evolve from monkeys,

379
00:19:28,640 --> 00:19:31,840
Speaker 6: and so we have an ability to reimagine them. All

380
00:19:31,880 --> 00:19:34,920
Speaker 6: of the existing hardware we use in the world has

381
00:19:34,960 --> 00:19:38,560
Speaker 6: wheels for a reason. It just works better. It's easier,

382
00:19:38,600 --> 00:19:41,640
Speaker 6: there's less friction. That means there's less maintenance. That means

383
00:19:41,720 --> 00:19:46,040
Speaker 6: there's less energy output. It's efficiency. It's also easier for

384
00:19:46,160 --> 00:19:49,360
Speaker 6: us to manufacture that stuff at scale. So I think

385
00:19:49,400 --> 00:19:55,960
Speaker 6: long term, do robots all have legs?

386
00:19:56,080 --> 00:19:57,359
Speaker 5: Yeah, more or less.

387
00:19:57,400 --> 00:19:59,600
Speaker 6: The home robot does turn into the leg robot because

388
00:19:59,640 --> 00:20:01,520
Speaker 6: then it can with you in the car everything.

389
00:20:01,880 --> 00:20:03,160
Speaker 5: But I think the early.

390
00:20:02,880 --> 00:20:08,800
Speaker 6: Stages the wheels, because of their cheaperness, because of their reliability.

391
00:20:09,359 --> 00:20:12,840
Speaker 6: I think that will be what wins early stage. That's

392
00:20:12,880 --> 00:20:13,879
Speaker 6: where we started here.

393
00:20:14,440 --> 00:20:16,240
Speaker 7: Because the robot can go in the car with you.

394
00:20:16,640 --> 00:20:18,560
Speaker 7: What do you see people wanting to have a robot

395
00:20:18,560 --> 00:20:19,440
Speaker 7: in the car with them for?

396
00:20:19,920 --> 00:20:22,720
Speaker 5: I think it will just become basically the same way.

397
00:20:22,760 --> 00:20:25,640
Speaker 6: If you have enough money, a lot of people afford

398
00:20:25,800 --> 00:20:29,240
Speaker 6: like an assistant to come with them places.

399
00:20:29,800 --> 00:20:35,919
Speaker 7: It's because that seems like a niche market compared to household.

400
00:20:37,400 --> 00:20:39,760
Speaker 3: I think it's the barrier.

401
00:20:39,840 --> 00:20:42,960
Speaker 6: I think is because of the the cost and then

402
00:20:43,119 --> 00:20:44,800
Speaker 6: the humanness.

403
00:20:44,119 --> 00:20:46,240
Speaker 5: Like then you have to care for another human.

404
00:20:46,480 --> 00:20:51,159
Speaker 6: And whereas in this case it's kind of all positive sum.

405
00:20:51,200 --> 00:20:56,000
Speaker 6: And yeah, I guess it's wrong to try to say

406
00:20:57,119 --> 00:20:58,840
Speaker 6: majority of people.

407
00:20:59,320 --> 00:21:03,560
Speaker 5: But anyone who's you know in media, you.

408
00:21:03,520 --> 00:21:06,760
Speaker 6: Know they videographer will be something you use a robot

409
00:21:06,880 --> 00:21:09,280
Speaker 6: for to follow you around and take media and film

410
00:21:09,320 --> 00:21:09,560
Speaker 6: for you.

411
00:21:10,119 --> 00:21:10,800
Speaker 5: They won't get.

412
00:21:10,720 --> 00:21:13,240
Speaker 6: Tired and say, go grab me a drink or you know,

413
00:21:13,800 --> 00:21:15,240
Speaker 6: go figure that thing out.

414
00:21:16,240 --> 00:21:19,560
Speaker 7: But it also can't decide, oh, that's actually not a

415
00:21:19,600 --> 00:21:21,160
Speaker 7: good location to film from.

416
00:21:21,160 --> 00:21:22,280
Speaker 1: It's not going to look as good.

417
00:21:22,560 --> 00:21:24,359
Speaker 7: We need to get over here, or we need another

418
00:21:24,400 --> 00:21:26,359
Speaker 7: camera on this side here, we need to get like

419
00:21:26,880 --> 00:21:29,040
Speaker 7: different angles because we're going to want to edit this

420
00:21:29,119 --> 00:21:32,000
Speaker 7: together into a thing. And as a videographer, I'm not

421
00:21:32,119 --> 00:21:35,760
Speaker 7: just a machine. I'm a part of a collaborative creative enterprise.

422
00:21:37,080 --> 00:21:42,040
Speaker 6: I think we're starting to see just how artistic these

423
00:21:42,640 --> 00:21:43,520
Speaker 6: ais can be.

424
00:21:44,160 --> 00:21:46,320
Speaker 1: What's the best example of that using.

425
00:21:47,760 --> 00:21:49,960
Speaker 6: Well, I think the most used thing is this the

426
00:21:50,040 --> 00:21:53,760
Speaker 6: Jenai art And then you have some of the new

427
00:21:53,840 --> 00:21:58,600
Speaker 6: video models are pretty cool and they're using certain sort

428
00:21:58,640 --> 00:22:05,000
Speaker 6: of zoom in shots everything. I think they'll make just

429
00:22:05,320 --> 00:22:08,240
Speaker 6: as good of movies as humans. Oh, I think the

430
00:22:08,280 --> 00:22:12,640
Speaker 6: best reference in order to actually say that that's possible

431
00:22:12,760 --> 00:22:14,879
Speaker 6: is music. I don't know if you've played with the

432
00:22:14,920 --> 00:22:17,960
Speaker 6: most recent AI music. There's song GBT dot com.

433
00:22:18,080 --> 00:22:20,840
Speaker 7: I've heard some things people call music that are produced

434
00:22:20,880 --> 00:22:21,119
Speaker 7: by that.

435
00:22:21,280 --> 00:22:22,760
Speaker 1: Yeah, we can make.

436
00:22:22,600 --> 00:22:23,960
Speaker 5: One live right now that.

437
00:22:24,040 --> 00:22:27,720
Speaker 6: I don't know if you've heard like the latest models,

438
00:22:28,040 --> 00:22:30,360
Speaker 6: pick me a genre.

439
00:22:32,280 --> 00:22:37,760
Speaker 1: Irish spiritualist Scott. If you try Scott too, you.

440
00:22:37,720 --> 00:22:42,680
Speaker 6: Love SKA is like definitely probably niche stuff is where

441
00:22:42,680 --> 00:22:48,080
Speaker 6: it's gonna have a harder time, But sk s KA.

442
00:22:49,000 --> 00:22:51,840
Speaker 5: I wonder how much SKA data there is out there.

443
00:22:52,400 --> 00:22:54,160
Speaker 1: There's a lot of SKA music out there.

444
00:22:54,200 --> 00:22:55,480
Speaker 5: What should we make it about? Should we make it

445
00:22:55,480 --> 00:22:56,359
Speaker 5: about iHeart radio?

446
00:22:57,040 --> 00:22:57,320
Speaker 1: Sure?

447
00:22:57,640 --> 00:23:05,800
Speaker 8: iHeartRadio and communication and clear channel communications? All right, it's

448
00:23:05,840 --> 00:23:08,800
Speaker 8: here a scaw song. We're like, oh, it has to

449
00:23:08,840 --> 00:23:10,080
Speaker 8: load for like thirty seconds.

450
00:23:10,080 --> 00:23:13,480
Speaker 6: It feels weirdly like I'm upset that I have to

451
00:23:13,520 --> 00:23:15,960
Speaker 6: wait that long for something to load online.

452
00:23:16,880 --> 00:23:18,800
Speaker 9: It feels to you, huh, yeah, I guys what they're

453
00:23:18,800 --> 00:23:21,320
Speaker 9: playing with it a lot, But It's funny to think

454
00:23:21,320 --> 00:23:23,359
Speaker 9: about how much time and effort it does take to

455
00:23:23,440 --> 00:23:27,359
Speaker 9: like produce a song typically I am twenty seven.

456
00:23:27,920 --> 00:23:31,800
Speaker 1: Yeah, that's interesting, wouldn't I guess that?

457
00:23:33,680 --> 00:23:33,760
Speaker 6: What?

458
00:23:34,000 --> 00:23:35,199
Speaker 4: Uh is it?

459
00:23:35,320 --> 00:23:37,679
Speaker 7: One thing that's really compelling to me is your partner.

460
00:23:38,000 --> 00:23:40,080
Speaker 7: When I came in here was very very much talking

461
00:23:40,080 --> 00:23:42,800
Speaker 7: about the utility of this in terms of replacing human

462
00:23:42,840 --> 00:23:47,800
Speaker 7: beings and tasks that are generally unpleasant laundry, doing the dishes,

463
00:23:47,920 --> 00:23:51,160
Speaker 7: cleaning up trash. You seem a lot more bullish on

464
00:23:52,040 --> 00:23:55,439
Speaker 7: robots replacing human beings and what are generally considered to

465
00:23:55,480 --> 00:23:57,879
Speaker 7: be enterprises people want to do.

466
00:23:57,960 --> 00:23:58,640
Speaker 1: With their time.

467
00:23:59,359 --> 00:24:02,040
Speaker 7: Is that like a discrepancy that that you guys have

468
00:24:02,200 --> 00:24:04,359
Speaker 7: kind of talked about or do you think it's something

469
00:24:04,600 --> 00:24:06,800
Speaker 7: you guys are more on the same page with stuff.

470
00:24:06,800 --> 00:24:10,479
Speaker 6: From a business standpoint, we're one hundred percent going after

471
00:24:11,440 --> 00:24:16,840
Speaker 6: the dishes, laundry, uh, nursing practice of.

472
00:24:16,880 --> 00:24:21,640
Speaker 5: Just doing vitals, which is the very repetitive task. That's

473
00:24:21,680 --> 00:24:25,560
Speaker 5: the push. I was starting to just talk into.

474
00:24:25,240 --> 00:24:29,760
Speaker 6: The aspect of the legged robots and kind of imagining

475
00:24:29,800 --> 00:24:34,960
Speaker 6: why a legged version would have better utility or be

476
00:24:35,040 --> 00:24:38,520
Speaker 6: something someone wants to purchase. Scared rather than the wheeled

477
00:24:38,600 --> 00:24:41,640
Speaker 6: robot an yow stairs is definitely a.

478
00:24:41,520 --> 00:24:42,280
Speaker 5: Big one of those.

479
00:24:42,320 --> 00:24:45,960
Speaker 6: There are wheel types we're working on right now, would

480
00:24:45,960 --> 00:24:49,240
Speaker 6: have ability to climb like single stairs obviously easiest, and

481
00:24:49,280 --> 00:24:51,639
Speaker 6: that's what most people have in their home if they

482
00:24:51,640 --> 00:24:52,400
Speaker 6: do have stairs.

483
00:24:53,400 --> 00:24:55,280
Speaker 1: Oh, are we gonna listen to some robots?

484
00:24:55,280 --> 00:24:58,200
Speaker 5: Scott my heart listeners system.

485
00:25:09,720 --> 00:25:28,000
Speaker 4: Stop? Crazy's a scarf.

486
00:25:29,400 --> 00:25:31,120
Speaker 1: It's a pretty basic melody.

487
00:25:31,640 --> 00:25:33,720
Speaker 7: I mean, there's horns in it, but I feel like

488
00:25:33,800 --> 00:25:36,520
Speaker 7: it's kind of takes in a I think it Helia

489
00:25:36,600 --> 00:25:38,639
Speaker 7: is trying to do pot that it's just thrown some

490
00:25:38,760 --> 00:25:42,080
Speaker 7: horns in on. This is a little closer to SCA,

491
00:25:43,800 --> 00:26:05,639
Speaker 7: although it's still Yeah, it's not really singing, but I

492
00:26:05,720 --> 00:26:06,960
Speaker 7: guess that's a matter of pace.

493
00:26:10,800 --> 00:26:11,600
Speaker 1: What do you listen to?

494
00:26:11,800 --> 00:26:13,040
Speaker 5: This is the worst that's going to be.

495
00:26:13,960 --> 00:26:14,800
Speaker 3: I hear that a lot.

496
00:26:15,320 --> 00:26:19,320
Speaker 7: It's interesting because GPT four took fifty times as much

497
00:26:19,400 --> 00:26:22,239
Speaker 7: power as GPT three to train, and there's a lot

498
00:26:22,280 --> 00:26:25,800
Speaker 7: of mixed reactions on that. And we're entering into a

499
00:26:25,920 --> 00:26:30,080
Speaker 7: period where we're very likely looking at a recession venture

500
00:26:30,160 --> 00:26:31,040
Speaker 7: capital funding.

501
00:26:31,760 --> 00:26:33,399
Speaker 1: There's a chance it's not going to be what it

502
00:26:33,520 --> 00:26:34,040
Speaker 1: has been.

503
00:26:34,720 --> 00:26:37,080
Speaker 7: Is that concern you at all that, like this vaunted

504
00:26:37,240 --> 00:26:40,040
Speaker 7: next level for all of this stuff, the energy cost,

505
00:26:40,720 --> 00:26:43,800
Speaker 7: the investment cost is just not going to be borne

506
00:26:43,920 --> 00:26:46,919
Speaker 7: by a market that is not going to be as

507
00:26:47,119 --> 00:26:49,320
Speaker 7: strong tomorrow as it was today.

508
00:26:49,680 --> 00:26:50,760
Speaker 1: At least in the immediator.

509
00:26:51,720 --> 00:26:56,720
Speaker 6: I think even if we created no more energy as

510
00:26:56,760 --> 00:26:59,840
Speaker 6: a human species today, the amount of advanced once we

511
00:27:00,119 --> 00:27:06,160
Speaker 6: create would from an architectural standpoint, continue to advance.

512
00:27:06,480 --> 00:27:09,240
Speaker 5: So you have other models.

513
00:27:11,400 --> 00:27:14,159
Speaker 6: Like I think LAMA three point three, which has matched

514
00:27:14,240 --> 00:27:19,520
Speaker 6: four oh's capabilities and is I forget how many parameters,

515
00:27:19,600 --> 00:27:22,639
Speaker 6: but like super like much much much smaller and was

516
00:27:22,720 --> 00:27:24,960
Speaker 6: much cheaper to train, and like we're continuing to see

517
00:27:25,000 --> 00:27:30,680
Speaker 6: like smaller models that are just as effective and we're

518
00:27:30,800 --> 00:27:33,719
Speaker 6: much cheaper training runs. I think Deep Seek was one

519
00:27:33,760 --> 00:27:34,560
Speaker 6: of the newest ones.

520
00:27:35,080 --> 00:27:37,199
Speaker 7: What I'm what I'm concerned about is I'm looking at

521
00:27:37,280 --> 00:27:40,360
Speaker 7: the p and L right, I'm looking at open AI's PNL.

522
00:27:40,680 --> 00:27:43,080
Speaker 7: I'm looking at the fact that they're losing five or

523
00:27:43,119 --> 00:27:46,840
Speaker 7: six billion dollars last year and we're very good chance

524
00:27:46,880 --> 00:27:47,320
Speaker 7: it's going to.

525
00:27:47,320 --> 00:27:49,080
Speaker 1: Be somewhere in the neighborhood to double that this year.

526
00:27:49,600 --> 00:27:52,440
Speaker 7: And I'm it's not that there's nothing impressive there it's

527
00:27:52,440 --> 00:27:54,320
Speaker 7: not that I don't see like, oh, you can generate

528
00:27:54,560 --> 00:27:58,560
Speaker 7: a song that's got like guitar and and and trumpets

529
00:27:58,640 --> 00:28:00,920
Speaker 7: and vocals and stuff and you know a minute or so.

530
00:28:01,240 --> 00:28:02,000
Speaker 1: It's not that that's not.

531
00:28:02,080 --> 00:28:07,000
Speaker 7: Impressive, but like a pilier trick, isn't a trillion dollar business,

532
00:28:07,160 --> 00:28:09,200
Speaker 7: And that's the kind of investment they're looking at. And

533
00:28:09,280 --> 00:28:12,120
Speaker 7: I do wonder like, is it not much more reasonable

534
00:28:12,200 --> 00:28:13,800
Speaker 7: to focus on folding laundry?

535
00:28:14,400 --> 00:28:19,560
Speaker 6: Well, obviously, I personally am in the the boathouse of

536
00:28:19,640 --> 00:28:24,880
Speaker 6: focusing on allowing this intelligence to flourish and doing these

537
00:28:24,960 --> 00:28:28,480
Speaker 6: laborious tasks and getting them in the households. I do

538
00:28:28,640 --> 00:28:32,800
Speaker 6: think from opening eye standpoint, and the reason why vcs

539
00:28:32,880 --> 00:28:36,760
Speaker 6: and private investors will value them so highly is what's

540
00:28:36,880 --> 00:28:39,080
Speaker 6: next is white collar work.

541
00:28:39,600 --> 00:28:44,960
Speaker 5: A lot of the jobs online. That's what they do.

542
00:28:45,120 --> 00:28:49,360
Speaker 6: Have an internal model which is able to control the computer,

543
00:28:50,080 --> 00:28:51,880
Speaker 6: you know, the same way you would ask an executive

544
00:28:51,880 --> 00:28:54,240
Speaker 6: assistant to do certain things online.

545
00:28:55,480 --> 00:28:56,320
Speaker 5: Now it's just.

546
00:28:56,800 --> 00:28:59,920
Speaker 7: Jobey's handing along all of their emails now through AI,

547
00:29:01,760 --> 00:29:03,120
Speaker 7: which is you know, we'll see how.

548
00:29:03,080 --> 00:29:04,200
Speaker 1: Well that works on the long term.

549
00:29:04,240 --> 00:29:07,520
Speaker 7: There's been some interesting pulling on like the degree to

550
00:29:07,600 --> 00:29:13,720
Speaker 7: which customers and investors feel trust when somebody's responding to

551
00:29:13,800 --> 00:29:16,720
Speaker 7: them with an AI. But what's interesting, I mean more

552
00:29:16,760 --> 00:29:19,760
Speaker 7: here is the dichotomy between what I see here is

553
00:29:20,080 --> 00:29:22,320
Speaker 7: a very pragmatic choice, which is, we're not going to

554
00:29:22,400 --> 00:29:26,000
Speaker 7: try and remake a human being formed robot and.

555
00:29:26,040 --> 00:29:28,560
Speaker 1: Deal with like knees and hips and all of that stuff.

556
00:29:28,800 --> 00:29:29,400
Speaker 1: We don't need that.

557
00:29:29,520 --> 00:29:32,200
Speaker 7: We can have its turn up and down on this

558
00:29:32,360 --> 00:29:36,640
Speaker 7: platform and reach things the same way. Melded to what

559
00:29:36,760 --> 00:29:38,880
Speaker 7: I consider to be kind of a little more pie

560
00:29:38,960 --> 00:29:42,240
Speaker 7: in the sky. We're doing this as eventually something that

561
00:29:42,320 --> 00:29:46,640
Speaker 7: can take creative roles and think independently and make things,

562
00:29:47,120 --> 00:29:49,600
Speaker 7: which is it's interesting to me to see that in

563
00:29:49,680 --> 00:29:51,760
Speaker 7: a company's DNA of what you guys are eight months

564
00:29:51,800 --> 00:29:54,920
Speaker 7: out right now, yep, is that what you're more interested in?

565
00:29:55,520 --> 00:29:58,480
Speaker 5: I'd say, I tailor my pitch to the person I'm

566
00:29:58,520 --> 00:29:58,920
Speaker 5: talking to.

567
00:29:59,160 --> 00:29:59,400
Speaker 1: Uh huh.

568
00:30:00,960 --> 00:30:04,560
Speaker 6: So some people definitely enjoy thinking about more of the

569
00:30:04,640 --> 00:30:07,880
Speaker 6: sci fi futures that are coming, for example, the droids

570
00:30:07,920 --> 00:30:12,920
Speaker 6: building Droid's moment. It's when you know you are decreasing

571
00:30:13,000 --> 00:30:17,680
Speaker 6: your own manufacturing costs by using your own hardware to

572
00:30:17,760 --> 00:30:20,880
Speaker 6: build more of that hardware and parts are just being

573
00:30:20,920 --> 00:30:22,080
Speaker 6: shipped into the factory.

574
00:30:22,560 --> 00:30:22,960
Speaker 5: Obviously.

575
00:30:23,200 --> 00:30:26,800
Speaker 6: I think the first fully automated phone factory just came

576
00:30:26,840 --> 00:30:29,680
Speaker 6: out in China recently, which is like some cool press

577
00:30:29,720 --> 00:30:33,560
Speaker 6: in news, but the phone is separate from the actual

578
00:30:33,640 --> 00:30:34,880
Speaker 6: manufacturing process.

579
00:30:35,120 --> 00:30:38,960
Speaker 5: Yeah, so there's that like interesting component.

580
00:30:39,600 --> 00:30:45,000
Speaker 6: The exciting part of the idea that how do we

581
00:30:45,080 --> 00:30:49,080
Speaker 6: reach true abundance as a species of material and resources

582
00:30:49,280 --> 00:30:55,840
Speaker 6: is well, because GDP is a calculation of capita times productivity,

583
00:30:56,320 --> 00:31:01,240
Speaker 6: a robot really represents capita one in it of creation.

584
00:31:02,280 --> 00:31:05,880
Speaker 6: And I'd say that's where the sci fi thinking comes

585
00:31:05,960 --> 00:31:09,920
Speaker 6: into play, and it's it's not not worth going there

586
00:31:11,000 --> 00:31:14,440
Speaker 6: when just dreaming about the future robotics and talking about

587
00:31:14,440 --> 00:31:19,160
Speaker 6: it and having an interesting, engaging conversation, But definitely when

588
00:31:19,200 --> 00:31:21,440
Speaker 6: it comes to what are we doing from an engineering

589
00:31:21,520 --> 00:31:23,640
Speaker 6: standpoint on the day to day and how are we

590
00:31:23,720 --> 00:31:27,520
Speaker 6: trying to approach the market, those conversations are not at

591
00:31:27,600 --> 00:31:28,040
Speaker 6: being had.

592
00:31:29,200 --> 00:31:31,320
Speaker 1: Well, I appreciate your time and you gave me a lot.

593
00:31:31,320 --> 00:31:33,040
Speaker 1: I'm gonna let you get to the other peak. Thank you,

594
00:31:33,160 --> 00:31:34,200
Speaker 1: Thank you so much to meet you.

595
00:31:34,320 --> 00:31:34,480
Speaker 6: Jack.

596
00:31:35,720 --> 00:31:37,880
Speaker 3: Oh wow, that's super interesting.

597
00:31:37,920 --> 00:31:40,440
Speaker 1: I hope you all liked Jack Jay as much as

598
00:31:40,520 --> 00:31:41,320
Speaker 1: I didn't.

599
00:31:41,200 --> 00:31:43,840
Speaker 3: Getting to twenty seventy years old and not knowing what.

600
00:31:44,000 --> 00:31:46,240
Speaker 1: Scott is that old. I thought he was much younger,

601
00:31:46,680 --> 00:31:48,160
Speaker 1: like you thought he was like twenty two.

602
00:31:48,360 --> 00:31:50,400
Speaker 3: Yes, but the fact that he like he didn't know

603
00:31:50,440 --> 00:31:53,479
Speaker 3: what SKA was as a genre, he wasn't was unaware

604
00:31:53,520 --> 00:31:53,600
Speaker 3: of it.

605
00:31:53,680 --> 00:31:55,200
Speaker 1: I don't think he listens to music.

606
00:31:55,040 --> 00:31:57,200
Speaker 3: Well, he listens to AI generated hess.

607
00:31:56,880 --> 00:31:58,840
Speaker 1: To AI generated music, he's just as good.

608
00:31:59,040 --> 00:32:01,840
Speaker 3: He has the most, he's the most. I listen to

609
00:32:01,960 --> 00:32:05,880
Speaker 3: AI generated music vines. If anyone I've ever seen before,

610
00:32:06,240 --> 00:32:06,760
Speaker 3: just very.

611
00:32:06,680 --> 00:32:09,840
Speaker 1: Clearly does not have a soul. No, like like nothing,

612
00:32:10,080 --> 00:32:12,080
Speaker 1: nothing would leave the universe.

613
00:32:11,800 --> 00:32:15,160
Speaker 3: If he did, right, Like it's so opposite from the

614
00:32:15,200 --> 00:32:18,000
Speaker 3: first guy you talked to, who was it so like about? No,

615
00:32:18,960 --> 00:32:22,160
Speaker 3: I want to have smart, actual tasks that people don't enjoy. Yeah,

616
00:32:22,800 --> 00:32:26,800
Speaker 3: I love cinematography. I love I love filmmaking. I don't

617
00:32:27,000 --> 00:32:30,200
Speaker 3: First of all, I don't think a robot can can

618
00:32:30,360 --> 00:32:30,840
Speaker 3: replace this.

619
00:32:31,200 --> 00:32:31,240
Speaker 6: No.

620
00:32:31,520 --> 00:32:35,320
Speaker 1: I watched five different AI generated movies yesterday, and they

621
00:32:35,360 --> 00:32:36,440
Speaker 1: all looked like shit, even like.

622
00:32:36,400 --> 00:32:38,720
Speaker 3: A robot handling a physical camera to make like to

623
00:32:38,800 --> 00:32:41,520
Speaker 3: make like choices on like shot framing and composition, and.

624
00:32:41,560 --> 00:32:43,440
Speaker 1: Like it's one thing to be like, we want we

625
00:32:43,520 --> 00:32:45,680
Speaker 1: have a race cargoing, and so we've got this robot

626
00:32:45,720 --> 00:32:47,360
Speaker 1: on a track so we can go seventy miles an

627
00:32:47,400 --> 00:32:49,280
Speaker 1: hour and we're just kind of running on a street.

628
00:32:49,680 --> 00:32:51,760
Speaker 1: Follow it because a human being can move that. That's sure.

629
00:32:52,160 --> 00:32:53,959
Speaker 1: One thing we've left out of this up so far.

630
00:32:54,120 --> 00:32:57,120
Speaker 1: So this this machine that I described earlier, this robot

631
00:32:57,160 --> 00:32:59,040
Speaker 1: that goes up and down this rolling base has a

632
00:32:59,480 --> 00:33:02,960
Speaker 1: floppy Donald Trump mask over the over its head.

633
00:33:02,880 --> 00:33:04,320
Speaker 3: Which first attracted us to this.

634
00:33:04,600 --> 00:33:06,440
Speaker 1: Yeah, that's why we showed up there in the.

635
00:33:07,000 --> 00:33:10,800
Speaker 3: A robot moving its arms around wearing a Donald Trump mask.

636
00:33:10,880 --> 00:33:14,160
Speaker 3: And as Robert was interviewing this guy, the robot was

637
00:33:14,200 --> 00:33:16,600
Speaker 3: like moving around and like trying to simulate its washing

638
00:33:16,760 --> 00:33:20,400
Speaker 3: dishes capability, and it knocked over the same water bottle

639
00:33:20,480 --> 00:33:23,880
Speaker 3: about five times. It couldn't it couldn't pick it up consistently,

640
00:33:24,280 --> 00:33:26,680
Speaker 3: So I will not trust it with my fine china.

641
00:33:26,760 --> 00:33:27,240
Speaker 3: I'll say that.

642
00:33:27,400 --> 00:33:28,840
Speaker 1: As soon as I got up there, I asked, like,

643
00:33:28,880 --> 00:33:30,680
Speaker 1: I couldn't take my jacket off, now can it fold?

644
00:33:30,760 --> 00:33:32,880
Speaker 1: And He's like, well, we'd have to reprogram it. And

645
00:33:32,960 --> 00:33:34,880
Speaker 1: it was this when I talked to the guy, I

646
00:33:35,120 --> 00:33:37,040
Speaker 1: was like because he was like, yeah, we really see

647
00:33:37,080 --> 00:33:41,000
Speaker 1: this as being, you know, potentially good for elder care. Sure,

648
00:33:41,160 --> 00:33:42,960
Speaker 1: and you know, we had just seen the product we

649
00:33:43,000 --> 00:33:45,400
Speaker 1: talked about in the last episode, which, for all of

650
00:33:45,480 --> 00:33:47,520
Speaker 1: it's I don't know that I think it'll work. Was

651
00:33:48,000 --> 00:33:50,240
Speaker 1: a lot of thought and care went into it. I

652
00:33:50,400 --> 00:33:52,080
Speaker 1: was like, okay, so, like, what work have you done

653
00:33:52,160 --> 00:33:54,760
Speaker 1: to build a machine that can like communicate and be

654
00:33:54,840 --> 00:33:57,640
Speaker 1: helpful to like people who are dealing with health issues

655
00:33:57,720 --> 00:33:59,880
Speaker 1: in their their later years. And like, well that's why

656
00:34:00,160 --> 00:34:02,720
Speaker 1: it's open right, someone else will Yeah, it's open sources.

657
00:34:02,760 --> 00:34:03,520
Speaker 1: Someone else can do it.

658
00:34:03,920 --> 00:34:04,080
Speaker 5: Part.

659
00:34:04,120 --> 00:34:05,760
Speaker 1: See, you guys are just you guys are just saying

660
00:34:05,800 --> 00:34:09,400
Speaker 1: it can do everything because somebody could potentially code something

661
00:34:09,520 --> 00:34:09,759
Speaker 1: for it.

662
00:34:10,000 --> 00:34:12,120
Speaker 3: Yeah cool, there always could be code.

663
00:34:12,200 --> 00:34:15,439
Speaker 1: Yeah, there could be code. I mean again, the other guy,

664
00:34:16,320 --> 00:34:20,600
Speaker 1: the actual engineer, seemed very interested in the nuts and

665
00:34:20,719 --> 00:34:26,080
Speaker 1: bolts of making an affordable, reproducible machine that could handle

666
00:34:26,120 --> 00:34:30,600
Speaker 1: specific tasks, and Jack Jay had absolutely no interest in

667
00:34:30,680 --> 00:34:32,920
Speaker 1: the actual machine that they were making. This is clearly

668
00:34:32,960 --> 00:34:35,160
Speaker 1: could not be clear. This is just a stepping stone,

669
00:34:35,400 --> 00:34:37,560
Speaker 1: and he's kind of grossed out by it because it's

670
00:34:37,640 --> 00:34:40,160
Speaker 1: not replacing all human art with a machine that he owns.

671
00:34:40,280 --> 00:34:43,840
Speaker 3: He's a man completely fueled by Lex Friedman podcasts, and

672
00:34:43,960 --> 00:34:46,520
Speaker 3: he doesn't want to actually do any real work. He

673
00:34:46,640 --> 00:34:49,480
Speaker 3: just wants to talk about how AI is going to

674
00:34:49,800 --> 00:34:52,880
Speaker 3: take over everything and we have to welcome it in

675
00:34:53,400 --> 00:34:55,040
Speaker 3: and here listen to this is SKA.

676
00:34:55,440 --> 00:34:58,520
Speaker 1: He wants to take money by owning something that does

677
00:34:58,640 --> 00:35:02,120
Speaker 1: not provide anything and also put people out of work. Like,

678
00:35:02,800 --> 00:35:05,640
Speaker 1: at no point did he express a desire to do

679
00:35:06,000 --> 00:35:10,080
Speaker 1: anything other than replace something people were already doing with

680
00:35:10,239 --> 00:35:15,359
Speaker 1: something worse that tech guys could profit from. That's all

681
00:35:15,480 --> 00:35:17,800
Speaker 1: there is to this man. He's not a human.

682
00:35:18,040 --> 00:35:19,160
Speaker 3: It's so anti human.

683
00:35:19,400 --> 00:35:23,000
Speaker 1: Yeah, I cannot overemphasize the degree to which there was

684
00:35:23,080 --> 00:35:24,759
Speaker 1: nothing behind this boy's eyes.

685
00:35:25,880 --> 00:35:30,040
Speaker 3: Well, do you know what, there's also nothing super intelligent behind.

686
00:35:30,360 --> 00:35:32,440
Speaker 1: That's not true. All of our ads are sponsored by

687
00:35:32,520 --> 00:35:36,080
Speaker 1: real people, even if they're bad people, they're at least people.

688
00:35:36,600 --> 00:35:40,000
Speaker 1: They live and they love and they hate and you know,

689
00:35:40,120 --> 00:35:41,279
Speaker 1: maybe they have a promo code.

690
00:35:41,800 --> 00:35:57,560
Speaker 10: Let's see, all right, So after our lovely, Our Lovely Robotic.

691
00:35:57,360 --> 00:36:03,800
Speaker 1: Jack j Jessanowski Adventure, Oh god, Also the SKA was

692
00:36:03,880 --> 00:36:07,399
Speaker 1: Shiit not good? Not good? It did? It just kept

693
00:36:07,480 --> 00:36:08,840
Speaker 1: saying the words Scott.

694
00:36:09,040 --> 00:36:12,320
Speaker 3: He kept saying the word Scott music and saying the

695
00:36:12,400 --> 00:36:17,520
Speaker 3: word Robert Robert Scott, well, just doing random noises. After

696
00:36:17,520 --> 00:36:19,200
Speaker 3: we had our fill of that, we did walk around

697
00:36:19,239 --> 00:36:20,960
Speaker 3: the rest of Showstoppers.

698
00:36:21,360 --> 00:36:24,680
Speaker 1: He was so surprised that I wasn't. I wasn't impressed

699
00:36:24,920 --> 00:36:26,239
Speaker 1: by any of the He was like, you must have

700
00:36:26,400 --> 00:36:28,080
Speaker 1: heard the lady, man, I hear them.

701
00:36:28,800 --> 00:36:30,479
Speaker 3: It's not good.

702
00:36:30,680 --> 00:36:33,479
Speaker 1: It's like it's like I made this comparison a few times.

703
00:36:33,840 --> 00:36:36,839
Speaker 1: If somebody like walked in while I'm at a house

704
00:36:36,920 --> 00:36:40,000
Speaker 1: party was like, hey, man, I taught my dog to

705
00:36:40,200 --> 00:36:44,120
Speaker 1: masturbate to pornography with its with its pause. I would

706
00:36:44,160 --> 00:36:47,920
Speaker 1: be like, well, I mean that's like, I guess impressed.

707
00:36:47,920 --> 00:36:49,960
Speaker 1: I didn't think a dog could do that. Like I

708
00:36:50,080 --> 00:36:54,799
Speaker 1: am kind of impressed, I guess, But I don't want

709
00:36:54,920 --> 00:36:58,799
Speaker 1: this like this, this doesn't do anything for me. It's

710
00:37:00,360 --> 00:37:01,319
Speaker 1: you figured this out?

711
00:37:01,480 --> 00:37:03,279
Speaker 3: What what value does this?

712
00:37:03,719 --> 00:37:06,040
Speaker 1: How does the dog know who Farah Fawce it is?

713
00:37:06,120 --> 00:37:09,680
Speaker 1: I have questions, sure, but it doesn't give me anything

714
00:37:10,239 --> 00:37:14,200
Speaker 1: like no Farah Fauce it was Garrison.

715
00:37:14,320 --> 00:37:16,160
Speaker 3: No, goddamn, what do you think I do?

716
00:37:16,920 --> 00:37:19,160
Speaker 1: I don't know anymore?

717
00:37:20,560 --> 00:37:23,600
Speaker 3: Well, what I did is walk around the rest of showstoppers.

718
00:37:24,120 --> 00:37:26,279
Speaker 3: I saw this one booth that had like a like

719
00:37:26,320 --> 00:37:29,320
Speaker 3: an iPhone case with like a little like keyboard on

720
00:37:29,360 --> 00:37:32,080
Speaker 3: the bottom that like plugs in, and I started messing

721
00:37:32,120 --> 00:37:34,400
Speaker 3: around with it, and the guy that booth walked up

722
00:37:34,440 --> 00:37:35,759
Speaker 3: to me and made fun of me because he's like,

723
00:37:36,280 --> 00:37:41,360
Speaker 3: you've never you've never held a phone with He literally said, like,

724
00:37:41,719 --> 00:37:45,040
Speaker 3: you've never had a BlackBerry before, have you? Like no, Like, yeah,

725
00:37:45,560 --> 00:37:46,520
Speaker 3: you're typing all wrong.

726
00:37:46,600 --> 00:37:46,759
Speaker 6: On that.

727
00:37:47,280 --> 00:37:51,759
Speaker 1: There was a solid nine day news cycle when Barack Obama,

728
00:37:51,880 --> 00:37:54,600
Speaker 1: newly the president, revealed that he had a BlackBerry.

729
00:37:54,719 --> 00:37:59,160
Speaker 3: I remember that, which sounds like a lifetime There was a.

730
00:37:59,200 --> 00:38:02,680
Speaker 1: Company called r im once and they made a tablet

731
00:38:02,760 --> 00:38:05,640
Speaker 1: that was pretty good and we only made a couple

732
00:38:05,719 --> 00:38:08,360
Speaker 1: of rim job jokes about it, but it didn't do

733
00:38:08,640 --> 00:38:10,680
Speaker 1: very well, and so I gave it to my dad

734
00:38:10,760 --> 00:38:13,080
Speaker 1: and accidentally there was still a picture of my dick

735
00:38:13,120 --> 00:38:15,680
Speaker 1: on it. Anyway, that's a story for another day.

736
00:38:17,520 --> 00:38:17,840
Speaker 3: Cool.

737
00:38:19,960 --> 00:38:21,719
Speaker 1: These are the kind of things you get recording at

738
00:38:21,760 --> 00:38:23,520
Speaker 1: eleven fifty six pm.

739
00:38:23,400 --> 00:38:28,040
Speaker 3: And wet Tuesday, gott to get to bed, but no,

740
00:38:28,200 --> 00:38:29,560
Speaker 3: he made fun of me for not knowing how to

741
00:38:29,680 --> 00:38:31,440
Speaker 3: use a smartphone keyboard.

742
00:38:31,640 --> 00:38:32,319
Speaker 1: He did the right thing.

743
00:38:32,400 --> 00:38:33,800
Speaker 3: I don't need to use that because I have a

744
00:38:33,880 --> 00:38:36,520
Speaker 3: keyboard on my phone built in already. It's much faster.

745
00:38:36,800 --> 00:38:40,160
Speaker 3: So anyway, we stopped it at this company that makes

746
00:38:40,520 --> 00:38:44,360
Speaker 3: well now just makes the software to use in conjunction

747
00:38:44,480 --> 00:38:48,200
Speaker 3: with the augmented reality glasses and any like high powered laptop,

748
00:38:48,320 --> 00:38:50,680
Speaker 3: specifically the laptops that have like built in like you know,

749
00:38:50,800 --> 00:38:53,879
Speaker 3: like co pilots, because they require like higher processing power,

750
00:38:54,120 --> 00:38:56,880
Speaker 3: they have an NPU or something like that, like a

751
00:38:57,080 --> 00:39:00,600
Speaker 3: like an ampro processing unit is what they're calling AI

752
00:39:00,800 --> 00:39:05,080
Speaker 3: dedicated GPU thing. Effectively, it allows you to hook up

753
00:39:05,120 --> 00:39:09,400
Speaker 3: these glasses and run you know, possibly infinite amount of

754
00:39:09,440 --> 00:39:13,279
Speaker 3: monitors using AR. And we talked about this company last

755
00:39:13,360 --> 00:39:15,120
Speaker 3: year because we saw them a show stoppers.

756
00:39:15,200 --> 00:39:16,759
Speaker 1: You put on the glasses and it's like you've got

757
00:39:16,960 --> 00:39:19,560
Speaker 1: six monitors or whatever that are all full size, and.

758
00:39:19,560 --> 00:39:21,040
Speaker 3: It's actually really easy to use.

759
00:39:21,160 --> 00:39:23,960
Speaker 1: It works very well, seamlessly, it's nice.

760
00:39:23,840 --> 00:39:26,359
Speaker 3: It's it's it's good quality, easy to use, you can

761
00:39:26,400 --> 00:39:27,320
Speaker 3: move the monitors around.

762
00:39:27,440 --> 00:39:28,919
Speaker 1: It's an excellent, excellent game.

763
00:39:29,000 --> 00:39:30,640
Speaker 3: We talked to them last year and the main thing

764
00:39:30,719 --> 00:39:32,800
Speaker 3: that was holding this like holding us back on it

765
00:39:33,160 --> 00:39:35,239
Speaker 3: is that you needed to use their own proprietary lab.

766
00:39:35,320 --> 00:39:37,200
Speaker 1: It was their own laptop, and it wasn't a great one.

767
00:39:37,320 --> 00:39:39,600
Speaker 3: It was just like a Linux laptop. It didn't have

768
00:39:39,680 --> 00:39:42,240
Speaker 3: everything like I want out of my own personal laptop.

769
00:39:42,440 --> 00:39:43,800
Speaker 1: And we were still impressed with it.

770
00:39:43,880 --> 00:39:45,640
Speaker 3: Then it was still it was so good. Yeah, and

771
00:39:45,719 --> 00:39:47,680
Speaker 3: now you can just use any high power laptop with

772
00:39:47,760 --> 00:39:51,160
Speaker 3: it essentially. So it's lovely to see that improved. We

773
00:39:51,239 --> 00:39:55,400
Speaker 3: saw this lovely like like very small foldable projector.

774
00:39:55,760 --> 00:39:58,319
Speaker 1: Oh yeah, that was cool. What's the company? That company name,

775
00:39:58,320 --> 00:39:59,920
Speaker 1: because we should be we should be giving out the name.

776
00:40:00,520 --> 00:40:03,080
Speaker 3: Yes, the ar glasses and the software system is called

777
00:40:03,200 --> 00:40:07,560
Speaker 3: spacetop very good by a company called Sightful. It works

778
00:40:07,880 --> 00:40:11,280
Speaker 3: works great. But yeah, this this little folding folding projector

779
00:40:11,600 --> 00:40:15,640
Speaker 3: currently has a kickstarter. The company is called aura Zen yeah,

780
00:40:15,920 --> 00:40:20,240
Speaker 3: or as Zen specifically, it was the zip trifled projector.

781
00:40:20,920 --> 00:40:22,839
Speaker 3: Right now. It's it's a seven to twenty p very

782
00:40:22,920 --> 00:40:25,360
Speaker 3: small foldable projector. It has a whole it has like

783
00:40:25,520 --> 00:40:28,520
Speaker 3: like an auto focusing auto keystone. They're working to get

784
00:40:28,560 --> 00:40:31,400
Speaker 3: it up to ten DP, but they're running a kickstarter

785
00:40:31,560 --> 00:40:35,000
Speaker 3: right now to ship in about three months. Super good

786
00:40:35,080 --> 00:40:36,120
Speaker 3: quality stuff.

787
00:40:35,960 --> 00:40:37,920
Speaker 1: If you're a gadget rishina, Like it felt like a

788
00:40:38,200 --> 00:40:41,279
Speaker 1: quality piece of electronics in my hands, Like the way

789
00:40:41,360 --> 00:40:45,279
Speaker 1: it like snapped when it closed just felt good. I'm

790
00:40:45,360 --> 00:40:48,280
Speaker 1: I think I'm gonna buy one like it's it's exactly

791
00:40:48,360 --> 00:40:50,440
Speaker 1: what I want for traveling, which is the ability to

792
00:40:50,520 --> 00:40:52,400
Speaker 1: it goes up to like eighty inches of screen and

793
00:40:52,520 --> 00:40:55,040
Speaker 1: like very good resolution. The ability to just have that

794
00:40:55,719 --> 00:40:58,440
Speaker 1: plugged in to a battery or the wall and my

795
00:40:58,600 --> 00:41:01,759
Speaker 1: laptop and like wherever I happen to be, I've got

796
00:41:01,800 --> 00:41:03,719
Speaker 1: a movie screen that I don't have to worry about

797
00:41:03,719 --> 00:41:05,680
Speaker 1: the fucking hooking up a TV to my laptop or

798
00:41:05,719 --> 00:41:06,040
Speaker 1: some shit.

799
00:41:06,200 --> 00:41:08,480
Speaker 3: It doesn't need Wi Fi to work. It just can

800
00:41:08,600 --> 00:41:10,440
Speaker 3: cast from from your phone.

801
00:41:10,440 --> 00:41:14,920
Speaker 1: A u r ze and zip trifle projector Aura zen yep, yep.

802
00:41:14,960 --> 00:41:16,759
Speaker 1: I think they're selling them for two fifty right now.

803
00:41:17,160 --> 00:41:18,840
Speaker 3: That's for the for the Kickstarter.

804
00:41:18,920 --> 00:41:20,440
Speaker 1: For the kickstart, it will go up a little one.

805
00:41:20,480 --> 00:41:23,800
Speaker 1: That's a product. But we saw it. It works. They

806
00:41:23,880 --> 00:41:26,600
Speaker 1: had a lot of they had tracking and stuff so

807
00:41:26,680 --> 00:41:28,720
Speaker 1: it like automatically would focus on ship.

808
00:41:28,560 --> 00:41:32,040
Speaker 3: Auto focuses and at like it scales correctly forwards, projecting

809
00:41:32,760 --> 00:41:35,360
Speaker 3: it automatically like adjusts like the tilt of it so

810
00:41:35,560 --> 00:41:36,120
Speaker 3: that it you know.

811
00:41:36,280 --> 00:41:38,520
Speaker 1: Yeah, obviously this isn't the full review because we don't

812
00:41:38,520 --> 00:41:40,600
Speaker 1: own one, but form everything we could tell by looking

813
00:41:40,640 --> 00:41:42,160
Speaker 1: at it. In the moment we tried it out, I

814
00:41:42,239 --> 00:41:43,200
Speaker 1: hooked up my phone to it.

815
00:41:43,280 --> 00:41:45,320
Speaker 3: As I went to my phone screen, I realized I

816
00:41:45,480 --> 00:41:50,640
Speaker 3: have a slightly I would say, artful flute image of

817
00:41:50,760 --> 00:41:53,680
Speaker 3: an angel. I quickly swiped away from.

818
00:41:53,640 --> 00:41:55,760
Speaker 1: We shouldn't show your dick to your damn my home.

819
00:41:55,719 --> 00:41:59,480
Speaker 3: Screen of my phone. You know they're gonna worse.

820
00:41:59,520 --> 00:42:03,719
Speaker 1: Things always be worse. But I think where will end is?

821
00:42:04,000 --> 00:42:06,279
Speaker 1: And this actually is not entirely in order, because this

822
00:42:06,440 --> 00:42:08,920
Speaker 1: is the next after we had that conversation with our

823
00:42:08,920 --> 00:42:13,120
Speaker 1: friend Jack Jay, which just left me thinking about, like,

824
00:42:13,719 --> 00:42:16,080
Speaker 1: some people aren't really people, right, That's what I kind

825
00:42:16,160 --> 00:42:16,759
Speaker 1: the sole thing is.

826
00:42:16,960 --> 00:42:19,520
Speaker 3: It is a sham. It's all. It's all for rooms.

827
00:42:19,640 --> 00:42:20,400
Speaker 3: It's soulless.

828
00:42:20,880 --> 00:42:22,840
Speaker 1: We immediately walk over and we just kind of like

829
00:42:23,000 --> 00:42:26,480
Speaker 1: randomly turn a corner and there's like a human shin

830
00:42:27,080 --> 00:42:31,800
Speaker 1: like tibia amphibia basically with like a carbon fiber you know,

831
00:42:32,239 --> 00:42:34,719
Speaker 1: frame around it that's roughly the shape of like a

832
00:42:35,000 --> 00:42:40,200
Speaker 1: person's lower leg, lower leg, and it's called bio leg.

833
00:42:40,360 --> 00:42:44,600
Speaker 1: It's a powered microprocessor knee made in Japan, where it

834
00:42:44,719 --> 00:42:48,280
Speaker 1: is a prosthetic, but unlike most prosthetics, it is powered

835
00:42:48,400 --> 00:42:50,759
Speaker 1: and has a muscle built into it, so like when

836
00:42:50,760 --> 00:42:52,640
Speaker 1: you lift up your prosthetic, it doesn't hang and it

837
00:42:52,719 --> 00:42:55,360
Speaker 1: doesn't lock. It actually has a degree of motion and

838
00:42:55,440 --> 00:42:57,520
Speaker 1: it feels like what lifts the rest of the leg

839
00:42:57,719 --> 00:43:01,680
Speaker 1: what you're remaining muscles like it measures based on like

840
00:43:01,960 --> 00:43:04,000
Speaker 1: it can like take measurements from them and it can

841
00:43:04,200 --> 00:43:07,719
Speaker 1: act intelligently based on that. And I know that it

842
00:43:07,880 --> 00:43:11,200
Speaker 1: works because the inventor was there and he was a

843
00:43:11,280 --> 00:43:13,840
Speaker 1: man who was missing his leg below the knee and

844
00:43:14,000 --> 00:43:15,680
Speaker 1: had built this for himself.

845
00:43:16,360 --> 00:43:18,560
Speaker 3: You spent like ten years working on this, Yeah, eight years,

846
00:43:18,600 --> 00:43:19,120
Speaker 3: he said.

847
00:43:18,960 --> 00:43:22,120
Speaker 1: Eight years. And that's like really the thing that is

848
00:43:23,000 --> 00:43:26,000
Speaker 1: like so both like addictive and also like this like

849
00:43:26,200 --> 00:43:28,120
Speaker 1: very tonal whipless. You get at ces as you will

850
00:43:28,160 --> 00:43:31,120
Speaker 1: go from like this dead eyed con man trying to

851
00:43:31,200 --> 00:43:33,200
Speaker 1: scam the world so he can do god knows what

852
00:43:33,360 --> 00:43:37,080
Speaker 1: kinds of other harms with absolutely nothing, nothing inside him

853
00:43:37,080 --> 00:43:39,800
Speaker 1: at all, And then I lost my leg and I

854
00:43:39,920 --> 00:43:43,480
Speaker 1: built a better prosthetic to help the entire world, and

855
00:43:43,640 --> 00:43:47,360
Speaker 1: that's like thirty seconds between those two experiences.

856
00:43:46,880 --> 00:43:49,680
Speaker 3: And like that's that's like the dark magic of cees.

857
00:43:50,000 --> 00:43:53,320
Speaker 3: Like I don't like, I'm not like anti tech, like

858
00:43:53,400 --> 00:43:56,720
Speaker 3: I think there. I think technology can really improve people's

859
00:43:56,800 --> 00:43:59,400
Speaker 3: lives if used well. And sometimes I get kind of

860
00:43:59,440 --> 00:44:02,520
Speaker 3: black pilled walking around. See yes, but then we'll stumble

861
00:44:02,600 --> 00:44:05,879
Speaker 3: across this, like you know someone who like literally lost

862
00:44:05,920 --> 00:44:09,280
Speaker 3: a leg and made themselves their own better leg.

863
00:44:09,440 --> 00:44:12,200
Speaker 1: Eight years figuring out how to do this. Yeah, is

864
00:44:12,239 --> 00:44:12,880
Speaker 1: winning award.

865
00:44:12,960 --> 00:44:15,680
Speaker 3: It's like award winning, like tech innovation.

866
00:44:15,880 --> 00:44:18,279
Speaker 1: It's changing your as a person who has lost your

867
00:44:18,320 --> 00:44:20,239
Speaker 1: lower le, like changing being able to like have a

868
00:44:20,320 --> 00:44:24,920
Speaker 1: normal gait and balance again, like massive potential to improve

869
00:44:25,040 --> 00:44:26,640
Speaker 1: people's lives as a result of this.

870
00:44:27,640 --> 00:44:32,560
Speaker 3: Yeah, just steps away from Ai ska Ai and Donald

871
00:44:32,680 --> 00:44:36,759
Speaker 3: trump Man Lafle.

872
00:44:37,040 --> 00:44:40,839
Speaker 1: The company is again Bionic IM and it's the bio Leg.

873
00:44:40,960 --> 00:44:41,840
Speaker 3: Biolag is the product.

874
00:44:41,960 --> 00:44:44,120
Speaker 1: Yeah, the Bioleague is the product by Bionic M.

875
00:44:44,280 --> 00:44:46,160
Speaker 3: I'm going to try to check it out more tomorrow

876
00:44:46,400 --> 00:44:48,680
Speaker 3: at Eureka Park, which at this point you know that'll

877
00:44:48,719 --> 00:44:52,440
Speaker 3: be like maybe future episodes come next week. But I

878
00:44:52,480 --> 00:44:55,800
Speaker 3: guess this closes our actual like like we coadge.

879
00:44:56,440 --> 00:44:58,919
Speaker 1: Let's go get fucked up and eat Japanese food.

880
00:44:59,239 --> 00:45:01,640
Speaker 3: Oh, I'm here, I'm down, Let's do it.

881
00:45:05,120 --> 00:45:07,600
Speaker 1: It Could Happen Here is a production of cool Zone Media.

882
00:45:07,800 --> 00:45:10,800
Speaker 9: For more podcasts from cool Zone Media, visit our website

883
00:45:10,920 --> 00:45:14,439
Speaker 9: coolzonmedia dot com, or check us out on the iHeartRadio app,

884
00:45:14,560 --> 00:45:17,400
Speaker 9: Apple Podcasts, or wherever you listen to podcasts.

885
00:45:17,920 --> 00:45:19,799
Speaker 1: You can now find sources for it could Happen Here,

886
00:45:19,880 --> 00:45:21,640
Speaker 1: listed directly in episode descriptions.

887
00:45:22,040 --> 00:45:22,800
Speaker 2: Thanks for listening.